# coding: utf-8
from __future__ import absolute_import, unicode_literals
from datetime import datetime, timedelta
import json
import random
import string
import time
from cryptography.hazmat.backends import default_backend
from cryptography.hazmat.primitives import serialization
from cryptography.hazmat.primitives.asymmetric.rsa import RSAPrivateKey
import jwt
from six import binary_type, string_types, raise_from, text_type
from ..config import API
from ..exception import BoxOAuthException
from .oauth2 import OAuth2
from ..object.user import User
from ..util.compat import NoneType
class JWTAuth(OAuth2):
"""
Responsible for handling JWT Auth for Box Developer Edition. Can authenticate enterprise instances or app users.
"""
_GRANT_TYPE = 'urn:ietf:params:oauth:grant-type:jwt-bearer'
def __init__(
self,
client_id,
client_secret,
enterprise_id,
jwt_key_id,
rsa_private_key_file_sys_path=None,
rsa_private_key_passphrase=None,
user=None,
store_tokens=None,
box_device_id='0',
box_device_name='',
access_token=None,
session=None,
jwt_algorithm='RS256',
rsa_private_key_data=None,
**kwargs
):
"""Extends baseclass method.
Must pass exactly one of either `rsa_private_key_file_sys_path` or
`rsa_private_key_data`.
If both `enterprise_id` and `user` are non-`None`, the `user` takes
precedence when `refresh()` is called. This can be overruled with a
call to `authenticate_instance()`.
:param client_id:
Box API key used for identifying the application the user is authenticating with.
:type client_id:
`unicode`
:param client_secret:
Box API secret used for making OAuth2 requests.
:type client_secret:
`unicode`
:param enterprise_id:
The ID of the Box Developer Edition enterprise.
May be `None`, if the caller knows that it will not be
authenticating as an enterprise instance / service account.
If `user` is passed, this value is not used, unless
`authenticate_instance()` is called to clear the user and
authenticate as the enterprise instance.
:type enterprise_id:
`unicode` or `None`
:param jwt_key_id:
Key ID for the JWT assertion.
:type jwt_key_id:
`unicode`
:param rsa_private_key_file_sys_path:
(optional) Path to an RSA private key file, used for signing the JWT assertion.
:type rsa_private_key_file_sys_path:
`unicode`
:param rsa_private_key_passphrase:
Passphrase used to unlock the private key. Do not pass a unicode string - this must be bytes.
:type rsa_private_key_passphrase:
`bytes` or None
:param user:
(optional) The user to authenticate, expressed as a Box User ID or
as a :class:`User` instance.
This value is not required. But if it is provided, then the user
will be auto-authenticated at the time of the first API call or
when calling `authenticate_user()` without any arguments.
Should be `None` if the intention is to authenticate as the
enterprise instance / service account. If both `enterprise_id` and
`user` are non-`None`, the `user` takes precedense when `refresh()`
is called.
May be one of this application's created App User. Depending on the
configured User Access Level, may also be any other App User or
Managed User in the enterprise.
<https://developer.box.com/en/guides/applications/>
<https://developer.box.com/en/guides/authentication/select/>
:type user:
`unicode` or :class:`User` or `None`
:param store_tokens:
Optional callback for getting access to tokens for storing them.
:type store_tokens:
`callable`
:param box_device_id:
Optional unique ID of this device. Used for applications that want to support device-pinning.
:type box_device_id:
`unicode`
:param box_device_name:
Optional human readable name for this device.
:type box_device_name:
`unicode`
:param access_token:
Access token to use for auth until it expires.
:type access_token:
`unicode`
:param network_layer:
If specified, use it to make network requests. If not, the default network implementation will be used.
:type network_layer:
:class:`Network`
:param jwt_algorithm:
Which algorithm to use for signing the JWT assertion. Must be one of 'RS256', 'RS384', 'RS512'.
:type jwt_algorithm:
`unicode`
:param rsa_private_key_data:
(optional) Contents of RSA private key, used for signing the JWT assertion. Do not pass a
unicode string. Can pass a byte string, or a file-like object that returns bytes, or an
already-loaded `RSAPrivateKey` object.
:type rsa_private_key_data: `bytes` or :class:`io.IOBase` or :class:`RSAPrivateKey`
"""
user_id = self._normalize_user_id(user)
rsa_private_key = self._normalize_rsa_private_key(
file_sys_path=rsa_private_key_file_sys_path,
data=rsa_private_key_data,
passphrase=rsa_private_key_passphrase,
)
del rsa_private_key_data
del rsa_private_key_file_sys_path
super(JWTAuth, self).__init__(
client_id,
client_secret,
store_tokens=store_tokens,
box_device_id=box_device_id,
box_device_name=box_device_name,
access_token=access_token,
refresh_token=None,
session=session,
**kwargs
)
self._rsa_private_key = rsa_private_key
self._enterprise_id = enterprise_id
self._jwt_algorithm = jwt_algorithm
self._jwt_key_id = jwt_key_id
self._user_id = user_id
def _construct_and_send_jwt_auth(self, sub, sub_type, now_time=None):
"""
Construct the claims used for JWT auth and send a request to get a JWT.
Pass an enterprise ID to get an enterprise token (which can be used to provision/deprovision users),
or a user ID to get a user token.
:param sub:
The enterprise ID or user ID to auth.
:type sub:
`unicode`
:param sub_type:
Either 'enterprise' or 'user'
:type sub_type:
`unicode`
:param now_time:
Optional. The current UTC time is needed in order to construct the expiration time of the JWT claim.
If None, `datetime.utcnow()` will be used.
:type now_time:
`datetime` or None
:return:
The access token for the enterprise or app user.
:rtype:
`unicode`
"""
system_random = random.SystemRandom()
jti_length = system_random.randint(16, 128)
ascii_alphabet = string.ascii_letters + string.digits
ascii_len = len(ascii_alphabet)
jti = ''.join(ascii_alphabet[int(system_random.random() * ascii_len)] for _ in range(jti_length))
if now_time is None:
now_time = datetime.utcnow()
now_plus_30 = now_time + timedelta(seconds=30)
assertion = jwt.encode(
{
'iss': self._client_id,
'sub': sub,
'box_sub_type': sub_type,
'aud': 'https://api.box.com/oauth2/token',
'jti': jti,
'exp': int((now_plus_30 - datetime(1970, 1, 1)).total_seconds()),
},
self._rsa_private_key,
algorithm=self._jwt_algorithm,
headers={
'kid': self._jwt_key_id,
},
)
data = {
'grant_type': self._GRANT_TYPE,
'client_id': self._client_id,
'client_secret': self._client_secret,
'assertion': assertion,
}
if self._box_device_id:
data['box_device_id'] = self._box_device_id
if self._box_device_name:
data['box_device_name'] = self._box_device_name
return self.send_token_request(data, access_token=None, expect_refresh_token=False)[0]
def _auth_with_jwt(self, sub, sub_type):
"""
Auth with JWT.
If authorization fails because the expiration time is out of sync with the Box servers,
retry using the time returned in the error response.
Pass an enterprise ID to get an enterprise token (which can be used to provision/deprovision users),
or a user ID to get a user token.
:param sub:
The enterprise ID or user ID to auth.
:type sub:
`unicode`
:param sub_type:
Either 'enterprise' or 'user'
:type sub_type:
`unicode`
:return:
The access token for the enterprise or app user.
:rtype:
`unicode`
"""
attempt_number = 0
jwt_time = None
while True:
try:
return self._construct_and_send_jwt_auth(sub, sub_type, jwt_time)
except BoxOAuthException as ex:
network_response = ex.network_response
code = network_response.status_code # pylint: disable=maybe-no-member
box_datetime = self._get_date_header(network_response)
if attempt_number >= API.MAX_RETRY_ATTEMPTS:
raise ex
if (code == 429 or code >= 500):
jwt_time = None
elif box_datetime is not None and self._was_exp_claim_rejected_due_to_clock_skew(network_response):
jwt_time = box_datetime
else:
raise ex
time_delay = self._session.get_retry_after_time(attempt_number, network_response.headers.get('Retry-After', None)) # pylint: disable=maybe-no-member
time.sleep(time_delay)
attempt_number += 1
self._logger.debug('Retrying JWT request')
@staticmethod
def _get_date_header(network_response):
"""
Get datetime object for Date header, if the Date header is available.
:param network_response:
The response from the Box API that should include a Date header.
:type network_response:
:class:`Response`
:return:
The datetime parsed from the Date header, or None if the header is absent or if it couldn't be parsed.
:rtype:
`datetime` or `None`
"""
box_date_header = network_response.headers.get('Date', None)
if box_date_header is not None:
try:
return datetime.strptime(box_date_header, '%a, %d %b %Y %H:%M:%S %Z')
except ValueError:
pass
return None
@staticmethod
def _was_exp_claim_rejected_due_to_clock_skew(network_response):
"""
Determine whether the network response indicates that the authorization request was rejected because of
the exp claim. This can happen if the current system time is too different from the Box server time.
Returns True if the status code is 400, the error code is invalid_grant, and the error description indicates
a problem with the exp claim; False, otherwise.
:param network_response:
:type network_response:
:class:`Response`
:rtype:
`bool`
"""
status_code = network_response.status_code
try:
json_response = network_response.json()
except ValueError:
return False
error_code = json_response.get('error', '')
error_description = json_response.get('error_description', '')
return status_code == 400 and error_code == 'invalid_grant' and 'exp' in error_description
def authenticate_user(self, user=None):
"""
Get an access token for a User.
May be one of this application's created App User. Depending on the
configured User Access Level, may also be any other App User or Managed
User in the enterprise.
<https://developer.box.com/en/guides/applications/>
<https://developer.box.com/en/guides/authentication/select/>
:param user:
(optional) The user to authenticate, expressed as a Box User ID or
as a :class:`User` instance.
If not given, then the most recently provided user ID, if
available, will be used.
:type user:
`unicode` or :class:`User`
:raises:
:exc:`ValueError` if no user ID was passed and the object is not
currently configured with one.
:return:
The access token for the user.
:rtype:
`unicode`
"""
sub = self._normalize_user_id(user) or self._user_id
if not sub:
raise ValueError("authenticate_user: Requires the user ID, but it was not provided.")
self._user_id = sub
return self._auth_with_jwt(sub, 'user')
authenticate_app_user = authenticate_user
@classmethod
def _normalize_user_id(cls, user):
"""Get a Box user ID from a selection of supported param types.
:param user:
An object representing the user or user ID.
Currently supported types are `unicode` (which represents the user
ID) and :class:`User`.
If `None`, returns `None`.
:raises: :exc:`TypeError` for unsupported types.
:rtype: `unicode` or `None`
"""
if user is None:
return None
if isinstance(user, User):
return user.object_id
if isinstance(user, string_types):
return text_type(user)
raise TypeError("Got unsupported type {0!r} for user.".format(user.__class__.__name__))
def authenticate_instance(self, enterprise=None):
"""
Get an access token for a Box Developer Edition enterprise.
:param enterprise:
The ID of the Box Developer Edition enterprise.
Optional if the value was already given to `__init__`,
otherwise required.
:type enterprise: `unicode` or `None`
:raises:
:exc:`ValueError` if `None` was passed for the enterprise ID here
and in `__init__`, or if the non-`None` value passed here does not
match the non-`None` value passed to `__init__`.
:return:
The access token for the enterprise which can provision/deprovision app users.
:rtype:
`unicode`
"""
enterprises = [enterprise, self._enterprise_id]
if not any(enterprises):
raise ValueError("authenticate_instance: Requires the enterprise ID, but it was not provided.")
if all(enterprises) and (enterprise != self._enterprise_id):
raise ValueError(
"authenticate_instance: Given enterprise ID {given_enterprise!r}, but {auth} already has ID {existing_enterprise!r}"
.format(auth=self, given_enterprise=enterprise, existing_enterprise=self._enterprise_id)
)
if not self._enterprise_id:
self._enterprise_id = enterprise
self._user_id = None
return self._auth_with_jwt(self._enterprise_id, 'enterprise')
def _refresh(self, access_token):
"""
Base class override.
Instead of refreshing an access token using a refresh token, we just issue a new JWT request.
"""
# pylint:disable=unused-argument
if self._user_id is None:
new_access_token = self.authenticate_instance()
else:
new_access_token = self.authenticate_user()
return new_access_token, None
@classmethod
def _normalize_rsa_private_key(cls, file_sys_path, data, passphrase=None):
if len(list(filter(None, [file_sys_path, data]))) != 1:
raise TypeError("must pass exactly one of either rsa_private_key_file_sys_path or rsa_private_key_data")
if file_sys_path:
with open(file_sys_path, 'rb') as key_file:
data = key_file.read()
if hasattr(data, 'read') and callable(data.read):
data = data.read()
if isinstance(data, text_type):
try:
data = data.encode('ascii')
except UnicodeError:
raise_from(
TypeError("rsa_private_key_data must contain binary data (bytes/str), not a text/unicode string"),
None,
)
if isinstance(data, binary_type):
passphrase = cls._normalize_rsa_private_key_passphrase(passphrase)
return serialization.load_pem_private_key(
data,
password=passphrase,
backend=default_backend(),
)
if isinstance(data, RSAPrivateKey):
return data
raise TypeError(
'rsa_private_key_data must be binary data (bytes/str), '
'a file-like object with a read() method, '
'or an instance of RSAPrivateKey, '
'but got {0!r}'
.format(data.__class__.__name__)
)
@staticmethod
def _normalize_rsa_private_key_passphrase(passphrase):
if isinstance(passphrase, text_type):
try:
return passphrase.encode('ascii')
except UnicodeError:
raise_from(
TypeError("rsa_private_key_passphrase must contain binary data (bytes/str), not a text/unicode string"),
None,
)
if not isinstance(passphrase, (binary_type, NoneType)):
raise TypeError(
"rsa_private_key_passphrase must contain binary data (bytes/str), got {0!r}"
.format(passphrase.__class__.__name__)
)
return passphrase
@classmethod
def from_settings_dictionary(cls, settings_dictionary, **kwargs):
"""
Create an auth instance as defined by the given settings dictionary.
The dictionary should have the structure of the JSON file downloaded from the Box Developer Console.
:param settings_dictionary: Dictionary containing settings for configuring app auth.
:type settings_dictionary: `dict`
:return: Auth instance configured as specified by the config dictionary.
:rtype: :class:`JWTAuth`
"""
if 'boxAppSettings' not in settings_dictionary:
raise ValueError('boxAppSettings not present in configuration')
return cls(
client_id=settings_dictionary['boxAppSettings']['clientID'],
client_secret=settings_dictionary['boxAppSettings']['clientSecret'],
enterprise_id=settings_dictionary.get('enterpriseID', None),
jwt_key_id=settings_dictionary['boxAppSettings']['appAuth'].get('publicKeyID', None),
rsa_private_key_data=settings_dictionary['boxAppSettings']['appAuth'].get('privateKey', None),
rsa_private_key_passphrase=settings_dictionary['boxAppSettings']['appAuth'].get('passphrase', None),
**kwargs
)
@classmethod
def from_settings_file(cls, settings_file_sys_path, **kwargs):
"""
Create an auth instance as defined by a JSON file downloaded from the Box Developer Console.
See https://developer.box.com/en/guides/authentication/jwt/ for more information.
:param settings_file_sys_path: Path to the JSON file containing the configuration.
:type settings_file_sys_path: `unicode`
:return: Auth instance configured as specified by the JSON file.
:rtype: :class:`JWTAuth`
"""
with open(settings_file_sys_path) as config_file:
config_dictionary = json.load(config_file)
return cls.from_settings_dictionary(config_dictionary, **kwargs)
|
# coding: utf-8
from __future__ import absolute_import, unicode_literals
from contextlib import contextmanager
from datetime import datetime, timedelta
import io
from itertools import cycle, product
import json
import random
import string
from cryptography.hazmat.backends import default_backend
from cryptography.hazmat.primitives.asymmetric.rsa import RSAPrivateKey, generate_private_key as generate_rsa_private_key
from cryptography.hazmat.primitives import serialization
from mock import Mock, mock_open, patch, sentinel, call
import pytest
import pytz
import requests
from six import binary_type, string_types, text_type
from boxsdk.auth.jwt_auth import JWTAuth
from boxsdk.exception import BoxOAuthException
from boxsdk.config import API
from boxsdk.object.user import User
@pytest.fixture(params=[16, 32, 128])
def jti_length(request):
return request.param
@pytest.fixture(params=('RS256', 'RS512'))
def jwt_algorithm(request):
return request.param
@pytest.fixture(scope='module')
def jwt_key_id():
return 'jwt_key_id_1'
@pytest.fixture(scope='module')
def rsa_private_key_object():
return generate_rsa_private_key(public_exponent=65537, key_size=4096, backend=default_backend())
@pytest.fixture(params=(None, b'strong_password'))
def rsa_passphrase(request):
return request.param
@pytest.fixture
def rsa_private_key_bytes(rsa_private_key_object, rsa_passphrase):
encryption = serialization.BestAvailableEncryption(rsa_passphrase) if rsa_passphrase else serialization.NoEncryption()
return rsa_private_key_object.private_bytes(
encoding=serialization.Encoding.PEM,
format=serialization.PrivateFormat.PKCS8,
encryption_algorithm=encryption,
)
@pytest.fixture(scope='function')
def successful_token_response(successful_token_mock, successful_token_json_response):
# pylint:disable=redefined-outer-name
response = successful_token_json_response.copy()
del response['refresh_token']
successful_token_mock.json = Mock(return_value=response)
successful_token_mock.ok = True
successful_token_mock.content = json.dumps(response)
successful_token_mock.status_code = 200
return successful_token_mock
@pytest.mark.parametrize(('key_file', 'key_data'), [(None, None), ('fake sys path', 'fake key data')])
@pytest.mark.parametrize('rsa_passphrase', [None])
def test_jwt_auth_init_raises_type_error_unless_exactly_one_of_rsa_private_key_file_or_data_is_given(key_file, key_data, rsa_private_key_bytes):
kwargs = dict(
rsa_private_key_data=rsa_private_key_bytes,
client_id=None,
client_secret=None,
jwt_key_id=None,
enterprise_id=None,
)
JWTAuth(**kwargs)
kwargs.update(rsa_private_key_file_sys_path=key_file, rsa_private_key_data=key_data)
with pytest.raises(TypeError):
JWTAuth(**kwargs)
@pytest.mark.parametrize('key_data', [object(), u'ƒøø'])
@pytest.mark.parametrize('rsa_passphrase', [None])
def test_jwt_auth_init_raises_type_error_if_rsa_private_key_data_has_unexpected_type(key_data, rsa_private_key_bytes):
kwargs = dict(
rsa_private_key_data=rsa_private_key_bytes,
client_id=None,
client_secret=None,
jwt_key_id=None,
enterprise_id=None,
)
JWTAuth(**kwargs)
kwargs.update(rsa_private_key_data=key_data)
with pytest.raises(TypeError):
JWTAuth(**kwargs)
@pytest.mark.parametrize('rsa_private_key_data_type', [io.BytesIO, text_type, binary_type, RSAPrivateKey])
def test_jwt_auth_init_accepts_rsa_private_key_data(rsa_private_key_bytes, rsa_passphrase, rsa_private_key_data_type):
if rsa_private_key_data_type is text_type:
rsa_private_key_data = text_type(rsa_private_key_bytes.decode('ascii'))
elif rsa_private_key_data_type is RSAPrivateKey:
rsa_private_key_data = serialization.load_pem_private_key(
rsa_private_key_bytes,
password=rsa_passphrase,
backend=default_backend(),
)
else:
rsa_private_key_data = rsa_private_key_data_type(rsa_private_key_bytes)
JWTAuth(
rsa_private_key_data=rsa_private_key_data,
rsa_private_key_passphrase=rsa_passphrase,
client_id=None,
client_secret=None,
jwt_key_id=None,
enterprise_id=None,
)
@pytest.fixture(params=[False, True])
def pass_private_key_by_path(request):
"""For jwt_auth_init_mocks, whether to pass the private key via sys_path (True) or pass the data directly (False)."""
return request.param
@pytest.fixture
def jwt_auth_init_mocks(
mock_box_session,
successful_token_response,
jwt_algorithm,
jwt_key_id,
rsa_passphrase,
rsa_private_key_bytes,
pass_private_key_by_path,
):
# pylint:disable=redefined-outer-name
@contextmanager
def _jwt_auth_init_mocks(**kwargs):
assert_authed = kwargs.pop('assert_authed', True)
fake_client_id = 'fake_client_id'
fake_client_secret = 'fake_client_secret'
assertion = Mock()
data = {
'grant_type': JWTAuth._GRANT_TYPE, # pylint:disable=protected-access
'client_id': fake_client_id,
'client_secret': fake_client_secret,
'assertion': assertion,
'box_device_id': '0',
'box_device_name': 'my_awesome_device',
}
mock_box_session.request.return_value = successful_token_response
with patch('boxsdk.auth.jwt_auth.open', mock_open(read_data=rsa_private_key_bytes), create=True) as jwt_auth_open:
with patch('cryptography.hazmat.primitives.serialization.load_pem_private_key') as load_pem_private_key:
oauth = JWTAuth(
client_id=fake_client_id,
client_secret=fake_client_secret,
rsa_private_key_file_sys_path=(sentinel.rsa_path if pass_private_key_by_path else None),
rsa_private_key_data=(None if pass_private_key_by_path else rsa_private_key_bytes),
rsa_private_key_passphrase=rsa_passphrase,
session=mock_box_session,
box_device_name='my_awesome_device',
jwt_algorithm=jwt_algorithm,
jwt_key_id=jwt_key_id,
enterprise_id=kwargs.pop('enterprise_id', None),
**kwargs
)
if pass_private_key_by_path:
jwt_auth_open.assert_called_once_with(sentinel.rsa_path, 'rb')
jwt_auth_open.return_value.read.assert_called_once_with() # pylint:disable=no-member
else:
jwt_auth_open.assert_not_called()
load_pem_private_key.assert_called_once_with(
rsa_private_key_bytes,
password=rsa_passphrase,
backend=default_backend(),
)
yield oauth, assertion, fake_client_id, load_pem_private_key.return_value
if assert_authed:
mock_box_session.request.assert_called_once_with(
'POST',
'{0}/token'.format(API.OAUTH2_API_URL),
data=data,
headers={'content-type': 'application/x-www-form-urlencoded'},
access_token=None,
)
assert oauth.access_token == successful_token_response.json()['access_token']
return _jwt_auth_init_mocks
def test_refresh_authenticates_with_user_if_enterprise_id_and_user_both_passed_to_constructor(jwt_auth_init_and_auth_mocks):
user = 'fake_user_id'
with jwt_auth_init_and_auth_mocks(sub=user, sub_type='user', enterprise_id='fake_enterprise_id', user=user) as oauth:
oauth.refresh(None)
@pytest.mark.parametrize('jwt_auth_method_name', ['authenticate_user', 'authenticate_instance'])
def test_authenticate_raises_value_error_if_sub_was_never_given(jwt_auth_init_mocks, jwt_auth_method_name):
with jwt_auth_init_mocks(assert_authed=False) as params:
auth = params[0]
authenticate_method = getattr(auth, jwt_auth_method_name)
with pytest.raises(ValueError):
authenticate_method()
def test_jwt_auth_constructor_raises_type_error_if_user_is_unsupported_type(jwt_auth_init_mocks):
with pytest.raises(TypeError):
with jwt_auth_init_mocks(user=object()):
assert False
def test_authenticate_user_raises_type_error_if_user_is_unsupported_type(jwt_auth_init_mocks):
with jwt_auth_init_mocks(assert_authed=False) as params:
auth = params[0]
with pytest.raises(TypeError):
auth.authenticate_user(object())
@pytest.mark.parametrize('user_id_for_init', [None, 'fake_user_id_1'])
def test_authenticate_user_saves_user_id_for_future_calls(jwt_auth_init_and_auth_mocks, user_id_for_init, jwt_encode):
def assert_jwt_encode_call_args(user_id):
assert jwt_encode.call_args[0][0]['sub'] == user_id
assert jwt_encode.call_args[0][0]['box_sub_type'] == 'user'
jwt_encode.call_args = None
with jwt_auth_init_and_auth_mocks(sub=None, sub_type=None, assert_authed=False, user=user_id_for_init) as auth:
for new_user_id in ['fake_user_id_2', 'fake_user_id_3']:
auth.authenticate_user(new_user_id)
assert_jwt_encode_call_args(new_user_id)
auth.authenticate_user()
assert_jwt_encode_call_args(new_user_id)
def test_authenticate_instance_raises_value_error_if_different_enterprise_id_is_given(jwt_auth_init_mocks):
with jwt_auth_init_mocks(enterprise_id='fake_enterprise_id_1', assert_authed=False) as params:
auth = params[0]
with pytest.raises(ValueError):
auth.authenticate_instance('fake_enterprise_id_2')
def test_authenticate_instance_saves_enterprise_id_for_future_calls(jwt_auth_init_and_auth_mocks):
enterprise_id = 'fake_enterprise_id'
with jwt_auth_init_and_auth_mocks(sub=enterprise_id, sub_type='enterprise', assert_authed=False) as auth:
auth.authenticate_instance(enterprise_id)
auth.authenticate_instance()
auth.authenticate_instance(enterprise_id)
with pytest.raises(ValueError):
auth.authenticate_instance('fake_enterprise_id_2')
@pytest.yield_fixture
def jwt_encode():
with patch('jwt.encode') as patched_jwt_encode:
yield patched_jwt_encode
@pytest.fixture
def jwt_auth_auth_mocks(jti_length, jwt_algorithm, jwt_key_id, jwt_encode):
@contextmanager
def _jwt_auth_auth_mocks(sub, sub_type, oauth, assertion, client_id, secret, assert_authed=True):
# pylint:disable=redefined-outer-name
with patch('boxsdk.auth.jwt_auth.datetime') as mock_datetime:
with patch('boxsdk.auth.jwt_auth.random.SystemRandom') as mock_system_random:
jwt_encode.return_value = assertion
mock_datetime.utcnow.return_value = datetime(2015, 7, 6, 12, 1, 2)
mock_datetime.return_value = datetime(1970, 1, 1)
now_plus_30 = mock_datetime.utcnow.return_value + timedelta(seconds=30)
exp = int((now_plus_30 - datetime(1970, 1, 1)).total_seconds())
system_random = mock_system_random.return_value
system_random.randint.return_value = jti_length
random_choices = [random.random() for _ in range(jti_length)]
# Use cycle so that we can do auth more than once inside the context manager.
system_random.random.side_effect = cycle(random_choices)
ascii_alphabet = string.ascii_letters + string.digits
ascii_len = len(ascii_alphabet)
jti = ''.join(ascii_alphabet[int(r * ascii_len)] for r in random_choices)
yield oauth
if assert_authed:
system_random.randint.assert_called_once_with(16, 128)
assert len(system_random.random.mock_calls) == jti_length
jwt_encode.assert_called_once_with({
'iss': client_id,
'sub': sub,
'box_sub_type': sub_type,
'aud': 'https://api.box.com/oauth2/token',
'jti': jti,
'exp': exp,
}, secret, algorithm=jwt_algorithm, headers={'kid': jwt_key_id})
return _jwt_auth_auth_mocks
@pytest.fixture
def jwt_auth_init_and_auth_mocks(jwt_auth_init_mocks, jwt_auth_auth_mocks):
@contextmanager
def _jwt_auth_init_and_auth_mocks(sub, sub_type, *jwt_auth_init_mocks_args, **jwt_auth_init_mocks_kwargs):
assert_authed = jwt_auth_init_mocks_kwargs.pop('assert_authed', True)
with jwt_auth_init_mocks(*jwt_auth_init_mocks_args, assert_authed=assert_authed, **jwt_auth_init_mocks_kwargs) as params:
with jwt_auth_auth_mocks(sub, sub_type, *params, assert_authed=assert_authed) as oauth:
yield oauth
return _jwt_auth_init_and_auth_mocks
@pytest.mark.parametrize(
('user', 'pass_in_init'),
list(product([str('fake_user_id'), text_type('fake_user_id'), User(None, 'fake_user_id')], [False, True])),
)
def test_authenticate_user_sends_post_request_with_correct_params(jwt_auth_init_and_auth_mocks, user, pass_in_init):
# pylint:disable=redefined-outer-name
if isinstance(user, User):
user_id = user.object_id
elif isinstance(user, string_types):
user_id = user
else:
raise NotImplementedError
init_kwargs = {}
authenticate_params = []
if pass_in_init:
init_kwargs['user'] = user
else:
authenticate_params.append(user)
with jwt_auth_init_and_auth_mocks(user_id, 'user', **init_kwargs) as oauth:
oauth.authenticate_user(*authenticate_params)
@pytest.mark.parametrize(('pass_in_init', 'pass_in_auth'), [(True, False), (False, True), (True, True)])
def test_authenticate_instance_sends_post_request_with_correct_params(jwt_auth_init_and_auth_mocks, pass_in_init, pass_in_auth):
# pylint:disable=redefined-outer-name
enterprise_id = 'fake_enterprise_id'
init_kwargs = {}
auth_params = []
if pass_in_init:
init_kwargs['enterprise_id'] = enterprise_id
if pass_in_auth:
auth_params.append(enterprise_id)
with jwt_auth_init_and_auth_mocks(enterprise_id, 'enterprise', **init_kwargs) as oauth:
oauth.authenticate_instance(*auth_params)
def test_refresh_app_user_sends_post_request_with_correct_params(jwt_auth_init_and_auth_mocks):
# pylint:disable=redefined-outer-name
fake_user_id = 'fake_user_id'
with jwt_auth_init_and_auth_mocks(fake_user_id, 'user', user=fake_user_id) as oauth:
oauth.refresh(None)
def test_refresh_instance_sends_post_request_with_correct_params(jwt_auth_init_and_auth_mocks):
# pylint:disable=redefined-outer-name
enterprise_id = 'fake_enterprise_id'
with jwt_auth_init_and_auth_mocks(enterprise_id, 'enterprise', enterprise_id=enterprise_id) as oauth:
oauth.refresh(None)
@pytest.fixture()
def jwt_subclass_that_just_stores_params():
class StoreParamJWTAuth(JWTAuth):
def __init__(self, **kwargs):
self.kwargs = kwargs
super(StoreParamJWTAuth, self).__init__(**kwargs)
return StoreParamJWTAuth
@pytest.fixture
def fake_client_id():
return 'fake_client_id'
@pytest.fixture
def fake_client_secret():
return 'fake_client_secret'
@pytest.fixture
def fake_enterprise_id():
return 'fake_enterprise_id'
@pytest.fixture
def app_config_json_content(
fake_client_id,
fake_client_secret,
fake_enterprise_id,
jwt_key_id,
rsa_private_key_bytes,
rsa_passphrase,
):
template = r"""
{{
"boxAppSettings": {{
"clientID": "{client_id}",
"clientSecret": "{client_secret}",
"appAuth": {{
"publicKeyID": "{jwt_key_id}",
"privateKey": "{private_key}",
"passphrase": {passphrase}
}}
}},
"enterpriseID": {enterprise_id}
}}"""
return template.format(
client_id=fake_client_id,
client_secret=fake_client_secret,
jwt_key_id=jwt_key_id,
private_key=rsa_private_key_bytes.replace(b"\n", b"\\n").decode(),
passphrase=json.dumps(rsa_passphrase and rsa_passphrase.decode()),
enterprise_id=json.dumps(fake_enterprise_id),
)
@pytest.fixture()
def assert_jwt_kwargs_expected(
fake_client_id,
fake_client_secret,
fake_enterprise_id,
jwt_key_id,
rsa_private_key_bytes,
rsa_passphrase,
):
def _assert_jwt_kwargs_expected(jwt_auth):
assert jwt_auth.kwargs['client_id'] == fake_client_id
assert jwt_auth.kwargs['client_secret'] == fake_client_secret
assert jwt_auth.kwargs['enterprise_id'] == fake_enterprise_id
assert jwt_auth.kwargs['jwt_key_id'] == jwt_key_id
assert jwt_auth.kwargs['rsa_private_key_data'] == rsa_private_key_bytes.decode()
assert jwt_auth.kwargs['rsa_private_key_passphrase'] == (rsa_passphrase and rsa_passphrase.decode())
return _assert_jwt_kwargs_expected
def test_from_config_file(
jwt_subclass_that_just_stores_params,
app_config_json_content,
assert_jwt_kwargs_expected,
):
# pylint:disable=redefined-outer-name
with patch('boxsdk.auth.jwt_auth.open', mock_open(read_data=app_config_json_content), create=True):
jwt_auth_from_config_file = jwt_subclass_that_just_stores_params.from_settings_file('fake_config_file_sys_path')
assert_jwt_kwargs_expected(jwt_auth_from_config_file)
def test_from_settings_dictionary(
jwt_subclass_that_just_stores_params,
app_config_json_content,
assert_jwt_kwargs_expected,
):
jwt_auth_from_dictionary = jwt_subclass_that_just_stores_params.from_settings_dictionary(json.loads(app_config_json_content))
assert_jwt_kwargs_expected(jwt_auth_from_dictionary)
@pytest.fixture
def expect_auth_retry(status_code, error_description, include_date_header, error_code):
return status_code == 400 and 'exp' in error_description and include_date_header and error_code == 'invalid_grant'
@pytest.fixture
def box_datetime():
return datetime.now(tz=pytz.utc) - timedelta(100)
@pytest.fixture
def unsuccessful_jwt_response(box_datetime, status_code, error_description, include_date_header, error_code):
headers = {'Date': box_datetime.strftime('%a, %d %b %Y %H:%M:%S %Z')} if include_date_header else {}
unsuccessful_response = Mock(requests.Response(), headers=headers)
unsuccessful_response.json.return_value = {'error_description': error_description, 'error': error_code}
unsuccessful_response.status_code = status_code
unsuccessful_response.ok = False
return unsuccessful_response
@pytest.mark.parametrize('jwt_algorithm', ('RS512',))
@pytest.mark.parametrize('rsa_passphrase', (None,))
@pytest.mark.parametrize('pass_private_key_by_path', (False,))
@pytest.mark.parametrize('status_code', (400, 401))
@pytest.mark.parametrize('error_description', ('invalid box_sub_type claim', 'invalid kid', "check the 'exp' claim"))
@pytest.mark.parametrize('error_code', ('invalid_grant', 'bad_request'))
@pytest.mark.parametrize('include_date_header', (True, False))
def test_auth_retry_for_invalid_exp_claim(
jwt_auth_init_mocks,
expect_auth_retry,
unsuccessful_jwt_response,
box_datetime,
):
# pylint:disable=redefined-outer-name
enterprise_id = 'fake_enterprise_id'
with jwt_auth_init_mocks(assert_authed=False) as params:
auth = params[0]
with patch.object(auth, '_construct_and_send_jwt_auth') as mock_send_jwt:
mock_send_jwt.side_effect = [BoxOAuthException(400, network_response=unsuccessful_jwt_response), 'jwt_token']
if not expect_auth_retry:
with pytest.raises(BoxOAuthException):
auth.authenticate_instance(enterprise_id)
else:
auth.authenticate_instance(enterprise_id)
expected_calls = [call(enterprise_id, 'enterprise', None)]
if expect_auth_retry:
expected_calls.append(call(enterprise_id, 'enterprise', box_datetime.replace(microsecond=0, tzinfo=None)))
assert len(mock_send_jwt.mock_calls) == len(expected_calls)
mock_send_jwt.assert_has_calls(expected_calls)
@pytest.mark.parametrize('jwt_algorithm', ('RS512',))
@pytest.mark.parametrize('rsa_passphrase', (None,))
@pytest.mark.parametrize('pass_private_key_by_path', (False,))
@pytest.mark.parametrize('status_code', (429,))
@pytest.mark.parametrize('error_description', ('Request rate limit exceeded',))
@pytest.mark.parametrize('error_code', ('rate_limit_exceeded',))
@pytest.mark.parametrize('include_date_header', (False,))
def test_auth_retry_for_rate_limit_error(
jwt_auth_init_mocks,
unsuccessful_jwt_response,
):
# pylint:disable=redefined-outer-name
enterprise_id = 'fake_enterprise_id'
with jwt_auth_init_mocks(assert_authed=False) as params:
auth = params[0]
with patch.object(auth, '_construct_and_send_jwt_auth') as mock_send_jwt:
side_effect = []
expected_calls = []
# Retries multiple times, but less than max retries. Then succeeds when it gets a token.
for _ in range(API.MAX_RETRY_ATTEMPTS - 2):
side_effect.append(BoxOAuthException(429, network_response=unsuccessful_jwt_response))
expected_calls.append(call(enterprise_id, 'enterprise', None))
side_effect.append('jwt_token')
expected_calls.append(call(enterprise_id, 'enterprise', None))
mock_send_jwt.side_effect = side_effect
auth.authenticate_instance(enterprise_id)
assert len(mock_send_jwt.mock_calls) == len(expected_calls)
mock_send_jwt.assert_has_calls(expected_calls)
@pytest.mark.parametrize('jwt_algorithm', ('RS512',))
@pytest.mark.parametrize('rsa_passphrase', (None,))
@pytest.mark.parametrize('pass_private_key_by_path', (False,))
@pytest.mark.parametrize('status_code', (429,))
@pytest.mark.parametrize('error_description', ('Request rate limit exceeded',))
@pytest.mark.parametrize('error_code', ('rate_limit_exceeded',))
@pytest.mark.parametrize('include_date_header', (False,))
def test_auth_max_retries_for_rate_limit_error(
jwt_auth_init_mocks,
unsuccessful_jwt_response,
):
# pylint:disable=redefined-outer-name
enterprise_id = 'fake_enterprise_id'
with jwt_auth_init_mocks(assert_authed=False) as params:
auth = params[0]
with patch.object(auth, '_construct_and_send_jwt_auth') as mock_send_jwt:
side_effect = []
expected_calls = []
# Retries max number of times, then throws the error
for _ in range(API.MAX_RETRY_ATTEMPTS + 1):
side_effect.append(BoxOAuthException(429, network_response=unsuccessful_jwt_response))
expected_calls.append(call(enterprise_id, 'enterprise', None))
mock_send_jwt.side_effect = side_effect
with pytest.raises(BoxOAuthException) as error:
auth.authenticate_instance(enterprise_id)
assert error.value.status == 429
assert len(mock_send_jwt.mock_calls) == len(expected_calls)
mock_send_jwt.assert_has_calls(expected_calls)
@pytest.mark.parametrize('jwt_algorithm', ('RS512',))
@pytest.mark.parametrize('rsa_passphrase', (None,))
@pytest.mark.parametrize('pass_private_key_by_path', (False,))
@pytest.mark.parametrize('status_code', (500,))
@pytest.mark.parametrize('error_description', ('Internal Server Error',))
@pytest.mark.parametrize('error_code', ('internal_server_error',))
@pytest.mark.parametrize('include_date_header', (False,))
def test_auth_retry_for_internal_server_error(
jwt_auth_init_mocks,
unsuccessful_jwt_response,
):
# pylint:disable=redefined-outer-name
enterprise_id = 'fake_enterprise_id'
with jwt_auth_init_mocks(assert_authed=False) as params:
auth = params[0]
with patch.object(auth, '_construct_and_send_jwt_auth') as mock_send_jwt:
side_effect = []
expected_calls = []
# Retries multiple times, but less than max retries. Then succeeds when it gets a token.
for _ in range(API.MAX_RETRY_ATTEMPTS - 2):
side_effect.append(BoxOAuthException(500, network_response=unsuccessful_jwt_response))
expected_calls.append(call(enterprise_id, 'enterprise', None))
side_effect.append('jwt_token')
expected_calls.append(call(enterprise_id, 'enterprise', None))
mock_send_jwt.side_effect = side_effect
auth.authenticate_instance(enterprise_id)
assert len(mock_send_jwt.mock_calls) == len(expected_calls)
mock_send_jwt.assert_has_calls(expected_calls)
@pytest.mark.parametrize('jwt_algorithm', ('RS512',))
@pytest.mark.parametrize('rsa_passphrase', (None,))
@pytest.mark.parametrize('pass_private_key_by_path', (False,))
@pytest.mark.parametrize('status_code', (500,))
@pytest.mark.parametrize('error_description', ('Internal Server Error',))
@pytest.mark.parametrize('error_code', ('internal_server_error',))
@pytest.mark.parametrize('include_date_header', (False,))
def test_auth_max_retries_for_internal_server_error(
jwt_auth_init_mocks,
unsuccessful_jwt_response,
):
# pylint:disable=redefined-outer-name
enterprise_id = 'fake_enterprise_id'
with jwt_auth_init_mocks(assert_authed=False) as params:
auth = params[0]
with patch.object(auth, '_construct_and_send_jwt_auth') as mock_send_jwt:
side_effect = []
expected_calls = []
# Retries max number of times, then throws the error
for _ in range(API.MAX_RETRY_ATTEMPTS + 1):
side_effect.append(BoxOAuthException(500, network_response=unsuccessful_jwt_response))
expected_calls.append(call(enterprise_id, 'enterprise', None))
mock_send_jwt.side_effect = side_effect
with pytest.raises(BoxOAuthException) as error:
auth.authenticate_instance(enterprise_id)
assert error.value.status == 500
assert len(mock_send_jwt.mock_calls) == len(expected_calls)
mock_send_jwt.assert_has_calls(expected_calls)
|
|
test/unit/auth/test_jwt_auth.py
|
|
from typing import List, Iterator, Union
from collections import Counter
from KINCluster.core.item import Item
from KINCluster.lib.tokenizer import tokenizer
from KINCluster import settings
import numpy as np
from gensim.models import Doc2Vec
from gensim.models.doc2vec import TaggedDocument
from scipy.cluster import hierarchy as hcluster
class Cluster:
def __init__(self, **kwargs):
"""hyperparameters
:alpha = learning rate
:min_alph = minimum learning reate
:window = max value of vector
:size = vector size
:tokenizer = lambda document: str -> list or words: List[str]
"""
def getattrs(module):
keys = [k for k in dir(module) if not k.startswith('__')]
return {key: getattr(module, key) for key in keys}
if 'settings' not in kwargs:
self.settings = getattrs(settings)
else:
self.settings = kwargs['settings']
alpha = kwargs.get("alpha", self.settings['LEARNING_RATE'])
min_alpha = kwargs.get("min_alpha", self.settings['LEARNING_RATE_MIN'])
window = kwargs.get("window", self.settings['WINDOW'])
size = kwargs.get("size", self.settings['SIZE'])
self.trate = kwargs.get("trate", self.settings['TRANING_RATE'])
self.epoch = kwargs.get("epoch", self.settings['EPOCH'])
self.thresh = kwargs.get("thresh", self.settings['THRESHOLD'])
self.tokenizer = tokenizer.s[kwargs.get("tokenizer", self.settings['TOKENIZER'])]
self.model = Doc2Vec(alpha=alpha, min_alpha=min_alpha, window=window, size=size)
self._items = []
self._counters = []
self._vectors = []
self._clusters = []
self._dumps = []
def put_item(self, item: Item):
self._items.append(item)
def __vocabs(self) -> Iterator[TaggedDocument]:
for idx, item in enumerate(self._items):
token = self.tokenizer(repr(item))
self._counters.append(Counter(token))
yield TaggedDocument(token, ['line_%s' % idx])
def __documents(self) -> Iterator[TaggedDocument]:
for idx, item in enumerate(self._items):
yield TaggedDocument(self.tokenizer(str(item)), ['line_%s' % idx])
def __cluster(self, method, metric, criterion) -> np.ndarray:
return hcluster.fclusterdata(self._vectors, self.thresh,
method=method, metric=metric, criterion=criterion)
def cluster(self):
# COMMENT: Top keyword 만 잘라서 분류해보기
"""cluster process
: build vocab, using repr of item
: train items, using str of item
: get _vectors and _clusters
"""
self.model.build_vocab(self.__vocabs())
documents = list(self.__documents())
for _ in range(self.epoch):
self.model.train(documents)
self.model.alpha *= self.trate
self.model.min_alpha = self.model.alpha
self._vectors = np.array(self.model.docvecs)
self._clusters = self.__cluster(self.settings['METHOD'],
self.settings['METRIC'],
self.settings['CRITERION'])
dumps = {c: [] for c in self.unique}
for cluster, item, vector, counter in zip(self._clusters,
self._items,
self._vectors,
self._counters):
dumps[cluster].append((item, vector, counter))
self._dumps = list(dumps.values())
def similar(self, pos, neg=[], top=10):
return self.model.most_similar(positive=pos, negative=neg, topn=top)
@property
def items(self) -> List[Item]:
return self._items
@property
def vocab(self) -> List[str]:
return self.model.vocab
@property
def vocab_count(self) -> List[Counter]:
return self._counters
@property
def dumps(self) -> List[List[Union[Item, np.ndarray]]]:
return self._dumps
@property
def vectors(self) -> np.ndarray:
return self._vectors
@property
def unique(self) -> np.ndarray:
return np.unique(self._clusters)
@property
def clusters(self) -> np.ndarray:
return self._clusters
@property
def distribution(self) -> np.ndarray:
return Counter(self._clusters)
def __len__(self):
return len(self._clusters)
|
# -*- coding: utf-8 -*-
"""
tests.cluster
---------------
Test cluster of KINCluster
:author: MaybeS(maytryark@gmail.com)
"""
import pytest
from KINCluster.core.extractor import Extractor, extractable
from KINCluster.core.cluster import Cluster
from KINCluster.core.pipeline import Pipeline
from KINCluster.core.item import Item
from KINCluster.lib.tokenizer import tokenize, stemize
import codecs
test_text = ['2016헌나1.txt', '2014헌나1.txt']
test_keyword = ['헌법판결문', '헌법판결문']
class Pipeline(Pipeline):
def capture_item(self):
for text, keyword in zip(test_text, test_keyword):
with codecs.open('tests/data/' + text, 'r', 'utf-8') as f:
content = f.read()
yield Item(title=text,content=content,keyword=keyword,date='')
def test_extractor1():
cluster = Cluster(epoch=32, tokenizer="tokenize")
pipeline = Pipeline()
for item in pipeline.capture_item():
cluster.put_item(item)
cluster.cluster()
extractor = Extractor(cluster)
for idx, dump in enumerate(cluster.dumps):
items, vectors, counter = map(list, zip(*dump))
assert set(['items', 'vectors', 'counter', 'center', 'keywords']) == set(extractable.s.keys())
extracted = extractor.dump(idx)
assert isinstance(extracted, Item)
assert isinstance(extracted.keywords, list)
assert 32 == len(extracted.keywords)
|
|
|
KINCluster/core/cluster.py
|
# Copyright 2015 Cloudera Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from impala.error import Error as ImpylaError # noqa
from impala.error import HiveServer2Error as HS2Error # noqa
import impala.dbapi as impyla # noqa
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# Copyright 2015 Cloudera Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import pandas as pd
import pytest
import ibis
import ibis.expr.datatypes as dt
import ibis.expr.types as ir
from ibis.compat import unittest
from ibis.common import IbisTypeError
from ibis.impala.client import pandas_to_ibis_schema
from ibis.impala.tests.common import ImpalaE2E
functional_alltypes_with_nulls = pd.DataFrame({
'bigint_col': np.int64([0, 10, 20, 30, 40, 50, 60, 70, 80, 90]),
'bool_col': np.bool_([True, False, True, False, True, None,
True, False, True, False]),
'date_string_col': ['11/01/10', None, '11/01/10', '11/01/10',
'11/01/10', '11/01/10', '11/01/10', '11/01/10',
'11/01/10', '11/01/10'],
'double_col': np.float64([0.0, 10.1, None, 30.299999999999997,
40.399999999999999, 50.5, 60.599999999999994,
70.700000000000003, 80.799999999999997,
90.899999999999991]),
'float_col': np.float32([None, 1.1000000238418579, 2.2000000476837158,
3.2999999523162842, 4.4000000953674316, 5.5,
6.5999999046325684, 7.6999998092651367,
8.8000001907348633,
9.8999996185302734]),
'int_col': np.int32([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
'month': [11, 11, 11, 11, 2, 11, 11, 11, 11, 11],
'smallint_col': np.int16([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
'string_col': ['0', '1', None, '3', '4', '5', '6', '7', '8', '9'],
'timestamp_col': [pd.Timestamp('2010-11-01 00:00:00'),
None,
pd.Timestamp('2010-11-01 00:02:00.100000'),
pd.Timestamp('2010-11-01 00:03:00.300000'),
pd.Timestamp('2010-11-01 00:04:00.600000'),
pd.Timestamp('2010-11-01 00:05:00.100000'),
pd.Timestamp('2010-11-01 00:06:00.150000'),
pd.Timestamp('2010-11-01 00:07:00.210000'),
pd.Timestamp('2010-11-01 00:08:00.280000'),
pd.Timestamp('2010-11-01 00:09:00.360000')],
'tinyint_col': np.int8([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
'year': [2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010]})
class TestPandasTypeInterop(unittest.TestCase):
def test_series_to_ibis_literal(self):
values = [1, 2, 3, 4]
s = pd.Series(values)
expr = ir.as_value_expr(s)
expected = ir.sequence(list(s))
assert expr.equals(expected)
class TestPandasSchemaInference(unittest.TestCase):
def test_dtype_bool(self):
df = pd.DataFrame({'col': [True, False, False]})
inferred = pandas_to_ibis_schema(df)
expected = ibis.schema([('col', 'boolean')])
assert inferred == expected
def test_dtype_int8(self):
df = pd.DataFrame({'col': np.int8([-3, 9, 17])})
inferred = pandas_to_ibis_schema(df)
expected = ibis.schema([('col', 'int8')])
assert inferred == expected
def test_dtype_int16(self):
df = pd.DataFrame({'col': np.int16([-5, 0, 12])})
inferred = pandas_to_ibis_schema(df)
expected = ibis.schema([('col', 'int16')])
assert inferred == expected
def test_dtype_int32(self):
df = pd.DataFrame({'col': np.int32([-12, 3, 25000])})
inferred = pandas_to_ibis_schema(df)
expected = ibis.schema([('col', 'int32')])
assert inferred == expected
def test_dtype_int64(self):
df = pd.DataFrame({'col': np.int64([102, 67228734, -0])})
inferred = pandas_to_ibis_schema(df)
expected = ibis.schema([('col', 'int64')])
assert inferred == expected
def test_dtype_float32(self):
df = pd.DataFrame({'col': np.float32([45e-3, -0.4, 99.])})
inferred = pandas_to_ibis_schema(df)
expected = ibis.schema([('col', 'float')])
assert inferred == expected
def test_dtype_float64(self):
df = pd.DataFrame({'col': np.float64([-3e43, 43., 10000000.])})
inferred = pandas_to_ibis_schema(df)
expected = ibis.schema([('col', 'double')])
assert inferred == expected
def test_dtype_uint8(self):
df = pd.DataFrame({'col': np.uint8([3, 0, 16])})
inferred = pandas_to_ibis_schema(df)
expected = ibis.schema([('col', 'int16')])
assert inferred == expected
def test_dtype_uint16(self):
df = pd.DataFrame({'col': np.uint16([5569, 1, 33])})
inferred = pandas_to_ibis_schema(df)
expected = ibis.schema([('col', 'int32')])
assert inferred == expected
def test_dtype_uint32(self):
df = pd.DataFrame({'col': np.uint32([100, 0, 6])})
inferred = pandas_to_ibis_schema(df)
expected = ibis.schema([('col', 'int64')])
assert inferred == expected
def test_dtype_uint64(self):
df = pd.DataFrame({'col': np.uint64([666, 2, 3])})
with self.assertRaises(IbisTypeError):
inferred = pandas_to_ibis_schema(df) # noqa
def test_dtype_datetime64(self):
df = pd.DataFrame({
'col': [pd.Timestamp('2010-11-01 00:01:00'),
pd.Timestamp('2010-11-01 00:02:00.1000'),
pd.Timestamp('2010-11-01 00:03:00.300000')]})
inferred = pandas_to_ibis_schema(df)
expected = ibis.schema([('col', 'timestamp')])
assert inferred == expected
def test_dtype_timedelta64(self):
df = pd.DataFrame({
'col': [pd.Timedelta('1 days'),
pd.Timedelta('-1 days 2 min 3us'),
pd.Timedelta('-2 days +23:57:59.999997')]})
inferred = pandas_to_ibis_schema(df)
expected = ibis.schema([('col', 'int64')])
assert inferred == expected
def test_dtype_string(self):
df = pd.DataFrame({'col': ['foo', 'bar', 'hello']})
inferred = pandas_to_ibis_schema(df)
expected = ibis.schema([('col', 'string')])
assert inferred == expected
def test_dtype_categorical(self):
df = pd.DataFrame({'col': ['a', 'b', 'c', 'a']}, dtype='category')
inferred = pandas_to_ibis_schema(df)
expected = ibis.schema([('col', dt.Category(3))])
assert inferred == expected
class TestPandasRoundTrip(ImpalaE2E, unittest.TestCase):
def test_round_trip(self):
pytest.skip('fails')
df1 = self.alltypes.execute()
df2 = self.con.pandas(df1, 'bamboo', database=self.tmp_db).execute()
assert (df1.columns == df2.columns).all()
assert (df1.dtypes == df2.dtypes).all()
assert (df1 == df2).all().all()
def test_round_trip_non_int_missing_data(self):
pytest.skip('WM: hangs -- will investigate later')
df1 = functional_alltypes_with_nulls
table = self.con.pandas(df1, 'fawn', database=self.tmp_db)
df2 = table.execute()
assert (df1.columns == df2.columns).all()
assert (df1.dtypes == df2.dtypes).all()
# bool/int cols should be exact
assert (df1.bool_col == df2.bool_col).all()
assert (df1.tinyint_col == df2.tinyint_col).all()
assert (df1.smallint_col == df2.smallint_col).all()
assert (df1.int_col == df2.int_col).all()
assert (df1.bigint_col == df2.bigint_col).all()
assert (df1.month == df2.month).all()
assert (df1.year == df2.year).all()
# string cols should be equal everywhere except for the NULLs
assert ((df1.string_col == df2.string_col) ==
[1, 1, 0, 1, 1, 1, 1, 1, 1, 1]).all()
assert ((df1.date_string_col == df2.date_string_col) ==
[1, 0, 1, 1, 1, 1, 1, 1, 1, 1]).all()
# float cols within tolerance, and NULLs should be False
assert ((df1.double_col - df2.double_col < 1e-9) ==
[1, 1, 0, 1, 1, 1, 1, 1, 1, 1]).all()
assert ((df1.float_col - df2.float_col < 1e-9) ==
[0, 1, 1, 1, 1, 1, 1, 1, 1, 1]).all()
def test_round_trip_missing_type_promotion(self):
pytest.skip('unfinished')
# prepare Impala table with missing ints
# TODO: switch to self.con.raw_sql once #412 is fixed
create_query = ('CREATE TABLE {0}.missing_ints '
' (tinyint_col TINYINT, bigint_col BIGINT) '
'STORED AS PARQUET'.format(self.tmp_db))
insert_query = ('INSERT INTO {0}.missing_ints '
'VALUES (NULL, 3), (-5, NULL), (19, 444444)'.format(
self.tmp_db))
self.con.con.cursor.execute(create_query)
self.con.con.cursor.execute(insert_query)
table = self.con.table('missing_ints', database=self.tmp_db)
df = table.execute() # noqa # REMOVE LATER
# WHAT NOW?
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ibis/impala/tests/test_pandas_interop.py
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"""Component to integrate the Home Assistant cloud."""
from hass_nabucasa import Cloud
import voluptuous as vol
from homeassistant.components.alexa import const as alexa_const
from homeassistant.components.google_assistant import const as ga_c
from homeassistant.const import (
CONF_DESCRIPTION,
CONF_MODE,
CONF_NAME,
CONF_REGION,
EVENT_HOMEASSISTANT_STOP,
)
from homeassistant.core import callback
from homeassistant.exceptions import HomeAssistantError
from homeassistant.helpers import config_validation as cv, entityfilter
from homeassistant.loader import bind_hass
from homeassistant.util.aiohttp import MockRequest
from . import account_link, http_api
from .client import CloudClient
from .const import (
CONF_ACCOUNT_LINK_URL,
CONF_ACME_DIRECTORY_SERVER,
CONF_ALEXA,
CONF_ALEXA_ACCESS_TOKEN_URL,
CONF_ALIASES,
CONF_CLOUDHOOK_CREATE_URL,
CONF_COGNITO_CLIENT_ID,
CONF_ENTITY_CONFIG,
CONF_FILTER,
CONF_GOOGLE_ACTIONS,
CONF_GOOGLE_ACTIONS_REPORT_STATE_URL,
CONF_RELAYER,
CONF_REMOTE_API_URL,
CONF_SUBSCRIPTION_INFO_URL,
CONF_USER_POOL_ID,
CONF_VOICE_API_URL,
DOMAIN,
MODE_DEV,
MODE_PROD,
)
from .prefs import CloudPreferences
DEFAULT_MODE = MODE_PROD
SERVICE_REMOTE_CONNECT = "remote_connect"
SERVICE_REMOTE_DISCONNECT = "remote_disconnect"
ALEXA_ENTITY_SCHEMA = vol.Schema(
{
vol.Optional(CONF_DESCRIPTION): cv.string,
vol.Optional(alexa_const.CONF_DISPLAY_CATEGORIES): cv.string,
vol.Optional(CONF_NAME): cv.string,
}
)
GOOGLE_ENTITY_SCHEMA = vol.Schema(
{
vol.Optional(CONF_NAME): cv.string,
vol.Optional(CONF_ALIASES): vol.All(cv.ensure_list, [cv.string]),
vol.Optional(ga_c.CONF_ROOM_HINT): cv.string,
}
)
ASSISTANT_SCHEMA = vol.Schema(
{vol.Optional(CONF_FILTER, default=dict): entityfilter.FILTER_SCHEMA}
)
ALEXA_SCHEMA = ASSISTANT_SCHEMA.extend(
{vol.Optional(CONF_ENTITY_CONFIG): {cv.entity_id: ALEXA_ENTITY_SCHEMA}}
)
GACTIONS_SCHEMA = ASSISTANT_SCHEMA.extend(
{vol.Optional(CONF_ENTITY_CONFIG): {cv.entity_id: GOOGLE_ENTITY_SCHEMA}}
)
# pylint: disable=no-value-for-parameter
CONFIG_SCHEMA = vol.Schema(
{
DOMAIN: vol.Schema(
{
vol.Optional(CONF_MODE, default=DEFAULT_MODE): vol.In(
[MODE_DEV, MODE_PROD]
),
vol.Optional(CONF_COGNITO_CLIENT_ID): str,
vol.Optional(CONF_USER_POOL_ID): str,
vol.Optional(CONF_REGION): str,
vol.Optional(CONF_RELAYER): str,
vol.Optional(CONF_SUBSCRIPTION_INFO_URL): vol.Url(),
vol.Optional(CONF_CLOUDHOOK_CREATE_URL): vol.Url(),
vol.Optional(CONF_REMOTE_API_URL): vol.Url(),
vol.Optional(CONF_ACME_DIRECTORY_SERVER): vol.Url(),
vol.Optional(CONF_ALEXA): ALEXA_SCHEMA,
vol.Optional(CONF_GOOGLE_ACTIONS): GACTIONS_SCHEMA,
vol.Optional(CONF_ALEXA_ACCESS_TOKEN_URL): vol.Url(),
vol.Optional(CONF_GOOGLE_ACTIONS_REPORT_STATE_URL): vol.Url(),
vol.Optional(CONF_ACCOUNT_LINK_URL): vol.Url(),
vol.Optional(CONF_VOICE_API_URL): vol.Url(),
}
)
},
extra=vol.ALLOW_EXTRA,
)
class CloudNotAvailable(HomeAssistantError):
"""Raised when an action requires the cloud but it's not available."""
@bind_hass
@callback
def async_is_logged_in(hass) -> bool:
"""Test if user is logged in."""
return DOMAIN in hass.data and hass.data[DOMAIN].is_logged_in
@bind_hass
@callback
def async_active_subscription(hass) -> bool:
"""Test if user has an active subscription."""
return async_is_logged_in(hass) and not hass.data[DOMAIN].subscription_expired
@bind_hass
async def async_create_cloudhook(hass, webhook_id: str) -> str:
"""Create a cloudhook."""
if not async_is_logged_in(hass):
raise CloudNotAvailable
hook = await hass.data[DOMAIN].cloudhooks.async_create(webhook_id, True)
return hook["cloudhook_url"]
@bind_hass
async def async_delete_cloudhook(hass, webhook_id: str) -> None:
"""Delete a cloudhook."""
if DOMAIN not in hass.data:
raise CloudNotAvailable
await hass.data[DOMAIN].cloudhooks.async_delete(webhook_id)
@bind_hass
@callback
def async_remote_ui_url(hass) -> str:
"""Get the remote UI URL."""
if not async_is_logged_in(hass):
raise CloudNotAvailable
if not hass.data[DOMAIN].client.prefs.remote_enabled:
raise CloudNotAvailable
if not hass.data[DOMAIN].remote.instance_domain:
raise CloudNotAvailable
return f"https://{hass.data[DOMAIN].remote.instance_domain}"
def is_cloudhook_request(request):
"""Test if a request came from a cloudhook.
Async friendly.
"""
return isinstance(request, MockRequest)
async def async_setup(hass, config):
"""Initialize the Home Assistant cloud."""
# Process configs
if DOMAIN in config:
kwargs = dict(config[DOMAIN])
else:
kwargs = {CONF_MODE: DEFAULT_MODE}
# Alexa/Google custom config
alexa_conf = kwargs.pop(CONF_ALEXA, None) or ALEXA_SCHEMA({})
google_conf = kwargs.pop(CONF_GOOGLE_ACTIONS, None) or GACTIONS_SCHEMA({})
# Cloud settings
prefs = CloudPreferences(hass)
await prefs.async_initialize()
# Initialize Cloud
websession = hass.helpers.aiohttp_client.async_get_clientsession()
client = CloudClient(hass, prefs, websession, alexa_conf, google_conf)
cloud = hass.data[DOMAIN] = Cloud(client, **kwargs)
async def _shutdown(event):
"""Shutdown event."""
await cloud.stop()
hass.bus.async_listen_once(EVENT_HOMEASSISTANT_STOP, _shutdown)
async def _service_handler(service):
"""Handle service for cloud."""
if service.service == SERVICE_REMOTE_CONNECT:
await cloud.remote.connect()
await prefs.async_update(remote_enabled=True)
elif service.service == SERVICE_REMOTE_DISCONNECT:
await cloud.remote.disconnect()
await prefs.async_update(remote_enabled=False)
hass.helpers.service.async_register_admin_service(
DOMAIN, SERVICE_REMOTE_CONNECT, _service_handler
)
hass.helpers.service.async_register_admin_service(
DOMAIN, SERVICE_REMOTE_DISCONNECT, _service_handler
)
loaded = False
async def _on_connect():
"""Discover RemoteUI binary sensor."""
nonlocal loaded
# Prevent multiple discovery
if loaded:
return
loaded = True
await hass.helpers.discovery.async_load_platform(
"binary_sensor", DOMAIN, {}, config
)
await hass.helpers.discovery.async_load_platform("stt", DOMAIN, {}, config)
await hass.helpers.discovery.async_load_platform("tts", DOMAIN, {}, config)
cloud.iot.register_on_connect(_on_connect)
await cloud.start()
await http_api.async_setup(hass)
account_link.async_setup(hass)
return True
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"""The tests for the mochad light platform."""
import unittest.mock as mock
import pytest
from homeassistant.components import light
from homeassistant.components.mochad import light as mochad
from homeassistant.setup import async_setup_component
@pytest.fixture(autouse=True)
def pymochad_mock():
"""Mock pymochad."""
with mock.patch("homeassistant.components.mochad.light.device") as device:
yield device
@pytest.fixture
def light_mock(hass, brightness):
"""Mock light."""
controller_mock = mock.MagicMock()
dev_dict = {"address": "a1", "name": "fake_light", "brightness_levels": brightness}
return mochad.MochadLight(hass, controller_mock, dev_dict)
async def test_setup_adds_proper_devices(hass):
"""Test if setup adds devices."""
good_config = {
"mochad": {},
"light": {
"platform": "mochad",
"devices": [{"name": "Light1", "address": "a1"}],
},
}
assert await async_setup_component(hass, light.DOMAIN, good_config)
@pytest.mark.parametrize(
"brightness,expected", [(32, "on"), (256, "xdim 255"), (64, "xdim 63")]
)
async def test_turn_on_with_no_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on()
light_mock.light.send_cmd.assert_called_once_with(expected)
@pytest.mark.parametrize(
"brightness,expected",
[
(32, [mock.call("on"), mock.call("dim 25")]),
(256, [mock.call("xdim 45")]),
(64, [mock.call("xdim 11")]),
],
)
async def test_turn_on_with_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on(brightness=45)
light_mock.light.send_cmd.assert_has_calls(expected)
@pytest.mark.parametrize("brightness", [32])
async def test_turn_off(light_mock):
"""Test turn_off."""
light_mock.turn_off()
light_mock.light.send_cmd.assert_called_once_with("off")
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tests/components/mochad/test_light.py
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homeassistant/components/cloud/__init__.py
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"""Support for hydrological data from the Fed. Office for the Environment."""
from datetime import timedelta
import logging
from swisshydrodata import SwissHydroData
import voluptuous as vol
from homeassistant.components.sensor import PLATFORM_SCHEMA, SensorEntity
from homeassistant.const import ATTR_ATTRIBUTION, CONF_MONITORED_CONDITIONS
import homeassistant.helpers.config_validation as cv
from homeassistant.util import Throttle
_LOGGER = logging.getLogger(__name__)
ATTRIBUTION = "Data provided by the Swiss Federal Office for the Environment FOEN"
ATTR_DELTA_24H = "delta-24h"
ATTR_MAX_1H = "max-1h"
ATTR_MAX_24H = "max-24h"
ATTR_MEAN_1H = "mean-1h"
ATTR_MEAN_24H = "mean-24h"
ATTR_MIN_1H = "min-1h"
ATTR_MIN_24H = "min-24h"
ATTR_PREVIOUS_24H = "previous-24h"
ATTR_STATION = "station"
ATTR_STATION_UPDATE = "station_update"
ATTR_WATER_BODY = "water_body"
ATTR_WATER_BODY_TYPE = "water_body_type"
CONF_STATION = "station"
MIN_TIME_BETWEEN_UPDATES = timedelta(seconds=60)
SENSOR_DISCHARGE = "discharge"
SENSOR_LEVEL = "level"
SENSOR_TEMPERATURE = "temperature"
CONDITIONS = {
SENSOR_DISCHARGE: "mdi:waves",
SENSOR_LEVEL: "mdi:zodiac-aquarius",
SENSOR_TEMPERATURE: "mdi:oil-temperature",
}
CONDITION_DETAILS = [
ATTR_DELTA_24H,
ATTR_MAX_1H,
ATTR_MAX_24H,
ATTR_MEAN_1H,
ATTR_MEAN_24H,
ATTR_MIN_1H,
ATTR_MIN_24H,
ATTR_PREVIOUS_24H,
]
PLATFORM_SCHEMA = PLATFORM_SCHEMA.extend(
{
vol.Required(CONF_STATION): vol.Coerce(int),
vol.Optional(CONF_MONITORED_CONDITIONS, default=[SENSOR_TEMPERATURE]): vol.All(
cv.ensure_list, [vol.In(CONDITIONS)]
),
}
)
def setup_platform(hass, config, add_entities, discovery_info=None):
"""Set up the Swiss hydrological sensor."""
station = config.get(CONF_STATION)
monitored_conditions = config.get(CONF_MONITORED_CONDITIONS)
hydro_data = HydrologicalData(station)
hydro_data.update()
if hydro_data.data is None:
_LOGGER.error("The station doesn't exists: %s", station)
return
entities = []
for condition in monitored_conditions:
entities.append(SwissHydrologicalDataSensor(hydro_data, station, condition))
add_entities(entities, True)
class SwissHydrologicalDataSensor(SensorEntity):
"""Implementation of a Swiss hydrological sensor."""
def __init__(self, hydro_data, station, condition):
"""Initialize the Swiss hydrological sensor."""
self.hydro_data = hydro_data
self._condition = condition
self._data = self._state = self._unit_of_measurement = None
self._icon = CONDITIONS[condition]
self._station = station
@property
def name(self):
"""Return the name of the sensor."""
return "{} {}".format(self._data["water-body-name"], self._condition)
@property
def unique_id(self) -> str:
"""Return a unique, friendly identifier for this entity."""
return f"{self._station}_{self._condition}"
@property
def unit_of_measurement(self):
"""Return the unit of measurement of this entity, if any."""
if self._state is not None:
return self.hydro_data.data["parameters"][self._condition]["unit"]
return None
@property
def state(self):
"""Return the state of the sensor."""
if isinstance(self._state, (int, float)):
return round(self._state, 2)
return None
@property
def extra_state_attributes(self):
"""Return the device state attributes."""
attrs = {}
if not self._data:
attrs[ATTR_ATTRIBUTION] = ATTRIBUTION
return attrs
attrs[ATTR_WATER_BODY_TYPE] = self._data["water-body-type"]
attrs[ATTR_STATION] = self._data["name"]
attrs[ATTR_STATION_UPDATE] = self._data["parameters"][self._condition][
"datetime"
]
attrs[ATTR_ATTRIBUTION] = ATTRIBUTION
for entry in CONDITION_DETAILS:
attrs[entry.replace("-", "_")] = self._data["parameters"][self._condition][
entry
]
return attrs
@property
def icon(self):
"""Icon to use in the frontend."""
return self._icon
def update(self):
"""Get the latest data and update the state."""
self.hydro_data.update()
self._data = self.hydro_data.data
if self._data is None:
self._state = None
else:
self._state = self._data["parameters"][self._condition]["value"]
class HydrologicalData:
"""The Class for handling the data retrieval."""
def __init__(self, station):
"""Initialize the data object."""
self.station = station
self.data = None
@Throttle(MIN_TIME_BETWEEN_UPDATES)
def update(self):
"""Get the latest data."""
shd = SwissHydroData()
self.data = shd.get_station(self.station)
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"""The tests for the mochad light platform."""
import unittest.mock as mock
import pytest
from homeassistant.components import light
from homeassistant.components.mochad import light as mochad
from homeassistant.setup import async_setup_component
@pytest.fixture(autouse=True)
def pymochad_mock():
"""Mock pymochad."""
with mock.patch("homeassistant.components.mochad.light.device") as device:
yield device
@pytest.fixture
def light_mock(hass, brightness):
"""Mock light."""
controller_mock = mock.MagicMock()
dev_dict = {"address": "a1", "name": "fake_light", "brightness_levels": brightness}
return mochad.MochadLight(hass, controller_mock, dev_dict)
async def test_setup_adds_proper_devices(hass):
"""Test if setup adds devices."""
good_config = {
"mochad": {},
"light": {
"platform": "mochad",
"devices": [{"name": "Light1", "address": "a1"}],
},
}
assert await async_setup_component(hass, light.DOMAIN, good_config)
@pytest.mark.parametrize(
"brightness,expected", [(32, "on"), (256, "xdim 255"), (64, "xdim 63")]
)
async def test_turn_on_with_no_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on()
light_mock.light.send_cmd.assert_called_once_with(expected)
@pytest.mark.parametrize(
"brightness,expected",
[
(32, [mock.call("on"), mock.call("dim 25")]),
(256, [mock.call("xdim 45")]),
(64, [mock.call("xdim 11")]),
],
)
async def test_turn_on_with_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on(brightness=45)
light_mock.light.send_cmd.assert_has_calls(expected)
@pytest.mark.parametrize("brightness", [32])
async def test_turn_off(light_mock):
"""Test turn_off."""
light_mock.turn_off()
light_mock.light.send_cmd.assert_called_once_with("off")
|
|
tests/components/mochad/test_light.py
|
homeassistant/components/swiss_hydrological_data/sensor.py
|
"""Support for MQTT vacuums."""
import functools
import voluptuous as vol
from homeassistant.components.vacuum import DOMAIN
from homeassistant.helpers.reload import async_setup_reload_service
from .. import DOMAIN as MQTT_DOMAIN, PLATFORMS
from ..mixins import async_setup_entry_helper
from .schema import CONF_SCHEMA, LEGACY, MQTT_VACUUM_SCHEMA, STATE
from .schema_legacy import PLATFORM_SCHEMA_LEGACY, async_setup_entity_legacy
from .schema_state import PLATFORM_SCHEMA_STATE, async_setup_entity_state
def validate_mqtt_vacuum(value):
"""Validate MQTT vacuum schema."""
schemas = {LEGACY: PLATFORM_SCHEMA_LEGACY, STATE: PLATFORM_SCHEMA_STATE}
return schemas[value[CONF_SCHEMA]](value)
PLATFORM_SCHEMA = vol.All(
MQTT_VACUUM_SCHEMA.extend({}, extra=vol.ALLOW_EXTRA), validate_mqtt_vacuum
)
async def async_setup_platform(hass, config, async_add_entities, discovery_info=None):
"""Set up MQTT vacuum through configuration.yaml."""
await async_setup_reload_service(hass, MQTT_DOMAIN, PLATFORMS)
await _async_setup_entity(async_add_entities, config)
async def async_setup_entry(hass, config_entry, async_add_entities):
"""Set up MQTT vacuum dynamically through MQTT discovery."""
setup = functools.partial(
_async_setup_entity, async_add_entities, config_entry=config_entry
)
await async_setup_entry_helper(hass, DOMAIN, setup, PLATFORM_SCHEMA)
async def _async_setup_entity(
async_add_entities, config, config_entry=None, discovery_data=None
):
"""Set up the MQTT vacuum."""
setup_entity = {LEGACY: async_setup_entity_legacy, STATE: async_setup_entity_state}
await setup_entity[config[CONF_SCHEMA]](
config, async_add_entities, config_entry, discovery_data
)
|
"""The tests for the mochad light platform."""
import unittest.mock as mock
import pytest
from homeassistant.components import light
from homeassistant.components.mochad import light as mochad
from homeassistant.setup import async_setup_component
@pytest.fixture(autouse=True)
def pymochad_mock():
"""Mock pymochad."""
with mock.patch("homeassistant.components.mochad.light.device") as device:
yield device
@pytest.fixture
def light_mock(hass, brightness):
"""Mock light."""
controller_mock = mock.MagicMock()
dev_dict = {"address": "a1", "name": "fake_light", "brightness_levels": brightness}
return mochad.MochadLight(hass, controller_mock, dev_dict)
async def test_setup_adds_proper_devices(hass):
"""Test if setup adds devices."""
good_config = {
"mochad": {},
"light": {
"platform": "mochad",
"devices": [{"name": "Light1", "address": "a1"}],
},
}
assert await async_setup_component(hass, light.DOMAIN, good_config)
@pytest.mark.parametrize(
"brightness,expected", [(32, "on"), (256, "xdim 255"), (64, "xdim 63")]
)
async def test_turn_on_with_no_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on()
light_mock.light.send_cmd.assert_called_once_with(expected)
@pytest.mark.parametrize(
"brightness,expected",
[
(32, [mock.call("on"), mock.call("dim 25")]),
(256, [mock.call("xdim 45")]),
(64, [mock.call("xdim 11")]),
],
)
async def test_turn_on_with_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on(brightness=45)
light_mock.light.send_cmd.assert_has_calls(expected)
@pytest.mark.parametrize("brightness", [32])
async def test_turn_off(light_mock):
"""Test turn_off."""
light_mock.turn_off()
light_mock.light.send_cmd.assert_called_once_with("off")
|
|
tests/components/mochad/test_light.py
|
homeassistant/components/mqtt/vacuum/__init__.py
|
"""Reproduce an Input select state."""
from __future__ import annotations
import asyncio
import logging
from types import MappingProxyType
from typing import Any, Iterable
from homeassistant.const import ATTR_ENTITY_ID
from homeassistant.core import Context, HomeAssistant, State
from . import (
ATTR_OPTION,
ATTR_OPTIONS,
DOMAIN,
SERVICE_SELECT_OPTION,
SERVICE_SET_OPTIONS,
)
ATTR_GROUP = [ATTR_OPTION, ATTR_OPTIONS]
_LOGGER = logging.getLogger(__name__)
async def _async_reproduce_state(
hass: HomeAssistant,
state: State,
*,
context: Context | None = None,
reproduce_options: dict[str, Any] | None = None,
) -> None:
"""Reproduce a single state."""
cur_state = hass.states.get(state.entity_id)
# Return if we can't find entity
if cur_state is None:
_LOGGER.warning("Unable to find entity %s", state.entity_id)
return
# Return if we are already at the right state.
if cur_state.state == state.state and all(
check_attr_equal(cur_state.attributes, state.attributes, attr)
for attr in ATTR_GROUP
):
return
# Set service data
service_data = {ATTR_ENTITY_ID: state.entity_id}
# If options are specified, call SERVICE_SET_OPTIONS
if ATTR_OPTIONS in state.attributes:
service = SERVICE_SET_OPTIONS
service_data[ATTR_OPTIONS] = state.attributes[ATTR_OPTIONS]
await hass.services.async_call(
DOMAIN, service, service_data, context=context, blocking=True
)
# Remove ATTR_OPTIONS from service_data so we can reuse service_data in next call
del service_data[ATTR_OPTIONS]
# Call SERVICE_SELECT_OPTION
service = SERVICE_SELECT_OPTION
service_data[ATTR_OPTION] = state.state
await hass.services.async_call(
DOMAIN, service, service_data, context=context, blocking=True
)
async def async_reproduce_states(
hass: HomeAssistant,
states: Iterable[State],
*,
context: Context | None = None,
reproduce_options: dict[str, Any] | None = None,
) -> None:
"""Reproduce Input select states."""
# Reproduce states in parallel.
await asyncio.gather(
*(
_async_reproduce_state(
hass, state, context=context, reproduce_options=reproduce_options
)
for state in states
)
)
def check_attr_equal(
attr1: MappingProxyType, attr2: MappingProxyType, attr_str: str
) -> bool:
"""Return true if the given attributes are equal."""
return attr1.get(attr_str) == attr2.get(attr_str)
|
"""The tests for the mochad light platform."""
import unittest.mock as mock
import pytest
from homeassistant.components import light
from homeassistant.components.mochad import light as mochad
from homeassistant.setup import async_setup_component
@pytest.fixture(autouse=True)
def pymochad_mock():
"""Mock pymochad."""
with mock.patch("homeassistant.components.mochad.light.device") as device:
yield device
@pytest.fixture
def light_mock(hass, brightness):
"""Mock light."""
controller_mock = mock.MagicMock()
dev_dict = {"address": "a1", "name": "fake_light", "brightness_levels": brightness}
return mochad.MochadLight(hass, controller_mock, dev_dict)
async def test_setup_adds_proper_devices(hass):
"""Test if setup adds devices."""
good_config = {
"mochad": {},
"light": {
"platform": "mochad",
"devices": [{"name": "Light1", "address": "a1"}],
},
}
assert await async_setup_component(hass, light.DOMAIN, good_config)
@pytest.mark.parametrize(
"brightness,expected", [(32, "on"), (256, "xdim 255"), (64, "xdim 63")]
)
async def test_turn_on_with_no_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on()
light_mock.light.send_cmd.assert_called_once_with(expected)
@pytest.mark.parametrize(
"brightness,expected",
[
(32, [mock.call("on"), mock.call("dim 25")]),
(256, [mock.call("xdim 45")]),
(64, [mock.call("xdim 11")]),
],
)
async def test_turn_on_with_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on(brightness=45)
light_mock.light.send_cmd.assert_has_calls(expected)
@pytest.mark.parametrize("brightness", [32])
async def test_turn_off(light_mock):
"""Test turn_off."""
light_mock.turn_off()
light_mock.light.send_cmd.assert_called_once_with("off")
|
|
tests/components/mochad/test_light.py
|
homeassistant/components/input_select/reproduce_state.py
|
"""Support for OpenTherm Gateway devices."""
import asyncio
from datetime import date, datetime
import logging
import pyotgw
import pyotgw.vars as gw_vars
import voluptuous as vol
from homeassistant.components.binary_sensor import DOMAIN as COMP_BINARY_SENSOR
from homeassistant.components.climate import DOMAIN as COMP_CLIMATE
from homeassistant.components.sensor import DOMAIN as COMP_SENSOR
from homeassistant.config_entries import SOURCE_IMPORT
from homeassistant.const import (
ATTR_DATE,
ATTR_ID,
ATTR_MODE,
ATTR_TEMPERATURE,
ATTR_TIME,
CONF_DEVICE,
CONF_ID,
CONF_NAME,
EVENT_HOMEASSISTANT_STOP,
PRECISION_HALVES,
PRECISION_TENTHS,
PRECISION_WHOLE,
)
import homeassistant.helpers.config_validation as cv
from homeassistant.helpers.device_registry import (
async_get_registry as async_get_dev_reg,
)
from homeassistant.helpers.dispatcher import async_dispatcher_send
from .const import (
ATTR_CH_OVRD,
ATTR_DHW_OVRD,
ATTR_GW_ID,
ATTR_LEVEL,
CONF_CLIMATE,
CONF_FLOOR_TEMP,
CONF_PRECISION,
CONF_READ_PRECISION,
CONF_SET_PRECISION,
DATA_GATEWAYS,
DATA_OPENTHERM_GW,
DOMAIN,
SERVICE_RESET_GATEWAY,
SERVICE_SET_CH_OVRD,
SERVICE_SET_CLOCK,
SERVICE_SET_CONTROL_SETPOINT,
SERVICE_SET_GPIO_MODE,
SERVICE_SET_HOT_WATER_OVRD,
SERVICE_SET_HOT_WATER_SETPOINT,
SERVICE_SET_LED_MODE,
SERVICE_SET_MAX_MOD,
SERVICE_SET_OAT,
SERVICE_SET_SB_TEMP,
)
_LOGGER = logging.getLogger(__name__)
CLIMATE_SCHEMA = vol.Schema(
{
vol.Optional(CONF_PRECISION): vol.In(
[PRECISION_TENTHS, PRECISION_HALVES, PRECISION_WHOLE]
),
vol.Optional(CONF_FLOOR_TEMP, default=False): cv.boolean,
}
)
CONFIG_SCHEMA = vol.Schema(
{
DOMAIN: cv.schema_with_slug_keys(
{
vol.Required(CONF_DEVICE): cv.string,
vol.Optional(CONF_CLIMATE, default={}): CLIMATE_SCHEMA,
vol.Optional(CONF_NAME): cv.string,
}
)
},
extra=vol.ALLOW_EXTRA,
)
async def options_updated(hass, entry):
"""Handle options update."""
gateway = hass.data[DATA_OPENTHERM_GW][DATA_GATEWAYS][entry.data[CONF_ID]]
async_dispatcher_send(hass, gateway.options_update_signal, entry)
async def async_setup_entry(hass, config_entry):
"""Set up the OpenTherm Gateway component."""
if DATA_OPENTHERM_GW not in hass.data:
hass.data[DATA_OPENTHERM_GW] = {DATA_GATEWAYS: {}}
gateway = OpenThermGatewayDevice(hass, config_entry)
hass.data[DATA_OPENTHERM_GW][DATA_GATEWAYS][config_entry.data[CONF_ID]] = gateway
if config_entry.options.get(CONF_PRECISION):
migrate_options = dict(config_entry.options)
migrate_options.update(
{
CONF_READ_PRECISION: config_entry.options[CONF_PRECISION],
CONF_SET_PRECISION: config_entry.options[CONF_PRECISION],
}
)
del migrate_options[CONF_PRECISION]
hass.config_entries.async_update_entry(config_entry, options=migrate_options)
config_entry.add_update_listener(options_updated)
# Schedule directly on the loop to avoid blocking HA startup.
hass.loop.create_task(gateway.connect_and_subscribe())
for comp in [COMP_BINARY_SENSOR, COMP_CLIMATE, COMP_SENSOR]:
hass.async_create_task(
hass.config_entries.async_forward_entry_setup(config_entry, comp)
)
register_services(hass)
return True
async def async_setup(hass, config):
"""Set up the OpenTherm Gateway component."""
if not hass.config_entries.async_entries(DOMAIN) and DOMAIN in config:
conf = config[DOMAIN]
for device_id, device_config in conf.items():
device_config[CONF_ID] = device_id
hass.async_create_task(
hass.config_entries.flow.async_init(
DOMAIN, context={"source": SOURCE_IMPORT}, data=device_config
)
)
return True
def register_services(hass):
"""Register services for the component."""
service_reset_schema = vol.Schema(
{
vol.Required(ATTR_GW_ID): vol.All(
cv.string, vol.In(hass.data[DATA_OPENTHERM_GW][DATA_GATEWAYS])
)
}
)
service_set_central_heating_ovrd_schema = vol.Schema(
{
vol.Required(ATTR_GW_ID): vol.All(
cv.string, vol.In(hass.data[DATA_OPENTHERM_GW][DATA_GATEWAYS])
),
vol.Required(ATTR_CH_OVRD): cv.boolean,
}
)
service_set_clock_schema = vol.Schema(
{
vol.Required(ATTR_GW_ID): vol.All(
cv.string, vol.In(hass.data[DATA_OPENTHERM_GW][DATA_GATEWAYS])
),
vol.Optional(ATTR_DATE, default=date.today()): cv.date,
vol.Optional(ATTR_TIME, default=datetime.now().time()): cv.time,
}
)
service_set_control_setpoint_schema = vol.Schema(
{
vol.Required(ATTR_GW_ID): vol.All(
cv.string, vol.In(hass.data[DATA_OPENTHERM_GW][DATA_GATEWAYS])
),
vol.Required(ATTR_TEMPERATURE): vol.All(
vol.Coerce(float), vol.Range(min=0, max=90)
),
}
)
service_set_hot_water_setpoint_schema = service_set_control_setpoint_schema
service_set_hot_water_ovrd_schema = vol.Schema(
{
vol.Required(ATTR_GW_ID): vol.All(
cv.string, vol.In(hass.data[DATA_OPENTHERM_GW][DATA_GATEWAYS])
),
vol.Required(ATTR_DHW_OVRD): vol.Any(
vol.Equal("A"), vol.All(vol.Coerce(int), vol.Range(min=0, max=1))
),
}
)
service_set_gpio_mode_schema = vol.Schema(
vol.Any(
vol.Schema(
{
vol.Required(ATTR_GW_ID): vol.All(
cv.string, vol.In(hass.data[DATA_OPENTHERM_GW][DATA_GATEWAYS])
),
vol.Required(ATTR_ID): vol.Equal("A"),
vol.Required(ATTR_MODE): vol.All(
vol.Coerce(int), vol.Range(min=0, max=6)
),
}
),
vol.Schema(
{
vol.Required(ATTR_GW_ID): vol.All(
cv.string, vol.In(hass.data[DATA_OPENTHERM_GW][DATA_GATEWAYS])
),
vol.Required(ATTR_ID): vol.Equal("B"),
vol.Required(ATTR_MODE): vol.All(
vol.Coerce(int), vol.Range(min=0, max=7)
),
}
),
)
)
service_set_led_mode_schema = vol.Schema(
{
vol.Required(ATTR_GW_ID): vol.All(
cv.string, vol.In(hass.data[DATA_OPENTHERM_GW][DATA_GATEWAYS])
),
vol.Required(ATTR_ID): vol.In("ABCDEF"),
vol.Required(ATTR_MODE): vol.In("RXTBOFHWCEMP"),
}
)
service_set_max_mod_schema = vol.Schema(
{
vol.Required(ATTR_GW_ID): vol.All(
cv.string, vol.In(hass.data[DATA_OPENTHERM_GW][DATA_GATEWAYS])
),
vol.Required(ATTR_LEVEL): vol.All(
vol.Coerce(int), vol.Range(min=-1, max=100)
),
}
)
service_set_oat_schema = vol.Schema(
{
vol.Required(ATTR_GW_ID): vol.All(
cv.string, vol.In(hass.data[DATA_OPENTHERM_GW][DATA_GATEWAYS])
),
vol.Required(ATTR_TEMPERATURE): vol.All(
vol.Coerce(float), vol.Range(min=-40, max=99)
),
}
)
service_set_sb_temp_schema = vol.Schema(
{
vol.Required(ATTR_GW_ID): vol.All(
cv.string, vol.In(hass.data[DATA_OPENTHERM_GW][DATA_GATEWAYS])
),
vol.Required(ATTR_TEMPERATURE): vol.All(
vol.Coerce(float), vol.Range(min=0, max=30)
),
}
)
async def reset_gateway(call):
"""Reset the OpenTherm Gateway."""
gw_dev = hass.data[DATA_OPENTHERM_GW][DATA_GATEWAYS][call.data[ATTR_GW_ID]]
mode_rst = gw_vars.OTGW_MODE_RESET
status = await gw_dev.gateway.set_mode(mode_rst)
gw_dev.status = status
async_dispatcher_send(hass, gw_dev.update_signal, gw_dev.status)
hass.services.async_register(
DOMAIN, SERVICE_RESET_GATEWAY, reset_gateway, service_reset_schema
)
async def set_ch_ovrd(call):
"""Set the central heating override on the OpenTherm Gateway."""
gw_dev = hass.data[DATA_OPENTHERM_GW][DATA_GATEWAYS][call.data[ATTR_GW_ID]]
await gw_dev.gateway.set_ch_enable_bit(1 if call.data[ATTR_CH_OVRD] else 0)
hass.services.async_register(
DOMAIN,
SERVICE_SET_CH_OVRD,
set_ch_ovrd,
service_set_central_heating_ovrd_schema,
)
async def set_control_setpoint(call):
"""Set the control setpoint on the OpenTherm Gateway."""
gw_dev = hass.data[DATA_OPENTHERM_GW][DATA_GATEWAYS][call.data[ATTR_GW_ID]]
gw_var = gw_vars.DATA_CONTROL_SETPOINT
value = await gw_dev.gateway.set_control_setpoint(call.data[ATTR_TEMPERATURE])
gw_dev.status.update({gw_var: value})
async_dispatcher_send(hass, gw_dev.update_signal, gw_dev.status)
hass.services.async_register(
DOMAIN,
SERVICE_SET_CONTROL_SETPOINT,
set_control_setpoint,
service_set_control_setpoint_schema,
)
async def set_dhw_ovrd(call):
"""Set the domestic hot water override on the OpenTherm Gateway."""
gw_dev = hass.data[DATA_OPENTHERM_GW][DATA_GATEWAYS][call.data[ATTR_GW_ID]]
gw_var = gw_vars.OTGW_DHW_OVRD
value = await gw_dev.gateway.set_hot_water_ovrd(call.data[ATTR_DHW_OVRD])
gw_dev.status.update({gw_var: value})
async_dispatcher_send(hass, gw_dev.update_signal, gw_dev.status)
hass.services.async_register(
DOMAIN,
SERVICE_SET_HOT_WATER_OVRD,
set_dhw_ovrd,
service_set_hot_water_ovrd_schema,
)
async def set_dhw_setpoint(call):
"""Set the domestic hot water setpoint on the OpenTherm Gateway."""
gw_dev = hass.data[DATA_OPENTHERM_GW][DATA_GATEWAYS][call.data[ATTR_GW_ID]]
gw_var = gw_vars.DATA_DHW_SETPOINT
value = await gw_dev.gateway.set_dhw_setpoint(call.data[ATTR_TEMPERATURE])
gw_dev.status.update({gw_var: value})
async_dispatcher_send(hass, gw_dev.update_signal, gw_dev.status)
hass.services.async_register(
DOMAIN,
SERVICE_SET_HOT_WATER_SETPOINT,
set_dhw_setpoint,
service_set_hot_water_setpoint_schema,
)
async def set_device_clock(call):
"""Set the clock on the OpenTherm Gateway."""
gw_dev = hass.data[DATA_OPENTHERM_GW][DATA_GATEWAYS][call.data[ATTR_GW_ID]]
attr_date = call.data[ATTR_DATE]
attr_time = call.data[ATTR_TIME]
await gw_dev.gateway.set_clock(datetime.combine(attr_date, attr_time))
hass.services.async_register(
DOMAIN, SERVICE_SET_CLOCK, set_device_clock, service_set_clock_schema
)
async def set_gpio_mode(call):
"""Set the OpenTherm Gateway GPIO modes."""
gw_dev = hass.data[DATA_OPENTHERM_GW][DATA_GATEWAYS][call.data[ATTR_GW_ID]]
gpio_id = call.data[ATTR_ID]
gpio_mode = call.data[ATTR_MODE]
mode = await gw_dev.gateway.set_gpio_mode(gpio_id, gpio_mode)
gpio_var = getattr(gw_vars, f"OTGW_GPIO_{gpio_id}")
gw_dev.status.update({gpio_var: mode})
async_dispatcher_send(hass, gw_dev.update_signal, gw_dev.status)
hass.services.async_register(
DOMAIN, SERVICE_SET_GPIO_MODE, set_gpio_mode, service_set_gpio_mode_schema
)
async def set_led_mode(call):
"""Set the OpenTherm Gateway LED modes."""
gw_dev = hass.data[DATA_OPENTHERM_GW][DATA_GATEWAYS][call.data[ATTR_GW_ID]]
led_id = call.data[ATTR_ID]
led_mode = call.data[ATTR_MODE]
mode = await gw_dev.gateway.set_led_mode(led_id, led_mode)
led_var = getattr(gw_vars, f"OTGW_LED_{led_id}")
gw_dev.status.update({led_var: mode})
async_dispatcher_send(hass, gw_dev.update_signal, gw_dev.status)
hass.services.async_register(
DOMAIN, SERVICE_SET_LED_MODE, set_led_mode, service_set_led_mode_schema
)
async def set_max_mod(call):
"""Set the max modulation level."""
gw_dev = hass.data[DATA_OPENTHERM_GW][DATA_GATEWAYS][call.data[ATTR_GW_ID]]
gw_var = gw_vars.DATA_SLAVE_MAX_RELATIVE_MOD
level = call.data[ATTR_LEVEL]
if level == -1:
# Backend only clears setting on non-numeric values.
level = "-"
value = await gw_dev.gateway.set_max_relative_mod(level)
gw_dev.status.update({gw_var: value})
async_dispatcher_send(hass, gw_dev.update_signal, gw_dev.status)
hass.services.async_register(
DOMAIN, SERVICE_SET_MAX_MOD, set_max_mod, service_set_max_mod_schema
)
async def set_outside_temp(call):
"""Provide the outside temperature to the OpenTherm Gateway."""
gw_dev = hass.data[DATA_OPENTHERM_GW][DATA_GATEWAYS][call.data[ATTR_GW_ID]]
gw_var = gw_vars.DATA_OUTSIDE_TEMP
value = await gw_dev.gateway.set_outside_temp(call.data[ATTR_TEMPERATURE])
gw_dev.status.update({gw_var: value})
async_dispatcher_send(hass, gw_dev.update_signal, gw_dev.status)
hass.services.async_register(
DOMAIN, SERVICE_SET_OAT, set_outside_temp, service_set_oat_schema
)
async def set_setback_temp(call):
"""Set the OpenTherm Gateway SetBack temperature."""
gw_dev = hass.data[DATA_OPENTHERM_GW][DATA_GATEWAYS][call.data[ATTR_GW_ID]]
gw_var = gw_vars.OTGW_SB_TEMP
value = await gw_dev.gateway.set_setback_temp(call.data[ATTR_TEMPERATURE])
gw_dev.status.update({gw_var: value})
async_dispatcher_send(hass, gw_dev.update_signal, gw_dev.status)
hass.services.async_register(
DOMAIN, SERVICE_SET_SB_TEMP, set_setback_temp, service_set_sb_temp_schema
)
async def async_unload_entry(hass, entry):
"""Cleanup and disconnect from gateway."""
await asyncio.gather(
hass.config_entries.async_forward_entry_unload(entry, COMP_BINARY_SENSOR),
hass.config_entries.async_forward_entry_unload(entry, COMP_CLIMATE),
hass.config_entries.async_forward_entry_unload(entry, COMP_SENSOR),
)
gateway = hass.data[DATA_OPENTHERM_GW][DATA_GATEWAYS][entry.data[CONF_ID]]
await gateway.cleanup()
return True
class OpenThermGatewayDevice:
"""OpenTherm Gateway device class."""
def __init__(self, hass, config_entry):
"""Initialize the OpenTherm Gateway."""
self.hass = hass
self.device_path = config_entry.data[CONF_DEVICE]
self.gw_id = config_entry.data[CONF_ID]
self.name = config_entry.data[CONF_NAME]
self.climate_config = config_entry.options
self.config_entry_id = config_entry.entry_id
self.status = {}
self.update_signal = f"{DATA_OPENTHERM_GW}_{self.gw_id}_update"
self.options_update_signal = f"{DATA_OPENTHERM_GW}_{self.gw_id}_options_update"
self.gateway = pyotgw.pyotgw()
self.gw_version = None
async def cleanup(self, event=None):
"""Reset overrides on the gateway."""
await self.gateway.set_control_setpoint(0)
await self.gateway.set_max_relative_mod("-")
await self.gateway.disconnect()
async def connect_and_subscribe(self):
"""Connect to serial device and subscribe report handler."""
self.status = await self.gateway.connect(self.hass.loop, self.device_path)
version_string = self.status[gw_vars.OTGW].get(gw_vars.OTGW_ABOUT)
self.gw_version = version_string[18:] if version_string else None
_LOGGER.debug(
"Connected to OpenTherm Gateway %s at %s", self.gw_version, self.device_path
)
dev_reg = await async_get_dev_reg(self.hass)
gw_dev = dev_reg.async_get_or_create(
config_entry_id=self.config_entry_id,
identifiers={(DOMAIN, self.gw_id)},
name=self.name,
manufacturer="Schelte Bron",
model="OpenTherm Gateway",
sw_version=self.gw_version,
)
if gw_dev.sw_version != self.gw_version:
dev_reg.async_update_device(gw_dev.id, sw_version=self.gw_version)
self.hass.bus.async_listen(EVENT_HOMEASSISTANT_STOP, self.cleanup)
async def handle_report(status):
"""Handle reports from the OpenTherm Gateway."""
_LOGGER.debug("Received report: %s", status)
self.status = status
async_dispatcher_send(self.hass, self.update_signal, status)
self.gateway.subscribe(handle_report)
|
"""The tests for the mochad light platform."""
import unittest.mock as mock
import pytest
from homeassistant.components import light
from homeassistant.components.mochad import light as mochad
from homeassistant.setup import async_setup_component
@pytest.fixture(autouse=True)
def pymochad_mock():
"""Mock pymochad."""
with mock.patch("homeassistant.components.mochad.light.device") as device:
yield device
@pytest.fixture
def light_mock(hass, brightness):
"""Mock light."""
controller_mock = mock.MagicMock()
dev_dict = {"address": "a1", "name": "fake_light", "brightness_levels": brightness}
return mochad.MochadLight(hass, controller_mock, dev_dict)
async def test_setup_adds_proper_devices(hass):
"""Test if setup adds devices."""
good_config = {
"mochad": {},
"light": {
"platform": "mochad",
"devices": [{"name": "Light1", "address": "a1"}],
},
}
assert await async_setup_component(hass, light.DOMAIN, good_config)
@pytest.mark.parametrize(
"brightness,expected", [(32, "on"), (256, "xdim 255"), (64, "xdim 63")]
)
async def test_turn_on_with_no_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on()
light_mock.light.send_cmd.assert_called_once_with(expected)
@pytest.mark.parametrize(
"brightness,expected",
[
(32, [mock.call("on"), mock.call("dim 25")]),
(256, [mock.call("xdim 45")]),
(64, [mock.call("xdim 11")]),
],
)
async def test_turn_on_with_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on(brightness=45)
light_mock.light.send_cmd.assert_has_calls(expected)
@pytest.mark.parametrize("brightness", [32])
async def test_turn_off(light_mock):
"""Test turn_off."""
light_mock.turn_off()
light_mock.light.send_cmd.assert_called_once_with("off")
|
|
tests/components/mochad/test_light.py
|
homeassistant/components/opentherm_gw/__init__.py
|
"""Constants for the AVM Fritz!Box integration."""
import logging
ATTR_STATE_BATTERY_LOW = "battery_low"
ATTR_STATE_DEVICE_LOCKED = "device_locked"
ATTR_STATE_HOLIDAY_MODE = "holiday_mode"
ATTR_STATE_LOCKED = "locked"
ATTR_STATE_SUMMER_MODE = "summer_mode"
ATTR_STATE_WINDOW_OPEN = "window_open"
ATTR_TEMPERATURE_UNIT = "temperature_unit"
ATTR_TOTAL_CONSUMPTION = "total_consumption"
ATTR_TOTAL_CONSUMPTION_UNIT = "total_consumption_unit"
CONF_CONNECTIONS = "connections"
DEFAULT_HOST = "fritz.box"
DEFAULT_USERNAME = "admin"
DOMAIN = "fritzbox"
LOGGER = logging.getLogger(__package__)
PLATFORMS = ["binary_sensor", "climate", "switch", "sensor"]
|
"""The tests for the mochad light platform."""
import unittest.mock as mock
import pytest
from homeassistant.components import light
from homeassistant.components.mochad import light as mochad
from homeassistant.setup import async_setup_component
@pytest.fixture(autouse=True)
def pymochad_mock():
"""Mock pymochad."""
with mock.patch("homeassistant.components.mochad.light.device") as device:
yield device
@pytest.fixture
def light_mock(hass, brightness):
"""Mock light."""
controller_mock = mock.MagicMock()
dev_dict = {"address": "a1", "name": "fake_light", "brightness_levels": brightness}
return mochad.MochadLight(hass, controller_mock, dev_dict)
async def test_setup_adds_proper_devices(hass):
"""Test if setup adds devices."""
good_config = {
"mochad": {},
"light": {
"platform": "mochad",
"devices": [{"name": "Light1", "address": "a1"}],
},
}
assert await async_setup_component(hass, light.DOMAIN, good_config)
@pytest.mark.parametrize(
"brightness,expected", [(32, "on"), (256, "xdim 255"), (64, "xdim 63")]
)
async def test_turn_on_with_no_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on()
light_mock.light.send_cmd.assert_called_once_with(expected)
@pytest.mark.parametrize(
"brightness,expected",
[
(32, [mock.call("on"), mock.call("dim 25")]),
(256, [mock.call("xdim 45")]),
(64, [mock.call("xdim 11")]),
],
)
async def test_turn_on_with_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on(brightness=45)
light_mock.light.send_cmd.assert_has_calls(expected)
@pytest.mark.parametrize("brightness", [32])
async def test_turn_off(light_mock):
"""Test turn_off."""
light_mock.turn_off()
light_mock.light.send_cmd.assert_called_once_with("off")
|
|
tests/components/mochad/test_light.py
|
homeassistant/components/fritzbox/const.py
|
"""Support for ESPHome lights."""
from __future__ import annotations
from aioesphomeapi import LightInfo, LightState
from homeassistant.components.light import (
ATTR_BRIGHTNESS,
ATTR_COLOR_TEMP,
ATTR_EFFECT,
ATTR_FLASH,
ATTR_HS_COLOR,
ATTR_TRANSITION,
ATTR_WHITE_VALUE,
FLASH_LONG,
FLASH_SHORT,
SUPPORT_BRIGHTNESS,
SUPPORT_COLOR,
SUPPORT_COLOR_TEMP,
SUPPORT_EFFECT,
SUPPORT_FLASH,
SUPPORT_TRANSITION,
SUPPORT_WHITE_VALUE,
LightEntity,
)
from homeassistant.config_entries import ConfigEntry
from homeassistant.helpers.typing import HomeAssistantType
import homeassistant.util.color as color_util
from . import EsphomeEntity, esphome_state_property, platform_async_setup_entry
FLASH_LENGTHS = {FLASH_SHORT: 2, FLASH_LONG: 10}
async def async_setup_entry(
hass: HomeAssistantType, entry: ConfigEntry, async_add_entities
) -> None:
"""Set up ESPHome lights based on a config entry."""
await platform_async_setup_entry(
hass,
entry,
async_add_entities,
component_key="light",
info_type=LightInfo,
entity_type=EsphomeLight,
state_type=LightState,
)
class EsphomeLight(EsphomeEntity, LightEntity):
"""A switch implementation for ESPHome."""
@property
def _static_info(self) -> LightInfo:
return super()._static_info
@property
def _state(self) -> LightState | None:
return super()._state
# https://github.com/PyCQA/pylint/issues/3150 for all @esphome_state_property
# pylint: disable=invalid-overridden-method
@esphome_state_property
def is_on(self) -> bool | None:
"""Return true if the switch is on."""
return self._state.state
async def async_turn_on(self, **kwargs) -> None:
"""Turn the entity on."""
data = {"key": self._static_info.key, "state": True}
if ATTR_HS_COLOR in kwargs:
hue, sat = kwargs[ATTR_HS_COLOR]
red, green, blue = color_util.color_hsv_to_RGB(hue, sat, 100)
data["rgb"] = (red / 255, green / 255, blue / 255)
if ATTR_FLASH in kwargs:
data["flash_length"] = FLASH_LENGTHS[kwargs[ATTR_FLASH]]
if ATTR_TRANSITION in kwargs:
data["transition_length"] = kwargs[ATTR_TRANSITION]
if ATTR_BRIGHTNESS in kwargs:
data["brightness"] = kwargs[ATTR_BRIGHTNESS] / 255
if ATTR_COLOR_TEMP in kwargs:
data["color_temperature"] = kwargs[ATTR_COLOR_TEMP]
if ATTR_EFFECT in kwargs:
data["effect"] = kwargs[ATTR_EFFECT]
if ATTR_WHITE_VALUE in kwargs:
data["white"] = kwargs[ATTR_WHITE_VALUE] / 255
await self._client.light_command(**data)
async def async_turn_off(self, **kwargs) -> None:
"""Turn the entity off."""
data = {"key": self._static_info.key, "state": False}
if ATTR_FLASH in kwargs:
data["flash_length"] = FLASH_LENGTHS[kwargs[ATTR_FLASH]]
if ATTR_TRANSITION in kwargs:
data["transition_length"] = kwargs[ATTR_TRANSITION]
await self._client.light_command(**data)
@esphome_state_property
def brightness(self) -> int | None:
"""Return the brightness of this light between 0..255."""
return round(self._state.brightness * 255)
@esphome_state_property
def hs_color(self) -> tuple[float, float] | None:
"""Return the hue and saturation color value [float, float]."""
return color_util.color_RGB_to_hs(
self._state.red * 255, self._state.green * 255, self._state.blue * 255
)
@esphome_state_property
def color_temp(self) -> float | None:
"""Return the CT color value in mireds."""
return self._state.color_temperature
@esphome_state_property
def white_value(self) -> int | None:
"""Return the white value of this light between 0..255."""
return round(self._state.white * 255)
@esphome_state_property
def effect(self) -> str | None:
"""Return the current effect."""
return self._state.effect
@property
def supported_features(self) -> int:
"""Flag supported features."""
flags = SUPPORT_FLASH
if self._static_info.supports_brightness:
flags |= SUPPORT_BRIGHTNESS
flags |= SUPPORT_TRANSITION
if self._static_info.supports_rgb:
flags |= SUPPORT_COLOR
if self._static_info.supports_white_value:
flags |= SUPPORT_WHITE_VALUE
if self._static_info.supports_color_temperature:
flags |= SUPPORT_COLOR_TEMP
if self._static_info.effects:
flags |= SUPPORT_EFFECT
return flags
@property
def effect_list(self) -> list[str]:
"""Return the list of supported effects."""
return self._static_info.effects
@property
def min_mireds(self) -> float:
"""Return the coldest color_temp that this light supports."""
return self._static_info.min_mireds
@property
def max_mireds(self) -> float:
"""Return the warmest color_temp that this light supports."""
return self._static_info.max_mireds
|
"""The tests for the mochad light platform."""
import unittest.mock as mock
import pytest
from homeassistant.components import light
from homeassistant.components.mochad import light as mochad
from homeassistant.setup import async_setup_component
@pytest.fixture(autouse=True)
def pymochad_mock():
"""Mock pymochad."""
with mock.patch("homeassistant.components.mochad.light.device") as device:
yield device
@pytest.fixture
def light_mock(hass, brightness):
"""Mock light."""
controller_mock = mock.MagicMock()
dev_dict = {"address": "a1", "name": "fake_light", "brightness_levels": brightness}
return mochad.MochadLight(hass, controller_mock, dev_dict)
async def test_setup_adds_proper_devices(hass):
"""Test if setup adds devices."""
good_config = {
"mochad": {},
"light": {
"platform": "mochad",
"devices": [{"name": "Light1", "address": "a1"}],
},
}
assert await async_setup_component(hass, light.DOMAIN, good_config)
@pytest.mark.parametrize(
"brightness,expected", [(32, "on"), (256, "xdim 255"), (64, "xdim 63")]
)
async def test_turn_on_with_no_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on()
light_mock.light.send_cmd.assert_called_once_with(expected)
@pytest.mark.parametrize(
"brightness,expected",
[
(32, [mock.call("on"), mock.call("dim 25")]),
(256, [mock.call("xdim 45")]),
(64, [mock.call("xdim 11")]),
],
)
async def test_turn_on_with_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on(brightness=45)
light_mock.light.send_cmd.assert_has_calls(expected)
@pytest.mark.parametrize("brightness", [32])
async def test_turn_off(light_mock):
"""Test turn_off."""
light_mock.turn_off()
light_mock.light.send_cmd.assert_called_once_with("off")
|
|
tests/components/mochad/test_light.py
|
homeassistant/components/esphome/light.py
|
"""This platform provides support for sensor data from RainMachine."""
from functools import partial
from typing import Callable
from regenmaschine.controller import Controller
from homeassistant.components.sensor import SensorEntity
from homeassistant.config_entries import ConfigEntry
from homeassistant.const import TEMP_CELSIUS, VOLUME_CUBIC_METERS
from homeassistant.core import HomeAssistant, callback
from homeassistant.helpers.update_coordinator import DataUpdateCoordinator
from . import RainMachineEntity
from .const import (
DATA_CONTROLLER,
DATA_COORDINATOR,
DATA_PROVISION_SETTINGS,
DATA_RESTRICTIONS_UNIVERSAL,
DOMAIN,
)
TYPE_FLOW_SENSOR_CLICK_M3 = "flow_sensor_clicks_cubic_meter"
TYPE_FLOW_SENSOR_CONSUMED_LITERS = "flow_sensor_consumed_liters"
TYPE_FLOW_SENSOR_START_INDEX = "flow_sensor_start_index"
TYPE_FLOW_SENSOR_WATERING_CLICKS = "flow_sensor_watering_clicks"
TYPE_FREEZE_TEMP = "freeze_protect_temp"
SENSORS = {
TYPE_FLOW_SENSOR_CLICK_M3: (
"Flow Sensor Clicks",
"mdi:water-pump",
f"clicks/{VOLUME_CUBIC_METERS}",
None,
False,
DATA_PROVISION_SETTINGS,
),
TYPE_FLOW_SENSOR_CONSUMED_LITERS: (
"Flow Sensor Consumed Liters",
"mdi:water-pump",
"liter",
None,
False,
DATA_PROVISION_SETTINGS,
),
TYPE_FLOW_SENSOR_START_INDEX: (
"Flow Sensor Start Index",
"mdi:water-pump",
"index",
None,
False,
DATA_PROVISION_SETTINGS,
),
TYPE_FLOW_SENSOR_WATERING_CLICKS: (
"Flow Sensor Clicks",
"mdi:water-pump",
"clicks",
None,
False,
DATA_PROVISION_SETTINGS,
),
TYPE_FREEZE_TEMP: (
"Freeze Protect Temperature",
"mdi:thermometer",
TEMP_CELSIUS,
"temperature",
True,
DATA_RESTRICTIONS_UNIVERSAL,
),
}
async def async_setup_entry(
hass: HomeAssistant, entry: ConfigEntry, async_add_entities: Callable
) -> None:
"""Set up RainMachine sensors based on a config entry."""
controller = hass.data[DOMAIN][DATA_CONTROLLER][entry.entry_id]
coordinators = hass.data[DOMAIN][DATA_COORDINATOR][entry.entry_id]
@callback
def async_get_sensor(api_category: str) -> partial:
"""Generate the appropriate sensor object for an API category."""
if api_category == DATA_PROVISION_SETTINGS:
return partial(
ProvisionSettingsSensor,
coordinators[DATA_PROVISION_SETTINGS],
)
return partial(
UniversalRestrictionsSensor,
coordinators[DATA_RESTRICTIONS_UNIVERSAL],
)
async_add_entities(
[
async_get_sensor(api_category)(
controller,
sensor_type,
name,
icon,
unit,
device_class,
enabled_by_default,
)
for (
sensor_type,
(name, icon, unit, device_class, enabled_by_default, api_category),
) in SENSORS.items()
]
)
class RainMachineSensor(RainMachineEntity, SensorEntity):
"""Define a general RainMachine sensor."""
def __init__(
self,
coordinator: DataUpdateCoordinator,
controller: Controller,
sensor_type: str,
name: str,
icon: str,
unit: str,
device_class: str,
enabled_by_default: bool,
) -> None:
"""Initialize."""
super().__init__(coordinator, controller)
self._device_class = device_class
self._enabled_by_default = enabled_by_default
self._icon = icon
self._name = name
self._sensor_type = sensor_type
self._state = None
self._unit = unit
@property
def entity_registry_enabled_default(self) -> bool:
"""Determine whether an entity is enabled by default."""
return self._enabled_by_default
@property
def icon(self) -> str:
"""Return the icon."""
return self._icon
@property
def state(self) -> str:
"""Return the name of the entity."""
return self._state
@property
def unique_id(self) -> str:
"""Return a unique, Home Assistant friendly identifier for this entity."""
return f"{self._unique_id}_{self._sensor_type}"
@property
def unit_of_measurement(self) -> str:
"""Return the unit the value is expressed in."""
return self._unit
class ProvisionSettingsSensor(RainMachineSensor):
"""Define a sensor that handles provisioning data."""
@callback
def update_from_latest_data(self) -> None:
"""Update the state."""
if self._sensor_type == TYPE_FLOW_SENSOR_CLICK_M3:
self._state = self.coordinator.data["system"].get(
"flowSensorClicksPerCubicMeter"
)
elif self._sensor_type == TYPE_FLOW_SENSOR_CONSUMED_LITERS:
clicks = self.coordinator.data["system"].get("flowSensorWateringClicks")
clicks_per_m3 = self.coordinator.data["system"].get(
"flowSensorClicksPerCubicMeter"
)
if clicks and clicks_per_m3:
self._state = (clicks * 1000) / clicks_per_m3
else:
self._state = None
elif self._sensor_type == TYPE_FLOW_SENSOR_START_INDEX:
self._state = self.coordinator.data["system"].get("flowSensorStartIndex")
elif self._sensor_type == TYPE_FLOW_SENSOR_WATERING_CLICKS:
self._state = self.coordinator.data["system"].get(
"flowSensorWateringClicks"
)
class UniversalRestrictionsSensor(RainMachineSensor):
"""Define a sensor that handles universal restrictions data."""
@callback
def update_from_latest_data(self) -> None:
"""Update the state."""
if self._sensor_type == TYPE_FREEZE_TEMP:
self._state = self.coordinator.data["freezeProtectTemp"]
|
"""The tests for the mochad light platform."""
import unittest.mock as mock
import pytest
from homeassistant.components import light
from homeassistant.components.mochad import light as mochad
from homeassistant.setup import async_setup_component
@pytest.fixture(autouse=True)
def pymochad_mock():
"""Mock pymochad."""
with mock.patch("homeassistant.components.mochad.light.device") as device:
yield device
@pytest.fixture
def light_mock(hass, brightness):
"""Mock light."""
controller_mock = mock.MagicMock()
dev_dict = {"address": "a1", "name": "fake_light", "brightness_levels": brightness}
return mochad.MochadLight(hass, controller_mock, dev_dict)
async def test_setup_adds_proper_devices(hass):
"""Test if setup adds devices."""
good_config = {
"mochad": {},
"light": {
"platform": "mochad",
"devices": [{"name": "Light1", "address": "a1"}],
},
}
assert await async_setup_component(hass, light.DOMAIN, good_config)
@pytest.mark.parametrize(
"brightness,expected", [(32, "on"), (256, "xdim 255"), (64, "xdim 63")]
)
async def test_turn_on_with_no_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on()
light_mock.light.send_cmd.assert_called_once_with(expected)
@pytest.mark.parametrize(
"brightness,expected",
[
(32, [mock.call("on"), mock.call("dim 25")]),
(256, [mock.call("xdim 45")]),
(64, [mock.call("xdim 11")]),
],
)
async def test_turn_on_with_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on(brightness=45)
light_mock.light.send_cmd.assert_has_calls(expected)
@pytest.mark.parametrize("brightness", [32])
async def test_turn_off(light_mock):
"""Test turn_off."""
light_mock.turn_off()
light_mock.light.send_cmd.assert_called_once_with("off")
|
|
tests/components/mochad/test_light.py
|
homeassistant/components/rainmachine/sensor.py
|
"""Define a config flow manager for AirVisual."""
import asyncio
from pyairvisual import CloudAPI, NodeSamba
from pyairvisual.errors import (
AirVisualError,
InvalidKeyError,
NodeProError,
NotFoundError,
)
import voluptuous as vol
from homeassistant import config_entries
from homeassistant.const import (
CONF_API_KEY,
CONF_IP_ADDRESS,
CONF_LATITUDE,
CONF_LONGITUDE,
CONF_PASSWORD,
CONF_SHOW_ON_MAP,
CONF_STATE,
)
from homeassistant.core import callback
from homeassistant.helpers import aiohttp_client, config_validation as cv
from . import async_get_geography_id
from .const import (
CONF_CITY,
CONF_COUNTRY,
CONF_INTEGRATION_TYPE,
DOMAIN,
INTEGRATION_TYPE_GEOGRAPHY_COORDS,
INTEGRATION_TYPE_GEOGRAPHY_NAME,
INTEGRATION_TYPE_NODE_PRO,
LOGGER,
)
API_KEY_DATA_SCHEMA = vol.Schema({vol.Required(CONF_API_KEY): cv.string})
GEOGRAPHY_NAME_SCHEMA = API_KEY_DATA_SCHEMA.extend(
{
vol.Required(CONF_CITY): cv.string,
vol.Required(CONF_STATE): cv.string,
vol.Required(CONF_COUNTRY): cv.string,
}
)
NODE_PRO_SCHEMA = vol.Schema(
{vol.Required(CONF_IP_ADDRESS): str, vol.Required(CONF_PASSWORD): cv.string}
)
PICK_INTEGRATION_TYPE_SCHEMA = vol.Schema(
{
vol.Required("type"): vol.In(
[
INTEGRATION_TYPE_GEOGRAPHY_COORDS,
INTEGRATION_TYPE_GEOGRAPHY_NAME,
INTEGRATION_TYPE_NODE_PRO,
]
)
}
)
class AirVisualFlowHandler(config_entries.ConfigFlow, domain=DOMAIN):
"""Handle an AirVisual config flow."""
VERSION = 2
CONNECTION_CLASS = config_entries.CONN_CLASS_CLOUD_POLL
def __init__(self):
"""Initialize the config flow."""
self._entry_data_for_reauth = None
self._geo_id = None
@property
def geography_coords_schema(self):
"""Return the data schema for the cloud API."""
return API_KEY_DATA_SCHEMA.extend(
{
vol.Required(
CONF_LATITUDE, default=self.hass.config.latitude
): cv.latitude,
vol.Required(
CONF_LONGITUDE, default=self.hass.config.longitude
): cv.longitude,
}
)
async def _async_finish_geography(self, user_input, integration_type):
"""Validate a Cloud API key."""
websession = aiohttp_client.async_get_clientsession(self.hass)
cloud_api = CloudAPI(user_input[CONF_API_KEY], session=websession)
# If this is the first (and only the first) time we've seen this API key, check
# that it's valid:
valid_keys = self.hass.data.setdefault("airvisual_checked_api_keys", set())
valid_keys_lock = self.hass.data.setdefault(
"airvisual_checked_api_keys_lock", asyncio.Lock()
)
if integration_type == INTEGRATION_TYPE_GEOGRAPHY_COORDS:
coro = cloud_api.air_quality.nearest_city()
error_schema = self.geography_coords_schema
error_step = "geography_by_coords"
else:
coro = cloud_api.air_quality.city(
user_input[CONF_CITY], user_input[CONF_STATE], user_input[CONF_COUNTRY]
)
error_schema = GEOGRAPHY_NAME_SCHEMA
error_step = "geography_by_name"
async with valid_keys_lock:
if user_input[CONF_API_KEY] not in valid_keys:
try:
await coro
except InvalidKeyError:
return self.async_show_form(
step_id=error_step,
data_schema=error_schema,
errors={CONF_API_KEY: "invalid_api_key"},
)
except NotFoundError:
return self.async_show_form(
step_id=error_step,
data_schema=error_schema,
errors={CONF_CITY: "location_not_found"},
)
except AirVisualError as err:
LOGGER.error(err)
return self.async_show_form(
step_id=error_step,
data_schema=error_schema,
errors={"base": "unknown"},
)
valid_keys.add(user_input[CONF_API_KEY])
existing_entry = await self.async_set_unique_id(self._geo_id)
if existing_entry:
self.hass.config_entries.async_update_entry(existing_entry, data=user_input)
return self.async_abort(reason="reauth_successful")
return self.async_create_entry(
title=f"Cloud API ({self._geo_id})",
data={**user_input, CONF_INTEGRATION_TYPE: integration_type},
)
async def _async_init_geography(self, user_input, integration_type):
"""Handle the initialization of the integration via the cloud API."""
self._geo_id = async_get_geography_id(user_input)
await self._async_set_unique_id(self._geo_id)
self._abort_if_unique_id_configured()
return await self._async_finish_geography(user_input, integration_type)
async def _async_set_unique_id(self, unique_id):
"""Set the unique ID of the config flow and abort if it already exists."""
await self.async_set_unique_id(unique_id)
self._abort_if_unique_id_configured()
@staticmethod
@callback
def async_get_options_flow(config_entry):
"""Define the config flow to handle options."""
return AirVisualOptionsFlowHandler(config_entry)
async def async_step_geography_by_coords(self, user_input=None):
"""Handle the initialization of the cloud API based on latitude/longitude."""
if not user_input:
return self.async_show_form(
step_id="geography_by_coords", data_schema=self.geography_coords_schema
)
return await self._async_init_geography(
user_input, INTEGRATION_TYPE_GEOGRAPHY_COORDS
)
async def async_step_geography_by_name(self, user_input=None):
"""Handle the initialization of the cloud API based on city/state/country."""
if not user_input:
return self.async_show_form(
step_id="geography_by_name", data_schema=GEOGRAPHY_NAME_SCHEMA
)
return await self._async_init_geography(
user_input, INTEGRATION_TYPE_GEOGRAPHY_NAME
)
async def async_step_node_pro(self, user_input=None):
"""Handle the initialization of the integration with a Node/Pro."""
if not user_input:
return self.async_show_form(step_id="node_pro", data_schema=NODE_PRO_SCHEMA)
await self._async_set_unique_id(user_input[CONF_IP_ADDRESS])
node = NodeSamba(user_input[CONF_IP_ADDRESS], user_input[CONF_PASSWORD])
try:
await node.async_connect()
except NodeProError as err:
LOGGER.error("Error connecting to Node/Pro unit: %s", err)
return self.async_show_form(
step_id="node_pro",
data_schema=NODE_PRO_SCHEMA,
errors={CONF_IP_ADDRESS: "cannot_connect"},
)
await node.async_disconnect()
return self.async_create_entry(
title=f"Node/Pro ({user_input[CONF_IP_ADDRESS]})",
data={**user_input, CONF_INTEGRATION_TYPE: INTEGRATION_TYPE_NODE_PRO},
)
async def async_step_reauth(self, data):
"""Handle configuration by re-auth."""
self._entry_data_for_reauth = data
self._geo_id = async_get_geography_id(data)
return await self.async_step_reauth_confirm()
async def async_step_reauth_confirm(self, user_input=None):
"""Handle re-auth completion."""
if not user_input:
return self.async_show_form(
step_id="reauth_confirm", data_schema=API_KEY_DATA_SCHEMA
)
conf = {CONF_API_KEY: user_input[CONF_API_KEY], **self._entry_data_for_reauth}
return await self._async_finish_geography(
conf, self._entry_data_for_reauth[CONF_INTEGRATION_TYPE]
)
async def async_step_user(self, user_input=None):
"""Handle the start of the config flow."""
if not user_input:
return self.async_show_form(
step_id="user", data_schema=PICK_INTEGRATION_TYPE_SCHEMA
)
if user_input["type"] == INTEGRATION_TYPE_GEOGRAPHY_COORDS:
return await self.async_step_geography_by_coords()
if user_input["type"] == INTEGRATION_TYPE_GEOGRAPHY_NAME:
return await self.async_step_geography_by_name()
return await self.async_step_node_pro()
class AirVisualOptionsFlowHandler(config_entries.OptionsFlow):
"""Handle an AirVisual options flow."""
def __init__(self, config_entry):
"""Initialize."""
self.config_entry = config_entry
async def async_step_init(self, user_input=None):
"""Manage the options."""
if user_input is not None:
return self.async_create_entry(title="", data=user_input)
return self.async_show_form(
step_id="init",
data_schema=vol.Schema(
{
vol.Required(
CONF_SHOW_ON_MAP,
default=self.config_entry.options.get(CONF_SHOW_ON_MAP),
): bool
}
),
)
|
"""The tests for the mochad light platform."""
import unittest.mock as mock
import pytest
from homeassistant.components import light
from homeassistant.components.mochad import light as mochad
from homeassistant.setup import async_setup_component
@pytest.fixture(autouse=True)
def pymochad_mock():
"""Mock pymochad."""
with mock.patch("homeassistant.components.mochad.light.device") as device:
yield device
@pytest.fixture
def light_mock(hass, brightness):
"""Mock light."""
controller_mock = mock.MagicMock()
dev_dict = {"address": "a1", "name": "fake_light", "brightness_levels": brightness}
return mochad.MochadLight(hass, controller_mock, dev_dict)
async def test_setup_adds_proper_devices(hass):
"""Test if setup adds devices."""
good_config = {
"mochad": {},
"light": {
"platform": "mochad",
"devices": [{"name": "Light1", "address": "a1"}],
},
}
assert await async_setup_component(hass, light.DOMAIN, good_config)
@pytest.mark.parametrize(
"brightness,expected", [(32, "on"), (256, "xdim 255"), (64, "xdim 63")]
)
async def test_turn_on_with_no_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on()
light_mock.light.send_cmd.assert_called_once_with(expected)
@pytest.mark.parametrize(
"brightness,expected",
[
(32, [mock.call("on"), mock.call("dim 25")]),
(256, [mock.call("xdim 45")]),
(64, [mock.call("xdim 11")]),
],
)
async def test_turn_on_with_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on(brightness=45)
light_mock.light.send_cmd.assert_has_calls(expected)
@pytest.mark.parametrize("brightness", [32])
async def test_turn_off(light_mock):
"""Test turn_off."""
light_mock.turn_off()
light_mock.light.send_cmd.assert_called_once_with("off")
|
|
tests/components/mochad/test_light.py
|
homeassistant/components/airvisual/config_flow.py
|
"""Brother helpers functions."""
import logging
import pysnmp.hlapi.asyncio as hlapi
from pysnmp.hlapi.asyncio.cmdgen import lcd
from homeassistant.const import EVENT_HOMEASSISTANT_STOP
from homeassistant.core import callback
from homeassistant.helpers import singleton
from .const import DOMAIN, SNMP
_LOGGER = logging.getLogger(__name__)
@singleton.singleton("snmp_engine")
def get_snmp_engine(hass):
"""Get SNMP engine."""
_LOGGER.debug("Creating SNMP engine")
snmp_engine = hlapi.SnmpEngine()
@callback
def shutdown_listener(ev):
if hass.data.get(DOMAIN):
_LOGGER.debug("Unconfiguring SNMP engine")
lcd.unconfigure(hass.data[DOMAIN][SNMP], None)
hass.bus.async_listen_once(EVENT_HOMEASSISTANT_STOP, shutdown_listener)
return snmp_engine
|
"""The tests for the mochad light platform."""
import unittest.mock as mock
import pytest
from homeassistant.components import light
from homeassistant.components.mochad import light as mochad
from homeassistant.setup import async_setup_component
@pytest.fixture(autouse=True)
def pymochad_mock():
"""Mock pymochad."""
with mock.patch("homeassistant.components.mochad.light.device") as device:
yield device
@pytest.fixture
def light_mock(hass, brightness):
"""Mock light."""
controller_mock = mock.MagicMock()
dev_dict = {"address": "a1", "name": "fake_light", "brightness_levels": brightness}
return mochad.MochadLight(hass, controller_mock, dev_dict)
async def test_setup_adds_proper_devices(hass):
"""Test if setup adds devices."""
good_config = {
"mochad": {},
"light": {
"platform": "mochad",
"devices": [{"name": "Light1", "address": "a1"}],
},
}
assert await async_setup_component(hass, light.DOMAIN, good_config)
@pytest.mark.parametrize(
"brightness,expected", [(32, "on"), (256, "xdim 255"), (64, "xdim 63")]
)
async def test_turn_on_with_no_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on()
light_mock.light.send_cmd.assert_called_once_with(expected)
@pytest.mark.parametrize(
"brightness,expected",
[
(32, [mock.call("on"), mock.call("dim 25")]),
(256, [mock.call("xdim 45")]),
(64, [mock.call("xdim 11")]),
],
)
async def test_turn_on_with_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on(brightness=45)
light_mock.light.send_cmd.assert_has_calls(expected)
@pytest.mark.parametrize("brightness", [32])
async def test_turn_off(light_mock):
"""Test turn_off."""
light_mock.turn_off()
light_mock.light.send_cmd.assert_called_once_with("off")
|
|
tests/components/mochad/test_light.py
|
homeassistant/components/brother/utils.py
|
"""The DirecTV integration."""
from __future__ import annotations
import asyncio
from datetime import timedelta
from typing import Any
from directv import DIRECTV, DIRECTVError
from homeassistant.config_entries import ConfigEntry
from homeassistant.const import ATTR_NAME, CONF_HOST
from homeassistant.core import HomeAssistant
from homeassistant.exceptions import ConfigEntryNotReady
from homeassistant.helpers import config_validation as cv
from homeassistant.helpers.aiohttp_client import async_get_clientsession
from homeassistant.helpers.entity import Entity
from .const import (
ATTR_IDENTIFIERS,
ATTR_MANUFACTURER,
ATTR_MODEL,
ATTR_SOFTWARE_VERSION,
ATTR_VIA_DEVICE,
DOMAIN,
)
CONFIG_SCHEMA = cv.deprecated(DOMAIN)
PLATFORMS = ["media_player", "remote"]
SCAN_INTERVAL = timedelta(seconds=30)
async def async_setup_entry(hass: HomeAssistant, entry: ConfigEntry) -> bool:
"""Set up DirecTV from a config entry."""
dtv = DIRECTV(entry.data[CONF_HOST], session=async_get_clientsession(hass))
try:
await dtv.update()
except DIRECTVError as err:
raise ConfigEntryNotReady from err
hass.data.setdefault(DOMAIN, {})
hass.data[DOMAIN][entry.entry_id] = dtv
for platform in PLATFORMS:
hass.async_create_task(
hass.config_entries.async_forward_entry_setup(entry, platform)
)
return True
async def async_unload_entry(hass: HomeAssistant, entry: ConfigEntry) -> bool:
"""Unload a config entry."""
unload_ok = all(
await asyncio.gather(
*[
hass.config_entries.async_forward_entry_unload(entry, platform)
for platform in PLATFORMS
]
)
)
if unload_ok:
hass.data[DOMAIN].pop(entry.entry_id)
return unload_ok
class DIRECTVEntity(Entity):
"""Defines a base DirecTV entity."""
def __init__(self, *, dtv: DIRECTV, name: str, address: str = "0") -> None:
"""Initialize the DirecTV entity."""
self._address = address
self._device_id = address if address != "0" else dtv.device.info.receiver_id
self._is_client = address != "0"
self._name = name
self.dtv = dtv
@property
def name(self) -> str:
"""Return the name of the entity."""
return self._name
@property
def device_info(self) -> dict[str, Any]:
"""Return device information about this DirecTV receiver."""
return {
ATTR_IDENTIFIERS: {(DOMAIN, self._device_id)},
ATTR_NAME: self.name,
ATTR_MANUFACTURER: self.dtv.device.info.brand,
ATTR_MODEL: None,
ATTR_SOFTWARE_VERSION: self.dtv.device.info.version,
ATTR_VIA_DEVICE: (DOMAIN, self.dtv.device.info.receiver_id),
}
|
"""The tests for the mochad light platform."""
import unittest.mock as mock
import pytest
from homeassistant.components import light
from homeassistant.components.mochad import light as mochad
from homeassistant.setup import async_setup_component
@pytest.fixture(autouse=True)
def pymochad_mock():
"""Mock pymochad."""
with mock.patch("homeassistant.components.mochad.light.device") as device:
yield device
@pytest.fixture
def light_mock(hass, brightness):
"""Mock light."""
controller_mock = mock.MagicMock()
dev_dict = {"address": "a1", "name": "fake_light", "brightness_levels": brightness}
return mochad.MochadLight(hass, controller_mock, dev_dict)
async def test_setup_adds_proper_devices(hass):
"""Test if setup adds devices."""
good_config = {
"mochad": {},
"light": {
"platform": "mochad",
"devices": [{"name": "Light1", "address": "a1"}],
},
}
assert await async_setup_component(hass, light.DOMAIN, good_config)
@pytest.mark.parametrize(
"brightness,expected", [(32, "on"), (256, "xdim 255"), (64, "xdim 63")]
)
async def test_turn_on_with_no_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on()
light_mock.light.send_cmd.assert_called_once_with(expected)
@pytest.mark.parametrize(
"brightness,expected",
[
(32, [mock.call("on"), mock.call("dim 25")]),
(256, [mock.call("xdim 45")]),
(64, [mock.call("xdim 11")]),
],
)
async def test_turn_on_with_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on(brightness=45)
light_mock.light.send_cmd.assert_has_calls(expected)
@pytest.mark.parametrize("brightness", [32])
async def test_turn_off(light_mock):
"""Test turn_off."""
light_mock.turn_off()
light_mock.light.send_cmd.assert_called_once_with("off")
|
|
tests/components/mochad/test_light.py
|
homeassistant/components/directv/__init__.py
|
"""Support for interface with a Gree climate systems."""
from __future__ import annotations
from homeassistant.components.switch import DEVICE_CLASS_SWITCH, SwitchEntity
from homeassistant.helpers.device_registry import CONNECTION_NETWORK_MAC
from homeassistant.helpers.update_coordinator import CoordinatorEntity
from .const import COORDINATOR, DOMAIN
async def async_setup_entry(hass, config_entry, async_add_entities):
"""Set up the Gree HVAC device from a config entry."""
async_add_entities(
[
GreeSwitchEntity(coordinator)
for coordinator in hass.data[DOMAIN][COORDINATOR]
]
)
class GreeSwitchEntity(CoordinatorEntity, SwitchEntity):
"""Representation of a Gree HVAC device."""
def __init__(self, coordinator):
"""Initialize the Gree device."""
super().__init__(coordinator)
self._name = coordinator.device.device_info.name + " Panel Light"
self._mac = coordinator.device.device_info.mac
@property
def name(self) -> str:
"""Return the name of the device."""
return self._name
@property
def unique_id(self) -> str:
"""Return a unique id for the device."""
return f"{self._mac}-panel-light"
@property
def icon(self) -> str | None:
"""Return the icon for the device."""
return "mdi:lightbulb"
@property
def device_info(self):
"""Return device specific attributes."""
return {
"name": self._name,
"identifiers": {(DOMAIN, self._mac)},
"manufacturer": "Gree",
"connections": {(CONNECTION_NETWORK_MAC, self._mac)},
}
@property
def device_class(self):
"""Return the class of this device, from component DEVICE_CLASSES."""
return DEVICE_CLASS_SWITCH
@property
def is_on(self) -> bool:
"""Return if the light is turned on."""
return self.coordinator.device.light
async def async_turn_on(self, **kwargs):
"""Turn the entity on."""
self.coordinator.device.light = True
await self.coordinator.push_state_update()
self.async_write_ha_state()
async def async_turn_off(self, **kwargs):
"""Turn the entity off."""
self.coordinator.device.light = False
await self.coordinator.push_state_update()
self.async_write_ha_state()
|
"""The tests for the mochad light platform."""
import unittest.mock as mock
import pytest
from homeassistant.components import light
from homeassistant.components.mochad import light as mochad
from homeassistant.setup import async_setup_component
@pytest.fixture(autouse=True)
def pymochad_mock():
"""Mock pymochad."""
with mock.patch("homeassistant.components.mochad.light.device") as device:
yield device
@pytest.fixture
def light_mock(hass, brightness):
"""Mock light."""
controller_mock = mock.MagicMock()
dev_dict = {"address": "a1", "name": "fake_light", "brightness_levels": brightness}
return mochad.MochadLight(hass, controller_mock, dev_dict)
async def test_setup_adds_proper_devices(hass):
"""Test if setup adds devices."""
good_config = {
"mochad": {},
"light": {
"platform": "mochad",
"devices": [{"name": "Light1", "address": "a1"}],
},
}
assert await async_setup_component(hass, light.DOMAIN, good_config)
@pytest.mark.parametrize(
"brightness,expected", [(32, "on"), (256, "xdim 255"), (64, "xdim 63")]
)
async def test_turn_on_with_no_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on()
light_mock.light.send_cmd.assert_called_once_with(expected)
@pytest.mark.parametrize(
"brightness,expected",
[
(32, [mock.call("on"), mock.call("dim 25")]),
(256, [mock.call("xdim 45")]),
(64, [mock.call("xdim 11")]),
],
)
async def test_turn_on_with_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on(brightness=45)
light_mock.light.send_cmd.assert_has_calls(expected)
@pytest.mark.parametrize("brightness", [32])
async def test_turn_off(light_mock):
"""Test turn_off."""
light_mock.turn_off()
light_mock.light.send_cmd.assert_called_once_with("off")
|
|
tests/components/mochad/test_light.py
|
homeassistant/components/gree/switch.py
|
"""Implement the Google Smart Home traits."""
from __future__ import annotations
import logging
from homeassistant.components import (
alarm_control_panel,
binary_sensor,
camera,
cover,
fan,
group,
input_boolean,
input_select,
light,
lock,
media_player,
scene,
script,
sensor,
switch,
vacuum,
)
from homeassistant.components.climate import const as climate
from homeassistant.components.humidifier import const as humidifier
from homeassistant.const import (
ATTR_ASSUMED_STATE,
ATTR_CODE,
ATTR_DEVICE_CLASS,
ATTR_ENTITY_ID,
ATTR_MODE,
ATTR_SUPPORTED_FEATURES,
ATTR_TEMPERATURE,
CAST_APP_ID_HOMEASSISTANT,
SERVICE_ALARM_ARM_AWAY,
SERVICE_ALARM_ARM_CUSTOM_BYPASS,
SERVICE_ALARM_ARM_HOME,
SERVICE_ALARM_ARM_NIGHT,
SERVICE_ALARM_DISARM,
SERVICE_ALARM_TRIGGER,
SERVICE_TURN_OFF,
SERVICE_TURN_ON,
STATE_ALARM_ARMED_AWAY,
STATE_ALARM_ARMED_CUSTOM_BYPASS,
STATE_ALARM_ARMED_HOME,
STATE_ALARM_ARMED_NIGHT,
STATE_ALARM_DISARMED,
STATE_ALARM_PENDING,
STATE_ALARM_TRIGGERED,
STATE_IDLE,
STATE_LOCKED,
STATE_OFF,
STATE_ON,
STATE_PAUSED,
STATE_PLAYING,
STATE_STANDBY,
STATE_UNAVAILABLE,
STATE_UNKNOWN,
TEMP_CELSIUS,
TEMP_FAHRENHEIT,
)
from homeassistant.core import DOMAIN as HA_DOMAIN
from homeassistant.helpers.network import get_url
from homeassistant.util import color as color_util, dt, temperature as temp_util
from .const import (
CHALLENGE_ACK_NEEDED,
CHALLENGE_FAILED_PIN_NEEDED,
CHALLENGE_PIN_NEEDED,
ERR_ALREADY_ARMED,
ERR_ALREADY_DISARMED,
ERR_ALREADY_STOPPED,
ERR_CHALLENGE_NOT_SETUP,
ERR_NOT_SUPPORTED,
ERR_UNSUPPORTED_INPUT,
ERR_VALUE_OUT_OF_RANGE,
)
from .error import ChallengeNeeded, SmartHomeError
_LOGGER = logging.getLogger(__name__)
PREFIX_TRAITS = "action.devices.traits."
TRAIT_CAMERA_STREAM = f"{PREFIX_TRAITS}CameraStream"
TRAIT_ONOFF = f"{PREFIX_TRAITS}OnOff"
TRAIT_DOCK = f"{PREFIX_TRAITS}Dock"
TRAIT_STARTSTOP = f"{PREFIX_TRAITS}StartStop"
TRAIT_BRIGHTNESS = f"{PREFIX_TRAITS}Brightness"
TRAIT_COLOR_SETTING = f"{PREFIX_TRAITS}ColorSetting"
TRAIT_SCENE = f"{PREFIX_TRAITS}Scene"
TRAIT_TEMPERATURE_SETTING = f"{PREFIX_TRAITS}TemperatureSetting"
TRAIT_LOCKUNLOCK = f"{PREFIX_TRAITS}LockUnlock"
TRAIT_FANSPEED = f"{PREFIX_TRAITS}FanSpeed"
TRAIT_MODES = f"{PREFIX_TRAITS}Modes"
TRAIT_INPUTSELECTOR = f"{PREFIX_TRAITS}InputSelector"
TRAIT_OPENCLOSE = f"{PREFIX_TRAITS}OpenClose"
TRAIT_VOLUME = f"{PREFIX_TRAITS}Volume"
TRAIT_ARMDISARM = f"{PREFIX_TRAITS}ArmDisarm"
TRAIT_HUMIDITY_SETTING = f"{PREFIX_TRAITS}HumiditySetting"
TRAIT_TRANSPORT_CONTROL = f"{PREFIX_TRAITS}TransportControl"
TRAIT_MEDIA_STATE = f"{PREFIX_TRAITS}MediaState"
PREFIX_COMMANDS = "action.devices.commands."
COMMAND_ONOFF = f"{PREFIX_COMMANDS}OnOff"
COMMAND_GET_CAMERA_STREAM = f"{PREFIX_COMMANDS}GetCameraStream"
COMMAND_DOCK = f"{PREFIX_COMMANDS}Dock"
COMMAND_STARTSTOP = f"{PREFIX_COMMANDS}StartStop"
COMMAND_PAUSEUNPAUSE = f"{PREFIX_COMMANDS}PauseUnpause"
COMMAND_BRIGHTNESS_ABSOLUTE = f"{PREFIX_COMMANDS}BrightnessAbsolute"
COMMAND_COLOR_ABSOLUTE = f"{PREFIX_COMMANDS}ColorAbsolute"
COMMAND_ACTIVATE_SCENE = f"{PREFIX_COMMANDS}ActivateScene"
COMMAND_THERMOSTAT_TEMPERATURE_SETPOINT = (
f"{PREFIX_COMMANDS}ThermostatTemperatureSetpoint"
)
COMMAND_THERMOSTAT_TEMPERATURE_SET_RANGE = (
f"{PREFIX_COMMANDS}ThermostatTemperatureSetRange"
)
COMMAND_THERMOSTAT_SET_MODE = f"{PREFIX_COMMANDS}ThermostatSetMode"
COMMAND_LOCKUNLOCK = f"{PREFIX_COMMANDS}LockUnlock"
COMMAND_FANSPEED = f"{PREFIX_COMMANDS}SetFanSpeed"
COMMAND_MODES = f"{PREFIX_COMMANDS}SetModes"
COMMAND_INPUT = f"{PREFIX_COMMANDS}SetInput"
COMMAND_NEXT_INPUT = f"{PREFIX_COMMANDS}NextInput"
COMMAND_PREVIOUS_INPUT = f"{PREFIX_COMMANDS}PreviousInput"
COMMAND_OPENCLOSE = f"{PREFIX_COMMANDS}OpenClose"
COMMAND_OPENCLOSE_RELATIVE = f"{PREFIX_COMMANDS}OpenCloseRelative"
COMMAND_SET_VOLUME = f"{PREFIX_COMMANDS}setVolume"
COMMAND_VOLUME_RELATIVE = f"{PREFIX_COMMANDS}volumeRelative"
COMMAND_MUTE = f"{PREFIX_COMMANDS}mute"
COMMAND_ARMDISARM = f"{PREFIX_COMMANDS}ArmDisarm"
COMMAND_MEDIA_NEXT = f"{PREFIX_COMMANDS}mediaNext"
COMMAND_MEDIA_PAUSE = f"{PREFIX_COMMANDS}mediaPause"
COMMAND_MEDIA_PREVIOUS = f"{PREFIX_COMMANDS}mediaPrevious"
COMMAND_MEDIA_RESUME = f"{PREFIX_COMMANDS}mediaResume"
COMMAND_MEDIA_SEEK_RELATIVE = f"{PREFIX_COMMANDS}mediaSeekRelative"
COMMAND_MEDIA_SEEK_TO_POSITION = f"{PREFIX_COMMANDS}mediaSeekToPosition"
COMMAND_MEDIA_SHUFFLE = f"{PREFIX_COMMANDS}mediaShuffle"
COMMAND_MEDIA_STOP = f"{PREFIX_COMMANDS}mediaStop"
COMMAND_SET_HUMIDITY = f"{PREFIX_COMMANDS}SetHumidity"
TRAITS = []
def register_trait(trait):
"""Decorate a function to register a trait."""
TRAITS.append(trait)
return trait
def _google_temp_unit(units):
"""Return Google temperature unit."""
if units == TEMP_FAHRENHEIT:
return "F"
return "C"
def _next_selected(items: list[str], selected: str | None) -> str | None:
"""Return the next item in a item list starting at given value.
If selected is missing in items, None is returned
"""
try:
index = items.index(selected)
except ValueError:
return None
next_item = 0 if index == len(items) - 1 else index + 1
return items[next_item]
class _Trait:
"""Represents a Trait inside Google Assistant skill."""
commands = []
@staticmethod
def might_2fa(domain, features, device_class):
"""Return if the trait might ask for 2FA."""
return False
def __init__(self, hass, state, config):
"""Initialize a trait for a state."""
self.hass = hass
self.state = state
self.config = config
def sync_attributes(self):
"""Return attributes for a sync request."""
raise NotImplementedError
def query_attributes(self):
"""Return the attributes of this trait for this entity."""
raise NotImplementedError
def can_execute(self, command, params):
"""Test if command can be executed."""
return command in self.commands
async def execute(self, command, data, params, challenge):
"""Execute a trait command."""
raise NotImplementedError
@register_trait
class BrightnessTrait(_Trait):
"""Trait to control brightness of a device.
https://developers.google.com/actions/smarthome/traits/brightness
"""
name = TRAIT_BRIGHTNESS
commands = [COMMAND_BRIGHTNESS_ABSOLUTE]
@staticmethod
def supported(domain, features, device_class):
"""Test if state is supported."""
if domain == light.DOMAIN:
return features & light.SUPPORT_BRIGHTNESS
return False
def sync_attributes(self):
"""Return brightness attributes for a sync request."""
return {}
def query_attributes(self):
"""Return brightness query attributes."""
domain = self.state.domain
response = {}
if domain == light.DOMAIN:
brightness = self.state.attributes.get(light.ATTR_BRIGHTNESS)
if brightness is not None:
response["brightness"] = int(100 * (brightness / 255))
else:
response["brightness"] = 0
return response
async def execute(self, command, data, params, challenge):
"""Execute a brightness command."""
domain = self.state.domain
if domain == light.DOMAIN:
await self.hass.services.async_call(
light.DOMAIN,
light.SERVICE_TURN_ON,
{
ATTR_ENTITY_ID: self.state.entity_id,
light.ATTR_BRIGHTNESS_PCT: params["brightness"],
},
blocking=True,
context=data.context,
)
@register_trait
class CameraStreamTrait(_Trait):
"""Trait to stream from cameras.
https://developers.google.com/actions/smarthome/traits/camerastream
"""
name = TRAIT_CAMERA_STREAM
commands = [COMMAND_GET_CAMERA_STREAM]
stream_info = None
@staticmethod
def supported(domain, features, device_class):
"""Test if state is supported."""
if domain == camera.DOMAIN:
return features & camera.SUPPORT_STREAM
return False
def sync_attributes(self):
"""Return stream attributes for a sync request."""
return {
"cameraStreamSupportedProtocols": ["hls"],
"cameraStreamNeedAuthToken": False,
"cameraStreamNeedDrmEncryption": False,
}
def query_attributes(self):
"""Return camera stream attributes."""
return self.stream_info or {}
async def execute(self, command, data, params, challenge):
"""Execute a get camera stream command."""
url = await self.hass.components.camera.async_request_stream(
self.state.entity_id, "hls"
)
self.stream_info = {
"cameraStreamAccessUrl": f"{get_url(self.hass)}{url}",
"cameraStreamReceiverAppId": CAST_APP_ID_HOMEASSISTANT,
}
@register_trait
class OnOffTrait(_Trait):
"""Trait to offer basic on and off functionality.
https://developers.google.com/actions/smarthome/traits/onoff
"""
name = TRAIT_ONOFF
commands = [COMMAND_ONOFF]
@staticmethod
def supported(domain, features, device_class):
"""Test if state is supported."""
return domain in (
group.DOMAIN,
input_boolean.DOMAIN,
switch.DOMAIN,
fan.DOMAIN,
light.DOMAIN,
media_player.DOMAIN,
humidifier.DOMAIN,
)
def sync_attributes(self):
"""Return OnOff attributes for a sync request."""
if self.state.attributes.get(ATTR_ASSUMED_STATE, False):
return {"commandOnlyOnOff": True}
return {}
def query_attributes(self):
"""Return OnOff query attributes."""
return {"on": self.state.state not in (STATE_OFF, STATE_UNKNOWN)}
async def execute(self, command, data, params, challenge):
"""Execute an OnOff command."""
domain = self.state.domain
if domain == group.DOMAIN:
service_domain = HA_DOMAIN
service = SERVICE_TURN_ON if params["on"] else SERVICE_TURN_OFF
else:
service_domain = domain
service = SERVICE_TURN_ON if params["on"] else SERVICE_TURN_OFF
await self.hass.services.async_call(
service_domain,
service,
{ATTR_ENTITY_ID: self.state.entity_id},
blocking=True,
context=data.context,
)
@register_trait
class ColorSettingTrait(_Trait):
"""Trait to offer color temperature functionality.
https://developers.google.com/actions/smarthome/traits/colortemperature
"""
name = TRAIT_COLOR_SETTING
commands = [COMMAND_COLOR_ABSOLUTE]
@staticmethod
def supported(domain, features, device_class):
"""Test if state is supported."""
if domain != light.DOMAIN:
return False
return features & light.SUPPORT_COLOR_TEMP or features & light.SUPPORT_COLOR
def sync_attributes(self):
"""Return color temperature attributes for a sync request."""
attrs = self.state.attributes
features = attrs.get(ATTR_SUPPORTED_FEATURES, 0)
response = {}
if features & light.SUPPORT_COLOR:
response["colorModel"] = "hsv"
if features & light.SUPPORT_COLOR_TEMP:
# Max Kelvin is Min Mireds K = 1000000 / mireds
# Min Kelvin is Max Mireds K = 1000000 / mireds
response["colorTemperatureRange"] = {
"temperatureMaxK": color_util.color_temperature_mired_to_kelvin(
attrs.get(light.ATTR_MIN_MIREDS)
),
"temperatureMinK": color_util.color_temperature_mired_to_kelvin(
attrs.get(light.ATTR_MAX_MIREDS)
),
}
return response
def query_attributes(self):
"""Return color temperature query attributes."""
features = self.state.attributes.get(ATTR_SUPPORTED_FEATURES, 0)
color = {}
if features & light.SUPPORT_COLOR:
color_hs = self.state.attributes.get(light.ATTR_HS_COLOR)
brightness = self.state.attributes.get(light.ATTR_BRIGHTNESS, 1)
if color_hs is not None:
color["spectrumHsv"] = {
"hue": color_hs[0],
"saturation": color_hs[1] / 100,
"value": brightness / 255,
}
if features & light.SUPPORT_COLOR_TEMP:
temp = self.state.attributes.get(light.ATTR_COLOR_TEMP)
# Some faulty integrations might put 0 in here, raising exception.
if temp == 0:
_LOGGER.warning(
"Entity %s has incorrect color temperature %s",
self.state.entity_id,
temp,
)
elif temp is not None:
color["temperatureK"] = color_util.color_temperature_mired_to_kelvin(
temp
)
response = {}
if color:
response["color"] = color
return response
async def execute(self, command, data, params, challenge):
"""Execute a color temperature command."""
if "temperature" in params["color"]:
temp = color_util.color_temperature_kelvin_to_mired(
params["color"]["temperature"]
)
min_temp = self.state.attributes[light.ATTR_MIN_MIREDS]
max_temp = self.state.attributes[light.ATTR_MAX_MIREDS]
if temp < min_temp or temp > max_temp:
raise SmartHomeError(
ERR_VALUE_OUT_OF_RANGE,
f"Temperature should be between {min_temp} and {max_temp}",
)
await self.hass.services.async_call(
light.DOMAIN,
SERVICE_TURN_ON,
{ATTR_ENTITY_ID: self.state.entity_id, light.ATTR_COLOR_TEMP: temp},
blocking=True,
context=data.context,
)
elif "spectrumRGB" in params["color"]:
# Convert integer to hex format and left pad with 0's till length 6
hex_value = f"{params['color']['spectrumRGB']:06x}"
color = color_util.color_RGB_to_hs(
*color_util.rgb_hex_to_rgb_list(hex_value)
)
await self.hass.services.async_call(
light.DOMAIN,
SERVICE_TURN_ON,
{ATTR_ENTITY_ID: self.state.entity_id, light.ATTR_HS_COLOR: color},
blocking=True,
context=data.context,
)
elif "spectrumHSV" in params["color"]:
color = params["color"]["spectrumHSV"]
saturation = color["saturation"] * 100
brightness = color["value"] * 255
await self.hass.services.async_call(
light.DOMAIN,
SERVICE_TURN_ON,
{
ATTR_ENTITY_ID: self.state.entity_id,
light.ATTR_HS_COLOR: [color["hue"], saturation],
light.ATTR_BRIGHTNESS: brightness,
},
blocking=True,
context=data.context,
)
@register_trait
class SceneTrait(_Trait):
"""Trait to offer scene functionality.
https://developers.google.com/actions/smarthome/traits/scene
"""
name = TRAIT_SCENE
commands = [COMMAND_ACTIVATE_SCENE]
@staticmethod
def supported(domain, features, device_class):
"""Test if state is supported."""
return domain in (scene.DOMAIN, script.DOMAIN)
def sync_attributes(self):
"""Return scene attributes for a sync request."""
# Neither supported domain can support sceneReversible
return {}
def query_attributes(self):
"""Return scene query attributes."""
return {}
async def execute(self, command, data, params, challenge):
"""Execute a scene command."""
# Don't block for scripts as they can be slow.
await self.hass.services.async_call(
self.state.domain,
SERVICE_TURN_ON,
{ATTR_ENTITY_ID: self.state.entity_id},
blocking=self.state.domain != script.DOMAIN,
context=data.context,
)
@register_trait
class DockTrait(_Trait):
"""Trait to offer dock functionality.
https://developers.google.com/actions/smarthome/traits/dock
"""
name = TRAIT_DOCK
commands = [COMMAND_DOCK]
@staticmethod
def supported(domain, features, device_class):
"""Test if state is supported."""
return domain == vacuum.DOMAIN
def sync_attributes(self):
"""Return dock attributes for a sync request."""
return {}
def query_attributes(self):
"""Return dock query attributes."""
return {"isDocked": self.state.state == vacuum.STATE_DOCKED}
async def execute(self, command, data, params, challenge):
"""Execute a dock command."""
await self.hass.services.async_call(
self.state.domain,
vacuum.SERVICE_RETURN_TO_BASE,
{ATTR_ENTITY_ID: self.state.entity_id},
blocking=True,
context=data.context,
)
@register_trait
class StartStopTrait(_Trait):
"""Trait to offer StartStop functionality.
https://developers.google.com/actions/smarthome/traits/startstop
"""
name = TRAIT_STARTSTOP
commands = [COMMAND_STARTSTOP, COMMAND_PAUSEUNPAUSE]
@staticmethod
def supported(domain, features, device_class):
"""Test if state is supported."""
if domain == vacuum.DOMAIN:
return True
if domain == cover.DOMAIN and features & cover.SUPPORT_STOP:
return True
return False
def sync_attributes(self):
"""Return StartStop attributes for a sync request."""
domain = self.state.domain
if domain == vacuum.DOMAIN:
return {
"pausable": self.state.attributes.get(ATTR_SUPPORTED_FEATURES, 0)
& vacuum.SUPPORT_PAUSE
!= 0
}
if domain == cover.DOMAIN:
return {}
def query_attributes(self):
"""Return StartStop query attributes."""
domain = self.state.domain
state = self.state.state
if domain == vacuum.DOMAIN:
return {
"isRunning": state == vacuum.STATE_CLEANING,
"isPaused": state == vacuum.STATE_PAUSED,
}
if domain == cover.DOMAIN:
return {"isRunning": state in (cover.STATE_CLOSING, cover.STATE_OPENING)}
async def execute(self, command, data, params, challenge):
"""Execute a StartStop command."""
domain = self.state.domain
if domain == vacuum.DOMAIN:
return await self._execute_vacuum(command, data, params, challenge)
if domain == cover.DOMAIN:
return await self._execute_cover(command, data, params, challenge)
async def _execute_vacuum(self, command, data, params, challenge):
"""Execute a StartStop command."""
if command == COMMAND_STARTSTOP:
if params["start"]:
await self.hass.services.async_call(
self.state.domain,
vacuum.SERVICE_START,
{ATTR_ENTITY_ID: self.state.entity_id},
blocking=True,
context=data.context,
)
else:
await self.hass.services.async_call(
self.state.domain,
vacuum.SERVICE_STOP,
{ATTR_ENTITY_ID: self.state.entity_id},
blocking=True,
context=data.context,
)
elif command == COMMAND_PAUSEUNPAUSE:
if params["pause"]:
await self.hass.services.async_call(
self.state.domain,
vacuum.SERVICE_PAUSE,
{ATTR_ENTITY_ID: self.state.entity_id},
blocking=True,
context=data.context,
)
else:
await self.hass.services.async_call(
self.state.domain,
vacuum.SERVICE_START,
{ATTR_ENTITY_ID: self.state.entity_id},
blocking=True,
context=data.context,
)
async def _execute_cover(self, command, data, params, challenge):
"""Execute a StartStop command."""
if command == COMMAND_STARTSTOP:
if params["start"] is False:
if (
self.state.state
in (
cover.STATE_CLOSING,
cover.STATE_OPENING,
)
or self.state.attributes.get(ATTR_ASSUMED_STATE)
):
await self.hass.services.async_call(
self.state.domain,
cover.SERVICE_STOP_COVER,
{ATTR_ENTITY_ID: self.state.entity_id},
blocking=True,
context=data.context,
)
else:
raise SmartHomeError(
ERR_ALREADY_STOPPED, "Cover is already stopped"
)
else:
raise SmartHomeError(
ERR_NOT_SUPPORTED, "Starting a cover is not supported"
)
else:
raise SmartHomeError(
ERR_NOT_SUPPORTED, f"Command {command} is not supported"
)
@register_trait
class TemperatureSettingTrait(_Trait):
"""Trait to offer handling both temperature point and modes functionality.
https://developers.google.com/actions/smarthome/traits/temperaturesetting
"""
name = TRAIT_TEMPERATURE_SETTING
commands = [
COMMAND_THERMOSTAT_TEMPERATURE_SETPOINT,
COMMAND_THERMOSTAT_TEMPERATURE_SET_RANGE,
COMMAND_THERMOSTAT_SET_MODE,
]
# We do not support "on" as we are unable to know how to restore
# the last mode.
hvac_to_google = {
climate.HVAC_MODE_HEAT: "heat",
climate.HVAC_MODE_COOL: "cool",
climate.HVAC_MODE_OFF: "off",
climate.HVAC_MODE_AUTO: "auto",
climate.HVAC_MODE_HEAT_COOL: "heatcool",
climate.HVAC_MODE_FAN_ONLY: "fan-only",
climate.HVAC_MODE_DRY: "dry",
}
google_to_hvac = {value: key for key, value in hvac_to_google.items()}
preset_to_google = {climate.PRESET_ECO: "eco"}
google_to_preset = {value: key for key, value in preset_to_google.items()}
@staticmethod
def supported(domain, features, device_class):
"""Test if state is supported."""
if domain == climate.DOMAIN:
return True
return (
domain == sensor.DOMAIN and device_class == sensor.DEVICE_CLASS_TEMPERATURE
)
@property
def climate_google_modes(self):
"""Return supported Google modes."""
modes = []
attrs = self.state.attributes
for mode in attrs.get(climate.ATTR_HVAC_MODES, []):
google_mode = self.hvac_to_google.get(mode)
if google_mode and google_mode not in modes:
modes.append(google_mode)
for preset in attrs.get(climate.ATTR_PRESET_MODES, []):
google_mode = self.preset_to_google.get(preset)
if google_mode and google_mode not in modes:
modes.append(google_mode)
return modes
def sync_attributes(self):
"""Return temperature point and modes attributes for a sync request."""
response = {}
attrs = self.state.attributes
domain = self.state.domain
response["thermostatTemperatureUnit"] = _google_temp_unit(
self.hass.config.units.temperature_unit
)
if domain == sensor.DOMAIN:
device_class = attrs.get(ATTR_DEVICE_CLASS)
if device_class == sensor.DEVICE_CLASS_TEMPERATURE:
response["queryOnlyTemperatureSetting"] = True
elif domain == climate.DOMAIN:
modes = self.climate_google_modes
# Some integrations don't support modes (e.g. opentherm), but Google doesn't
# support changing the temperature if we don't have any modes. If there's
# only one Google doesn't support changing it, so the default mode here is
# only cosmetic.
if len(modes) == 0:
modes.append("heat")
if "off" in modes and any(
mode in modes for mode in ("heatcool", "heat", "cool")
):
modes.append("on")
response["availableThermostatModes"] = modes
return response
def query_attributes(self):
"""Return temperature point and modes query attributes."""
response = {}
attrs = self.state.attributes
domain = self.state.domain
unit = self.hass.config.units.temperature_unit
if domain == sensor.DOMAIN:
device_class = attrs.get(ATTR_DEVICE_CLASS)
if device_class == sensor.DEVICE_CLASS_TEMPERATURE:
current_temp = self.state.state
if current_temp not in (STATE_UNKNOWN, STATE_UNAVAILABLE):
response["thermostatTemperatureAmbient"] = round(
temp_util.convert(float(current_temp), unit, TEMP_CELSIUS), 1
)
elif domain == climate.DOMAIN:
operation = self.state.state
preset = attrs.get(climate.ATTR_PRESET_MODE)
supported = attrs.get(ATTR_SUPPORTED_FEATURES, 0)
if preset in self.preset_to_google:
response["thermostatMode"] = self.preset_to_google[preset]
else:
response["thermostatMode"] = self.hvac_to_google.get(operation)
current_temp = attrs.get(climate.ATTR_CURRENT_TEMPERATURE)
if current_temp is not None:
response["thermostatTemperatureAmbient"] = round(
temp_util.convert(current_temp, unit, TEMP_CELSIUS), 1
)
current_humidity = attrs.get(climate.ATTR_CURRENT_HUMIDITY)
if current_humidity is not None:
response["thermostatHumidityAmbient"] = current_humidity
if operation in (climate.HVAC_MODE_AUTO, climate.HVAC_MODE_HEAT_COOL):
if supported & climate.SUPPORT_TARGET_TEMPERATURE_RANGE:
response["thermostatTemperatureSetpointHigh"] = round(
temp_util.convert(
attrs[climate.ATTR_TARGET_TEMP_HIGH], unit, TEMP_CELSIUS
),
1,
)
response["thermostatTemperatureSetpointLow"] = round(
temp_util.convert(
attrs[climate.ATTR_TARGET_TEMP_LOW], unit, TEMP_CELSIUS
),
1,
)
else:
target_temp = attrs.get(ATTR_TEMPERATURE)
if target_temp is not None:
target_temp = round(
temp_util.convert(target_temp, unit, TEMP_CELSIUS), 1
)
response["thermostatTemperatureSetpointHigh"] = target_temp
response["thermostatTemperatureSetpointLow"] = target_temp
else:
target_temp = attrs.get(ATTR_TEMPERATURE)
if target_temp is not None:
response["thermostatTemperatureSetpoint"] = round(
temp_util.convert(target_temp, unit, TEMP_CELSIUS), 1
)
return response
async def execute(self, command, data, params, challenge):
"""Execute a temperature point or mode command."""
domain = self.state.domain
if domain == sensor.DOMAIN:
raise SmartHomeError(
ERR_NOT_SUPPORTED, "Execute is not supported by sensor"
)
# All sent in temperatures are always in Celsius
unit = self.hass.config.units.temperature_unit
min_temp = self.state.attributes[climate.ATTR_MIN_TEMP]
max_temp = self.state.attributes[climate.ATTR_MAX_TEMP]
if command == COMMAND_THERMOSTAT_TEMPERATURE_SETPOINT:
temp = temp_util.convert(
params["thermostatTemperatureSetpoint"], TEMP_CELSIUS, unit
)
if unit == TEMP_FAHRENHEIT:
temp = round(temp)
if temp < min_temp or temp > max_temp:
raise SmartHomeError(
ERR_VALUE_OUT_OF_RANGE,
f"Temperature should be between {min_temp} and {max_temp}",
)
await self.hass.services.async_call(
climate.DOMAIN,
climate.SERVICE_SET_TEMPERATURE,
{ATTR_ENTITY_ID: self.state.entity_id, ATTR_TEMPERATURE: temp},
blocking=True,
context=data.context,
)
elif command == COMMAND_THERMOSTAT_TEMPERATURE_SET_RANGE:
temp_high = temp_util.convert(
params["thermostatTemperatureSetpointHigh"], TEMP_CELSIUS, unit
)
if unit == TEMP_FAHRENHEIT:
temp_high = round(temp_high)
if temp_high < min_temp or temp_high > max_temp:
raise SmartHomeError(
ERR_VALUE_OUT_OF_RANGE,
(
f"Upper bound for temperature range should be between "
f"{min_temp} and {max_temp}"
),
)
temp_low = temp_util.convert(
params["thermostatTemperatureSetpointLow"], TEMP_CELSIUS, unit
)
if unit == TEMP_FAHRENHEIT:
temp_low = round(temp_low)
if temp_low < min_temp or temp_low > max_temp:
raise SmartHomeError(
ERR_VALUE_OUT_OF_RANGE,
(
f"Lower bound for temperature range should be between "
f"{min_temp} and {max_temp}"
),
)
supported = self.state.attributes.get(ATTR_SUPPORTED_FEATURES)
svc_data = {ATTR_ENTITY_ID: self.state.entity_id}
if supported & climate.SUPPORT_TARGET_TEMPERATURE_RANGE:
svc_data[climate.ATTR_TARGET_TEMP_HIGH] = temp_high
svc_data[climate.ATTR_TARGET_TEMP_LOW] = temp_low
else:
svc_data[ATTR_TEMPERATURE] = (temp_high + temp_low) / 2
await self.hass.services.async_call(
climate.DOMAIN,
climate.SERVICE_SET_TEMPERATURE,
svc_data,
blocking=True,
context=data.context,
)
elif command == COMMAND_THERMOSTAT_SET_MODE:
target_mode = params["thermostatMode"]
supported = self.state.attributes.get(ATTR_SUPPORTED_FEATURES)
if target_mode == "on":
await self.hass.services.async_call(
climate.DOMAIN,
SERVICE_TURN_ON,
{ATTR_ENTITY_ID: self.state.entity_id},
blocking=True,
context=data.context,
)
return
if target_mode == "off":
await self.hass.services.async_call(
climate.DOMAIN,
SERVICE_TURN_OFF,
{ATTR_ENTITY_ID: self.state.entity_id},
blocking=True,
context=data.context,
)
return
if target_mode in self.google_to_preset:
await self.hass.services.async_call(
climate.DOMAIN,
climate.SERVICE_SET_PRESET_MODE,
{
climate.ATTR_PRESET_MODE: self.google_to_preset[target_mode],
ATTR_ENTITY_ID: self.state.entity_id,
},
blocking=True,
context=data.context,
)
return
await self.hass.services.async_call(
climate.DOMAIN,
climate.SERVICE_SET_HVAC_MODE,
{
ATTR_ENTITY_ID: self.state.entity_id,
climate.ATTR_HVAC_MODE: self.google_to_hvac[target_mode],
},
blocking=True,
context=data.context,
)
@register_trait
class HumiditySettingTrait(_Trait):
"""Trait to offer humidity setting functionality.
https://developers.google.com/actions/smarthome/traits/humiditysetting
"""
name = TRAIT_HUMIDITY_SETTING
commands = [COMMAND_SET_HUMIDITY]
@staticmethod
def supported(domain, features, device_class):
"""Test if state is supported."""
if domain == humidifier.DOMAIN:
return True
return domain == sensor.DOMAIN and device_class == sensor.DEVICE_CLASS_HUMIDITY
def sync_attributes(self):
"""Return humidity attributes for a sync request."""
response = {}
attrs = self.state.attributes
domain = self.state.domain
if domain == sensor.DOMAIN:
device_class = attrs.get(ATTR_DEVICE_CLASS)
if device_class == sensor.DEVICE_CLASS_HUMIDITY:
response["queryOnlyHumiditySetting"] = True
elif domain == humidifier.DOMAIN:
response["humiditySetpointRange"] = {
"minPercent": round(
float(self.state.attributes[humidifier.ATTR_MIN_HUMIDITY])
),
"maxPercent": round(
float(self.state.attributes[humidifier.ATTR_MAX_HUMIDITY])
),
}
return response
def query_attributes(self):
"""Return humidity query attributes."""
response = {}
attrs = self.state.attributes
domain = self.state.domain
if domain == sensor.DOMAIN:
device_class = attrs.get(ATTR_DEVICE_CLASS)
if device_class == sensor.DEVICE_CLASS_HUMIDITY:
current_humidity = self.state.state
if current_humidity not in (STATE_UNKNOWN, STATE_UNAVAILABLE):
response["humidityAmbientPercent"] = round(float(current_humidity))
elif domain == humidifier.DOMAIN:
target_humidity = attrs.get(humidifier.ATTR_HUMIDITY)
if target_humidity is not None:
response["humiditySetpointPercent"] = round(float(target_humidity))
return response
async def execute(self, command, data, params, challenge):
"""Execute a humidity command."""
domain = self.state.domain
if domain == sensor.DOMAIN:
raise SmartHomeError(
ERR_NOT_SUPPORTED, "Execute is not supported by sensor"
)
if command == COMMAND_SET_HUMIDITY:
await self.hass.services.async_call(
humidifier.DOMAIN,
humidifier.SERVICE_SET_HUMIDITY,
{
ATTR_ENTITY_ID: self.state.entity_id,
humidifier.ATTR_HUMIDITY: params["humidity"],
},
blocking=True,
context=data.context,
)
@register_trait
class LockUnlockTrait(_Trait):
"""Trait to lock or unlock a lock.
https://developers.google.com/actions/smarthome/traits/lockunlock
"""
name = TRAIT_LOCKUNLOCK
commands = [COMMAND_LOCKUNLOCK]
@staticmethod
def supported(domain, features, device_class):
"""Test if state is supported."""
return domain == lock.DOMAIN
@staticmethod
def might_2fa(domain, features, device_class):
"""Return if the trait might ask for 2FA."""
return True
def sync_attributes(self):
"""Return LockUnlock attributes for a sync request."""
return {}
def query_attributes(self):
"""Return LockUnlock query attributes."""
return {"isLocked": self.state.state == STATE_LOCKED}
async def execute(self, command, data, params, challenge):
"""Execute an LockUnlock command."""
if params["lock"]:
service = lock.SERVICE_LOCK
else:
_verify_pin_challenge(data, self.state, challenge)
service = lock.SERVICE_UNLOCK
await self.hass.services.async_call(
lock.DOMAIN,
service,
{ATTR_ENTITY_ID: self.state.entity_id},
blocking=True,
context=data.context,
)
@register_trait
class ArmDisArmTrait(_Trait):
"""Trait to Arm or Disarm a Security System.
https://developers.google.com/actions/smarthome/traits/armdisarm
"""
name = TRAIT_ARMDISARM
commands = [COMMAND_ARMDISARM]
state_to_service = {
STATE_ALARM_ARMED_HOME: SERVICE_ALARM_ARM_HOME,
STATE_ALARM_ARMED_AWAY: SERVICE_ALARM_ARM_AWAY,
STATE_ALARM_ARMED_NIGHT: SERVICE_ALARM_ARM_NIGHT,
STATE_ALARM_ARMED_CUSTOM_BYPASS: SERVICE_ALARM_ARM_CUSTOM_BYPASS,
STATE_ALARM_TRIGGERED: SERVICE_ALARM_TRIGGER,
}
state_to_support = {
STATE_ALARM_ARMED_HOME: alarm_control_panel.const.SUPPORT_ALARM_ARM_HOME,
STATE_ALARM_ARMED_AWAY: alarm_control_panel.const.SUPPORT_ALARM_ARM_AWAY,
STATE_ALARM_ARMED_NIGHT: alarm_control_panel.const.SUPPORT_ALARM_ARM_NIGHT,
STATE_ALARM_ARMED_CUSTOM_BYPASS: alarm_control_panel.const.SUPPORT_ALARM_ARM_CUSTOM_BYPASS,
STATE_ALARM_TRIGGERED: alarm_control_panel.const.SUPPORT_ALARM_TRIGGER,
}
@staticmethod
def supported(domain, features, device_class):
"""Test if state is supported."""
return domain == alarm_control_panel.DOMAIN
@staticmethod
def might_2fa(domain, features, device_class):
"""Return if the trait might ask for 2FA."""
return True
def _supported_states(self):
"""Return supported states."""
features = self.state.attributes.get(ATTR_SUPPORTED_FEATURES, 0)
return [
state
for state, required_feature in self.state_to_support.items()
if features & required_feature != 0
]
def sync_attributes(self):
"""Return ArmDisarm attributes for a sync request."""
response = {}
levels = []
for state in self._supported_states():
# level synonyms are generated from state names
# 'armed_away' becomes 'armed away' or 'away'
level_synonym = [state.replace("_", " ")]
if state != STATE_ALARM_TRIGGERED:
level_synonym.append(state.split("_")[1])
level = {
"level_name": state,
"level_values": [{"level_synonym": level_synonym, "lang": "en"}],
}
levels.append(level)
response["availableArmLevels"] = {"levels": levels, "ordered": False}
return response
def query_attributes(self):
"""Return ArmDisarm query attributes."""
if "next_state" in self.state.attributes:
armed_state = self.state.attributes["next_state"]
else:
armed_state = self.state.state
response = {"isArmed": armed_state in self.state_to_service}
if response["isArmed"]:
response.update({"currentArmLevel": armed_state})
return response
async def execute(self, command, data, params, challenge):
"""Execute an ArmDisarm command."""
if params["arm"] and not params.get("cancel"):
arm_level = params.get("armLevel")
# If no arm level given, we can only arm it if there is
# only one supported arm type. We never default to triggered.
if not arm_level:
states = self._supported_states()
if STATE_ALARM_TRIGGERED in states:
states.remove(STATE_ALARM_TRIGGERED)
if len(states) != 1:
raise SmartHomeError(ERR_NOT_SUPPORTED, "ArmLevel missing")
arm_level = states[0]
if self.state.state == arm_level:
raise SmartHomeError(ERR_ALREADY_ARMED, "System is already armed")
if self.state.attributes["code_arm_required"]:
_verify_pin_challenge(data, self.state, challenge)
service = self.state_to_service[arm_level]
# disarm the system without asking for code when
# 'cancel' arming action is received while current status is pending
elif (
params["arm"]
and params.get("cancel")
and self.state.state == STATE_ALARM_PENDING
):
service = SERVICE_ALARM_DISARM
else:
if self.state.state == STATE_ALARM_DISARMED:
raise SmartHomeError(ERR_ALREADY_DISARMED, "System is already disarmed")
_verify_pin_challenge(data, self.state, challenge)
service = SERVICE_ALARM_DISARM
await self.hass.services.async_call(
alarm_control_panel.DOMAIN,
service,
{
ATTR_ENTITY_ID: self.state.entity_id,
ATTR_CODE: data.config.secure_devices_pin,
},
blocking=True,
context=data.context,
)
@register_trait
class FanSpeedTrait(_Trait):
"""Trait to control speed of Fan.
https://developers.google.com/actions/smarthome/traits/fanspeed
"""
name = TRAIT_FANSPEED
commands = [COMMAND_FANSPEED]
speed_synonyms = {
fan.SPEED_OFF: ["stop", "off"],
fan.SPEED_LOW: ["slow", "low", "slowest", "lowest"],
fan.SPEED_MEDIUM: ["medium", "mid", "middle"],
fan.SPEED_HIGH: ["high", "max", "fast", "highest", "fastest", "maximum"],
}
@staticmethod
def supported(domain, features, device_class):
"""Test if state is supported."""
if domain == fan.DOMAIN:
return features & fan.SUPPORT_SET_SPEED
if domain == climate.DOMAIN:
return features & climate.SUPPORT_FAN_MODE
return False
def sync_attributes(self):
"""Return speed point and modes attributes for a sync request."""
domain = self.state.domain
speeds = []
reversible = False
if domain == fan.DOMAIN:
modes = self.state.attributes.get(fan.ATTR_SPEED_LIST, [])
for mode in modes:
if mode not in self.speed_synonyms:
continue
speed = {
"speed_name": mode,
"speed_values": [
{"speed_synonym": self.speed_synonyms.get(mode), "lang": "en"}
],
}
speeds.append(speed)
reversible = bool(
self.state.attributes.get(ATTR_SUPPORTED_FEATURES, 0)
& fan.SUPPORT_DIRECTION
)
elif domain == climate.DOMAIN:
modes = self.state.attributes.get(climate.ATTR_FAN_MODES, [])
for mode in modes:
speed = {
"speed_name": mode,
"speed_values": [{"speed_synonym": [mode], "lang": "en"}],
}
speeds.append(speed)
return {
"availableFanSpeeds": {"speeds": speeds, "ordered": True},
"reversible": reversible,
"supportsFanSpeedPercent": True,
}
def query_attributes(self):
"""Return speed point and modes query attributes."""
attrs = self.state.attributes
domain = self.state.domain
response = {}
if domain == climate.DOMAIN:
speed = attrs.get(climate.ATTR_FAN_MODE)
if speed is not None:
response["currentFanSpeedSetting"] = speed
if domain == fan.DOMAIN:
speed = attrs.get(fan.ATTR_SPEED)
percent = attrs.get(fan.ATTR_PERCENTAGE) or 0
if speed is not None:
response["on"] = speed != fan.SPEED_OFF
response["currentFanSpeedSetting"] = speed
response["currentFanSpeedPercent"] = percent
return response
async def execute(self, command, data, params, challenge):
"""Execute an SetFanSpeed command."""
domain = self.state.domain
if domain == climate.DOMAIN:
await self.hass.services.async_call(
climate.DOMAIN,
climate.SERVICE_SET_FAN_MODE,
{
ATTR_ENTITY_ID: self.state.entity_id,
climate.ATTR_FAN_MODE: params["fanSpeed"],
},
blocking=True,
context=data.context,
)
if domain == fan.DOMAIN:
service_params = {
ATTR_ENTITY_ID: self.state.entity_id,
}
if "fanSpeedPercent" in params:
service = fan.SERVICE_SET_PERCENTAGE
service_params[fan.ATTR_PERCENTAGE] = params["fanSpeedPercent"]
else:
service = fan.SERVICE_SET_SPEED
service_params[fan.ATTR_SPEED] = params["fanSpeed"]
await self.hass.services.async_call(
fan.DOMAIN,
service,
service_params,
blocking=True,
context=data.context,
)
@register_trait
class ModesTrait(_Trait):
"""Trait to set modes.
https://developers.google.com/actions/smarthome/traits/modes
"""
name = TRAIT_MODES
commands = [COMMAND_MODES]
SYNONYMS = {
"sound mode": ["sound mode", "effects"],
"option": ["option", "setting", "mode", "value"],
}
@staticmethod
def supported(domain, features, device_class):
"""Test if state is supported."""
if domain == input_select.DOMAIN:
return True
if domain == humidifier.DOMAIN and features & humidifier.SUPPORT_MODES:
return True
if domain == light.DOMAIN and features & light.SUPPORT_EFFECT:
return True
if domain != media_player.DOMAIN:
return False
return features & media_player.SUPPORT_SELECT_SOUND_MODE
def _generate(self, name, settings):
"""Generate a list of modes."""
mode = {
"name": name,
"name_values": [
{"name_synonym": self.SYNONYMS.get(name, [name]), "lang": "en"}
],
"settings": [],
"ordered": False,
}
for setting in settings:
mode["settings"].append(
{
"setting_name": setting,
"setting_values": [
{
"setting_synonym": self.SYNONYMS.get(setting, [setting]),
"lang": "en",
}
],
}
)
return mode
def sync_attributes(self):
"""Return mode attributes for a sync request."""
modes = []
for domain, attr, name in (
(media_player.DOMAIN, media_player.ATTR_SOUND_MODE_LIST, "sound mode"),
(input_select.DOMAIN, input_select.ATTR_OPTIONS, "option"),
(humidifier.DOMAIN, humidifier.ATTR_AVAILABLE_MODES, "mode"),
(light.DOMAIN, light.ATTR_EFFECT_LIST, "effect"),
):
if self.state.domain != domain:
continue
items = self.state.attributes.get(attr)
if items is not None:
modes.append(self._generate(name, items))
# Shortcut since all domains are currently unique
break
payload = {"availableModes": modes}
return payload
def query_attributes(self):
"""Return current modes."""
attrs = self.state.attributes
response = {}
mode_settings = {}
if self.state.domain == media_player.DOMAIN:
if media_player.ATTR_SOUND_MODE_LIST in attrs:
mode_settings["sound mode"] = attrs.get(media_player.ATTR_SOUND_MODE)
elif self.state.domain == input_select.DOMAIN:
mode_settings["option"] = self.state.state
elif self.state.domain == humidifier.DOMAIN:
if ATTR_MODE in attrs:
mode_settings["mode"] = attrs.get(ATTR_MODE)
elif self.state.domain == light.DOMAIN and light.ATTR_EFFECT in attrs:
mode_settings["effect"] = attrs.get(light.ATTR_EFFECT)
if mode_settings:
response["on"] = self.state.state not in (STATE_OFF, STATE_UNKNOWN)
response["currentModeSettings"] = mode_settings
return response
async def execute(self, command, data, params, challenge):
"""Execute a SetModes command."""
settings = params.get("updateModeSettings")
if self.state.domain == input_select.DOMAIN:
option = params["updateModeSettings"]["option"]
await self.hass.services.async_call(
input_select.DOMAIN,
input_select.SERVICE_SELECT_OPTION,
{
ATTR_ENTITY_ID: self.state.entity_id,
input_select.ATTR_OPTION: option,
},
blocking=True,
context=data.context,
)
return
if self.state.domain == humidifier.DOMAIN:
requested_mode = settings["mode"]
await self.hass.services.async_call(
humidifier.DOMAIN,
humidifier.SERVICE_SET_MODE,
{
ATTR_MODE: requested_mode,
ATTR_ENTITY_ID: self.state.entity_id,
},
blocking=True,
context=data.context,
)
return
if self.state.domain == light.DOMAIN:
requested_effect = settings["effect"]
await self.hass.services.async_call(
light.DOMAIN,
SERVICE_TURN_ON,
{
ATTR_ENTITY_ID: self.state.entity_id,
light.ATTR_EFFECT: requested_effect,
},
blocking=True,
context=data.context,
)
return
if self.state.domain != media_player.DOMAIN:
_LOGGER.info(
"Received an Options command for unrecognised domain %s",
self.state.domain,
)
return
sound_mode = settings.get("sound mode")
if sound_mode:
await self.hass.services.async_call(
media_player.DOMAIN,
media_player.SERVICE_SELECT_SOUND_MODE,
{
ATTR_ENTITY_ID: self.state.entity_id,
media_player.ATTR_SOUND_MODE: sound_mode,
},
blocking=True,
context=data.context,
)
@register_trait
class InputSelectorTrait(_Trait):
"""Trait to set modes.
https://developers.google.com/assistant/smarthome/traits/inputselector
"""
name = TRAIT_INPUTSELECTOR
commands = [COMMAND_INPUT, COMMAND_NEXT_INPUT, COMMAND_PREVIOUS_INPUT]
SYNONYMS = {}
@staticmethod
def supported(domain, features, device_class):
"""Test if state is supported."""
if domain == media_player.DOMAIN and (
features & media_player.SUPPORT_SELECT_SOURCE
):
return True
return False
def sync_attributes(self):
"""Return mode attributes for a sync request."""
attrs = self.state.attributes
inputs = [
{"key": source, "names": [{"name_synonym": [source], "lang": "en"}]}
for source in attrs.get(media_player.ATTR_INPUT_SOURCE_LIST, [])
]
payload = {"availableInputs": inputs, "orderedInputs": True}
return payload
def query_attributes(self):
"""Return current modes."""
attrs = self.state.attributes
return {"currentInput": attrs.get(media_player.ATTR_INPUT_SOURCE, "")}
async def execute(self, command, data, params, challenge):
"""Execute an SetInputSource command."""
sources = self.state.attributes.get(media_player.ATTR_INPUT_SOURCE_LIST) or []
source = self.state.attributes.get(media_player.ATTR_INPUT_SOURCE)
if command == COMMAND_INPUT:
requested_source = params.get("newInput")
elif command == COMMAND_NEXT_INPUT:
requested_source = _next_selected(sources, source)
elif command == COMMAND_PREVIOUS_INPUT:
requested_source = _next_selected(list(reversed(sources)), source)
else:
raise SmartHomeError(ERR_NOT_SUPPORTED, "Unsupported command")
if requested_source not in sources:
raise SmartHomeError(ERR_UNSUPPORTED_INPUT, "Unsupported input")
await self.hass.services.async_call(
media_player.DOMAIN,
media_player.SERVICE_SELECT_SOURCE,
{
ATTR_ENTITY_ID: self.state.entity_id,
media_player.ATTR_INPUT_SOURCE: requested_source,
},
blocking=True,
context=data.context,
)
@register_trait
class OpenCloseTrait(_Trait):
"""Trait to open and close a cover.
https://developers.google.com/actions/smarthome/traits/openclose
"""
# Cover device classes that require 2FA
COVER_2FA = (
cover.DEVICE_CLASS_DOOR,
cover.DEVICE_CLASS_GARAGE,
cover.DEVICE_CLASS_GATE,
)
name = TRAIT_OPENCLOSE
commands = [COMMAND_OPENCLOSE, COMMAND_OPENCLOSE_RELATIVE]
@staticmethod
def supported(domain, features, device_class):
"""Test if state is supported."""
if domain == cover.DOMAIN:
return True
return domain == binary_sensor.DOMAIN and device_class in (
binary_sensor.DEVICE_CLASS_DOOR,
binary_sensor.DEVICE_CLASS_GARAGE_DOOR,
binary_sensor.DEVICE_CLASS_LOCK,
binary_sensor.DEVICE_CLASS_OPENING,
binary_sensor.DEVICE_CLASS_WINDOW,
)
@staticmethod
def might_2fa(domain, features, device_class):
"""Return if the trait might ask for 2FA."""
return domain == cover.DOMAIN and device_class in OpenCloseTrait.COVER_2FA
def sync_attributes(self):
"""Return opening direction."""
response = {}
features = self.state.attributes.get(ATTR_SUPPORTED_FEATURES, 0)
if self.state.domain == binary_sensor.DOMAIN:
response["queryOnlyOpenClose"] = True
response["discreteOnlyOpenClose"] = True
elif (
self.state.domain == cover.DOMAIN
and features & cover.SUPPORT_SET_POSITION == 0
):
response["discreteOnlyOpenClose"] = True
if (
features & cover.SUPPORT_OPEN == 0
and features & cover.SUPPORT_CLOSE == 0
):
response["queryOnlyOpenClose"] = True
if self.state.attributes.get(ATTR_ASSUMED_STATE):
response["commandOnlyOpenClose"] = True
return response
def query_attributes(self):
"""Return state query attributes."""
domain = self.state.domain
response = {}
# When it's an assumed state, we will return empty state
# This shouldn't happen because we set `commandOnlyOpenClose`
# but Google still queries. Erroring here will cause device
# to show up offline.
if self.state.attributes.get(ATTR_ASSUMED_STATE):
return response
if domain == cover.DOMAIN:
if self.state.state == STATE_UNKNOWN:
raise SmartHomeError(
ERR_NOT_SUPPORTED, "Querying state is not supported"
)
position = self.state.attributes.get(cover.ATTR_CURRENT_POSITION)
if position is not None:
response["openPercent"] = position
elif self.state.state != cover.STATE_CLOSED:
response["openPercent"] = 100
else:
response["openPercent"] = 0
elif domain == binary_sensor.DOMAIN:
if self.state.state == STATE_ON:
response["openPercent"] = 100
else:
response["openPercent"] = 0
return response
async def execute(self, command, data, params, challenge):
"""Execute an Open, close, Set position command."""
domain = self.state.domain
features = self.state.attributes.get(ATTR_SUPPORTED_FEATURES, 0)
if domain == cover.DOMAIN:
svc_params = {ATTR_ENTITY_ID: self.state.entity_id}
should_verify = False
if command == COMMAND_OPENCLOSE_RELATIVE:
position = self.state.attributes.get(cover.ATTR_CURRENT_POSITION)
if position is None:
raise SmartHomeError(
ERR_NOT_SUPPORTED,
"Current position not know for relative command",
)
position = max(0, min(100, position + params["openRelativePercent"]))
else:
position = params["openPercent"]
if position == 0:
service = cover.SERVICE_CLOSE_COVER
should_verify = False
elif position == 100:
service = cover.SERVICE_OPEN_COVER
should_verify = True
elif features & cover.SUPPORT_SET_POSITION:
service = cover.SERVICE_SET_COVER_POSITION
if position > 0:
should_verify = True
svc_params[cover.ATTR_POSITION] = position
else:
raise SmartHomeError(
ERR_NOT_SUPPORTED, "No support for partial open close"
)
if (
should_verify
and self.state.attributes.get(ATTR_DEVICE_CLASS)
in OpenCloseTrait.COVER_2FA
):
_verify_pin_challenge(data, self.state, challenge)
await self.hass.services.async_call(
cover.DOMAIN, service, svc_params, blocking=True, context=data.context
)
@register_trait
class VolumeTrait(_Trait):
"""Trait to control volume of a device.
https://developers.google.com/actions/smarthome/traits/volume
"""
name = TRAIT_VOLUME
commands = [COMMAND_SET_VOLUME, COMMAND_VOLUME_RELATIVE, COMMAND_MUTE]
@staticmethod
def supported(domain, features, device_class):
"""Test if trait is supported."""
if domain == media_player.DOMAIN:
return features & (
media_player.SUPPORT_VOLUME_SET | media_player.SUPPORT_VOLUME_STEP
)
return False
def sync_attributes(self):
"""Return volume attributes for a sync request."""
features = self.state.attributes.get(ATTR_SUPPORTED_FEATURES, 0)
return {
"volumeCanMuteAndUnmute": bool(features & media_player.SUPPORT_VOLUME_MUTE),
"commandOnlyVolume": self.state.attributes.get(ATTR_ASSUMED_STATE, False),
# Volume amounts in SET_VOLUME and VOLUME_RELATIVE are on a scale
# from 0 to this value.
"volumeMaxLevel": 100,
# Default change for queries like "Hey Google, volume up".
# 10% corresponds to the default behavior for the
# media_player.volume{up,down} services.
"levelStepSize": 10,
}
def query_attributes(self):
"""Return volume query attributes."""
response = {}
level = self.state.attributes.get(media_player.ATTR_MEDIA_VOLUME_LEVEL)
if level is not None:
# Convert 0.0-1.0 to 0-100
response["currentVolume"] = int(level * 100)
muted = self.state.attributes.get(media_player.ATTR_MEDIA_VOLUME_MUTED)
if muted is not None:
response["isMuted"] = bool(muted)
return response
async def _set_volume_absolute(self, data, level):
await self.hass.services.async_call(
media_player.DOMAIN,
media_player.SERVICE_VOLUME_SET,
{
ATTR_ENTITY_ID: self.state.entity_id,
media_player.ATTR_MEDIA_VOLUME_LEVEL: level,
},
blocking=True,
context=data.context,
)
async def _execute_set_volume(self, data, params):
level = max(0, min(100, params["volumeLevel"]))
if not (
self.state.attributes.get(ATTR_SUPPORTED_FEATURES, 0)
& media_player.SUPPORT_VOLUME_SET
):
raise SmartHomeError(ERR_NOT_SUPPORTED, "Command not supported")
await self._set_volume_absolute(data, level / 100)
async def _execute_volume_relative(self, data, params):
relative = params["relativeSteps"]
features = self.state.attributes.get(ATTR_SUPPORTED_FEATURES, 0)
if features & media_player.SUPPORT_VOLUME_SET:
current = self.state.attributes.get(media_player.ATTR_MEDIA_VOLUME_LEVEL)
target = max(0.0, min(1.0, current + relative / 100))
await self._set_volume_absolute(data, target)
elif features & media_player.SUPPORT_VOLUME_STEP:
svc = media_player.SERVICE_VOLUME_UP
if relative < 0:
svc = media_player.SERVICE_VOLUME_DOWN
relative = -relative
for _ in range(relative):
await self.hass.services.async_call(
media_player.DOMAIN,
svc,
{ATTR_ENTITY_ID: self.state.entity_id},
blocking=True,
context=data.context,
)
else:
raise SmartHomeError(ERR_NOT_SUPPORTED, "Command not supported")
async def _execute_mute(self, data, params):
mute = params["mute"]
if not (
self.state.attributes.get(ATTR_SUPPORTED_FEATURES, 0)
& media_player.SUPPORT_VOLUME_MUTE
):
raise SmartHomeError(ERR_NOT_SUPPORTED, "Command not supported")
await self.hass.services.async_call(
media_player.DOMAIN,
media_player.SERVICE_VOLUME_MUTE,
{
ATTR_ENTITY_ID: self.state.entity_id,
media_player.ATTR_MEDIA_VOLUME_MUTED: mute,
},
blocking=True,
context=data.context,
)
async def execute(self, command, data, params, challenge):
"""Execute a volume command."""
if command == COMMAND_SET_VOLUME:
await self._execute_set_volume(data, params)
elif command == COMMAND_VOLUME_RELATIVE:
await self._execute_volume_relative(data, params)
elif command == COMMAND_MUTE:
await self._execute_mute(data, params)
else:
raise SmartHomeError(ERR_NOT_SUPPORTED, "Command not supported")
def _verify_pin_challenge(data, state, challenge):
"""Verify a pin challenge."""
if not data.config.should_2fa(state):
return
if not data.config.secure_devices_pin:
raise SmartHomeError(ERR_CHALLENGE_NOT_SETUP, "Challenge is not set up")
if not challenge:
raise ChallengeNeeded(CHALLENGE_PIN_NEEDED)
pin = challenge.get("pin")
if pin != data.config.secure_devices_pin:
raise ChallengeNeeded(CHALLENGE_FAILED_PIN_NEEDED)
def _verify_ack_challenge(data, state, challenge):
"""Verify an ack challenge."""
if not data.config.should_2fa(state):
return
if not challenge or not challenge.get("ack"):
raise ChallengeNeeded(CHALLENGE_ACK_NEEDED)
MEDIA_COMMAND_SUPPORT_MAPPING = {
COMMAND_MEDIA_NEXT: media_player.SUPPORT_NEXT_TRACK,
COMMAND_MEDIA_PAUSE: media_player.SUPPORT_PAUSE,
COMMAND_MEDIA_PREVIOUS: media_player.SUPPORT_PREVIOUS_TRACK,
COMMAND_MEDIA_RESUME: media_player.SUPPORT_PLAY,
COMMAND_MEDIA_SEEK_RELATIVE: media_player.SUPPORT_SEEK,
COMMAND_MEDIA_SEEK_TO_POSITION: media_player.SUPPORT_SEEK,
COMMAND_MEDIA_SHUFFLE: media_player.SUPPORT_SHUFFLE_SET,
COMMAND_MEDIA_STOP: media_player.SUPPORT_STOP,
}
MEDIA_COMMAND_ATTRIBUTES = {
COMMAND_MEDIA_NEXT: "NEXT",
COMMAND_MEDIA_PAUSE: "PAUSE",
COMMAND_MEDIA_PREVIOUS: "PREVIOUS",
COMMAND_MEDIA_RESUME: "RESUME",
COMMAND_MEDIA_SEEK_RELATIVE: "SEEK_RELATIVE",
COMMAND_MEDIA_SEEK_TO_POSITION: "SEEK_TO_POSITION",
COMMAND_MEDIA_SHUFFLE: "SHUFFLE",
COMMAND_MEDIA_STOP: "STOP",
}
@register_trait
class TransportControlTrait(_Trait):
"""Trait to control media playback.
https://developers.google.com/actions/smarthome/traits/transportcontrol
"""
name = TRAIT_TRANSPORT_CONTROL
commands = [
COMMAND_MEDIA_NEXT,
COMMAND_MEDIA_PAUSE,
COMMAND_MEDIA_PREVIOUS,
COMMAND_MEDIA_RESUME,
COMMAND_MEDIA_SEEK_RELATIVE,
COMMAND_MEDIA_SEEK_TO_POSITION,
COMMAND_MEDIA_SHUFFLE,
COMMAND_MEDIA_STOP,
]
@staticmethod
def supported(domain, features, device_class):
"""Test if state is supported."""
if domain == media_player.DOMAIN:
for feature in MEDIA_COMMAND_SUPPORT_MAPPING.values():
if features & feature:
return True
return False
def sync_attributes(self):
"""Return opening direction."""
response = {}
if self.state.domain == media_player.DOMAIN:
features = self.state.attributes.get(ATTR_SUPPORTED_FEATURES, 0)
support = []
for command, feature in MEDIA_COMMAND_SUPPORT_MAPPING.items():
if features & feature:
support.append(MEDIA_COMMAND_ATTRIBUTES[command])
response["transportControlSupportedCommands"] = support
return response
def query_attributes(self):
"""Return the attributes of this trait for this entity."""
return {}
async def execute(self, command, data, params, challenge):
"""Execute a media command."""
service_attrs = {ATTR_ENTITY_ID: self.state.entity_id}
if command == COMMAND_MEDIA_SEEK_RELATIVE:
service = media_player.SERVICE_MEDIA_SEEK
rel_position = params["relativePositionMs"] / 1000
seconds_since = 0 # Default to 0 seconds
if self.state.state == STATE_PLAYING:
now = dt.utcnow()
upd_at = self.state.attributes.get(
media_player.ATTR_MEDIA_POSITION_UPDATED_AT, now
)
seconds_since = (now - upd_at).total_seconds()
position = self.state.attributes.get(media_player.ATTR_MEDIA_POSITION, 0)
max_position = self.state.attributes.get(
media_player.ATTR_MEDIA_DURATION, 0
)
service_attrs[media_player.ATTR_MEDIA_SEEK_POSITION] = min(
max(position + seconds_since + rel_position, 0), max_position
)
elif command == COMMAND_MEDIA_SEEK_TO_POSITION:
service = media_player.SERVICE_MEDIA_SEEK
max_position = self.state.attributes.get(
media_player.ATTR_MEDIA_DURATION, 0
)
service_attrs[media_player.ATTR_MEDIA_SEEK_POSITION] = min(
max(params["absPositionMs"] / 1000, 0), max_position
)
elif command == COMMAND_MEDIA_NEXT:
service = media_player.SERVICE_MEDIA_NEXT_TRACK
elif command == COMMAND_MEDIA_PAUSE:
service = media_player.SERVICE_MEDIA_PAUSE
elif command == COMMAND_MEDIA_PREVIOUS:
service = media_player.SERVICE_MEDIA_PREVIOUS_TRACK
elif command == COMMAND_MEDIA_RESUME:
service = media_player.SERVICE_MEDIA_PLAY
elif command == COMMAND_MEDIA_SHUFFLE:
service = media_player.SERVICE_SHUFFLE_SET
# Google Assistant only supports enabling shuffle
service_attrs[media_player.ATTR_MEDIA_SHUFFLE] = True
elif command == COMMAND_MEDIA_STOP:
service = media_player.SERVICE_MEDIA_STOP
else:
raise SmartHomeError(ERR_NOT_SUPPORTED, "Command not supported")
await self.hass.services.async_call(
media_player.DOMAIN,
service,
service_attrs,
blocking=True,
context=data.context,
)
@register_trait
class MediaStateTrait(_Trait):
"""Trait to get media playback state.
https://developers.google.com/actions/smarthome/traits/mediastate
"""
name = TRAIT_MEDIA_STATE
commands = []
activity_lookup = {
STATE_OFF: "INACTIVE",
STATE_IDLE: "STANDBY",
STATE_PLAYING: "ACTIVE",
STATE_ON: "STANDBY",
STATE_PAUSED: "STANDBY",
STATE_STANDBY: "STANDBY",
STATE_UNAVAILABLE: "INACTIVE",
STATE_UNKNOWN: "INACTIVE",
}
playback_lookup = {
STATE_OFF: "STOPPED",
STATE_IDLE: "STOPPED",
STATE_PLAYING: "PLAYING",
STATE_ON: "STOPPED",
STATE_PAUSED: "PAUSED",
STATE_STANDBY: "STOPPED",
STATE_UNAVAILABLE: "STOPPED",
STATE_UNKNOWN: "STOPPED",
}
@staticmethod
def supported(domain, features, device_class):
"""Test if state is supported."""
return domain == media_player.DOMAIN
def sync_attributes(self):
"""Return attributes for a sync request."""
return {"supportActivityState": True, "supportPlaybackState": True}
def query_attributes(self):
"""Return the attributes of this trait for this entity."""
return {
"activityState": self.activity_lookup.get(self.state.state, "INACTIVE"),
"playbackState": self.playback_lookup.get(self.state.state, "STOPPED"),
}
|
"""The tests for the mochad light platform."""
import unittest.mock as mock
import pytest
from homeassistant.components import light
from homeassistant.components.mochad import light as mochad
from homeassistant.setup import async_setup_component
@pytest.fixture(autouse=True)
def pymochad_mock():
"""Mock pymochad."""
with mock.patch("homeassistant.components.mochad.light.device") as device:
yield device
@pytest.fixture
def light_mock(hass, brightness):
"""Mock light."""
controller_mock = mock.MagicMock()
dev_dict = {"address": "a1", "name": "fake_light", "brightness_levels": brightness}
return mochad.MochadLight(hass, controller_mock, dev_dict)
async def test_setup_adds_proper_devices(hass):
"""Test if setup adds devices."""
good_config = {
"mochad": {},
"light": {
"platform": "mochad",
"devices": [{"name": "Light1", "address": "a1"}],
},
}
assert await async_setup_component(hass, light.DOMAIN, good_config)
@pytest.mark.parametrize(
"brightness,expected", [(32, "on"), (256, "xdim 255"), (64, "xdim 63")]
)
async def test_turn_on_with_no_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on()
light_mock.light.send_cmd.assert_called_once_with(expected)
@pytest.mark.parametrize(
"brightness,expected",
[
(32, [mock.call("on"), mock.call("dim 25")]),
(256, [mock.call("xdim 45")]),
(64, [mock.call("xdim 11")]),
],
)
async def test_turn_on_with_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on(brightness=45)
light_mock.light.send_cmd.assert_has_calls(expected)
@pytest.mark.parametrize("brightness", [32])
async def test_turn_off(light_mock):
"""Test turn_off."""
light_mock.turn_off()
light_mock.light.send_cmd.assert_called_once_with("off")
|
|
tests/components/mochad/test_light.py
|
homeassistant/components/google_assistant/trait.py
|
"""Support for Z-Wave lights."""
from __future__ import annotations
import logging
from typing import Any, Callable
from zwave_js_server.client import Client as ZwaveClient
from zwave_js_server.const import ColorComponent, CommandClass
from homeassistant.components.light import (
ATTR_BRIGHTNESS,
ATTR_COLOR_TEMP,
ATTR_HS_COLOR,
ATTR_TRANSITION,
ATTR_WHITE_VALUE,
DOMAIN as LIGHT_DOMAIN,
SUPPORT_BRIGHTNESS,
SUPPORT_COLOR,
SUPPORT_COLOR_TEMP,
SUPPORT_TRANSITION,
SUPPORT_WHITE_VALUE,
LightEntity,
)
from homeassistant.config_entries import ConfigEntry
from homeassistant.core import HomeAssistant, callback
from homeassistant.helpers.dispatcher import async_dispatcher_connect
import homeassistant.util.color as color_util
from .const import DATA_CLIENT, DATA_UNSUBSCRIBE, DOMAIN
from .discovery import ZwaveDiscoveryInfo
from .entity import ZWaveBaseEntity
LOGGER = logging.getLogger(__name__)
MULTI_COLOR_MAP = {
ColorComponent.WARM_WHITE: "warmWhite",
ColorComponent.COLD_WHITE: "coldWhite",
ColorComponent.RED: "red",
ColorComponent.GREEN: "green",
ColorComponent.BLUE: "blue",
ColorComponent.AMBER: "amber",
ColorComponent.CYAN: "cyan",
ColorComponent.PURPLE: "purple",
}
async def async_setup_entry(
hass: HomeAssistant, config_entry: ConfigEntry, async_add_entities: Callable
) -> None:
"""Set up Z-Wave Light from Config Entry."""
client: ZwaveClient = hass.data[DOMAIN][config_entry.entry_id][DATA_CLIENT]
@callback
def async_add_light(info: ZwaveDiscoveryInfo) -> None:
"""Add Z-Wave Light."""
light = ZwaveLight(config_entry, client, info)
async_add_entities([light])
hass.data[DOMAIN][config_entry.entry_id][DATA_UNSUBSCRIBE].append(
async_dispatcher_connect(
hass,
f"{DOMAIN}_{config_entry.entry_id}_add_{LIGHT_DOMAIN}",
async_add_light,
)
)
def byte_to_zwave_brightness(value: int) -> int:
"""Convert brightness in 0-255 scale to 0-99 scale.
`value` -- (int) Brightness byte value from 0-255.
"""
if value > 0:
return max(1, round((value / 255) * 99))
return 0
class ZwaveLight(ZWaveBaseEntity, LightEntity):
"""Representation of a Z-Wave light."""
def __init__(
self, config_entry: ConfigEntry, client: ZwaveClient, info: ZwaveDiscoveryInfo
) -> None:
"""Initialize the light."""
super().__init__(config_entry, client, info)
self._supports_color = False
self._supports_white_value = False
self._supports_color_temp = False
self._hs_color: tuple[float, float] | None = None
self._white_value: int | None = None
self._color_temp: int | None = None
self._min_mireds = 153 # 6500K as a safe default
self._max_mireds = 370 # 2700K as a safe default
self._supported_features = SUPPORT_BRIGHTNESS
# get additional (optional) values and set features
self._target_value = self.get_zwave_value("targetValue")
self._dimming_duration = self.get_zwave_value("duration")
if self._dimming_duration is not None:
self._supported_features |= SUPPORT_TRANSITION
self._calculate_color_values()
if self._supports_color:
self._supported_features |= SUPPORT_COLOR
if self._supports_color_temp:
self._supported_features |= SUPPORT_COLOR_TEMP
if self._supports_white_value:
self._supported_features |= SUPPORT_WHITE_VALUE
@callback
def on_value_update(self) -> None:
"""Call when a watched value is added or updated."""
self._calculate_color_values()
@property
def brightness(self) -> int:
"""Return the brightness of this light between 0..255.
Z-Wave multilevel switches use a range of [0, 99] to control brightness.
"""
if self.info.primary_value.value is not None:
return round((self.info.primary_value.value / 99) * 255)
return 0
@property
def is_on(self) -> bool:
"""Return true if device is on (brightness above 0)."""
return self.brightness > 0
@property
def hs_color(self) -> tuple[float, float] | None:
"""Return the hs color."""
return self._hs_color
@property
def white_value(self) -> int | None:
"""Return the white value of this light between 0..255."""
return self._white_value
@property
def color_temp(self) -> int | None:
"""Return the color temperature."""
return self._color_temp
@property
def min_mireds(self) -> int:
"""Return the coldest color_temp that this light supports."""
return self._min_mireds
@property
def max_mireds(self) -> int:
"""Return the warmest color_temp that this light supports."""
return self._max_mireds
@property
def supported_features(self) -> int:
"""Flag supported features."""
return self._supported_features
async def async_turn_on(self, **kwargs: Any) -> None:
"""Turn the device on."""
# RGB/HS color
hs_color = kwargs.get(ATTR_HS_COLOR)
if hs_color is not None and self._supports_color:
red, green, blue = color_util.color_hs_to_RGB(*hs_color)
colors = {
ColorComponent.RED: red,
ColorComponent.GREEN: green,
ColorComponent.BLUE: blue,
}
if self._supports_color_temp:
# turn of white leds when setting rgb
colors[ColorComponent.WARM_WHITE] = 0
colors[ColorComponent.COLD_WHITE] = 0
await self._async_set_colors(colors)
# Color temperature
color_temp = kwargs.get(ATTR_COLOR_TEMP)
if color_temp is not None and self._supports_color_temp:
# Limit color temp to min/max values
cold = max(
0,
min(
255,
round(
(self._max_mireds - color_temp)
/ (self._max_mireds - self._min_mireds)
* 255
),
),
)
warm = 255 - cold
await self._async_set_colors(
{
# turn off color leds when setting color temperature
ColorComponent.RED: 0,
ColorComponent.GREEN: 0,
ColorComponent.BLUE: 0,
ColorComponent.WARM_WHITE: warm,
ColorComponent.COLD_WHITE: cold,
}
)
# White value
white_value = kwargs.get(ATTR_WHITE_VALUE)
if white_value is not None and self._supports_white_value:
# white led brightness is controlled by white level
# rgb leds (if any) can be on at the same time
await self._async_set_colors(
{
ColorComponent.WARM_WHITE: white_value,
ColorComponent.COLD_WHITE: white_value,
}
)
# set brightness
await self._async_set_brightness(
kwargs.get(ATTR_BRIGHTNESS), kwargs.get(ATTR_TRANSITION)
)
async def async_turn_off(self, **kwargs: Any) -> None:
"""Turn the light off."""
await self._async_set_brightness(0, kwargs.get(ATTR_TRANSITION))
async def _async_set_colors(self, colors: dict[ColorComponent, int]) -> None:
"""Set (multiple) defined colors to given value(s)."""
# prefer the (new) combined color property
# https://github.com/zwave-js/node-zwave-js/pull/1782
combined_color_val = self.get_zwave_value(
"targetColor",
CommandClass.SWITCH_COLOR,
value_property_key=None,
)
if combined_color_val and isinstance(combined_color_val.value, dict):
colors_dict = {}
for color, value in colors.items():
color_name = MULTI_COLOR_MAP[color]
colors_dict[color_name] = value
# set updated color object
await self.info.node.async_set_value(combined_color_val, colors_dict)
return
# fallback to setting the color(s) one by one if multicolor fails
# not sure this is needed at all, but just in case
for color, value in colors.items():
await self._async_set_color(color, value)
async def _async_set_color(self, color: ColorComponent, new_value: int) -> None:
"""Set defined color to given value."""
# actually set the new color value
target_zwave_value = self.get_zwave_value(
"targetColor",
CommandClass.SWITCH_COLOR,
value_property_key=color.value,
)
if target_zwave_value is None:
# guard for unsupported color
return
await self.info.node.async_set_value(target_zwave_value, new_value)
async def _async_set_brightness(
self, brightness: int | None, transition: int | None = None
) -> None:
"""Set new brightness to light."""
if brightness is None:
# Level 255 means to set it to previous value.
zwave_brightness = 255
else:
# Zwave multilevel switches use a range of [0, 99] to control brightness.
zwave_brightness = byte_to_zwave_brightness(brightness)
# set transition value before sending new brightness
await self._async_set_transition_duration(transition)
# setting a value requires setting targetValue
await self.info.node.async_set_value(self._target_value, zwave_brightness)
async def _async_set_transition_duration(self, duration: int | None = None) -> None:
"""Set the transition time for the brightness value."""
if self._dimming_duration is None:
return
# pylint: disable=fixme,unreachable
# TODO: setting duration needs to be fixed upstream
# https://github.com/zwave-js/node-zwave-js/issues/1321
return
if duration is None: # type: ignore
# no transition specified by user, use defaults
duration = 7621 # anything over 7620 uses the factory default
else: # pragma: no cover
# transition specified by user
transition = duration
if transition <= 127:
duration = transition
else:
minutes = round(transition / 60)
LOGGER.debug(
"Transition rounded to %d minutes for %s",
minutes,
self.entity_id,
)
duration = minutes + 128
# only send value if it differs from current
# this prevents sending a command for nothing
if self._dimming_duration.value != duration: # pragma: no cover
await self.info.node.async_set_value(self._dimming_duration, duration)
@callback
def _calculate_color_values(self) -> None:
"""Calculate light colors."""
# NOTE: We lookup all values here (instead of relying on the multicolor one)
# to find out what colors are supported
# as this is a simple lookup by key, this not heavy
red_val = self.get_zwave_value(
"currentColor",
CommandClass.SWITCH_COLOR,
value_property_key=ColorComponent.RED.value,
)
green_val = self.get_zwave_value(
"currentColor",
CommandClass.SWITCH_COLOR,
value_property_key=ColorComponent.GREEN.value,
)
blue_val = self.get_zwave_value(
"currentColor",
CommandClass.SWITCH_COLOR,
value_property_key=ColorComponent.BLUE.value,
)
ww_val = self.get_zwave_value(
"currentColor",
CommandClass.SWITCH_COLOR,
value_property_key=ColorComponent.WARM_WHITE.value,
)
cw_val = self.get_zwave_value(
"currentColor",
CommandClass.SWITCH_COLOR,
value_property_key=ColorComponent.COLD_WHITE.value,
)
# prefer the (new) combined color property
# https://github.com/zwave-js/node-zwave-js/pull/1782
combined_color_val = self.get_zwave_value(
"currentColor",
CommandClass.SWITCH_COLOR,
value_property_key=None,
)
if combined_color_val and isinstance(combined_color_val.value, dict):
multi_color = combined_color_val.value
else:
multi_color = {}
# RGB support
if red_val and green_val and blue_val:
# prefer values from the multicolor property
red = multi_color.get("red", red_val.value)
green = multi_color.get("green", green_val.value)
blue = multi_color.get("blue", blue_val.value)
self._supports_color = True
# convert to HS
self._hs_color = color_util.color_RGB_to_hs(red, green, blue)
# color temperature support
if ww_val and cw_val:
self._supports_color_temp = True
warm_white = multi_color.get("warmWhite", ww_val.value)
cold_white = multi_color.get("coldWhite", cw_val.value)
# Calculate color temps based on whites
if cold_white or warm_white:
self._color_temp = round(
self._max_mireds
- ((cold_white / 255) * (self._max_mireds - self._min_mireds))
)
else:
self._color_temp = None
# only one white channel (warm white) = white_level support
elif ww_val:
self._supports_white_value = True
self._white_value = multi_color.get("warmWhite", ww_val.value)
# only one white channel (cool white) = white_level support
elif cw_val:
self._supports_white_value = True
self._white_value = multi_color.get("coldWhite", cw_val.value)
|
"""The tests for the mochad light platform."""
import unittest.mock as mock
import pytest
from homeassistant.components import light
from homeassistant.components.mochad import light as mochad
from homeassistant.setup import async_setup_component
@pytest.fixture(autouse=True)
def pymochad_mock():
"""Mock pymochad."""
with mock.patch("homeassistant.components.mochad.light.device") as device:
yield device
@pytest.fixture
def light_mock(hass, brightness):
"""Mock light."""
controller_mock = mock.MagicMock()
dev_dict = {"address": "a1", "name": "fake_light", "brightness_levels": brightness}
return mochad.MochadLight(hass, controller_mock, dev_dict)
async def test_setup_adds_proper_devices(hass):
"""Test if setup adds devices."""
good_config = {
"mochad": {},
"light": {
"platform": "mochad",
"devices": [{"name": "Light1", "address": "a1"}],
},
}
assert await async_setup_component(hass, light.DOMAIN, good_config)
@pytest.mark.parametrize(
"brightness,expected", [(32, "on"), (256, "xdim 255"), (64, "xdim 63")]
)
async def test_turn_on_with_no_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on()
light_mock.light.send_cmd.assert_called_once_with(expected)
@pytest.mark.parametrize(
"brightness,expected",
[
(32, [mock.call("on"), mock.call("dim 25")]),
(256, [mock.call("xdim 45")]),
(64, [mock.call("xdim 11")]),
],
)
async def test_turn_on_with_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on(brightness=45)
light_mock.light.send_cmd.assert_has_calls(expected)
@pytest.mark.parametrize("brightness", [32])
async def test_turn_off(light_mock):
"""Test turn_off."""
light_mock.turn_off()
light_mock.light.send_cmd.assert_called_once_with("off")
|
|
tests/components/mochad/test_light.py
|
homeassistant/components/zwave_js/light.py
|
"""Support for Acmeda Roller Blinds."""
from homeassistant.components.cover import (
ATTR_POSITION,
SUPPORT_CLOSE,
SUPPORT_CLOSE_TILT,
SUPPORT_OPEN,
SUPPORT_OPEN_TILT,
SUPPORT_SET_POSITION,
SUPPORT_SET_TILT_POSITION,
SUPPORT_STOP,
SUPPORT_STOP_TILT,
CoverEntity,
)
from homeassistant.core import callback
from homeassistant.helpers.dispatcher import async_dispatcher_connect
from .base import AcmedaBase
from .const import ACMEDA_HUB_UPDATE, DOMAIN
from .helpers import async_add_acmeda_entities
async def async_setup_entry(hass, config_entry, async_add_entities):
"""Set up the Acmeda Rollers from a config entry."""
hub = hass.data[DOMAIN][config_entry.entry_id]
current = set()
@callback
def async_add_acmeda_covers():
async_add_acmeda_entities(
hass, AcmedaCover, config_entry, current, async_add_entities
)
hub.cleanup_callbacks.append(
async_dispatcher_connect(
hass,
ACMEDA_HUB_UPDATE.format(config_entry.entry_id),
async_add_acmeda_covers,
)
)
class AcmedaCover(AcmedaBase, CoverEntity):
"""Representation of a Acmeda cover device."""
@property
def current_cover_position(self):
"""Return the current position of the roller blind.
None is unknown, 0 is closed, 100 is fully open.
"""
position = None
if self.roller.type != 7:
position = 100 - self.roller.closed_percent
return position
@property
def current_cover_tilt_position(self):
"""Return the current tilt of the roller blind.
None is unknown, 0 is closed, 100 is fully open.
"""
position = None
if self.roller.type in [7, 10]:
position = 100 - self.roller.closed_percent
return position
@property
def supported_features(self):
"""Flag supported features."""
supported_features = 0
if self.current_cover_position is not None:
supported_features |= (
SUPPORT_OPEN | SUPPORT_CLOSE | SUPPORT_STOP | SUPPORT_SET_POSITION
)
if self.current_cover_tilt_position is not None:
supported_features |= (
SUPPORT_OPEN_TILT
| SUPPORT_CLOSE_TILT
| SUPPORT_STOP_TILT
| SUPPORT_SET_TILT_POSITION
)
return supported_features
@property
def is_closed(self):
"""Return if the cover is closed."""
return self.roller.closed_percent == 100
async def async_close_cover(self, **kwargs):
"""Close the roller."""
await self.roller.move_down()
async def async_open_cover(self, **kwargs):
"""Open the roller."""
await self.roller.move_up()
async def async_stop_cover(self, **kwargs):
"""Stop the roller."""
await self.roller.move_stop()
async def async_set_cover_position(self, **kwargs):
"""Move the roller shutter to a specific position."""
await self.roller.move_to(100 - kwargs[ATTR_POSITION])
async def async_close_cover_tilt(self, **kwargs):
"""Close the roller."""
await self.roller.move_down()
async def async_open_cover_tilt(self, **kwargs):
"""Open the roller."""
await self.roller.move_up()
async def async_stop_cover_tilt(self, **kwargs):
"""Stop the roller."""
await self.roller.move_stop()
async def async_set_cover_tilt(self, **kwargs):
"""Tilt the roller shutter to a specific position."""
await self.roller.move_to(100 - kwargs[ATTR_POSITION])
|
"""The tests for the mochad light platform."""
import unittest.mock as mock
import pytest
from homeassistant.components import light
from homeassistant.components.mochad import light as mochad
from homeassistant.setup import async_setup_component
@pytest.fixture(autouse=True)
def pymochad_mock():
"""Mock pymochad."""
with mock.patch("homeassistant.components.mochad.light.device") as device:
yield device
@pytest.fixture
def light_mock(hass, brightness):
"""Mock light."""
controller_mock = mock.MagicMock()
dev_dict = {"address": "a1", "name": "fake_light", "brightness_levels": brightness}
return mochad.MochadLight(hass, controller_mock, dev_dict)
async def test_setup_adds_proper_devices(hass):
"""Test if setup adds devices."""
good_config = {
"mochad": {},
"light": {
"platform": "mochad",
"devices": [{"name": "Light1", "address": "a1"}],
},
}
assert await async_setup_component(hass, light.DOMAIN, good_config)
@pytest.mark.parametrize(
"brightness,expected", [(32, "on"), (256, "xdim 255"), (64, "xdim 63")]
)
async def test_turn_on_with_no_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on()
light_mock.light.send_cmd.assert_called_once_with(expected)
@pytest.mark.parametrize(
"brightness,expected",
[
(32, [mock.call("on"), mock.call("dim 25")]),
(256, [mock.call("xdim 45")]),
(64, [mock.call("xdim 11")]),
],
)
async def test_turn_on_with_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on(brightness=45)
light_mock.light.send_cmd.assert_has_calls(expected)
@pytest.mark.parametrize("brightness", [32])
async def test_turn_off(light_mock):
"""Test turn_off."""
light_mock.turn_off()
light_mock.light.send_cmd.assert_called_once_with("off")
|
|
tests/components/mochad/test_light.py
|
homeassistant/components/acmeda/cover.py
|
"""Support for Nest Cameras."""
from datetime import timedelta
import logging
import requests
from homeassistant.components.camera import PLATFORM_SCHEMA, SUPPORT_ON_OFF, Camera
from homeassistant.util.dt import utcnow
from .const import DATA_NEST, DOMAIN
_LOGGER = logging.getLogger(__name__)
NEST_BRAND = "Nest"
PLATFORM_SCHEMA = PLATFORM_SCHEMA.extend({})
def setup_platform(hass, config, add_entities, discovery_info=None):
"""Set up a Nest Cam.
No longer in use.
"""
async def async_setup_legacy_entry(hass, entry, async_add_entities):
"""Set up a Nest sensor based on a config entry."""
camera_devices = await hass.async_add_executor_job(hass.data[DATA_NEST].cameras)
cameras = [NestCamera(structure, device) for structure, device in camera_devices]
async_add_entities(cameras, True)
class NestCamera(Camera):
"""Representation of a Nest Camera."""
def __init__(self, structure, device):
"""Initialize a Nest Camera."""
super().__init__()
self.structure = structure
self.device = device
self._location = None
self._name = None
self._online = None
self._is_streaming = None
self._is_video_history_enabled = False
# Default to non-NestAware subscribed, but will be fixed during update
self._time_between_snapshots = timedelta(seconds=30)
self._last_image = None
self._next_snapshot_at = None
@property
def name(self):
"""Return the name of the nest, if any."""
return self._name
@property
def unique_id(self):
"""Return the serial number."""
return self.device.device_id
@property
def device_info(self):
"""Return information about the device."""
return {
"identifiers": {(DOMAIN, self.device.device_id)},
"name": self.device.name_long,
"manufacturer": "Nest Labs",
"model": "Camera",
}
@property
def should_poll(self):
"""Nest camera should poll periodically."""
return True
@property
def is_recording(self):
"""Return true if the device is recording."""
return self._is_streaming
@property
def brand(self):
"""Return the brand of the camera."""
return NEST_BRAND
@property
def supported_features(self):
"""Nest Cam support turn on and off."""
return SUPPORT_ON_OFF
@property
def is_on(self):
"""Return true if on."""
return self._online and self._is_streaming
def turn_off(self):
"""Turn off camera."""
_LOGGER.debug("Turn off camera %s", self._name)
# Calling Nest API in is_streaming setter.
# device.is_streaming would not immediately change until the process
# finished in Nest Cam.
self.device.is_streaming = False
def turn_on(self):
"""Turn on camera."""
if not self._online:
_LOGGER.error("Camera %s is offline", self._name)
return
_LOGGER.debug("Turn on camera %s", self._name)
# Calling Nest API in is_streaming setter.
# device.is_streaming would not immediately change until the process
# finished in Nest Cam.
self.device.is_streaming = True
def update(self):
"""Cache value from Python-nest."""
self._location = self.device.where
self._name = self.device.name
self._online = self.device.online
self._is_streaming = self.device.is_streaming
self._is_video_history_enabled = self.device.is_video_history_enabled
if self._is_video_history_enabled:
# NestAware allowed 10/min
self._time_between_snapshots = timedelta(seconds=6)
else:
# Otherwise, 2/min
self._time_between_snapshots = timedelta(seconds=30)
def _ready_for_snapshot(self, now):
return self._next_snapshot_at is None or now > self._next_snapshot_at
def camera_image(self):
"""Return a still image response from the camera."""
now = utcnow()
if self._ready_for_snapshot(now):
url = self.device.snapshot_url
try:
response = requests.get(url)
except requests.exceptions.RequestException as error:
_LOGGER.error("Error getting camera image: %s", error)
return None
self._next_snapshot_at = now + self._time_between_snapshots
self._last_image = response.content
return self._last_image
|
"""The tests for the mochad light platform."""
import unittest.mock as mock
import pytest
from homeassistant.components import light
from homeassistant.components.mochad import light as mochad
from homeassistant.setup import async_setup_component
@pytest.fixture(autouse=True)
def pymochad_mock():
"""Mock pymochad."""
with mock.patch("homeassistant.components.mochad.light.device") as device:
yield device
@pytest.fixture
def light_mock(hass, brightness):
"""Mock light."""
controller_mock = mock.MagicMock()
dev_dict = {"address": "a1", "name": "fake_light", "brightness_levels": brightness}
return mochad.MochadLight(hass, controller_mock, dev_dict)
async def test_setup_adds_proper_devices(hass):
"""Test if setup adds devices."""
good_config = {
"mochad": {},
"light": {
"platform": "mochad",
"devices": [{"name": "Light1", "address": "a1"}],
},
}
assert await async_setup_component(hass, light.DOMAIN, good_config)
@pytest.mark.parametrize(
"brightness,expected", [(32, "on"), (256, "xdim 255"), (64, "xdim 63")]
)
async def test_turn_on_with_no_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on()
light_mock.light.send_cmd.assert_called_once_with(expected)
@pytest.mark.parametrize(
"brightness,expected",
[
(32, [mock.call("on"), mock.call("dim 25")]),
(256, [mock.call("xdim 45")]),
(64, [mock.call("xdim 11")]),
],
)
async def test_turn_on_with_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on(brightness=45)
light_mock.light.send_cmd.assert_has_calls(expected)
@pytest.mark.parametrize("brightness", [32])
async def test_turn_off(light_mock):
"""Test turn_off."""
light_mock.turn_off()
light_mock.light.send_cmd.assert_called_once_with("off")
|
|
tests/components/mochad/test_light.py
|
homeassistant/components/nest/legacy/camera.py
|
"""KIRA interface to receive UDP packets from an IR-IP bridge."""
import logging
import os
import pykira
import voluptuous as vol
from voluptuous.error import Error as VoluptuousError
import yaml
from homeassistant.const import (
CONF_CODE,
CONF_DEVICE,
CONF_HOST,
CONF_NAME,
CONF_PORT,
CONF_REPEAT,
CONF_SENSORS,
CONF_TYPE,
EVENT_HOMEASSISTANT_STOP,
STATE_UNKNOWN,
)
from homeassistant.helpers import discovery
import homeassistant.helpers.config_validation as cv
DOMAIN = "kira"
_LOGGER = logging.getLogger(__name__)
DEFAULT_HOST = "0.0.0.0"
DEFAULT_PORT = 65432
CONF_REMOTES = "remotes"
CONF_SENSOR = "sensor"
CONF_REMOTE = "remote"
CODES_YAML = f"{DOMAIN}_codes.yaml"
CODE_SCHEMA = vol.Schema(
{
vol.Required(CONF_NAME): cv.string,
vol.Required(CONF_CODE): cv.string,
vol.Optional(CONF_TYPE): cv.string,
vol.Optional(CONF_DEVICE): cv.string,
vol.Optional(CONF_REPEAT): cv.positive_int,
}
)
SENSOR_SCHEMA = vol.Schema(
{
vol.Optional(CONF_NAME, default=DOMAIN): vol.Exclusive(cv.string, "sensors"),
vol.Optional(CONF_HOST, default=DEFAULT_HOST): cv.string,
vol.Optional(CONF_PORT, default=DEFAULT_PORT): cv.port,
}
)
REMOTE_SCHEMA = vol.Schema(
{
vol.Optional(CONF_NAME, default=DOMAIN): vol.Exclusive(cv.string, "remotes"),
vol.Required(CONF_HOST): cv.string,
vol.Optional(CONF_PORT, default=DEFAULT_PORT): cv.port,
}
)
CONFIG_SCHEMA = vol.Schema(
{
DOMAIN: vol.Schema(
{
vol.Optional(CONF_SENSORS): [SENSOR_SCHEMA],
vol.Optional(CONF_REMOTES): [REMOTE_SCHEMA],
}
)
},
extra=vol.ALLOW_EXTRA,
)
def load_codes(path):
"""Load KIRA codes from specified file."""
codes = []
if os.path.exists(path):
with open(path) as code_file:
data = yaml.safe_load(code_file) or []
for code in data:
try:
codes.append(CODE_SCHEMA(code))
except VoluptuousError as exception:
# keep going
_LOGGER.warning("KIRA code invalid data: %s", exception)
else:
with open(path, "w") as code_file:
code_file.write("")
return codes
def setup(hass, config):
"""Set up the KIRA component."""
sensors = config.get(DOMAIN, {}).get(CONF_SENSORS, [])
remotes = config.get(DOMAIN, {}).get(CONF_REMOTES, [])
# If no sensors or remotes were specified, add a sensor
if not (sensors or remotes):
sensors.append({})
codes = load_codes(hass.config.path(CODES_YAML))
hass.data[DOMAIN] = {CONF_SENSOR: {}, CONF_REMOTE: {}}
def load_module(platform, idx, module_conf):
"""Set up the KIRA module and load platform."""
# note: module_name is not the HA device name. it's just a unique name
# to ensure the component and platform can share information
module_name = ("%s_%d" % (DOMAIN, idx)) if idx else DOMAIN
device_name = module_conf.get(CONF_NAME, DOMAIN)
port = module_conf.get(CONF_PORT, DEFAULT_PORT)
host = module_conf.get(CONF_HOST, DEFAULT_HOST)
if platform == CONF_SENSOR:
module = pykira.KiraReceiver(host, port)
module.start()
else:
module = pykira.KiraModule(host, port)
hass.data[DOMAIN][platform][module_name] = module
for code in codes:
code_tuple = (code.get(CONF_NAME), code.get(CONF_DEVICE, STATE_UNKNOWN))
module.registerCode(code_tuple, code.get(CONF_CODE))
discovery.load_platform(
hass, platform, DOMAIN, {"name": module_name, "device": device_name}, config
)
for idx, module_conf in enumerate(sensors):
load_module(CONF_SENSOR, idx, module_conf)
for idx, module_conf in enumerate(remotes):
load_module(CONF_REMOTE, idx, module_conf)
def _stop_kira(_event):
"""Stop the KIRA receiver."""
for receiver in hass.data[DOMAIN][CONF_SENSOR].values():
receiver.stop()
_LOGGER.info("Terminated receivers")
hass.bus.listen_once(EVENT_HOMEASSISTANT_STOP, _stop_kira)
return True
|
"""The tests for the mochad light platform."""
import unittest.mock as mock
import pytest
from homeassistant.components import light
from homeassistant.components.mochad import light as mochad
from homeassistant.setup import async_setup_component
@pytest.fixture(autouse=True)
def pymochad_mock():
"""Mock pymochad."""
with mock.patch("homeassistant.components.mochad.light.device") as device:
yield device
@pytest.fixture
def light_mock(hass, brightness):
"""Mock light."""
controller_mock = mock.MagicMock()
dev_dict = {"address": "a1", "name": "fake_light", "brightness_levels": brightness}
return mochad.MochadLight(hass, controller_mock, dev_dict)
async def test_setup_adds_proper_devices(hass):
"""Test if setup adds devices."""
good_config = {
"mochad": {},
"light": {
"platform": "mochad",
"devices": [{"name": "Light1", "address": "a1"}],
},
}
assert await async_setup_component(hass, light.DOMAIN, good_config)
@pytest.mark.parametrize(
"brightness,expected", [(32, "on"), (256, "xdim 255"), (64, "xdim 63")]
)
async def test_turn_on_with_no_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on()
light_mock.light.send_cmd.assert_called_once_with(expected)
@pytest.mark.parametrize(
"brightness,expected",
[
(32, [mock.call("on"), mock.call("dim 25")]),
(256, [mock.call("xdim 45")]),
(64, [mock.call("xdim 11")]),
],
)
async def test_turn_on_with_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on(brightness=45)
light_mock.light.send_cmd.assert_has_calls(expected)
@pytest.mark.parametrize("brightness", [32])
async def test_turn_off(light_mock):
"""Test turn_off."""
light_mock.turn_off()
light_mock.light.send_cmd.assert_called_once_with("off")
|
|
tests/components/mochad/test_light.py
|
homeassistant/components/kira/__init__.py
|
"""Errors for the cert_expiry integration."""
from homeassistant.exceptions import HomeAssistantError
class CertExpiryException(HomeAssistantError):
"""Base class for cert_expiry exceptions."""
class TemporaryFailure(CertExpiryException):
"""Temporary failure has occurred."""
class ValidationFailure(CertExpiryException):
"""Certificate validation failure has occurred."""
class ResolveFailed(TemporaryFailure):
"""Name resolution failed."""
class ConnectionTimeout(TemporaryFailure):
"""Network connection timed out."""
class ConnectionRefused(TemporaryFailure):
"""Network connection refused."""
|
"""The tests for the mochad light platform."""
import unittest.mock as mock
import pytest
from homeassistant.components import light
from homeassistant.components.mochad import light as mochad
from homeassistant.setup import async_setup_component
@pytest.fixture(autouse=True)
def pymochad_mock():
"""Mock pymochad."""
with mock.patch("homeassistant.components.mochad.light.device") as device:
yield device
@pytest.fixture
def light_mock(hass, brightness):
"""Mock light."""
controller_mock = mock.MagicMock()
dev_dict = {"address": "a1", "name": "fake_light", "brightness_levels": brightness}
return mochad.MochadLight(hass, controller_mock, dev_dict)
async def test_setup_adds_proper_devices(hass):
"""Test if setup adds devices."""
good_config = {
"mochad": {},
"light": {
"platform": "mochad",
"devices": [{"name": "Light1", "address": "a1"}],
},
}
assert await async_setup_component(hass, light.DOMAIN, good_config)
@pytest.mark.parametrize(
"brightness,expected", [(32, "on"), (256, "xdim 255"), (64, "xdim 63")]
)
async def test_turn_on_with_no_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on()
light_mock.light.send_cmd.assert_called_once_with(expected)
@pytest.mark.parametrize(
"brightness,expected",
[
(32, [mock.call("on"), mock.call("dim 25")]),
(256, [mock.call("xdim 45")]),
(64, [mock.call("xdim 11")]),
],
)
async def test_turn_on_with_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on(brightness=45)
light_mock.light.send_cmd.assert_has_calls(expected)
@pytest.mark.parametrize("brightness", [32])
async def test_turn_off(light_mock):
"""Test turn_off."""
light_mock.turn_off()
light_mock.light.send_cmd.assert_called_once_with("off")
|
|
tests/components/mochad/test_light.py
|
homeassistant/components/cert_expiry/errors.py
|
"""Config flow for UpCloud."""
import logging
import requests.exceptions
import upcloud_api
import voluptuous as vol
from homeassistant import config_entries
from homeassistant.const import CONF_PASSWORD, CONF_SCAN_INTERVAL, CONF_USERNAME
from homeassistant.core import callback
from .const import DEFAULT_SCAN_INTERVAL, DOMAIN
_LOGGER = logging.getLogger(__name__)
class UpCloudConfigFlow(config_entries.ConfigFlow, domain=DOMAIN):
"""UpCloud config flow."""
VERSION = 1
CONNECTION_CLASS = config_entries.CONN_CLASS_CLOUD_POLL
username: str
password: str
async def async_step_user(self, user_input=None):
"""Handle user initiated flow."""
if user_input is None:
return self._async_show_form(step_id="user")
await self.async_set_unique_id(user_input[CONF_USERNAME])
manager = upcloud_api.CloudManager(
user_input[CONF_USERNAME], user_input[CONF_PASSWORD]
)
errors = {}
try:
await self.hass.async_add_executor_job(manager.authenticate)
except upcloud_api.UpCloudAPIError:
errors["base"] = "invalid_auth"
_LOGGER.debug("invalid_auth", exc_info=True)
except requests.exceptions.RequestException:
errors["base"] = "cannot_connect"
_LOGGER.debug("cannot_connect", exc_info=True)
if errors:
return self._async_show_form(
step_id="user", user_input=user_input, errors=errors
)
return self.async_create_entry(title=user_input[CONF_USERNAME], data=user_input)
async def async_step_import(self, user_input=None):
"""Handle import initiated flow."""
await self.async_set_unique_id(user_input[CONF_USERNAME])
self._abort_if_unique_id_configured()
return await self.async_step_user(user_input=user_input)
@callback
def _async_show_form(self, step_id, user_input=None, errors=None):
"""Show our form."""
if user_input is None:
user_input = {}
return self.async_show_form(
step_id=step_id,
data_schema=vol.Schema(
{
vol.Required(
CONF_USERNAME, default=user_input.get(CONF_USERNAME, "")
): str,
vol.Required(
CONF_PASSWORD, default=user_input.get(CONF_PASSWORD, "")
): str,
}
),
errors=errors or {},
)
@staticmethod
@callback
def async_get_options_flow(config_entry):
"""Get options flow."""
return UpCloudOptionsFlow(config_entry)
class UpCloudOptionsFlow(config_entries.OptionsFlow):
"""UpCloud options flow."""
def __init__(self, config_entry: config_entries.ConfigEntry):
"""Initialize options flow."""
self.config_entry = config_entry
async def async_step_init(self, user_input=None):
"""Handle options flow."""
if user_input is not None:
return self.async_create_entry(title="", data=user_input)
data_schema = vol.Schema(
{
vol.Optional(
CONF_SCAN_INTERVAL,
default=self.config_entry.options.get(CONF_SCAN_INTERVAL)
or DEFAULT_SCAN_INTERVAL.seconds,
): vol.All(vol.Coerce(int), vol.Range(min=30)),
}
)
return self.async_show_form(step_id="init", data_schema=data_schema)
|
"""The tests for the mochad light platform."""
import unittest.mock as mock
import pytest
from homeassistant.components import light
from homeassistant.components.mochad import light as mochad
from homeassistant.setup import async_setup_component
@pytest.fixture(autouse=True)
def pymochad_mock():
"""Mock pymochad."""
with mock.patch("homeassistant.components.mochad.light.device") as device:
yield device
@pytest.fixture
def light_mock(hass, brightness):
"""Mock light."""
controller_mock = mock.MagicMock()
dev_dict = {"address": "a1", "name": "fake_light", "brightness_levels": brightness}
return mochad.MochadLight(hass, controller_mock, dev_dict)
async def test_setup_adds_proper_devices(hass):
"""Test if setup adds devices."""
good_config = {
"mochad": {},
"light": {
"platform": "mochad",
"devices": [{"name": "Light1", "address": "a1"}],
},
}
assert await async_setup_component(hass, light.DOMAIN, good_config)
@pytest.mark.parametrize(
"brightness,expected", [(32, "on"), (256, "xdim 255"), (64, "xdim 63")]
)
async def test_turn_on_with_no_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on()
light_mock.light.send_cmd.assert_called_once_with(expected)
@pytest.mark.parametrize(
"brightness,expected",
[
(32, [mock.call("on"), mock.call("dim 25")]),
(256, [mock.call("xdim 45")]),
(64, [mock.call("xdim 11")]),
],
)
async def test_turn_on_with_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on(brightness=45)
light_mock.light.send_cmd.assert_has_calls(expected)
@pytest.mark.parametrize("brightness", [32])
async def test_turn_off(light_mock):
"""Test turn_off."""
light_mock.turn_off()
light_mock.light.send_cmd.assert_called_once_with("off")
|
|
tests/components/mochad/test_light.py
|
homeassistant/components/upcloud/config_flow.py
|
"""Support for Snips on-device ASR and NLU."""
from datetime import timedelta
import json
import logging
import voluptuous as vol
from homeassistant.components import mqtt
from homeassistant.core import callback
from homeassistant.helpers import config_validation as cv, intent
DOMAIN = "snips"
CONF_INTENTS = "intents"
CONF_ACTION = "action"
CONF_FEEDBACK = "feedback_sounds"
CONF_PROBABILITY = "probability_threshold"
CONF_SITE_IDS = "site_ids"
SERVICE_SAY = "say"
SERVICE_SAY_ACTION = "say_action"
SERVICE_FEEDBACK_ON = "feedback_on"
SERVICE_FEEDBACK_OFF = "feedback_off"
INTENT_TOPIC = "hermes/intent/#"
FEEDBACK_ON_TOPIC = "hermes/feedback/sound/toggleOn"
FEEDBACK_OFF_TOPIC = "hermes/feedback/sound/toggleOff"
ATTR_TEXT = "text"
ATTR_SITE_ID = "site_id"
ATTR_CUSTOM_DATA = "custom_data"
ATTR_CAN_BE_ENQUEUED = "can_be_enqueued"
ATTR_INTENT_FILTER = "intent_filter"
_LOGGER = logging.getLogger(__name__)
CONFIG_SCHEMA = vol.Schema(
{
DOMAIN: vol.Schema(
{
vol.Optional(CONF_FEEDBACK): cv.boolean,
vol.Optional(CONF_PROBABILITY, default=0): vol.Coerce(float),
vol.Optional(CONF_SITE_IDS, default=["default"]): vol.All(
cv.ensure_list, [cv.string]
),
}
)
},
extra=vol.ALLOW_EXTRA,
)
INTENT_SCHEMA = vol.Schema(
{
vol.Required("input"): str,
vol.Required("intent"): {vol.Required("intentName"): str},
vol.Optional("slots"): [
{
vol.Required("slotName"): str,
vol.Required("value"): {
vol.Required("kind"): str,
vol.Optional("value"): cv.match_all,
vol.Optional("rawValue"): cv.match_all,
},
}
],
},
extra=vol.ALLOW_EXTRA,
)
SERVICE_SCHEMA_SAY = vol.Schema(
{
vol.Required(ATTR_TEXT): str,
vol.Optional(ATTR_SITE_ID, default="default"): str,
vol.Optional(ATTR_CUSTOM_DATA, default=""): str,
}
)
SERVICE_SCHEMA_SAY_ACTION = vol.Schema(
{
vol.Required(ATTR_TEXT): str,
vol.Optional(ATTR_SITE_ID, default="default"): str,
vol.Optional(ATTR_CUSTOM_DATA, default=""): str,
vol.Optional(ATTR_CAN_BE_ENQUEUED, default=True): cv.boolean,
vol.Optional(ATTR_INTENT_FILTER): vol.All(cv.ensure_list),
}
)
SERVICE_SCHEMA_FEEDBACK = vol.Schema(
{vol.Optional(ATTR_SITE_ID, default="default"): str}
)
async def async_setup(hass, config):
"""Activate Snips component."""
@callback
def async_set_feedback(site_ids, state):
"""Set Feedback sound state."""
site_ids = site_ids if site_ids else config[DOMAIN].get(CONF_SITE_IDS)
topic = FEEDBACK_ON_TOPIC if state else FEEDBACK_OFF_TOPIC
for site_id in site_ids:
payload = json.dumps({"siteId": site_id})
hass.components.mqtt.async_publish(
FEEDBACK_ON_TOPIC, "", qos=0, retain=False
)
hass.components.mqtt.async_publish(
topic, payload, qos=int(state), retain=state
)
if CONF_FEEDBACK in config[DOMAIN]:
async_set_feedback(None, config[DOMAIN][CONF_FEEDBACK])
async def message_received(msg):
"""Handle new messages on MQTT."""
_LOGGER.debug("New intent: %s", msg.payload)
try:
request = json.loads(msg.payload)
except TypeError:
_LOGGER.error("Received invalid JSON: %s", msg.payload)
return
if request["intent"]["confidenceScore"] < config[DOMAIN].get(CONF_PROBABILITY):
_LOGGER.warning(
"Intent below probaility threshold %s < %s",
request["intent"]["confidenceScore"],
config[DOMAIN].get(CONF_PROBABILITY),
)
return
try:
request = INTENT_SCHEMA(request)
except vol.Invalid as err:
_LOGGER.error("Intent has invalid schema: %s. %s", err, request)
return
if request["intent"]["intentName"].startswith("user_"):
intent_type = request["intent"]["intentName"].split("__")[-1]
else:
intent_type = request["intent"]["intentName"].split(":")[-1]
slots = {}
for slot in request.get("slots", []):
slots[slot["slotName"]] = {"value": resolve_slot_values(slot)}
slots["{}_raw".format(slot["slotName"])] = {"value": slot["rawValue"]}
slots["site_id"] = {"value": request.get("siteId")}
slots["session_id"] = {"value": request.get("sessionId")}
slots["confidenceScore"] = {"value": request["intent"]["confidenceScore"]}
try:
intent_response = await intent.async_handle(
hass, DOMAIN, intent_type, slots, request["input"]
)
notification = {"sessionId": request.get("sessionId", "default")}
if "plain" in intent_response.speech:
notification["text"] = intent_response.speech["plain"]["speech"]
_LOGGER.debug("send_response %s", json.dumps(notification))
mqtt.async_publish(
hass, "hermes/dialogueManager/endSession", json.dumps(notification)
)
except intent.UnknownIntent:
_LOGGER.warning(
"Received unknown intent %s", request["intent"]["intentName"]
)
except intent.IntentError:
_LOGGER.exception("Error while handling intent: %s", intent_type)
await hass.components.mqtt.async_subscribe(INTENT_TOPIC, message_received)
async def snips_say(call):
"""Send a Snips notification message."""
notification = {
"siteId": call.data.get(ATTR_SITE_ID, "default"),
"customData": call.data.get(ATTR_CUSTOM_DATA, ""),
"init": {"type": "notification", "text": call.data.get(ATTR_TEXT)},
}
mqtt.async_publish(
hass, "hermes/dialogueManager/startSession", json.dumps(notification)
)
return
async def snips_say_action(call):
"""Send a Snips action message."""
notification = {
"siteId": call.data.get(ATTR_SITE_ID, "default"),
"customData": call.data.get(ATTR_CUSTOM_DATA, ""),
"init": {
"type": "action",
"text": call.data.get(ATTR_TEXT),
"canBeEnqueued": call.data.get(ATTR_CAN_BE_ENQUEUED, True),
"intentFilter": call.data.get(ATTR_INTENT_FILTER, []),
},
}
mqtt.async_publish(
hass, "hermes/dialogueManager/startSession", json.dumps(notification)
)
return
async def feedback_on(call):
"""Turn feedback sounds on."""
async_set_feedback(call.data.get(ATTR_SITE_ID), True)
async def feedback_off(call):
"""Turn feedback sounds off."""
async_set_feedback(call.data.get(ATTR_SITE_ID), False)
hass.services.async_register(
DOMAIN, SERVICE_SAY, snips_say, schema=SERVICE_SCHEMA_SAY
)
hass.services.async_register(
DOMAIN, SERVICE_SAY_ACTION, snips_say_action, schema=SERVICE_SCHEMA_SAY_ACTION
)
hass.services.async_register(
DOMAIN, SERVICE_FEEDBACK_ON, feedback_on, schema=SERVICE_SCHEMA_FEEDBACK
)
hass.services.async_register(
DOMAIN, SERVICE_FEEDBACK_OFF, feedback_off, schema=SERVICE_SCHEMA_FEEDBACK
)
return True
def resolve_slot_values(slot):
"""Convert snips builtin types to usable values."""
if "value" in slot["value"]:
value = slot["value"]["value"]
else:
value = slot["rawValue"]
if slot.get("entity") == "snips/duration":
delta = timedelta(
weeks=slot["value"]["weeks"],
days=slot["value"]["days"],
hours=slot["value"]["hours"],
minutes=slot["value"]["minutes"],
seconds=slot["value"]["seconds"],
)
value = delta.seconds
return value
|
"""The tests for the mochad light platform."""
import unittest.mock as mock
import pytest
from homeassistant.components import light
from homeassistant.components.mochad import light as mochad
from homeassistant.setup import async_setup_component
@pytest.fixture(autouse=True)
def pymochad_mock():
"""Mock pymochad."""
with mock.patch("homeassistant.components.mochad.light.device") as device:
yield device
@pytest.fixture
def light_mock(hass, brightness):
"""Mock light."""
controller_mock = mock.MagicMock()
dev_dict = {"address": "a1", "name": "fake_light", "brightness_levels": brightness}
return mochad.MochadLight(hass, controller_mock, dev_dict)
async def test_setup_adds_proper_devices(hass):
"""Test if setup adds devices."""
good_config = {
"mochad": {},
"light": {
"platform": "mochad",
"devices": [{"name": "Light1", "address": "a1"}],
},
}
assert await async_setup_component(hass, light.DOMAIN, good_config)
@pytest.mark.parametrize(
"brightness,expected", [(32, "on"), (256, "xdim 255"), (64, "xdim 63")]
)
async def test_turn_on_with_no_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on()
light_mock.light.send_cmd.assert_called_once_with(expected)
@pytest.mark.parametrize(
"brightness,expected",
[
(32, [mock.call("on"), mock.call("dim 25")]),
(256, [mock.call("xdim 45")]),
(64, [mock.call("xdim 11")]),
],
)
async def test_turn_on_with_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on(brightness=45)
light_mock.light.send_cmd.assert_has_calls(expected)
@pytest.mark.parametrize("brightness", [32])
async def test_turn_off(light_mock):
"""Test turn_off."""
light_mock.turn_off()
light_mock.light.send_cmd.assert_called_once_with("off")
|
|
tests/components/mochad/test_light.py
|
homeassistant/components/snips/__init__.py
|
"""Config flow for Network UPS Tools (NUT) integration."""
import logging
import voluptuous as vol
from homeassistant import config_entries, core, exceptions
from homeassistant.const import (
CONF_ALIAS,
CONF_BASE,
CONF_HOST,
CONF_PASSWORD,
CONF_PORT,
CONF_RESOURCES,
CONF_SCAN_INTERVAL,
CONF_USERNAME,
)
from homeassistant.core import callback
import homeassistant.helpers.config_validation as cv
from . import PyNUTData, find_resources_in_config_entry
from .const import (
DEFAULT_HOST,
DEFAULT_PORT,
DEFAULT_SCAN_INTERVAL,
DOMAIN,
KEY_STATUS,
KEY_STATUS_DISPLAY,
SENSOR_NAME,
SENSOR_TYPES,
)
_LOGGER = logging.getLogger(__name__)
SENSOR_DICT = {
sensor_id: sensor_spec[SENSOR_NAME]
for sensor_id, sensor_spec in SENSOR_TYPES.items()
}
def _base_schema(discovery_info):
"""Generate base schema."""
base_schema = {}
if not discovery_info:
base_schema.update(
{
vol.Optional(CONF_HOST, default=DEFAULT_HOST): str,
vol.Optional(CONF_PORT, default=DEFAULT_PORT): int,
}
)
base_schema.update(
{vol.Optional(CONF_USERNAME): str, vol.Optional(CONF_PASSWORD): str}
)
return vol.Schema(base_schema)
def _resource_schema_base(available_resources, selected_resources):
"""Resource selection schema."""
known_available_resources = {
sensor_id: sensor[SENSOR_NAME]
for sensor_id, sensor in SENSOR_TYPES.items()
if sensor_id in available_resources
}
if KEY_STATUS in known_available_resources:
known_available_resources[KEY_STATUS_DISPLAY] = SENSOR_TYPES[
KEY_STATUS_DISPLAY
][SENSOR_NAME]
return {
vol.Required(CONF_RESOURCES, default=selected_resources): cv.multi_select(
known_available_resources
)
}
def _ups_schema(ups_list):
"""UPS selection schema."""
return vol.Schema({vol.Required(CONF_ALIAS): vol.In(ups_list)})
async def validate_input(hass: core.HomeAssistant, data):
"""Validate the user input allows us to connect.
Data has the keys from _base_schema with values provided by the user.
"""
host = data[CONF_HOST]
port = data[CONF_PORT]
alias = data.get(CONF_ALIAS)
username = data.get(CONF_USERNAME)
password = data.get(CONF_PASSWORD)
data = PyNUTData(host, port, alias, username, password)
await hass.async_add_executor_job(data.update)
status = data.status
if not status:
raise CannotConnect
return {"ups_list": data.ups_list, "available_resources": status}
def _format_host_port_alias(user_input):
"""Format a host, port, and alias so it can be used for comparison or display."""
host = user_input[CONF_HOST]
port = user_input[CONF_PORT]
alias = user_input.get(CONF_ALIAS)
if alias:
return f"{alias}@{host}:{port}"
return f"{host}:{port}"
class ConfigFlow(config_entries.ConfigFlow, domain=DOMAIN):
"""Handle a config flow for Network UPS Tools (NUT)."""
VERSION = 1
CONNECTION_CLASS = config_entries.CONN_CLASS_LOCAL_POLL
def __init__(self):
"""Initialize the nut config flow."""
self.nut_config = {}
self.available_resources = {}
self.discovery_info = {}
self.ups_list = None
self.title = None
async def async_step_zeroconf(self, discovery_info):
"""Prepare configuration for a discovered nut device."""
self.discovery_info = discovery_info
await self._async_handle_discovery_without_unique_id()
self.context["title_placeholders"] = {
CONF_PORT: discovery_info.get(CONF_PORT, DEFAULT_PORT),
CONF_HOST: discovery_info[CONF_HOST],
}
return await self.async_step_user()
async def async_step_user(self, user_input=None):
"""Handle the user input."""
errors = {}
if user_input is not None:
if self.discovery_info:
user_input.update(
{
CONF_HOST: self.discovery_info[CONF_HOST],
CONF_PORT: self.discovery_info.get(CONF_PORT, DEFAULT_PORT),
}
)
info, errors = await self._async_validate_or_error(user_input)
if not errors:
self.nut_config.update(user_input)
if len(info["ups_list"]) > 1:
self.ups_list = info["ups_list"]
return await self.async_step_ups()
if self._host_port_alias_already_configured(self.nut_config):
return self.async_abort(reason="already_configured")
self.available_resources.update(info["available_resources"])
return await self.async_step_resources()
return self.async_show_form(
step_id="user", data_schema=_base_schema(self.discovery_info), errors=errors
)
async def async_step_ups(self, user_input=None):
"""Handle the picking the ups."""
errors = {}
if user_input is not None:
self.nut_config.update(user_input)
if self._host_port_alias_already_configured(self.nut_config):
return self.async_abort(reason="already_configured")
info, errors = await self._async_validate_or_error(self.nut_config)
if not errors:
self.available_resources.update(info["available_resources"])
return await self.async_step_resources()
return self.async_show_form(
step_id="ups",
data_schema=_ups_schema(self.ups_list),
errors=errors,
)
async def async_step_resources(self, user_input=None):
"""Handle the picking the resources."""
if user_input is None:
return self.async_show_form(
step_id="resources",
data_schema=vol.Schema(
_resource_schema_base(self.available_resources, [])
),
)
self.nut_config.update(user_input)
title = _format_host_port_alias(self.nut_config)
return self.async_create_entry(title=title, data=self.nut_config)
def _host_port_alias_already_configured(self, user_input):
"""See if we already have a nut entry matching user input configured."""
existing_host_port_aliases = {
_format_host_port_alias(entry.data)
for entry in self._async_current_entries()
if CONF_HOST in entry.data
}
return _format_host_port_alias(user_input) in existing_host_port_aliases
async def _async_validate_or_error(self, config):
errors = {}
info = {}
try:
info = await validate_input(self.hass, config)
except CannotConnect:
errors[CONF_BASE] = "cannot_connect"
except Exception: # pylint: disable=broad-except
_LOGGER.exception("Unexpected exception")
errors[CONF_BASE] = "unknown"
return info, errors
@staticmethod
@callback
def async_get_options_flow(config_entry):
"""Get the options flow for this handler."""
return OptionsFlowHandler(config_entry)
class OptionsFlowHandler(config_entries.OptionsFlow):
"""Handle a option flow for nut."""
def __init__(self, config_entry: config_entries.ConfigEntry):
"""Initialize options flow."""
self.config_entry = config_entry
async def async_step_init(self, user_input=None):
"""Handle options flow."""
if user_input is not None:
return self.async_create_entry(title="", data=user_input)
resources = find_resources_in_config_entry(self.config_entry)
scan_interval = self.config_entry.options.get(
CONF_SCAN_INTERVAL, DEFAULT_SCAN_INTERVAL
)
errors = {}
try:
info = await validate_input(self.hass, self.config_entry.data)
except CannotConnect:
errors[CONF_BASE] = "cannot_connect"
except Exception: # pylint: disable=broad-except
_LOGGER.exception("Unexpected exception")
errors[CONF_BASE] = "unknown"
if errors:
return self.async_show_form(step_id="abort", errors=errors)
base_schema = _resource_schema_base(info["available_resources"], resources)
base_schema[
vol.Optional(CONF_SCAN_INTERVAL, default=scan_interval)
] = cv.positive_int
return self.async_show_form(
step_id="init", data_schema=vol.Schema(base_schema), errors=errors
)
async def async_step_abort(self, user_input=None):
"""Abort options flow."""
return self.async_create_entry(title="", data=self.config_entry.options)
class CannotConnect(exceptions.HomeAssistantError):
"""Error to indicate we cannot connect."""
|
"""The tests for the mochad light platform."""
import unittest.mock as mock
import pytest
from homeassistant.components import light
from homeassistant.components.mochad import light as mochad
from homeassistant.setup import async_setup_component
@pytest.fixture(autouse=True)
def pymochad_mock():
"""Mock pymochad."""
with mock.patch("homeassistant.components.mochad.light.device") as device:
yield device
@pytest.fixture
def light_mock(hass, brightness):
"""Mock light."""
controller_mock = mock.MagicMock()
dev_dict = {"address": "a1", "name": "fake_light", "brightness_levels": brightness}
return mochad.MochadLight(hass, controller_mock, dev_dict)
async def test_setup_adds_proper_devices(hass):
"""Test if setup adds devices."""
good_config = {
"mochad": {},
"light": {
"platform": "mochad",
"devices": [{"name": "Light1", "address": "a1"}],
},
}
assert await async_setup_component(hass, light.DOMAIN, good_config)
@pytest.mark.parametrize(
"brightness,expected", [(32, "on"), (256, "xdim 255"), (64, "xdim 63")]
)
async def test_turn_on_with_no_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on()
light_mock.light.send_cmd.assert_called_once_with(expected)
@pytest.mark.parametrize(
"brightness,expected",
[
(32, [mock.call("on"), mock.call("dim 25")]),
(256, [mock.call("xdim 45")]),
(64, [mock.call("xdim 11")]),
],
)
async def test_turn_on_with_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on(brightness=45)
light_mock.light.send_cmd.assert_has_calls(expected)
@pytest.mark.parametrize("brightness", [32])
async def test_turn_off(light_mock):
"""Test turn_off."""
light_mock.turn_off()
light_mock.light.send_cmd.assert_called_once_with("off")
|
|
tests/components/mochad/test_light.py
|
homeassistant/components/nut/config_flow.py
|
"""The fritzbox_callmonitor integration."""
from asyncio import gather
import logging
from fritzconnection.core.exceptions import FritzConnectionException, FritzSecurityError
from requests.exceptions import ConnectionError as RequestsConnectionError
from homeassistant.const import CONF_HOST, CONF_PASSWORD, CONF_USERNAME
from homeassistant.exceptions import ConfigEntryNotReady
from .base import FritzBoxPhonebook
from .const import (
CONF_PHONEBOOK,
CONF_PREFIXES,
DOMAIN,
FRITZBOX_PHONEBOOK,
PLATFORMS,
UNDO_UPDATE_LISTENER,
)
_LOGGER = logging.getLogger(__name__)
async def async_setup_entry(hass, config_entry):
"""Set up the fritzbox_callmonitor platforms."""
fritzbox_phonebook = FritzBoxPhonebook(
host=config_entry.data[CONF_HOST],
username=config_entry.data[CONF_USERNAME],
password=config_entry.data[CONF_PASSWORD],
phonebook_id=config_entry.data[CONF_PHONEBOOK],
prefixes=config_entry.options.get(CONF_PREFIXES),
)
try:
await hass.async_add_executor_job(fritzbox_phonebook.init_phonebook)
except FritzSecurityError as ex:
_LOGGER.error(
"User has insufficient permissions to access AVM FRITZ!Box settings and its phonebooks: %s",
ex,
)
return False
except FritzConnectionException as ex:
_LOGGER.error("Invalid authentication: %s", ex)
return False
except RequestsConnectionError as ex:
_LOGGER.error("Unable to connect to AVM FRITZ!Box call monitor: %s", ex)
raise ConfigEntryNotReady from ex
undo_listener = config_entry.add_update_listener(update_listener)
hass.data.setdefault(DOMAIN, {})
hass.data[DOMAIN][config_entry.entry_id] = {
FRITZBOX_PHONEBOOK: fritzbox_phonebook,
UNDO_UPDATE_LISTENER: undo_listener,
}
for platform in PLATFORMS:
hass.async_create_task(
hass.config_entries.async_forward_entry_setup(config_entry, platform)
)
return True
async def async_unload_entry(hass, config_entry):
"""Unloading the fritzbox_callmonitor platforms."""
unload_ok = all(
await gather(
*[
hass.config_entries.async_forward_entry_unload(config_entry, platform)
for platform in PLATFORMS
]
)
)
hass.data[DOMAIN][config_entry.entry_id][UNDO_UPDATE_LISTENER]()
if unload_ok:
hass.data[DOMAIN].pop(config_entry.entry_id)
return unload_ok
async def update_listener(hass, config_entry):
"""Update listener to reload after option has changed."""
await hass.config_entries.async_reload(config_entry.entry_id)
|
"""The tests for the mochad light platform."""
import unittest.mock as mock
import pytest
from homeassistant.components import light
from homeassistant.components.mochad import light as mochad
from homeassistant.setup import async_setup_component
@pytest.fixture(autouse=True)
def pymochad_mock():
"""Mock pymochad."""
with mock.patch("homeassistant.components.mochad.light.device") as device:
yield device
@pytest.fixture
def light_mock(hass, brightness):
"""Mock light."""
controller_mock = mock.MagicMock()
dev_dict = {"address": "a1", "name": "fake_light", "brightness_levels": brightness}
return mochad.MochadLight(hass, controller_mock, dev_dict)
async def test_setup_adds_proper_devices(hass):
"""Test if setup adds devices."""
good_config = {
"mochad": {},
"light": {
"platform": "mochad",
"devices": [{"name": "Light1", "address": "a1"}],
},
}
assert await async_setup_component(hass, light.DOMAIN, good_config)
@pytest.mark.parametrize(
"brightness,expected", [(32, "on"), (256, "xdim 255"), (64, "xdim 63")]
)
async def test_turn_on_with_no_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on()
light_mock.light.send_cmd.assert_called_once_with(expected)
@pytest.mark.parametrize(
"brightness,expected",
[
(32, [mock.call("on"), mock.call("dim 25")]),
(256, [mock.call("xdim 45")]),
(64, [mock.call("xdim 11")]),
],
)
async def test_turn_on_with_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on(brightness=45)
light_mock.light.send_cmd.assert_has_calls(expected)
@pytest.mark.parametrize("brightness", [32])
async def test_turn_off(light_mock):
"""Test turn_off."""
light_mock.turn_off()
light_mock.light.send_cmd.assert_called_once_with("off")
|
|
tests/components/mochad/test_light.py
|
homeassistant/components/fritzbox_callmonitor/__init__.py
|
"""Support for Axis binary sensors."""
from datetime import timedelta
from axis.event_stream import (
CLASS_INPUT,
CLASS_LIGHT,
CLASS_MOTION,
CLASS_OUTPUT,
CLASS_PTZ,
CLASS_SOUND,
FenceGuard,
LoiteringGuard,
MotionGuard,
ObjectAnalytics,
Vmd4,
)
from homeassistant.components.binary_sensor import (
DEVICE_CLASS_CONNECTIVITY,
DEVICE_CLASS_LIGHT,
DEVICE_CLASS_MOTION,
DEVICE_CLASS_SOUND,
BinarySensorEntity,
)
from homeassistant.core import callback
from homeassistant.helpers.dispatcher import async_dispatcher_connect
from homeassistant.helpers.event import async_track_point_in_utc_time
from homeassistant.util.dt import utcnow
from .axis_base import AxisEventBase
from .const import DOMAIN as AXIS_DOMAIN
DEVICE_CLASS = {
CLASS_INPUT: DEVICE_CLASS_CONNECTIVITY,
CLASS_LIGHT: DEVICE_CLASS_LIGHT,
CLASS_MOTION: DEVICE_CLASS_MOTION,
CLASS_SOUND: DEVICE_CLASS_SOUND,
}
async def async_setup_entry(hass, config_entry, async_add_entities):
"""Set up a Axis binary sensor."""
device = hass.data[AXIS_DOMAIN][config_entry.unique_id]
@callback
def async_add_sensor(event_id):
"""Add binary sensor from Axis device."""
event = device.api.event[event_id]
if event.CLASS not in (CLASS_OUTPUT, CLASS_PTZ) and not (
event.CLASS == CLASS_LIGHT and event.TYPE == "Light"
):
async_add_entities([AxisBinarySensor(event, device)])
device.listeners.append(
async_dispatcher_connect(hass, device.signal_new_event, async_add_sensor)
)
class AxisBinarySensor(AxisEventBase, BinarySensorEntity):
"""Representation of a binary Axis event."""
def __init__(self, event, device):
"""Initialize the Axis binary sensor."""
super().__init__(event, device)
self.cancel_scheduled_update = None
@callback
def update_callback(self, no_delay=False):
"""Update the sensor's state, if needed.
Parameter no_delay is True when device_event_reachable is sent.
"""
@callback
def scheduled_update(now):
"""Timer callback for sensor update."""
self.cancel_scheduled_update = None
self.async_write_ha_state()
if self.cancel_scheduled_update is not None:
self.cancel_scheduled_update()
self.cancel_scheduled_update = None
if self.is_on or self.device.option_trigger_time == 0 or no_delay:
self.async_write_ha_state()
return
self.cancel_scheduled_update = async_track_point_in_utc_time(
self.hass,
scheduled_update,
utcnow() + timedelta(seconds=self.device.option_trigger_time),
)
@property
def is_on(self):
"""Return true if event is active."""
return self.event.is_tripped
@property
def name(self):
"""Return the name of the event."""
if (
self.event.CLASS == CLASS_INPUT
and self.event.id in self.device.api.vapix.ports
and self.device.api.vapix.ports[self.event.id].name
):
return (
f"{self.device.name} {self.device.api.vapix.ports[self.event.id].name}"
)
if self.event.CLASS == CLASS_MOTION:
for event_class, event_data in (
(FenceGuard, self.device.api.vapix.fence_guard),
(LoiteringGuard, self.device.api.vapix.loitering_guard),
(MotionGuard, self.device.api.vapix.motion_guard),
(ObjectAnalytics, self.device.api.vapix.object_analytics),
(Vmd4, self.device.api.vapix.vmd4),
):
if (
isinstance(self.event, event_class)
and event_data
and self.event.id in event_data
):
return f"{self.device.name} {self.event.TYPE} {event_data[self.event.id].name}"
return super().name
@property
def device_class(self):
"""Return the class of the sensor."""
return DEVICE_CLASS.get(self.event.CLASS)
|
"""The tests for the mochad light platform."""
import unittest.mock as mock
import pytest
from homeassistant.components import light
from homeassistant.components.mochad import light as mochad
from homeassistant.setup import async_setup_component
@pytest.fixture(autouse=True)
def pymochad_mock():
"""Mock pymochad."""
with mock.patch("homeassistant.components.mochad.light.device") as device:
yield device
@pytest.fixture
def light_mock(hass, brightness):
"""Mock light."""
controller_mock = mock.MagicMock()
dev_dict = {"address": "a1", "name": "fake_light", "brightness_levels": brightness}
return mochad.MochadLight(hass, controller_mock, dev_dict)
async def test_setup_adds_proper_devices(hass):
"""Test if setup adds devices."""
good_config = {
"mochad": {},
"light": {
"platform": "mochad",
"devices": [{"name": "Light1", "address": "a1"}],
},
}
assert await async_setup_component(hass, light.DOMAIN, good_config)
@pytest.mark.parametrize(
"brightness,expected", [(32, "on"), (256, "xdim 255"), (64, "xdim 63")]
)
async def test_turn_on_with_no_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on()
light_mock.light.send_cmd.assert_called_once_with(expected)
@pytest.mark.parametrize(
"brightness,expected",
[
(32, [mock.call("on"), mock.call("dim 25")]),
(256, [mock.call("xdim 45")]),
(64, [mock.call("xdim 11")]),
],
)
async def test_turn_on_with_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on(brightness=45)
light_mock.light.send_cmd.assert_has_calls(expected)
@pytest.mark.parametrize("brightness", [32])
async def test_turn_off(light_mock):
"""Test turn_off."""
light_mock.turn_off()
light_mock.light.send_cmd.assert_called_once_with("off")
|
|
tests/components/mochad/test_light.py
|
homeassistant/components/axis/binary_sensor.py
|
"""Support for Axis lights."""
from axis.event_stream import CLASS_LIGHT
from homeassistant.components.light import (
ATTR_BRIGHTNESS,
SUPPORT_BRIGHTNESS,
LightEntity,
)
from homeassistant.core import callback
from homeassistant.helpers.dispatcher import async_dispatcher_connect
from .axis_base import AxisEventBase
from .const import DOMAIN as AXIS_DOMAIN
async def async_setup_entry(hass, config_entry, async_add_entities):
"""Set up a Axis light."""
device = hass.data[AXIS_DOMAIN][config_entry.unique_id]
if (
device.api.vapix.light_control is None
or len(device.api.vapix.light_control) == 0
):
return
@callback
def async_add_sensor(event_id):
"""Add light from Axis device."""
event = device.api.event[event_id]
if event.CLASS == CLASS_LIGHT and event.TYPE == "Light":
async_add_entities([AxisLight(event, device)])
device.listeners.append(
async_dispatcher_connect(hass, device.signal_new_event, async_add_sensor)
)
class AxisLight(AxisEventBase, LightEntity):
"""Representation of a light Axis event."""
def __init__(self, event, device):
"""Initialize the Axis light."""
super().__init__(event, device)
self.light_id = f"led{self.event.id}"
self.current_intensity = 0
self.max_intensity = 0
self._features = SUPPORT_BRIGHTNESS
async def async_added_to_hass(self) -> None:
"""Subscribe lights events."""
await super().async_added_to_hass()
current_intensity = (
await self.device.api.vapix.light_control.get_current_intensity(
self.light_id
)
)
self.current_intensity = current_intensity["data"]["intensity"]
max_intensity = await self.device.api.vapix.light_control.get_valid_intensity(
self.light_id
)
self.max_intensity = max_intensity["data"]["ranges"][0]["high"]
@property
def supported_features(self):
"""Flag supported features."""
return self._features
@property
def name(self):
"""Return the name of the light."""
light_type = self.device.api.vapix.light_control[self.light_id].light_type
return f"{self.device.name} {light_type} {self.event.TYPE} {self.event.id}"
@property
def is_on(self):
"""Return true if light is on."""
return self.event.is_tripped
@property
def brightness(self):
"""Return the brightness of this light between 0..255."""
return int((self.current_intensity / self.max_intensity) * 255)
async def async_turn_on(self, **kwargs):
"""Turn on light."""
if not self.is_on:
await self.device.api.vapix.light_control.activate_light(self.light_id)
if ATTR_BRIGHTNESS in kwargs:
intensity = int((kwargs[ATTR_BRIGHTNESS] / 255) * self.max_intensity)
await self.device.api.vapix.light_control.set_manual_intensity(
self.light_id, intensity
)
async def async_turn_off(self, **kwargs):
"""Turn off light."""
if self.is_on:
await self.device.api.vapix.light_control.deactivate_light(self.light_id)
async def async_update(self):
"""Update brightness."""
current_intensity = (
await self.device.api.vapix.light_control.get_current_intensity(
self.light_id
)
)
self.current_intensity = current_intensity["data"]["intensity"]
@property
def should_poll(self):
"""Brightness needs polling."""
return True
|
"""The tests for the mochad light platform."""
import unittest.mock as mock
import pytest
from homeassistant.components import light
from homeassistant.components.mochad import light as mochad
from homeassistant.setup import async_setup_component
@pytest.fixture(autouse=True)
def pymochad_mock():
"""Mock pymochad."""
with mock.patch("homeassistant.components.mochad.light.device") as device:
yield device
@pytest.fixture
def light_mock(hass, brightness):
"""Mock light."""
controller_mock = mock.MagicMock()
dev_dict = {"address": "a1", "name": "fake_light", "brightness_levels": brightness}
return mochad.MochadLight(hass, controller_mock, dev_dict)
async def test_setup_adds_proper_devices(hass):
"""Test if setup adds devices."""
good_config = {
"mochad": {},
"light": {
"platform": "mochad",
"devices": [{"name": "Light1", "address": "a1"}],
},
}
assert await async_setup_component(hass, light.DOMAIN, good_config)
@pytest.mark.parametrize(
"brightness,expected", [(32, "on"), (256, "xdim 255"), (64, "xdim 63")]
)
async def test_turn_on_with_no_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on()
light_mock.light.send_cmd.assert_called_once_with(expected)
@pytest.mark.parametrize(
"brightness,expected",
[
(32, [mock.call("on"), mock.call("dim 25")]),
(256, [mock.call("xdim 45")]),
(64, [mock.call("xdim 11")]),
],
)
async def test_turn_on_with_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on(brightness=45)
light_mock.light.send_cmd.assert_has_calls(expected)
@pytest.mark.parametrize("brightness", [32])
async def test_turn_off(light_mock):
"""Test turn_off."""
light_mock.turn_off()
light_mock.light.send_cmd.assert_called_once_with("off")
|
|
tests/components/mochad/test_light.py
|
homeassistant/components/axis/light.py
|
"""The Keenetic Client class."""
import logging
from homeassistant.components.binary_sensor import (
DEVICE_CLASS_CONNECTIVITY,
BinarySensorEntity,
)
from homeassistant.config_entries import ConfigEntry
from homeassistant.core import HomeAssistant
from homeassistant.helpers.dispatcher import async_dispatcher_connect
from . import KeeneticRouter
from .const import DOMAIN, ROUTER
_LOGGER = logging.getLogger(__name__)
async def async_setup_entry(
hass: HomeAssistant, config_entry: ConfigEntry, async_add_entities
):
"""Set up device tracker for Keenetic NDMS2 component."""
router: KeeneticRouter = hass.data[DOMAIN][config_entry.entry_id][ROUTER]
async_add_entities([RouterOnlineBinarySensor(router)])
class RouterOnlineBinarySensor(BinarySensorEntity):
"""Representation router connection status."""
def __init__(self, router: KeeneticRouter):
"""Initialize the APCUPSd binary device."""
self._router = router
@property
def name(self):
"""Return the name of the online status sensor."""
return f"{self._router.name} Online"
@property
def unique_id(self) -> str:
"""Return a unique identifier for this device."""
return f"online_{self._router.config_entry.entry_id}"
@property
def is_on(self):
"""Return true if the UPS is online, else false."""
return self._router.available
@property
def device_class(self):
"""Return the class of this device, from component DEVICE_CLASSES."""
return DEVICE_CLASS_CONNECTIVITY
@property
def should_poll(self) -> bool:
"""Return False since entity pushes its state to HA."""
return False
@property
def device_info(self):
"""Return a client description for device registry."""
return self._router.device_info
async def async_added_to_hass(self):
"""Client entity created."""
self.async_on_remove(
async_dispatcher_connect(
self.hass,
self._router.signal_update,
self.async_write_ha_state,
)
)
|
"""The tests for the mochad light platform."""
import unittest.mock as mock
import pytest
from homeassistant.components import light
from homeassistant.components.mochad import light as mochad
from homeassistant.setup import async_setup_component
@pytest.fixture(autouse=True)
def pymochad_mock():
"""Mock pymochad."""
with mock.patch("homeassistant.components.mochad.light.device") as device:
yield device
@pytest.fixture
def light_mock(hass, brightness):
"""Mock light."""
controller_mock = mock.MagicMock()
dev_dict = {"address": "a1", "name": "fake_light", "brightness_levels": brightness}
return mochad.MochadLight(hass, controller_mock, dev_dict)
async def test_setup_adds_proper_devices(hass):
"""Test if setup adds devices."""
good_config = {
"mochad": {},
"light": {
"platform": "mochad",
"devices": [{"name": "Light1", "address": "a1"}],
},
}
assert await async_setup_component(hass, light.DOMAIN, good_config)
@pytest.mark.parametrize(
"brightness,expected", [(32, "on"), (256, "xdim 255"), (64, "xdim 63")]
)
async def test_turn_on_with_no_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on()
light_mock.light.send_cmd.assert_called_once_with(expected)
@pytest.mark.parametrize(
"brightness,expected",
[
(32, [mock.call("on"), mock.call("dim 25")]),
(256, [mock.call("xdim 45")]),
(64, [mock.call("xdim 11")]),
],
)
async def test_turn_on_with_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on(brightness=45)
light_mock.light.send_cmd.assert_has_calls(expected)
@pytest.mark.parametrize("brightness", [32])
async def test_turn_off(light_mock):
"""Test turn_off."""
light_mock.turn_off()
light_mock.light.send_cmd.assert_called_once_with("off")
|
|
tests/components/mochad/test_light.py
|
homeassistant/components/keenetic_ndms2/binary_sensor.py
|
"""Handler for Hass.io."""
import asyncio
import logging
import os
import aiohttp
from homeassistant.components.http import (
CONF_SERVER_HOST,
CONF_SERVER_PORT,
CONF_SSL_CERTIFICATE,
)
from homeassistant.const import HTTP_BAD_REQUEST, HTTP_OK, SERVER_PORT
from .const import X_HASSIO
_LOGGER = logging.getLogger(__name__)
class HassioAPIError(RuntimeError):
"""Return if a API trow a error."""
def _api_bool(funct):
"""Return a boolean."""
async def _wrapper(*argv, **kwargs):
"""Wrap function."""
try:
data = await funct(*argv, **kwargs)
return data["result"] == "ok"
except HassioAPIError:
return False
return _wrapper
def api_data(funct):
"""Return data of an api."""
async def _wrapper(*argv, **kwargs):
"""Wrap function."""
data = await funct(*argv, **kwargs)
if data["result"] == "ok":
return data["data"]
raise HassioAPIError(data["message"])
return _wrapper
class HassIO:
"""Small API wrapper for Hass.io."""
def __init__(
self,
loop: asyncio.AbstractEventLoop,
websession: aiohttp.ClientSession,
ip: str,
) -> None:
"""Initialize Hass.io API."""
self.loop = loop
self.websession = websession
self._ip = ip
@_api_bool
def is_connected(self):
"""Return true if it connected to Hass.io supervisor.
This method return a coroutine.
"""
return self.send_command("/supervisor/ping", method="get", timeout=15)
@api_data
def get_info(self):
"""Return generic Supervisor information.
This method return a coroutine.
"""
return self.send_command("/info", method="get")
@api_data
def get_host_info(self):
"""Return data for Host.
This method return a coroutine.
"""
return self.send_command("/host/info", method="get")
@api_data
def get_os_info(self):
"""Return data for the OS.
This method return a coroutine.
"""
return self.send_command("/os/info", method="get")
@api_data
def get_core_info(self):
"""Return data for Home Asssistant Core.
This method returns a coroutine.
"""
return self.send_command("/core/info", method="get")
@api_data
def get_supervisor_info(self):
"""Return data for the Supervisor.
This method returns a coroutine.
"""
return self.send_command("/supervisor/info", method="get")
@api_data
def get_addon_info(self, addon):
"""Return data for a Add-on.
This method return a coroutine.
"""
return self.send_command(f"/addons/{addon}/info", method="get")
@api_data
def get_ingress_panels(self):
"""Return data for Add-on ingress panels.
This method return a coroutine.
"""
return self.send_command("/ingress/panels", method="get")
@_api_bool
def restart_homeassistant(self):
"""Restart Home-Assistant container.
This method return a coroutine.
"""
return self.send_command("/homeassistant/restart")
@_api_bool
def stop_homeassistant(self):
"""Stop Home-Assistant container.
This method return a coroutine.
"""
return self.send_command("/homeassistant/stop")
@api_data
def retrieve_discovery_messages(self):
"""Return all discovery data from Hass.io API.
This method return a coroutine.
"""
return self.send_command("/discovery", method="get", timeout=60)
@api_data
def get_discovery_message(self, uuid):
"""Return a single discovery data message.
This method return a coroutine.
"""
return self.send_command(f"/discovery/{uuid}", method="get")
@_api_bool
async def update_hass_api(self, http_config, refresh_token):
"""Update Home Assistant API data on Hass.io."""
port = http_config.get(CONF_SERVER_PORT) or SERVER_PORT
options = {
"ssl": CONF_SSL_CERTIFICATE in http_config,
"port": port,
"watchdog": True,
"refresh_token": refresh_token.token,
}
if http_config.get(CONF_SERVER_HOST) is not None:
options["watchdog"] = False
_LOGGER.warning(
"Found incompatible HTTP option 'server_host'. Watchdog feature disabled"
)
return await self.send_command("/homeassistant/options", payload=options)
@_api_bool
def update_hass_timezone(self, timezone):
"""Update Home-Assistant timezone data on Hass.io.
This method return a coroutine.
"""
return self.send_command("/supervisor/options", payload={"timezone": timezone})
@_api_bool
def update_diagnostics(self, diagnostics: bool):
"""Update Supervisor diagnostics setting.
This method return a coroutine.
"""
return self.send_command(
"/supervisor/options", payload={"diagnostics": diagnostics}
)
async def send_command(self, command, method="post", payload=None, timeout=10):
"""Send API command to Hass.io.
This method is a coroutine.
"""
try:
request = await self.websession.request(
method,
f"http://{self._ip}{command}",
json=payload,
headers={X_HASSIO: os.environ.get("HASSIO_TOKEN", "")},
timeout=aiohttp.ClientTimeout(total=timeout),
)
if request.status not in (HTTP_OK, HTTP_BAD_REQUEST):
_LOGGER.error("%s return code %d", command, request.status)
raise HassioAPIError()
answer = await request.json()
return answer
except asyncio.TimeoutError:
_LOGGER.error("Timeout on %s request", command)
except aiohttp.ClientError as err:
_LOGGER.error("Client error on %s request %s", command, err)
raise HassioAPIError()
|
"""The tests for the mochad light platform."""
import unittest.mock as mock
import pytest
from homeassistant.components import light
from homeassistant.components.mochad import light as mochad
from homeassistant.setup import async_setup_component
@pytest.fixture(autouse=True)
def pymochad_mock():
"""Mock pymochad."""
with mock.patch("homeassistant.components.mochad.light.device") as device:
yield device
@pytest.fixture
def light_mock(hass, brightness):
"""Mock light."""
controller_mock = mock.MagicMock()
dev_dict = {"address": "a1", "name": "fake_light", "brightness_levels": brightness}
return mochad.MochadLight(hass, controller_mock, dev_dict)
async def test_setup_adds_proper_devices(hass):
"""Test if setup adds devices."""
good_config = {
"mochad": {},
"light": {
"platform": "mochad",
"devices": [{"name": "Light1", "address": "a1"}],
},
}
assert await async_setup_component(hass, light.DOMAIN, good_config)
@pytest.mark.parametrize(
"brightness,expected", [(32, "on"), (256, "xdim 255"), (64, "xdim 63")]
)
async def test_turn_on_with_no_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on()
light_mock.light.send_cmd.assert_called_once_with(expected)
@pytest.mark.parametrize(
"brightness,expected",
[
(32, [mock.call("on"), mock.call("dim 25")]),
(256, [mock.call("xdim 45")]),
(64, [mock.call("xdim 11")]),
],
)
async def test_turn_on_with_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on(brightness=45)
light_mock.light.send_cmd.assert_has_calls(expected)
@pytest.mark.parametrize("brightness", [32])
async def test_turn_off(light_mock):
"""Test turn_off."""
light_mock.turn_off()
light_mock.light.send_cmd.assert_called_once_with("off")
|
|
tests/components/mochad/test_light.py
|
homeassistant/components/hassio/handler.py
|
"""Support to select an option from a list."""
from __future__ import annotations
import logging
import voluptuous as vol
from homeassistant.const import (
ATTR_EDITABLE,
ATTR_OPTION,
CONF_ICON,
CONF_ID,
CONF_NAME,
SERVICE_RELOAD,
)
from homeassistant.core import HomeAssistant, callback
from homeassistant.helpers import collection
import homeassistant.helpers.config_validation as cv
from homeassistant.helpers.entity_component import EntityComponent
from homeassistant.helpers.restore_state import RestoreEntity
import homeassistant.helpers.service
from homeassistant.helpers.storage import Store
from homeassistant.helpers.typing import ConfigType, ServiceCallType
_LOGGER = logging.getLogger(__name__)
DOMAIN = "input_select"
CONF_INITIAL = "initial"
CONF_OPTIONS = "options"
ATTR_OPTIONS = "options"
ATTR_CYCLE = "cycle"
SERVICE_SELECT_OPTION = "select_option"
SERVICE_SELECT_NEXT = "select_next"
SERVICE_SELECT_PREVIOUS = "select_previous"
SERVICE_SELECT_FIRST = "select_first"
SERVICE_SELECT_LAST = "select_last"
SERVICE_SET_OPTIONS = "set_options"
STORAGE_KEY = DOMAIN
STORAGE_VERSION = 1
CREATE_FIELDS = {
vol.Required(CONF_NAME): vol.All(str, vol.Length(min=1)),
vol.Required(CONF_OPTIONS): vol.All(cv.ensure_list, vol.Length(min=1), [cv.string]),
vol.Optional(CONF_INITIAL): cv.string,
vol.Optional(CONF_ICON): cv.icon,
}
UPDATE_FIELDS = {
vol.Optional(CONF_NAME): cv.string,
vol.Optional(CONF_OPTIONS): vol.All(cv.ensure_list, vol.Length(min=1), [cv.string]),
vol.Optional(CONF_INITIAL): cv.string,
vol.Optional(CONF_ICON): cv.icon,
}
def _cv_input_select(cfg):
"""Configure validation helper for input select (voluptuous)."""
options = cfg[CONF_OPTIONS]
initial = cfg.get(CONF_INITIAL)
if initial is not None and initial not in options:
raise vol.Invalid(
f"initial state {initial} is not part of the options: {','.join(options)}"
)
return cfg
CONFIG_SCHEMA = vol.Schema(
{
DOMAIN: cv.schema_with_slug_keys(
vol.All(
{
vol.Optional(CONF_NAME): cv.string,
vol.Required(CONF_OPTIONS): vol.All(
cv.ensure_list, vol.Length(min=1), [cv.string]
),
vol.Optional(CONF_INITIAL): cv.string,
vol.Optional(CONF_ICON): cv.icon,
},
_cv_input_select,
)
)
},
extra=vol.ALLOW_EXTRA,
)
RELOAD_SERVICE_SCHEMA = vol.Schema({})
async def async_setup(hass: HomeAssistant, config: ConfigType) -> bool:
"""Set up an input select."""
component = EntityComponent(_LOGGER, DOMAIN, hass)
id_manager = collection.IDManager()
yaml_collection = collection.YamlCollection(
logging.getLogger(f"{__name__}.yaml_collection"), id_manager
)
collection.sync_entity_lifecycle(
hass, DOMAIN, DOMAIN, component, yaml_collection, InputSelect.from_yaml
)
storage_collection = InputSelectStorageCollection(
Store(hass, STORAGE_VERSION, STORAGE_KEY),
logging.getLogger(f"{__name__}.storage_collection"),
id_manager,
)
collection.sync_entity_lifecycle(
hass, DOMAIN, DOMAIN, component, storage_collection, InputSelect
)
await yaml_collection.async_load(
[{CONF_ID: id_, **cfg} for id_, cfg in config.get(DOMAIN, {}).items()]
)
await storage_collection.async_load()
collection.StorageCollectionWebsocket(
storage_collection, DOMAIN, DOMAIN, CREATE_FIELDS, UPDATE_FIELDS
).async_setup(hass)
async def reload_service_handler(service_call: ServiceCallType) -> None:
"""Reload yaml entities."""
conf = await component.async_prepare_reload(skip_reset=True)
if conf is None:
conf = {DOMAIN: {}}
await yaml_collection.async_load(
[{CONF_ID: id_, **cfg} for id_, cfg in conf.get(DOMAIN, {}).items()]
)
homeassistant.helpers.service.async_register_admin_service(
hass,
DOMAIN,
SERVICE_RELOAD,
reload_service_handler,
schema=RELOAD_SERVICE_SCHEMA,
)
component.async_register_entity_service(
SERVICE_SELECT_OPTION,
{vol.Required(ATTR_OPTION): cv.string},
"async_select_option",
)
component.async_register_entity_service(
SERVICE_SELECT_NEXT,
{vol.Optional(ATTR_CYCLE, default=True): bool},
"async_next",
)
component.async_register_entity_service(
SERVICE_SELECT_PREVIOUS,
{vol.Optional(ATTR_CYCLE, default=True): bool},
"async_previous",
)
component.async_register_entity_service(
SERVICE_SELECT_FIRST,
{},
callback(lambda entity, call: entity.async_select_index(0)),
)
component.async_register_entity_service(
SERVICE_SELECT_LAST,
{},
callback(lambda entity, call: entity.async_select_index(-1)),
)
component.async_register_entity_service(
SERVICE_SET_OPTIONS,
{
vol.Required(ATTR_OPTIONS): vol.All(
cv.ensure_list, vol.Length(min=1), [cv.string]
)
},
"async_set_options",
)
return True
class InputSelectStorageCollection(collection.StorageCollection):
"""Input storage based collection."""
CREATE_SCHEMA = vol.Schema(vol.All(CREATE_FIELDS, _cv_input_select))
UPDATE_SCHEMA = vol.Schema(UPDATE_FIELDS)
async def _process_create_data(self, data: dict) -> dict:
"""Validate the config is valid."""
return self.CREATE_SCHEMA(data)
@callback
def _get_suggested_id(self, info: dict) -> str:
"""Suggest an ID based on the config."""
return info[CONF_NAME]
async def _update_data(self, data: dict, update_data: dict) -> dict:
"""Return a new updated data object."""
update_data = self.UPDATE_SCHEMA(update_data)
return _cv_input_select({**data, **update_data})
class InputSelect(RestoreEntity):
"""Representation of a select input."""
def __init__(self, config: dict):
"""Initialize a select input."""
self._config = config
self.editable = True
self._current_option = config.get(CONF_INITIAL)
@classmethod
def from_yaml(cls, config: dict) -> InputSelect:
"""Return entity instance initialized from yaml storage."""
input_select = cls(config)
input_select.entity_id = f"{DOMAIN}.{config[CONF_ID]}"
input_select.editable = False
return input_select
async def async_added_to_hass(self):
"""Run when entity about to be added."""
await super().async_added_to_hass()
if self._current_option is not None:
return
state = await self.async_get_last_state()
if not state or state.state not in self._options:
self._current_option = self._options[0]
else:
self._current_option = state.state
@property
def should_poll(self):
"""If entity should be polled."""
return False
@property
def name(self):
"""Return the name of the select input."""
return self._config.get(CONF_NAME)
@property
def icon(self):
"""Return the icon to be used for this entity."""
return self._config.get(CONF_ICON)
@property
def _options(self) -> list[str]:
"""Return a list of selection options."""
return self._config[CONF_OPTIONS]
@property
def state(self):
"""Return the state of the component."""
return self._current_option
@property
def extra_state_attributes(self):
"""Return the state attributes."""
return {ATTR_OPTIONS: self._config[ATTR_OPTIONS], ATTR_EDITABLE: self.editable}
@property
def unique_id(self) -> str | None:
"""Return unique id for the entity."""
return self._config[CONF_ID]
@callback
def async_select_option(self, option):
"""Select new option."""
if option not in self._options:
_LOGGER.warning(
"Invalid option: %s (possible options: %s)",
option,
", ".join(self._options),
)
return
self._current_option = option
self.async_write_ha_state()
@callback
def async_select_index(self, idx):
"""Select new option by index."""
new_index = idx % len(self._options)
self._current_option = self._options[new_index]
self.async_write_ha_state()
@callback
def async_offset_index(self, offset, cycle):
"""Offset current index."""
current_index = self._options.index(self._current_option)
new_index = current_index + offset
if cycle:
new_index = new_index % len(self._options)
else:
if new_index < 0:
new_index = 0
elif new_index >= len(self._options):
new_index = len(self._options) - 1
self._current_option = self._options[new_index]
self.async_write_ha_state()
@callback
def async_next(self, cycle):
"""Select next option."""
self.async_offset_index(1, cycle)
@callback
def async_previous(self, cycle):
"""Select previous option."""
self.async_offset_index(-1, cycle)
@callback
def async_set_options(self, options):
"""Set options."""
self._current_option = options[0]
self._config[CONF_OPTIONS] = options
self.async_write_ha_state()
async def async_update_config(self, config: dict) -> None:
"""Handle when the config is updated."""
self._config = config
self.async_write_ha_state()
|
"""The tests for the mochad light platform."""
import unittest.mock as mock
import pytest
from homeassistant.components import light
from homeassistant.components.mochad import light as mochad
from homeassistant.setup import async_setup_component
@pytest.fixture(autouse=True)
def pymochad_mock():
"""Mock pymochad."""
with mock.patch("homeassistant.components.mochad.light.device") as device:
yield device
@pytest.fixture
def light_mock(hass, brightness):
"""Mock light."""
controller_mock = mock.MagicMock()
dev_dict = {"address": "a1", "name": "fake_light", "brightness_levels": brightness}
return mochad.MochadLight(hass, controller_mock, dev_dict)
async def test_setup_adds_proper_devices(hass):
"""Test if setup adds devices."""
good_config = {
"mochad": {},
"light": {
"platform": "mochad",
"devices": [{"name": "Light1", "address": "a1"}],
},
}
assert await async_setup_component(hass, light.DOMAIN, good_config)
@pytest.mark.parametrize(
"brightness,expected", [(32, "on"), (256, "xdim 255"), (64, "xdim 63")]
)
async def test_turn_on_with_no_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on()
light_mock.light.send_cmd.assert_called_once_with(expected)
@pytest.mark.parametrize(
"brightness,expected",
[
(32, [mock.call("on"), mock.call("dim 25")]),
(256, [mock.call("xdim 45")]),
(64, [mock.call("xdim 11")]),
],
)
async def test_turn_on_with_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on(brightness=45)
light_mock.light.send_cmd.assert_has_calls(expected)
@pytest.mark.parametrize("brightness", [32])
async def test_turn_off(light_mock):
"""Test turn_off."""
light_mock.turn_off()
light_mock.light.send_cmd.assert_called_once_with("off")
|
|
tests/components/mochad/test_light.py
|
homeassistant/components/input_select/__init__.py
|
"""BleBox climate entity."""
from homeassistant.components.climate import ClimateEntity
from homeassistant.components.climate.const import (
CURRENT_HVAC_HEAT,
CURRENT_HVAC_IDLE,
CURRENT_HVAC_OFF,
HVAC_MODE_HEAT,
HVAC_MODE_OFF,
SUPPORT_TARGET_TEMPERATURE,
)
from homeassistant.const import ATTR_TEMPERATURE, TEMP_CELSIUS
from . import BleBoxEntity, create_blebox_entities
async def async_setup_entry(hass, config_entry, async_add_entities):
"""Set up a BleBox climate entity."""
create_blebox_entities(
hass, config_entry, async_add_entities, BleBoxClimateEntity, "climates"
)
class BleBoxClimateEntity(BleBoxEntity, ClimateEntity):
"""Representation of a BleBox climate feature (saunaBox)."""
@property
def supported_features(self):
"""Return the supported climate features."""
return SUPPORT_TARGET_TEMPERATURE
@property
def hvac_mode(self):
"""Return the desired HVAC mode."""
if self._feature.is_on is None:
return None
return HVAC_MODE_HEAT if self._feature.is_on else HVAC_MODE_OFF
@property
def hvac_action(self):
"""Return the actual current HVAC action."""
is_on = self._feature.is_on
if not is_on:
return None if is_on is None else CURRENT_HVAC_OFF
# NOTE: In practice, there's no need to handle case when is_heating is None
return CURRENT_HVAC_HEAT if self._feature.is_heating else CURRENT_HVAC_IDLE
@property
def hvac_modes(self):
"""Return a list of possible HVAC modes."""
return [HVAC_MODE_OFF, HVAC_MODE_HEAT]
@property
def temperature_unit(self):
"""Return the temperature unit."""
return TEMP_CELSIUS
@property
def max_temp(self):
"""Return the maximum temperature supported."""
return self._feature.max_temp
@property
def min_temp(self):
"""Return the maximum temperature supported."""
return self._feature.min_temp
@property
def current_temperature(self):
"""Return the current temperature."""
return self._feature.current
@property
def target_temperature(self):
"""Return the desired thermostat temperature."""
return self._feature.desired
async def async_set_hvac_mode(self, hvac_mode):
"""Set the climate entity mode."""
if hvac_mode == HVAC_MODE_HEAT:
await self._feature.async_on()
return
await self._feature.async_off()
async def async_set_temperature(self, **kwargs):
"""Set the thermostat temperature."""
value = kwargs[ATTR_TEMPERATURE]
await self._feature.async_set_temperature(value)
|
"""The tests for the mochad light platform."""
import unittest.mock as mock
import pytest
from homeassistant.components import light
from homeassistant.components.mochad import light as mochad
from homeassistant.setup import async_setup_component
@pytest.fixture(autouse=True)
def pymochad_mock():
"""Mock pymochad."""
with mock.patch("homeassistant.components.mochad.light.device") as device:
yield device
@pytest.fixture
def light_mock(hass, brightness):
"""Mock light."""
controller_mock = mock.MagicMock()
dev_dict = {"address": "a1", "name": "fake_light", "brightness_levels": brightness}
return mochad.MochadLight(hass, controller_mock, dev_dict)
async def test_setup_adds_proper_devices(hass):
"""Test if setup adds devices."""
good_config = {
"mochad": {},
"light": {
"platform": "mochad",
"devices": [{"name": "Light1", "address": "a1"}],
},
}
assert await async_setup_component(hass, light.DOMAIN, good_config)
@pytest.mark.parametrize(
"brightness,expected", [(32, "on"), (256, "xdim 255"), (64, "xdim 63")]
)
async def test_turn_on_with_no_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on()
light_mock.light.send_cmd.assert_called_once_with(expected)
@pytest.mark.parametrize(
"brightness,expected",
[
(32, [mock.call("on"), mock.call("dim 25")]),
(256, [mock.call("xdim 45")]),
(64, [mock.call("xdim 11")]),
],
)
async def test_turn_on_with_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on(brightness=45)
light_mock.light.send_cmd.assert_has_calls(expected)
@pytest.mark.parametrize("brightness", [32])
async def test_turn_off(light_mock):
"""Test turn_off."""
light_mock.turn_off()
light_mock.light.send_cmd.assert_called_once_with("off")
|
|
tests/components/mochad/test_light.py
|
homeassistant/components/blebox/climate.py
|
"""Component to embed TP-Link smart home devices."""
import logging
import voluptuous as vol
from homeassistant import config_entries
from homeassistant.const import CONF_HOST
import homeassistant.helpers.config_validation as cv
from homeassistant.helpers.typing import ConfigType, HomeAssistantType
from .common import (
ATTR_CONFIG,
CONF_DIMMER,
CONF_DISCOVERY,
CONF_LIGHT,
CONF_STRIP,
CONF_SWITCH,
SmartDevices,
async_discover_devices,
get_static_devices,
)
_LOGGER = logging.getLogger(__name__)
DOMAIN = "tplink"
TPLINK_HOST_SCHEMA = vol.Schema({vol.Required(CONF_HOST): cv.string})
CONFIG_SCHEMA = vol.Schema(
{
DOMAIN: vol.Schema(
{
vol.Optional(CONF_LIGHT, default=[]): vol.All(
cv.ensure_list, [TPLINK_HOST_SCHEMA]
),
vol.Optional(CONF_SWITCH, default=[]): vol.All(
cv.ensure_list, [TPLINK_HOST_SCHEMA]
),
vol.Optional(CONF_STRIP, default=[]): vol.All(
cv.ensure_list, [TPLINK_HOST_SCHEMA]
),
vol.Optional(CONF_DIMMER, default=[]): vol.All(
cv.ensure_list, [TPLINK_HOST_SCHEMA]
),
vol.Optional(CONF_DISCOVERY, default=True): cv.boolean,
}
)
},
extra=vol.ALLOW_EXTRA,
)
async def async_setup(hass, config):
"""Set up the TP-Link component."""
conf = config.get(DOMAIN)
hass.data[DOMAIN] = {}
hass.data[DOMAIN][ATTR_CONFIG] = conf
if conf is not None:
hass.async_create_task(
hass.config_entries.flow.async_init(
DOMAIN, context={"source": config_entries.SOURCE_IMPORT}
)
)
return True
async def async_setup_entry(hass: HomeAssistantType, config_entry: ConfigType):
"""Set up TPLink from a config entry."""
config_data = hass.data[DOMAIN].get(ATTR_CONFIG)
# These will contain the initialized devices
lights = hass.data[DOMAIN][CONF_LIGHT] = []
switches = hass.data[DOMAIN][CONF_SWITCH] = []
# Add static devices
static_devices = SmartDevices()
if config_data is not None:
static_devices = get_static_devices(config_data)
lights.extend(static_devices.lights)
switches.extend(static_devices.switches)
# Add discovered devices
if config_data is None or config_data[CONF_DISCOVERY]:
discovered_devices = await async_discover_devices(hass, static_devices)
lights.extend(discovered_devices.lights)
switches.extend(discovered_devices.switches)
forward_setup = hass.config_entries.async_forward_entry_setup
if lights:
_LOGGER.debug(
"Got %s lights: %s", len(lights), ", ".join([d.host for d in lights])
)
hass.async_create_task(forward_setup(config_entry, "light"))
if switches:
_LOGGER.debug(
"Got %s switches: %s", len(switches), ", ".join([d.host for d in switches])
)
hass.async_create_task(forward_setup(config_entry, "switch"))
return True
async def async_unload_entry(hass, entry):
"""Unload a config entry."""
forward_unload = hass.config_entries.async_forward_entry_unload
remove_lights = remove_switches = False
if hass.data[DOMAIN][CONF_LIGHT]:
remove_lights = await forward_unload(entry, "light")
if hass.data[DOMAIN][CONF_SWITCH]:
remove_switches = await forward_unload(entry, "switch")
if remove_lights or remove_switches:
hass.data[DOMAIN].clear()
return True
# We were not able to unload the platforms, either because there
# were none or one of the forward_unloads failed.
return False
|
"""The tests for the mochad light platform."""
import unittest.mock as mock
import pytest
from homeassistant.components import light
from homeassistant.components.mochad import light as mochad
from homeassistant.setup import async_setup_component
@pytest.fixture(autouse=True)
def pymochad_mock():
"""Mock pymochad."""
with mock.patch("homeassistant.components.mochad.light.device") as device:
yield device
@pytest.fixture
def light_mock(hass, brightness):
"""Mock light."""
controller_mock = mock.MagicMock()
dev_dict = {"address": "a1", "name": "fake_light", "brightness_levels": brightness}
return mochad.MochadLight(hass, controller_mock, dev_dict)
async def test_setup_adds_proper_devices(hass):
"""Test if setup adds devices."""
good_config = {
"mochad": {},
"light": {
"platform": "mochad",
"devices": [{"name": "Light1", "address": "a1"}],
},
}
assert await async_setup_component(hass, light.DOMAIN, good_config)
@pytest.mark.parametrize(
"brightness,expected", [(32, "on"), (256, "xdim 255"), (64, "xdim 63")]
)
async def test_turn_on_with_no_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on()
light_mock.light.send_cmd.assert_called_once_with(expected)
@pytest.mark.parametrize(
"brightness,expected",
[
(32, [mock.call("on"), mock.call("dim 25")]),
(256, [mock.call("xdim 45")]),
(64, [mock.call("xdim 11")]),
],
)
async def test_turn_on_with_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on(brightness=45)
light_mock.light.send_cmd.assert_has_calls(expected)
@pytest.mark.parametrize("brightness", [32])
async def test_turn_off(light_mock):
"""Test turn_off."""
light_mock.turn_off()
light_mock.light.send_cmd.assert_called_once_with("off")
|
|
tests/components/mochad/test_light.py
|
homeassistant/components/tplink/__init__.py
|
"""Support for the Daikin HVAC."""
import logging
import voluptuous as vol
from homeassistant.components.climate import PLATFORM_SCHEMA, ClimateEntity
from homeassistant.components.climate.const import (
ATTR_FAN_MODE,
ATTR_HVAC_MODE,
ATTR_PRESET_MODE,
ATTR_SWING_MODE,
HVAC_MODE_COOL,
HVAC_MODE_DRY,
HVAC_MODE_FAN_ONLY,
HVAC_MODE_HEAT,
HVAC_MODE_HEAT_COOL,
HVAC_MODE_OFF,
PRESET_AWAY,
PRESET_BOOST,
PRESET_ECO,
PRESET_NONE,
SUPPORT_FAN_MODE,
SUPPORT_PRESET_MODE,
SUPPORT_SWING_MODE,
SUPPORT_TARGET_TEMPERATURE,
)
from homeassistant.const import ATTR_TEMPERATURE, CONF_HOST, CONF_NAME, TEMP_CELSIUS
import homeassistant.helpers.config_validation as cv
from . import DOMAIN as DAIKIN_DOMAIN
from .const import (
ATTR_INSIDE_TEMPERATURE,
ATTR_OUTSIDE_TEMPERATURE,
ATTR_STATE_OFF,
ATTR_STATE_ON,
ATTR_TARGET_TEMPERATURE,
)
_LOGGER = logging.getLogger(__name__)
PLATFORM_SCHEMA = PLATFORM_SCHEMA.extend(
{vol.Required(CONF_HOST): cv.string, vol.Optional(CONF_NAME): cv.string}
)
HA_STATE_TO_DAIKIN = {
HVAC_MODE_FAN_ONLY: "fan",
HVAC_MODE_DRY: "dry",
HVAC_MODE_COOL: "cool",
HVAC_MODE_HEAT: "hot",
HVAC_MODE_HEAT_COOL: "auto",
HVAC_MODE_OFF: "off",
}
DAIKIN_TO_HA_STATE = {
"fan": HVAC_MODE_FAN_ONLY,
"dry": HVAC_MODE_DRY,
"cool": HVAC_MODE_COOL,
"hot": HVAC_MODE_HEAT,
"auto": HVAC_MODE_HEAT_COOL,
"off": HVAC_MODE_OFF,
}
HA_PRESET_TO_DAIKIN = {
PRESET_AWAY: "on",
PRESET_NONE: "off",
PRESET_BOOST: "powerful",
PRESET_ECO: "econo",
}
HA_ATTR_TO_DAIKIN = {
ATTR_PRESET_MODE: "en_hol",
ATTR_HVAC_MODE: "mode",
ATTR_FAN_MODE: "f_rate",
ATTR_SWING_MODE: "f_dir",
ATTR_INSIDE_TEMPERATURE: "htemp",
ATTR_OUTSIDE_TEMPERATURE: "otemp",
ATTR_TARGET_TEMPERATURE: "stemp",
}
DAIKIN_ATTR_ADVANCED = "adv"
async def async_setup_platform(hass, config, async_add_entities, discovery_info=None):
"""Old way of setting up the Daikin HVAC platform.
Can only be called when a user accidentally mentions the platform in their
config. But even in that case it would have been ignored.
"""
async def async_setup_entry(hass, entry, async_add_entities):
"""Set up Daikin climate based on config_entry."""
daikin_api = hass.data[DAIKIN_DOMAIN].get(entry.entry_id)
async_add_entities([DaikinClimate(daikin_api)], update_before_add=True)
class DaikinClimate(ClimateEntity):
"""Representation of a Daikin HVAC."""
def __init__(self, api):
"""Initialize the climate device."""
self._api = api
self._list = {
ATTR_HVAC_MODE: list(HA_STATE_TO_DAIKIN),
ATTR_FAN_MODE: self._api.device.fan_rate,
ATTR_SWING_MODE: self._api.device.swing_modes,
}
self._supported_features = SUPPORT_TARGET_TEMPERATURE
if (
self._api.device.support_away_mode
or self._api.device.support_advanced_modes
):
self._supported_features |= SUPPORT_PRESET_MODE
if self._api.device.support_fan_rate:
self._supported_features |= SUPPORT_FAN_MODE
if self._api.device.support_swing_mode:
self._supported_features |= SUPPORT_SWING_MODE
async def _set(self, settings):
"""Set device settings using API."""
values = {}
for attr in [ATTR_TEMPERATURE, ATTR_FAN_MODE, ATTR_SWING_MODE, ATTR_HVAC_MODE]:
value = settings.get(attr)
if value is None:
continue
daikin_attr = HA_ATTR_TO_DAIKIN.get(attr)
if daikin_attr is not None:
if attr == ATTR_HVAC_MODE:
values[daikin_attr] = HA_STATE_TO_DAIKIN[value]
elif value in self._list[attr]:
values[daikin_attr] = value.lower()
else:
_LOGGER.error("Invalid value %s for %s", attr, value)
# temperature
elif attr == ATTR_TEMPERATURE:
try:
values[HA_ATTR_TO_DAIKIN[ATTR_TARGET_TEMPERATURE]] = str(int(value))
except ValueError:
_LOGGER.error("Invalid temperature %s", value)
if values:
await self._api.device.set(values)
@property
def supported_features(self):
"""Return the list of supported features."""
return self._supported_features
@property
def name(self):
"""Return the name of the thermostat, if any."""
return self._api.name
@property
def unique_id(self):
"""Return a unique ID."""
return self._api.device.mac
@property
def temperature_unit(self):
"""Return the unit of measurement which this thermostat uses."""
return TEMP_CELSIUS
@property
def current_temperature(self):
"""Return the current temperature."""
return self._api.device.inside_temperature
@property
def target_temperature(self):
"""Return the temperature we try to reach."""
return self._api.device.target_temperature
@property
def target_temperature_step(self):
"""Return the supported step of target temperature."""
return 1
async def async_set_temperature(self, **kwargs):
"""Set new target temperature."""
await self._set(kwargs)
@property
def hvac_mode(self):
"""Return current operation ie. heat, cool, idle."""
daikin_mode = self._api.device.represent(HA_ATTR_TO_DAIKIN[ATTR_HVAC_MODE])[1]
return DAIKIN_TO_HA_STATE.get(daikin_mode, HVAC_MODE_HEAT_COOL)
@property
def hvac_modes(self):
"""Return the list of available operation modes."""
return self._list.get(ATTR_HVAC_MODE)
async def async_set_hvac_mode(self, hvac_mode):
"""Set HVAC mode."""
await self._set({ATTR_HVAC_MODE: hvac_mode})
@property
def fan_mode(self):
"""Return the fan setting."""
return self._api.device.represent(HA_ATTR_TO_DAIKIN[ATTR_FAN_MODE])[1].title()
async def async_set_fan_mode(self, fan_mode):
"""Set fan mode."""
await self._set({ATTR_FAN_MODE: fan_mode})
@property
def fan_modes(self):
"""List of available fan modes."""
return self._list.get(ATTR_FAN_MODE)
@property
def swing_mode(self):
"""Return the fan setting."""
return self._api.device.represent(HA_ATTR_TO_DAIKIN[ATTR_SWING_MODE])[1].title()
async def async_set_swing_mode(self, swing_mode):
"""Set new target temperature."""
await self._set({ATTR_SWING_MODE: swing_mode})
@property
def swing_modes(self):
"""List of available swing modes."""
return self._list.get(ATTR_SWING_MODE)
@property
def preset_mode(self):
"""Return the preset_mode."""
if (
self._api.device.represent(HA_ATTR_TO_DAIKIN[ATTR_PRESET_MODE])[1]
== HA_PRESET_TO_DAIKIN[PRESET_AWAY]
):
return PRESET_AWAY
if (
HA_PRESET_TO_DAIKIN[PRESET_BOOST]
in self._api.device.represent(DAIKIN_ATTR_ADVANCED)[1]
):
return PRESET_BOOST
if (
HA_PRESET_TO_DAIKIN[PRESET_ECO]
in self._api.device.represent(DAIKIN_ATTR_ADVANCED)[1]
):
return PRESET_ECO
return PRESET_NONE
async def async_set_preset_mode(self, preset_mode):
"""Set preset mode."""
if preset_mode == PRESET_AWAY:
await self._api.device.set_holiday(ATTR_STATE_ON)
elif preset_mode == PRESET_BOOST:
await self._api.device.set_advanced_mode(
HA_PRESET_TO_DAIKIN[PRESET_BOOST], ATTR_STATE_ON
)
elif preset_mode == PRESET_ECO:
await self._api.device.set_advanced_mode(
HA_PRESET_TO_DAIKIN[PRESET_ECO], ATTR_STATE_ON
)
else:
if self.preset_mode == PRESET_AWAY:
await self._api.device.set_holiday(ATTR_STATE_OFF)
elif self.preset_mode == PRESET_BOOST:
await self._api.device.set_advanced_mode(
HA_PRESET_TO_DAIKIN[PRESET_BOOST], ATTR_STATE_OFF
)
elif self.preset_mode == PRESET_ECO:
await self._api.device.set_advanced_mode(
HA_PRESET_TO_DAIKIN[PRESET_ECO], ATTR_STATE_OFF
)
@property
def preset_modes(self):
"""List of available preset modes."""
ret = [PRESET_NONE]
if self._api.device.support_away_mode:
ret.append(PRESET_AWAY)
if self._api.device.support_advanced_modes:
ret += [PRESET_ECO, PRESET_BOOST]
return ret
async def async_update(self):
"""Retrieve latest state."""
await self._api.async_update()
async def async_turn_on(self):
"""Turn device on."""
await self._api.device.set({})
async def async_turn_off(self):
"""Turn device off."""
await self._api.device.set(
{HA_ATTR_TO_DAIKIN[ATTR_HVAC_MODE]: HA_STATE_TO_DAIKIN[HVAC_MODE_OFF]}
)
@property
def device_info(self):
"""Return a device description for device registry."""
return self._api.device_info
|
"""The tests for the mochad light platform."""
import unittest.mock as mock
import pytest
from homeassistant.components import light
from homeassistant.components.mochad import light as mochad
from homeassistant.setup import async_setup_component
@pytest.fixture(autouse=True)
def pymochad_mock():
"""Mock pymochad."""
with mock.patch("homeassistant.components.mochad.light.device") as device:
yield device
@pytest.fixture
def light_mock(hass, brightness):
"""Mock light."""
controller_mock = mock.MagicMock()
dev_dict = {"address": "a1", "name": "fake_light", "brightness_levels": brightness}
return mochad.MochadLight(hass, controller_mock, dev_dict)
async def test_setup_adds_proper_devices(hass):
"""Test if setup adds devices."""
good_config = {
"mochad": {},
"light": {
"platform": "mochad",
"devices": [{"name": "Light1", "address": "a1"}],
},
}
assert await async_setup_component(hass, light.DOMAIN, good_config)
@pytest.mark.parametrize(
"brightness,expected", [(32, "on"), (256, "xdim 255"), (64, "xdim 63")]
)
async def test_turn_on_with_no_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on()
light_mock.light.send_cmd.assert_called_once_with(expected)
@pytest.mark.parametrize(
"brightness,expected",
[
(32, [mock.call("on"), mock.call("dim 25")]),
(256, [mock.call("xdim 45")]),
(64, [mock.call("xdim 11")]),
],
)
async def test_turn_on_with_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on(brightness=45)
light_mock.light.send_cmd.assert_has_calls(expected)
@pytest.mark.parametrize("brightness", [32])
async def test_turn_off(light_mock):
"""Test turn_off."""
light_mock.turn_off()
light_mock.light.send_cmd.assert_called_once_with("off")
|
|
tests/components/mochad/test_light.py
|
homeassistant/components/daikin/climate.py
|
"""Describe group states."""
from homeassistant.components.group import GroupIntegrationRegistry
from homeassistant.const import STATE_OFF
from homeassistant.core import callback
from homeassistant.helpers.typing import HomeAssistantType
from . import (
STATE_ECO,
STATE_ELECTRIC,
STATE_GAS,
STATE_HEAT_PUMP,
STATE_HIGH_DEMAND,
STATE_PERFORMANCE,
)
@callback
def async_describe_on_off_states(
hass: HomeAssistantType, registry: GroupIntegrationRegistry
) -> None:
"""Describe group on off states."""
registry.on_off_states(
{
STATE_ECO,
STATE_ELECTRIC,
STATE_PERFORMANCE,
STATE_HIGH_DEMAND,
STATE_HEAT_PUMP,
STATE_GAS,
},
STATE_OFF,
)
|
"""The tests for the mochad light platform."""
import unittest.mock as mock
import pytest
from homeassistant.components import light
from homeassistant.components.mochad import light as mochad
from homeassistant.setup import async_setup_component
@pytest.fixture(autouse=True)
def pymochad_mock():
"""Mock pymochad."""
with mock.patch("homeassistant.components.mochad.light.device") as device:
yield device
@pytest.fixture
def light_mock(hass, brightness):
"""Mock light."""
controller_mock = mock.MagicMock()
dev_dict = {"address": "a1", "name": "fake_light", "brightness_levels": brightness}
return mochad.MochadLight(hass, controller_mock, dev_dict)
async def test_setup_adds_proper_devices(hass):
"""Test if setup adds devices."""
good_config = {
"mochad": {},
"light": {
"platform": "mochad",
"devices": [{"name": "Light1", "address": "a1"}],
},
}
assert await async_setup_component(hass, light.DOMAIN, good_config)
@pytest.mark.parametrize(
"brightness,expected", [(32, "on"), (256, "xdim 255"), (64, "xdim 63")]
)
async def test_turn_on_with_no_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on()
light_mock.light.send_cmd.assert_called_once_with(expected)
@pytest.mark.parametrize(
"brightness,expected",
[
(32, [mock.call("on"), mock.call("dim 25")]),
(256, [mock.call("xdim 45")]),
(64, [mock.call("xdim 11")]),
],
)
async def test_turn_on_with_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on(brightness=45)
light_mock.light.send_cmd.assert_has_calls(expected)
@pytest.mark.parametrize("brightness", [32])
async def test_turn_off(light_mock):
"""Test turn_off."""
light_mock.turn_off()
light_mock.light.send_cmd.assert_called_once_with("off")
|
|
tests/components/mochad/test_light.py
|
homeassistant/components/water_heater/group.py
|
"""Describe logbook events."""
from homeassistant.core import callback
from .const import DOMAIN, EVENT_ALEXA_SMART_HOME
@callback
def async_describe_events(hass, async_describe_event):
"""Describe logbook events."""
@callback
def async_describe_logbook_event(event):
"""Describe a logbook event."""
data = event.data
entity_id = data["request"].get("entity_id")
if entity_id:
state = hass.states.get(entity_id)
name = state.name if state else entity_id
message = f"send command {data['request']['namespace']}/{data['request']['name']} for {name}"
else:
message = (
f"send command {data['request']['namespace']}/{data['request']['name']}"
)
return {"name": "Amazon Alexa", "message": message, "entity_id": entity_id}
async_describe_event(DOMAIN, EVENT_ALEXA_SMART_HOME, async_describe_logbook_event)
|
"""The tests for the mochad light platform."""
import unittest.mock as mock
import pytest
from homeassistant.components import light
from homeassistant.components.mochad import light as mochad
from homeassistant.setup import async_setup_component
@pytest.fixture(autouse=True)
def pymochad_mock():
"""Mock pymochad."""
with mock.patch("homeassistant.components.mochad.light.device") as device:
yield device
@pytest.fixture
def light_mock(hass, brightness):
"""Mock light."""
controller_mock = mock.MagicMock()
dev_dict = {"address": "a1", "name": "fake_light", "brightness_levels": brightness}
return mochad.MochadLight(hass, controller_mock, dev_dict)
async def test_setup_adds_proper_devices(hass):
"""Test if setup adds devices."""
good_config = {
"mochad": {},
"light": {
"platform": "mochad",
"devices": [{"name": "Light1", "address": "a1"}],
},
}
assert await async_setup_component(hass, light.DOMAIN, good_config)
@pytest.mark.parametrize(
"brightness,expected", [(32, "on"), (256, "xdim 255"), (64, "xdim 63")]
)
async def test_turn_on_with_no_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on()
light_mock.light.send_cmd.assert_called_once_with(expected)
@pytest.mark.parametrize(
"brightness,expected",
[
(32, [mock.call("on"), mock.call("dim 25")]),
(256, [mock.call("xdim 45")]),
(64, [mock.call("xdim 11")]),
],
)
async def test_turn_on_with_brightness(light_mock, expected):
"""Test turn_on."""
light_mock.turn_on(brightness=45)
light_mock.light.send_cmd.assert_has_calls(expected)
@pytest.mark.parametrize("brightness", [32])
async def test_turn_off(light_mock):
"""Test turn_off."""
light_mock.turn_off()
light_mock.light.send_cmd.assert_called_once_with("off")
|
|
tests/components/mochad/test_light.py
|
homeassistant/components/alexa/logbook.py
|
# -*- coding: utf-8 -*-
# Copyright (C) 2014-2016 Andrey Antukh <niwi@niwi.be>
# Copyright (C) 2014-2016 Jesús Espino <jespinog@gmail.com>
# Copyright (C) 2014-2016 David Barragán <bameda@dbarragan.com>
# Copyright (C) 2014-2016 Alejandro Alonso <alejandro.alonso@kaleidos.net>
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import os
from django.db.models.fields.files import FieldFile
from taiga.base.utils.urls import get_absolute_url
from easy_thumbnails.files import get_thumbnailer
from easy_thumbnails.exceptions import InvalidImageFormatError
def get_thumbnail_url(file_obj, thumbnailer_size):
# Ugly hack to temporary ignore tiff files
relative_name = file_obj
if isinstance(file_obj, FieldFile):
relative_name = file_obj.name
source_extension = os.path.splitext(relative_name)[1][1:]
if source_extension == "tiff":
return None
try:
path_url = get_thumbnailer(file_obj)[thumbnailer_size].url
thumb_url = get_absolute_url(path_url)
except InvalidImageFormatError:
thumb_url = None
return thumb_url
|
# -*- coding: utf-8 -*-
# Copyright (C) 2014-2016 Andrey Antukh <niwi@niwi.nz>
# Copyright (C) 2014-2016 Jesús Espino <jespinog@gmail.com>
# Copyright (C) 2014-2016 David Barragán <bameda@dbarragan.com>
# Copyright (C) 2014-2016 Alejandro Alonso <alejandro.alonso@kaleidos.net>
# Copyright (C) 2014-2016 Anler Hernández <hello@anler.me>
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import pytest
from unittest.mock import patch
from unittest.mock import Mock
from .. import factories as f
from taiga.projects.history import services
pytestmark = pytest.mark.django_db(transaction=True)
from taiga.base.utils import json
def test_webhooks_when_create_wiki_page(settings):
settings.WEBHOOKS_ENABLED = True
project = f.ProjectFactory()
f.WebhookFactory.create(project=project)
f.WebhookFactory.create(project=project)
obj = f.WikiPageFactory.create(project=project)
with patch('taiga.webhooks.tasks._send_request') as send_request_mock:
services.take_snapshot(obj, user=obj.owner)
assert send_request_mock.call_count == 2
(webhook_id, url, key, data) = send_request_mock.call_args[0]
assert data["action"] == "create"
assert data["type"] == "wikipage"
assert data["by"]["id"] == obj.owner.id
assert "date" in data
assert data["data"]["id"] == obj.id
def test_webhooks_when_update_wiki_page(settings):
settings.WEBHOOKS_ENABLED = True
project = f.ProjectFactory()
f.WebhookFactory.create(project=project)
f.WebhookFactory.create(project=project)
obj = f.WikiPageFactory.create(project=project)
with patch('taiga.webhooks.tasks._send_request') as send_request_mock:
services.take_snapshot(obj, user=obj.owner)
assert send_request_mock.call_count == 2
obj.content = "test webhook update"
obj.save()
with patch('taiga.webhooks.tasks._send_request') as send_request_mock:
services.take_snapshot(obj, user=obj.owner, comment="test_comment")
assert send_request_mock.call_count == 2
(webhook_id, url, key, data) = send_request_mock.call_args[0]
assert data["action"] == "change"
assert data["type"] == "wikipage"
assert data["by"]["id"] == obj.owner.id
assert "date" in data
assert data["data"]["id"] == obj.id
assert data["data"]["content"] == obj.content
assert data["change"]["comment"] == "test_comment"
assert data["change"]["diff"]["content_html"]["from"] != data["change"]["diff"]["content_html"]["to"]
assert obj.content in data["change"]["diff"]["content_html"]["to"]
def test_webhooks_when_delete_wiki_page(settings):
settings.WEBHOOKS_ENABLED = True
project = f.ProjectFactory()
f.WebhookFactory.create(project=project)
f.WebhookFactory.create(project=project)
obj = f.WikiPageFactory.create(project=project)
with patch('taiga.webhooks.tasks._send_request') as send_request_mock:
services.take_snapshot(obj, user=obj.owner, delete=True)
assert send_request_mock.call_count == 2
(webhook_id, url, key, data) = send_request_mock.call_args[0]
assert data["action"] == "delete"
assert data["type"] == "wikipage"
assert data["by"]["id"] == obj.owner.id
assert "date" in data
assert "data" in data
def test_webhooks_when_update_wiki_page_attachments(settings):
settings.WEBHOOKS_ENABLED = True
project = f.ProjectFactory()
f.WebhookFactory.create(project=project)
f.WebhookFactory.create(project=project)
obj = f.WikiPageFactory.create(project=project)
with patch('taiga.webhooks.tasks._send_request') as send_request_mock:
services.take_snapshot(obj, user=obj.owner)
assert send_request_mock.call_count == 2
# Create attachments
attachment1 = f.WikiAttachmentFactory(project=obj.project, content_object=obj, owner=obj.owner)
attachment2 = f.WikiAttachmentFactory(project=obj.project, content_object=obj, owner=obj.owner)
with patch('taiga.webhooks.tasks._send_request') as send_request_mock:
services.take_snapshot(obj, user=obj.owner, comment="test_comment")
assert send_request_mock.call_count == 2
(webhook_id, url, key, data) = send_request_mock.call_args[0]
assert data["action"] == "change"
assert data["type"] == "wikipage"
assert data["by"]["id"] == obj.owner.id
assert "date" in data
assert data["data"]["id"] == obj.id
assert data["change"]["comment"] == "test_comment"
assert len(data["change"]["diff"]["attachments"]["new"]) == 2
assert len(data["change"]["diff"]["attachments"]["changed"]) == 0
assert len(data["change"]["diff"]["attachments"]["deleted"]) == 0
# Update attachment
attachment1.description = "new attachment description"
attachment1.save()
with patch('taiga.webhooks.tasks._send_request') as send_request_mock:
services.take_snapshot(obj, user=obj.owner, comment="test_comment")
assert send_request_mock.call_count == 2
(webhook_id, url, key, data) = send_request_mock.call_args[0]
assert data["action"] == "change"
assert data["type"] == "wikipage"
assert data["by"]["id"] == obj.owner.id
assert "date" in data
assert data["data"]["id"] == obj.id
assert data["change"]["comment"] == "test_comment"
assert len(data["change"]["diff"]["attachments"]["new"]) == 0
assert len(data["change"]["diff"]["attachments"]["changed"]) == 1
assert len(data["change"]["diff"]["attachments"]["deleted"]) == 0
# Delete attachment
attachment2.delete()
with patch('taiga.webhooks.tasks._send_request') as send_request_mock:
services.take_snapshot(obj, user=obj.owner, comment="test_comment")
assert send_request_mock.call_count == 2
(webhook_id, url, key, data) = send_request_mock.call_args[0]
assert data["action"] == "change"
assert data["type"] == "wikipage"
assert data["by"]["id"] == obj.owner.id
assert "date" in data
assert data["data"]["id"] == obj.id
assert data["change"]["comment"] == "test_comment"
assert len(data["change"]["diff"]["attachments"]["new"]) == 0
assert len(data["change"]["diff"]["attachments"]["changed"]) == 0
assert len(data["change"]["diff"]["attachments"]["deleted"]) == 1
|
xdevelsistemas/taiga-back-community
|
tests/integration/test_webhooks_wikipages.py
|
taiga/base/utils/thumbnails.py
|
from collections.abc import Iterable
from numbers import Integral
import subprocess
import openmc
def _run(args, output, cwd):
# Launch a subprocess
p = subprocess.Popen(args, cwd=cwd, stdout=subprocess.PIPE,
stderr=subprocess.STDOUT, universal_newlines=True)
# Capture and re-print OpenMC output in real-time
lines = []
while True:
# If OpenMC is finished, break loop
line = p.stdout.readline()
if not line and p.poll() is not None:
break
lines.append(line)
if output:
# If user requested output, print to screen
print(line, end='')
# Raise an exception if return status is non-zero
if p.returncode != 0:
raise subprocess.CalledProcessError(p.returncode, ' '.join(args),
''.join(lines))
def plot_geometry(output=True, openmc_exec='openmc', cwd='.'):
"""Run OpenMC in plotting mode
Parameters
----------
output : bool, optional
Capture OpenMC output from standard out
openmc_exec : str, optional
Path to OpenMC executable
cwd : str, optional
Path to working directory to run in
Raises
------
subprocess.CalledProcessError
If the `openmc` executable returns a non-zero status
"""
_run([openmc_exec, '-p'], output, cwd)
def plot_inline(plots, openmc_exec='openmc', cwd='.', convert_exec='convert'):
"""Display plots inline in a Jupyter notebook.
This function requires that you have a program installed to convert PPM
files to PNG files. Typically, that would be `ImageMagick
<https://www.imagemagick.org>`_ which includes a `convert` command.
Parameters
----------
plots : Iterable of openmc.Plot
Plots to display
openmc_exec : str
Path to OpenMC executable
cwd : str, optional
Path to working directory to run in
convert_exec : str, optional
Command that can convert PPM files into PNG files
Raises
------
subprocess.CalledProcessError
If the `openmc` executable returns a non-zero status
"""
from IPython.display import Image, display
if not isinstance(plots, Iterable):
plots = [plots]
# Create plots.xml
openmc.Plots(plots).export_to_xml()
# Run OpenMC in geometry plotting mode
plot_geometry(False, openmc_exec, cwd)
images = []
if plots is not None:
for p in plots:
if p.filename is not None:
ppm_file = '{}.ppm'.format(p.filename)
else:
ppm_file = 'plot_{}.ppm'.format(p.id)
png_file = ppm_file.replace('.ppm', '.png')
subprocess.check_call([convert_exec, ppm_file, png_file])
images.append(Image(png_file))
display(*images)
def calculate_volumes(threads=None, output=True, cwd='.',
openmc_exec='openmc', mpi_args=None):
"""Run stochastic volume calculations in OpenMC.
This function runs OpenMC in stochastic volume calculation mode. To specify
the parameters of a volume calculation, one must first create a
:class:`openmc.VolumeCalculation` instance and assign it to
:attr:`openmc.Settings.volume_calculations`. For example:
>>> vol = openmc.VolumeCalculation(domains=[cell1, cell2], samples=100000)
>>> settings = openmc.Settings()
>>> settings.volume_calculations = [vol]
>>> settings.export_to_xml()
>>> openmc.calculate_volumes()
Parameters
----------
threads : int, optional
Number of OpenMP threads. If OpenMC is compiled with OpenMP threading
enabled, the default is implementation-dependent but is usually equal to
the number of hardware threads available (or a value set by the
:envvar:`OMP_NUM_THREADS` environment variable).
output : bool, optional
Capture OpenMC output from standard out
openmc_exec : str, optional
Path to OpenMC executable. Defaults to 'openmc'.
mpi_args : list of str, optional
MPI execute command and any additional MPI arguments to pass,
e.g. ['mpiexec', '-n', '8'].
cwd : str, optional
Path to working directory to run in. Defaults to the current working
directory.
Raises
------
subprocess.CalledProcessError
If the `openmc` executable returns a non-zero status
See Also
--------
openmc.VolumeCalculation
"""
args = [openmc_exec, '--volume']
if isinstance(threads, Integral) and threads > 0:
args += ['-s', str(threads)]
if mpi_args is not None:
args = mpi_args + args
_run(args, output, cwd)
def run(particles=None, threads=None, geometry_debug=False,
restart_file=None, tracks=False, output=True, cwd='.',
openmc_exec='openmc', mpi_args=None, event_based=False):
"""Run an OpenMC simulation.
Parameters
----------
particles : int, optional
Number of particles to simulate per generation.
threads : int, optional
Number of OpenMP threads. If OpenMC is compiled with OpenMP threading
enabled, the default is implementation-dependent but is usually equal to
the number of hardware threads available (or a value set by the
:envvar:`OMP_NUM_THREADS` environment variable).
geometry_debug : bool, optional
Turn on geometry debugging during simulation. Defaults to False.
restart_file : str, optional
Path to restart file to use
tracks : bool, optional
Write tracks for all particles. Defaults to False.
output : bool
Capture OpenMC output from standard out
cwd : str, optional
Path to working directory to run in. Defaults to the current working
directory.
openmc_exec : str, optional
Path to OpenMC executable. Defaults to 'openmc'.
mpi_args : list of str, optional
MPI execute command and any additional MPI arguments to pass,
e.g. ['mpiexec', '-n', '8'].
event_based : bool, optional
Turns on event-based parallelism, instead of default history-based
.. versionadded:: 0.12
Raises
------
subprocess.CalledProcessError
If the `openmc` executable returns a non-zero status
"""
args = [openmc_exec]
if isinstance(particles, Integral) and particles > 0:
args += ['-n', str(particles)]
if isinstance(threads, Integral) and threads > 0:
args += ['-s', str(threads)]
if geometry_debug:
args.append('-g')
if event_based:
args.append('-e')
if isinstance(restart_file, str):
args += ['-r', restart_file]
if tracks:
args.append('-t')
if mpi_args is not None:
args = mpi_args + args
_run(args, output, cwd)
|
from random import uniform
import numpy as np
import openmc
import pytest
def test_rectangular_parallelepiped():
xmin = uniform(-5., 5.)
xmax = xmin + uniform(0., 5.)
ymin = uniform(-5., 5.)
ymax = ymin + uniform(0., 5.)
zmin = uniform(-5., 5.)
zmax = zmin + uniform(0., 5.)
s = openmc.model.RectangularParallelepiped(xmin, xmax, ymin, ymax, zmin, zmax)
assert isinstance(s.xmin, openmc.XPlane)
assert isinstance(s.xmax, openmc.XPlane)
assert isinstance(s.ymin, openmc.YPlane)
assert isinstance(s.ymax, openmc.YPlane)
assert isinstance(s.zmin, openmc.ZPlane)
assert isinstance(s.zmax, openmc.ZPlane)
# Make sure boundary condition propagates
s.boundary_type = 'reflective'
assert s.boundary_type == 'reflective'
for axis in 'xyz':
assert getattr(s, '{}min'.format(axis)).boundary_type == 'reflective'
assert getattr(s, '{}max'.format(axis)).boundary_type == 'reflective'
# Check bounding box
ll, ur = (+s).bounding_box
assert np.all(np.isinf(ll))
assert np.all(np.isinf(ur))
ll, ur = (-s).bounding_box
assert ur == pytest.approx((xmax, ymax, zmax))
assert ll == pytest.approx((xmin, ymin, zmin))
# __contains__ on associated half-spaces
assert (xmin - 0.1, 0., 0.) in +s
assert (xmin - 0.1, 0., 0.) not in -s
dx, dy, dz = xmax - xmin, ymax - ymin, zmax - zmin
assert (xmin + dx/2, ymin + dy/2, zmin + dz/2) in -s
assert (xmin + dx/2, ymin + dy/2, zmin + dz/2) not in +s
# translate method
t = uniform(-5.0, 5.0)
s_t = s.translate((t, t, t))
ll_t, ur_t = (-s_t).bounding_box
assert ur_t == pytest.approx(ur + t)
assert ll_t == pytest.approx(ll + t)
# Make sure repr works
repr(s)
@pytest.mark.parametrize(
"axis, indices", [
("X", [0, 1, 2]),
("Y", [1, 2, 0]),
("Z", [2, 0, 1]),
]
)
def test_right_circular_cylinder(axis, indices):
x, y, z = 1.0, -2.5, 3.0
h, r = 5.0, 3.0
s = openmc.model.RightCircularCylinder((x, y, z), h, r, axis=axis.lower())
assert isinstance(s.cyl, getattr(openmc, axis + "Cylinder"))
assert isinstance(s.top, getattr(openmc, axis + "Plane"))
assert isinstance(s.bottom, getattr(openmc, axis + "Plane"))
# Make sure boundary condition propagates
s.boundary_type = 'reflective'
assert s.boundary_type == 'reflective'
assert s.cyl.boundary_type == 'reflective'
assert s.bottom.boundary_type == 'reflective'
assert s.top.boundary_type == 'reflective'
# Check bounding box
ll, ur = (+s).bounding_box
assert np.all(np.isinf(ll))
assert np.all(np.isinf(ur))
ll, ur = (-s).bounding_box
assert ll == pytest.approx((x, y, z) + np.roll([0, -r, -r], indices[0]))
assert ur == pytest.approx((x, y, z) + np.roll([h, r, r], indices[0]))
# __contains__ on associated half-spaces
point_pos = (x, y, z) + np.roll([h/2, r+1, r+1], indices[0])
assert point_pos in +s
assert point_pos not in -s
point_neg = (x, y, z) + np.roll([h/2, 0, 0], indices[0])
assert point_neg in -s
assert point_neg not in +s
# translate method
t = uniform(-5.0, 5.0)
s_t = s.translate((t, t, t))
ll_t, ur_t = (-s_t).bounding_box
assert ur_t == pytest.approx(ur + t)
assert ll_t == pytest.approx(ll + t)
# Make sure repr works
repr(s)
@pytest.mark.parametrize(
"axis, point_pos, point_neg, ll_true", [
("X", (8., 0., 0.), (12., 0., 0.), (10., -np.inf, -np.inf)),
("Y", (10., -2., 0.), (10., 2., 0.), (-np.inf, 0., -np.inf)),
("Z", (10., 0., -3.), (10., 0., 3.), (-np.inf, -np.inf, 0.))
]
)
def test_cone_one_sided(axis, point_pos, point_neg, ll_true):
cone_oneside = getattr(openmc.model, axis + "ConeOneSided")
cone_twoside = getattr(openmc, axis + "Cone")
plane = getattr(openmc, axis + "Plane")
x, y, z = 10., 0., 0.
r2 = 4.
s = cone_oneside(x, y, z, r2, True)
assert isinstance(s.cone, cone_twoside)
assert isinstance(s.plane, plane)
assert s.up
# Make sure boundary condition propagates
s.boundary_type = 'reflective'
assert s.boundary_type == 'reflective'
assert s.cone.boundary_type == 'reflective'
assert s.plane.boundary_type == 'transmission'
# Check bounding box
ll, ur = (+s).bounding_box
assert np.all(np.isinf(ll))
assert np.all(np.isinf(ur))
ll, ur = (-s).bounding_box
assert np.all(np.isinf(ur))
assert ll == pytest.approx(ll_true)
# __contains__ on associated half-spaces
assert point_pos in +s
assert point_pos not in -s
assert point_neg in -s
assert point_neg not in +s
# translate method
t = uniform(-5.0, 5.0)
s_t = s.translate((t, t, t))
ll_t, ur_t = (-s_t).bounding_box
assert ur_t == pytest.approx(ur + t)
assert ll_t == pytest.approx(ll + t)
# Make sure repr works
repr(s)
|
|
tests/unit_tests/test_surface_composite.py
|
|
from collections import OrderedDict
from collections.abc import Iterable
from copy import deepcopy
from math import cos, sin, pi
from numbers import Real
from xml.etree import ElementTree as ET
import numpy as np
from uncertainties import UFloat
import openmc
import openmc.checkvalue as cv
from ._xml import get_text
from .mixin import IDManagerMixin
from .region import Region, Complement
from .surface import Halfspace
class Cell(IDManagerMixin):
r"""A region of space defined as the intersection of half-space created by
quadric surfaces.
Parameters
----------
cell_id : int, optional
Unique identifier for the cell. If not specified, an identifier will
automatically be assigned.
name : str, optional
Name of the cell. If not specified, the name is the empty string.
fill : openmc.Material or openmc.Universe or openmc.Lattice or None or iterable of openmc.Material, optional
Indicates what the region of space is filled with
region : openmc.Region, optional
Region of space that is assigned to the cell.
Attributes
----------
id : int
Unique identifier for the cell
name : str
Name of the cell
fill : openmc.Material or openmc.Universe or openmc.Lattice or None or iterable of openmc.Material
Indicates what the region of space is filled with. If None, the cell is
treated as a void. An iterable of materials is used to fill repeated
instances of a cell with different materials.
fill_type : {'material', 'universe', 'lattice', 'distribmat', 'void'}
Indicates what the cell is filled with.
region : openmc.Region or None
Region of space that is assigned to the cell.
rotation : Iterable of float
If the cell is filled with a universe, this array specifies the angles
in degrees about the x, y, and z axes that the filled universe should be
rotated. The rotation applied is an intrinsic rotation with specified
Tait-Bryan angles. That is to say, if the angles are :math:`(\phi,
\theta, \psi)`, then the rotation matrix applied is :math:`R_z(\psi)
R_y(\theta) R_x(\phi)` or
.. math::
\left [ \begin{array}{ccc} \cos\theta \cos\psi & -\cos\phi \sin\psi
+ \sin\phi \sin\theta \cos\psi & \sin\phi \sin\psi + \cos\phi
\sin\theta \cos\psi \\ \cos\theta \sin\psi & \cos\phi \cos\psi +
\sin\phi \sin\theta \sin\psi & -\sin\phi \cos\psi + \cos\phi
\sin\theta \sin\psi \\ -\sin\theta & \sin\phi \cos\theta & \cos\phi
\cos\theta \end{array} \right ]
A rotation matrix can also be specified directly by setting this
attribute to a nested list (or 2D numpy array) that specifies each
element of the matrix.
rotation_matrix : numpy.ndarray
The rotation matrix defined by the angles specified in the
:attr:`Cell.rotation` property.
temperature : float or iterable of float
Temperature of the cell in Kelvin. Multiple temperatures can be given
to give each distributed cell instance a unique temperature.
translation : Iterable of float
If the cell is filled with a universe, this array specifies a vector
that is used to translate (shift) the universe.
paths : list of str
The paths traversed through the CSG tree to reach each cell
instance. This property is initialized by calling the
:meth:`Geometry.determine_paths` method.
num_instances : int
The number of instances of this cell throughout the geometry.
volume : float
Volume of the cell in cm^3. This can either be set manually or
calculated in a stochastic volume calculation and added via the
:meth:`Cell.add_volume_information` method. For 'distribmat' cells
it is the total volume of all instances.
atoms : collections.OrderedDict
Mapping of nuclides to the total number of atoms for each nuclide
present in the cell, or in all of its instances for a 'distribmat'
fill. For example, {'U235': 1.0e22, 'U238': 5.0e22, ...}.
.. versionadded:: 0.12
"""
next_id = 1
used_ids = set()
def __init__(self, cell_id=None, name='', fill=None, region=None):
# Initialize Cell class attributes
self.id = cell_id
self.name = name
self.fill = fill
self.region = region
self._rotation = None
self._rotation_matrix = None
self._temperature = None
self._translation = None
self._paths = None
self._num_instances = None
self._volume = None
self._atoms = None
def __contains__(self, point):
if self.region is None:
return True
else:
return point in self.region
def __repr__(self):
string = 'Cell\n'
string += '{: <16}=\t{}\n'.format('\tID', self.id)
string += '{: <16}=\t{}\n'.format('\tName', self.name)
if self.fill_type == 'material':
string += '{: <16}=\tMaterial {}\n'.format('\tFill', self.fill.id)
elif self.fill_type == 'void':
string += '{: <16}=\tNone\n'.format('\tFill')
elif self.fill_type == 'distribmat':
string += '{: <16}=\t{}\n'.format('\tFill', list(map(
lambda m: m if m is None else m.id, self.fill)))
else:
string += '{: <16}=\t{}\n'.format('\tFill', self.fill.id)
string += '{: <16}=\t{}\n'.format('\tRegion', self.region)
string += '{: <16}=\t{}\n'.format('\tRotation', self.rotation)
if self.fill_type == 'material':
string += '\t{0: <15}=\t{1}\n'.format('Temperature',
self.temperature)
string += '{: <16}=\t{}\n'.format('\tTranslation', self.translation)
string += '{: <16}=\t{}\n'.format('\tVolume', self.volume)
return string
@property
def name(self):
return self._name
@property
def fill(self):
return self._fill
@property
def fill_type(self):
if isinstance(self.fill, openmc.Material):
return 'material'
elif isinstance(self.fill, openmc.Universe):
return 'universe'
elif isinstance(self.fill, openmc.Lattice):
return 'lattice'
elif isinstance(self.fill, Iterable):
return 'distribmat'
else:
return 'void'
@property
def region(self):
return self._region
@property
def rotation(self):
return self._rotation
@property
def rotation_matrix(self):
return self._rotation_matrix
@property
def temperature(self):
return self._temperature
@property
def translation(self):
return self._translation
@property
def volume(self):
return self._volume
@property
def atoms(self):
if self._atoms is None:
if self._volume is None:
msg = ('Cannot calculate atom content becouse no volume '
'is set. Use Cell.volume to provide it or perform '
'a stochastic volume calculation.')
raise ValueError(msg)
elif self.fill_type == 'void':
msg = ('Cell is filled with void. It contains no atoms. '
'Material must be set to calculate atom content.')
raise ValueError(msg)
elif self.fill_type in ['lattice', 'universe']:
msg = ('Universe and Lattice cells can contain multiple '
'materials in diffrent proportions. Atom content must '
'be calculated with stochastic volume calculation.')
raise ValueError(msg)
elif self.fill_type == 'material':
# Get atomic densities
self._atoms = self._fill.get_nuclide_atom_densities()
# Convert to total number of atoms
for key, nuclide in self._atoms.items():
atom = nuclide[1] * self._volume * 1.0e+24
self._atoms[key] = atom
elif self.fill_type == 'distribmat':
# Assumes that volume is total volume of all instances
# Also assumes that all instances have the same volume
partial_volume = self.volume / len(self.fill)
self._atoms = OrderedDict()
for mat in self.fill:
for key, nuclide in mat.get_nuclide_atom_densities().items():
# To account for overlap of nuclides between distribmat
# we need to append new atoms to any existing value
# hence it is necessary to ask for default.
atom = self._atoms.setdefault(key, 0)
atom += nuclide[1] * partial_volume * 1.0e+24
self._atoms[key] = atom
else:
msg = 'Unrecognized fill_type: {}'.format(self.fill_type)
raise ValueError(msg)
return self._atoms
@property
def paths(self):
if self._paths is None:
raise ValueError('Cell instance paths have not been determined. '
'Call the Geometry.determine_paths() method.')
return self._paths
@property
def bounding_box(self):
if self.region is not None:
return self.region.bounding_box
else:
return (np.array([-np.inf, -np.inf, -np.inf]),
np.array([np.inf, np.inf, np.inf]))
@property
def num_instances(self):
if self._num_instances is None:
raise ValueError(
'Number of cell instances have not been determined. Call the '
'Geometry.determine_paths() method.')
return self._num_instances
@name.setter
def name(self, name):
if name is not None:
cv.check_type('cell name', name, str)
self._name = name
else:
self._name = ''
@fill.setter
def fill(self, fill):
if fill is not None:
if isinstance(fill, Iterable):
for i, f in enumerate(fill):
if f is not None:
cv.check_type('cell.fill[i]', f, openmc.Material)
elif not isinstance(fill, (openmc.Material, openmc.Lattice,
openmc.Universe)):
msg = ('Unable to set Cell ID="{0}" to use a non-Material or '
'Universe fill "{1}"'.format(self._id, fill))
raise ValueError(msg)
self._fill = fill
# Info about atom content can now be invalid
# (since fill has just changed)
self._atoms = None
@rotation.setter
def rotation(self, rotation):
cv.check_length('cell rotation', rotation, 3)
self._rotation = np.asarray(rotation)
# Save rotation matrix -- the reason we do this instead of having it be
# automatically calculated when the rotation_matrix property is accessed
# is so that plotting on a rotated geometry can be done faster.
if self._rotation.ndim == 2:
# User specified rotation matrix directly
self._rotation_matrix = self._rotation
else:
phi, theta, psi = self.rotation*(-pi/180.)
c3, s3 = cos(phi), sin(phi)
c2, s2 = cos(theta), sin(theta)
c1, s1 = cos(psi), sin(psi)
self._rotation_matrix = np.array([
[c1*c2, c1*s2*s3 - c3*s1, s1*s3 + c1*c3*s2],
[c2*s1, c1*c3 + s1*s2*s3, c3*s1*s2 - c1*s3],
[-s2, c2*s3, c2*c3]])
@translation.setter
def translation(self, translation):
cv.check_type('cell translation', translation, Iterable, Real)
cv.check_length('cell translation', translation, 3)
self._translation = np.asarray(translation)
@temperature.setter
def temperature(self, temperature):
# Make sure temperatures are positive
cv.check_type('cell temperature', temperature, (Iterable, Real))
if isinstance(temperature, Iterable):
cv.check_type('cell temperature', temperature, Iterable, Real)
for T in temperature:
cv.check_greater_than('cell temperature', T, 0.0, True)
else:
cv.check_greater_than('cell temperature', temperature, 0.0, True)
# If this cell is filled with a universe or lattice, propagate
# temperatures to all cells contained. Otherwise, simply assign it.
if self.fill_type in ('universe', 'lattice'):
for c in self.get_all_cells().values():
if c.fill_type == 'material':
c._temperature = temperature
else:
self._temperature = temperature
@region.setter
def region(self, region):
if region is not None:
cv.check_type('cell region', region, Region)
self._region = region
@volume.setter
def volume(self, volume):
if volume is not None:
cv.check_type('cell volume', volume, (Real, UFloat))
cv.check_greater_than('cell volume', volume, 0.0, equality=True)
self._volume = volume
# Info about atom content can now be invalid
# (sice volume has just changed)
self._atoms = None
def add_volume_information(self, volume_calc):
"""Add volume information to a cell.
Parameters
----------
volume_calc : openmc.VolumeCalculation
Results from a stochastic volume calculation
"""
if volume_calc.domain_type == 'cell':
if self.id in volume_calc.volumes:
self._volume = volume_calc.volumes[self.id].n
self._atoms = volume_calc.atoms[self.id]
else:
raise ValueError('No volume information found for this cell.')
else:
raise ValueError('No volume information found for this cell.')
def get_nuclides(self):
"""Returns all nuclides in the cell
Returns
-------
nuclides : list of str
List of nuclide names
"""
return self.fill.get_nuclides() if self.fill_type != 'void' else []
def get_nuclide_densities(self):
"""Return all nuclides contained in the cell and their densities
Returns
-------
nuclides : collections.OrderedDict
Dictionary whose keys are nuclide names and values are 2-tuples of
(nuclide, density)
"""
nuclides = OrderedDict()
if self.fill_type == 'material':
nuclides.update(self.fill.get_nuclide_densities())
elif self.fill_type == 'void':
pass
else:
if self._atoms is not None:
volume = self.volume
for name, atoms in self._atoms.items():
nuclide = openmc.Nuclide(name)
density = 1.0e-24 * atoms.n/volume # density in atoms/b-cm
nuclides[name] = (nuclide, density)
else:
raise RuntimeError(
'Volume information is needed to calculate microscopic cross '
'sections for cell {}. This can be done by running a '
'stochastic volume calculation via the '
'openmc.VolumeCalculation object'.format(self.id))
return nuclides
def get_all_cells(self, memo=None):
"""Return all cells that are contained within this one if it is filled with a
universe or lattice
Returns
-------
cells : collections.orderedDict
Dictionary whose keys are cell IDs and values are :class:`Cell`
instances
"""
cells = OrderedDict()
if memo and self in memo:
return cells
if memo is not None:
memo.add(self)
if self.fill_type in ('universe', 'lattice'):
cells.update(self.fill.get_all_cells(memo))
return cells
def get_all_materials(self, memo=None):
"""Return all materials that are contained within the cell
Returns
-------
materials : collections.OrderedDict
Dictionary whose keys are material IDs and values are
:class:`Material` instances
"""
materials = OrderedDict()
if self.fill_type == 'material':
materials[self.fill.id] = self.fill
elif self.fill_type == 'distribmat':
for m in self.fill:
if m is not None:
materials[m.id] = m
else:
# Append all Cells in each Cell in the Universe to the dictionary
cells = self.get_all_cells(memo)
for cell in cells.values():
materials.update(cell.get_all_materials(memo))
return materials
def get_all_universes(self):
"""Return all universes that are contained within this one if any of
its cells are filled with a universe or lattice.
Returns
-------
universes : collections.OrderedDict
Dictionary whose keys are universe IDs and values are
:class:`Universe` instances
"""
universes = OrderedDict()
if self.fill_type == 'universe':
universes[self.fill.id] = self.fill
universes.update(self.fill.get_all_universes())
elif self.fill_type == 'lattice':
universes.update(self.fill.get_all_universes())
return universes
def clone(self, clone_materials=True, clone_regions=True, memo=None):
"""Create a copy of this cell with a new unique ID, and clones
the cell's region and fill.
Parameters
----------
clone_materials : bool
Whether to create separate copies of the materials filling cells
contained in this cell, or the material filling this cell.
clone_regions : bool
Whether to create separate copies of the regions bounding cells
contained in this cell, and the region bounding this cell.
memo : dict or None
A nested dictionary of previously cloned objects. This parameter
is used internally and should not be specified by the user.
Returns
-------
clone : openmc.Cell
The clone of this cell
"""
if memo is None:
memo = {}
# If no memoize'd clone exists, instantiate one
if self not in memo:
# Temporarily remove paths
paths = self._paths
self._paths = None
clone = deepcopy(self)
clone.id = None
clone._num_instances = None
# Restore paths on original instance
self._paths = paths
if self.region is not None:
if clone_regions:
clone.region = self.region.clone(memo)
else:
clone.region = self.region
if self.fill is not None:
if self.fill_type == 'distribmat':
if not clone_materials:
clone.fill = self.fill
else:
clone.fill = [fill.clone(memo) if fill is not None else
None for fill in self.fill]
elif self.fill_type == 'material':
if not clone_materials:
clone.fill = self.fill
else:
clone.fill = self.fill.clone(memo)
else:
clone.fill = self.fill.clone(clone_materials,
clone_regions, memo)
# Memoize the clone
memo[self] = clone
return memo[self]
def create_xml_subelement(self, xml_element, memo=None):
"""Add the cell's xml representation to an incoming xml element
Parameters
----------
xml_element : xml.etree.ElementTree.Element
XML element to be added to
memo : set or None
A set of object IDs representing geometry entities already
written to ``xml_element``. This parameter is used internally
and should not be specified by users.
Returns
-------
None
"""
element = ET.Element("cell")
element.set("id", str(self.id))
if len(self._name) > 0:
element.set("name", str(self.name))
if self.fill_type == 'void':
element.set("material", "void")
elif self.fill_type == 'material':
element.set("material", str(self.fill.id))
elif self.fill_type == 'distribmat':
element.set("material", ' '.join(['void' if m is None else str(m.id)
for m in self.fill]))
elif self.fill_type in ('universe', 'lattice'):
element.set("fill", str(self.fill.id))
self.fill.create_xml_subelement(xml_element, memo)
if self.region is not None:
# Set the region attribute with the region specification
region = str(self.region)
if region.startswith('('):
region = region[1:-1]
if len(region) > 0:
element.set("region", region)
# Only surfaces that appear in a region are added to the geometry
# file, so the appropriate check is performed here. First we create
# a function which is called recursively to navigate through the CSG
# tree. When it reaches a leaf (a Halfspace), it creates a <surface>
# element for the corresponding surface if none has been created
# thus far.
def create_surface_elements(node, element, memo=None):
if isinstance(node, Halfspace):
if memo and node.surface in memo:
return
if memo is not None:
memo.add(node.surface)
xml_element.append(node.surface.to_xml_element())
elif isinstance(node, Complement):
create_surface_elements(node.node, element, memo)
else:
for subnode in node:
create_surface_elements(subnode, element, memo)
# Call the recursive function from the top node
create_surface_elements(self.region, xml_element, memo)
if self.temperature is not None:
if isinstance(self.temperature, Iterable):
element.set("temperature", ' '.join(
str(t) for t in self.temperature))
else:
element.set("temperature", str(self.temperature))
if self.translation is not None:
element.set("translation", ' '.join(map(str, self.translation)))
if self.rotation is not None:
element.set("rotation", ' '.join(map(str, self.rotation.ravel())))
return element
@classmethod
def from_xml_element(cls, elem, surfaces, materials, get_universe):
"""Generate cell from XML element
Parameters
----------
elem : xml.etree.ElementTree.Element
`<cell>` element
surfaces : dict
Dictionary mapping surface IDs to :class:`openmc.Surface` instances
materials : dict
Dictionary mapping material IDs to :class:`openmc.Material`
instances (defined in :math:`openmc.Geometry.from_xml`)
get_universe : function
Function returning universe (defined in
:meth:`openmc.Geometry.from_xml`)
Returns
-------
openmc.Cell
Cell instance
"""
cell_id = int(get_text(elem, 'id'))
name = get_text(elem, 'name')
c = cls(cell_id, name)
# Assign material/distributed materials or fill
mat_text = get_text(elem, 'material')
if mat_text is not None:
mat_ids = mat_text.split()
if len(mat_ids) > 1:
c.fill = [materials[i] for i in mat_ids]
else:
c.fill = materials[mat_ids[0]]
else:
fill_id = int(get_text(elem, 'fill'))
c.fill = get_universe(fill_id)
# Assign region
region = get_text(elem, 'region')
if region is not None:
c.region = Region.from_expression(region, surfaces)
# Check for other attributes
t = get_text(elem, 'temperature')
if t is not None:
if ' ' in t:
c.temperature = [float(t_i) for t_i in t.split()]
else:
c.temperature = float(t)
for key in ('temperature', 'rotation', 'translation'):
value = get_text(elem, key)
if value is not None:
setattr(c, key, [float(x) for x in value.split()])
# Add this cell to appropriate universe
univ_id = int(get_text(elem, 'universe', 0))
get_universe(univ_id).add_cell(c)
return c
|
from random import uniform
import numpy as np
import openmc
import pytest
def test_rectangular_parallelepiped():
xmin = uniform(-5., 5.)
xmax = xmin + uniform(0., 5.)
ymin = uniform(-5., 5.)
ymax = ymin + uniform(0., 5.)
zmin = uniform(-5., 5.)
zmax = zmin + uniform(0., 5.)
s = openmc.model.RectangularParallelepiped(xmin, xmax, ymin, ymax, zmin, zmax)
assert isinstance(s.xmin, openmc.XPlane)
assert isinstance(s.xmax, openmc.XPlane)
assert isinstance(s.ymin, openmc.YPlane)
assert isinstance(s.ymax, openmc.YPlane)
assert isinstance(s.zmin, openmc.ZPlane)
assert isinstance(s.zmax, openmc.ZPlane)
# Make sure boundary condition propagates
s.boundary_type = 'reflective'
assert s.boundary_type == 'reflective'
for axis in 'xyz':
assert getattr(s, '{}min'.format(axis)).boundary_type == 'reflective'
assert getattr(s, '{}max'.format(axis)).boundary_type == 'reflective'
# Check bounding box
ll, ur = (+s).bounding_box
assert np.all(np.isinf(ll))
assert np.all(np.isinf(ur))
ll, ur = (-s).bounding_box
assert ur == pytest.approx((xmax, ymax, zmax))
assert ll == pytest.approx((xmin, ymin, zmin))
# __contains__ on associated half-spaces
assert (xmin - 0.1, 0., 0.) in +s
assert (xmin - 0.1, 0., 0.) not in -s
dx, dy, dz = xmax - xmin, ymax - ymin, zmax - zmin
assert (xmin + dx/2, ymin + dy/2, zmin + dz/2) in -s
assert (xmin + dx/2, ymin + dy/2, zmin + dz/2) not in +s
# translate method
t = uniform(-5.0, 5.0)
s_t = s.translate((t, t, t))
ll_t, ur_t = (-s_t).bounding_box
assert ur_t == pytest.approx(ur + t)
assert ll_t == pytest.approx(ll + t)
# Make sure repr works
repr(s)
@pytest.mark.parametrize(
"axis, indices", [
("X", [0, 1, 2]),
("Y", [1, 2, 0]),
("Z", [2, 0, 1]),
]
)
def test_right_circular_cylinder(axis, indices):
x, y, z = 1.0, -2.5, 3.0
h, r = 5.0, 3.0
s = openmc.model.RightCircularCylinder((x, y, z), h, r, axis=axis.lower())
assert isinstance(s.cyl, getattr(openmc, axis + "Cylinder"))
assert isinstance(s.top, getattr(openmc, axis + "Plane"))
assert isinstance(s.bottom, getattr(openmc, axis + "Plane"))
# Make sure boundary condition propagates
s.boundary_type = 'reflective'
assert s.boundary_type == 'reflective'
assert s.cyl.boundary_type == 'reflective'
assert s.bottom.boundary_type == 'reflective'
assert s.top.boundary_type == 'reflective'
# Check bounding box
ll, ur = (+s).bounding_box
assert np.all(np.isinf(ll))
assert np.all(np.isinf(ur))
ll, ur = (-s).bounding_box
assert ll == pytest.approx((x, y, z) + np.roll([0, -r, -r], indices[0]))
assert ur == pytest.approx((x, y, z) + np.roll([h, r, r], indices[0]))
# __contains__ on associated half-spaces
point_pos = (x, y, z) + np.roll([h/2, r+1, r+1], indices[0])
assert point_pos in +s
assert point_pos not in -s
point_neg = (x, y, z) + np.roll([h/2, 0, 0], indices[0])
assert point_neg in -s
assert point_neg not in +s
# translate method
t = uniform(-5.0, 5.0)
s_t = s.translate((t, t, t))
ll_t, ur_t = (-s_t).bounding_box
assert ur_t == pytest.approx(ur + t)
assert ll_t == pytest.approx(ll + t)
# Make sure repr works
repr(s)
@pytest.mark.parametrize(
"axis, point_pos, point_neg, ll_true", [
("X", (8., 0., 0.), (12., 0., 0.), (10., -np.inf, -np.inf)),
("Y", (10., -2., 0.), (10., 2., 0.), (-np.inf, 0., -np.inf)),
("Z", (10., 0., -3.), (10., 0., 3.), (-np.inf, -np.inf, 0.))
]
)
def test_cone_one_sided(axis, point_pos, point_neg, ll_true):
cone_oneside = getattr(openmc.model, axis + "ConeOneSided")
cone_twoside = getattr(openmc, axis + "Cone")
plane = getattr(openmc, axis + "Plane")
x, y, z = 10., 0., 0.
r2 = 4.
s = cone_oneside(x, y, z, r2, True)
assert isinstance(s.cone, cone_twoside)
assert isinstance(s.plane, plane)
assert s.up
# Make sure boundary condition propagates
s.boundary_type = 'reflective'
assert s.boundary_type == 'reflective'
assert s.cone.boundary_type == 'reflective'
assert s.plane.boundary_type == 'transmission'
# Check bounding box
ll, ur = (+s).bounding_box
assert np.all(np.isinf(ll))
assert np.all(np.isinf(ur))
ll, ur = (-s).bounding_box
assert np.all(np.isinf(ur))
assert ll == pytest.approx(ll_true)
# __contains__ on associated half-spaces
assert point_pos in +s
assert point_pos not in -s
assert point_neg in -s
assert point_neg not in +s
# translate method
t = uniform(-5.0, 5.0)
s_t = s.translate((t, t, t))
ll_t, ur_t = (-s_t).bounding_box
assert ur_t == pytest.approx(ur + t)
assert ll_t == pytest.approx(ll + t)
# Make sure repr works
repr(s)
|
|
tests/unit_tests/test_surface_composite.py
|
|
# -*- coding: utf-8 -*-
'''Base TestCase class for OSF unittests. Uses a temporary MongoDB database.'''
import abc
import datetime as dt
import functools
import logging
import os
import re
import shutil
import tempfile
import unittest
import uuid
import blinker
import httpretty
import mock
import pytest
from addons.wiki.models import NodeWikiPage
from django.test.utils import override_settings
from django.test import TestCase as DjangoTestCase
from faker import Factory
from framework.auth import User
from framework.auth.core import Auth
from framework.celery_tasks.handlers import celery_before_request
from framework.django.handlers import handlers as django_handlers
from framework.flask import rm_handlers
from framework.guid.model import Guid
from framework.mongo import client as client_proxy
from framework.mongo import database as database_proxy
from framework.sessions.model import Session
from framework.transactions import commands, messages, utils
from pymongo.errors import OperationFailure
from website import settings
from website.app import init_app
from website.notifications.listeners import (subscribe_contributor,
subscribe_creator)
from website.project.model import (MetaSchema, Node, NodeLog, Tag, WatchConfig,
ensure_schemas)
from website.project.signals import contributor_added, project_created
from website.project.views.contributor import notify_added_contributor
from website.signals import ALL_SIGNALS
from webtest_plus import TestApp
from .json_api_test_app import JSONAPITestApp
from nose.tools import * # noqa (PEP8 asserts); noqa (PEP8 asserts)
logger = logging.getLogger(__name__)
def get_default_metaschema():
"""This needs to be a method so it gets called after the test database is set up"""
try:
return MetaSchema.find()[0]
except IndexError:
ensure_schemas()
return MetaSchema.find()[0]
try:
test_app = init_app(routes=True, set_backends=False)
except AssertionError: # Routes have already been set up
test_app = init_app(routes=False, set_backends=False)
rm_handlers(test_app, django_handlers)
test_app.testing = True
# Silence some 3rd-party logging and some "loud" internal loggers
SILENT_LOGGERS = [
'api.caching.tasks',
'factory.generate',
'factory.containers',
'framework.analytics',
'framework.auth.core',
'framework.celery_tasks.signals',
'website.app',
'website.archiver.tasks',
'website.mails',
'website.notifications.listeners',
'website.search.elastic_search',
'website.search_migration.migrate',
'website.util.paths',
'requests_oauthlib.oauth2_session',
'raven.base.Client',
'raven.contrib.django.client.DjangoClient',
]
for logger_name in SILENT_LOGGERS:
logging.getLogger(logger_name).setLevel(logging.CRITICAL)
# Fake factory
fake = Factory.create()
# All Models
MODELS = (User, Node, NodeLog, NodeWikiPage,
Tag, WatchConfig, Session, Guid)
def teardown_database(client=None, database=None):
client = client or client_proxy
database = database or database_proxy
try:
commands.rollback(database)
except OperationFailure as error:
message = utils.get_error_message(error)
if messages.NO_TRANSACTION_ERROR not in message:
raise
client.drop_database(database)
@pytest.mark.django_db
class DbTestCase(unittest.TestCase):
"""Base `TestCase` for tests that require a scratch database.
"""
DB_NAME = getattr(settings, 'TEST_DB_NAME', 'osf_test')
@classmethod
def setUpClass(cls):
super(DbTestCase, cls).setUpClass()
# cls._original_db_name = settings.DB_NAME
# settings.DB_NAME = cls.DB_NAME
cls._original_enable_email_subscriptions = settings.ENABLE_EMAIL_SUBSCRIPTIONS
settings.ENABLE_EMAIL_SUBSCRIPTIONS = False
cls._original_bcrypt_log_rounds = settings.BCRYPT_LOG_ROUNDS
settings.BCRYPT_LOG_ROUNDS = 4
# teardown_database(database=database_proxy._get_current_object())
# TODO: With `database` as a `LocalProxy`, we should be able to simply
# this logic
# set_up_storage(
# website.models.MODELS,
# storage.MongoStorage,
# addons=settings.ADDONS_AVAILABLE,
# )
# cls.db = database_proxy
@classmethod
def tearDownClass(cls):
super(DbTestCase, cls).tearDownClass()
# teardown_database(database=database_proxy._get_current_object())
# settings.DB_NAME = cls._original_db_name
settings.ENABLE_EMAIL_SUBSCRIPTIONS = cls._original_enable_email_subscriptions
settings.BCRYPT_LOG_ROUNDS = cls._original_bcrypt_log_rounds
class AppTestCase(unittest.TestCase):
"""Base `TestCase` for OSF tests that require the WSGI app (but no database).
"""
PUSH_CONTEXT = True
DISCONNECTED_SIGNALS = {
# disconnect notify_add_contributor so that add_contributor does not send "fake" emails in tests
contributor_added: [notify_added_contributor]
}
def setUp(self):
super(AppTestCase, self).setUp()
self.app = TestApp(test_app)
if not self.PUSH_CONTEXT:
return
self.context = test_app.test_request_context(headers={
'Remote-Addr': '146.9.219.56',
'User-Agent': 'Mozilla/5.0 (X11; U; SunOS sun4u; en-US; rv:0.9.4.1) Gecko/20020518 Netscape6/6.2.3'
})
self.context.push()
with self.context:
celery_before_request()
for signal in self.DISCONNECTED_SIGNALS:
for receiver in self.DISCONNECTED_SIGNALS[signal]:
signal.disconnect(receiver)
def tearDown(self):
super(AppTestCase, self).tearDown()
if not self.PUSH_CONTEXT:
return
with mock.patch('website.mailchimp_utils.get_mailchimp_api'):
self.context.pop()
for signal in self.DISCONNECTED_SIGNALS:
for receiver in self.DISCONNECTED_SIGNALS[signal]:
signal.connect(receiver)
class ApiAppTestCase(unittest.TestCase):
"""Base `TestCase` for OSF API v2 tests that require the WSGI app (but no database).
"""
allow_database_queries = True
def setUp(self):
super(ApiAppTestCase, self).setUp()
self.app = JSONAPITestApp()
class SearchTestCase(unittest.TestCase):
def setUp(self):
settings.ELASTIC_INDEX = uuid.uuid4().hex
settings.ELASTIC_TIMEOUT = 60
from website.search import elastic_search
elastic_search.INDEX = settings.ELASTIC_INDEX
elastic_search.create_index(settings.ELASTIC_INDEX)
# NOTE: Super is called last to ensure the ES connection can be established before
# the httpretty module patches the socket.
super(SearchTestCase, self).setUp()
def tearDown(self):
super(SearchTestCase, self).tearDown()
from website.search import elastic_search
elastic_search.delete_index(settings.ELASTIC_INDEX)
methods = [
httpretty.GET,
httpretty.PUT,
httpretty.HEAD,
httpretty.POST,
httpretty.PATCH,
httpretty.DELETE,
]
def kill(*args, **kwargs):
logger.error('httppretty.kill: %s - %s', args, kwargs)
raise httpretty.errors.UnmockedError()
class MockRequestTestCase(unittest.TestCase):
DISABLE_OUTGOING_CONNECTIONS = False
def setUp(self):
super(MockRequestTestCase, self).setUp()
if self.DISABLE_OUTGOING_CONNECTIONS:
httpretty.enable()
for method in methods:
httpretty.register_uri(
method,
re.compile(r'.*'),
body=kill,
priority=-1,
)
def tearDown(self):
super(MockRequestTestCase, self).tearDown()
httpretty.reset()
httpretty.disable()
class OsfTestCase(DbTestCase, AppTestCase, SearchTestCase, MockRequestTestCase):
"""Base `TestCase` for tests that require both scratch databases and the OSF
application. Note: superclasses must call `super` in order for all setup and
teardown methods to be called correctly.
"""
pass
class ApiTestCase(DbTestCase, ApiAppTestCase, SearchTestCase, MockRequestTestCase):
"""Base `TestCase` for tests that require both scratch databases and the OSF
API application. Note: superclasses must call `super` in order for all setup and
teardown methods to be called correctly.
"""
def setUp(self):
super(ApiTestCase, self).setUp()
settings.USE_EMAIL = False
class ApiAddonTestCase(ApiTestCase):
"""Base `TestCase` for tests that require interaction with addons.
"""
DISABLE_OUTGOING_CONNECTIONS = True
@abc.abstractproperty
def short_name(self):
pass
@abc.abstractproperty
def addon_type(self):
pass
@abc.abstractmethod
def _apply_auth_configuration(self):
pass
@abc.abstractmethod
def _set_urls(self):
pass
def _settings_kwargs(self, node, user_settings):
return {
'user_settings': self.user_settings,
'folder_id': '1234567890',
'owner': self.node
}
def setUp(self):
super(ApiAddonTestCase, self).setUp()
from osf_tests.factories import (
ProjectFactory,
AuthUserFactory,
)
from addons.base.models import (
BaseOAuthNodeSettings,
BaseOAuthUserSettings
)
assert self.addon_type in ('CONFIGURABLE', 'OAUTH', 'UNMANAGEABLE', 'INVALID')
self.account = None
self.node_settings = None
self.user_settings = None
self.user = AuthUserFactory()
self.auth = Auth(self.user)
self.node = ProjectFactory(creator=self.user)
if self.addon_type not in ('UNMANAGEABLE', 'INVALID'):
if self.addon_type in ('OAUTH', 'CONFIGURABLE'):
self.account = self.AccountFactory()
self.user.external_accounts.add(self.account)
self.user.save()
self.user_settings = self.user.get_or_add_addon(self.short_name)
self.node_settings = self.node.get_or_add_addon(self.short_name, auth=self.auth)
if self.addon_type in ('OAUTH', 'CONFIGURABLE'):
self.node_settings.set_auth(self.account, self.user)
self._apply_auth_configuration()
if self.addon_type in ('OAUTH', 'CONFIGURABLE'):
assert isinstance(self.node_settings, BaseOAuthNodeSettings)
assert isinstance(self.user_settings, BaseOAuthUserSettings)
self.node_settings.reload()
self.user_settings.reload()
self.account_id = self.account._id if self.account else None
self.set_urls()
def tearDown(self):
super(ApiAddonTestCase, self).tearDown()
self.user.remove()
self.node.remove()
if self.node_settings:
self.node_settings.remove()
if self.user_settings:
self.user_settings.remove()
if self.account:
self.account.remove()
@override_settings(ROOT_URLCONF='admin.base.urls')
class AdminTestCase(DbTestCase, DjangoTestCase, SearchTestCase, MockRequestTestCase):
pass
class NotificationTestCase(OsfTestCase):
"""An `OsfTestCase` to use when testing specific subscription behavior.
Use when you'd like to manually create all Node subscriptions and subscriptions
for added contributors yourself, and not rely on automatically added ones.
"""
DISCONNECTED_SIGNALS = {
# disconnect signals so that add_contributor does not send "fake" emails in tests
contributor_added: [notify_added_contributor, subscribe_contributor],
project_created: [subscribe_creator]
}
def setUp(self):
super(NotificationTestCase, self).setUp()
def tearDown(self):
super(NotificationTestCase, self).tearDown()
class ApiWikiTestCase(ApiTestCase):
def setUp(self):
from osf_tests.factories import AuthUserFactory
super(ApiWikiTestCase, self).setUp()
self.user = AuthUserFactory()
self.non_contributor = AuthUserFactory()
def _add_project_wiki_page(self, node, user):
from addons.wiki.tests.factories import NodeWikiFactory
# API will only return current wiki pages
# Mock out update_search. TODO: Remove when StoredFileNode is implemented
with mock.patch('osf.models.AbstractNode.update_search'):
return NodeWikiFactory(node=node, user=user)
# From Flask-Security: https://github.com/mattupstate/flask-security/blob/develop/flask_security/utils.py
class CaptureSignals(object):
"""Testing utility for capturing blinker signals.
Context manager which mocks out selected signals and registers which
are `sent` on and what arguments were sent. Instantiate with a list of
blinker `NamedSignals` to patch. Each signal has its `send` mocked out.
"""
def __init__(self, signals):
"""Patch all given signals and make them available as attributes.
:param signals: list of signals
"""
self._records = {}
self._receivers = {}
for signal in signals:
self._records[signal] = []
self._receivers[signal] = functools.partial(self._record, signal)
def __getitem__(self, signal):
"""All captured signals are available via `ctxt[signal]`.
"""
if isinstance(signal, blinker.base.NamedSignal):
return self._records[signal]
else:
super(CaptureSignals, self).__setitem__(signal)
def _record(self, signal, *args, **kwargs):
self._records[signal].append((args, kwargs))
def __enter__(self):
for signal, receiver in self._receivers.items():
signal.connect(receiver)
return self
def __exit__(self, type, value, traceback):
for signal, receiver in self._receivers.items():
signal.disconnect(receiver)
def signals_sent(self):
"""Return a set of the signals sent.
:rtype: list of blinker `NamedSignals`.
"""
return set([signal for signal, _ in self._records.items() if self._records[signal]])
def capture_signals():
"""Factory method that creates a ``CaptureSignals`` with all OSF signals."""
return CaptureSignals(ALL_SIGNALS)
def assert_is_redirect(response, msg='Response is a redirect.'):
assert 300 <= response.status_code < 400, msg
def assert_before(lst, item1, item2):
"""Assert that item1 appears before item2 in lst."""
assert_less(lst.index(item1), lst.index(item2),
'{0!r} appears before {1!r}'.format(item1, item2))
def assert_datetime_equal(dt1, dt2, allowance=500):
"""Assert that two datetimes are about equal."""
assert abs(dt1 - dt2) < dt.timedelta(milliseconds=allowance)
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Views tests for the OSF."""
from __future__ import absolute_import
import datetime as dt
import httplib as http
import json
import time
import pytz
import unittest
from flask import request
import mock
import pytest
from nose.tools import * # noqa PEP8 asserts
from django.utils import timezone
from django.apps import apps
from modularodm import Q
from modularodm.exceptions import ValidationError
from addons.github.tests.factories import GitHubAccountFactory
from framework.auth import cas
from framework.auth.core import generate_verification_key
from framework import auth
from framework.auth.campaigns import get_campaigns, is_institution_login, is_native_login, is_proxy_login, campaign_url_for
from framework.auth import Auth
from framework.auth.cas import get_login_url
from framework.auth.core import generate_verification_key
from framework.auth.exceptions import InvalidTokenError
from framework.auth.utils import impute_names_model, ensure_external_identity_uniqueness
from framework.auth.views import login_and_register_handler
from framework.celery_tasks import handlers
from framework.exceptions import HTTPError
from framework.transactions.handlers import no_auto_transaction
from tests.factories import MockAddonNodeSettings
from website import mailchimp_utils
from website import mails, settings
from addons.osfstorage import settings as osfstorage_settings
from website.models import Node, NodeLog, Pointer
from website.profile.utils import add_contributor_json, serialize_unregistered
from website.profile.views import fmt_date_or_none, update_osf_help_mails_subscription
from website.project.decorators import check_can_access
from website.project.model import has_anonymous_link
from website.project.signals import contributor_added
from website.project.views.contributor import (
deserialize_contributors,
notify_added_contributor,
send_claim_email,
send_claim_registered_email,
)
from website.project.views.node import _should_show_wiki_widget, _view_project, abbrev_authors
from website.util import api_url_for, web_url_for
from website.util import permissions, rubeus
from website.views import index
from osf.models import Comment
from osf.models import OSFUser as User
from tests.base import (
assert_is_redirect,
capture_signals,
fake,
get_default_metaschema,
OsfTestCase,
assert_datetime_equal,
)
from tests.base import test_app as mock_app
pytestmark = pytest.mark.django_db
from osf.models import NodeRelation
from osf_tests.factories import (
UserFactory,
UnconfirmedUserFactory,
UnregUserFactory,
AuthUserFactory,
PrivateLinkFactory,
ProjectFactory,
NodeFactory,
CommentFactory,
CollectionFactory,
InstitutionFactory,
RegistrationFactory,
ApiOAuth2ApplicationFactory,
ApiOAuth2PersonalTokenFactory,
ProjectWithAddonFactory,
PreprintFactory,
PreprintProviderFactory,
)
class Addon(MockAddonNodeSettings):
@property
def complete(self):
return True
def archive_errors(self):
return 'Error'
class Addon2(MockAddonNodeSettings):
@property
def complete(self):
return True
def archive_errors(self):
return 'Error'
@mock_app.route('/errorexc')
def error_exc():
UserFactory()
raise RuntimeError
@mock_app.route('/error500')
def error500():
UserFactory()
return 'error', 500
@mock_app.route('/noautotransact')
@no_auto_transaction
def no_auto_transact():
UserFactory()
return 'error', 500
class TestViewsAreAtomic(OsfTestCase):
def test_error_response_rolls_back_transaction(self):
original_user_count = User.objects.count()
self.app.get('/error500', expect_errors=True)
assert_equal(User.objects.count(), original_user_count)
# Need to set debug = False in order to rollback transactions in transaction_teardown_request
mock_app.debug = False
try:
self.app.get('/errorexc', expect_errors=True)
except RuntimeError:
pass
mock_app.debug = True
self.app.get('/noautotransact', expect_errors=True)
assert_equal(User.objects.count(), original_user_count + 1)
class TestViewingProjectWithPrivateLink(OsfTestCase):
def setUp(self):
super(TestViewingProjectWithPrivateLink, self).setUp()
self.user = AuthUserFactory() # Is NOT a contributor
self.project = ProjectFactory(is_public=False)
self.link = PrivateLinkFactory()
self.link.nodes.add(self.project)
self.link.save()
self.project_url = self.project.web_url_for('view_project')
def test_edit_private_link_empty(self):
node = ProjectFactory(creator=self.user)
link = PrivateLinkFactory()
link.nodes.add(node)
link.save()
url = node.api_url_for('project_private_link_edit')
res = self.app.put_json(url, {'pk': link._id, 'value': ''}, auth=self.user.auth, expect_errors=True)
assert_equal(res.status_code, 400)
assert_in('Title cannot be blank', res.body)
def test_edit_private_link_invalid(self):
node = ProjectFactory(creator=self.user)
link = PrivateLinkFactory()
link.nodes.add(node)
link.save()
url = node.api_url_for('project_private_link_edit')
res = self.app.put_json(url, {'pk': link._id, 'value': '<a></a>'}, auth=self.user.auth, expect_errors=True)
assert_equal(res.status_code, 400)
assert_in('Invalid link name.', res.body)
@mock.patch('framework.auth.core.Auth.private_link')
def test_can_be_anonymous_for_public_project(self, mock_property):
mock_property.return_value(mock.MagicMock())
mock_property.anonymous = True
anonymous_link = PrivateLinkFactory(anonymous=True)
anonymous_link.nodes.add(self.project)
anonymous_link.save()
self.project.set_privacy('public')
self.project.save()
self.project.reload()
auth = Auth(user=self.user, private_key=anonymous_link.key)
assert_true(has_anonymous_link(self.project, auth))
def test_has_private_link_key(self):
res = self.app.get(self.project_url, {'view_only': self.link.key})
assert_equal(res.status_code, 200)
def test_not_logged_in_no_key(self):
res = self.app.get(self.project_url, {'view_only': None})
assert_is_redirect(res)
res = res.follow(expect_errors=True)
assert_equal(res.status_code, 301)
assert_equal(
res.request.path,
'/login'
)
def test_logged_in_no_private_key(self):
res = self.app.get(self.project_url, {'view_only': None}, auth=self.user.auth,
expect_errors=True)
assert_equal(res.status_code, http.FORBIDDEN)
def test_logged_in_has_key(self):
res = self.app.get(
self.project_url, {'view_only': self.link.key}, auth=self.user.auth)
assert_equal(res.status_code, 200)
@unittest.skip('Skipping for now until we find a way to mock/set the referrer')
def test_prepare_private_key(self):
res = self.app.get(self.project_url, {'key': self.link.key})
res = res.click('Registrations')
assert_is_redirect(res)
res = res.follow()
assert_equal(res.status_code, 200)
assert_equal(res.request.GET['key'], self.link.key)
def test_cannot_access_registrations_or_forks_with_anon_key(self):
anonymous_link = PrivateLinkFactory(anonymous=True)
anonymous_link.nodes.add(self.project)
anonymous_link.save()
self.project.is_public = False
self.project.save()
url = self.project_url + 'registrations/?view_only={}'.format(anonymous_link.key)
res = self.app.get(url, expect_errors=True)
assert_equal(res.status_code, 401)
url = self.project_url + 'forks/?view_only={}'.format(anonymous_link.key)
res = self.app.get(url, expect_errors=True)
assert_equal(res.status_code, 401)
def test_can_access_registrations_and_forks_with_not_anon_key(self):
link = PrivateLinkFactory(anonymous=False)
link.nodes.add(self.project)
link.save()
self.project.is_public = False
self.project.save()
url = self.project_url + 'registrations/?view_only={}'.format(self.link.key)
res = self.app.get(url)
assert_equal(res.status_code, 200)
url = self.project_url + 'forks/?view_only={}'.format(self.link.key)
res = self.app.get(url)
assert_equal(res.status_code, 200)
def test_check_can_access_valid(self):
contributor = AuthUserFactory()
self.project.add_contributor(contributor, auth=Auth(self.project.creator))
self.project.save()
assert_true(check_can_access(self.project, contributor))
def test_check_user_access_invalid(self):
noncontrib = AuthUserFactory()
with assert_raises(HTTPError):
check_can_access(self.project, noncontrib)
def test_check_user_access_if_user_is_None(self):
assert_false(check_can_access(self.project, None))
class TestProjectViews(OsfTestCase):
def setUp(self):
super(TestProjectViews, self).setUp()
self.user1 = AuthUserFactory()
self.user1.save()
self.consolidate_auth1 = Auth(user=self.user1)
self.auth = self.user1.auth
self.user2 = AuthUserFactory()
self.auth2 = self.user2.auth
# A project has 2 contributors
self.project = ProjectFactory(
title='Ham',
description='Honey-baked',
creator=self.user1
)
self.project.add_contributor(self.user2, auth=Auth(self.user1))
self.project.save()
self.project2 = ProjectFactory(
title='Tofu',
description='Glazed',
creator=self.user1
)
self.project2.add_contributor(self.user2, auth=Auth(self.user1))
self.project2.save()
def test_node_setting_with_multiple_matched_institution_email_domains(self):
# User has alternate emails matching more than one institution's email domains
inst1 = InstitutionFactory(email_domains=['foo.bar'])
inst2 = InstitutionFactory(email_domains=['baz.qux'])
user = AuthUserFactory()
user.emails.append('queen@foo.bar')
user.emails.append('brian@baz.qux')
user.save()
project = ProjectFactory(creator=user)
# node settings page loads without error
url = project.web_url_for('node_setting')
res = self.app.get(url, auth=user.auth)
assert_equal(res.status_code, 200)
# user is automatically affiliated with institutions
# that matched email domains
user.reload()
assert_in(inst1, user.affiliated_institutions.all())
assert_in(inst2, user.affiliated_institutions.all())
def test_edit_title_empty(self):
node = ProjectFactory(creator=self.user1)
url = node.api_url_for('edit_node')
res = self.app.post_json(url, {'name': 'title', 'value': ''}, auth=self.user1.auth, expect_errors=True)
assert_equal(res.status_code, 400)
assert_in('Title cannot be blank', res.body)
def test_edit_title_invalid(self):
node = ProjectFactory(creator=self.user1)
url = node.api_url_for('edit_node')
res = self.app.post_json(url, {'name': 'title', 'value': '<a></a>'}, auth=self.user1.auth, expect_errors=True)
assert_equal(res.status_code, 400)
assert_in('Invalid title.', res.body)
def test_cannot_remove_only_visible_contributor(self):
user1_contrib = self.project.contributor_set.get(user=self.user1)
user1_contrib.visible = False
user1_contrib.save()
url = self.project.api_url_for('project_remove_contributor')
res = self.app.post_json(
url, {'contributorID': self.user2._id,
'nodeIDs': [self.project._id]}, auth=self.auth, expect_errors=True
)
assert_equal(res.status_code, http.FORBIDDEN)
assert_equal(res.json['message_long'], 'Must have at least one bibliographic contributor')
assert_true(self.project.is_contributor(self.user2))
def test_remove_only_visible_contributor_return_false(self):
user1_contrib = self.project.contributor_set.get(user=self.user1)
user1_contrib.visible = False
user1_contrib.save()
ret = self.project.remove_contributor(contributor=self.user2, auth=self.consolidate_auth1)
assert_false(ret)
self.project.reload()
assert_true(self.project.is_contributor(self.user2))
def test_can_view_nested_project_as_admin(self):
self.parent_project = NodeFactory(
title='parent project',
category='project',
parent=self.project,
is_public=False
)
self.parent_project.save()
self.child_project = NodeFactory(
title='child project',
category='project',
parent=self.parent_project,
is_public=False
)
self.child_project.save()
url = self.child_project.web_url_for('view_project')
res = self.app.get(url, auth=self.auth)
assert_not_in('Private Project', res.body)
assert_in('parent project', res.body)
def test_edit_description(self):
url = '/api/v1/project/{0}/edit/'.format(self.project._id)
self.app.post_json(url,
{'name': 'description', 'value': 'Deep-fried'},
auth=self.auth)
self.project.reload()
assert_equal(self.project.description, 'Deep-fried')
def test_project_api_url(self):
url = self.project.api_url
res = self.app.get(url, auth=self.auth)
data = res.json
assert_equal(data['node']['category'], 'Project')
assert_equal(data['node']['node_type'], 'project')
assert_equal(data['node']['title'], self.project.title)
assert_equal(data['node']['is_public'], self.project.is_public)
assert_equal(data['node']['is_registration'], False)
assert_equal(data['node']['id'], self.project._primary_key)
assert_true(data['user']['is_contributor'])
assert_equal(data['node']['description'], self.project.description)
assert_equal(data['node']['url'], self.project.url)
assert_equal(data['node']['tags'], list(self.project.tags.values_list('name', flat=True)))
assert_in('forked_date', data['node'])
assert_in('registered_from_url', data['node'])
# TODO: Test "parent" and "user" output
def test_add_contributor_post(self):
# Two users are added as a contributor via a POST request
project = ProjectFactory(creator=self.user1, is_public=True)
user2 = UserFactory()
user3 = UserFactory()
url = '/api/v1/project/{0}/contributors/'.format(project._id)
dict2 = add_contributor_json(user2)
dict3 = add_contributor_json(user3)
dict2.update({
'permission': 'admin',
'visible': True,
})
dict3.update({
'permission': 'write',
'visible': False,
})
self.app.post_json(
url,
{
'users': [dict2, dict3],
'node_ids': [project._id],
},
content_type='application/json',
auth=self.auth,
).maybe_follow()
project.reload()
assert_in(user2, project.contributors)
# A log event was added
assert_equal(project.logs.latest().action, 'contributor_added')
assert_equal(len(project.contributors), 3)
assert_equal(project.get_permissions(user2), ['read', 'write', 'admin'])
assert_equal(project.get_permissions(user3), ['read', 'write'])
def test_manage_permissions(self):
url = self.project.api_url + 'contributors/manage/'
self.app.post_json(
url,
{
'contributors': [
{'id': self.project.creator._id, 'permission': 'admin',
'registered': True, 'visible': True},
{'id': self.user1._id, 'permission': 'read',
'registered': True, 'visible': True},
{'id': self.user2._id, 'permission': 'admin',
'registered': True, 'visible': True},
]
},
auth=self.auth,
)
self.project.reload()
assert_equal(self.project.get_permissions(self.user1), ['read'])
assert_equal(self.project.get_permissions(self.user2), ['read', 'write', 'admin'])
def test_manage_permissions_again(self):
url = self.project.api_url + 'contributors/manage/'
self.app.post_json(
url,
{
'contributors': [
{'id': self.user1._id, 'permission': 'admin',
'registered': True, 'visible': True},
{'id': self.user2._id, 'permission': 'admin',
'registered': True, 'visible': True},
]
},
auth=self.auth,
)
self.project.reload()
self.app.post_json(
url,
{
'contributors': [
{'id': self.user1._id, 'permission': 'admin',
'registered': True, 'visible': True},
{'id': self.user2._id, 'permission': 'read',
'registered': True, 'visible': True},
]
},
auth=self.auth,
)
self.project.reload()
assert_equal(self.project.get_permissions(self.user2), ['read'])
assert_equal(self.project.get_permissions(self.user1), ['read', 'write', 'admin'])
def test_contributor_manage_reorder(self):
# Two users are added as a contributor via a POST request
project = ProjectFactory(creator=self.user1, is_public=True)
reg_user1, reg_user2 = UserFactory(), UserFactory()
project.add_contributors(
[
{'user': reg_user1, 'permissions': [
'read', 'write', 'admin'], 'visible': True},
{'user': reg_user2, 'permissions': [
'read', 'write', 'admin'], 'visible': False},
]
)
# Add a non-registered user
unregistered_user = project.add_unregistered_contributor(
fullname=fake.name(), email=fake.email(),
auth=self.consolidate_auth1,
save=True,
)
url = project.api_url + 'contributors/manage/'
self.app.post_json(
url,
{
'contributors': [
{'id': reg_user2._id, 'permission': 'admin',
'registered': True, 'visible': False},
{'id': project.creator._id, 'permission': 'admin',
'registered': True, 'visible': True},
{'id': unregistered_user._id, 'permission': 'admin',
'registered': False, 'visible': True},
{'id': reg_user1._id, 'permission': 'admin',
'registered': True, 'visible': True},
]
},
auth=self.auth,
)
project.reload()
assert_equal(
# Note: Cast ForeignList to list for comparison
list(project.contributors),
[reg_user2, project.creator, unregistered_user, reg_user1]
)
assert_equal(
list(project.visible_contributors),
[project.creator, unregistered_user, reg_user1]
)
def test_project_remove_contributor(self):
url = self.project.api_url_for('project_remove_contributor')
# User 1 removes user2
payload = {'contributorID': self.user2._id,
'nodeIDs': [self.project._id]}
self.app.post(url, json.dumps(payload),
content_type='application/json',
auth=self.auth).maybe_follow()
self.project.reload()
assert_not_in(self.user2._id, self.project.contributors)
# A log event was added
assert_equal(self.project.logs.latest().action, 'contributor_removed')
def test_multiple_project_remove_contributor(self):
url = self.project.api_url_for('project_remove_contributor')
# User 1 removes user2
payload = {'contributorID': self.user2._id,
'nodeIDs': [self.project._id, self.project2._id]}
res = self.app.post(url, json.dumps(payload),
content_type='application/json',
auth=self.auth).maybe_follow()
self.project.reload()
self.project2.reload()
assert_not_in(self.user2._id, self.project.contributors)
assert_not_in('/dashboard/', res.json)
assert_not_in(self.user2._id, self.project2.contributors)
# A log event was added
assert_equal(self.project.logs.latest().action, 'contributor_removed')
def test_private_project_remove_self_not_admin(self):
url = self.project.api_url_for('project_remove_contributor')
# user2 removes self
payload = {"contributorID": self.user2._id,
"nodeIDs": [self.project._id]}
res = self.app.post(url, json.dumps(payload),
content_type="application/json",
auth=self.auth2).maybe_follow()
self.project.reload()
assert_equal(res.status_code, 200)
assert_equal(res.json['redirectUrl'], '/dashboard/')
assert_not_in(self.user2._id, self.project.contributors)
def test_public_project_remove_self_not_admin(self):
url = self.project.api_url_for('project_remove_contributor')
# user2 removes self
self.public_project = ProjectFactory(creator=self.user1, is_public=True)
self.public_project.add_contributor(self.user2, auth=Auth(self.user1))
self.public_project.save()
payload = {"contributorID": self.user2._id,
"nodeIDs": [self.public_project._id]}
res = self.app.post(url, json.dumps(payload),
content_type="application/json",
auth=self.auth2).maybe_follow()
self.public_project.reload()
assert_equal(res.status_code, 200)
assert_equal(res.json['redirectUrl'], '/' + self.public_project._id + '/')
assert_not_in(self.user2._id, self.public_project.contributors)
def test_project_remove_other_not_admin(self):
url = self.project.api_url_for('project_remove_contributor')
# User 1 removes user2
payload = {"contributorID": self.user1._id,
"nodeIDs": [self.project._id]}
res = self.app.post(url, json.dumps(payload),
content_type="application/json",
expect_errors=True,
auth=self.auth2).maybe_follow()
self.project.reload()
assert_equal(res.status_code, 403)
assert_equal(res.json['message_long'],
'You do not have permission to perform this action. '
'If this should not have occurred and the issue persists, '
'please report it to <a href="mailto:support@osf.io">support@osf.io</a>.'
)
assert_in(self.user1, self.project.contributors)
def test_project_remove_fake_contributor(self):
url = self.project.api_url_for('project_remove_contributor')
# User 1 removes user2
payload = {'contributorID': 'badid',
'nodeIDs': [self.project._id]}
res = self.app.post(url, json.dumps(payload),
content_type='application/json',
expect_errors=True,
auth=self.auth).maybe_follow()
self.project.reload()
# Assert the contributor id was invalid
assert_equal(res.status_code, 400)
assert_equal(res.json['message_long'], 'Contributor not found.')
assert_not_in('badid', self.project.contributors)
def test_project_remove_self_only_admin(self):
url = self.project.api_url_for('project_remove_contributor')
# User 1 removes user2
payload = {'contributorID': self.user1._id,
'nodeIDs': [self.project._id]}
res = self.app.post(url, json.dumps(payload),
content_type='application/json',
expect_errors=True,
auth=self.auth).maybe_follow()
self.project.reload()
assert_equal(res.status_code, 400)
assert_equal(res.json['message_long'], 'Could not remove contributor.')
assert_in(self.user1, self.project.contributors)
def test_get_contributors_abbrev(self):
# create a project with 3 registered contributors
project = ProjectFactory(creator=self.user1, is_public=True)
reg_user1, reg_user2 = UserFactory(), UserFactory()
project.add_contributors(
[
{'user': reg_user1, 'permissions': [
'read', 'write', 'admin'], 'visible': True},
{'user': reg_user2, 'permissions': [
'read', 'write', 'admin'], 'visible': True},
]
)
# add an unregistered contributor
project.add_unregistered_contributor(
fullname=fake.name(), email=fake.email(),
auth=self.consolidate_auth1,
save=True,
)
url = project.api_url_for('get_node_contributors_abbrev')
res = self.app.get(url, auth=self.auth)
assert_equal(len(project.contributors), 4)
assert_equal(len(res.json['contributors']), 3)
assert_equal(len(res.json['others_count']), 1)
assert_equal(res.json['contributors'][0]['separator'], ',')
assert_equal(res.json['contributors'][1]['separator'], ',')
assert_equal(res.json['contributors'][2]['separator'], ' &')
def test_edit_node_title(self):
url = '/api/v1/project/{0}/edit/'.format(self.project._id)
# The title is changed though posting form data
self.app.post_json(url, {'name': 'title', 'value': 'Bacon'},
auth=self.auth).maybe_follow()
self.project.reload()
# The title was changed
assert_equal(self.project.title, 'Bacon')
# A log event was saved
assert_equal(self.project.logs.latest().action, 'edit_title')
def test_make_public(self):
self.project.is_public = False
self.project.save()
url = "/api/v1/project/{0}/permissions/public/".format(self.project._id)
res = self.app.post_json(url, {}, auth=self.auth)
self.project.reload()
assert_true(self.project.is_public)
assert_equal(res.json['status'], 'success')
def test_make_private(self):
self.project.is_public = True
self.project.save()
url = "/api/v1/project/{0}/permissions/private/".format(self.project._id)
res = self.app.post_json(url, {}, auth=self.auth)
self.project.reload()
assert_false(self.project.is_public)
assert_equal(res.json['status'], 'success')
def test_cant_make_public_if_not_admin(self):
non_admin = AuthUserFactory()
self.project.add_contributor(non_admin, permissions=['read', 'write'])
self.project.is_public = False
self.project.save()
url = "/api/v1/project/{0}/permissions/public/".format(self.project._id)
res = self.app.post_json(
url, {}, auth=non_admin.auth,
expect_errors=True,
)
assert_equal(res.status_code, http.FORBIDDEN)
assert_false(self.project.is_public)
def test_cant_make_private_if_not_admin(self):
non_admin = AuthUserFactory()
self.project.add_contributor(non_admin, permissions=['read', 'write'])
self.project.is_public = True
self.project.save()
url = "/api/v1/project/{0}/permissions/private/".format(self.project._id)
res = self.app.post_json(
url, {}, auth=non_admin.auth,
expect_errors=True,
)
assert_equal(res.status_code, http.FORBIDDEN)
assert_true(self.project.is_public)
def test_add_tag(self):
url = self.project.api_url_for('project_add_tag')
self.app.post_json(url, {'tag': "foo'ta#@%#%^&g?"}, auth=self.auth)
self.project.reload()
assert_in("foo'ta#@%#%^&g?", self.project.tags.values_list('name', flat=True))
assert_equal("foo'ta#@%#%^&g?", self.project.logs.latest().params['tag'])
def test_remove_tag(self):
self.project.add_tag("foo'ta#@%#%^&g?", auth=self.consolidate_auth1, save=True)
assert_in("foo'ta#@%#%^&g?", self.project.tags.values_list('name', flat=True))
url = self.project.api_url_for('project_remove_tag')
self.app.delete_json(url, {'tag': "foo'ta#@%#%^&g?"}, auth=self.auth)
self.project.reload()
assert_not_in("foo'ta#@%#%^&g?", self.project.tags.values_list('name', flat=True))
latest_log = self.project.logs.latest()
assert_equal('tag_removed', latest_log.action)
assert_equal("foo'ta#@%#%^&g?", latest_log.params['tag'])
# Regression test for #OSF-5257
def test_removal_empty_tag_throws_error(self):
url = self.project.api_url_for('project_remove_tag')
res = self.app.delete_json(url, {'tag': ''}, auth=self.auth, expect_errors=True)
assert_equal(res.status_code, http.BAD_REQUEST)
# Regression test for #OSF-5257
def test_removal_unknown_tag_throws_error(self):
self.project.add_tag('narf', auth=self.consolidate_auth1, save=True)
url = self.project.api_url_for('project_remove_tag')
res = self.app.delete_json(url, {'tag': 'troz'}, auth=self.auth, expect_errors=True)
assert_equal(res.status_code, http.CONFLICT)
def test_remove_project(self):
url = self.project.api_url
res = self.app.delete_json(url, {}, auth=self.auth).maybe_follow()
self.project.reload()
assert_equal(self.project.is_deleted, True)
assert_in('url', res.json)
assert_equal(res.json['url'], '/dashboard/')
def test_suspended_project(self):
node = NodeFactory(parent=self.project, creator=self.user1)
node.remove_node(Auth(self.user1))
node.suspended = True
node.save()
url = node.api_url
res = self.app.get(url, auth=Auth(self.user1), expect_errors=True)
assert_equal(res.status_code, 451)
def test_private_link_edit_name(self):
link = PrivateLinkFactory(name='link')
link.nodes.add(self.project)
link.save()
assert_equal(link.name, 'link')
url = self.project.api_url + 'private_link/edit/'
self.app.put_json(
url,
{'pk': link._id, 'value': 'new name'},
auth=self.auth,
).maybe_follow()
self.project.reload()
link.reload()
assert_equal(link.name, 'new name')
def test_remove_private_link(self):
link = PrivateLinkFactory()
link.nodes.add(self.project)
link.save()
url = self.project.api_url_for('remove_private_link')
self.app.delete_json(
url,
{'private_link_id': link._id},
auth=self.auth,
).maybe_follow()
self.project.reload()
link.reload()
assert_true(link.is_deleted)
def test_remove_component(self):
node = NodeFactory(parent=self.project, creator=self.user1)
url = node.api_url
res = self.app.delete_json(url, {}, auth=self.auth).maybe_follow()
node.reload()
assert_equal(node.is_deleted, True)
assert_in('url', res.json)
assert_equal(res.json['url'], self.project.url)
def test_cant_remove_component_if_not_admin(self):
node = NodeFactory(parent=self.project, creator=self.user1)
non_admin = AuthUserFactory()
node.add_contributor(
non_admin,
permissions=['read', 'write'],
save=True,
)
url = node.api_url
res = self.app.delete_json(
url, {}, auth=non_admin.auth,
expect_errors=True,
).maybe_follow()
assert_equal(res.status_code, http.FORBIDDEN)
assert_false(node.is_deleted)
def test_view_project_returns_whether_to_show_wiki_widget(self):
user = AuthUserFactory()
project = ProjectFactory(creator=user, is_public=True)
project.add_contributor(user)
project.save()
url = project.api_url_for('view_project')
res = self.app.get(url, auth=user.auth)
assert_equal(res.status_code, http.OK)
assert_in('show_wiki_widget', res.json['user'])
def test_fork_count_does_not_include_deleted_forks(self):
user = AuthUserFactory()
project = ProjectFactory(creator=user)
auth = Auth(project.creator)
fork = project.fork_node(auth)
project.save()
fork.remove_node(auth)
fork.save()
url = project.api_url_for('view_project')
res = self.app.get(url, auth=user.auth)
assert_in('fork_count', res.json['node'])
assert_equal(0, res.json['node']['fork_count'])
def test_statistic_page_redirect(self):
url = self.project.web_url_for('project_statistics_redirect')
res = self.app.get(url, auth=self.auth)
assert_equal(res.status_code, 302)
assert_in(self.project.web_url_for('project_statistics', _guid=True), res.location)
def test_registration_retraction_redirect(self):
url = self.project.web_url_for('node_registration_retraction_redirect')
res = self.app.get(url, auth=self.auth)
assert_equal(res.status_code, 302)
assert_in(self.project.web_url_for('node_registration_retraction_get', _guid=True), res.location)
def test_update_node(self):
url = self.project.api_url_for('update_node')
res = self.app.put_json(url, {'title': 'newtitle'}, auth=self.auth)
assert_equal(res.status_code, 200)
self.project.reload()
assert_equal(self.project.title, 'newtitle')
# Regression test
def test_update_node_with_tags(self):
self.project.add_tag('cheezebørger', auth=Auth(self.project.creator), save=True)
url = self.project.api_url_for('update_node')
res = self.app.put_json(url, {'title': 'newtitle'}, auth=self.auth)
assert_equal(res.status_code, 200)
self.project.reload()
assert_equal(self.project.title, 'newtitle')
class TestEditableChildrenViews(OsfTestCase):
def setUp(self):
OsfTestCase.setUp(self)
self.user = AuthUserFactory()
self.project = ProjectFactory(creator=self.user, is_public=False)
self.child = ProjectFactory(parent=self.project, creator=self.user, is_public=True)
self.grandchild = ProjectFactory(parent=self.child, creator=self.user, is_public=False)
self.great_grandchild = ProjectFactory(parent=self.grandchild, creator=self.user, is_public=True)
self.great_great_grandchild = ProjectFactory(parent=self.great_grandchild, creator=self.user, is_public=False)
url = self.project.api_url_for('get_editable_children')
self.project_results = self.app.get(url, auth=self.user.auth).json
def test_get_editable_children(self):
assert_equal(len(self.project_results['children']), 4)
assert_equal(self.project_results['node']['id'], self.project._id)
def test_editable_children_order(self):
assert_equal(self.project_results['children'][0]['id'], self.child._id)
assert_equal(self.project_results['children'][1]['id'], self.grandchild._id)
assert_equal(self.project_results['children'][2]['id'], self.great_grandchild._id)
assert_equal(self.project_results['children'][3]['id'], self.great_great_grandchild._id)
def test_editable_children_indents(self):
assert_equal(self.project_results['children'][0]['indent'], 0)
assert_equal(self.project_results['children'][1]['indent'], 1)
assert_equal(self.project_results['children'][2]['indent'], 2)
assert_equal(self.project_results['children'][3]['indent'], 3)
def test_editable_children_parents(self):
assert_equal(self.project_results['children'][0]['parent_id'], self.project._id)
assert_equal(self.project_results['children'][1]['parent_id'], self.child._id)
assert_equal(self.project_results['children'][2]['parent_id'], self.grandchild._id)
assert_equal(self.project_results['children'][3]['parent_id'], self.great_grandchild._id)
def test_editable_children_privacy(self):
assert_false(self.project_results['node']['is_public'])
assert_true(self.project_results['children'][0]['is_public'])
assert_false(self.project_results['children'][1]['is_public'])
assert_true(self.project_results['children'][2]['is_public'])
assert_false(self.project_results['children'][3]['is_public'])
def test_editable_children_titles(self):
assert_equal(self.project_results['node']['title'], self.project.title)
assert_equal(self.project_results['children'][0]['title'], self.child.title)
assert_equal(self.project_results['children'][1]['title'], self.grandchild.title)
assert_equal(self.project_results['children'][2]['title'], self.great_grandchild.title)
assert_equal(self.project_results['children'][3]['title'], self.great_great_grandchild.title)
class TestGetNodeTree(OsfTestCase):
def setUp(self):
OsfTestCase.setUp(self)
self.user = AuthUserFactory()
self.user2 = AuthUserFactory()
def test_get_single_node(self):
project = ProjectFactory(creator=self.user)
# child = NodeFactory(parent=project, creator=self.user)
url = project.api_url_for('get_node_tree')
res = self.app.get(url, auth=self.user.auth)
node_id = res.json[0]['node']['id']
assert_equal(node_id, project._primary_key)
def test_get_node_with_children(self):
project = ProjectFactory(creator=self.user)
child1 = NodeFactory(parent=project, creator=self.user)
child2 = NodeFactory(parent=project, creator=self.user2)
child3 = NodeFactory(parent=project, creator=self.user)
url = project.api_url_for('get_node_tree')
res = self.app.get(url, auth=self.user.auth)
tree = res.json[0]
parent_node_id = tree['node']['id']
child1_id = tree['children'][0]['node']['id']
child2_id = tree['children'][1]['node']['id']
child3_id = tree['children'][2]['node']['id']
assert_equal(parent_node_id, project._primary_key)
assert_equal(child1_id, child1._primary_key)
assert_equal(child2_id, child2._primary_key)
assert_equal(child3_id, child3._primary_key)
def test_get_node_with_child_linked_to_parent(self):
project = ProjectFactory(creator=self.user)
child1 = NodeFactory(parent=project, creator=self.user)
child1.add_pointer(project, Auth(self.user))
child1.save()
url = project.api_url_for('get_node_tree')
res = self.app.get(url, auth=self.user.auth)
tree = res.json[0]
parent_node_id = tree['node']['id']
child1_id = tree['children'][0]['node']['id']
assert_equal(child1_id, child1._primary_key)
def test_get_node_not_parent_owner(self):
project = ProjectFactory(creator=self.user2)
child = NodeFactory(parent=project, creator=self.user2)
url = project.api_url_for('get_node_tree')
res = self.app.get(url, auth=self.user.auth, expect_errors=True)
assert_equal(res.status_code, 200)
assert_equal(res.json, [])
# Parent node should show because of user2 read access, the children should not
def test_get_node_parent_not_admin(self):
project = ProjectFactory(creator=self.user)
project.add_contributor(self.user2, auth=Auth(self.user))
project.save()
child1 = NodeFactory(parent=project, creator=self.user)
child2 = NodeFactory(parent=project, creator=self.user)
child3 = NodeFactory(parent=project, creator=self.user)
url = project.api_url_for('get_node_tree')
res = self.app.get(url, auth=self.user2.auth)
tree = res.json[0]
parent_node_id = tree['node']['id']
children = tree['children']
assert_equal(parent_node_id, project._primary_key)
assert_equal(children, [])
class TestUserProfile(OsfTestCase):
def setUp(self):
super(TestUserProfile, self).setUp()
self.user = AuthUserFactory()
def test_sanitization_of_edit_profile(self):
url = api_url_for('edit_profile', uid=self.user._id)
post_data = {'name': 'fullname', 'value': 'new<b> name</b> '}
request = self.app.post(url, post_data, auth=self.user.auth)
assert_equal('new name', request.json['name'])
def test_fmt_date_or_none(self):
with assert_raises(HTTPError) as cm:
#enter a date before 1900
fmt_date_or_none(dt.datetime(1890, 10, 31, 18, 23, 29, 227))
# error should be raised because date is before 1900
assert_equal(cm.exception.code, http.BAD_REQUEST)
def test_unserialize_social(self):
url = api_url_for('unserialize_social')
payload = {
'profileWebsites': ['http://frozen.pizza.com/reviews'],
'twitter': 'howtopizza',
'github': 'frozenpizzacode',
}
self.app.put_json(
url,
payload,
auth=self.user.auth,
)
self.user.reload()
for key, value in payload.iteritems():
assert_equal(self.user.social[key], value)
assert_true(self.user.social['researcherId'] is None)
# Regression test for help-desk ticket
def test_making_email_primary_is_not_case_sensitive(self):
user = AuthUserFactory(username='fred@queen.test')
# make confirmed email have different casing
user.emails[0] = user.emails[0].capitalize()
user.save()
url = api_url_for('update_user')
res = self.app.put_json(
url,
{'id': user._id, 'emails': [{'address': 'fred@queen.test', 'primary': True, 'confirmed': True}]},
auth=user.auth
)
assert_equal(res.status_code, 200)
def test_unserialize_social_validation_failure(self):
url = api_url_for('unserialize_social')
# profileWebsites URL is invalid
payload = {
'profileWebsites': ['http://goodurl.com', 'http://invalidurl'],
'twitter': 'howtopizza',
'github': 'frozenpizzacode',
}
res = self.app.put_json(
url,
payload,
auth=self.user.auth,
expect_errors=True
)
assert_equal(res.status_code, 400)
assert_equal(res.json['message_long'], 'Invalid personal URL.')
def test_serialize_social_editable(self):
self.user.social['twitter'] = 'howtopizza'
self.user.social['profileWebsites'] = ['http://www.cos.io', 'http://www.osf.io', 'http://www.wordup.com']
self.user.save()
url = api_url_for('serialize_social')
res = self.app.get(
url,
auth=self.user.auth,
)
assert_equal(res.json.get('twitter'), 'howtopizza')
assert_equal(res.json.get('profileWebsites'), ['http://www.cos.io', 'http://www.osf.io', 'http://www.wordup.com'])
assert_true(res.json.get('github') is None)
assert_true(res.json['editable'])
def test_serialize_social_not_editable(self):
user2 = AuthUserFactory()
self.user.social['twitter'] = 'howtopizza'
self.user.social['profileWebsites'] = ['http://www.cos.io', 'http://www.osf.io', 'http://www.wordup.com']
self.user.save()
url = api_url_for('serialize_social', uid=self.user._id)
res = self.app.get(
url,
auth=user2.auth,
)
assert_equal(res.json.get('twitter'), 'howtopizza')
assert_equal(res.json.get('profileWebsites'), ['http://www.cos.io', 'http://www.osf.io', 'http://www.wordup.com'])
assert_true(res.json.get('github') is None)
assert_false(res.json['editable'])
def test_serialize_social_addons_editable(self):
self.user.add_addon('github')
github_account = GitHubAccountFactory()
github_account.save()
self.user.external_accounts.add(github_account)
self.user.save()
url = api_url_for('serialize_social')
res = self.app.get(
url,
auth=self.user.auth,
)
assert_equal(
res.json['addons']['github'],
'abc'
)
def test_serialize_social_addons_not_editable(self):
user2 = AuthUserFactory()
self.user.add_addon('github')
github_account = GitHubAccountFactory()
github_account.save()
self.user.external_accounts.add(github_account)
self.user.save()
url = api_url_for('serialize_social', uid=self.user._id)
res = self.app.get(
url,
auth=user2.auth,
)
assert_not_in('addons', res.json)
def test_unserialize_and_serialize_jobs(self):
jobs = [{
'institution': 'an institution',
'department': 'a department',
'title': 'a title',
'startMonth': 'January',
'startYear': '2001',
'endMonth': 'March',
'endYear': '2001',
'ongoing': False,
}, {
'institution': 'another institution',
'department': None,
'title': None,
'startMonth': 'May',
'startYear': '2001',
'endMonth': None,
'endYear': None,
'ongoing': True,
}]
payload = {'contents': jobs}
url = api_url_for('unserialize_jobs')
self.app.put_json(url, payload, auth=self.user.auth)
self.user.reload()
assert_equal(len(self.user.jobs), 2)
url = api_url_for('serialize_jobs')
res = self.app.get(
url,
auth=self.user.auth,
)
for i, job in enumerate(jobs):
assert_equal(job, res.json['contents'][i])
def test_unserialize_and_serialize_schools(self):
schools = [{
'institution': 'an institution',
'department': 'a department',
'degree': 'a degree',
'startMonth': 1,
'startYear': '2001',
'endMonth': 5,
'endYear': '2001',
'ongoing': False,
}, {
'institution': 'another institution',
'department': None,
'degree': None,
'startMonth': 5,
'startYear': '2001',
'endMonth': None,
'endYear': None,
'ongoing': True,
}]
payload = {'contents': schools}
url = api_url_for('unserialize_schools')
self.app.put_json(url, payload, auth=self.user.auth)
self.user.reload()
assert_equal(len(self.user.schools), 2)
url = api_url_for('serialize_schools')
res = self.app.get(
url,
auth=self.user.auth,
)
for i, job in enumerate(schools):
assert_equal(job, res.json['contents'][i])
def test_unserialize_jobs(self):
jobs = [
{
'institution': fake.company(),
'department': fake.catch_phrase(),
'title': fake.bs(),
'startMonth': 5,
'startYear': '2013',
'endMonth': 3,
'endYear': '2014',
'ongoing': False,
}
]
payload = {'contents': jobs}
url = api_url_for('unserialize_jobs')
res = self.app.put_json(url, payload, auth=self.user.auth)
assert_equal(res.status_code, 200)
self.user.reload()
# jobs field is updated
assert_equal(self.user.jobs, jobs)
def test_unserialize_names(self):
fake_fullname_w_spaces = ' {} '.format(fake.name())
names = {
'full': fake_fullname_w_spaces,
'given': 'Tea',
'middle': 'Gray',
'family': 'Pot',
'suffix': 'Ms.',
}
url = api_url_for('unserialize_names')
res = self.app.put_json(url, names, auth=self.user.auth)
assert_equal(res.status_code, 200)
self.user.reload()
# user is updated
assert_equal(self.user.fullname, fake_fullname_w_spaces.strip())
assert_equal(self.user.given_name, names['given'])
assert_equal(self.user.middle_names, names['middle'])
assert_equal(self.user.family_name, names['family'])
assert_equal(self.user.suffix, names['suffix'])
def test_unserialize_schools(self):
schools = [
{
'institution': fake.company(),
'department': fake.catch_phrase(),
'degree': fake.bs(),
'startMonth': 5,
'startYear': '2013',
'endMonth': 3,
'endYear': '2014',
'ongoing': False,
}
]
payload = {'contents': schools}
url = api_url_for('unserialize_schools')
res = self.app.put_json(url, payload, auth=self.user.auth)
assert_equal(res.status_code, 200)
self.user.reload()
# schools field is updated
assert_equal(self.user.schools, schools)
def test_unserialize_jobs_valid(self):
jobs = [
{
'institution': fake.company(),
'department': fake.catch_phrase(),
'title': fake.bs(),
'startMonth': 5,
'startYear': '2013',
'endMonth': 3,
'endYear': '2014',
'ongoing': False,
}
]
payload = {'contents': jobs}
url = api_url_for('unserialize_jobs')
res = self.app.put_json(url, payload, auth=self.user.auth)
assert_equal(res.status_code, 200)
def test_get_current_user_gravatar_default_size(self):
url = api_url_for('current_user_gravatar')
res = self.app.get(url, auth=self.user.auth)
current_user_gravatar = res.json['gravatar_url']
assert_true(current_user_gravatar is not None)
url = api_url_for('get_gravatar', uid=self.user._id)
res = self.app.get(url, auth=self.user.auth)
my_user_gravatar = res.json['gravatar_url']
assert_equal(current_user_gravatar, my_user_gravatar)
def test_get_other_user_gravatar_default_size(self):
user2 = AuthUserFactory()
url = api_url_for('current_user_gravatar')
res = self.app.get(url, auth=self.user.auth)
current_user_gravatar = res.json['gravatar_url']
url = api_url_for('get_gravatar', uid=user2._id)
res = self.app.get(url, auth=self.user.auth)
user2_gravatar = res.json['gravatar_url']
assert_true(user2_gravatar is not None)
assert_not_equal(current_user_gravatar, user2_gravatar)
def test_get_current_user_gravatar_specific_size(self):
url = api_url_for('current_user_gravatar')
res = self.app.get(url, auth=self.user.auth)
current_user_default_gravatar = res.json['gravatar_url']
url = api_url_for('current_user_gravatar', size=11)
res = self.app.get(url, auth=self.user.auth)
current_user_small_gravatar = res.json['gravatar_url']
assert_true(current_user_small_gravatar is not None)
assert_not_equal(current_user_default_gravatar, current_user_small_gravatar)
def test_get_other_user_gravatar_specific_size(self):
user2 = AuthUserFactory()
url = api_url_for('get_gravatar', uid=user2._id)
res = self.app.get(url, auth=self.user.auth)
gravatar_default_size = res.json['gravatar_url']
url = api_url_for('get_gravatar', uid=user2._id, size=11)
res = self.app.get(url, auth=self.user.auth)
gravatar_small = res.json['gravatar_url']
assert_true(gravatar_small is not None)
assert_not_equal(gravatar_default_size, gravatar_small)
def test_update_user_timezone(self):
assert_equal(self.user.timezone, 'Etc/UTC')
payload = {'timezone': 'America/New_York', 'id': self.user._id}
url = api_url_for('update_user', uid=self.user._id)
self.app.put_json(url, payload, auth=self.user.auth)
self.user.reload()
assert_equal(self.user.timezone, 'America/New_York')
def test_update_user_locale(self):
assert_equal(self.user.locale, 'en_US')
payload = {'locale': 'de_DE', 'id': self.user._id}
url = api_url_for('update_user', uid=self.user._id)
self.app.put_json(url, payload, auth=self.user.auth)
self.user.reload()
assert_equal(self.user.locale, 'de_DE')
def test_update_user_locale_none(self):
assert_equal(self.user.locale, 'en_US')
payload = {'locale': None, 'id': self.user._id}
url = api_url_for('update_user', uid=self.user._id)
self.app.put_json(url, payload, auth=self.user.auth)
self.user.reload()
assert_equal(self.user.locale, 'en_US')
def test_update_user_locale_empty_string(self):
assert_equal(self.user.locale, 'en_US')
payload = {'locale': '', 'id': self.user._id}
url = api_url_for('update_user', uid=self.user._id)
self.app.put_json(url, payload, auth=self.user.auth)
self.user.reload()
assert_equal(self.user.locale, 'en_US')
def test_cannot_update_user_without_user_id(self):
user1 = AuthUserFactory()
url = api_url_for('update_user')
header = {'emails': [{'address': user1.username}]}
res = self.app.put_json(url, header, auth=user1.auth, expect_errors=True)
assert_equal(res.status_code, 400)
assert_equal(res.json['message_long'], '"id" is required')
@mock.patch('framework.auth.views.mails.send_mail')
def test_add_emails_return_emails(self, send_mail):
user1 = AuthUserFactory()
url = api_url_for('update_user')
email = 'test@cos.io'
header = {'id': user1._id,
'emails': [{'address': user1.username, 'primary': True, 'confirmed': True},
{'address': email, 'primary': False, 'confirmed': False}
]}
res = self.app.put_json(url, header, auth=user1.auth)
assert_equal(res.status_code, 200)
assert_in('emails', res.json['profile'])
assert_equal(len(res.json['profile']['emails']), 2)
@mock.patch('framework.auth.views.mails.send_mail')
def test_resend_confirmation_return_emails(self, send_mail):
user1 = AuthUserFactory()
url = api_url_for('resend_confirmation')
email = 'test@cos.io'
header = {'id': user1._id,
'email': {'address': email, 'primary': False, 'confirmed': False}
}
res = self.app.put_json(url, header, auth=user1.auth)
assert_equal(res.status_code, 200)
assert_in('emails', res.json['profile'])
assert_equal(len(res.json['profile']['emails']), 2)
@mock.patch('framework.auth.views.mails.send_mail')
@mock.patch('website.mailchimp_utils.get_mailchimp_api')
def test_update_user_mailing_lists(self, mock_get_mailchimp_api, send_mail):
email = fake.email()
self.user.emails.append(email)
list_name = 'foo'
self.user.mailchimp_mailing_lists[list_name] = True
self.user.save()
mock_client = mock.MagicMock()
mock_get_mailchimp_api.return_value = mock_client
mock_client.lists.list.return_value = {'data': [{'id': 1, 'list_name': list_name}]}
list_id = mailchimp_utils.get_list_id_from_name(list_name)
url = api_url_for('update_user', uid=self.user._id)
emails = [
{'address': self.user.username, 'primary': False, 'confirmed': True},
{'address': email, 'primary': True, 'confirmed': True}]
payload = {'locale': '', 'id': self.user._id, 'emails': emails}
self.app.put_json(url, payload, auth=self.user.auth)
assert mock_client.lists.unsubscribe.called
mock_client.lists.unsubscribe.assert_called_with(
id=list_id,
email={'email': self.user.username},
send_goodbye=True
)
mock_client.lists.subscribe.assert_called_with(
id=list_id,
email={'email': email},
merge_vars={
'fname': self.user.given_name,
'lname': self.user.family_name,
},
double_optin=False,
update_existing=True
)
handlers.celery_teardown_request()
@mock.patch('framework.auth.views.mails.send_mail')
@mock.patch('website.mailchimp_utils.get_mailchimp_api')
def test_unsubscribe_mailchimp_not_called_if_user_not_subscribed(self, mock_get_mailchimp_api, send_mail):
email = fake.email()
self.user.emails.append(email)
list_name = 'foo'
self.user.mailchimp_mailing_lists[list_name] = False
self.user.save()
mock_client = mock.MagicMock()
mock_get_mailchimp_api.return_value = mock_client
mock_client.lists.list.return_value = {'data': [{'id': 1, 'list_name': list_name}]}
url = api_url_for('update_user', uid=self.user._id)
emails = [
{'address': self.user.username, 'primary': False, 'confirmed': True},
{'address': email, 'primary': True, 'confirmed': True}]
payload = {'locale': '', 'id': self.user._id, 'emails': emails}
self.app.put_json(url, payload, auth=self.user.auth)
assert_equal(mock_client.lists.unsubscribe.call_count, 0)
assert_equal(mock_client.lists.subscribe.call_count, 0)
handlers.celery_teardown_request()
# TODO: Uncomment once outstanding issues with this feature are addressed
# def test_twitter_redirect_success(self):
# self.user.social['twitter'] = fake.last_name()
# self.user.save()
# res = self.app.get(web_url_for('redirect_to_twitter', twitter_handle=self.user.social['twitter']))
# assert_equals(res.status_code, http.FOUND)
# assert_in(self.user.url, res.location)
# def test_twitter_redirect_is_case_insensitive(self):
# self.user.social['twitter'] = fake.last_name()
# self.user.save()
# res1 = self.app.get(web_url_for('redirect_to_twitter', twitter_handle=self.user.social['twitter']))
# res2 = self.app.get(web_url_for('redirect_to_twitter', twitter_handle=self.user.social['twitter'].lower()))
# assert_equal(res1.location, res2.location)
# def test_twitter_redirect_unassociated_twitter_handle_returns_404(self):
# unassociated_handle = fake.last_name()
# expected_error = 'There is no active user associated with the Twitter handle: {0}.'.format(unassociated_handle)
# res = self.app.get(
# web_url_for('redirect_to_twitter', twitter_handle=unassociated_handle),
# expect_errors=True
# )
# assert_equal(res.status_code, http.NOT_FOUND)
# assert_true(expected_error in res.body)
# def test_twitter_redirect_handle_with_multiple_associated_accounts_redirects_to_selection_page(self):
# self.user.social['twitter'] = fake.last_name()
# self.user.save()
# user2 = AuthUserFactory()
# user2.social['twitter'] = self.user.social['twitter']
# user2.save()
# expected_error = 'There are multiple OSF accounts associated with the Twitter handle: <strong>{0}</strong>.'.format(self.user.social['twitter'])
# res = self.app.get(
# web_url_for(
# 'redirect_to_twitter',
# twitter_handle=self.user.social['twitter'],
# expect_error=True
# )
# )
# assert_equal(res.status_code, http.MULTIPLE_CHOICES)
# assert_true(expected_error in res.body)
# assert_true(self.user.url in res.body)
# assert_true(user2.url in res.body)
class TestUserProfileApplicationsPage(OsfTestCase):
def setUp(self):
super(TestUserProfileApplicationsPage, self).setUp()
self.user = AuthUserFactory()
self.user2 = AuthUserFactory()
self.platform_app = ApiOAuth2ApplicationFactory(owner=self.user)
self.detail_url = web_url_for('oauth_application_detail', client_id=self.platform_app.client_id)
def test_non_owner_cant_access_detail_page(self):
res = self.app.get(self.detail_url, auth=self.user2.auth, expect_errors=True)
assert_equal(res.status_code, http.FORBIDDEN)
def test_owner_cant_access_deleted_application(self):
self.platform_app.is_active = False
self.platform_app.save()
res = self.app.get(self.detail_url, auth=self.user.auth, expect_errors=True)
assert_equal(res.status_code, http.GONE)
def test_owner_cant_access_nonexistent_application(self):
url = web_url_for('oauth_application_detail', client_id='nonexistent')
res = self.app.get(url, auth=self.user.auth, expect_errors=True)
assert_equal(res.status_code, http.NOT_FOUND)
def test_url_has_not_broken(self):
assert_equal(self.platform_app.url, self.detail_url)
class TestUserProfileTokensPage(OsfTestCase):
def setUp(self):
super(TestUserProfileTokensPage, self).setUp()
self.user = AuthUserFactory()
self.token = ApiOAuth2PersonalTokenFactory()
self.detail_url = web_url_for('personal_access_token_detail', _id=self.token._id)
def test_url_has_not_broken(self):
assert_equal(self.token.url, self.detail_url)
class TestUserAccount(OsfTestCase):
def setUp(self):
super(TestUserAccount, self).setUp()
self.user = AuthUserFactory()
self.user.set_password('password')
self.user.auth = (self.user.username, 'password')
self.user.save()
@mock.patch('website.profile.views.push_status_message')
def test_password_change_valid(self,
mock_push_status_message,
old_password='password',
new_password='Pa$$w0rd',
confirm_password='Pa$$w0rd'):
url = web_url_for('user_account_password')
post_data = {
'old_password': old_password,
'new_password': new_password,
'confirm_password': confirm_password,
}
res = self.app.post(url, post_data, auth=(self.user.username, old_password))
assert_true(302, res.status_code)
res = res.follow(auth=(self.user.username, new_password))
assert_true(200, res.status_code)
self.user.reload()
assert_true(self.user.check_password(new_password))
assert_true(mock_push_status_message.called)
assert_in('Password updated successfully', mock_push_status_message.mock_calls[0][1][0])
@mock.patch('website.profile.views.push_status_message')
def test_password_change_invalid(self, mock_push_status_message, old_password='', new_password='',
confirm_password='', error_message='Old password is invalid'):
url = web_url_for('user_account_password')
post_data = {
'old_password': old_password,
'new_password': new_password,
'confirm_password': confirm_password,
}
res = self.app.post(url, post_data, auth=self.user.auth)
assert_true(302, res.status_code)
res = res.follow(auth=self.user.auth)
assert_true(200, res.status_code)
self.user.reload()
assert_false(self.user.check_password(new_password))
assert_true(mock_push_status_message.called)
error_strings = [e[1][0] for e in mock_push_status_message.mock_calls]
assert_in(error_message, error_strings)
def test_password_change_invalid_old_password(self):
self.test_password_change_invalid(
old_password='invalid old password',
new_password='new password',
confirm_password='new password',
error_message='Old password is invalid',
)
def test_password_change_invalid_confirm_password(self):
self.test_password_change_invalid(
old_password='password',
new_password='new password',
confirm_password='invalid confirm password',
error_message='Password does not match the confirmation',
)
def test_password_change_invalid_new_password_length(self):
self.test_password_change_invalid(
old_password='password',
new_password='1234567',
confirm_password='1234567',
error_message='Password should be at least eight characters',
)
def test_password_change_valid_new_password_length(self):
self.test_password_change_valid(
old_password='password',
new_password='12345678',
confirm_password='12345678',
)
def test_password_change_invalid_blank_password(self, old_password='', new_password='', confirm_password=''):
self.test_password_change_invalid(
old_password=old_password,
new_password=new_password,
confirm_password=confirm_password,
error_message='Passwords cannot be blank',
)
def test_password_change_invalid_blank_new_password(self):
for password in ('', ' '):
self.test_password_change_invalid_blank_password('password', password, 'new password')
def test_password_change_invalid_blank_confirm_password(self):
for password in ('', ' '):
self.test_password_change_invalid_blank_password('password', 'new password', password)
@mock.patch('framework.auth.views.mails.send_mail')
def test_user_cannot_request_account_export_before_throttle_expires(self, send_mail):
url = api_url_for('request_export')
self.app.post(url, auth=self.user.auth)
assert_true(send_mail.called)
res = self.app.post(url, auth=self.user.auth, expect_errors=True)
assert_equal(res.status_code, 400)
assert_equal(send_mail.call_count, 1)
@mock.patch('framework.auth.views.mails.send_mail')
def test_user_cannot_request_account_deactivation_before_throttle_expires(self, send_mail):
url = api_url_for('request_deactivation')
self.app.post(url, auth=self.user.auth)
assert_true(send_mail.called)
res = self.app.post(url, auth=self.user.auth, expect_errors=True)
assert_equal(res.status_code, 400)
assert_equal(send_mail.call_count, 1)
def test_get_unconfirmed_emails_exclude_external_identity(self):
external_identity = {
'service': {
'AFI': 'LINK'
}
}
self.user.add_unconfirmed_email("james@steward.com")
self.user.add_unconfirmed_email("steward@james.com", external_identity=external_identity)
self.user.save()
unconfirmed_emails = self.user.get_unconfirmed_emails_exclude_external_identity()
assert_in("james@steward.com", unconfirmed_emails)
assert_not_in("steward@james.com", unconfirmed_emails)
class TestAddingContributorViews(OsfTestCase):
def setUp(self):
super(TestAddingContributorViews, self).setUp()
self.creator = AuthUserFactory()
self.project = ProjectFactory(creator=self.creator)
self.auth = Auth(self.project.creator)
# Authenticate all requests
self.app.authenticate(*self.creator.auth)
contributor_added.connect(notify_added_contributor)
def test_serialize_unregistered_without_record(self):
name, email = fake.name(), fake.email()
res = serialize_unregistered(fullname=name, email=email)
assert_equal(res['fullname'], name)
assert_equal(res['email'], email)
assert_equal(res['id'], None)
assert_false(res['registered'])
assert_true(res['gravatar'])
assert_false(res['active'])
def test_deserialize_contributors(self):
contrib = UserFactory()
unreg = UnregUserFactory()
name, email = fake.name(), fake.email()
unreg_no_record = serialize_unregistered(name, email)
contrib_data = [
add_contributor_json(contrib),
serialize_unregistered(fake.name(), unreg.username),
unreg_no_record
]
contrib_data[0]['permission'] = 'admin'
contrib_data[1]['permission'] = 'write'
contrib_data[2]['permission'] = 'read'
contrib_data[0]['visible'] = True
contrib_data[1]['visible'] = True
contrib_data[2]['visible'] = True
res = deserialize_contributors(
self.project,
contrib_data,
auth=Auth(self.creator))
assert_equal(len(res), len(contrib_data))
assert_true(res[0]['user'].is_registered)
assert_false(res[1]['user'].is_registered)
assert_true(res[1]['user']._id)
assert_false(res[2]['user'].is_registered)
assert_true(res[2]['user']._id)
def test_deserialize_contributors_validates_fullname(self):
name = "<img src=1 onerror=console.log(1)>"
email = fake.email()
unreg_no_record = serialize_unregistered(name, email)
contrib_data = [unreg_no_record]
contrib_data[0]['permission'] = 'admin'
contrib_data[0]['visible'] = True
with assert_raises(ValidationError):
deserialize_contributors(
self.project,
contrib_data,
auth=Auth(self.creator),
validate=True)
def test_deserialize_contributors_validates_email(self):
name = fake.name()
email = "!@#$%%^&*"
unreg_no_record = serialize_unregistered(name, email)
contrib_data = [unreg_no_record]
contrib_data[0]['permission'] = 'admin'
contrib_data[0]['visible'] = True
with assert_raises(ValidationError):
deserialize_contributors(
self.project,
contrib_data,
auth=Auth(self.creator),
validate=True)
def test_serialize_unregistered_with_record(self):
name, email = fake.name(), fake.email()
user = self.project.add_unregistered_contributor(fullname=name,
email=email, auth=Auth(self.project.creator))
self.project.save()
res = serialize_unregistered(
fullname=name,
email=email
)
assert_false(res['active'])
assert_false(res['registered'])
assert_equal(res['id'], user._primary_key)
assert_true(res['gravatar_url'])
assert_equal(res['fullname'], name)
assert_equal(res['email'], email)
def test_add_contributor_with_unreg_contribs_and_reg_contribs(self):
n_contributors_pre = len(self.project.contributors)
reg_user = UserFactory()
name, email = fake.name(), fake.email()
pseudouser = {
'id': None,
'registered': False,
'fullname': name,
'email': email,
'permission': 'admin',
'visible': True,
}
reg_dict = add_contributor_json(reg_user)
reg_dict['permission'] = 'admin'
reg_dict['visible'] = True
payload = {
'users': [reg_dict, pseudouser],
'node_ids': []
}
url = self.project.api_url_for('project_contributors_post')
self.app.post_json(url, payload).maybe_follow()
self.project.reload()
assert_equal(len(self.project.contributors),
n_contributors_pre + len(payload['users']))
new_unreg = auth.get_user(email=email)
assert_false(new_unreg.is_registered)
# unclaimed record was added
new_unreg.reload()
assert_in(self.project._primary_key, new_unreg.unclaimed_records)
rec = new_unreg.get_unclaimed_record(self.project._primary_key)
assert_equal(rec['name'], name)
assert_equal(rec['email'], email)
@mock.patch('website.project.views.contributor.send_claim_email')
def test_add_contributors_post_only_sends_one_email_to_unreg_user(
self, mock_send_claim_email):
# Project has components
comp1, comp2 = NodeFactory(
creator=self.creator), NodeFactory(creator=self.creator)
NodeRelation.objects.create(parent=self.project, child=comp1)
NodeRelation.objects.create(parent=self.project, child=comp2)
self.project.save()
# An unreg user is added to the project AND its components
unreg_user = { # dict because user has not previous unreg record
'id': None,
'registered': False,
'fullname': fake.name(),
'email': fake.email(),
'permission': 'admin',
'visible': True,
}
payload = {
'users': [unreg_user],
'node_ids': [comp1._primary_key, comp2._primary_key]
}
# send request
url = self.project.api_url_for('project_contributors_post')
assert_true(self.project.can_edit(user=self.creator))
self.app.post_json(url, payload, auth=self.creator.auth)
# finalize_invitation should only have been called once
assert_equal(mock_send_claim_email.call_count, 1)
@mock.patch('website.mails.send_mail')
def test_add_contributors_post_only_sends_one_email_to_registered_user(self, mock_send_mail):
# Project has components
comp1 = NodeFactory(creator=self.creator, parent=self.project)
comp2 = NodeFactory(creator=self.creator, parent=self.project)
# A registered user is added to the project AND its components
user = UserFactory()
user_dict = {
'id': user._id,
'fullname': user.fullname,
'email': user.username,
'permission': 'write',
'visible': True}
payload = {
'users': [user_dict],
'node_ids': [comp1._primary_key, comp2._primary_key]
}
# send request
url = self.project.api_url_for('project_contributors_post')
assert self.project.can_edit(user=self.creator)
self.app.post_json(url, payload, auth=self.creator.auth)
# send_mail should only have been called once
assert_equal(mock_send_mail.call_count, 1)
@mock.patch('website.mails.send_mail')
def test_add_contributors_post_sends_email_if_user_not_contributor_on_parent_node(self, mock_send_mail):
# Project has a component with a sub-component
component = NodeFactory(creator=self.creator, parent=self.project)
sub_component = NodeFactory(creator=self.creator, parent=component)
# A registered user is added to the project and the sub-component, but NOT the component
user = UserFactory()
user_dict = {
'id': user._id,
'fullname': user.fullname,
'email': user.username,
'permission': 'write',
'visible': True}
payload = {
'users': [user_dict],
'node_ids': [sub_component._primary_key]
}
# send request
url = self.project.api_url_for('project_contributors_post')
assert self.project.can_edit(user=self.creator)
self.app.post_json(url, payload, auth=self.creator.auth)
# send_mail is called for both the project and the sub-component
assert_equal(mock_send_mail.call_count, 2)
@mock.patch('website.project.views.contributor.send_claim_email')
def test_email_sent_when_unreg_user_is_added(self, send_mail):
name, email = fake.name(), fake.email()
pseudouser = {
'id': None,
'registered': False,
'fullname': name,
'email': email,
'permission': 'admin',
'visible': True,
}
payload = {
'users': [pseudouser],
'node_ids': []
}
url = self.project.api_url_for('project_contributors_post')
self.app.post_json(url, payload).maybe_follow()
assert_true(send_mail.called)
assert_true(send_mail.called_with(email=email))
@mock.patch('website.mails.send_mail')
def test_email_sent_when_reg_user_is_added(self, send_mail):
contributor = UserFactory()
contributors = [{
'user': contributor,
'visible': True,
'permissions': ['read', 'write']
}]
project = ProjectFactory(creator=self.auth.user)
project.add_contributors(contributors, auth=self.auth)
project.save()
assert_true(send_mail.called)
send_mail.assert_called_with(
contributor.username,
mails.CONTRIBUTOR_ADDED_DEFAULT,
user=contributor,
node=project,
referrer_name=self.auth.user.fullname,
all_global_subscriptions_none=False,
branded_service_name=None,
)
assert_almost_equal(contributor.contributor_added_email_records[project._id]['last_sent'], int(time.time()), delta=1)
@mock.patch('website.mails.send_mail')
def test_contributor_added_email_sent_to_unreg_user(self, send_mail):
unreg_user = UnregUserFactory()
project = ProjectFactory()
project.add_unregistered_contributor(fullname=unreg_user.fullname, email=unreg_user.email, auth=Auth(project.creator))
project.save()
assert_true(send_mail.called)
@mock.patch('website.mails.send_mail')
def test_forking_project_does_not_send_contributor_added_email(self, send_mail):
project = ProjectFactory()
project.fork_node(auth=Auth(project.creator))
assert_false(send_mail.called)
@mock.patch('website.mails.send_mail')
def test_templating_project_does_not_send_contributor_added_email(self, send_mail):
project = ProjectFactory()
project.use_as_template(auth=Auth(project.creator))
assert_false(send_mail.called)
@mock.patch('website.archiver.tasks.archive')
@mock.patch('website.mails.send_mail')
def test_registering_project_does_not_send_contributor_added_email(self, send_mail, mock_archive):
project = ProjectFactory()
project.register_node(get_default_metaschema(), Auth(user=project.creator), '', None)
assert_false(send_mail.called)
@mock.patch('website.mails.send_mail')
def test_notify_contributor_email_does_not_send_before_throttle_expires(self, send_mail):
contributor = UserFactory()
project = ProjectFactory()
auth = Auth(project.creator)
notify_added_contributor(project, contributor, auth)
assert_true(send_mail.called)
# 2nd call does not send email because throttle period has not expired
notify_added_contributor(project, contributor, auth)
assert_equal(send_mail.call_count, 1)
@mock.patch('website.mails.send_mail')
def test_notify_contributor_email_sends_after_throttle_expires(self, send_mail):
throttle = 0.5
contributor = UserFactory()
project = ProjectFactory()
auth = Auth(project.creator)
notify_added_contributor(project, contributor, auth, throttle=throttle)
assert_true(send_mail.called)
time.sleep(1) # throttle period expires
notify_added_contributor(project, contributor, auth, throttle=throttle)
assert_equal(send_mail.call_count, 2)
def test_add_multiple_contributors_only_adds_one_log(self):
n_logs_pre = self.project.logs.count()
reg_user = UserFactory()
name = fake.name()
pseudouser = {
'id': None,
'registered': False,
'fullname': name,
'email': fake.email(),
'permission': 'write',
'visible': True,
}
reg_dict = add_contributor_json(reg_user)
reg_dict['permission'] = 'admin'
reg_dict['visible'] = True
payload = {
'users': [reg_dict, pseudouser],
'node_ids': []
}
url = self.project.api_url_for('project_contributors_post')
self.app.post_json(url, payload).maybe_follow()
self.project.reload()
assert_equal(self.project.logs.count(), n_logs_pre + 1)
def test_add_contribs_to_multiple_nodes(self):
child = NodeFactory(parent=self.project, creator=self.creator)
n_contributors_pre = child.contributors.count()
reg_user = UserFactory()
name, email = fake.name(), fake.email()
pseudouser = {
'id': None,
'registered': False,
'fullname': name,
'email': email,
'permission': 'admin',
'visible': True,
}
reg_dict = add_contributor_json(reg_user)
reg_dict['permission'] = 'admin'
reg_dict['visible'] = True
payload = {
'users': [reg_dict, pseudouser],
'node_ids': [self.project._primary_key, child._primary_key]
}
url = '/api/v1/project/{0}/contributors/'.format(self.project._id)
self.app.post_json(url, payload).maybe_follow()
child.reload()
assert_equal(child.contributors.count(),
n_contributors_pre + len(payload['users']))
def tearDown(self):
super(TestAddingContributorViews, self).tearDown()
contributor_added.disconnect(notify_added_contributor)
class TestUserInviteViews(OsfTestCase):
def setUp(self):
super(TestUserInviteViews, self).setUp()
self.user = AuthUserFactory()
self.project = ProjectFactory(creator=self.user)
self.invite_url = '/api/v1/project/{0}/invite_contributor/'.format(
self.project._primary_key)
def test_invite_contributor_post_if_not_in_db(self):
name, email = fake.name(), fake.email()
res = self.app.post_json(
self.invite_url,
{'fullname': name, 'email': email},
auth=self.user.auth,
)
contrib = res.json['contributor']
assert_true(contrib['id'] is None)
assert_equal(contrib['fullname'], name)
assert_equal(contrib['email'], email)
def test_invite_contributor_post_if_unreg_already_in_db(self):
# A n unreg user is added to a different project
name, email = fake.name(), fake.email()
project2 = ProjectFactory()
unreg_user = project2.add_unregistered_contributor(fullname=name, email=email,
auth=Auth(project2.creator))
project2.save()
res = self.app.post_json(self.invite_url,
{'fullname': name, 'email': email}, auth=self.user.auth)
expected = add_contributor_json(unreg_user)
expected['fullname'] = name
expected['email'] = email
assert_equal(res.json['contributor'], expected)
def test_invite_contributor_post_if_emaiL_already_registered(self):
reg_user = UserFactory()
# Tries to invite user that is already regiestered
res = self.app.post_json(self.invite_url,
{'fullname': fake.name(), 'email': reg_user.username},
auth=self.user.auth, expect_errors=True)
assert_equal(res.status_code, http.BAD_REQUEST)
def test_invite_contributor_post_if_user_is_already_contributor(self):
unreg_user = self.project.add_unregistered_contributor(
fullname=fake.name(), email=fake.email(),
auth=Auth(self.project.creator)
)
self.project.save()
# Tries to invite unreg user that is already a contributor
res = self.app.post_json(self.invite_url,
{'fullname': fake.name(), 'email': unreg_user.username},
auth=self.user.auth, expect_errors=True)
assert_equal(res.status_code, http.BAD_REQUEST)
def test_invite_contributor_with_no_email(self):
name = fake.name()
res = self.app.post_json(self.invite_url,
{'fullname': name, 'email': None}, auth=self.user.auth)
assert_equal(res.status_code, http.OK)
data = res.json
assert_equal(data['status'], 'success')
assert_equal(data['contributor']['fullname'], name)
assert_true(data['contributor']['email'] is None)
assert_false(data['contributor']['registered'])
def test_invite_contributor_requires_fullname(self):
res = self.app.post_json(self.invite_url,
{'email': 'brian@queen.com', 'fullname': ''}, auth=self.user.auth,
expect_errors=True)
assert_equal(res.status_code, http.BAD_REQUEST)
@mock.patch('website.project.views.contributor.mails.send_mail')
def test_send_claim_email_to_given_email(self, send_mail):
project = ProjectFactory()
given_email = fake.email()
unreg_user = project.add_unregistered_contributor(
fullname=fake.name(),
email=given_email,
auth=Auth(project.creator),
)
project.save()
send_claim_email(email=given_email, unclaimed_user=unreg_user, node=project)
assert_true(send_mail.called)
assert_true(send_mail.called_with(
to_addr=given_email,
mail=mails.INVITE_DEFAULT
))
@mock.patch('website.project.views.contributor.mails.send_mail')
def test_send_claim_email_to_referrer(self, send_mail):
project = ProjectFactory()
referrer = project.creator
given_email, real_email = fake.email(), fake.email()
unreg_user = project.add_unregistered_contributor(fullname=fake.name(),
email=given_email, auth=Auth(
referrer)
)
project.save()
send_claim_email(email=real_email, unclaimed_user=unreg_user, node=project)
assert_true(send_mail.called)
# email was sent to referrer
send_mail.assert_called_with(
referrer.username,
mails.FORWARD_INVITE,
user=unreg_user,
referrer=referrer,
claim_url=unreg_user.get_claim_url(project._id, external=True),
email=real_email.lower().strip(),
fullname=unreg_user.get_unclaimed_record(project._id)['name'],
node=project,
branded_service_name=None
)
@mock.patch('website.project.views.contributor.mails.send_mail')
def test_send_claim_email_before_throttle_expires(self, send_mail):
project = ProjectFactory()
given_email = fake.email()
unreg_user = project.add_unregistered_contributor(
fullname=fake.name(),
email=given_email,
auth=Auth(project.creator),
)
project.save()
send_claim_email(email=fake.email(), unclaimed_user=unreg_user, node=project)
send_mail.reset_mock()
# 2nd call raises error because throttle hasn't expired
with assert_raises(HTTPError):
send_claim_email(email=fake.email(), unclaimed_user=unreg_user, node=project)
assert_false(send_mail.called)
class TestClaimViews(OsfTestCase):
def setUp(self):
super(TestClaimViews, self).setUp()
self.referrer = AuthUserFactory()
self.project = ProjectFactory(creator=self.referrer, is_public=True)
self.given_name = fake.name()
self.given_email = fake.email()
self.user = self.project.add_unregistered_contributor(
fullname=self.given_name,
email=self.given_email,
auth=Auth(user=self.referrer)
)
self.project.save()
@mock.patch('website.project.views.contributor.send_claim_email')
def test_claim_user_already_registered_redirects_to_claim_user_registered(self, claim_email):
name = fake.name()
email = fake.email()
# project contributor adds an unregistered contributor (without an email) on public project
unregistered_user = self.project.add_unregistered_contributor(
fullname=name,
email=None,
auth=Auth(user=self.referrer)
)
assert_in(unregistered_user, self.project.contributors)
# unregistered user comes along and claims themselves on the public project, entering an email
invite_url = self.project.api_url_for('claim_user_post', uid='undefined')
self.app.post_json(invite_url, {
'pk': unregistered_user._primary_key,
'value': email
})
assert_equal(claim_email.call_count, 1)
# set unregistered record email since we are mocking send_claim_email()
unclaimed_record = unregistered_user.get_unclaimed_record(self.project._primary_key)
unclaimed_record.update({'email': email})
unregistered_user.save()
# unregistered user then goes and makes an account with same email, before claiming themselves as contributor
UserFactory(username=email, fullname=name)
# claim link for the now registered email is accessed while not logged in
token = unregistered_user.get_unclaimed_record(self.project._primary_key)['token']
claim_url = '/user/{uid}/{pid}/claim/?token={token}'.format(
uid=unregistered_user._id,
pid=self.project._id,
token=token
)
res = self.app.get(claim_url)
# should redirect to 'claim_user_registered' view
claim_registered_url = '/user/{uid}/{pid}/claim/verify/{token}/'.format(
uid=unregistered_user._id,
pid=self.project._id,
token=token
)
assert_equal(res.status_code, 302)
assert_in(claim_registered_url, res.headers.get('Location'))
@mock.patch('website.project.views.contributor.send_claim_email')
def test_claim_user_already_registered_secondary_email_redirects_to_claim_user_registered(self, claim_email):
name = fake.name()
email = fake.email()
secondary_email = fake.email()
# project contributor adds an unregistered contributor (without an email) on public project
unregistered_user = self.project.add_unregistered_contributor(
fullname=name,
email=None,
auth=Auth(user=self.referrer)
)
assert_in(unregistered_user, self.project.contributors)
# unregistered user comes along and claims themselves on the public project, entering an email
invite_url = self.project.api_url_for('claim_user_post', uid='undefined')
self.app.post_json(invite_url, {
'pk': unregistered_user._primary_key,
'value': secondary_email
})
assert_equal(claim_email.call_count, 1)
# set unregistered record email since we are mocking send_claim_email()
unclaimed_record = unregistered_user.get_unclaimed_record(self.project._primary_key)
unclaimed_record.update({'email': secondary_email})
unregistered_user.save()
# unregistered user then goes and makes an account with same email, before claiming themselves as contributor
registered_user = UserFactory(username=email, fullname=name)
registered_user.emails.append(secondary_email)
registered_user.save()
# claim link for the now registered email is accessed while not logged in
token = unregistered_user.get_unclaimed_record(self.project._primary_key)['token']
claim_url = '/user/{uid}/{pid}/claim/?token={token}'.format(
uid=unregistered_user._id,
pid=self.project._id,
token=token
)
res = self.app.get(claim_url)
# should redirect to 'claim_user_registered' view
claim_registered_url = '/user/{uid}/{pid}/claim/verify/{token}/'.format(
uid=unregistered_user._id,
pid=self.project._id,
token=token
)
assert_equal(res.status_code, 302)
assert_in(claim_registered_url, res.headers.get('Location'))
def test_claim_user_invited_with_no_email_posts_to_claim_form(self):
given_name = fake.name()
invited_user = self.project.add_unregistered_contributor(
fullname=given_name,
email=None,
auth=Auth(user=self.referrer)
)
self.project.save()
url = invited_user.get_claim_url(self.project._primary_key)
res = self.app.post(url, {
'password': 'bohemianrhap',
'password2': 'bohemianrhap'
}, expect_errors=True)
assert_equal(res.status_code, 400)
@mock.patch('website.project.views.contributor.mails.send_mail')
def test_claim_user_post_with_registered_user_id(self, send_mail):
# registered user who is attempting to claim the unclaimed contributor
reg_user = UserFactory()
payload = {
# pk of unreg user record
'pk': self.user._primary_key,
'claimerId': reg_user._primary_key
}
url = '/api/v1/user/{uid}/{pid}/claim/email/'.format(
uid=self.user._primary_key,
pid=self.project._primary_key,
)
res = self.app.post_json(url, payload)
# mail was sent
assert_equal(send_mail.call_count, 2)
# ... to the correct address
referrer_call = send_mail.call_args_list[0]
claimer_call = send_mail.call_args_list[1]
args, _ = referrer_call
assert_equal(args[0], self.referrer.username)
args, _ = claimer_call
assert_equal(args[0], reg_user.username)
# view returns the correct JSON
assert_equal(res.json, {
'status': 'success',
'email': reg_user.username,
'fullname': self.given_name,
})
@mock.patch('website.project.views.contributor.mails.send_mail')
def test_send_claim_registered_email(self, mock_send_mail):
reg_user = UserFactory()
send_claim_registered_email(
claimer=reg_user,
unclaimed_user=self.user,
node=self.project
)
assert_equal(mock_send_mail.call_count, 2)
first_call_args = mock_send_mail.call_args_list[0][0]
assert_equal(first_call_args[0], self.referrer.username)
second_call_args = mock_send_mail.call_args_list[1][0]
assert_equal(second_call_args[0], reg_user.username)
@mock.patch('website.project.views.contributor.mails.send_mail')
def test_send_claim_registered_email_before_throttle_expires(self, mock_send_mail):
reg_user = UserFactory()
send_claim_registered_email(
claimer=reg_user,
unclaimed_user=self.user,
node=self.project,
)
mock_send_mail.reset_mock()
# second call raises error because it was called before throttle period
with assert_raises(HTTPError):
send_claim_registered_email(
claimer=reg_user,
unclaimed_user=self.user,
node=self.project,
)
assert_false(mock_send_mail.called)
@mock.patch('website.project.views.contributor.send_claim_registered_email')
def test_claim_user_post_with_email_already_registered_sends_correct_email(
self, send_claim_registered_email):
reg_user = UserFactory()
payload = {
'value': reg_user.username,
'pk': self.user._primary_key
}
url = self.project.api_url_for('claim_user_post', uid=self.user._id)
self.app.post_json(url, payload)
assert_true(send_claim_registered_email.called)
def test_user_with_removed_unclaimed_url_claiming(self):
""" Tests that when an unclaimed user is removed from a project, the
unregistered user object does not retain the token.
"""
self.project.remove_contributor(self.user, Auth(user=self.referrer))
assert_not_in(
self.project._primary_key,
self.user.unclaimed_records.keys()
)
def test_user_with_claim_url_cannot_claim_twice(self):
""" Tests that when an unclaimed user is replaced on a project with a
claimed user, the unregistered user object does not retain the token.
"""
reg_user = AuthUserFactory()
self.project.replace_contributor(self.user, reg_user)
assert_not_in(
self.project._primary_key,
self.user.unclaimed_records.keys()
)
def test_claim_user_form_redirects_to_password_confirm_page_if_user_is_logged_in(self):
reg_user = AuthUserFactory()
url = self.user.get_claim_url(self.project._primary_key)
res = self.app.get(url, auth=reg_user.auth)
assert_equal(res.status_code, 302)
res = res.follow(auth=reg_user.auth)
token = self.user.get_unclaimed_record(self.project._primary_key)['token']
expected = self.project.web_url_for(
'claim_user_registered',
uid=self.user._id,
token=token,
)
assert_equal(res.request.path, expected)
def test_get_valid_form(self):
url = self.user.get_claim_url(self.project._primary_key)
res = self.app.get(url).maybe_follow()
assert_equal(res.status_code, 200)
def test_invalid_claim_form_raise_400(self):
uid = self.user._primary_key
pid = self.project._primary_key
url = '/user/{uid}/{pid}/claim/?token=badtoken'.format(**locals())
res = self.app.get(url, expect_errors=True).maybe_follow()
assert_equal(res.status_code, 400)
@mock.patch('framework.auth.core.User.update_search_nodes')
def test_posting_to_claim_form_with_valid_data(self, mock_update_search_nodes):
url = self.user.get_claim_url(self.project._primary_key)
res = self.app.post(url, {
'username': self.user.username,
'password': 'killerqueen',
'password2': 'killerqueen'
})
assert_equal(res.status_code, 302)
location = res.headers.get('Location')
assert_in('login?service=', location)
assert_in('username', location)
assert_in('verification_key', location)
assert_in(self.project._primary_key, location)
self.user.reload()
assert_true(self.user.is_registered)
assert_true(self.user.is_active)
assert_not_in(self.project._primary_key, self.user.unclaimed_records)
@mock.patch('framework.auth.core.User.update_search_nodes')
def test_posting_to_claim_form_removes_all_unclaimed_data(self, mock_update_search_nodes):
# user has multiple unclaimed records
p2 = ProjectFactory(creator=self.referrer)
self.user.add_unclaimed_record(node=p2, referrer=self.referrer,
given_name=fake.name())
self.user.save()
assert_true(len(self.user.unclaimed_records.keys()) > 1) # sanity check
url = self.user.get_claim_url(self.project._primary_key)
self.app.post(url, {
'username': self.given_email,
'password': 'bohemianrhap',
'password2': 'bohemianrhap'
})
self.user.reload()
assert_equal(self.user.unclaimed_records, {})
@mock.patch('framework.auth.core.User.update_search_nodes')
def test_posting_to_claim_form_sets_fullname_to_given_name(self, mock_update_search_nodes):
# User is created with a full name
original_name = fake.name()
unreg = UnregUserFactory(fullname=original_name)
# User invited with a different name
different_name = fake.name()
new_user = self.project.add_unregistered_contributor(
email=unreg.username,
fullname=different_name,
auth=Auth(self.project.creator),
)
self.project.save()
# Goes to claim url
claim_url = new_user.get_claim_url(self.project._id)
self.app.post(claim_url, {
'username': unreg.username,
'password': 'killerqueen', 'password2': 'killerqueen'
})
unreg.reload()
# Full name was set correctly
assert_equal(unreg.fullname, different_name)
# CSL names were set correctly
parsed_name = impute_names_model(different_name)
assert_equal(unreg.given_name, parsed_name['given_name'])
assert_equal(unreg.family_name, parsed_name['family_name'])
@mock.patch('website.project.views.contributor.mails.send_mail')
def test_claim_user_post_returns_fullname(self, send_mail):
url = '/api/v1/user/{0}/{1}/claim/email/'.format(self.user._primary_key,
self.project._primary_key)
res = self.app.post_json(url,
{'value': self.given_email,
'pk': self.user._primary_key},
auth=self.referrer.auth)
assert_equal(res.json['fullname'], self.given_name)
assert_true(send_mail.called)
assert_true(send_mail.called_with(to_addr=self.given_email))
@mock.patch('website.project.views.contributor.mails.send_mail')
def test_claim_user_post_if_email_is_different_from_given_email(self, send_mail):
email = fake.email() # email that is different from the one the referrer gave
url = '/api/v1/user/{0}/{1}/claim/email/'.format(self.user._primary_key,
self.project._primary_key)
self.app.post_json(url,
{'value': email, 'pk': self.user._primary_key}
)
assert_true(send_mail.called)
assert_equal(send_mail.call_count, 2)
call_to_invited = send_mail.mock_calls[0]
assert_true(call_to_invited.called_with(
to_addr=email
))
call_to_referrer = send_mail.mock_calls[1]
assert_true(call_to_referrer.called_with(
to_addr=self.given_email
))
def test_claim_url_with_bad_token_returns_400(self):
url = self.project.web_url_for(
'claim_user_registered',
uid=self.user._id,
token='badtoken',
)
res = self.app.get(url, auth=self.referrer.auth, expect_errors=400)
assert_equal(res.status_code, 400)
def test_cannot_claim_user_with_user_who_is_already_contributor(self):
# user who is already a contirbutor to the project
contrib = AuthUserFactory()
self.project.add_contributor(contrib, auth=Auth(self.project.creator))
self.project.save()
# Claiming user goes to claim url, but contrib is already logged in
url = self.user.get_claim_url(self.project._primary_key)
res = self.app.get(
url,
auth=contrib.auth,
).follow(
auth=contrib.auth,
expect_errors=True,
)
# Response is a 400
assert_equal(res.status_code, 400)
@pytest.mark.skip('Watching no longer supported')
class TestWatchViews(OsfTestCase):
def setUp(self):
super(TestWatchViews, self).setUp()
self.user = AuthUserFactory()
self.consolidate_auth = Auth(user=self.user)
self.auth = self.user.auth # used for requests auth
# A public project
self.project = ProjectFactory(is_public=True)
self.project.save()
# Manually reset log date to 100 days ago so it won't show up in feed
latest_log = self.project.logs.latest()
latest_log.date = timezone.now() - dt.timedelta(days=100)
latest_log.save()
# A log added now
self.last_log = self.project.add_log(
NodeLog.TAG_ADDED,
params={'node': self.project._primary_key},
auth=self.consolidate_auth,
log_date=timezone.now(),
save=True,
)
# Clear watched list
WatchConfig = apps.get_model('osf.WatchConfig')
WatchConfig.objects.filter(user=self.user).delete()
def test_watching_a_project_appends_to_users_watched_list(self):
n_watched_then = self.user.watched.count()
url = '/api/v1/project/{0}/watch/'.format(self.project._id)
res = self.app.post_json(url,
params={'digest': True},
auth=self.auth)
assert_equal(res.json['watchCount'], 1)
self.user.reload()
n_watched_now = self.user.watched.count()
assert_equal(res.status_code, 200)
assert_equal(n_watched_now, n_watched_then + 1)
assert_true(self.user.watched.last().digest)
def test_watching_project_twice_returns_400(self):
url = '/api/v1/project/{0}/watch/'.format(self.project._id)
res = self.app.post_json(url,
params={},
auth=self.auth)
assert_equal(res.status_code, 200)
# User tries to watch a node she's already watching
res2 = self.app.post_json(url,
params={},
auth=self.auth,
expect_errors=True)
assert_equal(res2.status_code, http.BAD_REQUEST)
def test_unwatching_a_project_removes_from_watched_list(self):
# The user has already watched a project
watch_config = WatchConfigFactory(node=self.project)
self.user.watch(watch_config)
self.user.save()
n_watched_then = len(self.user.watched)
url = '/api/v1/project/{0}/unwatch/'.format(self.project._id)
res = self.app.post_json(url, {}, auth=self.auth)
self.user.reload()
n_watched_now = len(self.user.watched)
assert_equal(res.status_code, 200)
assert_equal(n_watched_now, n_watched_then - 1)
assert_false(self.user.is_watching(self.project))
def test_toggle_watch(self):
# The user is not watching project
assert_false(self.user.is_watching(self.project))
url = '/api/v1/project/{0}/togglewatch/'.format(self.project._id)
res = self.app.post_json(url, {}, auth=self.auth)
# The response json has a watchcount and watched property
assert_equal(res.json['watchCount'], 1)
assert_true(res.json['watched'])
assert_equal(res.status_code, 200)
self.user.reload()
# The user is now watching the project
assert_true(res.json['watched'])
assert_true(self.user.is_watching(self.project))
def test_toggle_watch_node(self):
# The project has a public sub-node
node = NodeFactory(creator=self.user, parent=self.project, is_public=True)
url = "/api/v1/project/{}/node/{}/togglewatch/".format(self.project._id,
node._id)
res = self.app.post_json(url, {}, auth=self.auth)
assert_equal(res.status_code, 200)
self.user.reload()
# The user is now watching the sub-node
assert_true(res.json['watched'])
assert_true(self.user.is_watching(node))
class TestPointerViews(OsfTestCase):
def setUp(self):
super(TestPointerViews, self).setUp()
self.user = AuthUserFactory()
self.consolidate_auth = Auth(user=self.user)
self.project = ProjectFactory(creator=self.user)
def _make_pointer_only_user_can_see(self, user, project, save=False):
node = ProjectFactory(creator=user)
project.add_pointer(node, auth=Auth(user=user), save=save)
def test_pointer_list_write_contributor_can_remove_private_component_entry(self):
"""Ensure that write contributors see the button to delete a pointer,
even if they cannot see what it is pointing at"""
url = web_url_for('view_project', pid=self.project._id)
user2 = AuthUserFactory()
self.project.add_contributor(user2,
auth=Auth(self.project.creator),
permissions=permissions.DEFAULT_CONTRIBUTOR_PERMISSIONS)
self._make_pointer_only_user_can_see(user2, self.project)
self.project.save()
res = self.app.get(url, auth=self.user.auth).maybe_follow()
assert_equal(res.status_code, 200)
has_controls = res.lxml.xpath('//li[@node_id]/p[starts-with(normalize-space(text()), "Private Link")]//i[contains(@class, "remove-pointer")]')
assert_true(has_controls)
def test_pointer_list_write_contributor_can_remove_public_component_entry(self):
url = web_url_for('view_project', pid=self.project._id)
for i in xrange(3):
self.project.add_pointer(ProjectFactory(creator=self.user),
auth=Auth(user=self.user))
self.project.save()
res = self.app.get(url, auth=self.user.auth).maybe_follow()
assert_equal(res.status_code, 200)
has_controls = res.lxml.xpath(
'//li[@node_id]//i[contains(@class, "remove-pointer")]')
assert_equal(len(has_controls), 3)
def test_pointer_list_read_contributor_cannot_remove_private_component_entry(self):
url = web_url_for('view_project', pid=self.project._id)
user2 = AuthUserFactory()
self.project.add_contributor(user2,
auth=Auth(self.project.creator),
permissions=[permissions.READ])
self._make_pointer_only_user_can_see(user2, self.project)
self.project.save()
res = self.app.get(url, auth=user2.auth).maybe_follow()
assert_equal(res.status_code, 200)
pointer_nodes = res.lxml.xpath('//li[@node_id]')
has_controls = res.lxml.xpath('//li[@node_id]/p[starts-with(normalize-space(text()), "Private Link")]//i[contains(@class, "remove-pointer")]')
assert_equal(len(pointer_nodes), 1)
assert_false(has_controls)
def test_pointer_list_read_contributor_cannot_remove_public_component_entry(self):
url = web_url_for('view_project', pid=self.project._id)
self.project.add_pointer(ProjectFactory(creator=self.user,
is_public=True),
auth=Auth(user=self.user))
user2 = AuthUserFactory()
self.project.add_contributor(user2,
auth=Auth(self.project.creator),
permissions=[permissions.READ])
self.project.save()
res = self.app.get(url, auth=user2.auth).maybe_follow()
assert_equal(res.status_code, 200)
pointer_nodes = res.lxml.xpath('//li[@node_id]')
has_controls = res.lxml.xpath(
'//li[@node_id]//i[contains(@class, "remove-pointer")]')
assert_equal(len(pointer_nodes), 1)
assert_equal(len(has_controls), 0)
# https://github.com/CenterForOpenScience/openscienceframework.org/issues/1109
def test_get_pointed_excludes_folders(self):
pointer_project = ProjectFactory(is_public=True) # project that points to another project
pointed_project = ProjectFactory(creator=self.user) # project that other project points to
pointer_project.add_pointer(pointed_project, Auth(pointer_project.creator), save=True)
# Project is in an organizer collection
collection = CollectionFactory(creator=pointed_project.creator)
collection.add_pointer(pointed_project, Auth(pointed_project.creator), save=True)
url = pointed_project.api_url_for('get_pointed')
res = self.app.get(url, auth=self.user.auth)
assert_equal(res.status_code, 200)
# pointer_project's id is included in response, but folder's id is not
pointer_ids = [each['id'] for each in res.json['pointed']]
assert_in(pointer_project._id, pointer_ids)
assert_not_in(collection._id, pointer_ids)
def test_add_pointers(self):
url = self.project.api_url + 'pointer/'
node_ids = [
NodeFactory()._id
for _ in range(5)
]
self.app.post_json(
url,
{'nodeIds': node_ids},
auth=self.user.auth,
).maybe_follow()
self.project.reload()
assert_equal(
self.project.nodes_active.count(),
5
)
def test_add_the_same_pointer_more_than_once(self):
url = self.project.api_url + 'pointer/'
double_node = NodeFactory()
self.app.post_json(
url,
{'nodeIds': [double_node._id]},
auth=self.user.auth,
)
res = self.app.post_json(
url,
{'nodeIds': [double_node._id]},
auth=self.user.auth,
expect_errors=True
)
assert_equal(res.status_code, 400)
def test_add_pointers_no_user_logg_in(self):
url = self.project.api_url_for('add_pointers')
node_ids = [
NodeFactory()._id
for _ in range(5)
]
res = self.app.post_json(
url,
{'nodeIds': node_ids},
auth=None,
expect_errors=True
)
assert_equal(res.status_code, 401)
def test_add_pointers_public_non_contributor(self):
project2 = ProjectFactory()
project2.set_privacy('public')
project2.save()
url = self.project.api_url_for('add_pointers')
self.app.post_json(
url,
{'nodeIds': [project2._id]},
auth=self.user.auth,
).maybe_follow()
self.project.reload()
assert_equal(
self.project.nodes_active.count(),
1
)
def test_add_pointers_contributor(self):
user2 = AuthUserFactory()
self.project.add_contributor(user2)
self.project.save()
url = self.project.api_url_for('add_pointers')
node_ids = [
NodeFactory()._id
for _ in range(5)
]
self.app.post_json(
url,
{'nodeIds': node_ids},
auth=user2.auth,
).maybe_follow()
self.project.reload()
assert_equal(
self.project.linked_nodes.count(),
5
)
def test_add_pointers_not_provided(self):
url = self.project.api_url + 'pointer/'
res = self.app.post_json(url, {}, auth=self.user.auth, expect_errors=True)
assert_equal(res.status_code, 400)
def test_move_pointers(self):
project_two = ProjectFactory(creator=self.user)
url = api_url_for('move_pointers')
node = NodeFactory()
pointer = self.project.add_pointer(node, auth=self.consolidate_auth)
assert_equal(self.project.linked_nodes.count(), 1)
assert_equal(project_two.linked_nodes.count(), 0)
user_auth = self.user.auth
move_request = \
{
'fromNodeId': self.project._id,
'toNodeId': project_two._id,
'pointerIds': [pointer._id],
}
self.app.post_json(
url,
move_request,
auth=user_auth,
).maybe_follow()
self.project.reload()
project_two.reload()
assert_equal(self.project.linked_nodes.count(), 0)
assert_equal(project_two.linked_nodes.count(), 1)
def test_remove_pointer(self):
url = self.project.api_url + 'pointer/'
node = NodeFactory()
pointer = self.project.add_pointer(node, auth=self.consolidate_auth)
self.app.delete_json(
url,
{'pointerId': pointer.node._id},
auth=self.user.auth,
)
self.project.reload()
assert_equal(
len(list(self.project.nodes)),
0
)
def test_remove_pointer_not_provided(self):
url = self.project.api_url + 'pointer/'
res = self.app.delete_json(url, {}, auth=self.user.auth, expect_errors=True)
assert_equal(res.status_code, 400)
def test_remove_pointer_not_found(self):
url = self.project.api_url + 'pointer/'
res = self.app.delete_json(
url,
{'pointerId': None},
auth=self.user.auth,
expect_errors=True
)
assert_equal(res.status_code, 400)
def test_remove_pointer_not_in_nodes(self):
url = self.project.api_url + 'pointer/'
node = NodeFactory()
pointer = Pointer()
res = self.app.delete_json(
url,
{'pointerId': pointer._id},
auth=self.user.auth,
expect_errors=True
)
assert_equal(res.status_code, 400)
def test_fork_pointer(self):
url = self.project.api_url + 'pointer/fork/'
node = NodeFactory(creator=self.user)
pointer = self.project.add_pointer(node, auth=self.consolidate_auth)
self.app.post_json(
url,
{'pointerId': pointer._id},
auth=self.user.auth
)
def test_fork_pointer_not_provided(self):
url = self.project.api_url + 'pointer/fork/'
res = self.app.post_json(url, {}, auth=self.user.auth,
expect_errors=True)
assert_equal(res.status_code, 400)
def test_fork_pointer_not_found(self):
url = self.project.api_url + 'pointer/fork/'
res = self.app.post_json(
url,
{'pointerId': None},
auth=self.user.auth,
expect_errors=True
)
assert_equal(res.status_code, 400)
def test_fork_pointer_not_in_nodes(self):
url = self.project.api_url + 'pointer/fork/'
node = NodeFactory()
pointer = Pointer()
res = self.app.post_json(
url,
{'pointerId': pointer._id},
auth=self.user.auth,
expect_errors=True
)
assert_equal(res.status_code, 400)
def test_before_register_with_pointer(self):
# Assert that link warning appears in before register callback.
node = NodeFactory()
self.project.add_pointer(node, auth=self.consolidate_auth)
url = self.project.api_url + 'fork/before/'
res = self.app.get(url, auth=self.user.auth).maybe_follow()
prompts = [
prompt
for prompt in res.json['prompts']
if 'Links will be copied into your fork' in prompt
]
assert_equal(len(prompts), 1)
def test_before_fork_with_pointer(self):
"""Assert that link warning appears in before fork callback."""
node = NodeFactory()
self.project.add_pointer(node, auth=self.consolidate_auth)
url = self.project.api_url + 'beforeregister/'
res = self.app.get(url, auth=self.user.auth).maybe_follow()
prompts = [
prompt
for prompt in res.json['prompts']
if 'Links will be copied into your registration' in prompt
]
assert_equal(len(prompts), 1)
def test_before_register_no_pointer(self):
"""Assert that link warning does not appear in before register callback."""
url = self.project.api_url + 'fork/before/'
res = self.app.get(url, auth=self.user.auth).maybe_follow()
prompts = [
prompt
for prompt in res.json['prompts']
if 'Links will be copied into your fork' in prompt
]
assert_equal(len(prompts), 0)
def test_before_fork_no_pointer(self):
"""Assert that link warning does not appear in before fork callback."""
url = self.project.api_url + 'beforeregister/'
res = self.app.get(url, auth=self.user.auth).maybe_follow()
prompts = [
prompt
for prompt in res.json['prompts']
if 'Links will be copied into your registration' in prompt
]
assert_equal(len(prompts), 0)
def test_get_pointed(self):
pointing_node = ProjectFactory(creator=self.user)
pointing_node.add_pointer(self.project, auth=Auth(self.user))
url = self.project.api_url_for('get_pointed')
res = self.app.get(url, auth=self.user.auth)
pointed = res.json['pointed']
assert_equal(len(pointed), 1)
assert_equal(pointed[0]['url'], pointing_node.url)
assert_equal(pointed[0]['title'], pointing_node.title)
assert_equal(pointed[0]['authorShort'], abbrev_authors(pointing_node))
def test_get_pointed_private(self):
secret_user = UserFactory()
pointing_node = ProjectFactory(creator=secret_user)
pointing_node.add_pointer(self.project, auth=Auth(secret_user))
url = self.project.api_url_for('get_pointed')
res = self.app.get(url, auth=self.user.auth)
pointed = res.json['pointed']
assert_equal(len(pointed), 1)
assert_equal(pointed[0]['url'], None)
assert_equal(pointed[0]['title'], 'Private Component')
assert_equal(pointed[0]['authorShort'], 'Private Author(s)')
class TestPublicViews(OsfTestCase):
def test_explore(self):
res = self.app.get("/explore/").maybe_follow()
assert_equal(res.status_code, 200)
class TestAuthViews(OsfTestCase):
def setUp(self):
super(TestAuthViews, self).setUp()
self.user = AuthUserFactory()
self.auth = self.user.auth
@mock.patch('framework.auth.views.mails.send_mail')
def test_register_ok(self, _):
url = api_url_for('register_user')
name, email, password = fake.name(), fake.email(), 'underpressure'
self.app.post_json(
url,
{
'fullName': name,
'email1': email,
'email2': email,
'password': password,
}
)
user = User.find_one(Q('username', 'eq', email))
assert_equal(user.fullname, name)
# Regression test for https://github.com/CenterForOpenScience/osf.io/issues/2902
@mock.patch('framework.auth.views.mails.send_mail')
def test_register_email_case_insensitive(self, _):
url = api_url_for('register_user')
name, email, password = fake.name(), fake.email(), 'underpressure'
self.app.post_json(
url,
{
'fullName': name,
'email1': email,
'email2': str(email).upper(),
'password': password,
}
)
user = User.find_one(Q('username', 'eq', email))
assert_equal(user.fullname, name)
@mock.patch('framework.auth.views.send_confirm_email')
def test_register_scrubs_username(self, _):
url = api_url_for('register_user')
name = "<i>Eunice</i> O' \"Cornwallis\"<script type='text/javascript' src='http://www.cornify.com/js/cornify.js'></script><script type='text/javascript'>cornify_add()</script>"
email, password = fake.email(), 'underpressure'
res = self.app.post_json(
url,
{
'fullName': name,
'email1': email,
'email2': email,
'password': password,
}
)
expected_scrub_username = "Eunice O' \"Cornwallis\"cornify_add()"
user = User.find_one(Q('username', 'eq', email))
assert_equal(res.status_code, http.OK)
assert_equal(user.fullname, expected_scrub_username)
def test_register_email_mismatch(self):
url = api_url_for('register_user')
name, email, password = fake.name(), fake.email(), 'underpressure'
res = self.app.post_json(
url,
{
'fullName': name,
'email1': email,
'email2': email + 'lol',
'password': password,
},
expect_errors=True,
)
assert_equal(res.status_code, http.BAD_REQUEST)
users = User.find(Q('username', 'eq', email))
assert_equal(users.count(), 0)
def test_register_blacklisted_email_domain(self):
url = api_url_for('register_user')
name, email, password = fake.name(), 'bad@mailinator.com', 'agreatpasswordobviously'
res = self.app.post_json(
url, {
'fullName': name,
'email1': email,
'email2': email,
'password': password
},
expect_errors=True
)
assert_equal(res.status_code, http.BAD_REQUEST)
users = User.find(Q('username', 'eq', email))
assert_equal(users.count(), 0)
@mock.patch('framework.auth.views.validate_recaptcha', return_value=True)
@mock.patch('framework.auth.views.mails.send_mail')
def test_register_good_captcha(self, _, validate_recaptcha):
url = api_url_for('register_user')
name, email, password = fake.name(), fake.email(), 'underpressure'
captcha = 'some valid captcha'
with mock.patch.object(settings, 'RECAPTCHA_SITE_KEY', 'some_value'):
resp = self.app.post_json(
url,
{
'fullName': name,
'email1': email,
'email2': str(email).upper(),
'password': password,
'g-recaptcha-response': captcha,
}
)
validate_recaptcha.assert_called_with(captcha, remote_ip=None)
assert_equal(resp.status_code, http.OK)
user = User.find_one(Q('username', 'eq', email))
assert_equal(user.fullname, name)
@mock.patch('framework.auth.views.validate_recaptcha', return_value=False)
@mock.patch('framework.auth.views.mails.send_mail')
def test_register_missing_captcha(self, _, validate_recaptcha):
url = api_url_for('register_user')
name, email, password = fake.name(), fake.email(), 'underpressure'
with mock.patch.object(settings, 'RECAPTCHA_SITE_KEY', 'some_value'):
resp = self.app.post_json(
url,
{
'fullName': name,
'email1': email,
'email2': str(email).upper(),
'password': password,
# 'g-recaptcha-response': 'supposed to be None',
},
expect_errors=True
)
validate_recaptcha.assert_called_with(None, remote_ip=None)
assert_equal(resp.status_code, http.BAD_REQUEST)
@mock.patch('framework.auth.views.validate_recaptcha', return_value=False)
@mock.patch('framework.auth.views.mails.send_mail')
def test_register_bad_captcha(self, _, validate_recaptcha):
url = api_url_for('register_user')
name, email, password = fake.name(), fake.email(), 'underpressure'
with mock.patch.object(settings, 'RECAPTCHA_SITE_KEY', 'some_value'):
resp = self.app.post_json(
url,
{
'fullName': name,
'email1': email,
'email2': str(email).upper(),
'password': password,
'g-recaptcha-response': 'bad captcha',
},
expect_errors=True
)
assert_equal(resp.status_code, http.BAD_REQUEST)
@mock.patch('framework.auth.core.User.update_search_nodes')
def test_register_after_being_invited_as_unreg_contributor(self, mock_update_search_nodes):
# Regression test for:
# https://github.com/CenterForOpenScience/openscienceframework.org/issues/861
# https://github.com/CenterForOpenScience/openscienceframework.org/issues/1021
# https://github.com/CenterForOpenScience/openscienceframework.org/issues/1026
# A user is invited as an unregistered contributor
project = ProjectFactory()
name, email = fake.name(), fake.email()
project.add_unregistered_contributor(fullname=name, email=email, auth=Auth(project.creator))
project.save()
# The new, unregistered user
new_user = User.find_one(Q('username', 'eq', email))
# Instead of following the invitation link, they register at the regular
# registration page
# They use a different name when they register, but same email
real_name = fake.name()
password = 'myprecious'
url = api_url_for('register_user')
payload = {
'fullName': real_name,
'email1': email,
'email2': email,
'password': password,
}
# Send registration request
self.app.post_json(url, payload)
new_user.reload()
# New user confirms by following confirmation link
confirm_url = new_user.get_confirmation_url(email, external=False)
self.app.get(confirm_url)
new_user.reload()
# Password and fullname should be updated
assert_true(new_user.is_confirmed)
assert_true(new_user.check_password(password))
assert_equal(new_user.fullname, real_name)
@mock.patch('framework.auth.views.send_confirm_email')
def test_register_sends_user_registered_signal(self, mock_send_confirm_email):
url = api_url_for('register_user')
name, email, password = fake.name(), fake.email(), 'underpressure'
with capture_signals() as mock_signals:
self.app.post_json(
url,
{
'fullName': name,
'email1': email,
'email2': email,
'password': password,
}
)
assert_equal(mock_signals.signals_sent(), set([auth.signals.user_registered,
auth.signals.unconfirmed_user_created]))
assert_true(mock_send_confirm_email.called)
@mock.patch('framework.auth.views.mails.send_mail')
def test_resend_confirmation(self, send_mail):
email = 'test@mail.com'
token = self.user.add_unconfirmed_email(email)
self.user.save()
url = api_url_for('resend_confirmation')
header = {'address': email, 'primary': False, 'confirmed': False}
self.app.put_json(url, {'id': self.user._id, 'email': header}, auth=self.user.auth)
assert_true(send_mail.called)
assert_true(send_mail.called_with(
to_addr=email
))
self.user.reload()
assert_not_equal(token, self.user.get_confirmation_token(email))
with assert_raises(InvalidTokenError):
self.user.get_unconfirmed_email_for_token(token)
@mock.patch('framework.auth.views.mails.send_mail')
def test_click_confirmation_email(self, send_mail):
email = 'test@mail.com'
token = self.user.add_unconfirmed_email(email)
self.user.save()
self.user.reload()
assert_equal(self.user.email_verifications[token]['confirmed'], False)
url = '/confirm/{}/{}/?logout=1'.format(self.user._id, token, self.user.username)
res = self.app.get(url)
self.user.reload()
assert_equal(self.user.email_verifications[token]['confirmed'], True)
assert_equal(res.status_code, 302)
login_url = 'login?service'
assert_in(login_url, res.body)
def test_get_email_to_add_no_email(self):
email_verifications = self.user.unconfirmed_email_info
assert_equal(email_verifications, [])
def test_get_unconfirmed_email(self):
email = 'test@mail.com'
self.user.add_unconfirmed_email(email)
self.user.save()
self.user.reload()
email_verifications = self.user.unconfirmed_email_info
assert_equal(email_verifications, [])
def test_get_email_to_add(self):
email = 'test@mail.com'
token = self.user.add_unconfirmed_email(email)
self.user.save()
self.user.reload()
assert_equal(self.user.email_verifications[token]['confirmed'], False)
url = '/confirm/{}/{}/?logout=1'.format(self.user._id, token, self.user.username)
self.app.get(url)
self.user.reload()
assert_equal(self.user.email_verifications[token]['confirmed'], True)
email_verifications = self.user.unconfirmed_email_info
assert_equal(email_verifications[0]['address'], 'test@mail.com')
def test_add_email(self):
email = 'test@mail.com'
token = self.user.add_unconfirmed_email(email)
self.user.save()
self.user.reload()
assert_equal(self.user.email_verifications[token]['confirmed'], False)
url = '/confirm/{}/{}/?logout=1'.format(self.user._id, token)
self.app.get(url)
self.user.reload()
email_verifications = self.user.unconfirmed_email_info
put_email_url = api_url_for('unconfirmed_email_add')
res = self.app.put_json(put_email_url, email_verifications[0], auth=self.user.auth)
self.user.reload()
assert_equal(res.json_body['status'], 'success')
assert_equal(self.user.emails[1], 'test@mail.com')
def test_remove_email(self):
email = 'test@mail.com'
token = self.user.add_unconfirmed_email(email)
self.user.save()
self.user.reload()
url = '/confirm/{}/{}/?logout=1'.format(self.user._id, token)
self.app.get(url)
self.user.reload()
email_verifications = self.user.unconfirmed_email_info
remove_email_url = api_url_for('unconfirmed_email_remove')
remove_res = self.app.delete_json(remove_email_url, email_verifications[0], auth=self.user.auth)
self.user.reload()
assert_equal(remove_res.json_body['status'], 'success')
assert_equal(self.user.unconfirmed_email_info, [])
def test_add_expired_email(self):
# Do not return expired token and removes it from user.email_verifications
email = 'test@mail.com'
token = self.user.add_unconfirmed_email(email)
self.user.email_verifications[token]['expiration'] = timezone.now() - dt.timedelta(days=100)
self.user.save()
self.user.reload()
assert_equal(self.user.email_verifications[token]['email'], email)
self.user.clean_email_verifications(given_token=token)
unconfirmed_emails = self.user.unconfirmed_email_info
assert_equal(unconfirmed_emails, [])
assert_equal(self.user.email_verifications, {})
def test_clean_email_verifications(self):
# Do not return bad token and removes it from user.email_verifications
email = 'test@mail.com'
token = 'blahblahblah'
self.user.email_verifications[token] = {'expiration': timezone.now() + dt.timedelta(days=1),
'email': email,
'confirmed': False }
self.user.save()
self.user.reload()
assert_equal(self.user.email_verifications[token]['email'], email)
self.user.clean_email_verifications(given_token=token)
unconfirmed_emails = self.user.unconfirmed_email_info
assert_equal(unconfirmed_emails, [])
assert_equal(self.user.email_verifications, {})
def test_clean_email_verifications_when_email_verifications_is_an_empty_dict(self):
self.user.email_verifications = {}
self.user.save()
ret = self.user.clean_email_verifications()
assert_equal(ret, None)
assert_equal(self.user.email_verifications, {})
def test_add_invalid_email(self):
# Do not return expired token and removes it from user.email_verifications
email = u'\u0000\u0008\u000b\u000c\u000e\u001f\ufffe\uffffHello@yourmom.com'
# illegal_str = u'\u0000\u0008\u000b\u000c\u000e\u001f\ufffe\uffffHello'
# illegal_str += unichr(0xd800) + unichr(0xdbff) + ' World'
# email = 'test@mail.com'
with assert_raises(ValidationError):
self.user.add_unconfirmed_email(email)
def test_add_email_merge(self):
email = "copy@cat.com"
dupe = UserFactory(
username=email,
emails=[email]
)
dupe.save()
token = self.user.add_unconfirmed_email(email)
self.user.save()
self.user.reload()
assert_equal(self.user.email_verifications[token]['confirmed'], False)
url = '/confirm/{}/{}/?logout=1'.format(self.user._id, token)
self.app.get(url)
self.user.reload()
email_verifications = self.user.unconfirmed_email_info
put_email_url = api_url_for('unconfirmed_email_add')
res = self.app.put_json(put_email_url, email_verifications[0], auth=self.user.auth)
self.user.reload()
assert_equal(res.json_body['status'], 'success')
assert_equal(self.user.emails[1], 'copy@cat.com')
def test_resend_confirmation_without_user_id(self):
email = 'test@mail.com'
url = api_url_for('resend_confirmation')
header = {'address': email, 'primary': False, 'confirmed': False}
res = self.app.put_json(url, {'email': header}, auth=self.user.auth, expect_errors=True)
assert_equal(res.status_code, 400)
assert_equal(res.json['message_long'], '"id" is required')
def test_resend_confirmation_without_email(self):
url = api_url_for('resend_confirmation')
res = self.app.put_json(url, {'id': self.user._id}, auth=self.user.auth, expect_errors=True)
assert_equal(res.status_code, 400)
def test_resend_confirmation_not_work_for_primary_email(self):
email = 'test@mail.com'
url = api_url_for('resend_confirmation')
header = {'address': email, 'primary': True, 'confirmed': False}
res = self.app.put_json(url, {'id': self.user._id, 'email': header}, auth=self.user.auth, expect_errors=True)
assert_equal(res.status_code, 400)
assert_equal(res.json['message_long'], 'Cannnot resend confirmation for confirmed emails')
def test_resend_confirmation_not_work_for_confirmed_email(self):
email = 'test@mail.com'
url = api_url_for('resend_confirmation')
header = {'address': email, 'primary': False, 'confirmed': True}
res = self.app.put_json(url, {'id': self.user._id, 'email': header}, auth=self.user.auth, expect_errors=True)
assert_equal(res.status_code, 400)
assert_equal(res.json['message_long'], 'Cannnot resend confirmation for confirmed emails')
@mock.patch('framework.auth.views.mails.send_mail')
def test_resend_confirmation_does_not_send_before_throttle_expires(self, send_mail):
email = 'test@mail.com'
self.user.save()
url = api_url_for('resend_confirmation')
header = {'address': email, 'primary': False, 'confirmed': False}
self.app.put_json(url, {'id': self.user._id, 'email': header}, auth=self.user.auth)
assert_true(send_mail.called)
# 2nd call does not send email because throttle period has not expired
res = self.app.put_json(url, {'id': self.user._id, 'email': header}, auth=self.user.auth, expect_errors=True)
assert_equal(res.status_code, 400)
def test_confirm_email_clears_unclaimed_records_and_revokes_token(self):
unclaimed_user = UnconfirmedUserFactory()
# unclaimed user has been invited to a project.
referrer = UserFactory()
project = ProjectFactory(creator=referrer)
unclaimed_user.add_unclaimed_record(project, referrer, 'foo')
unclaimed_user.save()
# sanity check
assert_equal(len(unclaimed_user.email_verifications.keys()), 1)
# user goes to email confirmation link
token = unclaimed_user.get_confirmation_token(unclaimed_user.username)
url = web_url_for('confirm_email_get', uid=unclaimed_user._id, token=token)
res = self.app.get(url)
assert_equal(res.status_code, 302)
# unclaimed records and token are cleared
unclaimed_user.reload()
assert_equal(unclaimed_user.unclaimed_records, {})
assert_equal(len(unclaimed_user.email_verifications.keys()), 0)
def test_confirmation_link_registers_user(self):
user = User.create_unconfirmed('brian@queen.com', 'bicycle123', 'Brian May')
assert_false(user.is_registered) # sanity check
user.save()
confirmation_url = user.get_confirmation_url('brian@queen.com', external=False)
res = self.app.get(confirmation_url)
assert_equal(res.status_code, 302, 'redirects to settings page')
res = res.follow()
user.reload()
assert_true(user.is_registered)
class TestAuthLoginAndRegisterLogic(OsfTestCase):
def setUp(self):
super(TestAuthLoginAndRegisterLogic, self).setUp()
self.no_auth = Auth()
self.user_auth = AuthUserFactory()
self.auth = Auth(user=self.user_auth)
self.next_url = web_url_for('my_projects', _absolute=True)
self.invalid_campaign = 'invalid_campaign'
def test_osf_login_with_auth(self):
# login: user with auth
data = login_and_register_handler(self.auth)
assert_equal(data.get('status_code'), http.FOUND)
assert_equal(data.get('next_url'), web_url_for('dashboard', _absolute=True))
def test_osf_login_without_auth(self):
# login: user without auth
data = login_and_register_handler(self.no_auth)
assert_equal(data.get('status_code'), http.FOUND)
assert_equal(data.get('next_url'), web_url_for('dashboard', _absolute=True))
def test_osf_register_with_auth(self):
# register: user with auth
data = login_and_register_handler(self.auth, login=False)
assert_equal(data.get('status_code'), http.FOUND)
assert_equal(data.get('next_url'), web_url_for('dashboard', _absolute=True))
def test_osf_register_without_auth(self):
# register: user without auth
data = login_and_register_handler(self.no_auth, login=False)
assert_equal(data.get('status_code'), http.OK)
assert_equal(data.get('next_url'), web_url_for('dashboard', _absolute=True))
def test_next_url_login_with_auth(self):
# next_url login: user with auth
data = login_and_register_handler(self.auth, next_url=self.next_url)
assert_equal(data.get('status_code'), http.FOUND)
assert_equal(data.get('next_url'), self.next_url)
def test_next_url_login_without_auth(self):
# login: user without auth
request.url = web_url_for('auth_login', next=self.next_url, _absolute=True)
data = login_and_register_handler(self.no_auth, next_url=self.next_url)
assert_equal(data.get('status_code'), http.FOUND)
assert_equal(data.get('next_url'), get_login_url(request.url))
def test_next_url_register_with_auth(self):
# register: user with auth
data = login_and_register_handler(self.auth, login=False, next_url=self.next_url)
assert_equal(data.get('status_code'), http.FOUND)
assert_equal(data.get('next_url'), self.next_url)
def test_next_url_register_without_auth(self):
# register: user without auth
data = login_and_register_handler(self.no_auth, login=False, next_url=self.next_url)
assert_equal(data.get('status_code'), http.OK)
assert_equal(data.get('next_url'), request.url)
def test_institution_login_and_register(self):
pass
def test_institution_login_with_auth(self):
# institution login: user with auth
data = login_and_register_handler(self.auth, campaign='institution')
assert_equal(data.get('status_code'), http.FOUND)
assert_equal(data.get('next_url'), web_url_for('dashboard', _absolute=True))
def test_institution_login_without_auth(self):
# institution login: user without auth
data = login_and_register_handler(self.no_auth, campaign='institution')
assert_equal(data.get('status_code'), http.FOUND)
assert_equal(
data.get('next_url'),
get_login_url(web_url_for('dashboard', _absolute=True), campaign='institution'))
def test_institution_regsiter_with_auth(self):
# institution register: user with auth
data = login_and_register_handler(self.auth, login=False, campaign='institution')
assert_equal(data.get('status_code'), http.FOUND)
assert_equal(data.get('next_url'), web_url_for('dashboard', _absolute=True))
def test_institution_register_without_auth(self):
# institution register: user without auth
data = login_and_register_handler(self.no_auth, login=False, campaign='institution')
assert_equal(data.get('status_code'), http.FOUND)
assert_equal(
data.get('next_url'),
get_login_url(web_url_for('dashboard', _absolute=True), campaign='institution')
)
def test_campaign_login_with_auth(self):
for campaign in get_campaigns():
if is_institution_login(campaign):
continue
# campaign login: user with auth
data = login_and_register_handler(self.auth, campaign=campaign)
assert_equal(data.get('status_code'), http.FOUND)
assert_equal(data.get('next_url'), campaign_url_for(campaign))
def test_campaign_login_without_auth(self):
for campaign in get_campaigns():
if is_institution_login(campaign):
continue
# campaign login: user without auth
data = login_and_register_handler(self.no_auth, campaign=campaign)
assert_equal(data.get('status_code'), http.FOUND)
assert_equal(
data.get('next_url'),
web_url_for('auth_register', campaign=campaign, next=campaign_url_for(campaign))
)
def test_campaign_register_with_auth(self):
for campaign in get_campaigns():
if is_institution_login(campaign):
continue
# campaign register: user with auth
data = login_and_register_handler(self.auth, login=False, campaign=campaign)
assert_equal(data.get('status_code'), http.FOUND)
assert_equal(data.get('next_url'), campaign_url_for(campaign))
def test_campaign_register_without_auth(self):
for campaign in get_campaigns():
if is_institution_login(campaign):
continue
# campaign register: user without auth
data = login_and_register_handler(self.no_auth, login=False, campaign=campaign)
assert_equal(data.get('status_code'), http.OK)
if is_native_login(campaign):
# native campaign: prereg and erpc
assert_equal(data.get('next_url'), campaign_url_for(campaign))
elif is_proxy_login(campaign):
# proxy campaign: preprints and branded ones
assert_equal(
data.get('next_url'),
web_url_for('auth_login', next=campaign_url_for(campaign), _absolute=True)
)
def test_campaign_next_url_login_with_auth(self):
for campaign in get_campaigns():
if is_institution_login(campaign):
continue
# campaign login: user with auth
next_url = campaign_url_for(campaign)
data = login_and_register_handler(self.auth, campaign=campaign, next_url=next_url)
assert_equal(data.get('status_code'), http.FOUND)
assert_equal(data.get('next_url'), next_url)
def test_campaign_next_url_login_without_auth(self):
for campaign in get_campaigns():
if is_institution_login(campaign):
continue
# campaign login: user without auth
next_url = campaign_url_for(campaign)
data = login_and_register_handler(self.no_auth, campaign=campaign, next_url=next_url)
assert_equal(data.get('status_code'), http.FOUND)
assert_equal(
data.get('next_url'),
web_url_for('auth_register', campaign=campaign, next=next_url)
)
def test_campaign_next_url_register_with_auth(self):
for campaign in get_campaigns():
if is_institution_login(campaign):
continue
# campaign register: user with auth
next_url = campaign_url_for(campaign)
data = login_and_register_handler(self.auth, login=False, campaign=campaign, next_url=next_url)
assert_equal(data.get('status_code'), http.FOUND)
assert_equal(data.get('next_url'), next_url)
def test_campaign_next_url_register_without_auth(self):
for campaign in get_campaigns():
if is_institution_login(campaign):
continue
# campaign register: user without auth
next_url = campaign_url_for(campaign)
data = login_and_register_handler(self.no_auth, login=False, campaign=campaign, next_url=next_url)
assert_equal(data.get('status_code'), http.OK)
if is_native_login(campaign):
# native campaign: prereg and erpc
assert_equal(data.get('next_url'), next_url)
elif is_proxy_login(campaign):
# proxy campaign: preprints and branded ones
assert_equal(
data.get('next_url'),
web_url_for('auth_login', next= next_url, _absolute=True)
)
def test_invalid_campaign_login_without_auth(self):
data = login_and_register_handler(
self.no_auth,
login=True,
campaign=self.invalid_campaign,
next_url=self.next_url
)
redirect_url = web_url_for('auth_login', campaigns=None, next=self.next_url)
assert_equal(data['status_code'], http.FOUND)
assert_equal(data['next_url'], redirect_url)
assert_equal(data['campaign'], None)
def test_invalid_campaign_register_without_auth(self):
data = login_and_register_handler(
self.no_auth,
login=False,
campaign=self.invalid_campaign,
next_url=self.next_url
)
redirect_url = web_url_for('auth_register', campaigns=None, next=self.next_url)
assert_equal(data['status_code'], http.FOUND)
assert_equal(data['next_url'], redirect_url)
assert_equal(data['campaign'], None)
# The following two tests handles the special case for `claim_user_registered`
# When an authenticated user clicks the claim confirmation clink, there are two ways to trigger this flow:
# 1. If the authenticated user is already a contributor to the project, OSF will ask the user to sign out
# by providing a "logout" link.
# 2. If the authenticated user is not a contributor but decides not to claim contributor under this account,
# OSF provides a link "not <username>?" for the user to logout.
# Both links will land user onto the register page with "MUST LOGIN" push notification.
def test_register_logout_flag_with_auth(self):
# when user click the "logout" or "not <username>?" link, first step is to log user out
data = login_and_register_handler(self.auth, login=False, campaign=None, next_url=self.next_url, logout=True)
assert_equal(data.get('status_code'), 'auth_logout')
assert_equal(data.get('next_url'), self.next_url)
def test_register_logout_flage_without(self):
# the second step is to land user on register page with "MUST LOGIN" warning
data = login_and_register_handler(self.no_auth, login=False, campaign=None, next_url=self.next_url, logout=True)
assert_equal(data.get('status_code'), http.OK)
assert_equal(data.get('next_url'), self.next_url)
assert_true(data.get('must_login_warning'))
class TestAuthLogout(OsfTestCase):
def setUp(self):
super(TestAuthLogout, self).setUp()
self.goodbye_url = web_url_for('goodbye', _absolute=True)
self.redirect_url = web_url_for('forgot_password_get', _absolute=True)
self.valid_next_url = web_url_for('dashboard', _absolute=True)
self.invalid_next_url = 'http://localhost:1234/abcde'
self.auth_user = AuthUserFactory()
def tearDown(self):
super(TestAuthLogout, self).tearDown()
User.objects.all().delete()
assert_equal(User.objects.count(), 0)
def test_logout_with_valid_next_url_logged_in(self):
logout_url = web_url_for('auth_logout', _absolute=True, next=self.valid_next_url)
resp = self.app.get(logout_url, auth=self.auth_user.auth)
assert_equal(resp.status_code, http.FOUND)
assert_equal(cas.get_logout_url(logout_url), resp.headers['Location'])
def test_logout_with_valid_next_url_logged_out(self):
logout_url = web_url_for('auth_logout', _absolute=True, next=self.valid_next_url)
resp = self.app.get(logout_url, auth=None)
assert_equal(resp.status_code, http.FOUND)
assert_equal(self.valid_next_url, resp.headers['Location'])
def test_logout_with_invalid_next_url_logged_in(self):
logout_url = web_url_for('auth_logout', _absolute=True, next=self.invalid_next_url)
resp = self.app.get(logout_url, auth=self.auth_user.auth)
assert_equal(resp.status_code, http.FOUND)
assert_equal(cas.get_logout_url(self.goodbye_url), resp.headers['Location'])
def test_logout_with_invalid_next_url_logged_out(self):
logout_url = web_url_for('auth_logout', _absolute=True, next=self.invalid_next_url)
resp = self.app.get(logout_url, auth=None)
assert_equal(resp.status_code, http.FOUND)
assert_equal(cas.get_logout_url(self.goodbye_url), resp.headers['Location'])
def test_logout_with_redirect_url(self):
logout_url = web_url_for('auth_logout', _absolute=True, redirect_url=self.redirect_url)
resp = self.app.get(logout_url, auth=self.auth_user.auth)
assert_equal(resp.status_code, http.FOUND)
assert_equal(cas.get_logout_url(self.redirect_url), resp.headers['Location'])
def test_logout_with_no_parameter(self):
logout_url = web_url_for('auth_logout', _absolute=True)
resp = self.app.get(logout_url, auth=None)
assert_equal(resp.status_code, http.FOUND)
assert_equal(cas.get_logout_url(self.goodbye_url), resp.headers['Location'])
class TestExternalAuthViews(OsfTestCase):
def setUp(self):
super(TestExternalAuthViews, self).setUp()
name, email = fake.name(), fake.email()
self.provider_id = fake.ean()
external_identity = {
'service': {
self.provider_id: 'CREATE'
}
}
self.user = User.create_unconfirmed(
username=email,
password=str(fake.password()),
fullname=name,
external_identity=external_identity,
)
self.user.save()
self.auth = Auth(self.user)
def test_external_login_email_get_with_invalid_session(self):
url = web_url_for('external_login_email_get')
resp = self.app.get(url, expect_errors=True)
assert_equal(resp.status_code, 401)
def test_external_login_confirm_email_get_with_another_user_logged_in(self):
another_user = AuthUserFactory()
url = self.user.get_confirmation_url(self.user.username, external_id_provider='service', destination='dashboard')
res = self.app.get(url, auth=another_user.auth)
assert_equal(res.status_code, 302, 'redirects to cas logout')
assert_in('/logout?service=', res.location)
assert_in(url, res.location)
def test_external_login_confirm_email_get_without_destination(self):
url = self.user.get_confirmation_url(self.user.username, external_id_provider='service')
res = self.app.get(url, auth=self.auth, expect_errors=True)
assert_equal(res.status_code, 400, 'bad request')
@mock.patch('website.mails.send_mail')
def test_external_login_confirm_email_get_create(self, mock_welcome):
assert_false(self.user.is_registered)
url = self.user.get_confirmation_url(self.user.username, external_id_provider='service', destination='dashboard')
res = self.app.get(url, auth=self.auth)
assert_equal(res.status_code, 302, 'redirects to cas login')
assert_in('/login?service=', res.location)
assert_in('new=true', res.location)
assert_equal(mock_welcome.call_count, 1)
self.user.reload()
assert_equal(self.user.external_identity['service'][self.provider_id], 'VERIFIED')
assert_true(self.user.is_registered)
assert_true(self.user.has_usable_password())
@mock.patch('website.mails.send_mail')
def test_external_login_confirm_email_get_link(self, mock_link_confirm):
self.user.external_identity['service'][self.provider_id] = 'LINK'
self.user.save()
assert_false(self.user.is_registered)
url = self.user.get_confirmation_url(self.user.username, external_id_provider='service', destination='dashboard')
res = self.app.get(url, auth=self.auth)
assert_equal(res.status_code, 302, 'redirects to cas login')
assert_in('/login?service=', res.location)
assert_not_in('new=true', res.location)
assert_equal(mock_link_confirm.call_count, 1)
self.user.reload()
assert_equal(self.user.external_identity['service'][self.provider_id], 'VERIFIED')
assert_true(self.user.is_registered)
assert_true(self.user.has_usable_password())
@mock.patch('website.mails.send_mail')
def test_external_login_confirm_email_get_duped_id(self, mock_confirm):
dupe_user = UserFactory(external_identity={'service': {self.provider_id: 'CREATE'}})
assert_equal(dupe_user.external_identity, self.user.external_identity)
url = self.user.get_confirmation_url(self.user.username, external_id_provider='service', destination='dashboard')
res = self.app.get(url, auth=self.auth)
assert_equal(res.status_code, 302, 'redirects to cas login')
assert_in('/login?service=', res.location)
assert_equal(mock_confirm.call_count, 1)
self.user.reload()
dupe_user.reload()
assert_equal(self.user.external_identity['service'][self.provider_id], 'VERIFIED')
assert_equal(dupe_user.external_identity, {})
@mock.patch('website.mails.send_mail')
def test_external_login_confirm_email_get_duping_id(self, mock_confirm):
dupe_user = UserFactory(external_identity={'service': {self.provider_id: 'VERIFIED'}})
url = self.user.get_confirmation_url(self.user.username, external_id_provider='service', destination='dashboard')
res = self.app.get(url, auth=self.auth, expect_errors=True)
assert_equal(res.status_code, 403, 'only allows one user to link an id')
assert_equal(mock_confirm.call_count, 0)
self.user.reload()
dupe_user.reload()
assert_equal(dupe_user.external_identity['service'][self.provider_id], 'VERIFIED')
assert_equal(self.user.external_identity, {})
def test_ensure_external_identity_uniqueness_unverified(self):
dupe_user = UserFactory(external_identity={'service': {self.provider_id: 'CREATE'}})
assert_equal(dupe_user.external_identity, self.user.external_identity)
ensure_external_identity_uniqueness('service', self.provider_id, self.user)
dupe_user.reload()
self.user.reload()
assert_equal(dupe_user.external_identity, {})
assert_equal(self.user.external_identity, {'service': {self.provider_id: 'CREATE'}})
def test_ensure_external_identity_uniqueness_verified(self):
dupe_user = UserFactory(external_identity={'service': {self.provider_id: 'VERIFIED'}})
assert_equal(dupe_user.external_identity, {'service': {self.provider_id: 'VERIFIED'}})
assert_not_equal(dupe_user.external_identity, self.user.external_identity)
with assert_raises(ValidationError):
ensure_external_identity_uniqueness('service', self.provider_id, self.user)
dupe_user.reload()
self.user.reload()
assert_equal(dupe_user.external_identity, {'service': {self.provider_id: 'VERIFIED'}})
assert_equal(self.user.external_identity, {})
def test_ensure_external_identity_uniqueness_multiple(self):
dupe_user = UserFactory(external_identity={'service': {self.provider_id: 'CREATE'}})
assert_equal(dupe_user.external_identity, self.user.external_identity)
ensure_external_identity_uniqueness('service', self.provider_id)
dupe_user.reload()
self.user.reload()
assert_equal(dupe_user.external_identity, {})
assert_equal(self.user.external_identity, {})
# TODO: Use mock add-on
class TestAddonUserViews(OsfTestCase):
def setUp(self):
super(TestAddonUserViews, self).setUp()
self.user = AuthUserFactory()
def test_choose_addons_add(self):
"""Add add-ons; assert that add-ons are attached to project.
"""
url = '/api/v1/settings/addons/'
self.app.post_json(
url,
{'github': True},
auth=self.user.auth,
).maybe_follow()
self.user.reload()
assert_true(self.user.get_addon('github'))
def test_choose_addons_remove(self):
# Add, then delete, add-ons; assert that add-ons are not attached to
# project.
url = '/api/v1/settings/addons/'
self.app.post_json(
url,
{'github': True},
auth=self.user.auth,
).maybe_follow()
self.app.post_json(
url,
{'github': False},
auth=self.user.auth
).maybe_follow()
self.user.reload()
assert_false(self.user.get_addon('github'))
class TestConfigureMailingListViews(OsfTestCase):
@classmethod
def setUpClass(cls):
super(TestConfigureMailingListViews, cls).setUpClass()
cls._original_enable_email_subscriptions = settings.ENABLE_EMAIL_SUBSCRIPTIONS
settings.ENABLE_EMAIL_SUBSCRIPTIONS = True
def test_user_unsubscribe_and_subscribe_help_mailing_list(self):
user = AuthUserFactory()
url = api_url_for('user_choose_mailing_lists')
payload = {settings.OSF_HELP_LIST: False}
res = self.app.post_json(url, payload, auth=user.auth)
user.reload()
assert_false(user.osf_mailing_lists[settings.OSF_HELP_LIST])
payload = {settings.OSF_HELP_LIST: True}
res = self.app.post_json(url, payload, auth=user.auth)
user.reload()
assert_true(user.osf_mailing_lists[settings.OSF_HELP_LIST])
def test_get_notifications(self):
user = AuthUserFactory()
mailing_lists = dict(user.osf_mailing_lists.items() + user.mailchimp_mailing_lists.items())
url = api_url_for('user_notifications')
res = self.app.get(url, auth=user.auth)
assert_equal(mailing_lists, res.json['mailing_lists'])
def test_osf_help_mails_subscribe(self):
user = UserFactory()
user.osf_mailing_lists[settings.OSF_HELP_LIST] = False
user.save()
update_osf_help_mails_subscription(user, True)
assert_true(user.osf_mailing_lists[settings.OSF_HELP_LIST])
def test_osf_help_mails_unsubscribe(self):
user = UserFactory()
user.osf_mailing_lists[settings.OSF_HELP_LIST] = True
user.save()
update_osf_help_mails_subscription(user, False)
assert_false(user.osf_mailing_lists[settings.OSF_HELP_LIST])
@unittest.skipIf(settings.USE_CELERY, 'Subscription must happen synchronously for this test')
@mock.patch('website.mailchimp_utils.get_mailchimp_api')
def test_user_choose_mailing_lists_updates_user_dict(self, mock_get_mailchimp_api):
user = AuthUserFactory()
list_name = 'OSF General'
mock_client = mock.MagicMock()
mock_get_mailchimp_api.return_value = mock_client
mock_client.lists.list.return_value = {'data': [{'id': 1, 'list_name': list_name}]}
list_id = mailchimp_utils.get_list_id_from_name(list_name)
payload = {settings.MAILCHIMP_GENERAL_LIST: True}
url = api_url_for('user_choose_mailing_lists')
res = self.app.post_json(url, payload, auth=user.auth)
user.reload()
# check user.mailing_lists is updated
assert_true(user.mailchimp_mailing_lists[settings.MAILCHIMP_GENERAL_LIST])
assert_equal(
user.mailchimp_mailing_lists[settings.MAILCHIMP_GENERAL_LIST],
payload[settings.MAILCHIMP_GENERAL_LIST]
)
# check that user is subscribed
mock_client.lists.subscribe.assert_called_with(id=list_id,
email={'email': user.username},
merge_vars={
'fname': user.given_name,
'lname': user.family_name,
},
double_optin=False,
update_existing=True)
def test_get_mailchimp_get_endpoint_returns_200(self):
url = api_url_for('mailchimp_get_endpoint')
res = self.app.get(url)
assert_equal(res.status_code, 200)
@mock.patch('website.mailchimp_utils.get_mailchimp_api')
def test_mailchimp_webhook_subscribe_action_does_not_change_user(self, mock_get_mailchimp_api):
""" Test that 'subscribe' actions sent to the OSF via mailchimp
webhooks update the OSF database.
"""
list_id = '12345'
list_name = 'OSF General'
mock_client = mock.MagicMock()
mock_get_mailchimp_api.return_value = mock_client
mock_client.lists.list.return_value = {'data': [{'id': list_id, 'name': list_name}]}
# user is not subscribed to a list
user = AuthUserFactory()
user.mailchimp_mailing_lists = {'OSF General': False}
user.save()
# user subscribes and webhook sends request to OSF
data = {
'type': 'subscribe',
'data[list_id]': list_id,
'data[email]': user.username
}
url = api_url_for('sync_data_from_mailchimp') + '?key=' + settings.MAILCHIMP_WEBHOOK_SECRET_KEY
res = self.app.post(url,
data,
content_type="application/x-www-form-urlencoded",
auth=user.auth)
# user field is updated on the OSF
user.reload()
assert_true(user.mailchimp_mailing_lists[list_name])
@mock.patch('website.mailchimp_utils.get_mailchimp_api')
def test_mailchimp_webhook_profile_action_does_not_change_user(self, mock_get_mailchimp_api):
""" Test that 'profile' actions sent to the OSF via mailchimp
webhooks do not cause any database changes.
"""
list_id = '12345'
list_name = 'OSF General'
mock_client = mock.MagicMock()
mock_get_mailchimp_api.return_value = mock_client
mock_client.lists.list.return_value = {'data': [{'id': list_id, 'name': list_name}]}
# user is subscribed to a list
user = AuthUserFactory()
user.mailchimp_mailing_lists = {'OSF General': True}
user.save()
# user hits subscribe again, which will update the user's existing info on mailchimp
# webhook sends request (when configured to update on changes made through the API)
data = {
'type': 'profile',
'data[list_id]': list_id,
'data[email]': user.username
}
url = api_url_for('sync_data_from_mailchimp') + '?key=' + settings.MAILCHIMP_WEBHOOK_SECRET_KEY
res = self.app.post(url,
data,
content_type="application/x-www-form-urlencoded",
auth=user.auth)
# user field does not change
user.reload()
assert_true(user.mailchimp_mailing_lists[list_name])
@mock.patch('website.mailchimp_utils.get_mailchimp_api')
def test_sync_data_from_mailchimp_unsubscribes_user(self, mock_get_mailchimp_api):
list_id = '12345'
list_name = 'OSF General'
mock_client = mock.MagicMock()
mock_get_mailchimp_api.return_value = mock_client
mock_client.lists.list.return_value = {'data': [{'id': list_id, 'name': list_name}]}
# user is subscribed to a list
user = AuthUserFactory()
user.mailchimp_mailing_lists = {'OSF General': True}
user.save()
# user unsubscribes through mailchimp and webhook sends request
data = {
'type': 'unsubscribe',
'data[list_id]': list_id,
'data[email]': user.username
}
url = api_url_for('sync_data_from_mailchimp') + '?key=' + settings.MAILCHIMP_WEBHOOK_SECRET_KEY
res = self.app.post(url,
data,
content_type="application/x-www-form-urlencoded",
auth=user.auth)
# user field is updated on the OSF
user.reload()
assert_false(user.mailchimp_mailing_lists[list_name])
def test_sync_data_from_mailchimp_fails_without_secret_key(self):
user = AuthUserFactory()
payload = {'values': {'type': 'unsubscribe',
'data': {'list_id': '12345',
'email': 'freddie@cos.io'}}}
url = api_url_for('sync_data_from_mailchimp')
res = self.app.post_json(url, payload, auth=user.auth, expect_errors=True)
assert_equal(res.status_code, http.UNAUTHORIZED)
@classmethod
def tearDownClass(cls):
super(TestConfigureMailingListViews, cls).tearDownClass()
settings.ENABLE_EMAIL_SUBSCRIPTIONS = cls._original_enable_email_subscriptions
# TODO: Move to OSF Storage
class TestFileViews(OsfTestCase):
def setUp(self):
super(TestFileViews, self).setUp()
self.user = AuthUserFactory()
self.project = ProjectFactory(creator=self.user, is_public=True)
self.project.add_contributor(self.user)
self.project.save()
def test_files_get(self):
url = self.project.api_url_for('collect_file_trees')
res = self.app.get(url, auth=self.user.auth)
expected = _view_project(self.project, auth=Auth(user=self.user))
assert_equal(res.status_code, http.OK)
assert_equal(res.json['node'], expected['node'])
assert_in('tree_js', res.json)
assert_in('tree_css', res.json)
def test_grid_data(self):
url = self.project.api_url_for('grid_data')
res = self.app.get(url, auth=self.user.auth).maybe_follow()
assert_equal(res.status_code, http.OK)
expected = rubeus.to_hgrid(self.project, auth=Auth(self.user))
data = res.json['data']
assert_equal(len(data), len(expected))
class TestTagViews(OsfTestCase):
def setUp(self):
super(TestTagViews, self).setUp()
self.user = AuthUserFactory()
self.project = ProjectFactory(creator=self.user)
@unittest.skip('Tags endpoint disabled for now.')
def test_tag_get_returns_200(self):
url = web_url_for('project_tag', tag='foo')
res = self.app.get(url)
assert_equal(res.status_code, 200)
class TestReorderComponents(OsfTestCase):
def setUp(self):
super(TestReorderComponents, self).setUp()
self.creator = AuthUserFactory()
self.contrib = AuthUserFactory()
# Project is public
self.project = ProjectFactory.create(creator=self.creator, is_public=True)
self.project.add_contributor(self.contrib, auth=Auth(self.creator))
# subcomponent that only creator can see
self.public_component = NodeFactory(creator=self.creator, is_public=True)
self.private_component = NodeFactory(creator=self.creator, is_public=False)
NodeRelation.objects.create(parent=self.project, child=self.public_component)
NodeRelation.objects.create(parent=self.project, child=self.private_component)
self.project.save()
# https://github.com/CenterForOpenScience/openscienceframework.org/issues/489
def test_reorder_components_with_private_component(self):
# contrib tries to reorder components
payload = {
'new_list': [
'{0}'.format(self.private_component._id),
'{0}'.format(self.public_component._id),
]
}
url = self.project.api_url_for('project_reorder_components')
res = self.app.post_json(url, payload, auth=self.contrib.auth)
assert_equal(res.status_code, 200)
class TestWikiWidgetViews(OsfTestCase):
def setUp(self):
super(TestWikiWidgetViews, self).setUp()
# project with no home wiki page
self.project = ProjectFactory()
self.read_only_contrib = AuthUserFactory()
self.project.add_contributor(self.read_only_contrib, permissions='read')
self.noncontributor = AuthUserFactory()
# project with no home wiki content
self.project2 = ProjectFactory(creator=self.project.creator)
self.project2.add_contributor(self.read_only_contrib, permissions='read')
self.project2.update_node_wiki(name='home', content='', auth=Auth(self.project.creator))
def test_show_wiki_for_contributors_when_no_wiki_or_content(self):
assert_true(_should_show_wiki_widget(self.project, self.project.creator))
assert_true(_should_show_wiki_widget(self.project2, self.project.creator))
def test_show_wiki_is_false_for_read_contributors_when_no_wiki_or_content(self):
assert_false(_should_show_wiki_widget(self.project, self.read_only_contrib))
assert_false(_should_show_wiki_widget(self.project2, self.read_only_contrib))
def test_show_wiki_is_false_for_noncontributors_when_no_wiki_or_content(self):
assert_false(_should_show_wiki_widget(self.project, self.noncontributor))
assert_false(_should_show_wiki_widget(self.project2, self.read_only_contrib))
class TestProjectCreation(OsfTestCase):
def setUp(self):
super(TestProjectCreation, self).setUp()
self.creator = AuthUserFactory()
self.url = api_url_for('project_new_post')
self.user1 = AuthUserFactory()
self.user2 = AuthUserFactory()
self.project = ProjectFactory(creator=self.user1)
self.project.add_contributor(self.user2, auth=Auth(self.user1))
self.project.save()
def tearDown(self):
super(TestProjectCreation, self).tearDown()
def test_needs_title(self):
res = self.app.post_json(self.url, {}, auth=self.creator.auth, expect_errors=True)
assert_equal(res.status_code, 400)
def test_create_component_strips_html(self):
user = AuthUserFactory()
project = ProjectFactory(creator=user)
url = web_url_for('project_new_node', pid=project._id)
post_data = {'title': '<b>New <blink>Component</blink> Title</b>', 'category': ''}
request = self.app.post(url, post_data, auth=user.auth).follow()
project.reload()
child = project.nodes[0]
# HTML has been stripped
assert_equal(child.title, 'New Component Title')
def test_strip_html_from_title(self):
payload = {
'title': 'no html <b>here</b>'
}
res = self.app.post_json(self.url, payload, auth=self.creator.auth)
node = Node.load(res.json['projectUrl'].replace('/', ''))
assert_true(node)
assert_equal('no html here', node.title)
def test_only_needs_title(self):
payload = {
'title': 'Im a real title'
}
res = self.app.post_json(self.url, payload, auth=self.creator.auth)
assert_equal(res.status_code, 201)
def test_title_must_be_one_long(self):
payload = {
'title': ''
}
res = self.app.post_json(
self.url, payload, auth=self.creator.auth, expect_errors=True)
assert_equal(res.status_code, 400)
def test_title_must_be_less_than_200(self):
payload = {
'title': ''.join([str(x) for x in xrange(0, 250)])
}
res = self.app.post_json(
self.url, payload, auth=self.creator.auth, expect_errors=True)
assert_equal(res.status_code, 400)
def test_fails_to_create_project_with_whitespace_title(self):
payload = {
'title': ' '
}
res = self.app.post_json(
self.url, payload, auth=self.creator.auth, expect_errors=True)
assert_equal(res.status_code, 400)
def test_creates_a_project(self):
payload = {
'title': 'Im a real title'
}
res = self.app.post_json(self.url, payload, auth=self.creator.auth)
assert_equal(res.status_code, 201)
node = Node.load(res.json['projectUrl'].replace('/', ''))
assert_true(node)
assert_true(node.title, 'Im a real title')
def test_create_component_add_contributors_admin(self):
url = web_url_for('project_new_node', pid=self.project._id)
post_data = {'title': 'New Component With Contributors Title', 'category': '', 'inherit_contributors': True}
res = self.app.post(url, post_data, auth=self.user1.auth)
self.project.reload()
child = self.project.nodes[0]
assert_equal(child.title, 'New Component With Contributors Title')
assert_in(self.user1, child.contributors)
assert_in(self.user2, child.contributors)
# check redirect url
assert_in('/contributors/', res.location)
def test_create_component_with_contributors_read_write(self):
url = web_url_for('project_new_node', pid=self.project._id)
non_admin = AuthUserFactory()
self.project.add_contributor(non_admin, permissions=['read', 'write'])
self.project.save()
post_data = {'title': 'New Component With Contributors Title', 'category': '', 'inherit_contributors': True}
res = self.app.post(url, post_data, auth=non_admin.auth)
self.project.reload()
child = self.project.nodes[0]
assert_equal(child.title, 'New Component With Contributors Title')
assert_in(non_admin, child.contributors)
assert_in(self.user1, child.contributors)
assert_in(self.user2, child.contributors)
assert_equal(child.get_permissions(non_admin), ['read', 'write', 'admin'])
# check redirect url
assert_in('/contributors/', res.location)
def test_create_component_with_contributors_read(self):
url = web_url_for('project_new_node', pid=self.project._id)
non_admin = AuthUserFactory()
self.project.add_contributor(non_admin, permissions=['read'])
self.project.save()
post_data = {'title': 'New Component With Contributors Title', 'category': '', 'inherit_contributors': True}
res = self.app.post(url, post_data, auth=non_admin.auth, expect_errors=True)
assert_equal(res.status_code, 403)
def test_create_component_add_no_contributors(self):
url = web_url_for('project_new_node', pid=self.project._id)
post_data = {'title': 'New Component With Contributors Title', 'category': ''}
res = self.app.post(url, post_data, auth=self.user1.auth)
self.project.reload()
child = self.project.nodes[0]
assert_equal(child.title, 'New Component With Contributors Title')
assert_in(self.user1, child.contributors)
assert_not_in(self.user2, child.contributors)
# check redirect url
assert_not_in('/contributors/', res.location)
def test_new_project_returns_serialized_node_data(self):
payload = {
'title': 'Im a real title'
}
res = self.app.post_json(self.url, payload, auth=self.creator.auth)
assert_equal(res.status_code, 201)
node = res.json['newNode']
assert_true(node)
assert_equal(node['title'], 'Im a real title')
def test_description_works(self):
payload = {
'title': 'Im a real title',
'description': 'I describe things!'
}
res = self.app.post_json(self.url, payload, auth=self.creator.auth)
assert_equal(res.status_code, 201)
node = Node.load(res.json['projectUrl'].replace('/', ''))
assert_true(node)
assert_true(node.description, 'I describe things!')
def test_can_template(self):
other_node = ProjectFactory(creator=self.creator)
payload = {
'title': 'Im a real title',
'template': other_node._id
}
res = self.app.post_json(self.url, payload, auth=self.creator.auth)
assert_equal(res.status_code, 201)
node = Node.load(res.json['projectUrl'].replace('/', ''))
assert_true(node)
assert_true(node.template_node, other_node)
def test_project_before_template_no_addons(self):
project = ProjectFactory()
res = self.app.get(project.api_url_for('project_before_template'), auth=project.creator.auth)
assert_equal(res.json['prompts'], [])
def test_project_before_template_with_addons(self):
project = ProjectWithAddonFactory(addon='box')
res = self.app.get(project.api_url_for('project_before_template'), auth=project.creator.auth)
assert_in('Box', res.json['prompts'])
def test_project_new_from_template_non_user(self):
project = ProjectFactory()
url = api_url_for('project_new_from_template', nid=project._id)
res = self.app.post(url, auth=None)
assert_equal(res.status_code, 302)
res2 = res.follow(expect_errors=True)
assert_equal(res2.status_code, 301)
assert_equal(res2.request.path, '/login')
def test_project_new_from_template_public_non_contributor(self):
non_contributor = AuthUserFactory()
project = ProjectFactory(is_public=True)
url = api_url_for('project_new_from_template', nid=project._id)
res = self.app.post(url, auth=non_contributor.auth)
assert_equal(res.status_code, 201)
def test_project_new_from_template_contributor(self):
contributor = AuthUserFactory()
project = ProjectFactory(is_public=False)
project.add_contributor(contributor)
project.save()
url = api_url_for('project_new_from_template', nid=project._id)
res = self.app.post(url, auth=contributor.auth)
assert_equal(res.status_code, 201)
class TestUnconfirmedUserViews(OsfTestCase):
def test_can_view_profile(self):
user = UnconfirmedUserFactory()
url = web_url_for('profile_view_id', uid=user._id)
res = self.app.get(url, expect_errors=True)
assert_equal(res.status_code, http.BAD_REQUEST)
class TestStaticFileViews(OsfTestCase):
def test_robots_dot_txt(self):
res = self.app.get('/robots.txt')
assert_equal(res.status_code, 200)
assert_in('User-agent', res)
assert_in('text/plain', res.headers['Content-Type'])
def test_favicon(self):
res = self.app.get('/favicon.ico')
assert_equal(res.status_code, 200)
assert_in('image/vnd.microsoft.icon', res.headers['Content-Type'])
def test_getting_started_page(self):
res = self.app.get('/getting-started/')
assert_equal(res.status_code, 302)
assert_equal(res.location, 'http://help.osf.io/')
def test_help_redirect(self):
res = self.app.get('/help/')
assert_equal(res.status_code,302)
class TestUserConfirmSignal(OsfTestCase):
def test_confirm_user_signal_called_when_user_claims_account(self):
unclaimed_user = UnconfirmedUserFactory()
# unclaimed user has been invited to a project.
referrer = UserFactory()
project = ProjectFactory(creator=referrer)
unclaimed_user.add_unclaimed_record(project, referrer, 'foo', email=fake.email())
unclaimed_user.save()
token = unclaimed_user.get_unclaimed_record(project._primary_key)['token']
with capture_signals() as mock_signals:
url = web_url_for('claim_user_form', pid=project._id, uid=unclaimed_user._id, token=token)
payload = {'username': unclaimed_user.username,
'password': 'password',
'password2': 'password'}
res = self.app.post(url, payload)
assert_equal(res.status_code, 302)
assert_equal(mock_signals.signals_sent(), set([auth.signals.user_confirmed]))
def test_confirm_user_signal_called_when_user_confirms_email(self):
unconfirmed_user = UnconfirmedUserFactory()
unconfirmed_user.save()
# user goes to email confirmation link
token = unconfirmed_user.get_confirmation_token(unconfirmed_user.username)
with capture_signals() as mock_signals:
url = web_url_for('confirm_email_get', uid=unconfirmed_user._id, token=token)
res = self.app.get(url)
assert_equal(res.status_code, 302)
assert_equal(mock_signals.signals_sent(), set([auth.signals.user_confirmed]))
# copied from tests/test_comments.py
class TestCommentViews(OsfTestCase):
def setUp(self):
super(TestCommentViews, self).setUp()
self.project = ProjectFactory(is_public=True)
self.user = AuthUserFactory()
self.project.add_contributor(self.user)
self.project.save()
self.user.save()
def test_view_project_comments_updates_user_comments_view_timestamp(self):
url = self.project.api_url_for('update_comments_timestamp')
res = self.app.put_json(url, {
'page': 'node',
'rootId': self.project._id
}, auth=self.user.auth)
self.user.reload()
user_timestamp = self.user.comments_viewed_timestamp[self.project._id]
view_timestamp = timezone.now()
assert_datetime_equal(user_timestamp, view_timestamp)
def test_confirm_non_contrib_viewers_dont_have_pid_in_comments_view_timestamp(self):
non_contributor = AuthUserFactory()
url = self.project.api_url_for('update_comments_timestamp')
res = self.app.put_json(url, {
'page': 'node',
'rootId': self.project._id
}, auth=self.user.auth)
non_contributor.reload()
assert_not_in(self.project._id, non_contributor.comments_viewed_timestamp)
def test_view_comments_updates_user_comments_view_timestamp_files(self):
osfstorage = self.project.get_addon('osfstorage')
root_node = osfstorage.get_root()
test_file = root_node.append_file('test_file')
test_file.create_version(self.user, {
'object': '06d80e',
'service': 'cloud',
osfstorage_settings.WATERBUTLER_RESOURCE: 'osf',
}, {
'size': 1337,
'contentType': 'img/png'
}).save()
url = self.project.api_url_for('update_comments_timestamp')
res = self.app.put_json(url, {
'page': 'files',
'rootId': test_file._id
}, auth=self.user.auth)
self.user.reload()
user_timestamp = self.user.comments_viewed_timestamp[test_file._id]
view_timestamp = timezone.now()
assert_datetime_equal(user_timestamp, view_timestamp)
# Regression test for https://openscience.atlassian.net/browse/OSF-5193
# moved from tests/test_comments.py
def test_find_unread_includes_edited_comments(self):
project = ProjectFactory()
user = AuthUserFactory()
project.add_contributor(user, save=True)
comment = CommentFactory(node=project, user=project.creator)
n_unread = Comment.find_n_unread(user=user, node=project, page='node')
assert n_unread == 1
url = project.api_url_for('update_comments_timestamp')
payload = {'page': 'node', 'rootId': project._id}
self.app.put_json(url, payload, auth=user.auth)
user.reload()
n_unread = Comment.find_n_unread(user=user, node=project, page='node')
assert n_unread == 0
# Edit previously read comment
comment.edit(
auth=Auth(project.creator),
content='edited',
save=True
)
n_unread = Comment.find_n_unread(user=user, node=project, page='node')
assert n_unread == 1
class TestResetPassword(OsfTestCase):
def setUp(self):
super(TestResetPassword, self).setUp()
self.user = AuthUserFactory()
self.another_user = AuthUserFactory()
self.osf_key_v2 = generate_verification_key(verification_type='password')
self.user.verification_key_v2 = self.osf_key_v2
self.user.verification_key = None
self.user.save()
self.get_url = web_url_for(
'reset_password_get',
uid=self.user._id,
token=self.osf_key_v2['token']
)
self.get_url_invalid_key = web_url_for(
'reset_password_get',
uid=self.user._id,
token=generate_verification_key()
)
self.get_url_invalid_user = web_url_for(
'reset_password_get',
uid=self.another_user._id,
token=self.osf_key_v2['token']
)
# successfully load reset password page
def test_reset_password_view_returns_200(self):
res = self.app.get(self.get_url)
assert_equal(res.status_code, 200)
# raise http 400 error
def test_reset_password_view_raises_400(self):
res = self.app.get(self.get_url_invalid_key, expect_errors=True)
assert_equal(res.status_code, 400)
res = self.app.get(self.get_url_invalid_user, expect_errors=True)
assert_equal(res.status_code, 400)
self.user.verification_key_v2['expires'] = timezone.now()
self.user.save()
res = self.app.get(self.get_url, expect_errors=True)
assert_equal(res.status_code, 400)
# successfully reset password
@mock.patch('framework.auth.cas.CasClient.service_validate')
def test_can_reset_password_if_form_success(self, mock_service_validate):
# load reset password page and submit email
res = self.app.get(self.get_url)
form = res.forms['resetPasswordForm']
form['password'] = 'newpassword'
form['password2'] = 'newpassword'
res = form.submit()
# check request URL is /resetpassword with username and new verification_key_v2 token
request_url_path = res.request.path
assert_in('resetpassword', request_url_path)
assert_in(self.user._id, request_url_path)
assert_not_in(self.user.verification_key_v2['token'], request_url_path)
# check verification_key_v2 for OSF is destroyed and verification_key for CAS is in place
self.user.reload()
assert_equal(self.user.verification_key_v2, {})
assert_not_equal(self.user.verification_key, None)
# check redirection to CAS login with username and the new verification_key(CAS)
assert_equal(res.status_code, 302)
location = res.headers.get('Location')
assert_true('login?service=' in location)
assert_true('username={}'.format(self.user.username) in location)
assert_true('verification_key={}'.format(self.user.verification_key) in location)
# check if password was updated
self.user.reload()
assert_true(self.user.check_password('newpassword'))
# check if verification_key is destroyed after service validation
mock_service_validate.return_value = cas.CasResponse(
authenticated=True,
user=self.user._id,
attributes={'accessToken': fake.md5()}
)
ticket = fake.md5()
service_url = 'http://accounts.osf.io/?ticket=' + ticket
cas.make_response_from_ticket(ticket, service_url)
self.user.reload()
assert_equal(self.user.verification_key, None)
# log users out before they land on reset password page
def test_reset_password_logs_out_user(self):
# visit reset password link while another user is logged in
res = self.app.get(self.get_url, auth=self.another_user.auth)
# check redirection to CAS logout
assert_equal(res.status_code, 302)
location = res.headers.get('Location')
assert_not_in('reauth', location)
assert_in('logout?service=', location)
assert_in('resetpassword', location)
@unittest.skip('Unskip when institution hiding code is reimplemented')
class TestIndexView(OsfTestCase):
def setUp(self):
super(TestIndexView, self).setUp()
self.inst_one = InstitutionFactory()
self.inst_two = InstitutionFactory()
self.inst_three = InstitutionFactory()
self.inst_four = InstitutionFactory()
self.inst_five = InstitutionFactory()
self.user = AuthUserFactory()
self.user.affiliated_institutions.add(self.inst_one)
self.user.affiliated_institutions.add(self.inst_two)
self.user.save()
# tests 5 affiliated, non-registered, public projects
for i in range(settings.INSTITUTION_DISPLAY_NODE_THRESHOLD):
node = ProjectFactory(creator=self.user, is_public=True)
node.affiliated_institutions.add(self.inst_one)
node.save()
# tests 4 affiliated, non-registered, public projects
for i in range(settings.INSTITUTION_DISPLAY_NODE_THRESHOLD - 1):
node = ProjectFactory(creator=self.user, is_public=True)
node.affiliated_institutions.add(self.inst_two)
node.save()
# tests 5 affiliated, registered, public projects
for i in range(settings.INSTITUTION_DISPLAY_NODE_THRESHOLD):
registration = RegistrationFactory(creator=self.user, is_public=True)
registration.affiliated_institutions.add(self.inst_three)
registration.save()
# tests 5 affiliated, non-registered public components
for i in range(settings.INSTITUTION_DISPLAY_NODE_THRESHOLD):
node = NodeFactory(creator=self.user, is_public=True)
node.affiliated_institutions.add(self.inst_four)
node.save()
# tests 5 affiliated, non-registered, private projects
for i in range(settings.INSTITUTION_DISPLAY_NODE_THRESHOLD):
node = ProjectFactory(creator=self.user)
node.affiliated_institutions.add(self.inst_five)
node.save()
def test_dashboard_institutions(self):
dashboard_institutions = index()['dashboard_institutions']
assert_equal(len(dashboard_institutions), 1)
assert_equal(dashboard_institutions[0]['id'], self.inst_one._id)
assert_not_equal(dashboard_institutions[0]['id'], self.inst_two._id)
assert_not_equal(dashboard_institutions[0]['id'], self.inst_three._id)
assert_not_equal(dashboard_institutions[0]['id'], self.inst_four._id)
assert_not_equal(dashboard_institutions[0]['id'], self.inst_five._id)
class TestResolveGuid(OsfTestCase):
def setUp(self):
super(TestResolveGuid, self).setUp()
def test_preprint_provider_without_domain(self):
provider = PreprintProviderFactory(domain='')
preprint = PreprintFactory(provider=provider)
url = web_url_for('resolve_guid', _guid=True, guid=preprint._id)
res = self.app.get(url)
assert_equal(res.status_code, 200)
assert_equal(
res.request.path,
'/{}/'.format(preprint._id)
)
def test_preprint_provider_with_domain_without_redirect(self):
domain = 'https://test.com/'
provider = PreprintProviderFactory(_id='test', domain=domain, domain_redirect_enabled=False)
preprint = PreprintFactory(provider=provider)
url = web_url_for('resolve_guid', _guid=True, guid=preprint._id)
res = self.app.get(url)
assert_equal(res.status_code, 200)
assert_equal(
res.request.path,
'/{}/'.format(preprint._id)
)
def test_preprint_provider_with_domain_with_redirect(self):
domain = 'https://test.com/'
provider = PreprintProviderFactory(_id='test', domain=domain, domain_redirect_enabled=True)
preprint = PreprintFactory(provider=provider)
url = web_url_for('resolve_guid', _guid=True, guid=preprint._id)
res = self.app.get(url)
assert_is_redirect(res)
assert_equal(res.status_code, 301)
assert_equal(
res.headers['location'],
'{}{}/'.format(domain, preprint._id)
)
assert_equal(
res.request.path,
'/{}/'.format(preprint._id)
)
def test_preprint_provider_with_osf_domain(self):
provider = PreprintProviderFactory(_id='osf', domain='https://osf.io/')
preprint = PreprintFactory(provider=provider)
url = web_url_for('resolve_guid', _guid=True, guid=preprint._id)
res = self.app.get(url)
assert_equal(res.status_code, 200)
assert_equal(
res.request.path,
'/{}/'.format(preprint._id)
)
class TestConfirmationViewBlockBingPreview(OsfTestCase):
def setUp(self):
super(TestConfirmationViewBlockBingPreview, self).setUp()
self.user_agent = 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/534+ (KHTML, like Gecko) BingPreview/1.0b'
# reset password link should fail with BingPreview
def test_reset_password_get_returns_403(self):
user = UserFactory()
osf_key_v2 = generate_verification_key(verification_type='password')
user.verification_key_v2 = osf_key_v2
user.verification_key = None
user.save()
reset_password_get_url = web_url_for(
'reset_password_get',
uid=user._id,
token=osf_key_v2['token']
)
res = self.app.get(
reset_password_get_url,
expect_errors=True,
headers={
'User-Agent': self.user_agent,
}
)
assert_equal(res.status_code, 403)
# new user confirm account should fail with BingPreview
def test_confirm_email_get_new_user_returns_403(self):
user = User.create_unconfirmed('unconfirmed@cos.io', 'abCD12#$', 'Unconfirmed User')
user.save()
confirm_url = user.get_confirmation_url('unconfirmed@cos.io', external=False)
res = self.app.get(
confirm_url,
expect_errors=True,
headers={
'User-Agent': self.user_agent,
}
)
assert_equal(res.status_code, 403)
# confirmation for adding new email should fail with BingPreview
def test_confirm_email_add_email_returns_403(self):
user = UserFactory()
user.add_unconfirmed_email('unconfirmed@cos.io')
user.save()
confirm_url = user.get_confirmation_url('unconfirmed@cos.io', external=False) + '?logout=1'
res = self.app.get(
confirm_url,
expect_errors=True,
headers={
'User-Agent': self.user_agent,
}
)
assert_equal(res.status_code, 403)
# confirmation for merging accounts should fail with BingPreview
def test_confirm_email_merge_account_returns_403(self):
user = UserFactory()
user_to_be_merged = UserFactory()
user.add_unconfirmed_email(user_to_be_merged.username)
user.save()
confirm_url = user.get_confirmation_url(user_to_be_merged.username, external=False) + '?logout=1'
res = self.app.get(
confirm_url,
expect_errors=True,
headers={
'User-Agent': self.user_agent,
}
)
assert_equal(res.status_code, 403)
# confirmation for new user claiming contributor should fail with BingPreview
def test_claim_user_form_new_user(self):
referrer = AuthUserFactory()
project = ProjectFactory(creator=referrer, is_public=True)
given_name = fake.name()
given_email = fake.email()
user = project.add_unregistered_contributor(
fullname=given_name,
email=given_email,
auth=Auth(user=referrer)
)
project.save()
claim_url = user.get_claim_url(project._primary_key)
res = self.app.get(
claim_url,
expect_errors=True,
headers={
'User-Agent': self.user_agent,
}
)
assert_equal(res.status_code, 403)
# confirmation for existing user claiming contributor should fail with BingPreview
def test_claim_user_form_existing_user(self):
referrer = AuthUserFactory()
project = ProjectFactory(creator=referrer, is_public=True)
auth_user = AuthUserFactory()
pending_user = project.add_unregistered_contributor(
fullname=auth_user.fullname,
email=None,
auth=Auth(user=referrer)
)
project.save()
claim_url = pending_user.get_claim_url(project._primary_key)
res = self.app.get(
claim_url,
auth = auth_user.auth,
expect_errors=True,
headers={
'User-Agent': self.user_agent,
}
)
assert_equal(res.status_code, 403)
# account creation confirmation for ORCiD login should fail with BingPreview
def test_external_login_confirm_email_get_create_user(self):
name, email = fake.name(), fake.email()
provider_id = fake.ean()
external_identity = {
'service': {
provider_id: 'CREATE'
}
}
user = User.create_unconfirmed(
username=email,
password=str(fake.password()),
fullname=name,
external_identity=external_identity,
)
user.save()
create_url = user.get_confirmation_url(
user.username,
external_id_provider='service',
destination='dashboard'
)
res = self.app.get(
create_url,
expect_errors=True,
headers={
'User-Agent': self.user_agent,
}
)
assert_equal(res.status_code, 403)
# account linking confirmation for ORCiD login should fail with BingPreview
def test_external_login_confirm_email_get_link_user(self):
user = UserFactory()
provider_id = fake.ean()
user.external_identity = {
'service': {
provider_id: 'LINK'
}
}
user.add_unconfirmed_email(user.username, external_identity='service')
user.save()
link_url = user.get_confirmation_url(
user.username,
external_id_provider='service',
destination='dashboard'
)
res = self.app.get(
link_url,
expect_errors=True,
headers={
'User-Agent': self.user_agent,
}
)
assert_equal(res.status_code, 403)
if __name__ == '__main__':
unittest.main()
|
|
|
|
# -*- coding: utf-8 -*-
#
# Copyright 2014 Thomas Amland <thomas.amland@gmail.com>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from time import time
from watchdog.utils.delayed_queue import DelayedQueue
def test_get():
q = DelayedQueue(2)
q.put("")
inserted = time()
q.get()
elapsed = time() - inserted
assert 2.01 > elapsed > 1.99
|
# -*- coding: utf-8 -*-
#
# Copyright 2014 Thomas Amland <thomas.amland@gmail.com>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import unicode_literals
import os
import time
import pytest
import logging
from tests import Queue
from functools import partial
from .shell import mkdir, touch, mv, rm, mkdtemp
from watchdog.utils import platform
from watchdog.utils.unicode_paths import str_cls
from watchdog.events import *
from watchdog.observers.api import ObservedWatch
pytestmark = pytest.mark.skipif(not platform.is_linux() and not platform.is_darwin(), reason="")
if platform.is_linux():
from watchdog.observers.inotify import InotifyEmitter as Emitter
elif platform.is_darwin():
from watchdog.observers.fsevents2 import FSEventsEmitter as Emitter
logging.basicConfig(level=logging.DEBUG)
def setup_function(function):
global p, event_queue
tmpdir = os.path.realpath(mkdtemp())
p = partial(os.path.join, tmpdir)
event_queue = Queue()
def start_watching(path=None):
path = p('') if path is None else path
global emitter
emitter = Emitter(event_queue, ObservedWatch(path, recursive=True))
if platform.is_darwin():
# FSEvents will report old evens (like create for mkdtemp in test
# setup. Waiting for a considerable time seems to 'flush' the events.
time.sleep(10)
emitter.start()
def teardown_function(function):
emitter.stop()
emitter.join(5)
rm(p(''), recursive=True)
assert not emitter.is_alive()
def test_create():
start_watching()
open(p('a'), 'a').close()
event = event_queue.get(timeout=5)[0]
assert event.src_path == p('a')
assert isinstance(event, FileCreatedEvent)
event = event_queue.get(timeout=5)[0]
assert os.path.normpath(event.src_path) == os.path.normpath(p(''))
assert isinstance(event, DirModifiedEvent)
def test_delete():
touch(p('a'))
start_watching()
rm(p('a'))
event = event_queue.get(timeout=5)[0]
assert event.src_path == p('a')
assert isinstance(event, FileDeletedEvent)
event = event_queue.get(timeout=5)[0]
assert os.path.normpath(event.src_path) == os.path.normpath(p(''))
assert isinstance(event, DirModifiedEvent)
def test_modify():
touch(p('a'))
start_watching()
touch(p('a'))
event = event_queue.get(timeout=5)[0]
assert event.src_path == p('a')
assert isinstance(event, FileModifiedEvent)
def test_move():
mkdir(p('dir1'))
mkdir(p('dir2'))
touch(p('dir1', 'a'))
start_watching()
mv(p('dir1', 'a'), p('dir2', 'b'))
event = event_queue.get(timeout=5)[0]
assert event.src_path == p('dir1', 'a')
assert event.dest_path == p('dir2', 'b')
assert isinstance(event, FileMovedEvent)
event = event_queue.get(timeout=5)[0]
assert event.src_path == p('dir1')
assert isinstance(event, DirModifiedEvent)
event = event_queue.get(timeout=5)[0]
assert event.src_path == p('dir2')
assert isinstance(event, DirModifiedEvent)
def test_move_to():
mkdir(p('dir1'))
mkdir(p('dir2'))
touch(p('dir1', 'a'))
start_watching(p('dir2'))
mv(p('dir1', 'a'), p('dir2', 'b'))
event = event_queue.get(timeout=5)[0]
assert isinstance(event, FileCreatedEvent)
assert event.src_path == p('dir2', 'b')
def test_move_from():
mkdir(p('dir1'))
mkdir(p('dir2'))
touch(p('dir1', 'a'))
start_watching(p('dir1'))
mv(p('dir1', 'a'), p('dir2', 'b'))
event = event_queue.get(timeout=5)[0]
assert isinstance(event, FileDeletedEvent)
assert event.src_path == p('dir1', 'a')
def test_separate_consecutive_moves():
mkdir(p('dir1'))
touch(p('dir1', 'a'))
touch(p('b'))
start_watching(p('dir1'))
mv(p('dir1', 'a'), p('c'))
mv(p('b'), p('dir1', 'd'))
event = event_queue.get(timeout=5)[0]
assert isinstance(event, FileDeletedEvent)
assert event.src_path == p('dir1', 'a')
assert isinstance(event_queue.get(timeout=5)[0], DirModifiedEvent)
event = event_queue.get(timeout=5)[0]
assert isinstance(event, FileCreatedEvent)
assert event.src_path == p('dir1', 'd')
assert isinstance(event_queue.get(timeout=5)[0], DirModifiedEvent)
@pytest.mark.skipif(platform.is_linux(), reason="bug. inotify will deadlock")
def test_delete_self():
mkdir(p('dir1'))
start_watching(p('dir1'))
rm(p('dir1'), True)
event_queue.get(timeout=5)[0]
def test_passing_unicode_should_give_unicode():
start_watching(p(''))
touch(p('a'))
event = event_queue.get(timeout=5)[0]
assert isinstance(event.src_path, str_cls)
def test_passing_bytes_should_give_bytes():
start_watching(p('').encode())
touch(p('a'))
event = event_queue.get(timeout=5)[0]
assert isinstance(event.src_path, bytes)
|
|
|
tests/test_delayed_queue.py
|
# Licensed under a 3-clause BSD style license - see LICENSE.rst
import sys
import traceback
import warnings
import socketserver
import xmlrpc.client as xmlrpc
from xmlrpc.server import SimpleXMLRPCRequestHandler, SimpleXMLRPCServer
from .constants import SAMP_ICON
from .errors import SAMPWarning
__all__ = []
class SAMPSimpleXMLRPCRequestHandler(SimpleXMLRPCRequestHandler):
"""
XMLRPC handler of Standard Profile requests.
"""
def do_GET(self):
if self.path == '/samp/icon':
self.send_response(200, 'OK')
self.send_header('Content-Type', 'image/png')
self.end_headers()
self.wfile.write(SAMP_ICON)
def do_POST(self):
"""
Handles the HTTP POST request.
Attempts to interpret all HTTP POST requests as XML-RPC calls,
which are forwarded to the server's ``_dispatch`` method for
handling.
"""
# Check that the path is legal
if not self.is_rpc_path_valid():
self.report_404()
return
try:
# Get arguments by reading body of request.
# We read this in chunks to avoid straining
# socket.read(); around the 10 or 15Mb mark, some platforms
# begin to have problems (bug #792570).
max_chunk_size = 10 * 1024 * 1024
size_remaining = int(self.headers["content-length"])
L = []
while size_remaining:
chunk_size = min(size_remaining, max_chunk_size)
L.append(self.rfile.read(chunk_size))
size_remaining -= len(L[-1])
data = b''.join(L)
params, method = xmlrpc.loads(data)
if method == "samp.webhub.register":
params = list(params)
params.append(self.client_address)
if 'Origin' in self.headers:
params.append(self.headers.get('Origin'))
else:
params.append('unknown')
params = tuple(params)
data = xmlrpc.dumps(params, methodname=method)
elif method in ('samp.hub.notify', 'samp.hub.notifyAll',
'samp.hub.call', 'samp.hub.callAll',
'samp.hub.callAndWait'):
user = "unknown"
if method == 'samp.hub.callAndWait':
params[2]["host"] = self.address_string()
params[2]["user"] = user
else:
params[-1]["host"] = self.address_string()
params[-1]["user"] = user
data = xmlrpc.dumps(params, methodname=method)
data = self.decode_request_content(data)
if data is None:
return # response has been sent
# In previous versions of SimpleXMLRPCServer, _dispatch
# could be overridden in this class, instead of in
# SimpleXMLRPCDispatcher. To maintain backwards compatibility,
# check to see if a subclass implements _dispatch and dispatch
# using that method if present.
response = self.server._marshaled_dispatch(
data, getattr(self, '_dispatch', None), self.path
)
except Exception as e:
# This should only happen if the module is buggy
# internal error, report as HTTP server error
self.send_response(500)
# Send information about the exception if requested
if hasattr(self.server, '_send_traceback_header') and \
self.server._send_traceback_header:
self.send_header("X-exception", str(e))
trace = traceback.format_exc()
trace = str(trace.encode('ASCII', 'backslashreplace'), 'ASCII')
self.send_header("X-traceback", trace)
self.send_header("Content-length", "0")
self.end_headers()
else:
# got a valid XML RPC response
self.send_response(200)
self.send_header("Content-type", "text/xml")
if self.encode_threshold is not None:
if len(response) > self.encode_threshold:
q = self.accept_encodings().get("gzip", 0)
if q:
try:
response = xmlrpc.gzip_encode(response)
self.send_header("Content-Encoding", "gzip")
except NotImplementedError:
pass
self.send_header("Content-length", str(len(response)))
self.end_headers()
self.wfile.write(response)
class ThreadingXMLRPCServer(socketserver.ThreadingMixIn, SimpleXMLRPCServer):
"""
Asynchronous multithreaded XMLRPC server.
"""
def __init__(self, addr, log=None,
requestHandler=SAMPSimpleXMLRPCRequestHandler,
logRequests=True, allow_none=True, encoding=None):
self.log = log
SimpleXMLRPCServer.__init__(self, addr, requestHandler,
logRequests, allow_none, encoding)
def handle_error(self, request, client_address):
if self.log is None:
socketserver.BaseServer.handle_error(self, request, client_address)
else:
warnings.warn("Exception happened during processing of request "
"from {}: {}".format(client_address, sys.exc_info()[1]),
SAMPWarning)
|
# Licensed under a 3-clause BSD style license - see LICENSE.rst
import inspect
import pytest
import numpy as np
from astropy.tests.helper import catch_warnings
from astropy.utils.exceptions import AstropyUserWarning
from astropy import units as u
from astropy.nddata.nddata import NDData
from astropy.nddata.decorators import support_nddata
class CCDData(NDData):
pass
@support_nddata
def wrapped_function_1(data, wcs=None, unit=None):
return data, wcs, unit
def test_pass_numpy():
data_in = np.array([1, 2, 3])
data_out, wcs_out, unit_out = wrapped_function_1(data=data_in)
assert data_out is data_in
assert wcs_out is None
assert unit_out is None
def test_pass_all_separate():
data_in = np.array([1, 2, 3])
wcs_in = "the wcs"
unit_in = u.Jy
data_out, wcs_out, unit_out = wrapped_function_1(data=data_in, wcs=wcs_in, unit=unit_in)
assert data_out is data_in
assert wcs_out is wcs_in
assert unit_out is unit_in
def test_pass_nddata():
data_in = np.array([1, 2, 3])
wcs_in = "the wcs"
unit_in = u.Jy
nddata_in = NDData(data_in, wcs=wcs_in, unit=unit_in)
data_out, wcs_out, unit_out = wrapped_function_1(nddata_in)
assert data_out is data_in
assert wcs_out is wcs_in
assert unit_out is unit_in
def test_pass_nddata_and_explicit():
data_in = np.array([1, 2, 3])
wcs_in = "the wcs"
unit_in = u.Jy
unit_in_alt = u.mJy
nddata_in = NDData(data_in, wcs=wcs_in, unit=unit_in)
with catch_warnings() as w:
data_out, wcs_out, unit_out = wrapped_function_1(nddata_in, unit=unit_in_alt)
assert data_out is data_in
assert wcs_out is wcs_in
assert unit_out is unit_in_alt
assert len(w) == 1
assert str(w[0].message) == ("Property unit has been passed explicitly and as "
"an NDData property, using explicitly specified value")
def test_pass_nddata_ignored():
data_in = np.array([1, 2, 3])
wcs_in = "the wcs"
unit_in = u.Jy
nddata_in = NDData(data_in, wcs=wcs_in, unit=unit_in, mask=[0, 1, 0])
with catch_warnings() as w:
data_out, wcs_out, unit_out = wrapped_function_1(nddata_in)
assert data_out is data_in
assert wcs_out is wcs_in
assert unit_out is unit_in
assert len(w) == 1
assert str(w[0].message) == ("The following attributes were set on the data "
"object, but will be ignored by the function: mask")
def test_incorrect_first_argument():
with pytest.raises(ValueError) as exc:
@support_nddata
def wrapped_function_2(something, wcs=None, unit=None):
pass
assert exc.value.args[0] == "Can only wrap functions whose first positional argument is `data`"
with pytest.raises(ValueError) as exc:
@support_nddata
def wrapped_function_3(something, data, wcs=None, unit=None):
pass
assert exc.value.args[0] == "Can only wrap functions whose first positional argument is `data`"
with pytest.raises(ValueError) as exc:
@support_nddata
def wrapped_function_4(wcs=None, unit=None):
pass
assert exc.value.args[0] == "Can only wrap functions whose first positional argument is `data`"
def test_wrap_function_no_kwargs():
@support_nddata
def wrapped_function_5(data, other_data):
return data
data_in = np.array([1, 2, 3])
nddata_in = NDData(data_in)
assert wrapped_function_5(nddata_in, [1, 2, 3]) is data_in
def test_wrap_function_repack_valid():
@support_nddata(repack=True, returns=['data'])
def wrapped_function_5(data, other_data):
return data
data_in = np.array([1, 2, 3])
nddata_in = NDData(data_in)
nddata_out = wrapped_function_5(nddata_in, [1, 2, 3])
assert isinstance(nddata_out, NDData)
assert nddata_out.data is data_in
def test_wrap_function_accepts():
class MyData(NDData):
pass
@support_nddata(accepts=MyData)
def wrapped_function_5(data, other_data):
return data
data_in = np.array([1, 2, 3])
nddata_in = NDData(data_in)
mydata_in = MyData(data_in)
assert wrapped_function_5(mydata_in, [1, 2, 3]) is data_in
with pytest.raises(TypeError) as exc:
wrapped_function_5(nddata_in, [1, 2, 3])
assert exc.value.args[0] == "Only NDData sub-classes that inherit from MyData can be used by this function"
def test_wrap_preserve_signature_docstring():
@support_nddata
def wrapped_function_6(data, wcs=None, unit=None):
"""
An awesome function
"""
pass
if wrapped_function_6.__doc__ is not None:
assert wrapped_function_6.__doc__.strip() == "An awesome function"
signature = inspect.signature(wrapped_function_6)
assert str(signature) == "(data, wcs=None, unit=None)"
def test_setup_failures1():
# repack but no returns
with pytest.raises(ValueError):
support_nddata(repack=True)
def test_setup_failures2():
# returns but no repack
with pytest.raises(ValueError):
support_nddata(returns=['data'])
def test_setup_failures9():
# keeps but no repack
with pytest.raises(ValueError):
support_nddata(keeps=['unit'])
def test_setup_failures3():
# same attribute in keeps and returns
with pytest.raises(ValueError):
support_nddata(repack=True, keeps=['mask'], returns=['data', 'mask'])
def test_setup_failures4():
# function accepts *args
with pytest.raises(ValueError):
@support_nddata
def test(data, *args):
pass
def test_setup_failures10():
# function accepts **kwargs
with pytest.raises(ValueError):
@support_nddata
def test(data, **kwargs):
pass
def test_setup_failures5():
# function accepts *args (or **kwargs)
with pytest.raises(ValueError):
@support_nddata
def test(data, *args):
pass
def test_setup_failures6():
# First argument is not data
with pytest.raises(ValueError):
@support_nddata
def test(img):
pass
def test_setup_failures7():
# accepts CCDData but was given just an NDData
with pytest.raises(TypeError):
@support_nddata(accepts=CCDData)
def test(data):
pass
test(NDData(np.ones((3, 3))))
def test_setup_failures8():
# function returns a different amount of arguments than specified. Using
# NDData here so we don't get into troubles when creating a CCDData without
# unit!
with pytest.raises(ValueError):
@support_nddata(repack=True, returns=['data', 'mask'])
def test(data):
return 10
test(NDData(np.ones((3, 3)))) # do NOT use CCDData here.
def test_setup_failures11():
# function accepts no arguments
with pytest.raises(ValueError):
@support_nddata
def test():
pass
def test_setup_numpyarray_default():
# It should be possible (even if it's not advisable to use mutable
# defaults) to have a numpy array as default value.
@support_nddata
def func(data, wcs=np.array([1, 2, 3])):
return wcs
def test_still_accepts_other_input():
@support_nddata(repack=True, returns=['data'])
def test(data):
return data
assert isinstance(test(NDData(np.ones((3, 3)))), NDData)
assert isinstance(test(10), int)
assert isinstance(test([1, 2, 3]), list)
def test_accepting_property_normal():
# Accepts a mask attribute and takes it from the input
@support_nddata
def test(data, mask=None):
return mask
ndd = NDData(np.ones((3, 3)))
assert test(ndd) is None
ndd._mask = np.zeros((3, 3))
assert np.all(test(ndd) == 0)
# Use the explicitly given one (raises a Warning)
with catch_warnings(AstropyUserWarning) as w:
assert test(ndd, mask=10) == 10
assert len(w) == 1
def test_parameter_default_identical_to_explicit_passed_argument():
# If the default is identical to the explicitly passed argument this
# should still raise a Warning and use the explicit one.
@support_nddata
def func(data, wcs=[1, 2, 3]):
return wcs
with catch_warnings(AstropyUserWarning) as w:
assert func(NDData(1, wcs=[1, 2]), [1, 2, 3]) == [1, 2, 3]
assert len(w) == 1
with catch_warnings(AstropyUserWarning) as w:
assert func(NDData(1, wcs=[1, 2])) == [1, 2]
assert len(w) == 0
def test_accepting_property_notexist():
# Accepts flags attribute but NDData doesn't have one
@support_nddata
def test(data, flags=10):
return flags
ndd = NDData(np.ones((3, 3)))
test(ndd)
def test_accepting_property_translated():
# Accepts a error attribute and we want to pass in uncertainty!
@support_nddata(mask='masked')
def test(data, masked=None):
return masked
ndd = NDData(np.ones((3, 3)))
assert test(ndd) is None
ndd._mask = np.zeros((3, 3))
assert np.all(test(ndd) == 0)
# Use the explicitly given one (raises a Warning)
with catch_warnings(AstropyUserWarning) as w:
assert test(ndd, masked=10) == 10
assert len(w) == 1
def test_accepting_property_meta_empty():
# Meta is always set (OrderedDict) so it has a special case that it's
# ignored if it's empty but not None
@support_nddata
def test(data, meta=None):
return meta
ndd = NDData(np.ones((3, 3)))
assert test(ndd) is None
ndd._meta = {'a': 10}
assert test(ndd) == {'a': 10}
|
|
astropy/nddata/tests/test_decorators.py
|
astropy/samp/standard_profile.py
|
# Licensed under a 3-clause BSD style license - see LICENSE.rst
import numpy as np
from astropy import units as u
from .wcs import WCS, WCSSUB_LONGITUDE, WCSSUB_LATITUDE, WCSSUB_CELESTIAL
__doctest_skip__ = ['wcs_to_celestial_frame', 'celestial_frame_to_wcs']
__all__ = ['add_stokes_axis_to_wcs', 'celestial_frame_to_wcs',
'wcs_to_celestial_frame', 'proj_plane_pixel_scales',
'proj_plane_pixel_area', 'is_proj_plane_distorted',
'non_celestial_pixel_scales', 'skycoord_to_pixel',
'pixel_to_skycoord', 'custom_wcs_to_frame_mappings',
'custom_frame_to_wcs_mappings']
def add_stokes_axis_to_wcs(wcs, add_before_ind):
"""
Add a new Stokes axis that is uncorrelated with any other axes.
Parameters
----------
wcs : `~astropy.wcs.WCS`
The WCS to add to
add_before_ind : int
Index of the WCS to insert the new Stokes axis in front of.
To add at the end, do add_before_ind = wcs.wcs.naxis
The beginning is at position 0.
Returns
-------
A new `~astropy.wcs.WCS` instance with an additional axis
"""
inds = [i + 1 for i in range(wcs.wcs.naxis)]
inds.insert(add_before_ind, 0)
newwcs = wcs.sub(inds)
newwcs.wcs.ctype[add_before_ind] = 'STOKES'
newwcs.wcs.cname[add_before_ind] = 'STOKES'
return newwcs
def _wcs_to_celestial_frame_builtin(wcs):
# Import astropy.coordinates here to avoid circular imports
from astropy.coordinates import FK4, FK4NoETerms, FK5, ICRS, ITRS, Galactic
# Import astropy.time here otherwise setup.py fails before extensions are compiled
from astropy.time import Time
if wcs.wcs.lng == -1 or wcs.wcs.lat == -1:
return None
radesys = wcs.wcs.radesys
if np.isnan(wcs.wcs.equinox):
equinox = None
else:
equinox = wcs.wcs.equinox
xcoord = wcs.wcs.ctype[wcs.wcs.lng][:4]
ycoord = wcs.wcs.ctype[wcs.wcs.lat][:4]
# Apply logic from FITS standard to determine the default radesys
if radesys == '' and xcoord == 'RA--' and ycoord == 'DEC-':
if equinox is None:
radesys = "ICRS"
elif equinox < 1984.:
radesys = "FK4"
else:
radesys = "FK5"
if radesys == 'FK4':
if equinox is not None:
equinox = Time(equinox, format='byear')
frame = FK4(equinox=equinox)
elif radesys == 'FK4-NO-E':
if equinox is not None:
equinox = Time(equinox, format='byear')
frame = FK4NoETerms(equinox=equinox)
elif radesys == 'FK5':
if equinox is not None:
equinox = Time(equinox, format='jyear')
frame = FK5(equinox=equinox)
elif radesys == 'ICRS':
frame = ICRS()
else:
if xcoord == 'GLON' and ycoord == 'GLAT':
frame = Galactic()
elif xcoord == 'TLON' and ycoord == 'TLAT':
frame = ITRS(obstime=wcs.wcs.dateobs or None)
else:
frame = None
return frame
def _celestial_frame_to_wcs_builtin(frame, projection='TAN'):
# Import astropy.coordinates here to avoid circular imports
from astropy.coordinates import BaseRADecFrame, FK4, FK4NoETerms, FK5, ICRS, ITRS, Galactic
# Create a 2-dimensional WCS
wcs = WCS(naxis=2)
if isinstance(frame, BaseRADecFrame):
xcoord = 'RA--'
ycoord = 'DEC-'
if isinstance(frame, ICRS):
wcs.wcs.radesys = 'ICRS'
elif isinstance(frame, FK4NoETerms):
wcs.wcs.radesys = 'FK4-NO-E'
wcs.wcs.equinox = frame.equinox.byear
elif isinstance(frame, FK4):
wcs.wcs.radesys = 'FK4'
wcs.wcs.equinox = frame.equinox.byear
elif isinstance(frame, FK5):
wcs.wcs.radesys = 'FK5'
wcs.wcs.equinox = frame.equinox.jyear
else:
return None
elif isinstance(frame, Galactic):
xcoord = 'GLON'
ycoord = 'GLAT'
elif isinstance(frame, ITRS):
xcoord = 'TLON'
ycoord = 'TLAT'
wcs.wcs.radesys = 'ITRS'
wcs.wcs.dateobs = frame.obstime.utc.isot
else:
return None
wcs.wcs.ctype = [xcoord + '-' + projection, ycoord + '-' + projection]
return wcs
WCS_FRAME_MAPPINGS = [[_wcs_to_celestial_frame_builtin]]
FRAME_WCS_MAPPINGS = [[_celestial_frame_to_wcs_builtin]]
class custom_wcs_to_frame_mappings:
def __init__(self, mappings=[]):
if hasattr(mappings, '__call__'):
mappings = [mappings]
WCS_FRAME_MAPPINGS.append(mappings)
def __enter__(self):
pass
def __exit__(self, type, value, tb):
WCS_FRAME_MAPPINGS.pop()
# Backward-compatibility
custom_frame_mappings = custom_wcs_to_frame_mappings
class custom_frame_to_wcs_mappings:
def __init__(self, mappings=[]):
if hasattr(mappings, '__call__'):
mappings = [mappings]
FRAME_WCS_MAPPINGS.append(mappings)
def __enter__(self):
pass
def __exit__(self, type, value, tb):
FRAME_WCS_MAPPINGS.pop()
def wcs_to_celestial_frame(wcs):
"""
For a given WCS, return the coordinate frame that matches the celestial
component of the WCS.
Parameters
----------
wcs : :class:`~astropy.wcs.WCS` instance
The WCS to find the frame for
Returns
-------
frame : :class:`~astropy.coordinates.baseframe.BaseCoordinateFrame` subclass instance
An instance of a :class:`~astropy.coordinates.baseframe.BaseCoordinateFrame`
subclass instance that best matches the specified WCS.
Notes
-----
To extend this function to frames not defined in astropy.coordinates, you
can write your own function which should take a :class:`~astropy.wcs.WCS`
instance and should return either an instance of a frame, or `None` if no
matching frame was found. You can register this function temporarily with::
>>> from astropy.wcs.utils import wcs_to_celestial_frame, custom_wcs_to_frame_mappings
>>> with custom_wcs_to_frame_mappings(my_function):
... wcs_to_celestial_frame(...)
"""
for mapping_set in WCS_FRAME_MAPPINGS:
for func in mapping_set:
frame = func(wcs)
if frame is not None:
return frame
raise ValueError("Could not determine celestial frame corresponding to "
"the specified WCS object")
def celestial_frame_to_wcs(frame, projection='TAN'):
"""
For a given coordinate frame, return the corresponding WCS object.
Note that the returned WCS object has only the elements corresponding to
coordinate frames set (e.g. ctype, equinox, radesys).
Parameters
----------
frame : :class:`~astropy.coordinates.baseframe.BaseCoordinateFrame` subclass instance
An instance of a :class:`~astropy.coordinates.baseframe.BaseCoordinateFrame`
subclass instance for which to find the WCS
projection : str
Projection code to use in ctype, if applicable
Returns
-------
wcs : :class:`~astropy.wcs.WCS` instance
The corresponding WCS object
Examples
--------
::
>>> from astropy.wcs.utils import celestial_frame_to_wcs
>>> from astropy.coordinates import FK5
>>> frame = FK5(equinox='J2010')
>>> wcs = celestial_frame_to_wcs(frame)
>>> wcs.to_header()
WCSAXES = 2 / Number of coordinate axes
CRPIX1 = 0.0 / Pixel coordinate of reference point
CRPIX2 = 0.0 / Pixel coordinate of reference point
CDELT1 = 1.0 / [deg] Coordinate increment at reference point
CDELT2 = 1.0 / [deg] Coordinate increment at reference point
CUNIT1 = 'deg' / Units of coordinate increment and value
CUNIT2 = 'deg' / Units of coordinate increment and value
CTYPE1 = 'RA---TAN' / Right ascension, gnomonic projection
CTYPE2 = 'DEC--TAN' / Declination, gnomonic projection
CRVAL1 = 0.0 / [deg] Coordinate value at reference point
CRVAL2 = 0.0 / [deg] Coordinate value at reference point
LONPOLE = 180.0 / [deg] Native longitude of celestial pole
LATPOLE = 0.0 / [deg] Native latitude of celestial pole
RADESYS = 'FK5' / Equatorial coordinate system
EQUINOX = 2010.0 / [yr] Equinox of equatorial coordinates
Notes
-----
To extend this function to frames not defined in astropy.coordinates, you
can write your own function which should take a
:class:`~astropy.coordinates.baseframe.BaseCoordinateFrame` subclass
instance and a projection (given as a string) and should return either a WCS
instance, or `None` if the WCS could not be determined. You can register
this function temporarily with::
>>> from astropy.wcs.utils import celestial_frame_to_wcs, custom_frame_to_wcs_mappings
>>> with custom_frame_to_wcs_mappings(my_function):
... celestial_frame_to_wcs(...)
"""
for mapping_set in FRAME_WCS_MAPPINGS:
for func in mapping_set:
wcs = func(frame, projection=projection)
if wcs is not None:
return wcs
raise ValueError("Could not determine WCS corresponding to the specified "
"coordinate frame.")
def proj_plane_pixel_scales(wcs):
"""
For a WCS returns pixel scales along each axis of the image pixel at
the ``CRPIX`` location once it is projected onto the
"plane of intermediate world coordinates" as defined in
`Greisen & Calabretta 2002, A&A, 395, 1061 <http://adsabs.harvard.edu/abs/2002A%26A...395.1061G>`_.
.. note::
This function is concerned **only** about the transformation
"image plane"->"projection plane" and **not** about the
transformation "celestial sphere"->"projection plane"->"image plane".
Therefore, this function ignores distortions arising due to
non-linear nature of most projections.
.. note::
In order to compute the scales corresponding to celestial axes only,
make sure that the input `~astropy.wcs.WCS` object contains
celestial axes only, e.g., by passing in the
`~astropy.wcs.WCS.celestial` WCS object.
Parameters
----------
wcs : `~astropy.wcs.WCS`
A world coordinate system object.
Returns
-------
scale : `~numpy.ndarray`
A vector (`~numpy.ndarray`) of projection plane increments
corresponding to each pixel side (axis). The units of the returned
results are the same as the units of `~astropy.wcs.Wcsprm.cdelt`,
`~astropy.wcs.Wcsprm.crval`, and `~astropy.wcs.Wcsprm.cd` for
the celestial WCS and can be obtained by inquiring the value
of `~astropy.wcs.Wcsprm.cunit` property of the input
`~astropy.wcs.WCS` WCS object.
See Also
--------
astropy.wcs.utils.proj_plane_pixel_area
"""
return np.sqrt((wcs.pixel_scale_matrix**2).sum(axis=0, dtype=float))
def proj_plane_pixel_area(wcs):
"""
For a **celestial** WCS (see `astropy.wcs.WCS.celestial`) returns pixel
area of the image pixel at the ``CRPIX`` location once it is projected
onto the "plane of intermediate world coordinates" as defined in
`Greisen & Calabretta 2002, A&A, 395, 1061 <http://adsabs.harvard.edu/abs/2002A%26A...395.1061G>`_.
.. note::
This function is concerned **only** about the transformation
"image plane"->"projection plane" and **not** about the
transformation "celestial sphere"->"projection plane"->"image plane".
Therefore, this function ignores distortions arising due to
non-linear nature of most projections.
.. note::
In order to compute the area of pixels corresponding to celestial
axes only, this function uses the `~astropy.wcs.WCS.celestial` WCS
object of the input ``wcs``. This is different from the
`~astropy.wcs.utils.proj_plane_pixel_scales` function
that computes the scales for the axes of the input WCS itself.
Parameters
----------
wcs : `~astropy.wcs.WCS`
A world coordinate system object.
Returns
-------
area : float
Area (in the projection plane) of the pixel at ``CRPIX`` location.
The units of the returned result are the same as the units of
the `~astropy.wcs.Wcsprm.cdelt`, `~astropy.wcs.Wcsprm.crval`,
and `~astropy.wcs.Wcsprm.cd` for the celestial WCS and can be
obtained by inquiring the value of `~astropy.wcs.Wcsprm.cunit`
property of the `~astropy.wcs.WCS.celestial` WCS object.
Raises
------
ValueError
Pixel area is defined only for 2D pixels. Most likely the
`~astropy.wcs.Wcsprm.cd` matrix of the `~astropy.wcs.WCS.celestial`
WCS is not a square matrix of second order.
Notes
-----
Depending on the application, square root of the pixel area can be used to
represent a single pixel scale of an equivalent square pixel
whose area is equal to the area of a generally non-square pixel.
See Also
--------
astropy.wcs.utils.proj_plane_pixel_scales
"""
psm = wcs.celestial.pixel_scale_matrix
if psm.shape != (2, 2):
raise ValueError("Pixel area is defined only for 2D pixels.")
return np.abs(np.linalg.det(psm))
def is_proj_plane_distorted(wcs, maxerr=1.0e-5):
r"""
For a WCS returns `False` if square image (detector) pixels stay square
when projected onto the "plane of intermediate world coordinates"
as defined in
`Greisen & Calabretta 2002, A&A, 395, 1061 <http://adsabs.harvard.edu/abs/2002A%26A...395.1061G>`_.
It will return `True` if transformation from image (detector) coordinates
to the focal plane coordinates is non-orthogonal or if WCS contains
non-linear (e.g., SIP) distortions.
.. note::
Since this function is concerned **only** about the transformation
"image plane"->"focal plane" and **not** about the transformation
"celestial sphere"->"focal plane"->"image plane",
this function ignores distortions arising due to non-linear nature
of most projections.
Let's denote by *C* either the original or the reconstructed
(from ``PC`` and ``CDELT``) CD matrix. `is_proj_plane_distorted`
verifies that the transformation from image (detector) coordinates
to the focal plane coordinates is orthogonal using the following
check:
.. math::
\left \| \frac{C \cdot C^{\mathrm{T}}}
{| det(C)|} - I \right \|_{\mathrm{max}} < \epsilon .
Parameters
----------
wcs : `~astropy.wcs.WCS`
World coordinate system object
maxerr : float, optional
Accuracy to which the CD matrix, **normalized** such
that :math:`|det(CD)|=1`, should be close to being an
orthogonal matrix as described in the above equation
(see :math:`\epsilon`).
Returns
-------
distorted : bool
Returns `True` if focal (projection) plane is distorted and `False`
otherwise.
"""
cwcs = wcs.celestial
return (not _is_cd_orthogonal(cwcs.pixel_scale_matrix, maxerr) or
_has_distortion(cwcs))
def _is_cd_orthogonal(cd, maxerr):
shape = cd.shape
if not (len(shape) == 2 and shape[0] == shape[1]):
raise ValueError("CD (or PC) matrix must be a 2D square matrix.")
pixarea = np.abs(np.linalg.det(cd))
if (pixarea == 0.0):
raise ValueError("CD (or PC) matrix is singular.")
# NOTE: Technically, below we should use np.dot(cd, np.conjugate(cd.T))
# However, I am not aware of complex CD/PC matrices...
I = np.dot(cd, cd.T) / pixarea
cd_unitary_err = np.amax(np.abs(I - np.eye(shape[0])))
return (cd_unitary_err < maxerr)
def non_celestial_pixel_scales(inwcs):
"""
Calculate the pixel scale along each axis of a non-celestial WCS,
for example one with mixed spectral and spatial axes.
Parameters
----------
inwcs : `~astropy.wcs.WCS`
The world coordinate system object.
Returns
-------
scale : `numpy.ndarray`
The pixel scale along each axis.
"""
if inwcs.is_celestial:
raise ValueError("WCS is celestial, use celestial_pixel_scales instead")
pccd = inwcs.pixel_scale_matrix
if np.allclose(np.extract(1-np.eye(*pccd.shape), pccd), 0):
return np.abs(np.diagonal(pccd))*u.deg
else:
raise ValueError("WCS is rotated, cannot determine consistent pixel scales")
def _has_distortion(wcs):
"""
`True` if contains any SIP or image distortion components.
"""
return any(getattr(wcs, dist_attr) is not None
for dist_attr in ['cpdis1', 'cpdis2', 'det2im1', 'det2im2', 'sip'])
# TODO: in future, we should think about how the following two functions can be
# integrated better into the WCS class.
def skycoord_to_pixel(coords, wcs, origin=0, mode='all'):
"""
Convert a set of SkyCoord coordinates into pixels.
Parameters
----------
coords : `~astropy.coordinates.SkyCoord`
The coordinates to convert.
wcs : `~astropy.wcs.WCS`
The WCS transformation to use.
origin : int
Whether to return 0 or 1-based pixel coordinates.
mode : 'all' or 'wcs'
Whether to do the transformation including distortions (``'all'``) or
only including only the core WCS transformation (``'wcs'``).
Returns
-------
xp, yp : `numpy.ndarray`
The pixel coordinates
See Also
--------
astropy.coordinates.SkyCoord.from_pixel
"""
if _has_distortion(wcs) and wcs.naxis != 2:
raise ValueError("Can only handle WCS with distortions for 2-dimensional WCS")
# Keep only the celestial part of the axes, also re-orders lon/lat
wcs = wcs.sub([WCSSUB_LONGITUDE, WCSSUB_LATITUDE])
if wcs.naxis != 2:
raise ValueError("WCS should contain celestial component")
# Check which frame the WCS uses
frame = wcs_to_celestial_frame(wcs)
# Check what unit the WCS needs
xw_unit = u.Unit(wcs.wcs.cunit[0])
yw_unit = u.Unit(wcs.wcs.cunit[1])
# Convert positions to frame
coords = coords.transform_to(frame)
# Extract longitude and latitude. We first try and use lon/lat directly,
# but if the representation is not spherical or unit spherical this will
# fail. We should then force the use of the unit spherical
# representation. We don't do that directly to make sure that we preserve
# custom lon/lat representations if available.
try:
lon = coords.data.lon.to(xw_unit)
lat = coords.data.lat.to(yw_unit)
except AttributeError:
lon = coords.spherical.lon.to(xw_unit)
lat = coords.spherical.lat.to(yw_unit)
# Convert to pixel coordinates
if mode == 'all':
xp, yp = wcs.all_world2pix(lon.value, lat.value, origin)
elif mode == 'wcs':
xp, yp = wcs.wcs_world2pix(lon.value, lat.value, origin)
else:
raise ValueError("mode should be either 'all' or 'wcs'")
return xp, yp
def pixel_to_skycoord(xp, yp, wcs, origin=0, mode='all', cls=None):
"""
Convert a set of pixel coordinates into a `~astropy.coordinates.SkyCoord`
coordinate.
Parameters
----------
xp, yp : float or `numpy.ndarray`
The coordinates to convert.
wcs : `~astropy.wcs.WCS`
The WCS transformation to use.
origin : int
Whether to return 0 or 1-based pixel coordinates.
mode : 'all' or 'wcs'
Whether to do the transformation including distortions (``'all'``) or
only including only the core WCS transformation (``'wcs'``).
cls : class or None
The class of object to create. Should be a
`~astropy.coordinates.SkyCoord` subclass. If None, defaults to
`~astropy.coordinates.SkyCoord`.
Returns
-------
coords : Whatever ``cls`` is (a subclass of `~astropy.coordinates.SkyCoord`)
The celestial coordinates
See Also
--------
astropy.coordinates.SkyCoord.from_pixel
"""
# Import astropy.coordinates here to avoid circular imports
from astropy.coordinates import SkyCoord, UnitSphericalRepresentation
# we have to do this instead of actually setting the default to SkyCoord
# because importing SkyCoord at the module-level leads to circular
# dependencies.
if cls is None:
cls = SkyCoord
if _has_distortion(wcs) and wcs.naxis != 2:
raise ValueError("Can only handle WCS with distortions for 2-dimensional WCS")
# Keep only the celestial part of the axes, also re-orders lon/lat
wcs = wcs.sub([WCSSUB_LONGITUDE, WCSSUB_LATITUDE])
if wcs.naxis != 2:
raise ValueError("WCS should contain celestial component")
# Check which frame the WCS uses
frame = wcs_to_celestial_frame(wcs)
# Check what unit the WCS gives
lon_unit = u.Unit(wcs.wcs.cunit[0])
lat_unit = u.Unit(wcs.wcs.cunit[1])
# Convert pixel coordinates to celestial coordinates
if mode == 'all':
lon, lat = wcs.all_pix2world(xp, yp, origin)
elif mode == 'wcs':
lon, lat = wcs.wcs_pix2world(xp, yp, origin)
else:
raise ValueError("mode should be either 'all' or 'wcs'")
# Add units to longitude/latitude
lon = lon * lon_unit
lat = lat * lat_unit
# Create a SkyCoord-like object
data = UnitSphericalRepresentation(lon=lon, lat=lat)
coords = cls(frame.realize_frame(data))
return coords
|
# Licensed under a 3-clause BSD style license - see LICENSE.rst
import inspect
import pytest
import numpy as np
from astropy.tests.helper import catch_warnings
from astropy.utils.exceptions import AstropyUserWarning
from astropy import units as u
from astropy.nddata.nddata import NDData
from astropy.nddata.decorators import support_nddata
class CCDData(NDData):
pass
@support_nddata
def wrapped_function_1(data, wcs=None, unit=None):
return data, wcs, unit
def test_pass_numpy():
data_in = np.array([1, 2, 3])
data_out, wcs_out, unit_out = wrapped_function_1(data=data_in)
assert data_out is data_in
assert wcs_out is None
assert unit_out is None
def test_pass_all_separate():
data_in = np.array([1, 2, 3])
wcs_in = "the wcs"
unit_in = u.Jy
data_out, wcs_out, unit_out = wrapped_function_1(data=data_in, wcs=wcs_in, unit=unit_in)
assert data_out is data_in
assert wcs_out is wcs_in
assert unit_out is unit_in
def test_pass_nddata():
data_in = np.array([1, 2, 3])
wcs_in = "the wcs"
unit_in = u.Jy
nddata_in = NDData(data_in, wcs=wcs_in, unit=unit_in)
data_out, wcs_out, unit_out = wrapped_function_1(nddata_in)
assert data_out is data_in
assert wcs_out is wcs_in
assert unit_out is unit_in
def test_pass_nddata_and_explicit():
data_in = np.array([1, 2, 3])
wcs_in = "the wcs"
unit_in = u.Jy
unit_in_alt = u.mJy
nddata_in = NDData(data_in, wcs=wcs_in, unit=unit_in)
with catch_warnings() as w:
data_out, wcs_out, unit_out = wrapped_function_1(nddata_in, unit=unit_in_alt)
assert data_out is data_in
assert wcs_out is wcs_in
assert unit_out is unit_in_alt
assert len(w) == 1
assert str(w[0].message) == ("Property unit has been passed explicitly and as "
"an NDData property, using explicitly specified value")
def test_pass_nddata_ignored():
data_in = np.array([1, 2, 3])
wcs_in = "the wcs"
unit_in = u.Jy
nddata_in = NDData(data_in, wcs=wcs_in, unit=unit_in, mask=[0, 1, 0])
with catch_warnings() as w:
data_out, wcs_out, unit_out = wrapped_function_1(nddata_in)
assert data_out is data_in
assert wcs_out is wcs_in
assert unit_out is unit_in
assert len(w) == 1
assert str(w[0].message) == ("The following attributes were set on the data "
"object, but will be ignored by the function: mask")
def test_incorrect_first_argument():
with pytest.raises(ValueError) as exc:
@support_nddata
def wrapped_function_2(something, wcs=None, unit=None):
pass
assert exc.value.args[0] == "Can only wrap functions whose first positional argument is `data`"
with pytest.raises(ValueError) as exc:
@support_nddata
def wrapped_function_3(something, data, wcs=None, unit=None):
pass
assert exc.value.args[0] == "Can only wrap functions whose first positional argument is `data`"
with pytest.raises(ValueError) as exc:
@support_nddata
def wrapped_function_4(wcs=None, unit=None):
pass
assert exc.value.args[0] == "Can only wrap functions whose first positional argument is `data`"
def test_wrap_function_no_kwargs():
@support_nddata
def wrapped_function_5(data, other_data):
return data
data_in = np.array([1, 2, 3])
nddata_in = NDData(data_in)
assert wrapped_function_5(nddata_in, [1, 2, 3]) is data_in
def test_wrap_function_repack_valid():
@support_nddata(repack=True, returns=['data'])
def wrapped_function_5(data, other_data):
return data
data_in = np.array([1, 2, 3])
nddata_in = NDData(data_in)
nddata_out = wrapped_function_5(nddata_in, [1, 2, 3])
assert isinstance(nddata_out, NDData)
assert nddata_out.data is data_in
def test_wrap_function_accepts():
class MyData(NDData):
pass
@support_nddata(accepts=MyData)
def wrapped_function_5(data, other_data):
return data
data_in = np.array([1, 2, 3])
nddata_in = NDData(data_in)
mydata_in = MyData(data_in)
assert wrapped_function_5(mydata_in, [1, 2, 3]) is data_in
with pytest.raises(TypeError) as exc:
wrapped_function_5(nddata_in, [1, 2, 3])
assert exc.value.args[0] == "Only NDData sub-classes that inherit from MyData can be used by this function"
def test_wrap_preserve_signature_docstring():
@support_nddata
def wrapped_function_6(data, wcs=None, unit=None):
"""
An awesome function
"""
pass
if wrapped_function_6.__doc__ is not None:
assert wrapped_function_6.__doc__.strip() == "An awesome function"
signature = inspect.signature(wrapped_function_6)
assert str(signature) == "(data, wcs=None, unit=None)"
def test_setup_failures1():
# repack but no returns
with pytest.raises(ValueError):
support_nddata(repack=True)
def test_setup_failures2():
# returns but no repack
with pytest.raises(ValueError):
support_nddata(returns=['data'])
def test_setup_failures9():
# keeps but no repack
with pytest.raises(ValueError):
support_nddata(keeps=['unit'])
def test_setup_failures3():
# same attribute in keeps and returns
with pytest.raises(ValueError):
support_nddata(repack=True, keeps=['mask'], returns=['data', 'mask'])
def test_setup_failures4():
# function accepts *args
with pytest.raises(ValueError):
@support_nddata
def test(data, *args):
pass
def test_setup_failures10():
# function accepts **kwargs
with pytest.raises(ValueError):
@support_nddata
def test(data, **kwargs):
pass
def test_setup_failures5():
# function accepts *args (or **kwargs)
with pytest.raises(ValueError):
@support_nddata
def test(data, *args):
pass
def test_setup_failures6():
# First argument is not data
with pytest.raises(ValueError):
@support_nddata
def test(img):
pass
def test_setup_failures7():
# accepts CCDData but was given just an NDData
with pytest.raises(TypeError):
@support_nddata(accepts=CCDData)
def test(data):
pass
test(NDData(np.ones((3, 3))))
def test_setup_failures8():
# function returns a different amount of arguments than specified. Using
# NDData here so we don't get into troubles when creating a CCDData without
# unit!
with pytest.raises(ValueError):
@support_nddata(repack=True, returns=['data', 'mask'])
def test(data):
return 10
test(NDData(np.ones((3, 3)))) # do NOT use CCDData here.
def test_setup_failures11():
# function accepts no arguments
with pytest.raises(ValueError):
@support_nddata
def test():
pass
def test_setup_numpyarray_default():
# It should be possible (even if it's not advisable to use mutable
# defaults) to have a numpy array as default value.
@support_nddata
def func(data, wcs=np.array([1, 2, 3])):
return wcs
def test_still_accepts_other_input():
@support_nddata(repack=True, returns=['data'])
def test(data):
return data
assert isinstance(test(NDData(np.ones((3, 3)))), NDData)
assert isinstance(test(10), int)
assert isinstance(test([1, 2, 3]), list)
def test_accepting_property_normal():
# Accepts a mask attribute and takes it from the input
@support_nddata
def test(data, mask=None):
return mask
ndd = NDData(np.ones((3, 3)))
assert test(ndd) is None
ndd._mask = np.zeros((3, 3))
assert np.all(test(ndd) == 0)
# Use the explicitly given one (raises a Warning)
with catch_warnings(AstropyUserWarning) as w:
assert test(ndd, mask=10) == 10
assert len(w) == 1
def test_parameter_default_identical_to_explicit_passed_argument():
# If the default is identical to the explicitly passed argument this
# should still raise a Warning and use the explicit one.
@support_nddata
def func(data, wcs=[1, 2, 3]):
return wcs
with catch_warnings(AstropyUserWarning) as w:
assert func(NDData(1, wcs=[1, 2]), [1, 2, 3]) == [1, 2, 3]
assert len(w) == 1
with catch_warnings(AstropyUserWarning) as w:
assert func(NDData(1, wcs=[1, 2])) == [1, 2]
assert len(w) == 0
def test_accepting_property_notexist():
# Accepts flags attribute but NDData doesn't have one
@support_nddata
def test(data, flags=10):
return flags
ndd = NDData(np.ones((3, 3)))
test(ndd)
def test_accepting_property_translated():
# Accepts a error attribute and we want to pass in uncertainty!
@support_nddata(mask='masked')
def test(data, masked=None):
return masked
ndd = NDData(np.ones((3, 3)))
assert test(ndd) is None
ndd._mask = np.zeros((3, 3))
assert np.all(test(ndd) == 0)
# Use the explicitly given one (raises a Warning)
with catch_warnings(AstropyUserWarning) as w:
assert test(ndd, masked=10) == 10
assert len(w) == 1
def test_accepting_property_meta_empty():
# Meta is always set (OrderedDict) so it has a special case that it's
# ignored if it's empty but not None
@support_nddata
def test(data, meta=None):
return meta
ndd = NDData(np.ones((3, 3)))
assert test(ndd) is None
ndd._meta = {'a': 10}
assert test(ndd) == {'a': 10}
|
|
astropy/nddata/tests/test_decorators.py
|
|
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
This module includes a fast iterator-based XML parser.
"""
# STDLIB
import contextlib
import io
import sys
# ASTROPY
from astropy.utils import data
__all__ = ['get_xml_iterator', 'get_xml_encoding', 'xml_readlines']
@contextlib.contextmanager
def _convert_to_fd_or_read_function(fd):
"""
Returns a function suitable for streaming input, or a file object.
This function is only useful if passing off to C code where:
- If it's a real file object, we want to use it as a real
C file object to avoid the Python overhead.
- If it's not a real file object, it's much handier to just
have a Python function to call.
This is somewhat quirky behavior, of course, which is why it is
private. For a more useful version of similar behavior, see
`astropy.utils.misc.get_readable_fileobj`.
Parameters
----------
fd : object
May be:
- a file object. If the file is uncompressed, this raw
file object is returned verbatim. Otherwise, the read
method is returned.
- a function that reads from a stream, in which case it is
returned verbatim.
- a file path, in which case it is opened. Again, like a
file object, if it's uncompressed, a raw file object is
returned, otherwise its read method.
- an object with a :meth:`read` method, in which case that
method is returned.
Returns
-------
fd : context-dependent
See above.
"""
if callable(fd):
yield fd
return
with data.get_readable_fileobj(fd, encoding='binary') as new_fd:
if sys.platform.startswith('win'):
yield new_fd.read
else:
if isinstance(new_fd, io.FileIO):
yield new_fd
else:
yield new_fd.read
def _fast_iterparse(fd, buffersize=2 ** 10):
from xml.parsers import expat
if not callable(fd):
read = fd.read
else:
read = fd
queue = []
text = []
def start(name, attr):
queue.append((True, name, attr,
(parser.CurrentLineNumber, parser.CurrentColumnNumber)))
del text[:]
def end(name):
queue.append((False, name, ''.join(text).strip(),
(parser.CurrentLineNumber, parser.CurrentColumnNumber)))
parser = expat.ParserCreate()
parser.specified_attributes = True
parser.StartElementHandler = start
parser.EndElementHandler = end
parser.CharacterDataHandler = text.append
Parse = parser.Parse
data = read(buffersize)
while data:
Parse(data, False)
for elem in queue:
yield elem
del queue[:]
data = read(buffersize)
Parse('', True)
for elem in queue:
yield elem
# Try to import the C version of the iterparser, otherwise fall back
# to the Python implementation above.
_slow_iterparse = _fast_iterparse
try:
from . import _iterparser
_fast_iterparse = _iterparser.IterParser
except ImportError:
pass
@contextlib.contextmanager
def get_xml_iterator(source, _debug_python_based_parser=False):
"""
Returns an iterator over the elements of an XML file.
The iterator doesn't ever build a tree, so it is much more memory
and time efficient than the alternative in ``cElementTree``.
Parameters
----------
fd : readable file-like object or read function
Returns
-------
parts : iterator
The iterator returns 4-tuples (*start*, *tag*, *data*, *pos*):
- *start*: when `True` is a start element event, otherwise
an end element event.
- *tag*: The name of the element
- *data*: Depends on the value of *event*:
- if *start* == `True`, data is a dictionary of
attributes
- if *start* == `False`, data is a string containing
the text content of the element
- *pos*: Tuple (*line*, *col*) indicating the source of the
event.
"""
with _convert_to_fd_or_read_function(source) as fd:
if _debug_python_based_parser:
context = _slow_iterparse(fd)
else:
context = _fast_iterparse(fd)
yield iter(context)
def get_xml_encoding(source):
"""
Determine the encoding of an XML file by reading its header.
Parameters
----------
source : readable file-like object, read function or str path
Returns
-------
encoding : str
"""
with get_xml_iterator(source) as iterator:
start, tag, data, pos = next(iterator)
if not start or tag != 'xml':
raise OSError('Invalid XML file')
# The XML spec says that no encoding === utf-8
return data.get('encoding') or 'utf-8'
def xml_readlines(source):
"""
Get the lines from a given XML file. Correctly determines the
encoding and always returns unicode.
Parameters
----------
source : readable file-like object, read function or str path
Returns
-------
lines : list of unicode
"""
encoding = get_xml_encoding(source)
with data.get_readable_fileobj(source, encoding=encoding) as input:
input.seek(0)
xml_lines = input.readlines()
return xml_lines
|
# Licensed under a 3-clause BSD style license - see LICENSE.rst
import inspect
import pytest
import numpy as np
from astropy.tests.helper import catch_warnings
from astropy.utils.exceptions import AstropyUserWarning
from astropy import units as u
from astropy.nddata.nddata import NDData
from astropy.nddata.decorators import support_nddata
class CCDData(NDData):
pass
@support_nddata
def wrapped_function_1(data, wcs=None, unit=None):
return data, wcs, unit
def test_pass_numpy():
data_in = np.array([1, 2, 3])
data_out, wcs_out, unit_out = wrapped_function_1(data=data_in)
assert data_out is data_in
assert wcs_out is None
assert unit_out is None
def test_pass_all_separate():
data_in = np.array([1, 2, 3])
wcs_in = "the wcs"
unit_in = u.Jy
data_out, wcs_out, unit_out = wrapped_function_1(data=data_in, wcs=wcs_in, unit=unit_in)
assert data_out is data_in
assert wcs_out is wcs_in
assert unit_out is unit_in
def test_pass_nddata():
data_in = np.array([1, 2, 3])
wcs_in = "the wcs"
unit_in = u.Jy
nddata_in = NDData(data_in, wcs=wcs_in, unit=unit_in)
data_out, wcs_out, unit_out = wrapped_function_1(nddata_in)
assert data_out is data_in
assert wcs_out is wcs_in
assert unit_out is unit_in
def test_pass_nddata_and_explicit():
data_in = np.array([1, 2, 3])
wcs_in = "the wcs"
unit_in = u.Jy
unit_in_alt = u.mJy
nddata_in = NDData(data_in, wcs=wcs_in, unit=unit_in)
with catch_warnings() as w:
data_out, wcs_out, unit_out = wrapped_function_1(nddata_in, unit=unit_in_alt)
assert data_out is data_in
assert wcs_out is wcs_in
assert unit_out is unit_in_alt
assert len(w) == 1
assert str(w[0].message) == ("Property unit has been passed explicitly and as "
"an NDData property, using explicitly specified value")
def test_pass_nddata_ignored():
data_in = np.array([1, 2, 3])
wcs_in = "the wcs"
unit_in = u.Jy
nddata_in = NDData(data_in, wcs=wcs_in, unit=unit_in, mask=[0, 1, 0])
with catch_warnings() as w:
data_out, wcs_out, unit_out = wrapped_function_1(nddata_in)
assert data_out is data_in
assert wcs_out is wcs_in
assert unit_out is unit_in
assert len(w) == 1
assert str(w[0].message) == ("The following attributes were set on the data "
"object, but will be ignored by the function: mask")
def test_incorrect_first_argument():
with pytest.raises(ValueError) as exc:
@support_nddata
def wrapped_function_2(something, wcs=None, unit=None):
pass
assert exc.value.args[0] == "Can only wrap functions whose first positional argument is `data`"
with pytest.raises(ValueError) as exc:
@support_nddata
def wrapped_function_3(something, data, wcs=None, unit=None):
pass
assert exc.value.args[0] == "Can only wrap functions whose first positional argument is `data`"
with pytest.raises(ValueError) as exc:
@support_nddata
def wrapped_function_4(wcs=None, unit=None):
pass
assert exc.value.args[0] == "Can only wrap functions whose first positional argument is `data`"
def test_wrap_function_no_kwargs():
@support_nddata
def wrapped_function_5(data, other_data):
return data
data_in = np.array([1, 2, 3])
nddata_in = NDData(data_in)
assert wrapped_function_5(nddata_in, [1, 2, 3]) is data_in
def test_wrap_function_repack_valid():
@support_nddata(repack=True, returns=['data'])
def wrapped_function_5(data, other_data):
return data
data_in = np.array([1, 2, 3])
nddata_in = NDData(data_in)
nddata_out = wrapped_function_5(nddata_in, [1, 2, 3])
assert isinstance(nddata_out, NDData)
assert nddata_out.data is data_in
def test_wrap_function_accepts():
class MyData(NDData):
pass
@support_nddata(accepts=MyData)
def wrapped_function_5(data, other_data):
return data
data_in = np.array([1, 2, 3])
nddata_in = NDData(data_in)
mydata_in = MyData(data_in)
assert wrapped_function_5(mydata_in, [1, 2, 3]) is data_in
with pytest.raises(TypeError) as exc:
wrapped_function_5(nddata_in, [1, 2, 3])
assert exc.value.args[0] == "Only NDData sub-classes that inherit from MyData can be used by this function"
def test_wrap_preserve_signature_docstring():
@support_nddata
def wrapped_function_6(data, wcs=None, unit=None):
"""
An awesome function
"""
pass
if wrapped_function_6.__doc__ is not None:
assert wrapped_function_6.__doc__.strip() == "An awesome function"
signature = inspect.signature(wrapped_function_6)
assert str(signature) == "(data, wcs=None, unit=None)"
def test_setup_failures1():
# repack but no returns
with pytest.raises(ValueError):
support_nddata(repack=True)
def test_setup_failures2():
# returns but no repack
with pytest.raises(ValueError):
support_nddata(returns=['data'])
def test_setup_failures9():
# keeps but no repack
with pytest.raises(ValueError):
support_nddata(keeps=['unit'])
def test_setup_failures3():
# same attribute in keeps and returns
with pytest.raises(ValueError):
support_nddata(repack=True, keeps=['mask'], returns=['data', 'mask'])
def test_setup_failures4():
# function accepts *args
with pytest.raises(ValueError):
@support_nddata
def test(data, *args):
pass
def test_setup_failures10():
# function accepts **kwargs
with pytest.raises(ValueError):
@support_nddata
def test(data, **kwargs):
pass
def test_setup_failures5():
# function accepts *args (or **kwargs)
with pytest.raises(ValueError):
@support_nddata
def test(data, *args):
pass
def test_setup_failures6():
# First argument is not data
with pytest.raises(ValueError):
@support_nddata
def test(img):
pass
def test_setup_failures7():
# accepts CCDData but was given just an NDData
with pytest.raises(TypeError):
@support_nddata(accepts=CCDData)
def test(data):
pass
test(NDData(np.ones((3, 3))))
def test_setup_failures8():
# function returns a different amount of arguments than specified. Using
# NDData here so we don't get into troubles when creating a CCDData without
# unit!
with pytest.raises(ValueError):
@support_nddata(repack=True, returns=['data', 'mask'])
def test(data):
return 10
test(NDData(np.ones((3, 3)))) # do NOT use CCDData here.
def test_setup_failures11():
# function accepts no arguments
with pytest.raises(ValueError):
@support_nddata
def test():
pass
def test_setup_numpyarray_default():
# It should be possible (even if it's not advisable to use mutable
# defaults) to have a numpy array as default value.
@support_nddata
def func(data, wcs=np.array([1, 2, 3])):
return wcs
def test_still_accepts_other_input():
@support_nddata(repack=True, returns=['data'])
def test(data):
return data
assert isinstance(test(NDData(np.ones((3, 3)))), NDData)
assert isinstance(test(10), int)
assert isinstance(test([1, 2, 3]), list)
def test_accepting_property_normal():
# Accepts a mask attribute and takes it from the input
@support_nddata
def test(data, mask=None):
return mask
ndd = NDData(np.ones((3, 3)))
assert test(ndd) is None
ndd._mask = np.zeros((3, 3))
assert np.all(test(ndd) == 0)
# Use the explicitly given one (raises a Warning)
with catch_warnings(AstropyUserWarning) as w:
assert test(ndd, mask=10) == 10
assert len(w) == 1
def test_parameter_default_identical_to_explicit_passed_argument():
# If the default is identical to the explicitly passed argument this
# should still raise a Warning and use the explicit one.
@support_nddata
def func(data, wcs=[1, 2, 3]):
return wcs
with catch_warnings(AstropyUserWarning) as w:
assert func(NDData(1, wcs=[1, 2]), [1, 2, 3]) == [1, 2, 3]
assert len(w) == 1
with catch_warnings(AstropyUserWarning) as w:
assert func(NDData(1, wcs=[1, 2])) == [1, 2]
assert len(w) == 0
def test_accepting_property_notexist():
# Accepts flags attribute but NDData doesn't have one
@support_nddata
def test(data, flags=10):
return flags
ndd = NDData(np.ones((3, 3)))
test(ndd)
def test_accepting_property_translated():
# Accepts a error attribute and we want to pass in uncertainty!
@support_nddata(mask='masked')
def test(data, masked=None):
return masked
ndd = NDData(np.ones((3, 3)))
assert test(ndd) is None
ndd._mask = np.zeros((3, 3))
assert np.all(test(ndd) == 0)
# Use the explicitly given one (raises a Warning)
with catch_warnings(AstropyUserWarning) as w:
assert test(ndd, masked=10) == 10
assert len(w) == 1
def test_accepting_property_meta_empty():
# Meta is always set (OrderedDict) so it has a special case that it's
# ignored if it's empty but not None
@support_nddata
def test(data, meta=None):
return meta
ndd = NDData(np.ones((3, 3)))
assert test(ndd) is None
ndd._meta = {'a': 10}
assert test(ndd) == {'a': 10}
|
|
astropy/nddata/tests/test_decorators.py
|
astropy/utils/xml/iterparser.py
|
# Licensed under a 3-clause BSD style license - see LICENSE.rst
# -*- coding: utf-8 -*-
import numpy as np
from asdf import tagged
from asdf import yamlutil
from asdf.tags.core.ndarray import NDArrayType
from astropy import table
from astropy.io.misc.asdf.types import AstropyType, AstropyAsdfType
class TableType:
"""
This class defines to_tree and from_tree methods that are used by both the
AstropyTableType and the AsdfTableType defined below. The behavior is
differentiated by the ``_compat`` class attribute. When ``_compat==True``,
the behavior will conform to the table schema defined by the ASDF Standard.
Otherwise, the behavior will conform to the custom table schema defined by
Astropy.
"""
_compat = False
@classmethod
def from_tree(cls, node, ctx):
# This is getting meta, guys
meta = node.get('meta', {})
# This enables us to support files that use the table definition from
# the ASDF Standard, rather than the custom one that Astropy defines.
if cls._compat:
columns = [
yamlutil.tagged_tree_to_custom_tree(col, ctx)
for col in node['columns']
]
return table.Table(columns, meta=meta)
if node.get('qtable', False):
t = table.QTable(meta=node.get('meta', {}))
else:
t = table.Table(meta=node.get('meta', {}))
for name, col in zip(node['colnames'], node['columns']):
t[name] = yamlutil.tagged_tree_to_custom_tree(col, ctx)
return t
@classmethod
def to_tree(cls, data, ctx):
columns = []
for name in data.colnames:
thiscol = data[name]
column = yamlutil.custom_tree_to_tagged_tree(thiscol, ctx)
columns.append(column)
node = dict(columns=columns)
# Files that use the table definition from the ASDF Standard (instead
# of the one defined by Astropy) will not contain these fields
if not cls._compat:
node['colnames'] = data.colnames
node['qtable'] = isinstance(data, table.QTable)
if data.meta:
node['meta'] = data.meta
return node
@classmethod
def assert_equal(cls, old, new):
assert old.meta == new.meta
try:
NDArrayType.assert_equal(np.array(old), np.array(new))
except (AttributeError, TypeError, ValueError):
for col0, col1 in zip(old, new):
try:
NDArrayType.assert_equal(np.array(col0), np.array(col1))
except (AttributeError, TypeError, ValueError):
assert col0 == col1
class AstropyTableType(TableType, AstropyType):
"""
This tag class reads and writes tables that conform to the custom schema
that is defined by Astropy (in contrast to the one that is defined by the
ASDF Standard). The primary reason for differentiating is to enable the
support of Astropy mixin columns, which are not supported by the ASDF
Standard.
"""
name = 'table/table'
types = ['astropy.table.Table']
requires = ['astropy']
class AsdfTableType(TableType, AstropyAsdfType):
"""
This tag class allows Astropy to read (and write) ASDF files that use the
table definition that is provided by the ASDF Standard (instead of the
custom one defined by Astropy). This is important to maintain for
cross-compatibility.
"""
name = 'core/table'
types = ['astropy.table.Table']
requires = ['astropy']
_compat = True
class ColumnType(AstropyAsdfType):
name = 'core/column'
types = ['astropy.table.Column', 'astropy.table.MaskedColumn']
requires = ['astropy']
handle_dynamic_subclasses = True
@classmethod
def from_tree(cls, node, ctx):
data = yamlutil.tagged_tree_to_custom_tree(
node['data'], ctx)
name = node['name']
description = node.get('description')
unit = node.get('unit')
meta = node.get('meta', None)
return table.Column(
data=data._make_array(), name=name, description=description,
unit=unit, meta=meta)
@classmethod
def to_tree(cls, data, ctx):
node = {
'data': yamlutil.custom_tree_to_tagged_tree(
data.data, ctx),
'name': data.name
}
if data.description:
node['description'] = data.description
if data.unit:
node['unit'] = yamlutil.custom_tree_to_tagged_tree(
data.unit, ctx)
if data.meta:
node['meta'] = data.meta
return node
@classmethod
def assert_equal(cls, old, new):
assert old.meta == new.meta
assert old.description == new.description
assert old.unit == new.unit
NDArrayType.assert_equal(np.array(old), np.array(new))
|
# Licensed under a 3-clause BSD style license - see LICENSE.rst
import inspect
import pytest
import numpy as np
from astropy.tests.helper import catch_warnings
from astropy.utils.exceptions import AstropyUserWarning
from astropy import units as u
from astropy.nddata.nddata import NDData
from astropy.nddata.decorators import support_nddata
class CCDData(NDData):
pass
@support_nddata
def wrapped_function_1(data, wcs=None, unit=None):
return data, wcs, unit
def test_pass_numpy():
data_in = np.array([1, 2, 3])
data_out, wcs_out, unit_out = wrapped_function_1(data=data_in)
assert data_out is data_in
assert wcs_out is None
assert unit_out is None
def test_pass_all_separate():
data_in = np.array([1, 2, 3])
wcs_in = "the wcs"
unit_in = u.Jy
data_out, wcs_out, unit_out = wrapped_function_1(data=data_in, wcs=wcs_in, unit=unit_in)
assert data_out is data_in
assert wcs_out is wcs_in
assert unit_out is unit_in
def test_pass_nddata():
data_in = np.array([1, 2, 3])
wcs_in = "the wcs"
unit_in = u.Jy
nddata_in = NDData(data_in, wcs=wcs_in, unit=unit_in)
data_out, wcs_out, unit_out = wrapped_function_1(nddata_in)
assert data_out is data_in
assert wcs_out is wcs_in
assert unit_out is unit_in
def test_pass_nddata_and_explicit():
data_in = np.array([1, 2, 3])
wcs_in = "the wcs"
unit_in = u.Jy
unit_in_alt = u.mJy
nddata_in = NDData(data_in, wcs=wcs_in, unit=unit_in)
with catch_warnings() as w:
data_out, wcs_out, unit_out = wrapped_function_1(nddata_in, unit=unit_in_alt)
assert data_out is data_in
assert wcs_out is wcs_in
assert unit_out is unit_in_alt
assert len(w) == 1
assert str(w[0].message) == ("Property unit has been passed explicitly and as "
"an NDData property, using explicitly specified value")
def test_pass_nddata_ignored():
data_in = np.array([1, 2, 3])
wcs_in = "the wcs"
unit_in = u.Jy
nddata_in = NDData(data_in, wcs=wcs_in, unit=unit_in, mask=[0, 1, 0])
with catch_warnings() as w:
data_out, wcs_out, unit_out = wrapped_function_1(nddata_in)
assert data_out is data_in
assert wcs_out is wcs_in
assert unit_out is unit_in
assert len(w) == 1
assert str(w[0].message) == ("The following attributes were set on the data "
"object, but will be ignored by the function: mask")
def test_incorrect_first_argument():
with pytest.raises(ValueError) as exc:
@support_nddata
def wrapped_function_2(something, wcs=None, unit=None):
pass
assert exc.value.args[0] == "Can only wrap functions whose first positional argument is `data`"
with pytest.raises(ValueError) as exc:
@support_nddata
def wrapped_function_3(something, data, wcs=None, unit=None):
pass
assert exc.value.args[0] == "Can only wrap functions whose first positional argument is `data`"
with pytest.raises(ValueError) as exc:
@support_nddata
def wrapped_function_4(wcs=None, unit=None):
pass
assert exc.value.args[0] == "Can only wrap functions whose first positional argument is `data`"
def test_wrap_function_no_kwargs():
@support_nddata
def wrapped_function_5(data, other_data):
return data
data_in = np.array([1, 2, 3])
nddata_in = NDData(data_in)
assert wrapped_function_5(nddata_in, [1, 2, 3]) is data_in
def test_wrap_function_repack_valid():
@support_nddata(repack=True, returns=['data'])
def wrapped_function_5(data, other_data):
return data
data_in = np.array([1, 2, 3])
nddata_in = NDData(data_in)
nddata_out = wrapped_function_5(nddata_in, [1, 2, 3])
assert isinstance(nddata_out, NDData)
assert nddata_out.data is data_in
def test_wrap_function_accepts():
class MyData(NDData):
pass
@support_nddata(accepts=MyData)
def wrapped_function_5(data, other_data):
return data
data_in = np.array([1, 2, 3])
nddata_in = NDData(data_in)
mydata_in = MyData(data_in)
assert wrapped_function_5(mydata_in, [1, 2, 3]) is data_in
with pytest.raises(TypeError) as exc:
wrapped_function_5(nddata_in, [1, 2, 3])
assert exc.value.args[0] == "Only NDData sub-classes that inherit from MyData can be used by this function"
def test_wrap_preserve_signature_docstring():
@support_nddata
def wrapped_function_6(data, wcs=None, unit=None):
"""
An awesome function
"""
pass
if wrapped_function_6.__doc__ is not None:
assert wrapped_function_6.__doc__.strip() == "An awesome function"
signature = inspect.signature(wrapped_function_6)
assert str(signature) == "(data, wcs=None, unit=None)"
def test_setup_failures1():
# repack but no returns
with pytest.raises(ValueError):
support_nddata(repack=True)
def test_setup_failures2():
# returns but no repack
with pytest.raises(ValueError):
support_nddata(returns=['data'])
def test_setup_failures9():
# keeps but no repack
with pytest.raises(ValueError):
support_nddata(keeps=['unit'])
def test_setup_failures3():
# same attribute in keeps and returns
with pytest.raises(ValueError):
support_nddata(repack=True, keeps=['mask'], returns=['data', 'mask'])
def test_setup_failures4():
# function accepts *args
with pytest.raises(ValueError):
@support_nddata
def test(data, *args):
pass
def test_setup_failures10():
# function accepts **kwargs
with pytest.raises(ValueError):
@support_nddata
def test(data, **kwargs):
pass
def test_setup_failures5():
# function accepts *args (or **kwargs)
with pytest.raises(ValueError):
@support_nddata
def test(data, *args):
pass
def test_setup_failures6():
# First argument is not data
with pytest.raises(ValueError):
@support_nddata
def test(img):
pass
def test_setup_failures7():
# accepts CCDData but was given just an NDData
with pytest.raises(TypeError):
@support_nddata(accepts=CCDData)
def test(data):
pass
test(NDData(np.ones((3, 3))))
def test_setup_failures8():
# function returns a different amount of arguments than specified. Using
# NDData here so we don't get into troubles when creating a CCDData without
# unit!
with pytest.raises(ValueError):
@support_nddata(repack=True, returns=['data', 'mask'])
def test(data):
return 10
test(NDData(np.ones((3, 3)))) # do NOT use CCDData here.
def test_setup_failures11():
# function accepts no arguments
with pytest.raises(ValueError):
@support_nddata
def test():
pass
def test_setup_numpyarray_default():
# It should be possible (even if it's not advisable to use mutable
# defaults) to have a numpy array as default value.
@support_nddata
def func(data, wcs=np.array([1, 2, 3])):
return wcs
def test_still_accepts_other_input():
@support_nddata(repack=True, returns=['data'])
def test(data):
return data
assert isinstance(test(NDData(np.ones((3, 3)))), NDData)
assert isinstance(test(10), int)
assert isinstance(test([1, 2, 3]), list)
def test_accepting_property_normal():
# Accepts a mask attribute and takes it from the input
@support_nddata
def test(data, mask=None):
return mask
ndd = NDData(np.ones((3, 3)))
assert test(ndd) is None
ndd._mask = np.zeros((3, 3))
assert np.all(test(ndd) == 0)
# Use the explicitly given one (raises a Warning)
with catch_warnings(AstropyUserWarning) as w:
assert test(ndd, mask=10) == 10
assert len(w) == 1
def test_parameter_default_identical_to_explicit_passed_argument():
# If the default is identical to the explicitly passed argument this
# should still raise a Warning and use the explicit one.
@support_nddata
def func(data, wcs=[1, 2, 3]):
return wcs
with catch_warnings(AstropyUserWarning) as w:
assert func(NDData(1, wcs=[1, 2]), [1, 2, 3]) == [1, 2, 3]
assert len(w) == 1
with catch_warnings(AstropyUserWarning) as w:
assert func(NDData(1, wcs=[1, 2])) == [1, 2]
assert len(w) == 0
def test_accepting_property_notexist():
# Accepts flags attribute but NDData doesn't have one
@support_nddata
def test(data, flags=10):
return flags
ndd = NDData(np.ones((3, 3)))
test(ndd)
def test_accepting_property_translated():
# Accepts a error attribute and we want to pass in uncertainty!
@support_nddata(mask='masked')
def test(data, masked=None):
return masked
ndd = NDData(np.ones((3, 3)))
assert test(ndd) is None
ndd._mask = np.zeros((3, 3))
assert np.all(test(ndd) == 0)
# Use the explicitly given one (raises a Warning)
with catch_warnings(AstropyUserWarning) as w:
assert test(ndd, masked=10) == 10
assert len(w) == 1
def test_accepting_property_meta_empty():
# Meta is always set (OrderedDict) so it has a special case that it's
# ignored if it's empty but not None
@support_nddata
def test(data, meta=None):
return meta
ndd = NDData(np.ones((3, 3)))
assert test(ndd) is None
ndd._meta = {'a': 10}
assert test(ndd) == {'a': 10}
|
|
astropy/nddata/tests/test_decorators.py
|
astropy/io/misc/asdf/tags/table/table.py
|
# -*- coding: utf-8 -*-
# Licensed under a 3-clause BSD style license - see LICENSE.rst
from copy import deepcopy
import numpy as np
from astropy import units as u
from astropy.tests.helper import (catch_warnings, pytest,
assert_quantity_allclose as assert_allclose)
from astropy.utils import OrderedDescriptorContainer
from astropy.utils.compat import NUMPY_LT_1_14
from astropy.utils.exceptions import AstropyWarning
from astropy.coordinates import representation as r
from astropy.coordinates.representation import REPRESENTATION_CLASSES
from astropy.units import allclose
from .test_representation import unitphysics # this fixture is used below
def setup_function(func):
func.REPRESENTATION_CLASSES_ORIG = deepcopy(REPRESENTATION_CLASSES)
def teardown_function(func):
REPRESENTATION_CLASSES.clear()
REPRESENTATION_CLASSES.update(func.REPRESENTATION_CLASSES_ORIG)
def test_frame_attribute_descriptor():
""" Unit tests of the Attribute descriptor """
from astropy.coordinates.attributes import Attribute
class TestAttributes(metaclass=OrderedDescriptorContainer):
attr_none = Attribute()
attr_2 = Attribute(default=2)
attr_3_attr2 = Attribute(default=3, secondary_attribute='attr_2')
attr_none_attr2 = Attribute(default=None, secondary_attribute='attr_2')
attr_none_nonexist = Attribute(default=None, secondary_attribute='nonexist')
t = TestAttributes()
# Defaults
assert t.attr_none is None
assert t.attr_2 == 2
assert t.attr_3_attr2 == 3
assert t.attr_none_attr2 == t.attr_2
assert t.attr_none_nonexist is None # No default and non-existent secondary attr
# Setting values via '_'-prefixed internal vars (as would normally done in __init__)
t._attr_none = 10
assert t.attr_none == 10
t._attr_2 = 20
assert t.attr_2 == 20
assert t.attr_3_attr2 == 3
assert t.attr_none_attr2 == t.attr_2
t._attr_none_attr2 = 40
assert t.attr_none_attr2 == 40
# Make sure setting values via public attribute fails
with pytest.raises(AttributeError) as err:
t.attr_none = 5
assert 'Cannot set frame attribute' in str(err.value)
def test_frame_subclass_attribute_descriptor():
from astropy.coordinates.builtin_frames import FK4
from astropy.coordinates.attributes import Attribute, TimeAttribute
from astropy.time import Time
_EQUINOX_B1980 = Time('B1980', scale='tai')
class MyFK4(FK4):
# equinox inherited from FK4, obstime overridden, and newattr is new
obstime = TimeAttribute(default=_EQUINOX_B1980)
newattr = Attribute(default='newattr')
mfk4 = MyFK4()
assert mfk4.equinox.value == 'B1950.000'
assert mfk4.obstime.value == 'B1980.000'
assert mfk4.newattr == 'newattr'
assert set(mfk4.get_frame_attr_names()) == set(['equinox', 'obstime', 'newattr'])
mfk4 = MyFK4(equinox='J1980.0', obstime='J1990.0', newattr='world')
assert mfk4.equinox.value == 'J1980.000'
assert mfk4.obstime.value == 'J1990.000'
assert mfk4.newattr == 'world'
def test_create_data_frames():
from astropy.coordinates.builtin_frames import ICRS
# from repr
i1 = ICRS(r.SphericalRepresentation(1*u.deg, 2*u.deg, 3*u.kpc))
i2 = ICRS(r.UnitSphericalRepresentation(lon=1*u.deg, lat=2*u.deg))
# from preferred name
i3 = ICRS(ra=1*u.deg, dec=2*u.deg, distance=3*u.kpc)
i4 = ICRS(ra=1*u.deg, dec=2*u.deg)
assert i1.data.lat == i3.data.lat
assert i1.data.lon == i3.data.lon
assert i1.data.distance == i3.data.distance
assert i2.data.lat == i4.data.lat
assert i2.data.lon == i4.data.lon
# now make sure the preferred names work as properties
assert_allclose(i1.ra, i3.ra)
assert_allclose(i2.ra, i4.ra)
assert_allclose(i1.distance, i3.distance)
with pytest.raises(AttributeError):
i1.ra = [11.]*u.deg
def test_create_orderered_data():
from astropy.coordinates.builtin_frames import ICRS, Galactic, AltAz
TOL = 1e-10*u.deg
i = ICRS(1*u.deg, 2*u.deg)
assert (i.ra - 1*u.deg) < TOL
assert (i.dec - 2*u.deg) < TOL
g = Galactic(1*u.deg, 2*u.deg)
assert (g.l - 1*u.deg) < TOL
assert (g.b - 2*u.deg) < TOL
a = AltAz(1*u.deg, 2*u.deg)
assert (a.az - 1*u.deg) < TOL
assert (a.alt - 2*u.deg) < TOL
with pytest.raises(TypeError):
ICRS(1*u.deg, 2*u.deg, 1*u.deg, 2*u.deg)
with pytest.raises(TypeError):
sph = r.SphericalRepresentation(1*u.deg, 2*u.deg, 3*u.kpc)
ICRS(sph, 1*u.deg, 2*u.deg)
def test_create_nodata_frames():
from astropy.coordinates.builtin_frames import ICRS, FK4, FK5
i = ICRS()
assert len(i.get_frame_attr_names()) == 0
f5 = FK5()
assert f5.equinox == FK5.get_frame_attr_names()['equinox']
f4 = FK4()
assert f4.equinox == FK4.get_frame_attr_names()['equinox']
# obstime is special because it's a property that uses equinox if obstime is not set
assert f4.obstime in (FK4.get_frame_attr_names()['obstime'],
FK4.get_frame_attr_names()['equinox'])
def test_no_data_nonscalar_frames():
from astropy.coordinates.builtin_frames import AltAz
from astropy.time import Time
a1 = AltAz(obstime=Time('2012-01-01') + np.arange(10.) * u.day,
temperature=np.ones((3, 1)) * u.deg_C)
assert a1.obstime.shape == (3, 10)
assert a1.temperature.shape == (3, 10)
assert a1.shape == (3, 10)
with pytest.raises(ValueError) as exc:
AltAz(obstime=Time('2012-01-01') + np.arange(10.) * u.day,
temperature=np.ones((3,)) * u.deg_C)
assert 'inconsistent shapes' in str(exc.value)
def test_frame_repr():
from astropy.coordinates.builtin_frames import ICRS, FK5
i = ICRS()
assert repr(i) == '<ICRS Frame>'
f5 = FK5()
assert repr(f5).startswith('<FK5 Frame (equinox=')
i2 = ICRS(ra=1*u.deg, dec=2*u.deg)
i3 = ICRS(ra=1*u.deg, dec=2*u.deg, distance=3*u.kpc)
assert repr(i2) == ('<ICRS Coordinate: (ra, dec) in deg\n'
' ({})>').format(' 1., 2.' if NUMPY_LT_1_14
else '1., 2.')
assert repr(i3) == ('<ICRS Coordinate: (ra, dec, distance) in (deg, deg, kpc)\n'
' ({})>').format(' 1., 2., 3.' if NUMPY_LT_1_14
else '1., 2., 3.')
# try with arrays
i2 = ICRS(ra=[1.1, 2.1]*u.deg, dec=[2.1, 3.1]*u.deg)
i3 = ICRS(ra=[1.1, 2.1]*u.deg, dec=[-15.6, 17.1]*u.deg, distance=[11., 21.]*u.kpc)
assert repr(i2) == ('<ICRS Coordinate: (ra, dec) in deg\n'
' [{}]>').format('( 1.1, 2.1), ( 2.1, 3.1)'
if NUMPY_LT_1_14 else
'(1.1, 2.1), (2.1, 3.1)')
if NUMPY_LT_1_14:
assert repr(i3) == ('<ICRS Coordinate: (ra, dec, distance) in (deg, deg, kpc)\n'
' [( 1.1, -15.6, 11.), ( 2.1, 17.1, 21.)]>')
else:
assert repr(i3) == ('<ICRS Coordinate: (ra, dec, distance) in (deg, deg, kpc)\n'
' [(1.1, -15.6, 11.), (2.1, 17.1, 21.)]>')
def test_frame_repr_vels():
from astropy.coordinates.builtin_frames import ICRS
i = ICRS(ra=1*u.deg, dec=2*u.deg,
pm_ra_cosdec=1*u.marcsec/u.yr, pm_dec=2*u.marcsec/u.yr)
# unit comes out as mas/yr because of the preferred units defined in the
# frame RepresentationMapping
assert repr(i) == ('<ICRS Coordinate: (ra, dec) in deg\n'
' ({0})\n'
' (pm_ra_cosdec, pm_dec) in mas / yr\n'
' ({0})>').format(' 1., 2.' if NUMPY_LT_1_14 else
'1., 2.')
def test_converting_units():
import re
from astropy.coordinates.baseframe import RepresentationMapping
from astropy.coordinates.builtin_frames import ICRS, FK5
# this is a regular expression that with split (see below) removes what's
# the decimal point to fix rounding problems
rexrepr = re.compile(r'(.*?=\d\.).*?( .*?=\d\.).*?( .*)')
# Use values that aren't subject to rounding down to X.9999...
i2 = ICRS(ra=2.*u.deg, dec=2.*u.deg)
i2_many = ICRS(ra=[2., 4.]*u.deg, dec=[2., -8.1]*u.deg)
# converting from FK5 to ICRS and back changes the *internal* representation,
# but it should still come out in the preferred form
i4 = i2.transform_to(FK5).transform_to(ICRS)
i4_many = i2_many.transform_to(FK5).transform_to(ICRS)
ri2 = ''.join(rexrepr.split(repr(i2)))
ri4 = ''.join(rexrepr.split(repr(i4)))
assert ri2 == ri4
assert i2.data.lon.unit != i4.data.lon.unit # Internal repr changed
ri2_many = ''.join(rexrepr.split(repr(i2_many)))
ri4_many = ''.join(rexrepr.split(repr(i4_many)))
assert ri2_many == ri4_many
assert i2_many.data.lon.unit != i4_many.data.lon.unit # Internal repr changed
# but that *shouldn't* hold if we turn off units for the representation
class FakeICRS(ICRS):
frame_specific_representation_info = {
'spherical': [RepresentationMapping('lon', 'ra', u.hourangle),
RepresentationMapping('lat', 'dec', None),
RepresentationMapping('distance', 'distance')] # should fall back to default of None unit
}
fi = FakeICRS(i4.data)
ri2 = ''.join(rexrepr.split(repr(i2)))
rfi = ''.join(rexrepr.split(repr(fi)))
rfi = re.sub('FakeICRS', 'ICRS', rfi) # Force frame name to match
assert ri2 != rfi
# the attributes should also get the right units
assert i2.dec.unit == i4.dec.unit
# unless no/explicitly given units
assert i2.dec.unit != fi.dec.unit
assert i2.ra.unit != fi.ra.unit
assert fi.ra.unit == u.hourangle
def test_representation_info():
from astropy.coordinates.baseframe import RepresentationMapping
from astropy.coordinates.builtin_frames import ICRS
class NewICRS1(ICRS):
frame_specific_representation_info = {
r.SphericalRepresentation: [
RepresentationMapping('lon', 'rara', u.hourangle),
RepresentationMapping('lat', 'decdec', u.degree),
RepresentationMapping('distance', 'distance', u.kpc)]
}
i1 = NewICRS1(rara=10*u.degree, decdec=-12*u.deg, distance=1000*u.pc,
pm_rara_cosdecdec=100*u.mas/u.yr,
pm_decdec=17*u.mas/u.yr,
radial_velocity=10*u.km/u.s)
assert allclose(i1.rara, 10*u.deg)
assert i1.rara.unit == u.hourangle
assert allclose(i1.decdec, -12*u.deg)
assert allclose(i1.distance, 1000*u.pc)
assert i1.distance.unit == u.kpc
assert allclose(i1.pm_rara_cosdecdec, 100*u.mas/u.yr)
assert allclose(i1.pm_decdec, 17*u.mas/u.yr)
# this should auto-set the names of UnitSpherical:
i1.set_representation_cls(r.UnitSphericalRepresentation,
s=r.UnitSphericalCosLatDifferential)
assert allclose(i1.rara, 10*u.deg)
assert allclose(i1.decdec, -12*u.deg)
assert allclose(i1.pm_rara_cosdecdec, 100*u.mas/u.yr)
assert allclose(i1.pm_decdec, 17*u.mas/u.yr)
# For backwards compatibility, we also support the string name in the
# representation info dictionary:
class NewICRS2(ICRS):
frame_specific_representation_info = {
'spherical': [
RepresentationMapping('lon', 'ang1', u.hourangle),
RepresentationMapping('lat', 'ang2', u.degree),
RepresentationMapping('distance', 'howfar', u.kpc)]
}
i2 = NewICRS2(ang1=10*u.degree, ang2=-12*u.deg, howfar=1000*u.pc)
assert allclose(i2.ang1, 10*u.deg)
assert i2.ang1.unit == u.hourangle
assert allclose(i2.ang2, -12*u.deg)
assert allclose(i2.howfar, 1000*u.pc)
assert i2.howfar.unit == u.kpc
# Test that the differential kwargs get overridden
class NewICRS3(ICRS):
frame_specific_representation_info = {
r.SphericalCosLatDifferential: [
RepresentationMapping('d_lon_coslat', 'pm_ang1', u.hourangle/u.year),
RepresentationMapping('d_lat', 'pm_ang2'),
RepresentationMapping('d_distance', 'vlos', u.kpc/u.Myr)]
}
i3 = NewICRS3(lon=10*u.degree, lat=-12*u.deg, distance=1000*u.pc,
pm_ang1=1*u.mas/u.yr, pm_ang2=2*u.mas/u.yr,
vlos=100*u.km/u.s)
assert allclose(i3.pm_ang1, 1*u.mas/u.yr)
assert i3.pm_ang1.unit == u.hourangle/u.year
assert allclose(i3.pm_ang2, 2*u.mas/u.yr)
assert allclose(i3.vlos, 100*u.km/u.s)
assert i3.vlos.unit == u.kpc/u.Myr
def test_realizing():
from astropy.coordinates.builtin_frames import ICRS, FK5
from astropy.time import Time
rep = r.SphericalRepresentation(1*u.deg, 2*u.deg, 3*u.kpc)
i = ICRS()
i2 = i.realize_frame(rep)
assert not i.has_data
assert i2.has_data
f = FK5(equinox=Time('J2001'))
f2 = f.realize_frame(rep)
assert not f.has_data
assert f2.has_data
assert f2.equinox == f.equinox
assert f2.equinox != FK5.get_frame_attr_names()['equinox']
# Check that a nicer error message is returned:
with pytest.raises(TypeError) as excinfo:
f.realize_frame(f.representation_type)
assert ('Class passed as data instead of a representation' in
excinfo.value.args[0])
def test_replicating():
from astropy.coordinates.builtin_frames import ICRS, AltAz
from astropy.time import Time
i = ICRS(ra=[1]*u.deg, dec=[2]*u.deg)
icopy = i.replicate(copy=True)
irepl = i.replicate(copy=False)
i.data._lat[:] = 0*u.deg
assert np.all(i.data.lat == irepl.data.lat)
assert np.all(i.data.lat != icopy.data.lat)
iclone = i.replicate_without_data()
assert i.has_data
assert not iclone.has_data
aa = AltAz(alt=1*u.deg, az=2*u.deg, obstime=Time('J2000'))
aaclone = aa.replicate_without_data(obstime=Time('J2001'))
assert not aaclone.has_data
assert aa.obstime != aaclone.obstime
assert aa.pressure == aaclone.pressure
assert aa.obswl == aaclone.obswl
def test_getitem():
from astropy.coordinates.builtin_frames import ICRS
rep = r.SphericalRepresentation(
[1, 2, 3]*u.deg, [4, 5, 6]*u.deg, [7, 8, 9]*u.kpc)
i = ICRS(rep)
assert len(i.ra) == 3
iidx = i[1:]
assert len(iidx.ra) == 2
iidx2 = i[0]
assert iidx2.ra.isscalar
def test_transform():
"""
This test just makes sure the transform architecture works, but does *not*
actually test all the builtin transforms themselves are accurate
"""
from astropy.coordinates.builtin_frames import ICRS, FK4, FK5, Galactic
from astropy.time import Time
i = ICRS(ra=[1, 2]*u.deg, dec=[3, 4]*u.deg)
f = i.transform_to(FK5)
i2 = f.transform_to(ICRS)
assert i2.data.__class__ == r.UnitSphericalRepresentation
assert_allclose(i.ra, i2.ra)
assert_allclose(i.dec, i2.dec)
i = ICRS(ra=[1, 2]*u.deg, dec=[3, 4]*u.deg, distance=[5, 6]*u.kpc)
f = i.transform_to(FK5)
i2 = f.transform_to(ICRS)
assert i2.data.__class__ != r.UnitSphericalRepresentation
f = FK5(ra=1*u.deg, dec=2*u.deg, equinox=Time('J2001'))
f4 = f.transform_to(FK4)
f4_2 = f.transform_to(FK4(equinox=f.equinox))
# make sure attributes are copied over correctly
assert f4.equinox == FK4.get_frame_attr_names()['equinox']
assert f4_2.equinox == f.equinox
# make sure self-transforms also work
i = ICRS(ra=[1, 2]*u.deg, dec=[3, 4]*u.deg)
i2 = i.transform_to(ICRS)
assert_allclose(i.ra, i2.ra)
assert_allclose(i.dec, i2.dec)
f = FK5(ra=1*u.deg, dec=2*u.deg, equinox=Time('J2001'))
f2 = f.transform_to(FK5) # default equinox, so should be *different*
assert f2.equinox == FK5().equinox
with pytest.raises(AssertionError):
assert_allclose(f.ra, f2.ra)
with pytest.raises(AssertionError):
assert_allclose(f.dec, f2.dec)
# finally, check Galactic round-tripping
i1 = ICRS(ra=[1, 2]*u.deg, dec=[3, 4]*u.deg)
i2 = i1.transform_to(Galactic).transform_to(ICRS)
assert_allclose(i1.ra, i2.ra)
assert_allclose(i1.dec, i2.dec)
def test_transform_to_nonscalar_nodata_frame():
# https://github.com/astropy/astropy/pull/5254#issuecomment-241592353
from astropy.coordinates.builtin_frames import ICRS, FK5
from astropy.time import Time
times = Time('2016-08-23') + np.linspace(0, 10, 12)*u.day
coo1 = ICRS(ra=[[0.], [10.], [20.]]*u.deg,
dec=[[-30.], [30.], [60.]]*u.deg)
coo2 = coo1.transform_to(FK5(equinox=times))
assert coo2.shape == (3, 12)
def test_sep():
from astropy.coordinates.builtin_frames import ICRS
i1 = ICRS(ra=0*u.deg, dec=1*u.deg)
i2 = ICRS(ra=0*u.deg, dec=2*u.deg)
sep = i1.separation(i2)
assert sep.deg == 1
i3 = ICRS(ra=[1, 2]*u.deg, dec=[3, 4]*u.deg, distance=[5, 6]*u.kpc)
i4 = ICRS(ra=[1, 2]*u.deg, dec=[3, 4]*u.deg, distance=[4, 5]*u.kpc)
sep3d = i3.separation_3d(i4)
assert_allclose(sep3d.to(u.kpc), np.array([1, 1])*u.kpc)
# check that it works even with velocities
i5 = ICRS(ra=[1, 2]*u.deg, dec=[3, 4]*u.deg, distance=[5, 6]*u.kpc,
pm_ra_cosdec=[1, 2]*u.mas/u.yr, pm_dec=[3, 4]*u.mas/u.yr,
radial_velocity=[5, 6]*u.km/u.s)
i6 = ICRS(ra=[1, 2]*u.deg, dec=[3, 4]*u.deg, distance=[7, 8]*u.kpc,
pm_ra_cosdec=[1, 2]*u.mas/u.yr, pm_dec=[3, 4]*u.mas/u.yr,
radial_velocity=[5, 6]*u.km/u.s)
sep3d = i5.separation_3d(i6)
assert_allclose(sep3d.to(u.kpc), np.array([2, 2])*u.kpc)
# 3d separations of dimensionless distances should still work
i7 = ICRS(ra=1*u.deg, dec=2*u.deg, distance=3*u.one)
i8 = ICRS(ra=1*u.deg, dec=2*u.deg, distance=4*u.one)
sep3d = i7.separation_3d(i8)
assert_allclose(sep3d, 1*u.one)
# but should fail with non-dimensionless
with pytest.raises(ValueError):
i7.separation_3d(i3)
def test_time_inputs():
"""
Test validation and conversion of inputs for equinox and obstime attributes.
"""
from astropy.time import Time
from astropy.coordinates.builtin_frames import FK4
c = FK4(1 * u.deg, 2 * u.deg, equinox='J2001.5', obstime='2000-01-01 12:00:00')
assert c.equinox == Time('J2001.5')
assert c.obstime == Time('2000-01-01 12:00:00')
with pytest.raises(ValueError) as err:
c = FK4(1 * u.deg, 2 * u.deg, equinox=1.5)
assert 'Invalid time input' in str(err.value)
with pytest.raises(ValueError) as err:
c = FK4(1 * u.deg, 2 * u.deg, obstime='hello')
assert 'Invalid time input' in str(err.value)
# A vector time should work if the shapes match, but we don't automatically
# broadcast the basic data (just like time).
FK4([1, 2] * u.deg, [2, 3] * u.deg, obstime=['J2000', 'J2001'])
with pytest.raises(ValueError) as err:
FK4(1 * u.deg, 2 * u.deg, obstime=['J2000', 'J2001'])
assert 'shape' in str(err.value)
def test_is_frame_attr_default():
"""
Check that the `is_frame_attr_default` machinery works as expected
"""
from astropy.time import Time
from astropy.coordinates.builtin_frames import FK5
c1 = FK5(ra=1*u.deg, dec=1*u.deg)
c2 = FK5(ra=1*u.deg, dec=1*u.deg, equinox=FK5.get_frame_attr_names()['equinox'])
c3 = FK5(ra=1*u.deg, dec=1*u.deg, equinox=Time('J2001.5'))
assert c1.equinox == c2.equinox
assert c1.equinox != c3.equinox
assert c1.is_frame_attr_default('equinox')
assert not c2.is_frame_attr_default('equinox')
assert not c3.is_frame_attr_default('equinox')
c4 = c1.realize_frame(r.UnitSphericalRepresentation(3*u.deg, 4*u.deg))
c5 = c2.realize_frame(r.UnitSphericalRepresentation(3*u.deg, 4*u.deg))
assert c4.is_frame_attr_default('equinox')
assert not c5.is_frame_attr_default('equinox')
def test_altaz_attributes():
from astropy.time import Time
from astropy.coordinates import EarthLocation, AltAz
aa = AltAz(1*u.deg, 2*u.deg)
assert aa.obstime is None
assert aa.location is None
aa2 = AltAz(1*u.deg, 2*u.deg, obstime='J2000')
assert aa2.obstime == Time('J2000')
aa3 = AltAz(1*u.deg, 2*u.deg, location=EarthLocation(0*u.deg, 0*u.deg, 0*u.m))
assert isinstance(aa3.location, EarthLocation)
def test_representation():
"""
Test the getter and setter properties for `representation`
"""
from astropy.coordinates.builtin_frames import ICRS
# Create the frame object.
icrs = ICRS(ra=1*u.deg, dec=1*u.deg)
data = icrs.data
# Create some representation objects.
icrs_cart = icrs.cartesian
icrs_spher = icrs.spherical
icrs_cyl = icrs.cylindrical
# Testing when `_representation` set to `CartesianRepresentation`.
icrs.representation_type = r.CartesianRepresentation
assert icrs.representation_type == r.CartesianRepresentation
assert icrs_cart.x == icrs.x
assert icrs_cart.y == icrs.y
assert icrs_cart.z == icrs.z
assert icrs.data == data
# Testing that an ICRS object in CartesianRepresentation must not have spherical attributes.
for attr in ('ra', 'dec', 'distance'):
with pytest.raises(AttributeError) as err:
getattr(icrs, attr)
assert 'object has no attribute' in str(err.value)
# Testing when `_representation` set to `CylindricalRepresentation`.
icrs.representation_type = r.CylindricalRepresentation
assert icrs.representation_type == r.CylindricalRepresentation
assert icrs.data == data
# Testing setter input using text argument for spherical.
icrs.representation_type = 'spherical'
assert icrs.representation_type is r.SphericalRepresentation
assert icrs_spher.lat == icrs.dec
assert icrs_spher.lon == icrs.ra
assert icrs_spher.distance == icrs.distance
assert icrs.data == data
# Testing that an ICRS object in SphericalRepresentation must not have cartesian attributes.
for attr in ('x', 'y', 'z'):
with pytest.raises(AttributeError) as err:
getattr(icrs, attr)
assert 'object has no attribute' in str(err.value)
# Testing setter input using text argument for cylindrical.
icrs.representation_type = 'cylindrical'
assert icrs.representation_type is r.CylindricalRepresentation
assert icrs_cyl.rho == icrs.rho
assert icrs_cyl.phi == icrs.phi
assert icrs_cyl.z == icrs.z
assert icrs.data == data
# Testing that an ICRS object in CylindricalRepresentation must not have spherical attributes.
for attr in ('ra', 'dec', 'distance'):
with pytest.raises(AttributeError) as err:
getattr(icrs, attr)
assert 'object has no attribute' in str(err.value)
with pytest.raises(ValueError) as err:
icrs.representation_type = 'WRONG'
assert 'but must be a BaseRepresentation class' in str(err.value)
with pytest.raises(ValueError) as err:
icrs.representation_type = ICRS
assert 'but must be a BaseRepresentation class' in str(err.value)
def test_represent_as():
from astropy.coordinates.builtin_frames import ICRS
icrs = ICRS(ra=1*u.deg, dec=1*u.deg)
cart1 = icrs.represent_as('cartesian')
cart2 = icrs.represent_as(r.CartesianRepresentation)
cart1.x == cart2.x
cart1.y == cart2.y
cart1.z == cart2.z
# now try with velocities
icrs = ICRS(ra=0*u.deg, dec=0*u.deg, distance=10*u.kpc,
pm_ra_cosdec=0*u.mas/u.yr, pm_dec=0*u.mas/u.yr,
radial_velocity=1*u.km/u.s)
# single string
rep2 = icrs.represent_as('cylindrical')
assert isinstance(rep2, r.CylindricalRepresentation)
assert isinstance(rep2.differentials['s'], r.CylindricalDifferential)
# single class with positional in_frame_units, verify that warning raised
with catch_warnings() as w:
icrs.represent_as(r.CylindricalRepresentation, False)
assert len(w) == 1
assert w[0].category == AstropyWarning
assert 'argument position' in str(w[0].message)
# TODO: this should probably fail in the future once we figure out a better
# workaround for dealing with UnitSphericalRepresentation's with
# RadialDifferential's
# two classes
# rep2 = icrs.represent_as(r.CartesianRepresentation,
# r.SphericalCosLatDifferential)
# assert isinstance(rep2, r.CartesianRepresentation)
# assert isinstance(rep2.differentials['s'], r.SphericalCosLatDifferential)
with pytest.raises(ValueError):
icrs.represent_as('odaigahara')
def test_shorthand_representations():
from astropy.coordinates.builtin_frames import ICRS
rep = r.CartesianRepresentation([1, 2, 3]*u.pc)
dif = r.CartesianDifferential([1, 2, 3]*u.km/u.s)
rep = rep.with_differentials(dif)
icrs = ICRS(rep)
cyl = icrs.cylindrical
assert isinstance(cyl, r.CylindricalRepresentation)
assert isinstance(cyl.differentials['s'], r.CylindricalDifferential)
sph = icrs.spherical
assert isinstance(sph, r.SphericalRepresentation)
assert isinstance(sph.differentials['s'], r.SphericalDifferential)
sph = icrs.sphericalcoslat
assert isinstance(sph, r.SphericalRepresentation)
assert isinstance(sph.differentials['s'], r.SphericalCosLatDifferential)
def test_dynamic_attrs():
from astropy.coordinates.builtin_frames import ICRS
c = ICRS(1*u.deg, 2*u.deg)
assert 'ra' in dir(c)
assert 'dec' in dir(c)
with pytest.raises(AttributeError) as err:
c.blahblah
assert "object has no attribute 'blahblah'" in str(err.value)
with pytest.raises(AttributeError) as err:
c.ra = 1
assert "Cannot set any frame attribute" in str(err.value)
c.blahblah = 1
assert c.blahblah == 1
def test_nodata_error():
from astropy.coordinates.builtin_frames import ICRS
i = ICRS()
with pytest.raises(ValueError) as excinfo:
i.data
assert 'does not have associated data' in str(excinfo.value)
def test_len0_data():
from astropy.coordinates.builtin_frames import ICRS
i = ICRS([]*u.deg, []*u.deg)
assert i.has_data
repr(i)
def test_quantity_attributes():
from astropy.coordinates.builtin_frames import GCRS
# make sure we can create a GCRS frame with valid inputs
GCRS(obstime='J2002', obsgeoloc=[1, 2, 3]*u.km, obsgeovel=[4, 5, 6]*u.km/u.s)
# make sure it fails for invalid lovs or vels
with pytest.raises(TypeError):
GCRS(obsgeoloc=[1, 2, 3]) # no unit
with pytest.raises(u.UnitsError):
GCRS(obsgeoloc=[1, 2, 3]*u.km/u.s) # incorrect unit
with pytest.raises(ValueError):
GCRS(obsgeoloc=[1, 3]*u.km) # incorrect shape
@pytest.mark.remote_data
def test_eloc_attributes():
from astropy.coordinates import AltAz, ITRS, GCRS, EarthLocation
el = EarthLocation(lon=12.3*u.deg, lat=45.6*u.deg, height=1*u.km)
it = ITRS(r.SphericalRepresentation(lon=12.3*u.deg, lat=45.6*u.deg, distance=1*u.km))
gc = GCRS(ra=12.3*u.deg, dec=45.6*u.deg, distance=6375*u.km)
el1 = AltAz(location=el).location
assert isinstance(el1, EarthLocation)
# these should match *exactly* because the EarthLocation
assert el1.lat == el.lat
assert el1.lon == el.lon
assert el1.height == el.height
el2 = AltAz(location=it).location
assert isinstance(el2, EarthLocation)
# these should *not* match because giving something in Spherical ITRS is
# *not* the same as giving it as an EarthLocation: EarthLocation is on an
# elliptical geoid. So the longitude should match (because flattening is
# only along the z-axis), but latitude should not. Also, height is relative
# to the *surface* in EarthLocation, but the ITRS distance is relative to
# the center of the Earth
assert not allclose(el2.lat, it.spherical.lat)
assert allclose(el2.lon, it.spherical.lon)
assert el2.height < -6000*u.km
el3 = AltAz(location=gc).location
# GCRS inputs implicitly get transformed to ITRS and then onto
# EarthLocation's elliptical geoid. So both lat and lon shouldn't match
assert isinstance(el3, EarthLocation)
assert not allclose(el3.lat, gc.dec)
assert not allclose(el3.lon, gc.ra)
assert np.abs(el3.height) < 500*u.km
def test_equivalent_frames():
from astropy.coordinates import SkyCoord
from astropy.coordinates.builtin_frames import ICRS, FK4, FK5, AltAz
i = ICRS()
i2 = ICRS(1*u.deg, 2*u.deg)
assert i.is_equivalent_frame(i)
assert i.is_equivalent_frame(i2)
with pytest.raises(TypeError):
assert i.is_equivalent_frame(10)
with pytest.raises(TypeError):
assert i2.is_equivalent_frame(SkyCoord(i2))
f0 = FK5() # this J2000 is TT
f1 = FK5(equinox='J2000')
f2 = FK5(1*u.deg, 2*u.deg, equinox='J2000')
f3 = FK5(equinox='J2010')
f4 = FK4(equinox='J2010')
assert f1.is_equivalent_frame(f1)
assert not i.is_equivalent_frame(f1)
assert f0.is_equivalent_frame(f1)
assert f1.is_equivalent_frame(f2)
assert not f1.is_equivalent_frame(f3)
assert not f3.is_equivalent_frame(f4)
aa1 = AltAz()
aa2 = AltAz(obstime='J2010')
assert aa2.is_equivalent_frame(aa2)
assert not aa1.is_equivalent_frame(i)
assert not aa1.is_equivalent_frame(aa2)
def test_representation_subclass():
# Regression test for #3354
from astropy.coordinates.builtin_frames import FK5
# Normally when instantiating a frame without a distance the frame will try
# and use UnitSphericalRepresentation internally instead of
# SphericalRepresentation.
frame = FK5(representation_type=r.SphericalRepresentation, ra=32 * u.deg, dec=20 * u.deg)
assert type(frame._data) == r.UnitSphericalRepresentation
assert frame.representation_type == r.SphericalRepresentation
# If using a SphericalRepresentation class this used to not work, so we
# test here that this is now fixed.
class NewSphericalRepresentation(r.SphericalRepresentation):
attr_classes = r.SphericalRepresentation.attr_classes
frame = FK5(representation_type=NewSphericalRepresentation, lon=32 * u.deg, lat=20 * u.deg)
assert type(frame._data) == r.UnitSphericalRepresentation
assert frame.representation_type == NewSphericalRepresentation
# A similar issue then happened in __repr__ with subclasses of
# SphericalRepresentation.
assert repr(frame) == ("<FK5 Coordinate (equinox=J2000.000): (lon, lat) in deg\n"
" ({})>").format(' 32., 20.' if NUMPY_LT_1_14
else '32., 20.')
# A more subtle issue is when specifying a custom
# UnitSphericalRepresentation subclass for the data and
# SphericalRepresentation or a subclass for the representation.
class NewUnitSphericalRepresentation(r.UnitSphericalRepresentation):
attr_classes = r.UnitSphericalRepresentation.attr_classes
def __repr__(self):
return "<NewUnitSphericalRepresentation: spam spam spam>"
frame = FK5(NewUnitSphericalRepresentation(lon=32 * u.deg, lat=20 * u.deg),
representation_type=NewSphericalRepresentation)
assert repr(frame) == "<FK5 Coordinate (equinox=J2000.000): spam spam spam>"
def test_getitem_representation():
"""
Make sure current representation survives __getitem__ even if different
from data representation.
"""
from astropy.coordinates.builtin_frames import ICRS
c = ICRS([1, 1] * u.deg, [2, 2] * u.deg)
c.representation_type = 'cartesian'
assert c[0].representation_type is r.CartesianRepresentation
def test_component_error_useful():
"""
Check that a data-less frame gives useful error messages about not having
data when the attributes asked for are possible coordinate components
"""
from astropy.coordinates.builtin_frames import ICRS
i = ICRS()
with pytest.raises(ValueError) as excinfo:
i.ra
assert 'does not have associated data' in str(excinfo.value)
with pytest.raises(AttributeError) as excinfo1:
i.foobar
with pytest.raises(AttributeError) as excinfo2:
i.lon # lon is *not* the component name despite being the underlying representation's name
assert "object has no attribute 'foobar'" in str(excinfo1.value)
assert "object has no attribute 'lon'" in str(excinfo2.value)
def test_cache_clear():
from astropy.coordinates.builtin_frames import ICRS
i = ICRS(1*u.deg, 2*u.deg)
# Add an in frame units version of the rep to the cache.
repr(i)
assert len(i.cache['representation']) == 2
i.cache.clear()
assert len(i.cache['representation']) == 0
def test_inplace_array():
from astropy.coordinates.builtin_frames import ICRS
i = ICRS([[1, 2], [3, 4]]*u.deg, [[10, 20], [30, 40]]*u.deg)
# Add an in frame units version of the rep to the cache.
repr(i)
# Check that repr() has added a rep to the cache
assert len(i.cache['representation']) == 2
# Modify the data
i.data.lon[:, 0] = [100, 200]*u.deg
# Clear the cache
i.cache.clear()
# This will use a second (potentially cached rep)
assert_allclose(i.ra, [[100, 2], [200, 4]]*u.deg)
assert_allclose(i.dec, [[10, 20], [30, 40]]*u.deg)
def test_inplace_change():
from astropy.coordinates.builtin_frames import ICRS
i = ICRS(1*u.deg, 2*u.deg)
# Add an in frame units version of the rep to the cache.
repr(i)
# Check that repr() has added a rep to the cache
assert len(i.cache['representation']) == 2
# Modify the data
i.data.lon[()] = 10*u.deg
# Clear the cache
i.cache.clear()
# This will use a second (potentially cached rep)
assert i.ra == 10 * u.deg
assert i.dec == 2 * u.deg
def test_representation_with_multiple_differentials():
from astropy.coordinates.builtin_frames import ICRS
dif1 = r.CartesianDifferential([1, 2, 3]*u.km/u.s)
dif2 = r.CartesianDifferential([1, 2, 3]*u.km/u.s**2)
rep = r.CartesianRepresentation([1, 2, 3]*u.pc,
differentials={'s': dif1, 's2': dif2})
# check warning is raised for a scalar
with pytest.raises(ValueError):
ICRS(rep)
def test_representation_arg_backwards_compatibility():
# TODO: this test can be removed when the `representation` argument is
# removed from the BaseCoordinateFrame initializer.
from astropy.coordinates.builtin_frames import ICRS
c1 = ICRS(x=1*u.pc, y=2*u.pc, z=3*u.pc,
representation_type=r.CartesianRepresentation)
c2 = ICRS(x=1*u.pc, y=2*u.pc, z=3*u.pc,
representation_type=r.CartesianRepresentation)
c3 = ICRS(x=1*u.pc, y=2*u.pc, z=3*u.pc,
representation_type='cartesian')
assert c1.x == c2.x
assert c1.y == c2.y
assert c1.z == c2.z
assert c1.x == c3.x
assert c1.y == c3.y
assert c1.z == c3.z
assert c1.representation_type == c1.representation_type
with pytest.raises(ValueError):
ICRS(x=1*u.pc, y=2*u.pc, z=3*u.pc,
representation_type='cartesian',
representation='cartesian')
def test_missing_component_error_names():
"""
This test checks that the component names are frame component names, not
representation or differential names, when referenced in an exception raised
when not passing in enough data. For example:
ICRS(ra=10*u.deg)
should state:
TypeError: __init__() missing 1 required positional argument: 'dec'
"""
from astropy.coordinates.builtin_frames import ICRS
with pytest.raises(TypeError) as e:
ICRS(ra=150 * u.deg)
assert "missing 1 required positional argument: 'dec'" in str(e.value)
with pytest.raises(TypeError) as e:
ICRS(ra=150*u.deg, dec=-11*u.deg,
pm_ra=100*u.mas/u.yr, pm_dec=10*u.mas/u.yr)
assert "pm_ra_cosdec" in str(e.value)
def test_non_spherical_representation_unit_creation(unitphysics):
from astropy.coordinates.builtin_frames import ICRS
class PhysicsICRS(ICRS):
default_representation = r.PhysicsSphericalRepresentation
pic = PhysicsICRS(phi=1*u.deg, theta=25*u.deg, r=1*u.kpc)
assert isinstance(pic.data, r.PhysicsSphericalRepresentation)
picu = PhysicsICRS(phi=1*u.deg, theta=25*u.deg)
assert isinstance(picu.data, unitphysics)
def test_attribute_repr():
from astropy.coordinates.attributes import Attribute
from astropy.coordinates.baseframe import BaseCoordinateFrame
class Spam:
def _astropy_repr_in_frame(self):
return "TEST REPR"
class TestFrame(BaseCoordinateFrame):
attrtest = Attribute(default=Spam())
assert "TEST REPR" in repr(TestFrame())
def test_component_names_repr():
from astropy.coordinates.baseframe import BaseCoordinateFrame, RepresentationMapping
# Frame class with new component names that includes a name swap
class NameChangeFrame(BaseCoordinateFrame):
default_representation = r.PhysicsSphericalRepresentation
frame_specific_representation_info = {
r.PhysicsSphericalRepresentation: [
RepresentationMapping('phi', 'theta', u.deg),
RepresentationMapping('theta', 'phi', u.arcsec),
RepresentationMapping('r', 'JUSTONCE', u.AU)]
}
frame = NameChangeFrame(0*u.deg, 0*u.arcsec, 0*u.AU)
# Check for the new names in the Frame repr
assert "(theta, phi, JUSTONCE)" in repr(frame)
# Check that the letter "r" has not been replaced more than once in the Frame repr
assert repr(frame).count("JUSTONCE") == 1
|
# Licensed under a 3-clause BSD style license - see LICENSE.rst
import inspect
import pytest
import numpy as np
from astropy.tests.helper import catch_warnings
from astropy.utils.exceptions import AstropyUserWarning
from astropy import units as u
from astropy.nddata.nddata import NDData
from astropy.nddata.decorators import support_nddata
class CCDData(NDData):
pass
@support_nddata
def wrapped_function_1(data, wcs=None, unit=None):
return data, wcs, unit
def test_pass_numpy():
data_in = np.array([1, 2, 3])
data_out, wcs_out, unit_out = wrapped_function_1(data=data_in)
assert data_out is data_in
assert wcs_out is None
assert unit_out is None
def test_pass_all_separate():
data_in = np.array([1, 2, 3])
wcs_in = "the wcs"
unit_in = u.Jy
data_out, wcs_out, unit_out = wrapped_function_1(data=data_in, wcs=wcs_in, unit=unit_in)
assert data_out is data_in
assert wcs_out is wcs_in
assert unit_out is unit_in
def test_pass_nddata():
data_in = np.array([1, 2, 3])
wcs_in = "the wcs"
unit_in = u.Jy
nddata_in = NDData(data_in, wcs=wcs_in, unit=unit_in)
data_out, wcs_out, unit_out = wrapped_function_1(nddata_in)
assert data_out is data_in
assert wcs_out is wcs_in
assert unit_out is unit_in
def test_pass_nddata_and_explicit():
data_in = np.array([1, 2, 3])
wcs_in = "the wcs"
unit_in = u.Jy
unit_in_alt = u.mJy
nddata_in = NDData(data_in, wcs=wcs_in, unit=unit_in)
with catch_warnings() as w:
data_out, wcs_out, unit_out = wrapped_function_1(nddata_in, unit=unit_in_alt)
assert data_out is data_in
assert wcs_out is wcs_in
assert unit_out is unit_in_alt
assert len(w) == 1
assert str(w[0].message) == ("Property unit has been passed explicitly and as "
"an NDData property, using explicitly specified value")
def test_pass_nddata_ignored():
data_in = np.array([1, 2, 3])
wcs_in = "the wcs"
unit_in = u.Jy
nddata_in = NDData(data_in, wcs=wcs_in, unit=unit_in, mask=[0, 1, 0])
with catch_warnings() as w:
data_out, wcs_out, unit_out = wrapped_function_1(nddata_in)
assert data_out is data_in
assert wcs_out is wcs_in
assert unit_out is unit_in
assert len(w) == 1
assert str(w[0].message) == ("The following attributes were set on the data "
"object, but will be ignored by the function: mask")
def test_incorrect_first_argument():
with pytest.raises(ValueError) as exc:
@support_nddata
def wrapped_function_2(something, wcs=None, unit=None):
pass
assert exc.value.args[0] == "Can only wrap functions whose first positional argument is `data`"
with pytest.raises(ValueError) as exc:
@support_nddata
def wrapped_function_3(something, data, wcs=None, unit=None):
pass
assert exc.value.args[0] == "Can only wrap functions whose first positional argument is `data`"
with pytest.raises(ValueError) as exc:
@support_nddata
def wrapped_function_4(wcs=None, unit=None):
pass
assert exc.value.args[0] == "Can only wrap functions whose first positional argument is `data`"
def test_wrap_function_no_kwargs():
@support_nddata
def wrapped_function_5(data, other_data):
return data
data_in = np.array([1, 2, 3])
nddata_in = NDData(data_in)
assert wrapped_function_5(nddata_in, [1, 2, 3]) is data_in
def test_wrap_function_repack_valid():
@support_nddata(repack=True, returns=['data'])
def wrapped_function_5(data, other_data):
return data
data_in = np.array([1, 2, 3])
nddata_in = NDData(data_in)
nddata_out = wrapped_function_5(nddata_in, [1, 2, 3])
assert isinstance(nddata_out, NDData)
assert nddata_out.data is data_in
def test_wrap_function_accepts():
class MyData(NDData):
pass
@support_nddata(accepts=MyData)
def wrapped_function_5(data, other_data):
return data
data_in = np.array([1, 2, 3])
nddata_in = NDData(data_in)
mydata_in = MyData(data_in)
assert wrapped_function_5(mydata_in, [1, 2, 3]) is data_in
with pytest.raises(TypeError) as exc:
wrapped_function_5(nddata_in, [1, 2, 3])
assert exc.value.args[0] == "Only NDData sub-classes that inherit from MyData can be used by this function"
def test_wrap_preserve_signature_docstring():
@support_nddata
def wrapped_function_6(data, wcs=None, unit=None):
"""
An awesome function
"""
pass
if wrapped_function_6.__doc__ is not None:
assert wrapped_function_6.__doc__.strip() == "An awesome function"
signature = inspect.signature(wrapped_function_6)
assert str(signature) == "(data, wcs=None, unit=None)"
def test_setup_failures1():
# repack but no returns
with pytest.raises(ValueError):
support_nddata(repack=True)
def test_setup_failures2():
# returns but no repack
with pytest.raises(ValueError):
support_nddata(returns=['data'])
def test_setup_failures9():
# keeps but no repack
with pytest.raises(ValueError):
support_nddata(keeps=['unit'])
def test_setup_failures3():
# same attribute in keeps and returns
with pytest.raises(ValueError):
support_nddata(repack=True, keeps=['mask'], returns=['data', 'mask'])
def test_setup_failures4():
# function accepts *args
with pytest.raises(ValueError):
@support_nddata
def test(data, *args):
pass
def test_setup_failures10():
# function accepts **kwargs
with pytest.raises(ValueError):
@support_nddata
def test(data, **kwargs):
pass
def test_setup_failures5():
# function accepts *args (or **kwargs)
with pytest.raises(ValueError):
@support_nddata
def test(data, *args):
pass
def test_setup_failures6():
# First argument is not data
with pytest.raises(ValueError):
@support_nddata
def test(img):
pass
def test_setup_failures7():
# accepts CCDData but was given just an NDData
with pytest.raises(TypeError):
@support_nddata(accepts=CCDData)
def test(data):
pass
test(NDData(np.ones((3, 3))))
def test_setup_failures8():
# function returns a different amount of arguments than specified. Using
# NDData here so we don't get into troubles when creating a CCDData without
# unit!
with pytest.raises(ValueError):
@support_nddata(repack=True, returns=['data', 'mask'])
def test(data):
return 10
test(NDData(np.ones((3, 3)))) # do NOT use CCDData here.
def test_setup_failures11():
# function accepts no arguments
with pytest.raises(ValueError):
@support_nddata
def test():
pass
def test_setup_numpyarray_default():
# It should be possible (even if it's not advisable to use mutable
# defaults) to have a numpy array as default value.
@support_nddata
def func(data, wcs=np.array([1, 2, 3])):
return wcs
def test_still_accepts_other_input():
@support_nddata(repack=True, returns=['data'])
def test(data):
return data
assert isinstance(test(NDData(np.ones((3, 3)))), NDData)
assert isinstance(test(10), int)
assert isinstance(test([1, 2, 3]), list)
def test_accepting_property_normal():
# Accepts a mask attribute and takes it from the input
@support_nddata
def test(data, mask=None):
return mask
ndd = NDData(np.ones((3, 3)))
assert test(ndd) is None
ndd._mask = np.zeros((3, 3))
assert np.all(test(ndd) == 0)
# Use the explicitly given one (raises a Warning)
with catch_warnings(AstropyUserWarning) as w:
assert test(ndd, mask=10) == 10
assert len(w) == 1
def test_parameter_default_identical_to_explicit_passed_argument():
# If the default is identical to the explicitly passed argument this
# should still raise a Warning and use the explicit one.
@support_nddata
def func(data, wcs=[1, 2, 3]):
return wcs
with catch_warnings(AstropyUserWarning) as w:
assert func(NDData(1, wcs=[1, 2]), [1, 2, 3]) == [1, 2, 3]
assert len(w) == 1
with catch_warnings(AstropyUserWarning) as w:
assert func(NDData(1, wcs=[1, 2])) == [1, 2]
assert len(w) == 0
def test_accepting_property_notexist():
# Accepts flags attribute but NDData doesn't have one
@support_nddata
def test(data, flags=10):
return flags
ndd = NDData(np.ones((3, 3)))
test(ndd)
def test_accepting_property_translated():
# Accepts a error attribute and we want to pass in uncertainty!
@support_nddata(mask='masked')
def test(data, masked=None):
return masked
ndd = NDData(np.ones((3, 3)))
assert test(ndd) is None
ndd._mask = np.zeros((3, 3))
assert np.all(test(ndd) == 0)
# Use the explicitly given one (raises a Warning)
with catch_warnings(AstropyUserWarning) as w:
assert test(ndd, masked=10) == 10
assert len(w) == 1
def test_accepting_property_meta_empty():
# Meta is always set (OrderedDict) so it has a special case that it's
# ignored if it's empty but not None
@support_nddata
def test(data, meta=None):
return meta
ndd = NDData(np.ones((3, 3)))
assert test(ndd) is None
ndd._meta = {'a': 10}
assert test(ndd) == {'a': 10}
|
|
astropy/nddata/tests/test_decorators.py
|
astropy/coordinates/tests/test_frames.py
|
from traitlets import Unicode
from .. import utils
from . import NbGraderPreprocessor
class ClearSolutions(NbGraderPreprocessor):
code_stub = Unicode(
"# YOUR CODE HERE\nraise NotImplementedError()",
config=True,
help="The code snippet that will replace code solutions")
text_stub = Unicode(
"YOUR ANSWER HERE",
config=True,
help="The text snippet that will replace written solutions")
comment_mark = Unicode(
"#",
config=True,
help="The comment mark to prefix solution delimiters")
begin_solution_delimeter = Unicode(
"## BEGIN SOLUTION",
config=True,
help="The delimiter marking the beginning of a solution (excluding comment mark)")
end_solution_delimeter = Unicode(
"## END SOLUTION",
config=True,
help="The delimiter marking the end of a solution (excluding comment mark)")
@property
def begin_solution(self):
return "{}{}".format(self.comment_mark, self.begin_solution_delimeter)
@property
def end_solution(self):
return "{}{}".format(self.comment_mark, self.end_solution_delimeter)
def _replace_solution_region(self, cell):
"""Find a region in the cell that is delimeted by
`self.begin_solution` and `self.end_solution` (e.g. ### BEGIN
SOLUTION and ### END SOLUTION). Replace that region either
with the code stub or text stub, depending the cell type.
This modifies the cell in place, and then returns True if a
solution region was replaced, and False otherwise.
"""
# pull out the cell input/source
lines = cell.source.split("\n")
if cell.cell_type == "code":
stub_lines = self.code_stub.split("\n")
else:
stub_lines = self.text_stub.split("\n")
new_lines = []
in_solution = False
replaced_solution = False
for line in lines:
# begin the solution area
if line.strip() == self.begin_solution:
# check to make sure this isn't a nested BEGIN
# SOLUTION region
if in_solution:
raise RuntimeError(
"encountered nested begin solution statements")
in_solution = True
replaced_solution = True
# replace it with the stub, indented as necessary
indent = line[:line.find(self.begin_solution)]
for stub_line in stub_lines:
new_lines.append(indent + stub_line)
# end the solution area
elif line.strip() == self.end_solution:
in_solution = False
# add lines as long as it's not in the solution area
elif not in_solution:
new_lines.append(line)
# we finished going through all the lines, but didn't find a
# matching END SOLUTION statment
if in_solution:
raise RuntimeError("no end solution statement found")
# replace the cell source
cell.source = "\n".join(new_lines)
return replaced_solution
def preprocess(self, nb, resources):
nb, resources = super(ClearSolutions, self).preprocess(nb, resources)
if 'celltoolbar' in nb.metadata:
del nb.metadata['celltoolbar']
return nb, resources
def preprocess_cell(self, cell, resources, cell_index):
# replace solution regions with the relevant stubs
replaced_solution = self._replace_solution_region(cell)
# determine whether the cell is a solution/grade cell
is_solution = utils.is_solution(cell)
# check that it is marked as a solution cell if we replaced a solution
# region -- if it's not, then this is a problem, because the cell needs
# to be given an id
if not is_solution and replaced_solution:
raise RuntimeError(
"Solution region detected in a non-solution cell; "
"please make sure all solution regions are within "
"solution cells")
# replace solution cells with the code/text stub -- but not if
# we already replaced a solution region, because that means
# there are parts of the cells that should be preserved
if is_solution and not replaced_solution:
if cell.cell_type == 'code':
cell.source = self.code_stub
else:
cell.source = self.text_stub
return cell, resources
|
import pytest
from textwrap import dedent
from ...preprocessors import LimitOutput
from .base import BaseTestPreprocessor
from .. import create_code_cell, create_text_cell
@pytest.fixture
def preprocessor():
return LimitOutput()
class TestLimitOutput(BaseTestPreprocessor):
def test_long_output(self):
nb = self._read_nb("files/long-output.ipynb")
cell, = nb.cells
output, = cell.outputs
assert len(output.text.split("\n")) > 1000
pp = LimitOutput()
nb, resources = pp.preprocess(nb, {})
cell, = nb.cells
output, = cell.outputs
assert len(output.text.split("\n")) == 1000
def test_infinite_recursion(self):
nb = self._read_nb("files/infinite-recursion.ipynb")
pp = LimitOutput()
nb, resources = pp.preprocess(nb, {})
cell, = nb.cells
output, = cell.outputs
assert len(output.traceback) == 100
|
|
nbgrader/tests/preprocessors/test_limitoutput.py
|
nbgrader/preprocessors/clearsolutions.py
|
# -*- coding: utf-8 -*-
import os
import itertools
import furl
import requests
from framework.exceptions import HTTPError
class BaseClient(object):
@property
def _auth(self):
return None
@property
def _default_headers(self):
return {}
@property
def _default_params(self):
return {}
def _make_request(self, method, url, **kwargs):
expects = kwargs.pop('expects', None)
throws = kwargs.pop('throws', None)
kwargs['headers'] = self._build_defaults(self._default_headers, **kwargs.get('headers', {}))
kwargs['params'] = self._build_defaults(self._default_params, **kwargs.get('params', {}))
response = requests.request(method, url, auth=self._auth, **kwargs)
if expects and response.status_code not in expects:
raise throws if throws else HTTPError(response.status_code, message=response.content)
return response
def _build_defaults(self, defaults, **kwargs):
defaults.update(kwargs)
return {
key: value
for key, value in defaults.items()
if value is not None
}
def _build_url(self, base, *segments):
url = furl.furl(base)
segments = filter(
lambda segment: segment,
map(
lambda segment: str(segment).strip('/'),
itertools.chain(url.path.segments, segments)
)
)
url.path = os.path.join(*segments)
return url.url
|
import mock
import pytest
from nose.tools import * # flake8: noqa
from framework.auth.core import Auth
from website.models import Node
from website.util import permissions
from api.base.settings.defaults import API_BASE
from tests.base import ApiTestCase
from osf_tests.factories import (
NodeFactory,
ProjectFactory,
RegistrationFactory,
AuthUserFactory,
WithdrawnRegistrationFactory,
ForkFactory
)
class TestRegistrationForksList(ApiTestCase):
def setUp(self):
super(TestRegistrationForksList, self).setUp()
self.user = AuthUserFactory()
self.private_project = ProjectFactory(creator=self.user)
self.private_project.save()
self.component = NodeFactory(parent=self.private_project, creator=self.user)
self.pointer = ProjectFactory(creator=self.user)
self.private_project.add_pointer(self.pointer, auth=Auth(self.user), save=True)
self.private_registration = RegistrationFactory(project=self.private_project, creator=self.user)
self.private_fork = ForkFactory(project=self.private_registration, user=self.user)
self.private_registration_url = '/{}registrations/{}/forks/'.format(API_BASE, self.private_registration._id)
self.public_project = ProjectFactory(is_public=True, creator=self.user)
self.public_project.save()
self.public_component = NodeFactory(parent=self.public_project, creator=self.user, is_public=True)
self.public_registration = RegistrationFactory(project = self.public_project, creator=self.user, is_public=True)
self.public_registration_url = '/{}registrations/{}/forks/'.format(API_BASE, self.public_registration._id)
self.public_fork = ForkFactory(project=self.public_registration, user=self.user)
self.user_two = AuthUserFactory()
def test_can_access_public_registration_forks_list_when_unauthenticated(self):
res = self.app.get(self.public_registration_url)
assert_equal(len(res.json['data']), 0)
# Fork defaults to private
assert_equal(self.public_fork.is_public, False)
self.public_fork.is_public = True
self.public_fork.save()
res = self.app.get(self.public_registration_url)
assert_equal(res.status_code, 200)
assert_equal(len(res.json['data']), 1)
assert_equal(self.public_fork.is_public, True)
data = res.json['data'][0]
assert_equal(data['attributes']['title'], 'Fork of ' + self.public_registration.title)
assert_equal(data['id'], self.public_fork._id)
assert_equal(data['attributes']['registration'], False)
assert_equal(data['attributes']['fork'], True)
def test_can_access_public_registration_forks_list_authenticated_contributor(self):
res = self.app.get(self.public_registration_url, auth=self.user.auth)
assert_equal(res.status_code, 200)
assert_equal(self.public_fork.is_public, False)
assert_equal(len(res.json['data']), 1)
data = res.json['data'][0]
assert_equal(data['attributes']['title'], 'Fork of ' + self.public_project.title)
assert_equal(data['id'], self.public_fork._id)
assert_equal(data['attributes']['registration'], False)
assert_equal(data['attributes']['fork'], True)
def test_can_access_public_registration_forks_list_authenticated_non_contributor(self):
res = self.app.get(self.public_registration_url, auth=self.user_two.auth)
assert_equal(res.status_code, 200)
assert_equal(len(res.json['data']), 0)
# Fork defaults to private
assert_equal(self.public_fork.is_public, False)
self.public_fork.is_public = True
self.public_fork.save()
res = self.app.get(self.public_registration_url)
assert_equal(len(res.json['data']), 1)
assert_equal(self.public_fork.is_public, True)
data = res.json['data'][0]
assert_equal(data['attributes']['title'], 'Fork of ' + self.public_project.title)
assert_equal(data['id'], self.public_fork._id)
assert_equal(data['attributes']['registration'], False)
assert_equal(data['attributes']['fork'], True)
def test_cannot_access_private_registration_forks_list_unauthenticated(self):
res = self.app.get(self.private_registration_url, expect_errors=True)
assert_equal(res.status_code, 401)
assert_equal(res.json['errors'][0]['detail'], 'Authentication credentials were not provided.')
def test_authenticated_contributor_can_access_private_registration_forks_list(self):
res = self.app.get(self.private_registration_url + '?embed=children&embed=node_links&embed=logs&embed=contributors&embed=forked_from', auth=self.user.auth)
assert_equal(res.status_code, 200)
assert_equal(len(res.json['data']), 1)
data = res.json['data'][0]
assert_equal(data['attributes']['title'], 'Fork of ' + self.private_project.title)
assert_equal(data['id'], self.private_fork._id)
fork_contributors = data['embeds']['contributors']['data'][0]['embeds']['users']['data']
assert_equal(fork_contributors['attributes']['family_name'], self.user.family_name)
assert_equal(fork_contributors['id'], self.user._id)
forked_children = data['embeds']['children']['data'][0]
assert_equal(forked_children['id'], self.private_registration.forks.first().nodes.first()._id)
assert_equal(forked_children['attributes']['title'], self.component.title)
forked_node_links = data['embeds']['node_links']['data'][0]['embeds']['target_node']['data']
assert_equal(forked_node_links['id'], self.pointer._id)
assert_equal(forked_node_links['attributes']['title'], self.pointer.title)
assert_equal(data['attributes']['registration'], False)
assert_equal(data['attributes']['fork'], True)
expected_logs = list(self.private_registration.logs.values_list('action', flat=True))
expected_logs.append(self.private_registration.nodes.first().logs.latest().action)
expected_logs.append('node_forked')
expected_logs.append('node_forked')
forked_logs = data['embeds']['logs']['data']
assert_equal(set(expected_logs), set(log['attributes']['action'] for log in forked_logs))
assert_equal(len(forked_logs), 6)
forked_from = data['embeds']['forked_from']['data']
assert_equal(forked_from['id'], self.private_registration._id)
def test_authenticated_non_contributor_cannot_access_private_registration_forks_list(self):
res = self.app.get(self.private_registration_url, auth=self.user_two.auth, expect_errors=True)
assert_equal(res.status_code, 403)
assert_equal(res.json['errors'][0]['detail'], 'You do not have permission to perform this action.')
class TestRegistrationForkCreate(ApiTestCase):
def setUp(self):
super(TestRegistrationForkCreate, self).setUp()
self.user = AuthUserFactory()
self.user_two = AuthUserFactory()
self.user_three = AuthUserFactory()
self.private_project = ProjectFactory(creator=self.user)
private_pointer = ProjectFactory(creator=self.user_two)
actual_pointer = self.private_project.add_pointer(private_pointer, auth=Auth(self.user_two), save=True)
self.private_registration = RegistrationFactory(creator=self.user, project=self.private_project)
self.fork_data = {
'data': {
'type': 'nodes'
}
}
self.fork_data_with_title = {
'data': {
'type': 'nodes',
'attributes':
{'title': 'My Forked Project'}
}
}
self.private_registration_url = '/{}registrations/{}/forks/'.format(API_BASE, self.private_registration._id)
self.public_project = ProjectFactory(is_public=True, creator=self.user)
self.public_registration = RegistrationFactory(creator=self.user, project=self.public_project, is_public=True)
self.public_registration_url = '/{}registrations/{}/forks/'.format(API_BASE, self.public_registration._id)
def test_create_fork_from_public_registration_with_new_title(self):
res = self.app.post_json_api(self.public_registration_url, self.fork_data_with_title, auth=self.user.auth)
assert_equal(res.status_code, 201)
data = res.json['data']
assert_equal(data['id'], self.public_registration.forks.first()._id)
assert_equal(data['attributes']['title'], self.fork_data_with_title['data']['attributes']['title'])
assert_equal(data['attributes']['registration'], False)
assert_equal(data['attributes']['fork'], True)
def test_create_fork_from_private_registration_with_new_title(self):
res = self.app.post_json_api(self.private_registration_url, self.fork_data_with_title, auth=self.user.auth)
assert_equal(res.status_code, 201)
data = res.json['data']
assert_equal(data['id'], self.private_registration.forks.first()._id)
assert_equal(data['attributes']['title'], self.fork_data_with_title['data']['attributes']['title'])
assert_equal(data['attributes']['registration'], False)
assert_equal(data['attributes']['fork'], True)
def test_can_fork_public_registration_logged_in(self):
res = self.app.post_json_api(self.public_registration_url, self.fork_data, auth=self.user_two.auth)
assert_equal(res.status_code, 201)
data = res.json['data']
assert_equal(data['id'], self.public_registration.forks.first()._id)
assert_equal(data['attributes']['title'], 'Fork of ' + self.public_registration.title)
assert_equal(data['attributes']['registration'], False)
assert_equal(data['attributes']['fork'], True)
def test_cannot_fork_public_registration_logged_out(self):
res = self.app.post_json_api(self.public_registration_url, self.fork_data, expect_errors=True)
assert_equal(res.status_code, 401)
assert_equal(res.json['errors'][0]['detail'], 'Authentication credentials were not provided.')
def test_can_fork_public_registration_logged_in_contributor(self):
res = self.app.post_json_api(self.public_registration_url, self.fork_data, auth=self.user.auth)
assert_equal(res.status_code, 201)
data = res.json['data']
assert_equal(data['id'], self.public_registration.forks.first()._id)
assert_equal(data['attributes']['title'], 'Fork of ' + self.public_registration.title)
assert_equal(data['attributes']['registration'], False)
assert_equal(data['attributes']['fork'], True)
def test_cannot_fork_private_registration_logged_out(self):
res = self.app.post_json_api(self.private_registration_url, self.fork_data, expect_errors=True)
assert_equal(res.status_code, 401)
assert_equal(res.json['errors'][0]['detail'], 'Authentication credentials were not provided.')
def test_cannot_fork_private_registration_logged_in_non_contributor(self):
res = self.app.post_json_api(self.private_registration_url, self.fork_data, auth=self.user_two.auth, expect_errors=True)
assert_equal(res.status_code, 403)
assert_equal(res.json['errors'][0]['detail'], 'You do not have permission to perform this action.')
def test_can_fork_private_registration_logged_in_contributor(self):
res = self.app.post_json_api(self.private_registration_url + '?embed=children&embed=node_links&embed=logs&embed=contributors&embed=forked_from', self.fork_data, auth=self.user.auth)
assert_equal(res.status_code, 201)
data = res.json['data']
assert_equal(data['attributes']['title'], 'Fork of ' + self.private_registration.title)
assert_equal(data['attributes']['registration'], False)
assert_equal(data['attributes']['fork'], True)
fork_contributors = data['embeds']['contributors']['data'][0]['embeds']['users']['data']
assert_equal(fork_contributors['attributes']['family_name'], self.user.family_name)
assert_equal(fork_contributors['id'], self.user._id)
forked_from = data['embeds']['forked_from']['data']
assert_equal(forked_from['id'], self.private_registration._id)
def test_fork_private_components_no_access(self):
url = self.public_registration_url + '?embed=children'
private_component = NodeFactory(parent=self.public_registration, creator=self.user_two, is_public=False)
res = self.app.post_json_api(url, self.fork_data, auth=self.user_three.auth)
assert_equal(res.status_code, 201)
# Private components that you do not have access to are not forked
assert_equal(res.json['data']['embeds']['children']['links']['meta']['total'], 0)
def test_fork_components_you_can_access(self):
url = self.private_registration_url + '?embed=children'
new_component = NodeFactory(parent=self.private_registration, creator=self.user)
res = self.app.post_json_api(url, self.fork_data, auth=self.user.auth)
assert_equal(res.status_code, 201)
assert_equal(res.json['data']['embeds']['children']['links']['meta']['total'], 1)
assert_equal(res.json['data']['embeds']['children']['data'][0]['id'], new_component.forks.first()._id)
def test_fork_private_node_links(self):
url = self.private_registration_url + '?embed=node_links'
# Node link is forked, but shows up as a private node link
res = self.app.post_json_api(url, self.fork_data, auth=self.user.auth)
assert_equal(res.json['data']['embeds']['node_links']['data'][0]['embeds']['target_node']['errors'][0]['detail'],
'You do not have permission to perform this action.')
assert_equal(res.json['data']['embeds']['node_links']['links']['meta']['total'], 1)
def test_fork_node_links_you_can_access(self):
pointer = ProjectFactory(creator=self.user)
self.private_project.add_pointer(pointer, auth=Auth(self.user), save=True)
new_registration = RegistrationFactory(project = self.private_project, creator=self.user)
url = '/{}registrations/{}/forks/'.format(API_BASE, new_registration._id) + '?embed=node_links'
res = self.app.post_json_api(url, self.fork_data, auth=self.user.auth)
assert_equal(res.json['data']['embeds']['node_links']['data'][1]['embeds']['target_node']['data']['id'], pointer._id)
assert_equal(res.json['data']['embeds']['node_links']['links']['meta']['total'], 2)
def test_cannot_fork_retractions(self):
with mock.patch('osf.models.AbstractNode.update_search'):
retraction = WithdrawnRegistrationFactory(registration=self.private_registration, user=self.user)
url = '/{}registrations/{}/forks/'.format(API_BASE, self.private_registration._id) + '?embed=forked_from'
res = self.app.post_json_api(url, self.fork_data, auth=self.user.auth, expect_errors=True)
assert_equal(res.status_code, 403)
|
|
api_tests/registrations/views/test_registration_forks.py
|
|
"""
DataFrame
---------
An efficient 2D container for potentially mixed-type time series or other
labeled data series.
Similar to its R counterpart, data.frame, except providing automatic data
alignment and a host of useful data manipulation methods having to do with the
labeling information
"""
import collections
from collections import OrderedDict, abc
import functools
from io import StringIO
import itertools
import sys
import warnings
from textwrap import dedent
from typing import FrozenSet, List, Optional, Set, Type, Union
import numpy as np
import numpy.ma as ma
from pandas._config import get_option
from pandas._libs import lib, algos as libalgos
from pandas.util._decorators import (Appender, Substitution,
rewrite_axis_style_signature,
deprecate_kwarg)
from pandas.util._validators import (validate_bool_kwarg,
validate_axis_style_args)
from pandas.compat import PY36, raise_with_traceback
from pandas.compat.numpy import function as nv
from pandas.core.arrays.sparse import SparseFrameAccessor
from pandas.core.dtypes.cast import (
maybe_upcast,
cast_scalar_to_array,
infer_dtype_from_scalar,
maybe_cast_to_datetime,
maybe_infer_to_datetimelike,
maybe_convert_platform,
maybe_downcast_to_dtype,
invalidate_string_dtypes,
coerce_to_dtypes,
maybe_upcast_putmask,
find_common_type)
from pandas.core.dtypes.common import (
is_dict_like,
is_datetime64tz_dtype,
is_object_dtype,
is_extension_type,
is_extension_array_dtype,
is_datetime64_any_dtype,
is_bool_dtype,
is_integer_dtype,
is_float_dtype,
is_integer,
is_scalar,
is_dtype_equal,
needs_i8_conversion,
infer_dtype_from_object,
ensure_float64,
ensure_int64,
ensure_platform_int,
is_list_like,
is_nested_list_like,
is_iterator,
is_sequence,
is_named_tuple)
from pandas.core.dtypes.generic import (
ABCSeries, ABCDataFrame, ABCIndexClass, ABCMultiIndex)
from pandas.core.dtypes.missing import isna, notna
from pandas.core import algorithms
from pandas.core import common as com
from pandas.core import nanops
from pandas.core import ops
from pandas.core.accessor import CachedAccessor
from pandas.core.arrays import Categorical, ExtensionArray
from pandas.core.arrays.datetimelike import (
DatetimeLikeArrayMixin as DatetimeLikeArray
)
from pandas.core.generic import NDFrame, _shared_docs
from pandas.core.index import (Index, MultiIndex, ensure_index,
ensure_index_from_sequences)
from pandas.core.indexes import base as ibase
from pandas.core.indexes.datetimes import DatetimeIndex
from pandas.core.indexes.period import PeriodIndex
from pandas.core.indexing import (maybe_droplevels, convert_to_index_sliceable,
check_bool_indexer)
from pandas.core.internals import BlockManager
from pandas.core.internals.construction import (
masked_rec_array_to_mgr, get_names_from_index, to_arrays,
reorder_arrays, init_ndarray, init_dict,
arrays_to_mgr, sanitize_index)
from pandas.core.series import Series
from pandas.io.formats import console
from pandas.io.formats import format as fmt
from pandas.io.formats.printing import pprint_thing
import pandas.plotting
# ---------------------------------------------------------------------
# Docstring templates
_shared_doc_kwargs = dict(
axes='index, columns', klass='DataFrame',
axes_single_arg="{0 or 'index', 1 or 'columns'}",
axis="""axis : {0 or 'index', 1 or 'columns'}, default 0
If 0 or 'index': apply function to each column.
If 1 or 'columns': apply function to each row.""",
optional_by="""
by : str or list of str
Name or list of names to sort by.
- if `axis` is 0 or `'index'` then `by` may contain index
levels and/or column labels
- if `axis` is 1 or `'columns'` then `by` may contain column
levels and/or index labels
.. versionchanged:: 0.23.0
Allow specifying index or column level names.""",
versionadded_to_excel='',
optional_labels="""labels : array-like, optional
New labels / index to conform the axis specified by 'axis' to.""",
optional_axis="""axis : int or str, optional
Axis to target. Can be either the axis name ('index', 'columns')
or number (0, 1).""",
)
_numeric_only_doc = """numeric_only : boolean, default None
Include only float, int, boolean data. If None, will attempt to use
everything, then use only numeric data
"""
_merge_doc = """
Merge DataFrame or named Series objects with a database-style join.
The join is done on columns or indexes. If joining columns on
columns, the DataFrame indexes *will be ignored*. Otherwise if joining indexes
on indexes or indexes on a column or columns, the index will be passed on.
Parameters
----------%s
right : DataFrame or named Series
Object to merge with.
how : {'left', 'right', 'outer', 'inner'}, default 'inner'
Type of merge to be performed.
* left: use only keys from left frame, similar to a SQL left outer join;
preserve key order.
* right: use only keys from right frame, similar to a SQL right outer join;
preserve key order.
* outer: use union of keys from both frames, similar to a SQL full outer
join; sort keys lexicographically.
* inner: use intersection of keys from both frames, similar to a SQL inner
join; preserve the order of the left keys.
on : label or list
Column or index level names to join on. These must be found in both
DataFrames. If `on` is None and not merging on indexes then this defaults
to the intersection of the columns in both DataFrames.
left_on : label or list, or array-like
Column or index level names to join on in the left DataFrame. Can also
be an array or list of arrays of the length of the left DataFrame.
These arrays are treated as if they are columns.
right_on : label or list, or array-like
Column or index level names to join on in the right DataFrame. Can also
be an array or list of arrays of the length of the right DataFrame.
These arrays are treated as if they are columns.
left_index : bool, default False
Use the index from the left DataFrame as the join key(s). If it is a
MultiIndex, the number of keys in the other DataFrame (either the index
or a number of columns) must match the number of levels.
right_index : bool, default False
Use the index from the right DataFrame as the join key. Same caveats as
left_index.
sort : bool, default False
Sort the join keys lexicographically in the result DataFrame. If False,
the order of the join keys depends on the join type (how keyword).
suffixes : tuple of (str, str), default ('_x', '_y')
Suffix to apply to overlapping column names in the left and right
side, respectively. To raise an exception on overlapping columns use
(False, False).
copy : bool, default True
If False, avoid copy if possible.
indicator : bool or str, default False
If True, adds a column to output DataFrame called "_merge" with
information on the source of each row.
If string, column with information on source of each row will be added to
output DataFrame, and column will be named value of string.
Information column is Categorical-type and takes on a value of "left_only"
for observations whose merge key only appears in 'left' DataFrame,
"right_only" for observations whose merge key only appears in 'right'
DataFrame, and "both" if the observation's merge key is found in both.
validate : str, optional
If specified, checks if merge is of specified type.
* "one_to_one" or "1:1": check if merge keys are unique in both
left and right datasets.
* "one_to_many" or "1:m": check if merge keys are unique in left
dataset.
* "many_to_one" or "m:1": check if merge keys are unique in right
dataset.
* "many_to_many" or "m:m": allowed, but does not result in checks.
.. versionadded:: 0.21.0
Returns
-------
DataFrame
A DataFrame of the two merged objects.
See Also
--------
merge_ordered : Merge with optional filling/interpolation.
merge_asof : Merge on nearest keys.
DataFrame.join : Similar method using indices.
Notes
-----
Support for specifying index levels as the `on`, `left_on`, and
`right_on` parameters was added in version 0.23.0
Support for merging named Series objects was added in version 0.24.0
Examples
--------
>>> df1 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],
... 'value': [1, 2, 3, 5]})
>>> df2 = pd.DataFrame({'rkey': ['foo', 'bar', 'baz', 'foo'],
... 'value': [5, 6, 7, 8]})
>>> df1
lkey value
0 foo 1
1 bar 2
2 baz 3
3 foo 5
>>> df2
rkey value
0 foo 5
1 bar 6
2 baz 7
3 foo 8
Merge df1 and df2 on the lkey and rkey columns. The value columns have
the default suffixes, _x and _y, appended.
>>> df1.merge(df2, left_on='lkey', right_on='rkey')
lkey value_x rkey value_y
0 foo 1 foo 5
1 foo 1 foo 8
2 foo 5 foo 5
3 foo 5 foo 8
4 bar 2 bar 6
5 baz 3 baz 7
Merge DataFrames df1 and df2 with specified left and right suffixes
appended to any overlapping columns.
>>> df1.merge(df2, left_on='lkey', right_on='rkey',
... suffixes=('_left', '_right'))
lkey value_left rkey value_right
0 foo 1 foo 5
1 foo 1 foo 8
2 foo 5 foo 5
3 foo 5 foo 8
4 bar 2 bar 6
5 baz 3 baz 7
Merge DataFrames df1 and df2, but raise an exception if the DataFrames have
any overlapping columns.
>>> df1.merge(df2, left_on='lkey', right_on='rkey', suffixes=(False, False))
Traceback (most recent call last):
...
ValueError: columns overlap but no suffix specified:
Index(['value'], dtype='object')
"""
# -----------------------------------------------------------------------
# DataFrame class
class DataFrame(NDFrame):
"""
Two-dimensional size-mutable, potentially heterogeneous tabular data
structure with labeled axes (rows and columns). Arithmetic operations
align on both row and column labels. Can be thought of as a dict-like
container for Series objects. The primary pandas data structure.
Parameters
----------
data : ndarray (structured or homogeneous), Iterable, dict, or DataFrame
Dict can contain Series, arrays, constants, or list-like objects
.. versionchanged :: 0.23.0
If data is a dict, argument order is maintained for Python 3.6
and later.
index : Index or array-like
Index to use for resulting frame. Will default to RangeIndex if
no indexing information part of input data and no index provided
columns : Index or array-like
Column labels to use for resulting frame. Will default to
RangeIndex (0, 1, 2, ..., n) if no column labels are provided
dtype : dtype, default None
Data type to force. Only a single dtype is allowed. If None, infer
copy : boolean, default False
Copy data from inputs. Only affects DataFrame / 2d ndarray input
See Also
--------
DataFrame.from_records : Constructor from tuples, also record arrays.
DataFrame.from_dict : From dicts of Series, arrays, or dicts.
DataFrame.from_items : From sequence of (key, value) pairs
read_csv, pandas.read_table, pandas.read_clipboard.
Examples
--------
Constructing DataFrame from a dictionary.
>>> d = {'col1': [1, 2], 'col2': [3, 4]}
>>> df = pd.DataFrame(data=d)
>>> df
col1 col2
0 1 3
1 2 4
Notice that the inferred dtype is int64.
>>> df.dtypes
col1 int64
col2 int64
dtype: object
To enforce a single dtype:
>>> df = pd.DataFrame(data=d, dtype=np.int8)
>>> df.dtypes
col1 int8
col2 int8
dtype: object
Constructing DataFrame from numpy ndarray:
>>> df2 = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),
... columns=['a', 'b', 'c'])
>>> df2
a b c
0 1 2 3
1 4 5 6
2 7 8 9
"""
@property
def _constructor(self):
return DataFrame
_constructor_sliced = Series # type: Type[Series]
_deprecations = NDFrame._deprecations | frozenset([
'get_value', 'set_value', 'from_csv', 'from_items'
]) # type: FrozenSet[str]
_accessors = set() # type: Set[str]
@property
def _constructor_expanddim(self):
raise NotImplementedError("Not supported for DataFrames!")
# ----------------------------------------------------------------------
# Constructors
def __init__(self, data=None, index=None, columns=None, dtype=None,
copy=False):
if data is None:
data = {}
if dtype is not None:
dtype = self._validate_dtype(dtype)
if isinstance(data, DataFrame):
data = data._data
if isinstance(data, BlockManager):
mgr = self._init_mgr(data, axes=dict(index=index, columns=columns),
dtype=dtype, copy=copy)
elif isinstance(data, dict):
mgr = init_dict(data, index, columns, dtype=dtype)
elif isinstance(data, ma.MaskedArray):
import numpy.ma.mrecords as mrecords
# masked recarray
if isinstance(data, mrecords.MaskedRecords):
mgr = masked_rec_array_to_mgr(data, index, columns, dtype,
copy)
# a masked array
else:
mask = ma.getmaskarray(data)
if mask.any():
data, fill_value = maybe_upcast(data, copy=True)
data.soften_mask() # set hardmask False if it was True
data[mask] = fill_value
else:
data = data.copy()
mgr = init_ndarray(data, index, columns, dtype=dtype,
copy=copy)
elif isinstance(data, (np.ndarray, Series, Index)):
if data.dtype.names:
data_columns = list(data.dtype.names)
data = {k: data[k] for k in data_columns}
if columns is None:
columns = data_columns
mgr = init_dict(data, index, columns, dtype=dtype)
elif getattr(data, 'name', None) is not None:
mgr = init_dict({data.name: data}, index, columns,
dtype=dtype)
else:
mgr = init_ndarray(data, index, columns, dtype=dtype,
copy=copy)
# For data is list-like, or Iterable (will consume into list)
elif (isinstance(data, abc.Iterable) and
not isinstance(data, (str, bytes))):
if not isinstance(data, abc.Sequence):
data = list(data)
if len(data) > 0:
if is_list_like(data[0]) and getattr(data[0], 'ndim', 1) == 1:
if is_named_tuple(data[0]) and columns is None:
columns = data[0]._fields
arrays, columns = to_arrays(data, columns, dtype=dtype)
columns = ensure_index(columns)
# set the index
if index is None:
if isinstance(data[0], Series):
index = get_names_from_index(data)
elif isinstance(data[0], Categorical):
index = ibase.default_index(len(data[0]))
else:
index = ibase.default_index(len(data))
mgr = arrays_to_mgr(arrays, columns, index, columns,
dtype=dtype)
else:
mgr = init_ndarray(data, index, columns, dtype=dtype,
copy=copy)
else:
mgr = init_dict({}, index, columns, dtype=dtype)
else:
try:
arr = np.array(data, dtype=dtype, copy=copy)
except (ValueError, TypeError) as e:
exc = TypeError('DataFrame constructor called with '
'incompatible data and dtype: {e}'.format(e=e))
raise_with_traceback(exc)
if arr.ndim == 0 and index is not None and columns is not None:
values = cast_scalar_to_array((len(index), len(columns)),
data, dtype=dtype)
mgr = init_ndarray(values, index, columns,
dtype=values.dtype, copy=False)
else:
raise ValueError('DataFrame constructor not properly called!')
NDFrame.__init__(self, mgr, fastpath=True)
# ----------------------------------------------------------------------
@property
def axes(self):
"""
Return a list representing the axes of the DataFrame.
It has the row axis labels and column axis labels as the only members.
They are returned in that order.
Examples
--------
>>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df.axes
[RangeIndex(start=0, stop=2, step=1), Index(['col1', 'col2'],
dtype='object')]
"""
return [self.index, self.columns]
@property
def shape(self):
"""
Return a tuple representing the dimensionality of the DataFrame.
See Also
--------
ndarray.shape
Examples
--------
>>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df.shape
(2, 2)
>>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4],
... 'col3': [5, 6]})
>>> df.shape
(2, 3)
"""
return len(self.index), len(self.columns)
@property
def _is_homogeneous_type(self):
"""
Whether all the columns in a DataFrame have the same type.
Returns
-------
bool
Examples
--------
>>> DataFrame({"A": [1, 2], "B": [3, 4]})._is_homogeneous_type
True
>>> DataFrame({"A": [1, 2], "B": [3.0, 4.0]})._is_homogeneous_type
False
Items with the same type but different sizes are considered
different types.
>>> DataFrame({
... "A": np.array([1, 2], dtype=np.int32),
... "B": np.array([1, 2], dtype=np.int64)})._is_homogeneous_type
False
"""
if self._data.any_extension_types:
return len({block.dtype for block in self._data.blocks}) == 1
else:
return not self._data.is_mixed_type
# ----------------------------------------------------------------------
# Rendering Methods
def _repr_fits_vertical_(self):
"""
Check length against max_rows.
"""
max_rows = get_option("display.max_rows")
return len(self) <= max_rows
def _repr_fits_horizontal_(self, ignore_width=False):
"""
Check if full repr fits in horizontal boundaries imposed by the display
options width and max_columns.
In case off non-interactive session, no boundaries apply.
`ignore_width` is here so ipnb+HTML output can behave the way
users expect. display.max_columns remains in effect.
GH3541, GH3573
"""
width, height = console.get_console_size()
max_columns = get_option("display.max_columns")
nb_columns = len(self.columns)
# exceed max columns
if ((max_columns and nb_columns > max_columns) or
((not ignore_width) and width and nb_columns > (width // 2))):
return False
# used by repr_html under IPython notebook or scripts ignore terminal
# dims
if ignore_width or not console.in_interactive_session():
return True
if (get_option('display.width') is not None or
console.in_ipython_frontend()):
# check at least the column row for excessive width
max_rows = 1
else:
max_rows = get_option("display.max_rows")
# when auto-detecting, so width=None and not in ipython front end
# check whether repr fits horizontal by actually checking
# the width of the rendered repr
buf = StringIO()
# only care about the stuff we'll actually print out
# and to_string on entire frame may be expensive
d = self
if not (max_rows is None): # unlimited rows
# min of two, where one may be None
d = d.iloc[:min(max_rows, len(d))]
else:
return True
d.to_string(buf=buf)
value = buf.getvalue()
repr_width = max(len(l) for l in value.split('\n'))
return repr_width < width
def _info_repr(self):
"""
True if the repr should show the info view.
"""
info_repr_option = (get_option("display.large_repr") == "info")
return info_repr_option and not (self._repr_fits_horizontal_() and
self._repr_fits_vertical_())
def __repr__(self):
"""
Return a string representation for a particular DataFrame.
"""
buf = StringIO("")
if self._info_repr():
self.info(buf=buf)
return buf.getvalue()
max_rows = get_option("display.max_rows")
max_cols = get_option("display.max_columns")
show_dimensions = get_option("display.show_dimensions")
if get_option("display.expand_frame_repr"):
width, _ = console.get_console_size()
else:
width = None
self.to_string(buf=buf, max_rows=max_rows, max_cols=max_cols,
line_width=width, show_dimensions=show_dimensions)
return buf.getvalue()
def _repr_html_(self):
"""
Return a html representation for a particular DataFrame.
Mainly for IPython notebook.
"""
if self._info_repr():
buf = StringIO("")
self.info(buf=buf)
# need to escape the <class>, should be the first line.
val = buf.getvalue().replace('<', r'<', 1)
val = val.replace('>', r'>', 1)
return '<pre>' + val + '</pre>'
if get_option("display.notebook_repr_html"):
max_rows = get_option("display.max_rows")
max_cols = get_option("display.max_columns")
show_dimensions = get_option("display.show_dimensions")
return self.to_html(max_rows=max_rows, max_cols=max_cols,
show_dimensions=show_dimensions, notebook=True)
else:
return None
@Substitution(header='Write out the column names. If a list of strings '
'is given, it is assumed to be aliases for the '
'column names',
col_space_type='int',
col_space='The minimum width of each column')
@Substitution(shared_params=fmt.common_docstring,
returns=fmt.return_docstring)
def to_string(self, buf=None, columns=None, col_space=None, header=True,
index=True, na_rep='NaN', formatters=None, float_format=None,
sparsify=None, index_names=True, justify=None,
max_rows=None, max_cols=None, show_dimensions=False,
decimal='.', line_width=None):
"""
Render a DataFrame to a console-friendly tabular output.
%(shared_params)s
line_width : int, optional
Width to wrap a line in characters.
%(returns)s
See Also
--------
to_html : Convert DataFrame to HTML.
Examples
--------
>>> d = {'col1': [1, 2, 3], 'col2': [4, 5, 6]}
>>> df = pd.DataFrame(d)
>>> print(df.to_string())
col1 col2
0 1 4
1 2 5
2 3 6
"""
formatter = fmt.DataFrameFormatter(self, buf=buf, columns=columns,
col_space=col_space, na_rep=na_rep,
formatters=formatters,
float_format=float_format,
sparsify=sparsify, justify=justify,
index_names=index_names,
header=header, index=index,
max_rows=max_rows,
max_cols=max_cols,
show_dimensions=show_dimensions,
decimal=decimal,
line_width=line_width)
formatter.to_string()
if buf is None:
result = formatter.buf.getvalue()
return result
# ----------------------------------------------------------------------
@property
def style(self):
"""
Property returning a Styler object containing methods for
building a styled HTML representation fo the DataFrame.
See Also
--------
io.formats.style.Styler
"""
from pandas.io.formats.style import Styler
return Styler(self)
def iteritems(self):
r"""
Iterator over (column name, Series) pairs.
Iterates over the DataFrame columns, returning a tuple with
the column name and the content as a Series.
Yields
------
label : object
The column names for the DataFrame being iterated over.
content : Series
The column entries belonging to each label, as a Series.
See Also
--------
DataFrame.iterrows : Iterate over DataFrame rows as
(index, Series) pairs.
DataFrame.itertuples : Iterate over DataFrame rows as namedtuples
of the values.
Examples
--------
>>> df = pd.DataFrame({'species': ['bear', 'bear', 'marsupial'],
... 'population': [1864, 22000, 80000]},
... index=['panda', 'polar', 'koala'])
>>> df
species population
panda bear 1864
polar bear 22000
koala marsupial 80000
>>> for label, content in df.iteritems():
... print('label:', label)
... print('content:', content, sep='\n')
...
label: species
content:
panda bear
polar bear
koala marsupial
Name: species, dtype: object
label: population
content:
panda 1864
polar 22000
koala 80000
Name: population, dtype: int64
"""
if self.columns.is_unique and hasattr(self, '_item_cache'):
for k in self.columns:
yield k, self._get_item_cache(k)
else:
for i, k in enumerate(self.columns):
yield k, self._ixs(i, axis=1)
def iterrows(self):
"""
Iterate over DataFrame rows as (index, Series) pairs.
Yields
------
index : label or tuple of label
The index of the row. A tuple for a `MultiIndex`.
data : Series
The data of the row as a Series.
it : generator
A generator that iterates over the rows of the frame.
See Also
--------
itertuples : Iterate over DataFrame rows as namedtuples of the values.
iteritems : Iterate over (column name, Series) pairs.
Notes
-----
1. Because ``iterrows`` returns a Series for each row,
it does **not** preserve dtypes across the rows (dtypes are
preserved across columns for DataFrames). For example,
>>> df = pd.DataFrame([[1, 1.5]], columns=['int', 'float'])
>>> row = next(df.iterrows())[1]
>>> row
int 1.0
float 1.5
Name: 0, dtype: float64
>>> print(row['int'].dtype)
float64
>>> print(df['int'].dtype)
int64
To preserve dtypes while iterating over the rows, it is better
to use :meth:`itertuples` which returns namedtuples of the values
and which is generally faster than ``iterrows``.
2. You should **never modify** something you are iterating over.
This is not guaranteed to work in all cases. Depending on the
data types, the iterator returns a copy and not a view, and writing
to it will have no effect.
"""
columns = self.columns
klass = self._constructor_sliced
for k, v in zip(self.index, self.values):
s = klass(v, index=columns, name=k)
yield k, s
def itertuples(self, index=True, name="Pandas"):
"""
Iterate over DataFrame rows as namedtuples.
Parameters
----------
index : bool, default True
If True, return the index as the first element of the tuple.
name : str or None, default "Pandas"
The name of the returned namedtuples or None to return regular
tuples.
Returns
-------
iterator
An object to iterate over namedtuples for each row in the
DataFrame with the first field possibly being the index and
following fields being the column values.
See Also
--------
DataFrame.iterrows : Iterate over DataFrame rows as (index, Series)
pairs.
DataFrame.iteritems : Iterate over (column name, Series) pairs.
Notes
-----
The column names will be renamed to positional names if they are
invalid Python identifiers, repeated, or start with an underscore.
With a large number of columns (>255), regular tuples are returned.
Examples
--------
>>> df = pd.DataFrame({'num_legs': [4, 2], 'num_wings': [0, 2]},
... index=['dog', 'hawk'])
>>> df
num_legs num_wings
dog 4 0
hawk 2 2
>>> for row in df.itertuples():
... print(row)
...
Pandas(Index='dog', num_legs=4, num_wings=0)
Pandas(Index='hawk', num_legs=2, num_wings=2)
By setting the `index` parameter to False we can remove the index
as the first element of the tuple:
>>> for row in df.itertuples(index=False):
... print(row)
...
Pandas(num_legs=4, num_wings=0)
Pandas(num_legs=2, num_wings=2)
With the `name` parameter set we set a custom name for the yielded
namedtuples:
>>> for row in df.itertuples(name='Animal'):
... print(row)
...
Animal(Index='dog', num_legs=4, num_wings=0)
Animal(Index='hawk', num_legs=2, num_wings=2)
"""
arrays = []
fields = list(self.columns)
if index:
arrays.append(self.index)
fields.insert(0, "Index")
# use integer indexing because of possible duplicate column names
arrays.extend(self.iloc[:, k] for k in range(len(self.columns)))
# Python 3 supports at most 255 arguments to constructor
if name is not None and len(self.columns) + index < 256:
itertuple = collections.namedtuple(name, fields, rename=True)
return map(itertuple._make, zip(*arrays))
# fallback to regular tuples
return zip(*arrays)
items = iteritems
def __len__(self):
"""
Returns length of info axis, but here we use the index.
"""
return len(self.index)
def dot(self, other):
"""
Compute the matrix multiplication between the DataFrame and other.
This method computes the matrix product between the DataFrame and the
values of an other Series, DataFrame or a numpy array.
It can also be called using ``self @ other`` in Python >= 3.5.
Parameters
----------
other : Series, DataFrame or array-like
The other object to compute the matrix product with.
Returns
-------
Series or DataFrame
If other is a Series, return the matrix product between self and
other as a Serie. If other is a DataFrame or a numpy.array, return
the matrix product of self and other in a DataFrame of a np.array.
See Also
--------
Series.dot: Similar method for Series.
Notes
-----
The dimensions of DataFrame and other must be compatible in order to
compute the matrix multiplication. In addition, the column names of
DataFrame and the index of other must contain the same values, as they
will be aligned prior to the multiplication.
The dot method for Series computes the inner product, instead of the
matrix product here.
Examples
--------
Here we multiply a DataFrame with a Series.
>>> df = pd.DataFrame([[0, 1, -2, -1], [1, 1, 1, 1]])
>>> s = pd.Series([1, 1, 2, 1])
>>> df.dot(s)
0 -4
1 5
dtype: int64
Here we multiply a DataFrame with another DataFrame.
>>> other = pd.DataFrame([[0, 1], [1, 2], [-1, -1], [2, 0]])
>>> df.dot(other)
0 1
0 1 4
1 2 2
Note that the dot method give the same result as @
>>> df @ other
0 1
0 1 4
1 2 2
The dot method works also if other is an np.array.
>>> arr = np.array([[0, 1], [1, 2], [-1, -1], [2, 0]])
>>> df.dot(arr)
0 1
0 1 4
1 2 2
Note how shuffling of the objects does not change the result.
>>> s2 = s.reindex([1, 0, 2, 3])
>>> df.dot(s2)
0 -4
1 5
dtype: int64
"""
if isinstance(other, (Series, DataFrame)):
common = self.columns.union(other.index)
if (len(common) > len(self.columns) or
len(common) > len(other.index)):
raise ValueError('matrices are not aligned')
left = self.reindex(columns=common, copy=False)
right = other.reindex(index=common, copy=False)
lvals = left.values
rvals = right.values
else:
left = self
lvals = self.values
rvals = np.asarray(other)
if lvals.shape[1] != rvals.shape[0]:
raise ValueError('Dot product shape mismatch, '
'{s} vs {r}'.format(s=lvals.shape,
r=rvals.shape))
if isinstance(other, DataFrame):
return self._constructor(np.dot(lvals, rvals), index=left.index,
columns=other.columns)
elif isinstance(other, Series):
return Series(np.dot(lvals, rvals), index=left.index)
elif isinstance(rvals, (np.ndarray, Index)):
result = np.dot(lvals, rvals)
if result.ndim == 2:
return self._constructor(result, index=left.index)
else:
return Series(result, index=left.index)
else: # pragma: no cover
raise TypeError('unsupported type: {oth}'.format(oth=type(other)))
def __matmul__(self, other):
"""
Matrix multiplication using binary `@` operator in Python>=3.5.
"""
return self.dot(other)
def __rmatmul__(self, other):
"""
Matrix multiplication using binary `@` operator in Python>=3.5.
"""
return self.T.dot(np.transpose(other)).T
# ----------------------------------------------------------------------
# IO methods (to / from other formats)
@classmethod
def from_dict(cls, data, orient='columns', dtype=None, columns=None):
"""
Construct DataFrame from dict of array-like or dicts.
Creates DataFrame object from dictionary by columns or by index
allowing dtype specification.
Parameters
----------
data : dict
Of the form {field : array-like} or {field : dict}.
orient : {'columns', 'index'}, default 'columns'
The "orientation" of the data. If the keys of the passed dict
should be the columns of the resulting DataFrame, pass 'columns'
(default). Otherwise if the keys should be rows, pass 'index'.
dtype : dtype, default None
Data type to force, otherwise infer.
columns : list, default None
Column labels to use when ``orient='index'``. Raises a ValueError
if used with ``orient='columns'``.
.. versionadded:: 0.23.0
Returns
-------
DataFrame
See Also
--------
DataFrame.from_records : DataFrame from ndarray (structured
dtype), list of tuples, dict, or DataFrame.
DataFrame : DataFrame object creation using constructor.
Examples
--------
By default the keys of the dict become the DataFrame columns:
>>> data = {'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']}
>>> pd.DataFrame.from_dict(data)
col_1 col_2
0 3 a
1 2 b
2 1 c
3 0 d
Specify ``orient='index'`` to create the DataFrame using dictionary
keys as rows:
>>> data = {'row_1': [3, 2, 1, 0], 'row_2': ['a', 'b', 'c', 'd']}
>>> pd.DataFrame.from_dict(data, orient='index')
0 1 2 3
row_1 3 2 1 0
row_2 a b c d
When using the 'index' orientation, the column names can be
specified manually:
>>> pd.DataFrame.from_dict(data, orient='index',
... columns=['A', 'B', 'C', 'D'])
A B C D
row_1 3 2 1 0
row_2 a b c d
"""
index = None
orient = orient.lower()
if orient == 'index':
if len(data) > 0:
# TODO speed up Series case
if isinstance(list(data.values())[0], (Series, dict)):
data = _from_nested_dict(data)
else:
data, index = list(data.values()), list(data.keys())
elif orient == 'columns':
if columns is not None:
raise ValueError("cannot use columns parameter with "
"orient='columns'")
else: # pragma: no cover
raise ValueError('only recognize index or columns for orient')
return cls(data, index=index, columns=columns, dtype=dtype)
def to_numpy(self, dtype=None, copy=False):
"""
Convert the DataFrame to a NumPy array.
.. versionadded:: 0.24.0
By default, the dtype of the returned array will be the common NumPy
dtype of all types in the DataFrame. For example, if the dtypes are
``float16`` and ``float32``, the results dtype will be ``float32``.
This may require copying data and coercing values, which may be
expensive.
Parameters
----------
dtype : str or numpy.dtype, optional
The dtype to pass to :meth:`numpy.asarray`
copy : bool, default False
Whether to ensure that the returned value is a not a view on
another array. Note that ``copy=False`` does not *ensure* that
``to_numpy()`` is no-copy. Rather, ``copy=True`` ensure that
a copy is made, even if not strictly necessary.
Returns
-------
numpy.ndarray
See Also
--------
Series.to_numpy : Similar method for Series.
Examples
--------
>>> pd.DataFrame({"A": [1, 2], "B": [3, 4]}).to_numpy()
array([[1, 3],
[2, 4]])
With heterogenous data, the lowest common type will have to
be used.
>>> df = pd.DataFrame({"A": [1, 2], "B": [3.0, 4.5]})
>>> df.to_numpy()
array([[1. , 3. ],
[2. , 4.5]])
For a mix of numeric and non-numeric types, the output array will
have object dtype.
>>> df['C'] = pd.date_range('2000', periods=2)
>>> df.to_numpy()
array([[1, 3.0, Timestamp('2000-01-01 00:00:00')],
[2, 4.5, Timestamp('2000-01-02 00:00:00')]], dtype=object)
"""
result = np.array(self.values, dtype=dtype, copy=copy)
return result
def to_dict(self, orient='dict', into=dict):
"""
Convert the DataFrame to a dictionary.
The type of the key-value pairs can be customized with the parameters
(see below).
Parameters
----------
orient : str {'dict', 'list', 'series', 'split', 'records', 'index'}
Determines the type of the values of the dictionary.
- 'dict' (default) : dict like {column -> {index -> value}}
- 'list' : dict like {column -> [values]}
- 'series' : dict like {column -> Series(values)}
- 'split' : dict like
{'index' -> [index], 'columns' -> [columns], 'data' -> [values]}
- 'records' : list like
[{column -> value}, ... , {column -> value}]
- 'index' : dict like {index -> {column -> value}}
Abbreviations are allowed. `s` indicates `series` and `sp`
indicates `split`.
into : class, default dict
The collections.abc.Mapping subclass used for all Mappings
in the return value. Can be the actual class or an empty
instance of the mapping type you want. If you want a
collections.defaultdict, you must pass it initialized.
.. versionadded:: 0.21.0
Returns
-------
dict, list or collections.abc.Mapping
Return a collections.abc.Mapping object representing the DataFrame.
The resulting transformation depends on the `orient` parameter.
See Also
--------
DataFrame.from_dict: Create a DataFrame from a dictionary.
DataFrame.to_json: Convert a DataFrame to JSON format.
Examples
--------
>>> df = pd.DataFrame({'col1': [1, 2],
... 'col2': [0.5, 0.75]},
... index=['row1', 'row2'])
>>> df
col1 col2
row1 1 0.50
row2 2 0.75
>>> df.to_dict()
{'col1': {'row1': 1, 'row2': 2}, 'col2': {'row1': 0.5, 'row2': 0.75}}
You can specify the return orientation.
>>> df.to_dict('series')
{'col1': row1 1
row2 2
Name: col1, dtype: int64,
'col2': row1 0.50
row2 0.75
Name: col2, dtype: float64}
>>> df.to_dict('split')
{'index': ['row1', 'row2'], 'columns': ['col1', 'col2'],
'data': [[1, 0.5], [2, 0.75]]}
>>> df.to_dict('records')
[{'col1': 1, 'col2': 0.5}, {'col1': 2, 'col2': 0.75}]
>>> df.to_dict('index')
{'row1': {'col1': 1, 'col2': 0.5}, 'row2': {'col1': 2, 'col2': 0.75}}
You can also specify the mapping type.
>>> from collections import OrderedDict, defaultdict
>>> df.to_dict(into=OrderedDict)
OrderedDict([('col1', OrderedDict([('row1', 1), ('row2', 2)])),
('col2', OrderedDict([('row1', 0.5), ('row2', 0.75)]))])
If you want a `defaultdict`, you need to initialize it:
>>> dd = defaultdict(list)
>>> df.to_dict('records', into=dd)
[defaultdict(<class 'list'>, {'col1': 1, 'col2': 0.5}),
defaultdict(<class 'list'>, {'col1': 2, 'col2': 0.75})]
"""
if not self.columns.is_unique:
warnings.warn("DataFrame columns are not unique, some "
"columns will be omitted.", UserWarning,
stacklevel=2)
# GH16122
into_c = com.standardize_mapping(into)
if orient.lower().startswith('d'):
return into_c(
(k, v.to_dict(into)) for k, v in self.items())
elif orient.lower().startswith('l'):
return into_c((k, v.tolist()) for k, v in self.items())
elif orient.lower().startswith('sp'):
return into_c((('index', self.index.tolist()),
('columns', self.columns.tolist()),
('data', [
list(map(com.maybe_box_datetimelike, t))
for t in self.itertuples(index=False, name=None)
])))
elif orient.lower().startswith('s'):
return into_c((k, com.maybe_box_datetimelike(v))
for k, v in self.items())
elif orient.lower().startswith('r'):
columns = self.columns.tolist()
rows = (dict(zip(columns, row))
for row in self.itertuples(index=False, name=None))
return [
into_c((k, com.maybe_box_datetimelike(v))
for k, v in row.items())
for row in rows]
elif orient.lower().startswith('i'):
if not self.index.is_unique:
raise ValueError(
"DataFrame index must be unique for orient='index'."
)
return into_c((t[0], dict(zip(self.columns, t[1:])))
for t in self.itertuples(name=None))
else:
raise ValueError("orient '{o}' not understood".format(o=orient))
def to_gbq(self, destination_table, project_id=None, chunksize=None,
reauth=False, if_exists='fail', auth_local_webserver=False,
table_schema=None, location=None, progress_bar=True,
credentials=None, verbose=None, private_key=None):
"""
Write a DataFrame to a Google BigQuery table.
This function requires the `pandas-gbq package
<https://pandas-gbq.readthedocs.io>`__.
See the `How to authenticate with Google BigQuery
<https://pandas-gbq.readthedocs.io/en/latest/howto/authentication.html>`__
guide for authentication instructions.
Parameters
----------
destination_table : str
Name of table to be written, in the form ``dataset.tablename``.
project_id : str, optional
Google BigQuery Account project ID. Optional when available from
the environment.
chunksize : int, optional
Number of rows to be inserted in each chunk from the dataframe.
Set to ``None`` to load the whole dataframe at once.
reauth : bool, default False
Force Google BigQuery to re-authenticate the user. This is useful
if multiple accounts are used.
if_exists : str, default 'fail'
Behavior when the destination table exists. Value can be one of:
``'fail'``
If table exists, do nothing.
``'replace'``
If table exists, drop it, recreate it, and insert data.
``'append'``
If table exists, insert data. Create if does not exist.
auth_local_webserver : bool, default False
Use the `local webserver flow`_ instead of the `console flow`_
when getting user credentials.
.. _local webserver flow:
http://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_local_server
.. _console flow:
http://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_console
*New in version 0.2.0 of pandas-gbq*.
table_schema : list of dicts, optional
List of BigQuery table fields to which according DataFrame
columns conform to, e.g. ``[{'name': 'col1', 'type':
'STRING'},...]``. If schema is not provided, it will be
generated according to dtypes of DataFrame columns. See
BigQuery API documentation on available names of a field.
*New in version 0.3.1 of pandas-gbq*.
location : str, optional
Location where the load job should run. See the `BigQuery locations
documentation
<https://cloud.google.com/bigquery/docs/dataset-locations>`__ for a
list of available locations. The location must match that of the
target dataset.
*New in version 0.5.0 of pandas-gbq*.
progress_bar : bool, default True
Use the library `tqdm` to show the progress bar for the upload,
chunk by chunk.
*New in version 0.5.0 of pandas-gbq*.
credentials : google.auth.credentials.Credentials, optional
Credentials for accessing Google APIs. Use this parameter to
override default credentials, such as to use Compute Engine
:class:`google.auth.compute_engine.Credentials` or Service
Account :class:`google.oauth2.service_account.Credentials`
directly.
*New in version 0.8.0 of pandas-gbq*.
.. versionadded:: 0.24.0
verbose : bool, deprecated
Deprecated in pandas-gbq version 0.4.0. Use the `logging module
to adjust verbosity instead
<https://pandas-gbq.readthedocs.io/en/latest/intro.html#logging>`__.
private_key : str, deprecated
Deprecated in pandas-gbq version 0.8.0. Use the ``credentials``
parameter and
:func:`google.oauth2.service_account.Credentials.from_service_account_info`
or
:func:`google.oauth2.service_account.Credentials.from_service_account_file`
instead.
Service account private key in JSON format. Can be file path
or string contents. This is useful for remote server
authentication (eg. Jupyter/IPython notebook on remote host).
See Also
--------
pandas_gbq.to_gbq : This function in the pandas-gbq library.
read_gbq : Read a DataFrame from Google BigQuery.
"""
from pandas.io import gbq
gbq.to_gbq(self, destination_table, project_id=project_id,
chunksize=chunksize, reauth=reauth, if_exists=if_exists,
auth_local_webserver=auth_local_webserver,
table_schema=table_schema, location=location,
progress_bar=progress_bar, credentials=credentials,
verbose=verbose, private_key=private_key)
@classmethod
def from_records(cls, data, index=None, exclude=None, columns=None,
coerce_float=False, nrows=None):
"""
Convert structured or record ndarray to DataFrame.
Parameters
----------
data : ndarray (structured dtype), list of tuples, dict, or DataFrame
index : string, list of fields, array-like
Field of array to use as the index, alternately a specific set of
input labels to use
exclude : sequence, default None
Columns or fields to exclude
columns : sequence, default None
Column names to use. If the passed data do not have names
associated with them, this argument provides names for the
columns. Otherwise this argument indicates the order of the columns
in the result (any names not found in the data will become all-NA
columns)
coerce_float : boolean, default False
Attempt to convert values of non-string, non-numeric objects (like
decimal.Decimal) to floating point, useful for SQL result sets
nrows : int, default None
Number of rows to read if data is an iterator
Returns
-------
DataFrame
"""
# Make a copy of the input columns so we can modify it
if columns is not None:
columns = ensure_index(columns)
if is_iterator(data):
if nrows == 0:
return cls()
try:
first_row = next(data)
except StopIteration:
return cls(index=index, columns=columns)
dtype = None
if hasattr(first_row, 'dtype') and first_row.dtype.names:
dtype = first_row.dtype
values = [first_row]
if nrows is None:
values += data
else:
values.extend(itertools.islice(data, nrows - 1))
if dtype is not None:
data = np.array(values, dtype=dtype)
else:
data = values
if isinstance(data, dict):
if columns is None:
columns = arr_columns = ensure_index(sorted(data))
arrays = [data[k] for k in columns]
else:
arrays = []
arr_columns = []
for k, v in data.items():
if k in columns:
arr_columns.append(k)
arrays.append(v)
arrays, arr_columns = reorder_arrays(arrays, arr_columns,
columns)
elif isinstance(data, (np.ndarray, DataFrame)):
arrays, columns = to_arrays(data, columns)
if columns is not None:
columns = ensure_index(columns)
arr_columns = columns
else:
arrays, arr_columns = to_arrays(data, columns,
coerce_float=coerce_float)
arr_columns = ensure_index(arr_columns)
if columns is not None:
columns = ensure_index(columns)
else:
columns = arr_columns
if exclude is None:
exclude = set()
else:
exclude = set(exclude)
result_index = None
if index is not None:
if (isinstance(index, str) or
not hasattr(index, "__iter__")):
i = columns.get_loc(index)
exclude.add(index)
if len(arrays) > 0:
result_index = Index(arrays[i], name=index)
else:
result_index = Index([], name=index)
else:
try:
index_data = [arrays[arr_columns.get_loc(field)]
for field in index]
result_index = ensure_index_from_sequences(index_data,
names=index)
exclude.update(index)
except Exception:
result_index = index
if any(exclude):
arr_exclude = [x for x in exclude if x in arr_columns]
to_remove = [arr_columns.get_loc(col) for col in arr_exclude]
arrays = [v for i, v in enumerate(arrays) if i not in to_remove]
arr_columns = arr_columns.drop(arr_exclude)
columns = columns.drop(exclude)
mgr = arrays_to_mgr(arrays, arr_columns, result_index, columns)
return cls(mgr)
def to_records(self, index=True, convert_datetime64=None,
column_dtypes=None, index_dtypes=None):
"""
Convert DataFrame to a NumPy record array.
Index will be included as the first field of the record array if
requested.
Parameters
----------
index : bool, default True
Include index in resulting record array, stored in 'index'
field or using the index label, if set.
convert_datetime64 : bool, default None
.. deprecated:: 0.23.0
Whether to convert the index to datetime.datetime if it is a
DatetimeIndex.
column_dtypes : str, type, dict, default None
.. versionadded:: 0.24.0
If a string or type, the data type to store all columns. If
a dictionary, a mapping of column names and indices (zero-indexed)
to specific data types.
index_dtypes : str, type, dict, default None
.. versionadded:: 0.24.0
If a string or type, the data type to store all index levels. If
a dictionary, a mapping of index level names and indices
(zero-indexed) to specific data types.
This mapping is applied only if `index=True`.
Returns
-------
numpy.recarray
NumPy ndarray with the DataFrame labels as fields and each row
of the DataFrame as entries.
See Also
--------
DataFrame.from_records: Convert structured or record ndarray
to DataFrame.
numpy.recarray: An ndarray that allows field access using
attributes, analogous to typed columns in a
spreadsheet.
Examples
--------
>>> df = pd.DataFrame({'A': [1, 2], 'B': [0.5, 0.75]},
... index=['a', 'b'])
>>> df
A B
a 1 0.50
b 2 0.75
>>> df.to_records()
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
dtype=[('index', 'O'), ('A', '<i8'), ('B', '<f8')])
If the DataFrame index has no label then the recarray field name
is set to 'index'. If the index has a label then this is used as the
field name:
>>> df.index = df.index.rename("I")
>>> df.to_records()
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
dtype=[('I', 'O'), ('A', '<i8'), ('B', '<f8')])
The index can be excluded from the record array:
>>> df.to_records(index=False)
rec.array([(1, 0.5 ), (2, 0.75)],
dtype=[('A', '<i8'), ('B', '<f8')])
Data types can be specified for the columns:
>>> df.to_records(column_dtypes={"A": "int32"})
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
dtype=[('I', 'O'), ('A', '<i4'), ('B', '<f8')])
As well as for the index:
>>> df.to_records(index_dtypes="<S2")
rec.array([(b'a', 1, 0.5 ), (b'b', 2, 0.75)],
dtype=[('I', 'S2'), ('A', '<i8'), ('B', '<f8')])
>>> index_dtypes = "<S{}".format(df.index.str.len().max())
>>> df.to_records(index_dtypes=index_dtypes)
rec.array([(b'a', 1, 0.5 ), (b'b', 2, 0.75)],
dtype=[('I', 'S1'), ('A', '<i8'), ('B', '<f8')])
"""
if convert_datetime64 is not None:
warnings.warn("The 'convert_datetime64' parameter is "
"deprecated and will be removed in a future "
"version",
FutureWarning, stacklevel=2)
if index:
if is_datetime64_any_dtype(self.index) and convert_datetime64:
ix_vals = [self.index.to_pydatetime()]
else:
if isinstance(self.index, MultiIndex):
# array of tuples to numpy cols. copy copy copy
ix_vals = list(map(np.array, zip(*self.index.values)))
else:
ix_vals = [self.index.values]
arrays = ix_vals + [self[c].get_values() for c in self.columns]
count = 0
index_names = list(self.index.names)
if isinstance(self.index, MultiIndex):
for i, n in enumerate(index_names):
if n is None:
index_names[i] = 'level_%d' % count
count += 1
elif index_names[0] is None:
index_names = ['index']
names = [str(name) for name in itertools.chain(index_names,
self.columns)]
else:
arrays = [self[c].get_values() for c in self.columns]
names = [str(c) for c in self.columns]
index_names = []
index_len = len(index_names)
formats = []
for i, v in enumerate(arrays):
index = i
# When the names and arrays are collected, we
# first collect those in the DataFrame's index,
# followed by those in its columns.
#
# Thus, the total length of the array is:
# len(index_names) + len(DataFrame.columns).
#
# This check allows us to see whether we are
# handling a name / array in the index or column.
if index < index_len:
dtype_mapping = index_dtypes
name = index_names[index]
else:
index -= index_len
dtype_mapping = column_dtypes
name = self.columns[index]
# We have a dictionary, so we get the data type
# associated with the index or column (which can
# be denoted by its name in the DataFrame or its
# position in DataFrame's array of indices or
# columns, whichever is applicable.
if is_dict_like(dtype_mapping):
if name in dtype_mapping:
dtype_mapping = dtype_mapping[name]
elif index in dtype_mapping:
dtype_mapping = dtype_mapping[index]
else:
dtype_mapping = None
# If no mapping can be found, use the array's
# dtype attribute for formatting.
#
# A valid dtype must either be a type or
# string naming a type.
if dtype_mapping is None:
formats.append(v.dtype)
elif isinstance(dtype_mapping, (type, np.dtype, str)):
formats.append(dtype_mapping)
else:
element = "row" if i < index_len else "column"
msg = ("Invalid dtype {dtype} specified for "
"{element} {name}").format(dtype=dtype_mapping,
element=element, name=name)
raise ValueError(msg)
return np.rec.fromarrays(
arrays,
dtype={'names': names, 'formats': formats}
)
@classmethod
def from_items(cls, items, columns=None, orient='columns'):
"""
Construct a DataFrame from a list of tuples.
.. deprecated:: 0.23.0
`from_items` is deprecated and will be removed in a future version.
Use :meth:`DataFrame.from_dict(dict(items)) <DataFrame.from_dict>`
instead.
:meth:`DataFrame.from_dict(OrderedDict(items)) <DataFrame.from_dict>`
may be used to preserve the key order.
Convert (key, value) pairs to DataFrame. The keys will be the axis
index (usually the columns, but depends on the specified
orientation). The values should be arrays or Series.
Parameters
----------
items : sequence of (key, value) pairs
Values should be arrays or Series.
columns : sequence of column labels, optional
Must be passed if orient='index'.
orient : {'columns', 'index'}, default 'columns'
The "orientation" of the data. If the keys of the
input correspond to column labels, pass 'columns'
(default). Otherwise if the keys correspond to the index,
pass 'index'.
Returns
-------
DataFrame
"""
warnings.warn("from_items is deprecated. Please use "
"DataFrame.from_dict(dict(items), ...) instead. "
"DataFrame.from_dict(OrderedDict(items)) may be used to "
"preserve the key order.",
FutureWarning, stacklevel=2)
keys, values = zip(*items)
if orient == 'columns':
if columns is not None:
columns = ensure_index(columns)
idict = dict(items)
if len(idict) < len(items):
if not columns.equals(ensure_index(keys)):
raise ValueError('With non-unique item names, passed '
'columns must be identical')
arrays = values
else:
arrays = [idict[k] for k in columns if k in idict]
else:
columns = ensure_index(keys)
arrays = values
# GH 17312
# Provide more informative error msg when scalar values passed
try:
return cls._from_arrays(arrays, columns, None)
except ValueError:
if not is_nested_list_like(values):
raise ValueError('The value in each (key, value) pair '
'must be an array, Series, or dict')
elif orient == 'index':
if columns is None:
raise TypeError("Must pass columns with orient='index'")
keys = ensure_index(keys)
# GH 17312
# Provide more informative error msg when scalar values passed
try:
arr = np.array(values, dtype=object).T
data = [lib.maybe_convert_objects(v) for v in arr]
return cls._from_arrays(data, columns, keys)
except TypeError:
if not is_nested_list_like(values):
raise ValueError('The value in each (key, value) pair '
'must be an array, Series, or dict')
else: # pragma: no cover
raise ValueError("'orient' must be either 'columns' or 'index'")
@classmethod
def _from_arrays(cls, arrays, columns, index, dtype=None):
mgr = arrays_to_mgr(arrays, columns, index, columns, dtype=dtype)
return cls(mgr)
@classmethod
def from_csv(cls, path, header=0, sep=',', index_col=0, parse_dates=True,
encoding=None, tupleize_cols=None,
infer_datetime_format=False):
"""
Read CSV file.
.. deprecated:: 0.21.0
Use :func:`read_csv` instead.
It is preferable to use the more powerful :func:`read_csv`
for most general purposes, but ``from_csv`` makes for an easy
roundtrip to and from a file (the exact counterpart of
``to_csv``), especially with a DataFrame of time series data.
This method only differs from the preferred :func:`read_csv`
in some defaults:
- `index_col` is ``0`` instead of ``None`` (take first column as index
by default)
- `parse_dates` is ``True`` instead of ``False`` (try parsing the index
as datetime by default)
So a ``pd.DataFrame.from_csv(path)`` can be replaced by
``pd.read_csv(path, index_col=0, parse_dates=True)``.
Parameters
----------
path : string file path or file handle / StringIO
header : int, default 0
Row to use as header (skip prior rows)
sep : string, default ','
Field delimiter
index_col : int or sequence, default 0
Column to use for index. If a sequence is given, a MultiIndex
is used. Different default from read_table
parse_dates : boolean, default True
Parse dates. Different default from read_table
tupleize_cols : boolean, default False
write multi_index columns as a list of tuples (if True)
or new (expanded format) if False)
infer_datetime_format : boolean, default False
If True and `parse_dates` is True for a column, try to infer the
datetime format based on the first datetime string. If the format
can be inferred, there often will be a large parsing speed-up.
Returns
-------
DataFrame
See Also
--------
read_csv
"""
warnings.warn("from_csv is deprecated. Please use read_csv(...) "
"instead. Note that some of the default arguments are "
"different, so please refer to the documentation "
"for from_csv when changing your function calls",
FutureWarning, stacklevel=2)
from pandas.io.parsers import read_csv
return read_csv(path, header=header, sep=sep,
parse_dates=parse_dates, index_col=index_col,
encoding=encoding, tupleize_cols=tupleize_cols,
infer_datetime_format=infer_datetime_format)
def to_sparse(self, fill_value=None, kind='block'):
"""
Convert to SparseDataFrame.
.. deprecated:: 0.25.0
Implement the sparse version of the DataFrame meaning that any data
matching a specific value it's omitted in the representation.
The sparse DataFrame allows for a more efficient storage.
Parameters
----------
fill_value : float, default None
The specific value that should be omitted in the representation.
kind : {'block', 'integer'}, default 'block'
The kind of the SparseIndex tracking where data is not equal to
the fill value:
- 'block' tracks only the locations and sizes of blocks of data.
- 'integer' keeps an array with all the locations of the data.
In most cases 'block' is recommended, since it's more memory
efficient.
Returns
-------
SparseDataFrame
The sparse representation of the DataFrame.
See Also
--------
DataFrame.to_dense :
Converts the DataFrame back to the its dense form.
Examples
--------
>>> df = pd.DataFrame([(np.nan, np.nan),
... (1., np.nan),
... (np.nan, 1.)])
>>> df
0 1
0 NaN NaN
1 1.0 NaN
2 NaN 1.0
>>> type(df)
<class 'pandas.core.frame.DataFrame'>
>>> sdf = df.to_sparse() # doctest: +SKIP
>>> sdf # doctest: +SKIP
0 1
0 NaN NaN
1 1.0 NaN
2 NaN 1.0
>>> type(sdf) # doctest: +SKIP
<class 'pandas.core.sparse.frame.SparseDataFrame'>
"""
warnings.warn("DataFrame.to_sparse is deprecated and will be removed "
"in a future version", FutureWarning, stacklevel=2)
from pandas.core.sparse.api import SparseDataFrame
with warnings.catch_warnings():
warnings.filterwarnings("ignore", message="SparseDataFrame")
return SparseDataFrame(self._series, index=self.index,
columns=self.columns, default_kind=kind,
default_fill_value=fill_value)
@deprecate_kwarg(old_arg_name='encoding', new_arg_name=None)
def to_stata(self, fname, convert_dates=None, write_index=True,
encoding="latin-1", byteorder=None, time_stamp=None,
data_label=None, variable_labels=None, version=114,
convert_strl=None):
"""
Export DataFrame object to Stata dta format.
Writes the DataFrame to a Stata dataset file.
"dta" files contain a Stata dataset.
Parameters
----------
fname : str, buffer or path object
String, path object (pathlib.Path or py._path.local.LocalPath) or
object implementing a binary write() function. If using a buffer
then the buffer will not be automatically closed after the file
data has been written.
convert_dates : dict
Dictionary mapping columns containing datetime types to stata
internal format to use when writing the dates. Options are 'tc',
'td', 'tm', 'tw', 'th', 'tq', 'ty'. Column can be either an integer
or a name. Datetime columns that do not have a conversion type
specified will be converted to 'tc'. Raises NotImplementedError if
a datetime column has timezone information.
write_index : bool
Write the index to Stata dataset.
encoding : str
Default is latin-1. Unicode is not supported.
byteorder : str
Can be ">", "<", "little", or "big". default is `sys.byteorder`.
time_stamp : datetime
A datetime to use as file creation date. Default is the current
time.
data_label : str, optional
A label for the data set. Must be 80 characters or smaller.
variable_labels : dict
Dictionary containing columns as keys and variable labels as
values. Each label must be 80 characters or smaller.
.. versionadded:: 0.19.0
version : {114, 117}, default 114
Version to use in the output dta file. Version 114 can be used
read by Stata 10 and later. Version 117 can be read by Stata 13
or later. Version 114 limits string variables to 244 characters or
fewer while 117 allows strings with lengths up to 2,000,000
characters.
.. versionadded:: 0.23.0
convert_strl : list, optional
List of column names to convert to string columns to Stata StrL
format. Only available if version is 117. Storing strings in the
StrL format can produce smaller dta files if strings have more than
8 characters and values are repeated.
.. versionadded:: 0.23.0
Raises
------
NotImplementedError
* If datetimes contain timezone information
* Column dtype is not representable in Stata
ValueError
* Columns listed in convert_dates are neither datetime64[ns]
or datetime.datetime
* Column listed in convert_dates is not in DataFrame
* Categorical label contains more than 32,000 characters
.. versionadded:: 0.19.0
See Also
--------
read_stata : Import Stata data files.
io.stata.StataWriter : Low-level writer for Stata data files.
io.stata.StataWriter117 : Low-level writer for version 117 files.
Examples
--------
>>> df = pd.DataFrame({'animal': ['falcon', 'parrot', 'falcon',
... 'parrot'],
... 'speed': [350, 18, 361, 15]})
>>> df.to_stata('animals.dta') # doctest: +SKIP
"""
kwargs = {}
if version not in (114, 117):
raise ValueError('Only formats 114 and 117 supported.')
if version == 114:
if convert_strl is not None:
raise ValueError('strl support is only available when using '
'format 117')
from pandas.io.stata import StataWriter as statawriter
else:
from pandas.io.stata import StataWriter117 as statawriter
kwargs['convert_strl'] = convert_strl
writer = statawriter(fname, self, convert_dates=convert_dates,
byteorder=byteorder, time_stamp=time_stamp,
data_label=data_label, write_index=write_index,
variable_labels=variable_labels, **kwargs)
writer.write_file()
def to_feather(self, fname):
"""
Write out the binary feather-format for DataFrames.
.. versionadded:: 0.20.0
Parameters
----------
fname : str
string file path
"""
from pandas.io.feather_format import to_feather
to_feather(self, fname)
def to_parquet(self, fname, engine='auto', compression='snappy',
index=None, partition_cols=None, **kwargs):
"""
Write a DataFrame to the binary parquet format.
.. versionadded:: 0.21.0
This function writes the dataframe as a `parquet file
<https://parquet.apache.org/>`_. You can choose different parquet
backends, and have the option of compression. See
:ref:`the user guide <io.parquet>` for more details.
Parameters
----------
fname : str
File path or Root Directory path. Will be used as Root Directory
path while writing a partitioned dataset.
.. versionchanged:: 0.24.0
engine : {'auto', 'pyarrow', 'fastparquet'}, default 'auto'
Parquet library to use. If 'auto', then the option
``io.parquet.engine`` is used. The default ``io.parquet.engine``
behavior is to try 'pyarrow', falling back to 'fastparquet' if
'pyarrow' is unavailable.
compression : {'snappy', 'gzip', 'brotli', None}, default 'snappy'
Name of the compression to use. Use ``None`` for no compression.
index : bool, default None
If ``True``, include the dataframe's index(es) in the file output.
If ``False``, they will not be written to the file. If ``None``,
the behavior depends on the chosen engine.
.. versionadded:: 0.24.0
partition_cols : list, optional, default None
Column names by which to partition the dataset
Columns are partitioned in the order they are given
.. versionadded:: 0.24.0
**kwargs
Additional arguments passed to the parquet library. See
:ref:`pandas io <io.parquet>` for more details.
See Also
--------
read_parquet : Read a parquet file.
DataFrame.to_csv : Write a csv file.
DataFrame.to_sql : Write to a sql table.
DataFrame.to_hdf : Write to hdf.
Notes
-----
This function requires either the `fastparquet
<https://pypi.org/project/fastparquet>`_ or `pyarrow
<https://arrow.apache.org/docs/python/>`_ library.
Examples
--------
>>> df = pd.DataFrame(data={'col1': [1, 2], 'col2': [3, 4]})
>>> df.to_parquet('df.parquet.gzip',
... compression='gzip') # doctest: +SKIP
>>> pd.read_parquet('df.parquet.gzip') # doctest: +SKIP
col1 col2
0 1 3
1 2 4
"""
from pandas.io.parquet import to_parquet
to_parquet(self, fname, engine,
compression=compression, index=index,
partition_cols=partition_cols, **kwargs)
@Substitution(header='Whether to print column labels, default True',
col_space_type='str or int',
col_space='The minimum width of each column in CSS length '
'units. An int is assumed to be px units.\n\n'
' .. versionadded:: 0.25.0\n'
' Ability to use str')
@Substitution(shared_params=fmt.common_docstring,
returns=fmt.return_docstring)
def to_html(self, buf=None, columns=None, col_space=None, header=True,
index=True, na_rep='NaN', formatters=None, float_format=None,
sparsify=None, index_names=True, justify=None, max_rows=None,
max_cols=None, show_dimensions=False, decimal='.',
bold_rows=True, classes=None, escape=True, notebook=False,
border=None, table_id=None, render_links=False):
"""
Render a DataFrame as an HTML table.
%(shared_params)s
bold_rows : bool, default True
Make the row labels bold in the output.
classes : str or list or tuple, default None
CSS class(es) to apply to the resulting html table.
escape : bool, default True
Convert the characters <, >, and & to HTML-safe sequences.
notebook : {True, False}, default False
Whether the generated HTML is for IPython Notebook.
border : int
A ``border=border`` attribute is included in the opening
`<table>` tag. Default ``pd.options.display.html.border``.
.. versionadded:: 0.19.0
table_id : str, optional
A css id is included in the opening `<table>` tag if specified.
.. versionadded:: 0.23.0
render_links : bool, default False
Convert URLs to HTML links.
.. versionadded:: 0.24.0
%(returns)s
See Also
--------
to_string : Convert DataFrame to a string.
"""
if (justify is not None and
justify not in fmt._VALID_JUSTIFY_PARAMETERS):
raise ValueError("Invalid value for justify parameter")
formatter = fmt.DataFrameFormatter(self, buf=buf, columns=columns,
col_space=col_space, na_rep=na_rep,
formatters=formatters,
float_format=float_format,
sparsify=sparsify, justify=justify,
index_names=index_names,
header=header, index=index,
bold_rows=bold_rows, escape=escape,
max_rows=max_rows,
max_cols=max_cols,
show_dimensions=show_dimensions,
decimal=decimal, table_id=table_id,
render_links=render_links)
# TODO: a generic formatter wld b in DataFrameFormatter
formatter.to_html(classes=classes, notebook=notebook, border=border)
if buf is None:
return formatter.buf.getvalue()
# ----------------------------------------------------------------------
def info(self, verbose=None, buf=None, max_cols=None, memory_usage=None,
null_counts=None):
"""
Print a concise summary of a DataFrame.
This method prints information about a DataFrame including
the index dtype and column dtypes, non-null values and memory usage.
Parameters
----------
verbose : bool, optional
Whether to print the full summary. By default, the setting in
``pandas.options.display.max_info_columns`` is followed.
buf : writable buffer, defaults to sys.stdout
Where to send the output. By default, the output is printed to
sys.stdout. Pass a writable buffer if you need to further process
the output.
max_cols : int, optional
When to switch from the verbose to the truncated output. If the
DataFrame has more than `max_cols` columns, the truncated output
is used. By default, the setting in
``pandas.options.display.max_info_columns`` is used.
memory_usage : bool, str, optional
Specifies whether total memory usage of the DataFrame
elements (including the index) should be displayed. By default,
this follows the ``pandas.options.display.memory_usage`` setting.
True always show memory usage. False never shows memory usage.
A value of 'deep' is equivalent to "True with deep introspection".
Memory usage is shown in human-readable units (base-2
representation). Without deep introspection a memory estimation is
made based in column dtype and number of rows assuming values
consume the same memory amount for corresponding dtypes. With deep
memory introspection, a real memory usage calculation is performed
at the cost of computational resources.
null_counts : bool, optional
Whether to show the non-null counts. By default, this is shown
only if the frame is smaller than
``pandas.options.display.max_info_rows`` and
``pandas.options.display.max_info_columns``. A value of True always
shows the counts, and False never shows the counts.
Returns
-------
None
This method prints a summary of a DataFrame and returns None.
See Also
--------
DataFrame.describe: Generate descriptive statistics of DataFrame
columns.
DataFrame.memory_usage: Memory usage of DataFrame columns.
Examples
--------
>>> int_values = [1, 2, 3, 4, 5]
>>> text_values = ['alpha', 'beta', 'gamma', 'delta', 'epsilon']
>>> float_values = [0.0, 0.25, 0.5, 0.75, 1.0]
>>> df = pd.DataFrame({"int_col": int_values, "text_col": text_values,
... "float_col": float_values})
>>> df
int_col text_col float_col
0 1 alpha 0.00
1 2 beta 0.25
2 3 gamma 0.50
3 4 delta 0.75
4 5 epsilon 1.00
Prints information of all columns:
>>> df.info(verbose=True)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Data columns (total 3 columns):
int_col 5 non-null int64
text_col 5 non-null object
float_col 5 non-null float64
dtypes: float64(1), int64(1), object(1)
memory usage: 248.0+ bytes
Prints a summary of columns count and its dtypes but not per column
information:
>>> df.info(verbose=False)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Columns: 3 entries, int_col to float_col
dtypes: float64(1), int64(1), object(1)
memory usage: 248.0+ bytes
Pipe output of DataFrame.info to buffer instead of sys.stdout, get
buffer content and writes to a text file:
>>> import io
>>> buffer = io.StringIO()
>>> df.info(buf=buffer)
>>> s = buffer.getvalue()
>>> with open("df_info.txt", "w",
... encoding="utf-8") as f: # doctest: +SKIP
... f.write(s)
260
The `memory_usage` parameter allows deep introspection mode, specially
useful for big DataFrames and fine-tune memory optimization:
>>> random_strings_array = np.random.choice(['a', 'b', 'c'], 10 ** 6)
>>> df = pd.DataFrame({
... 'column_1': np.random.choice(['a', 'b', 'c'], 10 ** 6),
... 'column_2': np.random.choice(['a', 'b', 'c'], 10 ** 6),
... 'column_3': np.random.choice(['a', 'b', 'c'], 10 ** 6)
... })
>>> df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000000 entries, 0 to 999999
Data columns (total 3 columns):
column_1 1000000 non-null object
column_2 1000000 non-null object
column_3 1000000 non-null object
dtypes: object(3)
memory usage: 22.9+ MB
>>> df.info(memory_usage='deep')
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000000 entries, 0 to 999999
Data columns (total 3 columns):
column_1 1000000 non-null object
column_2 1000000 non-null object
column_3 1000000 non-null object
dtypes: object(3)
memory usage: 188.8 MB
"""
if buf is None: # pragma: no cover
buf = sys.stdout
lines = []
lines.append(str(type(self)))
lines.append(self.index._summary())
if len(self.columns) == 0:
lines.append('Empty {name}'.format(name=type(self).__name__))
fmt.buffer_put_lines(buf, lines)
return
cols = self.columns
# hack
if max_cols is None:
max_cols = get_option('display.max_info_columns',
len(self.columns) + 1)
max_rows = get_option('display.max_info_rows', len(self) + 1)
if null_counts is None:
show_counts = ((len(self.columns) <= max_cols) and
(len(self) < max_rows))
else:
show_counts = null_counts
exceeds_info_cols = len(self.columns) > max_cols
def _verbose_repr():
lines.append('Data columns (total %d columns):' %
len(self.columns))
space = max(len(pprint_thing(k)) for k in self.columns) + 4
counts = None
tmpl = "{count}{dtype}"
if show_counts:
counts = self.count()
if len(cols) != len(counts): # pragma: no cover
raise AssertionError(
'Columns must equal counts '
'({cols:d} != {counts:d})'.format(
cols=len(cols), counts=len(counts)))
tmpl = "{count} non-null {dtype}"
dtypes = self.dtypes
for i, col in enumerate(self.columns):
dtype = dtypes.iloc[i]
col = pprint_thing(col)
count = ""
if show_counts:
count = counts.iloc[i]
lines.append(_put_str(col, space) + tmpl.format(count=count,
dtype=dtype))
def _non_verbose_repr():
lines.append(self.columns._summary(name='Columns'))
def _sizeof_fmt(num, size_qualifier):
# returns size in human readable format
for x in ['bytes', 'KB', 'MB', 'GB', 'TB']:
if num < 1024.0:
return ("{num:3.1f}{size_q} "
"{x}".format(num=num, size_q=size_qualifier, x=x))
num /= 1024.0
return "{num:3.1f}{size_q} {pb}".format(num=num,
size_q=size_qualifier,
pb='PB')
if verbose:
_verbose_repr()
elif verbose is False: # specifically set to False, not nesc None
_non_verbose_repr()
else:
if exceeds_info_cols:
_non_verbose_repr()
else:
_verbose_repr()
counts = self.get_dtype_counts()
dtypes = ['{k}({kk:d})'.format(k=k[0], kk=k[1]) for k
in sorted(counts.items())]
lines.append('dtypes: {types}'.format(types=', '.join(dtypes)))
if memory_usage is None:
memory_usage = get_option('display.memory_usage')
if memory_usage:
# append memory usage of df to display
size_qualifier = ''
if memory_usage == 'deep':
deep = True
else:
# size_qualifier is just a best effort; not guaranteed to catch
# all cases (e.g., it misses categorical data even with object
# categories)
deep = False
if ('object' in counts or
self.index._is_memory_usage_qualified()):
size_qualifier = '+'
mem_usage = self.memory_usage(index=True, deep=deep).sum()
lines.append("memory usage: {mem}\n".format(
mem=_sizeof_fmt(mem_usage, size_qualifier)))
fmt.buffer_put_lines(buf, lines)
def memory_usage(self, index=True, deep=False):
"""
Return the memory usage of each column in bytes.
The memory usage can optionally include the contribution of
the index and elements of `object` dtype.
This value is displayed in `DataFrame.info` by default. This can be
suppressed by setting ``pandas.options.display.memory_usage`` to False.
Parameters
----------
index : bool, default True
Specifies whether to include the memory usage of the DataFrame's
index in returned Series. If ``index=True``, the memory usage of
the index is the first item in the output.
deep : bool, default False
If True, introspect the data deeply by interrogating
`object` dtypes for system-level memory consumption, and include
it in the returned values.
Returns
-------
Series
A Series whose index is the original column names and whose values
is the memory usage of each column in bytes.
See Also
--------
numpy.ndarray.nbytes : Total bytes consumed by the elements of an
ndarray.
Series.memory_usage : Bytes consumed by a Series.
Categorical : Memory-efficient array for string values with
many repeated values.
DataFrame.info : Concise summary of a DataFrame.
Examples
--------
>>> dtypes = ['int64', 'float64', 'complex128', 'object', 'bool']
>>> data = dict([(t, np.ones(shape=5000).astype(t))
... for t in dtypes])
>>> df = pd.DataFrame(data)
>>> df.head()
int64 float64 complex128 object bool
0 1 1.0 1.0+0.0j 1 True
1 1 1.0 1.0+0.0j 1 True
2 1 1.0 1.0+0.0j 1 True
3 1 1.0 1.0+0.0j 1 True
4 1 1.0 1.0+0.0j 1 True
>>> df.memory_usage()
Index 128
int64 40000
float64 40000
complex128 80000
object 40000
bool 5000
dtype: int64
>>> df.memory_usage(index=False)
int64 40000
float64 40000
complex128 80000
object 40000
bool 5000
dtype: int64
The memory footprint of `object` dtype columns is ignored by default:
>>> df.memory_usage(deep=True)
Index 128
int64 40000
float64 40000
complex128 80000
object 160000
bool 5000
dtype: int64
Use a Categorical for efficient storage of an object-dtype column with
many repeated values.
>>> df['object'].astype('category').memory_usage(deep=True)
5216
"""
result = Series([c.memory_usage(index=False, deep=deep)
for col, c in self.iteritems()], index=self.columns)
if index:
result = Series(self.index.memory_usage(deep=deep),
index=['Index']).append(result)
return result
def transpose(self, *args, **kwargs):
"""
Transpose index and columns.
Reflect the DataFrame over its main diagonal by writing rows as columns
and vice-versa. The property :attr:`.T` is an accessor to the method
:meth:`transpose`.
Parameters
----------
copy : bool, default False
If True, the underlying data is copied. Otherwise (default), no
copy is made if possible.
*args, **kwargs
Additional keywords have no effect but might be accepted for
compatibility with numpy.
Returns
-------
DataFrame
The transposed DataFrame.
See Also
--------
numpy.transpose : Permute the dimensions of a given array.
Notes
-----
Transposing a DataFrame with mixed dtypes will result in a homogeneous
DataFrame with the `object` dtype. In such a case, a copy of the data
is always made.
Examples
--------
**Square DataFrame with homogeneous dtype**
>>> d1 = {'col1': [1, 2], 'col2': [3, 4]}
>>> df1 = pd.DataFrame(data=d1)
>>> df1
col1 col2
0 1 3
1 2 4
>>> df1_transposed = df1.T # or df1.transpose()
>>> df1_transposed
0 1
col1 1 2
col2 3 4
When the dtype is homogeneous in the original DataFrame, we get a
transposed DataFrame with the same dtype:
>>> df1.dtypes
col1 int64
col2 int64
dtype: object
>>> df1_transposed.dtypes
0 int64
1 int64
dtype: object
**Non-square DataFrame with mixed dtypes**
>>> d2 = {'name': ['Alice', 'Bob'],
... 'score': [9.5, 8],
... 'employed': [False, True],
... 'kids': [0, 0]}
>>> df2 = pd.DataFrame(data=d2)
>>> df2
name score employed kids
0 Alice 9.5 False 0
1 Bob 8.0 True 0
>>> df2_transposed = df2.T # or df2.transpose()
>>> df2_transposed
0 1
name Alice Bob
score 9.5 8
employed False True
kids 0 0
When the DataFrame has mixed dtypes, we get a transposed DataFrame with
the `object` dtype:
>>> df2.dtypes
name object
score float64
employed bool
kids int64
dtype: object
>>> df2_transposed.dtypes
0 object
1 object
dtype: object
"""
nv.validate_transpose(args, dict())
return super().transpose(1, 0, **kwargs)
T = property(transpose)
# ----------------------------------------------------------------------
# Picklability
# legacy pickle formats
def _unpickle_frame_compat(self, state): # pragma: no cover
if len(state) == 2: # pragma: no cover
series, idx = state
columns = sorted(series)
else:
series, cols, idx = state
columns = com._unpickle_array(cols)
index = com._unpickle_array(idx)
self._data = self._init_dict(series, index, columns, None)
def _unpickle_matrix_compat(self, state): # pragma: no cover
# old unpickling
(vals, idx, cols), object_state = state
index = com._unpickle_array(idx)
dm = DataFrame(vals, index=index, columns=com._unpickle_array(cols),
copy=False)
if object_state is not None:
ovals, _, ocols = object_state
objects = DataFrame(ovals, index=index,
columns=com._unpickle_array(ocols), copy=False)
dm = dm.join(objects)
self._data = dm._data
# ----------------------------------------------------------------------
# Getting and setting elements
def get_value(self, index, col, takeable=False):
"""
Quickly retrieve single value at passed column and index.
.. deprecated:: 0.21.0
Use .at[] or .iat[] accessors instead.
Parameters
----------
index : row label
col : column label
takeable : interpret the index/col as indexers, default False
Returns
-------
scalar
"""
warnings.warn("get_value is deprecated and will be removed "
"in a future release. Please use "
".at[] or .iat[] accessors instead", FutureWarning,
stacklevel=2)
return self._get_value(index, col, takeable=takeable)
def _get_value(self, index, col, takeable=False):
if takeable:
series = self._iget_item_cache(col)
return com.maybe_box_datetimelike(series._values[index])
series = self._get_item_cache(col)
engine = self.index._engine
try:
return engine.get_value(series._values, index)
except KeyError:
# GH 20629
if self.index.nlevels > 1:
# partial indexing forbidden
raise
except (TypeError, ValueError):
pass
# we cannot handle direct indexing
# use positional
col = self.columns.get_loc(col)
index = self.index.get_loc(index)
return self._get_value(index, col, takeable=True)
_get_value.__doc__ = get_value.__doc__
def set_value(self, index, col, value, takeable=False):
"""
Put single value at passed column and index.
.. deprecated:: 0.21.0
Use .at[] or .iat[] accessors instead.
Parameters
----------
index : row label
col : column label
value : scalar
takeable : interpret the index/col as indexers, default False
Returns
-------
DataFrame
If label pair is contained, will be reference to calling DataFrame,
otherwise a new object.
"""
warnings.warn("set_value is deprecated and will be removed "
"in a future release. Please use "
".at[] or .iat[] accessors instead", FutureWarning,
stacklevel=2)
return self._set_value(index, col, value, takeable=takeable)
def _set_value(self, index, col, value, takeable=False):
try:
if takeable is True:
series = self._iget_item_cache(col)
return series._set_value(index, value, takeable=True)
series = self._get_item_cache(col)
engine = self.index._engine
engine.set_value(series._values, index, value)
return self
except (KeyError, TypeError):
# set using a non-recursive method & reset the cache
if takeable:
self.iloc[index, col] = value
else:
self.loc[index, col] = value
self._item_cache.pop(col, None)
return self
_set_value.__doc__ = set_value.__doc__
def _ixs(self, i, axis=0):
"""
Parameters
----------
i : int, slice, or sequence of integers
axis : int
Notes
-----
If slice passed, the resulting data will be a view.
"""
# irow
if axis == 0:
if isinstance(i, slice):
return self[i]
else:
label = self.index[i]
if isinstance(label, Index):
# a location index by definition
result = self.take(i, axis=axis)
copy = True
else:
new_values = self._data.fast_xs(i)
if is_scalar(new_values):
return new_values
# if we are a copy, mark as such
copy = (isinstance(new_values, np.ndarray) and
new_values.base is None)
result = self._constructor_sliced(new_values,
index=self.columns,
name=self.index[i],
dtype=new_values.dtype)
result._set_is_copy(self, copy=copy)
return result
# icol
else:
label = self.columns[i]
if isinstance(i, slice):
# need to return view
lab_slice = slice(label[0], label[-1])
return self.loc[:, lab_slice]
else:
if isinstance(label, Index):
return self._take(i, axis=1)
index_len = len(self.index)
# if the values returned are not the same length
# as the index (iow a not found value), iget returns
# a 0-len ndarray. This is effectively catching
# a numpy error (as numpy should really raise)
values = self._data.iget(i)
if index_len and not len(values):
values = np.array([np.nan] * index_len, dtype=object)
result = self._box_col_values(values, label)
# this is a cached value, mark it so
result._set_as_cached(label, self)
return result
def __getitem__(self, key):
key = lib.item_from_zerodim(key)
key = com.apply_if_callable(key, self)
# shortcut if the key is in columns
try:
if self.columns.is_unique and key in self.columns:
if self.columns.nlevels > 1:
return self._getitem_multilevel(key)
return self._get_item_cache(key)
except (TypeError, ValueError):
# The TypeError correctly catches non hashable "key" (e.g. list)
# The ValueError can be removed once GH #21729 is fixed
pass
# Do we have a slicer (on rows)?
indexer = convert_to_index_sliceable(self, key)
if indexer is not None:
return self._slice(indexer, axis=0)
# Do we have a (boolean) DataFrame?
if isinstance(key, DataFrame):
return self._getitem_frame(key)
# Do we have a (boolean) 1d indexer?
if com.is_bool_indexer(key):
return self._getitem_bool_array(key)
# We are left with two options: a single key, and a collection of keys,
# We interpret tuples as collections only for non-MultiIndex
is_single_key = isinstance(key, tuple) or not is_list_like(key)
if is_single_key:
if self.columns.nlevels > 1:
return self._getitem_multilevel(key)
indexer = self.columns.get_loc(key)
if is_integer(indexer):
indexer = [indexer]
else:
if is_iterator(key):
key = list(key)
indexer = self.loc._convert_to_indexer(key, axis=1,
raise_missing=True)
# take() does not accept boolean indexers
if getattr(indexer, "dtype", None) == bool:
indexer = np.where(indexer)[0]
data = self._take(indexer, axis=1)
if is_single_key:
# What does looking for a single key in a non-unique index return?
# The behavior is inconsistent. It returns a Series, except when
# - the key itself is repeated (test on data.shape, #9519), or
# - we have a MultiIndex on columns (test on self.columns, #21309)
if data.shape[1] == 1 and not isinstance(self.columns, MultiIndex):
data = data[key]
return data
def _getitem_bool_array(self, key):
# also raises Exception if object array with NA values
# warning here just in case -- previously __setitem__ was
# reindexing but __getitem__ was not; it seems more reasonable to
# go with the __setitem__ behavior since that is more consistent
# with all other indexing behavior
if isinstance(key, Series) and not key.index.equals(self.index):
warnings.warn("Boolean Series key will be reindexed to match "
"DataFrame index.", UserWarning, stacklevel=3)
elif len(key) != len(self.index):
raise ValueError('Item wrong length %d instead of %d.' %
(len(key), len(self.index)))
# check_bool_indexer will throw exception if Series key cannot
# be reindexed to match DataFrame rows
key = check_bool_indexer(self.index, key)
indexer = key.nonzero()[0]
return self._take(indexer, axis=0)
def _getitem_multilevel(self, key):
loc = self.columns.get_loc(key)
if isinstance(loc, (slice, Series, np.ndarray, Index)):
new_columns = self.columns[loc]
result_columns = maybe_droplevels(new_columns, key)
if self._is_mixed_type:
result = self.reindex(columns=new_columns)
result.columns = result_columns
else:
new_values = self.values[:, loc]
result = self._constructor(new_values, index=self.index,
columns=result_columns)
result = result.__finalize__(self)
# If there is only one column being returned, and its name is
# either an empty string, or a tuple with an empty string as its
# first element, then treat the empty string as a placeholder
# and return the column as if the user had provided that empty
# string in the key. If the result is a Series, exclude the
# implied empty string from its name.
if len(result.columns) == 1:
top = result.columns[0]
if isinstance(top, tuple):
top = top[0]
if top == '':
result = result['']
if isinstance(result, Series):
result = self._constructor_sliced(result,
index=self.index,
name=key)
result._set_is_copy(self)
return result
else:
return self._get_item_cache(key)
def _getitem_frame(self, key):
if key.values.size and not is_bool_dtype(key.values):
raise ValueError('Must pass DataFrame with boolean values only')
return self.where(key)
def query(self, expr, inplace=False, **kwargs):
"""
Query the columns of a DataFrame with a boolean expression.
Parameters
----------
expr : str
The query string to evaluate. You can refer to variables
in the environment by prefixing them with an '@' character like
``@a + b``.
.. versionadded:: 0.25.0
You can refer to column names that contain spaces by surrounding
them in backticks.
For example, if one of your columns is called ``a a`` and you want
to sum it with ``b``, your query should be ```a a` + b``.
inplace : bool
Whether the query should modify the data in place or return
a modified copy.
**kwargs
See the documentation for :func:`eval` for complete details
on the keyword arguments accepted by :meth:`DataFrame.query`.
.. versionadded:: 0.18.0
Returns
-------
DataFrame
DataFrame resulting from the provided query expression.
See Also
--------
eval : Evaluate a string describing operations on
DataFrame columns.
DataFrame.eval : Evaluate a string describing operations on
DataFrame columns.
Notes
-----
The result of the evaluation of this expression is first passed to
:attr:`DataFrame.loc` and if that fails because of a
multidimensional key (e.g., a DataFrame) then the result will be passed
to :meth:`DataFrame.__getitem__`.
This method uses the top-level :func:`eval` function to
evaluate the passed query.
The :meth:`~pandas.DataFrame.query` method uses a slightly
modified Python syntax by default. For example, the ``&`` and ``|``
(bitwise) operators have the precedence of their boolean cousins,
:keyword:`and` and :keyword:`or`. This *is* syntactically valid Python,
however the semantics are different.
You can change the semantics of the expression by passing the keyword
argument ``parser='python'``. This enforces the same semantics as
evaluation in Python space. Likewise, you can pass ``engine='python'``
to evaluate an expression using Python itself as a backend. This is not
recommended as it is inefficient compared to using ``numexpr`` as the
engine.
The :attr:`DataFrame.index` and
:attr:`DataFrame.columns` attributes of the
:class:`~pandas.DataFrame` instance are placed in the query namespace
by default, which allows you to treat both the index and columns of the
frame as a column in the frame.
The identifier ``index`` is used for the frame index; you can also
use the name of the index to identify it in a query. Please note that
Python keywords may not be used as identifiers.
For further details and examples see the ``query`` documentation in
:ref:`indexing <indexing.query>`.
Examples
--------
>>> df = pd.DataFrame({'A': range(1, 6),
... 'B': range(10, 0, -2),
... 'C C': range(10, 5, -1)})
>>> df
A B C C
0 1 10 10
1 2 8 9
2 3 6 8
3 4 4 7
4 5 2 6
>>> df.query('A > B')
A B C C
4 5 2 6
The previous expression is equivalent to
>>> df[df.A > df.B]
A B C C
4 5 2 6
For columns with spaces in their name, you can use backtick quoting.
>>> df.query('B == `C C`')
A B C C
0 1 10 10
The previous expression is equivalent to
>>> df[df.B == df['C C']]
A B C C
0 1 10 10
"""
inplace = validate_bool_kwarg(inplace, 'inplace')
if not isinstance(expr, str):
msg = "expr must be a string to be evaluated, {0} given"
raise ValueError(msg.format(type(expr)))
kwargs['level'] = kwargs.pop('level', 0) + 1
kwargs['target'] = None
res = self.eval(expr, **kwargs)
try:
new_data = self.loc[res]
except ValueError:
# when res is multi-dimensional loc raises, but this is sometimes a
# valid query
new_data = self[res]
if inplace:
self._update_inplace(new_data)
else:
return new_data
def eval(self, expr, inplace=False, **kwargs):
"""
Evaluate a string describing operations on DataFrame columns.
Operates on columns only, not specific rows or elements. This allows
`eval` to run arbitrary code, which can make you vulnerable to code
injection if you pass user input to this function.
Parameters
----------
expr : str
The expression string to evaluate.
inplace : bool, default False
If the expression contains an assignment, whether to perform the
operation inplace and mutate the existing DataFrame. Otherwise,
a new DataFrame is returned.
.. versionadded:: 0.18.0.
kwargs : dict
See the documentation for :func:`eval` for complete details
on the keyword arguments accepted by
:meth:`~pandas.DataFrame.query`.
Returns
-------
ndarray, scalar, or pandas object
The result of the evaluation.
See Also
--------
DataFrame.query : Evaluates a boolean expression to query the columns
of a frame.
DataFrame.assign : Can evaluate an expression or function to create new
values for a column.
eval : Evaluate a Python expression as a string using various
backends.
Notes
-----
For more details see the API documentation for :func:`~eval`.
For detailed examples see :ref:`enhancing performance with eval
<enhancingperf.eval>`.
Examples
--------
>>> df = pd.DataFrame({'A': range(1, 6), 'B': range(10, 0, -2)})
>>> df
A B
0 1 10
1 2 8
2 3 6
3 4 4
4 5 2
>>> df.eval('A + B')
0 11
1 10
2 9
3 8
4 7
dtype: int64
Assignment is allowed though by default the original DataFrame is not
modified.
>>> df.eval('C = A + B')
A B C
0 1 10 11
1 2 8 10
2 3 6 9
3 4 4 8
4 5 2 7
>>> df
A B
0 1 10
1 2 8
2 3 6
3 4 4
4 5 2
Use ``inplace=True`` to modify the original DataFrame.
>>> df.eval('C = A + B', inplace=True)
>>> df
A B C
0 1 10 11
1 2 8 10
2 3 6 9
3 4 4 8
4 5 2 7
"""
from pandas.core.computation.eval import eval as _eval
inplace = validate_bool_kwarg(inplace, 'inplace')
resolvers = kwargs.pop('resolvers', None)
kwargs['level'] = kwargs.pop('level', 0) + 1
if resolvers is None:
index_resolvers = self._get_index_resolvers()
column_resolvers = \
self._get_space_character_free_column_resolvers()
resolvers = column_resolvers, index_resolvers
if 'target' not in kwargs:
kwargs['target'] = self
kwargs['resolvers'] = kwargs.get('resolvers', ()) + tuple(resolvers)
return _eval(expr, inplace=inplace, **kwargs)
def select_dtypes(self, include=None, exclude=None):
"""
Return a subset of the DataFrame's columns based on the column dtypes.
Parameters
----------
include, exclude : scalar or list-like
A selection of dtypes or strings to be included/excluded. At least
one of these parameters must be supplied.
Returns
-------
DataFrame
The subset of the frame including the dtypes in ``include`` and
excluding the dtypes in ``exclude``.
Raises
------
ValueError
* If both of ``include`` and ``exclude`` are empty
* If ``include`` and ``exclude`` have overlapping elements
* If any kind of string dtype is passed in.
Notes
-----
* To select all *numeric* types, use ``np.number`` or ``'number'``
* To select strings you must use the ``object`` dtype, but note that
this will return *all* object dtype columns
* See the `numpy dtype hierarchy
<http://docs.scipy.org/doc/numpy/reference/arrays.scalars.html>`__
* To select datetimes, use ``np.datetime64``, ``'datetime'`` or
``'datetime64'``
* To select timedeltas, use ``np.timedelta64``, ``'timedelta'`` or
``'timedelta64'``
* To select Pandas categorical dtypes, use ``'category'``
* To select Pandas datetimetz dtypes, use ``'datetimetz'`` (new in
0.20.0) or ``'datetime64[ns, tz]'``
Examples
--------
>>> df = pd.DataFrame({'a': [1, 2] * 3,
... 'b': [True, False] * 3,
... 'c': [1.0, 2.0] * 3})
>>> df
a b c
0 1 True 1.0
1 2 False 2.0
2 1 True 1.0
3 2 False 2.0
4 1 True 1.0
5 2 False 2.0
>>> df.select_dtypes(include='bool')
b
0 True
1 False
2 True
3 False
4 True
5 False
>>> df.select_dtypes(include=['float64'])
c
0 1.0
1 2.0
2 1.0
3 2.0
4 1.0
5 2.0
>>> df.select_dtypes(exclude=['int'])
b c
0 True 1.0
1 False 2.0
2 True 1.0
3 False 2.0
4 True 1.0
5 False 2.0
"""
def _get_info_slice(obj, indexer):
"""Slice the info axis of `obj` with `indexer`."""
if not hasattr(obj, '_info_axis_number'):
msg = 'object of type {typ!r} has no info axis'
raise TypeError(msg.format(typ=type(obj).__name__))
slices = [slice(None)] * obj.ndim
slices[obj._info_axis_number] = indexer
return tuple(slices)
if not is_list_like(include):
include = (include,) if include is not None else ()
if not is_list_like(exclude):
exclude = (exclude,) if exclude is not None else ()
selection = tuple(map(frozenset, (include, exclude)))
if not any(selection):
raise ValueError('at least one of include or exclude must be '
'nonempty')
# convert the myriad valid dtypes object to a single representation
include, exclude = map(
lambda x: frozenset(map(infer_dtype_from_object, x)), selection)
for dtypes in (include, exclude):
invalidate_string_dtypes(dtypes)
# can't both include AND exclude!
if not include.isdisjoint(exclude):
raise ValueError('include and exclude overlap on {inc_ex}'.format(
inc_ex=(include & exclude)))
# empty include/exclude -> defaults to True
# three cases (we've already raised if both are empty)
# case 1: empty include, nonempty exclude
# we have True, True, ... True for include, same for exclude
# in the loop below we get the excluded
# and when we call '&' below we get only the excluded
# case 2: nonempty include, empty exclude
# same as case 1, but with include
# case 3: both nonempty
# the "union" of the logic of case 1 and case 2:
# we get the included and excluded, and return their logical and
include_these = Series(not bool(include), index=self.columns)
exclude_these = Series(not bool(exclude), index=self.columns)
def is_dtype_instance_mapper(idx, dtype):
return idx, functools.partial(issubclass, dtype.type)
for idx, f in itertools.starmap(is_dtype_instance_mapper,
enumerate(self.dtypes)):
if include: # checks for the case of empty include or exclude
include_these.iloc[idx] = any(map(f, include))
if exclude:
exclude_these.iloc[idx] = not any(map(f, exclude))
dtype_indexer = include_these & exclude_these
return self.loc[_get_info_slice(self, dtype_indexer)]
def _box_item_values(self, key, values):
items = self.columns[self.columns.get_loc(key)]
if values.ndim == 2:
return self._constructor(values.T, columns=items, index=self.index)
else:
return self._box_col_values(values, items)
def _box_col_values(self, values, items):
"""
Provide boxed values for a column.
"""
klass = self._constructor_sliced
return klass(values, index=self.index, name=items, fastpath=True)
def __setitem__(self, key, value):
key = com.apply_if_callable(key, self)
# see if we can slice the rows
indexer = convert_to_index_sliceable(self, key)
if indexer is not None:
return self._setitem_slice(indexer, value)
if isinstance(key, DataFrame) or getattr(key, 'ndim', None) == 2:
self._setitem_frame(key, value)
elif isinstance(key, (Series, np.ndarray, list, Index)):
self._setitem_array(key, value)
else:
# set column
self._set_item(key, value)
def _setitem_slice(self, key, value):
self._check_setitem_copy()
self.loc._setitem_with_indexer(key, value)
def _setitem_array(self, key, value):
# also raises Exception if object array with NA values
if com.is_bool_indexer(key):
if len(key) != len(self.index):
raise ValueError('Item wrong length %d instead of %d!' %
(len(key), len(self.index)))
key = check_bool_indexer(self.index, key)
indexer = key.nonzero()[0]
self._check_setitem_copy()
self.loc._setitem_with_indexer(indexer, value)
else:
if isinstance(value, DataFrame):
if len(value.columns) != len(key):
raise ValueError('Columns must be same length as key')
for k1, k2 in zip(key, value.columns):
self[k1] = value[k2]
else:
indexer = self.loc._convert_to_indexer(key, axis=1)
self._check_setitem_copy()
self.loc._setitem_with_indexer((slice(None), indexer), value)
def _setitem_frame(self, key, value):
# support boolean setting with DataFrame input, e.g.
# df[df > df2] = 0
if isinstance(key, np.ndarray):
if key.shape != self.shape:
raise ValueError(
'Array conditional must be same shape as self'
)
key = self._constructor(key, **self._construct_axes_dict())
if key.values.size and not is_bool_dtype(key.values):
raise TypeError(
'Must pass DataFrame or 2-d ndarray with boolean values only'
)
self._check_inplace_setting(value)
self._check_setitem_copy()
self._where(-key, value, inplace=True)
def _ensure_valid_index(self, value):
"""
Ensure that if we don't have an index, that we can create one from the
passed value.
"""
# GH5632, make sure that we are a Series convertible
if not len(self.index) and is_list_like(value):
try:
value = Series(value)
except (ValueError, NotImplementedError, TypeError):
raise ValueError('Cannot set a frame with no defined index '
'and a value that cannot be converted to a '
'Series')
self._data = self._data.reindex_axis(value.index.copy(), axis=1,
fill_value=np.nan)
def _set_item(self, key, value):
"""
Add series to DataFrame in specified column.
If series is a numpy-array (not a Series/TimeSeries), it must be the
same length as the DataFrames index or an error will be thrown.
Series/TimeSeries will be conformed to the DataFrames index to
ensure homogeneity.
"""
self._ensure_valid_index(value)
value = self._sanitize_column(key, value)
NDFrame._set_item(self, key, value)
# check if we are modifying a copy
# try to set first as we want an invalid
# value exception to occur first
if len(self):
self._check_setitem_copy()
def insert(self, loc, column, value, allow_duplicates=False):
"""
Insert column into DataFrame at specified location.
Raises a ValueError if `column` is already contained in the DataFrame,
unless `allow_duplicates` is set to True.
Parameters
----------
loc : int
Insertion index. Must verify 0 <= loc <= len(columns)
column : string, number, or hashable object
label of the inserted column
value : int, Series, or array-like
allow_duplicates : bool, optional
"""
self._ensure_valid_index(value)
value = self._sanitize_column(column, value, broadcast=False)
self._data.insert(loc, column, value,
allow_duplicates=allow_duplicates)
def assign(self, **kwargs):
r"""
Assign new columns to a DataFrame.
Returns a new object with all original columns in addition to new ones.
Existing columns that are re-assigned will be overwritten.
Parameters
----------
**kwargs : dict of {str: callable or Series}
The column names are keywords. If the values are
callable, they are computed on the DataFrame and
assigned to the new columns. The callable must not
change input DataFrame (though pandas doesn't check it).
If the values are not callable, (e.g. a Series, scalar, or array),
they are simply assigned.
Returns
-------
DataFrame
A new DataFrame with the new columns in addition to
all the existing columns.
Notes
-----
Assigning multiple columns within the same ``assign`` is possible.
For Python 3.6 and above, later items in '\*\*kwargs' may refer to
newly created or modified columns in 'df'; items are computed and
assigned into 'df' in order. For Python 3.5 and below, the order of
keyword arguments is not specified, you cannot refer to newly created
or modified columns. All items are computed first, and then assigned
in alphabetical order.
.. versionchanged :: 0.23.0
Keyword argument order is maintained for Python 3.6 and later.
Examples
--------
>>> df = pd.DataFrame({'temp_c': [17.0, 25.0]},
... index=['Portland', 'Berkeley'])
>>> df
temp_c
Portland 17.0
Berkeley 25.0
Where the value is a callable, evaluated on `df`:
>>> df.assign(temp_f=lambda x: x.temp_c * 9 / 5 + 32)
temp_c temp_f
Portland 17.0 62.6
Berkeley 25.0 77.0
Alternatively, the same behavior can be achieved by directly
referencing an existing Series or sequence:
>>> df.assign(temp_f=df['temp_c'] * 9 / 5 + 32)
temp_c temp_f
Portland 17.0 62.6
Berkeley 25.0 77.0
In Python 3.6+, you can create multiple columns within the same assign
where one of the columns depends on another one defined within the same
assign:
>>> df.assign(temp_f=lambda x: x['temp_c'] * 9 / 5 + 32,
... temp_k=lambda x: (x['temp_f'] + 459.67) * 5 / 9)
temp_c temp_f temp_k
Portland 17.0 62.6 290.15
Berkeley 25.0 77.0 298.15
"""
data = self.copy()
# >= 3.6 preserve order of kwargs
if PY36:
for k, v in kwargs.items():
data[k] = com.apply_if_callable(v, data)
else:
# <= 3.5: do all calculations first...
results = OrderedDict()
for k, v in kwargs.items():
results[k] = com.apply_if_callable(v, data)
# <= 3.5 and earlier
results = sorted(results.items())
# ... and then assign
for k, v in results:
data[k] = v
return data
def _sanitize_column(self, key, value, broadcast=True):
"""
Ensures new columns (which go into the BlockManager as new blocks) are
always copied and converted into an array.
Parameters
----------
key : object
value : scalar, Series, or array-like
broadcast : bool, default True
If ``key`` matches multiple duplicate column names in the
DataFrame, this parameter indicates whether ``value`` should be
tiled so that the returned array contains a (duplicated) column for
each occurrence of the key. If False, ``value`` will not be tiled.
Returns
-------
numpy.ndarray
"""
def reindexer(value):
# reindex if necessary
if value.index.equals(self.index) or not len(self.index):
value = value._values.copy()
else:
# GH 4107
try:
value = value.reindex(self.index)._values
except Exception as e:
# duplicate axis
if not value.index.is_unique:
raise e
# other
raise TypeError('incompatible index of inserted column '
'with frame index')
return value
if isinstance(value, Series):
value = reindexer(value)
elif isinstance(value, DataFrame):
# align right-hand-side columns if self.columns
# is multi-index and self[key] is a sub-frame
if isinstance(self.columns, MultiIndex) and key in self.columns:
loc = self.columns.get_loc(key)
if isinstance(loc, (slice, Series, np.ndarray, Index)):
cols = maybe_droplevels(self.columns[loc], key)
if len(cols) and not cols.equals(value.columns):
value = value.reindex(cols, axis=1)
# now align rows
value = reindexer(value).T
elif isinstance(value, ExtensionArray):
# Explicitly copy here, instead of in sanitize_index,
# as sanitize_index won't copy an EA, even with copy=True
value = value.copy()
value = sanitize_index(value, self.index, copy=False)
elif isinstance(value, Index) or is_sequence(value):
# turn me into an ndarray
value = sanitize_index(value, self.index, copy=False)
if not isinstance(value, (np.ndarray, Index)):
if isinstance(value, list) and len(value) > 0:
value = maybe_convert_platform(value)
else:
value = com.asarray_tuplesafe(value)
elif value.ndim == 2:
value = value.copy().T
elif isinstance(value, Index):
value = value.copy(deep=True)
else:
value = value.copy()
# possibly infer to datetimelike
if is_object_dtype(value.dtype):
value = maybe_infer_to_datetimelike(value)
else:
# cast ignores pandas dtypes. so save the dtype first
infer_dtype, _ = infer_dtype_from_scalar(
value, pandas_dtype=True)
# upcast
value = cast_scalar_to_array(len(self.index), value)
value = maybe_cast_to_datetime(value, infer_dtype)
# return internal types directly
if is_extension_type(value) or is_extension_array_dtype(value):
return value
# broadcast across multiple columns if necessary
if broadcast and key in self.columns and value.ndim == 1:
if (not self.columns.is_unique or
isinstance(self.columns, MultiIndex)):
existing_piece = self[key]
if isinstance(existing_piece, DataFrame):
value = np.tile(value, (len(existing_piece.columns), 1))
return np.atleast_2d(np.asarray(value))
@property
def _series(self):
return {item: Series(self._data.iget(idx), index=self.index, name=item)
for idx, item in enumerate(self.columns)}
def lookup(self, row_labels, col_labels):
"""
Label-based "fancy indexing" function for DataFrame.
Given equal-length arrays of row and column labels, return an
array of the values corresponding to each (row, col) pair.
Parameters
----------
row_labels : sequence
The row labels to use for lookup
col_labels : sequence
The column labels to use for lookup
Returns
-------
numpy.ndarray
Notes
-----
Akin to::
result = [df.get_value(row, col)
for row, col in zip(row_labels, col_labels)]
Examples
--------
values : ndarray
The found values
"""
n = len(row_labels)
if n != len(col_labels):
raise ValueError('Row labels must have same size as column labels')
thresh = 1000
if not self._is_mixed_type or n > thresh:
values = self.values
ridx = self.index.get_indexer(row_labels)
cidx = self.columns.get_indexer(col_labels)
if (ridx == -1).any():
raise KeyError('One or more row labels was not found')
if (cidx == -1).any():
raise KeyError('One or more column labels was not found')
flat_index = ridx * len(self.columns) + cidx
result = values.flat[flat_index]
else:
result = np.empty(n, dtype='O')
for i, (r, c) in enumerate(zip(row_labels, col_labels)):
result[i] = self._get_value(r, c)
if is_object_dtype(result):
result = lib.maybe_convert_objects(result)
return result
# ----------------------------------------------------------------------
# Reindexing and alignment
def _reindex_axes(self, axes, level, limit, tolerance, method, fill_value,
copy):
frame = self
columns = axes['columns']
if columns is not None:
frame = frame._reindex_columns(columns, method, copy, level,
fill_value, limit, tolerance)
index = axes['index']
if index is not None:
frame = frame._reindex_index(index, method, copy, level,
fill_value, limit, tolerance)
return frame
def _reindex_index(self, new_index, method, copy, level, fill_value=np.nan,
limit=None, tolerance=None):
new_index, indexer = self.index.reindex(new_index, method=method,
level=level, limit=limit,
tolerance=tolerance)
return self._reindex_with_indexers({0: [new_index, indexer]},
copy=copy, fill_value=fill_value,
allow_dups=False)
def _reindex_columns(self, new_columns, method, copy, level,
fill_value=None, limit=None, tolerance=None):
new_columns, indexer = self.columns.reindex(new_columns, method=method,
level=level, limit=limit,
tolerance=tolerance)
return self._reindex_with_indexers({1: [new_columns, indexer]},
copy=copy, fill_value=fill_value,
allow_dups=False)
def _reindex_multi(self, axes, copy, fill_value):
"""
We are guaranteed non-Nones in the axes.
"""
new_index, row_indexer = self.index.reindex(axes['index'])
new_columns, col_indexer = self.columns.reindex(axes['columns'])
if row_indexer is not None and col_indexer is not None:
indexer = row_indexer, col_indexer
new_values = algorithms.take_2d_multi(self.values, indexer,
fill_value=fill_value)
return self._constructor(new_values, index=new_index,
columns=new_columns)
else:
return self._reindex_with_indexers({0: [new_index, row_indexer],
1: [new_columns, col_indexer]},
copy=copy,
fill_value=fill_value)
@Appender(_shared_docs['align'] % _shared_doc_kwargs)
def align(self, other, join='outer', axis=None, level=None, copy=True,
fill_value=None, method=None, limit=None, fill_axis=0,
broadcast_axis=None):
return super().align(other, join=join, axis=axis, level=level,
copy=copy, fill_value=fill_value, method=method,
limit=limit, fill_axis=fill_axis,
broadcast_axis=broadcast_axis)
@Substitution(**_shared_doc_kwargs)
@Appender(NDFrame.reindex.__doc__)
@rewrite_axis_style_signature('labels', [('method', None),
('copy', True),
('level', None),
('fill_value', np.nan),
('limit', None),
('tolerance', None)])
def reindex(self, *args, **kwargs):
axes = validate_axis_style_args(self, args, kwargs, 'labels',
'reindex')
kwargs.update(axes)
# Pop these, since the values are in `kwargs` under different names
kwargs.pop('axis', None)
kwargs.pop('labels', None)
return super().reindex(**kwargs)
@Appender(_shared_docs['reindex_axis'] % _shared_doc_kwargs)
def reindex_axis(self, labels, axis=0, method=None, level=None, copy=True,
limit=None, fill_value=np.nan):
return super().reindex_axis(labels=labels, axis=axis, method=method,
level=level, copy=copy, limit=limit,
fill_value=fill_value)
def drop(self, labels=None, axis=0, index=None, columns=None,
level=None, inplace=False, errors='raise'):
"""
Drop specified labels from rows or columns.
Remove rows or columns by specifying label names and corresponding
axis, or by specifying directly index or column names. When using a
multi-index, labels on different levels can be removed by specifying
the level.
Parameters
----------
labels : single label or list-like
Index or column labels to drop.
axis : {0 or 'index', 1 or 'columns'}, default 0
Whether to drop labels from the index (0 or 'index') or
columns (1 or 'columns').
index : single label or list-like
Alternative to specifying axis (``labels, axis=0``
is equivalent to ``index=labels``).
.. versionadded:: 0.21.0
columns : single label or list-like
Alternative to specifying axis (``labels, axis=1``
is equivalent to ``columns=labels``).
.. versionadded:: 0.21.0
level : int or level name, optional
For MultiIndex, level from which the labels will be removed.
inplace : bool, default False
If True, do operation inplace and return None.
errors : {'ignore', 'raise'}, default 'raise'
If 'ignore', suppress error and only existing labels are
dropped.
Returns
-------
DataFrame
DataFrame without the removed index or column labels.
Raises
------
KeyError
If any of the labels is not found in the selected axis.
See Also
--------
DataFrame.loc : Label-location based indexer for selection by label.
DataFrame.dropna : Return DataFrame with labels on given axis omitted
where (all or any) data are missing.
DataFrame.drop_duplicates : Return DataFrame with duplicate rows
removed, optionally only considering certain columns.
Series.drop : Return Series with specified index labels removed.
Examples
--------
>>> df = pd.DataFrame(np.arange(12).reshape(3, 4),
... columns=['A', 'B', 'C', 'D'])
>>> df
A B C D
0 0 1 2 3
1 4 5 6 7
2 8 9 10 11
Drop columns
>>> df.drop(['B', 'C'], axis=1)
A D
0 0 3
1 4 7
2 8 11
>>> df.drop(columns=['B', 'C'])
A D
0 0 3
1 4 7
2 8 11
Drop a row by index
>>> df.drop([0, 1])
A B C D
2 8 9 10 11
Drop columns and/or rows of MultiIndex DataFrame
>>> midx = pd.MultiIndex(levels=[['lama', 'cow', 'falcon'],
... ['speed', 'weight', 'length']],
... codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2],
... [0, 1, 2, 0, 1, 2, 0, 1, 2]])
>>> df = pd.DataFrame(index=midx, columns=['big', 'small'],
... data=[[45, 30], [200, 100], [1.5, 1], [30, 20],
... [250, 150], [1.5, 0.8], [320, 250],
... [1, 0.8], [0.3, 0.2]])
>>> df
big small
lama speed 45.0 30.0
weight 200.0 100.0
length 1.5 1.0
cow speed 30.0 20.0
weight 250.0 150.0
length 1.5 0.8
falcon speed 320.0 250.0
weight 1.0 0.8
length 0.3 0.2
>>> df.drop(index='cow', columns='small')
big
lama speed 45.0
weight 200.0
length 1.5
falcon speed 320.0
weight 1.0
length 0.3
>>> df.drop(index='length', level=1)
big small
lama speed 45.0 30.0
weight 200.0 100.0
cow speed 30.0 20.0
weight 250.0 150.0
falcon speed 320.0 250.0
weight 1.0 0.8
"""
return super().drop(labels=labels, axis=axis, index=index,
columns=columns, level=level, inplace=inplace,
errors=errors)
@rewrite_axis_style_signature('mapper', [('copy', True),
('inplace', False),
('level', None),
('errors', 'ignore')])
def rename(self, *args, **kwargs):
"""
Alter axes labels.
Function / dict values must be unique (1-to-1). Labels not contained in
a dict / Series will be left as-is. Extra labels listed don't throw an
error.
See the :ref:`user guide <basics.rename>` for more.
Parameters
----------
mapper : dict-like or function
Dict-like or functions transformations to apply to
that axis' values. Use either ``mapper`` and ``axis`` to
specify the axis to target with ``mapper``, or ``index`` and
``columns``.
index : dict-like or function
Alternative to specifying axis (``mapper, axis=0``
is equivalent to ``index=mapper``).
columns : dict-like or function
Alternative to specifying axis (``mapper, axis=1``
is equivalent to ``columns=mapper``).
axis : int or str
Axis to target with ``mapper``. Can be either the axis name
('index', 'columns') or number (0, 1). The default is 'index'.
copy : bool, default True
Also copy underlying data.
inplace : bool, default False
Whether to return a new DataFrame. If True then value of copy is
ignored.
level : int or level name, default None
In case of a MultiIndex, only rename labels in the specified
level.
errors : {'ignore', 'raise'}, default 'ignore'
If 'raise', raise a `KeyError` when a dict-like `mapper`, `index`,
or `columns` contains labels that are not present in the Index
being transformed.
If 'ignore', existing keys will be renamed and extra keys will be
ignored.
Returns
-------
DataFrame
DataFrame with the renamed axis labels.
Raises
------
KeyError
If any of the labels is not found in the selected axis and
"errors='raise'".
See Also
--------
DataFrame.rename_axis : Set the name of the axis.
Examples
--------
``DataFrame.rename`` supports two calling conventions
* ``(index=index_mapper, columns=columns_mapper, ...)``
* ``(mapper, axis={'index', 'columns'}, ...)``
We *highly* recommend using keyword arguments to clarify your
intent.
Rename columns using a mapping:
>>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
>>> df.rename(columns={"A": "a", "B": "c"})
a c
0 1 4
1 2 5
2 3 6
Rename index using a mapping:
>>> df.rename(index={0: "x", 1: "y", 2: "z"})
A B
x 1 4
y 2 5
z 3 6
Cast index labels to a different type:
>>> df.index
RangeIndex(start=0, stop=3, step=1)
>>> df.rename(index=str).index
Index(['0', '1', '2'], dtype='object')
>>> df.rename(columns={"A": "a", "B": "b", "C": "c"}, errors="raise")
Traceback (most recent call last):
KeyError: ['C'] not found in axis
Using axis-style parameters
>>> df.rename(str.lower, axis='columns')
a b
0 1 4
1 2 5
2 3 6
>>> df.rename({1: 2, 2: 4}, axis='index')
A B
0 1 4
2 2 5
4 3 6
"""
axes = validate_axis_style_args(self, args, kwargs, 'mapper', 'rename')
kwargs.update(axes)
# Pop these, since the values are in `kwargs` under different names
kwargs.pop('axis', None)
kwargs.pop('mapper', None)
return super().rename(**kwargs)
@Substitution(**_shared_doc_kwargs)
@Appender(NDFrame.fillna.__doc__)
def fillna(self, value=None, method=None, axis=None, inplace=False,
limit=None, downcast=None, **kwargs):
return super().fillna(value=value, method=method, axis=axis,
inplace=inplace, limit=limit, downcast=downcast,
**kwargs)
@Appender(_shared_docs['replace'] % _shared_doc_kwargs)
def replace(self, to_replace=None, value=None, inplace=False, limit=None,
regex=False, method='pad'):
return super().replace(to_replace=to_replace, value=value,
inplace=inplace, limit=limit, regex=regex,
method=method)
@Appender(_shared_docs['shift'] % _shared_doc_kwargs)
def shift(self, periods=1, freq=None, axis=0, fill_value=None):
return super().shift(periods=periods, freq=freq, axis=axis,
fill_value=fill_value)
def set_index(self, keys, drop=True, append=False, inplace=False,
verify_integrity=False):
"""
Set the DataFrame index using existing columns.
Set the DataFrame index (row labels) using one or more existing
columns or arrays (of the correct length). The index can replace the
existing index or expand on it.
Parameters
----------
keys : label or array-like or list of labels/arrays
This parameter can be either a single column key, a single array of
the same length as the calling DataFrame, or a list containing an
arbitrary combination of column keys and arrays. Here, "array"
encompasses :class:`Series`, :class:`Index`, ``np.ndarray``, and
instances of :class:`~collections.abc.Iterator`.
drop : bool, default True
Delete columns to be used as the new index.
append : bool, default False
Whether to append columns to existing index.
inplace : bool, default False
Modify the DataFrame in place (do not create a new object).
verify_integrity : bool, default False
Check the new index for duplicates. Otherwise defer the check until
necessary. Setting to False will improve the performance of this
method.
Returns
-------
DataFrame
Changed row labels.
See Also
--------
DataFrame.reset_index : Opposite of set_index.
DataFrame.reindex : Change to new indices or expand indices.
DataFrame.reindex_like : Change to same indices as other DataFrame.
Examples
--------
>>> df = pd.DataFrame({'month': [1, 4, 7, 10],
... 'year': [2012, 2014, 2013, 2014],
... 'sale': [55, 40, 84, 31]})
>>> df
month year sale
0 1 2012 55
1 4 2014 40
2 7 2013 84
3 10 2014 31
Set the index to become the 'month' column:
>>> df.set_index('month')
year sale
month
1 2012 55
4 2014 40
7 2013 84
10 2014 31
Create a MultiIndex using columns 'year' and 'month':
>>> df.set_index(['year', 'month'])
sale
year month
2012 1 55
2014 4 40
2013 7 84
2014 10 31
Create a MultiIndex using an Index and a column:
>>> df.set_index([pd.Index([1, 2, 3, 4]), 'year'])
month sale
year
1 2012 1 55
2 2014 4 40
3 2013 7 84
4 2014 10 31
Create a MultiIndex using two Series:
>>> s = pd.Series([1, 2, 3, 4])
>>> df.set_index([s, s**2])
month year sale
1 1 1 2012 55
2 4 4 2014 40
3 9 7 2013 84
4 16 10 2014 31
"""
inplace = validate_bool_kwarg(inplace, 'inplace')
if not isinstance(keys, list):
keys = [keys]
err_msg = ('The parameter "keys" may be a column key, one-dimensional '
'array, or a list containing only valid column keys and '
'one-dimensional arrays.')
missing = []
for col in keys:
if isinstance(col, (ABCIndexClass, ABCSeries, np.ndarray,
list, abc.Iterator)):
# arrays are fine as long as they are one-dimensional
# iterators get converted to list below
if getattr(col, 'ndim', 1) != 1:
raise ValueError(err_msg)
else:
# everything else gets tried as a key; see GH 24969
try:
found = col in self.columns
except TypeError:
raise TypeError(err_msg + ' Received column of '
'type {}'.format(type(col)))
else:
if not found:
missing.append(col)
if missing:
raise KeyError('None of {} are in the columns'.format(missing))
if inplace:
frame = self
else:
frame = self.copy()
arrays = []
names = []
if append:
names = [x for x in self.index.names]
if isinstance(self.index, ABCMultiIndex):
for i in range(self.index.nlevels):
arrays.append(self.index._get_level_values(i))
else:
arrays.append(self.index)
to_remove = []
for col in keys:
if isinstance(col, ABCMultiIndex):
for n in range(col.nlevels):
arrays.append(col._get_level_values(n))
names.extend(col.names)
elif isinstance(col, (ABCIndexClass, ABCSeries)):
# if Index then not MultiIndex (treated above)
arrays.append(col)
names.append(col.name)
elif isinstance(col, (list, np.ndarray)):
arrays.append(col)
names.append(None)
elif isinstance(col, abc.Iterator):
arrays.append(list(col))
names.append(None)
# from here, col can only be a column label
else:
arrays.append(frame[col]._values)
names.append(col)
if drop:
to_remove.append(col)
if len(arrays[-1]) != len(self):
# check newest element against length of calling frame, since
# ensure_index_from_sequences would not raise for append=False.
raise ValueError('Length mismatch: Expected {len_self} rows, '
'received array of length {len_col}'.format(
len_self=len(self),
len_col=len(arrays[-1])
))
index = ensure_index_from_sequences(arrays, names)
if verify_integrity and not index.is_unique:
duplicates = index[index.duplicated()].unique()
raise ValueError('Index has duplicate keys: {dup}'.format(
dup=duplicates))
# use set to handle duplicate column names gracefully in case of drop
for c in set(to_remove):
del frame[c]
# clear up memory usage
index._cleanup()
frame.index = index
if not inplace:
return frame
def reset_index(self, level=None, drop=False, inplace=False, col_level=0,
col_fill=''):
"""
Reset the index, or a level of it.
Reset the index of the DataFrame, and use the default one instead.
If the DataFrame has a MultiIndex, this method can remove one or more
levels.
Parameters
----------
level : int, str, tuple, or list, default None
Only remove the given levels from the index. Removes all levels by
default.
drop : bool, default False
Do not try to insert index into dataframe columns. This resets
the index to the default integer index.
inplace : bool, default False
Modify the DataFrame in place (do not create a new object).
col_level : int or str, default 0
If the columns have multiple levels, determines which level the
labels are inserted into. By default it is inserted into the first
level.
col_fill : object, default ''
If the columns have multiple levels, determines how the other
levels are named. If None then the index name is repeated.
Returns
-------
DataFrame
DataFrame with the new index.
See Also
--------
DataFrame.set_index : Opposite of reset_index.
DataFrame.reindex : Change to new indices or expand indices.
DataFrame.reindex_like : Change to same indices as other DataFrame.
Examples
--------
>>> df = pd.DataFrame([('bird', 389.0),
... ('bird', 24.0),
... ('mammal', 80.5),
... ('mammal', np.nan)],
... index=['falcon', 'parrot', 'lion', 'monkey'],
... columns=('class', 'max_speed'))
>>> df
class max_speed
falcon bird 389.0
parrot bird 24.0
lion mammal 80.5
monkey mammal NaN
When we reset the index, the old index is added as a column, and a
new sequential index is used:
>>> df.reset_index()
index class max_speed
0 falcon bird 389.0
1 parrot bird 24.0
2 lion mammal 80.5
3 monkey mammal NaN
We can use the `drop` parameter to avoid the old index being added as
a column:
>>> df.reset_index(drop=True)
class max_speed
0 bird 389.0
1 bird 24.0
2 mammal 80.5
3 mammal NaN
You can also use `reset_index` with `MultiIndex`.
>>> index = pd.MultiIndex.from_tuples([('bird', 'falcon'),
... ('bird', 'parrot'),
... ('mammal', 'lion'),
... ('mammal', 'monkey')],
... names=['class', 'name'])
>>> columns = pd.MultiIndex.from_tuples([('speed', 'max'),
... ('species', 'type')])
>>> df = pd.DataFrame([(389.0, 'fly'),
... ( 24.0, 'fly'),
... ( 80.5, 'run'),
... (np.nan, 'jump')],
... index=index,
... columns=columns)
>>> df
speed species
max type
class name
bird falcon 389.0 fly
parrot 24.0 fly
mammal lion 80.5 run
monkey NaN jump
If the index has multiple levels, we can reset a subset of them:
>>> df.reset_index(level='class')
class speed species
max type
name
falcon bird 389.0 fly
parrot bird 24.0 fly
lion mammal 80.5 run
monkey mammal NaN jump
If we are not dropping the index, by default, it is placed in the top
level. We can place it in another level:
>>> df.reset_index(level='class', col_level=1)
speed species
class max type
name
falcon bird 389.0 fly
parrot bird 24.0 fly
lion mammal 80.5 run
monkey mammal NaN jump
When the index is inserted under another level, we can specify under
which one with the parameter `col_fill`:
>>> df.reset_index(level='class', col_level=1, col_fill='species')
species speed species
class max type
name
falcon bird 389.0 fly
parrot bird 24.0 fly
lion mammal 80.5 run
monkey mammal NaN jump
If we specify a nonexistent level for `col_fill`, it is created:
>>> df.reset_index(level='class', col_level=1, col_fill='genus')
genus speed species
class max type
name
falcon bird 389.0 fly
parrot bird 24.0 fly
lion mammal 80.5 run
monkey mammal NaN jump
"""
inplace = validate_bool_kwarg(inplace, 'inplace')
if inplace:
new_obj = self
else:
new_obj = self.copy()
def _maybe_casted_values(index, labels=None):
values = index._values
if not isinstance(index, (PeriodIndex, DatetimeIndex)):
if values.dtype == np.object_:
values = lib.maybe_convert_objects(values)
# if we have the labels, extract the values with a mask
if labels is not None:
mask = labels == -1
# we can have situations where the whole mask is -1,
# meaning there is nothing found in labels, so make all nan's
if mask.all():
values = np.empty(len(mask))
values.fill(np.nan)
else:
values = values.take(labels)
# TODO(https://github.com/pandas-dev/pandas/issues/24206)
# Push this into maybe_upcast_putmask?
# We can't pass EAs there right now. Looks a bit
# complicated.
# So we unbox the ndarray_values, op, re-box.
values_type = type(values)
values_dtype = values.dtype
if issubclass(values_type, DatetimeLikeArray):
values = values._data
if mask.any():
values, changed = maybe_upcast_putmask(
values, mask, np.nan)
if issubclass(values_type, DatetimeLikeArray):
values = values_type(values, dtype=values_dtype)
return values
new_index = ibase.default_index(len(new_obj))
if level is not None:
if not isinstance(level, (tuple, list)):
level = [level]
level = [self.index._get_level_number(lev) for lev in level]
if len(level) < self.index.nlevels:
new_index = self.index.droplevel(level)
if not drop:
if isinstance(self.index, MultiIndex):
names = [n if n is not None else ('level_%d' % i)
for (i, n) in enumerate(self.index.names)]
to_insert = zip(self.index.levels, self.index.codes)
else:
default = 'index' if 'index' not in self else 'level_0'
names = ([default] if self.index.name is None
else [self.index.name])
to_insert = ((self.index, None),)
multi_col = isinstance(self.columns, MultiIndex)
for i, (lev, lab) in reversed(list(enumerate(to_insert))):
if not (level is None or i in level):
continue
name = names[i]
if multi_col:
col_name = (list(name) if isinstance(name, tuple)
else [name])
if col_fill is None:
if len(col_name) not in (1, self.columns.nlevels):
raise ValueError("col_fill=None is incompatible "
"with incomplete column name "
"{}".format(name))
col_fill = col_name[0]
lev_num = self.columns._get_level_number(col_level)
name_lst = [col_fill] * lev_num + col_name
missing = self.columns.nlevels - len(name_lst)
name_lst += [col_fill] * missing
name = tuple(name_lst)
# to ndarray and maybe infer different dtype
level_values = _maybe_casted_values(lev, lab)
new_obj.insert(0, name, level_values)
new_obj.index = new_index
if not inplace:
return new_obj
# ----------------------------------------------------------------------
# Reindex-based selection methods
@Appender(_shared_docs['isna'] % _shared_doc_kwargs)
def isna(self):
return super().isna()
@Appender(_shared_docs['isna'] % _shared_doc_kwargs)
def isnull(self):
return super().isnull()
@Appender(_shared_docs['notna'] % _shared_doc_kwargs)
def notna(self):
return super().notna()
@Appender(_shared_docs['notna'] % _shared_doc_kwargs)
def notnull(self):
return super().notnull()
def dropna(self, axis=0, how='any', thresh=None, subset=None,
inplace=False):
"""
Remove missing values.
See the :ref:`User Guide <missing_data>` for more on which values are
considered missing, and how to work with missing data.
Parameters
----------
axis : {0 or 'index', 1 or 'columns'}, default 0
Determine if rows or columns which contain missing values are
removed.
* 0, or 'index' : Drop rows which contain missing values.
* 1, or 'columns' : Drop columns which contain missing value.
.. deprecated:: 0.23.0
Pass tuple or list to drop on multiple axes.
Only a single axis is allowed.
how : {'any', 'all'}, default 'any'
Determine if row or column is removed from DataFrame, when we have
at least one NA or all NA.
* 'any' : If any NA values are present, drop that row or column.
* 'all' : If all values are NA, drop that row or column.
thresh : int, optional
Require that many non-NA values.
subset : array-like, optional
Labels along other axis to consider, e.g. if you are dropping rows
these would be a list of columns to include.
inplace : bool, default False
If True, do operation inplace and return None.
Returns
-------
DataFrame
DataFrame with NA entries dropped from it.
See Also
--------
DataFrame.isna: Indicate missing values.
DataFrame.notna : Indicate existing (non-missing) values.
DataFrame.fillna : Replace missing values.
Series.dropna : Drop missing values.
Index.dropna : Drop missing indices.
Examples
--------
>>> df = pd.DataFrame({"name": ['Alfred', 'Batman', 'Catwoman'],
... "toy": [np.nan, 'Batmobile', 'Bullwhip'],
... "born": [pd.NaT, pd.Timestamp("1940-04-25"),
... pd.NaT]})
>>> df
name toy born
0 Alfred NaN NaT
1 Batman Batmobile 1940-04-25
2 Catwoman Bullwhip NaT
Drop the rows where at least one element is missing.
>>> df.dropna()
name toy born
1 Batman Batmobile 1940-04-25
Drop the columns where at least one element is missing.
>>> df.dropna(axis='columns')
name
0 Alfred
1 Batman
2 Catwoman
Drop the rows where all elements are missing.
>>> df.dropna(how='all')
name toy born
0 Alfred NaN NaT
1 Batman Batmobile 1940-04-25
2 Catwoman Bullwhip NaT
Keep only the rows with at least 2 non-NA values.
>>> df.dropna(thresh=2)
name toy born
1 Batman Batmobile 1940-04-25
2 Catwoman Bullwhip NaT
Define in which columns to look for missing values.
>>> df.dropna(subset=['name', 'born'])
name toy born
1 Batman Batmobile 1940-04-25
Keep the DataFrame with valid entries in the same variable.
>>> df.dropna(inplace=True)
>>> df
name toy born
1 Batman Batmobile 1940-04-25
"""
inplace = validate_bool_kwarg(inplace, 'inplace')
if isinstance(axis, (tuple, list)):
# GH20987
msg = ("supplying multiple axes to axis is deprecated and "
"will be removed in a future version.")
warnings.warn(msg, FutureWarning, stacklevel=2)
result = self
for ax in axis:
result = result.dropna(how=how, thresh=thresh, subset=subset,
axis=ax)
else:
axis = self._get_axis_number(axis)
agg_axis = 1 - axis
agg_obj = self
if subset is not None:
ax = self._get_axis(agg_axis)
indices = ax.get_indexer_for(subset)
check = indices == -1
if check.any():
raise KeyError(list(np.compress(check, subset)))
agg_obj = self.take(indices, axis=agg_axis)
count = agg_obj.count(axis=agg_axis)
if thresh is not None:
mask = count >= thresh
elif how == 'any':
mask = count == len(agg_obj._get_axis(agg_axis))
elif how == 'all':
mask = count > 0
else:
if how is not None:
raise ValueError('invalid how option: {h}'.format(h=how))
else:
raise TypeError('must specify how or thresh')
result = self.loc(axis=axis)[mask]
if inplace:
self._update_inplace(result)
else:
return result
def drop_duplicates(self, subset=None, keep='first', inplace=False):
"""
Return DataFrame with duplicate rows removed, optionally only
considering certain columns. Indexes, including time indexes
are ignored.
Parameters
----------
subset : column label or sequence of labels, optional
Only consider certain columns for identifying duplicates, by
default use all of the columns
keep : {'first', 'last', False}, default 'first'
- ``first`` : Drop duplicates except for the first occurrence.
- ``last`` : Drop duplicates except for the last occurrence.
- False : Drop all duplicates.
inplace : boolean, default False
Whether to drop duplicates in place or to return a copy
Returns
-------
DataFrame
"""
if self.empty:
return self.copy()
inplace = validate_bool_kwarg(inplace, 'inplace')
duplicated = self.duplicated(subset, keep=keep)
if inplace:
inds, = (-duplicated)._ndarray_values.nonzero()
new_data = self._data.take(inds)
self._update_inplace(new_data)
else:
return self[-duplicated]
def duplicated(self, subset=None, keep='first'):
"""
Return boolean Series denoting duplicate rows, optionally only
considering certain columns.
Parameters
----------
subset : column label or sequence of labels, optional
Only consider certain columns for identifying duplicates, by
default use all of the columns
keep : {'first', 'last', False}, default 'first'
- ``first`` : Mark duplicates as ``True`` except for the
first occurrence.
- ``last`` : Mark duplicates as ``True`` except for the
last occurrence.
- False : Mark all duplicates as ``True``.
Returns
-------
Series
"""
from pandas.core.sorting import get_group_index
from pandas._libs.hashtable import duplicated_int64, _SIZE_HINT_LIMIT
if self.empty:
return Series(dtype=bool)
def f(vals):
labels, shape = algorithms.factorize(
vals, size_hint=min(len(self), _SIZE_HINT_LIMIT))
return labels.astype('i8', copy=False), len(shape)
if subset is None:
subset = self.columns
elif (not np.iterable(subset) or
isinstance(subset, str) or
isinstance(subset, tuple) and subset in self.columns):
subset = subset,
# Verify all columns in subset exist in the queried dataframe
# Otherwise, raise a KeyError, same as if you try to __getitem__ with a
# key that doesn't exist.
diff = Index(subset).difference(self.columns)
if not diff.empty:
raise KeyError(diff)
vals = (col.values for name, col in self.iteritems()
if name in subset)
labels, shape = map(list, zip(*map(f, vals)))
ids = get_group_index(labels, shape, sort=False, xnull=False)
return Series(duplicated_int64(ids, keep), index=self.index)
# ----------------------------------------------------------------------
# Sorting
@Substitution(**_shared_doc_kwargs)
@Appender(NDFrame.sort_values.__doc__)
def sort_values(self, by, axis=0, ascending=True, inplace=False,
kind='quicksort', na_position='last'):
inplace = validate_bool_kwarg(inplace, 'inplace')
axis = self._get_axis_number(axis)
if not isinstance(by, list):
by = [by]
if is_sequence(ascending) and len(by) != len(ascending):
raise ValueError('Length of ascending (%d) != length of by (%d)' %
(len(ascending), len(by)))
if len(by) > 1:
from pandas.core.sorting import lexsort_indexer
keys = [self._get_label_or_level_values(x, axis=axis)
for x in by]
indexer = lexsort_indexer(keys, orders=ascending,
na_position=na_position)
indexer = ensure_platform_int(indexer)
else:
from pandas.core.sorting import nargsort
by = by[0]
k = self._get_label_or_level_values(by, axis=axis)
if isinstance(ascending, (tuple, list)):
ascending = ascending[0]
indexer = nargsort(k, kind=kind, ascending=ascending,
na_position=na_position)
new_data = self._data.take(indexer,
axis=self._get_block_manager_axis(axis),
verify=False)
if inplace:
return self._update_inplace(new_data)
else:
return self._constructor(new_data).__finalize__(self)
@Substitution(**_shared_doc_kwargs)
@Appender(NDFrame.sort_index.__doc__)
def sort_index(self, axis=0, level=None, ascending=True, inplace=False,
kind='quicksort', na_position='last', sort_remaining=True,
by=None):
# TODO: this can be combined with Series.sort_index impl as
# almost identical
inplace = validate_bool_kwarg(inplace, 'inplace')
# 10726
if by is not None:
warnings.warn("by argument to sort_index is deprecated, "
"please use .sort_values(by=...)",
FutureWarning, stacklevel=2)
if level is not None:
raise ValueError("unable to simultaneously sort by and level")
return self.sort_values(by, axis=axis, ascending=ascending,
inplace=inplace)
axis = self._get_axis_number(axis)
labels = self._get_axis(axis)
# make sure that the axis is lexsorted to start
# if not we need to reconstruct to get the correct indexer
labels = labels._sort_levels_monotonic()
if level is not None:
new_axis, indexer = labels.sortlevel(level, ascending=ascending,
sort_remaining=sort_remaining)
elif isinstance(labels, MultiIndex):
from pandas.core.sorting import lexsort_indexer
indexer = lexsort_indexer(labels._get_codes_for_sorting(),
orders=ascending,
na_position=na_position)
else:
from pandas.core.sorting import nargsort
# Check monotonic-ness before sort an index
# GH11080
if ((ascending and labels.is_monotonic_increasing) or
(not ascending and labels.is_monotonic_decreasing)):
if inplace:
return
else:
return self.copy()
indexer = nargsort(labels, kind=kind, ascending=ascending,
na_position=na_position)
baxis = self._get_block_manager_axis(axis)
new_data = self._data.take(indexer,
axis=baxis,
verify=False)
# reconstruct axis if needed
new_data.axes[baxis] = new_data.axes[baxis]._sort_levels_monotonic()
if inplace:
return self._update_inplace(new_data)
else:
return self._constructor(new_data).__finalize__(self)
def nlargest(self, n, columns, keep='first'):
"""
Return the first `n` rows ordered by `columns` in descending order.
Return the first `n` rows with the largest values in `columns`, in
descending order. The columns that are not specified are returned as
well, but not used for ordering.
This method is equivalent to
``df.sort_values(columns, ascending=False).head(n)``, but more
performant.
Parameters
----------
n : int
Number of rows to return.
columns : label or list of labels
Column label(s) to order by.
keep : {'first', 'last', 'all'}, default 'first'
Where there are duplicate values:
- `first` : prioritize the first occurrence(s)
- `last` : prioritize the last occurrence(s)
- ``all`` : do not drop any duplicates, even it means
selecting more than `n` items.
.. versionadded:: 0.24.0
Returns
-------
DataFrame
The first `n` rows ordered by the given columns in descending
order.
See Also
--------
DataFrame.nsmallest : Return the first `n` rows ordered by `columns` in
ascending order.
DataFrame.sort_values : Sort DataFrame by the values.
DataFrame.head : Return the first `n` rows without re-ordering.
Notes
-----
This function cannot be used with all column types. For example, when
specifying columns with `object` or `category` dtypes, ``TypeError`` is
raised.
Examples
--------
>>> df = pd.DataFrame({'population': [59000000, 65000000, 434000,
... 434000, 434000, 337000, 11300,
... 11300, 11300],
... 'GDP': [1937894, 2583560 , 12011, 4520, 12128,
... 17036, 182, 38, 311],
... 'alpha-2': ["IT", "FR", "MT", "MV", "BN",
... "IS", "NR", "TV", "AI"]},
... index=["Italy", "France", "Malta",
... "Maldives", "Brunei", "Iceland",
... "Nauru", "Tuvalu", "Anguilla"])
>>> df
population GDP alpha-2
Italy 59000000 1937894 IT
France 65000000 2583560 FR
Malta 434000 12011 MT
Maldives 434000 4520 MV
Brunei 434000 12128 BN
Iceland 337000 17036 IS
Nauru 11300 182 NR
Tuvalu 11300 38 TV
Anguilla 11300 311 AI
In the following example, we will use ``nlargest`` to select the three
rows having the largest values in column "population".
>>> df.nlargest(3, 'population')
population GDP alpha-2
France 65000000 2583560 FR
Italy 59000000 1937894 IT
Malta 434000 12011 MT
When using ``keep='last'``, ties are resolved in reverse order:
>>> df.nlargest(3, 'population', keep='last')
population GDP alpha-2
France 65000000 2583560 FR
Italy 59000000 1937894 IT
Brunei 434000 12128 BN
When using ``keep='all'``, all duplicate items are maintained:
>>> df.nlargest(3, 'population', keep='all')
population GDP alpha-2
France 65000000 2583560 FR
Italy 59000000 1937894 IT
Malta 434000 12011 MT
Maldives 434000 4520 MV
Brunei 434000 12128 BN
To order by the largest values in column "population" and then "GDP",
we can specify multiple columns like in the next example.
>>> df.nlargest(3, ['population', 'GDP'])
population GDP alpha-2
France 65000000 2583560 FR
Italy 59000000 1937894 IT
Brunei 434000 12128 BN
"""
return algorithms.SelectNFrame(self,
n=n,
keep=keep,
columns=columns).nlargest()
def nsmallest(self, n, columns, keep='first'):
"""
Return the first `n` rows ordered by `columns` in ascending order.
Return the first `n` rows with the smallest values in `columns`, in
ascending order. The columns that are not specified are returned as
well, but not used for ordering.
This method is equivalent to
``df.sort_values(columns, ascending=True).head(n)``, but more
performant.
Parameters
----------
n : int
Number of items to retrieve.
columns : list or str
Column name or names to order by.
keep : {'first', 'last', 'all'}, default 'first'
Where there are duplicate values:
- ``first`` : take the first occurrence.
- ``last`` : take the last occurrence.
- ``all`` : do not drop any duplicates, even it means
selecting more than `n` items.
.. versionadded:: 0.24.0
Returns
-------
DataFrame
See Also
--------
DataFrame.nlargest : Return the first `n` rows ordered by `columns` in
descending order.
DataFrame.sort_values : Sort DataFrame by the values.
DataFrame.head : Return the first `n` rows without re-ordering.
Examples
--------
>>> df = pd.DataFrame({'population': [59000000, 65000000, 434000,
... 434000, 434000, 337000, 11300,
... 11300, 11300],
... 'GDP': [1937894, 2583560 , 12011, 4520, 12128,
... 17036, 182, 38, 311],
... 'alpha-2': ["IT", "FR", "MT", "MV", "BN",
... "IS", "NR", "TV", "AI"]},
... index=["Italy", "France", "Malta",
... "Maldives", "Brunei", "Iceland",
... "Nauru", "Tuvalu", "Anguilla"])
>>> df
population GDP alpha-2
Italy 59000000 1937894 IT
France 65000000 2583560 FR
Malta 434000 12011 MT
Maldives 434000 4520 MV
Brunei 434000 12128 BN
Iceland 337000 17036 IS
Nauru 11300 182 NR
Tuvalu 11300 38 TV
Anguilla 11300 311 AI
In the following example, we will use ``nsmallest`` to select the
three rows having the smallest values in column "a".
>>> df.nsmallest(3, 'population')
population GDP alpha-2
Nauru 11300 182 NR
Tuvalu 11300 38 TV
Anguilla 11300 311 AI
When using ``keep='last'``, ties are resolved in reverse order:
>>> df.nsmallest(3, 'population', keep='last')
population GDP alpha-2
Anguilla 11300 311 AI
Tuvalu 11300 38 TV
Nauru 11300 182 NR
When using ``keep='all'``, all duplicate items are maintained:
>>> df.nsmallest(3, 'population', keep='all')
population GDP alpha-2
Nauru 11300 182 NR
Tuvalu 11300 38 TV
Anguilla 11300 311 AI
To order by the largest values in column "a" and then "c", we can
specify multiple columns like in the next example.
>>> df.nsmallest(3, ['population', 'GDP'])
population GDP alpha-2
Tuvalu 11300 38 TV
Nauru 11300 182 NR
Anguilla 11300 311 AI
"""
return algorithms.SelectNFrame(self,
n=n,
keep=keep,
columns=columns).nsmallest()
def swaplevel(self, i=-2, j=-1, axis=0):
"""
Swap levels i and j in a MultiIndex on a particular axis.
Parameters
----------
i, j : int, string (can be mixed)
Level of index to be swapped. Can pass level name as string.
Returns
-------
DataFrame
.. versionchanged:: 0.18.1
The indexes ``i`` and ``j`` are now optional, and default to
the two innermost levels of the index.
"""
result = self.copy()
axis = self._get_axis_number(axis)
if axis == 0:
result.index = result.index.swaplevel(i, j)
else:
result.columns = result.columns.swaplevel(i, j)
return result
def reorder_levels(self, order, axis=0):
"""
Rearrange index levels using input order. May not drop or
duplicate levels.
Parameters
----------
order : list of int or list of str
List representing new level order. Reference level by number
(position) or by key (label).
axis : int
Where to reorder levels.
Returns
-------
type of caller (new object)
"""
axis = self._get_axis_number(axis)
if not isinstance(self._get_axis(axis),
MultiIndex): # pragma: no cover
raise TypeError('Can only reorder levels on a hierarchical axis.')
result = self.copy()
if axis == 0:
result.index = result.index.reorder_levels(order)
else:
result.columns = result.columns.reorder_levels(order)
return result
# ----------------------------------------------------------------------
# Arithmetic / combination related
def _combine_frame(self, other, func, fill_value=None, level=None):
this, other = self.align(other, join='outer', level=level, copy=False)
new_index, new_columns = this.index, this.columns
def _arith_op(left, right):
# for the mixed_type case where we iterate over columns,
# _arith_op(left, right) is equivalent to
# left._binop(right, func, fill_value=fill_value)
left, right = ops.fill_binop(left, right, fill_value)
return func(left, right)
if ops.should_series_dispatch(this, other, func):
# iterate over columns
return ops.dispatch_to_series(this, other, _arith_op)
else:
result = _arith_op(this.values, other.values)
return self._constructor(result,
index=new_index, columns=new_columns,
copy=False)
def _combine_match_index(self, other, func, level=None):
left, right = self.align(other, join='outer', axis=0, level=level,
copy=False)
assert left.index.equals(right.index)
if left._is_mixed_type or right._is_mixed_type:
# operate column-wise; avoid costly object-casting in `.values`
return ops.dispatch_to_series(left, right, func)
else:
# fastpath --> operate directly on values
with np.errstate(all="ignore"):
new_data = func(left.values.T, right.values).T
return self._constructor(new_data,
index=left.index, columns=self.columns,
copy=False)
def _combine_match_columns(self, other, func, level=None):
assert isinstance(other, Series)
left, right = self.align(other, join='outer', axis=1, level=level,
copy=False)
assert left.columns.equals(right.index)
return ops.dispatch_to_series(left, right, func, axis="columns")
def _combine_const(self, other, func):
assert lib.is_scalar(other) or np.ndim(other) == 0
return ops.dispatch_to_series(self, other, func)
def combine(self, other, func, fill_value=None, overwrite=True):
"""
Perform column-wise combine with another DataFrame.
Combines a DataFrame with `other` DataFrame using `func`
to element-wise combine columns. The row and column indexes of the
resulting DataFrame will be the union of the two.
Parameters
----------
other : DataFrame
The DataFrame to merge column-wise.
func : function
Function that takes two series as inputs and return a Series or a
scalar. Used to merge the two dataframes column by columns.
fill_value : scalar value, default None
The value to fill NaNs with prior to passing any column to the
merge func.
overwrite : bool, default True
If True, columns in `self` that do not exist in `other` will be
overwritten with NaNs.
Returns
-------
DataFrame
Combination of the provided DataFrames.
See Also
--------
DataFrame.combine_first : Combine two DataFrame objects and default to
non-null values in frame calling the method.
Examples
--------
Combine using a simple function that chooses the smaller column.
>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> take_smaller = lambda s1, s2: s1 if s1.sum() < s2.sum() else s2
>>> df1.combine(df2, take_smaller)
A B
0 0 3
1 0 3
Example using a true element-wise combine function.
>>> df1 = pd.DataFrame({'A': [5, 0], 'B': [2, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> df1.combine(df2, np.minimum)
A B
0 1 2
1 0 3
Using `fill_value` fills Nones prior to passing the column to the
merge function.
>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> df1.combine(df2, take_smaller, fill_value=-5)
A B
0 0 -5.0
1 0 4.0
However, if the same element in both dataframes is None, that None
is preserved
>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [None, 3]})
>>> df1.combine(df2, take_smaller, fill_value=-5)
A B
0 0 -5.0
1 0 3.0
Example that demonstrates the use of `overwrite` and behavior when
the axis differ between the dataframes.
>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})
>>> df2 = pd.DataFrame({'B': [3, 3], 'C': [-10, 1], }, index=[1, 2])
>>> df1.combine(df2, take_smaller)
A B C
0 NaN NaN NaN
1 NaN 3.0 -10.0
2 NaN 3.0 1.0
>>> df1.combine(df2, take_smaller, overwrite=False)
A B C
0 0.0 NaN NaN
1 0.0 3.0 -10.0
2 NaN 3.0 1.0
Demonstrating the preference of the passed in dataframe.
>>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1], }, index=[1, 2])
>>> df2.combine(df1, take_smaller)
A B C
0 0.0 NaN NaN
1 0.0 3.0 NaN
2 NaN 3.0 NaN
>>> df2.combine(df1, take_smaller, overwrite=False)
A B C
0 0.0 NaN NaN
1 0.0 3.0 1.0
2 NaN 3.0 1.0
"""
other_idxlen = len(other.index) # save for compare
this, other = self.align(other, copy=False)
new_index = this.index
if other.empty and len(new_index) == len(self.index):
return self.copy()
if self.empty and len(other) == other_idxlen:
return other.copy()
# sorts if possible
new_columns = this.columns.union(other.columns)
do_fill = fill_value is not None
result = {}
for col in new_columns:
series = this[col]
otherSeries = other[col]
this_dtype = series.dtype
other_dtype = otherSeries.dtype
this_mask = isna(series)
other_mask = isna(otherSeries)
# don't overwrite columns unnecessarily
# DO propagate if this column is not in the intersection
if not overwrite and other_mask.all():
result[col] = this[col].copy()
continue
if do_fill:
series = series.copy()
otherSeries = otherSeries.copy()
series[this_mask] = fill_value
otherSeries[other_mask] = fill_value
if col not in self.columns:
# If self DataFrame does not have col in other DataFrame,
# try to promote series, which is all NaN, as other_dtype.
new_dtype = other_dtype
try:
series = series.astype(new_dtype, copy=False)
except ValueError:
# e.g. new_dtype is integer types
pass
else:
# if we have different dtypes, possibly promote
new_dtype = find_common_type([this_dtype, other_dtype])
if not is_dtype_equal(this_dtype, new_dtype):
series = series.astype(new_dtype)
if not is_dtype_equal(other_dtype, new_dtype):
otherSeries = otherSeries.astype(new_dtype)
arr = func(series, otherSeries)
arr = maybe_downcast_to_dtype(arr, this_dtype)
result[col] = arr
# convert_objects just in case
return self._constructor(result, index=new_index,
columns=new_columns)
def combine_first(self, other):
"""
Update null elements with value in the same location in `other`.
Combine two DataFrame objects by filling null values in one DataFrame
with non-null values from other DataFrame. The row and column indexes
of the resulting DataFrame will be the union of the two.
Parameters
----------
other : DataFrame
Provided DataFrame to use to fill null values.
Returns
-------
DataFrame
See Also
--------
DataFrame.combine : Perform series-wise operation on two DataFrames
using a given function.
Examples
--------
>>> df1 = pd.DataFrame({'A': [None, 0], 'B': [None, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> df1.combine_first(df2)
A B
0 1.0 3.0
1 0.0 4.0
Null values still persist if the location of that null value
does not exist in `other`
>>> df1 = pd.DataFrame({'A': [None, 0], 'B': [4, None]})
>>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1]}, index=[1, 2])
>>> df1.combine_first(df2)
A B C
0 NaN 4.0 NaN
1 0.0 3.0 1.0
2 NaN 3.0 1.0
"""
import pandas.core.computation.expressions as expressions
def extract_values(arr):
# Does two things:
# 1. maybe gets the values from the Series / Index
# 2. convert datelike to i8
if isinstance(arr, (ABCIndexClass, ABCSeries)):
arr = arr._values
if needs_i8_conversion(arr):
if is_extension_array_dtype(arr.dtype):
arr = arr.asi8
else:
arr = arr.view('i8')
return arr
def combiner(x, y):
mask = isna(x)
if isinstance(mask, (ABCIndexClass, ABCSeries)):
mask = mask._values
x_values = extract_values(x)
y_values = extract_values(y)
# If the column y in other DataFrame is not in first DataFrame,
# just return y_values.
if y.name not in self.columns:
return y_values
return expressions.where(mask, y_values, x_values)
return self.combine(other, combiner, overwrite=False)
@deprecate_kwarg(old_arg_name='raise_conflict', new_arg_name='errors',
mapping={False: 'ignore', True: 'raise'})
def update(self, other, join='left', overwrite=True, filter_func=None,
errors='ignore'):
"""
Modify in place using non-NA values from another DataFrame.
Aligns on indices. There is no return value.
Parameters
----------
other : DataFrame, or object coercible into a DataFrame
Should have at least one matching index/column label
with the original DataFrame. If a Series is passed,
its name attribute must be set, and that will be
used as the column name to align with the original DataFrame.
join : {'left'}, default 'left'
Only left join is implemented, keeping the index and columns of the
original object.
overwrite : bool, default True
How to handle non-NA values for overlapping keys:
* True: overwrite original DataFrame's values
with values from `other`.
* False: only update values that are NA in
the original DataFrame.
filter_func : callable(1d-array) -> bool 1d-array, optional
Can choose to replace values other than NA. Return True for values
that should be updated.
errors : {'raise', 'ignore'}, default 'ignore'
If 'raise', will raise a ValueError if the DataFrame and `other`
both contain non-NA data in the same place.
.. versionchanged :: 0.24.0
Changed from `raise_conflict=False|True`
to `errors='ignore'|'raise'`.
Returns
-------
None : method directly changes calling object
Raises
------
ValueError
* When `errors='raise'` and there's overlapping non-NA data.
* When `errors` is not either `'ignore'` or `'raise'`
NotImplementedError
* If `join != 'left'`
See Also
--------
dict.update : Similar method for dictionaries.
DataFrame.merge : For column(s)-on-columns(s) operations.
Examples
--------
>>> df = pd.DataFrame({'A': [1, 2, 3],
... 'B': [400, 500, 600]})
>>> new_df = pd.DataFrame({'B': [4, 5, 6],
... 'C': [7, 8, 9]})
>>> df.update(new_df)
>>> df
A B
0 1 4
1 2 5
2 3 6
The DataFrame's length does not increase as a result of the update,
only values at matching index/column labels are updated.
>>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
... 'B': ['x', 'y', 'z']})
>>> new_df = pd.DataFrame({'B': ['d', 'e', 'f', 'g', 'h', 'i']})
>>> df.update(new_df)
>>> df
A B
0 a d
1 b e
2 c f
For Series, it's name attribute must be set.
>>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
... 'B': ['x', 'y', 'z']})
>>> new_column = pd.Series(['d', 'e'], name='B', index=[0, 2])
>>> df.update(new_column)
>>> df
A B
0 a d
1 b y
2 c e
>>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
... 'B': ['x', 'y', 'z']})
>>> new_df = pd.DataFrame({'B': ['d', 'e']}, index=[1, 2])
>>> df.update(new_df)
>>> df
A B
0 a x
1 b d
2 c e
If `other` contains NaNs the corresponding values are not updated
in the original dataframe.
>>> df = pd.DataFrame({'A': [1, 2, 3],
... 'B': [400, 500, 600]})
>>> new_df = pd.DataFrame({'B': [4, np.nan, 6]})
>>> df.update(new_df)
>>> df
A B
0 1 4.0
1 2 500.0
2 3 6.0
"""
import pandas.core.computation.expressions as expressions
# TODO: Support other joins
if join != 'left': # pragma: no cover
raise NotImplementedError("Only left join is supported")
if errors not in ['ignore', 'raise']:
raise ValueError("The parameter errors must be either "
"'ignore' or 'raise'")
if not isinstance(other, DataFrame):
other = DataFrame(other)
other = other.reindex_like(self)
for col in self.columns:
this = self[col]._values
that = other[col]._values
if filter_func is not None:
with np.errstate(all='ignore'):
mask = ~filter_func(this) | isna(that)
else:
if errors == 'raise':
mask_this = notna(that)
mask_that = notna(this)
if any(mask_this & mask_that):
raise ValueError("Data overlaps.")
if overwrite:
mask = isna(that)
else:
mask = notna(this)
# don't overwrite columns unnecessarily
if mask.all():
continue
self[col] = expressions.where(mask, this, that)
# ----------------------------------------------------------------------
# Data reshaping
_shared_docs['pivot'] = """
Return reshaped DataFrame organized by given index / column values.
Reshape data (produce a "pivot" table) based on column values. Uses
unique values from specified `index` / `columns` to form axes of the
resulting DataFrame. This function does not support data
aggregation, multiple values will result in a MultiIndex in the
columns. See the :ref:`User Guide <reshaping>` for more on reshaping.
Parameters
----------%s
index : string or object, optional
Column to use to make new frame's index. If None, uses
existing index.
columns : string or object
Column to use to make new frame's columns.
values : string, object or a list of the previous, optional
Column(s) to use for populating new frame's values. If not
specified, all remaining columns will be used and the result will
have hierarchically indexed columns.
.. versionchanged :: 0.23.0
Also accept list of column names.
Returns
-------
DataFrame
Returns reshaped DataFrame.
Raises
------
ValueError:
When there are any `index`, `columns` combinations with multiple
values. `DataFrame.pivot_table` when you need to aggregate.
See Also
--------
DataFrame.pivot_table : Generalization of pivot that can handle
duplicate values for one index/column pair.
DataFrame.unstack : Pivot based on the index values instead of a
column.
Notes
-----
For finer-tuned control, see hierarchical indexing documentation along
with the related stack/unstack methods.
Examples
--------
>>> df = pd.DataFrame({'foo': ['one', 'one', 'one', 'two', 'two',
... 'two'],
... 'bar': ['A', 'B', 'C', 'A', 'B', 'C'],
... 'baz': [1, 2, 3, 4, 5, 6],
... 'zoo': ['x', 'y', 'z', 'q', 'w', 't']})
>>> df
foo bar baz zoo
0 one A 1 x
1 one B 2 y
2 one C 3 z
3 two A 4 q
4 two B 5 w
5 two C 6 t
>>> df.pivot(index='foo', columns='bar', values='baz')
bar A B C
foo
one 1 2 3
two 4 5 6
>>> df.pivot(index='foo', columns='bar')['baz']
bar A B C
foo
one 1 2 3
two 4 5 6
>>> df.pivot(index='foo', columns='bar', values=['baz', 'zoo'])
baz zoo
bar A B C A B C
foo
one 1 2 3 x y z
two 4 5 6 q w t
A ValueError is raised if there are any duplicates.
>>> df = pd.DataFrame({"foo": ['one', 'one', 'two', 'two'],
... "bar": ['A', 'A', 'B', 'C'],
... "baz": [1, 2, 3, 4]})
>>> df
foo bar baz
0 one A 1
1 one A 2
2 two B 3
3 two C 4
Notice that the first two rows are the same for our `index`
and `columns` arguments.
>>> df.pivot(index='foo', columns='bar', values='baz')
Traceback (most recent call last):
...
ValueError: Index contains duplicate entries, cannot reshape
"""
@Substitution('')
@Appender(_shared_docs['pivot'])
def pivot(self, index=None, columns=None, values=None):
from pandas.core.reshape.pivot import pivot
return pivot(self, index=index, columns=columns, values=values)
_shared_docs['pivot_table'] = """
Create a spreadsheet-style pivot table as a DataFrame. The levels in
the pivot table will be stored in MultiIndex objects (hierarchical
indexes) on the index and columns of the result DataFrame.
Parameters
----------%s
values : column to aggregate, optional
index : column, Grouper, array, or list of the previous
If an array is passed, it must be the same length as the data. The
list can contain any of the other types (except list).
Keys to group by on the pivot table index. If an array is passed,
it is being used as the same manner as column values.
columns : column, Grouper, array, or list of the previous
If an array is passed, it must be the same length as the data. The
list can contain any of the other types (except list).
Keys to group by on the pivot table column. If an array is passed,
it is being used as the same manner as column values.
aggfunc : function, list of functions, dict, default numpy.mean
If list of functions passed, the resulting pivot table will have
hierarchical columns whose top level are the function names
(inferred from the function objects themselves)
If dict is passed, the key is column to aggregate and value
is function or list of functions
fill_value : scalar, default None
Value to replace missing values with
margins : boolean, default False
Add all row / columns (e.g. for subtotal / grand totals)
dropna : boolean, default True
Do not include columns whose entries are all NaN
margins_name : string, default 'All'
Name of the row / column that will contain the totals
when margins is True.
observed : boolean, default False
This only applies if any of the groupers are Categoricals.
If True: only show observed values for categorical groupers.
If False: show all values for categorical groupers.
.. versionchanged :: 0.25.0
Returns
-------
DataFrame
See Also
--------
DataFrame.pivot : Pivot without aggregation that can handle
non-numeric data.
Examples
--------
>>> df = pd.DataFrame({"A": ["foo", "foo", "foo", "foo", "foo",
... "bar", "bar", "bar", "bar"],
... "B": ["one", "one", "one", "two", "two",
... "one", "one", "two", "two"],
... "C": ["small", "large", "large", "small",
... "small", "large", "small", "small",
... "large"],
... "D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
... "E": [2, 4, 5, 5, 6, 6, 8, 9, 9]})
>>> df
A B C D E
0 foo one small 1 2
1 foo one large 2 4
2 foo one large 2 5
3 foo two small 3 5
4 foo two small 3 6
5 bar one large 4 6
6 bar one small 5 8
7 bar two small 6 9
8 bar two large 7 9
This first example aggregates values by taking the sum.
>>> table = pd.pivot_table(df, values='D', index=['A', 'B'],
... columns=['C'], aggfunc=np.sum)
>>> table
C large small
A B
bar one 4.0 5.0
two 7.0 6.0
foo one 4.0 1.0
two NaN 6.0
We can also fill missing values using the `fill_value` parameter.
>>> table = pd.pivot_table(df, values='D', index=['A', 'B'],
... columns=['C'], aggfunc=np.sum, fill_value=0)
>>> table
C large small
A B
bar one 4 5
two 7 6
foo one 4 1
two 0 6
The next example aggregates by taking the mean across multiple columns.
>>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],
... aggfunc={'D': np.mean,
... 'E': np.mean})
>>> table
D E
A C
bar large 5.500000 7.500000
small 5.500000 8.500000
foo large 2.000000 4.500000
small 2.333333 4.333333
We can also calculate multiple types of aggregations for any given
value column.
>>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],
... aggfunc={'D': np.mean,
... 'E': [min, max, np.mean]})
>>> table
D E
mean max mean min
A C
bar large 5.500000 9.0 7.500000 6.0
small 5.500000 9.0 8.500000 8.0
foo large 2.000000 5.0 4.500000 4.0
small 2.333333 6.0 4.333333 2.0
"""
@Substitution('')
@Appender(_shared_docs['pivot_table'])
def pivot_table(self, values=None, index=None, columns=None,
aggfunc='mean', fill_value=None, margins=False,
dropna=True, margins_name='All', observed=False):
from pandas.core.reshape.pivot import pivot_table
return pivot_table(self, values=values, index=index, columns=columns,
aggfunc=aggfunc, fill_value=fill_value,
margins=margins, dropna=dropna,
margins_name=margins_name, observed=observed)
def stack(self, level=-1, dropna=True):
"""
Stack the prescribed level(s) from columns to index.
Return a reshaped DataFrame or Series having a multi-level
index with one or more new inner-most levels compared to the current
DataFrame. The new inner-most levels are created by pivoting the
columns of the current dataframe:
- if the columns have a single level, the output is a Series;
- if the columns have multiple levels, the new index
level(s) is (are) taken from the prescribed level(s) and
the output is a DataFrame.
The new index levels are sorted.
Parameters
----------
level : int, str, list, default -1
Level(s) to stack from the column axis onto the index
axis, defined as one index or label, or a list of indices
or labels.
dropna : bool, default True
Whether to drop rows in the resulting Frame/Series with
missing values. Stacking a column level onto the index
axis can create combinations of index and column values
that are missing from the original dataframe. See Examples
section.
Returns
-------
DataFrame or Series
Stacked dataframe or series.
See Also
--------
DataFrame.unstack : Unstack prescribed level(s) from index axis
onto column axis.
DataFrame.pivot : Reshape dataframe from long format to wide
format.
DataFrame.pivot_table : Create a spreadsheet-style pivot table
as a DataFrame.
Notes
-----
The function is named by analogy with a collection of books
being reorganized from being side by side on a horizontal
position (the columns of the dataframe) to being stacked
vertically on top of each other (in the index of the
dataframe).
Examples
--------
**Single level columns**
>>> df_single_level_cols = pd.DataFrame([[0, 1], [2, 3]],
... index=['cat', 'dog'],
... columns=['weight', 'height'])
Stacking a dataframe with a single level column axis returns a Series:
>>> df_single_level_cols
weight height
cat 0 1
dog 2 3
>>> df_single_level_cols.stack()
cat weight 0
height 1
dog weight 2
height 3
dtype: int64
**Multi level columns: simple case**
>>> multicol1 = pd.MultiIndex.from_tuples([('weight', 'kg'),
... ('weight', 'pounds')])
>>> df_multi_level_cols1 = pd.DataFrame([[1, 2], [2, 4]],
... index=['cat', 'dog'],
... columns=multicol1)
Stacking a dataframe with a multi-level column axis:
>>> df_multi_level_cols1
weight
kg pounds
cat 1 2
dog 2 4
>>> df_multi_level_cols1.stack()
weight
cat kg 1
pounds 2
dog kg 2
pounds 4
**Missing values**
>>> multicol2 = pd.MultiIndex.from_tuples([('weight', 'kg'),
... ('height', 'm')])
>>> df_multi_level_cols2 = pd.DataFrame([[1.0, 2.0], [3.0, 4.0]],
... index=['cat', 'dog'],
... columns=multicol2)
It is common to have missing values when stacking a dataframe
with multi-level columns, as the stacked dataframe typically
has more values than the original dataframe. Missing values
are filled with NaNs:
>>> df_multi_level_cols2
weight height
kg m
cat 1.0 2.0
dog 3.0 4.0
>>> df_multi_level_cols2.stack()
height weight
cat kg NaN 1.0
m 2.0 NaN
dog kg NaN 3.0
m 4.0 NaN
**Prescribing the level(s) to be stacked**
The first parameter controls which level or levels are stacked:
>>> df_multi_level_cols2.stack(0)
kg m
cat height NaN 2.0
weight 1.0 NaN
dog height NaN 4.0
weight 3.0 NaN
>>> df_multi_level_cols2.stack([0, 1])
cat height m 2.0
weight kg 1.0
dog height m 4.0
weight kg 3.0
dtype: float64
**Dropping missing values**
>>> df_multi_level_cols3 = pd.DataFrame([[None, 1.0], [2.0, 3.0]],
... index=['cat', 'dog'],
... columns=multicol2)
Note that rows where all values are missing are dropped by
default but this behaviour can be controlled via the dropna
keyword parameter:
>>> df_multi_level_cols3
weight height
kg m
cat NaN 1.0
dog 2.0 3.0
>>> df_multi_level_cols3.stack(dropna=False)
height weight
cat kg NaN NaN
m 1.0 NaN
dog kg NaN 2.0
m 3.0 NaN
>>> df_multi_level_cols3.stack(dropna=True)
height weight
cat m 1.0 NaN
dog kg NaN 2.0
m 3.0 NaN
"""
from pandas.core.reshape.reshape import stack, stack_multiple
if isinstance(level, (tuple, list)):
return stack_multiple(self, level, dropna=dropna)
else:
return stack(self, level, dropna=dropna)
def unstack(self, level=-1, fill_value=None):
"""
Pivot a level of the (necessarily hierarchical) index labels, returning
a DataFrame having a new level of column labels whose inner-most level
consists of the pivoted index labels.
If the index is not a MultiIndex, the output will be a Series
(the analogue of stack when the columns are not a MultiIndex).
The level involved will automatically get sorted.
Parameters
----------
level : int, string, or list of these, default -1 (last level)
Level(s) of index to unstack, can pass level name
fill_value : replace NaN with this value if the unstack produces
missing values
.. versionadded:: 0.18.0
Returns
-------
Series or DataFrame
See Also
--------
DataFrame.pivot : Pivot a table based on column values.
DataFrame.stack : Pivot a level of the column labels (inverse operation
from `unstack`).
Examples
--------
>>> index = pd.MultiIndex.from_tuples([('one', 'a'), ('one', 'b'),
... ('two', 'a'), ('two', 'b')])
>>> s = pd.Series(np.arange(1.0, 5.0), index=index)
>>> s
one a 1.0
b 2.0
two a 3.0
b 4.0
dtype: float64
>>> s.unstack(level=-1)
a b
one 1.0 2.0
two 3.0 4.0
>>> s.unstack(level=0)
one two
a 1.0 3.0
b 2.0 4.0
>>> df = s.unstack(level=0)
>>> df.unstack()
one a 1.0
b 2.0
two a 3.0
b 4.0
dtype: float64
"""
from pandas.core.reshape.reshape import unstack
return unstack(self, level, fill_value)
_shared_docs['melt'] = ("""
Unpivot a DataFrame from wide format to long format, optionally
leaving identifier variables set.
This function is useful to massage a DataFrame into a format where one
or more columns are identifier variables (`id_vars`), while all other
columns, considered measured variables (`value_vars`), are "unpivoted" to
the row axis, leaving just two non-identifier columns, 'variable' and
'value'.
%(versionadded)s
Parameters
----------
frame : DataFrame
id_vars : tuple, list, or ndarray, optional
Column(s) to use as identifier variables.
value_vars : tuple, list, or ndarray, optional
Column(s) to unpivot. If not specified, uses all columns that
are not set as `id_vars`.
var_name : scalar
Name to use for the 'variable' column. If None it uses
``frame.columns.name`` or 'variable'.
value_name : scalar, default 'value'
Name to use for the 'value' column.
col_level : int or string, optional
If columns are a MultiIndex then use this level to melt.
Returns
-------
DataFrame
Unpivoted DataFrame.
See Also
--------
%(other)s
pivot_table
DataFrame.pivot
Examples
--------
>>> df = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'},
... 'B': {0: 1, 1: 3, 2: 5},
... 'C': {0: 2, 1: 4, 2: 6}})
>>> df
A B C
0 a 1 2
1 b 3 4
2 c 5 6
>>> %(caller)sid_vars=['A'], value_vars=['B'])
A variable value
0 a B 1
1 b B 3
2 c B 5
>>> %(caller)sid_vars=['A'], value_vars=['B', 'C'])
A variable value
0 a B 1
1 b B 3
2 c B 5
3 a C 2
4 b C 4
5 c C 6
The names of 'variable' and 'value' columns can be customized:
>>> %(caller)sid_vars=['A'], value_vars=['B'],
... var_name='myVarname', value_name='myValname')
A myVarname myValname
0 a B 1
1 b B 3
2 c B 5
If you have multi-index columns:
>>> df.columns = [list('ABC'), list('DEF')]
>>> df
A B C
D E F
0 a 1 2
1 b 3 4
2 c 5 6
>>> %(caller)scol_level=0, id_vars=['A'], value_vars=['B'])
A variable value
0 a B 1
1 b B 3
2 c B 5
>>> %(caller)sid_vars=[('A', 'D')], value_vars=[('B', 'E')])
(A, D) variable_0 variable_1 value
0 a B E 1
1 b B E 3
2 c B E 5
""")
@Appender(_shared_docs['melt'] %
dict(caller='df.melt(',
versionadded='.. versionadded:: 0.20.0\n',
other='melt'))
def melt(self, id_vars=None, value_vars=None, var_name=None,
value_name='value', col_level=None):
from pandas.core.reshape.melt import melt
return melt(self, id_vars=id_vars, value_vars=value_vars,
var_name=var_name, value_name=value_name,
col_level=col_level)
# ----------------------------------------------------------------------
# Time series-related
def diff(self, periods=1, axis=0):
"""
First discrete difference of element.
Calculates the difference of a DataFrame element compared with another
element in the DataFrame (default is the element in the same column
of the previous row).
Parameters
----------
periods : int, default 1
Periods to shift for calculating difference, accepts negative
values.
axis : {0 or 'index', 1 or 'columns'}, default 0
Take difference over rows (0) or columns (1).
.. versionadded:: 0.16.1.
Returns
-------
DataFrame
See Also
--------
Series.diff: First discrete difference for a Series.
DataFrame.pct_change: Percent change over given number of periods.
DataFrame.shift: Shift index by desired number of periods with an
optional time freq.
Examples
--------
Difference with previous row
>>> df = pd.DataFrame({'a': [1, 2, 3, 4, 5, 6],
... 'b': [1, 1, 2, 3, 5, 8],
... 'c': [1, 4, 9, 16, 25, 36]})
>>> df
a b c
0 1 1 1
1 2 1 4
2 3 2 9
3 4 3 16
4 5 5 25
5 6 8 36
>>> df.diff()
a b c
0 NaN NaN NaN
1 1.0 0.0 3.0
2 1.0 1.0 5.0
3 1.0 1.0 7.0
4 1.0 2.0 9.0
5 1.0 3.0 11.0
Difference with previous column
>>> df.diff(axis=1)
a b c
0 NaN 0.0 0.0
1 NaN -1.0 3.0
2 NaN -1.0 7.0
3 NaN -1.0 13.0
4 NaN 0.0 20.0
5 NaN 2.0 28.0
Difference with 3rd previous row
>>> df.diff(periods=3)
a b c
0 NaN NaN NaN
1 NaN NaN NaN
2 NaN NaN NaN
3 3.0 2.0 15.0
4 3.0 4.0 21.0
5 3.0 6.0 27.0
Difference with following row
>>> df.diff(periods=-1)
a b c
0 -1.0 0.0 -3.0
1 -1.0 -1.0 -5.0
2 -1.0 -1.0 -7.0
3 -1.0 -2.0 -9.0
4 -1.0 -3.0 -11.0
5 NaN NaN NaN
"""
bm_axis = self._get_block_manager_axis(axis)
new_data = self._data.diff(n=periods, axis=bm_axis)
return self._constructor(new_data)
# ----------------------------------------------------------------------
# Function application
def _gotitem(self,
key: Union[str, List[str]],
ndim: int,
subset: Optional[Union[Series, ABCDataFrame]] = None,
) -> Union[Series, ABCDataFrame]:
"""
Sub-classes to define. Return a sliced object.
Parameters
----------
key : string / list of selections
ndim : 1,2
requested ndim of result
subset : object, default None
subset to act on
"""
if subset is None:
subset = self
elif subset.ndim == 1: # is Series
return subset
# TODO: _shallow_copy(subset)?
return subset[key]
_agg_summary_and_see_also_doc = dedent("""
The aggregation operations are always performed over an axis, either the
index (default) or the column axis. This behavior is different from
`numpy` aggregation functions (`mean`, `median`, `prod`, `sum`, `std`,
`var`), where the default is to compute the aggregation of the flattened
array, e.g., ``numpy.mean(arr_2d)`` as opposed to
``numpy.mean(arr_2d, axis=0)``.
`agg` is an alias for `aggregate`. Use the alias.
See Also
--------
DataFrame.apply : Perform any type of operations.
DataFrame.transform : Perform transformation type operations.
core.groupby.GroupBy : Perform operations over groups.
core.resample.Resampler : Perform operations over resampled bins.
core.window.Rolling : Perform operations over rolling window.
core.window.Expanding : Perform operations over expanding window.
core.window.EWM : Perform operation over exponential weighted
window.
""")
_agg_examples_doc = dedent("""
Examples
--------
>>> df = pd.DataFrame([[1, 2, 3],
... [4, 5, 6],
... [7, 8, 9],
... [np.nan, np.nan, np.nan]],
... columns=['A', 'B', 'C'])
Aggregate these functions over the rows.
>>> df.agg(['sum', 'min'])
A B C
sum 12.0 15.0 18.0
min 1.0 2.0 3.0
Different aggregations per column.
>>> df.agg({'A' : ['sum', 'min'], 'B' : ['min', 'max']})
A B
max NaN 8.0
min 1.0 2.0
sum 12.0 NaN
Aggregate over the columns.
>>> df.agg("mean", axis="columns")
0 2.0
1 5.0
2 8.0
3 NaN
dtype: float64
""")
@Substitution(see_also=_agg_summary_and_see_also_doc,
examples=_agg_examples_doc,
versionadded='\n.. versionadded:: 0.20.0\n',
**_shared_doc_kwargs)
@Appender(_shared_docs['aggregate'])
def aggregate(self, func, axis=0, *args, **kwargs):
axis = self._get_axis_number(axis)
result = None
try:
result, how = self._aggregate(func, axis=axis, *args, **kwargs)
except TypeError:
pass
if result is None:
return self.apply(func, axis=axis, args=args, **kwargs)
return result
def _aggregate(self, arg, axis=0, *args, **kwargs):
if axis == 1:
# NDFrame.aggregate returns a tuple, and we need to transpose
# only result
result, how = self.T._aggregate(arg, *args, **kwargs)
result = result.T if result is not None else result
return result, how
return super()._aggregate(arg, *args, **kwargs)
agg = aggregate
@Appender(_shared_docs['transform'] % _shared_doc_kwargs)
def transform(self, func, axis=0, *args, **kwargs):
axis = self._get_axis_number(axis)
if axis == 1:
return self.T.transform(func, *args, **kwargs).T
return super().transform(func, *args, **kwargs)
def apply(self, func, axis=0, broadcast=None, raw=False, reduce=None,
result_type=None, args=(), **kwds):
"""
Apply a function along an axis of the DataFrame.
Objects passed to the function are Series objects whose index is
either the DataFrame's index (``axis=0``) or the DataFrame's columns
(``axis=1``). By default (``result_type=None``), the final return type
is inferred from the return type of the applied function. Otherwise,
it depends on the `result_type` argument.
Parameters
----------
func : function
Function to apply to each column or row.
axis : {0 or 'index', 1 or 'columns'}, default 0
Axis along which the function is applied:
* 0 or 'index': apply function to each column.
* 1 or 'columns': apply function to each row.
broadcast : bool, optional
Only relevant for aggregation functions:
* ``False`` or ``None`` : returns a Series whose length is the
length of the index or the number of columns (based on the
`axis` parameter)
* ``True`` : results will be broadcast to the original shape
of the frame, the original index and columns will be retained.
.. deprecated:: 0.23.0
This argument will be removed in a future version, replaced
by result_type='broadcast'.
raw : bool, default False
* ``False`` : passes each row or column as a Series to the
function.
* ``True`` : the passed function will receive ndarray objects
instead.
If you are just applying a NumPy reduction function this will
achieve much better performance.
reduce : bool or None, default None
Try to apply reduction procedures. If the DataFrame is empty,
`apply` will use `reduce` to determine whether the result
should be a Series or a DataFrame. If ``reduce=None`` (the
default), `apply`'s return value will be guessed by calling
`func` on an empty Series
(note: while guessing, exceptions raised by `func` will be
ignored).
If ``reduce=True`` a Series will always be returned, and if
``reduce=False`` a DataFrame will always be returned.
.. deprecated:: 0.23.0
This argument will be removed in a future version, replaced
by ``result_type='reduce'``.
result_type : {'expand', 'reduce', 'broadcast', None}, default None
These only act when ``axis=1`` (columns):
* 'expand' : list-like results will be turned into columns.
* 'reduce' : returns a Series if possible rather than expanding
list-like results. This is the opposite of 'expand'.
* 'broadcast' : results will be broadcast to the original shape
of the DataFrame, the original index and columns will be
retained.
The default behaviour (None) depends on the return value of the
applied function: list-like results will be returned as a Series
of those. However if the apply function returns a Series these
are expanded to columns.
.. versionadded:: 0.23.0
args : tuple
Positional arguments to pass to `func` in addition to the
array/series.
**kwds
Additional keyword arguments to pass as keywords arguments to
`func`.
Returns
-------
Series or DataFrame
Result of applying ``func`` along the given axis of the
DataFrame.
See Also
--------
DataFrame.applymap: For elementwise operations.
DataFrame.aggregate: Only perform aggregating type operations.
DataFrame.transform: Only perform transforming type operations.
Notes
-----
In the current implementation apply calls `func` twice on the
first column/row to decide whether it can take a fast or slow
code path. This can lead to unexpected behavior if `func` has
side-effects, as they will take effect twice for the first
column/row.
Examples
--------
>>> df = pd.DataFrame([[4, 9]] * 3, columns=['A', 'B'])
>>> df
A B
0 4 9
1 4 9
2 4 9
Using a numpy universal function (in this case the same as
``np.sqrt(df)``):
>>> df.apply(np.sqrt)
A B
0 2.0 3.0
1 2.0 3.0
2 2.0 3.0
Using a reducing function on either axis
>>> df.apply(np.sum, axis=0)
A 12
B 27
dtype: int64
>>> df.apply(np.sum, axis=1)
0 13
1 13
2 13
dtype: int64
Returning a list-like will result in a Series
>>> df.apply(lambda x: [1, 2], axis=1)
0 [1, 2]
1 [1, 2]
2 [1, 2]
dtype: object
Passing result_type='expand' will expand list-like results
to columns of a Dataframe
>>> df.apply(lambda x: [1, 2], axis=1, result_type='expand')
0 1
0 1 2
1 1 2
2 1 2
Returning a Series inside the function is similar to passing
``result_type='expand'``. The resulting column names
will be the Series index.
>>> df.apply(lambda x: pd.Series([1, 2], index=['foo', 'bar']), axis=1)
foo bar
0 1 2
1 1 2
2 1 2
Passing ``result_type='broadcast'`` will ensure the same shape
result, whether list-like or scalar is returned by the function,
and broadcast it along the axis. The resulting column names will
be the originals.
>>> df.apply(lambda x: [1, 2], axis=1, result_type='broadcast')
A B
0 1 2
1 1 2
2 1 2
"""
from pandas.core.apply import frame_apply
op = frame_apply(self,
func=func,
axis=axis,
broadcast=broadcast,
raw=raw,
reduce=reduce,
result_type=result_type,
args=args,
kwds=kwds)
return op.get_result()
def applymap(self, func):
"""
Apply a function to a Dataframe elementwise.
This method applies a function that accepts and returns a scalar
to every element of a DataFrame.
Parameters
----------
func : callable
Python function, returns a single value from a single value.
Returns
-------
DataFrame
Transformed DataFrame.
See Also
--------
DataFrame.apply : Apply a function along input axis of DataFrame.
Notes
-----
In the current implementation applymap calls `func` twice on the
first column/row to decide whether it can take a fast or slow
code path. This can lead to unexpected behavior if `func` has
side-effects, as they will take effect twice for the first
column/row.
Examples
--------
>>> df = pd.DataFrame([[1, 2.12], [3.356, 4.567]])
>>> df
0 1
0 1.000 2.120
1 3.356 4.567
>>> df.applymap(lambda x: len(str(x)))
0 1
0 3 4
1 5 5
Note that a vectorized version of `func` often exists, which will
be much faster. You could square each number elementwise.
>>> df.applymap(lambda x: x**2)
0 1
0 1.000000 4.494400
1 11.262736 20.857489
But it's better to avoid applymap in that case.
>>> df ** 2
0 1
0 1.000000 4.494400
1 11.262736 20.857489
"""
# if we have a dtype == 'M8[ns]', provide boxed values
def infer(x):
if x.empty:
return lib.map_infer(x, func)
return lib.map_infer(x.astype(object).values, func)
return self.apply(infer)
# ----------------------------------------------------------------------
# Merging / joining methods
def append(self, other, ignore_index=False,
verify_integrity=False, sort=None):
"""
Append rows of `other` to the end of caller, returning a new object.
Columns in `other` that are not in the caller are added as new columns.
Parameters
----------
other : DataFrame or Series/dict-like object, or list of these
The data to append.
ignore_index : boolean, default False
If True, do not use the index labels.
verify_integrity : boolean, default False
If True, raise ValueError on creating index with duplicates.
sort : boolean, default None
Sort columns if the columns of `self` and `other` are not aligned.
The default sorting is deprecated and will change to not-sorting
in a future version of pandas. Explicitly pass ``sort=True`` to
silence the warning and sort. Explicitly pass ``sort=False`` to
silence the warning and not sort.
.. versionadded:: 0.23.0
Returns
-------
DataFrame
See Also
--------
concat : General function to concatenate DataFrame or Series objects.
Notes
-----
If a list of dict/series is passed and the keys are all contained in
the DataFrame's index, the order of the columns in the resulting
DataFrame will be unchanged.
Iteratively appending rows to a DataFrame can be more computationally
intensive than a single concatenate. A better solution is to append
those rows to a list and then concatenate the list with the original
DataFrame all at once.
Examples
--------
>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=list('AB'))
>>> df
A B
0 1 2
1 3 4
>>> df2 = pd.DataFrame([[5, 6], [7, 8]], columns=list('AB'))
>>> df.append(df2)
A B
0 1 2
1 3 4
0 5 6
1 7 8
With `ignore_index` set to True:
>>> df.append(df2, ignore_index=True)
A B
0 1 2
1 3 4
2 5 6
3 7 8
The following, while not recommended methods for generating DataFrames,
show two ways to generate a DataFrame from multiple data sources.
Less efficient:
>>> df = pd.DataFrame(columns=['A'])
>>> for i in range(5):
... df = df.append({'A': i}, ignore_index=True)
>>> df
A
0 0
1 1
2 2
3 3
4 4
More efficient:
>>> pd.concat([pd.DataFrame([i], columns=['A']) for i in range(5)],
... ignore_index=True)
A
0 0
1 1
2 2
3 3
4 4
"""
if isinstance(other, (Series, dict)):
if isinstance(other, dict):
other = Series(other)
if other.name is None and not ignore_index:
raise TypeError('Can only append a Series if ignore_index=True'
' or if the Series has a name')
if other.name is None:
index = None
else:
# other must have the same index name as self, otherwise
# index name will be reset
index = Index([other.name], name=self.index.name)
idx_diff = other.index.difference(self.columns)
try:
combined_columns = self.columns.append(idx_diff)
except TypeError:
combined_columns = self.columns.astype(object).append(idx_diff)
other = other.reindex(combined_columns, copy=False)
other = DataFrame(other.values.reshape((1, len(other))),
index=index,
columns=combined_columns)
other = other._convert(datetime=True, timedelta=True)
if not self.columns.equals(combined_columns):
self = self.reindex(columns=combined_columns)
elif isinstance(other, list) and not isinstance(other[0], DataFrame):
other = DataFrame(other)
if (self.columns.get_indexer(other.columns) >= 0).all():
other = other.reindex(columns=self.columns)
from pandas.core.reshape.concat import concat
if isinstance(other, (list, tuple)):
to_concat = [self] + other
else:
to_concat = [self, other]
return concat(to_concat, ignore_index=ignore_index,
verify_integrity=verify_integrity,
sort=sort)
def join(self, other, on=None, how='left', lsuffix='', rsuffix='',
sort=False):
"""
Join columns of another DataFrame.
Join columns with `other` DataFrame either on index or on a key
column. Efficiently join multiple DataFrame objects by index at once by
passing a list.
Parameters
----------
other : DataFrame, Series, or list of DataFrame
Index should be similar to one of the columns in this one. If a
Series is passed, its name attribute must be set, and that will be
used as the column name in the resulting joined DataFrame.
on : str, list of str, or array-like, optional
Column or index level name(s) in the caller to join on the index
in `other`, otherwise joins index-on-index. If multiple
values given, the `other` DataFrame must have a MultiIndex. Can
pass an array as the join key if it is not already contained in
the calling DataFrame. Like an Excel VLOOKUP operation.
how : {'left', 'right', 'outer', 'inner'}, default 'left'
How to handle the operation of the two objects.
* left: use calling frame's index (or column if on is specified)
* right: use `other`'s index.
* outer: form union of calling frame's index (or column if on is
specified) with `other`'s index, and sort it.
lexicographically.
* inner: form intersection of calling frame's index (or column if
on is specified) with `other`'s index, preserving the order
of the calling's one.
lsuffix : str, default ''
Suffix to use from left frame's overlapping columns.
rsuffix : str, default ''
Suffix to use from right frame's overlapping columns.
sort : bool, default False
Order result DataFrame lexicographically by the join key. If False,
the order of the join key depends on the join type (how keyword).
Returns
-------
DataFrame
A dataframe containing columns from both the caller and `other`.
See Also
--------
DataFrame.merge : For column(s)-on-columns(s) operations.
Notes
-----
Parameters `on`, `lsuffix`, and `rsuffix` are not supported when
passing a list of `DataFrame` objects.
Support for specifying index levels as the `on` parameter was added
in version 0.23.0.
Examples
--------
>>> df = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3', 'K4', 'K5'],
... 'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})
>>> df
key A
0 K0 A0
1 K1 A1
2 K2 A2
3 K3 A3
4 K4 A4
5 K5 A5
>>> other = pd.DataFrame({'key': ['K0', 'K1', 'K2'],
... 'B': ['B0', 'B1', 'B2']})
>>> other
key B
0 K0 B0
1 K1 B1
2 K2 B2
Join DataFrames using their indexes.
>>> df.join(other, lsuffix='_caller', rsuffix='_other')
key_caller A key_other B
0 K0 A0 K0 B0
1 K1 A1 K1 B1
2 K2 A2 K2 B2
3 K3 A3 NaN NaN
4 K4 A4 NaN NaN
5 K5 A5 NaN NaN
If we want to join using the key columns, we need to set key to be
the index in both `df` and `other`. The joined DataFrame will have
key as its index.
>>> df.set_index('key').join(other.set_index('key'))
A B
key
K0 A0 B0
K1 A1 B1
K2 A2 B2
K3 A3 NaN
K4 A4 NaN
K5 A5 NaN
Another option to join using the key columns is to use the `on`
parameter. DataFrame.join always uses `other`'s index but we can use
any column in `df`. This method preserves the original DataFrame's
index in the result.
>>> df.join(other.set_index('key'), on='key')
key A B
0 K0 A0 B0
1 K1 A1 B1
2 K2 A2 B2
3 K3 A3 NaN
4 K4 A4 NaN
5 K5 A5 NaN
"""
# For SparseDataFrame's benefit
return self._join_compat(other, on=on, how=how, lsuffix=lsuffix,
rsuffix=rsuffix, sort=sort)
def _join_compat(self, other, on=None, how='left', lsuffix='', rsuffix='',
sort=False):
from pandas.core.reshape.merge import merge
from pandas.core.reshape.concat import concat
if isinstance(other, Series):
if other.name is None:
raise ValueError('Other Series must have a name')
other = DataFrame({other.name: other})
if isinstance(other, DataFrame):
return merge(self, other, left_on=on, how=how,
left_index=on is None, right_index=True,
suffixes=(lsuffix, rsuffix), sort=sort)
else:
if on is not None:
raise ValueError('Joining multiple DataFrames only supported'
' for joining on index')
frames = [self] + list(other)
can_concat = all(df.index.is_unique for df in frames)
# join indexes only using concat
if can_concat:
if how == 'left':
how = 'outer'
join_axes = [self.index]
else:
join_axes = None
return concat(frames, axis=1, join=how, join_axes=join_axes,
verify_integrity=True)
joined = frames[0]
for frame in frames[1:]:
joined = merge(joined, frame, how=how, left_index=True,
right_index=True)
return joined
@Substitution('')
@Appender(_merge_doc, indents=2)
def merge(self, right, how='inner', on=None, left_on=None, right_on=None,
left_index=False, right_index=False, sort=False,
suffixes=('_x', '_y'), copy=True, indicator=False,
validate=None):
from pandas.core.reshape.merge import merge
return merge(self, right, how=how, on=on, left_on=left_on,
right_on=right_on, left_index=left_index,
right_index=right_index, sort=sort, suffixes=suffixes,
copy=copy, indicator=indicator, validate=validate)
def round(self, decimals=0, *args, **kwargs):
"""
Round a DataFrame to a variable number of decimal places.
Parameters
----------
decimals : int, dict, Series
Number of decimal places to round each column to. If an int is
given, round each column to the same number of places.
Otherwise dict and Series round to variable numbers of places.
Column names should be in the keys if `decimals` is a
dict-like, or in the index if `decimals` is a Series. Any
columns not included in `decimals` will be left as is. Elements
of `decimals` which are not columns of the input will be
ignored.
*args
Additional keywords have no effect but might be accepted for
compatibility with numpy.
**kwargs
Additional keywords have no effect but might be accepted for
compatibility with numpy.
Returns
-------
DataFrame
A DataFrame with the affected columns rounded to the specified
number of decimal places.
See Also
--------
numpy.around : Round a numpy array to the given number of decimals.
Series.round : Round a Series to the given number of decimals.
Examples
--------
>>> df = pd.DataFrame([(.21, .32), (.01, .67), (.66, .03), (.21, .18)],
... columns=['dogs', 'cats'])
>>> df
dogs cats
0 0.21 0.32
1 0.01 0.67
2 0.66 0.03
3 0.21 0.18
By providing an integer each column is rounded to the same number
of decimal places
>>> df.round(1)
dogs cats
0 0.2 0.3
1 0.0 0.7
2 0.7 0.0
3 0.2 0.2
With a dict, the number of places for specific columns can be
specified with the column names as key and the number of decimal
places as value
>>> df.round({'dogs': 1, 'cats': 0})
dogs cats
0 0.2 0.0
1 0.0 1.0
2 0.7 0.0
3 0.2 0.0
Using a Series, the number of places for specific columns can be
specified with the column names as index and the number of
decimal places as value
>>> decimals = pd.Series([0, 1], index=['cats', 'dogs'])
>>> df.round(decimals)
dogs cats
0 0.2 0.0
1 0.0 1.0
2 0.7 0.0
3 0.2 0.0
"""
from pandas.core.reshape.concat import concat
def _dict_round(df, decimals):
for col, vals in df.iteritems():
try:
yield _series_round(vals, decimals[col])
except KeyError:
yield vals
def _series_round(s, decimals):
if is_integer_dtype(s) or is_float_dtype(s):
return s.round(decimals)
return s
nv.validate_round(args, kwargs)
if isinstance(decimals, (dict, Series)):
if isinstance(decimals, Series):
if not decimals.index.is_unique:
raise ValueError("Index of decimals must be unique")
new_cols = [col for col in _dict_round(self, decimals)]
elif is_integer(decimals):
# Dispatch to Series.round
new_cols = [_series_round(v, decimals)
for _, v in self.iteritems()]
else:
raise TypeError("decimals must be an integer, a dict-like or a "
"Series")
if len(new_cols) > 0:
return self._constructor(concat(new_cols, axis=1),
index=self.index,
columns=self.columns)
else:
return self
# ----------------------------------------------------------------------
# Statistical methods, etc.
def corr(self, method='pearson', min_periods=1):
"""
Compute pairwise correlation of columns, excluding NA/null values.
Parameters
----------
method : {'pearson', 'kendall', 'spearman'} or callable
* pearson : standard correlation coefficient
* kendall : Kendall Tau correlation coefficient
* spearman : Spearman rank correlation
* callable: callable with input two 1d ndarrays
and returning a float. Note that the returned matrix from corr
will have 1 along the diagonals and will be symmetric
regardless of the callable's behavior
.. versionadded:: 0.24.0
min_periods : int, optional
Minimum number of observations required per pair of columns
to have a valid result. Currently only available for Pearson
and Spearman correlation.
Returns
-------
DataFrame
Correlation matrix.
See Also
--------
DataFrame.corrwith
Series.corr
Examples
--------
>>> def histogram_intersection(a, b):
... v = np.minimum(a, b).sum().round(decimals=1)
... return v
>>> df = pd.DataFrame([(.2, .3), (.0, .6), (.6, .0), (.2, .1)],
... columns=['dogs', 'cats'])
>>> df.corr(method=histogram_intersection)
dogs cats
dogs 1.0 0.3
cats 0.3 1.0
"""
numeric_df = self._get_numeric_data()
cols = numeric_df.columns
idx = cols.copy()
mat = numeric_df.values
if method == 'pearson':
correl = libalgos.nancorr(ensure_float64(mat), minp=min_periods)
elif method == 'spearman':
correl = libalgos.nancorr_spearman(ensure_float64(mat),
minp=min_periods)
elif method == 'kendall' or callable(method):
if min_periods is None:
min_periods = 1
mat = ensure_float64(mat).T
corrf = nanops.get_corr_func(method)
K = len(cols)
correl = np.empty((K, K), dtype=float)
mask = np.isfinite(mat)
for i, ac in enumerate(mat):
for j, bc in enumerate(mat):
if i > j:
continue
valid = mask[i] & mask[j]
if valid.sum() < min_periods:
c = np.nan
elif i == j:
c = 1.
elif not valid.all():
c = corrf(ac[valid], bc[valid])
else:
c = corrf(ac, bc)
correl[i, j] = c
correl[j, i] = c
else:
raise ValueError("method must be either 'pearson', "
"'spearman', 'kendall', or a callable, "
"'{method}' was supplied".format(method=method))
return self._constructor(correl, index=idx, columns=cols)
def cov(self, min_periods=None):
"""
Compute pairwise covariance of columns, excluding NA/null values.
Compute the pairwise covariance among the series of a DataFrame.
The returned data frame is the `covariance matrix
<https://en.wikipedia.org/wiki/Covariance_matrix>`__ of the columns
of the DataFrame.
Both NA and null values are automatically excluded from the
calculation. (See the note below about bias from missing values.)
A threshold can be set for the minimum number of
observations for each value created. Comparisons with observations
below this threshold will be returned as ``NaN``.
This method is generally used for the analysis of time series data to
understand the relationship between different measures
across time.
Parameters
----------
min_periods : int, optional
Minimum number of observations required per pair of columns
to have a valid result.
Returns
-------
DataFrame
The covariance matrix of the series of the DataFrame.
See Also
--------
Series.cov : Compute covariance with another Series.
core.window.EWM.cov: Exponential weighted sample covariance.
core.window.Expanding.cov : Expanding sample covariance.
core.window.Rolling.cov : Rolling sample covariance.
Notes
-----
Returns the covariance matrix of the DataFrame's time series.
The covariance is normalized by N-1.
For DataFrames that have Series that are missing data (assuming that
data is `missing at random
<https://en.wikipedia.org/wiki/Missing_data#Missing_at_random>`__)
the returned covariance matrix will be an unbiased estimate
of the variance and covariance between the member Series.
However, for many applications this estimate may not be acceptable
because the estimate covariance matrix is not guaranteed to be positive
semi-definite. This could lead to estimate correlations having
absolute values which are greater than one, and/or a non-invertible
covariance matrix. See `Estimation of covariance matrices
<http://en.wikipedia.org/w/index.php?title=Estimation_of_covariance_
matrices>`__ for more details.
Examples
--------
>>> df = pd.DataFrame([(1, 2), (0, 3), (2, 0), (1, 1)],
... columns=['dogs', 'cats'])
>>> df.cov()
dogs cats
dogs 0.666667 -1.000000
cats -1.000000 1.666667
>>> np.random.seed(42)
>>> df = pd.DataFrame(np.random.randn(1000, 5),
... columns=['a', 'b', 'c', 'd', 'e'])
>>> df.cov()
a b c d e
a 0.998438 -0.020161 0.059277 -0.008943 0.014144
b -0.020161 1.059352 -0.008543 -0.024738 0.009826
c 0.059277 -0.008543 1.010670 -0.001486 -0.000271
d -0.008943 -0.024738 -0.001486 0.921297 -0.013692
e 0.014144 0.009826 -0.000271 -0.013692 0.977795
**Minimum number of periods**
This method also supports an optional ``min_periods`` keyword
that specifies the required minimum number of non-NA observations for
each column pair in order to have a valid result:
>>> np.random.seed(42)
>>> df = pd.DataFrame(np.random.randn(20, 3),
... columns=['a', 'b', 'c'])
>>> df.loc[df.index[:5], 'a'] = np.nan
>>> df.loc[df.index[5:10], 'b'] = np.nan
>>> df.cov(min_periods=12)
a b c
a 0.316741 NaN -0.150812
b NaN 1.248003 0.191417
c -0.150812 0.191417 0.895202
"""
numeric_df = self._get_numeric_data()
cols = numeric_df.columns
idx = cols.copy()
mat = numeric_df.values
if notna(mat).all():
if min_periods is not None and min_periods > len(mat):
baseCov = np.empty((mat.shape[1], mat.shape[1]))
baseCov.fill(np.nan)
else:
baseCov = np.cov(mat.T)
baseCov = baseCov.reshape((len(cols), len(cols)))
else:
baseCov = libalgos.nancorr(ensure_float64(mat), cov=True,
minp=min_periods)
return self._constructor(baseCov, index=idx, columns=cols)
def corrwith(self, other, axis=0, drop=False, method='pearson'):
"""
Compute pairwise correlation between rows or columns of DataFrame
with rows or columns of Series or DataFrame. DataFrames are first
aligned along both axes before computing the correlations.
Parameters
----------
other : DataFrame, Series
Object with which to compute correlations.
axis : {0 or 'index', 1 or 'columns'}, default 0
0 or 'index' to compute column-wise, 1 or 'columns' for row-wise.
drop : bool, default False
Drop missing indices from result.
method : {'pearson', 'kendall', 'spearman'} or callable
* pearson : standard correlation coefficient
* kendall : Kendall Tau correlation coefficient
* spearman : Spearman rank correlation
* callable: callable with input two 1d ndarrays
and returning a float
.. versionadded:: 0.24.0
Returns
-------
Series
Pairwise correlations.
See Also
--------
DataFrame.corr
"""
axis = self._get_axis_number(axis)
this = self._get_numeric_data()
if isinstance(other, Series):
return this.apply(lambda x: other.corr(x, method=method),
axis=axis)
other = other._get_numeric_data()
left, right = this.align(other, join='inner', copy=False)
if axis == 1:
left = left.T
right = right.T
if method == 'pearson':
# mask missing values
left = left + right * 0
right = right + left * 0
# demeaned data
ldem = left - left.mean()
rdem = right - right.mean()
num = (ldem * rdem).sum()
dom = (left.count() - 1) * left.std() * right.std()
correl = num / dom
elif method in ['kendall', 'spearman'] or callable(method):
def c(x):
return nanops.nancorr(x[0], x[1], method=method)
correl = Series(map(c,
zip(left.values.T, right.values.T)),
index=left.columns)
else:
raise ValueError("Invalid method {method} was passed, "
"valid methods are: 'pearson', 'kendall', "
"'spearman', or callable".
format(method=method))
if not drop:
# Find non-matching labels along the given axis
# and append missing correlations (GH 22375)
raxis = 1 if axis == 0 else 0
result_index = (this._get_axis(raxis).
union(other._get_axis(raxis)))
idx_diff = result_index.difference(correl.index)
if len(idx_diff) > 0:
correl = correl.append(Series([np.nan] * len(idx_diff),
index=idx_diff))
return correl
# ----------------------------------------------------------------------
# ndarray-like stats methods
def count(self, axis=0, level=None, numeric_only=False):
"""
Count non-NA cells for each column or row.
The values `None`, `NaN`, `NaT`, and optionally `numpy.inf` (depending
on `pandas.options.mode.use_inf_as_na`) are considered NA.
Parameters
----------
axis : {0 or 'index', 1 or 'columns'}, default 0
If 0 or 'index' counts are generated for each column.
If 1 or 'columns' counts are generated for each **row**.
level : int or str, optional
If the axis is a `MultiIndex` (hierarchical), count along a
particular `level`, collapsing into a `DataFrame`.
A `str` specifies the level name.
numeric_only : bool, default False
Include only `float`, `int` or `boolean` data.
Returns
-------
Series or DataFrame
For each column/row the number of non-NA/null entries.
If `level` is specified returns a `DataFrame`.
See Also
--------
Series.count: Number of non-NA elements in a Series.
DataFrame.shape: Number of DataFrame rows and columns (including NA
elements).
DataFrame.isna: Boolean same-sized DataFrame showing places of NA
elements.
Examples
--------
Constructing DataFrame from a dictionary:
>>> df = pd.DataFrame({"Person":
... ["John", "Myla", "Lewis", "John", "Myla"],
... "Age": [24., np.nan, 21., 33, 26],
... "Single": [False, True, True, True, False]})
>>> df
Person Age Single
0 John 24.0 False
1 Myla NaN True
2 Lewis 21.0 True
3 John 33.0 True
4 Myla 26.0 False
Notice the uncounted NA values:
>>> df.count()
Person 5
Age 4
Single 5
dtype: int64
Counts for each **row**:
>>> df.count(axis='columns')
0 3
1 2
2 3
3 3
4 3
dtype: int64
Counts for one level of a `MultiIndex`:
>>> df.set_index(["Person", "Single"]).count(level="Person")
Age
Person
John 2
Lewis 1
Myla 1
"""
axis = self._get_axis_number(axis)
if level is not None:
return self._count_level(level, axis=axis,
numeric_only=numeric_only)
if numeric_only:
frame = self._get_numeric_data()
else:
frame = self
# GH #423
if len(frame._get_axis(axis)) == 0:
result = Series(0, index=frame._get_agg_axis(axis))
else:
if frame._is_mixed_type or frame._data.any_extension_types:
# the or any_extension_types is really only hit for single-
# column frames with an extension array
result = notna(frame).sum(axis=axis)
else:
# GH13407
series_counts = notna(frame).sum(axis=axis)
counts = series_counts.values
result = Series(counts, index=frame._get_agg_axis(axis))
return result.astype('int64')
def _count_level(self, level, axis=0, numeric_only=False):
if numeric_only:
frame = self._get_numeric_data()
else:
frame = self
count_axis = frame._get_axis(axis)
agg_axis = frame._get_agg_axis(axis)
if not isinstance(count_axis, MultiIndex):
raise TypeError("Can only count levels on hierarchical "
"{ax}.".format(ax=self._get_axis_name(axis)))
if frame._is_mixed_type:
# Since we have mixed types, calling notna(frame.values) might
# upcast everything to object
mask = notna(frame).values
else:
# But use the speedup when we have homogeneous dtypes
mask = notna(frame.values)
if axis == 1:
# We're transposing the mask rather than frame to avoid potential
# upcasts to object, which induces a ~20x slowdown
mask = mask.T
if isinstance(level, str):
level = count_axis._get_level_number(level)
level_index = count_axis.levels[level]
level_codes = ensure_int64(count_axis.codes[level])
counts = lib.count_level_2d(mask, level_codes, len(level_index),
axis=0)
result = DataFrame(counts, index=level_index, columns=agg_axis)
if axis == 1:
# Undo our earlier transpose
return result.T
else:
return result
def _reduce(self, op, name, axis=0, skipna=True, numeric_only=None,
filter_type=None, **kwds):
if axis is None and filter_type == 'bool':
labels = None
constructor = None
else:
# TODO: Make other agg func handle axis=None properly
axis = self._get_axis_number(axis)
labels = self._get_agg_axis(axis)
constructor = self._constructor
def f(x):
return op(x, axis=axis, skipna=skipna, **kwds)
# exclude timedelta/datetime unless we are uniform types
if (axis == 1 and self._is_datelike_mixed_type
and (not self._is_homogeneous_type
and not is_datetime64tz_dtype(self.dtypes[0]))):
numeric_only = True
if numeric_only is None:
try:
values = self.values
result = f(values)
if (filter_type == 'bool' and is_object_dtype(values) and
axis is None):
# work around https://github.com/numpy/numpy/issues/10489
# TODO: combine with hasattr(result, 'dtype') further down
# hard since we don't have `values` down there.
result = np.bool_(result)
except Exception as e:
# try by-column first
if filter_type is None and axis == 0:
try:
# this can end up with a non-reduction
# but not always. if the types are mixed
# with datelike then need to make sure a series
# we only end up here if we have not specified
# numeric_only and yet we have tried a
# column-by-column reduction, where we have mixed type.
# So let's just do what we can
from pandas.core.apply import frame_apply
opa = frame_apply(self,
func=f,
result_type='expand',
ignore_failures=True)
result = opa.get_result()
if result.ndim == self.ndim:
result = result.iloc[0]
return result
except Exception:
pass
if filter_type is None or filter_type == 'numeric':
data = self._get_numeric_data()
elif filter_type == 'bool':
data = self._get_bool_data()
else: # pragma: no cover
e = NotImplementedError(
"Handling exception with filter_type {f} not"
"implemented.".format(f=filter_type))
raise_with_traceback(e)
with np.errstate(all='ignore'):
result = f(data.values)
labels = data._get_agg_axis(axis)
else:
if numeric_only:
if filter_type is None or filter_type == 'numeric':
data = self._get_numeric_data()
elif filter_type == 'bool':
# GH 25101, # GH 24434
data = self._get_bool_data() if axis == 0 else self
else: # pragma: no cover
msg = ("Generating numeric_only data with filter_type {f}"
"not supported.".format(f=filter_type))
raise NotImplementedError(msg)
values = data.values
labels = data._get_agg_axis(axis)
else:
values = self.values
result = f(values)
if hasattr(result, 'dtype') and is_object_dtype(result.dtype):
try:
if filter_type is None or filter_type == 'numeric':
result = result.astype(np.float64)
elif filter_type == 'bool' and notna(result).all():
result = result.astype(np.bool_)
except (ValueError, TypeError):
# try to coerce to the original dtypes item by item if we can
if axis == 0:
result = coerce_to_dtypes(result, self.dtypes)
if constructor is not None:
result = Series(result, index=labels)
return result
def nunique(self, axis=0, dropna=True):
"""
Count distinct observations over requested axis.
Return Series with number of distinct observations. Can ignore NaN
values.
.. versionadded:: 0.20.0
Parameters
----------
axis : {0 or 'index', 1 or 'columns'}, default 0
The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for
column-wise.
dropna : bool, default True
Don't include NaN in the counts.
Returns
-------
Series
See Also
--------
Series.nunique: Method nunique for Series.
DataFrame.count: Count non-NA cells for each column or row.
Examples
--------
>>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [1, 1, 1]})
>>> df.nunique()
A 3
B 1
dtype: int64
>>> df.nunique(axis=1)
0 1
1 2
2 2
dtype: int64
"""
return self.apply(Series.nunique, axis=axis, dropna=dropna)
def idxmin(self, axis=0, skipna=True):
"""
Return index of first occurrence of minimum over requested axis.
NA/null values are excluded.
Parameters
----------
axis : {0 or 'index', 1 or 'columns'}, default 0
0 or 'index' for row-wise, 1 or 'columns' for column-wise
skipna : boolean, default True
Exclude NA/null values. If an entire row/column is NA, the result
will be NA.
Returns
-------
Series
Indexes of minima along the specified axis.
Raises
------
ValueError
* If the row/column is empty
See Also
--------
Series.idxmin
Notes
-----
This method is the DataFrame version of ``ndarray.argmin``.
"""
axis = self._get_axis_number(axis)
indices = nanops.nanargmin(self.values, axis=axis, skipna=skipna)
index = self._get_axis(axis)
result = [index[i] if i >= 0 else np.nan for i in indices]
return Series(result, index=self._get_agg_axis(axis))
def idxmax(self, axis=0, skipna=True):
"""
Return index of first occurrence of maximum over requested axis.
NA/null values are excluded.
Parameters
----------
axis : {0 or 'index', 1 or 'columns'}, default 0
0 or 'index' for row-wise, 1 or 'columns' for column-wise
skipna : boolean, default True
Exclude NA/null values. If an entire row/column is NA, the result
will be NA.
Returns
-------
Series
Indexes of maxima along the specified axis.
Raises
------
ValueError
* If the row/column is empty
See Also
--------
Series.idxmax
Notes
-----
This method is the DataFrame version of ``ndarray.argmax``.
"""
axis = self._get_axis_number(axis)
indices = nanops.nanargmax(self.values, axis=axis, skipna=skipna)
index = self._get_axis(axis)
result = [index[i] if i >= 0 else np.nan for i in indices]
return Series(result, index=self._get_agg_axis(axis))
def _get_agg_axis(self, axis_num):
"""
Let's be explicit about this.
"""
if axis_num == 0:
return self.columns
elif axis_num == 1:
return self.index
else:
raise ValueError('Axis must be 0 or 1 (got %r)' % axis_num)
def mode(self, axis=0, numeric_only=False, dropna=True):
"""
Get the mode(s) of each element along the selected axis.
The mode of a set of values is the value that appears most often.
It can be multiple values.
Parameters
----------
axis : {0 or 'index', 1 or 'columns'}, default 0
The axis to iterate over while searching for the mode:
* 0 or 'index' : get mode of each column
* 1 or 'columns' : get mode of each row
numeric_only : bool, default False
If True, only apply to numeric columns.
dropna : bool, default True
Don't consider counts of NaN/NaT.
.. versionadded:: 0.24.0
Returns
-------
DataFrame
The modes of each column or row.
See Also
--------
Series.mode : Return the highest frequency value in a Series.
Series.value_counts : Return the counts of values in a Series.
Examples
--------
>>> df = pd.DataFrame([('bird', 2, 2),
... ('mammal', 4, np.nan),
... ('arthropod', 8, 0),
... ('bird', 2, np.nan)],
... index=('falcon', 'horse', 'spider', 'ostrich'),
... columns=('species', 'legs', 'wings'))
>>> df
species legs wings
falcon bird 2 2.0
horse mammal 4 NaN
spider arthropod 8 0.0
ostrich bird 2 NaN
By default, missing values are not considered, and the mode of wings
are both 0 and 2. The second row of species and legs contains ``NaN``,
because they have only one mode, but the DataFrame has two rows.
>>> df.mode()
species legs wings
0 bird 2.0 0.0
1 NaN NaN 2.0
Setting ``dropna=False`` ``NaN`` values are considered and they can be
the mode (like for wings).
>>> df.mode(dropna=False)
species legs wings
0 bird 2 NaN
Setting ``numeric_only=True``, only the mode of numeric columns is
computed, and columns of other types are ignored.
>>> df.mode(numeric_only=True)
legs wings
0 2.0 0.0
1 NaN 2.0
To compute the mode over columns and not rows, use the axis parameter:
>>> df.mode(axis='columns', numeric_only=True)
0 1
falcon 2.0 NaN
horse 4.0 NaN
spider 0.0 8.0
ostrich 2.0 NaN
"""
data = self if not numeric_only else self._get_numeric_data()
def f(s):
return s.mode(dropna=dropna)
return data.apply(f, axis=axis)
def quantile(self, q=0.5, axis=0, numeric_only=True,
interpolation='linear'):
"""
Return values at the given quantile over requested axis.
Parameters
----------
q : float or array-like, default 0.5 (50% quantile)
Value between 0 <= q <= 1, the quantile(s) to compute.
axis : {0, 1, 'index', 'columns'} (default 0)
Equals 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
numeric_only : bool, default True
If False, the quantile of datetime and timedelta data will be
computed as well.
interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
This optional parameter specifies the interpolation method to use,
when the desired quantile lies between two data points `i` and `j`:
* linear: `i + (j - i) * fraction`, where `fraction` is the
fractional part of the index surrounded by `i` and `j`.
* lower: `i`.
* higher: `j`.
* nearest: `i` or `j` whichever is nearest.
* midpoint: (`i` + `j`) / 2.
.. versionadded:: 0.18.0
Returns
-------
Series or DataFrame
If ``q`` is an array, a DataFrame will be returned where the
index is ``q``, the columns are the columns of self, and the
values are the quantiles.
If ``q`` is a float, a Series will be returned where the
index is the columns of self and the values are the quantiles.
See Also
--------
core.window.Rolling.quantile: Rolling quantile.
numpy.percentile: Numpy function to compute the percentile.
Examples
--------
>>> df = pd.DataFrame(np.array([[1, 1], [2, 10], [3, 100], [4, 100]]),
... columns=['a', 'b'])
>>> df.quantile(.1)
a 1.3
b 3.7
Name: 0.1, dtype: float64
>>> df.quantile([.1, .5])
a b
0.1 1.3 3.7
0.5 2.5 55.0
Specifying `numeric_only=False` will also compute the quantile of
datetime and timedelta data.
>>> df = pd.DataFrame({'A': [1, 2],
... 'B': [pd.Timestamp('2010'),
... pd.Timestamp('2011')],
... 'C': [pd.Timedelta('1 days'),
... pd.Timedelta('2 days')]})
>>> df.quantile(0.5, numeric_only=False)
A 1.5
B 2010-07-02 12:00:00
C 1 days 12:00:00
Name: 0.5, dtype: object
"""
self._check_percentile(q)
data = self._get_numeric_data() if numeric_only else self
axis = self._get_axis_number(axis)
is_transposed = axis == 1
if is_transposed:
data = data.T
result = data._data.quantile(qs=q,
axis=1,
interpolation=interpolation,
transposed=is_transposed)
if result.ndim == 2:
result = self._constructor(result)
else:
result = self._constructor_sliced(result, name=q)
if is_transposed:
result = result.T
return result
def to_timestamp(self, freq=None, how='start', axis=0, copy=True):
"""
Cast to DatetimeIndex of timestamps, at *beginning* of period.
Parameters
----------
freq : str, default frequency of PeriodIndex
Desired frequency.
how : {'s', 'e', 'start', 'end'}
Convention for converting period to timestamp; start of period
vs. end.
axis : {0 or 'index', 1 or 'columns'}, default 0
The axis to convert (the index by default).
copy : bool, default True
If False then underlying input data is not copied.
Returns
-------
DataFrame with DatetimeIndex
"""
new_data = self._data
if copy:
new_data = new_data.copy()
axis = self._get_axis_number(axis)
if axis == 0:
new_data.set_axis(1, self.index.to_timestamp(freq=freq, how=how))
elif axis == 1:
new_data.set_axis(0, self.columns.to_timestamp(freq=freq, how=how))
else: # pragma: no cover
raise AssertionError('Axis must be 0 or 1. Got {ax!s}'.format(
ax=axis))
return self._constructor(new_data)
def to_period(self, freq=None, axis=0, copy=True):
"""
Convert DataFrame from DatetimeIndex to PeriodIndex with desired
frequency (inferred from index if not passed).
Parameters
----------
freq : str, default
Frequency of the PeriodIndex.
axis : {0 or 'index', 1 or 'columns'}, default 0
The axis to convert (the index by default).
copy : bool, default True
If False then underlying input data is not copied.
Returns
-------
TimeSeries with PeriodIndex
"""
new_data = self._data
if copy:
new_data = new_data.copy()
axis = self._get_axis_number(axis)
if axis == 0:
new_data.set_axis(1, self.index.to_period(freq=freq))
elif axis == 1:
new_data.set_axis(0, self.columns.to_period(freq=freq))
else: # pragma: no cover
raise AssertionError('Axis must be 0 or 1. Got {ax!s}'.format(
ax=axis))
return self._constructor(new_data)
def isin(self, values):
"""
Whether each element in the DataFrame is contained in values.
Parameters
----------
values : iterable, Series, DataFrame or dict
The result will only be true at a location if all the
labels match. If `values` is a Series, that's the index. If
`values` is a dict, the keys must be the column names,
which must match. If `values` is a DataFrame,
then both the index and column labels must match.
Returns
-------
DataFrame
DataFrame of booleans showing whether each element in the DataFrame
is contained in values.
See Also
--------
DataFrame.eq: Equality test for DataFrame.
Series.isin: Equivalent method on Series.
Series.str.contains: Test if pattern or regex is contained within a
string of a Series or Index.
Examples
--------
>>> df = pd.DataFrame({'num_legs': [2, 4], 'num_wings': [2, 0]},
... index=['falcon', 'dog'])
>>> df
num_legs num_wings
falcon 2 2
dog 4 0
When ``values`` is a list check whether every value in the DataFrame
is present in the list (which animals have 0 or 2 legs or wings)
>>> df.isin([0, 2])
num_legs num_wings
falcon True True
dog False True
When ``values`` is a dict, we can pass values to check for each
column separately:
>>> df.isin({'num_wings': [0, 3]})
num_legs num_wings
falcon False False
dog False True
When ``values`` is a Series or DataFrame the index and column must
match. Note that 'falcon' does not match based on the number of legs
in df2.
>>> other = pd.DataFrame({'num_legs': [8, 2], 'num_wings': [0, 2]},
... index=['spider', 'falcon'])
>>> df.isin(other)
num_legs num_wings
falcon True True
dog False False
"""
if isinstance(values, dict):
from pandas.core.reshape.concat import concat
values = collections.defaultdict(list, values)
return concat((self.iloc[:, [i]].isin(values[col])
for i, col in enumerate(self.columns)), axis=1)
elif isinstance(values, Series):
if not values.index.is_unique:
raise ValueError("cannot compute isin with "
"a duplicate axis.")
return self.eq(values.reindex_like(self), axis='index')
elif isinstance(values, DataFrame):
if not (values.columns.is_unique and values.index.is_unique):
raise ValueError("cannot compute isin with "
"a duplicate axis.")
return self.eq(values.reindex_like(self))
else:
if not is_list_like(values):
raise TypeError("only list-like or dict-like objects are "
"allowed to be passed to DataFrame.isin(), "
"you passed a "
"{0!r}".format(type(values).__name__))
return DataFrame(
algorithms.isin(self.values.ravel(),
values).reshape(self.shape), self.index,
self.columns)
# ----------------------------------------------------------------------
# Add plotting methods to DataFrame
plot = CachedAccessor("plot", pandas.plotting.FramePlotMethods)
hist = pandas.plotting.hist_frame
boxplot = pandas.plotting.boxplot_frame
sparse = CachedAccessor("sparse", SparseFrameAccessor)
DataFrame._setup_axes(['index', 'columns'], info_axis=1, stat_axis=0,
axes_are_reversed=True, aliases={'rows': 0},
docs={
'index': 'The index (row labels) of the DataFrame.',
'columns': 'The column labels of the DataFrame.'})
DataFrame._add_numeric_operations()
DataFrame._add_series_or_dataframe_operations()
ops.add_flex_arithmetic_methods(DataFrame)
ops.add_special_arithmetic_methods(DataFrame)
def _from_nested_dict(data):
# TODO: this should be seriously cythonized
new_data = OrderedDict()
for index, s in data.items():
for col, v in s.items():
new_data[col] = new_data.get(col, OrderedDict())
new_data[col][index] = v
return new_data
def _put_str(s, space):
return '{s}'.format(s=s)[:space].ljust(space)
|
from copy import deepcopy
from distutils.version import LooseVersion
from operator import methodcaller
import numpy as np
import pytest
import pandas.util._test_decorators as td
import pandas as pd
from pandas import DataFrame, MultiIndex, Series, date_range
import pandas.util.testing as tm
from pandas.util.testing import (
assert_almost_equal, assert_frame_equal, assert_series_equal)
from .test_generic import Generic
try:
import xarray
_XARRAY_INSTALLED = True
except ImportError:
_XARRAY_INSTALLED = False
class TestDataFrame(Generic):
_typ = DataFrame
_comparator = lambda self, x, y: assert_frame_equal(x, y)
def test_rename_mi(self):
df = DataFrame([
11, 21, 31
], index=MultiIndex.from_tuples([("A", x) for x in ["a", "B", "c"]]))
df.rename(str.lower)
def test_set_axis_name(self):
df = pd.DataFrame([[1, 2], [3, 4]])
funcs = ['_set_axis_name', 'rename_axis']
for func in funcs:
result = methodcaller(func, 'foo')(df)
assert df.index.name is None
assert result.index.name == 'foo'
result = methodcaller(func, 'cols', axis=1)(df)
assert df.columns.name is None
assert result.columns.name == 'cols'
def test_set_axis_name_mi(self):
df = DataFrame(
np.empty((3, 3)),
index=MultiIndex.from_tuples([("A", x) for x in list('aBc')]),
columns=MultiIndex.from_tuples([('C', x) for x in list('xyz')])
)
level_names = ['L1', 'L2']
funcs = ['_set_axis_name', 'rename_axis']
for func in funcs:
result = methodcaller(func, level_names)(df)
assert result.index.names == level_names
assert result.columns.names == [None, None]
result = methodcaller(func, level_names, axis=1)(df)
assert result.columns.names == ["L1", "L2"]
assert result.index.names == [None, None]
def test_nonzero_single_element(self):
# allow single item via bool method
df = DataFrame([[True]])
assert df.bool()
df = DataFrame([[False]])
assert not df.bool()
df = DataFrame([[False, False]])
with pytest.raises(ValueError):
df.bool()
with pytest.raises(ValueError):
bool(df)
def test_get_numeric_data_preserve_dtype(self):
# get the numeric data
o = DataFrame({'A': [1, '2', 3.]})
result = o._get_numeric_data()
expected = DataFrame(index=[0, 1, 2], dtype=object)
self._compare(result, expected)
def test_metadata_propagation_indiv(self):
# groupby
df = DataFrame(
{'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'],
'B': ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three'],
'C': np.random.randn(8),
'D': np.random.randn(8)})
result = df.groupby('A').sum()
self.check_metadata(df, result)
# resample
df = DataFrame(np.random.randn(1000, 2),
index=date_range('20130101', periods=1000, freq='s'))
result = df.resample('1T')
self.check_metadata(df, result)
# merging with override
# GH 6923
_metadata = DataFrame._metadata
_finalize = DataFrame.__finalize__
np.random.seed(10)
df1 = DataFrame(np.random.randint(0, 4, (3, 2)), columns=['a', 'b'])
df2 = DataFrame(np.random.randint(0, 4, (3, 2)), columns=['c', 'd'])
DataFrame._metadata = ['filename']
df1.filename = 'fname1.csv'
df2.filename = 'fname2.csv'
def finalize(self, other, method=None, **kwargs):
for name in self._metadata:
if method == 'merge':
left, right = other.left, other.right
value = getattr(left, name, '') + '|' + getattr(right,
name, '')
object.__setattr__(self, name, value)
else:
object.__setattr__(self, name, getattr(other, name, ''))
return self
DataFrame.__finalize__ = finalize
result = df1.merge(df2, left_on=['a'], right_on=['c'], how='inner')
assert result.filename == 'fname1.csv|fname2.csv'
# concat
# GH 6927
DataFrame._metadata = ['filename']
df1 = DataFrame(np.random.randint(0, 4, (3, 2)), columns=list('ab'))
df1.filename = 'foo'
def finalize(self, other, method=None, **kwargs):
for name in self._metadata:
if method == 'concat':
value = '+'.join([getattr(
o, name) for o in other.objs if getattr(o, name, None)
])
object.__setattr__(self, name, value)
else:
object.__setattr__(self, name, getattr(other, name, None))
return self
DataFrame.__finalize__ = finalize
result = pd.concat([df1, df1])
assert result.filename == 'foo+foo'
# reset
DataFrame._metadata = _metadata
DataFrame.__finalize__ = _finalize
def test_set_attribute(self):
# Test for consistent setattr behavior when an attribute and a column
# have the same name (Issue #8994)
df = DataFrame({'x': [1, 2, 3]})
df.y = 2
df['y'] = [2, 4, 6]
df.y = 5
assert df.y == 5
assert_series_equal(df['y'], Series([2, 4, 6], name='y'))
@pytest.mark.skipif(not _XARRAY_INSTALLED or _XARRAY_INSTALLED and
LooseVersion(xarray.__version__) <
LooseVersion('0.10.0'),
reason='xarray >= 0.10.0 required')
@pytest.mark.parametrize(
"index", ['FloatIndex', 'IntIndex',
'StringIndex', 'UnicodeIndex',
'DateIndex', 'PeriodIndex',
'CategoricalIndex', 'TimedeltaIndex'])
def test_to_xarray_index_types(self, index):
from xarray import Dataset
index = getattr(tm, 'make{}'.format(index))
df = DataFrame({'a': list('abc'),
'b': list(range(1, 4)),
'c': np.arange(3, 6).astype('u1'),
'd': np.arange(4.0, 7.0, dtype='float64'),
'e': [True, False, True],
'f': pd.Categorical(list('abc')),
'g': pd.date_range('20130101', periods=3),
'h': pd.date_range('20130101',
periods=3,
tz='US/Eastern')}
)
df.index = index(3)
df.index.name = 'foo'
df.columns.name = 'bar'
result = df.to_xarray()
assert result.dims['foo'] == 3
assert len(result.coords) == 1
assert len(result.data_vars) == 8
assert_almost_equal(list(result.coords.keys()), ['foo'])
assert isinstance(result, Dataset)
# idempotency
# categoricals are not preserved
# datetimes w/tz are not preserved
# column names are lost
expected = df.copy()
expected['f'] = expected['f'].astype(object)
expected['h'] = expected['h'].astype('datetime64[ns]')
expected.columns.name = None
assert_frame_equal(result.to_dataframe(), expected,
check_index_type=False, check_categorical=False)
@td.skip_if_no('xarray', min_version='0.7.0')
def test_to_xarray(self):
from xarray import Dataset
df = DataFrame({'a': list('abc'),
'b': list(range(1, 4)),
'c': np.arange(3, 6).astype('u1'),
'd': np.arange(4.0, 7.0, dtype='float64'),
'e': [True, False, True],
'f': pd.Categorical(list('abc')),
'g': pd.date_range('20130101', periods=3),
'h': pd.date_range('20130101',
periods=3,
tz='US/Eastern')}
)
df.index.name = 'foo'
result = df[0:0].to_xarray()
assert result.dims['foo'] == 0
assert isinstance(result, Dataset)
# available in 0.7.1
# MultiIndex
df.index = pd.MultiIndex.from_product([['a'], range(3)],
names=['one', 'two'])
result = df.to_xarray()
assert result.dims['one'] == 1
assert result.dims['two'] == 3
assert len(result.coords) == 2
assert len(result.data_vars) == 8
assert_almost_equal(list(result.coords.keys()), ['one', 'two'])
assert isinstance(result, Dataset)
result = result.to_dataframe()
expected = df.copy()
expected['f'] = expected['f'].astype(object)
expected['h'] = expected['h'].astype('datetime64[ns]')
expected.columns.name = None
assert_frame_equal(result,
expected,
check_index_type=False)
def test_deepcopy_empty(self):
# This test covers empty frame copying with non-empty column sets
# as reported in issue GH15370
empty_frame = DataFrame(data=[], index=[], columns=['A'])
empty_frame_copy = deepcopy(empty_frame)
self._compare(empty_frame_copy, empty_frame)
|
|
pandas/tests/generic/test_frame.py
|
|
""" manage PyTables query interface via Expressions """
import ast
from functools import partial
import numpy as np
from pandas._libs.tslibs import Timedelta, Timestamp
from pandas.compat.chainmap import DeepChainMap
from pandas.core.dtypes.common import is_list_like
import pandas as pd
from pandas.core.base import StringMixin
import pandas.core.common as com
from pandas.core.computation import expr, ops
from pandas.core.computation.common import _ensure_decoded
from pandas.core.computation.expr import BaseExprVisitor
from pandas.core.computation.ops import UndefinedVariableError, is_term
from pandas.io.formats.printing import pprint_thing, pprint_thing_encoded
class Scope(expr.Scope):
__slots__ = 'queryables',
def __init__(self, level, global_dict=None, local_dict=None,
queryables=None):
super().__init__(level + 1,
global_dict=global_dict,
local_dict=local_dict)
self.queryables = queryables or dict()
class Term(ops.Term):
def __new__(cls, name, env, side=None, encoding=None):
klass = Constant if not isinstance(name, str) else cls
supr_new = StringMixin.__new__
return supr_new(klass)
def __init__(self, name, env, side=None, encoding=None):
super().__init__(name, env, side=side, encoding=encoding)
def _resolve_name(self):
# must be a queryables
if self.side == 'left':
if self.name not in self.env.queryables:
raise NameError('name {name!r} is not defined'
.format(name=self.name))
return self.name
# resolve the rhs (and allow it to be None)
try:
return self.env.resolve(self.name, is_local=False)
except UndefinedVariableError:
return self.name
# read-only property overwriting read/write property
@property # type: ignore
def value(self):
return self._value
class Constant(Term):
def __init__(self, value, env, side=None, encoding=None):
super().__init__(value, env, side=side, encoding=encoding)
def _resolve_name(self):
return self._name
class BinOp(ops.BinOp):
_max_selectors = 31
def __init__(self, op, lhs, rhs, queryables, encoding):
super().__init__(op, lhs, rhs)
self.queryables = queryables
self.encoding = encoding
self.filter = None
self.condition = None
def _disallow_scalar_only_bool_ops(self):
pass
def prune(self, klass):
def pr(left, right):
""" create and return a new specialized BinOp from myself """
if left is None:
return right
elif right is None:
return left
k = klass
if isinstance(left, ConditionBinOp):
if (isinstance(left, ConditionBinOp) and
isinstance(right, ConditionBinOp)):
k = JointConditionBinOp
elif isinstance(left, k):
return left
elif isinstance(right, k):
return right
elif isinstance(left, FilterBinOp):
if (isinstance(left, FilterBinOp) and
isinstance(right, FilterBinOp)):
k = JointFilterBinOp
elif isinstance(left, k):
return left
elif isinstance(right, k):
return right
return k(self.op, left, right, queryables=self.queryables,
encoding=self.encoding).evaluate()
left, right = self.lhs, self.rhs
if is_term(left) and is_term(right):
res = pr(left.value, right.value)
elif not is_term(left) and is_term(right):
res = pr(left.prune(klass), right.value)
elif is_term(left) and not is_term(right):
res = pr(left.value, right.prune(klass))
elif not (is_term(left) or is_term(right)):
res = pr(left.prune(klass), right.prune(klass))
return res
def conform(self, rhs):
""" inplace conform rhs """
if not is_list_like(rhs):
rhs = [rhs]
if isinstance(rhs, np.ndarray):
rhs = rhs.ravel()
return rhs
@property
def is_valid(self):
""" return True if this is a valid field """
return self.lhs in self.queryables
@property
def is_in_table(self):
""" return True if this is a valid column name for generation (e.g. an
actual column in the table) """
return self.queryables.get(self.lhs) is not None
@property
def kind(self):
""" the kind of my field """
return getattr(self.queryables.get(self.lhs), 'kind', None)
@property
def meta(self):
""" the meta of my field """
return getattr(self.queryables.get(self.lhs), 'meta', None)
@property
def metadata(self):
""" the metadata of my field """
return getattr(self.queryables.get(self.lhs), 'metadata', None)
def generate(self, v):
""" create and return the op string for this TermValue """
val = v.tostring(self.encoding)
return "({lhs} {op} {val})".format(lhs=self.lhs, op=self.op, val=val)
def convert_value(self, v):
""" convert the expression that is in the term to something that is
accepted by pytables """
def stringify(value):
if self.encoding is not None:
encoder = partial(pprint_thing_encoded,
encoding=self.encoding)
else:
encoder = pprint_thing
return encoder(value)
kind = _ensure_decoded(self.kind)
meta = _ensure_decoded(self.meta)
if kind == 'datetime64' or kind == 'datetime':
if isinstance(v, (int, float)):
v = stringify(v)
v = _ensure_decoded(v)
v = Timestamp(v)
if v.tz is not None:
v = v.tz_convert('UTC')
return TermValue(v, v.value, kind)
elif kind == 'timedelta64' or kind == 'timedelta':
v = Timedelta(v, unit='s').value
return TermValue(int(v), v, kind)
elif meta == 'category':
metadata = com.values_from_object(self.metadata)
result = metadata.searchsorted(v, side='left')
# result returns 0 if v is first element or if v is not in metadata
# check that metadata contains v
if not result and v not in metadata:
result = -1
return TermValue(result, result, 'integer')
elif kind == 'integer':
v = int(float(v))
return TermValue(v, v, kind)
elif kind == 'float':
v = float(v)
return TermValue(v, v, kind)
elif kind == 'bool':
if isinstance(v, str):
v = not v.strip().lower() in ['false', 'f', 'no',
'n', 'none', '0',
'[]', '{}', '']
else:
v = bool(v)
return TermValue(v, v, kind)
elif isinstance(v, str):
# string quoting
return TermValue(v, stringify(v), 'string')
else:
raise TypeError("Cannot compare {v} of type {typ} to {kind} column"
.format(v=v, typ=type(v), kind=kind))
def convert_values(self):
pass
class FilterBinOp(BinOp):
def __str__(self):
return pprint_thing("[Filter : [{lhs}] -> [{op}]"
.format(lhs=self.filter[0], op=self.filter[1]))
def invert(self):
""" invert the filter """
if self.filter is not None:
f = list(self.filter)
f[1] = self.generate_filter_op(invert=True)
self.filter = tuple(f)
return self
def format(self):
""" return the actual filter format """
return [self.filter]
def evaluate(self):
if not self.is_valid:
raise ValueError("query term is not valid [{slf}]"
.format(slf=self))
rhs = self.conform(self.rhs)
values = [TermValue(v, v, self.kind).value for v in rhs]
if self.is_in_table:
# if too many values to create the expression, use a filter instead
if self.op in ['==', '!='] and len(values) > self._max_selectors:
filter_op = self.generate_filter_op()
self.filter = (
self.lhs,
filter_op,
pd.Index(values))
return self
return None
# equality conditions
if self.op in ['==', '!=']:
filter_op = self.generate_filter_op()
self.filter = (
self.lhs,
filter_op,
pd.Index(values))
else:
raise TypeError("passing a filterable condition to a non-table "
"indexer [{slf}]".format(slf=self))
return self
def generate_filter_op(self, invert=False):
if (self.op == '!=' and not invert) or (self.op == '==' and invert):
return lambda axis, vals: ~axis.isin(vals)
else:
return lambda axis, vals: axis.isin(vals)
class JointFilterBinOp(FilterBinOp):
def format(self):
raise NotImplementedError("unable to collapse Joint Filters")
def evaluate(self):
return self
class ConditionBinOp(BinOp):
def __str__(self):
return pprint_thing("[Condition : [{cond}]]"
.format(cond=self.condition))
def invert(self):
""" invert the condition """
# if self.condition is not None:
# self.condition = "~(%s)" % self.condition
# return self
raise NotImplementedError("cannot use an invert condition when "
"passing to numexpr")
def format(self):
""" return the actual ne format """
return self.condition
def evaluate(self):
if not self.is_valid:
raise ValueError("query term is not valid [{slf}]"
.format(slf=self))
# convert values if we are in the table
if not self.is_in_table:
return None
rhs = self.conform(self.rhs)
values = [self.convert_value(v) for v in rhs]
# equality conditions
if self.op in ['==', '!=']:
# too many values to create the expression?
if len(values) <= self._max_selectors:
vs = [self.generate(v) for v in values]
self.condition = "({cond})".format(cond=' | '.join(vs))
# use a filter after reading
else:
return None
else:
self.condition = self.generate(values[0])
return self
class JointConditionBinOp(ConditionBinOp):
def evaluate(self):
self.condition = "({lhs} {op} {rhs})".format(lhs=self.lhs.condition,
op=self.op,
rhs=self.rhs.condition)
return self
class UnaryOp(ops.UnaryOp):
def prune(self, klass):
if self.op != '~':
raise NotImplementedError("UnaryOp only support invert type ops")
operand = self.operand
operand = operand.prune(klass)
if operand is not None:
if issubclass(klass, ConditionBinOp):
if operand.condition is not None:
return operand.invert()
elif issubclass(klass, FilterBinOp):
if operand.filter is not None:
return operand.invert()
return None
_op_classes = {'unary': UnaryOp}
class ExprVisitor(BaseExprVisitor):
const_type = Constant
term_type = Term
def __init__(self, env, engine, parser, **kwargs):
super().__init__(env, engine, parser)
for bin_op in self.binary_ops:
bin_node = self.binary_op_nodes_map[bin_op]
setattr(self, 'visit_{node}'.format(node=bin_node),
lambda node, bin_op=bin_op: partial(BinOp, bin_op,
**kwargs))
def visit_UnaryOp(self, node, **kwargs):
if isinstance(node.op, (ast.Not, ast.Invert)):
return UnaryOp('~', self.visit(node.operand))
elif isinstance(node.op, ast.USub):
return self.const_type(-self.visit(node.operand).value, self.env)
elif isinstance(node.op, ast.UAdd):
raise NotImplementedError('Unary addition not supported')
def visit_Index(self, node, **kwargs):
return self.visit(node.value).value
def visit_Assign(self, node, **kwargs):
cmpr = ast.Compare(ops=[ast.Eq()], left=node.targets[0],
comparators=[node.value])
return self.visit(cmpr)
def visit_Subscript(self, node, **kwargs):
# only allow simple subscripts
value = self.visit(node.value)
slobj = self.visit(node.slice)
try:
value = value.value
except AttributeError:
pass
try:
return self.const_type(value[slobj], self.env)
except TypeError:
raise ValueError("cannot subscript {value!r} with "
"{slobj!r}".format(value=value, slobj=slobj))
def visit_Attribute(self, node, **kwargs):
attr = node.attr
value = node.value
ctx = node.ctx.__class__
if ctx == ast.Load:
# resolve the value
resolved = self.visit(value)
# try to get the value to see if we are another expression
try:
resolved = resolved.value
except (AttributeError):
pass
try:
return self.term_type(getattr(resolved, attr), self.env)
except AttributeError:
# something like datetime.datetime where scope is overridden
if isinstance(value, ast.Name) and value.id == attr:
return resolved
raise ValueError("Invalid Attribute context {name}"
.format(name=ctx.__name__))
def translate_In(self, op):
return ast.Eq() if isinstance(op, ast.In) else op
def _rewrite_membership_op(self, node, left, right):
return self.visit(node.op), node.op, left, right
def _validate_where(w):
"""
Validate that the where statement is of the right type.
The type may either be String, Expr, or list-like of Exprs.
Parameters
----------
w : String term expression, Expr, or list-like of Exprs.
Returns
-------
where : The original where clause if the check was successful.
Raises
------
TypeError : An invalid data type was passed in for w (e.g. dict).
"""
if not (isinstance(w, (Expr, str)) or is_list_like(w)):
raise TypeError("where must be passed as a string, Expr, "
"or list-like of Exprs")
return w
class Expr(expr.Expr):
""" hold a pytables like expression, comprised of possibly multiple 'terms'
Parameters
----------
where : string term expression, Expr, or list-like of Exprs
queryables : a "kinds" map (dict of column name -> kind), or None if column
is non-indexable
encoding : an encoding that will encode the query terms
Returns
-------
an Expr object
Examples
--------
'index>=date'
"columns=['A', 'D']"
'columns=A'
'columns==A'
"~(columns=['A','B'])"
'index>df.index[3] & string="bar"'
'(index>df.index[3] & index<=df.index[6]) | string="bar"'
"ts>=Timestamp('2012-02-01')"
"major_axis>=20130101"
"""
def __init__(self, where, queryables=None, encoding=None, scope_level=0):
where = _validate_where(where)
self.encoding = encoding
self.condition = None
self.filter = None
self.terms = None
self._visitor = None
# capture the environment if needed
local_dict = DeepChainMap()
if isinstance(where, Expr):
local_dict = where.env.scope
where = where.expr
elif isinstance(where, (list, tuple)):
for idx, w in enumerate(where):
if isinstance(w, Expr):
local_dict = w.env.scope
else:
w = _validate_where(w)
where[idx] = w
where = ' & '.join(map('({})'.format, com.flatten(where))) # noqa
self.expr = where
self.env = Scope(scope_level + 1, local_dict=local_dict)
if queryables is not None and isinstance(self.expr, str):
self.env.queryables.update(queryables)
self._visitor = ExprVisitor(self.env, queryables=queryables,
parser='pytables', engine='pytables',
encoding=encoding)
self.terms = self.parse()
def __str__(self):
if self.terms is not None:
return pprint_thing(self.terms)
return pprint_thing(self.expr)
def evaluate(self):
""" create and return the numexpr condition and filter """
try:
self.condition = self.terms.prune(ConditionBinOp)
except AttributeError:
raise ValueError("cannot process expression [{expr}], [{slf}] "
"is not a valid condition".format(expr=self.expr,
slf=self))
try:
self.filter = self.terms.prune(FilterBinOp)
except AttributeError:
raise ValueError("cannot process expression [{expr}], [{slf}] "
"is not a valid filter".format(expr=self.expr,
slf=self))
return self.condition, self.filter
class TermValue:
""" hold a term value the we use to construct a condition/filter """
def __init__(self, value, converted, kind):
self.value = value
self.converted = converted
self.kind = kind
def tostring(self, encoding):
""" quote the string if not encoded
else encode and return """
if self.kind == 'string':
if encoding is not None:
return self.converted
return '"{converted}"'.format(converted=self.converted)
elif self.kind == 'float':
# python 2 str(float) is not always
# round-trippable so use repr()
return repr(self.converted)
return self.converted
def maybe_expression(s):
""" loose checking if s is a pytables-acceptable expression """
if not isinstance(s, str):
return False
ops = ExprVisitor.binary_ops + ExprVisitor.unary_ops + ('=',)
# make sure we have an op at least
return any(op in s for op in ops)
|
from copy import deepcopy
from distutils.version import LooseVersion
from operator import methodcaller
import numpy as np
import pytest
import pandas.util._test_decorators as td
import pandas as pd
from pandas import DataFrame, MultiIndex, Series, date_range
import pandas.util.testing as tm
from pandas.util.testing import (
assert_almost_equal, assert_frame_equal, assert_series_equal)
from .test_generic import Generic
try:
import xarray
_XARRAY_INSTALLED = True
except ImportError:
_XARRAY_INSTALLED = False
class TestDataFrame(Generic):
_typ = DataFrame
_comparator = lambda self, x, y: assert_frame_equal(x, y)
def test_rename_mi(self):
df = DataFrame([
11, 21, 31
], index=MultiIndex.from_tuples([("A", x) for x in ["a", "B", "c"]]))
df.rename(str.lower)
def test_set_axis_name(self):
df = pd.DataFrame([[1, 2], [3, 4]])
funcs = ['_set_axis_name', 'rename_axis']
for func in funcs:
result = methodcaller(func, 'foo')(df)
assert df.index.name is None
assert result.index.name == 'foo'
result = methodcaller(func, 'cols', axis=1)(df)
assert df.columns.name is None
assert result.columns.name == 'cols'
def test_set_axis_name_mi(self):
df = DataFrame(
np.empty((3, 3)),
index=MultiIndex.from_tuples([("A", x) for x in list('aBc')]),
columns=MultiIndex.from_tuples([('C', x) for x in list('xyz')])
)
level_names = ['L1', 'L2']
funcs = ['_set_axis_name', 'rename_axis']
for func in funcs:
result = methodcaller(func, level_names)(df)
assert result.index.names == level_names
assert result.columns.names == [None, None]
result = methodcaller(func, level_names, axis=1)(df)
assert result.columns.names == ["L1", "L2"]
assert result.index.names == [None, None]
def test_nonzero_single_element(self):
# allow single item via bool method
df = DataFrame([[True]])
assert df.bool()
df = DataFrame([[False]])
assert not df.bool()
df = DataFrame([[False, False]])
with pytest.raises(ValueError):
df.bool()
with pytest.raises(ValueError):
bool(df)
def test_get_numeric_data_preserve_dtype(self):
# get the numeric data
o = DataFrame({'A': [1, '2', 3.]})
result = o._get_numeric_data()
expected = DataFrame(index=[0, 1, 2], dtype=object)
self._compare(result, expected)
def test_metadata_propagation_indiv(self):
# groupby
df = DataFrame(
{'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'],
'B': ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three'],
'C': np.random.randn(8),
'D': np.random.randn(8)})
result = df.groupby('A').sum()
self.check_metadata(df, result)
# resample
df = DataFrame(np.random.randn(1000, 2),
index=date_range('20130101', periods=1000, freq='s'))
result = df.resample('1T')
self.check_metadata(df, result)
# merging with override
# GH 6923
_metadata = DataFrame._metadata
_finalize = DataFrame.__finalize__
np.random.seed(10)
df1 = DataFrame(np.random.randint(0, 4, (3, 2)), columns=['a', 'b'])
df2 = DataFrame(np.random.randint(0, 4, (3, 2)), columns=['c', 'd'])
DataFrame._metadata = ['filename']
df1.filename = 'fname1.csv'
df2.filename = 'fname2.csv'
def finalize(self, other, method=None, **kwargs):
for name in self._metadata:
if method == 'merge':
left, right = other.left, other.right
value = getattr(left, name, '') + '|' + getattr(right,
name, '')
object.__setattr__(self, name, value)
else:
object.__setattr__(self, name, getattr(other, name, ''))
return self
DataFrame.__finalize__ = finalize
result = df1.merge(df2, left_on=['a'], right_on=['c'], how='inner')
assert result.filename == 'fname1.csv|fname2.csv'
# concat
# GH 6927
DataFrame._metadata = ['filename']
df1 = DataFrame(np.random.randint(0, 4, (3, 2)), columns=list('ab'))
df1.filename = 'foo'
def finalize(self, other, method=None, **kwargs):
for name in self._metadata:
if method == 'concat':
value = '+'.join([getattr(
o, name) for o in other.objs if getattr(o, name, None)
])
object.__setattr__(self, name, value)
else:
object.__setattr__(self, name, getattr(other, name, None))
return self
DataFrame.__finalize__ = finalize
result = pd.concat([df1, df1])
assert result.filename == 'foo+foo'
# reset
DataFrame._metadata = _metadata
DataFrame.__finalize__ = _finalize
def test_set_attribute(self):
# Test for consistent setattr behavior when an attribute and a column
# have the same name (Issue #8994)
df = DataFrame({'x': [1, 2, 3]})
df.y = 2
df['y'] = [2, 4, 6]
df.y = 5
assert df.y == 5
assert_series_equal(df['y'], Series([2, 4, 6], name='y'))
@pytest.mark.skipif(not _XARRAY_INSTALLED or _XARRAY_INSTALLED and
LooseVersion(xarray.__version__) <
LooseVersion('0.10.0'),
reason='xarray >= 0.10.0 required')
@pytest.mark.parametrize(
"index", ['FloatIndex', 'IntIndex',
'StringIndex', 'UnicodeIndex',
'DateIndex', 'PeriodIndex',
'CategoricalIndex', 'TimedeltaIndex'])
def test_to_xarray_index_types(self, index):
from xarray import Dataset
index = getattr(tm, 'make{}'.format(index))
df = DataFrame({'a': list('abc'),
'b': list(range(1, 4)),
'c': np.arange(3, 6).astype('u1'),
'd': np.arange(4.0, 7.0, dtype='float64'),
'e': [True, False, True],
'f': pd.Categorical(list('abc')),
'g': pd.date_range('20130101', periods=3),
'h': pd.date_range('20130101',
periods=3,
tz='US/Eastern')}
)
df.index = index(3)
df.index.name = 'foo'
df.columns.name = 'bar'
result = df.to_xarray()
assert result.dims['foo'] == 3
assert len(result.coords) == 1
assert len(result.data_vars) == 8
assert_almost_equal(list(result.coords.keys()), ['foo'])
assert isinstance(result, Dataset)
# idempotency
# categoricals are not preserved
# datetimes w/tz are not preserved
# column names are lost
expected = df.copy()
expected['f'] = expected['f'].astype(object)
expected['h'] = expected['h'].astype('datetime64[ns]')
expected.columns.name = None
assert_frame_equal(result.to_dataframe(), expected,
check_index_type=False, check_categorical=False)
@td.skip_if_no('xarray', min_version='0.7.0')
def test_to_xarray(self):
from xarray import Dataset
df = DataFrame({'a': list('abc'),
'b': list(range(1, 4)),
'c': np.arange(3, 6).astype('u1'),
'd': np.arange(4.0, 7.0, dtype='float64'),
'e': [True, False, True],
'f': pd.Categorical(list('abc')),
'g': pd.date_range('20130101', periods=3),
'h': pd.date_range('20130101',
periods=3,
tz='US/Eastern')}
)
df.index.name = 'foo'
result = df[0:0].to_xarray()
assert result.dims['foo'] == 0
assert isinstance(result, Dataset)
# available in 0.7.1
# MultiIndex
df.index = pd.MultiIndex.from_product([['a'], range(3)],
names=['one', 'two'])
result = df.to_xarray()
assert result.dims['one'] == 1
assert result.dims['two'] == 3
assert len(result.coords) == 2
assert len(result.data_vars) == 8
assert_almost_equal(list(result.coords.keys()), ['one', 'two'])
assert isinstance(result, Dataset)
result = result.to_dataframe()
expected = df.copy()
expected['f'] = expected['f'].astype(object)
expected['h'] = expected['h'].astype('datetime64[ns]')
expected.columns.name = None
assert_frame_equal(result,
expected,
check_index_type=False)
def test_deepcopy_empty(self):
# This test covers empty frame copying with non-empty column sets
# as reported in issue GH15370
empty_frame = DataFrame(data=[], index=[], columns=['A'])
empty_frame_copy = deepcopy(empty_frame)
self._compare(empty_frame_copy, empty_frame)
|
|
pandas/tests/generic/test_frame.py
|
pandas/core/computation/pytables.py
|
import numpy as np
import pytest
import pandas as pd
from pandas import Index, MultiIndex
@pytest.fixture
def idx():
# a MultiIndex used to test the general functionality of the
# general functionality of this object
major_axis = Index(['foo', 'bar', 'baz', 'qux'])
minor_axis = Index(['one', 'two'])
major_codes = np.array([0, 0, 1, 2, 3, 3])
minor_codes = np.array([0, 1, 0, 1, 0, 1])
index_names = ['first', 'second']
mi = MultiIndex(levels=[major_axis, minor_axis],
codes=[major_codes, minor_codes],
names=index_names, verify_integrity=False)
return mi
@pytest.fixture
def idx_dup():
# compare tests/indexes/multi/conftest.py
major_axis = Index(['foo', 'bar', 'baz', 'qux'])
minor_axis = Index(['one', 'two'])
major_codes = np.array([0, 0, 1, 0, 1, 1])
minor_codes = np.array([0, 1, 0, 1, 0, 1])
index_names = ['first', 'second']
mi = MultiIndex(levels=[major_axis, minor_axis],
codes=[major_codes, minor_codes],
names=index_names, verify_integrity=False)
return mi
@pytest.fixture
def index_names():
# names that match those in the idx fixture for testing equality of
# names assigned to the idx
return ['first', 'second']
@pytest.fixture
def holder():
# the MultiIndex constructor used to base compatibility with pickle
return MultiIndex
@pytest.fixture
def compat_props():
# a MultiIndex must have these properties associated with it
return ['shape', 'ndim', 'size']
@pytest.fixture
def narrow_multi_index():
"""
Return a MultiIndex that is narrower than the display (<80 characters).
"""
n = 1000
ci = pd.CategoricalIndex(list('a' * n) + (['abc'] * n))
dti = pd.date_range('2000-01-01', freq='s', periods=n * 2)
return pd.MultiIndex.from_arrays([ci, ci.codes + 9, dti],
names=['a', 'b', 'dti'])
@pytest.fixture
def wide_multi_index():
"""
Return a MultiIndex that is wider than the display (>80 characters).
"""
n = 1000
ci = pd.CategoricalIndex(list('a' * n) + (['abc'] * n))
dti = pd.date_range('2000-01-01', freq='s', periods=n * 2)
levels = [ci, ci.codes + 9, dti, dti, dti]
names = ['a', 'b', 'dti_1', 'dti_2', 'dti_3']
return pd.MultiIndex.from_arrays(levels, names=names)
|
from copy import deepcopy
from distutils.version import LooseVersion
from operator import methodcaller
import numpy as np
import pytest
import pandas.util._test_decorators as td
import pandas as pd
from pandas import DataFrame, MultiIndex, Series, date_range
import pandas.util.testing as tm
from pandas.util.testing import (
assert_almost_equal, assert_frame_equal, assert_series_equal)
from .test_generic import Generic
try:
import xarray
_XARRAY_INSTALLED = True
except ImportError:
_XARRAY_INSTALLED = False
class TestDataFrame(Generic):
_typ = DataFrame
_comparator = lambda self, x, y: assert_frame_equal(x, y)
def test_rename_mi(self):
df = DataFrame([
11, 21, 31
], index=MultiIndex.from_tuples([("A", x) for x in ["a", "B", "c"]]))
df.rename(str.lower)
def test_set_axis_name(self):
df = pd.DataFrame([[1, 2], [3, 4]])
funcs = ['_set_axis_name', 'rename_axis']
for func in funcs:
result = methodcaller(func, 'foo')(df)
assert df.index.name is None
assert result.index.name == 'foo'
result = methodcaller(func, 'cols', axis=1)(df)
assert df.columns.name is None
assert result.columns.name == 'cols'
def test_set_axis_name_mi(self):
df = DataFrame(
np.empty((3, 3)),
index=MultiIndex.from_tuples([("A", x) for x in list('aBc')]),
columns=MultiIndex.from_tuples([('C', x) for x in list('xyz')])
)
level_names = ['L1', 'L2']
funcs = ['_set_axis_name', 'rename_axis']
for func in funcs:
result = methodcaller(func, level_names)(df)
assert result.index.names == level_names
assert result.columns.names == [None, None]
result = methodcaller(func, level_names, axis=1)(df)
assert result.columns.names == ["L1", "L2"]
assert result.index.names == [None, None]
def test_nonzero_single_element(self):
# allow single item via bool method
df = DataFrame([[True]])
assert df.bool()
df = DataFrame([[False]])
assert not df.bool()
df = DataFrame([[False, False]])
with pytest.raises(ValueError):
df.bool()
with pytest.raises(ValueError):
bool(df)
def test_get_numeric_data_preserve_dtype(self):
# get the numeric data
o = DataFrame({'A': [1, '2', 3.]})
result = o._get_numeric_data()
expected = DataFrame(index=[0, 1, 2], dtype=object)
self._compare(result, expected)
def test_metadata_propagation_indiv(self):
# groupby
df = DataFrame(
{'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'],
'B': ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three'],
'C': np.random.randn(8),
'D': np.random.randn(8)})
result = df.groupby('A').sum()
self.check_metadata(df, result)
# resample
df = DataFrame(np.random.randn(1000, 2),
index=date_range('20130101', periods=1000, freq='s'))
result = df.resample('1T')
self.check_metadata(df, result)
# merging with override
# GH 6923
_metadata = DataFrame._metadata
_finalize = DataFrame.__finalize__
np.random.seed(10)
df1 = DataFrame(np.random.randint(0, 4, (3, 2)), columns=['a', 'b'])
df2 = DataFrame(np.random.randint(0, 4, (3, 2)), columns=['c', 'd'])
DataFrame._metadata = ['filename']
df1.filename = 'fname1.csv'
df2.filename = 'fname2.csv'
def finalize(self, other, method=None, **kwargs):
for name in self._metadata:
if method == 'merge':
left, right = other.left, other.right
value = getattr(left, name, '') + '|' + getattr(right,
name, '')
object.__setattr__(self, name, value)
else:
object.__setattr__(self, name, getattr(other, name, ''))
return self
DataFrame.__finalize__ = finalize
result = df1.merge(df2, left_on=['a'], right_on=['c'], how='inner')
assert result.filename == 'fname1.csv|fname2.csv'
# concat
# GH 6927
DataFrame._metadata = ['filename']
df1 = DataFrame(np.random.randint(0, 4, (3, 2)), columns=list('ab'))
df1.filename = 'foo'
def finalize(self, other, method=None, **kwargs):
for name in self._metadata:
if method == 'concat':
value = '+'.join([getattr(
o, name) for o in other.objs if getattr(o, name, None)
])
object.__setattr__(self, name, value)
else:
object.__setattr__(self, name, getattr(other, name, None))
return self
DataFrame.__finalize__ = finalize
result = pd.concat([df1, df1])
assert result.filename == 'foo+foo'
# reset
DataFrame._metadata = _metadata
DataFrame.__finalize__ = _finalize
def test_set_attribute(self):
# Test for consistent setattr behavior when an attribute and a column
# have the same name (Issue #8994)
df = DataFrame({'x': [1, 2, 3]})
df.y = 2
df['y'] = [2, 4, 6]
df.y = 5
assert df.y == 5
assert_series_equal(df['y'], Series([2, 4, 6], name='y'))
@pytest.mark.skipif(not _XARRAY_INSTALLED or _XARRAY_INSTALLED and
LooseVersion(xarray.__version__) <
LooseVersion('0.10.0'),
reason='xarray >= 0.10.0 required')
@pytest.mark.parametrize(
"index", ['FloatIndex', 'IntIndex',
'StringIndex', 'UnicodeIndex',
'DateIndex', 'PeriodIndex',
'CategoricalIndex', 'TimedeltaIndex'])
def test_to_xarray_index_types(self, index):
from xarray import Dataset
index = getattr(tm, 'make{}'.format(index))
df = DataFrame({'a': list('abc'),
'b': list(range(1, 4)),
'c': np.arange(3, 6).astype('u1'),
'd': np.arange(4.0, 7.0, dtype='float64'),
'e': [True, False, True],
'f': pd.Categorical(list('abc')),
'g': pd.date_range('20130101', periods=3),
'h': pd.date_range('20130101',
periods=3,
tz='US/Eastern')}
)
df.index = index(3)
df.index.name = 'foo'
df.columns.name = 'bar'
result = df.to_xarray()
assert result.dims['foo'] == 3
assert len(result.coords) == 1
assert len(result.data_vars) == 8
assert_almost_equal(list(result.coords.keys()), ['foo'])
assert isinstance(result, Dataset)
# idempotency
# categoricals are not preserved
# datetimes w/tz are not preserved
# column names are lost
expected = df.copy()
expected['f'] = expected['f'].astype(object)
expected['h'] = expected['h'].astype('datetime64[ns]')
expected.columns.name = None
assert_frame_equal(result.to_dataframe(), expected,
check_index_type=False, check_categorical=False)
@td.skip_if_no('xarray', min_version='0.7.0')
def test_to_xarray(self):
from xarray import Dataset
df = DataFrame({'a': list('abc'),
'b': list(range(1, 4)),
'c': np.arange(3, 6).astype('u1'),
'd': np.arange(4.0, 7.0, dtype='float64'),
'e': [True, False, True],
'f': pd.Categorical(list('abc')),
'g': pd.date_range('20130101', periods=3),
'h': pd.date_range('20130101',
periods=3,
tz='US/Eastern')}
)
df.index.name = 'foo'
result = df[0:0].to_xarray()
assert result.dims['foo'] == 0
assert isinstance(result, Dataset)
# available in 0.7.1
# MultiIndex
df.index = pd.MultiIndex.from_product([['a'], range(3)],
names=['one', 'two'])
result = df.to_xarray()
assert result.dims['one'] == 1
assert result.dims['two'] == 3
assert len(result.coords) == 2
assert len(result.data_vars) == 8
assert_almost_equal(list(result.coords.keys()), ['one', 'two'])
assert isinstance(result, Dataset)
result = result.to_dataframe()
expected = df.copy()
expected['f'] = expected['f'].astype(object)
expected['h'] = expected['h'].astype('datetime64[ns]')
expected.columns.name = None
assert_frame_equal(result,
expected,
check_index_type=False)
def test_deepcopy_empty(self):
# This test covers empty frame copying with non-empty column sets
# as reported in issue GH15370
empty_frame = DataFrame(data=[], index=[], columns=['A'])
empty_frame_copy = deepcopy(empty_frame)
self._compare(empty_frame_copy, empty_frame)
|
|
pandas/tests/generic/test_frame.py
|
pandas/tests/indexes/multi/conftest.py
|
import numpy as np
import pytest
from pandas._libs.tslib import iNaT
from pandas.core.dtypes.dtypes import CategoricalDtype
import pandas as pd
from pandas import (
CategoricalIndex, DatetimeIndex, Index, Int64Index, IntervalIndex,
MultiIndex, PeriodIndex, RangeIndex, Series, TimedeltaIndex, UInt64Index,
isna)
from pandas.core.indexes.base import InvalidIndexError
from pandas.core.indexes.datetimelike import DatetimeIndexOpsMixin
import pandas.util.testing as tm
class Base:
""" base class for index sub-class tests """
_holder = None
_compat_props = ['shape', 'ndim', 'size', 'nbytes']
def setup_indices(self):
for name, idx in self.indices.items():
setattr(self, name, idx)
def test_pickle_compat_construction(self):
# need an object to create with
msg = (r"Index\(\.\.\.\) must be called with a collection of some"
r" kind, None was passed|"
r"__new__\(\) missing 1 required positional argument: 'data'|"
r"__new__\(\) takes at least 2 arguments \(1 given\)")
with pytest.raises(TypeError, match=msg):
self._holder()
def test_to_series(self):
# assert that we are creating a copy of the index
idx = self.create_index()
s = idx.to_series()
assert s.values is not idx.values
assert s.index is not idx
assert s.name == idx.name
def test_to_series_with_arguments(self):
# GH18699
# index kwarg
idx = self.create_index()
s = idx.to_series(index=idx)
assert s.values is not idx.values
assert s.index is idx
assert s.name == idx.name
# name kwarg
idx = self.create_index()
s = idx.to_series(name='__test')
assert s.values is not idx.values
assert s.index is not idx
assert s.name != idx.name
@pytest.mark.parametrize("name", [None, "new_name"])
def test_to_frame(self, name):
# see GH-15230, GH-22580
idx = self.create_index()
if name:
idx_name = name
else:
idx_name = idx.name or 0
df = idx.to_frame(name=idx_name)
assert df.index is idx
assert len(df.columns) == 1
assert df.columns[0] == idx_name
assert df[idx_name].values is not idx.values
df = idx.to_frame(index=False, name=idx_name)
assert df.index is not idx
def test_to_frame_datetime_tz(self):
# GH 25809
idx = pd.date_range(start='2019-01-01', end='2019-01-30', freq='D')
idx = idx.tz_localize('UTC')
result = idx.to_frame()
expected = pd.DataFrame(idx, index=idx)
tm.assert_frame_equal(result, expected)
def test_shift(self):
# GH8083 test the base class for shift
idx = self.create_index()
msg = "Not supported for type {}".format(type(idx).__name__)
with pytest.raises(NotImplementedError, match=msg):
idx.shift(1)
with pytest.raises(NotImplementedError, match=msg):
idx.shift(1, 2)
def test_create_index_existing_name(self):
# GH11193, when an existing index is passed, and a new name is not
# specified, the new index should inherit the previous object name
expected = self.create_index()
if not isinstance(expected, MultiIndex):
expected.name = 'foo'
result = pd.Index(expected)
tm.assert_index_equal(result, expected)
result = pd.Index(expected, name='bar')
expected.name = 'bar'
tm.assert_index_equal(result, expected)
else:
expected.names = ['foo', 'bar']
result = pd.Index(expected)
tm.assert_index_equal(
result, Index(Index([('foo', 'one'), ('foo', 'two'),
('bar', 'one'), ('baz', 'two'),
('qux', 'one'), ('qux', 'two')],
dtype='object'),
names=['foo', 'bar']))
result = pd.Index(expected, names=['A', 'B'])
tm.assert_index_equal(
result,
Index(Index([('foo', 'one'), ('foo', 'two'), ('bar', 'one'),
('baz', 'two'), ('qux', 'one'), ('qux', 'two')],
dtype='object'), names=['A', 'B']))
def test_numeric_compat(self):
idx = self.create_index()
with pytest.raises(TypeError, match="cannot perform __mul__"):
idx * 1
with pytest.raises(TypeError, match="cannot perform __rmul__"):
1 * idx
div_err = "cannot perform __truediv__"
with pytest.raises(TypeError, match=div_err):
idx / 1
div_err = div_err.replace(' __', ' __r')
with pytest.raises(TypeError, match=div_err):
1 / idx
with pytest.raises(TypeError, match="cannot perform __floordiv__"):
idx // 1
with pytest.raises(TypeError, match="cannot perform __rfloordiv__"):
1 // idx
def test_logical_compat(self):
idx = self.create_index()
with pytest.raises(TypeError, match='cannot perform all'):
idx.all()
with pytest.raises(TypeError, match='cannot perform any'):
idx.any()
def test_boolean_context_compat(self):
# boolean context compat
idx = self.create_index()
with pytest.raises(ValueError, match='The truth value of a'):
if idx:
pass
def test_reindex_base(self):
idx = self.create_index()
expected = np.arange(idx.size, dtype=np.intp)
actual = idx.get_indexer(idx)
tm.assert_numpy_array_equal(expected, actual)
with pytest.raises(ValueError, match='Invalid fill method'):
idx.get_indexer(idx, method='invalid')
def test_get_indexer_consistency(self):
# See GH 16819
for name, index in self.indices.items():
if isinstance(index, IntervalIndex):
continue
if index.is_unique or isinstance(index, CategoricalIndex):
indexer = index.get_indexer(index[0:2])
assert isinstance(indexer, np.ndarray)
assert indexer.dtype == np.intp
else:
e = "Reindexing only valid with uniquely valued Index objects"
with pytest.raises(InvalidIndexError, match=e):
index.get_indexer(index[0:2])
indexer, _ = index.get_indexer_non_unique(index[0:2])
assert isinstance(indexer, np.ndarray)
assert indexer.dtype == np.intp
def test_ndarray_compat_properties(self):
idx = self.create_index()
assert idx.T.equals(idx)
assert idx.transpose().equals(idx)
values = idx.values
for prop in self._compat_props:
assert getattr(idx, prop) == getattr(values, prop)
# test for validity
idx.nbytes
idx.values.nbytes
def test_repr_roundtrip(self):
idx = self.create_index()
tm.assert_index_equal(eval(repr(idx)), idx)
def test_str(self):
# test the string repr
idx = self.create_index()
idx.name = 'foo'
assert "'foo'" in str(idx)
assert idx.__class__.__name__ in str(idx)
def test_repr_max_seq_item_setting(self):
# GH10182
idx = self.create_index()
idx = idx.repeat(50)
with pd.option_context("display.max_seq_items", None):
repr(idx)
assert '...' not in str(idx)
def test_copy_name(self):
# gh-12309: Check that the "name" argument
# passed at initialization is honored.
for name, index in self.indices.items():
if isinstance(index, MultiIndex):
continue
first = index.__class__(index, copy=True, name='mario')
second = first.__class__(first, copy=False)
# Even though "copy=False", we want a new object.
assert first is not second
# Not using tm.assert_index_equal() since names differ.
assert index.equals(first)
assert first.name == 'mario'
assert second.name == 'mario'
s1 = Series(2, index=first)
s2 = Series(3, index=second[:-1])
if not isinstance(index, CategoricalIndex):
# See gh-13365
s3 = s1 * s2
assert s3.index.name == 'mario'
def test_ensure_copied_data(self):
# Check the "copy" argument of each Index.__new__ is honoured
# GH12309
for name, index in self.indices.items():
init_kwargs = {}
if isinstance(index, PeriodIndex):
# Needs "freq" specification:
init_kwargs['freq'] = index.freq
elif isinstance(index, (RangeIndex, MultiIndex, CategoricalIndex)):
# RangeIndex cannot be initialized from data
# MultiIndex and CategoricalIndex are tested separately
continue
index_type = index.__class__
result = index_type(index.values, copy=True, **init_kwargs)
tm.assert_index_equal(index, result)
tm.assert_numpy_array_equal(index._ndarray_values,
result._ndarray_values,
check_same='copy')
if isinstance(index, PeriodIndex):
# .values an object array of Period, thus copied
result = index_type(ordinal=index.asi8, copy=False,
**init_kwargs)
tm.assert_numpy_array_equal(index._ndarray_values,
result._ndarray_values,
check_same='same')
elif isinstance(index, IntervalIndex):
# checked in test_interval.py
pass
else:
result = index_type(index.values, copy=False, **init_kwargs)
tm.assert_numpy_array_equal(index.values, result.values,
check_same='same')
tm.assert_numpy_array_equal(index._ndarray_values,
result._ndarray_values,
check_same='same')
def test_memory_usage(self):
for name, index in self.indices.items():
result = index.memory_usage()
if len(index):
index.get_loc(index[0])
result2 = index.memory_usage()
result3 = index.memory_usage(deep=True)
# RangeIndex, IntervalIndex
# don't have engines
if not isinstance(index, (RangeIndex, IntervalIndex)):
assert result2 > result
if index.inferred_type == 'object':
assert result3 > result2
else:
# we report 0 for no-length
assert result == 0
def test_argsort(self):
for k, ind in self.indices.items():
# separately tested
if k in ['catIndex']:
continue
result = ind.argsort()
expected = np.array(ind).argsort()
tm.assert_numpy_array_equal(result, expected, check_dtype=False)
def test_numpy_argsort(self):
for k, ind in self.indices.items():
result = np.argsort(ind)
expected = ind.argsort()
tm.assert_numpy_array_equal(result, expected)
# these are the only two types that perform
# pandas compatibility input validation - the
# rest already perform separate (or no) such
# validation via their 'values' attribute as
# defined in pandas.core.indexes/base.py - they
# cannot be changed at the moment due to
# backwards compatibility concerns
if isinstance(type(ind), (CategoricalIndex, RangeIndex)):
msg = "the 'axis' parameter is not supported"
with pytest.raises(ValueError, match=msg):
np.argsort(ind, axis=1)
msg = "the 'kind' parameter is not supported"
with pytest.raises(ValueError, match=msg):
np.argsort(ind, kind='mergesort')
msg = "the 'order' parameter is not supported"
with pytest.raises(ValueError, match=msg):
np.argsort(ind, order=('a', 'b'))
def test_take(self):
indexer = [4, 3, 0, 2]
for k, ind in self.indices.items():
# separate
if k in ['boolIndex', 'tuples', 'empty']:
continue
result = ind.take(indexer)
expected = ind[indexer]
assert result.equals(expected)
if not isinstance(ind,
(DatetimeIndex, PeriodIndex, TimedeltaIndex)):
# GH 10791
with pytest.raises(AttributeError):
ind.freq
def test_take_invalid_kwargs(self):
idx = self.create_index()
indices = [1, 2]
msg = r"take\(\) got an unexpected keyword argument 'foo'"
with pytest.raises(TypeError, match=msg):
idx.take(indices, foo=2)
msg = "the 'out' parameter is not supported"
with pytest.raises(ValueError, match=msg):
idx.take(indices, out=indices)
msg = "the 'mode' parameter is not supported"
with pytest.raises(ValueError, match=msg):
idx.take(indices, mode='clip')
def test_repeat(self):
rep = 2
i = self.create_index()
expected = pd.Index(i.values.repeat(rep), name=i.name)
tm.assert_index_equal(i.repeat(rep), expected)
i = self.create_index()
rep = np.arange(len(i))
expected = pd.Index(i.values.repeat(rep), name=i.name)
tm.assert_index_equal(i.repeat(rep), expected)
def test_numpy_repeat(self):
rep = 2
i = self.create_index()
expected = i.repeat(rep)
tm.assert_index_equal(np.repeat(i, rep), expected)
msg = "the 'axis' parameter is not supported"
with pytest.raises(ValueError, match=msg):
np.repeat(i, rep, axis=0)
@pytest.mark.parametrize('klass', [list, tuple, np.array, Series])
def test_where(self, klass):
i = self.create_index()
cond = [True] * len(i)
result = i.where(klass(cond))
expected = i
tm.assert_index_equal(result, expected)
cond = [False] + [True] * len(i[1:])
expected = pd.Index([i._na_value] + i[1:].tolist(), dtype=i.dtype)
result = i.where(klass(cond))
tm.assert_index_equal(result, expected)
@pytest.mark.parametrize("case", [0.5, "xxx"])
@pytest.mark.parametrize("method", ["intersection", "union",
"difference", "symmetric_difference"])
def test_set_ops_error_cases(self, case, method):
for name, idx in self.indices.items():
# non-iterable input
msg = "Input must be Index or array-like"
with pytest.raises(TypeError, match=msg):
getattr(idx, method)(case)
def test_intersection_base(self):
for name, idx in self.indices.items():
first = idx[:5]
second = idx[:3]
intersect = first.intersection(second)
if isinstance(idx, CategoricalIndex):
pass
else:
assert tm.equalContents(intersect, second)
# GH 10149
cases = [klass(second.values)
for klass in [np.array, Series, list]]
for case in cases:
if isinstance(idx, CategoricalIndex):
pass
else:
result = first.intersection(case)
assert tm.equalContents(result, second)
if isinstance(idx, MultiIndex):
msg = "other must be a MultiIndex or a list of tuples"
with pytest.raises(TypeError, match=msg):
first.intersection([1, 2, 3])
def test_union_base(self):
for name, idx in self.indices.items():
first = idx[3:]
second = idx[:5]
everything = idx
union = first.union(second)
assert tm.equalContents(union, everything)
# GH 10149
cases = [klass(second.values)
for klass in [np.array, Series, list]]
for case in cases:
if isinstance(idx, CategoricalIndex):
pass
else:
result = first.union(case)
assert tm.equalContents(result, everything)
if isinstance(idx, MultiIndex):
msg = "other must be a MultiIndex or a list of tuples"
with pytest.raises(TypeError, match=msg):
first.union([1, 2, 3])
@pytest.mark.parametrize("sort", [None, False])
def test_difference_base(self, sort):
for name, idx in self.indices.items():
first = idx[2:]
second = idx[:4]
answer = idx[4:]
result = first.difference(second, sort)
if isinstance(idx, CategoricalIndex):
pass
else:
assert tm.equalContents(result, answer)
# GH 10149
cases = [klass(second.values)
for klass in [np.array, Series, list]]
for case in cases:
if isinstance(idx, CategoricalIndex):
pass
elif isinstance(idx, (DatetimeIndex, TimedeltaIndex)):
assert result.__class__ == answer.__class__
tm.assert_numpy_array_equal(result.sort_values().asi8,
answer.sort_values().asi8)
else:
result = first.difference(case, sort)
assert tm.equalContents(result, answer)
if isinstance(idx, MultiIndex):
msg = "other must be a MultiIndex or a list of tuples"
with pytest.raises(TypeError, match=msg):
first.difference([1, 2, 3], sort)
def test_symmetric_difference(self):
for name, idx in self.indices.items():
first = idx[1:]
second = idx[:-1]
if isinstance(idx, CategoricalIndex):
pass
else:
answer = idx[[0, -1]]
result = first.symmetric_difference(second)
assert tm.equalContents(result, answer)
# GH 10149
cases = [klass(second.values)
for klass in [np.array, Series, list]]
for case in cases:
if isinstance(idx, CategoricalIndex):
pass
else:
result = first.symmetric_difference(case)
assert tm.equalContents(result, answer)
if isinstance(idx, MultiIndex):
msg = "other must be a MultiIndex or a list of tuples"
with pytest.raises(TypeError, match=msg):
first.symmetric_difference([1, 2, 3])
def test_insert_base(self):
for name, idx in self.indices.items():
result = idx[1:4]
if not len(idx):
continue
# test 0th element
assert idx[0:4].equals(result.insert(0, idx[0]))
def test_delete_base(self):
for name, idx in self.indices.items():
if not len(idx):
continue
if isinstance(idx, RangeIndex):
# tested in class
continue
expected = idx[1:]
result = idx.delete(0)
assert result.equals(expected)
assert result.name == expected.name
expected = idx[:-1]
result = idx.delete(-1)
assert result.equals(expected)
assert result.name == expected.name
with pytest.raises((IndexError, ValueError)):
# either depending on numpy version
idx.delete(len(idx))
def test_equals(self):
for name, idx in self.indices.items():
assert idx.equals(idx)
assert idx.equals(idx.copy())
assert idx.equals(idx.astype(object))
assert not idx.equals(list(idx))
assert not idx.equals(np.array(idx))
# Cannot pass in non-int64 dtype to RangeIndex
if not isinstance(idx, RangeIndex):
same_values = Index(idx, dtype=object)
assert idx.equals(same_values)
assert same_values.equals(idx)
if idx.nlevels == 1:
# do not test MultiIndex
assert not idx.equals(pd.Series(idx))
def test_equals_op(self):
# GH9947, GH10637
index_a = self.create_index()
if isinstance(index_a, PeriodIndex):
pytest.skip('Skip check for PeriodIndex')
n = len(index_a)
index_b = index_a[0:-1]
index_c = index_a[0:-1].append(index_a[-2:-1])
index_d = index_a[0:1]
msg = "Lengths must match|could not be broadcast"
with pytest.raises(ValueError, match=msg):
index_a == index_b
expected1 = np.array([True] * n)
expected2 = np.array([True] * (n - 1) + [False])
tm.assert_numpy_array_equal(index_a == index_a, expected1)
tm.assert_numpy_array_equal(index_a == index_c, expected2)
# test comparisons with numpy arrays
array_a = np.array(index_a)
array_b = np.array(index_a[0:-1])
array_c = np.array(index_a[0:-1].append(index_a[-2:-1]))
array_d = np.array(index_a[0:1])
with pytest.raises(ValueError, match=msg):
index_a == array_b
tm.assert_numpy_array_equal(index_a == array_a, expected1)
tm.assert_numpy_array_equal(index_a == array_c, expected2)
# test comparisons with Series
series_a = Series(array_a)
series_b = Series(array_b)
series_c = Series(array_c)
series_d = Series(array_d)
with pytest.raises(ValueError, match=msg):
index_a == series_b
tm.assert_numpy_array_equal(index_a == series_a, expected1)
tm.assert_numpy_array_equal(index_a == series_c, expected2)
# cases where length is 1 for one of them
with pytest.raises(ValueError, match="Lengths must match"):
index_a == index_d
with pytest.raises(ValueError, match="Lengths must match"):
index_a == series_d
with pytest.raises(ValueError, match="Lengths must match"):
index_a == array_d
msg = "Can only compare identically-labeled Series objects"
with pytest.raises(ValueError, match=msg):
series_a == series_d
with pytest.raises(ValueError, match="Lengths must match"):
series_a == array_d
# comparing with a scalar should broadcast; note that we are excluding
# MultiIndex because in this case each item in the index is a tuple of
# length 2, and therefore is considered an array of length 2 in the
# comparison instead of a scalar
if not isinstance(index_a, MultiIndex):
expected3 = np.array([False] * (len(index_a) - 2) + [True, False])
# assuming the 2nd to last item is unique in the data
item = index_a[-2]
tm.assert_numpy_array_equal(index_a == item, expected3)
tm.assert_series_equal(series_a == item, Series(expected3))
def test_hasnans_isnans(self):
# GH 11343, added tests for hasnans / isnans
for name, index in self.indices.items():
if isinstance(index, MultiIndex):
pass
else:
idx = index.copy()
# cases in indices doesn't include NaN
expected = np.array([False] * len(idx), dtype=bool)
tm.assert_numpy_array_equal(idx._isnan, expected)
assert idx.hasnans is False
idx = index.copy()
values = np.asarray(idx.values)
if len(index) == 0:
continue
elif isinstance(index, DatetimeIndexOpsMixin):
values[1] = iNaT
elif isinstance(index, (Int64Index, UInt64Index)):
continue
else:
values[1] = np.nan
if isinstance(index, PeriodIndex):
idx = index.__class__(values, freq=index.freq)
else:
idx = index.__class__(values)
expected = np.array([False] * len(idx), dtype=bool)
expected[1] = True
tm.assert_numpy_array_equal(idx._isnan, expected)
assert idx.hasnans is True
def test_fillna(self):
# GH 11343
for name, index in self.indices.items():
if len(index) == 0:
pass
elif isinstance(index, MultiIndex):
idx = index.copy()
msg = "isna is not defined for MultiIndex"
with pytest.raises(NotImplementedError, match=msg):
idx.fillna(idx[0])
else:
idx = index.copy()
result = idx.fillna(idx[0])
tm.assert_index_equal(result, idx)
assert result is not idx
msg = "'value' must be a scalar, passed: "
with pytest.raises(TypeError, match=msg):
idx.fillna([idx[0]])
idx = index.copy()
values = np.asarray(idx.values)
if isinstance(index, DatetimeIndexOpsMixin):
values[1] = iNaT
elif isinstance(index, (Int64Index, UInt64Index)):
continue
else:
values[1] = np.nan
if isinstance(index, PeriodIndex):
idx = index.__class__(values, freq=index.freq)
else:
idx = index.__class__(values)
expected = np.array([False] * len(idx), dtype=bool)
expected[1] = True
tm.assert_numpy_array_equal(idx._isnan, expected)
assert idx.hasnans is True
def test_nulls(self):
# this is really a smoke test for the methods
# as these are adequately tested for function elsewhere
for name, index in self.indices.items():
if len(index) == 0:
tm.assert_numpy_array_equal(
index.isna(), np.array([], dtype=bool))
elif isinstance(index, MultiIndex):
idx = index.copy()
msg = "isna is not defined for MultiIndex"
with pytest.raises(NotImplementedError, match=msg):
idx.isna()
else:
if not index.hasnans:
tm.assert_numpy_array_equal(
index.isna(), np.zeros(len(index), dtype=bool))
tm.assert_numpy_array_equal(
index.notna(), np.ones(len(index), dtype=bool))
else:
result = isna(index)
tm.assert_numpy_array_equal(index.isna(), result)
tm.assert_numpy_array_equal(index.notna(), ~result)
def test_empty(self):
# GH 15270
index = self.create_index()
assert not index.empty
assert index[:0].empty
def test_join_self_unique(self, join_type):
index = self.create_index()
if index.is_unique:
joined = index.join(index, how=join_type)
assert (index == joined).all()
def test_map(self):
# callable
index = self.create_index()
# we don't infer UInt64
if isinstance(index, pd.UInt64Index):
expected = index.astype('int64')
else:
expected = index
result = index.map(lambda x: x)
tm.assert_index_equal(result, expected)
@pytest.mark.parametrize(
"mapper",
[
lambda values, index: {i: e for e, i in zip(values, index)},
lambda values, index: pd.Series(values, index)])
def test_map_dictlike(self, mapper):
index = self.create_index()
if isinstance(index, (pd.CategoricalIndex, pd.IntervalIndex)):
pytest.skip("skipping tests for {}".format(type(index)))
identity = mapper(index.values, index)
# we don't infer to UInt64 for a dict
if isinstance(index, pd.UInt64Index) and isinstance(identity, dict):
expected = index.astype('int64')
else:
expected = index
result = index.map(identity)
tm.assert_index_equal(result, expected)
# empty mappable
expected = pd.Index([np.nan] * len(index))
result = index.map(mapper(expected, index))
tm.assert_index_equal(result, expected)
def test_putmask_with_wrong_mask(self):
# GH18368
index = self.create_index()
with pytest.raises(ValueError):
index.putmask(np.ones(len(index) + 1, np.bool), 1)
with pytest.raises(ValueError):
index.putmask(np.ones(len(index) - 1, np.bool), 1)
with pytest.raises(ValueError):
index.putmask('foo', 1)
@pytest.mark.parametrize('copy', [True, False])
@pytest.mark.parametrize('name', [None, 'foo'])
@pytest.mark.parametrize('ordered', [True, False])
def test_astype_category(self, copy, name, ordered):
# GH 18630
index = self.create_index()
if name:
index = index.rename(name)
# standard categories
dtype = CategoricalDtype(ordered=ordered)
result = index.astype(dtype, copy=copy)
expected = CategoricalIndex(index.values, name=name, ordered=ordered)
tm.assert_index_equal(result, expected)
# non-standard categories
dtype = CategoricalDtype(index.unique().tolist()[:-1], ordered)
result = index.astype(dtype, copy=copy)
expected = CategoricalIndex(index.values, name=name, dtype=dtype)
tm.assert_index_equal(result, expected)
if ordered is False:
# dtype='category' defaults to ordered=False, so only test once
result = index.astype('category', copy=copy)
expected = CategoricalIndex(index.values, name=name)
tm.assert_index_equal(result, expected)
def test_is_unique(self):
# initialize a unique index
index = self.create_index().drop_duplicates()
assert index.is_unique is True
# empty index should be unique
index_empty = index[:0]
assert index_empty.is_unique is True
# test basic dupes
index_dup = index.insert(0, index[0])
assert index_dup.is_unique is False
# single NA should be unique
index_na = index.insert(0, np.nan)
assert index_na.is_unique is True
# multiple NA should not be unique
index_na_dup = index_na.insert(0, np.nan)
assert index_na_dup.is_unique is False
|
from copy import deepcopy
from distutils.version import LooseVersion
from operator import methodcaller
import numpy as np
import pytest
import pandas.util._test_decorators as td
import pandas as pd
from pandas import DataFrame, MultiIndex, Series, date_range
import pandas.util.testing as tm
from pandas.util.testing import (
assert_almost_equal, assert_frame_equal, assert_series_equal)
from .test_generic import Generic
try:
import xarray
_XARRAY_INSTALLED = True
except ImportError:
_XARRAY_INSTALLED = False
class TestDataFrame(Generic):
_typ = DataFrame
_comparator = lambda self, x, y: assert_frame_equal(x, y)
def test_rename_mi(self):
df = DataFrame([
11, 21, 31
], index=MultiIndex.from_tuples([("A", x) for x in ["a", "B", "c"]]))
df.rename(str.lower)
def test_set_axis_name(self):
df = pd.DataFrame([[1, 2], [3, 4]])
funcs = ['_set_axis_name', 'rename_axis']
for func in funcs:
result = methodcaller(func, 'foo')(df)
assert df.index.name is None
assert result.index.name == 'foo'
result = methodcaller(func, 'cols', axis=1)(df)
assert df.columns.name is None
assert result.columns.name == 'cols'
def test_set_axis_name_mi(self):
df = DataFrame(
np.empty((3, 3)),
index=MultiIndex.from_tuples([("A", x) for x in list('aBc')]),
columns=MultiIndex.from_tuples([('C', x) for x in list('xyz')])
)
level_names = ['L1', 'L2']
funcs = ['_set_axis_name', 'rename_axis']
for func in funcs:
result = methodcaller(func, level_names)(df)
assert result.index.names == level_names
assert result.columns.names == [None, None]
result = methodcaller(func, level_names, axis=1)(df)
assert result.columns.names == ["L1", "L2"]
assert result.index.names == [None, None]
def test_nonzero_single_element(self):
# allow single item via bool method
df = DataFrame([[True]])
assert df.bool()
df = DataFrame([[False]])
assert not df.bool()
df = DataFrame([[False, False]])
with pytest.raises(ValueError):
df.bool()
with pytest.raises(ValueError):
bool(df)
def test_get_numeric_data_preserve_dtype(self):
# get the numeric data
o = DataFrame({'A': [1, '2', 3.]})
result = o._get_numeric_data()
expected = DataFrame(index=[0, 1, 2], dtype=object)
self._compare(result, expected)
def test_metadata_propagation_indiv(self):
# groupby
df = DataFrame(
{'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'],
'B': ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three'],
'C': np.random.randn(8),
'D': np.random.randn(8)})
result = df.groupby('A').sum()
self.check_metadata(df, result)
# resample
df = DataFrame(np.random.randn(1000, 2),
index=date_range('20130101', periods=1000, freq='s'))
result = df.resample('1T')
self.check_metadata(df, result)
# merging with override
# GH 6923
_metadata = DataFrame._metadata
_finalize = DataFrame.__finalize__
np.random.seed(10)
df1 = DataFrame(np.random.randint(0, 4, (3, 2)), columns=['a', 'b'])
df2 = DataFrame(np.random.randint(0, 4, (3, 2)), columns=['c', 'd'])
DataFrame._metadata = ['filename']
df1.filename = 'fname1.csv'
df2.filename = 'fname2.csv'
def finalize(self, other, method=None, **kwargs):
for name in self._metadata:
if method == 'merge':
left, right = other.left, other.right
value = getattr(left, name, '') + '|' + getattr(right,
name, '')
object.__setattr__(self, name, value)
else:
object.__setattr__(self, name, getattr(other, name, ''))
return self
DataFrame.__finalize__ = finalize
result = df1.merge(df2, left_on=['a'], right_on=['c'], how='inner')
assert result.filename == 'fname1.csv|fname2.csv'
# concat
# GH 6927
DataFrame._metadata = ['filename']
df1 = DataFrame(np.random.randint(0, 4, (3, 2)), columns=list('ab'))
df1.filename = 'foo'
def finalize(self, other, method=None, **kwargs):
for name in self._metadata:
if method == 'concat':
value = '+'.join([getattr(
o, name) for o in other.objs if getattr(o, name, None)
])
object.__setattr__(self, name, value)
else:
object.__setattr__(self, name, getattr(other, name, None))
return self
DataFrame.__finalize__ = finalize
result = pd.concat([df1, df1])
assert result.filename == 'foo+foo'
# reset
DataFrame._metadata = _metadata
DataFrame.__finalize__ = _finalize
def test_set_attribute(self):
# Test for consistent setattr behavior when an attribute and a column
# have the same name (Issue #8994)
df = DataFrame({'x': [1, 2, 3]})
df.y = 2
df['y'] = [2, 4, 6]
df.y = 5
assert df.y == 5
assert_series_equal(df['y'], Series([2, 4, 6], name='y'))
@pytest.mark.skipif(not _XARRAY_INSTALLED or _XARRAY_INSTALLED and
LooseVersion(xarray.__version__) <
LooseVersion('0.10.0'),
reason='xarray >= 0.10.0 required')
@pytest.mark.parametrize(
"index", ['FloatIndex', 'IntIndex',
'StringIndex', 'UnicodeIndex',
'DateIndex', 'PeriodIndex',
'CategoricalIndex', 'TimedeltaIndex'])
def test_to_xarray_index_types(self, index):
from xarray import Dataset
index = getattr(tm, 'make{}'.format(index))
df = DataFrame({'a': list('abc'),
'b': list(range(1, 4)),
'c': np.arange(3, 6).astype('u1'),
'd': np.arange(4.0, 7.0, dtype='float64'),
'e': [True, False, True],
'f': pd.Categorical(list('abc')),
'g': pd.date_range('20130101', periods=3),
'h': pd.date_range('20130101',
periods=3,
tz='US/Eastern')}
)
df.index = index(3)
df.index.name = 'foo'
df.columns.name = 'bar'
result = df.to_xarray()
assert result.dims['foo'] == 3
assert len(result.coords) == 1
assert len(result.data_vars) == 8
assert_almost_equal(list(result.coords.keys()), ['foo'])
assert isinstance(result, Dataset)
# idempotency
# categoricals are not preserved
# datetimes w/tz are not preserved
# column names are lost
expected = df.copy()
expected['f'] = expected['f'].astype(object)
expected['h'] = expected['h'].astype('datetime64[ns]')
expected.columns.name = None
assert_frame_equal(result.to_dataframe(), expected,
check_index_type=False, check_categorical=False)
@td.skip_if_no('xarray', min_version='0.7.0')
def test_to_xarray(self):
from xarray import Dataset
df = DataFrame({'a': list('abc'),
'b': list(range(1, 4)),
'c': np.arange(3, 6).astype('u1'),
'd': np.arange(4.0, 7.0, dtype='float64'),
'e': [True, False, True],
'f': pd.Categorical(list('abc')),
'g': pd.date_range('20130101', periods=3),
'h': pd.date_range('20130101',
periods=3,
tz='US/Eastern')}
)
df.index.name = 'foo'
result = df[0:0].to_xarray()
assert result.dims['foo'] == 0
assert isinstance(result, Dataset)
# available in 0.7.1
# MultiIndex
df.index = pd.MultiIndex.from_product([['a'], range(3)],
names=['one', 'two'])
result = df.to_xarray()
assert result.dims['one'] == 1
assert result.dims['two'] == 3
assert len(result.coords) == 2
assert len(result.data_vars) == 8
assert_almost_equal(list(result.coords.keys()), ['one', 'two'])
assert isinstance(result, Dataset)
result = result.to_dataframe()
expected = df.copy()
expected['f'] = expected['f'].astype(object)
expected['h'] = expected['h'].astype('datetime64[ns]')
expected.columns.name = None
assert_frame_equal(result,
expected,
check_index_type=False)
def test_deepcopy_empty(self):
# This test covers empty frame copying with non-empty column sets
# as reported in issue GH15370
empty_frame = DataFrame(data=[], index=[], columns=['A'])
empty_frame_copy = deepcopy(empty_frame)
self._compare(empty_frame_copy, empty_frame)
|
|
pandas/tests/generic/test_frame.py
|
pandas/tests/indexes/common.py
|
import numpy as np
from pandas._libs import algos as libalgos, index as libindex
import pandas.util.testing as tm
class TestNumericEngine:
def test_is_monotonic(self, numeric_indexing_engine_type_and_dtype):
engine_type, dtype = numeric_indexing_engine_type_and_dtype
num = 1000
arr = np.array([1] * num + [2] * num + [3] * num, dtype=dtype)
# monotonic increasing
engine = engine_type(lambda: arr, len(arr))
assert engine.is_monotonic_increasing is True
assert engine.is_monotonic_decreasing is False
# monotonic decreasing
engine = engine_type(lambda: arr[::-1], len(arr))
assert engine.is_monotonic_increasing is False
assert engine.is_monotonic_decreasing is True
# neither monotonic increasing or decreasing
arr = np.array([1] * num + [2] * num + [1] * num, dtype=dtype)
engine = engine_type(lambda: arr[::-1], len(arr))
assert engine.is_monotonic_increasing is False
assert engine.is_monotonic_decreasing is False
def test_is_unique(self, numeric_indexing_engine_type_and_dtype):
engine_type, dtype = numeric_indexing_engine_type_and_dtype
# unique
arr = np.array([1, 3, 2], dtype=dtype)
engine = engine_type(lambda: arr, len(arr))
assert engine.is_unique is True
# not unique
arr = np.array([1, 2, 1], dtype=dtype)
engine = engine_type(lambda: arr, len(arr))
assert engine.is_unique is False
def test_get_loc(self, numeric_indexing_engine_type_and_dtype):
engine_type, dtype = numeric_indexing_engine_type_and_dtype
# unique
arr = np.array([1, 2, 3], dtype=dtype)
engine = engine_type(lambda: arr, len(arr))
assert engine.get_loc(2) == 1
# monotonic
num = 1000
arr = np.array([1] * num + [2] * num + [3] * num, dtype=dtype)
engine = engine_type(lambda: arr, len(arr))
assert engine.get_loc(2) == slice(1000, 2000)
# not monotonic
arr = np.array([1, 2, 3] * num, dtype=dtype)
engine = engine_type(lambda: arr, len(arr))
expected = np.array([False, True, False] * num, dtype=bool)
result = engine.get_loc(2)
assert (result == expected).all()
def test_get_backfill_indexer(
self, numeric_indexing_engine_type_and_dtype):
engine_type, dtype = numeric_indexing_engine_type_and_dtype
arr = np.array([1, 5, 10], dtype=dtype)
engine = engine_type(lambda: arr, len(arr))
new = np.arange(12, dtype=dtype)
result = engine.get_backfill_indexer(new)
expected = libalgos.backfill(arr, new)
tm.assert_numpy_array_equal(result, expected)
def test_get_pad_indexer(
self, numeric_indexing_engine_type_and_dtype):
engine_type, dtype = numeric_indexing_engine_type_and_dtype
arr = np.array([1, 5, 10], dtype=dtype)
engine = engine_type(lambda: arr, len(arr))
new = np.arange(12, dtype=dtype)
result = engine.get_pad_indexer(new)
expected = libalgos.pad(arr, new)
tm.assert_numpy_array_equal(result, expected)
class TestObjectEngine:
engine_type = libindex.ObjectEngine
dtype = np.object_
values = list('abc')
def test_is_monotonic(self):
num = 1000
arr = np.array(['a'] * num + ['a'] * num + ['c'] * num,
dtype=self.dtype)
# monotonic increasing
engine = self.engine_type(lambda: arr, len(arr))
assert engine.is_monotonic_increasing is True
assert engine.is_monotonic_decreasing is False
# monotonic decreasing
engine = self.engine_type(lambda: arr[::-1], len(arr))
assert engine.is_monotonic_increasing is False
assert engine.is_monotonic_decreasing is True
# neither monotonic increasing or decreasing
arr = np.array(['a'] * num + ['b'] * num + ['a'] * num,
dtype=self.dtype)
engine = self.engine_type(lambda: arr[::-1], len(arr))
assert engine.is_monotonic_increasing is False
assert engine.is_monotonic_decreasing is False
def test_is_unique(self):
# unique
arr = np.array(self.values, dtype=self.dtype)
engine = self.engine_type(lambda: arr, len(arr))
assert engine.is_unique is True
# not unique
arr = np.array(['a', 'b', 'a'], dtype=self.dtype)
engine = self.engine_type(lambda: arr, len(arr))
assert engine.is_unique is False
def test_get_loc(self):
# unique
arr = np.array(self.values, dtype=self.dtype)
engine = self.engine_type(lambda: arr, len(arr))
assert engine.get_loc('b') == 1
# monotonic
num = 1000
arr = np.array(['a'] * num + ['b'] * num + ['c'] * num,
dtype=self.dtype)
engine = self.engine_type(lambda: arr, len(arr))
assert engine.get_loc('b') == slice(1000, 2000)
# not monotonic
arr = np.array(self.values * num, dtype=self.dtype)
engine = self.engine_type(lambda: arr, len(arr))
expected = np.array([False, True, False] * num, dtype=bool)
result = engine.get_loc('b')
assert (result == expected).all()
def test_get_backfill_indexer(self):
arr = np.array(['a', 'e', 'j'], dtype=self.dtype)
engine = self.engine_type(lambda: arr, len(arr))
new = np.array(list('abcdefghij'), dtype=self.dtype)
result = engine.get_backfill_indexer(new)
expected = libalgos.backfill["object"](arr, new)
tm.assert_numpy_array_equal(result, expected)
def test_get_pad_indexer(self):
arr = np.array(['a', 'e', 'j'], dtype=self.dtype)
engine = self.engine_type(lambda: arr, len(arr))
new = np.array(list('abcdefghij'), dtype=self.dtype)
result = engine.get_pad_indexer(new)
expected = libalgos.pad["object"](arr, new)
tm.assert_numpy_array_equal(result, expected)
|
from copy import deepcopy
from distutils.version import LooseVersion
from operator import methodcaller
import numpy as np
import pytest
import pandas.util._test_decorators as td
import pandas as pd
from pandas import DataFrame, MultiIndex, Series, date_range
import pandas.util.testing as tm
from pandas.util.testing import (
assert_almost_equal, assert_frame_equal, assert_series_equal)
from .test_generic import Generic
try:
import xarray
_XARRAY_INSTALLED = True
except ImportError:
_XARRAY_INSTALLED = False
class TestDataFrame(Generic):
_typ = DataFrame
_comparator = lambda self, x, y: assert_frame_equal(x, y)
def test_rename_mi(self):
df = DataFrame([
11, 21, 31
], index=MultiIndex.from_tuples([("A", x) for x in ["a", "B", "c"]]))
df.rename(str.lower)
def test_set_axis_name(self):
df = pd.DataFrame([[1, 2], [3, 4]])
funcs = ['_set_axis_name', 'rename_axis']
for func in funcs:
result = methodcaller(func, 'foo')(df)
assert df.index.name is None
assert result.index.name == 'foo'
result = methodcaller(func, 'cols', axis=1)(df)
assert df.columns.name is None
assert result.columns.name == 'cols'
def test_set_axis_name_mi(self):
df = DataFrame(
np.empty((3, 3)),
index=MultiIndex.from_tuples([("A", x) for x in list('aBc')]),
columns=MultiIndex.from_tuples([('C', x) for x in list('xyz')])
)
level_names = ['L1', 'L2']
funcs = ['_set_axis_name', 'rename_axis']
for func in funcs:
result = methodcaller(func, level_names)(df)
assert result.index.names == level_names
assert result.columns.names == [None, None]
result = methodcaller(func, level_names, axis=1)(df)
assert result.columns.names == ["L1", "L2"]
assert result.index.names == [None, None]
def test_nonzero_single_element(self):
# allow single item via bool method
df = DataFrame([[True]])
assert df.bool()
df = DataFrame([[False]])
assert not df.bool()
df = DataFrame([[False, False]])
with pytest.raises(ValueError):
df.bool()
with pytest.raises(ValueError):
bool(df)
def test_get_numeric_data_preserve_dtype(self):
# get the numeric data
o = DataFrame({'A': [1, '2', 3.]})
result = o._get_numeric_data()
expected = DataFrame(index=[0, 1, 2], dtype=object)
self._compare(result, expected)
def test_metadata_propagation_indiv(self):
# groupby
df = DataFrame(
{'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'],
'B': ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three'],
'C': np.random.randn(8),
'D': np.random.randn(8)})
result = df.groupby('A').sum()
self.check_metadata(df, result)
# resample
df = DataFrame(np.random.randn(1000, 2),
index=date_range('20130101', periods=1000, freq='s'))
result = df.resample('1T')
self.check_metadata(df, result)
# merging with override
# GH 6923
_metadata = DataFrame._metadata
_finalize = DataFrame.__finalize__
np.random.seed(10)
df1 = DataFrame(np.random.randint(0, 4, (3, 2)), columns=['a', 'b'])
df2 = DataFrame(np.random.randint(0, 4, (3, 2)), columns=['c', 'd'])
DataFrame._metadata = ['filename']
df1.filename = 'fname1.csv'
df2.filename = 'fname2.csv'
def finalize(self, other, method=None, **kwargs):
for name in self._metadata:
if method == 'merge':
left, right = other.left, other.right
value = getattr(left, name, '') + '|' + getattr(right,
name, '')
object.__setattr__(self, name, value)
else:
object.__setattr__(self, name, getattr(other, name, ''))
return self
DataFrame.__finalize__ = finalize
result = df1.merge(df2, left_on=['a'], right_on=['c'], how='inner')
assert result.filename == 'fname1.csv|fname2.csv'
# concat
# GH 6927
DataFrame._metadata = ['filename']
df1 = DataFrame(np.random.randint(0, 4, (3, 2)), columns=list('ab'))
df1.filename = 'foo'
def finalize(self, other, method=None, **kwargs):
for name in self._metadata:
if method == 'concat':
value = '+'.join([getattr(
o, name) for o in other.objs if getattr(o, name, None)
])
object.__setattr__(self, name, value)
else:
object.__setattr__(self, name, getattr(other, name, None))
return self
DataFrame.__finalize__ = finalize
result = pd.concat([df1, df1])
assert result.filename == 'foo+foo'
# reset
DataFrame._metadata = _metadata
DataFrame.__finalize__ = _finalize
def test_set_attribute(self):
# Test for consistent setattr behavior when an attribute and a column
# have the same name (Issue #8994)
df = DataFrame({'x': [1, 2, 3]})
df.y = 2
df['y'] = [2, 4, 6]
df.y = 5
assert df.y == 5
assert_series_equal(df['y'], Series([2, 4, 6], name='y'))
@pytest.mark.skipif(not _XARRAY_INSTALLED or _XARRAY_INSTALLED and
LooseVersion(xarray.__version__) <
LooseVersion('0.10.0'),
reason='xarray >= 0.10.0 required')
@pytest.mark.parametrize(
"index", ['FloatIndex', 'IntIndex',
'StringIndex', 'UnicodeIndex',
'DateIndex', 'PeriodIndex',
'CategoricalIndex', 'TimedeltaIndex'])
def test_to_xarray_index_types(self, index):
from xarray import Dataset
index = getattr(tm, 'make{}'.format(index))
df = DataFrame({'a': list('abc'),
'b': list(range(1, 4)),
'c': np.arange(3, 6).astype('u1'),
'd': np.arange(4.0, 7.0, dtype='float64'),
'e': [True, False, True],
'f': pd.Categorical(list('abc')),
'g': pd.date_range('20130101', periods=3),
'h': pd.date_range('20130101',
periods=3,
tz='US/Eastern')}
)
df.index = index(3)
df.index.name = 'foo'
df.columns.name = 'bar'
result = df.to_xarray()
assert result.dims['foo'] == 3
assert len(result.coords) == 1
assert len(result.data_vars) == 8
assert_almost_equal(list(result.coords.keys()), ['foo'])
assert isinstance(result, Dataset)
# idempotency
# categoricals are not preserved
# datetimes w/tz are not preserved
# column names are lost
expected = df.copy()
expected['f'] = expected['f'].astype(object)
expected['h'] = expected['h'].astype('datetime64[ns]')
expected.columns.name = None
assert_frame_equal(result.to_dataframe(), expected,
check_index_type=False, check_categorical=False)
@td.skip_if_no('xarray', min_version='0.7.0')
def test_to_xarray(self):
from xarray import Dataset
df = DataFrame({'a': list('abc'),
'b': list(range(1, 4)),
'c': np.arange(3, 6).astype('u1'),
'd': np.arange(4.0, 7.0, dtype='float64'),
'e': [True, False, True],
'f': pd.Categorical(list('abc')),
'g': pd.date_range('20130101', periods=3),
'h': pd.date_range('20130101',
periods=3,
tz='US/Eastern')}
)
df.index.name = 'foo'
result = df[0:0].to_xarray()
assert result.dims['foo'] == 0
assert isinstance(result, Dataset)
# available in 0.7.1
# MultiIndex
df.index = pd.MultiIndex.from_product([['a'], range(3)],
names=['one', 'two'])
result = df.to_xarray()
assert result.dims['one'] == 1
assert result.dims['two'] == 3
assert len(result.coords) == 2
assert len(result.data_vars) == 8
assert_almost_equal(list(result.coords.keys()), ['one', 'two'])
assert isinstance(result, Dataset)
result = result.to_dataframe()
expected = df.copy()
expected['f'] = expected['f'].astype(object)
expected['h'] = expected['h'].astype('datetime64[ns]')
expected.columns.name = None
assert_frame_equal(result,
expected,
check_index_type=False)
def test_deepcopy_empty(self):
# This test covers empty frame copying with non-empty column sets
# as reported in issue GH15370
empty_frame = DataFrame(data=[], index=[], columns=['A'])
empty_frame_copy = deepcopy(empty_frame)
self._compare(empty_frame_copy, empty_frame)
|
|
pandas/tests/generic/test_frame.py
|
pandas/tests/indexing/test_indexing_engines.py
|
"""
Arithmetic operations for PandasObjects
This is not a public API.
"""
import datetime
import operator
import textwrap
from typing import Dict, Optional
import warnings
import numpy as np
from pandas._libs import algos as libalgos, lib, ops as libops
from pandas.errors import NullFrequencyError
from pandas.util._decorators import Appender
from pandas.core.dtypes.cast import (
construct_1d_object_array_from_listlike, find_common_type,
maybe_upcast_putmask)
from pandas.core.dtypes.common import (
ensure_object, is_bool_dtype, is_categorical_dtype, is_datetime64_dtype,
is_datetime64tz_dtype, is_datetimelike_v_numeric, is_extension_array_dtype,
is_integer_dtype, is_list_like, is_object_dtype, is_period_dtype,
is_scalar, is_timedelta64_dtype, needs_i8_conversion)
from pandas.core.dtypes.generic import (
ABCDataFrame, ABCIndex, ABCIndexClass, ABCSeries, ABCSparseArray,
ABCSparseSeries)
from pandas.core.dtypes.missing import isna, notna
import pandas as pd
import pandas.core.common as com
import pandas.core.missing as missing
# -----------------------------------------------------------------------------
# Ops Wrapping Utilities
def get_op_result_name(left, right):
"""
Find the appropriate name to pin to an operation result. This result
should always be either an Index or a Series.
Parameters
----------
left : {Series, Index}
right : object
Returns
-------
name : object
Usually a string
"""
# `left` is always a pd.Series when called from within ops
if isinstance(right, (ABCSeries, pd.Index)):
name = _maybe_match_name(left, right)
else:
name = left.name
return name
def _maybe_match_name(a, b):
"""
Try to find a name to attach to the result of an operation between
a and b. If only one of these has a `name` attribute, return that
name. Otherwise return a consensus name if they match of None if
they have different names.
Parameters
----------
a : object
b : object
Returns
-------
name : str or None
See Also
--------
pandas.core.common.consensus_name_attr
"""
a_has = hasattr(a, 'name')
b_has = hasattr(b, 'name')
if a_has and b_has:
if a.name == b.name:
return a.name
else:
# TODO: what if they both have np.nan for their names?
return None
elif a_has:
return a.name
elif b_has:
return b.name
return None
def maybe_upcast_for_op(obj):
"""
Cast non-pandas objects to pandas types to unify behavior of arithmetic
and comparison operations.
Parameters
----------
obj: object
Returns
-------
out : object
Notes
-----
Be careful to call this *after* determining the `name` attribute to be
attached to the result of the arithmetic operation.
"""
if type(obj) is datetime.timedelta:
# GH#22390 cast up to Timedelta to rely on Timedelta
# implementation; otherwise operation against numeric-dtype
# raises TypeError
return pd.Timedelta(obj)
elif isinstance(obj, np.timedelta64) and not isna(obj):
# In particular non-nanosecond timedelta64 needs to be cast to
# nanoseconds, or else we get undesired behavior like
# np.timedelta64(3, 'D') / 2 == np.timedelta64(1, 'D')
# The isna check is to avoid casting timedelta64("NaT"), which would
# return NaT and incorrectly be treated as a datetime-NaT.
return pd.Timedelta(obj)
elif isinstance(obj, np.ndarray) and is_timedelta64_dtype(obj):
# GH#22390 Unfortunately we need to special-case right-hand
# timedelta64 dtypes because numpy casts integer dtypes to
# timedelta64 when operating with timedelta64
return pd.TimedeltaIndex(obj)
return obj
# -----------------------------------------------------------------------------
# Reversed Operations not available in the stdlib operator module.
# Defining these instead of using lambdas allows us to reference them by name.
def radd(left, right):
return right + left
def rsub(left, right):
return right - left
def rmul(left, right):
return right * left
def rdiv(left, right):
return right / left
def rtruediv(left, right):
return right / left
def rfloordiv(left, right):
return right // left
def rmod(left, right):
# check if right is a string as % is the string
# formatting operation; this is a TypeError
# otherwise perform the op
if isinstance(right, str):
raise TypeError("{typ} cannot perform the operation mod".format(
typ=type(left).__name__))
return right % left
def rdivmod(left, right):
return divmod(right, left)
def rpow(left, right):
return right ** left
def rand_(left, right):
return operator.and_(right, left)
def ror_(left, right):
return operator.or_(right, left)
def rxor(left, right):
return operator.xor(right, left)
# -----------------------------------------------------------------------------
def make_invalid_op(name):
"""
Return a binary method that always raises a TypeError.
Parameters
----------
name : str
Returns
-------
invalid_op : function
"""
def invalid_op(self, other=None):
raise TypeError("cannot perform {name} with this index type: "
"{typ}".format(name=name, typ=type(self).__name__))
invalid_op.__name__ = name
return invalid_op
def _gen_eval_kwargs(name):
"""
Find the keyword arguments to pass to numexpr for the given operation.
Parameters
----------
name : str
Returns
-------
eval_kwargs : dict
Examples
--------
>>> _gen_eval_kwargs("__add__")
{}
>>> _gen_eval_kwargs("rtruediv")
{'reversed': True, 'truediv': True}
"""
kwargs = {}
# Series appear to only pass __add__, __radd__, ...
# but DataFrame gets both these dunder names _and_ non-dunder names
# add, radd, ...
name = name.replace('__', '')
if name.startswith('r'):
if name not in ['radd', 'rand', 'ror', 'rxor']:
# Exclude commutative operations
kwargs['reversed'] = True
if name in ['truediv', 'rtruediv']:
kwargs['truediv'] = True
if name in ['ne']:
kwargs['masker'] = True
return kwargs
def _gen_fill_zeros(name):
"""
Find the appropriate fill value to use when filling in undefined values
in the results of the given operation caused by operating on
(generally dividing by) zero.
Parameters
----------
name : str
Returns
-------
fill_value : {None, np.nan, np.inf}
"""
name = name.strip('__')
if 'div' in name:
# truediv, floordiv, div, and reversed variants
fill_value = np.inf
elif 'mod' in name:
# mod, rmod
fill_value = np.nan
else:
fill_value = None
return fill_value
def _get_frame_op_default_axis(name):
"""
Only DataFrame cares about default_axis, specifically:
special methods have default_axis=None and flex methods
have default_axis='columns'.
Parameters
----------
name : str
Returns
-------
default_axis: str or None
"""
if name.replace('__r', '__') in ['__and__', '__or__', '__xor__']:
# bool methods
return 'columns'
elif name.startswith('__'):
# __add__, __mul__, ...
return None
else:
# add, mul, ...
return 'columns'
def _get_opstr(op, cls):
"""
Find the operation string, if any, to pass to numexpr for this
operation.
Parameters
----------
op : binary operator
cls : class
Returns
-------
op_str : string or None
"""
# numexpr is available for non-sparse classes
subtyp = getattr(cls, '_subtyp', '')
use_numexpr = 'sparse' not in subtyp
if not use_numexpr:
# if we're not using numexpr, then don't pass a str_rep
return None
return {operator.add: '+',
radd: '+',
operator.mul: '*',
rmul: '*',
operator.sub: '-',
rsub: '-',
operator.truediv: '/',
rtruediv: '/',
operator.floordiv: '//',
rfloordiv: '//',
operator.mod: None, # TODO: Why None for mod but '%' for rmod?
rmod: '%',
operator.pow: '**',
rpow: '**',
operator.eq: '==',
operator.ne: '!=',
operator.le: '<=',
operator.lt: '<',
operator.ge: '>=',
operator.gt: '>',
operator.and_: '&',
rand_: '&',
operator.or_: '|',
ror_: '|',
operator.xor: '^',
rxor: '^',
divmod: None,
rdivmod: None}[op]
def _get_op_name(op, special):
"""
Find the name to attach to this method according to conventions
for special and non-special methods.
Parameters
----------
op : binary operator
special : bool
Returns
-------
op_name : str
"""
opname = op.__name__.strip('_')
if special:
opname = '__{opname}__'.format(opname=opname)
return opname
# -----------------------------------------------------------------------------
# Docstring Generation and Templates
_add_example_SERIES = """
Examples
--------
>>> a = pd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a 1.0
b 1.0
c 1.0
d NaN
dtype: float64
>>> b = pd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a 1.0
b NaN
d 1.0
e NaN
dtype: float64
>>> a.add(b, fill_value=0)
a 2.0
b 1.0
c 1.0
d 1.0
e NaN
dtype: float64
"""
_sub_example_SERIES = """
Examples
--------
>>> a = pd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a 1.0
b 1.0
c 1.0
d NaN
dtype: float64
>>> b = pd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a 1.0
b NaN
d 1.0
e NaN
dtype: float64
>>> a.subtract(b, fill_value=0)
a 0.0
b 1.0
c 1.0
d -1.0
e NaN
dtype: float64
"""
_mul_example_SERIES = """
Examples
--------
>>> a = pd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a 1.0
b 1.0
c 1.0
d NaN
dtype: float64
>>> b = pd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a 1.0
b NaN
d 1.0
e NaN
dtype: float64
>>> a.multiply(b, fill_value=0)
a 1.0
b 0.0
c 0.0
d 0.0
e NaN
dtype: float64
"""
_div_example_SERIES = """
Examples
--------
>>> a = pd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a 1.0
b 1.0
c 1.0
d NaN
dtype: float64
>>> b = pd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a 1.0
b NaN
d 1.0
e NaN
dtype: float64
>>> a.divide(b, fill_value=0)
a 1.0
b inf
c inf
d 0.0
e NaN
dtype: float64
"""
_floordiv_example_SERIES = """
Examples
--------
>>> a = pd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a 1.0
b 1.0
c 1.0
d NaN
dtype: float64
>>> b = pd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a 1.0
b NaN
d 1.0
e NaN
dtype: float64
>>> a.floordiv(b, fill_value=0)
a 1.0
b NaN
c NaN
d 0.0
e NaN
dtype: float64
"""
_mod_example_SERIES = """
Examples
--------
>>> a = pd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a 1.0
b 1.0
c 1.0
d NaN
dtype: float64
>>> b = pd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a 1.0
b NaN
d 1.0
e NaN
dtype: float64
>>> a.mod(b, fill_value=0)
a 0.0
b NaN
c NaN
d 0.0
e NaN
dtype: float64
"""
_pow_example_SERIES = """
Examples
--------
>>> a = pd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a 1.0
b 1.0
c 1.0
d NaN
dtype: float64
>>> b = pd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a 1.0
b NaN
d 1.0
e NaN
dtype: float64
>>> a.pow(b, fill_value=0)
a 1.0
b 1.0
c 1.0
d 0.0
e NaN
dtype: float64
"""
_op_descriptions = {
# Arithmetic Operators
'add': {'op': '+',
'desc': 'Addition',
'reverse': 'radd',
'series_examples': _add_example_SERIES},
'sub': {'op': '-',
'desc': 'Subtraction',
'reverse': 'rsub',
'series_examples': _sub_example_SERIES},
'mul': {'op': '*',
'desc': 'Multiplication',
'reverse': 'rmul',
'series_examples': _mul_example_SERIES,
'df_examples': None},
'mod': {'op': '%',
'desc': 'Modulo',
'reverse': 'rmod',
'series_examples': _mod_example_SERIES},
'pow': {'op': '**',
'desc': 'Exponential power',
'reverse': 'rpow',
'series_examples': _pow_example_SERIES,
'df_examples': None},
'truediv': {'op': '/',
'desc': 'Floating division',
'reverse': 'rtruediv',
'series_examples': _div_example_SERIES,
'df_examples': None},
'floordiv': {'op': '//',
'desc': 'Integer division',
'reverse': 'rfloordiv',
'series_examples': _floordiv_example_SERIES,
'df_examples': None},
'divmod': {'op': 'divmod',
'desc': 'Integer division and modulo',
'reverse': 'rdivmod',
'series_examples': None,
'df_examples': None},
# Comparison Operators
'eq': {'op': '==',
'desc': 'Equal to',
'reverse': None,
'series_examples': None},
'ne': {'op': '!=',
'desc': 'Not equal to',
'reverse': None,
'series_examples': None},
'lt': {'op': '<',
'desc': 'Less than',
'reverse': None,
'series_examples': None},
'le': {'op': '<=',
'desc': 'Less than or equal to',
'reverse': None,
'series_examples': None},
'gt': {'op': '>',
'desc': 'Greater than',
'reverse': None,
'series_examples': None},
'ge': {'op': '>=',
'desc': 'Greater than or equal to',
'reverse': None,
'series_examples': None}
} # type: Dict[str, Dict[str, Optional[str]]]
_op_names = list(_op_descriptions.keys())
for key in _op_names:
reverse_op = _op_descriptions[key]['reverse']
if reverse_op is not None:
_op_descriptions[reverse_op] = _op_descriptions[key].copy()
_op_descriptions[reverse_op]['reverse'] = key
_flex_doc_SERIES = """
Return {desc} of series and other, element-wise (binary operator `{op_name}`).
Equivalent to ``{equiv}``, but with support to substitute a fill_value for
missing data in one of the inputs.
Parameters
----------
other : Series or scalar value
fill_value : None or float value, default None (NaN)
Fill existing missing (NaN) values, and any new element needed for
successful Series alignment, with this value before computation.
If data in both corresponding Series locations is missing
the result will be missing.
level : int or name
Broadcast across a level, matching Index values on the
passed MultiIndex level.
Returns
-------
Series
The result of the operation.
See Also
--------
Series.{reverse}
"""
_arith_doc_FRAME = """
Binary operator %s with support to substitute a fill_value for missing data in
one of the inputs
Parameters
----------
other : Series, DataFrame, or constant
axis : {0, 1, 'index', 'columns'}
For Series input, axis to match Series index on
fill_value : None or float value, default None
Fill existing missing (NaN) values, and any new element needed for
successful DataFrame alignment, with this value before computation.
If data in both corresponding DataFrame locations is missing
the result will be missing
level : int or name
Broadcast across a level, matching Index values on the
passed MultiIndex level
Returns
-------
result : DataFrame
Notes
-----
Mismatched indices will be unioned together
"""
_flex_doc_FRAME = """
Get {desc} of dataframe and other, element-wise (binary operator `{op_name}`).
Equivalent to ``{equiv}``, but with support to substitute a fill_value
for missing data in one of the inputs. With reverse version, `{reverse}`.
Among flexible wrappers (`add`, `sub`, `mul`, `div`, `mod`, `pow`) to
arithmetic operators: `+`, `-`, `*`, `/`, `//`, `%`, `**`.
Parameters
----------
other : scalar, sequence, Series, or DataFrame
Any single or multiple element data structure, or list-like object.
axis : {{0 or 'index', 1 or 'columns'}}
Whether to compare by the index (0 or 'index') or columns
(1 or 'columns'). For Series input, axis to match Series index on.
level : int or label
Broadcast across a level, matching Index values on the
passed MultiIndex level.
fill_value : float or None, default None
Fill existing missing (NaN) values, and any new element needed for
successful DataFrame alignment, with this value before computation.
If data in both corresponding DataFrame locations is missing
the result will be missing.
Returns
-------
DataFrame
Result of the arithmetic operation.
See Also
--------
DataFrame.add : Add DataFrames.
DataFrame.sub : Subtract DataFrames.
DataFrame.mul : Multiply DataFrames.
DataFrame.div : Divide DataFrames (float division).
DataFrame.truediv : Divide DataFrames (float division).
DataFrame.floordiv : Divide DataFrames (integer division).
DataFrame.mod : Calculate modulo (remainder after division).
DataFrame.pow : Calculate exponential power.
Notes
-----
Mismatched indices will be unioned together.
Examples
--------
>>> df = pd.DataFrame({{'angles': [0, 3, 4],
... 'degrees': [360, 180, 360]}},
... index=['circle', 'triangle', 'rectangle'])
>>> df
angles degrees
circle 0 360
triangle 3 180
rectangle 4 360
Add a scalar with operator version which return the same
results.
>>> df + 1
angles degrees
circle 1 361
triangle 4 181
rectangle 5 361
>>> df.add(1)
angles degrees
circle 1 361
triangle 4 181
rectangle 5 361
Divide by constant with reverse version.
>>> df.div(10)
angles degrees
circle 0.0 36.0
triangle 0.3 18.0
rectangle 0.4 36.0
>>> df.rdiv(10)
angles degrees
circle inf 0.027778
triangle 3.333333 0.055556
rectangle 2.500000 0.027778
Subtract a list and Series by axis with operator version.
>>> df - [1, 2]
angles degrees
circle -1 358
triangle 2 178
rectangle 3 358
>>> df.sub([1, 2], axis='columns')
angles degrees
circle -1 358
triangle 2 178
rectangle 3 358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
... axis='index')
angles degrees
circle -1 359
triangle 2 179
rectangle 3 359
Multiply a DataFrame of different shape with operator version.
>>> other = pd.DataFrame({{'angles': [0, 3, 4]}},
... index=['circle', 'triangle', 'rectangle'])
>>> other
angles
circle 0
triangle 3
rectangle 4
>>> df * other
angles degrees
circle 0 NaN
triangle 9 NaN
rectangle 16 NaN
>>> df.mul(other, fill_value=0)
angles degrees
circle 0 0.0
triangle 9 0.0
rectangle 16 0.0
Divide by a MultiIndex by level.
>>> df_multindex = pd.DataFrame({{'angles': [0, 3, 4, 4, 5, 6],
... 'degrees': [360, 180, 360, 360, 540, 720]}},
... index=[['A', 'A', 'A', 'B', 'B', 'B'],
... ['circle', 'triangle', 'rectangle',
... 'square', 'pentagon', 'hexagon']])
>>> df_multindex
angles degrees
A circle 0 360
triangle 3 180
rectangle 4 360
B square 4 360
pentagon 5 540
hexagon 6 720
>>> df.div(df_multindex, level=1, fill_value=0)
angles degrees
A circle NaN 1.0
triangle 1.0 1.0
rectangle 1.0 1.0
B square 0.0 0.0
pentagon 0.0 0.0
hexagon 0.0 0.0
"""
_flex_comp_doc_FRAME = """
Get {desc} of dataframe and other, element-wise (binary operator `{op_name}`).
Among flexible wrappers (`eq`, `ne`, `le`, `lt`, `ge`, `gt`) to comparison
operators.
Equivalent to `==`, `=!`, `<=`, `<`, `>=`, `>` with support to choose axis
(rows or columns) and level for comparison.
Parameters
----------
other : scalar, sequence, Series, or DataFrame
Any single or multiple element data structure, or list-like object.
axis : {{0 or 'index', 1 or 'columns'}}, default 'columns'
Whether to compare by the index (0 or 'index') or columns
(1 or 'columns').
level : int or label
Broadcast across a level, matching Index values on the passed
MultiIndex level.
Returns
-------
DataFrame of bool
Result of the comparison.
See Also
--------
DataFrame.eq : Compare DataFrames for equality elementwise.
DataFrame.ne : Compare DataFrames for inequality elementwise.
DataFrame.le : Compare DataFrames for less than inequality
or equality elementwise.
DataFrame.lt : Compare DataFrames for strictly less than
inequality elementwise.
DataFrame.ge : Compare DataFrames for greater than inequality
or equality elementwise.
DataFrame.gt : Compare DataFrames for strictly greater than
inequality elementwise.
Notes
-----
Mismatched indices will be unioned together.
`NaN` values are considered different (i.e. `NaN` != `NaN`).
Examples
--------
>>> df = pd.DataFrame({{'cost': [250, 150, 100],
... 'revenue': [100, 250, 300]}},
... index=['A', 'B', 'C'])
>>> df
cost revenue
A 250 100
B 150 250
C 100 300
Comparison with a scalar, using either the operator or method:
>>> df == 100
cost revenue
A False True
B False False
C True False
>>> df.eq(100)
cost revenue
A False True
B False False
C True False
When `other` is a :class:`Series`, the columns of a DataFrame are aligned
with the index of `other` and broadcast:
>>> df != pd.Series([100, 250], index=["cost", "revenue"])
cost revenue
A True True
B True False
C False True
Use the method to control the broadcast axis:
>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')
cost revenue
A True False
B True True
C True True
D True True
When comparing to an arbitrary sequence, the number of columns must
match the number elements in `other`:
>>> df == [250, 100]
cost revenue
A True True
B False False
C False False
Use the method to control the axis:
>>> df.eq([250, 250, 100], axis='index')
cost revenue
A True False
B False True
C True False
Compare to a DataFrame of different shape.
>>> other = pd.DataFrame({{'revenue': [300, 250, 100, 150]}},
... index=['A', 'B', 'C', 'D'])
>>> other
revenue
A 300
B 250
C 100
D 150
>>> df.gt(other)
cost revenue
A False False
B False False
C False True
D False False
Compare to a MultiIndex by level.
>>> df_multindex = pd.DataFrame({{'cost': [250, 150, 100, 150, 300, 220],
... 'revenue': [100, 250, 300, 200, 175, 225]}},
... index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
... ['A', 'B', 'C', 'A', 'B', 'C']])
>>> df_multindex
cost revenue
Q1 A 250 100
B 150 250
C 100 300
Q2 A 150 200
B 300 175
C 220 225
>>> df.le(df_multindex, level=1)
cost revenue
Q1 A True True
B True True
C True True
Q2 A False True
B True False
C True False
"""
def _make_flex_doc(op_name, typ):
"""
Make the appropriate substitutions for the given operation and class-typ
into either _flex_doc_SERIES or _flex_doc_FRAME to return the docstring
to attach to a generated method.
Parameters
----------
op_name : str {'__add__', '__sub__', ... '__eq__', '__ne__', ...}
typ : str {series, 'dataframe']}
Returns
-------
doc : str
"""
op_name = op_name.replace('__', '')
op_desc = _op_descriptions[op_name]
if op_name.startswith('r'):
equiv = 'other ' + op_desc['op'] + ' ' + typ
else:
equiv = typ + ' ' + op_desc['op'] + ' other'
if typ == 'series':
base_doc = _flex_doc_SERIES
doc_no_examples = base_doc.format(
desc=op_desc['desc'],
op_name=op_name,
equiv=equiv,
reverse=op_desc['reverse']
)
if op_desc['series_examples']:
doc = doc_no_examples + op_desc['series_examples']
else:
doc = doc_no_examples
elif typ == 'dataframe':
base_doc = _flex_doc_FRAME
doc = base_doc.format(
desc=op_desc['desc'],
op_name=op_name,
equiv=equiv,
reverse=op_desc['reverse']
)
else:
raise AssertionError('Invalid typ argument.')
return doc
# -----------------------------------------------------------------------------
# Masking NA values and fallbacks for operations numpy does not support
def fill_binop(left, right, fill_value):
"""
If a non-None fill_value is given, replace null entries in left and right
with this value, but only in positions where _one_ of left/right is null,
not both.
Parameters
----------
left : array-like
right : array-like
fill_value : object
Returns
-------
left : array-like
right : array-like
Notes
-----
Makes copies if fill_value is not None
"""
# TODO: can we make a no-copy implementation?
if fill_value is not None:
left_mask = isna(left)
right_mask = isna(right)
left = left.copy()
right = right.copy()
# one but not both
mask = left_mask ^ right_mask
left[left_mask & mask] = fill_value
right[right_mask & mask] = fill_value
return left, right
def mask_cmp_op(x, y, op):
"""
Apply the function `op` to only non-null points in x and y.
Parameters
----------
x : array-like
y : array-like
op : binary operation
Returns
-------
result : ndarray[bool]
"""
xrav = x.ravel()
result = np.empty(x.size, dtype=bool)
if isinstance(y, (np.ndarray, ABCSeries)):
yrav = y.ravel()
mask = notna(xrav) & notna(yrav)
result[mask] = op(np.array(list(xrav[mask])),
np.array(list(yrav[mask])))
else:
mask = notna(xrav)
result[mask] = op(np.array(list(xrav[mask])), y)
if op == operator.ne: # pragma: no cover
np.putmask(result, ~mask, True)
else:
np.putmask(result, ~mask, False)
result = result.reshape(x.shape)
return result
def masked_arith_op(x, y, op):
"""
If the given arithmetic operation fails, attempt it again on
only the non-null elements of the input array(s).
Parameters
----------
x : np.ndarray
y : np.ndarray, Series, Index
op : binary operator
"""
# For Series `x` is 1D so ravel() is a no-op; calling it anyway makes
# the logic valid for both Series and DataFrame ops.
xrav = x.ravel()
assert isinstance(x, (np.ndarray, ABCSeries)), type(x)
if isinstance(y, (np.ndarray, ABCSeries, ABCIndexClass)):
dtype = find_common_type([x.dtype, y.dtype])
result = np.empty(x.size, dtype=dtype)
# PeriodIndex.ravel() returns int64 dtype, so we have
# to work around that case. See GH#19956
yrav = y if is_period_dtype(y) else y.ravel()
mask = notna(xrav) & notna(yrav)
if yrav.shape != mask.shape:
# FIXME: GH#5284, GH#5035, GH#19448
# Without specifically raising here we get mismatched
# errors in Py3 (TypeError) vs Py2 (ValueError)
# Note: Only = an issue in DataFrame case
raise ValueError('Cannot broadcast operands together.')
if mask.any():
with np.errstate(all='ignore'):
result[mask] = op(xrav[mask],
com.values_from_object(yrav[mask]))
else:
assert is_scalar(y), type(y)
assert isinstance(x, np.ndarray), type(x)
# mask is only meaningful for x
result = np.empty(x.size, dtype=x.dtype)
mask = notna(xrav)
# 1 ** np.nan is 1. So we have to unmask those.
if op == pow:
mask = np.where(x == 1, False, mask)
elif op == rpow:
mask = np.where(y == 1, False, mask)
if mask.any():
with np.errstate(all='ignore'):
result[mask] = op(xrav[mask], y)
result, changed = maybe_upcast_putmask(result, ~mask, np.nan)
result = result.reshape(x.shape) # 2D compat
return result
def invalid_comparison(left, right, op):
"""
If a comparison has mismatched types and is not necessarily meaningful,
follow python3 conventions by:
- returning all-False for equality
- returning all-True for inequality
- raising TypeError otherwise
Parameters
----------
left : array-like
right : scalar, array-like
op : operator.{eq, ne, lt, le, gt}
Raises
------
TypeError : on inequality comparisons
"""
if op is operator.eq:
res_values = np.zeros(left.shape, dtype=bool)
elif op is operator.ne:
res_values = np.ones(left.shape, dtype=bool)
else:
raise TypeError("Invalid comparison between dtype={dtype} and {typ}"
.format(dtype=left.dtype, typ=type(right).__name__))
return res_values
# -----------------------------------------------------------------------------
# Dispatch logic
def should_series_dispatch(left, right, op):
"""
Identify cases where a DataFrame operation should dispatch to its
Series counterpart.
Parameters
----------
left : DataFrame
right : DataFrame
op : binary operator
Returns
-------
override : bool
"""
if left._is_mixed_type or right._is_mixed_type:
return True
if not len(left.columns) or not len(right.columns):
# ensure obj.dtypes[0] exists for each obj
return False
ldtype = left.dtypes.iloc[0]
rdtype = right.dtypes.iloc[0]
if ((is_timedelta64_dtype(ldtype) and is_integer_dtype(rdtype)) or
(is_timedelta64_dtype(rdtype) and is_integer_dtype(ldtype))):
# numpy integer dtypes as timedelta64 dtypes in this scenario
return True
if is_datetime64_dtype(ldtype) and is_object_dtype(rdtype):
# in particular case where right is an array of DateOffsets
return True
return False
def dispatch_to_series(left, right, func, str_rep=None, axis=None):
"""
Evaluate the frame operation func(left, right) by evaluating
column-by-column, dispatching to the Series implementation.
Parameters
----------
left : DataFrame
right : scalar or DataFrame
func : arithmetic or comparison operator
str_rep : str or None, default None
axis : {None, 0, 1, "index", "columns"}
Returns
-------
DataFrame
"""
# Note: we use iloc to access columns for compat with cases
# with non-unique columns.
import pandas.core.computation.expressions as expressions
right = lib.item_from_zerodim(right)
if lib.is_scalar(right) or np.ndim(right) == 0:
def column_op(a, b):
return {i: func(a.iloc[:, i], b)
for i in range(len(a.columns))}
elif isinstance(right, ABCDataFrame):
assert right._indexed_same(left)
def column_op(a, b):
return {i: func(a.iloc[:, i], b.iloc[:, i])
for i in range(len(a.columns))}
elif isinstance(right, ABCSeries) and axis == "columns":
# We only get here if called via left._combine_match_columns,
# in which case we specifically want to operate row-by-row
assert right.index.equals(left.columns)
def column_op(a, b):
return {i: func(a.iloc[:, i], b.iloc[i])
for i in range(len(a.columns))}
elif isinstance(right, ABCSeries):
assert right.index.equals(left.index) # Handle other cases later
def column_op(a, b):
return {i: func(a.iloc[:, i], b)
for i in range(len(a.columns))}
else:
# Remaining cases have less-obvious dispatch rules
raise NotImplementedError(right)
new_data = expressions.evaluate(column_op, str_rep, left, right)
result = left._constructor(new_data, index=left.index, copy=False)
# Pin columns instead of passing to constructor for compat with
# non-unique columns case
result.columns = left.columns
return result
def dispatch_to_index_op(op, left, right, index_class):
"""
Wrap Series left in the given index_class to delegate the operation op
to the index implementation. DatetimeIndex and TimedeltaIndex perform
type checking, timezone handling, overflow checks, etc.
Parameters
----------
op : binary operator (operator.add, operator.sub, ...)
left : Series
right : object
index_class : DatetimeIndex or TimedeltaIndex
Returns
-------
result : object, usually DatetimeIndex, TimedeltaIndex, or Series
"""
left_idx = index_class(left)
# avoid accidentally allowing integer add/sub. For datetime64[tz] dtypes,
# left_idx may inherit a freq from a cached DatetimeIndex.
# See discussion in GH#19147.
if getattr(left_idx, 'freq', None) is not None:
left_idx = left_idx._shallow_copy(freq=None)
try:
result = op(left_idx, right)
except NullFrequencyError:
# DatetimeIndex and TimedeltaIndex with freq == None raise ValueError
# on add/sub of integers (or int-like). We re-raise as a TypeError.
raise TypeError('incompatible type for a datetime/timedelta '
'operation [{name}]'.format(name=op.__name__))
return result
def dispatch_to_extension_op(op, left, right):
"""
Assume that left or right is a Series backed by an ExtensionArray,
apply the operator defined by op.
"""
# The op calls will raise TypeError if the op is not defined
# on the ExtensionArray
# unbox Series and Index to arrays
if isinstance(left, (ABCSeries, ABCIndexClass)):
new_left = left._values
else:
new_left = left
if isinstance(right, (ABCSeries, ABCIndexClass)):
new_right = right._values
else:
new_right = right
res_values = op(new_left, new_right)
res_name = get_op_result_name(left, right)
if op.__name__ in ['divmod', 'rdivmod']:
return _construct_divmod_result(
left, res_values, left.index, res_name)
return _construct_result(left, res_values, left.index, res_name)
# -----------------------------------------------------------------------------
# Functions that add arithmetic methods to objects, given arithmetic factory
# methods
def _get_method_wrappers(cls):
"""
Find the appropriate operation-wrappers to use when defining flex/special
arithmetic, boolean, and comparison operations with the given class.
Parameters
----------
cls : class
Returns
-------
arith_flex : function or None
comp_flex : function or None
arith_special : function
comp_special : function
bool_special : function
Notes
-----
None is only returned for SparseArray
"""
if issubclass(cls, ABCSparseSeries):
# Be sure to catch this before ABCSeries and ABCSparseArray,
# as they will both come see SparseSeries as a subclass
arith_flex = _flex_method_SERIES
comp_flex = _flex_method_SERIES
arith_special = _arith_method_SPARSE_SERIES
comp_special = _arith_method_SPARSE_SERIES
bool_special = _bool_method_SERIES
# TODO: I don't think the functions defined by bool_method are tested
elif issubclass(cls, ABCSeries):
# Just Series; SparseSeries is caught above
arith_flex = _flex_method_SERIES
comp_flex = _flex_method_SERIES
arith_special = _arith_method_SERIES
comp_special = _comp_method_SERIES
bool_special = _bool_method_SERIES
elif issubclass(cls, ABCSparseArray):
arith_flex = None
comp_flex = None
arith_special = _arith_method_SPARSE_ARRAY
comp_special = _arith_method_SPARSE_ARRAY
bool_special = _arith_method_SPARSE_ARRAY
elif issubclass(cls, ABCDataFrame):
# Same for DataFrame and SparseDataFrame
arith_flex = _arith_method_FRAME
comp_flex = _flex_comp_method_FRAME
arith_special = _arith_method_FRAME
comp_special = _comp_method_FRAME
bool_special = _arith_method_FRAME
return arith_flex, comp_flex, arith_special, comp_special, bool_special
def _create_methods(cls, arith_method, comp_method, bool_method, special):
# creates actual methods based upon arithmetic, comp and bool method
# constructors.
have_divmod = issubclass(cls, ABCSeries)
# divmod is available for Series and SparseSeries
# yapf: disable
new_methods = dict(
add=arith_method(cls, operator.add, special),
radd=arith_method(cls, radd, special),
sub=arith_method(cls, operator.sub, special),
mul=arith_method(cls, operator.mul, special),
truediv=arith_method(cls, operator.truediv, special),
floordiv=arith_method(cls, operator.floordiv, special),
# Causes a floating point exception in the tests when numexpr enabled,
# so for now no speedup
mod=arith_method(cls, operator.mod, special),
pow=arith_method(cls, operator.pow, special),
# not entirely sure why this is necessary, but previously was included
# so it's here to maintain compatibility
rmul=arith_method(cls, rmul, special),
rsub=arith_method(cls, rsub, special),
rtruediv=arith_method(cls, rtruediv, special),
rfloordiv=arith_method(cls, rfloordiv, special),
rpow=arith_method(cls, rpow, special),
rmod=arith_method(cls, rmod, special))
# yapf: enable
new_methods['div'] = new_methods['truediv']
new_methods['rdiv'] = new_methods['rtruediv']
if have_divmod:
# divmod doesn't have an op that is supported by numexpr
new_methods['divmod'] = arith_method(cls, divmod, special)
new_methods['rdivmod'] = arith_method(cls, rdivmod, special)
new_methods.update(dict(
eq=comp_method(cls, operator.eq, special),
ne=comp_method(cls, operator.ne, special),
lt=comp_method(cls, operator.lt, special),
gt=comp_method(cls, operator.gt, special),
le=comp_method(cls, operator.le, special),
ge=comp_method(cls, operator.ge, special)))
if bool_method:
new_methods.update(
dict(and_=bool_method(cls, operator.and_, special),
or_=bool_method(cls, operator.or_, special),
# For some reason ``^`` wasn't used in original.
xor=bool_method(cls, operator.xor, special),
rand_=bool_method(cls, rand_, special),
ror_=bool_method(cls, ror_, special),
rxor=bool_method(cls, rxor, special)))
if special:
dunderize = lambda x: '__{name}__'.format(name=x.strip('_'))
else:
dunderize = lambda x: x
new_methods = {dunderize(k): v for k, v in new_methods.items()}
return new_methods
def add_methods(cls, new_methods):
for name, method in new_methods.items():
# For most methods, if we find that the class already has a method
# of the same name, it is OK to over-write it. The exception is
# inplace methods (__iadd__, __isub__, ...) for SparseArray, which
# retain the np.ndarray versions.
force = not (issubclass(cls, ABCSparseArray) and
name.startswith('__i'))
if force or name not in cls.__dict__:
setattr(cls, name, method)
# ----------------------------------------------------------------------
# Arithmetic
def add_special_arithmetic_methods(cls):
"""
Adds the full suite of special arithmetic methods (``__add__``,
``__sub__``, etc.) to the class.
Parameters
----------
cls : class
special methods will be defined and pinned to this class
"""
_, _, arith_method, comp_method, bool_method = _get_method_wrappers(cls)
new_methods = _create_methods(cls, arith_method, comp_method, bool_method,
special=True)
# inplace operators (I feel like these should get passed an `inplace=True`
# or just be removed
def _wrap_inplace_method(method):
"""
return an inplace wrapper for this method
"""
def f(self, other):
result = method(self, other)
# this makes sure that we are aligned like the input
# we are updating inplace so we want to ignore is_copy
self._update_inplace(result.reindex_like(self, copy=False)._data,
verify_is_copy=False)
return self
f.__name__ = "__i{name}__".format(name=method.__name__.strip("__"))
return f
new_methods.update(
dict(__iadd__=_wrap_inplace_method(new_methods["__add__"]),
__isub__=_wrap_inplace_method(new_methods["__sub__"]),
__imul__=_wrap_inplace_method(new_methods["__mul__"]),
__itruediv__=_wrap_inplace_method(new_methods["__truediv__"]),
__ifloordiv__=_wrap_inplace_method(new_methods["__floordiv__"]),
__imod__=_wrap_inplace_method(new_methods["__mod__"]),
__ipow__=_wrap_inplace_method(new_methods["__pow__"])))
new_methods.update(
dict(__iand__=_wrap_inplace_method(new_methods["__and__"]),
__ior__=_wrap_inplace_method(new_methods["__or__"]),
__ixor__=_wrap_inplace_method(new_methods["__xor__"])))
add_methods(cls, new_methods=new_methods)
def add_flex_arithmetic_methods(cls):
"""
Adds the full suite of flex arithmetic methods (``pow``, ``mul``, ``add``)
to the class.
Parameters
----------
cls : class
flex methods will be defined and pinned to this class
"""
flex_arith_method, flex_comp_method, _, _, _ = _get_method_wrappers(cls)
new_methods = _create_methods(cls, flex_arith_method,
flex_comp_method, bool_method=None,
special=False)
new_methods.update(dict(multiply=new_methods['mul'],
subtract=new_methods['sub'],
divide=new_methods['div']))
# opt out of bool flex methods for now
assert not any(kname in new_methods for kname in ('ror_', 'rxor', 'rand_'))
add_methods(cls, new_methods=new_methods)
# -----------------------------------------------------------------------------
# Series
def _align_method_SERIES(left, right, align_asobject=False):
""" align lhs and rhs Series """
# ToDo: Different from _align_method_FRAME, list, tuple and ndarray
# are not coerced here
# because Series has inconsistencies described in #13637
if isinstance(right, ABCSeries):
# avoid repeated alignment
if not left.index.equals(right.index):
if align_asobject:
# to keep original value's dtype for bool ops
left = left.astype(object)
right = right.astype(object)
left, right = left.align(right, copy=False)
return left, right
def _construct_result(left, result, index, name, dtype=None):
"""
If the raw op result has a non-None name (e.g. it is an Index object) and
the name argument is None, then passing name to the constructor will
not be enough; we still need to override the name attribute.
"""
out = left._constructor(result, index=index, dtype=dtype)
out = out.__finalize__(left)
out.name = name
return out
def _construct_divmod_result(left, result, index, name, dtype=None):
"""divmod returns a tuple of like indexed series instead of a single series.
"""
return (
_construct_result(left, result[0], index=index, name=name,
dtype=dtype),
_construct_result(left, result[1], index=index, name=name,
dtype=dtype),
)
def _arith_method_SERIES(cls, op, special):
"""
Wrapper function for Series arithmetic operations, to avoid
code duplication.
"""
str_rep = _get_opstr(op, cls)
op_name = _get_op_name(op, special)
eval_kwargs = _gen_eval_kwargs(op_name)
fill_zeros = _gen_fill_zeros(op_name)
construct_result = (_construct_divmod_result
if op in [divmod, rdivmod] else _construct_result)
def na_op(x, y):
"""
Return the result of evaluating op on the passed in values.
If native types are not compatible, try coersion to object dtype.
Parameters
----------
x : array-like
y : array-like or scalar
Returns
-------
array-like
Raises
------
TypeError : invalid operation
"""
import pandas.core.computation.expressions as expressions
try:
result = expressions.evaluate(op, str_rep, x, y, **eval_kwargs)
except TypeError:
result = masked_arith_op(x, y, op)
except Exception: # TODO: more specific?
if is_object_dtype(x):
return libalgos.arrmap_object(x,
lambda val: op(val, y))
raise
result = missing.fill_zeros(result, x, y, op_name, fill_zeros)
return result
def wrapper(left, right):
if isinstance(right, ABCDataFrame):
return NotImplemented
left, right = _align_method_SERIES(left, right)
res_name = get_op_result_name(left, right)
right = maybe_upcast_for_op(right)
if is_categorical_dtype(left):
raise TypeError("{typ} cannot perform the operation "
"{op}".format(typ=type(left).__name__, op=str_rep))
elif is_datetime64_dtype(left) or is_datetime64tz_dtype(left):
# Give dispatch_to_index_op a chance for tests like
# test_dt64_series_add_intlike, which the index dispatching handles
# specifically.
result = dispatch_to_index_op(op, left, right, pd.DatetimeIndex)
return construct_result(left, result,
index=left.index, name=res_name,
dtype=result.dtype)
elif (is_extension_array_dtype(left) or
(is_extension_array_dtype(right) and not is_scalar(right))):
# GH#22378 disallow scalar to exclude e.g. "category", "Int64"
return dispatch_to_extension_op(op, left, right)
elif is_timedelta64_dtype(left):
result = dispatch_to_index_op(op, left, right, pd.TimedeltaIndex)
return construct_result(left, result,
index=left.index, name=res_name)
elif is_timedelta64_dtype(right):
# We should only get here with non-scalar or timedelta64('NaT')
# values for right
# Note: we cannot use dispatch_to_index_op because
# that may incorrectly raise TypeError when we
# should get NullFrequencyError
result = op(pd.Index(left), right)
return construct_result(left, result,
index=left.index, name=res_name,
dtype=result.dtype)
lvalues = left.values
rvalues = right
if isinstance(rvalues, ABCSeries):
rvalues = rvalues.values
with np.errstate(all='ignore'):
result = na_op(lvalues, rvalues)
return construct_result(left, result,
index=left.index, name=res_name, dtype=None)
wrapper.__name__ = op_name
return wrapper
def _comp_method_OBJECT_ARRAY(op, x, y):
if isinstance(y, list):
y = construct_1d_object_array_from_listlike(y)
if isinstance(y, (np.ndarray, ABCSeries, ABCIndex)):
if not is_object_dtype(y.dtype):
y = y.astype(np.object_)
if isinstance(y, (ABCSeries, ABCIndex)):
y = y.values
result = libops.vec_compare(x, y, op)
else:
result = libops.scalar_compare(x, y, op)
return result
def _comp_method_SERIES(cls, op, special):
"""
Wrapper function for Series arithmetic operations, to avoid
code duplication.
"""
op_name = _get_op_name(op, special)
masker = _gen_eval_kwargs(op_name).get('masker', False)
def na_op(x, y):
# TODO:
# should have guarantess on what x, y can be type-wise
# Extension Dtypes are not called here
# Checking that cases that were once handled here are no longer
# reachable.
assert not (is_categorical_dtype(y) and not is_scalar(y))
if is_object_dtype(x.dtype):
result = _comp_method_OBJECT_ARRAY(op, x, y)
elif is_datetimelike_v_numeric(x, y):
return invalid_comparison(x, y, op)
else:
# we want to compare like types
# we only want to convert to integer like if
# we are not NotImplemented, otherwise
# we would allow datetime64 (but viewed as i8) against
# integer comparisons
# we have a datetime/timedelta and may need to convert
assert not needs_i8_conversion(x)
mask = None
if not is_scalar(y) and needs_i8_conversion(y):
mask = isna(x) | isna(y)
y = y.view('i8')
x = x.view('i8')
method = getattr(x, op_name, None)
if method is not None:
with np.errstate(all='ignore'):
result = method(y)
if result is NotImplemented:
return invalid_comparison(x, y, op)
else:
result = op(x, y)
if mask is not None and mask.any():
result[mask] = masker
return result
def wrapper(self, other, axis=None):
# Validate the axis parameter
if axis is not None:
self._get_axis_number(axis)
res_name = get_op_result_name(self, other)
if isinstance(other, list):
# TODO: same for tuples?
other = np.asarray(other)
if isinstance(other, ABCDataFrame): # pragma: no cover
# Defer to DataFrame implementation; fail early
return NotImplemented
elif isinstance(other, ABCSeries) and not self._indexed_same(other):
raise ValueError("Can only compare identically-labeled "
"Series objects")
elif is_categorical_dtype(self):
# Dispatch to Categorical implementation; pd.CategoricalIndex
# behavior is non-canonical GH#19513
res_values = dispatch_to_index_op(op, self, other, pd.Categorical)
return self._constructor(res_values, index=self.index,
name=res_name)
elif is_datetime64_dtype(self) or is_datetime64tz_dtype(self):
# Dispatch to DatetimeIndex to ensure identical
# Series/Index behavior
if (isinstance(other, datetime.date) and
not isinstance(other, datetime.datetime)):
# https://github.com/pandas-dev/pandas/issues/21152
# Compatibility for difference between Series comparison w/
# datetime and date
msg = (
"Comparing Series of datetimes with 'datetime.date'. "
"Currently, the 'datetime.date' is coerced to a "
"datetime. In the future pandas will not coerce, "
"and {future}. "
"To retain the current behavior, "
"convert the 'datetime.date' to a datetime with "
"'pd.Timestamp'."
)
if op in {operator.lt, operator.le, operator.gt, operator.ge}:
future = "a TypeError will be raised"
else:
future = (
"'the values will not compare equal to the "
"'datetime.date'"
)
msg = '\n'.join(textwrap.wrap(msg.format(future=future)))
warnings.warn(msg, FutureWarning, stacklevel=2)
other = pd.Timestamp(other)
res_values = dispatch_to_index_op(op, self, other,
pd.DatetimeIndex)
return self._constructor(res_values, index=self.index,
name=res_name)
elif is_timedelta64_dtype(self):
res_values = dispatch_to_index_op(op, self, other,
pd.TimedeltaIndex)
return self._constructor(res_values, index=self.index,
name=res_name)
elif (is_extension_array_dtype(self) or
(is_extension_array_dtype(other) and not is_scalar(other))):
# Note: the `not is_scalar(other)` condition rules out
# e.g. other == "category"
return dispatch_to_extension_op(op, self, other)
elif isinstance(other, ABCSeries):
# By this point we have checked that self._indexed_same(other)
res_values = na_op(self.values, other.values)
# rename is needed in case res_name is None and res_values.name
# is not.
return self._constructor(res_values, index=self.index,
name=res_name).rename(res_name)
elif isinstance(other, (np.ndarray, pd.Index)):
# do not check length of zerodim array
# as it will broadcast
if other.ndim != 0 and len(self) != len(other):
raise ValueError('Lengths must match to compare')
res_values = na_op(self.values, np.asarray(other))
result = self._constructor(res_values, index=self.index)
# rename is needed in case res_name is None and self.name
# is not.
return result.__finalize__(self).rename(res_name)
elif is_scalar(other) and isna(other):
# numpy does not like comparisons vs None
if op is operator.ne:
res_values = np.ones(len(self), dtype=bool)
else:
res_values = np.zeros(len(self), dtype=bool)
return self._constructor(res_values, index=self.index,
name=res_name, dtype='bool')
else:
values = self.get_values()
with np.errstate(all='ignore'):
res = na_op(values, other)
if is_scalar(res):
raise TypeError('Could not compare {typ} type with Series'
.format(typ=type(other)))
# always return a full value series here
res_values = com.values_from_object(res)
return self._constructor(res_values, index=self.index,
name=res_name, dtype='bool')
wrapper.__name__ = op_name
return wrapper
def _bool_method_SERIES(cls, op, special):
"""
Wrapper function for Series arithmetic operations, to avoid
code duplication.
"""
op_name = _get_op_name(op, special)
def na_op(x, y):
try:
result = op(x, y)
except TypeError:
assert not isinstance(y, (list, ABCSeries, ABCIndexClass))
if isinstance(y, np.ndarray):
# bool-bool dtype operations should be OK, should not get here
assert not (is_bool_dtype(x) and is_bool_dtype(y))
x = ensure_object(x)
y = ensure_object(y)
result = libops.vec_binop(x, y, op)
else:
# let null fall thru
assert lib.is_scalar(y)
if not isna(y):
y = bool(y)
try:
result = libops.scalar_binop(x, y, op)
except (TypeError, ValueError, AttributeError,
OverflowError, NotImplementedError):
raise TypeError("cannot compare a dtyped [{dtype}] array "
"with a scalar of type [{typ}]"
.format(dtype=x.dtype,
typ=type(y).__name__))
return result
fill_int = lambda x: x.fillna(0)
fill_bool = lambda x: x.fillna(False).astype(bool)
def wrapper(self, other):
is_self_int_dtype = is_integer_dtype(self.dtype)
self, other = _align_method_SERIES(self, other, align_asobject=True)
res_name = get_op_result_name(self, other)
if isinstance(other, ABCDataFrame):
# Defer to DataFrame implementation; fail early
return NotImplemented
elif isinstance(other, (ABCSeries, ABCIndexClass)):
is_other_int_dtype = is_integer_dtype(other.dtype)
other = fill_int(other) if is_other_int_dtype else fill_bool(other)
ovalues = other.values
finalizer = lambda x: x
else:
# scalars, list, tuple, np.array
is_other_int_dtype = is_integer_dtype(np.asarray(other))
if is_list_like(other) and not isinstance(other, np.ndarray):
# TODO: Can we do this before the is_integer_dtype check?
# could the is_integer_dtype check be checking the wrong
# thing? e.g. other = [[0, 1], [2, 3], [4, 5]]?
other = construct_1d_object_array_from_listlike(other)
ovalues = other
finalizer = lambda x: x.__finalize__(self)
# For int vs int `^`, `|`, `&` are bitwise operators and return
# integer dtypes. Otherwise these are boolean ops
filler = (fill_int if is_self_int_dtype and is_other_int_dtype
else fill_bool)
res_values = na_op(self.values, ovalues)
unfilled = self._constructor(res_values,
index=self.index, name=res_name)
filled = filler(unfilled)
return finalizer(filled)
wrapper.__name__ = op_name
return wrapper
def _flex_method_SERIES(cls, op, special):
name = _get_op_name(op, special)
doc = _make_flex_doc(name, 'series')
@Appender(doc)
def flex_wrapper(self, other, level=None, fill_value=None, axis=0):
# validate axis
if axis is not None:
self._get_axis_number(axis)
if isinstance(other, ABCSeries):
return self._binop(other, op, level=level, fill_value=fill_value)
elif isinstance(other, (np.ndarray, list, tuple)):
if len(other) != len(self):
raise ValueError('Lengths must be equal')
other = self._constructor(other, self.index)
return self._binop(other, op, level=level, fill_value=fill_value)
else:
if fill_value is not None:
self = self.fillna(fill_value)
return self._constructor(op(self, other),
self.index).__finalize__(self)
flex_wrapper.__name__ = name
return flex_wrapper
# -----------------------------------------------------------------------------
# DataFrame
def _combine_series_frame(self, other, func, fill_value=None, axis=None,
level=None):
"""
Apply binary operator `func` to self, other using alignment and fill
conventions determined by the fill_value, axis, and level kwargs.
Parameters
----------
self : DataFrame
other : Series
func : binary operator
fill_value : object, default None
axis : {0, 1, 'columns', 'index', None}, default None
level : int or None, default None
Returns
-------
result : DataFrame
"""
if fill_value is not None:
raise NotImplementedError("fill_value {fill} not supported."
.format(fill=fill_value))
if axis is not None:
axis = self._get_axis_number(axis)
if axis == 0:
return self._combine_match_index(other, func, level=level)
else:
return self._combine_match_columns(other, func, level=level)
else:
if not len(other):
return self * np.nan
if not len(self):
# Ambiguous case, use _series so works with DataFrame
return self._constructor(data=self._series, index=self.index,
columns=self.columns)
# default axis is columns
return self._combine_match_columns(other, func, level=level)
def _align_method_FRAME(left, right, axis):
""" convert rhs to meet lhs dims if input is list, tuple or np.ndarray """
def to_series(right):
msg = ('Unable to coerce to Series, length must be {req_len}: '
'given {given_len}')
if axis is not None and left._get_axis_name(axis) == 'index':
if len(left.index) != len(right):
raise ValueError(msg.format(req_len=len(left.index),
given_len=len(right)))
right = left._constructor_sliced(right, index=left.index)
else:
if len(left.columns) != len(right):
raise ValueError(msg.format(req_len=len(left.columns),
given_len=len(right)))
right = left._constructor_sliced(right, index=left.columns)
return right
if isinstance(right, np.ndarray):
if right.ndim == 1:
right = to_series(right)
elif right.ndim == 2:
if right.shape == left.shape:
right = left._constructor(right, index=left.index,
columns=left.columns)
elif right.shape[0] == left.shape[0] and right.shape[1] == 1:
# Broadcast across columns
right = np.broadcast_to(right, left.shape)
right = left._constructor(right,
index=left.index,
columns=left.columns)
elif right.shape[1] == left.shape[1] and right.shape[0] == 1:
# Broadcast along rows
right = to_series(right[0, :])
else:
raise ValueError("Unable to coerce to DataFrame, shape "
"must be {req_shape}: given {given_shape}"
.format(req_shape=left.shape,
given_shape=right.shape))
elif right.ndim > 2:
raise ValueError('Unable to coerce to Series/DataFrame, dim '
'must be <= 2: {dim}'.format(dim=right.shape))
elif (is_list_like(right) and
not isinstance(right, (ABCSeries, ABCDataFrame))):
# GH17901
right = to_series(right)
return right
def _arith_method_FRAME(cls, op, special):
str_rep = _get_opstr(op, cls)
op_name = _get_op_name(op, special)
eval_kwargs = _gen_eval_kwargs(op_name)
fill_zeros = _gen_fill_zeros(op_name)
default_axis = _get_frame_op_default_axis(op_name)
def na_op(x, y):
import pandas.core.computation.expressions as expressions
try:
result = expressions.evaluate(op, str_rep, x, y, **eval_kwargs)
except TypeError:
result = masked_arith_op(x, y, op)
result = missing.fill_zeros(result, x, y, op_name, fill_zeros)
return result
if op_name in _op_descriptions:
# i.e. include "add" but not "__add__"
doc = _make_flex_doc(op_name, 'dataframe')
else:
doc = _arith_doc_FRAME % op_name
@Appender(doc)
def f(self, other, axis=default_axis, level=None, fill_value=None):
other = _align_method_FRAME(self, other, axis)
if isinstance(other, ABCDataFrame):
# Another DataFrame
pass_op = op if should_series_dispatch(self, other, op) else na_op
return self._combine_frame(other, pass_op, fill_value, level)
elif isinstance(other, ABCSeries):
# For these values of `axis`, we end up dispatching to Series op,
# so do not want the masked op.
pass_op = op if axis in [0, "columns", None] else na_op
return _combine_series_frame(self, other, pass_op,
fill_value=fill_value, axis=axis,
level=level)
else:
if fill_value is not None:
self = self.fillna(fill_value)
assert np.ndim(other) == 0
return self._combine_const(other, op)
f.__name__ = op_name
return f
def _flex_comp_method_FRAME(cls, op, special):
str_rep = _get_opstr(op, cls)
op_name = _get_op_name(op, special)
default_axis = _get_frame_op_default_axis(op_name)
def na_op(x, y):
try:
with np.errstate(invalid='ignore'):
result = op(x, y)
except TypeError:
result = mask_cmp_op(x, y, op)
return result
doc = _flex_comp_doc_FRAME.format(op_name=op_name,
desc=_op_descriptions[op_name]['desc'])
@Appender(doc)
def f(self, other, axis=default_axis, level=None):
other = _align_method_FRAME(self, other, axis)
if isinstance(other, ABCDataFrame):
# Another DataFrame
if not self._indexed_same(other):
self, other = self.align(other, 'outer',
level=level, copy=False)
return dispatch_to_series(self, other, na_op, str_rep)
elif isinstance(other, ABCSeries):
return _combine_series_frame(self, other, na_op,
fill_value=None, axis=axis,
level=level)
else:
assert np.ndim(other) == 0, other
return self._combine_const(other, na_op)
f.__name__ = op_name
return f
def _comp_method_FRAME(cls, func, special):
str_rep = _get_opstr(func, cls)
op_name = _get_op_name(func, special)
@Appender('Wrapper for comparison method {name}'.format(name=op_name))
def f(self, other):
other = _align_method_FRAME(self, other, axis=None)
if isinstance(other, ABCDataFrame):
# Another DataFrame
if not self._indexed_same(other):
raise ValueError('Can only compare identically-labeled '
'DataFrame objects')
return dispatch_to_series(self, other, func, str_rep)
elif isinstance(other, ABCSeries):
return _combine_series_frame(self, other, func,
fill_value=None, axis=None,
level=None)
else:
# straight boolean comparisons we want to allow all columns
# (regardless of dtype to pass thru) See #4537 for discussion.
res = self._combine_const(other, func)
return res.fillna(True).astype(bool)
f.__name__ = op_name
return f
# -----------------------------------------------------------------------------
# Sparse
def _cast_sparse_series_op(left, right, opname):
"""
For SparseSeries operation, coerce to float64 if the result is expected
to have NaN or inf values
Parameters
----------
left : SparseArray
right : SparseArray
opname : str
Returns
-------
left : SparseArray
right : SparseArray
"""
from pandas.core.sparse.api import SparseDtype
opname = opname.strip('_')
# TODO: This should be moved to the array?
if is_integer_dtype(left) and is_integer_dtype(right):
# series coerces to float64 if result should have NaN/inf
if opname in ('floordiv', 'mod') and (right.to_dense() == 0).any():
left = left.astype(SparseDtype(np.float64, left.fill_value))
right = right.astype(SparseDtype(np.float64, right.fill_value))
elif opname in ('rfloordiv', 'rmod') and (left.to_dense() == 0).any():
left = left.astype(SparseDtype(np.float64, left.fill_value))
right = right.astype(SparseDtype(np.float64, right.fill_value))
return left, right
def _arith_method_SPARSE_SERIES(cls, op, special):
"""
Wrapper function for Series arithmetic operations, to avoid
code duplication.
"""
op_name = _get_op_name(op, special)
def wrapper(self, other):
if isinstance(other, ABCDataFrame):
return NotImplemented
elif isinstance(other, ABCSeries):
if not isinstance(other, ABCSparseSeries):
other = other.to_sparse(fill_value=self.fill_value)
return _sparse_series_op(self, other, op, op_name)
elif is_scalar(other):
with np.errstate(all='ignore'):
new_values = op(self.values, other)
return self._constructor(new_values,
index=self.index,
name=self.name)
else: # pragma: no cover
raise TypeError('operation with {other} not supported'
.format(other=type(other)))
wrapper.__name__ = op_name
return wrapper
def _sparse_series_op(left, right, op, name):
left, right = left.align(right, join='outer', copy=False)
new_index = left.index
new_name = get_op_result_name(left, right)
from pandas.core.arrays.sparse import _sparse_array_op
lvalues, rvalues = _cast_sparse_series_op(left.values, right.values, name)
result = _sparse_array_op(lvalues, rvalues, op, name)
return left._constructor(result, index=new_index, name=new_name)
def _arith_method_SPARSE_ARRAY(cls, op, special):
"""
Wrapper function for Series arithmetic operations, to avoid
code duplication.
"""
op_name = _get_op_name(op, special)
def wrapper(self, other):
from pandas.core.arrays.sparse.array import (
SparseArray, _sparse_array_op, _wrap_result, _get_fill)
if isinstance(other, np.ndarray):
if len(self) != len(other):
raise AssertionError("length mismatch: {self} vs. {other}"
.format(self=len(self), other=len(other)))
if not isinstance(other, SparseArray):
dtype = getattr(other, 'dtype', None)
other = SparseArray(other, fill_value=self.fill_value,
dtype=dtype)
return _sparse_array_op(self, other, op, op_name)
elif is_scalar(other):
with np.errstate(all='ignore'):
fill = op(_get_fill(self), np.asarray(other))
result = op(self.sp_values, other)
return _wrap_result(op_name, result, self.sp_index, fill)
else: # pragma: no cover
raise TypeError('operation with {other} not supported'
.format(other=type(other)))
wrapper.__name__ = op_name
return wrapper
|
from copy import deepcopy
from distutils.version import LooseVersion
from operator import methodcaller
import numpy as np
import pytest
import pandas.util._test_decorators as td
import pandas as pd
from pandas import DataFrame, MultiIndex, Series, date_range
import pandas.util.testing as tm
from pandas.util.testing import (
assert_almost_equal, assert_frame_equal, assert_series_equal)
from .test_generic import Generic
try:
import xarray
_XARRAY_INSTALLED = True
except ImportError:
_XARRAY_INSTALLED = False
class TestDataFrame(Generic):
_typ = DataFrame
_comparator = lambda self, x, y: assert_frame_equal(x, y)
def test_rename_mi(self):
df = DataFrame([
11, 21, 31
], index=MultiIndex.from_tuples([("A", x) for x in ["a", "B", "c"]]))
df.rename(str.lower)
def test_set_axis_name(self):
df = pd.DataFrame([[1, 2], [3, 4]])
funcs = ['_set_axis_name', 'rename_axis']
for func in funcs:
result = methodcaller(func, 'foo')(df)
assert df.index.name is None
assert result.index.name == 'foo'
result = methodcaller(func, 'cols', axis=1)(df)
assert df.columns.name is None
assert result.columns.name == 'cols'
def test_set_axis_name_mi(self):
df = DataFrame(
np.empty((3, 3)),
index=MultiIndex.from_tuples([("A", x) for x in list('aBc')]),
columns=MultiIndex.from_tuples([('C', x) for x in list('xyz')])
)
level_names = ['L1', 'L2']
funcs = ['_set_axis_name', 'rename_axis']
for func in funcs:
result = methodcaller(func, level_names)(df)
assert result.index.names == level_names
assert result.columns.names == [None, None]
result = methodcaller(func, level_names, axis=1)(df)
assert result.columns.names == ["L1", "L2"]
assert result.index.names == [None, None]
def test_nonzero_single_element(self):
# allow single item via bool method
df = DataFrame([[True]])
assert df.bool()
df = DataFrame([[False]])
assert not df.bool()
df = DataFrame([[False, False]])
with pytest.raises(ValueError):
df.bool()
with pytest.raises(ValueError):
bool(df)
def test_get_numeric_data_preserve_dtype(self):
# get the numeric data
o = DataFrame({'A': [1, '2', 3.]})
result = o._get_numeric_data()
expected = DataFrame(index=[0, 1, 2], dtype=object)
self._compare(result, expected)
def test_metadata_propagation_indiv(self):
# groupby
df = DataFrame(
{'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'],
'B': ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three'],
'C': np.random.randn(8),
'D': np.random.randn(8)})
result = df.groupby('A').sum()
self.check_metadata(df, result)
# resample
df = DataFrame(np.random.randn(1000, 2),
index=date_range('20130101', periods=1000, freq='s'))
result = df.resample('1T')
self.check_metadata(df, result)
# merging with override
# GH 6923
_metadata = DataFrame._metadata
_finalize = DataFrame.__finalize__
np.random.seed(10)
df1 = DataFrame(np.random.randint(0, 4, (3, 2)), columns=['a', 'b'])
df2 = DataFrame(np.random.randint(0, 4, (3, 2)), columns=['c', 'd'])
DataFrame._metadata = ['filename']
df1.filename = 'fname1.csv'
df2.filename = 'fname2.csv'
def finalize(self, other, method=None, **kwargs):
for name in self._metadata:
if method == 'merge':
left, right = other.left, other.right
value = getattr(left, name, '') + '|' + getattr(right,
name, '')
object.__setattr__(self, name, value)
else:
object.__setattr__(self, name, getattr(other, name, ''))
return self
DataFrame.__finalize__ = finalize
result = df1.merge(df2, left_on=['a'], right_on=['c'], how='inner')
assert result.filename == 'fname1.csv|fname2.csv'
# concat
# GH 6927
DataFrame._metadata = ['filename']
df1 = DataFrame(np.random.randint(0, 4, (3, 2)), columns=list('ab'))
df1.filename = 'foo'
def finalize(self, other, method=None, **kwargs):
for name in self._metadata:
if method == 'concat':
value = '+'.join([getattr(
o, name) for o in other.objs if getattr(o, name, None)
])
object.__setattr__(self, name, value)
else:
object.__setattr__(self, name, getattr(other, name, None))
return self
DataFrame.__finalize__ = finalize
result = pd.concat([df1, df1])
assert result.filename == 'foo+foo'
# reset
DataFrame._metadata = _metadata
DataFrame.__finalize__ = _finalize
def test_set_attribute(self):
# Test for consistent setattr behavior when an attribute and a column
# have the same name (Issue #8994)
df = DataFrame({'x': [1, 2, 3]})
df.y = 2
df['y'] = [2, 4, 6]
df.y = 5
assert df.y == 5
assert_series_equal(df['y'], Series([2, 4, 6], name='y'))
@pytest.mark.skipif(not _XARRAY_INSTALLED or _XARRAY_INSTALLED and
LooseVersion(xarray.__version__) <
LooseVersion('0.10.0'),
reason='xarray >= 0.10.0 required')
@pytest.mark.parametrize(
"index", ['FloatIndex', 'IntIndex',
'StringIndex', 'UnicodeIndex',
'DateIndex', 'PeriodIndex',
'CategoricalIndex', 'TimedeltaIndex'])
def test_to_xarray_index_types(self, index):
from xarray import Dataset
index = getattr(tm, 'make{}'.format(index))
df = DataFrame({'a': list('abc'),
'b': list(range(1, 4)),
'c': np.arange(3, 6).astype('u1'),
'd': np.arange(4.0, 7.0, dtype='float64'),
'e': [True, False, True],
'f': pd.Categorical(list('abc')),
'g': pd.date_range('20130101', periods=3),
'h': pd.date_range('20130101',
periods=3,
tz='US/Eastern')}
)
df.index = index(3)
df.index.name = 'foo'
df.columns.name = 'bar'
result = df.to_xarray()
assert result.dims['foo'] == 3
assert len(result.coords) == 1
assert len(result.data_vars) == 8
assert_almost_equal(list(result.coords.keys()), ['foo'])
assert isinstance(result, Dataset)
# idempotency
# categoricals are not preserved
# datetimes w/tz are not preserved
# column names are lost
expected = df.copy()
expected['f'] = expected['f'].astype(object)
expected['h'] = expected['h'].astype('datetime64[ns]')
expected.columns.name = None
assert_frame_equal(result.to_dataframe(), expected,
check_index_type=False, check_categorical=False)
@td.skip_if_no('xarray', min_version='0.7.0')
def test_to_xarray(self):
from xarray import Dataset
df = DataFrame({'a': list('abc'),
'b': list(range(1, 4)),
'c': np.arange(3, 6).astype('u1'),
'd': np.arange(4.0, 7.0, dtype='float64'),
'e': [True, False, True],
'f': pd.Categorical(list('abc')),
'g': pd.date_range('20130101', periods=3),
'h': pd.date_range('20130101',
periods=3,
tz='US/Eastern')}
)
df.index.name = 'foo'
result = df[0:0].to_xarray()
assert result.dims['foo'] == 0
assert isinstance(result, Dataset)
# available in 0.7.1
# MultiIndex
df.index = pd.MultiIndex.from_product([['a'], range(3)],
names=['one', 'two'])
result = df.to_xarray()
assert result.dims['one'] == 1
assert result.dims['two'] == 3
assert len(result.coords) == 2
assert len(result.data_vars) == 8
assert_almost_equal(list(result.coords.keys()), ['one', 'two'])
assert isinstance(result, Dataset)
result = result.to_dataframe()
expected = df.copy()
expected['f'] = expected['f'].astype(object)
expected['h'] = expected['h'].astype('datetime64[ns]')
expected.columns.name = None
assert_frame_equal(result,
expected,
check_index_type=False)
def test_deepcopy_empty(self):
# This test covers empty frame copying with non-empty column sets
# as reported in issue GH15370
empty_frame = DataFrame(data=[], index=[], columns=['A'])
empty_frame_copy = deepcopy(empty_frame)
self._compare(empty_frame_copy, empty_frame)
|
|
pandas/tests/generic/test_frame.py
|
|
import requests
import json
import httplib as http
import celery
from celery.utils.log import get_task_logger
from framework.celery_tasks import app as celery_app
from framework.celery_tasks.utils import logged
from framework.exceptions import HTTPError
from api.base.utils import waterbutler_api_url_for
from website.archiver import (
ARCHIVER_SUCCESS,
ARCHIVER_FAILURE,
ARCHIVER_SIZE_EXCEEDED,
ARCHIVER_NETWORK_ERROR,
ARCHIVER_FILE_NOT_FOUND,
ARCHIVER_UNCAUGHT_ERROR,
NO_ARCHIVE_LIMIT,
AggregateStatResult,
)
from website.archiver import utils
from website.archiver import signals as archiver_signals
from website.project import signals as project_signals
from website import settings
from website.app import init_addons
from osf.models import (
ArchiveJob,
AbstractNode,
DraftRegistration,
)
def create_app_context():
try:
init_addons(settings)
except AssertionError: # ignore AssertionErrors
pass
logger = get_task_logger(__name__)
class ArchiverSizeExceeded(Exception):
def __init__(self, result, *args, **kwargs):
super(ArchiverSizeExceeded, self).__init__(*args, **kwargs)
self.result = result
class ArchiverStateError(Exception):
def __init__(self, info, *args, **kwargs):
super(ArchiverStateError, self).__init__(*args, **kwargs)
self.info = info
class ArchivedFileNotFound(Exception):
def __init__(self, registration, missing_files, *args, **kwargs):
super(ArchivedFileNotFound, self).__init__(*args, **kwargs)
self.draft_registration = DraftRegistration.objects.get(registered_node=registration)
self.missing_files = missing_files
class ArchiverTask(celery.Task):
abstract = True
max_retries = 0
ignore_result = False
def on_failure(self, exc, task_id, args, kwargs, einfo):
job = ArchiveJob.load(kwargs.get('job_pk'))
if not job:
raise ArchiverStateError({
'exception': exc,
'args': args,
'kwargs': kwargs,
'einfo': einfo,
})
if job.status == ARCHIVER_FAILURE:
# already captured
return
src, dst, user = job.info()
errors = []
if isinstance(exc, ArchiverSizeExceeded):
dst.archive_status = ARCHIVER_SIZE_EXCEEDED
errors = exc.result
elif isinstance(exc, HTTPError):
dst.archive_status = ARCHIVER_NETWORK_ERROR
errors = [
each for each in
dst.archive_job.target_info()
if each is not None
]
elif isinstance(exc, ArchivedFileNotFound):
dst.archive_status = ARCHIVER_FILE_NOT_FOUND
errors = {
'missing_files': exc.missing_files,
'draft': exc.draft_registration
}
else:
dst.archive_status = ARCHIVER_UNCAUGHT_ERROR
errors = [einfo] if einfo else []
dst.save()
archiver_signals.archive_fail.send(dst, errors=errors)
@celery_app.task(base=ArchiverTask, ignore_result=False)
@logged('stat_addon')
def stat_addon(addon_short_name, job_pk):
"""Collect metadata about the file tree of a given addon
:param addon_short_name: AddonConfig.short_name of the addon to be examined
:param job_pk: primary key of archive_job
:return: AggregateStatResult containing file tree metadata
"""
# Dataverse reqires special handling for draft and
# published content
addon_name = addon_short_name
version = None
if 'dataverse' in addon_short_name:
addon_name = 'dataverse'
version = 'latest' if addon_short_name.split('-')[-1] == 'draft' else 'latest-published'
create_app_context()
job = ArchiveJob.load(job_pk)
src, dst, user = job.info()
src_addon = src.get_addon(addon_name)
if hasattr(src_addon, 'configured') and not src_addon.configured:
# Addon enabled but not configured - no file trees, nothing to archive.
return AggregateStatResult(src_addon._id, addon_short_name)
try:
file_tree = src_addon._get_file_tree(user=user, version=version)
except HTTPError as e:
dst.archive_job.update_target(
addon_short_name,
ARCHIVER_NETWORK_ERROR,
errors=[e.data['error']],
)
raise
result = AggregateStatResult(
src_addon._id,
addon_short_name,
targets=[utils.aggregate_file_tree_metadata(addon_short_name, file_tree, user)],
)
return result
@celery_app.task(base=ArchiverTask, ignore_result=False)
@logged('make_copy_request')
def make_copy_request(job_pk, url, data):
"""Make the copy request to the WaterBulter API and handle
successful and failed responses
:param job_pk: primary key of ArchiveJob
:param url: URL to send request to
:param data: <dict> of setting to send in POST to WaterBulter API
:return: None
"""
create_app_context()
job = ArchiveJob.load(job_pk)
src, dst, user = job.info()
logger.info('Sending copy request for addon: {0} on node: {1}'.format(data['provider'], dst._id))
res = requests.post(url, data=json.dumps(data))
if res.status_code not in (http.OK, http.CREATED, http.ACCEPTED):
raise HTTPError(res.status_code)
def make_waterbutler_payload(dst_id, rename):
return {
'action': 'copy',
'path': '/',
'rename': rename.replace('/', '-'),
'resource': dst_id,
'provider': settings.ARCHIVE_PROVIDER,
}
@celery_app.task(base=ArchiverTask, ignore_result=False)
@logged('archive_addon')
def archive_addon(addon_short_name, job_pk):
"""Archive the contents of an addon by making a copy request to the
WaterBulter API
:param addon_short_name: AddonConfig.short_name of the addon to be archived
:param job_pk: primary key of ArchiveJob
:return: None
"""
create_app_context()
job = ArchiveJob.load(job_pk)
src, dst, user = job.info()
logger.info('Archiving addon: {0} on node: {1}'.format(addon_short_name, src._id))
cookie = user.get_or_create_cookie()
params = {'cookie': cookie}
rename_suffix = ''
# The dataverse API will not differentiate between published and draft files
# unless expcicitly asked. We need to create seperate folders for published and
# draft in the resulting archive.
#
# Additionally trying to run the archive without this distinction creates a race
# condition that non-deterministically caused archive jobs to fail.
if 'dataverse' in addon_short_name:
params['revision'] = 'latest' if addon_short_name.split('-')[-1] == 'draft' else 'latest-published'
rename_suffix = ' (draft)' if addon_short_name.split('-')[-1] == 'draft' else ' (published)'
addon_short_name = 'dataverse'
src_provider = src.get_addon(addon_short_name)
folder_name = src_provider.archive_folder_name
rename = '{}{}'.format(folder_name, rename_suffix)
url = waterbutler_api_url_for(src._id, addon_short_name, _internal=True, base_url=src.osfstorage_region.waterbutler_url, **params)
data = make_waterbutler_payload(dst._id, rename)
make_copy_request.delay(job_pk=job_pk, url=url, data=data)
@celery_app.task(base=ArchiverTask, ignore_result=False)
@logged('archive_node')
def archive_node(stat_results, job_pk):
"""First use the results of #stat_node to check disk usage of the
initiated registration, then either fail the registration or
create a celery.group group of subtasks to archive addons
:param results: results from the #stat_addon subtasks spawned in #stat_node
:param job_pk: primary key of ArchiveJob
:return: None
"""
create_app_context()
job = ArchiveJob.load(job_pk)
src, dst, user = job.info()
logger.info('Archiving node: {0}'.format(src._id))
if not isinstance(stat_results, list):
stat_results = [stat_results]
stat_result = AggregateStatResult(
dst._id,
dst.title,
targets=stat_results
)
if (NO_ARCHIVE_LIMIT not in job.initiator.system_tags) and (stat_result.disk_usage > settings.MAX_ARCHIVE_SIZE):
raise ArchiverSizeExceeded(result=stat_result)
else:
if not stat_result.targets:
job.status = ARCHIVER_SUCCESS
job.save()
for result in stat_result.targets:
if not result['num_files']:
job.update_target(result['target_name'], ARCHIVER_SUCCESS)
else:
archive_addon.delay(
addon_short_name=result['target_name'],
job_pk=job_pk
)
project_signals.archive_callback.send(dst)
def archive(job_pk):
"""Starts a celery.chord that runs stat_addon for each
complete addon attached to the Node, then runs
#archive_node with the result
:param job_pk: primary key of ArchiveJob
:return: None
"""
create_app_context()
job = ArchiveJob.load(job_pk)
src, dst, user = job.info()
logger = get_task_logger(__name__)
logger.info('Received archive task for Node: {0} into Node: {1}'.format(src._id, dst._id))
return celery.chain(
[
celery.group([
stat_addon.si(
addon_short_name=target.name,
job_pk=job_pk,
)
for target in job.target_addons.all()
]),
archive_node.s(
job_pk=job_pk
)
]
)
@celery_app.task(base=ArchiverTask, ignore_result=False)
@logged('archive_success')
def archive_success(dst_pk, job_pk):
"""Archiver's final callback. For the time being the use case for this task
is to rewrite references to files selected in a registration schema (the Prereg
Challenge being the first to expose this feature). The created references point
to files on the registered_from Node (needed for previewing schema data), and
must be re-associated with the corresponding files in the newly created registration.
:param str dst_pk: primary key of registration Node
note:: At first glance this task makes redundant calls to utils.get_file_map (which
returns a generator yielding (<sha256>, <file_metadata>) pairs) on the dst Node. Two
notes about utils.get_file_map: 1) this function memoizes previous results to reduce
overhead and 2) this function returns a generator that lazily fetches the file metadata
of child Nodes (it is possible for a selected file to belong to a child Node) using a
non-recursive DFS. Combined this allows for a relatively effient implementation with
seemingly redundant calls.
"""
create_app_context()
dst = AbstractNode.load(dst_pk)
# The filePicker extension addded with the Prereg Challenge registration schema
# allows users to select files in OSFStorage as their response to some schema
# questions. These files are references to files on the unregistered Node, and
# consequently we must migrate those file paths after archiver has run. Using
# sha256 hashes is a convenient way to identify files post-archival.
for schema in dst.registered_schema.all():
if schema.has_files:
utils.migrate_file_metadata(dst, schema)
job = ArchiveJob.load(job_pk)
if not job.sent:
job.sent = True
job.save()
dst.sanction.ask(dst.get_active_contributors_recursive(unique_users=True))
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Functional tests using WebTest."""
import datetime as dt
import httplib as http
import logging
import unittest
import markupsafe
import mock
import pytest
from nose.tools import * # flake8: noqa (PEP8 asserts)
import re
from addons.wiki.utils import to_mongo_key
from framework.auth import exceptions as auth_exc
from framework.auth.core import Auth
from tests.base import OsfTestCase
from tests.base import fake
from osf_tests.factories import (
fake_email,
AuthUserFactory,
NodeFactory,
PreprintFactory,
PreprintProviderFactory,
PrivateLinkFactory,
ProjectFactory,
RegistrationFactory,
SubjectFactory,
UserFactory,
UnconfirmedUserFactory,
UnregUserFactory,
)
from addons.wiki.models import WikiPage, WikiVersion
from addons.wiki.tests.factories import WikiFactory, WikiVersionFactory
from website import settings, language
from addons.osfstorage.models import OsfStorageFile
from website.util import web_url_for, api_url_for
from api_tests import utils as test_utils
logging.getLogger('website.project.model').setLevel(logging.ERROR)
def assert_in_html(member, container, **kwargs):
"""Looks for the specified member in markupsafe-escaped HTML output"""
member = markupsafe.escape(member)
return assert_in(member, container, **kwargs)
def assert_not_in_html(member, container, **kwargs):
"""Looks for the specified member in markupsafe-escaped HTML output"""
member = markupsafe.escape(member)
return assert_not_in(member, container, **kwargs)
class TestDisabledUser(OsfTestCase):
def setUp(self):
super(TestDisabledUser, self).setUp()
self.user = UserFactory()
self.user.set_password('Korben Dallas')
self.user.is_disabled = True
self.user.save()
def test_profile_disabled_returns_401(self):
res = self.app.get(self.user.url, expect_errors=True)
assert_equal(res.status_code, 410)
class TestAnUnregisteredUser(OsfTestCase):
def test_cant_see_profile_if_not_logged_in(self):
url = web_url_for('profile_view')
res = self.app.get(url)
res = res.follow()
assert_equal(res.status_code, 301)
assert_in('/login/', res.headers['Location'])
@pytest.mark.enable_bookmark_creation
@pytest.mark.enable_quickfiles_creation
class TestAUser(OsfTestCase):
def setUp(self):
super(TestAUser, self).setUp()
self.user = AuthUserFactory()
self.auth = self.user.auth
def test_can_see_profile_url(self):
res = self.app.get(self.user.url).maybe_follow()
assert_in(self.user.url, res)
# `GET /login/` without parameters is redirected to `/dashboard/` page which has `@must_be_logged_in` decorator
# if user is not logged in, she/he is further redirected to CAS login page
def test_is_redirected_to_cas_if_not_logged_in_at_login_page(self):
res = self.app.get('/login/').follow()
assert_equal(res.status_code, 302)
location = res.headers.get('Location')
assert_in('login?service=', location)
def test_is_redirected_to_dashboard_if_already_logged_in_at_login_page(self):
res = self.app.get('/login/', auth=self.user.auth)
assert_equal(res.status_code, 302)
assert 'dashboard' in res.headers.get('Location')
def test_register_page(self):
res = self.app.get('/register/')
assert_equal(res.status_code, 200)
def test_is_redirected_to_dashboard_if_already_logged_in_at_register_page(self):
res = self.app.get('/register/', auth=self.user.auth)
assert_equal(res.status_code, 302)
assert 'dashboard' in res.headers.get('Location')
def test_sees_projects_in_her_dashboard(self):
# the user already has a project
project = ProjectFactory(creator=self.user)
project.add_contributor(self.user)
project.save()
res = self.app.get('/myprojects/', auth=self.user.auth)
assert_in('Projects', res) # Projects heading
def test_does_not_see_osffiles_in_user_addon_settings(self):
res = self.app.get('/settings/addons/', auth=self.auth, auto_follow=True)
assert_not_in('OSF Storage', res)
def test_sees_osffiles_in_project_addon_settings(self):
project = ProjectFactory(creator=self.user)
project.add_contributor(
self.user,
permissions=['read', 'write', 'admin'],
save=True)
res = self.app.get('/{0}/addons/'.format(project._primary_key), auth=self.auth, auto_follow=True)
assert_in('OSF Storage', res)
def test_sees_correct_title_on_dashboard(self):
# User goes to dashboard
res = self.app.get('/myprojects/', auth=self.auth, auto_follow=True)
title = res.html.title.string
assert_equal('OSF | My Projects', title)
def test_can_see_make_public_button_if_admin(self):
# User is a contributor on a project
project = ProjectFactory()
project.add_contributor(
self.user,
permissions=['read', 'write', 'admin'],
save=True)
# User goes to the project page
res = self.app.get(project.url, auth=self.auth).maybe_follow()
assert_in('Make Public', res)
def test_cant_see_make_public_button_if_not_admin(self):
# User is a contributor on a project
project = ProjectFactory()
project.add_contributor(
self.user,
permissions=['read', 'write'],
save=True)
# User goes to the project page
res = self.app.get(project.url, auth=self.auth).maybe_follow()
assert_not_in('Make Public', res)
def test_can_see_make_private_button_if_admin(self):
# User is a contributor on a project
project = ProjectFactory(is_public=True)
project.add_contributor(
self.user,
permissions=['read', 'write', 'admin'],
save=True)
# User goes to the project page
res = self.app.get(project.url, auth=self.auth).maybe_follow()
assert_in('Make Private', res)
def test_cant_see_make_private_button_if_not_admin(self):
# User is a contributor on a project
project = ProjectFactory(is_public=True)
project.add_contributor(
self.user,
permissions=['read', 'write'],
save=True)
# User goes to the project page
res = self.app.get(project.url, auth=self.auth).maybe_follow()
assert_not_in('Make Private', res)
def test_sees_logs_on_a_project(self):
project = ProjectFactory(is_public=True)
# User goes to the project's page
res = self.app.get(project.url, auth=self.auth).maybe_follow()
# Can see log event
assert_in('created', res)
def test_no_wiki_content_message(self):
project = ProjectFactory(creator=self.user)
# Goes to project's wiki, where there is no content
res = self.app.get('/{0}/wiki/home/'.format(project._primary_key), auth=self.auth)
# Sees a message indicating no content
assert_in('Add important information, links, or images here to describe your project.', res)
# Sees that edit panel is open by default when home wiki has no content
assert_in('panelsUsed: ["view", "menu", "edit"]', res)
def test_wiki_content(self):
project = ProjectFactory(creator=self.user)
wiki_page_name = 'home'
wiki_content = 'Kittens'
wiki_page = WikiFactory(
user=self.user,
node=project,
)
wiki = WikiVersionFactory(
wiki_page=wiki_page,
content=wiki_content
)
res = self.app.get('/{0}/wiki/{1}/'.format(
project._primary_key,
wiki_page_name,
), auth=self.auth)
assert_not_in('Add important information, links, or images here to describe your project.', res)
assert_in(wiki_content, res)
assert_in('panelsUsed: ["view", "menu"]', res)
def test_wiki_page_name_non_ascii(self):
project = ProjectFactory(creator=self.user)
non_ascii = to_mongo_key('WöRlÐé')
WikiPage.objects.create_for_node(project, 'WöRlÐé', 'new content', Auth(self.user))
wv = WikiVersion.objects.get_for_node(project, non_ascii)
assert wv.wiki_page.page_name.upper() == non_ascii.decode('utf-8').upper()
def test_noncontributor_cannot_see_wiki_if_no_content(self):
user2 = UserFactory()
# user2 creates a public project and adds no wiki content
project = ProjectFactory(creator=user2, is_public=True)
# self navigates to project
res = self.app.get(project.url).maybe_follow()
# Should not see wiki widget (since non-contributor and no content)
assert_not_in('Add important information, links, or images here to describe your project.', res)
def test_wiki_does_not_exist(self):
project = ProjectFactory(creator=self.user)
res = self.app.get('/{0}/wiki/{1}/'.format(
project._primary_key,
'not a real page yet',
), auth=self.auth, expect_errors=True)
assert_in('Add important information, links, or images here to describe your project.', res)
def test_sees_own_profile(self):
res = self.app.get('/profile/', auth=self.auth)
td1 = res.html.find('td', text=re.compile(r'Public(.*?)Profile'))
td2 = td1.find_next_sibling('td')
assert_equal(td2.text, self.user.display_absolute_url)
def test_sees_another_profile(self):
user2 = UserFactory()
res = self.app.get(user2.url, auth=self.auth)
td1 = res.html.find('td', text=re.compile(r'Public(.*?)Profile'))
td2 = td1.find_next_sibling('td')
assert_equal(td2.text, user2.display_absolute_url)
@pytest.mark.enable_bookmark_creation
class TestComponents(OsfTestCase):
def setUp(self):
super(TestComponents, self).setUp()
self.user = AuthUserFactory()
self.consolidate_auth = Auth(user=self.user)
self.project = ProjectFactory(creator=self.user)
self.project.add_contributor(contributor=self.user, auth=self.consolidate_auth)
# A non-project componenet
self.component = NodeFactory(
category='hypothesis',
creator=self.user,
parent=self.project,
)
self.component.save()
self.component.set_privacy('public', self.consolidate_auth)
self.component.set_privacy('private', self.consolidate_auth)
self.project.save()
self.project_url = self.project.web_url_for('view_project')
def test_sees_parent(self):
res = self.app.get(self.component.url, auth=self.user.auth).maybe_follow()
parent_title = res.html.find_all('h2', class_='node-parent-title')
assert_equal(len(parent_title), 1)
assert_in(self.project.title, parent_title[0].text) # Bs4 will handle unescaping HTML here
def test_delete_project(self):
res = self.app.get(
self.component.url + 'settings/',
auth=self.user.auth
).maybe_follow()
assert_in(
'Delete {0}'.format(self.component.project_or_component),
res
)
def test_cant_delete_project_if_not_admin(self):
non_admin = AuthUserFactory()
self.component.add_contributor(
non_admin,
permissions=['read', 'write'],
auth=self.consolidate_auth,
save=True,
)
res = self.app.get(
self.component.url + 'settings/',
auth=non_admin.auth
).maybe_follow()
assert_not_in(
'Delete {0}'.format(self.component.project_or_component),
res
)
def test_can_configure_comments_if_admin(self):
res = self.app.get(
self.component.url + 'settings/',
auth=self.user.auth,
).maybe_follow()
assert_in('Commenting', res)
def test_cant_configure_comments_if_not_admin(self):
non_admin = AuthUserFactory()
self.component.add_contributor(
non_admin,
permissions=['read', 'write'],
auth=self.consolidate_auth,
save=True,
)
res = self.app.get(
self.component.url + 'settings/',
auth=non_admin.auth
).maybe_follow()
assert_not_in('Commenting', res)
def test_components_should_have_component_list(self):
res = self.app.get(self.component.url, auth=self.user.auth)
assert_in('Components', res)
@pytest.mark.enable_bookmark_creation
class TestPrivateLinkView(OsfTestCase):
def setUp(self):
super(TestPrivateLinkView, self).setUp()
self.user = AuthUserFactory() # Is NOT a contributor
self.project = ProjectFactory(is_public=False)
self.link = PrivateLinkFactory(anonymous=True)
self.link.nodes.add(self.project)
self.link.save()
self.project_url = self.project.web_url_for('view_project')
def test_anonymous_link_hide_contributor(self):
res = self.app.get(self.project_url, {'view_only': self.link.key})
assert_in('Anonymous Contributors', res.body)
assert_not_in(self.user.fullname, res)
def test_anonymous_link_hides_citations(self):
res = self.app.get(self.project_url, {'view_only': self.link.key})
assert_not_in('Citation:', res)
def test_no_warning_for_read_only_user_with_valid_link(self):
link2 = PrivateLinkFactory(anonymous=False)
link2.nodes.add(self.project)
link2.save()
self.project.add_contributor(
self.user,
permissions=['read'],
save=True,
)
res = self.app.get(self.project_url, {'view_only': link2.key},
auth=self.user.auth)
assert_not_in(
'is being viewed through a private, view-only link. '
'Anyone with the link can view this project. Keep '
'the link safe.',
res.body
)
def test_no_warning_for_read_only_user_with_invalid_link(self):
self.project.add_contributor(
self.user,
permissions=['read'],
save=True,
)
res = self.app.get(self.project_url, {'view_only': 'not_valid'},
auth=self.user.auth)
assert_not_in(
'is being viewed through a private, view-only link. '
'Anyone with the link can view this project. Keep '
'the link safe.',
res.body
)
@pytest.mark.enable_bookmark_creation
@pytest.mark.enable_quickfiles_creation
class TestMergingAccounts(OsfTestCase):
def setUp(self):
super(TestMergingAccounts, self).setUp()
self.user = UserFactory.build()
self.user.fullname = "tess' test string"
self.user.set_password('science')
self.user.save()
self.dupe = UserFactory.build()
self.dupe.set_password('example')
self.dupe.save()
def test_merged_user_is_not_shown_as_a_contributor(self):
project = ProjectFactory(is_public=True)
# Both the master and dupe are contributors
project.add_contributor(self.dupe, log=False)
project.add_contributor(self.user, log=False)
project.save()
# At the project page, both are listed as contributors
res = self.app.get(project.url).maybe_follow()
assert_in_html(self.user.fullname, res)
assert_in_html(self.dupe.fullname, res)
# The accounts are merged
self.user.merge_user(self.dupe)
self.user.save()
# Now only the master user is shown at the project page
res = self.app.get(project.url).maybe_follow()
assert_in_html(self.user.fullname, res)
assert_true(self.dupe.is_merged)
assert_not_in(self.dupe.fullname, res)
def test_merged_user_has_alert_message_on_profile(self):
# Master merges dupe
self.user.merge_user(self.dupe)
self.user.save()
# At the dupe user's profile there is an alert message at the top
# indicating that the user is merged
res = self.app.get('/profile/{0}/'.format(self.dupe._primary_key)).maybe_follow()
assert_in('This account has been merged', res)
@pytest.mark.enable_bookmark_creation
class TestShortUrls(OsfTestCase):
def setUp(self):
super(TestShortUrls, self).setUp()
self.user = AuthUserFactory()
self.auth = self.user.auth
self.consolidate_auth = Auth(user=self.user)
self.project = ProjectFactory(creator=self.user)
# A non-project componenet
self.component = NodeFactory(parent=self.project, category='hypothesis', creator=self.user)
# Hack: Add some logs to component; should be unnecessary pending
# improvements to factories from @rliebz
self.component.set_privacy('public', auth=self.consolidate_auth)
self.component.set_privacy('private', auth=self.consolidate_auth)
self.wiki = WikiFactory(
user=self.user,
node=self.component,
)
def _url_to_body(self, url):
return self.app.get(
url,
auth=self.auth
).maybe_follow(
auth=self.auth,
).normal_body
def test_project_url(self):
assert_equal(
self._url_to_body(self.project.deep_url),
self._url_to_body(self.project.url),
)
def test_component_url(self):
assert_equal(
self._url_to_body(self.component.deep_url),
self._url_to_body(self.component.url),
)
def test_wiki_url(self):
assert_equal(
self._url_to_body(self.wiki.deep_url),
self._url_to_body(self.wiki.url),
)
@pytest.mark.enable_bookmark_creation
@pytest.mark.enable_implicit_clean
class TestClaiming(OsfTestCase):
def setUp(self):
super(TestClaiming, self).setUp()
self.referrer = AuthUserFactory()
self.project = ProjectFactory(creator=self.referrer, is_public=True)
def test_correct_name_shows_in_contributor_list(self):
name1, email = fake.name(), fake_email()
UnregUserFactory(fullname=name1, email=email)
name2, email = fake.name(), fake_email()
# Added with different name
self.project.add_unregistered_contributor(fullname=name2,
email=email, auth=Auth(self.referrer))
self.project.save()
res = self.app.get(self.project.url, auth=self.referrer.auth)
# Correct name is shown
assert_in_html(name2, res)
assert_not_in(name1, res)
def test_user_can_set_password_on_claim_page(self):
name, email = fake.name(), fake_email()
new_user = self.project.add_unregistered_contributor(
email=email,
fullname=name,
auth=Auth(self.referrer)
)
self.project.save()
claim_url = new_user.get_claim_url(self.project._primary_key)
res = self.app.get(claim_url)
self.project.reload()
assert_in('Set Password', res)
form = res.forms['setPasswordForm']
#form['username'] = new_user.username #Removed as long as E-mail can't be updated.
form['password'] = 'killerqueen'
form['password2'] = 'killerqueen'
res = form.submit().follow()
new_user.reload()
assert_true(new_user.check_password('killerqueen'))
def test_sees_is_redirected_if_user_already_logged_in(self):
name, email = fake.name(), fake_email()
new_user = self.project.add_unregistered_contributor(
email=email,
fullname=name,
auth=Auth(self.referrer)
)
self.project.save()
existing = AuthUserFactory()
claim_url = new_user.get_claim_url(self.project._primary_key)
# a user is already logged in
res = self.app.get(claim_url, auth=existing.auth, expect_errors=True)
assert_equal(res.status_code, 302)
def test_unregistered_users_names_are_project_specific(self):
name1, name2, email = fake.name(), fake.name(), fake_email()
project2 = ProjectFactory(creator=self.referrer)
# different projects use different names for the same unreg contributor
self.project.add_unregistered_contributor(
email=email,
fullname=name1,
auth=Auth(self.referrer)
)
self.project.save()
project2.add_unregistered_contributor(
email=email,
fullname=name2,
auth=Auth(self.referrer)
)
project2.save()
self.app.authenticate(*self.referrer.auth)
# Each project displays a different name in the contributor list
res = self.app.get(self.project.url)
assert_in_html(name1, res)
res2 = self.app.get(project2.url)
assert_in_html(name2, res2)
@unittest.skip('as long as E-mails cannot be changed')
def test_cannot_set_email_to_a_user_that_already_exists(self):
reg_user = UserFactory()
name, email = fake.name(), fake_email()
new_user = self.project.add_unregistered_contributor(
email=email,
fullname=name,
auth=Auth(self.referrer)
)
self.project.save()
# Goes to claim url and successfully claims account
claim_url = new_user.get_claim_url(self.project._primary_key)
res = self.app.get(claim_url)
self.project.reload()
assert_in('Set Password', res)
form = res.forms['setPasswordForm']
# Fills out an email that is the username of another user
form['username'] = reg_user.username
form['password'] = 'killerqueen'
form['password2'] = 'killerqueen'
res = form.submit().maybe_follow(expect_errors=True)
assert_in(
language.ALREADY_REGISTERED.format(email=reg_user.username),
res
)
def test_correct_display_name_is_shown_at_claim_page(self):
original_name = fake.name()
unreg = UnregUserFactory(fullname=original_name)
different_name = fake.name()
new_user = self.project.add_unregistered_contributor(
email=unreg.username,
fullname=different_name,
auth=Auth(self.referrer),
)
self.project.save()
claim_url = new_user.get_claim_url(self.project._primary_key)
res = self.app.get(claim_url)
# Correct name (different_name) should be on page
assert_in_html(different_name, res)
class TestConfirmingEmail(OsfTestCase):
def setUp(self):
super(TestConfirmingEmail, self).setUp()
self.user = UnconfirmedUserFactory()
self.confirmation_url = self.user.get_confirmation_url(
self.user.username,
external=False,
)
self.confirmation_token = self.user.get_confirmation_token(
self.user.username
)
def test_cannot_remove_another_user_email(self):
user1 = AuthUserFactory()
user2 = AuthUserFactory()
url = api_url_for('update_user')
header = {'id': user1.username, 'emails': [{'address': user1.username}]}
res = self.app.put_json(url, header, auth=user2.auth, expect_errors=True)
assert_equal(res.status_code, 403)
def test_cannnot_make_primary_email_for_another_user(self):
user1 = AuthUserFactory()
user2 = AuthUserFactory()
email = 'test@cos.io'
user1.emails.create(address=email)
user1.save()
url = api_url_for('update_user')
header = {'id': user1.username,
'emails': [{'address': user1.username, 'primary': False, 'confirmed': True},
{'address': email, 'primary': True, 'confirmed': True}
]}
res = self.app.put_json(url, header, auth=user2.auth, expect_errors=True)
assert_equal(res.status_code, 403)
def test_cannnot_add_email_for_another_user(self):
user1 = AuthUserFactory()
user2 = AuthUserFactory()
email = 'test@cos.io'
url = api_url_for('update_user')
header = {'id': user1.username,
'emails': [{'address': user1.username, 'primary': True, 'confirmed': True},
{'address': email, 'primary': False, 'confirmed': False}
]}
res = self.app.put_json(url, header, auth=user2.auth, expect_errors=True)
assert_equal(res.status_code, 403)
def test_error_page_if_confirm_link_is_used(self):
self.user.confirm_email(self.confirmation_token)
self.user.save()
res = self.app.get(self.confirmation_url, expect_errors=True)
assert_in(auth_exc.InvalidTokenError.message_short, res)
assert_equal(res.status_code, http.BAD_REQUEST)
@pytest.mark.enable_implicit_clean
@pytest.mark.enable_bookmark_creation
class TestClaimingAsARegisteredUser(OsfTestCase):
def setUp(self):
super(TestClaimingAsARegisteredUser, self).setUp()
self.referrer = AuthUserFactory()
self.project = ProjectFactory(creator=self.referrer, is_public=True)
name, email = fake.name(), fake_email()
self.user = self.project.add_unregistered_contributor(
fullname=name,
email=email,
auth=Auth(user=self.referrer)
)
self.project.save()
def test_claim_user_registered_with_correct_password(self):
reg_user = AuthUserFactory() # NOTE: AuthUserFactory sets password as 'queenfan86'
url = self.user.get_claim_url(self.project._primary_key)
# Follow to password re-enter page
res = self.app.get(url, auth=reg_user.auth).follow(auth=reg_user.auth)
# verify that the "Claim Account" form is returned
assert_in('Claim Contributor', res.body)
form = res.forms['claimContributorForm']
form['password'] = 'queenfan86'
res = form.submit(auth=reg_user.auth)
res = res.follow(auth=reg_user.auth)
self.project.reload()
self.user.reload()
# user is now a contributor to the project
assert_in(reg_user, self.project.contributors)
# the unregistered user (self.user) is removed as a contributor, and their
assert_not_in(self.user, self.project.contributors)
# unclaimed record for the project has been deleted
assert_not_in(self.project, self.user.unclaimed_records)
@pytest.mark.enable_implicit_clean
class TestExplorePublicActivity(OsfTestCase):
def setUp(self):
super(TestExplorePublicActivity, self).setUp()
self.project = ProjectFactory(is_public=True)
self.registration = RegistrationFactory(project=self.project)
self.private_project = ProjectFactory(title='Test private project')
self.popular_project = ProjectFactory(is_public=True)
self.popular_registration = RegistrationFactory(project=self.project, is_public=True)
# Add project to new and noteworthy projects
self.new_and_noteworthy_links_node = ProjectFactory(is_public=True)
self.new_and_noteworthy_links_node._id = settings.NEW_AND_NOTEWORTHY_LINKS_NODE
self.new_and_noteworthy_links_node.add_pointer(self.project, auth=Auth(self.new_and_noteworthy_links_node.creator), save=True)
# Set up popular projects and registrations
self.popular_links_node = ProjectFactory(is_public=True)
settings.POPULAR_LINKS_NODE = self.popular_links_node._id
self.popular_links_node.add_pointer(self.popular_project, auth=Auth(self.popular_links_node.creator), save=True)
self.popular_links_registrations = ProjectFactory(is_public=True)
settings.POPULAR_LINKS_REGISTRATIONS = self.popular_links_registrations._id
self.popular_links_registrations.add_pointer(self.popular_registration, auth=Auth(self.popular_links_registrations.creator), save=True)
def test_explore_page_loads_when_settings_not_configured(self):
old_settings_values = settings.POPULAR_LINKS_NODE, settings.NEW_AND_NOTEWORTHY_LINKS_NODE, settings.POPULAR_LINKS_REGISTRATIONS
settings.POPULAR_LINKS_NODE = 'notanode'
settings.NEW_AND_NOTEWORTHY_LINKS_NODE = 'alsototallywrong'
settings.POPULAR_LINKS_REGISTRATIONS = 'nopenope'
url = self.project.web_url_for('activity')
res = self.app.get(url)
assert_equal(res.status_code, 200)
settings.POPULAR_LINKS_NODE, settings.NEW_AND_NOTEWORTHY_LINKS_NODE, settings.POPULAR_LINKS_REGISTRATIONS = old_settings_values
def test_new_and_noteworthy_and_popular_nodes_show_in_explore_activity(self):
url = self.project.web_url_for('activity')
res = self.app.get(url)
assert_equal(res.status_code, 200)
# New and Noteworthy
assert_in(str(self.project.title), res)
assert_in(str(self.project.created.date()), res)
assert_in(str(self.registration.title), res)
assert_in(str(self.registration.registered_date.date()), res)
assert_not_in(str(self.private_project.title), res)
# Popular Projects and Registrations
assert_in(str(self.popular_project.title), res)
assert_in(str(self.popular_project.created.date()), res)
assert_in(str(self.popular_registration.title), res)
assert_in(str(self.popular_registration.registered_date.date()), res)
class TestResendConfirmation(OsfTestCase):
def setUp(self):
super(TestResendConfirmation, self).setUp()
self.unconfirmed_user = UnconfirmedUserFactory()
self.confirmed_user = UserFactory()
self.get_url = web_url_for('resend_confirmation_get')
self.post_url = web_url_for('resend_confirmation_post')
# test that resend confirmation page is load correctly
def test_resend_confirmation_get(self):
res = self.app.get(self.get_url)
assert_equal(res.status_code, 200)
assert_in('Resend Confirmation', res.body)
assert_in('resendForm', res.forms)
# test that unconfirmed user can receive resend confirmation email
@mock.patch('framework.auth.views.mails.send_mail')
def test_can_receive_resend_confirmation_email(self, mock_send_mail):
# load resend confirmation page and submit email
res = self.app.get(self.get_url)
form = res.forms['resendForm']
form['email'] = self.unconfirmed_user.unconfirmed_emails[0]
res = form.submit()
# check email, request and response
assert_true(mock_send_mail.called)
assert_equal(res.status_code, 200)
assert_equal(res.request.path, self.post_url)
assert_in_html('If there is an OSF account', res)
# test that confirmed user cannot receive resend confirmation email
@mock.patch('framework.auth.views.mails.send_mail')
def test_cannot_receive_resend_confirmation_email_1(self, mock_send_mail):
# load resend confirmation page and submit email
res = self.app.get(self.get_url)
form = res.forms['resendForm']
form['email'] = self.confirmed_user.emails.first().address
res = form.submit()
# check email, request and response
assert_false(mock_send_mail.called)
assert_equal(res.status_code, 200)
assert_equal(res.request.path, self.post_url)
assert_in_html('has already been confirmed', res)
# test that non-existing user cannot receive resend confirmation email
@mock.patch('framework.auth.views.mails.send_mail')
def test_cannot_receive_resend_confirmation_email_2(self, mock_send_mail):
# load resend confirmation page and submit email
res = self.app.get(self.get_url)
form = res.forms['resendForm']
form['email'] = 'random@random.com'
res = form.submit()
# check email, request and response
assert_false(mock_send_mail.called)
assert_equal(res.status_code, 200)
assert_equal(res.request.path, self.post_url)
assert_in_html('If there is an OSF account', res)
# test that user cannot submit resend confirmation request too quickly
@mock.patch('framework.auth.views.mails.send_mail')
def test_cannot_resend_confirmation_twice_quickly(self, mock_send_mail):
# load resend confirmation page and submit email
res = self.app.get(self.get_url)
form = res.forms['resendForm']
form['email'] = self.unconfirmed_user.email
res = form.submit()
res = form.submit()
# check request and response
assert_equal(res.status_code, 200)
assert_in_html('Please wait', res)
class TestForgotPassword(OsfTestCase):
def setUp(self):
super(TestForgotPassword, self).setUp()
self.user = UserFactory()
self.auth_user = AuthUserFactory()
self.get_url = web_url_for('forgot_password_get')
self.post_url = web_url_for('forgot_password_post')
self.user.verification_key_v2 = {}
self.user.save()
# log users out before they land on forgot password page
def test_forgot_password_logs_out_user(self):
# visit forgot password link while another user is logged in
res = self.app.get(self.get_url, auth=self.auth_user.auth)
# check redirection to CAS logout
assert_equal(res.status_code, 302)
location = res.headers.get('Location')
assert_not_in('reauth', location)
assert_in('logout?service=', location)
assert_in('forgotpassword', location)
# test that forgot password page is loaded correctly
def test_get_forgot_password(self):
res = self.app.get(self.get_url)
assert_equal(res.status_code, 200)
assert_in('Forgot Password', res.body)
assert_in('forgotPasswordForm', res.forms)
# test that existing user can receive reset password email
@mock.patch('framework.auth.views.mails.send_mail')
def test_can_receive_reset_password_email(self, mock_send_mail):
# load forgot password page and submit email
res = self.app.get(self.get_url)
form = res.forms['forgotPasswordForm']
form['forgot_password-email'] = self.user.username
res = form.submit()
# check mail was sent
assert_true(mock_send_mail.called)
# check http 200 response
assert_equal(res.status_code, 200)
# check request URL is /forgotpassword
assert_equal(res.request.path, self.post_url)
# check push notification
assert_in_html('If there is an OSF account', res)
assert_not_in_html('Please wait', res)
# check verification_key_v2 is set
self.user.reload()
assert_not_equal(self.user.verification_key_v2, {})
# test that non-existing user cannot receive reset password email
@mock.patch('framework.auth.views.mails.send_mail')
def test_cannot_receive_reset_password_email(self, mock_send_mail):
# load forgot password page and submit email
res = self.app.get(self.get_url)
form = res.forms['forgotPasswordForm']
form['forgot_password-email'] = 'fake' + self.user.username
res = form.submit()
# check mail was not sent
assert_false(mock_send_mail.called)
# check http 200 response
assert_equal(res.status_code, 200)
# check request URL is /forgotpassword
assert_equal(res.request.path, self.post_url)
# check push notification
assert_in_html('If there is an OSF account', res)
assert_not_in_html('Please wait', res)
# check verification_key_v2 is not set
self.user.reload()
assert_equal(self.user.verification_key_v2, {})
# test that non-existing user cannot receive reset password email
@mock.patch('framework.auth.views.mails.send_mail')
def test_not_active_user_no_reset_password_email(self, mock_send_mail):
self.user.disable_account()
self.user.save()
# load forgot password page and submit email
res = self.app.get(self.get_url)
form = res.forms['forgotPasswordForm']
form['forgot_password-email'] = self.user.username
res = form.submit()
# check mail was not sent
assert_false(mock_send_mail.called)
# check http 200 response
assert_equal(res.status_code, 200)
# check request URL is /forgotpassword
assert_equal(res.request.path, self.post_url)
# check push notification
assert_in_html('If there is an OSF account', res)
assert_not_in_html('Please wait', res)
# check verification_key_v2 is not set
self.user.reload()
assert_equal(self.user.verification_key_v2, {})
# test that user cannot submit forgot password request too quickly
@mock.patch('framework.auth.views.mails.send_mail')
def test_cannot_reset_password_twice_quickly(self, mock_send_mail):
# load forgot password page and submit email
res = self.app.get(self.get_url)
form = res.forms['forgotPasswordForm']
form['forgot_password-email'] = self.user.username
res = form.submit()
res = form.submit()
# check http 200 response
assert_equal(res.status_code, 200)
# check push notification
assert_in_html('Please wait', res)
assert_not_in_html('If there is an OSF account', res)
@unittest.skip('Public projects/components are dynamically loaded now.')
class TestAUserProfile(OsfTestCase):
def setUp(self):
OsfTestCase.setUp(self)
self.user = AuthUserFactory()
self.me = AuthUserFactory()
self.project = ProjectFactory(creator=self.me, is_public=True, title=fake.bs())
self.component = NodeFactory(creator=self.me, parent=self.project, is_public=True, title=fake.bs())
# regression test for https://github.com/CenterForOpenScience/osf.io/issues/2623
def test_has_public_projects_and_components(self):
# I go to my own profile
url = web_url_for('profile_view_id', uid=self.me._primary_key)
# I see the title of both my project and component
res = self.app.get(url, auth=self.me.auth)
assert_in_html(self.component.title, res)
assert_in_html(self.project.title, res)
# Another user can also see my public project and component
url = web_url_for('profile_view_id', uid=self.me._primary_key)
# I see the title of both my project and component
res = self.app.get(url, auth=self.user.auth)
assert_in_html(self.component.title, res)
assert_in_html(self.project.title, res)
def test_shows_projects_with_many_contributors(self):
# My project has many contributors
for _ in range(5):
user = UserFactory()
self.project.add_contributor(user, auth=Auth(self.project.creator), save=True)
# I go to my own profile
url = web_url_for('profile_view_id', uid=self.me._primary_key)
res = self.app.get(url, auth=self.me.auth)
# I see '3 more' as a link
assert_in('3 more', res)
res = res.click('3 more')
assert_equal(res.request.path, self.project.url)
def test_has_no_public_projects_or_components_on_own_profile(self):
# User goes to their profile
url = web_url_for('profile_view_id', uid=self.user._id)
res = self.app.get(url, auth=self.user.auth)
# user has no public components/projects
assert_in('You have no public projects', res)
assert_in('You have no public components', res)
def test_user_no_public_projects_or_components(self):
# I go to other user's profile
url = web_url_for('profile_view_id', uid=self.user._id)
# User has no public components/projects
res = self.app.get(url, auth=self.me.auth)
assert_in('This user has no public projects', res)
assert_in('This user has no public components', res)
# regression test
def test_does_not_show_registrations(self):
project = ProjectFactory(creator=self.user)
component = NodeFactory(parent=project, creator=self.user, is_public=False)
# User has a registration with public components
reg = RegistrationFactory(project=component.parent_node, creator=self.user, is_public=True)
for each in reg.nodes:
each.is_public = True
each.save()
# I go to other user's profile
url = web_url_for('profile_view_id', uid=self.user._id)
# Registration does not appear on profile
res = self.app.get(url, auth=self.me.auth)
assert_in('This user has no public components', res)
assert_not_in(reg.title, res)
assert_not_in(reg.nodes[0].title, res)
@pytest.mark.enable_bookmark_creation
class TestPreprintBannerView(OsfTestCase):
def setUp(self):
super(TestPreprintBannerView, self).setUp()
self.admin = AuthUserFactory()
self.provider_one = PreprintProviderFactory()
self.provider_two = PreprintProviderFactory()
self.project_one = ProjectFactory(creator=self.admin, is_public=True)
self.project_two = ProjectFactory(creator=self.admin, is_public=True)
self.project_three = ProjectFactory(creator=self.admin, is_public=True)
self.subject_one = SubjectFactory()
self.subject_two = SubjectFactory()
self.file_one = test_utils.create_test_file(self.project_one, self.admin, 'mgla.pdf')
self.file_two = test_utils.create_test_file(self.project_two, self.admin, 'saor.pdf')
self.published_preprint = PreprintFactory(creator=self.admin, filename='mgla.pdf', provider=self.provider_one, subjects=[[self.subject_one._id]], project=self.project_one, is_published=True)
self.unpublished_preprint = PreprintFactory(creator=self.admin, filename='saor.pdf', provider=self.provider_two, subjects=[[self.subject_two._id]], project=self.project_two, is_published=False)
def test_public_project_published_preprint(self):
url = self.project_one.web_url_for('view_project')
res = self.app.get(url, auth=self.admin.auth)
assert_not_in('has a preprint, but has been made Private. Make your preprint discoverable by making this', res.body)
def test_private_project_published_preprint(self):
self.project_one.is_public = False
self.project_one.save()
url = self.project_one.web_url_for('view_project')
res = self.app.get(url, auth=self.admin.auth)
assert_in('has a preprint, but has been made Private. Make your preprint discoverable by making this', res.body)
def test_public_project_unpublished_preprint(self):
url = self.project_two.web_url_for('view_project')
res = self.app.get(url, auth=self.admin.auth)
assert_not_in('has a preprint, but has been made Private. Make your preprint discoverable by making this', res.body)
def test_private_project_unpublished_preprint(self):
# Do not show banner on unpublished preprints
self.project_two.is_public = False
self.project_two.save()
url = self.project_two.web_url_for('view_project')
res = self.app.get(url, auth=self.admin.auth)
assert_not_in('has a preprint, but has been made Private. Make your preprint discoverable by making this', res.body)
def test_public_project_no_preprint(self):
url = self.project_three.web_url_for('view_project')
res = self.app.get(url, auth=self.admin.auth)
assert_not_in('has a preprint, but has been made Private. Make your preprint discoverable by making this', res.body)
def test_private_project_no_preprint(self):
self.project_three.is_public = False
self.project_three.save()
url = self.project_three.web_url_for('view_project')
res = self.app.get(url, auth=self.admin.auth)
assert_not_in('has a preprint, but has been made Private. Make your preprint discoverable by making this', res.body)
if __name__ == '__main__':
unittest.main()
|
|
|
website/archiver/tasks.py
|
import os.path as osp
import numpy as np
# Import sile objects
from .sile import *
from sisl._internal import set_module
from sisl import Geometry, AtomUnknown, SuperCell
from sisl.utils import str_spec
__all__ = ['xsfSile', 'axsfSile']
@set_module("sisl.io")
class xsfSile(Sile):
""" XSF file for XCrySDen """
def _setup(self, *args, **kwargs):
""" Setup the `xsfSile` after initialization """
self._comment = ['#']
self._md_steps = kwargs.get('steps', None)
self._md_index = 0
def _step_md(self):
""" Step the MD counter """
self._md_index += 1
@sile_fh_open()
def write_supercell(self, sc, fmt='.8f'):
""" Writes the supercell to the contained file
Parameters
----------
sc : SuperCell
the supercell to be written
fmt : str, optional
used format for the precision of the data
"""
# Implementation notice!
# The XSF files are compatible with Vesta, but ONLY
# if there are no empty lines!
# Check that we can write to the file
sile_raise_write(self)
# Write out top-header stuff
from time import gmtime, strftime
self._write('# File created by: sisl {}\n#\n'.format(strftime("%Y-%m-%d", gmtime())))
# Print out the number of ANIMSTEPS (if required)
if not self._md_steps is None:
self._write(f'ANIMSTEPS {self._md_steps}\n')
self._write('CRYSTAL\n#\n')
if self._md_index == 1:
self._write('# Primitive lattice vectors:\n#\n')
if self._md_steps is None:
self._write('PRIMVEC\n')
else:
self._write(f'PRIMVEC {self._md_index}\n')
# We write the cell coordinates as the cell coordinates
fmt_str = f'{{:{fmt}}} ' * 3 + '\n'
for i in [0, 1, 2]:
self._write(fmt_str.format(*sc.cell[i, :]))
# Currently not written (we should convert the unit cell
# to a conventional cell (90-90-90))
# It seems this simply allows to store both formats in
# the same file.
self._write('#\n# Conventional lattice vectors:\n#\n')
if self._md_steps is None:
self._write('CONVVEC\n')
else:
self._write(f'CONVVEC {self._md_index}\n')
convcell = sc.toCuboid(True)._v
for i in [0, 1, 2]:
self._write(fmt_str.format(*convcell[i, :]))
@sile_fh_open()
def write_geometry(self, geometry, fmt='.8f', data=None):
""" Writes the geometry to the contained file
Parameters
----------
geometry : Geometry
the geometry to be written
fmt : str, optional
used format for the precision of the data
data : (geometry.na, 3), optional
auxiliary data associated with the geometry to be saved
along side. Internally in XCrySDen this data is named *Forces*
"""
self._step_md()
self.write_supercell(geometry.sc, fmt)
has_data = not data is None
if has_data:
data.shape = (-1, 3)
# The current geometry is currently only a single
# one, and does not write the convvec
# Is it a necessity?
if self._md_index == 1:
self._write('#\n# Atomic coordinates (in primitive coordinates)\n#\n')
if self._md_steps is None:
self._write('PRIMCOORD\n')
else:
self._write(f'PRIMCOORD {self._md_index}\n')
self._write('{} {}\n'.format(len(geometry), 1))
non_valid_Z = (geometry.atoms.Z <= 0).nonzero()[0]
if len(non_valid_Z) > 0:
geometry = geometry.remove(non_valid_Z)
if has_data:
fmt_str = '{{0:3d}} {{1:{0}}} {{2:{0}}} {{3:{0}}} {{4:{0}}} {{5:{0}}} {{6:{0}}}\n'.format(fmt)
for ia in geometry:
tmp = np.append(geometry.xyz[ia, :], data[ia, :])
self._write(fmt_str.format(geometry.atoms[ia].Z, *tmp))
else:
fmt_str = '{{0:3d}} {{1:{0}}} {{2:{0}}} {{3:{0}}}\n'.format(fmt)
for ia in geometry:
self._write(fmt_str.format(geometry.atoms[ia].Z, *geometry.xyz[ia, :]))
@sile_fh_open()
def read_geometry(self, data=False):
""" Returns Geometry object from the XSF file
Parameters
----------
data : bool, optional
in case the XSF file has auxiliary data, return that as well.
"""
# Prepare containers...
cell = np.zeros([3, 3], np.float64)
cell_set = False
atom = []
xyz = []
na = 0
line = ' '
while line != '':
# skip comments
line = self.readline()
key = line.strip()
# We prefer the primvec
if key.startswith('CONVVEC') and not cell_set:
for i in [0, 1, 2]:
line = self.readline()
cell[i, :] = [float(x) for x in line.split()]
elif key.startswith('PRIMVEC'):
cell_set = True
for i in [0, 1, 2]:
line = self.readline()
cell[i, :] = [float(x) for x in line.split()]
elif key.startswith('PRIMCOORD'):
# First read # of atoms
line = self.readline().split()
na = int(line[0])
# currently line[1] is unused!
for _ in range(na):
line = self.readline().split()
atom.append(int(line[0]))
xyz.append([float(x) for x in line[1:]])
xyz = np.array(xyz, np.float64)
if data:
dat = None
if xyz.shape[1] == 6:
dat = xyz[:, 3:]
xyz = xyz[:, :3]
if len(atom) == 0:
geom = Geometry(xyz, sc=SuperCell(cell))
elif len(atom) == 1 and atom[0] == -999:
geom = None
else:
geom = Geometry(xyz, atoms=atom, sc=SuperCell(cell))
if data:
return geom, dat
return geom
@sile_fh_open()
def write_grid(self, *args, **kwargs):
""" Store grid(s) data to an XSF file
Examples
--------
>>> g1 = Grid(0.1, sc=2.)
>>> g2 = Grid(0.1, sc=2.)
>>> get_sile('output.xsf', 'w').write_grid(g1, g2)
Parameters
----------
*args : Grid
a list of data-grids to be written to the XSF file.
Each argument gets the field name ``?grid_<>`` where <> starts
with the integer 0, and *?* is ``real_``/``imag_`` for complex
valued grids.
geometry : Geometry, optional
the geometry stored in the file, defaults to ``args[0].geometry``
fmt : str, optional
floating point format for data (.5e)
buffersize : int, optional
size of the buffer while writing the data, (6144)
"""
sile_raise_write(self)
geom = kwargs.get('geometry', args[0].geometry)
if geom is None:
geom = Geometry([0, 0, 0], AtomUnknown(999), sc=args[0].sc)
self.write_geometry(geom)
# Buffer size for writing
buffersize = kwargs.get('buffersize', min(6144, args[0].grid.size))
# Format for precision
fmt = kwargs.get('fmt', '.5e')
self._write('BEGIN_BLOCK_DATAGRID_3D\n')
name = kwargs.get('name', 'sisl_grid_{}'.format(len(args)))
# Transfer all spaces to underscores (no spaces allowed)
self._write(' ' + name.replace(' ', '_') + '\n')
_v3 = (('{:' + fmt + '} ') * 3).strip() + '\n'
def write_cell(grid):
# Now write the grid
self._write(' {} {} {}\n'.format(*grid.shape))
self._write(' ' + _v3.format(*grid.origo))
self._write(' ' + _v3.format(*grid.cell[0, :]))
self._write(' ' + _v3.format(*grid.cell[1, :]))
self._write(' ' + _v3.format(*grid.cell[2, :]))
for i, grid in enumerate(args):
is_complex = np.iscomplexobj(grid.grid)
name = kwargs.get('grid' + str(i), str(i))
if is_complex:
self._write(f' BEGIN_DATAGRID_3D_real_{name}\n')
else:
self._write(f' BEGIN_DATAGRID_3D_{name}\n')
write_cell(grid)
# for z
# for y
# for x
# write...
_fmt = '{:' + fmt + '}\n'
for x in np.nditer(np.asarray(grid.grid.real.T, order='C').reshape(-1), flags=['external_loop', 'buffered'],
op_flags=[['readonly']], order='C', buffersize=buffersize):
self._write((_fmt * x.shape[0]).format(*x.tolist()))
self._write(' END_DATAGRID_3D\n')
# Skip if not complex
if not is_complex:
continue
self._write(f' BEGIN_DATAGRID_3D_imag_{name}\n')
write_cell(grid)
for x in np.nditer(np.asarray(grid.grid.imag.T, order='C').reshape(-1), flags=['external_loop', 'buffered'],
op_flags=[['readonly']], order='C', buffersize=buffersize):
self._write((_fmt * x.shape[0]).format(*x.tolist()))
self._write(' END_DATAGRID_3D\n')
self._write('END_BLOCK_DATAGRID_3D\n')
def ArgumentParser(self, p=None, *args, **kwargs):
""" Returns the arguments that is available for this Sile """
newkw = Geometry._ArgumentParser_args_single()
newkw.update(kwargs)
return self.read_geometry().ArgumentParser(p, *args, **newkw)
def ArgumentParser_out(self, p, *args, **kwargs):
""" Adds arguments only if this file is an output file
Parameters
----------
p : ``argparse.ArgumentParser``
the parser which gets amended the additional output options.
"""
import argparse
ns = kwargs.get("namespace", None)
if ns is None:
class _():
pass
ns = _()
# We will add the vector data
class VectorNoScale(argparse.Action):
def __call__(self, parser, ns, no_value, option_string=None):
setattr(ns, "_vector_scale", False)
p.add_argument("--no-vector-scale", "-nsv", nargs=0,
action=VectorNoScale,
help='''Do not modify vector components (same as --vector-scale 1.)''')
# Default to scale the vectors
setattr(ns, "_vector_scale", True)
# We will add the vector data
class VectorScale(argparse.Action):
def __call__(self, parser, ns, value, option_string=None):
setattr(ns, '_vector_scale', float(value))
p.add_argument('--vector-scale', '-sv', metavar='SCALE',
action=VectorScale,
help='''Scale vector components by this factor.''')
# We will add the vector data
class Vectors(argparse.Action):
def __call__(self, parser, ns, values, option_string=None):
routine = values.pop(0)
# Default input file
input_file = getattr(ns, '_input_file', None)
# Figure out which of the segments are a file
for i, val in enumerate(values):
if osp.isfile(str_spec(val)[0]):
input_file = values.pop(i)
break
# Quick return if there is no input-file...
if input_file is None:
return
# Try and read the vector
from sisl.io import get_sile
input_sile = get_sile(input_file, mode='r')
vector = None
if hasattr(input_sile, f'read_{routine}'):
vector = getattr(input_sile, f'read_{routine}')(*values)
if vector is None:
# Try the read_data function
d = {routine: True}
vector = input_sile.read_data(*values, **d)
if vector is None and len(values) > 1:
# try and see if the first argument is a str, if
# so use that as a keyword
if isinstance(values[0], str):
d = {values[0]: True}
vector = input_sile.read_data(*values[1:], **d)
# Clean the sile
del input_sile
if vector is None:
# Use title to capitalize
raise ValueError('{} could not be read from file: {}.'.format(routine.title(), input_file))
if len(vector) != len(ns._geometry):
raise ValueError(f'read_{routine} could read from file: {input_file}, sizes does not conform to geometry.')
setattr(ns, '_vector', vector)
p.add_argument('--vector', '-v', metavar=('DATA', '*ARGS[, FILE]'), nargs='+',
action=Vectors,
help='''Adds vector arrows for each atom, first argument is type (force, moment, ...).
If the current input file contains the vectors no second argument is necessary, else
the file containing the data is required as the last input.
Any arguments inbetween are passed to the `read_data` function (in order).
By default the vectors scaled by 1 / max(|V|) such that the longest vector has length 1.
''')
@set_module("sisl.io")
class axsfSile(xsfSile):
""" AXSF file for XCrySDen
When creating an AXSF file one should denote how many MD steps to write out:
>>> axsf = axsfSile('file.axsf', steps=100)
>>> for i in range(100):
... axsf.write_geometry(geom)
"""
def _setup(self, *args, **kwargs):
# Correct number of steps
super()._setup(*args, **kwargs)
if not hasattr(self, '_md_steps'):
self._md_steps = 1
write_grid = None
add_sile('xsf', xsfSile, case=False, gzip=True)
add_sile('axsf', axsfSile, case=False, gzip=True)
|
import pytest
pytestmark = pytest.mark.spin
import math as m
import numpy as np
from sisl import Spin
def test_spin1():
for val in ['unpolarized', '', Spin.UNPOLARIZED,
'polarized', 'p', Spin.POLARIZED,
'non-collinear', 'nc', Spin.NONCOLINEAR,
'spin-orbit', 'so', Spin.SPINORBIT]:
s = Spin(val)
str(s)
s1 = s.copy()
assert s == s1
def test_spin2():
s1 = Spin()
s2 = Spin('p')
s3 = Spin('nc')
s4 = Spin('so')
assert s1.kind == Spin.UNPOLARIZED
assert s2.kind == Spin.POLARIZED
assert s3.kind == Spin.NONCOLINEAR
assert s4.kind == Spin.SPINORBIT
assert s1 == s1.copy()
assert s2 == s2.copy()
assert s3 == s3.copy()
assert s4 == s4.copy()
assert s1 < s2
assert s2 < s3
assert s3 < s4
assert s1 <= s2
assert s2 <= s3
assert s3 <= s4
assert s2 > s1
assert s3 > s2
assert s4 > s3
assert s2 >= s1
assert s3 >= s2
assert s4 >= s3
assert s1.is_unpolarized
assert not s1.is_polarized
assert not s1.is_noncolinear
assert not s1.is_spinorbit
assert not s2.is_unpolarized
assert s2.is_polarized
assert not s2.is_noncolinear
assert not s2.is_spinorbit
assert not s3.is_unpolarized
assert not s3.is_polarized
assert s3.is_noncolinear
assert not s3.is_spinorbit
assert not s4.is_unpolarized
assert not s4.is_polarized
assert not s4.is_noncolinear
assert s4.is_spinorbit
def test_spin3():
with pytest.raises(ValueError):
s = Spin('satoehus')
def test_spin4():
s1 = Spin(Spin.UNPOLARIZED)
S1 = Spin(Spin.UNPOLARIZED, np.complex64)
s2 = Spin(Spin.POLARIZED)
S2 = Spin(Spin.POLARIZED, np.complex64)
s3 = Spin(Spin.NONCOLINEAR)
S3 = Spin(Spin.NONCOLINEAR, np.complex64)
s4 = Spin(Spin.SPINORBIT)
S4 = Spin(Spin.SPINORBIT, np.complex64)
assert s1 == S1
assert s2 == S2
assert s3 == S3
assert s4 == S4
# real comparison
assert s1 < S2
assert s1 < S3
assert s1 < S4
assert s2 > S1
assert s2 < S3
assert s2 < S4
assert s3 > S1
assert s3 > S2
assert s3 < S4
assert s4 > S1
assert s4 > S2
assert s4 > S3
# complex complex
assert S1 < S2
assert S1 < S3
assert S1 < S4
assert S2 > S1
assert S2 < S3
assert S2 < S4
assert S3 > S1
assert S3 > S2
assert S3 < S4
assert S4 > S1
assert S4 > S2
assert S4 > S3
# real comparison
assert S1 < s2
assert S1 < s3
assert S1 < s4
assert S2 > s1
assert S2 < s3
assert S2 < s4
assert S3 > s1
assert S3 > s2
assert S3 < s4
assert S4 > s1
assert S4 > s2
assert S4 > s3
# complex complex
assert S1 < s2
assert S1 < s3
assert S1 < s4
assert S2 > s1
assert S2 < s3
assert S2 < s4
assert S3 > s1
assert S3 > s2
assert S3 < s4
assert S4 > s1
assert S4 > s2
assert S4 > s3
def test_pauli():
# just grab the default spin
S = Spin()
# Create a fictituous wave-function
sq2 = 2 ** .5
W = np.array([
[1/sq2, 1/sq2], # M_x = 1
[1/sq2, -1/sq2], # M_x = -1
[0.5 + 0.5j, 0.5 + 0.5j], # M_x = 1
[0.5 - 0.5j, -0.5 + 0.5j], # M_x = -1
[1/sq2, 1j/sq2], # M_y = 1
[1/sq2, -1j/sq2], # M_y = -1
[0.5 - 0.5j, 0.5 + 0.5j], # M_y = 1
[0.5 + 0.5j, 0.5 - 0.5j], # M_y = -1
[1, 0], # M_z = 1
[0, 1], # M_z = -1
])
x = np.array([1, -1, 1, -1, 0, 0, 0, 0, 0, 0])
assert np.allclose(x, (np.conj(W)*S.X.dot(W.T).T).sum(1).real)
y = np.array([0, 0, 0, 0, 1, -1, 1, -1, 0, 0])
assert np.allclose(y, (np.conj(W)*np.dot(S.Y, W.T).T).sum(1).real)
z = np.array([0, 0, 0, 0, 0, 0, 0, 0, 1, -1])
assert np.allclose(z, (np.conj(W)*np.dot(S.Z, W.T).T).sum(1).real)
def test_pickle():
import pickle as p
S = Spin('nc')
n = p.dumps(S)
s = p.loads(n)
assert S == s
|
|
sisl/physics/tests/test_spin.py
|
|
import warnings
from datetime import datetime
import numpy as np
import scipy as sp
from os.path import isfile
import itertools as itools
from ..sile import add_sile, get_sile_class, sile_fh_open, sile_raise_write, SileError
from .sile import SileSiesta
from .._help import *
from sisl._internal import set_module
from sisl import constant
from sisl.unit.siesta import units
import sisl._array as _a
from sisl._indices import indices_only
from sisl.utils.ranges import list2str
from sisl.messages import SislError, info, warn
from sisl.utils.mathematics import fnorm
from .binaries import tshsSileSiesta, tsdeSileSiesta
from .binaries import dmSileSiesta, hsxSileSiesta, onlysSileSiesta
from .eig import eigSileSiesta
from .fc import fcSileSiesta
from .fa import faSileSiesta
from .siesta_grid import gridncSileSiesta
from .siesta_nc import ncSileSiesta
from .basis import ionxmlSileSiesta, ionncSileSiesta
from .orb_indx import orbindxSileSiesta
from .xv import xvSileSiesta
from sisl import Geometry, Orbital, Atom, AtomGhost, Atoms, SuperCell, DynamicalMatrix
from sisl.utils.cmd import default_ArgumentParser, default_namespace
from sisl.utils.misc import merge_instances
from sisl.unit.siesta import unit_convert, unit_default, unit_group
__all__ = ['fdfSileSiesta']
_LOGICAL_TRUE = ['.true.', 'true', 'yes', 'y', 't']
_LOGICAL_FALSE = ['.false.', 'false', 'no', 'n', 'f']
_LOGICAL = _LOGICAL_FALSE + _LOGICAL_TRUE
Bohr2Ang = unit_convert('Bohr', 'Ang')
def _listify_str(arg):
if isinstance(arg, str):
return [arg]
return arg
def _track(method, msg):
if method.__self__.track:
info(f"{method.__self__.__class__.__name__}.{method.__name__}: {msg}")
def _track_file(method, f, msg=None):
if msg is None:
if f.is_file():
msg = f"reading file {f}"
else:
msg = f"could not find file {f}"
if method.__self__.track:
info(f"{method.__self__.__class__.__name__}.{method.__name__}: {msg}")
@set_module("sisl.io.siesta")
class fdfSileSiesta(SileSiesta):
""" FDF-input file
By supplying base you can reference files in other directories.
By default the ``base`` is the directory given in the file name.
Parameters
----------
filename: str
fdf file
mode : str, optional
opening mode, default to read-only
base : str, optional
base-directory to read output files from.
Examples
--------
>>> fdf = fdfSileSiesta('tmp/RUN.fdf') # reads output files in 'tmp/' folder
>>> fdf = fdfSileSiesta('tmp/RUN.fdf', base='.') # reads output files in './' folder
"""
def _setup(self, *args, **kwargs):
""" Setup the `fdfSileSiesta` after initialization """
self._comment = ['#', '!', ';']
# List of parent file-handles used while reading
# This is because fdf enables inclusion of other files
self._parent_fh = []
# Public key for printing information about where stuff comes from
self.track = kwargs.get("track", False)
def _pushfile(self, f):
if self.dir_file(f).is_file():
self._parent_fh.append(self.fh)
self.fh = self.dir_file(f).open(self._mode)
else:
warn(str(self) + f' is trying to include file: {f} but the file seems not to exist? Will disregard file!')
def _popfile(self):
if len(self._parent_fh) > 0:
self.fh.close()
self.fh = self._parent_fh.pop()
return True
return False
def _seek(self):
""" Closes all files, and starts over from beginning """
try:
while self._popfile():
pass
self.fh.seek(0)
except:
pass
@sile_fh_open()
def includes(self):
""" Return a list of all files that are *included* or otherwise necessary for reading the fdf file """
self._seek()
# In FDF files, %include marks files that progress
# down in a tree structure
def add(f):
f = self.dir_file(f)
if f not in includes:
includes.append(f)
# List of includes
includes = []
l = self.readline()
while l != '':
ls = l.split()
if '%include' == ls[0].lower():
add(ls[1])
self._pushfile(ls[1])
elif '<' in ls:
# TODO, in principle the < could contain
# include if this line is not a %block.
add(ls[ls.index('<')+1])
l = self.readline()
while l == '':
# last line of file
if self._popfile():
l = self.readline()
else:
break
return includes
@sile_fh_open()
def _read_label(self, label):
""" Try and read the first occurence of a key
This will take care of blocks, labels and piped in labels
Parameters
----------
label : str
label to find in the fdf file
"""
self._seek()
def tolabel(label):
return label.lower().replace('_', '').replace('-', '').replace('.', '')
labell = tolabel(label)
def valid_line(line):
ls = line.strip()
if len(ls) == 0:
return False
return not (ls[0] in self._comment)
def process_line(line):
# Split line by spaces
ls = line.split()
if len(ls) == 0:
return None
# Make a lower equivalent of ls
lsl = list(map(tolabel, ls))
# Check if there is a pipe in the line
if '<' in lsl:
idx = lsl.index('<')
# Now there are two cases
# 1. It is a block, in which case
# the full block is piped into the label
# %block Label < file
if lsl[0] == '%block' and lsl[1] == labell:
# Correct line found
# Read the file content, removing any empty and/or comment lines
lines = self.dir_file(ls[3]).open('r').readlines()
return [l.strip() for l in lines if valid_line(l)]
# 2. There are labels that should be read from a subsequent file
# Label1 Label2 < other.fdf
if labell in lsl[:idx]:
# Valid line, read key from other.fdf
return fdfSileSiesta(self.dir_file(ls[idx+1]), base=self._directory)._read_label(label)
# It is not in this line, either key is
# on the RHS of <, or the key could be "block". Say.
return None
# The last case is if the label is the first word on the line
# In that case we have found what we are looking for
if lsl[0] == labell:
return (' '.join(ls[1:])).strip()
elif lsl[0] == '%block':
if lsl[1] == labell:
# Read in the block content
lines = []
# Now read lines
l = self.readline().strip()
while not tolabel(l).startswith('%endblock'):
if len(l) > 0:
lines.append(l)
l = self.readline().strip()
return lines
elif lsl[0] == '%include':
# We have to open a new file
self._pushfile(ls[1])
return None
# Perform actual reading of line
l = self.readline().split('#')[0]
if len(l) == 0:
return None
l = process_line(l)
while l is None:
l = self.readline().split('#')[0]
if len(l) == 0:
if not self._popfile():
return None
l = process_line(l)
return l
@classmethod
def _type(cls, value):
""" Determine the type by the value
Parameters
----------
value : str or list or numpy.ndarray
the value to check for fdf-type
"""
if value is None:
return None
if isinstance(value, list):
# A block, %block ...
return 'B'
if isinstance(value, np.ndarray):
# A list, Label [...]
return 'a'
# Grab the entire line (beside the key)
values = value.split()
if len(values) == 1:
fdf = values[0].lower()
if fdf in _LOGICAL:
# logical
return 'b'
try:
float(fdf)
if '.' in fdf:
# a real number (otherwise an integer)
return 'r'
return 'i'
except:
pass
# fall-back to name with everything
elif len(values) == 2:
# possibly a physical value
try:
float(values[0])
return 'p'
except:
pass
return 'n'
@sile_fh_open()
def type(self, label):
""" Return the type of the fdf-keyword
Parameters
----------
label : str
the label to look-up
"""
self._seek()
return self._type(self._read_label(label))
@sile_fh_open()
def get(self, label, default=None, unit=None, with_unit=False):
""" Retrieve fdf-keyword from the file
Parameters
----------
label : str
the fdf-label to search for
default : optional
if the label is not found, this will be the returned value (default to ``None``)
unit : str, optional
unit of the physical quantity to return
with_unit : bool, optional
whether the physical quantity gets returned with the found unit in the fdf file.
Returns
-------
value : the value of the fdf-label. If the label is a block, a `list` is returned, for
a real value a `float` (or if the default is of `float`), for an integer, an
`int` is returned.
unit : if `with_unit` is true this will contain the associated unit if it is specified
Examples
--------
>>> print(open(...).readlines())
LabeleV 1. eV
LabelRy 1. Ry
Label name
FakeInt 1
%block Hello
line 1
line2
%endblock
>>> fdf.get('LabeleV') == 1. # default unit is eV
>>> fdf.get('LabelRy') == unit.siesta.unit_convert('Ry', 'eV')
>>> fdf.get('LabelRy', unit='Ry') == 1.
>>> fdf.get('LabelRy', with_unit=True) == (1., 'Ry')
>>> fdf.get('FakeInt', '0') == '1'
>>> fdf.get('LabeleV', with_unit=True) == (1., 'eV')
>>> fdf.get('Label', with_unit=True) == 'name' # no unit present on line
>>> fdf.get('Hello') == ['line 1', 'line2']
"""
# Try and read a line
value = self._read_label(label)
# Simply return the default value if not found
if value is None:
return default
# Figure out what it is
t = self._type(value)
# We will only do something if it is a real, int, or physical.
# Else we simply return, as-is
if t == 'r':
if default is None:
return float(value)
t = type(default)
return t(value)
elif t == 'i':
if default is None:
return int(value)
t = type(default)
return t(value)
elif t == 'p':
value = value.split()
if with_unit:
# Simply return, as is. Let the user do whatever.
return float(value[0]), value[1]
if unit is None:
default = unit_default(unit_group(value[1]))
else:
if unit_group(value[1]) != unit_group(unit):
raise ValueError(f"Requested unit for {label} is not the same type. "
"Found/Requested {value[1]}/{unit}'")
default = unit
return float(value[0]) * unit_convert(value[1], default)
elif t == 'b':
return value.lower() in _LOGICAL_TRUE
return value
def set(self, key, value, keep=True):
""" Add the key and value to the FDF file
Parameters
----------
key : str
the fdf-key value to be set in the fdf file
value : str or list of str
the value of the string. If a `str` is passed a regular
fdf-key is used, if a `list` it will be a %block.
keep : bool, optional
whether old flags will be kept in the fdf file. In this case
a time-stamp will be written to show when the key was overwritten.
"""
# To set a key we first need to figure out if it is
# already present, if so, we will add the new key, just above
# the already present key.
top_file = str(self.file)
# 1. find the old value, and thus the file in which it is found
with self:
try:
self.get(key)
# Get the file of the containing data
top_file = str(self.fh.name)
except:
pass
# Ensure that all files are closed
self._seek()
# Now we should re-read and edit the file
lines = open(top_file, 'r').readlines()
def write(fh, value):
if value is None:
return
if isinstance(value, str):
fh.write(self.print(key, value))
if '\n' not in value:
fh.write('\n')
else:
raise NotImplementedError('Currently blocks are not implemented in set!')
# Now loop, write and edit
do_write = True
lkey = key.lower()
with open(top_file, 'w') as fh:
for line in lines:
if self.line_has_key(line, lkey, case=False) and do_write:
write(fh, value)
if keep:
fh.write('# Old value ({})\n'.format(datetime.today().strftime('%Y-%m-%d %H:%M')))
fh.write(f'{line}')
do_write = False
else:
fh.write(line)
@staticmethod
def print(key, value):
""" Return a string which is pretty-printing the key+value """
if isinstance(value, list):
s = f'%block {key}'
# if the value has any new-values
has_nl = False
for v in value:
if '\n' in v:
has_nl = True
break
if has_nl:
# do not skip to next line in next segment
value[-1].replace('\n', '')
s += '\n{}'.format(''.join(value))
else:
s += '\n{} {}'.format(value[0], '\n'.join(value[1:]))
s += f'%endblock {key}'
else:
s = f'{key} {value}'
return s
@sile_fh_open()
def write_supercell(self, sc, fmt='.8f', *args, **kwargs):
""" Writes the supercell to the contained file """
# Check that we can write to the file
sile_raise_write(self)
fmt_str = ' {{0:{0}}} {{1:{0}}} {{2:{0}}}\n'.format(fmt)
unit = kwargs.get('unit', 'Ang').capitalize()
conv = 1.
if unit in ['Ang', 'Bohr']:
conv = unit_convert('Ang', unit)
else:
unit = 'Ang'
# Write out the cell
self._write(f'LatticeConstant 1.0 {unit}\n')
self._write('%block LatticeVectors\n')
self._write(fmt_str.format(*sc.cell[0, :] * conv))
self._write(fmt_str.format(*sc.cell[1, :] * conv))
self._write(fmt_str.format(*sc.cell[2, :] * conv))
self._write('%endblock LatticeVectors\n')
@sile_fh_open()
def write_geometry(self, geometry, fmt='.8f', *args, **kwargs):
""" Writes the geometry to the contained file """
# Check that we can write to the file
sile_raise_write(self)
self.write_supercell(geometry.sc, fmt, *args, **kwargs)
self._write('\n')
self._write(f'NumberOfAtoms {geometry.na}\n')
unit = kwargs.get('unit', 'Ang').capitalize()
is_fractional = unit in ['Frac', 'Fractional']
if is_fractional:
self._write('AtomicCoordinatesFormat Fractional\n')
else:
conv = unit_convert('Ang', unit)
self._write(f'AtomicCoordinatesFormat {unit}\n')
self._write('%block AtomicCoordinatesAndAtomicSpecies\n')
n_species = len(geometry.atoms.atom)
# Count for the species
if is_fractional:
xyz = geometry.fxyz
else:
xyz = geometry.xyz * conv
if fmt[0] == '.':
# Correct for a "same" length of all coordinates
c_max = len(str((f'{{:{fmt}}}').format(xyz.max())))
c_min = len(str((f'{{:{fmt}}}').format(xyz.min())))
fmt = str(max(c_min, c_max)) + fmt
fmt_str = ' {{3:{0}}} {{4:{0}}} {{5:{0}}} {{0}} # {{1:{1}d}}: {{2}}\n'.format(fmt, len(str(len(geometry))))
for ia, a, isp in geometry.iter_species():
self._write(fmt_str.format(isp + 1, ia + 1, a.tag, *xyz[ia, :]))
self._write('%endblock AtomicCoordinatesAndAtomicSpecies\n\n')
# Write out species
# First swap key and value
self._write(f'NumberOfSpecies {n_species}\n')
self._write('%block ChemicalSpeciesLabel\n')
for i, a in enumerate(geometry.atoms.atom):
if isinstance(a, AtomGhost):
self._write(' {} {} {}\n'.format(i + 1, -a.Z, a.tag))
else:
self._write(' {} {} {}\n'.format(i + 1, a.Z, a.tag))
self._write('%endblock ChemicalSpeciesLabel\n')
_write_block = True
def write_block(atoms, append, write_block):
if write_block:
self._write('\n# Constraints\n%block Geometry.Constraints\n')
write_block = False
self._write(f' atom [{atoms}]{append}\n')
return write_block
for d in range(4):
append = {0: '', 1: ' 1. 0. 0.', 2: ' 0. 1. 0.', 3: ' 0. 0. 1.'}.get(d)
n = 'CONSTRAIN' + {0: '', 1: '-x', 2: '-y', 3: '-z'}.get(d)
if n in geometry.names:
idx = list2str(geometry.names[n] + 1).replace('-', ' -- ')
if len(idx) > 200:
info(f"{str(self)}.write_geometry will not write the constraints for {n} (too long line).")
else:
_write_block = write_block(idx, append, _write_block)
if not _write_block:
self._write('%endblock\n')
@staticmethod
def _SpGeom_replace_geom(spgeom, geometry):
""" Replace all atoms in spgeom with the atom in geometry while retaining the number of orbitals
Currently we need some way of figuring out whether the number of atoms and orbitals are
consistent.
Parameters
----------
spgeom : SparseGeometry
the sparse object with attached geometry
geometry : Geometry
geometry to grab atoms from
full_replace : bool, optional
whether the full geometry may be replaced in case ``spgeom.na != geometry.na && spgeom.no == geometry.no``.
This is required when `spgeom` does not contain information about atoms.
"""
if spgeom.na != geometry.na and spgeom.no == geometry.no:
# In this case we cannot compare individiual atoms # of orbitals.
# I.e. we suspect the incoming geometry to be correct.
spgeom._geometry = geometry
return True
elif spgeom.na != geometry.na:
warn('cannot replace geometry due to insufficient information regarding number of '
'atoms and orbitals, ensuring correct geometry failed...')
no_no = spgeom.no == geometry.no
# Loop and make sure the number of orbitals is consistent
for a, idx in geometry.atoms.iter(True):
if len(idx) == 0:
continue
Sa = spgeom.geometry.atoms[idx[0]]
if Sa.no != a.no:
# Make sure the atom we replace with retains the same information
# *except* the number of orbitals.
a = a.__class__(a.Z, Sa.orbital, mass=a.mass, tag=a.tag)
spgeom.geometry.atoms.replace(idx, a)
spgeom.geometry.reduce()
return no_no
def read_supercell_nsc(self, *args, **kwargs):
""" Read supercell size using any method available
Raises
------
SislWarning if none of the files can be read
"""
order = _listify_str(kwargs.pop('order', ['nc', 'ORB_INDX']))
for f in order:
v = getattr(self, '_r_supercell_nsc_{}'.format(f.lower()))(*args, **kwargs)
if v is not None:
_track(self.read_supercell_nsc, f"found file {f}")
return v
warn('number of supercells could not be read from output files. Assuming molecule cell '
'(no supercell connections)')
return _a.onesi(3)
def _r_supercell_nsc_nc(self, *args, **kwargs):
f = self.dir_file(self.get('SystemLabel', default='siesta') + '.nc')
_track_file(self._r_supercell_nsc_nc, f)
if f.is_file():
return ncSileSiesta(f).read_supercell_nsc()
return None
def _r_supercell_nsc_orb_indx(self, *args, **kwargs):
f = self.dir_file(self.get('SystemLabel', default='siesta') + '.ORB_INDX')
_track_file(self._r_supercell_nsc_orb_indx, f)
if f.is_file():
return orbindxSileSiesta(f).read_supercell_nsc()
return None
def read_supercell(self, output=False, *args, **kwargs):
""" Returns SuperCell object by reading fdf or Siesta output related files.
One can limit the tried files to only one file by passing
only a single file ending.
Parameters
----------
output: bool, optional
whether to read supercell from output files (default to read from
the fdf file).
order: list of str, optional
the order of which to try and read the supercell.
By default this is ``['XV', 'nc', 'fdf']`` if `output` is true.
If `order` is present `output` is disregarded.
Examples
--------
>>> fdf = get_sile('RUN.fdf')
>>> fdf.read_supercell() # read from fdf
>>> fdf.read_supercell(True) # read from [XV, nc, fdf]
>>> fdf.read_supercell(order=['nc']) # read from [nc]
>>> fdf.read_supercell(True, order=['nc']) # read from [nc]
"""
if output:
order = _listify_str(kwargs.pop('order', ['XV', 'nc', 'fdf']))
else:
order = _listify_str(kwargs.pop('order', ['fdf']))
for f in order:
v = getattr(self, '_r_supercell_{}'.format(f.lower()))(*args, **kwargs)
if v is not None:
_track(self.read_supercell, f"found file {f}")
return v
return None
def _r_supercell_fdf(self, *args, **kwargs):
""" Returns `SuperCell` object from the FDF file """
s = self.get('LatticeConstant', unit='Ang')
if s is None:
raise SileError('Could not find LatticeConstant in file')
# Read in cell
cell = _a.emptyd([3, 3])
lc = self.get('LatticeVectors')
if lc:
for i in range(3):
cell[i, :] = [float(k) for k in lc[i].split()[:3]]
else:
lc = self.get('LatticeParameters')
if lc:
tmp = [float(k) for k in lc[0].split()[:6]]
cell = SuperCell.tocell(*tmp)
if lc is None:
# the fdf file contains neither the latticevectors or parameters
raise SileError('Could not find LatticeVectors or LatticeParameters block in file')
cell *= s
# When reading from the fdf, the warning should be suppressed
with warnings.catch_warnings():
warnings.simplefilter("ignore")
nsc = self.read_supercell_nsc()
return SuperCell(cell, nsc=nsc)
def _r_supercell_nc(self):
# Read supercell from <>.nc file
f = self.dir_file(self.get('SystemLabel', default='siesta') + '.nc')
_track_file(self._r_supercell_nc, f)
if f.is_file():
return ncSileSiesta(f).read_supercell()
return None
def _r_supercell_xv(self, *args, **kwargs):
""" Returns `SuperCell` object from the FDF file """
f = self.dir_file(self.get('SystemLabel', default='siesta') + '.XV')
_track_file(self._r_supercell_xv, f)
if f.is_file():
nsc = self.read_supercell_nsc()
sc = xvSileSiesta(f).read_supercell()
sc.set_nsc(nsc)
return sc
return None
def _r_supercell_tshs(self, *args, **kwargs):
f = self.dir_file(self.get('SystemLabel', default='siesta') + '.TSHS')
_track_file(self._r_supercell_tshs, f)
if f.is_file():
return tshsSileSiesta(f).read_supercell()
return None
def _r_supercell_onlys(self, *args, **kwargs):
f = self.dir_file(self.get('SystemLabel', default='siesta') + '.onlyS')
_track_file(self._r_supercell_onlys, f)
if f.is_file():
return onlysSileSiesta(f).read_supercell()
return None
def read_force(self, *args, **kwargs):
""" Read forces from the output of the calculation (forces are not defined in the input)
Parameters
----------
order : list of str, optional
the order of the forces we are trying to read, default to ``['FA', 'nc']``
Returns
-------
(*, 3) : vector with forces for each of the atoms
"""
order = _listify_str(kwargs.pop('order', ['FA', 'nc']))
for f in order:
v = getattr(self, '_r_force_{}'.format(f.lower()))(*args, **kwargs)
if v is not None:
if self.track:
info(f"{self.file}(read_force) found in file={f}")
return v
return None
def _r_force_fa(self, *args, **kwargs):
""" Read forces from the FA file """
f = self.dir_file(self.get('SystemLabel', default='siesta') + '.FA')
if f.is_file():
return faSileSiesta(f).read_force()
return None
def _r_force_fac(self, *args, **kwargs):
""" Read forces from the FAC file """
f = self.dir_file(self.get('SystemLabel', default='siesta') + '.FAC')
if f.is_file():
return faSileSiesta(f).read_force()
return None
def _r_force_nc(self, *args, **kwargs):
""" Read forces from the nc file """
f = self.dir_file(self.get('SystemLabel', default='siesta') + '.nc')
if f.is_file():
return ncSileSiesta(f).read_force()
return None
def read_force_constant(self, *args, **kwargs):
""" Read force constant from the output of the calculation
Returns
-------
force_constant : numpy.ndarray
vector [*, 3, 2, *, 3] with force constant element for each of the atomic displacements
"""
order = _listify_str(kwargs.pop('order', ['nc', 'FC']))
for f in order:
v = getattr(self, '_r_force_constant_{}'.format(f.lower()))(*args, **kwargs)
if v is not None:
if self.track:
info(f"{self.file}(read_force_constant) found in file={f}")
return v
return None
def _r_force_constant_nc(self, *args, **kwargs):
f = self.dir_file(self.get('SystemLabel', default='siesta') + '.nc')
if f.is_file():
if not 'FC' in ncSileSiesta(f).groups:
return None
fc = ncSileSiesta(f).read_force_constant()
return fc
return None
def _r_force_constant_fc(self, *args, **kwargs):
f = self.dir_file(self.get('SystemLabel', default='siesta') + '.FC')
if f.is_file():
na = self.get('NumberOfAtoms', default=None)
return fcSileSiesta(f).read_force_constant(na=na)
return None
def read_fermi_level(self, *args, **kwargs):
""" Read fermi-level from output of the calculation
Parameters
----------
order: list of str, optional
the order of which to try and read the fermi-level.
By default this is ``['nc', 'TSDE', 'TSHS', 'EIG']``.
Returns
-------
Ef : float
fermi-level
"""
order = _listify_str(kwargs.pop('order', ['nc', 'TSDE', 'TSHS', 'EIG']))
for f in order:
v = getattr(self, '_r_fermi_level_{}'.format(f.lower()))(*args, **kwargs)
if v is not None:
if self.track:
info(f"{self.file}(read_fermi_level) found in file={f}")
return v
return None
def _r_fermi_level_nc(self):
f = self.dir_file(self.get('SystemLabel', default='siesta') + '.nc')
if isfile(f):
return ncSileSiesta(f).read_fermi_level()
return None
def _r_fermi_level_tsde(self):
f = self.dir_file(self.get('SystemLabel', default='siesta') + '.TSDE')
if isfile(f):
return tsdeSileSiesta(f).read_fermi_level()
return None
def _r_fermi_level_tshs(self):
f = self.dir_file(self.get('SystemLabel', default='siesta') + '.TSHS')
if isfile(f):
return tshsSileSiesta(f).read_fermi_level()
return None
def _r_fermi_level_eig(self):
f = self.dir_file(self.get('SystemLabel', default='siesta') + '.EIG')
if isfile(f):
return eigSileSiesta(f).read_fermi_level()
return None
def read_dynamical_matrix(self, *args, **kwargs):
""" Read dynamical matrix from output of the calculation
Generally the mass is stored in the basis information output,
but for dynamical matrices it makes sense to let the user control this,
e.g. through the fdf file.
By default the mass will be read from the AtomicMass key in the fdf file
and _not_ from the basis set information.
Parameters
----------
order: list of str, optional
the order of which to try and read the dynamical matrix.
By default this is ``['nc', 'FC']``.
cutoff_dist : float, optional
cutoff value for the distance of the force-constants (everything farther than
`cutoff_dist` will be set to 0 Ang). Default, no cutoff.
cutoff : float, optional
absolute values below the cutoff are considered 0. Defaults to 0. eV/Ang**2.
trans_inv : bool, optional
if true (default), the force-constant matrix will be fixed so that translational
invariance will be enforced
sum0 : bool, optional
if true (default), the sum of forces on atoms for each displacement will be
forced to 0.
hermitian: bool, optional
if true (default), the returned dynamical matrix will be hermitian
Returns
-------
dynamic_matrix : DynamicalMatrix
the dynamical matrix
"""
order = _listify_str(kwargs.pop('order', ['nc', 'FC']))
for f in order:
v = getattr(self, '_r_dynamical_matrix_{}'.format(f.lower()))(*args, **kwargs)
if v is not None:
if self.track:
info(f"{self.file}(read_dynamical_matrix) found in file={f}")
return v
return None
def _r_dynamical_matrix_fc(self, *args, **kwargs):
FC = self.read_force_constant(*args, order="FC", **kwargs)
if FC is None:
return None
geom = self.read_geometry()
basis_fdf = self.read_basis(order="fdf")
for i, atom in enumerate(basis_fdf):
geom.atoms.replace(i, atom)
# Get list of FC atoms
FC_atoms = _a.arangei(self.get('MD.FCFirst', default=1) - 1, self.get('MD.FCLast', default=geom.na))
return self._dynamical_matrix_from_fc(geom, FC, FC_atoms, *args, **kwargs)
def _r_dynamical_matrix_nc(self, *args, **kwargs):
FC = self.read_force_constant(*args, order=['nc'], **kwargs)
if FC is None:
return None
geom = self.read_geometry(order=['nc'])
basis_fdf = self.read_basis(order="fdf")
for i, atom in enumerate(basis_fdf):
geom.atoms.replace(i, atom)
# Get list of FC atoms
# TODO change to read in from the NetCDF file
FC_atoms = _a.arangei(self.get('MD.FCFirst', default=1) - 1, self.get('MD.FCLast', default=geom.na))
return self._dynamical_matrix_from_fc(geom, FC, FC_atoms, *args, **kwargs)
def _dynamical_matrix_from_fc(self, geom, FC, FC_atoms, *args, **kwargs):
# We have the force constant matrix.
# Now handle it...
# FC(OLD) = (n_displ, 3, 2, na, 3)
# FC(NEW) = (n_displ, 3, na, 3)
# In fact, after averaging this becomes the Hessian
FC = FC.sum(axis=2) * 0.5
hermitian = kwargs.get("hermitian", True)
# Figure out the "original" periodic directions
periodic = geom.nsc > 1
# Create conversion from eV/Ang^2 to correct units
# Further down we are multiplying with [1 / amu]
scale = constant.hbar / units('Ang', 'm') / units('eV amu', 'J kg') ** 0.5
# Cut-off too small values
fc_cut = kwargs.get('cutoff', 0.)
FC = np.where(np.abs(FC) > fc_cut, FC, 0.)
# Convert the force constant such that a diagonalization returns eV ^ 2
# FC is in [eV / Ang^2]
# Convert the geometry to contain 3 orbitals per atom (x, y, z)
R = kwargs.get('cutoff_dist', -2.)
orbs = [Orbital(R / 2, tag=tag) for tag in 'xyz']
with warnings.catch_warnings():
warnings.simplefilter("ignore")
for atom, _ in geom.atoms.iter(True):
new_atom = atom.__class__(atom.Z, orbs, mass=atom.mass, tag=atom.tag)
geom.atoms.replace(atom, new_atom)
# Figure out the supercell indices
supercell = kwargs.get('supercell', [1, 1, 1])
if supercell is False:
supercell = [1] * 3
elif supercell is True:
_, supercell = geom.as_primary(FC.shape[0], ret_super=True)
info("{}.read_dynamical_matrix(FC) guessed on a [{}, {}, {}] "
"supercell calculation.".format(str(self), *supercell))
# Convert to integer array
supercell = _a.asarrayi(supercell)
# Reshape to supercell
FC.shape = (FC.shape[0], 3, *supercell, -1, 3)
na_fc = len(FC_atoms)
assert FC.shape[0] == len(FC_atoms)
assert FC.shape[5] == len(geom) // np.prod(supercell)
# Now we are in a problem since the tiling of the geometry
# is not necessarily in x, y, z order.
# Say for users who did:
# geom.tile(*, 2).tile(*, 1).tile(*, 0).write(...)
# then we need to pivot the data to be consistent with the
# supercell information
if np.any(supercell > 1):
# Re-arange FC before we use _fc_correct
# Now we need to figure out how the atoms are laid out.
# It *MUST* either be repeated or tiled (preferentially tiled).
# We have an actual supercell. Lets try and fix it.
# First lets recreate the smallest geometry
sc = geom.sc.cell.copy()
sc[0, :] /= supercell[0]
sc[1, :] /= supercell[1]
sc[2, :] /= supercell[2]
# Ensure nsc is at least an odd number, later down we will symmetrize the FC matrix
nsc = supercell + (supercell + 1) % 2
if R > 0:
# Correct for the optional radius
sc_norm = fnorm(sc)
# R is already "twice" the "orbital" range
nsc_R = 1 + 2 * np.ceil(R / sc_norm).astype(np.int32)
for i in range(3):
nsc[i] = min(nsc[i], nsc_R[i])
del nsc_R
# Construct the minimal unit-cell geometry
sc = SuperCell(sc, nsc=nsc)
# TODO check that the coordinates are in the cell
geom_small = Geometry(geom.xyz[FC_atoms], geom.atoms[FC_atoms], sc)
# Convert the big geometry's coordinates to fractional coordinates of the small unit-cell.
isc_xyz = (geom.xyz.dot(geom_small.sc.icell.T) -
np.tile(geom_small.fxyz, (np.product(supercell), 1)))
axis_tiling = []
offset = len(geom_small)
for _ in (supercell > 1).nonzero()[0]:
first_isc = (np.around(isc_xyz[FC_atoms + offset, :]) == 1.).sum(0)
axis_tiling.append(np.argmax(first_isc))
# Fix the offset
offset *= supercell[axis_tiling[-1]]
while len(axis_tiling) < 3:
for i in range(3):
if not i in axis_tiling:
axis_tiling.append(i)
# Now we have the tiling operation, check it sort of matches
geom_tile = geom_small.copy()
for axis in axis_tiling:
geom_tile = geom_tile.tile(supercell[axis], axis)
# Proximity check of 0.01 Ang (TODO add this as an argument)
if not np.allclose(geom_tile.xyz, geom.xyz, rtol=0, atol=0.01):
raise SislError(f"{str(self)}.read_dynamical_matrix(FC) could "
"not figure out the tiling method for the supercell")
# Convert the FC matrix to a "rollable" matrix
# This will make it easier to symmetrize
# 0. displaced atoms
# 1. x, y, z (displacements)
# 2. tile-axis_tiling[0]
# 3. tile-axis_tiling[1]
# 4. tile-axis_tiling[2]
# 5. na
# 6. x, y, z (force components)
FC.shape = (na_fc, 3, *supercell[axis_tiling], na_fc, 3)
# Now swap the [2, 3, 4] dimensions so that we get in order of lattice vectors
swap = np.array([2, 3, 4])[axis_tiling]
swap = (0, 1, *swap, 5, 6)
FC = np.transpose(FC, swap)
del axis_tiling
# Now FC is sorted according to the supercell tiling
# TODO this will probably fail if: FC_atoms.size != FC.shape[5]
from ._help import _fc_correct
FC = _fc_correct(FC, trans_inv=kwargs.get("trans_inv", True),
sum0=kwargs.get("sum0", True),
hermitian=hermitian)
# Remove ghost-atoms or atoms with 0 mass!
# TODO check if ghost-atoms should be taken into account in _fc_correct
idx = (geom.atoms.mass == 0.).nonzero()[0]
if len(idx) > 0:
FC = np.delete(FC, idx, axis=5)
geom = geom.remove(idx)
geom.set_nsc([1] * 3)
raise NotImplementedError(f"{self}.read_dynamical_matrix could not reduce geometry "
"since there are atoms with 0 mass.")
# Now we can build the dynamical matrix (it will always be real)
na = len(geom)
if np.all(supercell <= 1):
# also catches supercell == 0
D = sp.sparse.lil_matrix((geom.no, geom.no), dtype=np.float64)
FC = np.squeeze(FC, axis=(2, 3, 4))
# Instead of doing the sqrt in all D = FC (below) we do it here
m = scale / geom.atoms.mass ** 0.5
FC *= m[FC_atoms].reshape(-1, 1, 1, 1) * m.reshape(1, 1, -1, 1)
j_FC_atoms = FC_atoms
idx = _a.arangei(len(FC_atoms))
for ia, fia in enumerate(FC_atoms):
if R > 0:
# find distances between the other atoms to cut-off the distance
idx = geom.close(fia, R=R, atoms=FC_atoms)
idx = indices_only(FC_atoms, idx)
j_FC_atoms = FC_atoms[idx]
for ja, fja in zip(idx, j_FC_atoms):
D[ia*3:(ia+1)*3, ja*3:(ja+1)*3] = FC[ia, :, fja, :]
else:
geom = geom_small
if np.any(np.diff(FC_atoms) != 1):
raise SislError(f"{self}.read_dynamical_matrix(FC) requires the FC atoms to be consecutive!")
# Re-order FC matrix so the FC-atoms are first
if FC.shape[0] != FC.shape[5]:
ordered = _a.arangei(FC.shape[5])
ordered = np.concatenate(FC_atoms, np.delete(ordered, FC_atoms))
FC = FC[:, :, :, :, :, ordered, :]
FC_atoms = _a.arangei(len(FC_atoms))
if FC_atoms[0] != 0:
# TODO we could roll the axis such that the displaced atoms moves into the
# first elements
raise SislError(f"{self}.read_dynamical_matrix(FC) requires the displaced atoms to start from 1!")
# After having done this we can easily mass scale all FC components
m = scale / geom.atoms.mass ** 0.5
FC *= m.reshape(-1, 1, 1, 1, 1, 1, 1) * m.reshape(1, 1, 1, 1, 1, -1, 1)
# Check whether we need to "halve" the equivalent supercell
# This will be present in calculations done on an even number of supercells.
# I.e. for 4 supercells
# [0] [1] [2] [3]
# where in the supercell approach:
# *[2] [3] [0] [1] *[2]
# I.e. since we are double counting [2] we will halve it.
# This is not *exactly* true because depending on the range one should do the symmetry operations.
# However the FC does not contain such symmetry considerations.
for i in range(3):
if supercell[i] % 2 == 1:
# We don't need to do anything
continue
# Figure out the supercell to halve
halve_idx = supercell[i] // 2
if i == 0:
FC[:, :, halve_idx, :, :, :, :] *= 0.5
elif i == 1:
FC[:, :, :, halve_idx, :, :, :] *= 0.5
else:
FC[:, :, :, :, halve_idx, :, :] *= 0.5
# Now create the dynamical matrix
# Currently this will be in lil_matrix (changed in the end)
D = sp.sparse.lil_matrix((geom.no, geom.no_s), dtype=np.float64)
# When x, y, z are negative we simply look-up from the back of the array
# which is exactly what is required
isc_off = geom.sc.isc_off
nxyz, na = geom.no, geom.na
dist = geom.rij
# Now take all positive supercell connections (including inner cell)
nsc = geom.nsc // 2
list_nsc = [range(-x, x + 1) for x in nsc]
iter_FC_atoms = _a.arangei(len(FC_atoms))
iter_j_FC_atoms = iter_FC_atoms
for x, y, z in itools.product(*list_nsc):
isc = isc_off[x, y, z]
aoff = isc * na
joff = isc * nxyz
for ia in iter_FC_atoms:
# Reduce second loop based on radius cutoff
if R > 0:
iter_j_FC_atoms = iter_FC_atoms[dist(ia, aoff + iter_FC_atoms) <= R]
for ja in iter_j_FC_atoms:
D[ia*3:(ia+1)*3, joff+ja*3:joff+(ja+1)*3] = FC[ia, :, x, y, z, ja, :]
D = D.tocsr()
# Remove all zeros
D.eliminate_zeros()
D = DynamicalMatrix.fromsp(geom, D)
if hermitian:
D.finalize()
D = (D + D.transpose()) * 0.5
return D
def read_geometry(self, output=False, *args, **kwargs):
""" Returns Geometry object by reading fdf or Siesta output related files.
One can limit the tried files to only one file by passing
only a single file ending.
Parameters
----------
output: bool, optional
whether to read geometry from output files (default to read from
the fdf file).
order: list of str, optional
the order of which to try and read the geometry.
By default this is ``['XV', 'nc', 'fdf', 'TSHS']`` if `output` is true
If `order` is present `output` is disregarded.
Examples
--------
>>> fdf = get_sile('RUN.fdf')
>>> fdf.read_geometry() # read from fdf
>>> fdf.read_geometry(True) # read from [XV, nc, fdf]
>>> fdf.read_geometry(order=['nc']) # read from [nc]
>>> fdf.read_geometry(True, order=['nc']) # read from [nc]
"""
##
# NOTE
# When adding more capabilities please check the read_geometry(True, order=...) in this
# code to correct.
##
if output:
order = _listify_str(kwargs.pop('order', ['XV', 'nc', 'fdf', 'TSHS']))
else:
order = _listify_str(kwargs.pop('order', ['fdf']))
for f in order:
v = getattr(self, '_r_geometry_{}'.format(f.lower()))(*args, **kwargs)
if v is not None:
if self.track:
info(f"{self.file}(read_geometry) found in file={f}")
return v
return None
def _r_geometry_xv(self, *args, **kwargs):
""" Returns `SuperCell` object from the FDF file """
f = self.dir_file(self.get('SystemLabel', default='siesta') + '.XV')
geom = None
if f.is_file():
basis = self.read_basis()
if basis is None:
geom = xvSileSiesta(f).read_geometry()
else:
geom = xvSileSiesta(f).read_geometry(species_Z=True)
with warnings.catch_warnings():
warnings.simplefilter('ignore')
for atom, _ in geom.atoms.iter(True):
geom.atoms.replace(atom, basis[atom.Z-1])
geom.reduce()
nsc = self.read_supercell_nsc()
geom.set_nsc(nsc)
return geom
def _r_geometry_nc(self):
# Read geometry from <>.nc file
f = self.dir_file(self.get('SystemLabel', default='siesta') + '.nc')
if f.is_file():
return ncSileSiesta(f).read_geometry()
return None
def _r_geometry_tshs(self):
# Read geometry from <>.TSHS file
f = self.dir_file(self.get('SystemLabel', default='siesta') + '.TSHS')
if f.is_file():
# Default to a geometry with the correct atomic numbers etc.
return tshsSileSiesta(f).read_geometry(geometry=self.read_geometry(False))
return None
def _r_geometry_fdf(self, *args, **kwargs):
""" Returns Geometry object from the FDF file
NOTE: Interaction range of the Atoms are currently not read.
"""
sc = self.read_supercell(order='fdf')
# No fractional coordinates
is_frac = False
# Read atom scaling
lc = self.get('AtomicCoordinatesFormat', default='Bohr').lower()
if 'ang' in lc or 'notscaledcartesianang' in lc:
s = 1.
elif 'bohr' in lc or 'notscaledcartesianbohr' in lc:
s = Bohr2Ang
elif 'scaledcartesian' in lc:
# the same scaling as the lattice-vectors
s = self.get('LatticeConstant', unit='Ang')
elif 'fractional' in lc or 'scaledbylatticevectors' in lc:
# no scaling of coordinates as that is entirely
# done by the latticevectors
s = 1.
is_frac = True
# If the user requests a shifted geometry
# we correct for this
origo = _a.zerosd([3])
lor = self.get('AtomicCoordinatesOrigin')
if lor:
if kwargs.get('origin', True):
if isinstance(lor, str):
origo = lor.lower()
else:
origo = _a.asarrayd(list(map(float, lor[0].split()[:3]))) * s
# Origo cannot be interpreted with fractional coordinates
# hence, it is not transformed.
# Read atom block
atms = self.get('AtomicCoordinatesAndAtomicSpecies')
if atms is None:
raise SileError('AtomicCoordinatesAndAtomicSpecies block could not be found')
# Read number of atoms and block
# We default to the number of elements in the
# AtomicCoordinatesAndAtomicSpecies block
na = self.get('NumberOfAtoms', default=len(atms))
# Reduce space if number of atoms specified
if na < len(atms):
# align number of atoms and atms array
atms = atms[:na]
elif na > len(atms):
raise SileError('NumberOfAtoms is larger than the atoms defined in the blocks')
elif na == 0:
raise SileError('NumberOfAtoms has been determined to be zero, no atoms.')
# Create array
xyz = _a.emptyd([na, 3])
species = _a.emptyi([na])
for ia in range(na):
l = atms[ia].split()
xyz[ia, :] = [float(k) for k in l[:3]]
species[ia] = int(l[3]) - 1
if is_frac:
xyz = np.dot(xyz, sc.cell)
xyz *= s
# Read the block (not strictly needed, if so we simply set all atoms to H)
atoms = self.read_basis()
if atoms is None:
warn(SileWarning('Block ChemicalSpeciesLabel does not exist, cannot determine the basis (all Hydrogen).'))
# Default atom (hydrogen)
atoms = Atom(1)
else:
atoms = [atoms[i] for i in species]
atoms = Atoms(atoms, na=len(xyz))
if isinstance(origo, str):
opt = origo
if opt.startswith('cop'):
origo = sc.cell.sum(0) * 0.5 - np.average(xyz, 0)
elif opt.startswith('com'):
# TODO for ghost atoms its mass should not be used
w = atom.mass
w /= w.sum()
origo = sc.cell.sum(0) * 0.5 - np.average(xyz, 0, weights=w)
elif opt.startswith('min'):
origo = - np.amin(xyz, 0)
if len(opt) > 4:
opt = opt[4:]
if opt == 'x':
origo[1:] = 0.
elif opt == 'y':
origo[[0, 2]] = 0.
elif opt == 'z':
origo[:2] = 0.
elif opt == 'xy' or opt == 'yx':
origo[2] = 0.
elif opt == 'xz' or opt == 'zx':
origo[1] = 0.
elif opt == 'yz' or opt == 'zy':
origo[0] = 0.
xyz += origo
# Create and return geometry object
return Geometry(xyz, atoms, sc=sc)
def read_grid(self, name, *args, **kwargs):
""" Read grid related information from any of the output files
The order of the readed data is shown below.
One can limit the tried files to only one file by passing
only a single file ending.
Parameters
----------
name : str
name of data to read. The list of names correspond to the
Siesta output manual (Rho, TotalPotential, etc.), the strings are
case insensitive.
order: list of str, optional
the order of which to try and read the geometry.
By default this is ``['nc', 'grid.nc', 'bin']`` (bin refers to the binary files)
"""
order = _listify_str(kwargs.pop('order', ['nc', 'grid.nc', 'bin']))
for f in order:
v = getattr(self, '_r_grid_{}'.format(f.lower().replace('.', '_')))(name, *args, **kwargs)
if v is not None:
if self.track:
info(f"{self.file}(read_grid) found in file={f}")
return v
return None
def _r_grid_nc(self, name, *args, **kwargs):
# Read grid from the <>.nc file
f = self.dir_file(self.get('SystemLabel', default='siesta') + '.nc')
if f.is_file():
# Capitalize correctly
name = {'rho': 'Rho',
'rhoinit': 'RhoInit',
'vna': 'Vna',
'ioch': 'Chlocal',
'chlocal': 'Chlocal',
'toch': 'RhoTot',
'totalcharge': 'RhoTot',
'rhotot': 'RhoTot',
'drho': 'RhoDelta',
'deltarho': 'RhoDelta',
'rhodelta': 'RhoDelta',
'vh': 'Vh',
'electrostaticpotential': 'Vh',
'rhoxc': 'RhoXC',
'vt': 'Vt',
'totalpotential': 'Vt',
'bader': 'RhoBader',
'baderrho': 'RhoBader',
'rhobader': 'RhoBader'
}.get(name.lower())
return ncSileSiesta(f).read_grid(name, **kwargs)
return None
def _r_grid_grid_nc(self, name, *args, **kwargs):
# Read grid from the <>.nc file
name = {'rho': 'Rho',
'rhoinit': 'RhoInit',
'vna': 'Vna',
'ioch': 'Chlocal',
'chlocal': 'Chlocal',
'toch': 'TotalCharge',
'totalcharge': 'TotalCharge',
'rhotot': 'TotalCharge',
'drho': 'DeltaRho',
'deltarho': 'DeltaRho',
'rhodelta': 'DeltaRho',
'vh': 'ElectrostaticPotential',
'electrostaticpotential': 'ElectrostaticPotential',
'rhoxc': 'RhoXC',
'vt': 'TotalPotential',
'totalpotential': 'TotalPotential',
'bader': 'BaderCharge',
'baderrho': 'BaderCharge',
'rhobader': 'BaderCharge'
}.get(name.lower()) + '.grid.nc'
f = self.dir_file(name)
if f.is_file():
grid = gridncSileSiesta(f).read_grid(*args, **kwargs)
grid.set_geometry(self.read_geometry(True))
return grid
return None
def _r_grid_bin(self, name, *args, **kwargs):
# Read grid from the <>.VT/... file
name = {'rho': '.RHO',
'rhoinit': '.RHOINIT',
'vna': '.VNA',
'ioch': '.IOCH',
'chlocal': '.IOCH',
'toch': '.TOCH',
'totalcharge': '.TOCH',
'rhotot': '.TOCH',
'drho': '.DRHO',
'deltarho': '.DRHO',
'rhodelta': '.DRHO',
'vh': '.VH',
'electrostaticpotential': '.VH',
'rhoxc': '.RHOXC',
'vt': '.VT',
'totalpotential': '.VT',
'bader': '.BADER',
'baderrho': '.BADER',
'rhobader': '.BADER'
}.get(name.lower())
f = self.dir_file(self.get('SystemLabel', default='siesta') + name)
if f.is_file():
grid = get_sile_class(f)(f).read_grid(*args, **kwargs)
grid.set_geometry(self.read_geometry(True))
return grid
return None
def read_basis(self, *args, **kwargs):
""" Read the atomic species and figure out the number of atomic orbitals in their basis
The order of the read is shown below.
One can limit the tried files to only one file by passing
only a single file ending.
Parameters
----------
order: list of str, optional
the order of which to try and read the basis information.
By default this is ``['nc', 'ion', 'ORB_INDX', 'fdf']``
"""
order = _listify_str(kwargs.pop('order', ['nc', 'ion', 'ORB_INDX', 'fdf']))
for f in order:
v = getattr(self, '_r_basis_{}'.format(f.lower()))(*args, **kwargs)
if v is not None:
if self.track:
info(f"{self.file}(read_basis) found in file={f}")
return v
return None
def _r_basis_nc(self):
# Read basis from <>.nc file
f = self.dir_file(self.get('SystemLabel', default='siesta') + '.nc')
if f.is_file():
return ncSileSiesta(f).read_basis()
return None
def _r_basis_ion(self):
# Read basis from <>.ion.nc file or <>.ion.xml
spcs = self.get('ChemicalSpeciesLabel')
if spcs is None:
# We haven't found the chemical and species label
# so return nothing
return None
# Now spcs contains the block of the chemicalspecieslabel
atoms = [None] * len(spcs)
found_one = False
found_all = True
for spc in spcs:
idx, Z, lbl = spc.split()[:3]
idx = int(idx) - 1 # F-indexing
Z = int(Z)
lbl = lbl.strip()
f = self.dir_file(lbl + ".ext")
# now try and read the basis
if f.with_suffix('.ion.nc').is_file():
atoms[idx] = ionncSileSiesta(f.with_suffix('.ion.nc')).read_basis()
found_one = True
elif f.with_suffix('.ion.xml').is_file():
atoms[idx] = ionxmlSileSiesta(f.with_suffix('.ion.xml')).read_basis()
found_one = True
else:
# default the atom to not have a range, and no associated orbitals
atoms[idx] = Atom(Z=Z, tag=lbl)
found_all = False
if found_one and not found_all:
warn("Siesta basis information could not read all ion.nc/ion.xml files. "
"Only a subset of the basis information is accessible.")
elif not found_one:
return None
return atoms
def _r_basis_orb_indx(self):
f = self.dir_file(self.get('SystemLabel', default='siesta') + '.ORB_INDX')
if f.is_file():
info(f"Siesta basis information is read from {f}, the radial functions are not accessible.")
return orbindxSileSiesta(f).read_basis(atoms=self._r_basis_fdf())
return None
def _r_basis_fdf(self):
# Read basis from fdf file
spcs = self.get('ChemicalSpeciesLabel')
if spcs is None:
# We haven't found the chemical and species label
# so return nothing
return None
all_mass = self.get('AtomicMass', default=[])
# default mass
mass = None
# Now spcs contains the block of the chemicalspecieslabel
atoms = [None] * len(spcs)
for spc in spcs:
idx, Z, lbl = spc.split()[:3]
idx = int(idx) - 1 # F-indexing
Z = int(Z)
lbl = lbl.strip()
if len(all_mass) > 0:
for mass_line in all_mass:
s, mass = mass_line.split()
if int(s) - 1 == idx:
mass = float(mass)
break
else:
mass = None
atoms[idx] = Atom(Z=Z, mass=mass, tag=lbl)
return atoms
def _r_add_overlap(self, parent_call, M):
""" Internal routine to ensure that the overlap matrix is read and added to the matrix `M` """
try:
S = self.read_overlap()
# Check for the same sparsity pattern
if np.all(M._csr.col == S._csr.col):
M._csr._D[:, -1] = S._csr._D[:, 0]
else:
raise ValueError
except:
warn(str(self) + f' could not succesfully read the overlap matrix in {parent_call}.')
def read_density_matrix(self, *args, **kwargs):
""" Try and read density matrix by reading the <>.nc, <>.TSDE files, <>.DM (in that order)
One can limit the tried files to only one file by passing
only a single file ending.
Parameters
----------
order: list of str, optional
the order of which to try and read the density matrix
By default this is ``['nc', 'TSDE', 'DM']``.
"""
order = _listify_str(kwargs.pop('order', ['nc', 'TSDE', 'DM']))
for f in order:
DM = getattr(self, '_r_density_matrix_{}'.format(f.lower()))(*args, **kwargs)
if DM is not None:
_track(self.read_density_matrix, f"found file {f}")
return DM
return None
def _r_density_matrix_nc(self, *args, **kwargs):
""" Try and read the density matrix by reading the <>.nc """
f = self.dir_file(self.get('SystemLabel', default='siesta') + '.nc')
_track_file(self._r_density_matrix_nc, f)
DM = None
if f.is_file():
# this *should* also contain the overlap matrix
DM = ncSileSiesta(f).read_density_matrix(*args, **kwargs)
return DM
def _r_density_matrix_tsde(self, *args, **kwargs):
""" Read density matrix from the TSDE file """
f = self.dir_file(self.get('SystemLabel', default='siesta') + '.TSDE')
_track_file(self._r_density_matrix_tsde, f)
DM = None
if f.is_file():
if 'geometry' not in kwargs:
# to ensure we get the correct orbital count
kwargs['geometry'] = self.read_geometry(True, order=['nc', 'TSHS', 'fdf'])
DM = tsdeSileSiesta(f).read_density_matrix(*args, **kwargs)
self._r_add_overlap('_r_density_matrix_tsde', DM)
return DM
def _r_density_matrix_dm(self, *args, **kwargs):
""" Read density matrix from the DM file """
f = self.dir_file(self.get('SystemLabel', default='siesta') + '.DM')
_track_file(self._r_density_matrix_dm, f)
DM = None
if f.is_file():
if 'geometry' not in kwargs:
# to ensure we get the correct orbital count
kwargs['geometry'] = self.read_geometry(True, order=['nc', 'TSHS', 'fdf'])
DM = dmSileSiesta(f).read_density_matrix(*args, **kwargs)
self._r_add_overlap('_r_density_matrix_dm', DM)
return DM
def read_energy_density_matrix(self, *args, **kwargs):
""" Try and read energy density matrix by reading the <>.nc or <>.TSDE files (in that order)
One can limit the tried files to only one file by passing
only a single file ending.
Parameters
----------
order: list of str, optional
the order of which to try and read the density matrix
By default this is ``['nc', 'TSDE']``.
"""
order = _listify_str(kwargs.pop('order', ['nc', 'TSDE']))
for f in order:
EDM = getattr(self, '_r_energy_density_matrix_{}'.format(f.lower()))(*args, **kwargs)
if EDM is not None:
_track(self.read_energy_density_matrix, f"found file {f}")
return EDM
return None
def _r_energy_density_matrix_nc(self, *args, **kwargs):
""" Read energy density matrix by reading the <>.nc """
f = self.dir_file(self.get('SystemLabel', default='siesta') + '.nc')
_track_file(self._r_energy_density_matrix_nc, f)
if f.is_file():
return ncSileSiesta(f).read_energy_density_matrix(*args, **kwargs)
return None
def _r_energy_density_matrix_tsde(self, *args, **kwargs):
""" Read energy density matrix from the TSDE file """
f = self.dir_file(self.get('SystemLabel', default='siesta') + '.TSDE')
_track_file(self._r_energy_density_matrix_tsde, f)
EDM = None
if f.is_file():
if 'geometry' not in kwargs:
# to ensure we get the correct orbital count
kwargs['geometry'] = self.read_geometry(True, order=['nc', 'TSHS'])
EDM = tsdeSileSiesta(f).read_energy_density_matrix(*args, **kwargs)
self._r_add_overlap('_r_energy_density_matrix_tsde', EDM)
return EDM
def read_overlap(self, *args, **kwargs):
""" Try and read the overlap matrix by reading the <>.nc, <>.TSHS files, <>.HSX, <>.onlyS (in that order)
One can limit the tried files to only one file by passing
only a single file ending.
Parameters
----------
order: list of str, optional
the order of which to try and read the overlap matrix
By default this is ``['nc', 'TSHS', 'HSX', 'onlyS']``.
"""
order = _listify_str(kwargs.pop('order', ['nc', 'TSHS', 'HSX', 'onlyS']))
for f in order:
v = getattr(self, '_r_overlap_{}'.format(f.lower()))(*args, **kwargs)
if v is not None:
_track(self.read_overlap, f"found file {f}")
return v
return None
def _r_overlap_nc(self, *args, **kwargs):
""" Read overlap from the nc file """
f = self.dir_file(self.get('SystemLabel', default='siesta') + '.nc')
_track_file(self._r_overlap_nc, f)
if f.is_file():
return ncSileSiesta(f).read_overlap(*args, **kwargs)
return None
def _r_overlap_tshs(self, *args, **kwargs):
""" Read overlap from the TSHS file """
f = self.dir_file(self.get('SystemLabel', default='siesta') + '.TSHS')
_track_file(self._r_overlap_tshs, f)
S = None
if f.is_file():
if 'geometry' not in kwargs:
# to ensure we get the correct orbital count
kwargs['geometry'] = self.read_geometry(True, order=['nc', 'TSHS'])
S = tshsSileSiesta(f).read_overlap(*args, **kwargs)
return S
def _r_overlap_hsx(self, *args, **kwargs):
""" Read overlap from the HSX file """
f = self.dir_file(self.get('SystemLabel', default='siesta') + '.HSX')
_track_file(self._r_overlap_hsx, f)
S = None
if f.is_file():
if 'geometry' not in kwargs:
# to ensure we get the correct orbital count
kwargs['geometry'] = self.read_geometry(True, order=['nc', 'TSHS', 'fdf'])
S = hsxSileSiesta(f).read_overlap(*args, **kwargs)
return S
def _r_overlap_onlys(self, *args, **kwargs):
""" Read overlap from the onlyS file """
f = self.dir_file(self.get('SystemLabel', default='siesta') + '.onlyS')
_track_file(self._r_overlap_onlys, f)
S = None
if f.is_file():
if 'geometry' not in kwargs:
# to ensure we get the correct orbital count
kwargs['geometry'] = self.read_geometry(True, order=['nc', 'TSHS', 'fdf'])
S = onlysSileSiesta(f).read_overlap(*args, **kwargs)
return S
def read_hamiltonian(self, *args, **kwargs):
""" Try and read the Hamiltonian by reading the <>.nc, <>.TSHS files, <>.HSX (in that order)
One can limit the tried files to only one file by passing
only a single file ending.
Parameters
----------
order: list of str, optional
the order of which to try and read the Hamiltonian.
By default this is ``['nc', 'TSHS', 'HSX']``.
"""
order = _listify_str(kwargs.pop('order', ['nc', 'TSHS', 'HSX']))
for f in order:
H = getattr(self, '_r_hamiltonian_{}'.format(f.lower()))(*args, **kwargs)
if H is not None:
_track(self.read_hamiltonian, f"found file {f}")
return H
return None
def _r_hamiltonian_nc(self, *args, **kwargs):
""" Read Hamiltonian from the nc file """
f = self.dir_file(self.get('SystemLabel', default='siesta') + '.nc')
_track_file(self._r_hamiltonian_nc, f)
if f.is_file():
return ncSileSiesta(f).read_hamiltonian(*args, **kwargs)
return None
def _r_hamiltonian_tshs(self, *args, **kwargs):
""" Read Hamiltonian from the TSHS file """
f = self.dir_file(self.get('SystemLabel', default='siesta') + '.TSHS')
_track_file(self._r_hamiltonian_tshs, f)
H = None
if f.is_file():
if 'geometry' not in kwargs:
# to ensure we get the correct orbital count
kwargs['geometry'] = self.read_geometry(True, order=['nc', 'TSHS'])
H = tshsSileSiesta(f).read_hamiltonian(*args, **kwargs)
return H
def _r_hamiltonian_hsx(self, *args, **kwargs):
""" Read Hamiltonian from the HSX file """
f = self.dir_file(self.get('SystemLabel', default='siesta') + '.HSX')
_track_file(self._r_hamiltonian_hsx, f)
H = None
if f.is_file():
if 'geometry' not in kwargs:
# to ensure we get the correct orbital count
kwargs['geometry'] = self.read_geometry(True, order=['nc', 'TSHS', 'fdf'])
H = hsxSileSiesta(f).read_hamiltonian(*args, **kwargs)
Ef = self.read_fermi_level()
if Ef is None:
info(f"{str(self)}.read_hamiltonian from HSX file failed shifting to the Fermi-level.")
else:
H.shift(-Ef)
return H
@default_ArgumentParser(description="Manipulate a FDF file.")
def ArgumentParser(self, p=None, *args, **kwargs):
""" Returns the arguments that is available for this Sile """
import argparse
# We must by-pass this fdf-file for importing
import sisl.io.siesta as sis
# The fdf parser is more complicated
# It is based on different settings based on the
sp = p.add_subparsers(help="Determine which part of the fdf-file that should be processed.")
# Get the label which retains all the sub-modules
label = self.get('SystemLabel', default='siesta')
f_label = label + ".ext"
def label_file(suffix):
return self.dir_file(f_label).with_suffix(suffix)
# The default on all sub-parsers are the retrieval and setting
d = {
'_fdf': self,
'_fdf_first': True,
}
namespace = default_namespace(**d)
ep = sp.add_parser('edit',
help='Change or read and print data from the fdf file')
# As the fdf may provide additional stuff, we do not add EVERYTHING from
# the Geometry class.
class FDFAdd(argparse.Action):
def __call__(self, parser, ns, values, option_string=None):
key = values[0]
val = values[1]
if ns._fdf_first:
# Append to the end of the file
with ns._fdf as fd:
fd.write('\n\n# SISL added keywords\n')
setattr(ns, '_fdf_first', False)
ns._fdf.set(key, val)
ep.add_argument('--set', '-s', nargs=2, metavar=('KEY', 'VALUE'),
action=FDFAdd,
help='Add a key to the FDF file. If it already exists it will be overwritten')
class FDFGet(argparse.Action):
def __call__(self, parser, ns, value, option_string=None):
# Retrieve the value in standard units
# Currently, we write out the unit "as-is"
val = ns._fdf.get(value[0], with_unit=True)
if val is None:
print(f'# {value[0]} is currently not in the FDF file ')
return
if isinstance(val, tuple):
print(ns._fdf.print(value[0], '{} {}'.format(*val)))
else:
print(ns._fdf.print(value[0], val))
ep.add_argument('--get', '-g', nargs=1, metavar='KEY',
action=FDFGet,
help='Print (to stdout) the value of the key in the FDF file.')
# If the XV file exists, it has precedence
# of the contained geometry (we will issue
# a warning in that case)
f = label_file('.XV')
try:
geom = self.read_geometry(True)
tmp_p = sp.add_parser('geom',
help="Edit the contained geometry in the file")
tmp_p, tmp_ns = geom.ArgumentParser(tmp_p, *args, **kwargs)
namespace = merge_instances(namespace, tmp_ns)
except:
# Allowed pass due to pythonic reading
pass
f = label_file('.bands')
if f.is_file():
tmp_p = sp.add_parser('band',
help="Manipulate bands file from the Siesta simulation")
tmp_p, tmp_ns = sis.bandsSileSiesta(f).ArgumentParser(tmp_p, *args, **kwargs)
namespace = merge_instances(namespace, tmp_ns)
f = label_file('.PDOS.xml')
if f.is_file():
tmp_p = sp.add_parser('pdos',
help="Manipulate PDOS.xml file from the Siesta simulation")
tmp_p, tmp_ns = sis.pdosSileSiesta(f).ArgumentParser(tmp_p, *args, **kwargs)
namespace = merge_instances(namespace, tmp_ns)
f = label_file('.EIG')
if f.is_file():
tmp_p = sp.add_parser('eig',
help="Manipulate EIG file from the Siesta simulation")
tmp_p, tmp_ns = sis.eigSileSiesta(f).ArgumentParser(tmp_p, *args, **kwargs)
namespace = merge_instances(namespace, tmp_ns)
#f = label + '.FA'
#if isfile(f):
# tmp_p = sp.add_parser('force',
# help="Manipulate FA file from the Siesta simulation")
# tmp_p, tmp_ns = sis.faSileSiesta(f).ArgumentParser(tmp_p, *args, **kwargs)
# namespace = merge_instances(namespace, tmp_ns)
f = label_file('.TBT.nc')
if f.is_file():
tmp_p = sp.add_parser('tbt',
help="Manipulate tbtrans output file")
tmp_p, tmp_ns = sis.tbtncSileSiesta(f).ArgumentParser(tmp_p, *args, **kwargs)
namespace = merge_instances(namespace, tmp_ns)
f = label_file('.TBT.Proj.nc')
if f.is_file():
tmp_p = sp.add_parser('tbt-proj',
help="Manipulate tbtrans projection output file")
tmp_p, tmp_ns = sis.tbtprojncSileSiesta(f).ArgumentParser(tmp_p, *args, **kwargs)
namespace = merge_instances(namespace, tmp_ns)
f = label_file('.PHT.nc')
if f.is_file():
tmp_p = sp.add_parser('pht',
help="Manipulate the phtrans output file")
tmp_p, tmp_ns = sis.phtncSileSiesta(f).ArgumentParser(tmp_p, *args, **kwargs)
namespace = merge_instances(namespace, tmp_ns)
f = label_file('.PHT.Proj.nc')
if f.is_file():
tmp_p = sp.add_parser('pht-proj',
help="Manipulate phtrans projection output file")
tmp_p, tmp_ns = sis.phtprojncSileSiesta(f).ArgumentParser(tmp_p, *args, **kwargs)
namespace = merge_instances(namespace, tmp_ns)
f = label_file('.nc')
if f.is_file():
tmp_p = sp.add_parser('nc',
help="Manipulate Siesta NetCDF output file")
tmp_p, tmp_ns = sis.ncSileSiesta(f).ArgumentParser(tmp_p, *args, **kwargs)
namespace = merge_instances(namespace, tmp_ns)
return p, namespace
add_sile('fdf', fdfSileSiesta, case=False, gzip=True)
|
import pytest
pytestmark = pytest.mark.spin
import math as m
import numpy as np
from sisl import Spin
def test_spin1():
for val in ['unpolarized', '', Spin.UNPOLARIZED,
'polarized', 'p', Spin.POLARIZED,
'non-collinear', 'nc', Spin.NONCOLINEAR,
'spin-orbit', 'so', Spin.SPINORBIT]:
s = Spin(val)
str(s)
s1 = s.copy()
assert s == s1
def test_spin2():
s1 = Spin()
s2 = Spin('p')
s3 = Spin('nc')
s4 = Spin('so')
assert s1.kind == Spin.UNPOLARIZED
assert s2.kind == Spin.POLARIZED
assert s3.kind == Spin.NONCOLINEAR
assert s4.kind == Spin.SPINORBIT
assert s1 == s1.copy()
assert s2 == s2.copy()
assert s3 == s3.copy()
assert s4 == s4.copy()
assert s1 < s2
assert s2 < s3
assert s3 < s4
assert s1 <= s2
assert s2 <= s3
assert s3 <= s4
assert s2 > s1
assert s3 > s2
assert s4 > s3
assert s2 >= s1
assert s3 >= s2
assert s4 >= s3
assert s1.is_unpolarized
assert not s1.is_polarized
assert not s1.is_noncolinear
assert not s1.is_spinorbit
assert not s2.is_unpolarized
assert s2.is_polarized
assert not s2.is_noncolinear
assert not s2.is_spinorbit
assert not s3.is_unpolarized
assert not s3.is_polarized
assert s3.is_noncolinear
assert not s3.is_spinorbit
assert not s4.is_unpolarized
assert not s4.is_polarized
assert not s4.is_noncolinear
assert s4.is_spinorbit
def test_spin3():
with pytest.raises(ValueError):
s = Spin('satoehus')
def test_spin4():
s1 = Spin(Spin.UNPOLARIZED)
S1 = Spin(Spin.UNPOLARIZED, np.complex64)
s2 = Spin(Spin.POLARIZED)
S2 = Spin(Spin.POLARIZED, np.complex64)
s3 = Spin(Spin.NONCOLINEAR)
S3 = Spin(Spin.NONCOLINEAR, np.complex64)
s4 = Spin(Spin.SPINORBIT)
S4 = Spin(Spin.SPINORBIT, np.complex64)
assert s1 == S1
assert s2 == S2
assert s3 == S3
assert s4 == S4
# real comparison
assert s1 < S2
assert s1 < S3
assert s1 < S4
assert s2 > S1
assert s2 < S3
assert s2 < S4
assert s3 > S1
assert s3 > S2
assert s3 < S4
assert s4 > S1
assert s4 > S2
assert s4 > S3
# complex complex
assert S1 < S2
assert S1 < S3
assert S1 < S4
assert S2 > S1
assert S2 < S3
assert S2 < S4
assert S3 > S1
assert S3 > S2
assert S3 < S4
assert S4 > S1
assert S4 > S2
assert S4 > S3
# real comparison
assert S1 < s2
assert S1 < s3
assert S1 < s4
assert S2 > s1
assert S2 < s3
assert S2 < s4
assert S3 > s1
assert S3 > s2
assert S3 < s4
assert S4 > s1
assert S4 > s2
assert S4 > s3
# complex complex
assert S1 < s2
assert S1 < s3
assert S1 < s4
assert S2 > s1
assert S2 < s3
assert S2 < s4
assert S3 > s1
assert S3 > s2
assert S3 < s4
assert S4 > s1
assert S4 > s2
assert S4 > s3
def test_pauli():
# just grab the default spin
S = Spin()
# Create a fictituous wave-function
sq2 = 2 ** .5
W = np.array([
[1/sq2, 1/sq2], # M_x = 1
[1/sq2, -1/sq2], # M_x = -1
[0.5 + 0.5j, 0.5 + 0.5j], # M_x = 1
[0.5 - 0.5j, -0.5 + 0.5j], # M_x = -1
[1/sq2, 1j/sq2], # M_y = 1
[1/sq2, -1j/sq2], # M_y = -1
[0.5 - 0.5j, 0.5 + 0.5j], # M_y = 1
[0.5 + 0.5j, 0.5 - 0.5j], # M_y = -1
[1, 0], # M_z = 1
[0, 1], # M_z = -1
])
x = np.array([1, -1, 1, -1, 0, 0, 0, 0, 0, 0])
assert np.allclose(x, (np.conj(W)*S.X.dot(W.T).T).sum(1).real)
y = np.array([0, 0, 0, 0, 1, -1, 1, -1, 0, 0])
assert np.allclose(y, (np.conj(W)*np.dot(S.Y, W.T).T).sum(1).real)
z = np.array([0, 0, 0, 0, 0, 0, 0, 0, 1, -1])
assert np.allclose(z, (np.conj(W)*np.dot(S.Z, W.T).T).sum(1).real)
def test_pickle():
import pickle as p
S = Spin('nc')
n = p.dumps(S)
s = p.loads(n)
assert S == s
|
|
sisl/physics/tests/test_spin.py
|
|
"""Test deprecation and future warnings.
"""
import numpy as np
from numpy.testing import assert_warns
def test_qr_mode_full_future_warning():
"""Check mode='full' FutureWarning.
In numpy 1.8 the mode options 'full' and 'economic' in linalg.qr were
deprecated. The release date will probably be sometime in the summer
of 2013.
"""
a = np.eye(2)
assert_warns(DeprecationWarning, np.linalg.qr, a, mode='full')
assert_warns(DeprecationWarning, np.linalg.qr, a, mode='f')
assert_warns(DeprecationWarning, np.linalg.qr, a, mode='economic')
assert_warns(DeprecationWarning, np.linalg.qr, a, mode='e')
|
import platform
import pytest
from numpy import array
from numpy.testing import assert_, assert_raises
from . import util
class TestReturnReal(util.F2PyTest):
def check_function(self, t, tname):
if tname in ['t0', 't4', 's0', 's4']:
err = 1e-5
else:
err = 0.0
assert_(abs(t(234) - 234.0) <= err)
assert_(abs(t(234.6) - 234.6) <= err)
assert_(abs(t('234') - 234) <= err)
assert_(abs(t('234.6') - 234.6) <= err)
assert_(abs(t(-234) + 234) <= err)
assert_(abs(t([234]) - 234) <= err)
assert_(abs(t((234,)) - 234.) <= err)
assert_(abs(t(array(234)) - 234.) <= err)
assert_(abs(t(array([234])) - 234.) <= err)
assert_(abs(t(array([[234]])) - 234.) <= err)
assert_(abs(t(array([234], 'b')) + 22) <= err)
assert_(abs(t(array([234], 'h')) - 234.) <= err)
assert_(abs(t(array([234], 'i')) - 234.) <= err)
assert_(abs(t(array([234], 'l')) - 234.) <= err)
assert_(abs(t(array([234], 'B')) - 234.) <= err)
assert_(abs(t(array([234], 'f')) - 234.) <= err)
assert_(abs(t(array([234], 'd')) - 234.) <= err)
if tname in ['t0', 't4', 's0', 's4']:
assert_(t(1e200) == t(1e300)) # inf
#assert_raises(ValueError, t, array([234], 'S1'))
assert_raises(ValueError, t, 'abc')
assert_raises(IndexError, t, [])
assert_raises(IndexError, t, ())
assert_raises(Exception, t, t)
assert_raises(Exception, t, {})
try:
r = t(10 ** 400)
assert_(repr(r) in ['inf', 'Infinity'], repr(r))
except OverflowError:
pass
@pytest.mark.skipif(
platform.system() == 'Darwin',
reason="Prone to error when run with numpy/f2py/tests on mac os, "
"but not when run in isolation")
class TestCReturnReal(TestReturnReal):
suffix = ".pyf"
module_name = "c_ext_return_real"
code = """
python module c_ext_return_real
usercode \'\'\'
float t4(float value) { return value; }
void s4(float *t4, float value) { *t4 = value; }
double t8(double value) { return value; }
void s8(double *t8, double value) { *t8 = value; }
\'\'\'
interface
function t4(value)
real*4 intent(c) :: t4,value
end
function t8(value)
real*8 intent(c) :: t8,value
end
subroutine s4(t4,value)
intent(c) s4
real*4 intent(out) :: t4
real*4 intent(c) :: value
end
subroutine s8(t8,value)
intent(c) s8
real*8 intent(out) :: t8
real*8 intent(c) :: value
end
end interface
end python module c_ext_return_real
"""
@pytest.mark.parametrize('name', 't4,t8,s4,s8'.split(','))
def test_all(self, name):
self.check_function(getattr(self.module, name), name)
class TestF77ReturnReal(TestReturnReal):
code = """
function t0(value)
real value
real t0
t0 = value
end
function t4(value)
real*4 value
real*4 t4
t4 = value
end
function t8(value)
real*8 value
real*8 t8
t8 = value
end
function td(value)
double precision value
double precision td
td = value
end
subroutine s0(t0,value)
real value
real t0
cf2py intent(out) t0
t0 = value
end
subroutine s4(t4,value)
real*4 value
real*4 t4
cf2py intent(out) t4
t4 = value
end
subroutine s8(t8,value)
real*8 value
real*8 t8
cf2py intent(out) t8
t8 = value
end
subroutine sd(td,value)
double precision value
double precision td
cf2py intent(out) td
td = value
end
"""
@pytest.mark.parametrize('name', 't0,t4,t8,td,s0,s4,s8,sd'.split(','))
def test_all(self, name):
self.check_function(getattr(self.module, name), name)
class TestF90ReturnReal(TestReturnReal):
suffix = ".f90"
code = """
module f90_return_real
contains
function t0(value)
real :: value
real :: t0
t0 = value
end function t0
function t4(value)
real(kind=4) :: value
real(kind=4) :: t4
t4 = value
end function t4
function t8(value)
real(kind=8) :: value
real(kind=8) :: t8
t8 = value
end function t8
function td(value)
double precision :: value
double precision :: td
td = value
end function td
subroutine s0(t0,value)
real :: value
real :: t0
!f2py intent(out) t0
t0 = value
end subroutine s0
subroutine s4(t4,value)
real(kind=4) :: value
real(kind=4) :: t4
!f2py intent(out) t4
t4 = value
end subroutine s4
subroutine s8(t8,value)
real(kind=8) :: value
real(kind=8) :: t8
!f2py intent(out) t8
t8 = value
end subroutine s8
subroutine sd(td,value)
double precision :: value
double precision :: td
!f2py intent(out) td
td = value
end subroutine sd
end module f90_return_real
"""
@pytest.mark.parametrize('name', 't0,t4,t8,td,s0,s4,s8,sd'.split(','))
def test_all(self, name):
self.check_function(getattr(self.module.f90_return_real, name), name)
|
|
numpy/f2py/tests/test_return_real.py
|
numpy/linalg/tests/test_deprecations.py
|
import numpy as np
nd = np.array([[1, 2], [3, 4]])
# reshape
reveal_type(nd.reshape()) # E: numpy.ndarray
reveal_type(nd.reshape(4)) # E: numpy.ndarray
reveal_type(nd.reshape(2, 2)) # E: numpy.ndarray
reveal_type(nd.reshape((2, 2))) # E: numpy.ndarray
reveal_type(nd.reshape((2, 2), order="C")) # E: numpy.ndarray
reveal_type(nd.reshape(4, order="C")) # E: numpy.ndarray
# resize does not return a value
# transpose
reveal_type(nd.transpose()) # E: numpy.ndarray
reveal_type(nd.transpose(1, 0)) # E: numpy.ndarray
reveal_type(nd.transpose((1, 0))) # E: numpy.ndarray
# swapaxes
reveal_type(nd.swapaxes(0, 1)) # E: numpy.ndarray
# flatten
reveal_type(nd.flatten()) # E: numpy.ndarray
reveal_type(nd.flatten("C")) # E: numpy.ndarray
# ravel
reveal_type(nd.ravel()) # E: numpy.ndarray
reveal_type(nd.ravel("C")) # E: numpy.ndarray
# squeeze
reveal_type(nd.squeeze()) # E: numpy.ndarray
reveal_type(nd.squeeze(0)) # E: numpy.ndarray
reveal_type(nd.squeeze((0, 2))) # E: numpy.ndarray
|
import platform
import pytest
from numpy import array
from numpy.testing import assert_, assert_raises
from . import util
class TestReturnReal(util.F2PyTest):
def check_function(self, t, tname):
if tname in ['t0', 't4', 's0', 's4']:
err = 1e-5
else:
err = 0.0
assert_(abs(t(234) - 234.0) <= err)
assert_(abs(t(234.6) - 234.6) <= err)
assert_(abs(t('234') - 234) <= err)
assert_(abs(t('234.6') - 234.6) <= err)
assert_(abs(t(-234) + 234) <= err)
assert_(abs(t([234]) - 234) <= err)
assert_(abs(t((234,)) - 234.) <= err)
assert_(abs(t(array(234)) - 234.) <= err)
assert_(abs(t(array([234])) - 234.) <= err)
assert_(abs(t(array([[234]])) - 234.) <= err)
assert_(abs(t(array([234], 'b')) + 22) <= err)
assert_(abs(t(array([234], 'h')) - 234.) <= err)
assert_(abs(t(array([234], 'i')) - 234.) <= err)
assert_(abs(t(array([234], 'l')) - 234.) <= err)
assert_(abs(t(array([234], 'B')) - 234.) <= err)
assert_(abs(t(array([234], 'f')) - 234.) <= err)
assert_(abs(t(array([234], 'd')) - 234.) <= err)
if tname in ['t0', 't4', 's0', 's4']:
assert_(t(1e200) == t(1e300)) # inf
#assert_raises(ValueError, t, array([234], 'S1'))
assert_raises(ValueError, t, 'abc')
assert_raises(IndexError, t, [])
assert_raises(IndexError, t, ())
assert_raises(Exception, t, t)
assert_raises(Exception, t, {})
try:
r = t(10 ** 400)
assert_(repr(r) in ['inf', 'Infinity'], repr(r))
except OverflowError:
pass
@pytest.mark.skipif(
platform.system() == 'Darwin',
reason="Prone to error when run with numpy/f2py/tests on mac os, "
"but not when run in isolation")
class TestCReturnReal(TestReturnReal):
suffix = ".pyf"
module_name = "c_ext_return_real"
code = """
python module c_ext_return_real
usercode \'\'\'
float t4(float value) { return value; }
void s4(float *t4, float value) { *t4 = value; }
double t8(double value) { return value; }
void s8(double *t8, double value) { *t8 = value; }
\'\'\'
interface
function t4(value)
real*4 intent(c) :: t4,value
end
function t8(value)
real*8 intent(c) :: t8,value
end
subroutine s4(t4,value)
intent(c) s4
real*4 intent(out) :: t4
real*4 intent(c) :: value
end
subroutine s8(t8,value)
intent(c) s8
real*8 intent(out) :: t8
real*8 intent(c) :: value
end
end interface
end python module c_ext_return_real
"""
@pytest.mark.parametrize('name', 't4,t8,s4,s8'.split(','))
def test_all(self, name):
self.check_function(getattr(self.module, name), name)
class TestF77ReturnReal(TestReturnReal):
code = """
function t0(value)
real value
real t0
t0 = value
end
function t4(value)
real*4 value
real*4 t4
t4 = value
end
function t8(value)
real*8 value
real*8 t8
t8 = value
end
function td(value)
double precision value
double precision td
td = value
end
subroutine s0(t0,value)
real value
real t0
cf2py intent(out) t0
t0 = value
end
subroutine s4(t4,value)
real*4 value
real*4 t4
cf2py intent(out) t4
t4 = value
end
subroutine s8(t8,value)
real*8 value
real*8 t8
cf2py intent(out) t8
t8 = value
end
subroutine sd(td,value)
double precision value
double precision td
cf2py intent(out) td
td = value
end
"""
@pytest.mark.parametrize('name', 't0,t4,t8,td,s0,s4,s8,sd'.split(','))
def test_all(self, name):
self.check_function(getattr(self.module, name), name)
class TestF90ReturnReal(TestReturnReal):
suffix = ".f90"
code = """
module f90_return_real
contains
function t0(value)
real :: value
real :: t0
t0 = value
end function t0
function t4(value)
real(kind=4) :: value
real(kind=4) :: t4
t4 = value
end function t4
function t8(value)
real(kind=8) :: value
real(kind=8) :: t8
t8 = value
end function t8
function td(value)
double precision :: value
double precision :: td
td = value
end function td
subroutine s0(t0,value)
real :: value
real :: t0
!f2py intent(out) t0
t0 = value
end subroutine s0
subroutine s4(t4,value)
real(kind=4) :: value
real(kind=4) :: t4
!f2py intent(out) t4
t4 = value
end subroutine s4
subroutine s8(t8,value)
real(kind=8) :: value
real(kind=8) :: t8
!f2py intent(out) t8
t8 = value
end subroutine s8
subroutine sd(td,value)
double precision :: value
double precision :: td
!f2py intent(out) td
td = value
end subroutine sd
end module f90_return_real
"""
@pytest.mark.parametrize('name', 't0,t4,t8,td,s0,s4,s8,sd'.split(','))
def test_all(self, name):
self.check_function(getattr(self.module.f90_return_real, name), name)
|
|
numpy/f2py/tests/test_return_real.py
|
numpy/typing/tests/data/reveal/ndarray_shape_manipulation.py
|
import numpy as np
reveal_type(np.issctype(np.generic)) # E: bool
reveal_type(np.issctype("foo")) # E: bool
reveal_type(np.obj2sctype("S8")) # E: Union[numpy.generic, None]
reveal_type(np.obj2sctype("S8", default=None)) # E: Union[numpy.generic, None]
reveal_type(
np.obj2sctype("foo", default=int) # E: Union[numpy.generic, Type[builtins.int*]]
)
reveal_type(np.issubclass_(np.float64, float)) # E: bool
reveal_type(np.issubclass_(np.float64, (int, float))) # E: bool
reveal_type(np.sctype2char("S8")) # E: str
reveal_type(np.sctype2char(list)) # E: str
reveal_type(np.find_common_type([np.int64], [np.int64])) # E: numpy.dtype
reveal_type(np.cast[int]) # E: _CastFunc
reveal_type(np.cast["i8"]) # E: _CastFunc
reveal_type(np.cast[np.int64]) # E: _CastFunc
reveal_type(np.nbytes[int]) # E: int
reveal_type(np.nbytes["i8"]) # E: int
reveal_type(np.nbytes[np.int64]) # E: int
reveal_type(np.ScalarType) # E: Tuple
reveal_type(np.ScalarType[0]) # E: Type[builtins.int]
reveal_type(np.ScalarType[4]) # E: Type[builtins.bool]
reveal_type(np.ScalarType[9]) # E: Type[{csingle}]
reveal_type(np.ScalarType[11]) # E: Type[{clongdouble}]
reveal_type(np.typecodes["Character"]) # E: Literal['c']
reveal_type(np.typecodes["Complex"]) # E: Literal['FDG']
reveal_type(np.typecodes["All"]) # E: Literal['?bhilqpBHILQPefdgFDGSUVOMm']
|
import platform
import pytest
from numpy import array
from numpy.testing import assert_, assert_raises
from . import util
class TestReturnReal(util.F2PyTest):
def check_function(self, t, tname):
if tname in ['t0', 't4', 's0', 's4']:
err = 1e-5
else:
err = 0.0
assert_(abs(t(234) - 234.0) <= err)
assert_(abs(t(234.6) - 234.6) <= err)
assert_(abs(t('234') - 234) <= err)
assert_(abs(t('234.6') - 234.6) <= err)
assert_(abs(t(-234) + 234) <= err)
assert_(abs(t([234]) - 234) <= err)
assert_(abs(t((234,)) - 234.) <= err)
assert_(abs(t(array(234)) - 234.) <= err)
assert_(abs(t(array([234])) - 234.) <= err)
assert_(abs(t(array([[234]])) - 234.) <= err)
assert_(abs(t(array([234], 'b')) + 22) <= err)
assert_(abs(t(array([234], 'h')) - 234.) <= err)
assert_(abs(t(array([234], 'i')) - 234.) <= err)
assert_(abs(t(array([234], 'l')) - 234.) <= err)
assert_(abs(t(array([234], 'B')) - 234.) <= err)
assert_(abs(t(array([234], 'f')) - 234.) <= err)
assert_(abs(t(array([234], 'd')) - 234.) <= err)
if tname in ['t0', 't4', 's0', 's4']:
assert_(t(1e200) == t(1e300)) # inf
#assert_raises(ValueError, t, array([234], 'S1'))
assert_raises(ValueError, t, 'abc')
assert_raises(IndexError, t, [])
assert_raises(IndexError, t, ())
assert_raises(Exception, t, t)
assert_raises(Exception, t, {})
try:
r = t(10 ** 400)
assert_(repr(r) in ['inf', 'Infinity'], repr(r))
except OverflowError:
pass
@pytest.mark.skipif(
platform.system() == 'Darwin',
reason="Prone to error when run with numpy/f2py/tests on mac os, "
"but not when run in isolation")
class TestCReturnReal(TestReturnReal):
suffix = ".pyf"
module_name = "c_ext_return_real"
code = """
python module c_ext_return_real
usercode \'\'\'
float t4(float value) { return value; }
void s4(float *t4, float value) { *t4 = value; }
double t8(double value) { return value; }
void s8(double *t8, double value) { *t8 = value; }
\'\'\'
interface
function t4(value)
real*4 intent(c) :: t4,value
end
function t8(value)
real*8 intent(c) :: t8,value
end
subroutine s4(t4,value)
intent(c) s4
real*4 intent(out) :: t4
real*4 intent(c) :: value
end
subroutine s8(t8,value)
intent(c) s8
real*8 intent(out) :: t8
real*8 intent(c) :: value
end
end interface
end python module c_ext_return_real
"""
@pytest.mark.parametrize('name', 't4,t8,s4,s8'.split(','))
def test_all(self, name):
self.check_function(getattr(self.module, name), name)
class TestF77ReturnReal(TestReturnReal):
code = """
function t0(value)
real value
real t0
t0 = value
end
function t4(value)
real*4 value
real*4 t4
t4 = value
end
function t8(value)
real*8 value
real*8 t8
t8 = value
end
function td(value)
double precision value
double precision td
td = value
end
subroutine s0(t0,value)
real value
real t0
cf2py intent(out) t0
t0 = value
end
subroutine s4(t4,value)
real*4 value
real*4 t4
cf2py intent(out) t4
t4 = value
end
subroutine s8(t8,value)
real*8 value
real*8 t8
cf2py intent(out) t8
t8 = value
end
subroutine sd(td,value)
double precision value
double precision td
cf2py intent(out) td
td = value
end
"""
@pytest.mark.parametrize('name', 't0,t4,t8,td,s0,s4,s8,sd'.split(','))
def test_all(self, name):
self.check_function(getattr(self.module, name), name)
class TestF90ReturnReal(TestReturnReal):
suffix = ".f90"
code = """
module f90_return_real
contains
function t0(value)
real :: value
real :: t0
t0 = value
end function t0
function t4(value)
real(kind=4) :: value
real(kind=4) :: t4
t4 = value
end function t4
function t8(value)
real(kind=8) :: value
real(kind=8) :: t8
t8 = value
end function t8
function td(value)
double precision :: value
double precision :: td
td = value
end function td
subroutine s0(t0,value)
real :: value
real :: t0
!f2py intent(out) t0
t0 = value
end subroutine s0
subroutine s4(t4,value)
real(kind=4) :: value
real(kind=4) :: t4
!f2py intent(out) t4
t4 = value
end subroutine s4
subroutine s8(t8,value)
real(kind=8) :: value
real(kind=8) :: t8
!f2py intent(out) t8
t8 = value
end subroutine s8
subroutine sd(td,value)
double precision :: value
double precision :: td
!f2py intent(out) td
td = value
end subroutine sd
end module f90_return_real
"""
@pytest.mark.parametrize('name', 't0,t4,t8,td,s0,s4,s8,sd'.split(','))
def test_all(self, name):
self.check_function(getattr(self.module.f90_return_real, name), name)
|
|
numpy/f2py/tests/test_return_real.py
|
numpy/typing/tests/data/reveal/numerictypes.py
|
"""
Implementation of optimized einsum.
"""
import itertools
import operator
from numpy.core.multiarray import c_einsum
from numpy.core.numeric import asanyarray, tensordot
from numpy.core.overrides import array_function_dispatch
__all__ = ['einsum', 'einsum_path']
einsum_symbols = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'
einsum_symbols_set = set(einsum_symbols)
def _flop_count(idx_contraction, inner, num_terms, size_dictionary):
"""
Computes the number of FLOPS in the contraction.
Parameters
----------
idx_contraction : iterable
The indices involved in the contraction
inner : bool
Does this contraction require an inner product?
num_terms : int
The number of terms in a contraction
size_dictionary : dict
The size of each of the indices in idx_contraction
Returns
-------
flop_count : int
The total number of FLOPS required for the contraction.
Examples
--------
>>> _flop_count('abc', False, 1, {'a': 2, 'b':3, 'c':5})
30
>>> _flop_count('abc', True, 2, {'a': 2, 'b':3, 'c':5})
60
"""
overall_size = _compute_size_by_dict(idx_contraction, size_dictionary)
op_factor = max(1, num_terms - 1)
if inner:
op_factor += 1
return overall_size * op_factor
def _compute_size_by_dict(indices, idx_dict):
"""
Computes the product of the elements in indices based on the dictionary
idx_dict.
Parameters
----------
indices : iterable
Indices to base the product on.
idx_dict : dictionary
Dictionary of index sizes
Returns
-------
ret : int
The resulting product.
Examples
--------
>>> _compute_size_by_dict('abbc', {'a': 2, 'b':3, 'c':5})
90
"""
ret = 1
for i in indices:
ret *= idx_dict[i]
return ret
def _find_contraction(positions, input_sets, output_set):
"""
Finds the contraction for a given set of input and output sets.
Parameters
----------
positions : iterable
Integer positions of terms used in the contraction.
input_sets : list
List of sets that represent the lhs side of the einsum subscript
output_set : set
Set that represents the rhs side of the overall einsum subscript
Returns
-------
new_result : set
The indices of the resulting contraction
remaining : list
List of sets that have not been contracted, the new set is appended to
the end of this list
idx_removed : set
Indices removed from the entire contraction
idx_contraction : set
The indices used in the current contraction
Examples
--------
# A simple dot product test case
>>> pos = (0, 1)
>>> isets = [set('ab'), set('bc')]
>>> oset = set('ac')
>>> _find_contraction(pos, isets, oset)
({'a', 'c'}, [{'a', 'c'}], {'b'}, {'a', 'b', 'c'})
# A more complex case with additional terms in the contraction
>>> pos = (0, 2)
>>> isets = [set('abd'), set('ac'), set('bdc')]
>>> oset = set('ac')
>>> _find_contraction(pos, isets, oset)
({'a', 'c'}, [{'a', 'c'}, {'a', 'c'}], {'b', 'd'}, {'a', 'b', 'c', 'd'})
"""
idx_contract = set()
idx_remain = output_set.copy()
remaining = []
for ind, value in enumerate(input_sets):
if ind in positions:
idx_contract |= value
else:
remaining.append(value)
idx_remain |= value
new_result = idx_remain & idx_contract
idx_removed = (idx_contract - new_result)
remaining.append(new_result)
return (new_result, remaining, idx_removed, idx_contract)
def _optimal_path(input_sets, output_set, idx_dict, memory_limit):
"""
Computes all possible pair contractions, sieves the results based
on ``memory_limit`` and returns the lowest cost path. This algorithm
scales factorial with respect to the elements in the list ``input_sets``.
Parameters
----------
input_sets : list
List of sets that represent the lhs side of the einsum subscript
output_set : set
Set that represents the rhs side of the overall einsum subscript
idx_dict : dictionary
Dictionary of index sizes
memory_limit : int
The maximum number of elements in a temporary array
Returns
-------
path : list
The optimal contraction order within the memory limit constraint.
Examples
--------
>>> isets = [set('abd'), set('ac'), set('bdc')]
>>> oset = set()
>>> idx_sizes = {'a': 1, 'b':2, 'c':3, 'd':4}
>>> _optimal_path(isets, oset, idx_sizes, 5000)
[(0, 2), (0, 1)]
"""
full_results = [(0, [], input_sets)]
for iteration in range(len(input_sets) - 1):
iter_results = []
# Compute all unique pairs
for curr in full_results:
cost, positions, remaining = curr
for con in itertools.combinations(range(len(input_sets) - iteration), 2):
# Find the contraction
cont = _find_contraction(con, remaining, output_set)
new_result, new_input_sets, idx_removed, idx_contract = cont
# Sieve the results based on memory_limit
new_size = _compute_size_by_dict(new_result, idx_dict)
if new_size > memory_limit:
continue
# Build (total_cost, positions, indices_remaining)
total_cost = cost + _flop_count(idx_contract, idx_removed, len(con), idx_dict)
new_pos = positions + [con]
iter_results.append((total_cost, new_pos, new_input_sets))
# Update combinatorial list, if we did not find anything return best
# path + remaining contractions
if iter_results:
full_results = iter_results
else:
path = min(full_results, key=lambda x: x[0])[1]
path += [tuple(range(len(input_sets) - iteration))]
return path
# If we have not found anything return single einsum contraction
if len(full_results) == 0:
return [tuple(range(len(input_sets)))]
path = min(full_results, key=lambda x: x[0])[1]
return path
def _parse_possible_contraction(positions, input_sets, output_set, idx_dict, memory_limit, path_cost, naive_cost):
"""Compute the cost (removed size + flops) and resultant indices for
performing the contraction specified by ``positions``.
Parameters
----------
positions : tuple of int
The locations of the proposed tensors to contract.
input_sets : list of sets
The indices found on each tensors.
output_set : set
The output indices of the expression.
idx_dict : dict
Mapping of each index to its size.
memory_limit : int
The total allowed size for an intermediary tensor.
path_cost : int
The contraction cost so far.
naive_cost : int
The cost of the unoptimized expression.
Returns
-------
cost : (int, int)
A tuple containing the size of any indices removed, and the flop cost.
positions : tuple of int
The locations of the proposed tensors to contract.
new_input_sets : list of sets
The resulting new list of indices if this proposed contraction is performed.
"""
# Find the contraction
contract = _find_contraction(positions, input_sets, output_set)
idx_result, new_input_sets, idx_removed, idx_contract = contract
# Sieve the results based on memory_limit
new_size = _compute_size_by_dict(idx_result, idx_dict)
if new_size > memory_limit:
return None
# Build sort tuple
old_sizes = (_compute_size_by_dict(input_sets[p], idx_dict) for p in positions)
removed_size = sum(old_sizes) - new_size
# NB: removed_size used to be just the size of any removed indices i.e.:
# helpers.compute_size_by_dict(idx_removed, idx_dict)
cost = _flop_count(idx_contract, idx_removed, len(positions), idx_dict)
sort = (-removed_size, cost)
# Sieve based on total cost as well
if (path_cost + cost) > naive_cost:
return None
# Add contraction to possible choices
return [sort, positions, new_input_sets]
def _update_other_results(results, best):
"""Update the positions and provisional input_sets of ``results`` based on
performing the contraction result ``best``. Remove any involving the tensors
contracted.
Parameters
----------
results : list
List of contraction results produced by ``_parse_possible_contraction``.
best : list
The best contraction of ``results`` i.e. the one that will be performed.
Returns
-------
mod_results : list
The list of modified results, updated with outcome of ``best`` contraction.
"""
best_con = best[1]
bx, by = best_con
mod_results = []
for cost, (x, y), con_sets in results:
# Ignore results involving tensors just contracted
if x in best_con or y in best_con:
continue
# Update the input_sets
del con_sets[by - int(by > x) - int(by > y)]
del con_sets[bx - int(bx > x) - int(bx > y)]
con_sets.insert(-1, best[2][-1])
# Update the position indices
mod_con = x - int(x > bx) - int(x > by), y - int(y > bx) - int(y > by)
mod_results.append((cost, mod_con, con_sets))
return mod_results
def _greedy_path(input_sets, output_set, idx_dict, memory_limit):
"""
Finds the path by contracting the best pair until the input list is
exhausted. The best pair is found by minimizing the tuple
``(-prod(indices_removed), cost)``. What this amounts to is prioritizing
matrix multiplication or inner product operations, then Hadamard like
operations, and finally outer operations. Outer products are limited by
``memory_limit``. This algorithm scales cubically with respect to the
number of elements in the list ``input_sets``.
Parameters
----------
input_sets : list
List of sets that represent the lhs side of the einsum subscript
output_set : set
Set that represents the rhs side of the overall einsum subscript
idx_dict : dictionary
Dictionary of index sizes
memory_limit : int
The maximum number of elements in a temporary array
Returns
-------
path : list
The greedy contraction order within the memory limit constraint.
Examples
--------
>>> isets = [set('abd'), set('ac'), set('bdc')]
>>> oset = set()
>>> idx_sizes = {'a': 1, 'b':2, 'c':3, 'd':4}
>>> _greedy_path(isets, oset, idx_sizes, 5000)
[(0, 2), (0, 1)]
"""
# Handle trivial cases that leaked through
if len(input_sets) == 1:
return [(0,)]
elif len(input_sets) == 2:
return [(0, 1)]
# Build up a naive cost
contract = _find_contraction(range(len(input_sets)), input_sets, output_set)
idx_result, new_input_sets, idx_removed, idx_contract = contract
naive_cost = _flop_count(idx_contract, idx_removed, len(input_sets), idx_dict)
# Initially iterate over all pairs
comb_iter = itertools.combinations(range(len(input_sets)), 2)
known_contractions = []
path_cost = 0
path = []
for iteration in range(len(input_sets) - 1):
# Iterate over all pairs on first step, only previously found pairs on subsequent steps
for positions in comb_iter:
# Always initially ignore outer products
if input_sets[positions[0]].isdisjoint(input_sets[positions[1]]):
continue
result = _parse_possible_contraction(positions, input_sets, output_set, idx_dict, memory_limit, path_cost,
naive_cost)
if result is not None:
known_contractions.append(result)
# If we do not have a inner contraction, rescan pairs including outer products
if len(known_contractions) == 0:
# Then check the outer products
for positions in itertools.combinations(range(len(input_sets)), 2):
result = _parse_possible_contraction(positions, input_sets, output_set, idx_dict, memory_limit,
path_cost, naive_cost)
if result is not None:
known_contractions.append(result)
# If we still did not find any remaining contractions, default back to einsum like behavior
if len(known_contractions) == 0:
path.append(tuple(range(len(input_sets))))
break
# Sort based on first index
best = min(known_contractions, key=lambda x: x[0])
# Now propagate as many unused contractions as possible to next iteration
known_contractions = _update_other_results(known_contractions, best)
# Next iteration only compute contractions with the new tensor
# All other contractions have been accounted for
input_sets = best[2]
new_tensor_pos = len(input_sets) - 1
comb_iter = ((i, new_tensor_pos) for i in range(new_tensor_pos))
# Update path and total cost
path.append(best[1])
path_cost += best[0][1]
return path
def _can_dot(inputs, result, idx_removed):
"""
Checks if we can use BLAS (np.tensordot) call and its beneficial to do so.
Parameters
----------
inputs : list of str
Specifies the subscripts for summation.
result : str
Resulting summation.
idx_removed : set
Indices that are removed in the summation
Returns
-------
type : bool
Returns true if BLAS should and can be used, else False
Notes
-----
If the operations is BLAS level 1 or 2 and is not already aligned
we default back to einsum as the memory movement to copy is more
costly than the operation itself.
Examples
--------
# Standard GEMM operation
>>> _can_dot(['ij', 'jk'], 'ik', set('j'))
True
# Can use the standard BLAS, but requires odd data movement
>>> _can_dot(['ijj', 'jk'], 'ik', set('j'))
False
# DDOT where the memory is not aligned
>>> _can_dot(['ijk', 'ikj'], '', set('ijk'))
False
"""
# All `dot` calls remove indices
if len(idx_removed) == 0:
return False
# BLAS can only handle two operands
if len(inputs) != 2:
return False
input_left, input_right = inputs
for c in set(input_left + input_right):
# can't deal with repeated indices on same input or more than 2 total
nl, nr = input_left.count(c), input_right.count(c)
if (nl > 1) or (nr > 1) or (nl + nr > 2):
return False
# can't do implicit summation or dimension collapse e.g.
# "ab,bc->c" (implicitly sum over 'a')
# "ab,ca->ca" (take diagonal of 'a')
if nl + nr - 1 == int(c in result):
return False
# Build a few temporaries
set_left = set(input_left)
set_right = set(input_right)
keep_left = set_left - idx_removed
keep_right = set_right - idx_removed
rs = len(idx_removed)
# At this point we are a DOT, GEMV, or GEMM operation
# Handle inner products
# DDOT with aligned data
if input_left == input_right:
return True
# DDOT without aligned data (better to use einsum)
if set_left == set_right:
return False
# Handle the 4 possible (aligned) GEMV or GEMM cases
# GEMM or GEMV no transpose
if input_left[-rs:] == input_right[:rs]:
return True
# GEMM or GEMV transpose both
if input_left[:rs] == input_right[-rs:]:
return True
# GEMM or GEMV transpose right
if input_left[-rs:] == input_right[-rs:]:
return True
# GEMM or GEMV transpose left
if input_left[:rs] == input_right[:rs]:
return True
# Einsum is faster than GEMV if we have to copy data
if not keep_left or not keep_right:
return False
# We are a matrix-matrix product, but we need to copy data
return True
def _parse_einsum_input(operands):
"""
A reproduction of einsum c side einsum parsing in python.
Returns
-------
input_strings : str
Parsed input strings
output_string : str
Parsed output string
operands : list of array_like
The operands to use in the numpy contraction
Examples
--------
The operand list is simplified to reduce printing:
>>> np.random.seed(123)
>>> a = np.random.rand(4, 4)
>>> b = np.random.rand(4, 4, 4)
>>> _parse_einsum_input(('...a,...a->...', a, b))
('za,xza', 'xz', [a, b]) # may vary
>>> _parse_einsum_input((a, [Ellipsis, 0], b, [Ellipsis, 0]))
('za,xza', 'xz', [a, b]) # may vary
"""
if len(operands) == 0:
raise ValueError("No input operands")
if isinstance(operands[0], str):
subscripts = operands[0].replace(" ", "")
operands = [asanyarray(v) for v in operands[1:]]
# Ensure all characters are valid
for s in subscripts:
if s in '.,->':
continue
if s not in einsum_symbols:
raise ValueError("Character %s is not a valid symbol." % s)
else:
tmp_operands = list(operands)
operand_list = []
subscript_list = []
for p in range(len(operands) // 2):
operand_list.append(tmp_operands.pop(0))
subscript_list.append(tmp_operands.pop(0))
output_list = tmp_operands[-1] if len(tmp_operands) else None
operands = [asanyarray(v) for v in operand_list]
subscripts = ""
last = len(subscript_list) - 1
for num, sub in enumerate(subscript_list):
for s in sub:
if s is Ellipsis:
subscripts += "..."
else:
try:
s = operator.index(s)
except TypeError as e:
raise TypeError("For this input type lists must contain "
"either int or Ellipsis") from e
subscripts += einsum_symbols[s]
if num != last:
subscripts += ","
if output_list is not None:
subscripts += "->"
for s in output_list:
if s is Ellipsis:
subscripts += "..."
else:
try:
s = operator.index(s)
except TypeError as e:
raise TypeError("For this input type lists must contain "
"either int or Ellipsis") from e
subscripts += einsum_symbols[s]
# Check for proper "->"
if ("-" in subscripts) or (">" in subscripts):
invalid = (subscripts.count("-") > 1) or (subscripts.count(">") > 1)
if invalid or (subscripts.count("->") != 1):
raise ValueError("Subscripts can only contain one '->'.")
# Parse ellipses
if "." in subscripts:
used = subscripts.replace(".", "").replace(",", "").replace("->", "")
unused = list(einsum_symbols_set - set(used))
ellipse_inds = "".join(unused)
longest = 0
if "->" in subscripts:
input_tmp, output_sub = subscripts.split("->")
split_subscripts = input_tmp.split(",")
out_sub = True
else:
split_subscripts = subscripts.split(',')
out_sub = False
for num, sub in enumerate(split_subscripts):
if "." in sub:
if (sub.count(".") != 3) or (sub.count("...") != 1):
raise ValueError("Invalid Ellipses.")
# Take into account numerical values
if operands[num].shape == ():
ellipse_count = 0
else:
ellipse_count = max(operands[num].ndim, 1)
ellipse_count -= (len(sub) - 3)
if ellipse_count > longest:
longest = ellipse_count
if ellipse_count < 0:
raise ValueError("Ellipses lengths do not match.")
elif ellipse_count == 0:
split_subscripts[num] = sub.replace('...', '')
else:
rep_inds = ellipse_inds[-ellipse_count:]
split_subscripts[num] = sub.replace('...', rep_inds)
subscripts = ",".join(split_subscripts)
if longest == 0:
out_ellipse = ""
else:
out_ellipse = ellipse_inds[-longest:]
if out_sub:
subscripts += "->" + output_sub.replace("...", out_ellipse)
else:
# Special care for outputless ellipses
output_subscript = ""
tmp_subscripts = subscripts.replace(",", "")
for s in sorted(set(tmp_subscripts)):
if s not in (einsum_symbols):
raise ValueError("Character %s is not a valid symbol." % s)
if tmp_subscripts.count(s) == 1:
output_subscript += s
normal_inds = ''.join(sorted(set(output_subscript) -
set(out_ellipse)))
subscripts += "->" + out_ellipse + normal_inds
# Build output string if does not exist
if "->" in subscripts:
input_subscripts, output_subscript = subscripts.split("->")
else:
input_subscripts = subscripts
# Build output subscripts
tmp_subscripts = subscripts.replace(",", "")
output_subscript = ""
for s in sorted(set(tmp_subscripts)):
if s not in einsum_symbols:
raise ValueError("Character %s is not a valid symbol." % s)
if tmp_subscripts.count(s) == 1:
output_subscript += s
# Make sure output subscripts are in the input
for char in output_subscript:
if char not in input_subscripts:
raise ValueError("Output character %s did not appear in the input"
% char)
# Make sure number operands is equivalent to the number of terms
if len(input_subscripts.split(',')) != len(operands):
raise ValueError("Number of einsum subscripts must be equal to the "
"number of operands.")
return (input_subscripts, output_subscript, operands)
def _einsum_path_dispatcher(*operands, optimize=None, einsum_call=None):
# NOTE: technically, we should only dispatch on array-like arguments, not
# subscripts (given as strings). But separating operands into
# arrays/subscripts is a little tricky/slow (given einsum's two supported
# signatures), so as a practical shortcut we dispatch on everything.
# Strings will be ignored for dispatching since they don't define
# __array_function__.
return operands
@array_function_dispatch(_einsum_path_dispatcher, module='numpy')
def einsum_path(*operands, optimize='greedy', einsum_call=False):
"""
einsum_path(subscripts, *operands, optimize='greedy')
Evaluates the lowest cost contraction order for an einsum expression by
considering the creation of intermediate arrays.
Parameters
----------
subscripts : str
Specifies the subscripts for summation.
*operands : list of array_like
These are the arrays for the operation.
optimize : {bool, list, tuple, 'greedy', 'optimal'}
Choose the type of path. If a tuple is provided, the second argument is
assumed to be the maximum intermediate size created. If only a single
argument is provided the largest input or output array size is used
as a maximum intermediate size.
* if a list is given that starts with ``einsum_path``, uses this as the
contraction path
* if False no optimization is taken
* if True defaults to the 'greedy' algorithm
* 'optimal' An algorithm that combinatorially explores all possible
ways of contracting the listed tensors and choosest the least costly
path. Scales exponentially with the number of terms in the
contraction.
* 'greedy' An algorithm that chooses the best pair contraction
at each step. Effectively, this algorithm searches the largest inner,
Hadamard, and then outer products at each step. Scales cubically with
the number of terms in the contraction. Equivalent to the 'optimal'
path for most contractions.
Default is 'greedy'.
Returns
-------
path : list of tuples
A list representation of the einsum path.
string_repr : str
A printable representation of the einsum path.
Notes
-----
The resulting path indicates which terms of the input contraction should be
contracted first, the result of this contraction is then appended to the
end of the contraction list. This list can then be iterated over until all
intermediate contractions are complete.
See Also
--------
einsum, linalg.multi_dot
Examples
--------
We can begin with a chain dot example. In this case, it is optimal to
contract the ``b`` and ``c`` tensors first as represented by the first
element of the path ``(1, 2)``. The resulting tensor is added to the end
of the contraction and the remaining contraction ``(0, 1)`` is then
completed.
>>> np.random.seed(123)
>>> a = np.random.rand(2, 2)
>>> b = np.random.rand(2, 5)
>>> c = np.random.rand(5, 2)
>>> path_info = np.einsum_path('ij,jk,kl->il', a, b, c, optimize='greedy')
>>> print(path_info[0])
['einsum_path', (1, 2), (0, 1)]
>>> print(path_info[1])
Complete contraction: ij,jk,kl->il # may vary
Naive scaling: 4
Optimized scaling: 3
Naive FLOP count: 1.600e+02
Optimized FLOP count: 5.600e+01
Theoretical speedup: 2.857
Largest intermediate: 4.000e+00 elements
-------------------------------------------------------------------------
scaling current remaining
-------------------------------------------------------------------------
3 kl,jk->jl ij,jl->il
3 jl,ij->il il->il
A more complex index transformation example.
>>> I = np.random.rand(10, 10, 10, 10)
>>> C = np.random.rand(10, 10)
>>> path_info = np.einsum_path('ea,fb,abcd,gc,hd->efgh', C, C, I, C, C,
... optimize='greedy')
>>> print(path_info[0])
['einsum_path', (0, 2), (0, 3), (0, 2), (0, 1)]
>>> print(path_info[1])
Complete contraction: ea,fb,abcd,gc,hd->efgh # may vary
Naive scaling: 8
Optimized scaling: 5
Naive FLOP count: 8.000e+08
Optimized FLOP count: 8.000e+05
Theoretical speedup: 1000.000
Largest intermediate: 1.000e+04 elements
--------------------------------------------------------------------------
scaling current remaining
--------------------------------------------------------------------------
5 abcd,ea->bcde fb,gc,hd,bcde->efgh
5 bcde,fb->cdef gc,hd,cdef->efgh
5 cdef,gc->defg hd,defg->efgh
5 defg,hd->efgh efgh->efgh
"""
# Figure out what the path really is
path_type = optimize
if path_type is True:
path_type = 'greedy'
if path_type is None:
path_type = False
memory_limit = None
# No optimization or a named path algorithm
if (path_type is False) or isinstance(path_type, str):
pass
# Given an explicit path
elif len(path_type) and (path_type[0] == 'einsum_path'):
pass
# Path tuple with memory limit
elif ((len(path_type) == 2) and isinstance(path_type[0], str) and
isinstance(path_type[1], (int, float))):
memory_limit = int(path_type[1])
path_type = path_type[0]
else:
raise TypeError("Did not understand the path: %s" % str(path_type))
# Hidden option, only einsum should call this
einsum_call_arg = einsum_call
# Python side parsing
input_subscripts, output_subscript, operands = _parse_einsum_input(operands)
# Build a few useful list and sets
input_list = input_subscripts.split(',')
input_sets = [set(x) for x in input_list]
output_set = set(output_subscript)
indices = set(input_subscripts.replace(',', ''))
# Get length of each unique dimension and ensure all dimensions are correct
dimension_dict = {}
broadcast_indices = [[] for x in range(len(input_list))]
for tnum, term in enumerate(input_list):
sh = operands[tnum].shape
if len(sh) != len(term):
raise ValueError("Einstein sum subscript %s does not contain the "
"correct number of indices for operand %d."
% (input_subscripts[tnum], tnum))
for cnum, char in enumerate(term):
dim = sh[cnum]
# Build out broadcast indices
if dim == 1:
broadcast_indices[tnum].append(char)
if char in dimension_dict.keys():
# For broadcasting cases we always want the largest dim size
if dimension_dict[char] == 1:
dimension_dict[char] = dim
elif dim not in (1, dimension_dict[char]):
raise ValueError("Size of label '%s' for operand %d (%d) "
"does not match previous terms (%d)."
% (char, tnum, dimension_dict[char], dim))
else:
dimension_dict[char] = dim
# Convert broadcast inds to sets
broadcast_indices = [set(x) for x in broadcast_indices]
# Compute size of each input array plus the output array
size_list = [_compute_size_by_dict(term, dimension_dict)
for term in input_list + [output_subscript]]
max_size = max(size_list)
if memory_limit is None:
memory_arg = max_size
else:
memory_arg = memory_limit
# Compute naive cost
# This isn't quite right, need to look into exactly how einsum does this
inner_product = (sum(len(x) for x in input_sets) - len(indices)) > 0
naive_cost = _flop_count(indices, inner_product, len(input_list), dimension_dict)
# Compute the path
if (path_type is False) or (len(input_list) in [1, 2]) or (indices == output_set):
# Nothing to be optimized, leave it to einsum
path = [tuple(range(len(input_list)))]
elif path_type == "greedy":
path = _greedy_path(input_sets, output_set, dimension_dict, memory_arg)
elif path_type == "optimal":
path = _optimal_path(input_sets, output_set, dimension_dict, memory_arg)
elif path_type[0] == 'einsum_path':
path = path_type[1:]
else:
raise KeyError("Path name %s not found", path_type)
cost_list, scale_list, size_list, contraction_list = [], [], [], []
# Build contraction tuple (positions, gemm, einsum_str, remaining)
for cnum, contract_inds in enumerate(path):
# Make sure we remove inds from right to left
contract_inds = tuple(sorted(list(contract_inds), reverse=True))
contract = _find_contraction(contract_inds, input_sets, output_set)
out_inds, input_sets, idx_removed, idx_contract = contract
cost = _flop_count(idx_contract, idx_removed, len(contract_inds), dimension_dict)
cost_list.append(cost)
scale_list.append(len(idx_contract))
size_list.append(_compute_size_by_dict(out_inds, dimension_dict))
bcast = set()
tmp_inputs = []
for x in contract_inds:
tmp_inputs.append(input_list.pop(x))
bcast |= broadcast_indices.pop(x)
new_bcast_inds = bcast - idx_removed
# If we're broadcasting, nix blas
if not len(idx_removed & bcast):
do_blas = _can_dot(tmp_inputs, out_inds, idx_removed)
else:
do_blas = False
# Last contraction
if (cnum - len(path)) == -1:
idx_result = output_subscript
else:
sort_result = [(dimension_dict[ind], ind) for ind in out_inds]
idx_result = "".join([x[1] for x in sorted(sort_result)])
input_list.append(idx_result)
broadcast_indices.append(new_bcast_inds)
einsum_str = ",".join(tmp_inputs) + "->" + idx_result
contraction = (contract_inds, idx_removed, einsum_str, input_list[:], do_blas)
contraction_list.append(contraction)
opt_cost = sum(cost_list) + 1
if einsum_call_arg:
return (operands, contraction_list)
# Return the path along with a nice string representation
overall_contraction = input_subscripts + "->" + output_subscript
header = ("scaling", "current", "remaining")
speedup = naive_cost / opt_cost
max_i = max(size_list)
path_print = " Complete contraction: %s\n" % overall_contraction
path_print += " Naive scaling: %d\n" % len(indices)
path_print += " Optimized scaling: %d\n" % max(scale_list)
path_print += " Naive FLOP count: %.3e\n" % naive_cost
path_print += " Optimized FLOP count: %.3e\n" % opt_cost
path_print += " Theoretical speedup: %3.3f\n" % speedup
path_print += " Largest intermediate: %.3e elements\n" % max_i
path_print += "-" * 74 + "\n"
path_print += "%6s %24s %40s\n" % header
path_print += "-" * 74
for n, contraction in enumerate(contraction_list):
inds, idx_rm, einsum_str, remaining, blas = contraction
remaining_str = ",".join(remaining) + "->" + output_subscript
path_run = (scale_list[n], einsum_str, remaining_str)
path_print += "\n%4d %24s %40s" % path_run
path = ['einsum_path'] + path
return (path, path_print)
def _einsum_dispatcher(*operands, out=None, optimize=None, **kwargs):
# Arguably we dispatch on more arguments that we really should; see note in
# _einsum_path_dispatcher for why.
yield from operands
yield out
# Rewrite einsum to handle different cases
@array_function_dispatch(_einsum_dispatcher, module='numpy')
def einsum(*operands, out=None, optimize=False, **kwargs):
"""
einsum(subscripts, *operands, out=None, dtype=None, order='K',
casting='safe', optimize=False)
Evaluates the Einstein summation convention on the operands.
Using the Einstein summation convention, many common multi-dimensional,
linear algebraic array operations can be represented in a simple fashion.
In *implicit* mode `einsum` computes these values.
In *explicit* mode, `einsum` provides further flexibility to compute
other array operations that might not be considered classical Einstein
summation operations, by disabling, or forcing summation over specified
subscript labels.
See the notes and examples for clarification.
Parameters
----------
subscripts : str
Specifies the subscripts for summation as comma separated list of
subscript labels. An implicit (classical Einstein summation)
calculation is performed unless the explicit indicator '->' is
included as well as subscript labels of the precise output form.
operands : list of array_like
These are the arrays for the operation.
out : ndarray, optional
If provided, the calculation is done into this array.
dtype : {data-type, None}, optional
If provided, forces the calculation to use the data type specified.
Note that you may have to also give a more liberal `casting`
parameter to allow the conversions. Default is None.
order : {'C', 'F', 'A', 'K'}, optional
Controls the memory layout of the output. 'C' means it should
be C contiguous. 'F' means it should be Fortran contiguous,
'A' means it should be 'F' if the inputs are all 'F', 'C' otherwise.
'K' means it should be as close to the layout as the inputs as
is possible, including arbitrarily permuted axes.
Default is 'K'.
casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
Controls what kind of data casting may occur. Setting this to
'unsafe' is not recommended, as it can adversely affect accumulations.
* 'no' means the data types should not be cast at all.
* 'equiv' means only byte-order changes are allowed.
* 'safe' means only casts which can preserve values are allowed.
* 'same_kind' means only safe casts or casts within a kind,
like float64 to float32, are allowed.
* 'unsafe' means any data conversions may be done.
Default is 'safe'.
optimize : {False, True, 'greedy', 'optimal'}, optional
Controls if intermediate optimization should occur. No optimization
will occur if False and True will default to the 'greedy' algorithm.
Also accepts an explicit contraction list from the ``np.einsum_path``
function. See ``np.einsum_path`` for more details. Defaults to False.
Returns
-------
output : ndarray
The calculation based on the Einstein summation convention.
See Also
--------
einsum_path, dot, inner, outer, tensordot, linalg.multi_dot
einops :
similar verbose interface is provided by
`einops <https://github.com/arogozhnikov/einops>`_ package to cover
additional operations: transpose, reshape/flatten, repeat/tile,
squeeze/unsqueeze and reductions.
opt_einsum :
`opt_einsum <https://optimized-einsum.readthedocs.io/en/stable/>`_
optimizes contraction order for einsum-like expressions
in backend-agnostic manner.
Notes
-----
.. versionadded:: 1.6.0
The Einstein summation convention can be used to compute
many multi-dimensional, linear algebraic array operations. `einsum`
provides a succinct way of representing these.
A non-exhaustive list of these operations,
which can be computed by `einsum`, is shown below along with examples:
* Trace of an array, :py:func:`numpy.trace`.
* Return a diagonal, :py:func:`numpy.diag`.
* Array axis summations, :py:func:`numpy.sum`.
* Transpositions and permutations, :py:func:`numpy.transpose`.
* Matrix multiplication and dot product, :py:func:`numpy.matmul` :py:func:`numpy.dot`.
* Vector inner and outer products, :py:func:`numpy.inner` :py:func:`numpy.outer`.
* Broadcasting, element-wise and scalar multiplication, :py:func:`numpy.multiply`.
* Tensor contractions, :py:func:`numpy.tensordot`.
* Chained array operations, in efficient calculation order, :py:func:`numpy.einsum_path`.
The subscripts string is a comma-separated list of subscript labels,
where each label refers to a dimension of the corresponding operand.
Whenever a label is repeated it is summed, so ``np.einsum('i,i', a, b)``
is equivalent to :py:func:`np.inner(a,b) <numpy.inner>`. If a label
appears only once, it is not summed, so ``np.einsum('i', a)`` produces a
view of ``a`` with no changes. A further example ``np.einsum('ij,jk', a, b)``
describes traditional matrix multiplication and is equivalent to
:py:func:`np.matmul(a,b) <numpy.matmul>`. Repeated subscript labels in one
operand take the diagonal. For example, ``np.einsum('ii', a)`` is equivalent
to :py:func:`np.trace(a) <numpy.trace>`.
In *implicit mode*, the chosen subscripts are important
since the axes of the output are reordered alphabetically. This
means that ``np.einsum('ij', a)`` doesn't affect a 2D array, while
``np.einsum('ji', a)`` takes its transpose. Additionally,
``np.einsum('ij,jk', a, b)`` returns a matrix multiplication, while,
``np.einsum('ij,jh', a, b)`` returns the transpose of the
multiplication since subscript 'h' precedes subscript 'i'.
In *explicit mode* the output can be directly controlled by
specifying output subscript labels. This requires the
identifier '->' as well as the list of output subscript labels.
This feature increases the flexibility of the function since
summing can be disabled or forced when required. The call
``np.einsum('i->', a)`` is like :py:func:`np.sum(a, axis=-1) <numpy.sum>`,
and ``np.einsum('ii->i', a)`` is like :py:func:`np.diag(a) <numpy.diag>`.
The difference is that `einsum` does not allow broadcasting by default.
Additionally ``np.einsum('ij,jh->ih', a, b)`` directly specifies the
order of the output subscript labels and therefore returns matrix
multiplication, unlike the example above in implicit mode.
To enable and control broadcasting, use an ellipsis. Default
NumPy-style broadcasting is done by adding an ellipsis
to the left of each term, like ``np.einsum('...ii->...i', a)``.
To take the trace along the first and last axes,
you can do ``np.einsum('i...i', a)``, or to do a matrix-matrix
product with the left-most indices instead of rightmost, one can do
``np.einsum('ij...,jk...->ik...', a, b)``.
When there is only one operand, no axes are summed, and no output
parameter is provided, a view into the operand is returned instead
of a new array. Thus, taking the diagonal as ``np.einsum('ii->i', a)``
produces a view (changed in version 1.10.0).
`einsum` also provides an alternative way to provide the subscripts
and operands as ``einsum(op0, sublist0, op1, sublist1, ..., [sublistout])``.
If the output shape is not provided in this format `einsum` will be
calculated in implicit mode, otherwise it will be performed explicitly.
The examples below have corresponding `einsum` calls with the two
parameter methods.
.. versionadded:: 1.10.0
Views returned from einsum are now writeable whenever the input array
is writeable. For example, ``np.einsum('ijk...->kji...', a)`` will now
have the same effect as :py:func:`np.swapaxes(a, 0, 2) <numpy.swapaxes>`
and ``np.einsum('ii->i', a)`` will return a writeable view of the diagonal
of a 2D array.
.. versionadded:: 1.12.0
Added the ``optimize`` argument which will optimize the contraction order
of an einsum expression. For a contraction with three or more operands this
can greatly increase the computational efficiency at the cost of a larger
memory footprint during computation.
Typically a 'greedy' algorithm is applied which empirical tests have shown
returns the optimal path in the majority of cases. In some cases 'optimal'
will return the superlative path through a more expensive, exhaustive search.
For iterative calculations it may be advisable to calculate the optimal path
once and reuse that path by supplying it as an argument. An example is given
below.
See :py:func:`numpy.einsum_path` for more details.
Examples
--------
>>> a = np.arange(25).reshape(5,5)
>>> b = np.arange(5)
>>> c = np.arange(6).reshape(2,3)
Trace of a matrix:
>>> np.einsum('ii', a)
60
>>> np.einsum(a, [0,0])
60
>>> np.trace(a)
60
Extract the diagonal (requires explicit form):
>>> np.einsum('ii->i', a)
array([ 0, 6, 12, 18, 24])
>>> np.einsum(a, [0,0], [0])
array([ 0, 6, 12, 18, 24])
>>> np.diag(a)
array([ 0, 6, 12, 18, 24])
Sum over an axis (requires explicit form):
>>> np.einsum('ij->i', a)
array([ 10, 35, 60, 85, 110])
>>> np.einsum(a, [0,1], [0])
array([ 10, 35, 60, 85, 110])
>>> np.sum(a, axis=1)
array([ 10, 35, 60, 85, 110])
For higher dimensional arrays summing a single axis can be done with ellipsis:
>>> np.einsum('...j->...', a)
array([ 10, 35, 60, 85, 110])
>>> np.einsum(a, [Ellipsis,1], [Ellipsis])
array([ 10, 35, 60, 85, 110])
Compute a matrix transpose, or reorder any number of axes:
>>> np.einsum('ji', c)
array([[0, 3],
[1, 4],
[2, 5]])
>>> np.einsum('ij->ji', c)
array([[0, 3],
[1, 4],
[2, 5]])
>>> np.einsum(c, [1,0])
array([[0, 3],
[1, 4],
[2, 5]])
>>> np.transpose(c)
array([[0, 3],
[1, 4],
[2, 5]])
Vector inner products:
>>> np.einsum('i,i', b, b)
30
>>> np.einsum(b, [0], b, [0])
30
>>> np.inner(b,b)
30
Matrix vector multiplication:
>>> np.einsum('ij,j', a, b)
array([ 30, 80, 130, 180, 230])
>>> np.einsum(a, [0,1], b, [1])
array([ 30, 80, 130, 180, 230])
>>> np.dot(a, b)
array([ 30, 80, 130, 180, 230])
>>> np.einsum('...j,j', a, b)
array([ 30, 80, 130, 180, 230])
Broadcasting and scalar multiplication:
>>> np.einsum('..., ...', 3, c)
array([[ 0, 3, 6],
[ 9, 12, 15]])
>>> np.einsum(',ij', 3, c)
array([[ 0, 3, 6],
[ 9, 12, 15]])
>>> np.einsum(3, [Ellipsis], c, [Ellipsis])
array([[ 0, 3, 6],
[ 9, 12, 15]])
>>> np.multiply(3, c)
array([[ 0, 3, 6],
[ 9, 12, 15]])
Vector outer product:
>>> np.einsum('i,j', np.arange(2)+1, b)
array([[0, 1, 2, 3, 4],
[0, 2, 4, 6, 8]])
>>> np.einsum(np.arange(2)+1, [0], b, [1])
array([[0, 1, 2, 3, 4],
[0, 2, 4, 6, 8]])
>>> np.outer(np.arange(2)+1, b)
array([[0, 1, 2, 3, 4],
[0, 2, 4, 6, 8]])
Tensor contraction:
>>> a = np.arange(60.).reshape(3,4,5)
>>> b = np.arange(24.).reshape(4,3,2)
>>> np.einsum('ijk,jil->kl', a, b)
array([[4400., 4730.],
[4532., 4874.],
[4664., 5018.],
[4796., 5162.],
[4928., 5306.]])
>>> np.einsum(a, [0,1,2], b, [1,0,3], [2,3])
array([[4400., 4730.],
[4532., 4874.],
[4664., 5018.],
[4796., 5162.],
[4928., 5306.]])
>>> np.tensordot(a,b, axes=([1,0],[0,1]))
array([[4400., 4730.],
[4532., 4874.],
[4664., 5018.],
[4796., 5162.],
[4928., 5306.]])
Writeable returned arrays (since version 1.10.0):
>>> a = np.zeros((3, 3))
>>> np.einsum('ii->i', a)[:] = 1
>>> a
array([[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]])
Example of ellipsis use:
>>> a = np.arange(6).reshape((3,2))
>>> b = np.arange(12).reshape((4,3))
>>> np.einsum('ki,jk->ij', a, b)
array([[10, 28, 46, 64],
[13, 40, 67, 94]])
>>> np.einsum('ki,...k->i...', a, b)
array([[10, 28, 46, 64],
[13, 40, 67, 94]])
>>> np.einsum('k...,jk', a, b)
array([[10, 28, 46, 64],
[13, 40, 67, 94]])
Chained array operations. For more complicated contractions, speed ups
might be achieved by repeatedly computing a 'greedy' path or pre-computing the
'optimal' path and repeatedly applying it, using an
`einsum_path` insertion (since version 1.12.0). Performance improvements can be
particularly significant with larger arrays:
>>> a = np.ones(64).reshape(2,4,8)
Basic `einsum`: ~1520ms (benchmarked on 3.1GHz Intel i5.)
>>> for iteration in range(500):
... _ = np.einsum('ijk,ilm,njm,nlk,abc->',a,a,a,a,a)
Sub-optimal `einsum` (due to repeated path calculation time): ~330ms
>>> for iteration in range(500):
... _ = np.einsum('ijk,ilm,njm,nlk,abc->',a,a,a,a,a, optimize='optimal')
Greedy `einsum` (faster optimal path approximation): ~160ms
>>> for iteration in range(500):
... _ = np.einsum('ijk,ilm,njm,nlk,abc->',a,a,a,a,a, optimize='greedy')
Optimal `einsum` (best usage pattern in some use cases): ~110ms
>>> path = np.einsum_path('ijk,ilm,njm,nlk,abc->',a,a,a,a,a, optimize='optimal')[0]
>>> for iteration in range(500):
... _ = np.einsum('ijk,ilm,njm,nlk,abc->',a,a,a,a,a, optimize=path)
"""
# Special handling if out is specified
specified_out = out is not None
# If no optimization, run pure einsum
if optimize is False:
if specified_out:
kwargs['out'] = out
return c_einsum(*operands, **kwargs)
# Check the kwargs to avoid a more cryptic error later, without having to
# repeat default values here
valid_einsum_kwargs = ['dtype', 'order', 'casting']
unknown_kwargs = [k for (k, v) in kwargs.items() if
k not in valid_einsum_kwargs]
if len(unknown_kwargs):
raise TypeError("Did not understand the following kwargs: %s"
% unknown_kwargs)
# Build the contraction list and operand
operands, contraction_list = einsum_path(*operands, optimize=optimize,
einsum_call=True)
# Handle order kwarg for output array, c_einsum allows mixed case
output_order = kwargs.pop('order', 'K')
if output_order.upper() == 'A':
if all(arr.flags.f_contiguous for arr in operands):
output_order = 'F'
else:
output_order = 'C'
# Start contraction loop
for num, contraction in enumerate(contraction_list):
inds, idx_rm, einsum_str, remaining, blas = contraction
tmp_operands = [operands.pop(x) for x in inds]
# Do we need to deal with the output?
handle_out = specified_out and ((num + 1) == len(contraction_list))
# Call tensordot if still possible
if blas:
# Checks have already been handled
input_str, results_index = einsum_str.split('->')
input_left, input_right = input_str.split(',')
tensor_result = input_left + input_right
for s in idx_rm:
tensor_result = tensor_result.replace(s, "")
# Find indices to contract over
left_pos, right_pos = [], []
for s in sorted(idx_rm):
left_pos.append(input_left.find(s))
right_pos.append(input_right.find(s))
# Contract!
new_view = tensordot(*tmp_operands, axes=(tuple(left_pos), tuple(right_pos)))
# Build a new view if needed
if (tensor_result != results_index) or handle_out:
if handle_out:
kwargs["out"] = out
new_view = c_einsum(tensor_result + '->' + results_index, new_view, **kwargs)
# Call einsum
else:
# If out was specified
if handle_out:
kwargs["out"] = out
# Do the contraction
new_view = c_einsum(einsum_str, *tmp_operands, **kwargs)
# Append new items and dereference what we can
operands.append(new_view)
del tmp_operands, new_view
if specified_out:
return out
else:
return asanyarray(operands[0], order=output_order)
|
import platform
import pytest
from numpy import array
from numpy.testing import assert_, assert_raises
from . import util
class TestReturnReal(util.F2PyTest):
def check_function(self, t, tname):
if tname in ['t0', 't4', 's0', 's4']:
err = 1e-5
else:
err = 0.0
assert_(abs(t(234) - 234.0) <= err)
assert_(abs(t(234.6) - 234.6) <= err)
assert_(abs(t('234') - 234) <= err)
assert_(abs(t('234.6') - 234.6) <= err)
assert_(abs(t(-234) + 234) <= err)
assert_(abs(t([234]) - 234) <= err)
assert_(abs(t((234,)) - 234.) <= err)
assert_(abs(t(array(234)) - 234.) <= err)
assert_(abs(t(array([234])) - 234.) <= err)
assert_(abs(t(array([[234]])) - 234.) <= err)
assert_(abs(t(array([234], 'b')) + 22) <= err)
assert_(abs(t(array([234], 'h')) - 234.) <= err)
assert_(abs(t(array([234], 'i')) - 234.) <= err)
assert_(abs(t(array([234], 'l')) - 234.) <= err)
assert_(abs(t(array([234], 'B')) - 234.) <= err)
assert_(abs(t(array([234], 'f')) - 234.) <= err)
assert_(abs(t(array([234], 'd')) - 234.) <= err)
if tname in ['t0', 't4', 's0', 's4']:
assert_(t(1e200) == t(1e300)) # inf
#assert_raises(ValueError, t, array([234], 'S1'))
assert_raises(ValueError, t, 'abc')
assert_raises(IndexError, t, [])
assert_raises(IndexError, t, ())
assert_raises(Exception, t, t)
assert_raises(Exception, t, {})
try:
r = t(10 ** 400)
assert_(repr(r) in ['inf', 'Infinity'], repr(r))
except OverflowError:
pass
@pytest.mark.skipif(
platform.system() == 'Darwin',
reason="Prone to error when run with numpy/f2py/tests on mac os, "
"but not when run in isolation")
class TestCReturnReal(TestReturnReal):
suffix = ".pyf"
module_name = "c_ext_return_real"
code = """
python module c_ext_return_real
usercode \'\'\'
float t4(float value) { return value; }
void s4(float *t4, float value) { *t4 = value; }
double t8(double value) { return value; }
void s8(double *t8, double value) { *t8 = value; }
\'\'\'
interface
function t4(value)
real*4 intent(c) :: t4,value
end
function t8(value)
real*8 intent(c) :: t8,value
end
subroutine s4(t4,value)
intent(c) s4
real*4 intent(out) :: t4
real*4 intent(c) :: value
end
subroutine s8(t8,value)
intent(c) s8
real*8 intent(out) :: t8
real*8 intent(c) :: value
end
end interface
end python module c_ext_return_real
"""
@pytest.mark.parametrize('name', 't4,t8,s4,s8'.split(','))
def test_all(self, name):
self.check_function(getattr(self.module, name), name)
class TestF77ReturnReal(TestReturnReal):
code = """
function t0(value)
real value
real t0
t0 = value
end
function t4(value)
real*4 value
real*4 t4
t4 = value
end
function t8(value)
real*8 value
real*8 t8
t8 = value
end
function td(value)
double precision value
double precision td
td = value
end
subroutine s0(t0,value)
real value
real t0
cf2py intent(out) t0
t0 = value
end
subroutine s4(t4,value)
real*4 value
real*4 t4
cf2py intent(out) t4
t4 = value
end
subroutine s8(t8,value)
real*8 value
real*8 t8
cf2py intent(out) t8
t8 = value
end
subroutine sd(td,value)
double precision value
double precision td
cf2py intent(out) td
td = value
end
"""
@pytest.mark.parametrize('name', 't0,t4,t8,td,s0,s4,s8,sd'.split(','))
def test_all(self, name):
self.check_function(getattr(self.module, name), name)
class TestF90ReturnReal(TestReturnReal):
suffix = ".f90"
code = """
module f90_return_real
contains
function t0(value)
real :: value
real :: t0
t0 = value
end function t0
function t4(value)
real(kind=4) :: value
real(kind=4) :: t4
t4 = value
end function t4
function t8(value)
real(kind=8) :: value
real(kind=8) :: t8
t8 = value
end function t8
function td(value)
double precision :: value
double precision :: td
td = value
end function td
subroutine s0(t0,value)
real :: value
real :: t0
!f2py intent(out) t0
t0 = value
end subroutine s0
subroutine s4(t4,value)
real(kind=4) :: value
real(kind=4) :: t4
!f2py intent(out) t4
t4 = value
end subroutine s4
subroutine s8(t8,value)
real(kind=8) :: value
real(kind=8) :: t8
!f2py intent(out) t8
t8 = value
end subroutine s8
subroutine sd(td,value)
double precision :: value
double precision :: td
!f2py intent(out) td
td = value
end subroutine sd
end module f90_return_real
"""
@pytest.mark.parametrize('name', 't0,t4,t8,td,s0,s4,s8,sd'.split(','))
def test_all(self, name):
self.check_function(getattr(self.module.f90_return_real, name), name)
|
|
numpy/f2py/tests/test_return_real.py
|
|
# Licensed under a 3-clause BSD style license - see LICENSE.rst
from io import StringIO
import numpy as np
from astropy.io import ascii
from .common import assert_equal
def test_types_from_dat():
converters = {'a': [ascii.convert_numpy(float)],
'e': [ascii.convert_numpy(str)]}
dat = ascii.read(['a b c d e', '1 1 cat 2.1 4.2'],
Reader=ascii.Basic,
converters=converters)
assert dat['a'].dtype.kind == 'f'
assert dat['b'].dtype.kind == 'i'
assert dat['c'].dtype.kind in ('S', 'U')
assert dat['d'].dtype.kind == 'f'
assert dat['e'].dtype.kind in ('S', 'U')
def test_rdb_write_types():
dat = ascii.read(['a b c d', '1 1.0 cat 2.1'],
Reader=ascii.Basic)
out = StringIO()
ascii.write(dat, out, Writer=ascii.Rdb)
outs = out.getvalue().splitlines()
assert_equal(outs[1], 'N\tN\tS\tN')
def test_ipac_read_types():
table = r"""\
| ra | dec | sai |-----v2---| sptype |
| real | float | l | real | char |
| unit | unit | unit | unit | ergs |
| null | null | null | null | -999 |
2.09708 2956 73765 2.06000 B8IVpMnHg
"""
reader = ascii.get_reader(Reader=ascii.Ipac)
reader.read(table)
types = [ascii.FloatType,
ascii.FloatType,
ascii.IntType,
ascii.FloatType,
ascii.StrType]
for (col, expected_type) in zip(reader.cols, types):
assert_equal(col.type, expected_type)
def test_col_dtype_in_custom_class():
"""Test code in BaseOutputter._convert_vals to handle Column.dtype
attribute. See discussion in #11895."""
dtypes = [np.float32, np.int8, np.int16]
class TestDtypeHeader(ascii.BasicHeader):
def get_cols(self, lines):
super().get_cols(lines)
for col, dtype in zip(self.cols, dtypes):
col.dtype = dtype
class TestDtype(ascii.Basic):
"""
Basic table Data Reader with data type alternating float32, int8
"""
header_class = TestDtypeHeader
txt = """
a b c
1 2 3
"""
reader = ascii.get_reader(TestDtype)
t = reader.read(txt)
for col, dtype in zip(t.itercols(), dtypes):
assert col.dtype.type is dtype
|
# -*- coding: utf-8 -*-
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
This module tests some of the methods related to the ``HTML``
reader/writer and aims to document its functionality.
Requires `BeautifulSoup <http://www.crummy.com/software/BeautifulSoup/>`_
to be installed.
"""
from io import StringIO
from astropy.io.ascii import html
from astropy.io.ascii import core
from astropy.table import Table
import pytest
import numpy as np
from .common import setup_function, teardown_function # noqa
from astropy.io import ascii
from astropy.utils.compat.optional_deps import HAS_BLEACH, HAS_BS4 # noqa
if HAS_BS4:
from bs4 import BeautifulSoup, FeatureNotFound
@pytest.mark.skipif('not HAS_BS4')
def test_soupstring():
"""
Test to make sure the class SoupString behaves properly.
"""
soup = BeautifulSoup('<html><head></head><body><p>foo</p></body></html>',
'html.parser')
soup_str = html.SoupString(soup)
assert isinstance(soup_str, str)
assert isinstance(soup_str, html.SoupString)
assert soup_str == '<html><head></head><body><p>foo</p></body></html>'
assert soup_str.soup is soup
def test_listwriter():
"""
Test to make sure the class ListWriter behaves properly.
"""
lst = []
writer = html.ListWriter(lst)
for i in range(5):
writer.write(i)
for ch in 'abcde':
writer.write(ch)
assert lst == [0, 1, 2, 3, 4, 'a', 'b', 'c', 'd', 'e']
@pytest.mark.skipif('not HAS_BS4')
def test_identify_table():
"""
Test to make sure that identify_table() returns whether the
given BeautifulSoup tag is the correct table to process.
"""
# Should return False on non-<table> tags and None
soup = BeautifulSoup('<html><body></body></html>', 'html.parser')
assert html.identify_table(soup, {}, 0) is False
assert html.identify_table(None, {}, 0) is False
soup = BeautifulSoup('<table id="foo"><tr><th>A</th></tr><tr>'
'<td>B</td></tr></table>', 'html.parser').table
assert html.identify_table(soup, {}, 2) is False
assert html.identify_table(soup, {}, 1) is True # Default index of 1
# Same tests, but with explicit parameter
assert html.identify_table(soup, {'table_id': 2}, 1) is False
assert html.identify_table(soup, {'table_id': 1}, 1) is True
# Test identification by string ID
assert html.identify_table(soup, {'table_id': 'bar'}, 1) is False
assert html.identify_table(soup, {'table_id': 'foo'}, 1) is True
@pytest.mark.skipif('not HAS_BS4')
def test_missing_data():
"""
Test reading a table with missing data
"""
# First with default where blank => '0'
table_in = ['<table>',
'<tr><th>A</th></tr>',
'<tr><td></td></tr>',
'<tr><td>1</td></tr>',
'</table>']
dat = Table.read(table_in, format='ascii.html')
assert dat.masked is False
assert np.all(dat['A'].mask == [True, False])
assert dat['A'].dtype.kind == 'i'
# Now with a specific value '...' => missing
table_in = ['<table>',
'<tr><th>A</th></tr>',
'<tr><td>...</td></tr>',
'<tr><td>1</td></tr>',
'</table>']
dat = Table.read(table_in, format='ascii.html', fill_values=[('...', '0')])
assert dat.masked is False
assert np.all(dat['A'].mask == [True, False])
assert dat['A'].dtype.kind == 'i'
@pytest.mark.skipif('not HAS_BS4')
def test_rename_cols():
"""
Test reading a table and renaming cols
"""
table_in = ['<table>',
'<tr><th>A</th> <th>B</th></tr>',
'<tr><td>1</td><td>2</td></tr>',
'</table>']
# Swap column names
dat = Table.read(table_in, format='ascii.html', names=['B', 'A'])
assert dat.colnames == ['B', 'A']
assert len(dat) == 1
# Swap column names and only include A (the renamed version)
dat = Table.read(table_in, format='ascii.html', names=['B', 'A'], include_names=['A'])
assert dat.colnames == ['A']
assert len(dat) == 1
assert np.all(dat['A'] == 2)
@pytest.mark.skipif('not HAS_BS4')
def test_no_names():
"""
Test reading a table witn no column header
"""
table_in = ['<table>',
'<tr><td>1</td></tr>',
'<tr><td>2</td></tr>',
'</table>']
dat = Table.read(table_in, format='ascii.html')
assert dat.colnames == ['col1']
assert len(dat) == 2
dat = Table.read(table_in, format='ascii.html', names=['a'])
assert dat.colnames == ['a']
assert len(dat) == 2
@pytest.mark.skipif('not HAS_BS4')
def test_identify_table_fail():
"""
Raise an exception with an informative error message if table_id
is not found.
"""
table_in = ['<table id="foo"><tr><th>A</th></tr>',
'<tr><td>B</td></tr></table>']
with pytest.raises(core.InconsistentTableError) as err:
Table.read(table_in, format='ascii.html', htmldict={'table_id': 'bad_id'},
guess=False)
assert err.match("ERROR: HTML table id 'bad_id' not found$")
with pytest.raises(core.InconsistentTableError) as err:
Table.read(table_in, format='ascii.html', htmldict={'table_id': 3},
guess=False)
assert err.match("ERROR: HTML table number 3 not found$")
@pytest.mark.skipif('not HAS_BS4')
def test_backend_parsers():
"""
Make sure the user can specify which back-end parser to use
and that an error is raised if the parser is invalid.
"""
for parser in ('lxml', 'xml', 'html.parser', 'html5lib'):
try:
Table.read('data/html2.html', format='ascii.html',
htmldict={'parser': parser}, guess=False)
except FeatureNotFound:
if parser == 'html.parser':
raise
# otherwise ignore if the dependency isn't present
# reading should fail if the parser is invalid
with pytest.raises(FeatureNotFound):
Table.read('data/html2.html', format='ascii.html',
htmldict={'parser': 'foo'}, guess=False)
@pytest.mark.skipif('HAS_BS4')
def test_htmlinputter_no_bs4():
"""
This should return an OptionalTableImportError if BeautifulSoup
is not installed.
"""
inputter = html.HTMLInputter()
with pytest.raises(core.OptionalTableImportError):
inputter.process_lines([])
@pytest.mark.skipif('not HAS_BS4')
def test_htmlinputter():
"""
Test to ensure that HTMLInputter correctly converts input
into a list of SoupStrings representing table elements.
"""
f = 'data/html.html'
with open(f) as fd:
table = fd.read()
inputter = html.HTMLInputter()
inputter.html = {}
# In absence of table_id, defaults to the first table
expected = ['<tr><th>Column 1</th><th>Column 2</th><th>Column 3</th></tr>',
'<tr><td>1</td><td>a</td><td>1.05</td></tr>',
'<tr><td>2</td><td>b</td><td>2.75</td></tr>',
'<tr><td>3</td><td>c</td><td>-1.25</td></tr>']
assert [str(x) for x in inputter.get_lines(table)] == expected
# Should raise an InconsistentTableError if the table is not found
inputter.html = {'table_id': 4}
with pytest.raises(core.InconsistentTableError):
inputter.get_lines(table)
# Identification by string ID
inputter.html['table_id'] = 'second'
expected = ['<tr><th>Column A</th><th>Column B</th><th>Column C</th></tr>',
'<tr><td>4</td><td>d</td><td>10.5</td></tr>',
'<tr><td>5</td><td>e</td><td>27.5</td></tr>',
'<tr><td>6</td><td>f</td><td>-12.5</td></tr>']
assert [str(x) for x in inputter.get_lines(table)] == expected
# Identification by integer index
inputter.html['table_id'] = 3
expected = ['<tr><th>C1</th><th>C2</th><th>C3</th></tr>',
'<tr><td>7</td><td>g</td><td>105.0</td></tr>',
'<tr><td>8</td><td>h</td><td>275.0</td></tr>',
'<tr><td>9</td><td>i</td><td>-125.0</td></tr>']
assert [str(x) for x in inputter.get_lines(table)] == expected
@pytest.mark.skipif('not HAS_BS4')
def test_htmlsplitter():
"""
Test to make sure that HTMLSplitter correctly inputs lines
of type SoupString to return a generator that gives all
header and data elements.
"""
splitter = html.HTMLSplitter()
lines = [html.SoupString(BeautifulSoup('<table><tr><th>Col 1</th><th>Col 2</th></tr></table>',
'html.parser').tr),
html.SoupString(BeautifulSoup('<table><tr><td>Data 1</td><td>Data 2</td></tr></table>',
'html.parser').tr)]
expected_data = [['Col 1', 'Col 2'], ['Data 1', 'Data 2']]
assert list(splitter(lines)) == expected_data
# Make sure the presence of a non-SoupString triggers a TypeError
lines.append('<tr><td>Data 3</td><td>Data 4</td></tr>')
with pytest.raises(TypeError):
list(splitter(lines))
# Make sure that passing an empty list triggers an error
with pytest.raises(core.InconsistentTableError):
list(splitter([]))
@pytest.mark.skipif('not HAS_BS4')
def test_htmlheader_start():
"""
Test to ensure that the start_line method of HTMLHeader
returns the first line of header data. Uses t/html.html
for sample input.
"""
f = 'data/html.html'
with open(f) as fd:
table = fd.read()
inputter = html.HTMLInputter()
inputter.html = {}
header = html.HTMLHeader()
lines = inputter.get_lines(table)
assert str(lines[header.start_line(lines)]) == \
'<tr><th>Column 1</th><th>Column 2</th><th>Column 3</th></tr>'
inputter.html['table_id'] = 'second'
lines = inputter.get_lines(table)
assert str(lines[header.start_line(lines)]) == \
'<tr><th>Column A</th><th>Column B</th><th>Column C</th></tr>'
inputter.html['table_id'] = 3
lines = inputter.get_lines(table)
assert str(lines[header.start_line(lines)]) == \
'<tr><th>C1</th><th>C2</th><th>C3</th></tr>'
# start_line should return None if no valid header is found
lines = [html.SoupString(BeautifulSoup('<table><tr><td>Data</td></tr></table>',
'html.parser').tr),
html.SoupString(BeautifulSoup('<p>Text</p>', 'html.parser').p)]
assert header.start_line(lines) is None
# Should raise an error if a non-SoupString is present
lines.append('<tr><th>Header</th></tr>')
with pytest.raises(TypeError):
header.start_line(lines)
@pytest.mark.skipif('not HAS_BS4')
def test_htmldata():
"""
Test to ensure that the start_line and end_lines methods
of HTMLData returns the first line of table data. Uses
t/html.html for sample input.
"""
f = 'data/html.html'
with open(f) as fd:
table = fd.read()
inputter = html.HTMLInputter()
inputter.html = {}
data = html.HTMLData()
lines = inputter.get_lines(table)
assert str(lines[data.start_line(lines)]) == \
'<tr><td>1</td><td>a</td><td>1.05</td></tr>'
# end_line returns the index of the last data element + 1
assert str(lines[data.end_line(lines) - 1]) == \
'<tr><td>3</td><td>c</td><td>-1.25</td></tr>'
inputter.html['table_id'] = 'second'
lines = inputter.get_lines(table)
assert str(lines[data.start_line(lines)]) == \
'<tr><td>4</td><td>d</td><td>10.5</td></tr>'
assert str(lines[data.end_line(lines) - 1]) == \
'<tr><td>6</td><td>f</td><td>-12.5</td></tr>'
inputter.html['table_id'] = 3
lines = inputter.get_lines(table)
assert str(lines[data.start_line(lines)]) == \
'<tr><td>7</td><td>g</td><td>105.0</td></tr>'
assert str(lines[data.end_line(lines) - 1]) == \
'<tr><td>9</td><td>i</td><td>-125.0</td></tr>'
# start_line should raise an error if no table data exists
lines = [html.SoupString(BeautifulSoup('<div></div>', 'html.parser').div),
html.SoupString(BeautifulSoup('<p>Text</p>', 'html.parser').p)]
with pytest.raises(core.InconsistentTableError):
data.start_line(lines)
# end_line should return None if no table data exists
assert data.end_line(lines) is None
# Should raise an error if a non-SoupString is present
lines.append('<tr><td>Data</td></tr>')
with pytest.raises(TypeError):
data.start_line(lines)
with pytest.raises(TypeError):
data.end_line(lines)
def test_multicolumn_write():
"""
Test to make sure that the HTML writer writes multidimensional
columns (those with iterable elements) using the colspan
attribute of <th>.
"""
col1 = [1, 2, 3]
col2 = [(1.0, 1.0), (2.0, 2.0), (3.0, 3.0)]
col3 = [('a', 'a', 'a'), ('b', 'b', 'b'), ('c', 'c', 'c')]
table = Table([col1, col2, col3], names=('C1', 'C2', 'C3'))
expected = """\
<html>
<head>
<meta charset="utf-8"/>
<meta content="text/html;charset=UTF-8" http-equiv="Content-type"/>
</head>
<body>
<table>
<thead>
<tr>
<th>C1</th>
<th colspan="2">C2</th>
<th colspan="3">C3</th>
</tr>
</thead>
<tr>
<td>1</td>
<td>1.0</td>
<td>1.0</td>
<td>a</td>
<td>a</td>
<td>a</td>
</tr>
<tr>
<td>2</td>
<td>2.0</td>
<td>2.0</td>
<td>b</td>
<td>b</td>
<td>b</td>
</tr>
<tr>
<td>3</td>
<td>3.0</td>
<td>3.0</td>
<td>c</td>
<td>c</td>
<td>c</td>
</tr>
</table>
</body>
</html>
"""
out = html.HTML().write(table)[0].strip()
assert out == expected.strip()
@pytest.mark.skipif('not HAS_BLEACH')
def test_multicolumn_write_escape():
"""
Test to make sure that the HTML writer writes multidimensional
columns (those with iterable elements) using the colspan
attribute of <th>.
"""
col1 = [1, 2, 3]
col2 = [(1.0, 1.0), (2.0, 2.0), (3.0, 3.0)]
col3 = [('<a></a>', '<a></a>', 'a'), ('<b></b>', 'b', 'b'), ('c', 'c', 'c')]
table = Table([col1, col2, col3], names=('C1', 'C2', 'C3'))
expected = """\
<html>
<head>
<meta charset="utf-8"/>
<meta content="text/html;charset=UTF-8" http-equiv="Content-type"/>
</head>
<body>
<table>
<thead>
<tr>
<th>C1</th>
<th colspan="2">C2</th>
<th colspan="3">C3</th>
</tr>
</thead>
<tr>
<td>1</td>
<td>1.0</td>
<td>1.0</td>
<td><a></a></td>
<td><a></a></td>
<td>a</td>
</tr>
<tr>
<td>2</td>
<td>2.0</td>
<td>2.0</td>
<td><b></b></td>
<td>b</td>
<td>b</td>
</tr>
<tr>
<td>3</td>
<td>3.0</td>
<td>3.0</td>
<td>c</td>
<td>c</td>
<td>c</td>
</tr>
</table>
</body>
</html>
"""
out = html.HTML(htmldict={'raw_html_cols': 'C3'}).write(table)[0].strip()
assert out == expected.strip()
def test_write_no_multicols():
"""
Test to make sure that the HTML writer will not use
multi-dimensional columns if the multicol parameter
is False.
"""
col1 = [1, 2, 3]
col2 = [(1.0, 1.0), (2.0, 2.0), (3.0, 3.0)]
col3 = [('a', 'a', 'a'), ('b', 'b', 'b'), ('c', 'c', 'c')]
table = Table([col1, col2, col3], names=('C1', 'C2', 'C3'))
expected = """\
<html>
<head>
<meta charset="utf-8"/>
<meta content="text/html;charset=UTF-8" http-equiv="Content-type"/>
</head>
<body>
<table>
<thead>
<tr>
<th>C1</th>
<th>C2</th>
<th>C3</th>
</tr>
</thead>
<tr>
<td>1</td>
<td>1.0 .. 1.0</td>
<td>a .. a</td>
</tr>
<tr>
<td>2</td>
<td>2.0 .. 2.0</td>
<td>b .. b</td>
</tr>
<tr>
<td>3</td>
<td>3.0 .. 3.0</td>
<td>c .. c</td>
</tr>
</table>
</body>
</html>
"""
assert html.HTML({'multicol': False}).write(table)[0].strip() == \
expected.strip()
@pytest.mark.skipif('not HAS_BS4')
def test_multicolumn_read():
"""
Test to make sure that the HTML reader inputs multidimensional
columns (those with iterable elements) using the colspan
attribute of <th>.
Ensure that any string element within a multidimensional column
casts all elements to string prior to type conversion operations.
"""
table = Table.read('data/html2.html', format='ascii.html')
str_type = np.dtype((str, 21))
expected = Table(np.array([(['1', '2.5000000000000000001'], 3),
(['1a', '1'], 3.5)],
dtype=[('A', str_type, (2,)), ('B', '<f8')]))
assert np.all(table == expected)
@pytest.mark.skipif('not HAS_BLEACH')
def test_raw_html_write():
"""
Test that columns can contain raw HTML which is not escaped.
"""
t = Table([['<em>x</em>'], ['<em>y</em>']], names=['a', 'b'])
# One column contains raw HTML (string input)
out = StringIO()
t.write(out, format='ascii.html', htmldict={'raw_html_cols': 'a'})
expected = """\
<tr>
<td><em>x</em></td>
<td><em>y</em></td>
</tr>"""
assert expected in out.getvalue()
# One column contains raw HTML (list input)
out = StringIO()
t.write(out, format='ascii.html', htmldict={'raw_html_cols': ['a']})
assert expected in out.getvalue()
# Two columns contains raw HTML (list input)
out = StringIO()
t.write(out, format='ascii.html', htmldict={'raw_html_cols': ['a', 'b']})
expected = """\
<tr>
<td><em>x</em></td>
<td><em>y</em></td>
</tr>"""
assert expected in out.getvalue()
@pytest.mark.skipif('not HAS_BLEACH')
def test_raw_html_write_clean():
"""
Test that columns can contain raw HTML which is not escaped.
"""
import bleach # noqa
t = Table([['<script>x</script>'], ['<p>y</p>'], ['<em>y</em>']], names=['a', 'b', 'c'])
# Confirm that <script> and <p> get escaped but not <em>
out = StringIO()
t.write(out, format='ascii.html', htmldict={'raw_html_cols': t.colnames})
expected = """\
<tr>
<td><script>x</script></td>
<td><p>y</p></td>
<td><em>y</em></td>
</tr>"""
assert expected in out.getvalue()
# Confirm that we can whitelist <p>
out = StringIO()
t.write(out, format='ascii.html',
htmldict={'raw_html_cols': t.colnames,
'raw_html_clean_kwargs': {'tags': bleach.ALLOWED_TAGS + ['p']}})
expected = """\
<tr>
<td><script>x</script></td>
<td><p>y</p></td>
<td><em>y</em></td>
</tr>"""
assert expected in out.getvalue()
def test_write_table_html_fill_values():
"""
Test that passing fill_values should replace any matching row
"""
buffer_output = StringIO()
t = Table([[1], [2]], names=('a', 'b'))
ascii.write(t, buffer_output, fill_values=('1', 'Hello world'),
format='html')
t_expected = Table([['Hello world'], [2]], names=('a', 'b'))
buffer_expected = StringIO()
ascii.write(t_expected, buffer_expected, format='html')
assert buffer_output.getvalue() == buffer_expected.getvalue()
def test_write_table_html_fill_values_optional_columns():
"""
Test that passing optional column in fill_values should only replace
matching columns
"""
buffer_output = StringIO()
t = Table([[1], [1]], names=('a', 'b'))
ascii.write(t, buffer_output, fill_values=('1', 'Hello world', 'b'),
format='html')
t_expected = Table([[1], ['Hello world']], names=('a', 'b'))
buffer_expected = StringIO()
ascii.write(t_expected, buffer_expected, format='html')
assert buffer_output.getvalue() == buffer_expected.getvalue()
def test_write_table_html_fill_values_masked():
"""
Test that passing masked values in fill_values should only replace
masked columns or values
"""
buffer_output = StringIO()
t = Table([[1], [1]], names=('a', 'b'), masked=True, dtype=('i4', 'i8'))
t['a'] = np.ma.masked
ascii.write(t, buffer_output, fill_values=(ascii.masked, 'TEST'),
format='html')
t_expected = Table([['TEST'], [1]], names=('a', 'b'))
buffer_expected = StringIO()
ascii.write(t_expected, buffer_expected, format='html')
assert buffer_output.getvalue() == buffer_expected.getvalue()
def test_multicolumn_table_html_fill_values():
"""
Test to make sure that the HTML writer writes multidimensional
columns with correctly replaced fill_values.
"""
col1 = [1, 2, 3]
col2 = [(1.0, 1.0), (2.0, 2.0), (3.0, 3.0)]
col3 = [('a', 'a', 'a'), ('b', 'b', 'b'), ('c', 'c', 'c')]
buffer_output = StringIO()
t = Table([col1, col2, col3], names=('C1', 'C2', 'C3'))
ascii.write(t, buffer_output, fill_values=('a', 'z'),
format='html')
col1 = [1, 2, 3]
col2 = [(1.0, 1.0), (2.0, 2.0), (3.0, 3.0)]
col3 = [('z', 'z', 'z'), ('b', 'b', 'b'), ('c', 'c', 'c')]
buffer_expected = StringIO()
t_expected = Table([col1, col2, col3], names=('C1', 'C2', 'C3'))
ascii.write(t_expected, buffer_expected, format='html')
assert buffer_output.getvalue() == buffer_expected.getvalue()
def test_multi_column_write_table_html_fill_values_masked():
"""
Test that passing masked values in fill_values should only replace
masked columns or values for multidimensional tables
"""
buffer_output = StringIO()
t = Table([[1, 2, 3, 4], ['--', 'a', '--', 'b']], names=('a', 'b'), masked=True)
t['a'][0:2] = np.ma.masked
t['b'][0:2] = np.ma.masked
ascii.write(t, buffer_output, fill_values=[(ascii.masked, 'MASKED')],
format='html')
t_expected = Table([['MASKED', 'MASKED', 3, 4], [
'MASKED', 'MASKED', '--', 'b']], names=('a', 'b'))
buffer_expected = StringIO()
ascii.write(t_expected, buffer_expected, format='html')
print(buffer_expected.getvalue())
assert buffer_output.getvalue() == buffer_expected.getvalue()
@pytest.mark.skipif('not HAS_BS4')
def test_read_html_unicode():
"""
Test reading an HTML table with unicode values
"""
table_in = ['<table>',
'<tr><td>Δ</td></tr>',
'<tr><td>Δ</td></tr>',
'</table>']
dat = Table.read(table_in, format='ascii.html')
assert np.all(dat['col1'] == ['Δ', 'Δ'])
|
|
astropy/io/ascii/tests/test_html.py
|
astropy/io/ascii/tests/test_types.py
|
# Licensed under a 3-clause BSD style license - see PYFITS.rst
import os
import shutil
import stat
import tempfile
import time
from astropy.io import fits
class FitsTestCase:
def setup(self):
self.data_dir = os.path.join(os.path.dirname(__file__), 'data')
self.temp_dir = tempfile.mkdtemp(prefix='fits-test-')
# Restore global settings to defaults
# TODO: Replace this when there's a better way to in the config API to
# force config values to their defaults
fits.conf.enable_record_valued_keyword_cards = True
fits.conf.extension_name_case_sensitive = False
fits.conf.strip_header_whitespace = True
fits.conf.use_memmap = True
def teardown(self):
if hasattr(self, 'temp_dir') and os.path.exists(self.temp_dir):
tries = 3
while tries:
try:
shutil.rmtree(self.temp_dir)
break
except OSError:
# Probably couldn't delete the file because for whatever
# reason a handle to it is still open/hasn't been
# garbage-collected
time.sleep(0.5)
tries -= 1
fits.conf.reset('enable_record_valued_keyword_cards')
fits.conf.reset('extension_name_case_sensitive')
fits.conf.reset('strip_header_whitespace')
fits.conf.reset('use_memmap')
def copy_file(self, filename):
"""Copies a backup of a test data file to the temp dir and sets its
mode to writeable.
"""
shutil.copy(self.data(filename), self.temp(filename))
os.chmod(self.temp(filename), stat.S_IREAD | stat.S_IWRITE)
def data(self, filename):
"""Returns the path to a test data file."""
return os.path.join(self.data_dir, filename)
def temp(self, filename):
""" Returns the full path to a file in the test temp dir."""
return os.path.join(self.temp_dir, filename)
|
# -*- coding: utf-8 -*-
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
This module tests some of the methods related to the ``HTML``
reader/writer and aims to document its functionality.
Requires `BeautifulSoup <http://www.crummy.com/software/BeautifulSoup/>`_
to be installed.
"""
from io import StringIO
from astropy.io.ascii import html
from astropy.io.ascii import core
from astropy.table import Table
import pytest
import numpy as np
from .common import setup_function, teardown_function # noqa
from astropy.io import ascii
from astropy.utils.compat.optional_deps import HAS_BLEACH, HAS_BS4 # noqa
if HAS_BS4:
from bs4 import BeautifulSoup, FeatureNotFound
@pytest.mark.skipif('not HAS_BS4')
def test_soupstring():
"""
Test to make sure the class SoupString behaves properly.
"""
soup = BeautifulSoup('<html><head></head><body><p>foo</p></body></html>',
'html.parser')
soup_str = html.SoupString(soup)
assert isinstance(soup_str, str)
assert isinstance(soup_str, html.SoupString)
assert soup_str == '<html><head></head><body><p>foo</p></body></html>'
assert soup_str.soup is soup
def test_listwriter():
"""
Test to make sure the class ListWriter behaves properly.
"""
lst = []
writer = html.ListWriter(lst)
for i in range(5):
writer.write(i)
for ch in 'abcde':
writer.write(ch)
assert lst == [0, 1, 2, 3, 4, 'a', 'b', 'c', 'd', 'e']
@pytest.mark.skipif('not HAS_BS4')
def test_identify_table():
"""
Test to make sure that identify_table() returns whether the
given BeautifulSoup tag is the correct table to process.
"""
# Should return False on non-<table> tags and None
soup = BeautifulSoup('<html><body></body></html>', 'html.parser')
assert html.identify_table(soup, {}, 0) is False
assert html.identify_table(None, {}, 0) is False
soup = BeautifulSoup('<table id="foo"><tr><th>A</th></tr><tr>'
'<td>B</td></tr></table>', 'html.parser').table
assert html.identify_table(soup, {}, 2) is False
assert html.identify_table(soup, {}, 1) is True # Default index of 1
# Same tests, but with explicit parameter
assert html.identify_table(soup, {'table_id': 2}, 1) is False
assert html.identify_table(soup, {'table_id': 1}, 1) is True
# Test identification by string ID
assert html.identify_table(soup, {'table_id': 'bar'}, 1) is False
assert html.identify_table(soup, {'table_id': 'foo'}, 1) is True
@pytest.mark.skipif('not HAS_BS4')
def test_missing_data():
"""
Test reading a table with missing data
"""
# First with default where blank => '0'
table_in = ['<table>',
'<tr><th>A</th></tr>',
'<tr><td></td></tr>',
'<tr><td>1</td></tr>',
'</table>']
dat = Table.read(table_in, format='ascii.html')
assert dat.masked is False
assert np.all(dat['A'].mask == [True, False])
assert dat['A'].dtype.kind == 'i'
# Now with a specific value '...' => missing
table_in = ['<table>',
'<tr><th>A</th></tr>',
'<tr><td>...</td></tr>',
'<tr><td>1</td></tr>',
'</table>']
dat = Table.read(table_in, format='ascii.html', fill_values=[('...', '0')])
assert dat.masked is False
assert np.all(dat['A'].mask == [True, False])
assert dat['A'].dtype.kind == 'i'
@pytest.mark.skipif('not HAS_BS4')
def test_rename_cols():
"""
Test reading a table and renaming cols
"""
table_in = ['<table>',
'<tr><th>A</th> <th>B</th></tr>',
'<tr><td>1</td><td>2</td></tr>',
'</table>']
# Swap column names
dat = Table.read(table_in, format='ascii.html', names=['B', 'A'])
assert dat.colnames == ['B', 'A']
assert len(dat) == 1
# Swap column names and only include A (the renamed version)
dat = Table.read(table_in, format='ascii.html', names=['B', 'A'], include_names=['A'])
assert dat.colnames == ['A']
assert len(dat) == 1
assert np.all(dat['A'] == 2)
@pytest.mark.skipif('not HAS_BS4')
def test_no_names():
"""
Test reading a table witn no column header
"""
table_in = ['<table>',
'<tr><td>1</td></tr>',
'<tr><td>2</td></tr>',
'</table>']
dat = Table.read(table_in, format='ascii.html')
assert dat.colnames == ['col1']
assert len(dat) == 2
dat = Table.read(table_in, format='ascii.html', names=['a'])
assert dat.colnames == ['a']
assert len(dat) == 2
@pytest.mark.skipif('not HAS_BS4')
def test_identify_table_fail():
"""
Raise an exception with an informative error message if table_id
is not found.
"""
table_in = ['<table id="foo"><tr><th>A</th></tr>',
'<tr><td>B</td></tr></table>']
with pytest.raises(core.InconsistentTableError) as err:
Table.read(table_in, format='ascii.html', htmldict={'table_id': 'bad_id'},
guess=False)
assert err.match("ERROR: HTML table id 'bad_id' not found$")
with pytest.raises(core.InconsistentTableError) as err:
Table.read(table_in, format='ascii.html', htmldict={'table_id': 3},
guess=False)
assert err.match("ERROR: HTML table number 3 not found$")
@pytest.mark.skipif('not HAS_BS4')
def test_backend_parsers():
"""
Make sure the user can specify which back-end parser to use
and that an error is raised if the parser is invalid.
"""
for parser in ('lxml', 'xml', 'html.parser', 'html5lib'):
try:
Table.read('data/html2.html', format='ascii.html',
htmldict={'parser': parser}, guess=False)
except FeatureNotFound:
if parser == 'html.parser':
raise
# otherwise ignore if the dependency isn't present
# reading should fail if the parser is invalid
with pytest.raises(FeatureNotFound):
Table.read('data/html2.html', format='ascii.html',
htmldict={'parser': 'foo'}, guess=False)
@pytest.mark.skipif('HAS_BS4')
def test_htmlinputter_no_bs4():
"""
This should return an OptionalTableImportError if BeautifulSoup
is not installed.
"""
inputter = html.HTMLInputter()
with pytest.raises(core.OptionalTableImportError):
inputter.process_lines([])
@pytest.mark.skipif('not HAS_BS4')
def test_htmlinputter():
"""
Test to ensure that HTMLInputter correctly converts input
into a list of SoupStrings representing table elements.
"""
f = 'data/html.html'
with open(f) as fd:
table = fd.read()
inputter = html.HTMLInputter()
inputter.html = {}
# In absence of table_id, defaults to the first table
expected = ['<tr><th>Column 1</th><th>Column 2</th><th>Column 3</th></tr>',
'<tr><td>1</td><td>a</td><td>1.05</td></tr>',
'<tr><td>2</td><td>b</td><td>2.75</td></tr>',
'<tr><td>3</td><td>c</td><td>-1.25</td></tr>']
assert [str(x) for x in inputter.get_lines(table)] == expected
# Should raise an InconsistentTableError if the table is not found
inputter.html = {'table_id': 4}
with pytest.raises(core.InconsistentTableError):
inputter.get_lines(table)
# Identification by string ID
inputter.html['table_id'] = 'second'
expected = ['<tr><th>Column A</th><th>Column B</th><th>Column C</th></tr>',
'<tr><td>4</td><td>d</td><td>10.5</td></tr>',
'<tr><td>5</td><td>e</td><td>27.5</td></tr>',
'<tr><td>6</td><td>f</td><td>-12.5</td></tr>']
assert [str(x) for x in inputter.get_lines(table)] == expected
# Identification by integer index
inputter.html['table_id'] = 3
expected = ['<tr><th>C1</th><th>C2</th><th>C3</th></tr>',
'<tr><td>7</td><td>g</td><td>105.0</td></tr>',
'<tr><td>8</td><td>h</td><td>275.0</td></tr>',
'<tr><td>9</td><td>i</td><td>-125.0</td></tr>']
assert [str(x) for x in inputter.get_lines(table)] == expected
@pytest.mark.skipif('not HAS_BS4')
def test_htmlsplitter():
"""
Test to make sure that HTMLSplitter correctly inputs lines
of type SoupString to return a generator that gives all
header and data elements.
"""
splitter = html.HTMLSplitter()
lines = [html.SoupString(BeautifulSoup('<table><tr><th>Col 1</th><th>Col 2</th></tr></table>',
'html.parser').tr),
html.SoupString(BeautifulSoup('<table><tr><td>Data 1</td><td>Data 2</td></tr></table>',
'html.parser').tr)]
expected_data = [['Col 1', 'Col 2'], ['Data 1', 'Data 2']]
assert list(splitter(lines)) == expected_data
# Make sure the presence of a non-SoupString triggers a TypeError
lines.append('<tr><td>Data 3</td><td>Data 4</td></tr>')
with pytest.raises(TypeError):
list(splitter(lines))
# Make sure that passing an empty list triggers an error
with pytest.raises(core.InconsistentTableError):
list(splitter([]))
@pytest.mark.skipif('not HAS_BS4')
def test_htmlheader_start():
"""
Test to ensure that the start_line method of HTMLHeader
returns the first line of header data. Uses t/html.html
for sample input.
"""
f = 'data/html.html'
with open(f) as fd:
table = fd.read()
inputter = html.HTMLInputter()
inputter.html = {}
header = html.HTMLHeader()
lines = inputter.get_lines(table)
assert str(lines[header.start_line(lines)]) == \
'<tr><th>Column 1</th><th>Column 2</th><th>Column 3</th></tr>'
inputter.html['table_id'] = 'second'
lines = inputter.get_lines(table)
assert str(lines[header.start_line(lines)]) == \
'<tr><th>Column A</th><th>Column B</th><th>Column C</th></tr>'
inputter.html['table_id'] = 3
lines = inputter.get_lines(table)
assert str(lines[header.start_line(lines)]) == \
'<tr><th>C1</th><th>C2</th><th>C3</th></tr>'
# start_line should return None if no valid header is found
lines = [html.SoupString(BeautifulSoup('<table><tr><td>Data</td></tr></table>',
'html.parser').tr),
html.SoupString(BeautifulSoup('<p>Text</p>', 'html.parser').p)]
assert header.start_line(lines) is None
# Should raise an error if a non-SoupString is present
lines.append('<tr><th>Header</th></tr>')
with pytest.raises(TypeError):
header.start_line(lines)
@pytest.mark.skipif('not HAS_BS4')
def test_htmldata():
"""
Test to ensure that the start_line and end_lines methods
of HTMLData returns the first line of table data. Uses
t/html.html for sample input.
"""
f = 'data/html.html'
with open(f) as fd:
table = fd.read()
inputter = html.HTMLInputter()
inputter.html = {}
data = html.HTMLData()
lines = inputter.get_lines(table)
assert str(lines[data.start_line(lines)]) == \
'<tr><td>1</td><td>a</td><td>1.05</td></tr>'
# end_line returns the index of the last data element + 1
assert str(lines[data.end_line(lines) - 1]) == \
'<tr><td>3</td><td>c</td><td>-1.25</td></tr>'
inputter.html['table_id'] = 'second'
lines = inputter.get_lines(table)
assert str(lines[data.start_line(lines)]) == \
'<tr><td>4</td><td>d</td><td>10.5</td></tr>'
assert str(lines[data.end_line(lines) - 1]) == \
'<tr><td>6</td><td>f</td><td>-12.5</td></tr>'
inputter.html['table_id'] = 3
lines = inputter.get_lines(table)
assert str(lines[data.start_line(lines)]) == \
'<tr><td>7</td><td>g</td><td>105.0</td></tr>'
assert str(lines[data.end_line(lines) - 1]) == \
'<tr><td>9</td><td>i</td><td>-125.0</td></tr>'
# start_line should raise an error if no table data exists
lines = [html.SoupString(BeautifulSoup('<div></div>', 'html.parser').div),
html.SoupString(BeautifulSoup('<p>Text</p>', 'html.parser').p)]
with pytest.raises(core.InconsistentTableError):
data.start_line(lines)
# end_line should return None if no table data exists
assert data.end_line(lines) is None
# Should raise an error if a non-SoupString is present
lines.append('<tr><td>Data</td></tr>')
with pytest.raises(TypeError):
data.start_line(lines)
with pytest.raises(TypeError):
data.end_line(lines)
def test_multicolumn_write():
"""
Test to make sure that the HTML writer writes multidimensional
columns (those with iterable elements) using the colspan
attribute of <th>.
"""
col1 = [1, 2, 3]
col2 = [(1.0, 1.0), (2.0, 2.0), (3.0, 3.0)]
col3 = [('a', 'a', 'a'), ('b', 'b', 'b'), ('c', 'c', 'c')]
table = Table([col1, col2, col3], names=('C1', 'C2', 'C3'))
expected = """\
<html>
<head>
<meta charset="utf-8"/>
<meta content="text/html;charset=UTF-8" http-equiv="Content-type"/>
</head>
<body>
<table>
<thead>
<tr>
<th>C1</th>
<th colspan="2">C2</th>
<th colspan="3">C3</th>
</tr>
</thead>
<tr>
<td>1</td>
<td>1.0</td>
<td>1.0</td>
<td>a</td>
<td>a</td>
<td>a</td>
</tr>
<tr>
<td>2</td>
<td>2.0</td>
<td>2.0</td>
<td>b</td>
<td>b</td>
<td>b</td>
</tr>
<tr>
<td>3</td>
<td>3.0</td>
<td>3.0</td>
<td>c</td>
<td>c</td>
<td>c</td>
</tr>
</table>
</body>
</html>
"""
out = html.HTML().write(table)[0].strip()
assert out == expected.strip()
@pytest.mark.skipif('not HAS_BLEACH')
def test_multicolumn_write_escape():
"""
Test to make sure that the HTML writer writes multidimensional
columns (those with iterable elements) using the colspan
attribute of <th>.
"""
col1 = [1, 2, 3]
col2 = [(1.0, 1.0), (2.0, 2.0), (3.0, 3.0)]
col3 = [('<a></a>', '<a></a>', 'a'), ('<b></b>', 'b', 'b'), ('c', 'c', 'c')]
table = Table([col1, col2, col3], names=('C1', 'C2', 'C3'))
expected = """\
<html>
<head>
<meta charset="utf-8"/>
<meta content="text/html;charset=UTF-8" http-equiv="Content-type"/>
</head>
<body>
<table>
<thead>
<tr>
<th>C1</th>
<th colspan="2">C2</th>
<th colspan="3">C3</th>
</tr>
</thead>
<tr>
<td>1</td>
<td>1.0</td>
<td>1.0</td>
<td><a></a></td>
<td><a></a></td>
<td>a</td>
</tr>
<tr>
<td>2</td>
<td>2.0</td>
<td>2.0</td>
<td><b></b></td>
<td>b</td>
<td>b</td>
</tr>
<tr>
<td>3</td>
<td>3.0</td>
<td>3.0</td>
<td>c</td>
<td>c</td>
<td>c</td>
</tr>
</table>
</body>
</html>
"""
out = html.HTML(htmldict={'raw_html_cols': 'C3'}).write(table)[0].strip()
assert out == expected.strip()
def test_write_no_multicols():
"""
Test to make sure that the HTML writer will not use
multi-dimensional columns if the multicol parameter
is False.
"""
col1 = [1, 2, 3]
col2 = [(1.0, 1.0), (2.0, 2.0), (3.0, 3.0)]
col3 = [('a', 'a', 'a'), ('b', 'b', 'b'), ('c', 'c', 'c')]
table = Table([col1, col2, col3], names=('C1', 'C2', 'C3'))
expected = """\
<html>
<head>
<meta charset="utf-8"/>
<meta content="text/html;charset=UTF-8" http-equiv="Content-type"/>
</head>
<body>
<table>
<thead>
<tr>
<th>C1</th>
<th>C2</th>
<th>C3</th>
</tr>
</thead>
<tr>
<td>1</td>
<td>1.0 .. 1.0</td>
<td>a .. a</td>
</tr>
<tr>
<td>2</td>
<td>2.0 .. 2.0</td>
<td>b .. b</td>
</tr>
<tr>
<td>3</td>
<td>3.0 .. 3.0</td>
<td>c .. c</td>
</tr>
</table>
</body>
</html>
"""
assert html.HTML({'multicol': False}).write(table)[0].strip() == \
expected.strip()
@pytest.mark.skipif('not HAS_BS4')
def test_multicolumn_read():
"""
Test to make sure that the HTML reader inputs multidimensional
columns (those with iterable elements) using the colspan
attribute of <th>.
Ensure that any string element within a multidimensional column
casts all elements to string prior to type conversion operations.
"""
table = Table.read('data/html2.html', format='ascii.html')
str_type = np.dtype((str, 21))
expected = Table(np.array([(['1', '2.5000000000000000001'], 3),
(['1a', '1'], 3.5)],
dtype=[('A', str_type, (2,)), ('B', '<f8')]))
assert np.all(table == expected)
@pytest.mark.skipif('not HAS_BLEACH')
def test_raw_html_write():
"""
Test that columns can contain raw HTML which is not escaped.
"""
t = Table([['<em>x</em>'], ['<em>y</em>']], names=['a', 'b'])
# One column contains raw HTML (string input)
out = StringIO()
t.write(out, format='ascii.html', htmldict={'raw_html_cols': 'a'})
expected = """\
<tr>
<td><em>x</em></td>
<td><em>y</em></td>
</tr>"""
assert expected in out.getvalue()
# One column contains raw HTML (list input)
out = StringIO()
t.write(out, format='ascii.html', htmldict={'raw_html_cols': ['a']})
assert expected in out.getvalue()
# Two columns contains raw HTML (list input)
out = StringIO()
t.write(out, format='ascii.html', htmldict={'raw_html_cols': ['a', 'b']})
expected = """\
<tr>
<td><em>x</em></td>
<td><em>y</em></td>
</tr>"""
assert expected in out.getvalue()
@pytest.mark.skipif('not HAS_BLEACH')
def test_raw_html_write_clean():
"""
Test that columns can contain raw HTML which is not escaped.
"""
import bleach # noqa
t = Table([['<script>x</script>'], ['<p>y</p>'], ['<em>y</em>']], names=['a', 'b', 'c'])
# Confirm that <script> and <p> get escaped but not <em>
out = StringIO()
t.write(out, format='ascii.html', htmldict={'raw_html_cols': t.colnames})
expected = """\
<tr>
<td><script>x</script></td>
<td><p>y</p></td>
<td><em>y</em></td>
</tr>"""
assert expected in out.getvalue()
# Confirm that we can whitelist <p>
out = StringIO()
t.write(out, format='ascii.html',
htmldict={'raw_html_cols': t.colnames,
'raw_html_clean_kwargs': {'tags': bleach.ALLOWED_TAGS + ['p']}})
expected = """\
<tr>
<td><script>x</script></td>
<td><p>y</p></td>
<td><em>y</em></td>
</tr>"""
assert expected in out.getvalue()
def test_write_table_html_fill_values():
"""
Test that passing fill_values should replace any matching row
"""
buffer_output = StringIO()
t = Table([[1], [2]], names=('a', 'b'))
ascii.write(t, buffer_output, fill_values=('1', 'Hello world'),
format='html')
t_expected = Table([['Hello world'], [2]], names=('a', 'b'))
buffer_expected = StringIO()
ascii.write(t_expected, buffer_expected, format='html')
assert buffer_output.getvalue() == buffer_expected.getvalue()
def test_write_table_html_fill_values_optional_columns():
"""
Test that passing optional column in fill_values should only replace
matching columns
"""
buffer_output = StringIO()
t = Table([[1], [1]], names=('a', 'b'))
ascii.write(t, buffer_output, fill_values=('1', 'Hello world', 'b'),
format='html')
t_expected = Table([[1], ['Hello world']], names=('a', 'b'))
buffer_expected = StringIO()
ascii.write(t_expected, buffer_expected, format='html')
assert buffer_output.getvalue() == buffer_expected.getvalue()
def test_write_table_html_fill_values_masked():
"""
Test that passing masked values in fill_values should only replace
masked columns or values
"""
buffer_output = StringIO()
t = Table([[1], [1]], names=('a', 'b'), masked=True, dtype=('i4', 'i8'))
t['a'] = np.ma.masked
ascii.write(t, buffer_output, fill_values=(ascii.masked, 'TEST'),
format='html')
t_expected = Table([['TEST'], [1]], names=('a', 'b'))
buffer_expected = StringIO()
ascii.write(t_expected, buffer_expected, format='html')
assert buffer_output.getvalue() == buffer_expected.getvalue()
def test_multicolumn_table_html_fill_values():
"""
Test to make sure that the HTML writer writes multidimensional
columns with correctly replaced fill_values.
"""
col1 = [1, 2, 3]
col2 = [(1.0, 1.0), (2.0, 2.0), (3.0, 3.0)]
col3 = [('a', 'a', 'a'), ('b', 'b', 'b'), ('c', 'c', 'c')]
buffer_output = StringIO()
t = Table([col1, col2, col3], names=('C1', 'C2', 'C3'))
ascii.write(t, buffer_output, fill_values=('a', 'z'),
format='html')
col1 = [1, 2, 3]
col2 = [(1.0, 1.0), (2.0, 2.0), (3.0, 3.0)]
col3 = [('z', 'z', 'z'), ('b', 'b', 'b'), ('c', 'c', 'c')]
buffer_expected = StringIO()
t_expected = Table([col1, col2, col3], names=('C1', 'C2', 'C3'))
ascii.write(t_expected, buffer_expected, format='html')
assert buffer_output.getvalue() == buffer_expected.getvalue()
def test_multi_column_write_table_html_fill_values_masked():
"""
Test that passing masked values in fill_values should only replace
masked columns or values for multidimensional tables
"""
buffer_output = StringIO()
t = Table([[1, 2, 3, 4], ['--', 'a', '--', 'b']], names=('a', 'b'), masked=True)
t['a'][0:2] = np.ma.masked
t['b'][0:2] = np.ma.masked
ascii.write(t, buffer_output, fill_values=[(ascii.masked, 'MASKED')],
format='html')
t_expected = Table([['MASKED', 'MASKED', 3, 4], [
'MASKED', 'MASKED', '--', 'b']], names=('a', 'b'))
buffer_expected = StringIO()
ascii.write(t_expected, buffer_expected, format='html')
print(buffer_expected.getvalue())
assert buffer_output.getvalue() == buffer_expected.getvalue()
@pytest.mark.skipif('not HAS_BS4')
def test_read_html_unicode():
"""
Test reading an HTML table with unicode values
"""
table_in = ['<table>',
'<tr><td>Δ</td></tr>',
'<tr><td>Δ</td></tr>',
'</table>']
dat = Table.read(table_in, format='ascii.html')
assert np.all(dat['col1'] == ['Δ', 'Δ'])
|
|
astropy/io/ascii/tests/test_html.py
|
astropy/io/fits/tests/__init__.py
|
from pandas.compat import StringIO
from pandas import read_sas
import pandas.util.testing as tm
class TestSas(object):
def test_sas_buffer_format(self):
# see gh-14947
b = StringIO("")
msg = ("If this is a buffer object rather than a string "
"name, you must specify a format string")
with tm.assert_raises_regex(ValueError, msg):
read_sas(b)
|
# -*- coding: utf-8 -*-
# pylint: disable-msg=E1101,W0612
from operator import methodcaller
import pytest
import numpy as np
import pandas as pd
from distutils.version import LooseVersion
from pandas import Series, date_range, MultiIndex
from pandas.compat import range
from pandas.util.testing import (assert_series_equal,
assert_almost_equal)
import pandas.util.testing as tm
import pandas.util._test_decorators as td
from .test_generic import Generic
try:
import xarray
_XARRAY_INSTALLED = True
except ImportError:
_XARRAY_INSTALLED = False
class TestSeries(Generic):
_typ = Series
_comparator = lambda self, x, y: assert_series_equal(x, y)
def setup_method(self):
self.ts = tm.makeTimeSeries() # Was at top level in test_series
self.ts.name = 'ts'
self.series = tm.makeStringSeries()
self.series.name = 'series'
def test_rename_mi(self):
s = Series([11, 21, 31],
index=MultiIndex.from_tuples(
[("A", x) for x in ["a", "B", "c"]]))
s.rename(str.lower)
def test_set_axis_name(self):
s = Series([1, 2, 3], index=['a', 'b', 'c'])
funcs = ['rename_axis', '_set_axis_name']
name = 'foo'
for func in funcs:
result = methodcaller(func, name)(s)
assert s.index.name is None
assert result.index.name == name
def test_set_axis_name_mi(self):
s = Series([11, 21, 31], index=MultiIndex.from_tuples(
[("A", x) for x in ["a", "B", "c"]],
names=['l1', 'l2'])
)
funcs = ['rename_axis', '_set_axis_name']
for func in funcs:
result = methodcaller(func, ['L1', 'L2'])(s)
assert s.index.name is None
assert s.index.names == ['l1', 'l2']
assert result.index.name is None
assert result.index.names, ['L1', 'L2']
def test_set_axis_name_raises(self):
s = pd.Series([1])
with pytest.raises(ValueError):
s._set_axis_name(name='a', axis=1)
def test_get_numeric_data_preserve_dtype(self):
# get the numeric data
o = Series([1, 2, 3])
result = o._get_numeric_data()
self._compare(result, o)
o = Series([1, '2', 3.])
result = o._get_numeric_data()
expected = Series([], dtype=object, index=pd.Index([], dtype=object))
self._compare(result, expected)
o = Series([True, False, True])
result = o._get_numeric_data()
self._compare(result, o)
o = Series([True, False, True])
result = o._get_bool_data()
self._compare(result, o)
o = Series(date_range('20130101', periods=3))
result = o._get_numeric_data()
expected = Series([], dtype='M8[ns]', index=pd.Index([], dtype=object))
self._compare(result, expected)
def test_nonzero_single_element(self):
# allow single item via bool method
s = Series([True])
assert s.bool()
s = Series([False])
assert not s.bool()
# single item nan to raise
for s in [Series([np.nan]), Series([pd.NaT]), Series([True]),
Series([False])]:
pytest.raises(ValueError, lambda: bool(s))
for s in [Series([np.nan]), Series([pd.NaT])]:
pytest.raises(ValueError, lambda: s.bool())
# multiple bool are still an error
for s in [Series([True, True]), Series([False, False])]:
pytest.raises(ValueError, lambda: bool(s))
pytest.raises(ValueError, lambda: s.bool())
# single non-bool are an error
for s in [Series([1]), Series([0]), Series(['a']), Series([0.0])]:
pytest.raises(ValueError, lambda: bool(s))
pytest.raises(ValueError, lambda: s.bool())
def test_metadata_propagation_indiv(self):
# check that the metadata matches up on the resulting ops
o = Series(range(3), range(3))
o.name = 'foo'
o2 = Series(range(3), range(3))
o2.name = 'bar'
result = o.T
self.check_metadata(o, result)
# resample
ts = Series(np.random.rand(1000),
index=date_range('20130101', periods=1000, freq='s'),
name='foo')
result = ts.resample('1T').mean()
self.check_metadata(ts, result)
result = ts.resample('1T').min()
self.check_metadata(ts, result)
result = ts.resample('1T').apply(lambda x: x.sum())
self.check_metadata(ts, result)
_metadata = Series._metadata
_finalize = Series.__finalize__
Series._metadata = ['name', 'filename']
o.filename = 'foo'
o2.filename = 'bar'
def finalize(self, other, method=None, **kwargs):
for name in self._metadata:
if method == 'concat' and name == 'filename':
value = '+'.join([getattr(
o, name) for o in other.objs if getattr(o, name, None)
])
object.__setattr__(self, name, value)
else:
object.__setattr__(self, name, getattr(other, name, None))
return self
Series.__finalize__ = finalize
result = pd.concat([o, o2])
assert result.filename == 'foo+bar'
assert result.name is None
# reset
Series._metadata = _metadata
Series.__finalize__ = _finalize
@pytest.mark.skipif(not _XARRAY_INSTALLED or _XARRAY_INSTALLED and
LooseVersion(xarray.__version__) <
LooseVersion('0.10.0'),
reason='xarray >= 0.10.0 required')
@pytest.mark.parametrize(
"index",
['FloatIndex', 'IntIndex',
'StringIndex', 'UnicodeIndex',
'DateIndex', 'PeriodIndex',
'TimedeltaIndex', 'CategoricalIndex'])
def test_to_xarray_index_types(self, index):
from xarray import DataArray
index = getattr(tm, 'make{}'.format(index))
s = Series(range(6), index=index(6))
s.index.name = 'foo'
result = s.to_xarray()
repr(result)
assert len(result) == 6
assert len(result.coords) == 1
assert_almost_equal(list(result.coords.keys()), ['foo'])
assert isinstance(result, DataArray)
# idempotency
assert_series_equal(result.to_series(), s,
check_index_type=False,
check_categorical=True)
@td.skip_if_no('xarray', min_version='0.7.0')
def test_to_xarray(self):
from xarray import DataArray
s = Series([])
s.index.name = 'foo'
result = s.to_xarray()
assert len(result) == 0
assert len(result.coords) == 1
assert_almost_equal(list(result.coords.keys()), ['foo'])
assert isinstance(result, DataArray)
s = Series(range(6))
s.index.name = 'foo'
s.index = pd.MultiIndex.from_product([['a', 'b'], range(3)],
names=['one', 'two'])
result = s.to_xarray()
assert len(result) == 2
assert_almost_equal(list(result.coords.keys()), ['one', 'two'])
assert isinstance(result, DataArray)
assert_series_equal(result.to_series(), s)
def test_valid_deprecated(self):
# GH18800
with tm.assert_produces_warning(FutureWarning):
pd.Series([]).valid()
@pytest.mark.parametrize("s", [
Series([np.arange(5)]),
pd.date_range('1/1/2011', periods=24, freq='H'),
pd.Series(range(5), index=pd.date_range("2017", periods=5))
])
@pytest.mark.parametrize("shift_size", [0, 1, 2])
def test_shift_always_copy(self, s, shift_size):
# GH22397
assert s.shift(shift_size) is not s
@pytest.mark.parametrize("move_by_freq", [
pd.Timedelta('1D'),
pd.Timedelta('1M'),
])
def test_datetime_shift_always_copy(self, move_by_freq):
# GH22397
s = pd.Series(range(5), index=pd.date_range("2017", periods=5))
assert s.shift(freq=move_by_freq) is not s
|
|
pandas/tests/generic/test_series.py
|
pandas/tests/io/sas/test_sas.py
|
#!/usr/bin/env python
"""Top level ``eval`` module.
"""
import warnings
import tokenize
from pandas.io.formats.printing import pprint_thing
from pandas.core.computation.scope import _ensure_scope
from pandas.compat import string_types
from pandas.core.computation.engines import _engines
from pandas.util._validators import validate_bool_kwarg
def _check_engine(engine):
"""Make sure a valid engine is passed.
Parameters
----------
engine : str
Raises
------
KeyError
* If an invalid engine is passed
ImportError
* If numexpr was requested but doesn't exist
Returns
-------
string engine
"""
from pandas.core.computation.check import _NUMEXPR_INSTALLED
if engine is None:
if _NUMEXPR_INSTALLED:
engine = 'numexpr'
else:
engine = 'python'
if engine not in _engines:
valid = list(_engines.keys())
raise KeyError('Invalid engine {engine!r} passed, valid engines are'
' {valid}'.format(engine=engine, valid=valid))
# TODO: validate this in a more general way (thinking of future engines
# that won't necessarily be import-able)
# Could potentially be done on engine instantiation
if engine == 'numexpr':
if not _NUMEXPR_INSTALLED:
raise ImportError("'numexpr' is not installed or an "
"unsupported version. Cannot use "
"engine='numexpr' for query/eval "
"if 'numexpr' is not installed")
return engine
def _check_parser(parser):
"""Make sure a valid parser is passed.
Parameters
----------
parser : str
Raises
------
KeyError
* If an invalid parser is passed
"""
from pandas.core.computation.expr import _parsers
if parser not in _parsers:
raise KeyError('Invalid parser {parser!r} passed, valid parsers are'
' {valid}'.format(parser=parser, valid=_parsers.keys()))
def _check_resolvers(resolvers):
if resolvers is not None:
for resolver in resolvers:
if not hasattr(resolver, '__getitem__'):
name = type(resolver).__name__
raise TypeError('Resolver of type {name!r} does not implement '
'the __getitem__ method'.format(name=name))
def _check_expression(expr):
"""Make sure an expression is not an empty string
Parameters
----------
expr : object
An object that can be converted to a string
Raises
------
ValueError
* If expr is an empty string
"""
if not expr:
raise ValueError("expr cannot be an empty string")
def _convert_expression(expr):
"""Convert an object to an expression.
Thus function converts an object to an expression (a unicode string) and
checks to make sure it isn't empty after conversion. This is used to
convert operators to their string representation for recursive calls to
:func:`~pandas.eval`.
Parameters
----------
expr : object
The object to be converted to a string.
Returns
-------
s : unicode
The string representation of an object.
Raises
------
ValueError
* If the expression is empty.
"""
s = pprint_thing(expr)
_check_expression(s)
return s
def _check_for_locals(expr, stack_level, parser):
from pandas.core.computation.expr import tokenize_string
at_top_of_stack = stack_level == 0
not_pandas_parser = parser != 'pandas'
if not_pandas_parser:
msg = "The '@' prefix is only supported by the pandas parser"
elif at_top_of_stack:
msg = ("The '@' prefix is not allowed in "
"top-level eval calls, \nplease refer to "
"your variables by name without the '@' "
"prefix")
if at_top_of_stack or not_pandas_parser:
for toknum, tokval in tokenize_string(expr):
if toknum == tokenize.OP and tokval == '@':
raise SyntaxError(msg)
def eval(expr, parser='pandas', engine=None, truediv=True,
local_dict=None, global_dict=None, resolvers=(), level=0,
target=None, inplace=False):
"""Evaluate a Python expression as a string using various backends.
The following arithmetic operations are supported: ``+``, ``-``, ``*``,
``/``, ``**``, ``%``, ``//`` (python engine only) along with the following
boolean operations: ``|`` (or), ``&`` (and), and ``~`` (not).
Additionally, the ``'pandas'`` parser allows the use of :keyword:`and`,
:keyword:`or`, and :keyword:`not` with the same semantics as the
corresponding bitwise operators. :class:`~pandas.Series` and
:class:`~pandas.DataFrame` objects are supported and behave as they would
with plain ol' Python evaluation.
Parameters
----------
expr : str or unicode
The expression to evaluate. This string cannot contain any Python
`statements
<https://docs.python.org/3/reference/simple_stmts.html#simple-statements>`__,
only Python `expressions
<https://docs.python.org/3/reference/simple_stmts.html#expression-statements>`__.
parser : string, default 'pandas', {'pandas', 'python'}
The parser to use to construct the syntax tree from the expression. The
default of ``'pandas'`` parses code slightly different than standard
Python. Alternatively, you can parse an expression using the
``'python'`` parser to retain strict Python semantics. See the
:ref:`enhancing performance <enhancingperf.eval>` documentation for
more details.
engine : string or None, default 'numexpr', {'python', 'numexpr'}
The engine used to evaluate the expression. Supported engines are
- None : tries to use ``numexpr``, falls back to ``python``
- ``'numexpr'``: This default engine evaluates pandas objects using
numexpr for large speed ups in complex expressions
with large frames.
- ``'python'``: Performs operations as if you had ``eval``'d in top
level python. This engine is generally not that useful.
More backends may be available in the future.
truediv : bool, optional
Whether to use true division, like in Python >= 3
local_dict : dict or None, optional
A dictionary of local variables, taken from locals() by default.
global_dict : dict or None, optional
A dictionary of global variables, taken from globals() by default.
resolvers : list of dict-like or None, optional
A list of objects implementing the ``__getitem__`` special method that
you can use to inject an additional collection of namespaces to use for
variable lookup. For example, this is used in the
:meth:`~pandas.DataFrame.query` method to inject the
``DataFrame.index`` and ``DataFrame.columns``
variables that refer to their respective :class:`~pandas.DataFrame`
instance attributes.
level : int, optional
The number of prior stack frames to traverse and add to the current
scope. Most users will **not** need to change this parameter.
target : object, optional, default None
This is the target object for assignment. It is used when there is
variable assignment in the expression. If so, then `target` must
support item assignment with string keys, and if a copy is being
returned, it must also support `.copy()`.
inplace : bool, default False
If `target` is provided, and the expression mutates `target`, whether
to modify `target` inplace. Otherwise, return a copy of `target` with
the mutation.
Returns
-------
ndarray, numeric scalar, DataFrame, Series
Raises
------
ValueError
There are many instances where such an error can be raised:
- `target=None`, but the expression is multiline.
- The expression is multiline, but not all them have item assignment.
An example of such an arrangement is this:
a = b + 1
a + 2
Here, there are expressions on different lines, making it multiline,
but the last line has no variable assigned to the output of `a + 2`.
- `inplace=True`, but the expression is missing item assignment.
- Item assignment is provided, but the `target` does not support
string item assignment.
- Item assignment is provided and `inplace=False`, but the `target`
does not support the `.copy()` method
Notes
-----
The ``dtype`` of any objects involved in an arithmetic ``%`` operation are
recursively cast to ``float64``.
See the :ref:`enhancing performance <enhancingperf.eval>` documentation for
more details.
See Also
--------
pandas.DataFrame.query
pandas.DataFrame.eval
"""
from pandas.core.computation.expr import Expr
inplace = validate_bool_kwarg(inplace, "inplace")
if isinstance(expr, string_types):
_check_expression(expr)
exprs = [e.strip() for e in expr.splitlines() if e.strip() != '']
else:
exprs = [expr]
multi_line = len(exprs) > 1
if multi_line and target is None:
raise ValueError("multi-line expressions are only valid in the "
"context of data, use DataFrame.eval")
ret = None
first_expr = True
target_modified = False
for expr in exprs:
expr = _convert_expression(expr)
engine = _check_engine(engine)
_check_parser(parser)
_check_resolvers(resolvers)
_check_for_locals(expr, level, parser)
# get our (possibly passed-in) scope
env = _ensure_scope(level + 1, global_dict=global_dict,
local_dict=local_dict, resolvers=resolvers,
target=target)
parsed_expr = Expr(expr, engine=engine, parser=parser, env=env,
truediv=truediv)
# construct the engine and evaluate the parsed expression
eng = _engines[engine]
eng_inst = eng(parsed_expr)
ret = eng_inst.evaluate()
if parsed_expr.assigner is None:
if multi_line:
raise ValueError("Multi-line expressions are only valid"
" if all expressions contain an assignment")
elif inplace:
raise ValueError("Cannot operate inplace "
"if there is no assignment")
# assign if needed
assigner = parsed_expr.assigner
if env.target is not None and assigner is not None:
target_modified = True
# if returning a copy, copy only on the first assignment
if not inplace and first_expr:
try:
target = env.target.copy()
except AttributeError:
raise ValueError("Cannot return a copy of the target")
else:
target = env.target
# TypeError is most commonly raised (e.g. int, list), but you
# get IndexError if you try to do this assignment on np.ndarray.
# we will ignore numpy warnings here; e.g. if trying
# to use a non-numeric indexer
try:
with warnings.catch_warnings(record=True):
# TODO: Filter the warnings we actually care about here.
target[assigner] = ret
except (TypeError, IndexError):
raise ValueError("Cannot assign expression output to target")
if not resolvers:
resolvers = ({assigner: ret},)
else:
# existing resolver needs updated to handle
# case of mutating existing column in copy
for resolver in resolvers:
if assigner in resolver:
resolver[assigner] = ret
break
else:
resolvers += ({assigner: ret},)
ret = None
first_expr = False
# We want to exclude `inplace=None` as being False.
if inplace is False:
return target if target_modified else ret
|
# -*- coding: utf-8 -*-
# pylint: disable-msg=E1101,W0612
from operator import methodcaller
import pytest
import numpy as np
import pandas as pd
from distutils.version import LooseVersion
from pandas import Series, date_range, MultiIndex
from pandas.compat import range
from pandas.util.testing import (assert_series_equal,
assert_almost_equal)
import pandas.util.testing as tm
import pandas.util._test_decorators as td
from .test_generic import Generic
try:
import xarray
_XARRAY_INSTALLED = True
except ImportError:
_XARRAY_INSTALLED = False
class TestSeries(Generic):
_typ = Series
_comparator = lambda self, x, y: assert_series_equal(x, y)
def setup_method(self):
self.ts = tm.makeTimeSeries() # Was at top level in test_series
self.ts.name = 'ts'
self.series = tm.makeStringSeries()
self.series.name = 'series'
def test_rename_mi(self):
s = Series([11, 21, 31],
index=MultiIndex.from_tuples(
[("A", x) for x in ["a", "B", "c"]]))
s.rename(str.lower)
def test_set_axis_name(self):
s = Series([1, 2, 3], index=['a', 'b', 'c'])
funcs = ['rename_axis', '_set_axis_name']
name = 'foo'
for func in funcs:
result = methodcaller(func, name)(s)
assert s.index.name is None
assert result.index.name == name
def test_set_axis_name_mi(self):
s = Series([11, 21, 31], index=MultiIndex.from_tuples(
[("A", x) for x in ["a", "B", "c"]],
names=['l1', 'l2'])
)
funcs = ['rename_axis', '_set_axis_name']
for func in funcs:
result = methodcaller(func, ['L1', 'L2'])(s)
assert s.index.name is None
assert s.index.names == ['l1', 'l2']
assert result.index.name is None
assert result.index.names, ['L1', 'L2']
def test_set_axis_name_raises(self):
s = pd.Series([1])
with pytest.raises(ValueError):
s._set_axis_name(name='a', axis=1)
def test_get_numeric_data_preserve_dtype(self):
# get the numeric data
o = Series([1, 2, 3])
result = o._get_numeric_data()
self._compare(result, o)
o = Series([1, '2', 3.])
result = o._get_numeric_data()
expected = Series([], dtype=object, index=pd.Index([], dtype=object))
self._compare(result, expected)
o = Series([True, False, True])
result = o._get_numeric_data()
self._compare(result, o)
o = Series([True, False, True])
result = o._get_bool_data()
self._compare(result, o)
o = Series(date_range('20130101', periods=3))
result = o._get_numeric_data()
expected = Series([], dtype='M8[ns]', index=pd.Index([], dtype=object))
self._compare(result, expected)
def test_nonzero_single_element(self):
# allow single item via bool method
s = Series([True])
assert s.bool()
s = Series([False])
assert not s.bool()
# single item nan to raise
for s in [Series([np.nan]), Series([pd.NaT]), Series([True]),
Series([False])]:
pytest.raises(ValueError, lambda: bool(s))
for s in [Series([np.nan]), Series([pd.NaT])]:
pytest.raises(ValueError, lambda: s.bool())
# multiple bool are still an error
for s in [Series([True, True]), Series([False, False])]:
pytest.raises(ValueError, lambda: bool(s))
pytest.raises(ValueError, lambda: s.bool())
# single non-bool are an error
for s in [Series([1]), Series([0]), Series(['a']), Series([0.0])]:
pytest.raises(ValueError, lambda: bool(s))
pytest.raises(ValueError, lambda: s.bool())
def test_metadata_propagation_indiv(self):
# check that the metadata matches up on the resulting ops
o = Series(range(3), range(3))
o.name = 'foo'
o2 = Series(range(3), range(3))
o2.name = 'bar'
result = o.T
self.check_metadata(o, result)
# resample
ts = Series(np.random.rand(1000),
index=date_range('20130101', periods=1000, freq='s'),
name='foo')
result = ts.resample('1T').mean()
self.check_metadata(ts, result)
result = ts.resample('1T').min()
self.check_metadata(ts, result)
result = ts.resample('1T').apply(lambda x: x.sum())
self.check_metadata(ts, result)
_metadata = Series._metadata
_finalize = Series.__finalize__
Series._metadata = ['name', 'filename']
o.filename = 'foo'
o2.filename = 'bar'
def finalize(self, other, method=None, **kwargs):
for name in self._metadata:
if method == 'concat' and name == 'filename':
value = '+'.join([getattr(
o, name) for o in other.objs if getattr(o, name, None)
])
object.__setattr__(self, name, value)
else:
object.__setattr__(self, name, getattr(other, name, None))
return self
Series.__finalize__ = finalize
result = pd.concat([o, o2])
assert result.filename == 'foo+bar'
assert result.name is None
# reset
Series._metadata = _metadata
Series.__finalize__ = _finalize
@pytest.mark.skipif(not _XARRAY_INSTALLED or _XARRAY_INSTALLED and
LooseVersion(xarray.__version__) <
LooseVersion('0.10.0'),
reason='xarray >= 0.10.0 required')
@pytest.mark.parametrize(
"index",
['FloatIndex', 'IntIndex',
'StringIndex', 'UnicodeIndex',
'DateIndex', 'PeriodIndex',
'TimedeltaIndex', 'CategoricalIndex'])
def test_to_xarray_index_types(self, index):
from xarray import DataArray
index = getattr(tm, 'make{}'.format(index))
s = Series(range(6), index=index(6))
s.index.name = 'foo'
result = s.to_xarray()
repr(result)
assert len(result) == 6
assert len(result.coords) == 1
assert_almost_equal(list(result.coords.keys()), ['foo'])
assert isinstance(result, DataArray)
# idempotency
assert_series_equal(result.to_series(), s,
check_index_type=False,
check_categorical=True)
@td.skip_if_no('xarray', min_version='0.7.0')
def test_to_xarray(self):
from xarray import DataArray
s = Series([])
s.index.name = 'foo'
result = s.to_xarray()
assert len(result) == 0
assert len(result.coords) == 1
assert_almost_equal(list(result.coords.keys()), ['foo'])
assert isinstance(result, DataArray)
s = Series(range(6))
s.index.name = 'foo'
s.index = pd.MultiIndex.from_product([['a', 'b'], range(3)],
names=['one', 'two'])
result = s.to_xarray()
assert len(result) == 2
assert_almost_equal(list(result.coords.keys()), ['one', 'two'])
assert isinstance(result, DataArray)
assert_series_equal(result.to_series(), s)
def test_valid_deprecated(self):
# GH18800
with tm.assert_produces_warning(FutureWarning):
pd.Series([]).valid()
@pytest.mark.parametrize("s", [
Series([np.arange(5)]),
pd.date_range('1/1/2011', periods=24, freq='H'),
pd.Series(range(5), index=pd.date_range("2017", periods=5))
])
@pytest.mark.parametrize("shift_size", [0, 1, 2])
def test_shift_always_copy(self, s, shift_size):
# GH22397
assert s.shift(shift_size) is not s
@pytest.mark.parametrize("move_by_freq", [
pd.Timedelta('1D'),
pd.Timedelta('1M'),
])
def test_datetime_shift_always_copy(self, move_by_freq):
# GH22397
s = pd.Series(range(5), index=pd.date_range("2017", periods=5))
assert s.shift(freq=move_by_freq) is not s
|
|
pandas/tests/generic/test_series.py
|
pandas/core/computation/eval.py
|
"""
DataFrame
---------
An efficient 2D container for potentially mixed-type time series or other
labeled data series.
Similar to its R counterpart, data.frame, except providing automatic data
alignment and a host of useful data manipulation methods having to do with the
labeling information
"""
import collections
from collections import OrderedDict, abc
import functools
from io import StringIO
import itertools
import sys
import warnings
from textwrap import dedent
from typing import FrozenSet, List, Optional, Set, Type, Union
import numpy as np
import numpy.ma as ma
from pandas._config import get_option
from pandas._libs import lib, algos as libalgos
from pandas.util._decorators import (Appender, Substitution,
rewrite_axis_style_signature,
deprecate_kwarg)
from pandas.util._validators import (validate_bool_kwarg,
validate_axis_style_args)
from pandas.compat import PY36, raise_with_traceback
from pandas.compat.numpy import function as nv
from pandas.core.arrays.sparse import SparseFrameAccessor
from pandas.core.dtypes.cast import (
maybe_upcast,
cast_scalar_to_array,
infer_dtype_from_scalar,
maybe_cast_to_datetime,
maybe_infer_to_datetimelike,
maybe_convert_platform,
maybe_downcast_to_dtype,
invalidate_string_dtypes,
coerce_to_dtypes,
maybe_upcast_putmask,
find_common_type)
from pandas.core.dtypes.common import (
is_dict_like,
is_datetime64tz_dtype,
is_object_dtype,
is_extension_type,
is_extension_array_dtype,
is_datetime64_any_dtype,
is_bool_dtype,
is_integer_dtype,
is_float_dtype,
is_integer,
is_scalar,
is_dtype_equal,
needs_i8_conversion,
infer_dtype_from_object,
ensure_float64,
ensure_int64,
ensure_platform_int,
is_list_like,
is_nested_list_like,
is_iterator,
is_sequence,
is_named_tuple)
from pandas.core.dtypes.generic import (
ABCSeries, ABCDataFrame, ABCIndexClass, ABCMultiIndex)
from pandas.core.dtypes.missing import isna, notna
from pandas.core import algorithms
from pandas.core import common as com
from pandas.core import nanops
from pandas.core import ops
from pandas.core.accessor import CachedAccessor
from pandas.core.arrays import Categorical, ExtensionArray
from pandas.core.arrays.datetimelike import (
DatetimeLikeArrayMixin as DatetimeLikeArray
)
from pandas.core.generic import NDFrame, _shared_docs
from pandas.core.index import (Index, MultiIndex, ensure_index,
ensure_index_from_sequences)
from pandas.core.indexes import base as ibase
from pandas.core.indexes.datetimes import DatetimeIndex
from pandas.core.indexes.period import PeriodIndex
from pandas.core.indexing import (maybe_droplevels, convert_to_index_sliceable,
check_bool_indexer)
from pandas.core.internals import BlockManager
from pandas.core.internals.construction import (
masked_rec_array_to_mgr, get_names_from_index, to_arrays,
reorder_arrays, init_ndarray, init_dict,
arrays_to_mgr, sanitize_index)
from pandas.core.series import Series
from pandas.io.formats import console
from pandas.io.formats import format as fmt
from pandas.io.formats.printing import pprint_thing
import pandas.plotting
# ---------------------------------------------------------------------
# Docstring templates
_shared_doc_kwargs = dict(
axes='index, columns', klass='DataFrame',
axes_single_arg="{0 or 'index', 1 or 'columns'}",
axis="""axis : {0 or 'index', 1 or 'columns'}, default 0
If 0 or 'index': apply function to each column.
If 1 or 'columns': apply function to each row.""",
optional_by="""
by : str or list of str
Name or list of names to sort by.
- if `axis` is 0 or `'index'` then `by` may contain index
levels and/or column labels
- if `axis` is 1 or `'columns'` then `by` may contain column
levels and/or index labels
.. versionchanged:: 0.23.0
Allow specifying index or column level names.""",
versionadded_to_excel='',
optional_labels="""labels : array-like, optional
New labels / index to conform the axis specified by 'axis' to.""",
optional_axis="""axis : int or str, optional
Axis to target. Can be either the axis name ('index', 'columns')
or number (0, 1).""",
)
_numeric_only_doc = """numeric_only : boolean, default None
Include only float, int, boolean data. If None, will attempt to use
everything, then use only numeric data
"""
_merge_doc = """
Merge DataFrame or named Series objects with a database-style join.
The join is done on columns or indexes. If joining columns on
columns, the DataFrame indexes *will be ignored*. Otherwise if joining indexes
on indexes or indexes on a column or columns, the index will be passed on.
Parameters
----------%s
right : DataFrame or named Series
Object to merge with.
how : {'left', 'right', 'outer', 'inner'}, default 'inner'
Type of merge to be performed.
* left: use only keys from left frame, similar to a SQL left outer join;
preserve key order.
* right: use only keys from right frame, similar to a SQL right outer join;
preserve key order.
* outer: use union of keys from both frames, similar to a SQL full outer
join; sort keys lexicographically.
* inner: use intersection of keys from both frames, similar to a SQL inner
join; preserve the order of the left keys.
on : label or list
Column or index level names to join on. These must be found in both
DataFrames. If `on` is None and not merging on indexes then this defaults
to the intersection of the columns in both DataFrames.
left_on : label or list, or array-like
Column or index level names to join on in the left DataFrame. Can also
be an array or list of arrays of the length of the left DataFrame.
These arrays are treated as if they are columns.
right_on : label or list, or array-like
Column or index level names to join on in the right DataFrame. Can also
be an array or list of arrays of the length of the right DataFrame.
These arrays are treated as if they are columns.
left_index : bool, default False
Use the index from the left DataFrame as the join key(s). If it is a
MultiIndex, the number of keys in the other DataFrame (either the index
or a number of columns) must match the number of levels.
right_index : bool, default False
Use the index from the right DataFrame as the join key. Same caveats as
left_index.
sort : bool, default False
Sort the join keys lexicographically in the result DataFrame. If False,
the order of the join keys depends on the join type (how keyword).
suffixes : tuple of (str, str), default ('_x', '_y')
Suffix to apply to overlapping column names in the left and right
side, respectively. To raise an exception on overlapping columns use
(False, False).
copy : bool, default True
If False, avoid copy if possible.
indicator : bool or str, default False
If True, adds a column to output DataFrame called "_merge" with
information on the source of each row.
If string, column with information on source of each row will be added to
output DataFrame, and column will be named value of string.
Information column is Categorical-type and takes on a value of "left_only"
for observations whose merge key only appears in 'left' DataFrame,
"right_only" for observations whose merge key only appears in 'right'
DataFrame, and "both" if the observation's merge key is found in both.
validate : str, optional
If specified, checks if merge is of specified type.
* "one_to_one" or "1:1": check if merge keys are unique in both
left and right datasets.
* "one_to_many" or "1:m": check if merge keys are unique in left
dataset.
* "many_to_one" or "m:1": check if merge keys are unique in right
dataset.
* "many_to_many" or "m:m": allowed, but does not result in checks.
.. versionadded:: 0.21.0
Returns
-------
DataFrame
A DataFrame of the two merged objects.
See Also
--------
merge_ordered : Merge with optional filling/interpolation.
merge_asof : Merge on nearest keys.
DataFrame.join : Similar method using indices.
Notes
-----
Support for specifying index levels as the `on`, `left_on`, and
`right_on` parameters was added in version 0.23.0
Support for merging named Series objects was added in version 0.24.0
Examples
--------
>>> df1 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],
... 'value': [1, 2, 3, 5]})
>>> df2 = pd.DataFrame({'rkey': ['foo', 'bar', 'baz', 'foo'],
... 'value': [5, 6, 7, 8]})
>>> df1
lkey value
0 foo 1
1 bar 2
2 baz 3
3 foo 5
>>> df2
rkey value
0 foo 5
1 bar 6
2 baz 7
3 foo 8
Merge df1 and df2 on the lkey and rkey columns. The value columns have
the default suffixes, _x and _y, appended.
>>> df1.merge(df2, left_on='lkey', right_on='rkey')
lkey value_x rkey value_y
0 foo 1 foo 5
1 foo 1 foo 8
2 foo 5 foo 5
3 foo 5 foo 8
4 bar 2 bar 6
5 baz 3 baz 7
Merge DataFrames df1 and df2 with specified left and right suffixes
appended to any overlapping columns.
>>> df1.merge(df2, left_on='lkey', right_on='rkey',
... suffixes=('_left', '_right'))
lkey value_left rkey value_right
0 foo 1 foo 5
1 foo 1 foo 8
2 foo 5 foo 5
3 foo 5 foo 8
4 bar 2 bar 6
5 baz 3 baz 7
Merge DataFrames df1 and df2, but raise an exception if the DataFrames have
any overlapping columns.
>>> df1.merge(df2, left_on='lkey', right_on='rkey', suffixes=(False, False))
Traceback (most recent call last):
...
ValueError: columns overlap but no suffix specified:
Index(['value'], dtype='object')
"""
# -----------------------------------------------------------------------
# DataFrame class
class DataFrame(NDFrame):
"""
Two-dimensional size-mutable, potentially heterogeneous tabular data
structure with labeled axes (rows and columns). Arithmetic operations
align on both row and column labels. Can be thought of as a dict-like
container for Series objects. The primary pandas data structure.
Parameters
----------
data : ndarray (structured or homogeneous), Iterable, dict, or DataFrame
Dict can contain Series, arrays, constants, or list-like objects
.. versionchanged :: 0.23.0
If data is a dict, argument order is maintained for Python 3.6
and later.
index : Index or array-like
Index to use for resulting frame. Will default to RangeIndex if
no indexing information part of input data and no index provided
columns : Index or array-like
Column labels to use for resulting frame. Will default to
RangeIndex (0, 1, 2, ..., n) if no column labels are provided
dtype : dtype, default None
Data type to force. Only a single dtype is allowed. If None, infer
copy : boolean, default False
Copy data from inputs. Only affects DataFrame / 2d ndarray input
See Also
--------
DataFrame.from_records : Constructor from tuples, also record arrays.
DataFrame.from_dict : From dicts of Series, arrays, or dicts.
DataFrame.from_items : From sequence of (key, value) pairs
read_csv, pandas.read_table, pandas.read_clipboard.
Examples
--------
Constructing DataFrame from a dictionary.
>>> d = {'col1': [1, 2], 'col2': [3, 4]}
>>> df = pd.DataFrame(data=d)
>>> df
col1 col2
0 1 3
1 2 4
Notice that the inferred dtype is int64.
>>> df.dtypes
col1 int64
col2 int64
dtype: object
To enforce a single dtype:
>>> df = pd.DataFrame(data=d, dtype=np.int8)
>>> df.dtypes
col1 int8
col2 int8
dtype: object
Constructing DataFrame from numpy ndarray:
>>> df2 = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),
... columns=['a', 'b', 'c'])
>>> df2
a b c
0 1 2 3
1 4 5 6
2 7 8 9
"""
@property
def _constructor(self):
return DataFrame
_constructor_sliced = Series # type: Type[Series]
_deprecations = NDFrame._deprecations | frozenset([
'get_value', 'set_value', 'from_csv', 'from_items'
]) # type: FrozenSet[str]
_accessors = set() # type: Set[str]
@property
def _constructor_expanddim(self):
raise NotImplementedError("Not supported for DataFrames!")
# ----------------------------------------------------------------------
# Constructors
def __init__(self, data=None, index=None, columns=None, dtype=None,
copy=False):
if data is None:
data = {}
if dtype is not None:
dtype = self._validate_dtype(dtype)
if isinstance(data, DataFrame):
data = data._data
if isinstance(data, BlockManager):
mgr = self._init_mgr(data, axes=dict(index=index, columns=columns),
dtype=dtype, copy=copy)
elif isinstance(data, dict):
mgr = init_dict(data, index, columns, dtype=dtype)
elif isinstance(data, ma.MaskedArray):
import numpy.ma.mrecords as mrecords
# masked recarray
if isinstance(data, mrecords.MaskedRecords):
mgr = masked_rec_array_to_mgr(data, index, columns, dtype,
copy)
# a masked array
else:
mask = ma.getmaskarray(data)
if mask.any():
data, fill_value = maybe_upcast(data, copy=True)
data.soften_mask() # set hardmask False if it was True
data[mask] = fill_value
else:
data = data.copy()
mgr = init_ndarray(data, index, columns, dtype=dtype,
copy=copy)
elif isinstance(data, (np.ndarray, Series, Index)):
if data.dtype.names:
data_columns = list(data.dtype.names)
data = {k: data[k] for k in data_columns}
if columns is None:
columns = data_columns
mgr = init_dict(data, index, columns, dtype=dtype)
elif getattr(data, 'name', None) is not None:
mgr = init_dict({data.name: data}, index, columns,
dtype=dtype)
else:
mgr = init_ndarray(data, index, columns, dtype=dtype,
copy=copy)
# For data is list-like, or Iterable (will consume into list)
elif (isinstance(data, abc.Iterable) and
not isinstance(data, (str, bytes))):
if not isinstance(data, abc.Sequence):
data = list(data)
if len(data) > 0:
if is_list_like(data[0]) and getattr(data[0], 'ndim', 1) == 1:
if is_named_tuple(data[0]) and columns is None:
columns = data[0]._fields
arrays, columns = to_arrays(data, columns, dtype=dtype)
columns = ensure_index(columns)
# set the index
if index is None:
if isinstance(data[0], Series):
index = get_names_from_index(data)
elif isinstance(data[0], Categorical):
index = ibase.default_index(len(data[0]))
else:
index = ibase.default_index(len(data))
mgr = arrays_to_mgr(arrays, columns, index, columns,
dtype=dtype)
else:
mgr = init_ndarray(data, index, columns, dtype=dtype,
copy=copy)
else:
mgr = init_dict({}, index, columns, dtype=dtype)
else:
try:
arr = np.array(data, dtype=dtype, copy=copy)
except (ValueError, TypeError) as e:
exc = TypeError('DataFrame constructor called with '
'incompatible data and dtype: {e}'.format(e=e))
raise_with_traceback(exc)
if arr.ndim == 0 and index is not None and columns is not None:
values = cast_scalar_to_array((len(index), len(columns)),
data, dtype=dtype)
mgr = init_ndarray(values, index, columns,
dtype=values.dtype, copy=False)
else:
raise ValueError('DataFrame constructor not properly called!')
NDFrame.__init__(self, mgr, fastpath=True)
# ----------------------------------------------------------------------
@property
def axes(self):
"""
Return a list representing the axes of the DataFrame.
It has the row axis labels and column axis labels as the only members.
They are returned in that order.
Examples
--------
>>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df.axes
[RangeIndex(start=0, stop=2, step=1), Index(['col1', 'col2'],
dtype='object')]
"""
return [self.index, self.columns]
@property
def shape(self):
"""
Return a tuple representing the dimensionality of the DataFrame.
See Also
--------
ndarray.shape
Examples
--------
>>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df.shape
(2, 2)
>>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4],
... 'col3': [5, 6]})
>>> df.shape
(2, 3)
"""
return len(self.index), len(self.columns)
@property
def _is_homogeneous_type(self):
"""
Whether all the columns in a DataFrame have the same type.
Returns
-------
bool
Examples
--------
>>> DataFrame({"A": [1, 2], "B": [3, 4]})._is_homogeneous_type
True
>>> DataFrame({"A": [1, 2], "B": [3.0, 4.0]})._is_homogeneous_type
False
Items with the same type but different sizes are considered
different types.
>>> DataFrame({
... "A": np.array([1, 2], dtype=np.int32),
... "B": np.array([1, 2], dtype=np.int64)})._is_homogeneous_type
False
"""
if self._data.any_extension_types:
return len({block.dtype for block in self._data.blocks}) == 1
else:
return not self._data.is_mixed_type
# ----------------------------------------------------------------------
# Rendering Methods
def _repr_fits_vertical_(self):
"""
Check length against max_rows.
"""
max_rows = get_option("display.max_rows")
return len(self) <= max_rows
def _repr_fits_horizontal_(self, ignore_width=False):
"""
Check if full repr fits in horizontal boundaries imposed by the display
options width and max_columns.
In case off non-interactive session, no boundaries apply.
`ignore_width` is here so ipnb+HTML output can behave the way
users expect. display.max_columns remains in effect.
GH3541, GH3573
"""
width, height = console.get_console_size()
max_columns = get_option("display.max_columns")
nb_columns = len(self.columns)
# exceed max columns
if ((max_columns and nb_columns > max_columns) or
((not ignore_width) and width and nb_columns > (width // 2))):
return False
# used by repr_html under IPython notebook or scripts ignore terminal
# dims
if ignore_width or not console.in_interactive_session():
return True
if (get_option('display.width') is not None or
console.in_ipython_frontend()):
# check at least the column row for excessive width
max_rows = 1
else:
max_rows = get_option("display.max_rows")
# when auto-detecting, so width=None and not in ipython front end
# check whether repr fits horizontal by actually checking
# the width of the rendered repr
buf = StringIO()
# only care about the stuff we'll actually print out
# and to_string on entire frame may be expensive
d = self
if not (max_rows is None): # unlimited rows
# min of two, where one may be None
d = d.iloc[:min(max_rows, len(d))]
else:
return True
d.to_string(buf=buf)
value = buf.getvalue()
repr_width = max(len(l) for l in value.split('\n'))
return repr_width < width
def _info_repr(self):
"""
True if the repr should show the info view.
"""
info_repr_option = (get_option("display.large_repr") == "info")
return info_repr_option and not (self._repr_fits_horizontal_() and
self._repr_fits_vertical_())
def __repr__(self):
"""
Return a string representation for a particular DataFrame.
"""
buf = StringIO("")
if self._info_repr():
self.info(buf=buf)
return buf.getvalue()
max_rows = get_option("display.max_rows")
max_cols = get_option("display.max_columns")
show_dimensions = get_option("display.show_dimensions")
if get_option("display.expand_frame_repr"):
width, _ = console.get_console_size()
else:
width = None
self.to_string(buf=buf, max_rows=max_rows, max_cols=max_cols,
line_width=width, show_dimensions=show_dimensions)
return buf.getvalue()
def _repr_html_(self):
"""
Return a html representation for a particular DataFrame.
Mainly for IPython notebook.
"""
if self._info_repr():
buf = StringIO("")
self.info(buf=buf)
# need to escape the <class>, should be the first line.
val = buf.getvalue().replace('<', r'<', 1)
val = val.replace('>', r'>', 1)
return '<pre>' + val + '</pre>'
if get_option("display.notebook_repr_html"):
max_rows = get_option("display.max_rows")
max_cols = get_option("display.max_columns")
show_dimensions = get_option("display.show_dimensions")
return self.to_html(max_rows=max_rows, max_cols=max_cols,
show_dimensions=show_dimensions, notebook=True)
else:
return None
@Substitution(header='Write out the column names. If a list of strings '
'is given, it is assumed to be aliases for the '
'column names',
col_space_type='int',
col_space='The minimum width of each column')
@Substitution(shared_params=fmt.common_docstring,
returns=fmt.return_docstring)
def to_string(self, buf=None, columns=None, col_space=None, header=True,
index=True, na_rep='NaN', formatters=None, float_format=None,
sparsify=None, index_names=True, justify=None,
max_rows=None, max_cols=None, show_dimensions=False,
decimal='.', line_width=None):
"""
Render a DataFrame to a console-friendly tabular output.
%(shared_params)s
line_width : int, optional
Width to wrap a line in characters.
%(returns)s
See Also
--------
to_html : Convert DataFrame to HTML.
Examples
--------
>>> d = {'col1': [1, 2, 3], 'col2': [4, 5, 6]}
>>> df = pd.DataFrame(d)
>>> print(df.to_string())
col1 col2
0 1 4
1 2 5
2 3 6
"""
formatter = fmt.DataFrameFormatter(self, buf=buf, columns=columns,
col_space=col_space, na_rep=na_rep,
formatters=formatters,
float_format=float_format,
sparsify=sparsify, justify=justify,
index_names=index_names,
header=header, index=index,
max_rows=max_rows,
max_cols=max_cols,
show_dimensions=show_dimensions,
decimal=decimal,
line_width=line_width)
formatter.to_string()
if buf is None:
result = formatter.buf.getvalue()
return result
# ----------------------------------------------------------------------
@property
def style(self):
"""
Property returning a Styler object containing methods for
building a styled HTML representation fo the DataFrame.
See Also
--------
io.formats.style.Styler
"""
from pandas.io.formats.style import Styler
return Styler(self)
def iteritems(self):
r"""
Iterator over (column name, Series) pairs.
Iterates over the DataFrame columns, returning a tuple with
the column name and the content as a Series.
Yields
------
label : object
The column names for the DataFrame being iterated over.
content : Series
The column entries belonging to each label, as a Series.
See Also
--------
DataFrame.iterrows : Iterate over DataFrame rows as
(index, Series) pairs.
DataFrame.itertuples : Iterate over DataFrame rows as namedtuples
of the values.
Examples
--------
>>> df = pd.DataFrame({'species': ['bear', 'bear', 'marsupial'],
... 'population': [1864, 22000, 80000]},
... index=['panda', 'polar', 'koala'])
>>> df
species population
panda bear 1864
polar bear 22000
koala marsupial 80000
>>> for label, content in df.iteritems():
... print('label:', label)
... print('content:', content, sep='\n')
...
label: species
content:
panda bear
polar bear
koala marsupial
Name: species, dtype: object
label: population
content:
panda 1864
polar 22000
koala 80000
Name: population, dtype: int64
"""
if self.columns.is_unique and hasattr(self, '_item_cache'):
for k in self.columns:
yield k, self._get_item_cache(k)
else:
for i, k in enumerate(self.columns):
yield k, self._ixs(i, axis=1)
def iterrows(self):
"""
Iterate over DataFrame rows as (index, Series) pairs.
Yields
------
index : label or tuple of label
The index of the row. A tuple for a `MultiIndex`.
data : Series
The data of the row as a Series.
it : generator
A generator that iterates over the rows of the frame.
See Also
--------
itertuples : Iterate over DataFrame rows as namedtuples of the values.
iteritems : Iterate over (column name, Series) pairs.
Notes
-----
1. Because ``iterrows`` returns a Series for each row,
it does **not** preserve dtypes across the rows (dtypes are
preserved across columns for DataFrames). For example,
>>> df = pd.DataFrame([[1, 1.5]], columns=['int', 'float'])
>>> row = next(df.iterrows())[1]
>>> row
int 1.0
float 1.5
Name: 0, dtype: float64
>>> print(row['int'].dtype)
float64
>>> print(df['int'].dtype)
int64
To preserve dtypes while iterating over the rows, it is better
to use :meth:`itertuples` which returns namedtuples of the values
and which is generally faster than ``iterrows``.
2. You should **never modify** something you are iterating over.
This is not guaranteed to work in all cases. Depending on the
data types, the iterator returns a copy and not a view, and writing
to it will have no effect.
"""
columns = self.columns
klass = self._constructor_sliced
for k, v in zip(self.index, self.values):
s = klass(v, index=columns, name=k)
yield k, s
def itertuples(self, index=True, name="Pandas"):
"""
Iterate over DataFrame rows as namedtuples.
Parameters
----------
index : bool, default True
If True, return the index as the first element of the tuple.
name : str or None, default "Pandas"
The name of the returned namedtuples or None to return regular
tuples.
Returns
-------
iterator
An object to iterate over namedtuples for each row in the
DataFrame with the first field possibly being the index and
following fields being the column values.
See Also
--------
DataFrame.iterrows : Iterate over DataFrame rows as (index, Series)
pairs.
DataFrame.iteritems : Iterate over (column name, Series) pairs.
Notes
-----
The column names will be renamed to positional names if they are
invalid Python identifiers, repeated, or start with an underscore.
With a large number of columns (>255), regular tuples are returned.
Examples
--------
>>> df = pd.DataFrame({'num_legs': [4, 2], 'num_wings': [0, 2]},
... index=['dog', 'hawk'])
>>> df
num_legs num_wings
dog 4 0
hawk 2 2
>>> for row in df.itertuples():
... print(row)
...
Pandas(Index='dog', num_legs=4, num_wings=0)
Pandas(Index='hawk', num_legs=2, num_wings=2)
By setting the `index` parameter to False we can remove the index
as the first element of the tuple:
>>> for row in df.itertuples(index=False):
... print(row)
...
Pandas(num_legs=4, num_wings=0)
Pandas(num_legs=2, num_wings=2)
With the `name` parameter set we set a custom name for the yielded
namedtuples:
>>> for row in df.itertuples(name='Animal'):
... print(row)
...
Animal(Index='dog', num_legs=4, num_wings=0)
Animal(Index='hawk', num_legs=2, num_wings=2)
"""
arrays = []
fields = list(self.columns)
if index:
arrays.append(self.index)
fields.insert(0, "Index")
# use integer indexing because of possible duplicate column names
arrays.extend(self.iloc[:, k] for k in range(len(self.columns)))
# Python 3 supports at most 255 arguments to constructor
if name is not None and len(self.columns) + index < 256:
itertuple = collections.namedtuple(name, fields, rename=True)
return map(itertuple._make, zip(*arrays))
# fallback to regular tuples
return zip(*arrays)
items = iteritems
def __len__(self):
"""
Returns length of info axis, but here we use the index.
"""
return len(self.index)
def dot(self, other):
"""
Compute the matrix multiplication between the DataFrame and other.
This method computes the matrix product between the DataFrame and the
values of an other Series, DataFrame or a numpy array.
It can also be called using ``self @ other`` in Python >= 3.5.
Parameters
----------
other : Series, DataFrame or array-like
The other object to compute the matrix product with.
Returns
-------
Series or DataFrame
If other is a Series, return the matrix product between self and
other as a Serie. If other is a DataFrame or a numpy.array, return
the matrix product of self and other in a DataFrame of a np.array.
See Also
--------
Series.dot: Similar method for Series.
Notes
-----
The dimensions of DataFrame and other must be compatible in order to
compute the matrix multiplication. In addition, the column names of
DataFrame and the index of other must contain the same values, as they
will be aligned prior to the multiplication.
The dot method for Series computes the inner product, instead of the
matrix product here.
Examples
--------
Here we multiply a DataFrame with a Series.
>>> df = pd.DataFrame([[0, 1, -2, -1], [1, 1, 1, 1]])
>>> s = pd.Series([1, 1, 2, 1])
>>> df.dot(s)
0 -4
1 5
dtype: int64
Here we multiply a DataFrame with another DataFrame.
>>> other = pd.DataFrame([[0, 1], [1, 2], [-1, -1], [2, 0]])
>>> df.dot(other)
0 1
0 1 4
1 2 2
Note that the dot method give the same result as @
>>> df @ other
0 1
0 1 4
1 2 2
The dot method works also if other is an np.array.
>>> arr = np.array([[0, 1], [1, 2], [-1, -1], [2, 0]])
>>> df.dot(arr)
0 1
0 1 4
1 2 2
Note how shuffling of the objects does not change the result.
>>> s2 = s.reindex([1, 0, 2, 3])
>>> df.dot(s2)
0 -4
1 5
dtype: int64
"""
if isinstance(other, (Series, DataFrame)):
common = self.columns.union(other.index)
if (len(common) > len(self.columns) or
len(common) > len(other.index)):
raise ValueError('matrices are not aligned')
left = self.reindex(columns=common, copy=False)
right = other.reindex(index=common, copy=False)
lvals = left.values
rvals = right.values
else:
left = self
lvals = self.values
rvals = np.asarray(other)
if lvals.shape[1] != rvals.shape[0]:
raise ValueError('Dot product shape mismatch, '
'{s} vs {r}'.format(s=lvals.shape,
r=rvals.shape))
if isinstance(other, DataFrame):
return self._constructor(np.dot(lvals, rvals), index=left.index,
columns=other.columns)
elif isinstance(other, Series):
return Series(np.dot(lvals, rvals), index=left.index)
elif isinstance(rvals, (np.ndarray, Index)):
result = np.dot(lvals, rvals)
if result.ndim == 2:
return self._constructor(result, index=left.index)
else:
return Series(result, index=left.index)
else: # pragma: no cover
raise TypeError('unsupported type: {oth}'.format(oth=type(other)))
def __matmul__(self, other):
"""
Matrix multiplication using binary `@` operator in Python>=3.5.
"""
return self.dot(other)
def __rmatmul__(self, other):
"""
Matrix multiplication using binary `@` operator in Python>=3.5.
"""
return self.T.dot(np.transpose(other)).T
# ----------------------------------------------------------------------
# IO methods (to / from other formats)
@classmethod
def from_dict(cls, data, orient='columns', dtype=None, columns=None):
"""
Construct DataFrame from dict of array-like or dicts.
Creates DataFrame object from dictionary by columns or by index
allowing dtype specification.
Parameters
----------
data : dict
Of the form {field : array-like} or {field : dict}.
orient : {'columns', 'index'}, default 'columns'
The "orientation" of the data. If the keys of the passed dict
should be the columns of the resulting DataFrame, pass 'columns'
(default). Otherwise if the keys should be rows, pass 'index'.
dtype : dtype, default None
Data type to force, otherwise infer.
columns : list, default None
Column labels to use when ``orient='index'``. Raises a ValueError
if used with ``orient='columns'``.
.. versionadded:: 0.23.0
Returns
-------
DataFrame
See Also
--------
DataFrame.from_records : DataFrame from ndarray (structured
dtype), list of tuples, dict, or DataFrame.
DataFrame : DataFrame object creation using constructor.
Examples
--------
By default the keys of the dict become the DataFrame columns:
>>> data = {'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']}
>>> pd.DataFrame.from_dict(data)
col_1 col_2
0 3 a
1 2 b
2 1 c
3 0 d
Specify ``orient='index'`` to create the DataFrame using dictionary
keys as rows:
>>> data = {'row_1': [3, 2, 1, 0], 'row_2': ['a', 'b', 'c', 'd']}
>>> pd.DataFrame.from_dict(data, orient='index')
0 1 2 3
row_1 3 2 1 0
row_2 a b c d
When using the 'index' orientation, the column names can be
specified manually:
>>> pd.DataFrame.from_dict(data, orient='index',
... columns=['A', 'B', 'C', 'D'])
A B C D
row_1 3 2 1 0
row_2 a b c d
"""
index = None
orient = orient.lower()
if orient == 'index':
if len(data) > 0:
# TODO speed up Series case
if isinstance(list(data.values())[0], (Series, dict)):
data = _from_nested_dict(data)
else:
data, index = list(data.values()), list(data.keys())
elif orient == 'columns':
if columns is not None:
raise ValueError("cannot use columns parameter with "
"orient='columns'")
else: # pragma: no cover
raise ValueError('only recognize index or columns for orient')
return cls(data, index=index, columns=columns, dtype=dtype)
def to_numpy(self, dtype=None, copy=False):
"""
Convert the DataFrame to a NumPy array.
.. versionadded:: 0.24.0
By default, the dtype of the returned array will be the common NumPy
dtype of all types in the DataFrame. For example, if the dtypes are
``float16`` and ``float32``, the results dtype will be ``float32``.
This may require copying data and coercing values, which may be
expensive.
Parameters
----------
dtype : str or numpy.dtype, optional
The dtype to pass to :meth:`numpy.asarray`
copy : bool, default False
Whether to ensure that the returned value is a not a view on
another array. Note that ``copy=False`` does not *ensure* that
``to_numpy()`` is no-copy. Rather, ``copy=True`` ensure that
a copy is made, even if not strictly necessary.
Returns
-------
numpy.ndarray
See Also
--------
Series.to_numpy : Similar method for Series.
Examples
--------
>>> pd.DataFrame({"A": [1, 2], "B": [3, 4]}).to_numpy()
array([[1, 3],
[2, 4]])
With heterogenous data, the lowest common type will have to
be used.
>>> df = pd.DataFrame({"A": [1, 2], "B": [3.0, 4.5]})
>>> df.to_numpy()
array([[1. , 3. ],
[2. , 4.5]])
For a mix of numeric and non-numeric types, the output array will
have object dtype.
>>> df['C'] = pd.date_range('2000', periods=2)
>>> df.to_numpy()
array([[1, 3.0, Timestamp('2000-01-01 00:00:00')],
[2, 4.5, Timestamp('2000-01-02 00:00:00')]], dtype=object)
"""
result = np.array(self.values, dtype=dtype, copy=copy)
return result
def to_dict(self, orient='dict', into=dict):
"""
Convert the DataFrame to a dictionary.
The type of the key-value pairs can be customized with the parameters
(see below).
Parameters
----------
orient : str {'dict', 'list', 'series', 'split', 'records', 'index'}
Determines the type of the values of the dictionary.
- 'dict' (default) : dict like {column -> {index -> value}}
- 'list' : dict like {column -> [values]}
- 'series' : dict like {column -> Series(values)}
- 'split' : dict like
{'index' -> [index], 'columns' -> [columns], 'data' -> [values]}
- 'records' : list like
[{column -> value}, ... , {column -> value}]
- 'index' : dict like {index -> {column -> value}}
Abbreviations are allowed. `s` indicates `series` and `sp`
indicates `split`.
into : class, default dict
The collections.abc.Mapping subclass used for all Mappings
in the return value. Can be the actual class or an empty
instance of the mapping type you want. If you want a
collections.defaultdict, you must pass it initialized.
.. versionadded:: 0.21.0
Returns
-------
dict, list or collections.abc.Mapping
Return a collections.abc.Mapping object representing the DataFrame.
The resulting transformation depends on the `orient` parameter.
See Also
--------
DataFrame.from_dict: Create a DataFrame from a dictionary.
DataFrame.to_json: Convert a DataFrame to JSON format.
Examples
--------
>>> df = pd.DataFrame({'col1': [1, 2],
... 'col2': [0.5, 0.75]},
... index=['row1', 'row2'])
>>> df
col1 col2
row1 1 0.50
row2 2 0.75
>>> df.to_dict()
{'col1': {'row1': 1, 'row2': 2}, 'col2': {'row1': 0.5, 'row2': 0.75}}
You can specify the return orientation.
>>> df.to_dict('series')
{'col1': row1 1
row2 2
Name: col1, dtype: int64,
'col2': row1 0.50
row2 0.75
Name: col2, dtype: float64}
>>> df.to_dict('split')
{'index': ['row1', 'row2'], 'columns': ['col1', 'col2'],
'data': [[1, 0.5], [2, 0.75]]}
>>> df.to_dict('records')
[{'col1': 1, 'col2': 0.5}, {'col1': 2, 'col2': 0.75}]
>>> df.to_dict('index')
{'row1': {'col1': 1, 'col2': 0.5}, 'row2': {'col1': 2, 'col2': 0.75}}
You can also specify the mapping type.
>>> from collections import OrderedDict, defaultdict
>>> df.to_dict(into=OrderedDict)
OrderedDict([('col1', OrderedDict([('row1', 1), ('row2', 2)])),
('col2', OrderedDict([('row1', 0.5), ('row2', 0.75)]))])
If you want a `defaultdict`, you need to initialize it:
>>> dd = defaultdict(list)
>>> df.to_dict('records', into=dd)
[defaultdict(<class 'list'>, {'col1': 1, 'col2': 0.5}),
defaultdict(<class 'list'>, {'col1': 2, 'col2': 0.75})]
"""
if not self.columns.is_unique:
warnings.warn("DataFrame columns are not unique, some "
"columns will be omitted.", UserWarning,
stacklevel=2)
# GH16122
into_c = com.standardize_mapping(into)
if orient.lower().startswith('d'):
return into_c(
(k, v.to_dict(into)) for k, v in self.items())
elif orient.lower().startswith('l'):
return into_c((k, v.tolist()) for k, v in self.items())
elif orient.lower().startswith('sp'):
return into_c((('index', self.index.tolist()),
('columns', self.columns.tolist()),
('data', [
list(map(com.maybe_box_datetimelike, t))
for t in self.itertuples(index=False, name=None)
])))
elif orient.lower().startswith('s'):
return into_c((k, com.maybe_box_datetimelike(v))
for k, v in self.items())
elif orient.lower().startswith('r'):
columns = self.columns.tolist()
rows = (dict(zip(columns, row))
for row in self.itertuples(index=False, name=None))
return [
into_c((k, com.maybe_box_datetimelike(v))
for k, v in row.items())
for row in rows]
elif orient.lower().startswith('i'):
if not self.index.is_unique:
raise ValueError(
"DataFrame index must be unique for orient='index'."
)
return into_c((t[0], dict(zip(self.columns, t[1:])))
for t in self.itertuples(name=None))
else:
raise ValueError("orient '{o}' not understood".format(o=orient))
def to_gbq(self, destination_table, project_id=None, chunksize=None,
reauth=False, if_exists='fail', auth_local_webserver=False,
table_schema=None, location=None, progress_bar=True,
credentials=None, verbose=None, private_key=None):
"""
Write a DataFrame to a Google BigQuery table.
This function requires the `pandas-gbq package
<https://pandas-gbq.readthedocs.io>`__.
See the `How to authenticate with Google BigQuery
<https://pandas-gbq.readthedocs.io/en/latest/howto/authentication.html>`__
guide for authentication instructions.
Parameters
----------
destination_table : str
Name of table to be written, in the form ``dataset.tablename``.
project_id : str, optional
Google BigQuery Account project ID. Optional when available from
the environment.
chunksize : int, optional
Number of rows to be inserted in each chunk from the dataframe.
Set to ``None`` to load the whole dataframe at once.
reauth : bool, default False
Force Google BigQuery to re-authenticate the user. This is useful
if multiple accounts are used.
if_exists : str, default 'fail'
Behavior when the destination table exists. Value can be one of:
``'fail'``
If table exists, do nothing.
``'replace'``
If table exists, drop it, recreate it, and insert data.
``'append'``
If table exists, insert data. Create if does not exist.
auth_local_webserver : bool, default False
Use the `local webserver flow`_ instead of the `console flow`_
when getting user credentials.
.. _local webserver flow:
http://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_local_server
.. _console flow:
http://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_console
*New in version 0.2.0 of pandas-gbq*.
table_schema : list of dicts, optional
List of BigQuery table fields to which according DataFrame
columns conform to, e.g. ``[{'name': 'col1', 'type':
'STRING'},...]``. If schema is not provided, it will be
generated according to dtypes of DataFrame columns. See
BigQuery API documentation on available names of a field.
*New in version 0.3.1 of pandas-gbq*.
location : str, optional
Location where the load job should run. See the `BigQuery locations
documentation
<https://cloud.google.com/bigquery/docs/dataset-locations>`__ for a
list of available locations. The location must match that of the
target dataset.
*New in version 0.5.0 of pandas-gbq*.
progress_bar : bool, default True
Use the library `tqdm` to show the progress bar for the upload,
chunk by chunk.
*New in version 0.5.0 of pandas-gbq*.
credentials : google.auth.credentials.Credentials, optional
Credentials for accessing Google APIs. Use this parameter to
override default credentials, such as to use Compute Engine
:class:`google.auth.compute_engine.Credentials` or Service
Account :class:`google.oauth2.service_account.Credentials`
directly.
*New in version 0.8.0 of pandas-gbq*.
.. versionadded:: 0.24.0
verbose : bool, deprecated
Deprecated in pandas-gbq version 0.4.0. Use the `logging module
to adjust verbosity instead
<https://pandas-gbq.readthedocs.io/en/latest/intro.html#logging>`__.
private_key : str, deprecated
Deprecated in pandas-gbq version 0.8.0. Use the ``credentials``
parameter and
:func:`google.oauth2.service_account.Credentials.from_service_account_info`
or
:func:`google.oauth2.service_account.Credentials.from_service_account_file`
instead.
Service account private key in JSON format. Can be file path
or string contents. This is useful for remote server
authentication (eg. Jupyter/IPython notebook on remote host).
See Also
--------
pandas_gbq.to_gbq : This function in the pandas-gbq library.
read_gbq : Read a DataFrame from Google BigQuery.
"""
from pandas.io import gbq
gbq.to_gbq(self, destination_table, project_id=project_id,
chunksize=chunksize, reauth=reauth, if_exists=if_exists,
auth_local_webserver=auth_local_webserver,
table_schema=table_schema, location=location,
progress_bar=progress_bar, credentials=credentials,
verbose=verbose, private_key=private_key)
@classmethod
def from_records(cls, data, index=None, exclude=None, columns=None,
coerce_float=False, nrows=None):
"""
Convert structured or record ndarray to DataFrame.
Parameters
----------
data : ndarray (structured dtype), list of tuples, dict, or DataFrame
index : string, list of fields, array-like
Field of array to use as the index, alternately a specific set of
input labels to use
exclude : sequence, default None
Columns or fields to exclude
columns : sequence, default None
Column names to use. If the passed data do not have names
associated with them, this argument provides names for the
columns. Otherwise this argument indicates the order of the columns
in the result (any names not found in the data will become all-NA
columns)
coerce_float : boolean, default False
Attempt to convert values of non-string, non-numeric objects (like
decimal.Decimal) to floating point, useful for SQL result sets
nrows : int, default None
Number of rows to read if data is an iterator
Returns
-------
DataFrame
"""
# Make a copy of the input columns so we can modify it
if columns is not None:
columns = ensure_index(columns)
if is_iterator(data):
if nrows == 0:
return cls()
try:
first_row = next(data)
except StopIteration:
return cls(index=index, columns=columns)
dtype = None
if hasattr(first_row, 'dtype') and first_row.dtype.names:
dtype = first_row.dtype
values = [first_row]
if nrows is None:
values += data
else:
values.extend(itertools.islice(data, nrows - 1))
if dtype is not None:
data = np.array(values, dtype=dtype)
else:
data = values
if isinstance(data, dict):
if columns is None:
columns = arr_columns = ensure_index(sorted(data))
arrays = [data[k] for k in columns]
else:
arrays = []
arr_columns = []
for k, v in data.items():
if k in columns:
arr_columns.append(k)
arrays.append(v)
arrays, arr_columns = reorder_arrays(arrays, arr_columns,
columns)
elif isinstance(data, (np.ndarray, DataFrame)):
arrays, columns = to_arrays(data, columns)
if columns is not None:
columns = ensure_index(columns)
arr_columns = columns
else:
arrays, arr_columns = to_arrays(data, columns,
coerce_float=coerce_float)
arr_columns = ensure_index(arr_columns)
if columns is not None:
columns = ensure_index(columns)
else:
columns = arr_columns
if exclude is None:
exclude = set()
else:
exclude = set(exclude)
result_index = None
if index is not None:
if (isinstance(index, str) or
not hasattr(index, "__iter__")):
i = columns.get_loc(index)
exclude.add(index)
if len(arrays) > 0:
result_index = Index(arrays[i], name=index)
else:
result_index = Index([], name=index)
else:
try:
index_data = [arrays[arr_columns.get_loc(field)]
for field in index]
result_index = ensure_index_from_sequences(index_data,
names=index)
exclude.update(index)
except Exception:
result_index = index
if any(exclude):
arr_exclude = [x for x in exclude if x in arr_columns]
to_remove = [arr_columns.get_loc(col) for col in arr_exclude]
arrays = [v for i, v in enumerate(arrays) if i not in to_remove]
arr_columns = arr_columns.drop(arr_exclude)
columns = columns.drop(exclude)
mgr = arrays_to_mgr(arrays, arr_columns, result_index, columns)
return cls(mgr)
def to_records(self, index=True, convert_datetime64=None,
column_dtypes=None, index_dtypes=None):
"""
Convert DataFrame to a NumPy record array.
Index will be included as the first field of the record array if
requested.
Parameters
----------
index : bool, default True
Include index in resulting record array, stored in 'index'
field or using the index label, if set.
convert_datetime64 : bool, default None
.. deprecated:: 0.23.0
Whether to convert the index to datetime.datetime if it is a
DatetimeIndex.
column_dtypes : str, type, dict, default None
.. versionadded:: 0.24.0
If a string or type, the data type to store all columns. If
a dictionary, a mapping of column names and indices (zero-indexed)
to specific data types.
index_dtypes : str, type, dict, default None
.. versionadded:: 0.24.0
If a string or type, the data type to store all index levels. If
a dictionary, a mapping of index level names and indices
(zero-indexed) to specific data types.
This mapping is applied only if `index=True`.
Returns
-------
numpy.recarray
NumPy ndarray with the DataFrame labels as fields and each row
of the DataFrame as entries.
See Also
--------
DataFrame.from_records: Convert structured or record ndarray
to DataFrame.
numpy.recarray: An ndarray that allows field access using
attributes, analogous to typed columns in a
spreadsheet.
Examples
--------
>>> df = pd.DataFrame({'A': [1, 2], 'B': [0.5, 0.75]},
... index=['a', 'b'])
>>> df
A B
a 1 0.50
b 2 0.75
>>> df.to_records()
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
dtype=[('index', 'O'), ('A', '<i8'), ('B', '<f8')])
If the DataFrame index has no label then the recarray field name
is set to 'index'. If the index has a label then this is used as the
field name:
>>> df.index = df.index.rename("I")
>>> df.to_records()
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
dtype=[('I', 'O'), ('A', '<i8'), ('B', '<f8')])
The index can be excluded from the record array:
>>> df.to_records(index=False)
rec.array([(1, 0.5 ), (2, 0.75)],
dtype=[('A', '<i8'), ('B', '<f8')])
Data types can be specified for the columns:
>>> df.to_records(column_dtypes={"A": "int32"})
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
dtype=[('I', 'O'), ('A', '<i4'), ('B', '<f8')])
As well as for the index:
>>> df.to_records(index_dtypes="<S2")
rec.array([(b'a', 1, 0.5 ), (b'b', 2, 0.75)],
dtype=[('I', 'S2'), ('A', '<i8'), ('B', '<f8')])
>>> index_dtypes = "<S{}".format(df.index.str.len().max())
>>> df.to_records(index_dtypes=index_dtypes)
rec.array([(b'a', 1, 0.5 ), (b'b', 2, 0.75)],
dtype=[('I', 'S1'), ('A', '<i8'), ('B', '<f8')])
"""
if convert_datetime64 is not None:
warnings.warn("The 'convert_datetime64' parameter is "
"deprecated and will be removed in a future "
"version",
FutureWarning, stacklevel=2)
if index:
if is_datetime64_any_dtype(self.index) and convert_datetime64:
ix_vals = [self.index.to_pydatetime()]
else:
if isinstance(self.index, MultiIndex):
# array of tuples to numpy cols. copy copy copy
ix_vals = list(map(np.array, zip(*self.index.values)))
else:
ix_vals = [self.index.values]
arrays = ix_vals + [self[c].get_values() for c in self.columns]
count = 0
index_names = list(self.index.names)
if isinstance(self.index, MultiIndex):
for i, n in enumerate(index_names):
if n is None:
index_names[i] = 'level_%d' % count
count += 1
elif index_names[0] is None:
index_names = ['index']
names = [str(name) for name in itertools.chain(index_names,
self.columns)]
else:
arrays = [self[c].get_values() for c in self.columns]
names = [str(c) for c in self.columns]
index_names = []
index_len = len(index_names)
formats = []
for i, v in enumerate(arrays):
index = i
# When the names and arrays are collected, we
# first collect those in the DataFrame's index,
# followed by those in its columns.
#
# Thus, the total length of the array is:
# len(index_names) + len(DataFrame.columns).
#
# This check allows us to see whether we are
# handling a name / array in the index or column.
if index < index_len:
dtype_mapping = index_dtypes
name = index_names[index]
else:
index -= index_len
dtype_mapping = column_dtypes
name = self.columns[index]
# We have a dictionary, so we get the data type
# associated with the index or column (which can
# be denoted by its name in the DataFrame or its
# position in DataFrame's array of indices or
# columns, whichever is applicable.
if is_dict_like(dtype_mapping):
if name in dtype_mapping:
dtype_mapping = dtype_mapping[name]
elif index in dtype_mapping:
dtype_mapping = dtype_mapping[index]
else:
dtype_mapping = None
# If no mapping can be found, use the array's
# dtype attribute for formatting.
#
# A valid dtype must either be a type or
# string naming a type.
if dtype_mapping is None:
formats.append(v.dtype)
elif isinstance(dtype_mapping, (type, np.dtype, str)):
formats.append(dtype_mapping)
else:
element = "row" if i < index_len else "column"
msg = ("Invalid dtype {dtype} specified for "
"{element} {name}").format(dtype=dtype_mapping,
element=element, name=name)
raise ValueError(msg)
return np.rec.fromarrays(
arrays,
dtype={'names': names, 'formats': formats}
)
@classmethod
def from_items(cls, items, columns=None, orient='columns'):
"""
Construct a DataFrame from a list of tuples.
.. deprecated:: 0.23.0
`from_items` is deprecated and will be removed in a future version.
Use :meth:`DataFrame.from_dict(dict(items)) <DataFrame.from_dict>`
instead.
:meth:`DataFrame.from_dict(OrderedDict(items)) <DataFrame.from_dict>`
may be used to preserve the key order.
Convert (key, value) pairs to DataFrame. The keys will be the axis
index (usually the columns, but depends on the specified
orientation). The values should be arrays or Series.
Parameters
----------
items : sequence of (key, value) pairs
Values should be arrays or Series.
columns : sequence of column labels, optional
Must be passed if orient='index'.
orient : {'columns', 'index'}, default 'columns'
The "orientation" of the data. If the keys of the
input correspond to column labels, pass 'columns'
(default). Otherwise if the keys correspond to the index,
pass 'index'.
Returns
-------
DataFrame
"""
warnings.warn("from_items is deprecated. Please use "
"DataFrame.from_dict(dict(items), ...) instead. "
"DataFrame.from_dict(OrderedDict(items)) may be used to "
"preserve the key order.",
FutureWarning, stacklevel=2)
keys, values = zip(*items)
if orient == 'columns':
if columns is not None:
columns = ensure_index(columns)
idict = dict(items)
if len(idict) < len(items):
if not columns.equals(ensure_index(keys)):
raise ValueError('With non-unique item names, passed '
'columns must be identical')
arrays = values
else:
arrays = [idict[k] for k in columns if k in idict]
else:
columns = ensure_index(keys)
arrays = values
# GH 17312
# Provide more informative error msg when scalar values passed
try:
return cls._from_arrays(arrays, columns, None)
except ValueError:
if not is_nested_list_like(values):
raise ValueError('The value in each (key, value) pair '
'must be an array, Series, or dict')
elif orient == 'index':
if columns is None:
raise TypeError("Must pass columns with orient='index'")
keys = ensure_index(keys)
# GH 17312
# Provide more informative error msg when scalar values passed
try:
arr = np.array(values, dtype=object).T
data = [lib.maybe_convert_objects(v) for v in arr]
return cls._from_arrays(data, columns, keys)
except TypeError:
if not is_nested_list_like(values):
raise ValueError('The value in each (key, value) pair '
'must be an array, Series, or dict')
else: # pragma: no cover
raise ValueError("'orient' must be either 'columns' or 'index'")
@classmethod
def _from_arrays(cls, arrays, columns, index, dtype=None):
mgr = arrays_to_mgr(arrays, columns, index, columns, dtype=dtype)
return cls(mgr)
@classmethod
def from_csv(cls, path, header=0, sep=',', index_col=0, parse_dates=True,
encoding=None, tupleize_cols=None,
infer_datetime_format=False):
"""
Read CSV file.
.. deprecated:: 0.21.0
Use :func:`read_csv` instead.
It is preferable to use the more powerful :func:`read_csv`
for most general purposes, but ``from_csv`` makes for an easy
roundtrip to and from a file (the exact counterpart of
``to_csv``), especially with a DataFrame of time series data.
This method only differs from the preferred :func:`read_csv`
in some defaults:
- `index_col` is ``0`` instead of ``None`` (take first column as index
by default)
- `parse_dates` is ``True`` instead of ``False`` (try parsing the index
as datetime by default)
So a ``pd.DataFrame.from_csv(path)`` can be replaced by
``pd.read_csv(path, index_col=0, parse_dates=True)``.
Parameters
----------
path : string file path or file handle / StringIO
header : int, default 0
Row to use as header (skip prior rows)
sep : string, default ','
Field delimiter
index_col : int or sequence, default 0
Column to use for index. If a sequence is given, a MultiIndex
is used. Different default from read_table
parse_dates : boolean, default True
Parse dates. Different default from read_table
tupleize_cols : boolean, default False
write multi_index columns as a list of tuples (if True)
or new (expanded format) if False)
infer_datetime_format : boolean, default False
If True and `parse_dates` is True for a column, try to infer the
datetime format based on the first datetime string. If the format
can be inferred, there often will be a large parsing speed-up.
Returns
-------
DataFrame
See Also
--------
read_csv
"""
warnings.warn("from_csv is deprecated. Please use read_csv(...) "
"instead. Note that some of the default arguments are "
"different, so please refer to the documentation "
"for from_csv when changing your function calls",
FutureWarning, stacklevel=2)
from pandas.io.parsers import read_csv
return read_csv(path, header=header, sep=sep,
parse_dates=parse_dates, index_col=index_col,
encoding=encoding, tupleize_cols=tupleize_cols,
infer_datetime_format=infer_datetime_format)
def to_sparse(self, fill_value=None, kind='block'):
"""
Convert to SparseDataFrame.
.. deprecated:: 0.25.0
Implement the sparse version of the DataFrame meaning that any data
matching a specific value it's omitted in the representation.
The sparse DataFrame allows for a more efficient storage.
Parameters
----------
fill_value : float, default None
The specific value that should be omitted in the representation.
kind : {'block', 'integer'}, default 'block'
The kind of the SparseIndex tracking where data is not equal to
the fill value:
- 'block' tracks only the locations and sizes of blocks of data.
- 'integer' keeps an array with all the locations of the data.
In most cases 'block' is recommended, since it's more memory
efficient.
Returns
-------
SparseDataFrame
The sparse representation of the DataFrame.
See Also
--------
DataFrame.to_dense :
Converts the DataFrame back to the its dense form.
Examples
--------
>>> df = pd.DataFrame([(np.nan, np.nan),
... (1., np.nan),
... (np.nan, 1.)])
>>> df
0 1
0 NaN NaN
1 1.0 NaN
2 NaN 1.0
>>> type(df)
<class 'pandas.core.frame.DataFrame'>
>>> sdf = df.to_sparse() # doctest: +SKIP
>>> sdf # doctest: +SKIP
0 1
0 NaN NaN
1 1.0 NaN
2 NaN 1.0
>>> type(sdf) # doctest: +SKIP
<class 'pandas.core.sparse.frame.SparseDataFrame'>
"""
warnings.warn("DataFrame.to_sparse is deprecated and will be removed "
"in a future version", FutureWarning, stacklevel=2)
from pandas.core.sparse.api import SparseDataFrame
with warnings.catch_warnings():
warnings.filterwarnings("ignore", message="SparseDataFrame")
return SparseDataFrame(self._series, index=self.index,
columns=self.columns, default_kind=kind,
default_fill_value=fill_value)
@deprecate_kwarg(old_arg_name='encoding', new_arg_name=None)
def to_stata(self, fname, convert_dates=None, write_index=True,
encoding="latin-1", byteorder=None, time_stamp=None,
data_label=None, variable_labels=None, version=114,
convert_strl=None):
"""
Export DataFrame object to Stata dta format.
Writes the DataFrame to a Stata dataset file.
"dta" files contain a Stata dataset.
Parameters
----------
fname : str, buffer or path object
String, path object (pathlib.Path or py._path.local.LocalPath) or
object implementing a binary write() function. If using a buffer
then the buffer will not be automatically closed after the file
data has been written.
convert_dates : dict
Dictionary mapping columns containing datetime types to stata
internal format to use when writing the dates. Options are 'tc',
'td', 'tm', 'tw', 'th', 'tq', 'ty'. Column can be either an integer
or a name. Datetime columns that do not have a conversion type
specified will be converted to 'tc'. Raises NotImplementedError if
a datetime column has timezone information.
write_index : bool
Write the index to Stata dataset.
encoding : str
Default is latin-1. Unicode is not supported.
byteorder : str
Can be ">", "<", "little", or "big". default is `sys.byteorder`.
time_stamp : datetime
A datetime to use as file creation date. Default is the current
time.
data_label : str, optional
A label for the data set. Must be 80 characters or smaller.
variable_labels : dict
Dictionary containing columns as keys and variable labels as
values. Each label must be 80 characters or smaller.
.. versionadded:: 0.19.0
version : {114, 117}, default 114
Version to use in the output dta file. Version 114 can be used
read by Stata 10 and later. Version 117 can be read by Stata 13
or later. Version 114 limits string variables to 244 characters or
fewer while 117 allows strings with lengths up to 2,000,000
characters.
.. versionadded:: 0.23.0
convert_strl : list, optional
List of column names to convert to string columns to Stata StrL
format. Only available if version is 117. Storing strings in the
StrL format can produce smaller dta files if strings have more than
8 characters and values are repeated.
.. versionadded:: 0.23.0
Raises
------
NotImplementedError
* If datetimes contain timezone information
* Column dtype is not representable in Stata
ValueError
* Columns listed in convert_dates are neither datetime64[ns]
or datetime.datetime
* Column listed in convert_dates is not in DataFrame
* Categorical label contains more than 32,000 characters
.. versionadded:: 0.19.0
See Also
--------
read_stata : Import Stata data files.
io.stata.StataWriter : Low-level writer for Stata data files.
io.stata.StataWriter117 : Low-level writer for version 117 files.
Examples
--------
>>> df = pd.DataFrame({'animal': ['falcon', 'parrot', 'falcon',
... 'parrot'],
... 'speed': [350, 18, 361, 15]})
>>> df.to_stata('animals.dta') # doctest: +SKIP
"""
kwargs = {}
if version not in (114, 117):
raise ValueError('Only formats 114 and 117 supported.')
if version == 114:
if convert_strl is not None:
raise ValueError('strl support is only available when using '
'format 117')
from pandas.io.stata import StataWriter as statawriter
else:
from pandas.io.stata import StataWriter117 as statawriter
kwargs['convert_strl'] = convert_strl
writer = statawriter(fname, self, convert_dates=convert_dates,
byteorder=byteorder, time_stamp=time_stamp,
data_label=data_label, write_index=write_index,
variable_labels=variable_labels, **kwargs)
writer.write_file()
def to_feather(self, fname):
"""
Write out the binary feather-format for DataFrames.
.. versionadded:: 0.20.0
Parameters
----------
fname : str
string file path
"""
from pandas.io.feather_format import to_feather
to_feather(self, fname)
def to_parquet(self, fname, engine='auto', compression='snappy',
index=None, partition_cols=None, **kwargs):
"""
Write a DataFrame to the binary parquet format.
.. versionadded:: 0.21.0
This function writes the dataframe as a `parquet file
<https://parquet.apache.org/>`_. You can choose different parquet
backends, and have the option of compression. See
:ref:`the user guide <io.parquet>` for more details.
Parameters
----------
fname : str
File path or Root Directory path. Will be used as Root Directory
path while writing a partitioned dataset.
.. versionchanged:: 0.24.0
engine : {'auto', 'pyarrow', 'fastparquet'}, default 'auto'
Parquet library to use. If 'auto', then the option
``io.parquet.engine`` is used. The default ``io.parquet.engine``
behavior is to try 'pyarrow', falling back to 'fastparquet' if
'pyarrow' is unavailable.
compression : {'snappy', 'gzip', 'brotli', None}, default 'snappy'
Name of the compression to use. Use ``None`` for no compression.
index : bool, default None
If ``True``, include the dataframe's index(es) in the file output.
If ``False``, they will not be written to the file. If ``None``,
the behavior depends on the chosen engine.
.. versionadded:: 0.24.0
partition_cols : list, optional, default None
Column names by which to partition the dataset
Columns are partitioned in the order they are given
.. versionadded:: 0.24.0
**kwargs
Additional arguments passed to the parquet library. See
:ref:`pandas io <io.parquet>` for more details.
See Also
--------
read_parquet : Read a parquet file.
DataFrame.to_csv : Write a csv file.
DataFrame.to_sql : Write to a sql table.
DataFrame.to_hdf : Write to hdf.
Notes
-----
This function requires either the `fastparquet
<https://pypi.org/project/fastparquet>`_ or `pyarrow
<https://arrow.apache.org/docs/python/>`_ library.
Examples
--------
>>> df = pd.DataFrame(data={'col1': [1, 2], 'col2': [3, 4]})
>>> df.to_parquet('df.parquet.gzip',
... compression='gzip') # doctest: +SKIP
>>> pd.read_parquet('df.parquet.gzip') # doctest: +SKIP
col1 col2
0 1 3
1 2 4
"""
from pandas.io.parquet import to_parquet
to_parquet(self, fname, engine,
compression=compression, index=index,
partition_cols=partition_cols, **kwargs)
@Substitution(header='Whether to print column labels, default True',
col_space_type='str or int',
col_space='The minimum width of each column in CSS length '
'units. An int is assumed to be px units.\n\n'
' .. versionadded:: 0.25.0\n'
' Ability to use str')
@Substitution(shared_params=fmt.common_docstring,
returns=fmt.return_docstring)
def to_html(self, buf=None, columns=None, col_space=None, header=True,
index=True, na_rep='NaN', formatters=None, float_format=None,
sparsify=None, index_names=True, justify=None, max_rows=None,
max_cols=None, show_dimensions=False, decimal='.',
bold_rows=True, classes=None, escape=True, notebook=False,
border=None, table_id=None, render_links=False):
"""
Render a DataFrame as an HTML table.
%(shared_params)s
bold_rows : bool, default True
Make the row labels bold in the output.
classes : str or list or tuple, default None
CSS class(es) to apply to the resulting html table.
escape : bool, default True
Convert the characters <, >, and & to HTML-safe sequences.
notebook : {True, False}, default False
Whether the generated HTML is for IPython Notebook.
border : int
A ``border=border`` attribute is included in the opening
`<table>` tag. Default ``pd.options.display.html.border``.
.. versionadded:: 0.19.0
table_id : str, optional
A css id is included in the opening `<table>` tag if specified.
.. versionadded:: 0.23.0
render_links : bool, default False
Convert URLs to HTML links.
.. versionadded:: 0.24.0
%(returns)s
See Also
--------
to_string : Convert DataFrame to a string.
"""
if (justify is not None and
justify not in fmt._VALID_JUSTIFY_PARAMETERS):
raise ValueError("Invalid value for justify parameter")
formatter = fmt.DataFrameFormatter(self, buf=buf, columns=columns,
col_space=col_space, na_rep=na_rep,
formatters=formatters,
float_format=float_format,
sparsify=sparsify, justify=justify,
index_names=index_names,
header=header, index=index,
bold_rows=bold_rows, escape=escape,
max_rows=max_rows,
max_cols=max_cols,
show_dimensions=show_dimensions,
decimal=decimal, table_id=table_id,
render_links=render_links)
# TODO: a generic formatter wld b in DataFrameFormatter
formatter.to_html(classes=classes, notebook=notebook, border=border)
if buf is None:
return formatter.buf.getvalue()
# ----------------------------------------------------------------------
def info(self, verbose=None, buf=None, max_cols=None, memory_usage=None,
null_counts=None):
"""
Print a concise summary of a DataFrame.
This method prints information about a DataFrame including
the index dtype and column dtypes, non-null values and memory usage.
Parameters
----------
verbose : bool, optional
Whether to print the full summary. By default, the setting in
``pandas.options.display.max_info_columns`` is followed.
buf : writable buffer, defaults to sys.stdout
Where to send the output. By default, the output is printed to
sys.stdout. Pass a writable buffer if you need to further process
the output.
max_cols : int, optional
When to switch from the verbose to the truncated output. If the
DataFrame has more than `max_cols` columns, the truncated output
is used. By default, the setting in
``pandas.options.display.max_info_columns`` is used.
memory_usage : bool, str, optional
Specifies whether total memory usage of the DataFrame
elements (including the index) should be displayed. By default,
this follows the ``pandas.options.display.memory_usage`` setting.
True always show memory usage. False never shows memory usage.
A value of 'deep' is equivalent to "True with deep introspection".
Memory usage is shown in human-readable units (base-2
representation). Without deep introspection a memory estimation is
made based in column dtype and number of rows assuming values
consume the same memory amount for corresponding dtypes. With deep
memory introspection, a real memory usage calculation is performed
at the cost of computational resources.
null_counts : bool, optional
Whether to show the non-null counts. By default, this is shown
only if the frame is smaller than
``pandas.options.display.max_info_rows`` and
``pandas.options.display.max_info_columns``. A value of True always
shows the counts, and False never shows the counts.
Returns
-------
None
This method prints a summary of a DataFrame and returns None.
See Also
--------
DataFrame.describe: Generate descriptive statistics of DataFrame
columns.
DataFrame.memory_usage: Memory usage of DataFrame columns.
Examples
--------
>>> int_values = [1, 2, 3, 4, 5]
>>> text_values = ['alpha', 'beta', 'gamma', 'delta', 'epsilon']
>>> float_values = [0.0, 0.25, 0.5, 0.75, 1.0]
>>> df = pd.DataFrame({"int_col": int_values, "text_col": text_values,
... "float_col": float_values})
>>> df
int_col text_col float_col
0 1 alpha 0.00
1 2 beta 0.25
2 3 gamma 0.50
3 4 delta 0.75
4 5 epsilon 1.00
Prints information of all columns:
>>> df.info(verbose=True)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Data columns (total 3 columns):
int_col 5 non-null int64
text_col 5 non-null object
float_col 5 non-null float64
dtypes: float64(1), int64(1), object(1)
memory usage: 248.0+ bytes
Prints a summary of columns count and its dtypes but not per column
information:
>>> df.info(verbose=False)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Columns: 3 entries, int_col to float_col
dtypes: float64(1), int64(1), object(1)
memory usage: 248.0+ bytes
Pipe output of DataFrame.info to buffer instead of sys.stdout, get
buffer content and writes to a text file:
>>> import io
>>> buffer = io.StringIO()
>>> df.info(buf=buffer)
>>> s = buffer.getvalue()
>>> with open("df_info.txt", "w",
... encoding="utf-8") as f: # doctest: +SKIP
... f.write(s)
260
The `memory_usage` parameter allows deep introspection mode, specially
useful for big DataFrames and fine-tune memory optimization:
>>> random_strings_array = np.random.choice(['a', 'b', 'c'], 10 ** 6)
>>> df = pd.DataFrame({
... 'column_1': np.random.choice(['a', 'b', 'c'], 10 ** 6),
... 'column_2': np.random.choice(['a', 'b', 'c'], 10 ** 6),
... 'column_3': np.random.choice(['a', 'b', 'c'], 10 ** 6)
... })
>>> df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000000 entries, 0 to 999999
Data columns (total 3 columns):
column_1 1000000 non-null object
column_2 1000000 non-null object
column_3 1000000 non-null object
dtypes: object(3)
memory usage: 22.9+ MB
>>> df.info(memory_usage='deep')
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000000 entries, 0 to 999999
Data columns (total 3 columns):
column_1 1000000 non-null object
column_2 1000000 non-null object
column_3 1000000 non-null object
dtypes: object(3)
memory usage: 188.8 MB
"""
if buf is None: # pragma: no cover
buf = sys.stdout
lines = []
lines.append(str(type(self)))
lines.append(self.index._summary())
if len(self.columns) == 0:
lines.append('Empty {name}'.format(name=type(self).__name__))
fmt.buffer_put_lines(buf, lines)
return
cols = self.columns
# hack
if max_cols is None:
max_cols = get_option('display.max_info_columns',
len(self.columns) + 1)
max_rows = get_option('display.max_info_rows', len(self) + 1)
if null_counts is None:
show_counts = ((len(self.columns) <= max_cols) and
(len(self) < max_rows))
else:
show_counts = null_counts
exceeds_info_cols = len(self.columns) > max_cols
def _verbose_repr():
lines.append('Data columns (total %d columns):' %
len(self.columns))
space = max(len(pprint_thing(k)) for k in self.columns) + 4
counts = None
tmpl = "{count}{dtype}"
if show_counts:
counts = self.count()
if len(cols) != len(counts): # pragma: no cover
raise AssertionError(
'Columns must equal counts '
'({cols:d} != {counts:d})'.format(
cols=len(cols), counts=len(counts)))
tmpl = "{count} non-null {dtype}"
dtypes = self.dtypes
for i, col in enumerate(self.columns):
dtype = dtypes.iloc[i]
col = pprint_thing(col)
count = ""
if show_counts:
count = counts.iloc[i]
lines.append(_put_str(col, space) + tmpl.format(count=count,
dtype=dtype))
def _non_verbose_repr():
lines.append(self.columns._summary(name='Columns'))
def _sizeof_fmt(num, size_qualifier):
# returns size in human readable format
for x in ['bytes', 'KB', 'MB', 'GB', 'TB']:
if num < 1024.0:
return ("{num:3.1f}{size_q} "
"{x}".format(num=num, size_q=size_qualifier, x=x))
num /= 1024.0
return "{num:3.1f}{size_q} {pb}".format(num=num,
size_q=size_qualifier,
pb='PB')
if verbose:
_verbose_repr()
elif verbose is False: # specifically set to False, not nesc None
_non_verbose_repr()
else:
if exceeds_info_cols:
_non_verbose_repr()
else:
_verbose_repr()
counts = self.get_dtype_counts()
dtypes = ['{k}({kk:d})'.format(k=k[0], kk=k[1]) for k
in sorted(counts.items())]
lines.append('dtypes: {types}'.format(types=', '.join(dtypes)))
if memory_usage is None:
memory_usage = get_option('display.memory_usage')
if memory_usage:
# append memory usage of df to display
size_qualifier = ''
if memory_usage == 'deep':
deep = True
else:
# size_qualifier is just a best effort; not guaranteed to catch
# all cases (e.g., it misses categorical data even with object
# categories)
deep = False
if ('object' in counts or
self.index._is_memory_usage_qualified()):
size_qualifier = '+'
mem_usage = self.memory_usage(index=True, deep=deep).sum()
lines.append("memory usage: {mem}\n".format(
mem=_sizeof_fmt(mem_usage, size_qualifier)))
fmt.buffer_put_lines(buf, lines)
def memory_usage(self, index=True, deep=False):
"""
Return the memory usage of each column in bytes.
The memory usage can optionally include the contribution of
the index and elements of `object` dtype.
This value is displayed in `DataFrame.info` by default. This can be
suppressed by setting ``pandas.options.display.memory_usage`` to False.
Parameters
----------
index : bool, default True
Specifies whether to include the memory usage of the DataFrame's
index in returned Series. If ``index=True``, the memory usage of
the index is the first item in the output.
deep : bool, default False
If True, introspect the data deeply by interrogating
`object` dtypes for system-level memory consumption, and include
it in the returned values.
Returns
-------
Series
A Series whose index is the original column names and whose values
is the memory usage of each column in bytes.
See Also
--------
numpy.ndarray.nbytes : Total bytes consumed by the elements of an
ndarray.
Series.memory_usage : Bytes consumed by a Series.
Categorical : Memory-efficient array for string values with
many repeated values.
DataFrame.info : Concise summary of a DataFrame.
Examples
--------
>>> dtypes = ['int64', 'float64', 'complex128', 'object', 'bool']
>>> data = dict([(t, np.ones(shape=5000).astype(t))
... for t in dtypes])
>>> df = pd.DataFrame(data)
>>> df.head()
int64 float64 complex128 object bool
0 1 1.0 1.0+0.0j 1 True
1 1 1.0 1.0+0.0j 1 True
2 1 1.0 1.0+0.0j 1 True
3 1 1.0 1.0+0.0j 1 True
4 1 1.0 1.0+0.0j 1 True
>>> df.memory_usage()
Index 128
int64 40000
float64 40000
complex128 80000
object 40000
bool 5000
dtype: int64
>>> df.memory_usage(index=False)
int64 40000
float64 40000
complex128 80000
object 40000
bool 5000
dtype: int64
The memory footprint of `object` dtype columns is ignored by default:
>>> df.memory_usage(deep=True)
Index 128
int64 40000
float64 40000
complex128 80000
object 160000
bool 5000
dtype: int64
Use a Categorical for efficient storage of an object-dtype column with
many repeated values.
>>> df['object'].astype('category').memory_usage(deep=True)
5216
"""
result = Series([c.memory_usage(index=False, deep=deep)
for col, c in self.iteritems()], index=self.columns)
if index:
result = Series(self.index.memory_usage(deep=deep),
index=['Index']).append(result)
return result
def transpose(self, *args, **kwargs):
"""
Transpose index and columns.
Reflect the DataFrame over its main diagonal by writing rows as columns
and vice-versa. The property :attr:`.T` is an accessor to the method
:meth:`transpose`.
Parameters
----------
copy : bool, default False
If True, the underlying data is copied. Otherwise (default), no
copy is made if possible.
*args, **kwargs
Additional keywords have no effect but might be accepted for
compatibility with numpy.
Returns
-------
DataFrame
The transposed DataFrame.
See Also
--------
numpy.transpose : Permute the dimensions of a given array.
Notes
-----
Transposing a DataFrame with mixed dtypes will result in a homogeneous
DataFrame with the `object` dtype. In such a case, a copy of the data
is always made.
Examples
--------
**Square DataFrame with homogeneous dtype**
>>> d1 = {'col1': [1, 2], 'col2': [3, 4]}
>>> df1 = pd.DataFrame(data=d1)
>>> df1
col1 col2
0 1 3
1 2 4
>>> df1_transposed = df1.T # or df1.transpose()
>>> df1_transposed
0 1
col1 1 2
col2 3 4
When the dtype is homogeneous in the original DataFrame, we get a
transposed DataFrame with the same dtype:
>>> df1.dtypes
col1 int64
col2 int64
dtype: object
>>> df1_transposed.dtypes
0 int64
1 int64
dtype: object
**Non-square DataFrame with mixed dtypes**
>>> d2 = {'name': ['Alice', 'Bob'],
... 'score': [9.5, 8],
... 'employed': [False, True],
... 'kids': [0, 0]}
>>> df2 = pd.DataFrame(data=d2)
>>> df2
name score employed kids
0 Alice 9.5 False 0
1 Bob 8.0 True 0
>>> df2_transposed = df2.T # or df2.transpose()
>>> df2_transposed
0 1
name Alice Bob
score 9.5 8
employed False True
kids 0 0
When the DataFrame has mixed dtypes, we get a transposed DataFrame with
the `object` dtype:
>>> df2.dtypes
name object
score float64
employed bool
kids int64
dtype: object
>>> df2_transposed.dtypes
0 object
1 object
dtype: object
"""
nv.validate_transpose(args, dict())
return super().transpose(1, 0, **kwargs)
T = property(transpose)
# ----------------------------------------------------------------------
# Picklability
# legacy pickle formats
def _unpickle_frame_compat(self, state): # pragma: no cover
if len(state) == 2: # pragma: no cover
series, idx = state
columns = sorted(series)
else:
series, cols, idx = state
columns = com._unpickle_array(cols)
index = com._unpickle_array(idx)
self._data = self._init_dict(series, index, columns, None)
def _unpickle_matrix_compat(self, state): # pragma: no cover
# old unpickling
(vals, idx, cols), object_state = state
index = com._unpickle_array(idx)
dm = DataFrame(vals, index=index, columns=com._unpickle_array(cols),
copy=False)
if object_state is not None:
ovals, _, ocols = object_state
objects = DataFrame(ovals, index=index,
columns=com._unpickle_array(ocols), copy=False)
dm = dm.join(objects)
self._data = dm._data
# ----------------------------------------------------------------------
# Getting and setting elements
def get_value(self, index, col, takeable=False):
"""
Quickly retrieve single value at passed column and index.
.. deprecated:: 0.21.0
Use .at[] or .iat[] accessors instead.
Parameters
----------
index : row label
col : column label
takeable : interpret the index/col as indexers, default False
Returns
-------
scalar
"""
warnings.warn("get_value is deprecated and will be removed "
"in a future release. Please use "
".at[] or .iat[] accessors instead", FutureWarning,
stacklevel=2)
return self._get_value(index, col, takeable=takeable)
def _get_value(self, index, col, takeable=False):
if takeable:
series = self._iget_item_cache(col)
return com.maybe_box_datetimelike(series._values[index])
series = self._get_item_cache(col)
engine = self.index._engine
try:
return engine.get_value(series._values, index)
except KeyError:
# GH 20629
if self.index.nlevels > 1:
# partial indexing forbidden
raise
except (TypeError, ValueError):
pass
# we cannot handle direct indexing
# use positional
col = self.columns.get_loc(col)
index = self.index.get_loc(index)
return self._get_value(index, col, takeable=True)
_get_value.__doc__ = get_value.__doc__
def set_value(self, index, col, value, takeable=False):
"""
Put single value at passed column and index.
.. deprecated:: 0.21.0
Use .at[] or .iat[] accessors instead.
Parameters
----------
index : row label
col : column label
value : scalar
takeable : interpret the index/col as indexers, default False
Returns
-------
DataFrame
If label pair is contained, will be reference to calling DataFrame,
otherwise a new object.
"""
warnings.warn("set_value is deprecated and will be removed "
"in a future release. Please use "
".at[] or .iat[] accessors instead", FutureWarning,
stacklevel=2)
return self._set_value(index, col, value, takeable=takeable)
def _set_value(self, index, col, value, takeable=False):
try:
if takeable is True:
series = self._iget_item_cache(col)
return series._set_value(index, value, takeable=True)
series = self._get_item_cache(col)
engine = self.index._engine
engine.set_value(series._values, index, value)
return self
except (KeyError, TypeError):
# set using a non-recursive method & reset the cache
if takeable:
self.iloc[index, col] = value
else:
self.loc[index, col] = value
self._item_cache.pop(col, None)
return self
_set_value.__doc__ = set_value.__doc__
def _ixs(self, i, axis=0):
"""
Parameters
----------
i : int, slice, or sequence of integers
axis : int
Notes
-----
If slice passed, the resulting data will be a view.
"""
# irow
if axis == 0:
if isinstance(i, slice):
return self[i]
else:
label = self.index[i]
if isinstance(label, Index):
# a location index by definition
result = self.take(i, axis=axis)
copy = True
else:
new_values = self._data.fast_xs(i)
if is_scalar(new_values):
return new_values
# if we are a copy, mark as such
copy = (isinstance(new_values, np.ndarray) and
new_values.base is None)
result = self._constructor_sliced(new_values,
index=self.columns,
name=self.index[i],
dtype=new_values.dtype)
result._set_is_copy(self, copy=copy)
return result
# icol
else:
label = self.columns[i]
if isinstance(i, slice):
# need to return view
lab_slice = slice(label[0], label[-1])
return self.loc[:, lab_slice]
else:
if isinstance(label, Index):
return self._take(i, axis=1)
index_len = len(self.index)
# if the values returned are not the same length
# as the index (iow a not found value), iget returns
# a 0-len ndarray. This is effectively catching
# a numpy error (as numpy should really raise)
values = self._data.iget(i)
if index_len and not len(values):
values = np.array([np.nan] * index_len, dtype=object)
result = self._box_col_values(values, label)
# this is a cached value, mark it so
result._set_as_cached(label, self)
return result
def __getitem__(self, key):
key = lib.item_from_zerodim(key)
key = com.apply_if_callable(key, self)
# shortcut if the key is in columns
try:
if self.columns.is_unique and key in self.columns:
if self.columns.nlevels > 1:
return self._getitem_multilevel(key)
return self._get_item_cache(key)
except (TypeError, ValueError):
# The TypeError correctly catches non hashable "key" (e.g. list)
# The ValueError can be removed once GH #21729 is fixed
pass
# Do we have a slicer (on rows)?
indexer = convert_to_index_sliceable(self, key)
if indexer is not None:
return self._slice(indexer, axis=0)
# Do we have a (boolean) DataFrame?
if isinstance(key, DataFrame):
return self._getitem_frame(key)
# Do we have a (boolean) 1d indexer?
if com.is_bool_indexer(key):
return self._getitem_bool_array(key)
# We are left with two options: a single key, and a collection of keys,
# We interpret tuples as collections only for non-MultiIndex
is_single_key = isinstance(key, tuple) or not is_list_like(key)
if is_single_key:
if self.columns.nlevels > 1:
return self._getitem_multilevel(key)
indexer = self.columns.get_loc(key)
if is_integer(indexer):
indexer = [indexer]
else:
if is_iterator(key):
key = list(key)
indexer = self.loc._convert_to_indexer(key, axis=1,
raise_missing=True)
# take() does not accept boolean indexers
if getattr(indexer, "dtype", None) == bool:
indexer = np.where(indexer)[0]
data = self._take(indexer, axis=1)
if is_single_key:
# What does looking for a single key in a non-unique index return?
# The behavior is inconsistent. It returns a Series, except when
# - the key itself is repeated (test on data.shape, #9519), or
# - we have a MultiIndex on columns (test on self.columns, #21309)
if data.shape[1] == 1 and not isinstance(self.columns, MultiIndex):
data = data[key]
return data
def _getitem_bool_array(self, key):
# also raises Exception if object array with NA values
# warning here just in case -- previously __setitem__ was
# reindexing but __getitem__ was not; it seems more reasonable to
# go with the __setitem__ behavior since that is more consistent
# with all other indexing behavior
if isinstance(key, Series) and not key.index.equals(self.index):
warnings.warn("Boolean Series key will be reindexed to match "
"DataFrame index.", UserWarning, stacklevel=3)
elif len(key) != len(self.index):
raise ValueError('Item wrong length %d instead of %d.' %
(len(key), len(self.index)))
# check_bool_indexer will throw exception if Series key cannot
# be reindexed to match DataFrame rows
key = check_bool_indexer(self.index, key)
indexer = key.nonzero()[0]
return self._take(indexer, axis=0)
def _getitem_multilevel(self, key):
loc = self.columns.get_loc(key)
if isinstance(loc, (slice, Series, np.ndarray, Index)):
new_columns = self.columns[loc]
result_columns = maybe_droplevels(new_columns, key)
if self._is_mixed_type:
result = self.reindex(columns=new_columns)
result.columns = result_columns
else:
new_values = self.values[:, loc]
result = self._constructor(new_values, index=self.index,
columns=result_columns)
result = result.__finalize__(self)
# If there is only one column being returned, and its name is
# either an empty string, or a tuple with an empty string as its
# first element, then treat the empty string as a placeholder
# and return the column as if the user had provided that empty
# string in the key. If the result is a Series, exclude the
# implied empty string from its name.
if len(result.columns) == 1:
top = result.columns[0]
if isinstance(top, tuple):
top = top[0]
if top == '':
result = result['']
if isinstance(result, Series):
result = self._constructor_sliced(result,
index=self.index,
name=key)
result._set_is_copy(self)
return result
else:
return self._get_item_cache(key)
def _getitem_frame(self, key):
if key.values.size and not is_bool_dtype(key.values):
raise ValueError('Must pass DataFrame with boolean values only')
return self.where(key)
def query(self, expr, inplace=False, **kwargs):
"""
Query the columns of a DataFrame with a boolean expression.
Parameters
----------
expr : str
The query string to evaluate. You can refer to variables
in the environment by prefixing them with an '@' character like
``@a + b``.
.. versionadded:: 0.25.0
You can refer to column names that contain spaces by surrounding
them in backticks.
For example, if one of your columns is called ``a a`` and you want
to sum it with ``b``, your query should be ```a a` + b``.
inplace : bool
Whether the query should modify the data in place or return
a modified copy.
**kwargs
See the documentation for :func:`eval` for complete details
on the keyword arguments accepted by :meth:`DataFrame.query`.
.. versionadded:: 0.18.0
Returns
-------
DataFrame
DataFrame resulting from the provided query expression.
See Also
--------
eval : Evaluate a string describing operations on
DataFrame columns.
DataFrame.eval : Evaluate a string describing operations on
DataFrame columns.
Notes
-----
The result of the evaluation of this expression is first passed to
:attr:`DataFrame.loc` and if that fails because of a
multidimensional key (e.g., a DataFrame) then the result will be passed
to :meth:`DataFrame.__getitem__`.
This method uses the top-level :func:`eval` function to
evaluate the passed query.
The :meth:`~pandas.DataFrame.query` method uses a slightly
modified Python syntax by default. For example, the ``&`` and ``|``
(bitwise) operators have the precedence of their boolean cousins,
:keyword:`and` and :keyword:`or`. This *is* syntactically valid Python,
however the semantics are different.
You can change the semantics of the expression by passing the keyword
argument ``parser='python'``. This enforces the same semantics as
evaluation in Python space. Likewise, you can pass ``engine='python'``
to evaluate an expression using Python itself as a backend. This is not
recommended as it is inefficient compared to using ``numexpr`` as the
engine.
The :attr:`DataFrame.index` and
:attr:`DataFrame.columns` attributes of the
:class:`~pandas.DataFrame` instance are placed in the query namespace
by default, which allows you to treat both the index and columns of the
frame as a column in the frame.
The identifier ``index`` is used for the frame index; you can also
use the name of the index to identify it in a query. Please note that
Python keywords may not be used as identifiers.
For further details and examples see the ``query`` documentation in
:ref:`indexing <indexing.query>`.
Examples
--------
>>> df = pd.DataFrame({'A': range(1, 6),
... 'B': range(10, 0, -2),
... 'C C': range(10, 5, -1)})
>>> df
A B C C
0 1 10 10
1 2 8 9
2 3 6 8
3 4 4 7
4 5 2 6
>>> df.query('A > B')
A B C C
4 5 2 6
The previous expression is equivalent to
>>> df[df.A > df.B]
A B C C
4 5 2 6
For columns with spaces in their name, you can use backtick quoting.
>>> df.query('B == `C C`')
A B C C
0 1 10 10
The previous expression is equivalent to
>>> df[df.B == df['C C']]
A B C C
0 1 10 10
"""
inplace = validate_bool_kwarg(inplace, 'inplace')
if not isinstance(expr, str):
msg = "expr must be a string to be evaluated, {0} given"
raise ValueError(msg.format(type(expr)))
kwargs['level'] = kwargs.pop('level', 0) + 1
kwargs['target'] = None
res = self.eval(expr, **kwargs)
try:
new_data = self.loc[res]
except ValueError:
# when res is multi-dimensional loc raises, but this is sometimes a
# valid query
new_data = self[res]
if inplace:
self._update_inplace(new_data)
else:
return new_data
def eval(self, expr, inplace=False, **kwargs):
"""
Evaluate a string describing operations on DataFrame columns.
Operates on columns only, not specific rows or elements. This allows
`eval` to run arbitrary code, which can make you vulnerable to code
injection if you pass user input to this function.
Parameters
----------
expr : str
The expression string to evaluate.
inplace : bool, default False
If the expression contains an assignment, whether to perform the
operation inplace and mutate the existing DataFrame. Otherwise,
a new DataFrame is returned.
.. versionadded:: 0.18.0.
kwargs : dict
See the documentation for :func:`eval` for complete details
on the keyword arguments accepted by
:meth:`~pandas.DataFrame.query`.
Returns
-------
ndarray, scalar, or pandas object
The result of the evaluation.
See Also
--------
DataFrame.query : Evaluates a boolean expression to query the columns
of a frame.
DataFrame.assign : Can evaluate an expression or function to create new
values for a column.
eval : Evaluate a Python expression as a string using various
backends.
Notes
-----
For more details see the API documentation for :func:`~eval`.
For detailed examples see :ref:`enhancing performance with eval
<enhancingperf.eval>`.
Examples
--------
>>> df = pd.DataFrame({'A': range(1, 6), 'B': range(10, 0, -2)})
>>> df
A B
0 1 10
1 2 8
2 3 6
3 4 4
4 5 2
>>> df.eval('A + B')
0 11
1 10
2 9
3 8
4 7
dtype: int64
Assignment is allowed though by default the original DataFrame is not
modified.
>>> df.eval('C = A + B')
A B C
0 1 10 11
1 2 8 10
2 3 6 9
3 4 4 8
4 5 2 7
>>> df
A B
0 1 10
1 2 8
2 3 6
3 4 4
4 5 2
Use ``inplace=True`` to modify the original DataFrame.
>>> df.eval('C = A + B', inplace=True)
>>> df
A B C
0 1 10 11
1 2 8 10
2 3 6 9
3 4 4 8
4 5 2 7
"""
from pandas.core.computation.eval import eval as _eval
inplace = validate_bool_kwarg(inplace, 'inplace')
resolvers = kwargs.pop('resolvers', None)
kwargs['level'] = kwargs.pop('level', 0) + 1
if resolvers is None:
index_resolvers = self._get_index_resolvers()
column_resolvers = \
self._get_space_character_free_column_resolvers()
resolvers = column_resolvers, index_resolvers
if 'target' not in kwargs:
kwargs['target'] = self
kwargs['resolvers'] = kwargs.get('resolvers', ()) + tuple(resolvers)
return _eval(expr, inplace=inplace, **kwargs)
def select_dtypes(self, include=None, exclude=None):
"""
Return a subset of the DataFrame's columns based on the column dtypes.
Parameters
----------
include, exclude : scalar or list-like
A selection of dtypes or strings to be included/excluded. At least
one of these parameters must be supplied.
Returns
-------
DataFrame
The subset of the frame including the dtypes in ``include`` and
excluding the dtypes in ``exclude``.
Raises
------
ValueError
* If both of ``include`` and ``exclude`` are empty
* If ``include`` and ``exclude`` have overlapping elements
* If any kind of string dtype is passed in.
Notes
-----
* To select all *numeric* types, use ``np.number`` or ``'number'``
* To select strings you must use the ``object`` dtype, but note that
this will return *all* object dtype columns
* See the `numpy dtype hierarchy
<http://docs.scipy.org/doc/numpy/reference/arrays.scalars.html>`__
* To select datetimes, use ``np.datetime64``, ``'datetime'`` or
``'datetime64'``
* To select timedeltas, use ``np.timedelta64``, ``'timedelta'`` or
``'timedelta64'``
* To select Pandas categorical dtypes, use ``'category'``
* To select Pandas datetimetz dtypes, use ``'datetimetz'`` (new in
0.20.0) or ``'datetime64[ns, tz]'``
Examples
--------
>>> df = pd.DataFrame({'a': [1, 2] * 3,
... 'b': [True, False] * 3,
... 'c': [1.0, 2.0] * 3})
>>> df
a b c
0 1 True 1.0
1 2 False 2.0
2 1 True 1.0
3 2 False 2.0
4 1 True 1.0
5 2 False 2.0
>>> df.select_dtypes(include='bool')
b
0 True
1 False
2 True
3 False
4 True
5 False
>>> df.select_dtypes(include=['float64'])
c
0 1.0
1 2.0
2 1.0
3 2.0
4 1.0
5 2.0
>>> df.select_dtypes(exclude=['int'])
b c
0 True 1.0
1 False 2.0
2 True 1.0
3 False 2.0
4 True 1.0
5 False 2.0
"""
def _get_info_slice(obj, indexer):
"""Slice the info axis of `obj` with `indexer`."""
if not hasattr(obj, '_info_axis_number'):
msg = 'object of type {typ!r} has no info axis'
raise TypeError(msg.format(typ=type(obj).__name__))
slices = [slice(None)] * obj.ndim
slices[obj._info_axis_number] = indexer
return tuple(slices)
if not is_list_like(include):
include = (include,) if include is not None else ()
if not is_list_like(exclude):
exclude = (exclude,) if exclude is not None else ()
selection = tuple(map(frozenset, (include, exclude)))
if not any(selection):
raise ValueError('at least one of include or exclude must be '
'nonempty')
# convert the myriad valid dtypes object to a single representation
include, exclude = map(
lambda x: frozenset(map(infer_dtype_from_object, x)), selection)
for dtypes in (include, exclude):
invalidate_string_dtypes(dtypes)
# can't both include AND exclude!
if not include.isdisjoint(exclude):
raise ValueError('include and exclude overlap on {inc_ex}'.format(
inc_ex=(include & exclude)))
# empty include/exclude -> defaults to True
# three cases (we've already raised if both are empty)
# case 1: empty include, nonempty exclude
# we have True, True, ... True for include, same for exclude
# in the loop below we get the excluded
# and when we call '&' below we get only the excluded
# case 2: nonempty include, empty exclude
# same as case 1, but with include
# case 3: both nonempty
# the "union" of the logic of case 1 and case 2:
# we get the included and excluded, and return their logical and
include_these = Series(not bool(include), index=self.columns)
exclude_these = Series(not bool(exclude), index=self.columns)
def is_dtype_instance_mapper(idx, dtype):
return idx, functools.partial(issubclass, dtype.type)
for idx, f in itertools.starmap(is_dtype_instance_mapper,
enumerate(self.dtypes)):
if include: # checks for the case of empty include or exclude
include_these.iloc[idx] = any(map(f, include))
if exclude:
exclude_these.iloc[idx] = not any(map(f, exclude))
dtype_indexer = include_these & exclude_these
return self.loc[_get_info_slice(self, dtype_indexer)]
def _box_item_values(self, key, values):
items = self.columns[self.columns.get_loc(key)]
if values.ndim == 2:
return self._constructor(values.T, columns=items, index=self.index)
else:
return self._box_col_values(values, items)
def _box_col_values(self, values, items):
"""
Provide boxed values for a column.
"""
klass = self._constructor_sliced
return klass(values, index=self.index, name=items, fastpath=True)
def __setitem__(self, key, value):
key = com.apply_if_callable(key, self)
# see if we can slice the rows
indexer = convert_to_index_sliceable(self, key)
if indexer is not None:
return self._setitem_slice(indexer, value)
if isinstance(key, DataFrame) or getattr(key, 'ndim', None) == 2:
self._setitem_frame(key, value)
elif isinstance(key, (Series, np.ndarray, list, Index)):
self._setitem_array(key, value)
else:
# set column
self._set_item(key, value)
def _setitem_slice(self, key, value):
self._check_setitem_copy()
self.loc._setitem_with_indexer(key, value)
def _setitem_array(self, key, value):
# also raises Exception if object array with NA values
if com.is_bool_indexer(key):
if len(key) != len(self.index):
raise ValueError('Item wrong length %d instead of %d!' %
(len(key), len(self.index)))
key = check_bool_indexer(self.index, key)
indexer = key.nonzero()[0]
self._check_setitem_copy()
self.loc._setitem_with_indexer(indexer, value)
else:
if isinstance(value, DataFrame):
if len(value.columns) != len(key):
raise ValueError('Columns must be same length as key')
for k1, k2 in zip(key, value.columns):
self[k1] = value[k2]
else:
indexer = self.loc._convert_to_indexer(key, axis=1)
self._check_setitem_copy()
self.loc._setitem_with_indexer((slice(None), indexer), value)
def _setitem_frame(self, key, value):
# support boolean setting with DataFrame input, e.g.
# df[df > df2] = 0
if isinstance(key, np.ndarray):
if key.shape != self.shape:
raise ValueError(
'Array conditional must be same shape as self'
)
key = self._constructor(key, **self._construct_axes_dict())
if key.values.size and not is_bool_dtype(key.values):
raise TypeError(
'Must pass DataFrame or 2-d ndarray with boolean values only'
)
self._check_inplace_setting(value)
self._check_setitem_copy()
self._where(-key, value, inplace=True)
def _ensure_valid_index(self, value):
"""
Ensure that if we don't have an index, that we can create one from the
passed value.
"""
# GH5632, make sure that we are a Series convertible
if not len(self.index) and is_list_like(value):
try:
value = Series(value)
except (ValueError, NotImplementedError, TypeError):
raise ValueError('Cannot set a frame with no defined index '
'and a value that cannot be converted to a '
'Series')
self._data = self._data.reindex_axis(value.index.copy(), axis=1,
fill_value=np.nan)
def _set_item(self, key, value):
"""
Add series to DataFrame in specified column.
If series is a numpy-array (not a Series/TimeSeries), it must be the
same length as the DataFrames index or an error will be thrown.
Series/TimeSeries will be conformed to the DataFrames index to
ensure homogeneity.
"""
self._ensure_valid_index(value)
value = self._sanitize_column(key, value)
NDFrame._set_item(self, key, value)
# check if we are modifying a copy
# try to set first as we want an invalid
# value exception to occur first
if len(self):
self._check_setitem_copy()
def insert(self, loc, column, value, allow_duplicates=False):
"""
Insert column into DataFrame at specified location.
Raises a ValueError if `column` is already contained in the DataFrame,
unless `allow_duplicates` is set to True.
Parameters
----------
loc : int
Insertion index. Must verify 0 <= loc <= len(columns)
column : string, number, or hashable object
label of the inserted column
value : int, Series, or array-like
allow_duplicates : bool, optional
"""
self._ensure_valid_index(value)
value = self._sanitize_column(column, value, broadcast=False)
self._data.insert(loc, column, value,
allow_duplicates=allow_duplicates)
def assign(self, **kwargs):
r"""
Assign new columns to a DataFrame.
Returns a new object with all original columns in addition to new ones.
Existing columns that are re-assigned will be overwritten.
Parameters
----------
**kwargs : dict of {str: callable or Series}
The column names are keywords. If the values are
callable, they are computed on the DataFrame and
assigned to the new columns. The callable must not
change input DataFrame (though pandas doesn't check it).
If the values are not callable, (e.g. a Series, scalar, or array),
they are simply assigned.
Returns
-------
DataFrame
A new DataFrame with the new columns in addition to
all the existing columns.
Notes
-----
Assigning multiple columns within the same ``assign`` is possible.
For Python 3.6 and above, later items in '\*\*kwargs' may refer to
newly created or modified columns in 'df'; items are computed and
assigned into 'df' in order. For Python 3.5 and below, the order of
keyword arguments is not specified, you cannot refer to newly created
or modified columns. All items are computed first, and then assigned
in alphabetical order.
.. versionchanged :: 0.23.0
Keyword argument order is maintained for Python 3.6 and later.
Examples
--------
>>> df = pd.DataFrame({'temp_c': [17.0, 25.0]},
... index=['Portland', 'Berkeley'])
>>> df
temp_c
Portland 17.0
Berkeley 25.0
Where the value is a callable, evaluated on `df`:
>>> df.assign(temp_f=lambda x: x.temp_c * 9 / 5 + 32)
temp_c temp_f
Portland 17.0 62.6
Berkeley 25.0 77.0
Alternatively, the same behavior can be achieved by directly
referencing an existing Series or sequence:
>>> df.assign(temp_f=df['temp_c'] * 9 / 5 + 32)
temp_c temp_f
Portland 17.0 62.6
Berkeley 25.0 77.0
In Python 3.6+, you can create multiple columns within the same assign
where one of the columns depends on another one defined within the same
assign:
>>> df.assign(temp_f=lambda x: x['temp_c'] * 9 / 5 + 32,
... temp_k=lambda x: (x['temp_f'] + 459.67) * 5 / 9)
temp_c temp_f temp_k
Portland 17.0 62.6 290.15
Berkeley 25.0 77.0 298.15
"""
data = self.copy()
# >= 3.6 preserve order of kwargs
if PY36:
for k, v in kwargs.items():
data[k] = com.apply_if_callable(v, data)
else:
# <= 3.5: do all calculations first...
results = OrderedDict()
for k, v in kwargs.items():
results[k] = com.apply_if_callable(v, data)
# <= 3.5 and earlier
results = sorted(results.items())
# ... and then assign
for k, v in results:
data[k] = v
return data
def _sanitize_column(self, key, value, broadcast=True):
"""
Ensures new columns (which go into the BlockManager as new blocks) are
always copied and converted into an array.
Parameters
----------
key : object
value : scalar, Series, or array-like
broadcast : bool, default True
If ``key`` matches multiple duplicate column names in the
DataFrame, this parameter indicates whether ``value`` should be
tiled so that the returned array contains a (duplicated) column for
each occurrence of the key. If False, ``value`` will not be tiled.
Returns
-------
numpy.ndarray
"""
def reindexer(value):
# reindex if necessary
if value.index.equals(self.index) or not len(self.index):
value = value._values.copy()
else:
# GH 4107
try:
value = value.reindex(self.index)._values
except Exception as e:
# duplicate axis
if not value.index.is_unique:
raise e
# other
raise TypeError('incompatible index of inserted column '
'with frame index')
return value
if isinstance(value, Series):
value = reindexer(value)
elif isinstance(value, DataFrame):
# align right-hand-side columns if self.columns
# is multi-index and self[key] is a sub-frame
if isinstance(self.columns, MultiIndex) and key in self.columns:
loc = self.columns.get_loc(key)
if isinstance(loc, (slice, Series, np.ndarray, Index)):
cols = maybe_droplevels(self.columns[loc], key)
if len(cols) and not cols.equals(value.columns):
value = value.reindex(cols, axis=1)
# now align rows
value = reindexer(value).T
elif isinstance(value, ExtensionArray):
# Explicitly copy here, instead of in sanitize_index,
# as sanitize_index won't copy an EA, even with copy=True
value = value.copy()
value = sanitize_index(value, self.index, copy=False)
elif isinstance(value, Index) or is_sequence(value):
# turn me into an ndarray
value = sanitize_index(value, self.index, copy=False)
if not isinstance(value, (np.ndarray, Index)):
if isinstance(value, list) and len(value) > 0:
value = maybe_convert_platform(value)
else:
value = com.asarray_tuplesafe(value)
elif value.ndim == 2:
value = value.copy().T
elif isinstance(value, Index):
value = value.copy(deep=True)
else:
value = value.copy()
# possibly infer to datetimelike
if is_object_dtype(value.dtype):
value = maybe_infer_to_datetimelike(value)
else:
# cast ignores pandas dtypes. so save the dtype first
infer_dtype, _ = infer_dtype_from_scalar(
value, pandas_dtype=True)
# upcast
value = cast_scalar_to_array(len(self.index), value)
value = maybe_cast_to_datetime(value, infer_dtype)
# return internal types directly
if is_extension_type(value) or is_extension_array_dtype(value):
return value
# broadcast across multiple columns if necessary
if broadcast and key in self.columns and value.ndim == 1:
if (not self.columns.is_unique or
isinstance(self.columns, MultiIndex)):
existing_piece = self[key]
if isinstance(existing_piece, DataFrame):
value = np.tile(value, (len(existing_piece.columns), 1))
return np.atleast_2d(np.asarray(value))
@property
def _series(self):
return {item: Series(self._data.iget(idx), index=self.index, name=item)
for idx, item in enumerate(self.columns)}
def lookup(self, row_labels, col_labels):
"""
Label-based "fancy indexing" function for DataFrame.
Given equal-length arrays of row and column labels, return an
array of the values corresponding to each (row, col) pair.
Parameters
----------
row_labels : sequence
The row labels to use for lookup
col_labels : sequence
The column labels to use for lookup
Returns
-------
numpy.ndarray
Notes
-----
Akin to::
result = [df.get_value(row, col)
for row, col in zip(row_labels, col_labels)]
Examples
--------
values : ndarray
The found values
"""
n = len(row_labels)
if n != len(col_labels):
raise ValueError('Row labels must have same size as column labels')
thresh = 1000
if not self._is_mixed_type or n > thresh:
values = self.values
ridx = self.index.get_indexer(row_labels)
cidx = self.columns.get_indexer(col_labels)
if (ridx == -1).any():
raise KeyError('One or more row labels was not found')
if (cidx == -1).any():
raise KeyError('One or more column labels was not found')
flat_index = ridx * len(self.columns) + cidx
result = values.flat[flat_index]
else:
result = np.empty(n, dtype='O')
for i, (r, c) in enumerate(zip(row_labels, col_labels)):
result[i] = self._get_value(r, c)
if is_object_dtype(result):
result = lib.maybe_convert_objects(result)
return result
# ----------------------------------------------------------------------
# Reindexing and alignment
def _reindex_axes(self, axes, level, limit, tolerance, method, fill_value,
copy):
frame = self
columns = axes['columns']
if columns is not None:
frame = frame._reindex_columns(columns, method, copy, level,
fill_value, limit, tolerance)
index = axes['index']
if index is not None:
frame = frame._reindex_index(index, method, copy, level,
fill_value, limit, tolerance)
return frame
def _reindex_index(self, new_index, method, copy, level, fill_value=np.nan,
limit=None, tolerance=None):
new_index, indexer = self.index.reindex(new_index, method=method,
level=level, limit=limit,
tolerance=tolerance)
return self._reindex_with_indexers({0: [new_index, indexer]},
copy=copy, fill_value=fill_value,
allow_dups=False)
def _reindex_columns(self, new_columns, method, copy, level,
fill_value=None, limit=None, tolerance=None):
new_columns, indexer = self.columns.reindex(new_columns, method=method,
level=level, limit=limit,
tolerance=tolerance)
return self._reindex_with_indexers({1: [new_columns, indexer]},
copy=copy, fill_value=fill_value,
allow_dups=False)
def _reindex_multi(self, axes, copy, fill_value):
"""
We are guaranteed non-Nones in the axes.
"""
new_index, row_indexer = self.index.reindex(axes['index'])
new_columns, col_indexer = self.columns.reindex(axes['columns'])
if row_indexer is not None and col_indexer is not None:
indexer = row_indexer, col_indexer
new_values = algorithms.take_2d_multi(self.values, indexer,
fill_value=fill_value)
return self._constructor(new_values, index=new_index,
columns=new_columns)
else:
return self._reindex_with_indexers({0: [new_index, row_indexer],
1: [new_columns, col_indexer]},
copy=copy,
fill_value=fill_value)
@Appender(_shared_docs['align'] % _shared_doc_kwargs)
def align(self, other, join='outer', axis=None, level=None, copy=True,
fill_value=None, method=None, limit=None, fill_axis=0,
broadcast_axis=None):
return super().align(other, join=join, axis=axis, level=level,
copy=copy, fill_value=fill_value, method=method,
limit=limit, fill_axis=fill_axis,
broadcast_axis=broadcast_axis)
@Substitution(**_shared_doc_kwargs)
@Appender(NDFrame.reindex.__doc__)
@rewrite_axis_style_signature('labels', [('method', None),
('copy', True),
('level', None),
('fill_value', np.nan),
('limit', None),
('tolerance', None)])
def reindex(self, *args, **kwargs):
axes = validate_axis_style_args(self, args, kwargs, 'labels',
'reindex')
kwargs.update(axes)
# Pop these, since the values are in `kwargs` under different names
kwargs.pop('axis', None)
kwargs.pop('labels', None)
return super().reindex(**kwargs)
@Appender(_shared_docs['reindex_axis'] % _shared_doc_kwargs)
def reindex_axis(self, labels, axis=0, method=None, level=None, copy=True,
limit=None, fill_value=np.nan):
return super().reindex_axis(labels=labels, axis=axis, method=method,
level=level, copy=copy, limit=limit,
fill_value=fill_value)
def drop(self, labels=None, axis=0, index=None, columns=None,
level=None, inplace=False, errors='raise'):
"""
Drop specified labels from rows or columns.
Remove rows or columns by specifying label names and corresponding
axis, or by specifying directly index or column names. When using a
multi-index, labels on different levels can be removed by specifying
the level.
Parameters
----------
labels : single label or list-like
Index or column labels to drop.
axis : {0 or 'index', 1 or 'columns'}, default 0
Whether to drop labels from the index (0 or 'index') or
columns (1 or 'columns').
index : single label or list-like
Alternative to specifying axis (``labels, axis=0``
is equivalent to ``index=labels``).
.. versionadded:: 0.21.0
columns : single label or list-like
Alternative to specifying axis (``labels, axis=1``
is equivalent to ``columns=labels``).
.. versionadded:: 0.21.0
level : int or level name, optional
For MultiIndex, level from which the labels will be removed.
inplace : bool, default False
If True, do operation inplace and return None.
errors : {'ignore', 'raise'}, default 'raise'
If 'ignore', suppress error and only existing labels are
dropped.
Returns
-------
DataFrame
DataFrame without the removed index or column labels.
Raises
------
KeyError
If any of the labels is not found in the selected axis.
See Also
--------
DataFrame.loc : Label-location based indexer for selection by label.
DataFrame.dropna : Return DataFrame with labels on given axis omitted
where (all or any) data are missing.
DataFrame.drop_duplicates : Return DataFrame with duplicate rows
removed, optionally only considering certain columns.
Series.drop : Return Series with specified index labels removed.
Examples
--------
>>> df = pd.DataFrame(np.arange(12).reshape(3, 4),
... columns=['A', 'B', 'C', 'D'])
>>> df
A B C D
0 0 1 2 3
1 4 5 6 7
2 8 9 10 11
Drop columns
>>> df.drop(['B', 'C'], axis=1)
A D
0 0 3
1 4 7
2 8 11
>>> df.drop(columns=['B', 'C'])
A D
0 0 3
1 4 7
2 8 11
Drop a row by index
>>> df.drop([0, 1])
A B C D
2 8 9 10 11
Drop columns and/or rows of MultiIndex DataFrame
>>> midx = pd.MultiIndex(levels=[['lama', 'cow', 'falcon'],
... ['speed', 'weight', 'length']],
... codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2],
... [0, 1, 2, 0, 1, 2, 0, 1, 2]])
>>> df = pd.DataFrame(index=midx, columns=['big', 'small'],
... data=[[45, 30], [200, 100], [1.5, 1], [30, 20],
... [250, 150], [1.5, 0.8], [320, 250],
... [1, 0.8], [0.3, 0.2]])
>>> df
big small
lama speed 45.0 30.0
weight 200.0 100.0
length 1.5 1.0
cow speed 30.0 20.0
weight 250.0 150.0
length 1.5 0.8
falcon speed 320.0 250.0
weight 1.0 0.8
length 0.3 0.2
>>> df.drop(index='cow', columns='small')
big
lama speed 45.0
weight 200.0
length 1.5
falcon speed 320.0
weight 1.0
length 0.3
>>> df.drop(index='length', level=1)
big small
lama speed 45.0 30.0
weight 200.0 100.0
cow speed 30.0 20.0
weight 250.0 150.0
falcon speed 320.0 250.0
weight 1.0 0.8
"""
return super().drop(labels=labels, axis=axis, index=index,
columns=columns, level=level, inplace=inplace,
errors=errors)
@rewrite_axis_style_signature('mapper', [('copy', True),
('inplace', False),
('level', None),
('errors', 'ignore')])
def rename(self, *args, **kwargs):
"""
Alter axes labels.
Function / dict values must be unique (1-to-1). Labels not contained in
a dict / Series will be left as-is. Extra labels listed don't throw an
error.
See the :ref:`user guide <basics.rename>` for more.
Parameters
----------
mapper : dict-like or function
Dict-like or functions transformations to apply to
that axis' values. Use either ``mapper`` and ``axis`` to
specify the axis to target with ``mapper``, or ``index`` and
``columns``.
index : dict-like or function
Alternative to specifying axis (``mapper, axis=0``
is equivalent to ``index=mapper``).
columns : dict-like or function
Alternative to specifying axis (``mapper, axis=1``
is equivalent to ``columns=mapper``).
axis : int or str
Axis to target with ``mapper``. Can be either the axis name
('index', 'columns') or number (0, 1). The default is 'index'.
copy : bool, default True
Also copy underlying data.
inplace : bool, default False
Whether to return a new DataFrame. If True then value of copy is
ignored.
level : int or level name, default None
In case of a MultiIndex, only rename labels in the specified
level.
errors : {'ignore', 'raise'}, default 'ignore'
If 'raise', raise a `KeyError` when a dict-like `mapper`, `index`,
or `columns` contains labels that are not present in the Index
being transformed.
If 'ignore', existing keys will be renamed and extra keys will be
ignored.
Returns
-------
DataFrame
DataFrame with the renamed axis labels.
Raises
------
KeyError
If any of the labels is not found in the selected axis and
"errors='raise'".
See Also
--------
DataFrame.rename_axis : Set the name of the axis.
Examples
--------
``DataFrame.rename`` supports two calling conventions
* ``(index=index_mapper, columns=columns_mapper, ...)``
* ``(mapper, axis={'index', 'columns'}, ...)``
We *highly* recommend using keyword arguments to clarify your
intent.
Rename columns using a mapping:
>>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
>>> df.rename(columns={"A": "a", "B": "c"})
a c
0 1 4
1 2 5
2 3 6
Rename index using a mapping:
>>> df.rename(index={0: "x", 1: "y", 2: "z"})
A B
x 1 4
y 2 5
z 3 6
Cast index labels to a different type:
>>> df.index
RangeIndex(start=0, stop=3, step=1)
>>> df.rename(index=str).index
Index(['0', '1', '2'], dtype='object')
>>> df.rename(columns={"A": "a", "B": "b", "C": "c"}, errors="raise")
Traceback (most recent call last):
KeyError: ['C'] not found in axis
Using axis-style parameters
>>> df.rename(str.lower, axis='columns')
a b
0 1 4
1 2 5
2 3 6
>>> df.rename({1: 2, 2: 4}, axis='index')
A B
0 1 4
2 2 5
4 3 6
"""
axes = validate_axis_style_args(self, args, kwargs, 'mapper', 'rename')
kwargs.update(axes)
# Pop these, since the values are in `kwargs` under different names
kwargs.pop('axis', None)
kwargs.pop('mapper', None)
return super().rename(**kwargs)
@Substitution(**_shared_doc_kwargs)
@Appender(NDFrame.fillna.__doc__)
def fillna(self, value=None, method=None, axis=None, inplace=False,
limit=None, downcast=None, **kwargs):
return super().fillna(value=value, method=method, axis=axis,
inplace=inplace, limit=limit, downcast=downcast,
**kwargs)
@Appender(_shared_docs['replace'] % _shared_doc_kwargs)
def replace(self, to_replace=None, value=None, inplace=False, limit=None,
regex=False, method='pad'):
return super().replace(to_replace=to_replace, value=value,
inplace=inplace, limit=limit, regex=regex,
method=method)
@Appender(_shared_docs['shift'] % _shared_doc_kwargs)
def shift(self, periods=1, freq=None, axis=0, fill_value=None):
return super().shift(periods=periods, freq=freq, axis=axis,
fill_value=fill_value)
def set_index(self, keys, drop=True, append=False, inplace=False,
verify_integrity=False):
"""
Set the DataFrame index using existing columns.
Set the DataFrame index (row labels) using one or more existing
columns or arrays (of the correct length). The index can replace the
existing index or expand on it.
Parameters
----------
keys : label or array-like or list of labels/arrays
This parameter can be either a single column key, a single array of
the same length as the calling DataFrame, or a list containing an
arbitrary combination of column keys and arrays. Here, "array"
encompasses :class:`Series`, :class:`Index`, ``np.ndarray``, and
instances of :class:`~collections.abc.Iterator`.
drop : bool, default True
Delete columns to be used as the new index.
append : bool, default False
Whether to append columns to existing index.
inplace : bool, default False
Modify the DataFrame in place (do not create a new object).
verify_integrity : bool, default False
Check the new index for duplicates. Otherwise defer the check until
necessary. Setting to False will improve the performance of this
method.
Returns
-------
DataFrame
Changed row labels.
See Also
--------
DataFrame.reset_index : Opposite of set_index.
DataFrame.reindex : Change to new indices or expand indices.
DataFrame.reindex_like : Change to same indices as other DataFrame.
Examples
--------
>>> df = pd.DataFrame({'month': [1, 4, 7, 10],
... 'year': [2012, 2014, 2013, 2014],
... 'sale': [55, 40, 84, 31]})
>>> df
month year sale
0 1 2012 55
1 4 2014 40
2 7 2013 84
3 10 2014 31
Set the index to become the 'month' column:
>>> df.set_index('month')
year sale
month
1 2012 55
4 2014 40
7 2013 84
10 2014 31
Create a MultiIndex using columns 'year' and 'month':
>>> df.set_index(['year', 'month'])
sale
year month
2012 1 55
2014 4 40
2013 7 84
2014 10 31
Create a MultiIndex using an Index and a column:
>>> df.set_index([pd.Index([1, 2, 3, 4]), 'year'])
month sale
year
1 2012 1 55
2 2014 4 40
3 2013 7 84
4 2014 10 31
Create a MultiIndex using two Series:
>>> s = pd.Series([1, 2, 3, 4])
>>> df.set_index([s, s**2])
month year sale
1 1 1 2012 55
2 4 4 2014 40
3 9 7 2013 84
4 16 10 2014 31
"""
inplace = validate_bool_kwarg(inplace, 'inplace')
if not isinstance(keys, list):
keys = [keys]
err_msg = ('The parameter "keys" may be a column key, one-dimensional '
'array, or a list containing only valid column keys and '
'one-dimensional arrays.')
missing = []
for col in keys:
if isinstance(col, (ABCIndexClass, ABCSeries, np.ndarray,
list, abc.Iterator)):
# arrays are fine as long as they are one-dimensional
# iterators get converted to list below
if getattr(col, 'ndim', 1) != 1:
raise ValueError(err_msg)
else:
# everything else gets tried as a key; see GH 24969
try:
found = col in self.columns
except TypeError:
raise TypeError(err_msg + ' Received column of '
'type {}'.format(type(col)))
else:
if not found:
missing.append(col)
if missing:
raise KeyError('None of {} are in the columns'.format(missing))
if inplace:
frame = self
else:
frame = self.copy()
arrays = []
names = []
if append:
names = [x for x in self.index.names]
if isinstance(self.index, ABCMultiIndex):
for i in range(self.index.nlevels):
arrays.append(self.index._get_level_values(i))
else:
arrays.append(self.index)
to_remove = []
for col in keys:
if isinstance(col, ABCMultiIndex):
for n in range(col.nlevels):
arrays.append(col._get_level_values(n))
names.extend(col.names)
elif isinstance(col, (ABCIndexClass, ABCSeries)):
# if Index then not MultiIndex (treated above)
arrays.append(col)
names.append(col.name)
elif isinstance(col, (list, np.ndarray)):
arrays.append(col)
names.append(None)
elif isinstance(col, abc.Iterator):
arrays.append(list(col))
names.append(None)
# from here, col can only be a column label
else:
arrays.append(frame[col]._values)
names.append(col)
if drop:
to_remove.append(col)
if len(arrays[-1]) != len(self):
# check newest element against length of calling frame, since
# ensure_index_from_sequences would not raise for append=False.
raise ValueError('Length mismatch: Expected {len_self} rows, '
'received array of length {len_col}'.format(
len_self=len(self),
len_col=len(arrays[-1])
))
index = ensure_index_from_sequences(arrays, names)
if verify_integrity and not index.is_unique:
duplicates = index[index.duplicated()].unique()
raise ValueError('Index has duplicate keys: {dup}'.format(
dup=duplicates))
# use set to handle duplicate column names gracefully in case of drop
for c in set(to_remove):
del frame[c]
# clear up memory usage
index._cleanup()
frame.index = index
if not inplace:
return frame
def reset_index(self, level=None, drop=False, inplace=False, col_level=0,
col_fill=''):
"""
Reset the index, or a level of it.
Reset the index of the DataFrame, and use the default one instead.
If the DataFrame has a MultiIndex, this method can remove one or more
levels.
Parameters
----------
level : int, str, tuple, or list, default None
Only remove the given levels from the index. Removes all levels by
default.
drop : bool, default False
Do not try to insert index into dataframe columns. This resets
the index to the default integer index.
inplace : bool, default False
Modify the DataFrame in place (do not create a new object).
col_level : int or str, default 0
If the columns have multiple levels, determines which level the
labels are inserted into. By default it is inserted into the first
level.
col_fill : object, default ''
If the columns have multiple levels, determines how the other
levels are named. If None then the index name is repeated.
Returns
-------
DataFrame
DataFrame with the new index.
See Also
--------
DataFrame.set_index : Opposite of reset_index.
DataFrame.reindex : Change to new indices or expand indices.
DataFrame.reindex_like : Change to same indices as other DataFrame.
Examples
--------
>>> df = pd.DataFrame([('bird', 389.0),
... ('bird', 24.0),
... ('mammal', 80.5),
... ('mammal', np.nan)],
... index=['falcon', 'parrot', 'lion', 'monkey'],
... columns=('class', 'max_speed'))
>>> df
class max_speed
falcon bird 389.0
parrot bird 24.0
lion mammal 80.5
monkey mammal NaN
When we reset the index, the old index is added as a column, and a
new sequential index is used:
>>> df.reset_index()
index class max_speed
0 falcon bird 389.0
1 parrot bird 24.0
2 lion mammal 80.5
3 monkey mammal NaN
We can use the `drop` parameter to avoid the old index being added as
a column:
>>> df.reset_index(drop=True)
class max_speed
0 bird 389.0
1 bird 24.0
2 mammal 80.5
3 mammal NaN
You can also use `reset_index` with `MultiIndex`.
>>> index = pd.MultiIndex.from_tuples([('bird', 'falcon'),
... ('bird', 'parrot'),
... ('mammal', 'lion'),
... ('mammal', 'monkey')],
... names=['class', 'name'])
>>> columns = pd.MultiIndex.from_tuples([('speed', 'max'),
... ('species', 'type')])
>>> df = pd.DataFrame([(389.0, 'fly'),
... ( 24.0, 'fly'),
... ( 80.5, 'run'),
... (np.nan, 'jump')],
... index=index,
... columns=columns)
>>> df
speed species
max type
class name
bird falcon 389.0 fly
parrot 24.0 fly
mammal lion 80.5 run
monkey NaN jump
If the index has multiple levels, we can reset a subset of them:
>>> df.reset_index(level='class')
class speed species
max type
name
falcon bird 389.0 fly
parrot bird 24.0 fly
lion mammal 80.5 run
monkey mammal NaN jump
If we are not dropping the index, by default, it is placed in the top
level. We can place it in another level:
>>> df.reset_index(level='class', col_level=1)
speed species
class max type
name
falcon bird 389.0 fly
parrot bird 24.0 fly
lion mammal 80.5 run
monkey mammal NaN jump
When the index is inserted under another level, we can specify under
which one with the parameter `col_fill`:
>>> df.reset_index(level='class', col_level=1, col_fill='species')
species speed species
class max type
name
falcon bird 389.0 fly
parrot bird 24.0 fly
lion mammal 80.5 run
monkey mammal NaN jump
If we specify a nonexistent level for `col_fill`, it is created:
>>> df.reset_index(level='class', col_level=1, col_fill='genus')
genus speed species
class max type
name
falcon bird 389.0 fly
parrot bird 24.0 fly
lion mammal 80.5 run
monkey mammal NaN jump
"""
inplace = validate_bool_kwarg(inplace, 'inplace')
if inplace:
new_obj = self
else:
new_obj = self.copy()
def _maybe_casted_values(index, labels=None):
values = index._values
if not isinstance(index, (PeriodIndex, DatetimeIndex)):
if values.dtype == np.object_:
values = lib.maybe_convert_objects(values)
# if we have the labels, extract the values with a mask
if labels is not None:
mask = labels == -1
# we can have situations where the whole mask is -1,
# meaning there is nothing found in labels, so make all nan's
if mask.all():
values = np.empty(len(mask))
values.fill(np.nan)
else:
values = values.take(labels)
# TODO(https://github.com/pandas-dev/pandas/issues/24206)
# Push this into maybe_upcast_putmask?
# We can't pass EAs there right now. Looks a bit
# complicated.
# So we unbox the ndarray_values, op, re-box.
values_type = type(values)
values_dtype = values.dtype
if issubclass(values_type, DatetimeLikeArray):
values = values._data
if mask.any():
values, changed = maybe_upcast_putmask(
values, mask, np.nan)
if issubclass(values_type, DatetimeLikeArray):
values = values_type(values, dtype=values_dtype)
return values
new_index = ibase.default_index(len(new_obj))
if level is not None:
if not isinstance(level, (tuple, list)):
level = [level]
level = [self.index._get_level_number(lev) for lev in level]
if len(level) < self.index.nlevels:
new_index = self.index.droplevel(level)
if not drop:
if isinstance(self.index, MultiIndex):
names = [n if n is not None else ('level_%d' % i)
for (i, n) in enumerate(self.index.names)]
to_insert = zip(self.index.levels, self.index.codes)
else:
default = 'index' if 'index' not in self else 'level_0'
names = ([default] if self.index.name is None
else [self.index.name])
to_insert = ((self.index, None),)
multi_col = isinstance(self.columns, MultiIndex)
for i, (lev, lab) in reversed(list(enumerate(to_insert))):
if not (level is None or i in level):
continue
name = names[i]
if multi_col:
col_name = (list(name) if isinstance(name, tuple)
else [name])
if col_fill is None:
if len(col_name) not in (1, self.columns.nlevels):
raise ValueError("col_fill=None is incompatible "
"with incomplete column name "
"{}".format(name))
col_fill = col_name[0]
lev_num = self.columns._get_level_number(col_level)
name_lst = [col_fill] * lev_num + col_name
missing = self.columns.nlevels - len(name_lst)
name_lst += [col_fill] * missing
name = tuple(name_lst)
# to ndarray and maybe infer different dtype
level_values = _maybe_casted_values(lev, lab)
new_obj.insert(0, name, level_values)
new_obj.index = new_index
if not inplace:
return new_obj
# ----------------------------------------------------------------------
# Reindex-based selection methods
@Appender(_shared_docs['isna'] % _shared_doc_kwargs)
def isna(self):
return super().isna()
@Appender(_shared_docs['isna'] % _shared_doc_kwargs)
def isnull(self):
return super().isnull()
@Appender(_shared_docs['notna'] % _shared_doc_kwargs)
def notna(self):
return super().notna()
@Appender(_shared_docs['notna'] % _shared_doc_kwargs)
def notnull(self):
return super().notnull()
def dropna(self, axis=0, how='any', thresh=None, subset=None,
inplace=False):
"""
Remove missing values.
See the :ref:`User Guide <missing_data>` for more on which values are
considered missing, and how to work with missing data.
Parameters
----------
axis : {0 or 'index', 1 or 'columns'}, default 0
Determine if rows or columns which contain missing values are
removed.
* 0, or 'index' : Drop rows which contain missing values.
* 1, or 'columns' : Drop columns which contain missing value.
.. deprecated:: 0.23.0
Pass tuple or list to drop on multiple axes.
Only a single axis is allowed.
how : {'any', 'all'}, default 'any'
Determine if row or column is removed from DataFrame, when we have
at least one NA or all NA.
* 'any' : If any NA values are present, drop that row or column.
* 'all' : If all values are NA, drop that row or column.
thresh : int, optional
Require that many non-NA values.
subset : array-like, optional
Labels along other axis to consider, e.g. if you are dropping rows
these would be a list of columns to include.
inplace : bool, default False
If True, do operation inplace and return None.
Returns
-------
DataFrame
DataFrame with NA entries dropped from it.
See Also
--------
DataFrame.isna: Indicate missing values.
DataFrame.notna : Indicate existing (non-missing) values.
DataFrame.fillna : Replace missing values.
Series.dropna : Drop missing values.
Index.dropna : Drop missing indices.
Examples
--------
>>> df = pd.DataFrame({"name": ['Alfred', 'Batman', 'Catwoman'],
... "toy": [np.nan, 'Batmobile', 'Bullwhip'],
... "born": [pd.NaT, pd.Timestamp("1940-04-25"),
... pd.NaT]})
>>> df
name toy born
0 Alfred NaN NaT
1 Batman Batmobile 1940-04-25
2 Catwoman Bullwhip NaT
Drop the rows where at least one element is missing.
>>> df.dropna()
name toy born
1 Batman Batmobile 1940-04-25
Drop the columns where at least one element is missing.
>>> df.dropna(axis='columns')
name
0 Alfred
1 Batman
2 Catwoman
Drop the rows where all elements are missing.
>>> df.dropna(how='all')
name toy born
0 Alfred NaN NaT
1 Batman Batmobile 1940-04-25
2 Catwoman Bullwhip NaT
Keep only the rows with at least 2 non-NA values.
>>> df.dropna(thresh=2)
name toy born
1 Batman Batmobile 1940-04-25
2 Catwoman Bullwhip NaT
Define in which columns to look for missing values.
>>> df.dropna(subset=['name', 'born'])
name toy born
1 Batman Batmobile 1940-04-25
Keep the DataFrame with valid entries in the same variable.
>>> df.dropna(inplace=True)
>>> df
name toy born
1 Batman Batmobile 1940-04-25
"""
inplace = validate_bool_kwarg(inplace, 'inplace')
if isinstance(axis, (tuple, list)):
# GH20987
msg = ("supplying multiple axes to axis is deprecated and "
"will be removed in a future version.")
warnings.warn(msg, FutureWarning, stacklevel=2)
result = self
for ax in axis:
result = result.dropna(how=how, thresh=thresh, subset=subset,
axis=ax)
else:
axis = self._get_axis_number(axis)
agg_axis = 1 - axis
agg_obj = self
if subset is not None:
ax = self._get_axis(agg_axis)
indices = ax.get_indexer_for(subset)
check = indices == -1
if check.any():
raise KeyError(list(np.compress(check, subset)))
agg_obj = self.take(indices, axis=agg_axis)
count = agg_obj.count(axis=agg_axis)
if thresh is not None:
mask = count >= thresh
elif how == 'any':
mask = count == len(agg_obj._get_axis(agg_axis))
elif how == 'all':
mask = count > 0
else:
if how is not None:
raise ValueError('invalid how option: {h}'.format(h=how))
else:
raise TypeError('must specify how or thresh')
result = self.loc(axis=axis)[mask]
if inplace:
self._update_inplace(result)
else:
return result
def drop_duplicates(self, subset=None, keep='first', inplace=False):
"""
Return DataFrame with duplicate rows removed, optionally only
considering certain columns. Indexes, including time indexes
are ignored.
Parameters
----------
subset : column label or sequence of labels, optional
Only consider certain columns for identifying duplicates, by
default use all of the columns
keep : {'first', 'last', False}, default 'first'
- ``first`` : Drop duplicates except for the first occurrence.
- ``last`` : Drop duplicates except for the last occurrence.
- False : Drop all duplicates.
inplace : boolean, default False
Whether to drop duplicates in place or to return a copy
Returns
-------
DataFrame
"""
if self.empty:
return self.copy()
inplace = validate_bool_kwarg(inplace, 'inplace')
duplicated = self.duplicated(subset, keep=keep)
if inplace:
inds, = (-duplicated)._ndarray_values.nonzero()
new_data = self._data.take(inds)
self._update_inplace(new_data)
else:
return self[-duplicated]
def duplicated(self, subset=None, keep='first'):
"""
Return boolean Series denoting duplicate rows, optionally only
considering certain columns.
Parameters
----------
subset : column label or sequence of labels, optional
Only consider certain columns for identifying duplicates, by
default use all of the columns
keep : {'first', 'last', False}, default 'first'
- ``first`` : Mark duplicates as ``True`` except for the
first occurrence.
- ``last`` : Mark duplicates as ``True`` except for the
last occurrence.
- False : Mark all duplicates as ``True``.
Returns
-------
Series
"""
from pandas.core.sorting import get_group_index
from pandas._libs.hashtable import duplicated_int64, _SIZE_HINT_LIMIT
if self.empty:
return Series(dtype=bool)
def f(vals):
labels, shape = algorithms.factorize(
vals, size_hint=min(len(self), _SIZE_HINT_LIMIT))
return labels.astype('i8', copy=False), len(shape)
if subset is None:
subset = self.columns
elif (not np.iterable(subset) or
isinstance(subset, str) or
isinstance(subset, tuple) and subset in self.columns):
subset = subset,
# Verify all columns in subset exist in the queried dataframe
# Otherwise, raise a KeyError, same as if you try to __getitem__ with a
# key that doesn't exist.
diff = Index(subset).difference(self.columns)
if not diff.empty:
raise KeyError(diff)
vals = (col.values for name, col in self.iteritems()
if name in subset)
labels, shape = map(list, zip(*map(f, vals)))
ids = get_group_index(labels, shape, sort=False, xnull=False)
return Series(duplicated_int64(ids, keep), index=self.index)
# ----------------------------------------------------------------------
# Sorting
@Substitution(**_shared_doc_kwargs)
@Appender(NDFrame.sort_values.__doc__)
def sort_values(self, by, axis=0, ascending=True, inplace=False,
kind='quicksort', na_position='last'):
inplace = validate_bool_kwarg(inplace, 'inplace')
axis = self._get_axis_number(axis)
if not isinstance(by, list):
by = [by]
if is_sequence(ascending) and len(by) != len(ascending):
raise ValueError('Length of ascending (%d) != length of by (%d)' %
(len(ascending), len(by)))
if len(by) > 1:
from pandas.core.sorting import lexsort_indexer
keys = [self._get_label_or_level_values(x, axis=axis)
for x in by]
indexer = lexsort_indexer(keys, orders=ascending,
na_position=na_position)
indexer = ensure_platform_int(indexer)
else:
from pandas.core.sorting import nargsort
by = by[0]
k = self._get_label_or_level_values(by, axis=axis)
if isinstance(ascending, (tuple, list)):
ascending = ascending[0]
indexer = nargsort(k, kind=kind, ascending=ascending,
na_position=na_position)
new_data = self._data.take(indexer,
axis=self._get_block_manager_axis(axis),
verify=False)
if inplace:
return self._update_inplace(new_data)
else:
return self._constructor(new_data).__finalize__(self)
@Substitution(**_shared_doc_kwargs)
@Appender(NDFrame.sort_index.__doc__)
def sort_index(self, axis=0, level=None, ascending=True, inplace=False,
kind='quicksort', na_position='last', sort_remaining=True,
by=None):
# TODO: this can be combined with Series.sort_index impl as
# almost identical
inplace = validate_bool_kwarg(inplace, 'inplace')
# 10726
if by is not None:
warnings.warn("by argument to sort_index is deprecated, "
"please use .sort_values(by=...)",
FutureWarning, stacklevel=2)
if level is not None:
raise ValueError("unable to simultaneously sort by and level")
return self.sort_values(by, axis=axis, ascending=ascending,
inplace=inplace)
axis = self._get_axis_number(axis)
labels = self._get_axis(axis)
# make sure that the axis is lexsorted to start
# if not we need to reconstruct to get the correct indexer
labels = labels._sort_levels_monotonic()
if level is not None:
new_axis, indexer = labels.sortlevel(level, ascending=ascending,
sort_remaining=sort_remaining)
elif isinstance(labels, MultiIndex):
from pandas.core.sorting import lexsort_indexer
indexer = lexsort_indexer(labels._get_codes_for_sorting(),
orders=ascending,
na_position=na_position)
else:
from pandas.core.sorting import nargsort
# Check monotonic-ness before sort an index
# GH11080
if ((ascending and labels.is_monotonic_increasing) or
(not ascending and labels.is_monotonic_decreasing)):
if inplace:
return
else:
return self.copy()
indexer = nargsort(labels, kind=kind, ascending=ascending,
na_position=na_position)
baxis = self._get_block_manager_axis(axis)
new_data = self._data.take(indexer,
axis=baxis,
verify=False)
# reconstruct axis if needed
new_data.axes[baxis] = new_data.axes[baxis]._sort_levels_monotonic()
if inplace:
return self._update_inplace(new_data)
else:
return self._constructor(new_data).__finalize__(self)
def nlargest(self, n, columns, keep='first'):
"""
Return the first `n` rows ordered by `columns` in descending order.
Return the first `n` rows with the largest values in `columns`, in
descending order. The columns that are not specified are returned as
well, but not used for ordering.
This method is equivalent to
``df.sort_values(columns, ascending=False).head(n)``, but more
performant.
Parameters
----------
n : int
Number of rows to return.
columns : label or list of labels
Column label(s) to order by.
keep : {'first', 'last', 'all'}, default 'first'
Where there are duplicate values:
- `first` : prioritize the first occurrence(s)
- `last` : prioritize the last occurrence(s)
- ``all`` : do not drop any duplicates, even it means
selecting more than `n` items.
.. versionadded:: 0.24.0
Returns
-------
DataFrame
The first `n` rows ordered by the given columns in descending
order.
See Also
--------
DataFrame.nsmallest : Return the first `n` rows ordered by `columns` in
ascending order.
DataFrame.sort_values : Sort DataFrame by the values.
DataFrame.head : Return the first `n` rows without re-ordering.
Notes
-----
This function cannot be used with all column types. For example, when
specifying columns with `object` or `category` dtypes, ``TypeError`` is
raised.
Examples
--------
>>> df = pd.DataFrame({'population': [59000000, 65000000, 434000,
... 434000, 434000, 337000, 11300,
... 11300, 11300],
... 'GDP': [1937894, 2583560 , 12011, 4520, 12128,
... 17036, 182, 38, 311],
... 'alpha-2': ["IT", "FR", "MT", "MV", "BN",
... "IS", "NR", "TV", "AI"]},
... index=["Italy", "France", "Malta",
... "Maldives", "Brunei", "Iceland",
... "Nauru", "Tuvalu", "Anguilla"])
>>> df
population GDP alpha-2
Italy 59000000 1937894 IT
France 65000000 2583560 FR
Malta 434000 12011 MT
Maldives 434000 4520 MV
Brunei 434000 12128 BN
Iceland 337000 17036 IS
Nauru 11300 182 NR
Tuvalu 11300 38 TV
Anguilla 11300 311 AI
In the following example, we will use ``nlargest`` to select the three
rows having the largest values in column "population".
>>> df.nlargest(3, 'population')
population GDP alpha-2
France 65000000 2583560 FR
Italy 59000000 1937894 IT
Malta 434000 12011 MT
When using ``keep='last'``, ties are resolved in reverse order:
>>> df.nlargest(3, 'population', keep='last')
population GDP alpha-2
France 65000000 2583560 FR
Italy 59000000 1937894 IT
Brunei 434000 12128 BN
When using ``keep='all'``, all duplicate items are maintained:
>>> df.nlargest(3, 'population', keep='all')
population GDP alpha-2
France 65000000 2583560 FR
Italy 59000000 1937894 IT
Malta 434000 12011 MT
Maldives 434000 4520 MV
Brunei 434000 12128 BN
To order by the largest values in column "population" and then "GDP",
we can specify multiple columns like in the next example.
>>> df.nlargest(3, ['population', 'GDP'])
population GDP alpha-2
France 65000000 2583560 FR
Italy 59000000 1937894 IT
Brunei 434000 12128 BN
"""
return algorithms.SelectNFrame(self,
n=n,
keep=keep,
columns=columns).nlargest()
def nsmallest(self, n, columns, keep='first'):
"""
Return the first `n` rows ordered by `columns` in ascending order.
Return the first `n` rows with the smallest values in `columns`, in
ascending order. The columns that are not specified are returned as
well, but not used for ordering.
This method is equivalent to
``df.sort_values(columns, ascending=True).head(n)``, but more
performant.
Parameters
----------
n : int
Number of items to retrieve.
columns : list or str
Column name or names to order by.
keep : {'first', 'last', 'all'}, default 'first'
Where there are duplicate values:
- ``first`` : take the first occurrence.
- ``last`` : take the last occurrence.
- ``all`` : do not drop any duplicates, even it means
selecting more than `n` items.
.. versionadded:: 0.24.0
Returns
-------
DataFrame
See Also
--------
DataFrame.nlargest : Return the first `n` rows ordered by `columns` in
descending order.
DataFrame.sort_values : Sort DataFrame by the values.
DataFrame.head : Return the first `n` rows without re-ordering.
Examples
--------
>>> df = pd.DataFrame({'population': [59000000, 65000000, 434000,
... 434000, 434000, 337000, 11300,
... 11300, 11300],
... 'GDP': [1937894, 2583560 , 12011, 4520, 12128,
... 17036, 182, 38, 311],
... 'alpha-2': ["IT", "FR", "MT", "MV", "BN",
... "IS", "NR", "TV", "AI"]},
... index=["Italy", "France", "Malta",
... "Maldives", "Brunei", "Iceland",
... "Nauru", "Tuvalu", "Anguilla"])
>>> df
population GDP alpha-2
Italy 59000000 1937894 IT
France 65000000 2583560 FR
Malta 434000 12011 MT
Maldives 434000 4520 MV
Brunei 434000 12128 BN
Iceland 337000 17036 IS
Nauru 11300 182 NR
Tuvalu 11300 38 TV
Anguilla 11300 311 AI
In the following example, we will use ``nsmallest`` to select the
three rows having the smallest values in column "a".
>>> df.nsmallest(3, 'population')
population GDP alpha-2
Nauru 11300 182 NR
Tuvalu 11300 38 TV
Anguilla 11300 311 AI
When using ``keep='last'``, ties are resolved in reverse order:
>>> df.nsmallest(3, 'population', keep='last')
population GDP alpha-2
Anguilla 11300 311 AI
Tuvalu 11300 38 TV
Nauru 11300 182 NR
When using ``keep='all'``, all duplicate items are maintained:
>>> df.nsmallest(3, 'population', keep='all')
population GDP alpha-2
Nauru 11300 182 NR
Tuvalu 11300 38 TV
Anguilla 11300 311 AI
To order by the largest values in column "a" and then "c", we can
specify multiple columns like in the next example.
>>> df.nsmallest(3, ['population', 'GDP'])
population GDP alpha-2
Tuvalu 11300 38 TV
Nauru 11300 182 NR
Anguilla 11300 311 AI
"""
return algorithms.SelectNFrame(self,
n=n,
keep=keep,
columns=columns).nsmallest()
def swaplevel(self, i=-2, j=-1, axis=0):
"""
Swap levels i and j in a MultiIndex on a particular axis.
Parameters
----------
i, j : int, string (can be mixed)
Level of index to be swapped. Can pass level name as string.
Returns
-------
DataFrame
.. versionchanged:: 0.18.1
The indexes ``i`` and ``j`` are now optional, and default to
the two innermost levels of the index.
"""
result = self.copy()
axis = self._get_axis_number(axis)
if axis == 0:
result.index = result.index.swaplevel(i, j)
else:
result.columns = result.columns.swaplevel(i, j)
return result
def reorder_levels(self, order, axis=0):
"""
Rearrange index levels using input order. May not drop or
duplicate levels.
Parameters
----------
order : list of int or list of str
List representing new level order. Reference level by number
(position) or by key (label).
axis : int
Where to reorder levels.
Returns
-------
type of caller (new object)
"""
axis = self._get_axis_number(axis)
if not isinstance(self._get_axis(axis),
MultiIndex): # pragma: no cover
raise TypeError('Can only reorder levels on a hierarchical axis.')
result = self.copy()
if axis == 0:
result.index = result.index.reorder_levels(order)
else:
result.columns = result.columns.reorder_levels(order)
return result
# ----------------------------------------------------------------------
# Arithmetic / combination related
def _combine_frame(self, other, func, fill_value=None, level=None):
this, other = self.align(other, join='outer', level=level, copy=False)
new_index, new_columns = this.index, this.columns
def _arith_op(left, right):
# for the mixed_type case where we iterate over columns,
# _arith_op(left, right) is equivalent to
# left._binop(right, func, fill_value=fill_value)
left, right = ops.fill_binop(left, right, fill_value)
return func(left, right)
if ops.should_series_dispatch(this, other, func):
# iterate over columns
return ops.dispatch_to_series(this, other, _arith_op)
else:
result = _arith_op(this.values, other.values)
return self._constructor(result,
index=new_index, columns=new_columns,
copy=False)
def _combine_match_index(self, other, func, level=None):
left, right = self.align(other, join='outer', axis=0, level=level,
copy=False)
assert left.index.equals(right.index)
if left._is_mixed_type or right._is_mixed_type:
# operate column-wise; avoid costly object-casting in `.values`
return ops.dispatch_to_series(left, right, func)
else:
# fastpath --> operate directly on values
with np.errstate(all="ignore"):
new_data = func(left.values.T, right.values).T
return self._constructor(new_data,
index=left.index, columns=self.columns,
copy=False)
def _combine_match_columns(self, other, func, level=None):
assert isinstance(other, Series)
left, right = self.align(other, join='outer', axis=1, level=level,
copy=False)
assert left.columns.equals(right.index)
return ops.dispatch_to_series(left, right, func, axis="columns")
def _combine_const(self, other, func):
assert lib.is_scalar(other) or np.ndim(other) == 0
return ops.dispatch_to_series(self, other, func)
def combine(self, other, func, fill_value=None, overwrite=True):
"""
Perform column-wise combine with another DataFrame.
Combines a DataFrame with `other` DataFrame using `func`
to element-wise combine columns. The row and column indexes of the
resulting DataFrame will be the union of the two.
Parameters
----------
other : DataFrame
The DataFrame to merge column-wise.
func : function
Function that takes two series as inputs and return a Series or a
scalar. Used to merge the two dataframes column by columns.
fill_value : scalar value, default None
The value to fill NaNs with prior to passing any column to the
merge func.
overwrite : bool, default True
If True, columns in `self` that do not exist in `other` will be
overwritten with NaNs.
Returns
-------
DataFrame
Combination of the provided DataFrames.
See Also
--------
DataFrame.combine_first : Combine two DataFrame objects and default to
non-null values in frame calling the method.
Examples
--------
Combine using a simple function that chooses the smaller column.
>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> take_smaller = lambda s1, s2: s1 if s1.sum() < s2.sum() else s2
>>> df1.combine(df2, take_smaller)
A B
0 0 3
1 0 3
Example using a true element-wise combine function.
>>> df1 = pd.DataFrame({'A': [5, 0], 'B': [2, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> df1.combine(df2, np.minimum)
A B
0 1 2
1 0 3
Using `fill_value` fills Nones prior to passing the column to the
merge function.
>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> df1.combine(df2, take_smaller, fill_value=-5)
A B
0 0 -5.0
1 0 4.0
However, if the same element in both dataframes is None, that None
is preserved
>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [None, 3]})
>>> df1.combine(df2, take_smaller, fill_value=-5)
A B
0 0 -5.0
1 0 3.0
Example that demonstrates the use of `overwrite` and behavior when
the axis differ between the dataframes.
>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})
>>> df2 = pd.DataFrame({'B': [3, 3], 'C': [-10, 1], }, index=[1, 2])
>>> df1.combine(df2, take_smaller)
A B C
0 NaN NaN NaN
1 NaN 3.0 -10.0
2 NaN 3.0 1.0
>>> df1.combine(df2, take_smaller, overwrite=False)
A B C
0 0.0 NaN NaN
1 0.0 3.0 -10.0
2 NaN 3.0 1.0
Demonstrating the preference of the passed in dataframe.
>>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1], }, index=[1, 2])
>>> df2.combine(df1, take_smaller)
A B C
0 0.0 NaN NaN
1 0.0 3.0 NaN
2 NaN 3.0 NaN
>>> df2.combine(df1, take_smaller, overwrite=False)
A B C
0 0.0 NaN NaN
1 0.0 3.0 1.0
2 NaN 3.0 1.0
"""
other_idxlen = len(other.index) # save for compare
this, other = self.align(other, copy=False)
new_index = this.index
if other.empty and len(new_index) == len(self.index):
return self.copy()
if self.empty and len(other) == other_idxlen:
return other.copy()
# sorts if possible
new_columns = this.columns.union(other.columns)
do_fill = fill_value is not None
result = {}
for col in new_columns:
series = this[col]
otherSeries = other[col]
this_dtype = series.dtype
other_dtype = otherSeries.dtype
this_mask = isna(series)
other_mask = isna(otherSeries)
# don't overwrite columns unnecessarily
# DO propagate if this column is not in the intersection
if not overwrite and other_mask.all():
result[col] = this[col].copy()
continue
if do_fill:
series = series.copy()
otherSeries = otherSeries.copy()
series[this_mask] = fill_value
otherSeries[other_mask] = fill_value
if col not in self.columns:
# If self DataFrame does not have col in other DataFrame,
# try to promote series, which is all NaN, as other_dtype.
new_dtype = other_dtype
try:
series = series.astype(new_dtype, copy=False)
except ValueError:
# e.g. new_dtype is integer types
pass
else:
# if we have different dtypes, possibly promote
new_dtype = find_common_type([this_dtype, other_dtype])
if not is_dtype_equal(this_dtype, new_dtype):
series = series.astype(new_dtype)
if not is_dtype_equal(other_dtype, new_dtype):
otherSeries = otherSeries.astype(new_dtype)
arr = func(series, otherSeries)
arr = maybe_downcast_to_dtype(arr, this_dtype)
result[col] = arr
# convert_objects just in case
return self._constructor(result, index=new_index,
columns=new_columns)
def combine_first(self, other):
"""
Update null elements with value in the same location in `other`.
Combine two DataFrame objects by filling null values in one DataFrame
with non-null values from other DataFrame. The row and column indexes
of the resulting DataFrame will be the union of the two.
Parameters
----------
other : DataFrame
Provided DataFrame to use to fill null values.
Returns
-------
DataFrame
See Also
--------
DataFrame.combine : Perform series-wise operation on two DataFrames
using a given function.
Examples
--------
>>> df1 = pd.DataFrame({'A': [None, 0], 'B': [None, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> df1.combine_first(df2)
A B
0 1.0 3.0
1 0.0 4.0
Null values still persist if the location of that null value
does not exist in `other`
>>> df1 = pd.DataFrame({'A': [None, 0], 'B': [4, None]})
>>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1]}, index=[1, 2])
>>> df1.combine_first(df2)
A B C
0 NaN 4.0 NaN
1 0.0 3.0 1.0
2 NaN 3.0 1.0
"""
import pandas.core.computation.expressions as expressions
def extract_values(arr):
# Does two things:
# 1. maybe gets the values from the Series / Index
# 2. convert datelike to i8
if isinstance(arr, (ABCIndexClass, ABCSeries)):
arr = arr._values
if needs_i8_conversion(arr):
if is_extension_array_dtype(arr.dtype):
arr = arr.asi8
else:
arr = arr.view('i8')
return arr
def combiner(x, y):
mask = isna(x)
if isinstance(mask, (ABCIndexClass, ABCSeries)):
mask = mask._values
x_values = extract_values(x)
y_values = extract_values(y)
# If the column y in other DataFrame is not in first DataFrame,
# just return y_values.
if y.name not in self.columns:
return y_values
return expressions.where(mask, y_values, x_values)
return self.combine(other, combiner, overwrite=False)
@deprecate_kwarg(old_arg_name='raise_conflict', new_arg_name='errors',
mapping={False: 'ignore', True: 'raise'})
def update(self, other, join='left', overwrite=True, filter_func=None,
errors='ignore'):
"""
Modify in place using non-NA values from another DataFrame.
Aligns on indices. There is no return value.
Parameters
----------
other : DataFrame, or object coercible into a DataFrame
Should have at least one matching index/column label
with the original DataFrame. If a Series is passed,
its name attribute must be set, and that will be
used as the column name to align with the original DataFrame.
join : {'left'}, default 'left'
Only left join is implemented, keeping the index and columns of the
original object.
overwrite : bool, default True
How to handle non-NA values for overlapping keys:
* True: overwrite original DataFrame's values
with values from `other`.
* False: only update values that are NA in
the original DataFrame.
filter_func : callable(1d-array) -> bool 1d-array, optional
Can choose to replace values other than NA. Return True for values
that should be updated.
errors : {'raise', 'ignore'}, default 'ignore'
If 'raise', will raise a ValueError if the DataFrame and `other`
both contain non-NA data in the same place.
.. versionchanged :: 0.24.0
Changed from `raise_conflict=False|True`
to `errors='ignore'|'raise'`.
Returns
-------
None : method directly changes calling object
Raises
------
ValueError
* When `errors='raise'` and there's overlapping non-NA data.
* When `errors` is not either `'ignore'` or `'raise'`
NotImplementedError
* If `join != 'left'`
See Also
--------
dict.update : Similar method for dictionaries.
DataFrame.merge : For column(s)-on-columns(s) operations.
Examples
--------
>>> df = pd.DataFrame({'A': [1, 2, 3],
... 'B': [400, 500, 600]})
>>> new_df = pd.DataFrame({'B': [4, 5, 6],
... 'C': [7, 8, 9]})
>>> df.update(new_df)
>>> df
A B
0 1 4
1 2 5
2 3 6
The DataFrame's length does not increase as a result of the update,
only values at matching index/column labels are updated.
>>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
... 'B': ['x', 'y', 'z']})
>>> new_df = pd.DataFrame({'B': ['d', 'e', 'f', 'g', 'h', 'i']})
>>> df.update(new_df)
>>> df
A B
0 a d
1 b e
2 c f
For Series, it's name attribute must be set.
>>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
... 'B': ['x', 'y', 'z']})
>>> new_column = pd.Series(['d', 'e'], name='B', index=[0, 2])
>>> df.update(new_column)
>>> df
A B
0 a d
1 b y
2 c e
>>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
... 'B': ['x', 'y', 'z']})
>>> new_df = pd.DataFrame({'B': ['d', 'e']}, index=[1, 2])
>>> df.update(new_df)
>>> df
A B
0 a x
1 b d
2 c e
If `other` contains NaNs the corresponding values are not updated
in the original dataframe.
>>> df = pd.DataFrame({'A': [1, 2, 3],
... 'B': [400, 500, 600]})
>>> new_df = pd.DataFrame({'B': [4, np.nan, 6]})
>>> df.update(new_df)
>>> df
A B
0 1 4.0
1 2 500.0
2 3 6.0
"""
import pandas.core.computation.expressions as expressions
# TODO: Support other joins
if join != 'left': # pragma: no cover
raise NotImplementedError("Only left join is supported")
if errors not in ['ignore', 'raise']:
raise ValueError("The parameter errors must be either "
"'ignore' or 'raise'")
if not isinstance(other, DataFrame):
other = DataFrame(other)
other = other.reindex_like(self)
for col in self.columns:
this = self[col]._values
that = other[col]._values
if filter_func is not None:
with np.errstate(all='ignore'):
mask = ~filter_func(this) | isna(that)
else:
if errors == 'raise':
mask_this = notna(that)
mask_that = notna(this)
if any(mask_this & mask_that):
raise ValueError("Data overlaps.")
if overwrite:
mask = isna(that)
else:
mask = notna(this)
# don't overwrite columns unnecessarily
if mask.all():
continue
self[col] = expressions.where(mask, this, that)
# ----------------------------------------------------------------------
# Data reshaping
_shared_docs['pivot'] = """
Return reshaped DataFrame organized by given index / column values.
Reshape data (produce a "pivot" table) based on column values. Uses
unique values from specified `index` / `columns` to form axes of the
resulting DataFrame. This function does not support data
aggregation, multiple values will result in a MultiIndex in the
columns. See the :ref:`User Guide <reshaping>` for more on reshaping.
Parameters
----------%s
index : string or object, optional
Column to use to make new frame's index. If None, uses
existing index.
columns : string or object
Column to use to make new frame's columns.
values : string, object or a list of the previous, optional
Column(s) to use for populating new frame's values. If not
specified, all remaining columns will be used and the result will
have hierarchically indexed columns.
.. versionchanged :: 0.23.0
Also accept list of column names.
Returns
-------
DataFrame
Returns reshaped DataFrame.
Raises
------
ValueError:
When there are any `index`, `columns` combinations with multiple
values. `DataFrame.pivot_table` when you need to aggregate.
See Also
--------
DataFrame.pivot_table : Generalization of pivot that can handle
duplicate values for one index/column pair.
DataFrame.unstack : Pivot based on the index values instead of a
column.
Notes
-----
For finer-tuned control, see hierarchical indexing documentation along
with the related stack/unstack methods.
Examples
--------
>>> df = pd.DataFrame({'foo': ['one', 'one', 'one', 'two', 'two',
... 'two'],
... 'bar': ['A', 'B', 'C', 'A', 'B', 'C'],
... 'baz': [1, 2, 3, 4, 5, 6],
... 'zoo': ['x', 'y', 'z', 'q', 'w', 't']})
>>> df
foo bar baz zoo
0 one A 1 x
1 one B 2 y
2 one C 3 z
3 two A 4 q
4 two B 5 w
5 two C 6 t
>>> df.pivot(index='foo', columns='bar', values='baz')
bar A B C
foo
one 1 2 3
two 4 5 6
>>> df.pivot(index='foo', columns='bar')['baz']
bar A B C
foo
one 1 2 3
two 4 5 6
>>> df.pivot(index='foo', columns='bar', values=['baz', 'zoo'])
baz zoo
bar A B C A B C
foo
one 1 2 3 x y z
two 4 5 6 q w t
A ValueError is raised if there are any duplicates.
>>> df = pd.DataFrame({"foo": ['one', 'one', 'two', 'two'],
... "bar": ['A', 'A', 'B', 'C'],
... "baz": [1, 2, 3, 4]})
>>> df
foo bar baz
0 one A 1
1 one A 2
2 two B 3
3 two C 4
Notice that the first two rows are the same for our `index`
and `columns` arguments.
>>> df.pivot(index='foo', columns='bar', values='baz')
Traceback (most recent call last):
...
ValueError: Index contains duplicate entries, cannot reshape
"""
@Substitution('')
@Appender(_shared_docs['pivot'])
def pivot(self, index=None, columns=None, values=None):
from pandas.core.reshape.pivot import pivot
return pivot(self, index=index, columns=columns, values=values)
_shared_docs['pivot_table'] = """
Create a spreadsheet-style pivot table as a DataFrame. The levels in
the pivot table will be stored in MultiIndex objects (hierarchical
indexes) on the index and columns of the result DataFrame.
Parameters
----------%s
values : column to aggregate, optional
index : column, Grouper, array, or list of the previous
If an array is passed, it must be the same length as the data. The
list can contain any of the other types (except list).
Keys to group by on the pivot table index. If an array is passed,
it is being used as the same manner as column values.
columns : column, Grouper, array, or list of the previous
If an array is passed, it must be the same length as the data. The
list can contain any of the other types (except list).
Keys to group by on the pivot table column. If an array is passed,
it is being used as the same manner as column values.
aggfunc : function, list of functions, dict, default numpy.mean
If list of functions passed, the resulting pivot table will have
hierarchical columns whose top level are the function names
(inferred from the function objects themselves)
If dict is passed, the key is column to aggregate and value
is function or list of functions
fill_value : scalar, default None
Value to replace missing values with
margins : boolean, default False
Add all row / columns (e.g. for subtotal / grand totals)
dropna : boolean, default True
Do not include columns whose entries are all NaN
margins_name : string, default 'All'
Name of the row / column that will contain the totals
when margins is True.
observed : boolean, default False
This only applies if any of the groupers are Categoricals.
If True: only show observed values for categorical groupers.
If False: show all values for categorical groupers.
.. versionchanged :: 0.25.0
Returns
-------
DataFrame
See Also
--------
DataFrame.pivot : Pivot without aggregation that can handle
non-numeric data.
Examples
--------
>>> df = pd.DataFrame({"A": ["foo", "foo", "foo", "foo", "foo",
... "bar", "bar", "bar", "bar"],
... "B": ["one", "one", "one", "two", "two",
... "one", "one", "two", "two"],
... "C": ["small", "large", "large", "small",
... "small", "large", "small", "small",
... "large"],
... "D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
... "E": [2, 4, 5, 5, 6, 6, 8, 9, 9]})
>>> df
A B C D E
0 foo one small 1 2
1 foo one large 2 4
2 foo one large 2 5
3 foo two small 3 5
4 foo two small 3 6
5 bar one large 4 6
6 bar one small 5 8
7 bar two small 6 9
8 bar two large 7 9
This first example aggregates values by taking the sum.
>>> table = pd.pivot_table(df, values='D', index=['A', 'B'],
... columns=['C'], aggfunc=np.sum)
>>> table
C large small
A B
bar one 4.0 5.0
two 7.0 6.0
foo one 4.0 1.0
two NaN 6.0
We can also fill missing values using the `fill_value` parameter.
>>> table = pd.pivot_table(df, values='D', index=['A', 'B'],
... columns=['C'], aggfunc=np.sum, fill_value=0)
>>> table
C large small
A B
bar one 4 5
two 7 6
foo one 4 1
two 0 6
The next example aggregates by taking the mean across multiple columns.
>>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],
... aggfunc={'D': np.mean,
... 'E': np.mean})
>>> table
D E
A C
bar large 5.500000 7.500000
small 5.500000 8.500000
foo large 2.000000 4.500000
small 2.333333 4.333333
We can also calculate multiple types of aggregations for any given
value column.
>>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],
... aggfunc={'D': np.mean,
... 'E': [min, max, np.mean]})
>>> table
D E
mean max mean min
A C
bar large 5.500000 9.0 7.500000 6.0
small 5.500000 9.0 8.500000 8.0
foo large 2.000000 5.0 4.500000 4.0
small 2.333333 6.0 4.333333 2.0
"""
@Substitution('')
@Appender(_shared_docs['pivot_table'])
def pivot_table(self, values=None, index=None, columns=None,
aggfunc='mean', fill_value=None, margins=False,
dropna=True, margins_name='All', observed=False):
from pandas.core.reshape.pivot import pivot_table
return pivot_table(self, values=values, index=index, columns=columns,
aggfunc=aggfunc, fill_value=fill_value,
margins=margins, dropna=dropna,
margins_name=margins_name, observed=observed)
def stack(self, level=-1, dropna=True):
"""
Stack the prescribed level(s) from columns to index.
Return a reshaped DataFrame or Series having a multi-level
index with one or more new inner-most levels compared to the current
DataFrame. The new inner-most levels are created by pivoting the
columns of the current dataframe:
- if the columns have a single level, the output is a Series;
- if the columns have multiple levels, the new index
level(s) is (are) taken from the prescribed level(s) and
the output is a DataFrame.
The new index levels are sorted.
Parameters
----------
level : int, str, list, default -1
Level(s) to stack from the column axis onto the index
axis, defined as one index or label, or a list of indices
or labels.
dropna : bool, default True
Whether to drop rows in the resulting Frame/Series with
missing values. Stacking a column level onto the index
axis can create combinations of index and column values
that are missing from the original dataframe. See Examples
section.
Returns
-------
DataFrame or Series
Stacked dataframe or series.
See Also
--------
DataFrame.unstack : Unstack prescribed level(s) from index axis
onto column axis.
DataFrame.pivot : Reshape dataframe from long format to wide
format.
DataFrame.pivot_table : Create a spreadsheet-style pivot table
as a DataFrame.
Notes
-----
The function is named by analogy with a collection of books
being reorganized from being side by side on a horizontal
position (the columns of the dataframe) to being stacked
vertically on top of each other (in the index of the
dataframe).
Examples
--------
**Single level columns**
>>> df_single_level_cols = pd.DataFrame([[0, 1], [2, 3]],
... index=['cat', 'dog'],
... columns=['weight', 'height'])
Stacking a dataframe with a single level column axis returns a Series:
>>> df_single_level_cols
weight height
cat 0 1
dog 2 3
>>> df_single_level_cols.stack()
cat weight 0
height 1
dog weight 2
height 3
dtype: int64
**Multi level columns: simple case**
>>> multicol1 = pd.MultiIndex.from_tuples([('weight', 'kg'),
... ('weight', 'pounds')])
>>> df_multi_level_cols1 = pd.DataFrame([[1, 2], [2, 4]],
... index=['cat', 'dog'],
... columns=multicol1)
Stacking a dataframe with a multi-level column axis:
>>> df_multi_level_cols1
weight
kg pounds
cat 1 2
dog 2 4
>>> df_multi_level_cols1.stack()
weight
cat kg 1
pounds 2
dog kg 2
pounds 4
**Missing values**
>>> multicol2 = pd.MultiIndex.from_tuples([('weight', 'kg'),
... ('height', 'm')])
>>> df_multi_level_cols2 = pd.DataFrame([[1.0, 2.0], [3.0, 4.0]],
... index=['cat', 'dog'],
... columns=multicol2)
It is common to have missing values when stacking a dataframe
with multi-level columns, as the stacked dataframe typically
has more values than the original dataframe. Missing values
are filled with NaNs:
>>> df_multi_level_cols2
weight height
kg m
cat 1.0 2.0
dog 3.0 4.0
>>> df_multi_level_cols2.stack()
height weight
cat kg NaN 1.0
m 2.0 NaN
dog kg NaN 3.0
m 4.0 NaN
**Prescribing the level(s) to be stacked**
The first parameter controls which level or levels are stacked:
>>> df_multi_level_cols2.stack(0)
kg m
cat height NaN 2.0
weight 1.0 NaN
dog height NaN 4.0
weight 3.0 NaN
>>> df_multi_level_cols2.stack([0, 1])
cat height m 2.0
weight kg 1.0
dog height m 4.0
weight kg 3.0
dtype: float64
**Dropping missing values**
>>> df_multi_level_cols3 = pd.DataFrame([[None, 1.0], [2.0, 3.0]],
... index=['cat', 'dog'],
... columns=multicol2)
Note that rows where all values are missing are dropped by
default but this behaviour can be controlled via the dropna
keyword parameter:
>>> df_multi_level_cols3
weight height
kg m
cat NaN 1.0
dog 2.0 3.0
>>> df_multi_level_cols3.stack(dropna=False)
height weight
cat kg NaN NaN
m 1.0 NaN
dog kg NaN 2.0
m 3.0 NaN
>>> df_multi_level_cols3.stack(dropna=True)
height weight
cat m 1.0 NaN
dog kg NaN 2.0
m 3.0 NaN
"""
from pandas.core.reshape.reshape import stack, stack_multiple
if isinstance(level, (tuple, list)):
return stack_multiple(self, level, dropna=dropna)
else:
return stack(self, level, dropna=dropna)
def unstack(self, level=-1, fill_value=None):
"""
Pivot a level of the (necessarily hierarchical) index labels, returning
a DataFrame having a new level of column labels whose inner-most level
consists of the pivoted index labels.
If the index is not a MultiIndex, the output will be a Series
(the analogue of stack when the columns are not a MultiIndex).
The level involved will automatically get sorted.
Parameters
----------
level : int, string, or list of these, default -1 (last level)
Level(s) of index to unstack, can pass level name
fill_value : replace NaN with this value if the unstack produces
missing values
.. versionadded:: 0.18.0
Returns
-------
Series or DataFrame
See Also
--------
DataFrame.pivot : Pivot a table based on column values.
DataFrame.stack : Pivot a level of the column labels (inverse operation
from `unstack`).
Examples
--------
>>> index = pd.MultiIndex.from_tuples([('one', 'a'), ('one', 'b'),
... ('two', 'a'), ('two', 'b')])
>>> s = pd.Series(np.arange(1.0, 5.0), index=index)
>>> s
one a 1.0
b 2.0
two a 3.0
b 4.0
dtype: float64
>>> s.unstack(level=-1)
a b
one 1.0 2.0
two 3.0 4.0
>>> s.unstack(level=0)
one two
a 1.0 3.0
b 2.0 4.0
>>> df = s.unstack(level=0)
>>> df.unstack()
one a 1.0
b 2.0
two a 3.0
b 4.0
dtype: float64
"""
from pandas.core.reshape.reshape import unstack
return unstack(self, level, fill_value)
_shared_docs['melt'] = ("""
Unpivot a DataFrame from wide format to long format, optionally
leaving identifier variables set.
This function is useful to massage a DataFrame into a format where one
or more columns are identifier variables (`id_vars`), while all other
columns, considered measured variables (`value_vars`), are "unpivoted" to
the row axis, leaving just two non-identifier columns, 'variable' and
'value'.
%(versionadded)s
Parameters
----------
frame : DataFrame
id_vars : tuple, list, or ndarray, optional
Column(s) to use as identifier variables.
value_vars : tuple, list, or ndarray, optional
Column(s) to unpivot. If not specified, uses all columns that
are not set as `id_vars`.
var_name : scalar
Name to use for the 'variable' column. If None it uses
``frame.columns.name`` or 'variable'.
value_name : scalar, default 'value'
Name to use for the 'value' column.
col_level : int or string, optional
If columns are a MultiIndex then use this level to melt.
Returns
-------
DataFrame
Unpivoted DataFrame.
See Also
--------
%(other)s
pivot_table
DataFrame.pivot
Examples
--------
>>> df = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'},
... 'B': {0: 1, 1: 3, 2: 5},
... 'C': {0: 2, 1: 4, 2: 6}})
>>> df
A B C
0 a 1 2
1 b 3 4
2 c 5 6
>>> %(caller)sid_vars=['A'], value_vars=['B'])
A variable value
0 a B 1
1 b B 3
2 c B 5
>>> %(caller)sid_vars=['A'], value_vars=['B', 'C'])
A variable value
0 a B 1
1 b B 3
2 c B 5
3 a C 2
4 b C 4
5 c C 6
The names of 'variable' and 'value' columns can be customized:
>>> %(caller)sid_vars=['A'], value_vars=['B'],
... var_name='myVarname', value_name='myValname')
A myVarname myValname
0 a B 1
1 b B 3
2 c B 5
If you have multi-index columns:
>>> df.columns = [list('ABC'), list('DEF')]
>>> df
A B C
D E F
0 a 1 2
1 b 3 4
2 c 5 6
>>> %(caller)scol_level=0, id_vars=['A'], value_vars=['B'])
A variable value
0 a B 1
1 b B 3
2 c B 5
>>> %(caller)sid_vars=[('A', 'D')], value_vars=[('B', 'E')])
(A, D) variable_0 variable_1 value
0 a B E 1
1 b B E 3
2 c B E 5
""")
@Appender(_shared_docs['melt'] %
dict(caller='df.melt(',
versionadded='.. versionadded:: 0.20.0\n',
other='melt'))
def melt(self, id_vars=None, value_vars=None, var_name=None,
value_name='value', col_level=None):
from pandas.core.reshape.melt import melt
return melt(self, id_vars=id_vars, value_vars=value_vars,
var_name=var_name, value_name=value_name,
col_level=col_level)
# ----------------------------------------------------------------------
# Time series-related
def diff(self, periods=1, axis=0):
"""
First discrete difference of element.
Calculates the difference of a DataFrame element compared with another
element in the DataFrame (default is the element in the same column
of the previous row).
Parameters
----------
periods : int, default 1
Periods to shift for calculating difference, accepts negative
values.
axis : {0 or 'index', 1 or 'columns'}, default 0
Take difference over rows (0) or columns (1).
.. versionadded:: 0.16.1.
Returns
-------
DataFrame
See Also
--------
Series.diff: First discrete difference for a Series.
DataFrame.pct_change: Percent change over given number of periods.
DataFrame.shift: Shift index by desired number of periods with an
optional time freq.
Examples
--------
Difference with previous row
>>> df = pd.DataFrame({'a': [1, 2, 3, 4, 5, 6],
... 'b': [1, 1, 2, 3, 5, 8],
... 'c': [1, 4, 9, 16, 25, 36]})
>>> df
a b c
0 1 1 1
1 2 1 4
2 3 2 9
3 4 3 16
4 5 5 25
5 6 8 36
>>> df.diff()
a b c
0 NaN NaN NaN
1 1.0 0.0 3.0
2 1.0 1.0 5.0
3 1.0 1.0 7.0
4 1.0 2.0 9.0
5 1.0 3.0 11.0
Difference with previous column
>>> df.diff(axis=1)
a b c
0 NaN 0.0 0.0
1 NaN -1.0 3.0
2 NaN -1.0 7.0
3 NaN -1.0 13.0
4 NaN 0.0 20.0
5 NaN 2.0 28.0
Difference with 3rd previous row
>>> df.diff(periods=3)
a b c
0 NaN NaN NaN
1 NaN NaN NaN
2 NaN NaN NaN
3 3.0 2.0 15.0
4 3.0 4.0 21.0
5 3.0 6.0 27.0
Difference with following row
>>> df.diff(periods=-1)
a b c
0 -1.0 0.0 -3.0
1 -1.0 -1.0 -5.0
2 -1.0 -1.0 -7.0
3 -1.0 -2.0 -9.0
4 -1.0 -3.0 -11.0
5 NaN NaN NaN
"""
bm_axis = self._get_block_manager_axis(axis)
new_data = self._data.diff(n=periods, axis=bm_axis)
return self._constructor(new_data)
# ----------------------------------------------------------------------
# Function application
def _gotitem(self,
key: Union[str, List[str]],
ndim: int,
subset: Optional[Union[Series, ABCDataFrame]] = None,
) -> Union[Series, ABCDataFrame]:
"""
Sub-classes to define. Return a sliced object.
Parameters
----------
key : string / list of selections
ndim : 1,2
requested ndim of result
subset : object, default None
subset to act on
"""
if subset is None:
subset = self
elif subset.ndim == 1: # is Series
return subset
# TODO: _shallow_copy(subset)?
return subset[key]
_agg_summary_and_see_also_doc = dedent("""
The aggregation operations are always performed over an axis, either the
index (default) or the column axis. This behavior is different from
`numpy` aggregation functions (`mean`, `median`, `prod`, `sum`, `std`,
`var`), where the default is to compute the aggregation of the flattened
array, e.g., ``numpy.mean(arr_2d)`` as opposed to
``numpy.mean(arr_2d, axis=0)``.
`agg` is an alias for `aggregate`. Use the alias.
See Also
--------
DataFrame.apply : Perform any type of operations.
DataFrame.transform : Perform transformation type operations.
core.groupby.GroupBy : Perform operations over groups.
core.resample.Resampler : Perform operations over resampled bins.
core.window.Rolling : Perform operations over rolling window.
core.window.Expanding : Perform operations over expanding window.
core.window.EWM : Perform operation over exponential weighted
window.
""")
_agg_examples_doc = dedent("""
Examples
--------
>>> df = pd.DataFrame([[1, 2, 3],
... [4, 5, 6],
... [7, 8, 9],
... [np.nan, np.nan, np.nan]],
... columns=['A', 'B', 'C'])
Aggregate these functions over the rows.
>>> df.agg(['sum', 'min'])
A B C
sum 12.0 15.0 18.0
min 1.0 2.0 3.0
Different aggregations per column.
>>> df.agg({'A' : ['sum', 'min'], 'B' : ['min', 'max']})
A B
max NaN 8.0
min 1.0 2.0
sum 12.0 NaN
Aggregate over the columns.
>>> df.agg("mean", axis="columns")
0 2.0
1 5.0
2 8.0
3 NaN
dtype: float64
""")
@Substitution(see_also=_agg_summary_and_see_also_doc,
examples=_agg_examples_doc,
versionadded='\n.. versionadded:: 0.20.0\n',
**_shared_doc_kwargs)
@Appender(_shared_docs['aggregate'])
def aggregate(self, func, axis=0, *args, **kwargs):
axis = self._get_axis_number(axis)
result = None
try:
result, how = self._aggregate(func, axis=axis, *args, **kwargs)
except TypeError:
pass
if result is None:
return self.apply(func, axis=axis, args=args, **kwargs)
return result
def _aggregate(self, arg, axis=0, *args, **kwargs):
if axis == 1:
# NDFrame.aggregate returns a tuple, and we need to transpose
# only result
result, how = self.T._aggregate(arg, *args, **kwargs)
result = result.T if result is not None else result
return result, how
return super()._aggregate(arg, *args, **kwargs)
agg = aggregate
@Appender(_shared_docs['transform'] % _shared_doc_kwargs)
def transform(self, func, axis=0, *args, **kwargs):
axis = self._get_axis_number(axis)
if axis == 1:
return self.T.transform(func, *args, **kwargs).T
return super().transform(func, *args, **kwargs)
def apply(self, func, axis=0, broadcast=None, raw=False, reduce=None,
result_type=None, args=(), **kwds):
"""
Apply a function along an axis of the DataFrame.
Objects passed to the function are Series objects whose index is
either the DataFrame's index (``axis=0``) or the DataFrame's columns
(``axis=1``). By default (``result_type=None``), the final return type
is inferred from the return type of the applied function. Otherwise,
it depends on the `result_type` argument.
Parameters
----------
func : function
Function to apply to each column or row.
axis : {0 or 'index', 1 or 'columns'}, default 0
Axis along which the function is applied:
* 0 or 'index': apply function to each column.
* 1 or 'columns': apply function to each row.
broadcast : bool, optional
Only relevant for aggregation functions:
* ``False`` or ``None`` : returns a Series whose length is the
length of the index or the number of columns (based on the
`axis` parameter)
* ``True`` : results will be broadcast to the original shape
of the frame, the original index and columns will be retained.
.. deprecated:: 0.23.0
This argument will be removed in a future version, replaced
by result_type='broadcast'.
raw : bool, default False
* ``False`` : passes each row or column as a Series to the
function.
* ``True`` : the passed function will receive ndarray objects
instead.
If you are just applying a NumPy reduction function this will
achieve much better performance.
reduce : bool or None, default None
Try to apply reduction procedures. If the DataFrame is empty,
`apply` will use `reduce` to determine whether the result
should be a Series or a DataFrame. If ``reduce=None`` (the
default), `apply`'s return value will be guessed by calling
`func` on an empty Series
(note: while guessing, exceptions raised by `func` will be
ignored).
If ``reduce=True`` a Series will always be returned, and if
``reduce=False`` a DataFrame will always be returned.
.. deprecated:: 0.23.0
This argument will be removed in a future version, replaced
by ``result_type='reduce'``.
result_type : {'expand', 'reduce', 'broadcast', None}, default None
These only act when ``axis=1`` (columns):
* 'expand' : list-like results will be turned into columns.
* 'reduce' : returns a Series if possible rather than expanding
list-like results. This is the opposite of 'expand'.
* 'broadcast' : results will be broadcast to the original shape
of the DataFrame, the original index and columns will be
retained.
The default behaviour (None) depends on the return value of the
applied function: list-like results will be returned as a Series
of those. However if the apply function returns a Series these
are expanded to columns.
.. versionadded:: 0.23.0
args : tuple
Positional arguments to pass to `func` in addition to the
array/series.
**kwds
Additional keyword arguments to pass as keywords arguments to
`func`.
Returns
-------
Series or DataFrame
Result of applying ``func`` along the given axis of the
DataFrame.
See Also
--------
DataFrame.applymap: For elementwise operations.
DataFrame.aggregate: Only perform aggregating type operations.
DataFrame.transform: Only perform transforming type operations.
Notes
-----
In the current implementation apply calls `func` twice on the
first column/row to decide whether it can take a fast or slow
code path. This can lead to unexpected behavior if `func` has
side-effects, as they will take effect twice for the first
column/row.
Examples
--------
>>> df = pd.DataFrame([[4, 9]] * 3, columns=['A', 'B'])
>>> df
A B
0 4 9
1 4 9
2 4 9
Using a numpy universal function (in this case the same as
``np.sqrt(df)``):
>>> df.apply(np.sqrt)
A B
0 2.0 3.0
1 2.0 3.0
2 2.0 3.0
Using a reducing function on either axis
>>> df.apply(np.sum, axis=0)
A 12
B 27
dtype: int64
>>> df.apply(np.sum, axis=1)
0 13
1 13
2 13
dtype: int64
Returning a list-like will result in a Series
>>> df.apply(lambda x: [1, 2], axis=1)
0 [1, 2]
1 [1, 2]
2 [1, 2]
dtype: object
Passing result_type='expand' will expand list-like results
to columns of a Dataframe
>>> df.apply(lambda x: [1, 2], axis=1, result_type='expand')
0 1
0 1 2
1 1 2
2 1 2
Returning a Series inside the function is similar to passing
``result_type='expand'``. The resulting column names
will be the Series index.
>>> df.apply(lambda x: pd.Series([1, 2], index=['foo', 'bar']), axis=1)
foo bar
0 1 2
1 1 2
2 1 2
Passing ``result_type='broadcast'`` will ensure the same shape
result, whether list-like or scalar is returned by the function,
and broadcast it along the axis. The resulting column names will
be the originals.
>>> df.apply(lambda x: [1, 2], axis=1, result_type='broadcast')
A B
0 1 2
1 1 2
2 1 2
"""
from pandas.core.apply import frame_apply
op = frame_apply(self,
func=func,
axis=axis,
broadcast=broadcast,
raw=raw,
reduce=reduce,
result_type=result_type,
args=args,
kwds=kwds)
return op.get_result()
def applymap(self, func):
"""
Apply a function to a Dataframe elementwise.
This method applies a function that accepts and returns a scalar
to every element of a DataFrame.
Parameters
----------
func : callable
Python function, returns a single value from a single value.
Returns
-------
DataFrame
Transformed DataFrame.
See Also
--------
DataFrame.apply : Apply a function along input axis of DataFrame.
Notes
-----
In the current implementation applymap calls `func` twice on the
first column/row to decide whether it can take a fast or slow
code path. This can lead to unexpected behavior if `func` has
side-effects, as they will take effect twice for the first
column/row.
Examples
--------
>>> df = pd.DataFrame([[1, 2.12], [3.356, 4.567]])
>>> df
0 1
0 1.000 2.120
1 3.356 4.567
>>> df.applymap(lambda x: len(str(x)))
0 1
0 3 4
1 5 5
Note that a vectorized version of `func` often exists, which will
be much faster. You could square each number elementwise.
>>> df.applymap(lambda x: x**2)
0 1
0 1.000000 4.494400
1 11.262736 20.857489
But it's better to avoid applymap in that case.
>>> df ** 2
0 1
0 1.000000 4.494400
1 11.262736 20.857489
"""
# if we have a dtype == 'M8[ns]', provide boxed values
def infer(x):
if x.empty:
return lib.map_infer(x, func)
return lib.map_infer(x.astype(object).values, func)
return self.apply(infer)
# ----------------------------------------------------------------------
# Merging / joining methods
def append(self, other, ignore_index=False,
verify_integrity=False, sort=None):
"""
Append rows of `other` to the end of caller, returning a new object.
Columns in `other` that are not in the caller are added as new columns.
Parameters
----------
other : DataFrame or Series/dict-like object, or list of these
The data to append.
ignore_index : boolean, default False
If True, do not use the index labels.
verify_integrity : boolean, default False
If True, raise ValueError on creating index with duplicates.
sort : boolean, default None
Sort columns if the columns of `self` and `other` are not aligned.
The default sorting is deprecated and will change to not-sorting
in a future version of pandas. Explicitly pass ``sort=True`` to
silence the warning and sort. Explicitly pass ``sort=False`` to
silence the warning and not sort.
.. versionadded:: 0.23.0
Returns
-------
DataFrame
See Also
--------
concat : General function to concatenate DataFrame or Series objects.
Notes
-----
If a list of dict/series is passed and the keys are all contained in
the DataFrame's index, the order of the columns in the resulting
DataFrame will be unchanged.
Iteratively appending rows to a DataFrame can be more computationally
intensive than a single concatenate. A better solution is to append
those rows to a list and then concatenate the list with the original
DataFrame all at once.
Examples
--------
>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=list('AB'))
>>> df
A B
0 1 2
1 3 4
>>> df2 = pd.DataFrame([[5, 6], [7, 8]], columns=list('AB'))
>>> df.append(df2)
A B
0 1 2
1 3 4
0 5 6
1 7 8
With `ignore_index` set to True:
>>> df.append(df2, ignore_index=True)
A B
0 1 2
1 3 4
2 5 6
3 7 8
The following, while not recommended methods for generating DataFrames,
show two ways to generate a DataFrame from multiple data sources.
Less efficient:
>>> df = pd.DataFrame(columns=['A'])
>>> for i in range(5):
... df = df.append({'A': i}, ignore_index=True)
>>> df
A
0 0
1 1
2 2
3 3
4 4
More efficient:
>>> pd.concat([pd.DataFrame([i], columns=['A']) for i in range(5)],
... ignore_index=True)
A
0 0
1 1
2 2
3 3
4 4
"""
if isinstance(other, (Series, dict)):
if isinstance(other, dict):
other = Series(other)
if other.name is None and not ignore_index:
raise TypeError('Can only append a Series if ignore_index=True'
' or if the Series has a name')
if other.name is None:
index = None
else:
# other must have the same index name as self, otherwise
# index name will be reset
index = Index([other.name], name=self.index.name)
idx_diff = other.index.difference(self.columns)
try:
combined_columns = self.columns.append(idx_diff)
except TypeError:
combined_columns = self.columns.astype(object).append(idx_diff)
other = other.reindex(combined_columns, copy=False)
other = DataFrame(other.values.reshape((1, len(other))),
index=index,
columns=combined_columns)
other = other._convert(datetime=True, timedelta=True)
if not self.columns.equals(combined_columns):
self = self.reindex(columns=combined_columns)
elif isinstance(other, list) and not isinstance(other[0], DataFrame):
other = DataFrame(other)
if (self.columns.get_indexer(other.columns) >= 0).all():
other = other.reindex(columns=self.columns)
from pandas.core.reshape.concat import concat
if isinstance(other, (list, tuple)):
to_concat = [self] + other
else:
to_concat = [self, other]
return concat(to_concat, ignore_index=ignore_index,
verify_integrity=verify_integrity,
sort=sort)
def join(self, other, on=None, how='left', lsuffix='', rsuffix='',
sort=False):
"""
Join columns of another DataFrame.
Join columns with `other` DataFrame either on index or on a key
column. Efficiently join multiple DataFrame objects by index at once by
passing a list.
Parameters
----------
other : DataFrame, Series, or list of DataFrame
Index should be similar to one of the columns in this one. If a
Series is passed, its name attribute must be set, and that will be
used as the column name in the resulting joined DataFrame.
on : str, list of str, or array-like, optional
Column or index level name(s) in the caller to join on the index
in `other`, otherwise joins index-on-index. If multiple
values given, the `other` DataFrame must have a MultiIndex. Can
pass an array as the join key if it is not already contained in
the calling DataFrame. Like an Excel VLOOKUP operation.
how : {'left', 'right', 'outer', 'inner'}, default 'left'
How to handle the operation of the two objects.
* left: use calling frame's index (or column if on is specified)
* right: use `other`'s index.
* outer: form union of calling frame's index (or column if on is
specified) with `other`'s index, and sort it.
lexicographically.
* inner: form intersection of calling frame's index (or column if
on is specified) with `other`'s index, preserving the order
of the calling's one.
lsuffix : str, default ''
Suffix to use from left frame's overlapping columns.
rsuffix : str, default ''
Suffix to use from right frame's overlapping columns.
sort : bool, default False
Order result DataFrame lexicographically by the join key. If False,
the order of the join key depends on the join type (how keyword).
Returns
-------
DataFrame
A dataframe containing columns from both the caller and `other`.
See Also
--------
DataFrame.merge : For column(s)-on-columns(s) operations.
Notes
-----
Parameters `on`, `lsuffix`, and `rsuffix` are not supported when
passing a list of `DataFrame` objects.
Support for specifying index levels as the `on` parameter was added
in version 0.23.0.
Examples
--------
>>> df = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3', 'K4', 'K5'],
... 'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})
>>> df
key A
0 K0 A0
1 K1 A1
2 K2 A2
3 K3 A3
4 K4 A4
5 K5 A5
>>> other = pd.DataFrame({'key': ['K0', 'K1', 'K2'],
... 'B': ['B0', 'B1', 'B2']})
>>> other
key B
0 K0 B0
1 K1 B1
2 K2 B2
Join DataFrames using their indexes.
>>> df.join(other, lsuffix='_caller', rsuffix='_other')
key_caller A key_other B
0 K0 A0 K0 B0
1 K1 A1 K1 B1
2 K2 A2 K2 B2
3 K3 A3 NaN NaN
4 K4 A4 NaN NaN
5 K5 A5 NaN NaN
If we want to join using the key columns, we need to set key to be
the index in both `df` and `other`. The joined DataFrame will have
key as its index.
>>> df.set_index('key').join(other.set_index('key'))
A B
key
K0 A0 B0
K1 A1 B1
K2 A2 B2
K3 A3 NaN
K4 A4 NaN
K5 A5 NaN
Another option to join using the key columns is to use the `on`
parameter. DataFrame.join always uses `other`'s index but we can use
any column in `df`. This method preserves the original DataFrame's
index in the result.
>>> df.join(other.set_index('key'), on='key')
key A B
0 K0 A0 B0
1 K1 A1 B1
2 K2 A2 B2
3 K3 A3 NaN
4 K4 A4 NaN
5 K5 A5 NaN
"""
# For SparseDataFrame's benefit
return self._join_compat(other, on=on, how=how, lsuffix=lsuffix,
rsuffix=rsuffix, sort=sort)
def _join_compat(self, other, on=None, how='left', lsuffix='', rsuffix='',
sort=False):
from pandas.core.reshape.merge import merge
from pandas.core.reshape.concat import concat
if isinstance(other, Series):
if other.name is None:
raise ValueError('Other Series must have a name')
other = DataFrame({other.name: other})
if isinstance(other, DataFrame):
return merge(self, other, left_on=on, how=how,
left_index=on is None, right_index=True,
suffixes=(lsuffix, rsuffix), sort=sort)
else:
if on is not None:
raise ValueError('Joining multiple DataFrames only supported'
' for joining on index')
frames = [self] + list(other)
can_concat = all(df.index.is_unique for df in frames)
# join indexes only using concat
if can_concat:
if how == 'left':
how = 'outer'
join_axes = [self.index]
else:
join_axes = None
return concat(frames, axis=1, join=how, join_axes=join_axes,
verify_integrity=True)
joined = frames[0]
for frame in frames[1:]:
joined = merge(joined, frame, how=how, left_index=True,
right_index=True)
return joined
@Substitution('')
@Appender(_merge_doc, indents=2)
def merge(self, right, how='inner', on=None, left_on=None, right_on=None,
left_index=False, right_index=False, sort=False,
suffixes=('_x', '_y'), copy=True, indicator=False,
validate=None):
from pandas.core.reshape.merge import merge
return merge(self, right, how=how, on=on, left_on=left_on,
right_on=right_on, left_index=left_index,
right_index=right_index, sort=sort, suffixes=suffixes,
copy=copy, indicator=indicator, validate=validate)
def round(self, decimals=0, *args, **kwargs):
"""
Round a DataFrame to a variable number of decimal places.
Parameters
----------
decimals : int, dict, Series
Number of decimal places to round each column to. If an int is
given, round each column to the same number of places.
Otherwise dict and Series round to variable numbers of places.
Column names should be in the keys if `decimals` is a
dict-like, or in the index if `decimals` is a Series. Any
columns not included in `decimals` will be left as is. Elements
of `decimals` which are not columns of the input will be
ignored.
*args
Additional keywords have no effect but might be accepted for
compatibility with numpy.
**kwargs
Additional keywords have no effect but might be accepted for
compatibility with numpy.
Returns
-------
DataFrame
A DataFrame with the affected columns rounded to the specified
number of decimal places.
See Also
--------
numpy.around : Round a numpy array to the given number of decimals.
Series.round : Round a Series to the given number of decimals.
Examples
--------
>>> df = pd.DataFrame([(.21, .32), (.01, .67), (.66, .03), (.21, .18)],
... columns=['dogs', 'cats'])
>>> df
dogs cats
0 0.21 0.32
1 0.01 0.67
2 0.66 0.03
3 0.21 0.18
By providing an integer each column is rounded to the same number
of decimal places
>>> df.round(1)
dogs cats
0 0.2 0.3
1 0.0 0.7
2 0.7 0.0
3 0.2 0.2
With a dict, the number of places for specific columns can be
specified with the column names as key and the number of decimal
places as value
>>> df.round({'dogs': 1, 'cats': 0})
dogs cats
0 0.2 0.0
1 0.0 1.0
2 0.7 0.0
3 0.2 0.0
Using a Series, the number of places for specific columns can be
specified with the column names as index and the number of
decimal places as value
>>> decimals = pd.Series([0, 1], index=['cats', 'dogs'])
>>> df.round(decimals)
dogs cats
0 0.2 0.0
1 0.0 1.0
2 0.7 0.0
3 0.2 0.0
"""
from pandas.core.reshape.concat import concat
def _dict_round(df, decimals):
for col, vals in df.iteritems():
try:
yield _series_round(vals, decimals[col])
except KeyError:
yield vals
def _series_round(s, decimals):
if is_integer_dtype(s) or is_float_dtype(s):
return s.round(decimals)
return s
nv.validate_round(args, kwargs)
if isinstance(decimals, (dict, Series)):
if isinstance(decimals, Series):
if not decimals.index.is_unique:
raise ValueError("Index of decimals must be unique")
new_cols = [col for col in _dict_round(self, decimals)]
elif is_integer(decimals):
# Dispatch to Series.round
new_cols = [_series_round(v, decimals)
for _, v in self.iteritems()]
else:
raise TypeError("decimals must be an integer, a dict-like or a "
"Series")
if len(new_cols) > 0:
return self._constructor(concat(new_cols, axis=1),
index=self.index,
columns=self.columns)
else:
return self
# ----------------------------------------------------------------------
# Statistical methods, etc.
def corr(self, method='pearson', min_periods=1):
"""
Compute pairwise correlation of columns, excluding NA/null values.
Parameters
----------
method : {'pearson', 'kendall', 'spearman'} or callable
* pearson : standard correlation coefficient
* kendall : Kendall Tau correlation coefficient
* spearman : Spearman rank correlation
* callable: callable with input two 1d ndarrays
and returning a float. Note that the returned matrix from corr
will have 1 along the diagonals and will be symmetric
regardless of the callable's behavior
.. versionadded:: 0.24.0
min_periods : int, optional
Minimum number of observations required per pair of columns
to have a valid result. Currently only available for Pearson
and Spearman correlation.
Returns
-------
DataFrame
Correlation matrix.
See Also
--------
DataFrame.corrwith
Series.corr
Examples
--------
>>> def histogram_intersection(a, b):
... v = np.minimum(a, b).sum().round(decimals=1)
... return v
>>> df = pd.DataFrame([(.2, .3), (.0, .6), (.6, .0), (.2, .1)],
... columns=['dogs', 'cats'])
>>> df.corr(method=histogram_intersection)
dogs cats
dogs 1.0 0.3
cats 0.3 1.0
"""
numeric_df = self._get_numeric_data()
cols = numeric_df.columns
idx = cols.copy()
mat = numeric_df.values
if method == 'pearson':
correl = libalgos.nancorr(ensure_float64(mat), minp=min_periods)
elif method == 'spearman':
correl = libalgos.nancorr_spearman(ensure_float64(mat),
minp=min_periods)
elif method == 'kendall' or callable(method):
if min_periods is None:
min_periods = 1
mat = ensure_float64(mat).T
corrf = nanops.get_corr_func(method)
K = len(cols)
correl = np.empty((K, K), dtype=float)
mask = np.isfinite(mat)
for i, ac in enumerate(mat):
for j, bc in enumerate(mat):
if i > j:
continue
valid = mask[i] & mask[j]
if valid.sum() < min_periods:
c = np.nan
elif i == j:
c = 1.
elif not valid.all():
c = corrf(ac[valid], bc[valid])
else:
c = corrf(ac, bc)
correl[i, j] = c
correl[j, i] = c
else:
raise ValueError("method must be either 'pearson', "
"'spearman', 'kendall', or a callable, "
"'{method}' was supplied".format(method=method))
return self._constructor(correl, index=idx, columns=cols)
def cov(self, min_periods=None):
"""
Compute pairwise covariance of columns, excluding NA/null values.
Compute the pairwise covariance among the series of a DataFrame.
The returned data frame is the `covariance matrix
<https://en.wikipedia.org/wiki/Covariance_matrix>`__ of the columns
of the DataFrame.
Both NA and null values are automatically excluded from the
calculation. (See the note below about bias from missing values.)
A threshold can be set for the minimum number of
observations for each value created. Comparisons with observations
below this threshold will be returned as ``NaN``.
This method is generally used for the analysis of time series data to
understand the relationship between different measures
across time.
Parameters
----------
min_periods : int, optional
Minimum number of observations required per pair of columns
to have a valid result.
Returns
-------
DataFrame
The covariance matrix of the series of the DataFrame.
See Also
--------
Series.cov : Compute covariance with another Series.
core.window.EWM.cov: Exponential weighted sample covariance.
core.window.Expanding.cov : Expanding sample covariance.
core.window.Rolling.cov : Rolling sample covariance.
Notes
-----
Returns the covariance matrix of the DataFrame's time series.
The covariance is normalized by N-1.
For DataFrames that have Series that are missing data (assuming that
data is `missing at random
<https://en.wikipedia.org/wiki/Missing_data#Missing_at_random>`__)
the returned covariance matrix will be an unbiased estimate
of the variance and covariance between the member Series.
However, for many applications this estimate may not be acceptable
because the estimate covariance matrix is not guaranteed to be positive
semi-definite. This could lead to estimate correlations having
absolute values which are greater than one, and/or a non-invertible
covariance matrix. See `Estimation of covariance matrices
<http://en.wikipedia.org/w/index.php?title=Estimation_of_covariance_
matrices>`__ for more details.
Examples
--------
>>> df = pd.DataFrame([(1, 2), (0, 3), (2, 0), (1, 1)],
... columns=['dogs', 'cats'])
>>> df.cov()
dogs cats
dogs 0.666667 -1.000000
cats -1.000000 1.666667
>>> np.random.seed(42)
>>> df = pd.DataFrame(np.random.randn(1000, 5),
... columns=['a', 'b', 'c', 'd', 'e'])
>>> df.cov()
a b c d e
a 0.998438 -0.020161 0.059277 -0.008943 0.014144
b -0.020161 1.059352 -0.008543 -0.024738 0.009826
c 0.059277 -0.008543 1.010670 -0.001486 -0.000271
d -0.008943 -0.024738 -0.001486 0.921297 -0.013692
e 0.014144 0.009826 -0.000271 -0.013692 0.977795
**Minimum number of periods**
This method also supports an optional ``min_periods`` keyword
that specifies the required minimum number of non-NA observations for
each column pair in order to have a valid result:
>>> np.random.seed(42)
>>> df = pd.DataFrame(np.random.randn(20, 3),
... columns=['a', 'b', 'c'])
>>> df.loc[df.index[:5], 'a'] = np.nan
>>> df.loc[df.index[5:10], 'b'] = np.nan
>>> df.cov(min_periods=12)
a b c
a 0.316741 NaN -0.150812
b NaN 1.248003 0.191417
c -0.150812 0.191417 0.895202
"""
numeric_df = self._get_numeric_data()
cols = numeric_df.columns
idx = cols.copy()
mat = numeric_df.values
if notna(mat).all():
if min_periods is not None and min_periods > len(mat):
baseCov = np.empty((mat.shape[1], mat.shape[1]))
baseCov.fill(np.nan)
else:
baseCov = np.cov(mat.T)
baseCov = baseCov.reshape((len(cols), len(cols)))
else:
baseCov = libalgos.nancorr(ensure_float64(mat), cov=True,
minp=min_periods)
return self._constructor(baseCov, index=idx, columns=cols)
def corrwith(self, other, axis=0, drop=False, method='pearson'):
"""
Compute pairwise correlation between rows or columns of DataFrame
with rows or columns of Series or DataFrame. DataFrames are first
aligned along both axes before computing the correlations.
Parameters
----------
other : DataFrame, Series
Object with which to compute correlations.
axis : {0 or 'index', 1 or 'columns'}, default 0
0 or 'index' to compute column-wise, 1 or 'columns' for row-wise.
drop : bool, default False
Drop missing indices from result.
method : {'pearson', 'kendall', 'spearman'} or callable
* pearson : standard correlation coefficient
* kendall : Kendall Tau correlation coefficient
* spearman : Spearman rank correlation
* callable: callable with input two 1d ndarrays
and returning a float
.. versionadded:: 0.24.0
Returns
-------
Series
Pairwise correlations.
See Also
--------
DataFrame.corr
"""
axis = self._get_axis_number(axis)
this = self._get_numeric_data()
if isinstance(other, Series):
return this.apply(lambda x: other.corr(x, method=method),
axis=axis)
other = other._get_numeric_data()
left, right = this.align(other, join='inner', copy=False)
if axis == 1:
left = left.T
right = right.T
if method == 'pearson':
# mask missing values
left = left + right * 0
right = right + left * 0
# demeaned data
ldem = left - left.mean()
rdem = right - right.mean()
num = (ldem * rdem).sum()
dom = (left.count() - 1) * left.std() * right.std()
correl = num / dom
elif method in ['kendall', 'spearman'] or callable(method):
def c(x):
return nanops.nancorr(x[0], x[1], method=method)
correl = Series(map(c,
zip(left.values.T, right.values.T)),
index=left.columns)
else:
raise ValueError("Invalid method {method} was passed, "
"valid methods are: 'pearson', 'kendall', "
"'spearman', or callable".
format(method=method))
if not drop:
# Find non-matching labels along the given axis
# and append missing correlations (GH 22375)
raxis = 1 if axis == 0 else 0
result_index = (this._get_axis(raxis).
union(other._get_axis(raxis)))
idx_diff = result_index.difference(correl.index)
if len(idx_diff) > 0:
correl = correl.append(Series([np.nan] * len(idx_diff),
index=idx_diff))
return correl
# ----------------------------------------------------------------------
# ndarray-like stats methods
def count(self, axis=0, level=None, numeric_only=False):
"""
Count non-NA cells for each column or row.
The values `None`, `NaN`, `NaT`, and optionally `numpy.inf` (depending
on `pandas.options.mode.use_inf_as_na`) are considered NA.
Parameters
----------
axis : {0 or 'index', 1 or 'columns'}, default 0
If 0 or 'index' counts are generated for each column.
If 1 or 'columns' counts are generated for each **row**.
level : int or str, optional
If the axis is a `MultiIndex` (hierarchical), count along a
particular `level`, collapsing into a `DataFrame`.
A `str` specifies the level name.
numeric_only : bool, default False
Include only `float`, `int` or `boolean` data.
Returns
-------
Series or DataFrame
For each column/row the number of non-NA/null entries.
If `level` is specified returns a `DataFrame`.
See Also
--------
Series.count: Number of non-NA elements in a Series.
DataFrame.shape: Number of DataFrame rows and columns (including NA
elements).
DataFrame.isna: Boolean same-sized DataFrame showing places of NA
elements.
Examples
--------
Constructing DataFrame from a dictionary:
>>> df = pd.DataFrame({"Person":
... ["John", "Myla", "Lewis", "John", "Myla"],
... "Age": [24., np.nan, 21., 33, 26],
... "Single": [False, True, True, True, False]})
>>> df
Person Age Single
0 John 24.0 False
1 Myla NaN True
2 Lewis 21.0 True
3 John 33.0 True
4 Myla 26.0 False
Notice the uncounted NA values:
>>> df.count()
Person 5
Age 4
Single 5
dtype: int64
Counts for each **row**:
>>> df.count(axis='columns')
0 3
1 2
2 3
3 3
4 3
dtype: int64
Counts for one level of a `MultiIndex`:
>>> df.set_index(["Person", "Single"]).count(level="Person")
Age
Person
John 2
Lewis 1
Myla 1
"""
axis = self._get_axis_number(axis)
if level is not None:
return self._count_level(level, axis=axis,
numeric_only=numeric_only)
if numeric_only:
frame = self._get_numeric_data()
else:
frame = self
# GH #423
if len(frame._get_axis(axis)) == 0:
result = Series(0, index=frame._get_agg_axis(axis))
else:
if frame._is_mixed_type or frame._data.any_extension_types:
# the or any_extension_types is really only hit for single-
# column frames with an extension array
result = notna(frame).sum(axis=axis)
else:
# GH13407
series_counts = notna(frame).sum(axis=axis)
counts = series_counts.values
result = Series(counts, index=frame._get_agg_axis(axis))
return result.astype('int64')
def _count_level(self, level, axis=0, numeric_only=False):
if numeric_only:
frame = self._get_numeric_data()
else:
frame = self
count_axis = frame._get_axis(axis)
agg_axis = frame._get_agg_axis(axis)
if not isinstance(count_axis, MultiIndex):
raise TypeError("Can only count levels on hierarchical "
"{ax}.".format(ax=self._get_axis_name(axis)))
if frame._is_mixed_type:
# Since we have mixed types, calling notna(frame.values) might
# upcast everything to object
mask = notna(frame).values
else:
# But use the speedup when we have homogeneous dtypes
mask = notna(frame.values)
if axis == 1:
# We're transposing the mask rather than frame to avoid potential
# upcasts to object, which induces a ~20x slowdown
mask = mask.T
if isinstance(level, str):
level = count_axis._get_level_number(level)
level_index = count_axis.levels[level]
level_codes = ensure_int64(count_axis.codes[level])
counts = lib.count_level_2d(mask, level_codes, len(level_index),
axis=0)
result = DataFrame(counts, index=level_index, columns=agg_axis)
if axis == 1:
# Undo our earlier transpose
return result.T
else:
return result
def _reduce(self, op, name, axis=0, skipna=True, numeric_only=None,
filter_type=None, **kwds):
if axis is None and filter_type == 'bool':
labels = None
constructor = None
else:
# TODO: Make other agg func handle axis=None properly
axis = self._get_axis_number(axis)
labels = self._get_agg_axis(axis)
constructor = self._constructor
def f(x):
return op(x, axis=axis, skipna=skipna, **kwds)
# exclude timedelta/datetime unless we are uniform types
if (axis == 1 and self._is_datelike_mixed_type
and (not self._is_homogeneous_type
and not is_datetime64tz_dtype(self.dtypes[0]))):
numeric_only = True
if numeric_only is None:
try:
values = self.values
result = f(values)
if (filter_type == 'bool' and is_object_dtype(values) and
axis is None):
# work around https://github.com/numpy/numpy/issues/10489
# TODO: combine with hasattr(result, 'dtype') further down
# hard since we don't have `values` down there.
result = np.bool_(result)
except Exception as e:
# try by-column first
if filter_type is None and axis == 0:
try:
# this can end up with a non-reduction
# but not always. if the types are mixed
# with datelike then need to make sure a series
# we only end up here if we have not specified
# numeric_only and yet we have tried a
# column-by-column reduction, where we have mixed type.
# So let's just do what we can
from pandas.core.apply import frame_apply
opa = frame_apply(self,
func=f,
result_type='expand',
ignore_failures=True)
result = opa.get_result()
if result.ndim == self.ndim:
result = result.iloc[0]
return result
except Exception:
pass
if filter_type is None or filter_type == 'numeric':
data = self._get_numeric_data()
elif filter_type == 'bool':
data = self._get_bool_data()
else: # pragma: no cover
e = NotImplementedError(
"Handling exception with filter_type {f} not"
"implemented.".format(f=filter_type))
raise_with_traceback(e)
with np.errstate(all='ignore'):
result = f(data.values)
labels = data._get_agg_axis(axis)
else:
if numeric_only:
if filter_type is None or filter_type == 'numeric':
data = self._get_numeric_data()
elif filter_type == 'bool':
# GH 25101, # GH 24434
data = self._get_bool_data() if axis == 0 else self
else: # pragma: no cover
msg = ("Generating numeric_only data with filter_type {f}"
"not supported.".format(f=filter_type))
raise NotImplementedError(msg)
values = data.values
labels = data._get_agg_axis(axis)
else:
values = self.values
result = f(values)
if hasattr(result, 'dtype') and is_object_dtype(result.dtype):
try:
if filter_type is None or filter_type == 'numeric':
result = result.astype(np.float64)
elif filter_type == 'bool' and notna(result).all():
result = result.astype(np.bool_)
except (ValueError, TypeError):
# try to coerce to the original dtypes item by item if we can
if axis == 0:
result = coerce_to_dtypes(result, self.dtypes)
if constructor is not None:
result = Series(result, index=labels)
return result
def nunique(self, axis=0, dropna=True):
"""
Count distinct observations over requested axis.
Return Series with number of distinct observations. Can ignore NaN
values.
.. versionadded:: 0.20.0
Parameters
----------
axis : {0 or 'index', 1 or 'columns'}, default 0
The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for
column-wise.
dropna : bool, default True
Don't include NaN in the counts.
Returns
-------
Series
See Also
--------
Series.nunique: Method nunique for Series.
DataFrame.count: Count non-NA cells for each column or row.
Examples
--------
>>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [1, 1, 1]})
>>> df.nunique()
A 3
B 1
dtype: int64
>>> df.nunique(axis=1)
0 1
1 2
2 2
dtype: int64
"""
return self.apply(Series.nunique, axis=axis, dropna=dropna)
def idxmin(self, axis=0, skipna=True):
"""
Return index of first occurrence of minimum over requested axis.
NA/null values are excluded.
Parameters
----------
axis : {0 or 'index', 1 or 'columns'}, default 0
0 or 'index' for row-wise, 1 or 'columns' for column-wise
skipna : boolean, default True
Exclude NA/null values. If an entire row/column is NA, the result
will be NA.
Returns
-------
Series
Indexes of minima along the specified axis.
Raises
------
ValueError
* If the row/column is empty
See Also
--------
Series.idxmin
Notes
-----
This method is the DataFrame version of ``ndarray.argmin``.
"""
axis = self._get_axis_number(axis)
indices = nanops.nanargmin(self.values, axis=axis, skipna=skipna)
index = self._get_axis(axis)
result = [index[i] if i >= 0 else np.nan for i in indices]
return Series(result, index=self._get_agg_axis(axis))
def idxmax(self, axis=0, skipna=True):
"""
Return index of first occurrence of maximum over requested axis.
NA/null values are excluded.
Parameters
----------
axis : {0 or 'index', 1 or 'columns'}, default 0
0 or 'index' for row-wise, 1 or 'columns' for column-wise
skipna : boolean, default True
Exclude NA/null values. If an entire row/column is NA, the result
will be NA.
Returns
-------
Series
Indexes of maxima along the specified axis.
Raises
------
ValueError
* If the row/column is empty
See Also
--------
Series.idxmax
Notes
-----
This method is the DataFrame version of ``ndarray.argmax``.
"""
axis = self._get_axis_number(axis)
indices = nanops.nanargmax(self.values, axis=axis, skipna=skipna)
index = self._get_axis(axis)
result = [index[i] if i >= 0 else np.nan for i in indices]
return Series(result, index=self._get_agg_axis(axis))
def _get_agg_axis(self, axis_num):
"""
Let's be explicit about this.
"""
if axis_num == 0:
return self.columns
elif axis_num == 1:
return self.index
else:
raise ValueError('Axis must be 0 or 1 (got %r)' % axis_num)
def mode(self, axis=0, numeric_only=False, dropna=True):
"""
Get the mode(s) of each element along the selected axis.
The mode of a set of values is the value that appears most often.
It can be multiple values.
Parameters
----------
axis : {0 or 'index', 1 or 'columns'}, default 0
The axis to iterate over while searching for the mode:
* 0 or 'index' : get mode of each column
* 1 or 'columns' : get mode of each row
numeric_only : bool, default False
If True, only apply to numeric columns.
dropna : bool, default True
Don't consider counts of NaN/NaT.
.. versionadded:: 0.24.0
Returns
-------
DataFrame
The modes of each column or row.
See Also
--------
Series.mode : Return the highest frequency value in a Series.
Series.value_counts : Return the counts of values in a Series.
Examples
--------
>>> df = pd.DataFrame([('bird', 2, 2),
... ('mammal', 4, np.nan),
... ('arthropod', 8, 0),
... ('bird', 2, np.nan)],
... index=('falcon', 'horse', 'spider', 'ostrich'),
... columns=('species', 'legs', 'wings'))
>>> df
species legs wings
falcon bird 2 2.0
horse mammal 4 NaN
spider arthropod 8 0.0
ostrich bird 2 NaN
By default, missing values are not considered, and the mode of wings
are both 0 and 2. The second row of species and legs contains ``NaN``,
because they have only one mode, but the DataFrame has two rows.
>>> df.mode()
species legs wings
0 bird 2.0 0.0
1 NaN NaN 2.0
Setting ``dropna=False`` ``NaN`` values are considered and they can be
the mode (like for wings).
>>> df.mode(dropna=False)
species legs wings
0 bird 2 NaN
Setting ``numeric_only=True``, only the mode of numeric columns is
computed, and columns of other types are ignored.
>>> df.mode(numeric_only=True)
legs wings
0 2.0 0.0
1 NaN 2.0
To compute the mode over columns and not rows, use the axis parameter:
>>> df.mode(axis='columns', numeric_only=True)
0 1
falcon 2.0 NaN
horse 4.0 NaN
spider 0.0 8.0
ostrich 2.0 NaN
"""
data = self if not numeric_only else self._get_numeric_data()
def f(s):
return s.mode(dropna=dropna)
return data.apply(f, axis=axis)
def quantile(self, q=0.5, axis=0, numeric_only=True,
interpolation='linear'):
"""
Return values at the given quantile over requested axis.
Parameters
----------
q : float or array-like, default 0.5 (50% quantile)
Value between 0 <= q <= 1, the quantile(s) to compute.
axis : {0, 1, 'index', 'columns'} (default 0)
Equals 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
numeric_only : bool, default True
If False, the quantile of datetime and timedelta data will be
computed as well.
interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
This optional parameter specifies the interpolation method to use,
when the desired quantile lies between two data points `i` and `j`:
* linear: `i + (j - i) * fraction`, where `fraction` is the
fractional part of the index surrounded by `i` and `j`.
* lower: `i`.
* higher: `j`.
* nearest: `i` or `j` whichever is nearest.
* midpoint: (`i` + `j`) / 2.
.. versionadded:: 0.18.0
Returns
-------
Series or DataFrame
If ``q`` is an array, a DataFrame will be returned where the
index is ``q``, the columns are the columns of self, and the
values are the quantiles.
If ``q`` is a float, a Series will be returned where the
index is the columns of self and the values are the quantiles.
See Also
--------
core.window.Rolling.quantile: Rolling quantile.
numpy.percentile: Numpy function to compute the percentile.
Examples
--------
>>> df = pd.DataFrame(np.array([[1, 1], [2, 10], [3, 100], [4, 100]]),
... columns=['a', 'b'])
>>> df.quantile(.1)
a 1.3
b 3.7
Name: 0.1, dtype: float64
>>> df.quantile([.1, .5])
a b
0.1 1.3 3.7
0.5 2.5 55.0
Specifying `numeric_only=False` will also compute the quantile of
datetime and timedelta data.
>>> df = pd.DataFrame({'A': [1, 2],
... 'B': [pd.Timestamp('2010'),
... pd.Timestamp('2011')],
... 'C': [pd.Timedelta('1 days'),
... pd.Timedelta('2 days')]})
>>> df.quantile(0.5, numeric_only=False)
A 1.5
B 2010-07-02 12:00:00
C 1 days 12:00:00
Name: 0.5, dtype: object
"""
self._check_percentile(q)
data = self._get_numeric_data() if numeric_only else self
axis = self._get_axis_number(axis)
is_transposed = axis == 1
if is_transposed:
data = data.T
result = data._data.quantile(qs=q,
axis=1,
interpolation=interpolation,
transposed=is_transposed)
if result.ndim == 2:
result = self._constructor(result)
else:
result = self._constructor_sliced(result, name=q)
if is_transposed:
result = result.T
return result
def to_timestamp(self, freq=None, how='start', axis=0, copy=True):
"""
Cast to DatetimeIndex of timestamps, at *beginning* of period.
Parameters
----------
freq : str, default frequency of PeriodIndex
Desired frequency.
how : {'s', 'e', 'start', 'end'}
Convention for converting period to timestamp; start of period
vs. end.
axis : {0 or 'index', 1 or 'columns'}, default 0
The axis to convert (the index by default).
copy : bool, default True
If False then underlying input data is not copied.
Returns
-------
DataFrame with DatetimeIndex
"""
new_data = self._data
if copy:
new_data = new_data.copy()
axis = self._get_axis_number(axis)
if axis == 0:
new_data.set_axis(1, self.index.to_timestamp(freq=freq, how=how))
elif axis == 1:
new_data.set_axis(0, self.columns.to_timestamp(freq=freq, how=how))
else: # pragma: no cover
raise AssertionError('Axis must be 0 or 1. Got {ax!s}'.format(
ax=axis))
return self._constructor(new_data)
def to_period(self, freq=None, axis=0, copy=True):
"""
Convert DataFrame from DatetimeIndex to PeriodIndex with desired
frequency (inferred from index if not passed).
Parameters
----------
freq : str, default
Frequency of the PeriodIndex.
axis : {0 or 'index', 1 or 'columns'}, default 0
The axis to convert (the index by default).
copy : bool, default True
If False then underlying input data is not copied.
Returns
-------
TimeSeries with PeriodIndex
"""
new_data = self._data
if copy:
new_data = new_data.copy()
axis = self._get_axis_number(axis)
if axis == 0:
new_data.set_axis(1, self.index.to_period(freq=freq))
elif axis == 1:
new_data.set_axis(0, self.columns.to_period(freq=freq))
else: # pragma: no cover
raise AssertionError('Axis must be 0 or 1. Got {ax!s}'.format(
ax=axis))
return self._constructor(new_data)
def isin(self, values):
"""
Whether each element in the DataFrame is contained in values.
Parameters
----------
values : iterable, Series, DataFrame or dict
The result will only be true at a location if all the
labels match. If `values` is a Series, that's the index. If
`values` is a dict, the keys must be the column names,
which must match. If `values` is a DataFrame,
then both the index and column labels must match.
Returns
-------
DataFrame
DataFrame of booleans showing whether each element in the DataFrame
is contained in values.
See Also
--------
DataFrame.eq: Equality test for DataFrame.
Series.isin: Equivalent method on Series.
Series.str.contains: Test if pattern or regex is contained within a
string of a Series or Index.
Examples
--------
>>> df = pd.DataFrame({'num_legs': [2, 4], 'num_wings': [2, 0]},
... index=['falcon', 'dog'])
>>> df
num_legs num_wings
falcon 2 2
dog 4 0
When ``values`` is a list check whether every value in the DataFrame
is present in the list (which animals have 0 or 2 legs or wings)
>>> df.isin([0, 2])
num_legs num_wings
falcon True True
dog False True
When ``values`` is a dict, we can pass values to check for each
column separately:
>>> df.isin({'num_wings': [0, 3]})
num_legs num_wings
falcon False False
dog False True
When ``values`` is a Series or DataFrame the index and column must
match. Note that 'falcon' does not match based on the number of legs
in df2.
>>> other = pd.DataFrame({'num_legs': [8, 2], 'num_wings': [0, 2]},
... index=['spider', 'falcon'])
>>> df.isin(other)
num_legs num_wings
falcon True True
dog False False
"""
if isinstance(values, dict):
from pandas.core.reshape.concat import concat
values = collections.defaultdict(list, values)
return concat((self.iloc[:, [i]].isin(values[col])
for i, col in enumerate(self.columns)), axis=1)
elif isinstance(values, Series):
if not values.index.is_unique:
raise ValueError("cannot compute isin with "
"a duplicate axis.")
return self.eq(values.reindex_like(self), axis='index')
elif isinstance(values, DataFrame):
if not (values.columns.is_unique and values.index.is_unique):
raise ValueError("cannot compute isin with "
"a duplicate axis.")
return self.eq(values.reindex_like(self))
else:
if not is_list_like(values):
raise TypeError("only list-like or dict-like objects are "
"allowed to be passed to DataFrame.isin(), "
"you passed a "
"{0!r}".format(type(values).__name__))
return DataFrame(
algorithms.isin(self.values.ravel(),
values).reshape(self.shape), self.index,
self.columns)
# ----------------------------------------------------------------------
# Add plotting methods to DataFrame
plot = CachedAccessor("plot", pandas.plotting.FramePlotMethods)
hist = pandas.plotting.hist_frame
boxplot = pandas.plotting.boxplot_frame
sparse = CachedAccessor("sparse", SparseFrameAccessor)
DataFrame._setup_axes(['index', 'columns'], info_axis=1, stat_axis=0,
axes_are_reversed=True, aliases={'rows': 0},
docs={
'index': 'The index (row labels) of the DataFrame.',
'columns': 'The column labels of the DataFrame.'})
DataFrame._add_numeric_operations()
DataFrame._add_series_or_dataframe_operations()
ops.add_flex_arithmetic_methods(DataFrame)
ops.add_special_arithmetic_methods(DataFrame)
def _from_nested_dict(data):
# TODO: this should be seriously cythonized
new_data = OrderedDict()
for index, s in data.items():
for col, v in s.items():
new_data[col] = new_data.get(col, OrderedDict())
new_data[col][index] = v
return new_data
def _put_str(s, space):
return '{s}'.format(s=s)[:space].ljust(space)
|
from collections import OrderedDict
import numpy as np
from numpy import nan
from numpy.random import randn
import pytest
import pandas as pd
from pandas import DataFrame, Index, MultiIndex, Series
from pandas.core.reshape.concat import concat
from pandas.core.reshape.merge import merge
import pandas.util.testing as tm
@pytest.fixture
def left():
"""left dataframe (not multi-indexed) for multi-index join tests"""
# a little relevant example with NAs
key1 = ['bar', 'bar', 'bar', 'foo', 'foo', 'baz', 'baz', 'qux',
'qux', 'snap']
key2 = ['two', 'one', 'three', 'one', 'two', 'one', 'two', 'two',
'three', 'one']
data = np.random.randn(len(key1))
return DataFrame({'key1': key1, 'key2': key2, 'data': data})
@pytest.fixture
def right():
"""right dataframe (multi-indexed) for multi-index join tests"""
index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'],
['one', 'two', 'three']],
codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
[0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
names=['key1', 'key2'])
return DataFrame(np.random.randn(10, 3), index=index,
columns=['j_one', 'j_two', 'j_three'])
@pytest.fixture
def left_multi():
return (
DataFrame(
dict(Origin=['A', 'A', 'B', 'B', 'C'],
Destination=['A', 'B', 'A', 'C', 'A'],
Period=['AM', 'AM', 'IP', 'AM', 'OP'],
TripPurp=['hbw', 'nhb', 'hbo', 'nhb', 'hbw'],
Trips=[1987, 3647, 2470, 4296, 4444]),
columns=['Origin', 'Destination', 'Period',
'TripPurp', 'Trips'])
.set_index(['Origin', 'Destination', 'Period', 'TripPurp']))
@pytest.fixture
def right_multi():
return (
DataFrame(
dict(Origin=['A', 'A', 'B', 'B', 'C', 'C', 'E'],
Destination=['A', 'B', 'A', 'B', 'A', 'B', 'F'],
Period=['AM', 'AM', 'IP', 'AM', 'OP', 'IP', 'AM'],
LinkType=['a', 'b', 'c', 'b', 'a', 'b', 'a'],
Distance=[100, 80, 90, 80, 75, 35, 55]),
columns=['Origin', 'Destination', 'Period',
'LinkType', 'Distance'])
.set_index(['Origin', 'Destination', 'Period', 'LinkType']))
@pytest.fixture
def on_cols_multi():
return ['Origin', 'Destination', 'Period']
@pytest.fixture
def idx_cols_multi():
return ['Origin', 'Destination', 'Period', 'TripPurp', 'LinkType']
class TestMergeMulti:
def setup_method(self):
self.index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'],
['one', 'two', 'three']],
codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
[0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
names=['first', 'second'])
self.to_join = DataFrame(np.random.randn(10, 3), index=self.index,
columns=['j_one', 'j_two', 'j_three'])
# a little relevant example with NAs
key1 = ['bar', 'bar', 'bar', 'foo', 'foo', 'baz', 'baz', 'qux',
'qux', 'snap']
key2 = ['two', 'one', 'three', 'one', 'two', 'one', 'two', 'two',
'three', 'one']
data = np.random.randn(len(key1))
self.data = DataFrame({'key1': key1, 'key2': key2,
'data': data})
def test_merge_on_multikey(self, left, right, join_type):
on_cols = ['key1', 'key2']
result = (left.join(right, on=on_cols, how=join_type)
.reset_index(drop=True))
expected = pd.merge(left, right.reset_index(),
on=on_cols, how=join_type)
tm.assert_frame_equal(result, expected)
result = (left.join(right, on=on_cols, how=join_type, sort=True)
.reset_index(drop=True))
expected = pd.merge(left, right.reset_index(),
on=on_cols, how=join_type, sort=True)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("sort", [False, True])
def test_left_join_multi_index(self, left, right, sort):
icols = ['1st', '2nd', '3rd']
def bind_cols(df):
iord = lambda a: 0 if a != a else ord(a)
f = lambda ts: ts.map(iord) - ord('a')
return (f(df['1st']) + f(df['3rd']) * 1e2 +
df['2nd'].fillna(0) * 1e4)
def run_asserts(left, right, sort):
res = left.join(right, on=icols, how='left', sort=sort)
assert len(left) < len(res) + 1
assert not res['4th'].isna().any()
assert not res['5th'].isna().any()
tm.assert_series_equal(
res['4th'], - res['5th'], check_names=False)
result = bind_cols(res.iloc[:, :-2])
tm.assert_series_equal(res['4th'], result, check_names=False)
assert result.name is None
if sort:
tm.assert_frame_equal(
res, res.sort_values(icols, kind='mergesort'))
out = merge(left, right.reset_index(), on=icols,
sort=sort, how='left')
res.index = np.arange(len(res))
tm.assert_frame_equal(out, res)
lc = list(map(chr, np.arange(ord('a'), ord('z') + 1)))
left = DataFrame(np.random.choice(lc, (5000, 2)),
columns=['1st', '3rd'])
left.insert(1, '2nd', np.random.randint(0, 1000, len(left)))
i = np.random.permutation(len(left))
right = left.iloc[i].copy()
left['4th'] = bind_cols(left)
right['5th'] = - bind_cols(right)
right.set_index(icols, inplace=True)
run_asserts(left, right, sort)
# inject some nulls
left.loc[1::23, '1st'] = np.nan
left.loc[2::37, '2nd'] = np.nan
left.loc[3::43, '3rd'] = np.nan
left['4th'] = bind_cols(left)
i = np.random.permutation(len(left))
right = left.iloc[i, :-1]
right['5th'] = - bind_cols(right)
right.set_index(icols, inplace=True)
run_asserts(left, right, sort)
@pytest.mark.parametrize("sort", [False, True])
def test_merge_right_vs_left(self, left, right, sort):
# compare left vs right merge with multikey
on_cols = ['key1', 'key2']
merged_left_right = left.merge(right,
left_on=on_cols, right_index=True,
how='left', sort=sort)
merge_right_left = right.merge(left,
right_on=on_cols, left_index=True,
how='right', sort=sort)
# Reorder columns
merge_right_left = merge_right_left[merged_left_right.columns]
tm.assert_frame_equal(merged_left_right, merge_right_left)
def test_compress_group_combinations(self):
# ~ 40000000 possible unique groups
key1 = tm.rands_array(10, 10000)
key1 = np.tile(key1, 2)
key2 = key1[::-1]
df = DataFrame({'key1': key1, 'key2': key2,
'value1': np.random.randn(20000)})
df2 = DataFrame({'key1': key1[::2], 'key2': key2[::2],
'value2': np.random.randn(10000)})
# just to hit the label compression code path
merge(df, df2, how='outer')
def test_left_join_index_preserve_order(self):
on_cols = ['k1', 'k2']
left = DataFrame({'k1': [0, 1, 2] * 8,
'k2': ['foo', 'bar'] * 12,
'v': np.array(np.arange(24), dtype=np.int64)})
index = MultiIndex.from_tuples([(2, 'bar'), (1, 'foo')])
right = DataFrame({'v2': [5, 7]}, index=index)
result = left.join(right, on=on_cols)
expected = left.copy()
expected['v2'] = np.nan
expected.loc[(expected.k1 == 2) & (expected.k2 == 'bar'), 'v2'] = 5
expected.loc[(expected.k1 == 1) & (expected.k2 == 'foo'), 'v2'] = 7
tm.assert_frame_equal(result, expected)
result.sort_values(on_cols, kind='mergesort', inplace=True)
expected = left.join(right, on=on_cols, sort=True)
tm.assert_frame_equal(result, expected)
# test join with multi dtypes blocks
left = DataFrame({'k1': [0, 1, 2] * 8,
'k2': ['foo', 'bar'] * 12,
'k3': np.array([0, 1, 2] * 8, dtype=np.float32),
'v': np.array(np.arange(24), dtype=np.int32)})
index = MultiIndex.from_tuples([(2, 'bar'), (1, 'foo')])
right = DataFrame({'v2': [5, 7]}, index=index)
result = left.join(right, on=on_cols)
expected = left.copy()
expected['v2'] = np.nan
expected.loc[(expected.k1 == 2) & (expected.k2 == 'bar'), 'v2'] = 5
expected.loc[(expected.k1 == 1) & (expected.k2 == 'foo'), 'v2'] = 7
tm.assert_frame_equal(result, expected)
result = result.sort_values(on_cols, kind='mergesort')
expected = left.join(right, on=on_cols, sort=True)
tm.assert_frame_equal(result, expected)
def test_left_join_index_multi_match_multiindex(self):
left = DataFrame([
['X', 'Y', 'C', 'a'],
['W', 'Y', 'C', 'e'],
['V', 'Q', 'A', 'h'],
['V', 'R', 'D', 'i'],
['X', 'Y', 'D', 'b'],
['X', 'Y', 'A', 'c'],
['W', 'Q', 'B', 'f'],
['W', 'R', 'C', 'g'],
['V', 'Y', 'C', 'j'],
['X', 'Y', 'B', 'd']],
columns=['cola', 'colb', 'colc', 'tag'],
index=[3, 2, 0, 1, 7, 6, 4, 5, 9, 8])
right = (DataFrame([
['W', 'R', 'C', 0],
['W', 'Q', 'B', 3],
['W', 'Q', 'B', 8],
['X', 'Y', 'A', 1],
['X', 'Y', 'A', 4],
['X', 'Y', 'B', 5],
['X', 'Y', 'C', 6],
['X', 'Y', 'C', 9],
['X', 'Q', 'C', -6],
['X', 'R', 'C', -9],
['V', 'Y', 'C', 7],
['V', 'R', 'D', 2],
['V', 'R', 'D', -1],
['V', 'Q', 'A', -3]],
columns=['col1', 'col2', 'col3', 'val'])
.set_index(['col1', 'col2', 'col3']))
result = left.join(right, on=['cola', 'colb', 'colc'], how='left')
expected = DataFrame([
['X', 'Y', 'C', 'a', 6],
['X', 'Y', 'C', 'a', 9],
['W', 'Y', 'C', 'e', nan],
['V', 'Q', 'A', 'h', -3],
['V', 'R', 'D', 'i', 2],
['V', 'R', 'D', 'i', -1],
['X', 'Y', 'D', 'b', nan],
['X', 'Y', 'A', 'c', 1],
['X', 'Y', 'A', 'c', 4],
['W', 'Q', 'B', 'f', 3],
['W', 'Q', 'B', 'f', 8],
['W', 'R', 'C', 'g', 0],
['V', 'Y', 'C', 'j', 7],
['X', 'Y', 'B', 'd', 5]],
columns=['cola', 'colb', 'colc', 'tag', 'val'],
index=[3, 3, 2, 0, 1, 1, 7, 6, 6, 4, 4, 5, 9, 8])
tm.assert_frame_equal(result, expected)
result = left.join(right, on=['cola', 'colb', 'colc'],
how='left', sort=True)
expected = expected.sort_values(['cola', 'colb', 'colc'],
kind='mergesort')
tm.assert_frame_equal(result, expected)
def test_left_join_index_multi_match(self):
left = DataFrame([
['c', 0],
['b', 1],
['a', 2],
['b', 3]],
columns=['tag', 'val'],
index=[2, 0, 1, 3])
right = (DataFrame([
['a', 'v'],
['c', 'w'],
['c', 'x'],
['d', 'y'],
['a', 'z'],
['c', 'r'],
['e', 'q'],
['c', 's']],
columns=['tag', 'char'])
.set_index('tag'))
result = left.join(right, on='tag', how='left')
expected = DataFrame([
['c', 0, 'w'],
['c', 0, 'x'],
['c', 0, 'r'],
['c', 0, 's'],
['b', 1, nan],
['a', 2, 'v'],
['a', 2, 'z'],
['b', 3, nan]],
columns=['tag', 'val', 'char'],
index=[2, 2, 2, 2, 0, 1, 1, 3])
tm.assert_frame_equal(result, expected)
result = left.join(right, on='tag', how='left', sort=True)
expected2 = expected.sort_values('tag', kind='mergesort')
tm.assert_frame_equal(result, expected2)
# GH7331 - maintain left frame order in left merge
result = merge(left, right.reset_index(), how='left', on='tag')
expected.index = np.arange(len(expected))
tm.assert_frame_equal(result, expected)
def test_left_merge_na_buglet(self):
left = DataFrame({'id': list('abcde'), 'v1': randn(5),
'v2': randn(5), 'dummy': list('abcde'),
'v3': randn(5)},
columns=['id', 'v1', 'v2', 'dummy', 'v3'])
right = DataFrame({'id': ['a', 'b', np.nan, np.nan, np.nan],
'sv3': [1.234, 5.678, np.nan, np.nan, np.nan]})
result = merge(left, right, on='id', how='left')
rdf = right.drop(['id'], axis=1)
expected = left.join(rdf)
tm.assert_frame_equal(result, expected)
def test_merge_na_keys(self):
data = [[1950, "A", 1.5],
[1950, "B", 1.5],
[1955, "B", 1.5],
[1960, "B", np.nan],
[1970, "B", 4.],
[1950, "C", 4.],
[1960, "C", np.nan],
[1965, "C", 3.],
[1970, "C", 4.]]
frame = DataFrame(data, columns=["year", "panel", "data"])
other_data = [[1960, 'A', np.nan],
[1970, 'A', np.nan],
[1955, 'A', np.nan],
[1965, 'A', np.nan],
[1965, 'B', np.nan],
[1955, 'C', np.nan]]
other = DataFrame(other_data, columns=['year', 'panel', 'data'])
result = frame.merge(other, how='outer')
expected = frame.fillna(-999).merge(other.fillna(-999), how='outer')
expected = expected.replace(-999, np.nan)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("klass", [None, np.asarray, Series, Index])
def test_merge_datetime_index(self, klass):
# see gh-19038
df = DataFrame([1, 2, 3],
["2016-01-01", "2017-01-01", "2018-01-01"],
columns=["a"])
df.index = pd.to_datetime(df.index)
on_vector = df.index.year
if klass is not None:
on_vector = klass(on_vector)
expected = DataFrame(
OrderedDict([
("a", [1, 2, 3]),
("key_1", [2016, 2017, 2018]),
])
)
result = df.merge(df, on=["a", on_vector], how="inner")
tm.assert_frame_equal(result, expected)
expected = DataFrame(
OrderedDict([
("key_0", [2016, 2017, 2018]),
("a_x", [1, 2, 3]),
("a_y", [1, 2, 3]),
])
)
result = df.merge(df, on=[df.index.year], how="inner")
tm.assert_frame_equal(result, expected)
def test_join_multi_levels(self):
# GH 3662
# merge multi-levels
household = (
DataFrame(
dict(household_id=[1, 2, 3],
male=[0, 1, 0],
wealth=[196087.3, 316478.7, 294750]),
columns=['household_id', 'male', 'wealth'])
.set_index('household_id'))
portfolio = (
DataFrame(
dict(household_id=[1, 2, 2, 3, 3, 3, 4],
asset_id=["nl0000301109", "nl0000289783", "gb00b03mlx29",
"gb00b03mlx29", "lu0197800237", "nl0000289965",
np.nan],
name=["ABN Amro", "Robeco", "Royal Dutch Shell",
"Royal Dutch Shell",
"AAB Eastern Europe Equity Fund",
"Postbank BioTech Fonds", np.nan],
share=[1.0, 0.4, 0.6, 0.15, 0.6, 0.25, 1.0]),
columns=['household_id', 'asset_id', 'name', 'share'])
.set_index(['household_id', 'asset_id']))
result = household.join(portfolio, how='inner')
expected = (
DataFrame(
dict(male=[0, 1, 1, 0, 0, 0],
wealth=[196087.3, 316478.7, 316478.7,
294750.0, 294750.0, 294750.0],
name=['ABN Amro', 'Robeco', 'Royal Dutch Shell',
'Royal Dutch Shell',
'AAB Eastern Europe Equity Fund',
'Postbank BioTech Fonds'],
share=[1.00, 0.40, 0.60, 0.15, 0.60, 0.25],
household_id=[1, 2, 2, 3, 3, 3],
asset_id=['nl0000301109', 'nl0000289783', 'gb00b03mlx29',
'gb00b03mlx29', 'lu0197800237',
'nl0000289965']))
.set_index(['household_id', 'asset_id'])
.reindex(columns=['male', 'wealth', 'name', 'share']))
tm.assert_frame_equal(result, expected)
# equivalency
result = (merge(household.reset_index(), portfolio.reset_index(),
on=['household_id'], how='inner')
.set_index(['household_id', 'asset_id']))
tm.assert_frame_equal(result, expected)
result = household.join(portfolio, how='outer')
expected = (concat([
expected,
(DataFrame(
dict(share=[1.00]),
index=MultiIndex.from_tuples(
[(4, np.nan)],
names=['household_id', 'asset_id'])))
], axis=0, sort=True).reindex(columns=expected.columns))
tm.assert_frame_equal(result, expected)
# invalid cases
household.index.name = 'foo'
with pytest.raises(ValueError):
household.join(portfolio, how='inner')
portfolio2 = portfolio.copy()
portfolio2.index.set_names(['household_id', 'foo'])
with pytest.raises(ValueError):
portfolio2.join(portfolio, how='inner')
def test_join_multi_levels2(self):
# some more advanced merges
# GH6360
household = (
DataFrame(
dict(household_id=[1, 2, 2, 3, 3, 3, 4],
asset_id=["nl0000301109", "nl0000301109", "gb00b03mlx29",
"gb00b03mlx29", "lu0197800237", "nl0000289965",
np.nan],
share=[1.0, 0.4, 0.6, 0.15, 0.6, 0.25, 1.0]),
columns=['household_id', 'asset_id', 'share'])
.set_index(['household_id', 'asset_id']))
log_return = DataFrame(dict(
asset_id=["gb00b03mlx29", "gb00b03mlx29",
"gb00b03mlx29", "lu0197800237", "lu0197800237"],
t=[233, 234, 235, 180, 181],
log_return=[.09604978, -.06524096, .03532373, .03025441, .036997]
)).set_index(["asset_id", "t"])
expected = (
DataFrame(dict(
household_id=[2, 2, 2, 3, 3, 3, 3, 3],
asset_id=["gb00b03mlx29", "gb00b03mlx29",
"gb00b03mlx29", "gb00b03mlx29",
"gb00b03mlx29", "gb00b03mlx29",
"lu0197800237", "lu0197800237"],
t=[233, 234, 235, 233, 234, 235, 180, 181],
share=[0.6, 0.6, 0.6, 0.15, 0.15, 0.15, 0.6, 0.6],
log_return=[.09604978, -.06524096, .03532373,
.09604978, -.06524096, .03532373,
.03025441, .036997]
))
.set_index(["household_id", "asset_id", "t"])
.reindex(columns=['share', 'log_return']))
# this is the equivalency
result = (merge(household.reset_index(), log_return.reset_index(),
on=['asset_id'], how='inner')
.set_index(['household_id', 'asset_id', 't']))
tm.assert_frame_equal(result, expected)
expected = (
DataFrame(dict(
household_id=[1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 4],
asset_id=["nl0000301109", "nl0000301109", "gb00b03mlx29",
"gb00b03mlx29", "gb00b03mlx29",
"gb00b03mlx29", "gb00b03mlx29", "gb00b03mlx29",
"lu0197800237", "lu0197800237",
"nl0000289965", None],
t=[None, None, 233, 234, 235, 233, 234,
235, 180, 181, None, None],
share=[1.0, 0.4, 0.6, 0.6, 0.6, 0.15,
0.15, 0.15, 0.6, 0.6, 0.25, 1.0],
log_return=[None, None, .09604978, -.06524096, .03532373,
.09604978, -.06524096, .03532373,
.03025441, .036997, None, None]
))
.set_index(["household_id", "asset_id", "t"])
.reindex(columns=['share', 'log_return']))
result = (merge(household.reset_index(), log_return.reset_index(),
on=['asset_id'], how='outer')
.set_index(['household_id', 'asset_id', 't']))
tm.assert_frame_equal(result, expected)
class TestJoinMultiMulti:
def test_join_multi_multi(self, left_multi, right_multi, join_type,
on_cols_multi, idx_cols_multi):
# Multi-index join tests
expected = (pd.merge(left_multi.reset_index(),
right_multi.reset_index(),
how=join_type, on=on_cols_multi).
set_index(idx_cols_multi).sort_index())
result = left_multi.join(right_multi, how=join_type).sort_index()
tm.assert_frame_equal(result, expected)
def test_join_multi_empty_frames(self, left_multi, right_multi, join_type,
on_cols_multi, idx_cols_multi):
left_multi = left_multi.drop(columns=left_multi.columns)
right_multi = right_multi.drop(columns=right_multi.columns)
expected = (pd.merge(left_multi.reset_index(),
right_multi.reset_index(),
how=join_type, on=on_cols_multi)
.set_index(idx_cols_multi).sort_index())
result = left_multi.join(right_multi, how=join_type).sort_index()
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("box", [None, np.asarray, Series, Index])
def test_merge_datetime_index(self, box):
# see gh-19038
df = DataFrame([1, 2, 3],
["2016-01-01", "2017-01-01", "2018-01-01"],
columns=["a"])
df.index = pd.to_datetime(df.index)
on_vector = df.index.year
if box is not None:
on_vector = box(on_vector)
expected = DataFrame(
OrderedDict([
("a", [1, 2, 3]),
("key_1", [2016, 2017, 2018]),
])
)
result = df.merge(df, on=["a", on_vector], how="inner")
tm.assert_frame_equal(result, expected)
expected = DataFrame(
OrderedDict([
("key_0", [2016, 2017, 2018]),
("a_x", [1, 2, 3]),
("a_y", [1, 2, 3]),
])
)
result = df.merge(df, on=[df.index.year], how="inner")
tm.assert_frame_equal(result, expected)
def test_single_common_level(self):
index_left = pd.MultiIndex.from_tuples([('K0', 'X0'), ('K0', 'X1'),
('K1', 'X2')],
names=['key', 'X'])
left = pd.DataFrame({'A': ['A0', 'A1', 'A2'],
'B': ['B0', 'B1', 'B2']},
index=index_left)
index_right = pd.MultiIndex.from_tuples([('K0', 'Y0'), ('K1', 'Y1'),
('K2', 'Y2'), ('K2', 'Y3')],
names=['key', 'Y'])
right = pd.DataFrame({'C': ['C0', 'C1', 'C2', 'C3'],
'D': ['D0', 'D1', 'D2', 'D3']},
index=index_right)
result = left.join(right)
expected = (pd.merge(left.reset_index(), right.reset_index(),
on=['key'], how='inner')
.set_index(['key', 'X', 'Y']))
tm.assert_frame_equal(result, expected)
|
|
pandas/tests/reshape/merge/test_multi.py
|
|
""" manage PyTables query interface via Expressions """
import ast
from functools import partial
import numpy as np
from pandas._libs.tslibs import Timedelta, Timestamp
from pandas.compat.chainmap import DeepChainMap
from pandas.core.dtypes.common import is_list_like
import pandas as pd
from pandas.core.base import StringMixin
import pandas.core.common as com
from pandas.core.computation import expr, ops
from pandas.core.computation.common import _ensure_decoded
from pandas.core.computation.expr import BaseExprVisitor
from pandas.core.computation.ops import UndefinedVariableError, is_term
from pandas.io.formats.printing import pprint_thing, pprint_thing_encoded
class Scope(expr.Scope):
__slots__ = 'queryables',
def __init__(self, level, global_dict=None, local_dict=None,
queryables=None):
super().__init__(level + 1,
global_dict=global_dict,
local_dict=local_dict)
self.queryables = queryables or dict()
class Term(ops.Term):
def __new__(cls, name, env, side=None, encoding=None):
klass = Constant if not isinstance(name, str) else cls
supr_new = StringMixin.__new__
return supr_new(klass)
def __init__(self, name, env, side=None, encoding=None):
super().__init__(name, env, side=side, encoding=encoding)
def _resolve_name(self):
# must be a queryables
if self.side == 'left':
if self.name not in self.env.queryables:
raise NameError('name {name!r} is not defined'
.format(name=self.name))
return self.name
# resolve the rhs (and allow it to be None)
try:
return self.env.resolve(self.name, is_local=False)
except UndefinedVariableError:
return self.name
# read-only property overwriting read/write property
@property # type: ignore
def value(self):
return self._value
class Constant(Term):
def __init__(self, value, env, side=None, encoding=None):
super().__init__(value, env, side=side, encoding=encoding)
def _resolve_name(self):
return self._name
class BinOp(ops.BinOp):
_max_selectors = 31
def __init__(self, op, lhs, rhs, queryables, encoding):
super().__init__(op, lhs, rhs)
self.queryables = queryables
self.encoding = encoding
self.filter = None
self.condition = None
def _disallow_scalar_only_bool_ops(self):
pass
def prune(self, klass):
def pr(left, right):
""" create and return a new specialized BinOp from myself """
if left is None:
return right
elif right is None:
return left
k = klass
if isinstance(left, ConditionBinOp):
if (isinstance(left, ConditionBinOp) and
isinstance(right, ConditionBinOp)):
k = JointConditionBinOp
elif isinstance(left, k):
return left
elif isinstance(right, k):
return right
elif isinstance(left, FilterBinOp):
if (isinstance(left, FilterBinOp) and
isinstance(right, FilterBinOp)):
k = JointFilterBinOp
elif isinstance(left, k):
return left
elif isinstance(right, k):
return right
return k(self.op, left, right, queryables=self.queryables,
encoding=self.encoding).evaluate()
left, right = self.lhs, self.rhs
if is_term(left) and is_term(right):
res = pr(left.value, right.value)
elif not is_term(left) and is_term(right):
res = pr(left.prune(klass), right.value)
elif is_term(left) and not is_term(right):
res = pr(left.value, right.prune(klass))
elif not (is_term(left) or is_term(right)):
res = pr(left.prune(klass), right.prune(klass))
return res
def conform(self, rhs):
""" inplace conform rhs """
if not is_list_like(rhs):
rhs = [rhs]
if isinstance(rhs, np.ndarray):
rhs = rhs.ravel()
return rhs
@property
def is_valid(self):
""" return True if this is a valid field """
return self.lhs in self.queryables
@property
def is_in_table(self):
""" return True if this is a valid column name for generation (e.g. an
actual column in the table) """
return self.queryables.get(self.lhs) is not None
@property
def kind(self):
""" the kind of my field """
return getattr(self.queryables.get(self.lhs), 'kind', None)
@property
def meta(self):
""" the meta of my field """
return getattr(self.queryables.get(self.lhs), 'meta', None)
@property
def metadata(self):
""" the metadata of my field """
return getattr(self.queryables.get(self.lhs), 'metadata', None)
def generate(self, v):
""" create and return the op string for this TermValue """
val = v.tostring(self.encoding)
return "({lhs} {op} {val})".format(lhs=self.lhs, op=self.op, val=val)
def convert_value(self, v):
""" convert the expression that is in the term to something that is
accepted by pytables """
def stringify(value):
if self.encoding is not None:
encoder = partial(pprint_thing_encoded,
encoding=self.encoding)
else:
encoder = pprint_thing
return encoder(value)
kind = _ensure_decoded(self.kind)
meta = _ensure_decoded(self.meta)
if kind == 'datetime64' or kind == 'datetime':
if isinstance(v, (int, float)):
v = stringify(v)
v = _ensure_decoded(v)
v = Timestamp(v)
if v.tz is not None:
v = v.tz_convert('UTC')
return TermValue(v, v.value, kind)
elif kind == 'timedelta64' or kind == 'timedelta':
v = Timedelta(v, unit='s').value
return TermValue(int(v), v, kind)
elif meta == 'category':
metadata = com.values_from_object(self.metadata)
result = metadata.searchsorted(v, side='left')
# result returns 0 if v is first element or if v is not in metadata
# check that metadata contains v
if not result and v not in metadata:
result = -1
return TermValue(result, result, 'integer')
elif kind == 'integer':
v = int(float(v))
return TermValue(v, v, kind)
elif kind == 'float':
v = float(v)
return TermValue(v, v, kind)
elif kind == 'bool':
if isinstance(v, str):
v = not v.strip().lower() in ['false', 'f', 'no',
'n', 'none', '0',
'[]', '{}', '']
else:
v = bool(v)
return TermValue(v, v, kind)
elif isinstance(v, str):
# string quoting
return TermValue(v, stringify(v), 'string')
else:
raise TypeError("Cannot compare {v} of type {typ} to {kind} column"
.format(v=v, typ=type(v), kind=kind))
def convert_values(self):
pass
class FilterBinOp(BinOp):
def __str__(self):
return pprint_thing("[Filter : [{lhs}] -> [{op}]"
.format(lhs=self.filter[0], op=self.filter[1]))
def invert(self):
""" invert the filter """
if self.filter is not None:
f = list(self.filter)
f[1] = self.generate_filter_op(invert=True)
self.filter = tuple(f)
return self
def format(self):
""" return the actual filter format """
return [self.filter]
def evaluate(self):
if not self.is_valid:
raise ValueError("query term is not valid [{slf}]"
.format(slf=self))
rhs = self.conform(self.rhs)
values = [TermValue(v, v, self.kind).value for v in rhs]
if self.is_in_table:
# if too many values to create the expression, use a filter instead
if self.op in ['==', '!='] and len(values) > self._max_selectors:
filter_op = self.generate_filter_op()
self.filter = (
self.lhs,
filter_op,
pd.Index(values))
return self
return None
# equality conditions
if self.op in ['==', '!=']:
filter_op = self.generate_filter_op()
self.filter = (
self.lhs,
filter_op,
pd.Index(values))
else:
raise TypeError("passing a filterable condition to a non-table "
"indexer [{slf}]".format(slf=self))
return self
def generate_filter_op(self, invert=False):
if (self.op == '!=' and not invert) or (self.op == '==' and invert):
return lambda axis, vals: ~axis.isin(vals)
else:
return lambda axis, vals: axis.isin(vals)
class JointFilterBinOp(FilterBinOp):
def format(self):
raise NotImplementedError("unable to collapse Joint Filters")
def evaluate(self):
return self
class ConditionBinOp(BinOp):
def __str__(self):
return pprint_thing("[Condition : [{cond}]]"
.format(cond=self.condition))
def invert(self):
""" invert the condition """
# if self.condition is not None:
# self.condition = "~(%s)" % self.condition
# return self
raise NotImplementedError("cannot use an invert condition when "
"passing to numexpr")
def format(self):
""" return the actual ne format """
return self.condition
def evaluate(self):
if not self.is_valid:
raise ValueError("query term is not valid [{slf}]"
.format(slf=self))
# convert values if we are in the table
if not self.is_in_table:
return None
rhs = self.conform(self.rhs)
values = [self.convert_value(v) for v in rhs]
# equality conditions
if self.op in ['==', '!=']:
# too many values to create the expression?
if len(values) <= self._max_selectors:
vs = [self.generate(v) for v in values]
self.condition = "({cond})".format(cond=' | '.join(vs))
# use a filter after reading
else:
return None
else:
self.condition = self.generate(values[0])
return self
class JointConditionBinOp(ConditionBinOp):
def evaluate(self):
self.condition = "({lhs} {op} {rhs})".format(lhs=self.lhs.condition,
op=self.op,
rhs=self.rhs.condition)
return self
class UnaryOp(ops.UnaryOp):
def prune(self, klass):
if self.op != '~':
raise NotImplementedError("UnaryOp only support invert type ops")
operand = self.operand
operand = operand.prune(klass)
if operand is not None:
if issubclass(klass, ConditionBinOp):
if operand.condition is not None:
return operand.invert()
elif issubclass(klass, FilterBinOp):
if operand.filter is not None:
return operand.invert()
return None
_op_classes = {'unary': UnaryOp}
class ExprVisitor(BaseExprVisitor):
const_type = Constant
term_type = Term
def __init__(self, env, engine, parser, **kwargs):
super().__init__(env, engine, parser)
for bin_op in self.binary_ops:
bin_node = self.binary_op_nodes_map[bin_op]
setattr(self, 'visit_{node}'.format(node=bin_node),
lambda node, bin_op=bin_op: partial(BinOp, bin_op,
**kwargs))
def visit_UnaryOp(self, node, **kwargs):
if isinstance(node.op, (ast.Not, ast.Invert)):
return UnaryOp('~', self.visit(node.operand))
elif isinstance(node.op, ast.USub):
return self.const_type(-self.visit(node.operand).value, self.env)
elif isinstance(node.op, ast.UAdd):
raise NotImplementedError('Unary addition not supported')
def visit_Index(self, node, **kwargs):
return self.visit(node.value).value
def visit_Assign(self, node, **kwargs):
cmpr = ast.Compare(ops=[ast.Eq()], left=node.targets[0],
comparators=[node.value])
return self.visit(cmpr)
def visit_Subscript(self, node, **kwargs):
# only allow simple subscripts
value = self.visit(node.value)
slobj = self.visit(node.slice)
try:
value = value.value
except AttributeError:
pass
try:
return self.const_type(value[slobj], self.env)
except TypeError:
raise ValueError("cannot subscript {value!r} with "
"{slobj!r}".format(value=value, slobj=slobj))
def visit_Attribute(self, node, **kwargs):
attr = node.attr
value = node.value
ctx = node.ctx.__class__
if ctx == ast.Load:
# resolve the value
resolved = self.visit(value)
# try to get the value to see if we are another expression
try:
resolved = resolved.value
except (AttributeError):
pass
try:
return self.term_type(getattr(resolved, attr), self.env)
except AttributeError:
# something like datetime.datetime where scope is overridden
if isinstance(value, ast.Name) and value.id == attr:
return resolved
raise ValueError("Invalid Attribute context {name}"
.format(name=ctx.__name__))
def translate_In(self, op):
return ast.Eq() if isinstance(op, ast.In) else op
def _rewrite_membership_op(self, node, left, right):
return self.visit(node.op), node.op, left, right
def _validate_where(w):
"""
Validate that the where statement is of the right type.
The type may either be String, Expr, or list-like of Exprs.
Parameters
----------
w : String term expression, Expr, or list-like of Exprs.
Returns
-------
where : The original where clause if the check was successful.
Raises
------
TypeError : An invalid data type was passed in for w (e.g. dict).
"""
if not (isinstance(w, (Expr, str)) or is_list_like(w)):
raise TypeError("where must be passed as a string, Expr, "
"or list-like of Exprs")
return w
class Expr(expr.Expr):
""" hold a pytables like expression, comprised of possibly multiple 'terms'
Parameters
----------
where : string term expression, Expr, or list-like of Exprs
queryables : a "kinds" map (dict of column name -> kind), or None if column
is non-indexable
encoding : an encoding that will encode the query terms
Returns
-------
an Expr object
Examples
--------
'index>=date'
"columns=['A', 'D']"
'columns=A'
'columns==A'
"~(columns=['A','B'])"
'index>df.index[3] & string="bar"'
'(index>df.index[3] & index<=df.index[6]) | string="bar"'
"ts>=Timestamp('2012-02-01')"
"major_axis>=20130101"
"""
def __init__(self, where, queryables=None, encoding=None, scope_level=0):
where = _validate_where(where)
self.encoding = encoding
self.condition = None
self.filter = None
self.terms = None
self._visitor = None
# capture the environment if needed
local_dict = DeepChainMap()
if isinstance(where, Expr):
local_dict = where.env.scope
where = where.expr
elif isinstance(where, (list, tuple)):
for idx, w in enumerate(where):
if isinstance(w, Expr):
local_dict = w.env.scope
else:
w = _validate_where(w)
where[idx] = w
where = ' & '.join(map('({})'.format, com.flatten(where))) # noqa
self.expr = where
self.env = Scope(scope_level + 1, local_dict=local_dict)
if queryables is not None and isinstance(self.expr, str):
self.env.queryables.update(queryables)
self._visitor = ExprVisitor(self.env, queryables=queryables,
parser='pytables', engine='pytables',
encoding=encoding)
self.terms = self.parse()
def __str__(self):
if self.terms is not None:
return pprint_thing(self.terms)
return pprint_thing(self.expr)
def evaluate(self):
""" create and return the numexpr condition and filter """
try:
self.condition = self.terms.prune(ConditionBinOp)
except AttributeError:
raise ValueError("cannot process expression [{expr}], [{slf}] "
"is not a valid condition".format(expr=self.expr,
slf=self))
try:
self.filter = self.terms.prune(FilterBinOp)
except AttributeError:
raise ValueError("cannot process expression [{expr}], [{slf}] "
"is not a valid filter".format(expr=self.expr,
slf=self))
return self.condition, self.filter
class TermValue:
""" hold a term value the we use to construct a condition/filter """
def __init__(self, value, converted, kind):
self.value = value
self.converted = converted
self.kind = kind
def tostring(self, encoding):
""" quote the string if not encoded
else encode and return """
if self.kind == 'string':
if encoding is not None:
return self.converted
return '"{converted}"'.format(converted=self.converted)
elif self.kind == 'float':
# python 2 str(float) is not always
# round-trippable so use repr()
return repr(self.converted)
return self.converted
def maybe_expression(s):
""" loose checking if s is a pytables-acceptable expression """
if not isinstance(s, str):
return False
ops = ExprVisitor.binary_ops + ExprVisitor.unary_ops + ('=',)
# make sure we have an op at least
return any(op in s for op in ops)
|
from collections import OrderedDict
import numpy as np
from numpy import nan
from numpy.random import randn
import pytest
import pandas as pd
from pandas import DataFrame, Index, MultiIndex, Series
from pandas.core.reshape.concat import concat
from pandas.core.reshape.merge import merge
import pandas.util.testing as tm
@pytest.fixture
def left():
"""left dataframe (not multi-indexed) for multi-index join tests"""
# a little relevant example with NAs
key1 = ['bar', 'bar', 'bar', 'foo', 'foo', 'baz', 'baz', 'qux',
'qux', 'snap']
key2 = ['two', 'one', 'three', 'one', 'two', 'one', 'two', 'two',
'three', 'one']
data = np.random.randn(len(key1))
return DataFrame({'key1': key1, 'key2': key2, 'data': data})
@pytest.fixture
def right():
"""right dataframe (multi-indexed) for multi-index join tests"""
index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'],
['one', 'two', 'three']],
codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
[0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
names=['key1', 'key2'])
return DataFrame(np.random.randn(10, 3), index=index,
columns=['j_one', 'j_two', 'j_three'])
@pytest.fixture
def left_multi():
return (
DataFrame(
dict(Origin=['A', 'A', 'B', 'B', 'C'],
Destination=['A', 'B', 'A', 'C', 'A'],
Period=['AM', 'AM', 'IP', 'AM', 'OP'],
TripPurp=['hbw', 'nhb', 'hbo', 'nhb', 'hbw'],
Trips=[1987, 3647, 2470, 4296, 4444]),
columns=['Origin', 'Destination', 'Period',
'TripPurp', 'Trips'])
.set_index(['Origin', 'Destination', 'Period', 'TripPurp']))
@pytest.fixture
def right_multi():
return (
DataFrame(
dict(Origin=['A', 'A', 'B', 'B', 'C', 'C', 'E'],
Destination=['A', 'B', 'A', 'B', 'A', 'B', 'F'],
Period=['AM', 'AM', 'IP', 'AM', 'OP', 'IP', 'AM'],
LinkType=['a', 'b', 'c', 'b', 'a', 'b', 'a'],
Distance=[100, 80, 90, 80, 75, 35, 55]),
columns=['Origin', 'Destination', 'Period',
'LinkType', 'Distance'])
.set_index(['Origin', 'Destination', 'Period', 'LinkType']))
@pytest.fixture
def on_cols_multi():
return ['Origin', 'Destination', 'Period']
@pytest.fixture
def idx_cols_multi():
return ['Origin', 'Destination', 'Period', 'TripPurp', 'LinkType']
class TestMergeMulti:
def setup_method(self):
self.index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'],
['one', 'two', 'three']],
codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
[0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
names=['first', 'second'])
self.to_join = DataFrame(np.random.randn(10, 3), index=self.index,
columns=['j_one', 'j_two', 'j_three'])
# a little relevant example with NAs
key1 = ['bar', 'bar', 'bar', 'foo', 'foo', 'baz', 'baz', 'qux',
'qux', 'snap']
key2 = ['two', 'one', 'three', 'one', 'two', 'one', 'two', 'two',
'three', 'one']
data = np.random.randn(len(key1))
self.data = DataFrame({'key1': key1, 'key2': key2,
'data': data})
def test_merge_on_multikey(self, left, right, join_type):
on_cols = ['key1', 'key2']
result = (left.join(right, on=on_cols, how=join_type)
.reset_index(drop=True))
expected = pd.merge(left, right.reset_index(),
on=on_cols, how=join_type)
tm.assert_frame_equal(result, expected)
result = (left.join(right, on=on_cols, how=join_type, sort=True)
.reset_index(drop=True))
expected = pd.merge(left, right.reset_index(),
on=on_cols, how=join_type, sort=True)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("sort", [False, True])
def test_left_join_multi_index(self, left, right, sort):
icols = ['1st', '2nd', '3rd']
def bind_cols(df):
iord = lambda a: 0 if a != a else ord(a)
f = lambda ts: ts.map(iord) - ord('a')
return (f(df['1st']) + f(df['3rd']) * 1e2 +
df['2nd'].fillna(0) * 1e4)
def run_asserts(left, right, sort):
res = left.join(right, on=icols, how='left', sort=sort)
assert len(left) < len(res) + 1
assert not res['4th'].isna().any()
assert not res['5th'].isna().any()
tm.assert_series_equal(
res['4th'], - res['5th'], check_names=False)
result = bind_cols(res.iloc[:, :-2])
tm.assert_series_equal(res['4th'], result, check_names=False)
assert result.name is None
if sort:
tm.assert_frame_equal(
res, res.sort_values(icols, kind='mergesort'))
out = merge(left, right.reset_index(), on=icols,
sort=sort, how='left')
res.index = np.arange(len(res))
tm.assert_frame_equal(out, res)
lc = list(map(chr, np.arange(ord('a'), ord('z') + 1)))
left = DataFrame(np.random.choice(lc, (5000, 2)),
columns=['1st', '3rd'])
left.insert(1, '2nd', np.random.randint(0, 1000, len(left)))
i = np.random.permutation(len(left))
right = left.iloc[i].copy()
left['4th'] = bind_cols(left)
right['5th'] = - bind_cols(right)
right.set_index(icols, inplace=True)
run_asserts(left, right, sort)
# inject some nulls
left.loc[1::23, '1st'] = np.nan
left.loc[2::37, '2nd'] = np.nan
left.loc[3::43, '3rd'] = np.nan
left['4th'] = bind_cols(left)
i = np.random.permutation(len(left))
right = left.iloc[i, :-1]
right['5th'] = - bind_cols(right)
right.set_index(icols, inplace=True)
run_asserts(left, right, sort)
@pytest.mark.parametrize("sort", [False, True])
def test_merge_right_vs_left(self, left, right, sort):
# compare left vs right merge with multikey
on_cols = ['key1', 'key2']
merged_left_right = left.merge(right,
left_on=on_cols, right_index=True,
how='left', sort=sort)
merge_right_left = right.merge(left,
right_on=on_cols, left_index=True,
how='right', sort=sort)
# Reorder columns
merge_right_left = merge_right_left[merged_left_right.columns]
tm.assert_frame_equal(merged_left_right, merge_right_left)
def test_compress_group_combinations(self):
# ~ 40000000 possible unique groups
key1 = tm.rands_array(10, 10000)
key1 = np.tile(key1, 2)
key2 = key1[::-1]
df = DataFrame({'key1': key1, 'key2': key2,
'value1': np.random.randn(20000)})
df2 = DataFrame({'key1': key1[::2], 'key2': key2[::2],
'value2': np.random.randn(10000)})
# just to hit the label compression code path
merge(df, df2, how='outer')
def test_left_join_index_preserve_order(self):
on_cols = ['k1', 'k2']
left = DataFrame({'k1': [0, 1, 2] * 8,
'k2': ['foo', 'bar'] * 12,
'v': np.array(np.arange(24), dtype=np.int64)})
index = MultiIndex.from_tuples([(2, 'bar'), (1, 'foo')])
right = DataFrame({'v2': [5, 7]}, index=index)
result = left.join(right, on=on_cols)
expected = left.copy()
expected['v2'] = np.nan
expected.loc[(expected.k1 == 2) & (expected.k2 == 'bar'), 'v2'] = 5
expected.loc[(expected.k1 == 1) & (expected.k2 == 'foo'), 'v2'] = 7
tm.assert_frame_equal(result, expected)
result.sort_values(on_cols, kind='mergesort', inplace=True)
expected = left.join(right, on=on_cols, sort=True)
tm.assert_frame_equal(result, expected)
# test join with multi dtypes blocks
left = DataFrame({'k1': [0, 1, 2] * 8,
'k2': ['foo', 'bar'] * 12,
'k3': np.array([0, 1, 2] * 8, dtype=np.float32),
'v': np.array(np.arange(24), dtype=np.int32)})
index = MultiIndex.from_tuples([(2, 'bar'), (1, 'foo')])
right = DataFrame({'v2': [5, 7]}, index=index)
result = left.join(right, on=on_cols)
expected = left.copy()
expected['v2'] = np.nan
expected.loc[(expected.k1 == 2) & (expected.k2 == 'bar'), 'v2'] = 5
expected.loc[(expected.k1 == 1) & (expected.k2 == 'foo'), 'v2'] = 7
tm.assert_frame_equal(result, expected)
result = result.sort_values(on_cols, kind='mergesort')
expected = left.join(right, on=on_cols, sort=True)
tm.assert_frame_equal(result, expected)
def test_left_join_index_multi_match_multiindex(self):
left = DataFrame([
['X', 'Y', 'C', 'a'],
['W', 'Y', 'C', 'e'],
['V', 'Q', 'A', 'h'],
['V', 'R', 'D', 'i'],
['X', 'Y', 'D', 'b'],
['X', 'Y', 'A', 'c'],
['W', 'Q', 'B', 'f'],
['W', 'R', 'C', 'g'],
['V', 'Y', 'C', 'j'],
['X', 'Y', 'B', 'd']],
columns=['cola', 'colb', 'colc', 'tag'],
index=[3, 2, 0, 1, 7, 6, 4, 5, 9, 8])
right = (DataFrame([
['W', 'R', 'C', 0],
['W', 'Q', 'B', 3],
['W', 'Q', 'B', 8],
['X', 'Y', 'A', 1],
['X', 'Y', 'A', 4],
['X', 'Y', 'B', 5],
['X', 'Y', 'C', 6],
['X', 'Y', 'C', 9],
['X', 'Q', 'C', -6],
['X', 'R', 'C', -9],
['V', 'Y', 'C', 7],
['V', 'R', 'D', 2],
['V', 'R', 'D', -1],
['V', 'Q', 'A', -3]],
columns=['col1', 'col2', 'col3', 'val'])
.set_index(['col1', 'col2', 'col3']))
result = left.join(right, on=['cola', 'colb', 'colc'], how='left')
expected = DataFrame([
['X', 'Y', 'C', 'a', 6],
['X', 'Y', 'C', 'a', 9],
['W', 'Y', 'C', 'e', nan],
['V', 'Q', 'A', 'h', -3],
['V', 'R', 'D', 'i', 2],
['V', 'R', 'D', 'i', -1],
['X', 'Y', 'D', 'b', nan],
['X', 'Y', 'A', 'c', 1],
['X', 'Y', 'A', 'c', 4],
['W', 'Q', 'B', 'f', 3],
['W', 'Q', 'B', 'f', 8],
['W', 'R', 'C', 'g', 0],
['V', 'Y', 'C', 'j', 7],
['X', 'Y', 'B', 'd', 5]],
columns=['cola', 'colb', 'colc', 'tag', 'val'],
index=[3, 3, 2, 0, 1, 1, 7, 6, 6, 4, 4, 5, 9, 8])
tm.assert_frame_equal(result, expected)
result = left.join(right, on=['cola', 'colb', 'colc'],
how='left', sort=True)
expected = expected.sort_values(['cola', 'colb', 'colc'],
kind='mergesort')
tm.assert_frame_equal(result, expected)
def test_left_join_index_multi_match(self):
left = DataFrame([
['c', 0],
['b', 1],
['a', 2],
['b', 3]],
columns=['tag', 'val'],
index=[2, 0, 1, 3])
right = (DataFrame([
['a', 'v'],
['c', 'w'],
['c', 'x'],
['d', 'y'],
['a', 'z'],
['c', 'r'],
['e', 'q'],
['c', 's']],
columns=['tag', 'char'])
.set_index('tag'))
result = left.join(right, on='tag', how='left')
expected = DataFrame([
['c', 0, 'w'],
['c', 0, 'x'],
['c', 0, 'r'],
['c', 0, 's'],
['b', 1, nan],
['a', 2, 'v'],
['a', 2, 'z'],
['b', 3, nan]],
columns=['tag', 'val', 'char'],
index=[2, 2, 2, 2, 0, 1, 1, 3])
tm.assert_frame_equal(result, expected)
result = left.join(right, on='tag', how='left', sort=True)
expected2 = expected.sort_values('tag', kind='mergesort')
tm.assert_frame_equal(result, expected2)
# GH7331 - maintain left frame order in left merge
result = merge(left, right.reset_index(), how='left', on='tag')
expected.index = np.arange(len(expected))
tm.assert_frame_equal(result, expected)
def test_left_merge_na_buglet(self):
left = DataFrame({'id': list('abcde'), 'v1': randn(5),
'v2': randn(5), 'dummy': list('abcde'),
'v3': randn(5)},
columns=['id', 'v1', 'v2', 'dummy', 'v3'])
right = DataFrame({'id': ['a', 'b', np.nan, np.nan, np.nan],
'sv3': [1.234, 5.678, np.nan, np.nan, np.nan]})
result = merge(left, right, on='id', how='left')
rdf = right.drop(['id'], axis=1)
expected = left.join(rdf)
tm.assert_frame_equal(result, expected)
def test_merge_na_keys(self):
data = [[1950, "A", 1.5],
[1950, "B", 1.5],
[1955, "B", 1.5],
[1960, "B", np.nan],
[1970, "B", 4.],
[1950, "C", 4.],
[1960, "C", np.nan],
[1965, "C", 3.],
[1970, "C", 4.]]
frame = DataFrame(data, columns=["year", "panel", "data"])
other_data = [[1960, 'A', np.nan],
[1970, 'A', np.nan],
[1955, 'A', np.nan],
[1965, 'A', np.nan],
[1965, 'B', np.nan],
[1955, 'C', np.nan]]
other = DataFrame(other_data, columns=['year', 'panel', 'data'])
result = frame.merge(other, how='outer')
expected = frame.fillna(-999).merge(other.fillna(-999), how='outer')
expected = expected.replace(-999, np.nan)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("klass", [None, np.asarray, Series, Index])
def test_merge_datetime_index(self, klass):
# see gh-19038
df = DataFrame([1, 2, 3],
["2016-01-01", "2017-01-01", "2018-01-01"],
columns=["a"])
df.index = pd.to_datetime(df.index)
on_vector = df.index.year
if klass is not None:
on_vector = klass(on_vector)
expected = DataFrame(
OrderedDict([
("a", [1, 2, 3]),
("key_1", [2016, 2017, 2018]),
])
)
result = df.merge(df, on=["a", on_vector], how="inner")
tm.assert_frame_equal(result, expected)
expected = DataFrame(
OrderedDict([
("key_0", [2016, 2017, 2018]),
("a_x", [1, 2, 3]),
("a_y", [1, 2, 3]),
])
)
result = df.merge(df, on=[df.index.year], how="inner")
tm.assert_frame_equal(result, expected)
def test_join_multi_levels(self):
# GH 3662
# merge multi-levels
household = (
DataFrame(
dict(household_id=[1, 2, 3],
male=[0, 1, 0],
wealth=[196087.3, 316478.7, 294750]),
columns=['household_id', 'male', 'wealth'])
.set_index('household_id'))
portfolio = (
DataFrame(
dict(household_id=[1, 2, 2, 3, 3, 3, 4],
asset_id=["nl0000301109", "nl0000289783", "gb00b03mlx29",
"gb00b03mlx29", "lu0197800237", "nl0000289965",
np.nan],
name=["ABN Amro", "Robeco", "Royal Dutch Shell",
"Royal Dutch Shell",
"AAB Eastern Europe Equity Fund",
"Postbank BioTech Fonds", np.nan],
share=[1.0, 0.4, 0.6, 0.15, 0.6, 0.25, 1.0]),
columns=['household_id', 'asset_id', 'name', 'share'])
.set_index(['household_id', 'asset_id']))
result = household.join(portfolio, how='inner')
expected = (
DataFrame(
dict(male=[0, 1, 1, 0, 0, 0],
wealth=[196087.3, 316478.7, 316478.7,
294750.0, 294750.0, 294750.0],
name=['ABN Amro', 'Robeco', 'Royal Dutch Shell',
'Royal Dutch Shell',
'AAB Eastern Europe Equity Fund',
'Postbank BioTech Fonds'],
share=[1.00, 0.40, 0.60, 0.15, 0.60, 0.25],
household_id=[1, 2, 2, 3, 3, 3],
asset_id=['nl0000301109', 'nl0000289783', 'gb00b03mlx29',
'gb00b03mlx29', 'lu0197800237',
'nl0000289965']))
.set_index(['household_id', 'asset_id'])
.reindex(columns=['male', 'wealth', 'name', 'share']))
tm.assert_frame_equal(result, expected)
# equivalency
result = (merge(household.reset_index(), portfolio.reset_index(),
on=['household_id'], how='inner')
.set_index(['household_id', 'asset_id']))
tm.assert_frame_equal(result, expected)
result = household.join(portfolio, how='outer')
expected = (concat([
expected,
(DataFrame(
dict(share=[1.00]),
index=MultiIndex.from_tuples(
[(4, np.nan)],
names=['household_id', 'asset_id'])))
], axis=0, sort=True).reindex(columns=expected.columns))
tm.assert_frame_equal(result, expected)
# invalid cases
household.index.name = 'foo'
with pytest.raises(ValueError):
household.join(portfolio, how='inner')
portfolio2 = portfolio.copy()
portfolio2.index.set_names(['household_id', 'foo'])
with pytest.raises(ValueError):
portfolio2.join(portfolio, how='inner')
def test_join_multi_levels2(self):
# some more advanced merges
# GH6360
household = (
DataFrame(
dict(household_id=[1, 2, 2, 3, 3, 3, 4],
asset_id=["nl0000301109", "nl0000301109", "gb00b03mlx29",
"gb00b03mlx29", "lu0197800237", "nl0000289965",
np.nan],
share=[1.0, 0.4, 0.6, 0.15, 0.6, 0.25, 1.0]),
columns=['household_id', 'asset_id', 'share'])
.set_index(['household_id', 'asset_id']))
log_return = DataFrame(dict(
asset_id=["gb00b03mlx29", "gb00b03mlx29",
"gb00b03mlx29", "lu0197800237", "lu0197800237"],
t=[233, 234, 235, 180, 181],
log_return=[.09604978, -.06524096, .03532373, .03025441, .036997]
)).set_index(["asset_id", "t"])
expected = (
DataFrame(dict(
household_id=[2, 2, 2, 3, 3, 3, 3, 3],
asset_id=["gb00b03mlx29", "gb00b03mlx29",
"gb00b03mlx29", "gb00b03mlx29",
"gb00b03mlx29", "gb00b03mlx29",
"lu0197800237", "lu0197800237"],
t=[233, 234, 235, 233, 234, 235, 180, 181],
share=[0.6, 0.6, 0.6, 0.15, 0.15, 0.15, 0.6, 0.6],
log_return=[.09604978, -.06524096, .03532373,
.09604978, -.06524096, .03532373,
.03025441, .036997]
))
.set_index(["household_id", "asset_id", "t"])
.reindex(columns=['share', 'log_return']))
# this is the equivalency
result = (merge(household.reset_index(), log_return.reset_index(),
on=['asset_id'], how='inner')
.set_index(['household_id', 'asset_id', 't']))
tm.assert_frame_equal(result, expected)
expected = (
DataFrame(dict(
household_id=[1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 4],
asset_id=["nl0000301109", "nl0000301109", "gb00b03mlx29",
"gb00b03mlx29", "gb00b03mlx29",
"gb00b03mlx29", "gb00b03mlx29", "gb00b03mlx29",
"lu0197800237", "lu0197800237",
"nl0000289965", None],
t=[None, None, 233, 234, 235, 233, 234,
235, 180, 181, None, None],
share=[1.0, 0.4, 0.6, 0.6, 0.6, 0.15,
0.15, 0.15, 0.6, 0.6, 0.25, 1.0],
log_return=[None, None, .09604978, -.06524096, .03532373,
.09604978, -.06524096, .03532373,
.03025441, .036997, None, None]
))
.set_index(["household_id", "asset_id", "t"])
.reindex(columns=['share', 'log_return']))
result = (merge(household.reset_index(), log_return.reset_index(),
on=['asset_id'], how='outer')
.set_index(['household_id', 'asset_id', 't']))
tm.assert_frame_equal(result, expected)
class TestJoinMultiMulti:
def test_join_multi_multi(self, left_multi, right_multi, join_type,
on_cols_multi, idx_cols_multi):
# Multi-index join tests
expected = (pd.merge(left_multi.reset_index(),
right_multi.reset_index(),
how=join_type, on=on_cols_multi).
set_index(idx_cols_multi).sort_index())
result = left_multi.join(right_multi, how=join_type).sort_index()
tm.assert_frame_equal(result, expected)
def test_join_multi_empty_frames(self, left_multi, right_multi, join_type,
on_cols_multi, idx_cols_multi):
left_multi = left_multi.drop(columns=left_multi.columns)
right_multi = right_multi.drop(columns=right_multi.columns)
expected = (pd.merge(left_multi.reset_index(),
right_multi.reset_index(),
how=join_type, on=on_cols_multi)
.set_index(idx_cols_multi).sort_index())
result = left_multi.join(right_multi, how=join_type).sort_index()
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("box", [None, np.asarray, Series, Index])
def test_merge_datetime_index(self, box):
# see gh-19038
df = DataFrame([1, 2, 3],
["2016-01-01", "2017-01-01", "2018-01-01"],
columns=["a"])
df.index = pd.to_datetime(df.index)
on_vector = df.index.year
if box is not None:
on_vector = box(on_vector)
expected = DataFrame(
OrderedDict([
("a", [1, 2, 3]),
("key_1", [2016, 2017, 2018]),
])
)
result = df.merge(df, on=["a", on_vector], how="inner")
tm.assert_frame_equal(result, expected)
expected = DataFrame(
OrderedDict([
("key_0", [2016, 2017, 2018]),
("a_x", [1, 2, 3]),
("a_y", [1, 2, 3]),
])
)
result = df.merge(df, on=[df.index.year], how="inner")
tm.assert_frame_equal(result, expected)
def test_single_common_level(self):
index_left = pd.MultiIndex.from_tuples([('K0', 'X0'), ('K0', 'X1'),
('K1', 'X2')],
names=['key', 'X'])
left = pd.DataFrame({'A': ['A0', 'A1', 'A2'],
'B': ['B0', 'B1', 'B2']},
index=index_left)
index_right = pd.MultiIndex.from_tuples([('K0', 'Y0'), ('K1', 'Y1'),
('K2', 'Y2'), ('K2', 'Y3')],
names=['key', 'Y'])
right = pd.DataFrame({'C': ['C0', 'C1', 'C2', 'C3'],
'D': ['D0', 'D1', 'D2', 'D3']},
index=index_right)
result = left.join(right)
expected = (pd.merge(left.reset_index(), right.reset_index(),
on=['key'], how='inner')
.set_index(['key', 'X', 'Y']))
tm.assert_frame_equal(result, expected)
|
|
pandas/tests/reshape/merge/test_multi.py
|
pandas/core/computation/pytables.py
|
import numpy as np
import pytest
import pandas as pd
from pandas import Index, MultiIndex
@pytest.fixture
def idx():
# a MultiIndex used to test the general functionality of the
# general functionality of this object
major_axis = Index(['foo', 'bar', 'baz', 'qux'])
minor_axis = Index(['one', 'two'])
major_codes = np.array([0, 0, 1, 2, 3, 3])
minor_codes = np.array([0, 1, 0, 1, 0, 1])
index_names = ['first', 'second']
mi = MultiIndex(levels=[major_axis, minor_axis],
codes=[major_codes, minor_codes],
names=index_names, verify_integrity=False)
return mi
@pytest.fixture
def idx_dup():
# compare tests/indexes/multi/conftest.py
major_axis = Index(['foo', 'bar', 'baz', 'qux'])
minor_axis = Index(['one', 'two'])
major_codes = np.array([0, 0, 1, 0, 1, 1])
minor_codes = np.array([0, 1, 0, 1, 0, 1])
index_names = ['first', 'second']
mi = MultiIndex(levels=[major_axis, minor_axis],
codes=[major_codes, minor_codes],
names=index_names, verify_integrity=False)
return mi
@pytest.fixture
def index_names():
# names that match those in the idx fixture for testing equality of
# names assigned to the idx
return ['first', 'second']
@pytest.fixture
def holder():
# the MultiIndex constructor used to base compatibility with pickle
return MultiIndex
@pytest.fixture
def compat_props():
# a MultiIndex must have these properties associated with it
return ['shape', 'ndim', 'size']
@pytest.fixture
def narrow_multi_index():
"""
Return a MultiIndex that is narrower than the display (<80 characters).
"""
n = 1000
ci = pd.CategoricalIndex(list('a' * n) + (['abc'] * n))
dti = pd.date_range('2000-01-01', freq='s', periods=n * 2)
return pd.MultiIndex.from_arrays([ci, ci.codes + 9, dti],
names=['a', 'b', 'dti'])
@pytest.fixture
def wide_multi_index():
"""
Return a MultiIndex that is wider than the display (>80 characters).
"""
n = 1000
ci = pd.CategoricalIndex(list('a' * n) + (['abc'] * n))
dti = pd.date_range('2000-01-01', freq='s', periods=n * 2)
levels = [ci, ci.codes + 9, dti, dti, dti]
names = ['a', 'b', 'dti_1', 'dti_2', 'dti_3']
return pd.MultiIndex.from_arrays(levels, names=names)
|
from collections import OrderedDict
import numpy as np
from numpy import nan
from numpy.random import randn
import pytest
import pandas as pd
from pandas import DataFrame, Index, MultiIndex, Series
from pandas.core.reshape.concat import concat
from pandas.core.reshape.merge import merge
import pandas.util.testing as tm
@pytest.fixture
def left():
"""left dataframe (not multi-indexed) for multi-index join tests"""
# a little relevant example with NAs
key1 = ['bar', 'bar', 'bar', 'foo', 'foo', 'baz', 'baz', 'qux',
'qux', 'snap']
key2 = ['two', 'one', 'three', 'one', 'two', 'one', 'two', 'two',
'three', 'one']
data = np.random.randn(len(key1))
return DataFrame({'key1': key1, 'key2': key2, 'data': data})
@pytest.fixture
def right():
"""right dataframe (multi-indexed) for multi-index join tests"""
index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'],
['one', 'two', 'three']],
codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
[0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
names=['key1', 'key2'])
return DataFrame(np.random.randn(10, 3), index=index,
columns=['j_one', 'j_two', 'j_three'])
@pytest.fixture
def left_multi():
return (
DataFrame(
dict(Origin=['A', 'A', 'B', 'B', 'C'],
Destination=['A', 'B', 'A', 'C', 'A'],
Period=['AM', 'AM', 'IP', 'AM', 'OP'],
TripPurp=['hbw', 'nhb', 'hbo', 'nhb', 'hbw'],
Trips=[1987, 3647, 2470, 4296, 4444]),
columns=['Origin', 'Destination', 'Period',
'TripPurp', 'Trips'])
.set_index(['Origin', 'Destination', 'Period', 'TripPurp']))
@pytest.fixture
def right_multi():
return (
DataFrame(
dict(Origin=['A', 'A', 'B', 'B', 'C', 'C', 'E'],
Destination=['A', 'B', 'A', 'B', 'A', 'B', 'F'],
Period=['AM', 'AM', 'IP', 'AM', 'OP', 'IP', 'AM'],
LinkType=['a', 'b', 'c', 'b', 'a', 'b', 'a'],
Distance=[100, 80, 90, 80, 75, 35, 55]),
columns=['Origin', 'Destination', 'Period',
'LinkType', 'Distance'])
.set_index(['Origin', 'Destination', 'Period', 'LinkType']))
@pytest.fixture
def on_cols_multi():
return ['Origin', 'Destination', 'Period']
@pytest.fixture
def idx_cols_multi():
return ['Origin', 'Destination', 'Period', 'TripPurp', 'LinkType']
class TestMergeMulti:
def setup_method(self):
self.index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'],
['one', 'two', 'three']],
codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
[0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
names=['first', 'second'])
self.to_join = DataFrame(np.random.randn(10, 3), index=self.index,
columns=['j_one', 'j_two', 'j_three'])
# a little relevant example with NAs
key1 = ['bar', 'bar', 'bar', 'foo', 'foo', 'baz', 'baz', 'qux',
'qux', 'snap']
key2 = ['two', 'one', 'three', 'one', 'two', 'one', 'two', 'two',
'three', 'one']
data = np.random.randn(len(key1))
self.data = DataFrame({'key1': key1, 'key2': key2,
'data': data})
def test_merge_on_multikey(self, left, right, join_type):
on_cols = ['key1', 'key2']
result = (left.join(right, on=on_cols, how=join_type)
.reset_index(drop=True))
expected = pd.merge(left, right.reset_index(),
on=on_cols, how=join_type)
tm.assert_frame_equal(result, expected)
result = (left.join(right, on=on_cols, how=join_type, sort=True)
.reset_index(drop=True))
expected = pd.merge(left, right.reset_index(),
on=on_cols, how=join_type, sort=True)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("sort", [False, True])
def test_left_join_multi_index(self, left, right, sort):
icols = ['1st', '2nd', '3rd']
def bind_cols(df):
iord = lambda a: 0 if a != a else ord(a)
f = lambda ts: ts.map(iord) - ord('a')
return (f(df['1st']) + f(df['3rd']) * 1e2 +
df['2nd'].fillna(0) * 1e4)
def run_asserts(left, right, sort):
res = left.join(right, on=icols, how='left', sort=sort)
assert len(left) < len(res) + 1
assert not res['4th'].isna().any()
assert not res['5th'].isna().any()
tm.assert_series_equal(
res['4th'], - res['5th'], check_names=False)
result = bind_cols(res.iloc[:, :-2])
tm.assert_series_equal(res['4th'], result, check_names=False)
assert result.name is None
if sort:
tm.assert_frame_equal(
res, res.sort_values(icols, kind='mergesort'))
out = merge(left, right.reset_index(), on=icols,
sort=sort, how='left')
res.index = np.arange(len(res))
tm.assert_frame_equal(out, res)
lc = list(map(chr, np.arange(ord('a'), ord('z') + 1)))
left = DataFrame(np.random.choice(lc, (5000, 2)),
columns=['1st', '3rd'])
left.insert(1, '2nd', np.random.randint(0, 1000, len(left)))
i = np.random.permutation(len(left))
right = left.iloc[i].copy()
left['4th'] = bind_cols(left)
right['5th'] = - bind_cols(right)
right.set_index(icols, inplace=True)
run_asserts(left, right, sort)
# inject some nulls
left.loc[1::23, '1st'] = np.nan
left.loc[2::37, '2nd'] = np.nan
left.loc[3::43, '3rd'] = np.nan
left['4th'] = bind_cols(left)
i = np.random.permutation(len(left))
right = left.iloc[i, :-1]
right['5th'] = - bind_cols(right)
right.set_index(icols, inplace=True)
run_asserts(left, right, sort)
@pytest.mark.parametrize("sort", [False, True])
def test_merge_right_vs_left(self, left, right, sort):
# compare left vs right merge with multikey
on_cols = ['key1', 'key2']
merged_left_right = left.merge(right,
left_on=on_cols, right_index=True,
how='left', sort=sort)
merge_right_left = right.merge(left,
right_on=on_cols, left_index=True,
how='right', sort=sort)
# Reorder columns
merge_right_left = merge_right_left[merged_left_right.columns]
tm.assert_frame_equal(merged_left_right, merge_right_left)
def test_compress_group_combinations(self):
# ~ 40000000 possible unique groups
key1 = tm.rands_array(10, 10000)
key1 = np.tile(key1, 2)
key2 = key1[::-1]
df = DataFrame({'key1': key1, 'key2': key2,
'value1': np.random.randn(20000)})
df2 = DataFrame({'key1': key1[::2], 'key2': key2[::2],
'value2': np.random.randn(10000)})
# just to hit the label compression code path
merge(df, df2, how='outer')
def test_left_join_index_preserve_order(self):
on_cols = ['k1', 'k2']
left = DataFrame({'k1': [0, 1, 2] * 8,
'k2': ['foo', 'bar'] * 12,
'v': np.array(np.arange(24), dtype=np.int64)})
index = MultiIndex.from_tuples([(2, 'bar'), (1, 'foo')])
right = DataFrame({'v2': [5, 7]}, index=index)
result = left.join(right, on=on_cols)
expected = left.copy()
expected['v2'] = np.nan
expected.loc[(expected.k1 == 2) & (expected.k2 == 'bar'), 'v2'] = 5
expected.loc[(expected.k1 == 1) & (expected.k2 == 'foo'), 'v2'] = 7
tm.assert_frame_equal(result, expected)
result.sort_values(on_cols, kind='mergesort', inplace=True)
expected = left.join(right, on=on_cols, sort=True)
tm.assert_frame_equal(result, expected)
# test join with multi dtypes blocks
left = DataFrame({'k1': [0, 1, 2] * 8,
'k2': ['foo', 'bar'] * 12,
'k3': np.array([0, 1, 2] * 8, dtype=np.float32),
'v': np.array(np.arange(24), dtype=np.int32)})
index = MultiIndex.from_tuples([(2, 'bar'), (1, 'foo')])
right = DataFrame({'v2': [5, 7]}, index=index)
result = left.join(right, on=on_cols)
expected = left.copy()
expected['v2'] = np.nan
expected.loc[(expected.k1 == 2) & (expected.k2 == 'bar'), 'v2'] = 5
expected.loc[(expected.k1 == 1) & (expected.k2 == 'foo'), 'v2'] = 7
tm.assert_frame_equal(result, expected)
result = result.sort_values(on_cols, kind='mergesort')
expected = left.join(right, on=on_cols, sort=True)
tm.assert_frame_equal(result, expected)
def test_left_join_index_multi_match_multiindex(self):
left = DataFrame([
['X', 'Y', 'C', 'a'],
['W', 'Y', 'C', 'e'],
['V', 'Q', 'A', 'h'],
['V', 'R', 'D', 'i'],
['X', 'Y', 'D', 'b'],
['X', 'Y', 'A', 'c'],
['W', 'Q', 'B', 'f'],
['W', 'R', 'C', 'g'],
['V', 'Y', 'C', 'j'],
['X', 'Y', 'B', 'd']],
columns=['cola', 'colb', 'colc', 'tag'],
index=[3, 2, 0, 1, 7, 6, 4, 5, 9, 8])
right = (DataFrame([
['W', 'R', 'C', 0],
['W', 'Q', 'B', 3],
['W', 'Q', 'B', 8],
['X', 'Y', 'A', 1],
['X', 'Y', 'A', 4],
['X', 'Y', 'B', 5],
['X', 'Y', 'C', 6],
['X', 'Y', 'C', 9],
['X', 'Q', 'C', -6],
['X', 'R', 'C', -9],
['V', 'Y', 'C', 7],
['V', 'R', 'D', 2],
['V', 'R', 'D', -1],
['V', 'Q', 'A', -3]],
columns=['col1', 'col2', 'col3', 'val'])
.set_index(['col1', 'col2', 'col3']))
result = left.join(right, on=['cola', 'colb', 'colc'], how='left')
expected = DataFrame([
['X', 'Y', 'C', 'a', 6],
['X', 'Y', 'C', 'a', 9],
['W', 'Y', 'C', 'e', nan],
['V', 'Q', 'A', 'h', -3],
['V', 'R', 'D', 'i', 2],
['V', 'R', 'D', 'i', -1],
['X', 'Y', 'D', 'b', nan],
['X', 'Y', 'A', 'c', 1],
['X', 'Y', 'A', 'c', 4],
['W', 'Q', 'B', 'f', 3],
['W', 'Q', 'B', 'f', 8],
['W', 'R', 'C', 'g', 0],
['V', 'Y', 'C', 'j', 7],
['X', 'Y', 'B', 'd', 5]],
columns=['cola', 'colb', 'colc', 'tag', 'val'],
index=[3, 3, 2, 0, 1, 1, 7, 6, 6, 4, 4, 5, 9, 8])
tm.assert_frame_equal(result, expected)
result = left.join(right, on=['cola', 'colb', 'colc'],
how='left', sort=True)
expected = expected.sort_values(['cola', 'colb', 'colc'],
kind='mergesort')
tm.assert_frame_equal(result, expected)
def test_left_join_index_multi_match(self):
left = DataFrame([
['c', 0],
['b', 1],
['a', 2],
['b', 3]],
columns=['tag', 'val'],
index=[2, 0, 1, 3])
right = (DataFrame([
['a', 'v'],
['c', 'w'],
['c', 'x'],
['d', 'y'],
['a', 'z'],
['c', 'r'],
['e', 'q'],
['c', 's']],
columns=['tag', 'char'])
.set_index('tag'))
result = left.join(right, on='tag', how='left')
expected = DataFrame([
['c', 0, 'w'],
['c', 0, 'x'],
['c', 0, 'r'],
['c', 0, 's'],
['b', 1, nan],
['a', 2, 'v'],
['a', 2, 'z'],
['b', 3, nan]],
columns=['tag', 'val', 'char'],
index=[2, 2, 2, 2, 0, 1, 1, 3])
tm.assert_frame_equal(result, expected)
result = left.join(right, on='tag', how='left', sort=True)
expected2 = expected.sort_values('tag', kind='mergesort')
tm.assert_frame_equal(result, expected2)
# GH7331 - maintain left frame order in left merge
result = merge(left, right.reset_index(), how='left', on='tag')
expected.index = np.arange(len(expected))
tm.assert_frame_equal(result, expected)
def test_left_merge_na_buglet(self):
left = DataFrame({'id': list('abcde'), 'v1': randn(5),
'v2': randn(5), 'dummy': list('abcde'),
'v3': randn(5)},
columns=['id', 'v1', 'v2', 'dummy', 'v3'])
right = DataFrame({'id': ['a', 'b', np.nan, np.nan, np.nan],
'sv3': [1.234, 5.678, np.nan, np.nan, np.nan]})
result = merge(left, right, on='id', how='left')
rdf = right.drop(['id'], axis=1)
expected = left.join(rdf)
tm.assert_frame_equal(result, expected)
def test_merge_na_keys(self):
data = [[1950, "A", 1.5],
[1950, "B", 1.5],
[1955, "B", 1.5],
[1960, "B", np.nan],
[1970, "B", 4.],
[1950, "C", 4.],
[1960, "C", np.nan],
[1965, "C", 3.],
[1970, "C", 4.]]
frame = DataFrame(data, columns=["year", "panel", "data"])
other_data = [[1960, 'A', np.nan],
[1970, 'A', np.nan],
[1955, 'A', np.nan],
[1965, 'A', np.nan],
[1965, 'B', np.nan],
[1955, 'C', np.nan]]
other = DataFrame(other_data, columns=['year', 'panel', 'data'])
result = frame.merge(other, how='outer')
expected = frame.fillna(-999).merge(other.fillna(-999), how='outer')
expected = expected.replace(-999, np.nan)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("klass", [None, np.asarray, Series, Index])
def test_merge_datetime_index(self, klass):
# see gh-19038
df = DataFrame([1, 2, 3],
["2016-01-01", "2017-01-01", "2018-01-01"],
columns=["a"])
df.index = pd.to_datetime(df.index)
on_vector = df.index.year
if klass is not None:
on_vector = klass(on_vector)
expected = DataFrame(
OrderedDict([
("a", [1, 2, 3]),
("key_1", [2016, 2017, 2018]),
])
)
result = df.merge(df, on=["a", on_vector], how="inner")
tm.assert_frame_equal(result, expected)
expected = DataFrame(
OrderedDict([
("key_0", [2016, 2017, 2018]),
("a_x", [1, 2, 3]),
("a_y", [1, 2, 3]),
])
)
result = df.merge(df, on=[df.index.year], how="inner")
tm.assert_frame_equal(result, expected)
def test_join_multi_levels(self):
# GH 3662
# merge multi-levels
household = (
DataFrame(
dict(household_id=[1, 2, 3],
male=[0, 1, 0],
wealth=[196087.3, 316478.7, 294750]),
columns=['household_id', 'male', 'wealth'])
.set_index('household_id'))
portfolio = (
DataFrame(
dict(household_id=[1, 2, 2, 3, 3, 3, 4],
asset_id=["nl0000301109", "nl0000289783", "gb00b03mlx29",
"gb00b03mlx29", "lu0197800237", "nl0000289965",
np.nan],
name=["ABN Amro", "Robeco", "Royal Dutch Shell",
"Royal Dutch Shell",
"AAB Eastern Europe Equity Fund",
"Postbank BioTech Fonds", np.nan],
share=[1.0, 0.4, 0.6, 0.15, 0.6, 0.25, 1.0]),
columns=['household_id', 'asset_id', 'name', 'share'])
.set_index(['household_id', 'asset_id']))
result = household.join(portfolio, how='inner')
expected = (
DataFrame(
dict(male=[0, 1, 1, 0, 0, 0],
wealth=[196087.3, 316478.7, 316478.7,
294750.0, 294750.0, 294750.0],
name=['ABN Amro', 'Robeco', 'Royal Dutch Shell',
'Royal Dutch Shell',
'AAB Eastern Europe Equity Fund',
'Postbank BioTech Fonds'],
share=[1.00, 0.40, 0.60, 0.15, 0.60, 0.25],
household_id=[1, 2, 2, 3, 3, 3],
asset_id=['nl0000301109', 'nl0000289783', 'gb00b03mlx29',
'gb00b03mlx29', 'lu0197800237',
'nl0000289965']))
.set_index(['household_id', 'asset_id'])
.reindex(columns=['male', 'wealth', 'name', 'share']))
tm.assert_frame_equal(result, expected)
# equivalency
result = (merge(household.reset_index(), portfolio.reset_index(),
on=['household_id'], how='inner')
.set_index(['household_id', 'asset_id']))
tm.assert_frame_equal(result, expected)
result = household.join(portfolio, how='outer')
expected = (concat([
expected,
(DataFrame(
dict(share=[1.00]),
index=MultiIndex.from_tuples(
[(4, np.nan)],
names=['household_id', 'asset_id'])))
], axis=0, sort=True).reindex(columns=expected.columns))
tm.assert_frame_equal(result, expected)
# invalid cases
household.index.name = 'foo'
with pytest.raises(ValueError):
household.join(portfolio, how='inner')
portfolio2 = portfolio.copy()
portfolio2.index.set_names(['household_id', 'foo'])
with pytest.raises(ValueError):
portfolio2.join(portfolio, how='inner')
def test_join_multi_levels2(self):
# some more advanced merges
# GH6360
household = (
DataFrame(
dict(household_id=[1, 2, 2, 3, 3, 3, 4],
asset_id=["nl0000301109", "nl0000301109", "gb00b03mlx29",
"gb00b03mlx29", "lu0197800237", "nl0000289965",
np.nan],
share=[1.0, 0.4, 0.6, 0.15, 0.6, 0.25, 1.0]),
columns=['household_id', 'asset_id', 'share'])
.set_index(['household_id', 'asset_id']))
log_return = DataFrame(dict(
asset_id=["gb00b03mlx29", "gb00b03mlx29",
"gb00b03mlx29", "lu0197800237", "lu0197800237"],
t=[233, 234, 235, 180, 181],
log_return=[.09604978, -.06524096, .03532373, .03025441, .036997]
)).set_index(["asset_id", "t"])
expected = (
DataFrame(dict(
household_id=[2, 2, 2, 3, 3, 3, 3, 3],
asset_id=["gb00b03mlx29", "gb00b03mlx29",
"gb00b03mlx29", "gb00b03mlx29",
"gb00b03mlx29", "gb00b03mlx29",
"lu0197800237", "lu0197800237"],
t=[233, 234, 235, 233, 234, 235, 180, 181],
share=[0.6, 0.6, 0.6, 0.15, 0.15, 0.15, 0.6, 0.6],
log_return=[.09604978, -.06524096, .03532373,
.09604978, -.06524096, .03532373,
.03025441, .036997]
))
.set_index(["household_id", "asset_id", "t"])
.reindex(columns=['share', 'log_return']))
# this is the equivalency
result = (merge(household.reset_index(), log_return.reset_index(),
on=['asset_id'], how='inner')
.set_index(['household_id', 'asset_id', 't']))
tm.assert_frame_equal(result, expected)
expected = (
DataFrame(dict(
household_id=[1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 4],
asset_id=["nl0000301109", "nl0000301109", "gb00b03mlx29",
"gb00b03mlx29", "gb00b03mlx29",
"gb00b03mlx29", "gb00b03mlx29", "gb00b03mlx29",
"lu0197800237", "lu0197800237",
"nl0000289965", None],
t=[None, None, 233, 234, 235, 233, 234,
235, 180, 181, None, None],
share=[1.0, 0.4, 0.6, 0.6, 0.6, 0.15,
0.15, 0.15, 0.6, 0.6, 0.25, 1.0],
log_return=[None, None, .09604978, -.06524096, .03532373,
.09604978, -.06524096, .03532373,
.03025441, .036997, None, None]
))
.set_index(["household_id", "asset_id", "t"])
.reindex(columns=['share', 'log_return']))
result = (merge(household.reset_index(), log_return.reset_index(),
on=['asset_id'], how='outer')
.set_index(['household_id', 'asset_id', 't']))
tm.assert_frame_equal(result, expected)
class TestJoinMultiMulti:
def test_join_multi_multi(self, left_multi, right_multi, join_type,
on_cols_multi, idx_cols_multi):
# Multi-index join tests
expected = (pd.merge(left_multi.reset_index(),
right_multi.reset_index(),
how=join_type, on=on_cols_multi).
set_index(idx_cols_multi).sort_index())
result = left_multi.join(right_multi, how=join_type).sort_index()
tm.assert_frame_equal(result, expected)
def test_join_multi_empty_frames(self, left_multi, right_multi, join_type,
on_cols_multi, idx_cols_multi):
left_multi = left_multi.drop(columns=left_multi.columns)
right_multi = right_multi.drop(columns=right_multi.columns)
expected = (pd.merge(left_multi.reset_index(),
right_multi.reset_index(),
how=join_type, on=on_cols_multi)
.set_index(idx_cols_multi).sort_index())
result = left_multi.join(right_multi, how=join_type).sort_index()
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("box", [None, np.asarray, Series, Index])
def test_merge_datetime_index(self, box):
# see gh-19038
df = DataFrame([1, 2, 3],
["2016-01-01", "2017-01-01", "2018-01-01"],
columns=["a"])
df.index = pd.to_datetime(df.index)
on_vector = df.index.year
if box is not None:
on_vector = box(on_vector)
expected = DataFrame(
OrderedDict([
("a", [1, 2, 3]),
("key_1", [2016, 2017, 2018]),
])
)
result = df.merge(df, on=["a", on_vector], how="inner")
tm.assert_frame_equal(result, expected)
expected = DataFrame(
OrderedDict([
("key_0", [2016, 2017, 2018]),
("a_x", [1, 2, 3]),
("a_y", [1, 2, 3]),
])
)
result = df.merge(df, on=[df.index.year], how="inner")
tm.assert_frame_equal(result, expected)
def test_single_common_level(self):
index_left = pd.MultiIndex.from_tuples([('K0', 'X0'), ('K0', 'X1'),
('K1', 'X2')],
names=['key', 'X'])
left = pd.DataFrame({'A': ['A0', 'A1', 'A2'],
'B': ['B0', 'B1', 'B2']},
index=index_left)
index_right = pd.MultiIndex.from_tuples([('K0', 'Y0'), ('K1', 'Y1'),
('K2', 'Y2'), ('K2', 'Y3')],
names=['key', 'Y'])
right = pd.DataFrame({'C': ['C0', 'C1', 'C2', 'C3'],
'D': ['D0', 'D1', 'D2', 'D3']},
index=index_right)
result = left.join(right)
expected = (pd.merge(left.reset_index(), right.reset_index(),
on=['key'], how='inner')
.set_index(['key', 'X', 'Y']))
tm.assert_frame_equal(result, expected)
|
|
pandas/tests/reshape/merge/test_multi.py
|
pandas/tests/indexes/multi/conftest.py
|
import numpy as np
import pytest
from pandas._libs.tslib import iNaT
from pandas.core.dtypes.dtypes import CategoricalDtype
import pandas as pd
from pandas import (
CategoricalIndex, DatetimeIndex, Index, Int64Index, IntervalIndex,
MultiIndex, PeriodIndex, RangeIndex, Series, TimedeltaIndex, UInt64Index,
isna)
from pandas.core.indexes.base import InvalidIndexError
from pandas.core.indexes.datetimelike import DatetimeIndexOpsMixin
import pandas.util.testing as tm
class Base:
""" base class for index sub-class tests """
_holder = None
_compat_props = ['shape', 'ndim', 'size', 'nbytes']
def setup_indices(self):
for name, idx in self.indices.items():
setattr(self, name, idx)
def test_pickle_compat_construction(self):
# need an object to create with
msg = (r"Index\(\.\.\.\) must be called with a collection of some"
r" kind, None was passed|"
r"__new__\(\) missing 1 required positional argument: 'data'|"
r"__new__\(\) takes at least 2 arguments \(1 given\)")
with pytest.raises(TypeError, match=msg):
self._holder()
def test_to_series(self):
# assert that we are creating a copy of the index
idx = self.create_index()
s = idx.to_series()
assert s.values is not idx.values
assert s.index is not idx
assert s.name == idx.name
def test_to_series_with_arguments(self):
# GH18699
# index kwarg
idx = self.create_index()
s = idx.to_series(index=idx)
assert s.values is not idx.values
assert s.index is idx
assert s.name == idx.name
# name kwarg
idx = self.create_index()
s = idx.to_series(name='__test')
assert s.values is not idx.values
assert s.index is not idx
assert s.name != idx.name
@pytest.mark.parametrize("name", [None, "new_name"])
def test_to_frame(self, name):
# see GH-15230, GH-22580
idx = self.create_index()
if name:
idx_name = name
else:
idx_name = idx.name or 0
df = idx.to_frame(name=idx_name)
assert df.index is idx
assert len(df.columns) == 1
assert df.columns[0] == idx_name
assert df[idx_name].values is not idx.values
df = idx.to_frame(index=False, name=idx_name)
assert df.index is not idx
def test_to_frame_datetime_tz(self):
# GH 25809
idx = pd.date_range(start='2019-01-01', end='2019-01-30', freq='D')
idx = idx.tz_localize('UTC')
result = idx.to_frame()
expected = pd.DataFrame(idx, index=idx)
tm.assert_frame_equal(result, expected)
def test_shift(self):
# GH8083 test the base class for shift
idx = self.create_index()
msg = "Not supported for type {}".format(type(idx).__name__)
with pytest.raises(NotImplementedError, match=msg):
idx.shift(1)
with pytest.raises(NotImplementedError, match=msg):
idx.shift(1, 2)
def test_create_index_existing_name(self):
# GH11193, when an existing index is passed, and a new name is not
# specified, the new index should inherit the previous object name
expected = self.create_index()
if not isinstance(expected, MultiIndex):
expected.name = 'foo'
result = pd.Index(expected)
tm.assert_index_equal(result, expected)
result = pd.Index(expected, name='bar')
expected.name = 'bar'
tm.assert_index_equal(result, expected)
else:
expected.names = ['foo', 'bar']
result = pd.Index(expected)
tm.assert_index_equal(
result, Index(Index([('foo', 'one'), ('foo', 'two'),
('bar', 'one'), ('baz', 'two'),
('qux', 'one'), ('qux', 'two')],
dtype='object'),
names=['foo', 'bar']))
result = pd.Index(expected, names=['A', 'B'])
tm.assert_index_equal(
result,
Index(Index([('foo', 'one'), ('foo', 'two'), ('bar', 'one'),
('baz', 'two'), ('qux', 'one'), ('qux', 'two')],
dtype='object'), names=['A', 'B']))
def test_numeric_compat(self):
idx = self.create_index()
with pytest.raises(TypeError, match="cannot perform __mul__"):
idx * 1
with pytest.raises(TypeError, match="cannot perform __rmul__"):
1 * idx
div_err = "cannot perform __truediv__"
with pytest.raises(TypeError, match=div_err):
idx / 1
div_err = div_err.replace(' __', ' __r')
with pytest.raises(TypeError, match=div_err):
1 / idx
with pytest.raises(TypeError, match="cannot perform __floordiv__"):
idx // 1
with pytest.raises(TypeError, match="cannot perform __rfloordiv__"):
1 // idx
def test_logical_compat(self):
idx = self.create_index()
with pytest.raises(TypeError, match='cannot perform all'):
idx.all()
with pytest.raises(TypeError, match='cannot perform any'):
idx.any()
def test_boolean_context_compat(self):
# boolean context compat
idx = self.create_index()
with pytest.raises(ValueError, match='The truth value of a'):
if idx:
pass
def test_reindex_base(self):
idx = self.create_index()
expected = np.arange(idx.size, dtype=np.intp)
actual = idx.get_indexer(idx)
tm.assert_numpy_array_equal(expected, actual)
with pytest.raises(ValueError, match='Invalid fill method'):
idx.get_indexer(idx, method='invalid')
def test_get_indexer_consistency(self):
# See GH 16819
for name, index in self.indices.items():
if isinstance(index, IntervalIndex):
continue
if index.is_unique or isinstance(index, CategoricalIndex):
indexer = index.get_indexer(index[0:2])
assert isinstance(indexer, np.ndarray)
assert indexer.dtype == np.intp
else:
e = "Reindexing only valid with uniquely valued Index objects"
with pytest.raises(InvalidIndexError, match=e):
index.get_indexer(index[0:2])
indexer, _ = index.get_indexer_non_unique(index[0:2])
assert isinstance(indexer, np.ndarray)
assert indexer.dtype == np.intp
def test_ndarray_compat_properties(self):
idx = self.create_index()
assert idx.T.equals(idx)
assert idx.transpose().equals(idx)
values = idx.values
for prop in self._compat_props:
assert getattr(idx, prop) == getattr(values, prop)
# test for validity
idx.nbytes
idx.values.nbytes
def test_repr_roundtrip(self):
idx = self.create_index()
tm.assert_index_equal(eval(repr(idx)), idx)
def test_str(self):
# test the string repr
idx = self.create_index()
idx.name = 'foo'
assert "'foo'" in str(idx)
assert idx.__class__.__name__ in str(idx)
def test_repr_max_seq_item_setting(self):
# GH10182
idx = self.create_index()
idx = idx.repeat(50)
with pd.option_context("display.max_seq_items", None):
repr(idx)
assert '...' not in str(idx)
def test_copy_name(self):
# gh-12309: Check that the "name" argument
# passed at initialization is honored.
for name, index in self.indices.items():
if isinstance(index, MultiIndex):
continue
first = index.__class__(index, copy=True, name='mario')
second = first.__class__(first, copy=False)
# Even though "copy=False", we want a new object.
assert first is not second
# Not using tm.assert_index_equal() since names differ.
assert index.equals(first)
assert first.name == 'mario'
assert second.name == 'mario'
s1 = Series(2, index=first)
s2 = Series(3, index=second[:-1])
if not isinstance(index, CategoricalIndex):
# See gh-13365
s3 = s1 * s2
assert s3.index.name == 'mario'
def test_ensure_copied_data(self):
# Check the "copy" argument of each Index.__new__ is honoured
# GH12309
for name, index in self.indices.items():
init_kwargs = {}
if isinstance(index, PeriodIndex):
# Needs "freq" specification:
init_kwargs['freq'] = index.freq
elif isinstance(index, (RangeIndex, MultiIndex, CategoricalIndex)):
# RangeIndex cannot be initialized from data
# MultiIndex and CategoricalIndex are tested separately
continue
index_type = index.__class__
result = index_type(index.values, copy=True, **init_kwargs)
tm.assert_index_equal(index, result)
tm.assert_numpy_array_equal(index._ndarray_values,
result._ndarray_values,
check_same='copy')
if isinstance(index, PeriodIndex):
# .values an object array of Period, thus copied
result = index_type(ordinal=index.asi8, copy=False,
**init_kwargs)
tm.assert_numpy_array_equal(index._ndarray_values,
result._ndarray_values,
check_same='same')
elif isinstance(index, IntervalIndex):
# checked in test_interval.py
pass
else:
result = index_type(index.values, copy=False, **init_kwargs)
tm.assert_numpy_array_equal(index.values, result.values,
check_same='same')
tm.assert_numpy_array_equal(index._ndarray_values,
result._ndarray_values,
check_same='same')
def test_memory_usage(self):
for name, index in self.indices.items():
result = index.memory_usage()
if len(index):
index.get_loc(index[0])
result2 = index.memory_usage()
result3 = index.memory_usage(deep=True)
# RangeIndex, IntervalIndex
# don't have engines
if not isinstance(index, (RangeIndex, IntervalIndex)):
assert result2 > result
if index.inferred_type == 'object':
assert result3 > result2
else:
# we report 0 for no-length
assert result == 0
def test_argsort(self):
for k, ind in self.indices.items():
# separately tested
if k in ['catIndex']:
continue
result = ind.argsort()
expected = np.array(ind).argsort()
tm.assert_numpy_array_equal(result, expected, check_dtype=False)
def test_numpy_argsort(self):
for k, ind in self.indices.items():
result = np.argsort(ind)
expected = ind.argsort()
tm.assert_numpy_array_equal(result, expected)
# these are the only two types that perform
# pandas compatibility input validation - the
# rest already perform separate (or no) such
# validation via their 'values' attribute as
# defined in pandas.core.indexes/base.py - they
# cannot be changed at the moment due to
# backwards compatibility concerns
if isinstance(type(ind), (CategoricalIndex, RangeIndex)):
msg = "the 'axis' parameter is not supported"
with pytest.raises(ValueError, match=msg):
np.argsort(ind, axis=1)
msg = "the 'kind' parameter is not supported"
with pytest.raises(ValueError, match=msg):
np.argsort(ind, kind='mergesort')
msg = "the 'order' parameter is not supported"
with pytest.raises(ValueError, match=msg):
np.argsort(ind, order=('a', 'b'))
def test_take(self):
indexer = [4, 3, 0, 2]
for k, ind in self.indices.items():
# separate
if k in ['boolIndex', 'tuples', 'empty']:
continue
result = ind.take(indexer)
expected = ind[indexer]
assert result.equals(expected)
if not isinstance(ind,
(DatetimeIndex, PeriodIndex, TimedeltaIndex)):
# GH 10791
with pytest.raises(AttributeError):
ind.freq
def test_take_invalid_kwargs(self):
idx = self.create_index()
indices = [1, 2]
msg = r"take\(\) got an unexpected keyword argument 'foo'"
with pytest.raises(TypeError, match=msg):
idx.take(indices, foo=2)
msg = "the 'out' parameter is not supported"
with pytest.raises(ValueError, match=msg):
idx.take(indices, out=indices)
msg = "the 'mode' parameter is not supported"
with pytest.raises(ValueError, match=msg):
idx.take(indices, mode='clip')
def test_repeat(self):
rep = 2
i = self.create_index()
expected = pd.Index(i.values.repeat(rep), name=i.name)
tm.assert_index_equal(i.repeat(rep), expected)
i = self.create_index()
rep = np.arange(len(i))
expected = pd.Index(i.values.repeat(rep), name=i.name)
tm.assert_index_equal(i.repeat(rep), expected)
def test_numpy_repeat(self):
rep = 2
i = self.create_index()
expected = i.repeat(rep)
tm.assert_index_equal(np.repeat(i, rep), expected)
msg = "the 'axis' parameter is not supported"
with pytest.raises(ValueError, match=msg):
np.repeat(i, rep, axis=0)
@pytest.mark.parametrize('klass', [list, tuple, np.array, Series])
def test_where(self, klass):
i = self.create_index()
cond = [True] * len(i)
result = i.where(klass(cond))
expected = i
tm.assert_index_equal(result, expected)
cond = [False] + [True] * len(i[1:])
expected = pd.Index([i._na_value] + i[1:].tolist(), dtype=i.dtype)
result = i.where(klass(cond))
tm.assert_index_equal(result, expected)
@pytest.mark.parametrize("case", [0.5, "xxx"])
@pytest.mark.parametrize("method", ["intersection", "union",
"difference", "symmetric_difference"])
def test_set_ops_error_cases(self, case, method):
for name, idx in self.indices.items():
# non-iterable input
msg = "Input must be Index or array-like"
with pytest.raises(TypeError, match=msg):
getattr(idx, method)(case)
def test_intersection_base(self):
for name, idx in self.indices.items():
first = idx[:5]
second = idx[:3]
intersect = first.intersection(second)
if isinstance(idx, CategoricalIndex):
pass
else:
assert tm.equalContents(intersect, second)
# GH 10149
cases = [klass(second.values)
for klass in [np.array, Series, list]]
for case in cases:
if isinstance(idx, CategoricalIndex):
pass
else:
result = first.intersection(case)
assert tm.equalContents(result, second)
if isinstance(idx, MultiIndex):
msg = "other must be a MultiIndex or a list of tuples"
with pytest.raises(TypeError, match=msg):
first.intersection([1, 2, 3])
def test_union_base(self):
for name, idx in self.indices.items():
first = idx[3:]
second = idx[:5]
everything = idx
union = first.union(second)
assert tm.equalContents(union, everything)
# GH 10149
cases = [klass(second.values)
for klass in [np.array, Series, list]]
for case in cases:
if isinstance(idx, CategoricalIndex):
pass
else:
result = first.union(case)
assert tm.equalContents(result, everything)
if isinstance(idx, MultiIndex):
msg = "other must be a MultiIndex or a list of tuples"
with pytest.raises(TypeError, match=msg):
first.union([1, 2, 3])
@pytest.mark.parametrize("sort", [None, False])
def test_difference_base(self, sort):
for name, idx in self.indices.items():
first = idx[2:]
second = idx[:4]
answer = idx[4:]
result = first.difference(second, sort)
if isinstance(idx, CategoricalIndex):
pass
else:
assert tm.equalContents(result, answer)
# GH 10149
cases = [klass(second.values)
for klass in [np.array, Series, list]]
for case in cases:
if isinstance(idx, CategoricalIndex):
pass
elif isinstance(idx, (DatetimeIndex, TimedeltaIndex)):
assert result.__class__ == answer.__class__
tm.assert_numpy_array_equal(result.sort_values().asi8,
answer.sort_values().asi8)
else:
result = first.difference(case, sort)
assert tm.equalContents(result, answer)
if isinstance(idx, MultiIndex):
msg = "other must be a MultiIndex or a list of tuples"
with pytest.raises(TypeError, match=msg):
first.difference([1, 2, 3], sort)
def test_symmetric_difference(self):
for name, idx in self.indices.items():
first = idx[1:]
second = idx[:-1]
if isinstance(idx, CategoricalIndex):
pass
else:
answer = idx[[0, -1]]
result = first.symmetric_difference(second)
assert tm.equalContents(result, answer)
# GH 10149
cases = [klass(second.values)
for klass in [np.array, Series, list]]
for case in cases:
if isinstance(idx, CategoricalIndex):
pass
else:
result = first.symmetric_difference(case)
assert tm.equalContents(result, answer)
if isinstance(idx, MultiIndex):
msg = "other must be a MultiIndex or a list of tuples"
with pytest.raises(TypeError, match=msg):
first.symmetric_difference([1, 2, 3])
def test_insert_base(self):
for name, idx in self.indices.items():
result = idx[1:4]
if not len(idx):
continue
# test 0th element
assert idx[0:4].equals(result.insert(0, idx[0]))
def test_delete_base(self):
for name, idx in self.indices.items():
if not len(idx):
continue
if isinstance(idx, RangeIndex):
# tested in class
continue
expected = idx[1:]
result = idx.delete(0)
assert result.equals(expected)
assert result.name == expected.name
expected = idx[:-1]
result = idx.delete(-1)
assert result.equals(expected)
assert result.name == expected.name
with pytest.raises((IndexError, ValueError)):
# either depending on numpy version
idx.delete(len(idx))
def test_equals(self):
for name, idx in self.indices.items():
assert idx.equals(idx)
assert idx.equals(idx.copy())
assert idx.equals(idx.astype(object))
assert not idx.equals(list(idx))
assert not idx.equals(np.array(idx))
# Cannot pass in non-int64 dtype to RangeIndex
if not isinstance(idx, RangeIndex):
same_values = Index(idx, dtype=object)
assert idx.equals(same_values)
assert same_values.equals(idx)
if idx.nlevels == 1:
# do not test MultiIndex
assert not idx.equals(pd.Series(idx))
def test_equals_op(self):
# GH9947, GH10637
index_a = self.create_index()
if isinstance(index_a, PeriodIndex):
pytest.skip('Skip check for PeriodIndex')
n = len(index_a)
index_b = index_a[0:-1]
index_c = index_a[0:-1].append(index_a[-2:-1])
index_d = index_a[0:1]
msg = "Lengths must match|could not be broadcast"
with pytest.raises(ValueError, match=msg):
index_a == index_b
expected1 = np.array([True] * n)
expected2 = np.array([True] * (n - 1) + [False])
tm.assert_numpy_array_equal(index_a == index_a, expected1)
tm.assert_numpy_array_equal(index_a == index_c, expected2)
# test comparisons with numpy arrays
array_a = np.array(index_a)
array_b = np.array(index_a[0:-1])
array_c = np.array(index_a[0:-1].append(index_a[-2:-1]))
array_d = np.array(index_a[0:1])
with pytest.raises(ValueError, match=msg):
index_a == array_b
tm.assert_numpy_array_equal(index_a == array_a, expected1)
tm.assert_numpy_array_equal(index_a == array_c, expected2)
# test comparisons with Series
series_a = Series(array_a)
series_b = Series(array_b)
series_c = Series(array_c)
series_d = Series(array_d)
with pytest.raises(ValueError, match=msg):
index_a == series_b
tm.assert_numpy_array_equal(index_a == series_a, expected1)
tm.assert_numpy_array_equal(index_a == series_c, expected2)
# cases where length is 1 for one of them
with pytest.raises(ValueError, match="Lengths must match"):
index_a == index_d
with pytest.raises(ValueError, match="Lengths must match"):
index_a == series_d
with pytest.raises(ValueError, match="Lengths must match"):
index_a == array_d
msg = "Can only compare identically-labeled Series objects"
with pytest.raises(ValueError, match=msg):
series_a == series_d
with pytest.raises(ValueError, match="Lengths must match"):
series_a == array_d
# comparing with a scalar should broadcast; note that we are excluding
# MultiIndex because in this case each item in the index is a tuple of
# length 2, and therefore is considered an array of length 2 in the
# comparison instead of a scalar
if not isinstance(index_a, MultiIndex):
expected3 = np.array([False] * (len(index_a) - 2) + [True, False])
# assuming the 2nd to last item is unique in the data
item = index_a[-2]
tm.assert_numpy_array_equal(index_a == item, expected3)
tm.assert_series_equal(series_a == item, Series(expected3))
def test_hasnans_isnans(self):
# GH 11343, added tests for hasnans / isnans
for name, index in self.indices.items():
if isinstance(index, MultiIndex):
pass
else:
idx = index.copy()
# cases in indices doesn't include NaN
expected = np.array([False] * len(idx), dtype=bool)
tm.assert_numpy_array_equal(idx._isnan, expected)
assert idx.hasnans is False
idx = index.copy()
values = np.asarray(idx.values)
if len(index) == 0:
continue
elif isinstance(index, DatetimeIndexOpsMixin):
values[1] = iNaT
elif isinstance(index, (Int64Index, UInt64Index)):
continue
else:
values[1] = np.nan
if isinstance(index, PeriodIndex):
idx = index.__class__(values, freq=index.freq)
else:
idx = index.__class__(values)
expected = np.array([False] * len(idx), dtype=bool)
expected[1] = True
tm.assert_numpy_array_equal(idx._isnan, expected)
assert idx.hasnans is True
def test_fillna(self):
# GH 11343
for name, index in self.indices.items():
if len(index) == 0:
pass
elif isinstance(index, MultiIndex):
idx = index.copy()
msg = "isna is not defined for MultiIndex"
with pytest.raises(NotImplementedError, match=msg):
idx.fillna(idx[0])
else:
idx = index.copy()
result = idx.fillna(idx[0])
tm.assert_index_equal(result, idx)
assert result is not idx
msg = "'value' must be a scalar, passed: "
with pytest.raises(TypeError, match=msg):
idx.fillna([idx[0]])
idx = index.copy()
values = np.asarray(idx.values)
if isinstance(index, DatetimeIndexOpsMixin):
values[1] = iNaT
elif isinstance(index, (Int64Index, UInt64Index)):
continue
else:
values[1] = np.nan
if isinstance(index, PeriodIndex):
idx = index.__class__(values, freq=index.freq)
else:
idx = index.__class__(values)
expected = np.array([False] * len(idx), dtype=bool)
expected[1] = True
tm.assert_numpy_array_equal(idx._isnan, expected)
assert idx.hasnans is True
def test_nulls(self):
# this is really a smoke test for the methods
# as these are adequately tested for function elsewhere
for name, index in self.indices.items():
if len(index) == 0:
tm.assert_numpy_array_equal(
index.isna(), np.array([], dtype=bool))
elif isinstance(index, MultiIndex):
idx = index.copy()
msg = "isna is not defined for MultiIndex"
with pytest.raises(NotImplementedError, match=msg):
idx.isna()
else:
if not index.hasnans:
tm.assert_numpy_array_equal(
index.isna(), np.zeros(len(index), dtype=bool))
tm.assert_numpy_array_equal(
index.notna(), np.ones(len(index), dtype=bool))
else:
result = isna(index)
tm.assert_numpy_array_equal(index.isna(), result)
tm.assert_numpy_array_equal(index.notna(), ~result)
def test_empty(self):
# GH 15270
index = self.create_index()
assert not index.empty
assert index[:0].empty
def test_join_self_unique(self, join_type):
index = self.create_index()
if index.is_unique:
joined = index.join(index, how=join_type)
assert (index == joined).all()
def test_map(self):
# callable
index = self.create_index()
# we don't infer UInt64
if isinstance(index, pd.UInt64Index):
expected = index.astype('int64')
else:
expected = index
result = index.map(lambda x: x)
tm.assert_index_equal(result, expected)
@pytest.mark.parametrize(
"mapper",
[
lambda values, index: {i: e for e, i in zip(values, index)},
lambda values, index: pd.Series(values, index)])
def test_map_dictlike(self, mapper):
index = self.create_index()
if isinstance(index, (pd.CategoricalIndex, pd.IntervalIndex)):
pytest.skip("skipping tests for {}".format(type(index)))
identity = mapper(index.values, index)
# we don't infer to UInt64 for a dict
if isinstance(index, pd.UInt64Index) and isinstance(identity, dict):
expected = index.astype('int64')
else:
expected = index
result = index.map(identity)
tm.assert_index_equal(result, expected)
# empty mappable
expected = pd.Index([np.nan] * len(index))
result = index.map(mapper(expected, index))
tm.assert_index_equal(result, expected)
def test_putmask_with_wrong_mask(self):
# GH18368
index = self.create_index()
with pytest.raises(ValueError):
index.putmask(np.ones(len(index) + 1, np.bool), 1)
with pytest.raises(ValueError):
index.putmask(np.ones(len(index) - 1, np.bool), 1)
with pytest.raises(ValueError):
index.putmask('foo', 1)
@pytest.mark.parametrize('copy', [True, False])
@pytest.mark.parametrize('name', [None, 'foo'])
@pytest.mark.parametrize('ordered', [True, False])
def test_astype_category(self, copy, name, ordered):
# GH 18630
index = self.create_index()
if name:
index = index.rename(name)
# standard categories
dtype = CategoricalDtype(ordered=ordered)
result = index.astype(dtype, copy=copy)
expected = CategoricalIndex(index.values, name=name, ordered=ordered)
tm.assert_index_equal(result, expected)
# non-standard categories
dtype = CategoricalDtype(index.unique().tolist()[:-1], ordered)
result = index.astype(dtype, copy=copy)
expected = CategoricalIndex(index.values, name=name, dtype=dtype)
tm.assert_index_equal(result, expected)
if ordered is False:
# dtype='category' defaults to ordered=False, so only test once
result = index.astype('category', copy=copy)
expected = CategoricalIndex(index.values, name=name)
tm.assert_index_equal(result, expected)
def test_is_unique(self):
# initialize a unique index
index = self.create_index().drop_duplicates()
assert index.is_unique is True
# empty index should be unique
index_empty = index[:0]
assert index_empty.is_unique is True
# test basic dupes
index_dup = index.insert(0, index[0])
assert index_dup.is_unique is False
# single NA should be unique
index_na = index.insert(0, np.nan)
assert index_na.is_unique is True
# multiple NA should not be unique
index_na_dup = index_na.insert(0, np.nan)
assert index_na_dup.is_unique is False
|
from collections import OrderedDict
import numpy as np
from numpy import nan
from numpy.random import randn
import pytest
import pandas as pd
from pandas import DataFrame, Index, MultiIndex, Series
from pandas.core.reshape.concat import concat
from pandas.core.reshape.merge import merge
import pandas.util.testing as tm
@pytest.fixture
def left():
"""left dataframe (not multi-indexed) for multi-index join tests"""
# a little relevant example with NAs
key1 = ['bar', 'bar', 'bar', 'foo', 'foo', 'baz', 'baz', 'qux',
'qux', 'snap']
key2 = ['two', 'one', 'three', 'one', 'two', 'one', 'two', 'two',
'three', 'one']
data = np.random.randn(len(key1))
return DataFrame({'key1': key1, 'key2': key2, 'data': data})
@pytest.fixture
def right():
"""right dataframe (multi-indexed) for multi-index join tests"""
index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'],
['one', 'two', 'three']],
codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
[0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
names=['key1', 'key2'])
return DataFrame(np.random.randn(10, 3), index=index,
columns=['j_one', 'j_two', 'j_three'])
@pytest.fixture
def left_multi():
return (
DataFrame(
dict(Origin=['A', 'A', 'B', 'B', 'C'],
Destination=['A', 'B', 'A', 'C', 'A'],
Period=['AM', 'AM', 'IP', 'AM', 'OP'],
TripPurp=['hbw', 'nhb', 'hbo', 'nhb', 'hbw'],
Trips=[1987, 3647, 2470, 4296, 4444]),
columns=['Origin', 'Destination', 'Period',
'TripPurp', 'Trips'])
.set_index(['Origin', 'Destination', 'Period', 'TripPurp']))
@pytest.fixture
def right_multi():
return (
DataFrame(
dict(Origin=['A', 'A', 'B', 'B', 'C', 'C', 'E'],
Destination=['A', 'B', 'A', 'B', 'A', 'B', 'F'],
Period=['AM', 'AM', 'IP', 'AM', 'OP', 'IP', 'AM'],
LinkType=['a', 'b', 'c', 'b', 'a', 'b', 'a'],
Distance=[100, 80, 90, 80, 75, 35, 55]),
columns=['Origin', 'Destination', 'Period',
'LinkType', 'Distance'])
.set_index(['Origin', 'Destination', 'Period', 'LinkType']))
@pytest.fixture
def on_cols_multi():
return ['Origin', 'Destination', 'Period']
@pytest.fixture
def idx_cols_multi():
return ['Origin', 'Destination', 'Period', 'TripPurp', 'LinkType']
class TestMergeMulti:
def setup_method(self):
self.index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'],
['one', 'two', 'three']],
codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
[0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
names=['first', 'second'])
self.to_join = DataFrame(np.random.randn(10, 3), index=self.index,
columns=['j_one', 'j_two', 'j_three'])
# a little relevant example with NAs
key1 = ['bar', 'bar', 'bar', 'foo', 'foo', 'baz', 'baz', 'qux',
'qux', 'snap']
key2 = ['two', 'one', 'three', 'one', 'two', 'one', 'two', 'two',
'three', 'one']
data = np.random.randn(len(key1))
self.data = DataFrame({'key1': key1, 'key2': key2,
'data': data})
def test_merge_on_multikey(self, left, right, join_type):
on_cols = ['key1', 'key2']
result = (left.join(right, on=on_cols, how=join_type)
.reset_index(drop=True))
expected = pd.merge(left, right.reset_index(),
on=on_cols, how=join_type)
tm.assert_frame_equal(result, expected)
result = (left.join(right, on=on_cols, how=join_type, sort=True)
.reset_index(drop=True))
expected = pd.merge(left, right.reset_index(),
on=on_cols, how=join_type, sort=True)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("sort", [False, True])
def test_left_join_multi_index(self, left, right, sort):
icols = ['1st', '2nd', '3rd']
def bind_cols(df):
iord = lambda a: 0 if a != a else ord(a)
f = lambda ts: ts.map(iord) - ord('a')
return (f(df['1st']) + f(df['3rd']) * 1e2 +
df['2nd'].fillna(0) * 1e4)
def run_asserts(left, right, sort):
res = left.join(right, on=icols, how='left', sort=sort)
assert len(left) < len(res) + 1
assert not res['4th'].isna().any()
assert not res['5th'].isna().any()
tm.assert_series_equal(
res['4th'], - res['5th'], check_names=False)
result = bind_cols(res.iloc[:, :-2])
tm.assert_series_equal(res['4th'], result, check_names=False)
assert result.name is None
if sort:
tm.assert_frame_equal(
res, res.sort_values(icols, kind='mergesort'))
out = merge(left, right.reset_index(), on=icols,
sort=sort, how='left')
res.index = np.arange(len(res))
tm.assert_frame_equal(out, res)
lc = list(map(chr, np.arange(ord('a'), ord('z') + 1)))
left = DataFrame(np.random.choice(lc, (5000, 2)),
columns=['1st', '3rd'])
left.insert(1, '2nd', np.random.randint(0, 1000, len(left)))
i = np.random.permutation(len(left))
right = left.iloc[i].copy()
left['4th'] = bind_cols(left)
right['5th'] = - bind_cols(right)
right.set_index(icols, inplace=True)
run_asserts(left, right, sort)
# inject some nulls
left.loc[1::23, '1st'] = np.nan
left.loc[2::37, '2nd'] = np.nan
left.loc[3::43, '3rd'] = np.nan
left['4th'] = bind_cols(left)
i = np.random.permutation(len(left))
right = left.iloc[i, :-1]
right['5th'] = - bind_cols(right)
right.set_index(icols, inplace=True)
run_asserts(left, right, sort)
@pytest.mark.parametrize("sort", [False, True])
def test_merge_right_vs_left(self, left, right, sort):
# compare left vs right merge with multikey
on_cols = ['key1', 'key2']
merged_left_right = left.merge(right,
left_on=on_cols, right_index=True,
how='left', sort=sort)
merge_right_left = right.merge(left,
right_on=on_cols, left_index=True,
how='right', sort=sort)
# Reorder columns
merge_right_left = merge_right_left[merged_left_right.columns]
tm.assert_frame_equal(merged_left_right, merge_right_left)
def test_compress_group_combinations(self):
# ~ 40000000 possible unique groups
key1 = tm.rands_array(10, 10000)
key1 = np.tile(key1, 2)
key2 = key1[::-1]
df = DataFrame({'key1': key1, 'key2': key2,
'value1': np.random.randn(20000)})
df2 = DataFrame({'key1': key1[::2], 'key2': key2[::2],
'value2': np.random.randn(10000)})
# just to hit the label compression code path
merge(df, df2, how='outer')
def test_left_join_index_preserve_order(self):
on_cols = ['k1', 'k2']
left = DataFrame({'k1': [0, 1, 2] * 8,
'k2': ['foo', 'bar'] * 12,
'v': np.array(np.arange(24), dtype=np.int64)})
index = MultiIndex.from_tuples([(2, 'bar'), (1, 'foo')])
right = DataFrame({'v2': [5, 7]}, index=index)
result = left.join(right, on=on_cols)
expected = left.copy()
expected['v2'] = np.nan
expected.loc[(expected.k1 == 2) & (expected.k2 == 'bar'), 'v2'] = 5
expected.loc[(expected.k1 == 1) & (expected.k2 == 'foo'), 'v2'] = 7
tm.assert_frame_equal(result, expected)
result.sort_values(on_cols, kind='mergesort', inplace=True)
expected = left.join(right, on=on_cols, sort=True)
tm.assert_frame_equal(result, expected)
# test join with multi dtypes blocks
left = DataFrame({'k1': [0, 1, 2] * 8,
'k2': ['foo', 'bar'] * 12,
'k3': np.array([0, 1, 2] * 8, dtype=np.float32),
'v': np.array(np.arange(24), dtype=np.int32)})
index = MultiIndex.from_tuples([(2, 'bar'), (1, 'foo')])
right = DataFrame({'v2': [5, 7]}, index=index)
result = left.join(right, on=on_cols)
expected = left.copy()
expected['v2'] = np.nan
expected.loc[(expected.k1 == 2) & (expected.k2 == 'bar'), 'v2'] = 5
expected.loc[(expected.k1 == 1) & (expected.k2 == 'foo'), 'v2'] = 7
tm.assert_frame_equal(result, expected)
result = result.sort_values(on_cols, kind='mergesort')
expected = left.join(right, on=on_cols, sort=True)
tm.assert_frame_equal(result, expected)
def test_left_join_index_multi_match_multiindex(self):
left = DataFrame([
['X', 'Y', 'C', 'a'],
['W', 'Y', 'C', 'e'],
['V', 'Q', 'A', 'h'],
['V', 'R', 'D', 'i'],
['X', 'Y', 'D', 'b'],
['X', 'Y', 'A', 'c'],
['W', 'Q', 'B', 'f'],
['W', 'R', 'C', 'g'],
['V', 'Y', 'C', 'j'],
['X', 'Y', 'B', 'd']],
columns=['cola', 'colb', 'colc', 'tag'],
index=[3, 2, 0, 1, 7, 6, 4, 5, 9, 8])
right = (DataFrame([
['W', 'R', 'C', 0],
['W', 'Q', 'B', 3],
['W', 'Q', 'B', 8],
['X', 'Y', 'A', 1],
['X', 'Y', 'A', 4],
['X', 'Y', 'B', 5],
['X', 'Y', 'C', 6],
['X', 'Y', 'C', 9],
['X', 'Q', 'C', -6],
['X', 'R', 'C', -9],
['V', 'Y', 'C', 7],
['V', 'R', 'D', 2],
['V', 'R', 'D', -1],
['V', 'Q', 'A', -3]],
columns=['col1', 'col2', 'col3', 'val'])
.set_index(['col1', 'col2', 'col3']))
result = left.join(right, on=['cola', 'colb', 'colc'], how='left')
expected = DataFrame([
['X', 'Y', 'C', 'a', 6],
['X', 'Y', 'C', 'a', 9],
['W', 'Y', 'C', 'e', nan],
['V', 'Q', 'A', 'h', -3],
['V', 'R', 'D', 'i', 2],
['V', 'R', 'D', 'i', -1],
['X', 'Y', 'D', 'b', nan],
['X', 'Y', 'A', 'c', 1],
['X', 'Y', 'A', 'c', 4],
['W', 'Q', 'B', 'f', 3],
['W', 'Q', 'B', 'f', 8],
['W', 'R', 'C', 'g', 0],
['V', 'Y', 'C', 'j', 7],
['X', 'Y', 'B', 'd', 5]],
columns=['cola', 'colb', 'colc', 'tag', 'val'],
index=[3, 3, 2, 0, 1, 1, 7, 6, 6, 4, 4, 5, 9, 8])
tm.assert_frame_equal(result, expected)
result = left.join(right, on=['cola', 'colb', 'colc'],
how='left', sort=True)
expected = expected.sort_values(['cola', 'colb', 'colc'],
kind='mergesort')
tm.assert_frame_equal(result, expected)
def test_left_join_index_multi_match(self):
left = DataFrame([
['c', 0],
['b', 1],
['a', 2],
['b', 3]],
columns=['tag', 'val'],
index=[2, 0, 1, 3])
right = (DataFrame([
['a', 'v'],
['c', 'w'],
['c', 'x'],
['d', 'y'],
['a', 'z'],
['c', 'r'],
['e', 'q'],
['c', 's']],
columns=['tag', 'char'])
.set_index('tag'))
result = left.join(right, on='tag', how='left')
expected = DataFrame([
['c', 0, 'w'],
['c', 0, 'x'],
['c', 0, 'r'],
['c', 0, 's'],
['b', 1, nan],
['a', 2, 'v'],
['a', 2, 'z'],
['b', 3, nan]],
columns=['tag', 'val', 'char'],
index=[2, 2, 2, 2, 0, 1, 1, 3])
tm.assert_frame_equal(result, expected)
result = left.join(right, on='tag', how='left', sort=True)
expected2 = expected.sort_values('tag', kind='mergesort')
tm.assert_frame_equal(result, expected2)
# GH7331 - maintain left frame order in left merge
result = merge(left, right.reset_index(), how='left', on='tag')
expected.index = np.arange(len(expected))
tm.assert_frame_equal(result, expected)
def test_left_merge_na_buglet(self):
left = DataFrame({'id': list('abcde'), 'v1': randn(5),
'v2': randn(5), 'dummy': list('abcde'),
'v3': randn(5)},
columns=['id', 'v1', 'v2', 'dummy', 'v3'])
right = DataFrame({'id': ['a', 'b', np.nan, np.nan, np.nan],
'sv3': [1.234, 5.678, np.nan, np.nan, np.nan]})
result = merge(left, right, on='id', how='left')
rdf = right.drop(['id'], axis=1)
expected = left.join(rdf)
tm.assert_frame_equal(result, expected)
def test_merge_na_keys(self):
data = [[1950, "A", 1.5],
[1950, "B", 1.5],
[1955, "B", 1.5],
[1960, "B", np.nan],
[1970, "B", 4.],
[1950, "C", 4.],
[1960, "C", np.nan],
[1965, "C", 3.],
[1970, "C", 4.]]
frame = DataFrame(data, columns=["year", "panel", "data"])
other_data = [[1960, 'A', np.nan],
[1970, 'A', np.nan],
[1955, 'A', np.nan],
[1965, 'A', np.nan],
[1965, 'B', np.nan],
[1955, 'C', np.nan]]
other = DataFrame(other_data, columns=['year', 'panel', 'data'])
result = frame.merge(other, how='outer')
expected = frame.fillna(-999).merge(other.fillna(-999), how='outer')
expected = expected.replace(-999, np.nan)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("klass", [None, np.asarray, Series, Index])
def test_merge_datetime_index(self, klass):
# see gh-19038
df = DataFrame([1, 2, 3],
["2016-01-01", "2017-01-01", "2018-01-01"],
columns=["a"])
df.index = pd.to_datetime(df.index)
on_vector = df.index.year
if klass is not None:
on_vector = klass(on_vector)
expected = DataFrame(
OrderedDict([
("a", [1, 2, 3]),
("key_1", [2016, 2017, 2018]),
])
)
result = df.merge(df, on=["a", on_vector], how="inner")
tm.assert_frame_equal(result, expected)
expected = DataFrame(
OrderedDict([
("key_0", [2016, 2017, 2018]),
("a_x", [1, 2, 3]),
("a_y", [1, 2, 3]),
])
)
result = df.merge(df, on=[df.index.year], how="inner")
tm.assert_frame_equal(result, expected)
def test_join_multi_levels(self):
# GH 3662
# merge multi-levels
household = (
DataFrame(
dict(household_id=[1, 2, 3],
male=[0, 1, 0],
wealth=[196087.3, 316478.7, 294750]),
columns=['household_id', 'male', 'wealth'])
.set_index('household_id'))
portfolio = (
DataFrame(
dict(household_id=[1, 2, 2, 3, 3, 3, 4],
asset_id=["nl0000301109", "nl0000289783", "gb00b03mlx29",
"gb00b03mlx29", "lu0197800237", "nl0000289965",
np.nan],
name=["ABN Amro", "Robeco", "Royal Dutch Shell",
"Royal Dutch Shell",
"AAB Eastern Europe Equity Fund",
"Postbank BioTech Fonds", np.nan],
share=[1.0, 0.4, 0.6, 0.15, 0.6, 0.25, 1.0]),
columns=['household_id', 'asset_id', 'name', 'share'])
.set_index(['household_id', 'asset_id']))
result = household.join(portfolio, how='inner')
expected = (
DataFrame(
dict(male=[0, 1, 1, 0, 0, 0],
wealth=[196087.3, 316478.7, 316478.7,
294750.0, 294750.0, 294750.0],
name=['ABN Amro', 'Robeco', 'Royal Dutch Shell',
'Royal Dutch Shell',
'AAB Eastern Europe Equity Fund',
'Postbank BioTech Fonds'],
share=[1.00, 0.40, 0.60, 0.15, 0.60, 0.25],
household_id=[1, 2, 2, 3, 3, 3],
asset_id=['nl0000301109', 'nl0000289783', 'gb00b03mlx29',
'gb00b03mlx29', 'lu0197800237',
'nl0000289965']))
.set_index(['household_id', 'asset_id'])
.reindex(columns=['male', 'wealth', 'name', 'share']))
tm.assert_frame_equal(result, expected)
# equivalency
result = (merge(household.reset_index(), portfolio.reset_index(),
on=['household_id'], how='inner')
.set_index(['household_id', 'asset_id']))
tm.assert_frame_equal(result, expected)
result = household.join(portfolio, how='outer')
expected = (concat([
expected,
(DataFrame(
dict(share=[1.00]),
index=MultiIndex.from_tuples(
[(4, np.nan)],
names=['household_id', 'asset_id'])))
], axis=0, sort=True).reindex(columns=expected.columns))
tm.assert_frame_equal(result, expected)
# invalid cases
household.index.name = 'foo'
with pytest.raises(ValueError):
household.join(portfolio, how='inner')
portfolio2 = portfolio.copy()
portfolio2.index.set_names(['household_id', 'foo'])
with pytest.raises(ValueError):
portfolio2.join(portfolio, how='inner')
def test_join_multi_levels2(self):
# some more advanced merges
# GH6360
household = (
DataFrame(
dict(household_id=[1, 2, 2, 3, 3, 3, 4],
asset_id=["nl0000301109", "nl0000301109", "gb00b03mlx29",
"gb00b03mlx29", "lu0197800237", "nl0000289965",
np.nan],
share=[1.0, 0.4, 0.6, 0.15, 0.6, 0.25, 1.0]),
columns=['household_id', 'asset_id', 'share'])
.set_index(['household_id', 'asset_id']))
log_return = DataFrame(dict(
asset_id=["gb00b03mlx29", "gb00b03mlx29",
"gb00b03mlx29", "lu0197800237", "lu0197800237"],
t=[233, 234, 235, 180, 181],
log_return=[.09604978, -.06524096, .03532373, .03025441, .036997]
)).set_index(["asset_id", "t"])
expected = (
DataFrame(dict(
household_id=[2, 2, 2, 3, 3, 3, 3, 3],
asset_id=["gb00b03mlx29", "gb00b03mlx29",
"gb00b03mlx29", "gb00b03mlx29",
"gb00b03mlx29", "gb00b03mlx29",
"lu0197800237", "lu0197800237"],
t=[233, 234, 235, 233, 234, 235, 180, 181],
share=[0.6, 0.6, 0.6, 0.15, 0.15, 0.15, 0.6, 0.6],
log_return=[.09604978, -.06524096, .03532373,
.09604978, -.06524096, .03532373,
.03025441, .036997]
))
.set_index(["household_id", "asset_id", "t"])
.reindex(columns=['share', 'log_return']))
# this is the equivalency
result = (merge(household.reset_index(), log_return.reset_index(),
on=['asset_id'], how='inner')
.set_index(['household_id', 'asset_id', 't']))
tm.assert_frame_equal(result, expected)
expected = (
DataFrame(dict(
household_id=[1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 4],
asset_id=["nl0000301109", "nl0000301109", "gb00b03mlx29",
"gb00b03mlx29", "gb00b03mlx29",
"gb00b03mlx29", "gb00b03mlx29", "gb00b03mlx29",
"lu0197800237", "lu0197800237",
"nl0000289965", None],
t=[None, None, 233, 234, 235, 233, 234,
235, 180, 181, None, None],
share=[1.0, 0.4, 0.6, 0.6, 0.6, 0.15,
0.15, 0.15, 0.6, 0.6, 0.25, 1.0],
log_return=[None, None, .09604978, -.06524096, .03532373,
.09604978, -.06524096, .03532373,
.03025441, .036997, None, None]
))
.set_index(["household_id", "asset_id", "t"])
.reindex(columns=['share', 'log_return']))
result = (merge(household.reset_index(), log_return.reset_index(),
on=['asset_id'], how='outer')
.set_index(['household_id', 'asset_id', 't']))
tm.assert_frame_equal(result, expected)
class TestJoinMultiMulti:
def test_join_multi_multi(self, left_multi, right_multi, join_type,
on_cols_multi, idx_cols_multi):
# Multi-index join tests
expected = (pd.merge(left_multi.reset_index(),
right_multi.reset_index(),
how=join_type, on=on_cols_multi).
set_index(idx_cols_multi).sort_index())
result = left_multi.join(right_multi, how=join_type).sort_index()
tm.assert_frame_equal(result, expected)
def test_join_multi_empty_frames(self, left_multi, right_multi, join_type,
on_cols_multi, idx_cols_multi):
left_multi = left_multi.drop(columns=left_multi.columns)
right_multi = right_multi.drop(columns=right_multi.columns)
expected = (pd.merge(left_multi.reset_index(),
right_multi.reset_index(),
how=join_type, on=on_cols_multi)
.set_index(idx_cols_multi).sort_index())
result = left_multi.join(right_multi, how=join_type).sort_index()
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("box", [None, np.asarray, Series, Index])
def test_merge_datetime_index(self, box):
# see gh-19038
df = DataFrame([1, 2, 3],
["2016-01-01", "2017-01-01", "2018-01-01"],
columns=["a"])
df.index = pd.to_datetime(df.index)
on_vector = df.index.year
if box is not None:
on_vector = box(on_vector)
expected = DataFrame(
OrderedDict([
("a", [1, 2, 3]),
("key_1", [2016, 2017, 2018]),
])
)
result = df.merge(df, on=["a", on_vector], how="inner")
tm.assert_frame_equal(result, expected)
expected = DataFrame(
OrderedDict([
("key_0", [2016, 2017, 2018]),
("a_x", [1, 2, 3]),
("a_y", [1, 2, 3]),
])
)
result = df.merge(df, on=[df.index.year], how="inner")
tm.assert_frame_equal(result, expected)
def test_single_common_level(self):
index_left = pd.MultiIndex.from_tuples([('K0', 'X0'), ('K0', 'X1'),
('K1', 'X2')],
names=['key', 'X'])
left = pd.DataFrame({'A': ['A0', 'A1', 'A2'],
'B': ['B0', 'B1', 'B2']},
index=index_left)
index_right = pd.MultiIndex.from_tuples([('K0', 'Y0'), ('K1', 'Y1'),
('K2', 'Y2'), ('K2', 'Y3')],
names=['key', 'Y'])
right = pd.DataFrame({'C': ['C0', 'C1', 'C2', 'C3'],
'D': ['D0', 'D1', 'D2', 'D3']},
index=index_right)
result = left.join(right)
expected = (pd.merge(left.reset_index(), right.reset_index(),
on=['key'], how='inner')
.set_index(['key', 'X', 'Y']))
tm.assert_frame_equal(result, expected)
|
|
pandas/tests/reshape/merge/test_multi.py
|
pandas/tests/indexes/common.py
|
import numpy as np
from pandas._libs import algos as libalgos, index as libindex
import pandas.util.testing as tm
class TestNumericEngine:
def test_is_monotonic(self, numeric_indexing_engine_type_and_dtype):
engine_type, dtype = numeric_indexing_engine_type_and_dtype
num = 1000
arr = np.array([1] * num + [2] * num + [3] * num, dtype=dtype)
# monotonic increasing
engine = engine_type(lambda: arr, len(arr))
assert engine.is_monotonic_increasing is True
assert engine.is_monotonic_decreasing is False
# monotonic decreasing
engine = engine_type(lambda: arr[::-1], len(arr))
assert engine.is_monotonic_increasing is False
assert engine.is_monotonic_decreasing is True
# neither monotonic increasing or decreasing
arr = np.array([1] * num + [2] * num + [1] * num, dtype=dtype)
engine = engine_type(lambda: arr[::-1], len(arr))
assert engine.is_monotonic_increasing is False
assert engine.is_monotonic_decreasing is False
def test_is_unique(self, numeric_indexing_engine_type_and_dtype):
engine_type, dtype = numeric_indexing_engine_type_and_dtype
# unique
arr = np.array([1, 3, 2], dtype=dtype)
engine = engine_type(lambda: arr, len(arr))
assert engine.is_unique is True
# not unique
arr = np.array([1, 2, 1], dtype=dtype)
engine = engine_type(lambda: arr, len(arr))
assert engine.is_unique is False
def test_get_loc(self, numeric_indexing_engine_type_and_dtype):
engine_type, dtype = numeric_indexing_engine_type_and_dtype
# unique
arr = np.array([1, 2, 3], dtype=dtype)
engine = engine_type(lambda: arr, len(arr))
assert engine.get_loc(2) == 1
# monotonic
num = 1000
arr = np.array([1] * num + [2] * num + [3] * num, dtype=dtype)
engine = engine_type(lambda: arr, len(arr))
assert engine.get_loc(2) == slice(1000, 2000)
# not monotonic
arr = np.array([1, 2, 3] * num, dtype=dtype)
engine = engine_type(lambda: arr, len(arr))
expected = np.array([False, True, False] * num, dtype=bool)
result = engine.get_loc(2)
assert (result == expected).all()
def test_get_backfill_indexer(
self, numeric_indexing_engine_type_and_dtype):
engine_type, dtype = numeric_indexing_engine_type_and_dtype
arr = np.array([1, 5, 10], dtype=dtype)
engine = engine_type(lambda: arr, len(arr))
new = np.arange(12, dtype=dtype)
result = engine.get_backfill_indexer(new)
expected = libalgos.backfill(arr, new)
tm.assert_numpy_array_equal(result, expected)
def test_get_pad_indexer(
self, numeric_indexing_engine_type_and_dtype):
engine_type, dtype = numeric_indexing_engine_type_and_dtype
arr = np.array([1, 5, 10], dtype=dtype)
engine = engine_type(lambda: arr, len(arr))
new = np.arange(12, dtype=dtype)
result = engine.get_pad_indexer(new)
expected = libalgos.pad(arr, new)
tm.assert_numpy_array_equal(result, expected)
class TestObjectEngine:
engine_type = libindex.ObjectEngine
dtype = np.object_
values = list('abc')
def test_is_monotonic(self):
num = 1000
arr = np.array(['a'] * num + ['a'] * num + ['c'] * num,
dtype=self.dtype)
# monotonic increasing
engine = self.engine_type(lambda: arr, len(arr))
assert engine.is_monotonic_increasing is True
assert engine.is_monotonic_decreasing is False
# monotonic decreasing
engine = self.engine_type(lambda: arr[::-1], len(arr))
assert engine.is_monotonic_increasing is False
assert engine.is_monotonic_decreasing is True
# neither monotonic increasing or decreasing
arr = np.array(['a'] * num + ['b'] * num + ['a'] * num,
dtype=self.dtype)
engine = self.engine_type(lambda: arr[::-1], len(arr))
assert engine.is_monotonic_increasing is False
assert engine.is_monotonic_decreasing is False
def test_is_unique(self):
# unique
arr = np.array(self.values, dtype=self.dtype)
engine = self.engine_type(lambda: arr, len(arr))
assert engine.is_unique is True
# not unique
arr = np.array(['a', 'b', 'a'], dtype=self.dtype)
engine = self.engine_type(lambda: arr, len(arr))
assert engine.is_unique is False
def test_get_loc(self):
# unique
arr = np.array(self.values, dtype=self.dtype)
engine = self.engine_type(lambda: arr, len(arr))
assert engine.get_loc('b') == 1
# monotonic
num = 1000
arr = np.array(['a'] * num + ['b'] * num + ['c'] * num,
dtype=self.dtype)
engine = self.engine_type(lambda: arr, len(arr))
assert engine.get_loc('b') == slice(1000, 2000)
# not monotonic
arr = np.array(self.values * num, dtype=self.dtype)
engine = self.engine_type(lambda: arr, len(arr))
expected = np.array([False, True, False] * num, dtype=bool)
result = engine.get_loc('b')
assert (result == expected).all()
def test_get_backfill_indexer(self):
arr = np.array(['a', 'e', 'j'], dtype=self.dtype)
engine = self.engine_type(lambda: arr, len(arr))
new = np.array(list('abcdefghij'), dtype=self.dtype)
result = engine.get_backfill_indexer(new)
expected = libalgos.backfill["object"](arr, new)
tm.assert_numpy_array_equal(result, expected)
def test_get_pad_indexer(self):
arr = np.array(['a', 'e', 'j'], dtype=self.dtype)
engine = self.engine_type(lambda: arr, len(arr))
new = np.array(list('abcdefghij'), dtype=self.dtype)
result = engine.get_pad_indexer(new)
expected = libalgos.pad["object"](arr, new)
tm.assert_numpy_array_equal(result, expected)
|
from collections import OrderedDict
import numpy as np
from numpy import nan
from numpy.random import randn
import pytest
import pandas as pd
from pandas import DataFrame, Index, MultiIndex, Series
from pandas.core.reshape.concat import concat
from pandas.core.reshape.merge import merge
import pandas.util.testing as tm
@pytest.fixture
def left():
"""left dataframe (not multi-indexed) for multi-index join tests"""
# a little relevant example with NAs
key1 = ['bar', 'bar', 'bar', 'foo', 'foo', 'baz', 'baz', 'qux',
'qux', 'snap']
key2 = ['two', 'one', 'three', 'one', 'two', 'one', 'two', 'two',
'three', 'one']
data = np.random.randn(len(key1))
return DataFrame({'key1': key1, 'key2': key2, 'data': data})
@pytest.fixture
def right():
"""right dataframe (multi-indexed) for multi-index join tests"""
index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'],
['one', 'two', 'three']],
codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
[0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
names=['key1', 'key2'])
return DataFrame(np.random.randn(10, 3), index=index,
columns=['j_one', 'j_two', 'j_three'])
@pytest.fixture
def left_multi():
return (
DataFrame(
dict(Origin=['A', 'A', 'B', 'B', 'C'],
Destination=['A', 'B', 'A', 'C', 'A'],
Period=['AM', 'AM', 'IP', 'AM', 'OP'],
TripPurp=['hbw', 'nhb', 'hbo', 'nhb', 'hbw'],
Trips=[1987, 3647, 2470, 4296, 4444]),
columns=['Origin', 'Destination', 'Period',
'TripPurp', 'Trips'])
.set_index(['Origin', 'Destination', 'Period', 'TripPurp']))
@pytest.fixture
def right_multi():
return (
DataFrame(
dict(Origin=['A', 'A', 'B', 'B', 'C', 'C', 'E'],
Destination=['A', 'B', 'A', 'B', 'A', 'B', 'F'],
Period=['AM', 'AM', 'IP', 'AM', 'OP', 'IP', 'AM'],
LinkType=['a', 'b', 'c', 'b', 'a', 'b', 'a'],
Distance=[100, 80, 90, 80, 75, 35, 55]),
columns=['Origin', 'Destination', 'Period',
'LinkType', 'Distance'])
.set_index(['Origin', 'Destination', 'Period', 'LinkType']))
@pytest.fixture
def on_cols_multi():
return ['Origin', 'Destination', 'Period']
@pytest.fixture
def idx_cols_multi():
return ['Origin', 'Destination', 'Period', 'TripPurp', 'LinkType']
class TestMergeMulti:
def setup_method(self):
self.index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'],
['one', 'two', 'three']],
codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
[0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
names=['first', 'second'])
self.to_join = DataFrame(np.random.randn(10, 3), index=self.index,
columns=['j_one', 'j_two', 'j_three'])
# a little relevant example with NAs
key1 = ['bar', 'bar', 'bar', 'foo', 'foo', 'baz', 'baz', 'qux',
'qux', 'snap']
key2 = ['two', 'one', 'three', 'one', 'two', 'one', 'two', 'two',
'three', 'one']
data = np.random.randn(len(key1))
self.data = DataFrame({'key1': key1, 'key2': key2,
'data': data})
def test_merge_on_multikey(self, left, right, join_type):
on_cols = ['key1', 'key2']
result = (left.join(right, on=on_cols, how=join_type)
.reset_index(drop=True))
expected = pd.merge(left, right.reset_index(),
on=on_cols, how=join_type)
tm.assert_frame_equal(result, expected)
result = (left.join(right, on=on_cols, how=join_type, sort=True)
.reset_index(drop=True))
expected = pd.merge(left, right.reset_index(),
on=on_cols, how=join_type, sort=True)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("sort", [False, True])
def test_left_join_multi_index(self, left, right, sort):
icols = ['1st', '2nd', '3rd']
def bind_cols(df):
iord = lambda a: 0 if a != a else ord(a)
f = lambda ts: ts.map(iord) - ord('a')
return (f(df['1st']) + f(df['3rd']) * 1e2 +
df['2nd'].fillna(0) * 1e4)
def run_asserts(left, right, sort):
res = left.join(right, on=icols, how='left', sort=sort)
assert len(left) < len(res) + 1
assert not res['4th'].isna().any()
assert not res['5th'].isna().any()
tm.assert_series_equal(
res['4th'], - res['5th'], check_names=False)
result = bind_cols(res.iloc[:, :-2])
tm.assert_series_equal(res['4th'], result, check_names=False)
assert result.name is None
if sort:
tm.assert_frame_equal(
res, res.sort_values(icols, kind='mergesort'))
out = merge(left, right.reset_index(), on=icols,
sort=sort, how='left')
res.index = np.arange(len(res))
tm.assert_frame_equal(out, res)
lc = list(map(chr, np.arange(ord('a'), ord('z') + 1)))
left = DataFrame(np.random.choice(lc, (5000, 2)),
columns=['1st', '3rd'])
left.insert(1, '2nd', np.random.randint(0, 1000, len(left)))
i = np.random.permutation(len(left))
right = left.iloc[i].copy()
left['4th'] = bind_cols(left)
right['5th'] = - bind_cols(right)
right.set_index(icols, inplace=True)
run_asserts(left, right, sort)
# inject some nulls
left.loc[1::23, '1st'] = np.nan
left.loc[2::37, '2nd'] = np.nan
left.loc[3::43, '3rd'] = np.nan
left['4th'] = bind_cols(left)
i = np.random.permutation(len(left))
right = left.iloc[i, :-1]
right['5th'] = - bind_cols(right)
right.set_index(icols, inplace=True)
run_asserts(left, right, sort)
@pytest.mark.parametrize("sort", [False, True])
def test_merge_right_vs_left(self, left, right, sort):
# compare left vs right merge with multikey
on_cols = ['key1', 'key2']
merged_left_right = left.merge(right,
left_on=on_cols, right_index=True,
how='left', sort=sort)
merge_right_left = right.merge(left,
right_on=on_cols, left_index=True,
how='right', sort=sort)
# Reorder columns
merge_right_left = merge_right_left[merged_left_right.columns]
tm.assert_frame_equal(merged_left_right, merge_right_left)
def test_compress_group_combinations(self):
# ~ 40000000 possible unique groups
key1 = tm.rands_array(10, 10000)
key1 = np.tile(key1, 2)
key2 = key1[::-1]
df = DataFrame({'key1': key1, 'key2': key2,
'value1': np.random.randn(20000)})
df2 = DataFrame({'key1': key1[::2], 'key2': key2[::2],
'value2': np.random.randn(10000)})
# just to hit the label compression code path
merge(df, df2, how='outer')
def test_left_join_index_preserve_order(self):
on_cols = ['k1', 'k2']
left = DataFrame({'k1': [0, 1, 2] * 8,
'k2': ['foo', 'bar'] * 12,
'v': np.array(np.arange(24), dtype=np.int64)})
index = MultiIndex.from_tuples([(2, 'bar'), (1, 'foo')])
right = DataFrame({'v2': [5, 7]}, index=index)
result = left.join(right, on=on_cols)
expected = left.copy()
expected['v2'] = np.nan
expected.loc[(expected.k1 == 2) & (expected.k2 == 'bar'), 'v2'] = 5
expected.loc[(expected.k1 == 1) & (expected.k2 == 'foo'), 'v2'] = 7
tm.assert_frame_equal(result, expected)
result.sort_values(on_cols, kind='mergesort', inplace=True)
expected = left.join(right, on=on_cols, sort=True)
tm.assert_frame_equal(result, expected)
# test join with multi dtypes blocks
left = DataFrame({'k1': [0, 1, 2] * 8,
'k2': ['foo', 'bar'] * 12,
'k3': np.array([0, 1, 2] * 8, dtype=np.float32),
'v': np.array(np.arange(24), dtype=np.int32)})
index = MultiIndex.from_tuples([(2, 'bar'), (1, 'foo')])
right = DataFrame({'v2': [5, 7]}, index=index)
result = left.join(right, on=on_cols)
expected = left.copy()
expected['v2'] = np.nan
expected.loc[(expected.k1 == 2) & (expected.k2 == 'bar'), 'v2'] = 5
expected.loc[(expected.k1 == 1) & (expected.k2 == 'foo'), 'v2'] = 7
tm.assert_frame_equal(result, expected)
result = result.sort_values(on_cols, kind='mergesort')
expected = left.join(right, on=on_cols, sort=True)
tm.assert_frame_equal(result, expected)
def test_left_join_index_multi_match_multiindex(self):
left = DataFrame([
['X', 'Y', 'C', 'a'],
['W', 'Y', 'C', 'e'],
['V', 'Q', 'A', 'h'],
['V', 'R', 'D', 'i'],
['X', 'Y', 'D', 'b'],
['X', 'Y', 'A', 'c'],
['W', 'Q', 'B', 'f'],
['W', 'R', 'C', 'g'],
['V', 'Y', 'C', 'j'],
['X', 'Y', 'B', 'd']],
columns=['cola', 'colb', 'colc', 'tag'],
index=[3, 2, 0, 1, 7, 6, 4, 5, 9, 8])
right = (DataFrame([
['W', 'R', 'C', 0],
['W', 'Q', 'B', 3],
['W', 'Q', 'B', 8],
['X', 'Y', 'A', 1],
['X', 'Y', 'A', 4],
['X', 'Y', 'B', 5],
['X', 'Y', 'C', 6],
['X', 'Y', 'C', 9],
['X', 'Q', 'C', -6],
['X', 'R', 'C', -9],
['V', 'Y', 'C', 7],
['V', 'R', 'D', 2],
['V', 'R', 'D', -1],
['V', 'Q', 'A', -3]],
columns=['col1', 'col2', 'col3', 'val'])
.set_index(['col1', 'col2', 'col3']))
result = left.join(right, on=['cola', 'colb', 'colc'], how='left')
expected = DataFrame([
['X', 'Y', 'C', 'a', 6],
['X', 'Y', 'C', 'a', 9],
['W', 'Y', 'C', 'e', nan],
['V', 'Q', 'A', 'h', -3],
['V', 'R', 'D', 'i', 2],
['V', 'R', 'D', 'i', -1],
['X', 'Y', 'D', 'b', nan],
['X', 'Y', 'A', 'c', 1],
['X', 'Y', 'A', 'c', 4],
['W', 'Q', 'B', 'f', 3],
['W', 'Q', 'B', 'f', 8],
['W', 'R', 'C', 'g', 0],
['V', 'Y', 'C', 'j', 7],
['X', 'Y', 'B', 'd', 5]],
columns=['cola', 'colb', 'colc', 'tag', 'val'],
index=[3, 3, 2, 0, 1, 1, 7, 6, 6, 4, 4, 5, 9, 8])
tm.assert_frame_equal(result, expected)
result = left.join(right, on=['cola', 'colb', 'colc'],
how='left', sort=True)
expected = expected.sort_values(['cola', 'colb', 'colc'],
kind='mergesort')
tm.assert_frame_equal(result, expected)
def test_left_join_index_multi_match(self):
left = DataFrame([
['c', 0],
['b', 1],
['a', 2],
['b', 3]],
columns=['tag', 'val'],
index=[2, 0, 1, 3])
right = (DataFrame([
['a', 'v'],
['c', 'w'],
['c', 'x'],
['d', 'y'],
['a', 'z'],
['c', 'r'],
['e', 'q'],
['c', 's']],
columns=['tag', 'char'])
.set_index('tag'))
result = left.join(right, on='tag', how='left')
expected = DataFrame([
['c', 0, 'w'],
['c', 0, 'x'],
['c', 0, 'r'],
['c', 0, 's'],
['b', 1, nan],
['a', 2, 'v'],
['a', 2, 'z'],
['b', 3, nan]],
columns=['tag', 'val', 'char'],
index=[2, 2, 2, 2, 0, 1, 1, 3])
tm.assert_frame_equal(result, expected)
result = left.join(right, on='tag', how='left', sort=True)
expected2 = expected.sort_values('tag', kind='mergesort')
tm.assert_frame_equal(result, expected2)
# GH7331 - maintain left frame order in left merge
result = merge(left, right.reset_index(), how='left', on='tag')
expected.index = np.arange(len(expected))
tm.assert_frame_equal(result, expected)
def test_left_merge_na_buglet(self):
left = DataFrame({'id': list('abcde'), 'v1': randn(5),
'v2': randn(5), 'dummy': list('abcde'),
'v3': randn(5)},
columns=['id', 'v1', 'v2', 'dummy', 'v3'])
right = DataFrame({'id': ['a', 'b', np.nan, np.nan, np.nan],
'sv3': [1.234, 5.678, np.nan, np.nan, np.nan]})
result = merge(left, right, on='id', how='left')
rdf = right.drop(['id'], axis=1)
expected = left.join(rdf)
tm.assert_frame_equal(result, expected)
def test_merge_na_keys(self):
data = [[1950, "A", 1.5],
[1950, "B", 1.5],
[1955, "B", 1.5],
[1960, "B", np.nan],
[1970, "B", 4.],
[1950, "C", 4.],
[1960, "C", np.nan],
[1965, "C", 3.],
[1970, "C", 4.]]
frame = DataFrame(data, columns=["year", "panel", "data"])
other_data = [[1960, 'A', np.nan],
[1970, 'A', np.nan],
[1955, 'A', np.nan],
[1965, 'A', np.nan],
[1965, 'B', np.nan],
[1955, 'C', np.nan]]
other = DataFrame(other_data, columns=['year', 'panel', 'data'])
result = frame.merge(other, how='outer')
expected = frame.fillna(-999).merge(other.fillna(-999), how='outer')
expected = expected.replace(-999, np.nan)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("klass", [None, np.asarray, Series, Index])
def test_merge_datetime_index(self, klass):
# see gh-19038
df = DataFrame([1, 2, 3],
["2016-01-01", "2017-01-01", "2018-01-01"],
columns=["a"])
df.index = pd.to_datetime(df.index)
on_vector = df.index.year
if klass is not None:
on_vector = klass(on_vector)
expected = DataFrame(
OrderedDict([
("a", [1, 2, 3]),
("key_1", [2016, 2017, 2018]),
])
)
result = df.merge(df, on=["a", on_vector], how="inner")
tm.assert_frame_equal(result, expected)
expected = DataFrame(
OrderedDict([
("key_0", [2016, 2017, 2018]),
("a_x", [1, 2, 3]),
("a_y", [1, 2, 3]),
])
)
result = df.merge(df, on=[df.index.year], how="inner")
tm.assert_frame_equal(result, expected)
def test_join_multi_levels(self):
# GH 3662
# merge multi-levels
household = (
DataFrame(
dict(household_id=[1, 2, 3],
male=[0, 1, 0],
wealth=[196087.3, 316478.7, 294750]),
columns=['household_id', 'male', 'wealth'])
.set_index('household_id'))
portfolio = (
DataFrame(
dict(household_id=[1, 2, 2, 3, 3, 3, 4],
asset_id=["nl0000301109", "nl0000289783", "gb00b03mlx29",
"gb00b03mlx29", "lu0197800237", "nl0000289965",
np.nan],
name=["ABN Amro", "Robeco", "Royal Dutch Shell",
"Royal Dutch Shell",
"AAB Eastern Europe Equity Fund",
"Postbank BioTech Fonds", np.nan],
share=[1.0, 0.4, 0.6, 0.15, 0.6, 0.25, 1.0]),
columns=['household_id', 'asset_id', 'name', 'share'])
.set_index(['household_id', 'asset_id']))
result = household.join(portfolio, how='inner')
expected = (
DataFrame(
dict(male=[0, 1, 1, 0, 0, 0],
wealth=[196087.3, 316478.7, 316478.7,
294750.0, 294750.0, 294750.0],
name=['ABN Amro', 'Robeco', 'Royal Dutch Shell',
'Royal Dutch Shell',
'AAB Eastern Europe Equity Fund',
'Postbank BioTech Fonds'],
share=[1.00, 0.40, 0.60, 0.15, 0.60, 0.25],
household_id=[1, 2, 2, 3, 3, 3],
asset_id=['nl0000301109', 'nl0000289783', 'gb00b03mlx29',
'gb00b03mlx29', 'lu0197800237',
'nl0000289965']))
.set_index(['household_id', 'asset_id'])
.reindex(columns=['male', 'wealth', 'name', 'share']))
tm.assert_frame_equal(result, expected)
# equivalency
result = (merge(household.reset_index(), portfolio.reset_index(),
on=['household_id'], how='inner')
.set_index(['household_id', 'asset_id']))
tm.assert_frame_equal(result, expected)
result = household.join(portfolio, how='outer')
expected = (concat([
expected,
(DataFrame(
dict(share=[1.00]),
index=MultiIndex.from_tuples(
[(4, np.nan)],
names=['household_id', 'asset_id'])))
], axis=0, sort=True).reindex(columns=expected.columns))
tm.assert_frame_equal(result, expected)
# invalid cases
household.index.name = 'foo'
with pytest.raises(ValueError):
household.join(portfolio, how='inner')
portfolio2 = portfolio.copy()
portfolio2.index.set_names(['household_id', 'foo'])
with pytest.raises(ValueError):
portfolio2.join(portfolio, how='inner')
def test_join_multi_levels2(self):
# some more advanced merges
# GH6360
household = (
DataFrame(
dict(household_id=[1, 2, 2, 3, 3, 3, 4],
asset_id=["nl0000301109", "nl0000301109", "gb00b03mlx29",
"gb00b03mlx29", "lu0197800237", "nl0000289965",
np.nan],
share=[1.0, 0.4, 0.6, 0.15, 0.6, 0.25, 1.0]),
columns=['household_id', 'asset_id', 'share'])
.set_index(['household_id', 'asset_id']))
log_return = DataFrame(dict(
asset_id=["gb00b03mlx29", "gb00b03mlx29",
"gb00b03mlx29", "lu0197800237", "lu0197800237"],
t=[233, 234, 235, 180, 181],
log_return=[.09604978, -.06524096, .03532373, .03025441, .036997]
)).set_index(["asset_id", "t"])
expected = (
DataFrame(dict(
household_id=[2, 2, 2, 3, 3, 3, 3, 3],
asset_id=["gb00b03mlx29", "gb00b03mlx29",
"gb00b03mlx29", "gb00b03mlx29",
"gb00b03mlx29", "gb00b03mlx29",
"lu0197800237", "lu0197800237"],
t=[233, 234, 235, 233, 234, 235, 180, 181],
share=[0.6, 0.6, 0.6, 0.15, 0.15, 0.15, 0.6, 0.6],
log_return=[.09604978, -.06524096, .03532373,
.09604978, -.06524096, .03532373,
.03025441, .036997]
))
.set_index(["household_id", "asset_id", "t"])
.reindex(columns=['share', 'log_return']))
# this is the equivalency
result = (merge(household.reset_index(), log_return.reset_index(),
on=['asset_id'], how='inner')
.set_index(['household_id', 'asset_id', 't']))
tm.assert_frame_equal(result, expected)
expected = (
DataFrame(dict(
household_id=[1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 4],
asset_id=["nl0000301109", "nl0000301109", "gb00b03mlx29",
"gb00b03mlx29", "gb00b03mlx29",
"gb00b03mlx29", "gb00b03mlx29", "gb00b03mlx29",
"lu0197800237", "lu0197800237",
"nl0000289965", None],
t=[None, None, 233, 234, 235, 233, 234,
235, 180, 181, None, None],
share=[1.0, 0.4, 0.6, 0.6, 0.6, 0.15,
0.15, 0.15, 0.6, 0.6, 0.25, 1.0],
log_return=[None, None, .09604978, -.06524096, .03532373,
.09604978, -.06524096, .03532373,
.03025441, .036997, None, None]
))
.set_index(["household_id", "asset_id", "t"])
.reindex(columns=['share', 'log_return']))
result = (merge(household.reset_index(), log_return.reset_index(),
on=['asset_id'], how='outer')
.set_index(['household_id', 'asset_id', 't']))
tm.assert_frame_equal(result, expected)
class TestJoinMultiMulti:
def test_join_multi_multi(self, left_multi, right_multi, join_type,
on_cols_multi, idx_cols_multi):
# Multi-index join tests
expected = (pd.merge(left_multi.reset_index(),
right_multi.reset_index(),
how=join_type, on=on_cols_multi).
set_index(idx_cols_multi).sort_index())
result = left_multi.join(right_multi, how=join_type).sort_index()
tm.assert_frame_equal(result, expected)
def test_join_multi_empty_frames(self, left_multi, right_multi, join_type,
on_cols_multi, idx_cols_multi):
left_multi = left_multi.drop(columns=left_multi.columns)
right_multi = right_multi.drop(columns=right_multi.columns)
expected = (pd.merge(left_multi.reset_index(),
right_multi.reset_index(),
how=join_type, on=on_cols_multi)
.set_index(idx_cols_multi).sort_index())
result = left_multi.join(right_multi, how=join_type).sort_index()
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("box", [None, np.asarray, Series, Index])
def test_merge_datetime_index(self, box):
# see gh-19038
df = DataFrame([1, 2, 3],
["2016-01-01", "2017-01-01", "2018-01-01"],
columns=["a"])
df.index = pd.to_datetime(df.index)
on_vector = df.index.year
if box is not None:
on_vector = box(on_vector)
expected = DataFrame(
OrderedDict([
("a", [1, 2, 3]),
("key_1", [2016, 2017, 2018]),
])
)
result = df.merge(df, on=["a", on_vector], how="inner")
tm.assert_frame_equal(result, expected)
expected = DataFrame(
OrderedDict([
("key_0", [2016, 2017, 2018]),
("a_x", [1, 2, 3]),
("a_y", [1, 2, 3]),
])
)
result = df.merge(df, on=[df.index.year], how="inner")
tm.assert_frame_equal(result, expected)
def test_single_common_level(self):
index_left = pd.MultiIndex.from_tuples([('K0', 'X0'), ('K0', 'X1'),
('K1', 'X2')],
names=['key', 'X'])
left = pd.DataFrame({'A': ['A0', 'A1', 'A2'],
'B': ['B0', 'B1', 'B2']},
index=index_left)
index_right = pd.MultiIndex.from_tuples([('K0', 'Y0'), ('K1', 'Y1'),
('K2', 'Y2'), ('K2', 'Y3')],
names=['key', 'Y'])
right = pd.DataFrame({'C': ['C0', 'C1', 'C2', 'C3'],
'D': ['D0', 'D1', 'D2', 'D3']},
index=index_right)
result = left.join(right)
expected = (pd.merge(left.reset_index(), right.reset_index(),
on=['key'], how='inner')
.set_index(['key', 'X', 'Y']))
tm.assert_frame_equal(result, expected)
|
|
pandas/tests/reshape/merge/test_multi.py
|
pandas/tests/indexing/test_indexing_engines.py
|
"""
Arithmetic operations for PandasObjects
This is not a public API.
"""
import datetime
import operator
import textwrap
from typing import Dict, Optional
import warnings
import numpy as np
from pandas._libs import algos as libalgos, lib, ops as libops
from pandas.errors import NullFrequencyError
from pandas.util._decorators import Appender
from pandas.core.dtypes.cast import (
construct_1d_object_array_from_listlike, find_common_type,
maybe_upcast_putmask)
from pandas.core.dtypes.common import (
ensure_object, is_bool_dtype, is_categorical_dtype, is_datetime64_dtype,
is_datetime64tz_dtype, is_datetimelike_v_numeric, is_extension_array_dtype,
is_integer_dtype, is_list_like, is_object_dtype, is_period_dtype,
is_scalar, is_timedelta64_dtype, needs_i8_conversion)
from pandas.core.dtypes.generic import (
ABCDataFrame, ABCIndex, ABCIndexClass, ABCSeries, ABCSparseArray,
ABCSparseSeries)
from pandas.core.dtypes.missing import isna, notna
import pandas as pd
import pandas.core.common as com
import pandas.core.missing as missing
# -----------------------------------------------------------------------------
# Ops Wrapping Utilities
def get_op_result_name(left, right):
"""
Find the appropriate name to pin to an operation result. This result
should always be either an Index or a Series.
Parameters
----------
left : {Series, Index}
right : object
Returns
-------
name : object
Usually a string
"""
# `left` is always a pd.Series when called from within ops
if isinstance(right, (ABCSeries, pd.Index)):
name = _maybe_match_name(left, right)
else:
name = left.name
return name
def _maybe_match_name(a, b):
"""
Try to find a name to attach to the result of an operation between
a and b. If only one of these has a `name` attribute, return that
name. Otherwise return a consensus name if they match of None if
they have different names.
Parameters
----------
a : object
b : object
Returns
-------
name : str or None
See Also
--------
pandas.core.common.consensus_name_attr
"""
a_has = hasattr(a, 'name')
b_has = hasattr(b, 'name')
if a_has and b_has:
if a.name == b.name:
return a.name
else:
# TODO: what if they both have np.nan for their names?
return None
elif a_has:
return a.name
elif b_has:
return b.name
return None
def maybe_upcast_for_op(obj):
"""
Cast non-pandas objects to pandas types to unify behavior of arithmetic
and comparison operations.
Parameters
----------
obj: object
Returns
-------
out : object
Notes
-----
Be careful to call this *after* determining the `name` attribute to be
attached to the result of the arithmetic operation.
"""
if type(obj) is datetime.timedelta:
# GH#22390 cast up to Timedelta to rely on Timedelta
# implementation; otherwise operation against numeric-dtype
# raises TypeError
return pd.Timedelta(obj)
elif isinstance(obj, np.timedelta64) and not isna(obj):
# In particular non-nanosecond timedelta64 needs to be cast to
# nanoseconds, or else we get undesired behavior like
# np.timedelta64(3, 'D') / 2 == np.timedelta64(1, 'D')
# The isna check is to avoid casting timedelta64("NaT"), which would
# return NaT and incorrectly be treated as a datetime-NaT.
return pd.Timedelta(obj)
elif isinstance(obj, np.ndarray) and is_timedelta64_dtype(obj):
# GH#22390 Unfortunately we need to special-case right-hand
# timedelta64 dtypes because numpy casts integer dtypes to
# timedelta64 when operating with timedelta64
return pd.TimedeltaIndex(obj)
return obj
# -----------------------------------------------------------------------------
# Reversed Operations not available in the stdlib operator module.
# Defining these instead of using lambdas allows us to reference them by name.
def radd(left, right):
return right + left
def rsub(left, right):
return right - left
def rmul(left, right):
return right * left
def rdiv(left, right):
return right / left
def rtruediv(left, right):
return right / left
def rfloordiv(left, right):
return right // left
def rmod(left, right):
# check if right is a string as % is the string
# formatting operation; this is a TypeError
# otherwise perform the op
if isinstance(right, str):
raise TypeError("{typ} cannot perform the operation mod".format(
typ=type(left).__name__))
return right % left
def rdivmod(left, right):
return divmod(right, left)
def rpow(left, right):
return right ** left
def rand_(left, right):
return operator.and_(right, left)
def ror_(left, right):
return operator.or_(right, left)
def rxor(left, right):
return operator.xor(right, left)
# -----------------------------------------------------------------------------
def make_invalid_op(name):
"""
Return a binary method that always raises a TypeError.
Parameters
----------
name : str
Returns
-------
invalid_op : function
"""
def invalid_op(self, other=None):
raise TypeError("cannot perform {name} with this index type: "
"{typ}".format(name=name, typ=type(self).__name__))
invalid_op.__name__ = name
return invalid_op
def _gen_eval_kwargs(name):
"""
Find the keyword arguments to pass to numexpr for the given operation.
Parameters
----------
name : str
Returns
-------
eval_kwargs : dict
Examples
--------
>>> _gen_eval_kwargs("__add__")
{}
>>> _gen_eval_kwargs("rtruediv")
{'reversed': True, 'truediv': True}
"""
kwargs = {}
# Series appear to only pass __add__, __radd__, ...
# but DataFrame gets both these dunder names _and_ non-dunder names
# add, radd, ...
name = name.replace('__', '')
if name.startswith('r'):
if name not in ['radd', 'rand', 'ror', 'rxor']:
# Exclude commutative operations
kwargs['reversed'] = True
if name in ['truediv', 'rtruediv']:
kwargs['truediv'] = True
if name in ['ne']:
kwargs['masker'] = True
return kwargs
def _gen_fill_zeros(name):
"""
Find the appropriate fill value to use when filling in undefined values
in the results of the given operation caused by operating on
(generally dividing by) zero.
Parameters
----------
name : str
Returns
-------
fill_value : {None, np.nan, np.inf}
"""
name = name.strip('__')
if 'div' in name:
# truediv, floordiv, div, and reversed variants
fill_value = np.inf
elif 'mod' in name:
# mod, rmod
fill_value = np.nan
else:
fill_value = None
return fill_value
def _get_frame_op_default_axis(name):
"""
Only DataFrame cares about default_axis, specifically:
special methods have default_axis=None and flex methods
have default_axis='columns'.
Parameters
----------
name : str
Returns
-------
default_axis: str or None
"""
if name.replace('__r', '__') in ['__and__', '__or__', '__xor__']:
# bool methods
return 'columns'
elif name.startswith('__'):
# __add__, __mul__, ...
return None
else:
# add, mul, ...
return 'columns'
def _get_opstr(op, cls):
"""
Find the operation string, if any, to pass to numexpr for this
operation.
Parameters
----------
op : binary operator
cls : class
Returns
-------
op_str : string or None
"""
# numexpr is available for non-sparse classes
subtyp = getattr(cls, '_subtyp', '')
use_numexpr = 'sparse' not in subtyp
if not use_numexpr:
# if we're not using numexpr, then don't pass a str_rep
return None
return {operator.add: '+',
radd: '+',
operator.mul: '*',
rmul: '*',
operator.sub: '-',
rsub: '-',
operator.truediv: '/',
rtruediv: '/',
operator.floordiv: '//',
rfloordiv: '//',
operator.mod: None, # TODO: Why None for mod but '%' for rmod?
rmod: '%',
operator.pow: '**',
rpow: '**',
operator.eq: '==',
operator.ne: '!=',
operator.le: '<=',
operator.lt: '<',
operator.ge: '>=',
operator.gt: '>',
operator.and_: '&',
rand_: '&',
operator.or_: '|',
ror_: '|',
operator.xor: '^',
rxor: '^',
divmod: None,
rdivmod: None}[op]
def _get_op_name(op, special):
"""
Find the name to attach to this method according to conventions
for special and non-special methods.
Parameters
----------
op : binary operator
special : bool
Returns
-------
op_name : str
"""
opname = op.__name__.strip('_')
if special:
opname = '__{opname}__'.format(opname=opname)
return opname
# -----------------------------------------------------------------------------
# Docstring Generation and Templates
_add_example_SERIES = """
Examples
--------
>>> a = pd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a 1.0
b 1.0
c 1.0
d NaN
dtype: float64
>>> b = pd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a 1.0
b NaN
d 1.0
e NaN
dtype: float64
>>> a.add(b, fill_value=0)
a 2.0
b 1.0
c 1.0
d 1.0
e NaN
dtype: float64
"""
_sub_example_SERIES = """
Examples
--------
>>> a = pd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a 1.0
b 1.0
c 1.0
d NaN
dtype: float64
>>> b = pd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a 1.0
b NaN
d 1.0
e NaN
dtype: float64
>>> a.subtract(b, fill_value=0)
a 0.0
b 1.0
c 1.0
d -1.0
e NaN
dtype: float64
"""
_mul_example_SERIES = """
Examples
--------
>>> a = pd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a 1.0
b 1.0
c 1.0
d NaN
dtype: float64
>>> b = pd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a 1.0
b NaN
d 1.0
e NaN
dtype: float64
>>> a.multiply(b, fill_value=0)
a 1.0
b 0.0
c 0.0
d 0.0
e NaN
dtype: float64
"""
_div_example_SERIES = """
Examples
--------
>>> a = pd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a 1.0
b 1.0
c 1.0
d NaN
dtype: float64
>>> b = pd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a 1.0
b NaN
d 1.0
e NaN
dtype: float64
>>> a.divide(b, fill_value=0)
a 1.0
b inf
c inf
d 0.0
e NaN
dtype: float64
"""
_floordiv_example_SERIES = """
Examples
--------
>>> a = pd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a 1.0
b 1.0
c 1.0
d NaN
dtype: float64
>>> b = pd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a 1.0
b NaN
d 1.0
e NaN
dtype: float64
>>> a.floordiv(b, fill_value=0)
a 1.0
b NaN
c NaN
d 0.0
e NaN
dtype: float64
"""
_mod_example_SERIES = """
Examples
--------
>>> a = pd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a 1.0
b 1.0
c 1.0
d NaN
dtype: float64
>>> b = pd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a 1.0
b NaN
d 1.0
e NaN
dtype: float64
>>> a.mod(b, fill_value=0)
a 0.0
b NaN
c NaN
d 0.0
e NaN
dtype: float64
"""
_pow_example_SERIES = """
Examples
--------
>>> a = pd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a 1.0
b 1.0
c 1.0
d NaN
dtype: float64
>>> b = pd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a 1.0
b NaN
d 1.0
e NaN
dtype: float64
>>> a.pow(b, fill_value=0)
a 1.0
b 1.0
c 1.0
d 0.0
e NaN
dtype: float64
"""
_op_descriptions = {
# Arithmetic Operators
'add': {'op': '+',
'desc': 'Addition',
'reverse': 'radd',
'series_examples': _add_example_SERIES},
'sub': {'op': '-',
'desc': 'Subtraction',
'reverse': 'rsub',
'series_examples': _sub_example_SERIES},
'mul': {'op': '*',
'desc': 'Multiplication',
'reverse': 'rmul',
'series_examples': _mul_example_SERIES,
'df_examples': None},
'mod': {'op': '%',
'desc': 'Modulo',
'reverse': 'rmod',
'series_examples': _mod_example_SERIES},
'pow': {'op': '**',
'desc': 'Exponential power',
'reverse': 'rpow',
'series_examples': _pow_example_SERIES,
'df_examples': None},
'truediv': {'op': '/',
'desc': 'Floating division',
'reverse': 'rtruediv',
'series_examples': _div_example_SERIES,
'df_examples': None},
'floordiv': {'op': '//',
'desc': 'Integer division',
'reverse': 'rfloordiv',
'series_examples': _floordiv_example_SERIES,
'df_examples': None},
'divmod': {'op': 'divmod',
'desc': 'Integer division and modulo',
'reverse': 'rdivmod',
'series_examples': None,
'df_examples': None},
# Comparison Operators
'eq': {'op': '==',
'desc': 'Equal to',
'reverse': None,
'series_examples': None},
'ne': {'op': '!=',
'desc': 'Not equal to',
'reverse': None,
'series_examples': None},
'lt': {'op': '<',
'desc': 'Less than',
'reverse': None,
'series_examples': None},
'le': {'op': '<=',
'desc': 'Less than or equal to',
'reverse': None,
'series_examples': None},
'gt': {'op': '>',
'desc': 'Greater than',
'reverse': None,
'series_examples': None},
'ge': {'op': '>=',
'desc': 'Greater than or equal to',
'reverse': None,
'series_examples': None}
} # type: Dict[str, Dict[str, Optional[str]]]
_op_names = list(_op_descriptions.keys())
for key in _op_names:
reverse_op = _op_descriptions[key]['reverse']
if reverse_op is not None:
_op_descriptions[reverse_op] = _op_descriptions[key].copy()
_op_descriptions[reverse_op]['reverse'] = key
_flex_doc_SERIES = """
Return {desc} of series and other, element-wise (binary operator `{op_name}`).
Equivalent to ``{equiv}``, but with support to substitute a fill_value for
missing data in one of the inputs.
Parameters
----------
other : Series or scalar value
fill_value : None or float value, default None (NaN)
Fill existing missing (NaN) values, and any new element needed for
successful Series alignment, with this value before computation.
If data in both corresponding Series locations is missing
the result will be missing.
level : int or name
Broadcast across a level, matching Index values on the
passed MultiIndex level.
Returns
-------
Series
The result of the operation.
See Also
--------
Series.{reverse}
"""
_arith_doc_FRAME = """
Binary operator %s with support to substitute a fill_value for missing data in
one of the inputs
Parameters
----------
other : Series, DataFrame, or constant
axis : {0, 1, 'index', 'columns'}
For Series input, axis to match Series index on
fill_value : None or float value, default None
Fill existing missing (NaN) values, and any new element needed for
successful DataFrame alignment, with this value before computation.
If data in both corresponding DataFrame locations is missing
the result will be missing
level : int or name
Broadcast across a level, matching Index values on the
passed MultiIndex level
Returns
-------
result : DataFrame
Notes
-----
Mismatched indices will be unioned together
"""
_flex_doc_FRAME = """
Get {desc} of dataframe and other, element-wise (binary operator `{op_name}`).
Equivalent to ``{equiv}``, but with support to substitute a fill_value
for missing data in one of the inputs. With reverse version, `{reverse}`.
Among flexible wrappers (`add`, `sub`, `mul`, `div`, `mod`, `pow`) to
arithmetic operators: `+`, `-`, `*`, `/`, `//`, `%`, `**`.
Parameters
----------
other : scalar, sequence, Series, or DataFrame
Any single or multiple element data structure, or list-like object.
axis : {{0 or 'index', 1 or 'columns'}}
Whether to compare by the index (0 or 'index') or columns
(1 or 'columns'). For Series input, axis to match Series index on.
level : int or label
Broadcast across a level, matching Index values on the
passed MultiIndex level.
fill_value : float or None, default None
Fill existing missing (NaN) values, and any new element needed for
successful DataFrame alignment, with this value before computation.
If data in both corresponding DataFrame locations is missing
the result will be missing.
Returns
-------
DataFrame
Result of the arithmetic operation.
See Also
--------
DataFrame.add : Add DataFrames.
DataFrame.sub : Subtract DataFrames.
DataFrame.mul : Multiply DataFrames.
DataFrame.div : Divide DataFrames (float division).
DataFrame.truediv : Divide DataFrames (float division).
DataFrame.floordiv : Divide DataFrames (integer division).
DataFrame.mod : Calculate modulo (remainder after division).
DataFrame.pow : Calculate exponential power.
Notes
-----
Mismatched indices will be unioned together.
Examples
--------
>>> df = pd.DataFrame({{'angles': [0, 3, 4],
... 'degrees': [360, 180, 360]}},
... index=['circle', 'triangle', 'rectangle'])
>>> df
angles degrees
circle 0 360
triangle 3 180
rectangle 4 360
Add a scalar with operator version which return the same
results.
>>> df + 1
angles degrees
circle 1 361
triangle 4 181
rectangle 5 361
>>> df.add(1)
angles degrees
circle 1 361
triangle 4 181
rectangle 5 361
Divide by constant with reverse version.
>>> df.div(10)
angles degrees
circle 0.0 36.0
triangle 0.3 18.0
rectangle 0.4 36.0
>>> df.rdiv(10)
angles degrees
circle inf 0.027778
triangle 3.333333 0.055556
rectangle 2.500000 0.027778
Subtract a list and Series by axis with operator version.
>>> df - [1, 2]
angles degrees
circle -1 358
triangle 2 178
rectangle 3 358
>>> df.sub([1, 2], axis='columns')
angles degrees
circle -1 358
triangle 2 178
rectangle 3 358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
... axis='index')
angles degrees
circle -1 359
triangle 2 179
rectangle 3 359
Multiply a DataFrame of different shape with operator version.
>>> other = pd.DataFrame({{'angles': [0, 3, 4]}},
... index=['circle', 'triangle', 'rectangle'])
>>> other
angles
circle 0
triangle 3
rectangle 4
>>> df * other
angles degrees
circle 0 NaN
triangle 9 NaN
rectangle 16 NaN
>>> df.mul(other, fill_value=0)
angles degrees
circle 0 0.0
triangle 9 0.0
rectangle 16 0.0
Divide by a MultiIndex by level.
>>> df_multindex = pd.DataFrame({{'angles': [0, 3, 4, 4, 5, 6],
... 'degrees': [360, 180, 360, 360, 540, 720]}},
... index=[['A', 'A', 'A', 'B', 'B', 'B'],
... ['circle', 'triangle', 'rectangle',
... 'square', 'pentagon', 'hexagon']])
>>> df_multindex
angles degrees
A circle 0 360
triangle 3 180
rectangle 4 360
B square 4 360
pentagon 5 540
hexagon 6 720
>>> df.div(df_multindex, level=1, fill_value=0)
angles degrees
A circle NaN 1.0
triangle 1.0 1.0
rectangle 1.0 1.0
B square 0.0 0.0
pentagon 0.0 0.0
hexagon 0.0 0.0
"""
_flex_comp_doc_FRAME = """
Get {desc} of dataframe and other, element-wise (binary operator `{op_name}`).
Among flexible wrappers (`eq`, `ne`, `le`, `lt`, `ge`, `gt`) to comparison
operators.
Equivalent to `==`, `=!`, `<=`, `<`, `>=`, `>` with support to choose axis
(rows or columns) and level for comparison.
Parameters
----------
other : scalar, sequence, Series, or DataFrame
Any single or multiple element data structure, or list-like object.
axis : {{0 or 'index', 1 or 'columns'}}, default 'columns'
Whether to compare by the index (0 or 'index') or columns
(1 or 'columns').
level : int or label
Broadcast across a level, matching Index values on the passed
MultiIndex level.
Returns
-------
DataFrame of bool
Result of the comparison.
See Also
--------
DataFrame.eq : Compare DataFrames for equality elementwise.
DataFrame.ne : Compare DataFrames for inequality elementwise.
DataFrame.le : Compare DataFrames for less than inequality
or equality elementwise.
DataFrame.lt : Compare DataFrames for strictly less than
inequality elementwise.
DataFrame.ge : Compare DataFrames for greater than inequality
or equality elementwise.
DataFrame.gt : Compare DataFrames for strictly greater than
inequality elementwise.
Notes
-----
Mismatched indices will be unioned together.
`NaN` values are considered different (i.e. `NaN` != `NaN`).
Examples
--------
>>> df = pd.DataFrame({{'cost': [250, 150, 100],
... 'revenue': [100, 250, 300]}},
... index=['A', 'B', 'C'])
>>> df
cost revenue
A 250 100
B 150 250
C 100 300
Comparison with a scalar, using either the operator or method:
>>> df == 100
cost revenue
A False True
B False False
C True False
>>> df.eq(100)
cost revenue
A False True
B False False
C True False
When `other` is a :class:`Series`, the columns of a DataFrame are aligned
with the index of `other` and broadcast:
>>> df != pd.Series([100, 250], index=["cost", "revenue"])
cost revenue
A True True
B True False
C False True
Use the method to control the broadcast axis:
>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')
cost revenue
A True False
B True True
C True True
D True True
When comparing to an arbitrary sequence, the number of columns must
match the number elements in `other`:
>>> df == [250, 100]
cost revenue
A True True
B False False
C False False
Use the method to control the axis:
>>> df.eq([250, 250, 100], axis='index')
cost revenue
A True False
B False True
C True False
Compare to a DataFrame of different shape.
>>> other = pd.DataFrame({{'revenue': [300, 250, 100, 150]}},
... index=['A', 'B', 'C', 'D'])
>>> other
revenue
A 300
B 250
C 100
D 150
>>> df.gt(other)
cost revenue
A False False
B False False
C False True
D False False
Compare to a MultiIndex by level.
>>> df_multindex = pd.DataFrame({{'cost': [250, 150, 100, 150, 300, 220],
... 'revenue': [100, 250, 300, 200, 175, 225]}},
... index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
... ['A', 'B', 'C', 'A', 'B', 'C']])
>>> df_multindex
cost revenue
Q1 A 250 100
B 150 250
C 100 300
Q2 A 150 200
B 300 175
C 220 225
>>> df.le(df_multindex, level=1)
cost revenue
Q1 A True True
B True True
C True True
Q2 A False True
B True False
C True False
"""
def _make_flex_doc(op_name, typ):
"""
Make the appropriate substitutions for the given operation and class-typ
into either _flex_doc_SERIES or _flex_doc_FRAME to return the docstring
to attach to a generated method.
Parameters
----------
op_name : str {'__add__', '__sub__', ... '__eq__', '__ne__', ...}
typ : str {series, 'dataframe']}
Returns
-------
doc : str
"""
op_name = op_name.replace('__', '')
op_desc = _op_descriptions[op_name]
if op_name.startswith('r'):
equiv = 'other ' + op_desc['op'] + ' ' + typ
else:
equiv = typ + ' ' + op_desc['op'] + ' other'
if typ == 'series':
base_doc = _flex_doc_SERIES
doc_no_examples = base_doc.format(
desc=op_desc['desc'],
op_name=op_name,
equiv=equiv,
reverse=op_desc['reverse']
)
if op_desc['series_examples']:
doc = doc_no_examples + op_desc['series_examples']
else:
doc = doc_no_examples
elif typ == 'dataframe':
base_doc = _flex_doc_FRAME
doc = base_doc.format(
desc=op_desc['desc'],
op_name=op_name,
equiv=equiv,
reverse=op_desc['reverse']
)
else:
raise AssertionError('Invalid typ argument.')
return doc
# -----------------------------------------------------------------------------
# Masking NA values and fallbacks for operations numpy does not support
def fill_binop(left, right, fill_value):
"""
If a non-None fill_value is given, replace null entries in left and right
with this value, but only in positions where _one_ of left/right is null,
not both.
Parameters
----------
left : array-like
right : array-like
fill_value : object
Returns
-------
left : array-like
right : array-like
Notes
-----
Makes copies if fill_value is not None
"""
# TODO: can we make a no-copy implementation?
if fill_value is not None:
left_mask = isna(left)
right_mask = isna(right)
left = left.copy()
right = right.copy()
# one but not both
mask = left_mask ^ right_mask
left[left_mask & mask] = fill_value
right[right_mask & mask] = fill_value
return left, right
def mask_cmp_op(x, y, op):
"""
Apply the function `op` to only non-null points in x and y.
Parameters
----------
x : array-like
y : array-like
op : binary operation
Returns
-------
result : ndarray[bool]
"""
xrav = x.ravel()
result = np.empty(x.size, dtype=bool)
if isinstance(y, (np.ndarray, ABCSeries)):
yrav = y.ravel()
mask = notna(xrav) & notna(yrav)
result[mask] = op(np.array(list(xrav[mask])),
np.array(list(yrav[mask])))
else:
mask = notna(xrav)
result[mask] = op(np.array(list(xrav[mask])), y)
if op == operator.ne: # pragma: no cover
np.putmask(result, ~mask, True)
else:
np.putmask(result, ~mask, False)
result = result.reshape(x.shape)
return result
def masked_arith_op(x, y, op):
"""
If the given arithmetic operation fails, attempt it again on
only the non-null elements of the input array(s).
Parameters
----------
x : np.ndarray
y : np.ndarray, Series, Index
op : binary operator
"""
# For Series `x` is 1D so ravel() is a no-op; calling it anyway makes
# the logic valid for both Series and DataFrame ops.
xrav = x.ravel()
assert isinstance(x, (np.ndarray, ABCSeries)), type(x)
if isinstance(y, (np.ndarray, ABCSeries, ABCIndexClass)):
dtype = find_common_type([x.dtype, y.dtype])
result = np.empty(x.size, dtype=dtype)
# PeriodIndex.ravel() returns int64 dtype, so we have
# to work around that case. See GH#19956
yrav = y if is_period_dtype(y) else y.ravel()
mask = notna(xrav) & notna(yrav)
if yrav.shape != mask.shape:
# FIXME: GH#5284, GH#5035, GH#19448
# Without specifically raising here we get mismatched
# errors in Py3 (TypeError) vs Py2 (ValueError)
# Note: Only = an issue in DataFrame case
raise ValueError('Cannot broadcast operands together.')
if mask.any():
with np.errstate(all='ignore'):
result[mask] = op(xrav[mask],
com.values_from_object(yrav[mask]))
else:
assert is_scalar(y), type(y)
assert isinstance(x, np.ndarray), type(x)
# mask is only meaningful for x
result = np.empty(x.size, dtype=x.dtype)
mask = notna(xrav)
# 1 ** np.nan is 1. So we have to unmask those.
if op == pow:
mask = np.where(x == 1, False, mask)
elif op == rpow:
mask = np.where(y == 1, False, mask)
if mask.any():
with np.errstate(all='ignore'):
result[mask] = op(xrav[mask], y)
result, changed = maybe_upcast_putmask(result, ~mask, np.nan)
result = result.reshape(x.shape) # 2D compat
return result
def invalid_comparison(left, right, op):
"""
If a comparison has mismatched types and is not necessarily meaningful,
follow python3 conventions by:
- returning all-False for equality
- returning all-True for inequality
- raising TypeError otherwise
Parameters
----------
left : array-like
right : scalar, array-like
op : operator.{eq, ne, lt, le, gt}
Raises
------
TypeError : on inequality comparisons
"""
if op is operator.eq:
res_values = np.zeros(left.shape, dtype=bool)
elif op is operator.ne:
res_values = np.ones(left.shape, dtype=bool)
else:
raise TypeError("Invalid comparison between dtype={dtype} and {typ}"
.format(dtype=left.dtype, typ=type(right).__name__))
return res_values
# -----------------------------------------------------------------------------
# Dispatch logic
def should_series_dispatch(left, right, op):
"""
Identify cases where a DataFrame operation should dispatch to its
Series counterpart.
Parameters
----------
left : DataFrame
right : DataFrame
op : binary operator
Returns
-------
override : bool
"""
if left._is_mixed_type or right._is_mixed_type:
return True
if not len(left.columns) or not len(right.columns):
# ensure obj.dtypes[0] exists for each obj
return False
ldtype = left.dtypes.iloc[0]
rdtype = right.dtypes.iloc[0]
if ((is_timedelta64_dtype(ldtype) and is_integer_dtype(rdtype)) or
(is_timedelta64_dtype(rdtype) and is_integer_dtype(ldtype))):
# numpy integer dtypes as timedelta64 dtypes in this scenario
return True
if is_datetime64_dtype(ldtype) and is_object_dtype(rdtype):
# in particular case where right is an array of DateOffsets
return True
return False
def dispatch_to_series(left, right, func, str_rep=None, axis=None):
"""
Evaluate the frame operation func(left, right) by evaluating
column-by-column, dispatching to the Series implementation.
Parameters
----------
left : DataFrame
right : scalar or DataFrame
func : arithmetic or comparison operator
str_rep : str or None, default None
axis : {None, 0, 1, "index", "columns"}
Returns
-------
DataFrame
"""
# Note: we use iloc to access columns for compat with cases
# with non-unique columns.
import pandas.core.computation.expressions as expressions
right = lib.item_from_zerodim(right)
if lib.is_scalar(right) or np.ndim(right) == 0:
def column_op(a, b):
return {i: func(a.iloc[:, i], b)
for i in range(len(a.columns))}
elif isinstance(right, ABCDataFrame):
assert right._indexed_same(left)
def column_op(a, b):
return {i: func(a.iloc[:, i], b.iloc[:, i])
for i in range(len(a.columns))}
elif isinstance(right, ABCSeries) and axis == "columns":
# We only get here if called via left._combine_match_columns,
# in which case we specifically want to operate row-by-row
assert right.index.equals(left.columns)
def column_op(a, b):
return {i: func(a.iloc[:, i], b.iloc[i])
for i in range(len(a.columns))}
elif isinstance(right, ABCSeries):
assert right.index.equals(left.index) # Handle other cases later
def column_op(a, b):
return {i: func(a.iloc[:, i], b)
for i in range(len(a.columns))}
else:
# Remaining cases have less-obvious dispatch rules
raise NotImplementedError(right)
new_data = expressions.evaluate(column_op, str_rep, left, right)
result = left._constructor(new_data, index=left.index, copy=False)
# Pin columns instead of passing to constructor for compat with
# non-unique columns case
result.columns = left.columns
return result
def dispatch_to_index_op(op, left, right, index_class):
"""
Wrap Series left in the given index_class to delegate the operation op
to the index implementation. DatetimeIndex and TimedeltaIndex perform
type checking, timezone handling, overflow checks, etc.
Parameters
----------
op : binary operator (operator.add, operator.sub, ...)
left : Series
right : object
index_class : DatetimeIndex or TimedeltaIndex
Returns
-------
result : object, usually DatetimeIndex, TimedeltaIndex, or Series
"""
left_idx = index_class(left)
# avoid accidentally allowing integer add/sub. For datetime64[tz] dtypes,
# left_idx may inherit a freq from a cached DatetimeIndex.
# See discussion in GH#19147.
if getattr(left_idx, 'freq', None) is not None:
left_idx = left_idx._shallow_copy(freq=None)
try:
result = op(left_idx, right)
except NullFrequencyError:
# DatetimeIndex and TimedeltaIndex with freq == None raise ValueError
# on add/sub of integers (or int-like). We re-raise as a TypeError.
raise TypeError('incompatible type for a datetime/timedelta '
'operation [{name}]'.format(name=op.__name__))
return result
def dispatch_to_extension_op(op, left, right):
"""
Assume that left or right is a Series backed by an ExtensionArray,
apply the operator defined by op.
"""
# The op calls will raise TypeError if the op is not defined
# on the ExtensionArray
# unbox Series and Index to arrays
if isinstance(left, (ABCSeries, ABCIndexClass)):
new_left = left._values
else:
new_left = left
if isinstance(right, (ABCSeries, ABCIndexClass)):
new_right = right._values
else:
new_right = right
res_values = op(new_left, new_right)
res_name = get_op_result_name(left, right)
if op.__name__ in ['divmod', 'rdivmod']:
return _construct_divmod_result(
left, res_values, left.index, res_name)
return _construct_result(left, res_values, left.index, res_name)
# -----------------------------------------------------------------------------
# Functions that add arithmetic methods to objects, given arithmetic factory
# methods
def _get_method_wrappers(cls):
"""
Find the appropriate operation-wrappers to use when defining flex/special
arithmetic, boolean, and comparison operations with the given class.
Parameters
----------
cls : class
Returns
-------
arith_flex : function or None
comp_flex : function or None
arith_special : function
comp_special : function
bool_special : function
Notes
-----
None is only returned for SparseArray
"""
if issubclass(cls, ABCSparseSeries):
# Be sure to catch this before ABCSeries and ABCSparseArray,
# as they will both come see SparseSeries as a subclass
arith_flex = _flex_method_SERIES
comp_flex = _flex_method_SERIES
arith_special = _arith_method_SPARSE_SERIES
comp_special = _arith_method_SPARSE_SERIES
bool_special = _bool_method_SERIES
# TODO: I don't think the functions defined by bool_method are tested
elif issubclass(cls, ABCSeries):
# Just Series; SparseSeries is caught above
arith_flex = _flex_method_SERIES
comp_flex = _flex_method_SERIES
arith_special = _arith_method_SERIES
comp_special = _comp_method_SERIES
bool_special = _bool_method_SERIES
elif issubclass(cls, ABCSparseArray):
arith_flex = None
comp_flex = None
arith_special = _arith_method_SPARSE_ARRAY
comp_special = _arith_method_SPARSE_ARRAY
bool_special = _arith_method_SPARSE_ARRAY
elif issubclass(cls, ABCDataFrame):
# Same for DataFrame and SparseDataFrame
arith_flex = _arith_method_FRAME
comp_flex = _flex_comp_method_FRAME
arith_special = _arith_method_FRAME
comp_special = _comp_method_FRAME
bool_special = _arith_method_FRAME
return arith_flex, comp_flex, arith_special, comp_special, bool_special
def _create_methods(cls, arith_method, comp_method, bool_method, special):
# creates actual methods based upon arithmetic, comp and bool method
# constructors.
have_divmod = issubclass(cls, ABCSeries)
# divmod is available for Series and SparseSeries
# yapf: disable
new_methods = dict(
add=arith_method(cls, operator.add, special),
radd=arith_method(cls, radd, special),
sub=arith_method(cls, operator.sub, special),
mul=arith_method(cls, operator.mul, special),
truediv=arith_method(cls, operator.truediv, special),
floordiv=arith_method(cls, operator.floordiv, special),
# Causes a floating point exception in the tests when numexpr enabled,
# so for now no speedup
mod=arith_method(cls, operator.mod, special),
pow=arith_method(cls, operator.pow, special),
# not entirely sure why this is necessary, but previously was included
# so it's here to maintain compatibility
rmul=arith_method(cls, rmul, special),
rsub=arith_method(cls, rsub, special),
rtruediv=arith_method(cls, rtruediv, special),
rfloordiv=arith_method(cls, rfloordiv, special),
rpow=arith_method(cls, rpow, special),
rmod=arith_method(cls, rmod, special))
# yapf: enable
new_methods['div'] = new_methods['truediv']
new_methods['rdiv'] = new_methods['rtruediv']
if have_divmod:
# divmod doesn't have an op that is supported by numexpr
new_methods['divmod'] = arith_method(cls, divmod, special)
new_methods['rdivmod'] = arith_method(cls, rdivmod, special)
new_methods.update(dict(
eq=comp_method(cls, operator.eq, special),
ne=comp_method(cls, operator.ne, special),
lt=comp_method(cls, operator.lt, special),
gt=comp_method(cls, operator.gt, special),
le=comp_method(cls, operator.le, special),
ge=comp_method(cls, operator.ge, special)))
if bool_method:
new_methods.update(
dict(and_=bool_method(cls, operator.and_, special),
or_=bool_method(cls, operator.or_, special),
# For some reason ``^`` wasn't used in original.
xor=bool_method(cls, operator.xor, special),
rand_=bool_method(cls, rand_, special),
ror_=bool_method(cls, ror_, special),
rxor=bool_method(cls, rxor, special)))
if special:
dunderize = lambda x: '__{name}__'.format(name=x.strip('_'))
else:
dunderize = lambda x: x
new_methods = {dunderize(k): v for k, v in new_methods.items()}
return new_methods
def add_methods(cls, new_methods):
for name, method in new_methods.items():
# For most methods, if we find that the class already has a method
# of the same name, it is OK to over-write it. The exception is
# inplace methods (__iadd__, __isub__, ...) for SparseArray, which
# retain the np.ndarray versions.
force = not (issubclass(cls, ABCSparseArray) and
name.startswith('__i'))
if force or name not in cls.__dict__:
setattr(cls, name, method)
# ----------------------------------------------------------------------
# Arithmetic
def add_special_arithmetic_methods(cls):
"""
Adds the full suite of special arithmetic methods (``__add__``,
``__sub__``, etc.) to the class.
Parameters
----------
cls : class
special methods will be defined and pinned to this class
"""
_, _, arith_method, comp_method, bool_method = _get_method_wrappers(cls)
new_methods = _create_methods(cls, arith_method, comp_method, bool_method,
special=True)
# inplace operators (I feel like these should get passed an `inplace=True`
# or just be removed
def _wrap_inplace_method(method):
"""
return an inplace wrapper for this method
"""
def f(self, other):
result = method(self, other)
# this makes sure that we are aligned like the input
# we are updating inplace so we want to ignore is_copy
self._update_inplace(result.reindex_like(self, copy=False)._data,
verify_is_copy=False)
return self
f.__name__ = "__i{name}__".format(name=method.__name__.strip("__"))
return f
new_methods.update(
dict(__iadd__=_wrap_inplace_method(new_methods["__add__"]),
__isub__=_wrap_inplace_method(new_methods["__sub__"]),
__imul__=_wrap_inplace_method(new_methods["__mul__"]),
__itruediv__=_wrap_inplace_method(new_methods["__truediv__"]),
__ifloordiv__=_wrap_inplace_method(new_methods["__floordiv__"]),
__imod__=_wrap_inplace_method(new_methods["__mod__"]),
__ipow__=_wrap_inplace_method(new_methods["__pow__"])))
new_methods.update(
dict(__iand__=_wrap_inplace_method(new_methods["__and__"]),
__ior__=_wrap_inplace_method(new_methods["__or__"]),
__ixor__=_wrap_inplace_method(new_methods["__xor__"])))
add_methods(cls, new_methods=new_methods)
def add_flex_arithmetic_methods(cls):
"""
Adds the full suite of flex arithmetic methods (``pow``, ``mul``, ``add``)
to the class.
Parameters
----------
cls : class
flex methods will be defined and pinned to this class
"""
flex_arith_method, flex_comp_method, _, _, _ = _get_method_wrappers(cls)
new_methods = _create_methods(cls, flex_arith_method,
flex_comp_method, bool_method=None,
special=False)
new_methods.update(dict(multiply=new_methods['mul'],
subtract=new_methods['sub'],
divide=new_methods['div']))
# opt out of bool flex methods for now
assert not any(kname in new_methods for kname in ('ror_', 'rxor', 'rand_'))
add_methods(cls, new_methods=new_methods)
# -----------------------------------------------------------------------------
# Series
def _align_method_SERIES(left, right, align_asobject=False):
""" align lhs and rhs Series """
# ToDo: Different from _align_method_FRAME, list, tuple and ndarray
# are not coerced here
# because Series has inconsistencies described in #13637
if isinstance(right, ABCSeries):
# avoid repeated alignment
if not left.index.equals(right.index):
if align_asobject:
# to keep original value's dtype for bool ops
left = left.astype(object)
right = right.astype(object)
left, right = left.align(right, copy=False)
return left, right
def _construct_result(left, result, index, name, dtype=None):
"""
If the raw op result has a non-None name (e.g. it is an Index object) and
the name argument is None, then passing name to the constructor will
not be enough; we still need to override the name attribute.
"""
out = left._constructor(result, index=index, dtype=dtype)
out = out.__finalize__(left)
out.name = name
return out
def _construct_divmod_result(left, result, index, name, dtype=None):
"""divmod returns a tuple of like indexed series instead of a single series.
"""
return (
_construct_result(left, result[0], index=index, name=name,
dtype=dtype),
_construct_result(left, result[1], index=index, name=name,
dtype=dtype),
)
def _arith_method_SERIES(cls, op, special):
"""
Wrapper function for Series arithmetic operations, to avoid
code duplication.
"""
str_rep = _get_opstr(op, cls)
op_name = _get_op_name(op, special)
eval_kwargs = _gen_eval_kwargs(op_name)
fill_zeros = _gen_fill_zeros(op_name)
construct_result = (_construct_divmod_result
if op in [divmod, rdivmod] else _construct_result)
def na_op(x, y):
"""
Return the result of evaluating op on the passed in values.
If native types are not compatible, try coersion to object dtype.
Parameters
----------
x : array-like
y : array-like or scalar
Returns
-------
array-like
Raises
------
TypeError : invalid operation
"""
import pandas.core.computation.expressions as expressions
try:
result = expressions.evaluate(op, str_rep, x, y, **eval_kwargs)
except TypeError:
result = masked_arith_op(x, y, op)
except Exception: # TODO: more specific?
if is_object_dtype(x):
return libalgos.arrmap_object(x,
lambda val: op(val, y))
raise
result = missing.fill_zeros(result, x, y, op_name, fill_zeros)
return result
def wrapper(left, right):
if isinstance(right, ABCDataFrame):
return NotImplemented
left, right = _align_method_SERIES(left, right)
res_name = get_op_result_name(left, right)
right = maybe_upcast_for_op(right)
if is_categorical_dtype(left):
raise TypeError("{typ} cannot perform the operation "
"{op}".format(typ=type(left).__name__, op=str_rep))
elif is_datetime64_dtype(left) or is_datetime64tz_dtype(left):
# Give dispatch_to_index_op a chance for tests like
# test_dt64_series_add_intlike, which the index dispatching handles
# specifically.
result = dispatch_to_index_op(op, left, right, pd.DatetimeIndex)
return construct_result(left, result,
index=left.index, name=res_name,
dtype=result.dtype)
elif (is_extension_array_dtype(left) or
(is_extension_array_dtype(right) and not is_scalar(right))):
# GH#22378 disallow scalar to exclude e.g. "category", "Int64"
return dispatch_to_extension_op(op, left, right)
elif is_timedelta64_dtype(left):
result = dispatch_to_index_op(op, left, right, pd.TimedeltaIndex)
return construct_result(left, result,
index=left.index, name=res_name)
elif is_timedelta64_dtype(right):
# We should only get here with non-scalar or timedelta64('NaT')
# values for right
# Note: we cannot use dispatch_to_index_op because
# that may incorrectly raise TypeError when we
# should get NullFrequencyError
result = op(pd.Index(left), right)
return construct_result(left, result,
index=left.index, name=res_name,
dtype=result.dtype)
lvalues = left.values
rvalues = right
if isinstance(rvalues, ABCSeries):
rvalues = rvalues.values
with np.errstate(all='ignore'):
result = na_op(lvalues, rvalues)
return construct_result(left, result,
index=left.index, name=res_name, dtype=None)
wrapper.__name__ = op_name
return wrapper
def _comp_method_OBJECT_ARRAY(op, x, y):
if isinstance(y, list):
y = construct_1d_object_array_from_listlike(y)
if isinstance(y, (np.ndarray, ABCSeries, ABCIndex)):
if not is_object_dtype(y.dtype):
y = y.astype(np.object_)
if isinstance(y, (ABCSeries, ABCIndex)):
y = y.values
result = libops.vec_compare(x, y, op)
else:
result = libops.scalar_compare(x, y, op)
return result
def _comp_method_SERIES(cls, op, special):
"""
Wrapper function for Series arithmetic operations, to avoid
code duplication.
"""
op_name = _get_op_name(op, special)
masker = _gen_eval_kwargs(op_name).get('masker', False)
def na_op(x, y):
# TODO:
# should have guarantess on what x, y can be type-wise
# Extension Dtypes are not called here
# Checking that cases that were once handled here are no longer
# reachable.
assert not (is_categorical_dtype(y) and not is_scalar(y))
if is_object_dtype(x.dtype):
result = _comp_method_OBJECT_ARRAY(op, x, y)
elif is_datetimelike_v_numeric(x, y):
return invalid_comparison(x, y, op)
else:
# we want to compare like types
# we only want to convert to integer like if
# we are not NotImplemented, otherwise
# we would allow datetime64 (but viewed as i8) against
# integer comparisons
# we have a datetime/timedelta and may need to convert
assert not needs_i8_conversion(x)
mask = None
if not is_scalar(y) and needs_i8_conversion(y):
mask = isna(x) | isna(y)
y = y.view('i8')
x = x.view('i8')
method = getattr(x, op_name, None)
if method is not None:
with np.errstate(all='ignore'):
result = method(y)
if result is NotImplemented:
return invalid_comparison(x, y, op)
else:
result = op(x, y)
if mask is not None and mask.any():
result[mask] = masker
return result
def wrapper(self, other, axis=None):
# Validate the axis parameter
if axis is not None:
self._get_axis_number(axis)
res_name = get_op_result_name(self, other)
if isinstance(other, list):
# TODO: same for tuples?
other = np.asarray(other)
if isinstance(other, ABCDataFrame): # pragma: no cover
# Defer to DataFrame implementation; fail early
return NotImplemented
elif isinstance(other, ABCSeries) and not self._indexed_same(other):
raise ValueError("Can only compare identically-labeled "
"Series objects")
elif is_categorical_dtype(self):
# Dispatch to Categorical implementation; pd.CategoricalIndex
# behavior is non-canonical GH#19513
res_values = dispatch_to_index_op(op, self, other, pd.Categorical)
return self._constructor(res_values, index=self.index,
name=res_name)
elif is_datetime64_dtype(self) or is_datetime64tz_dtype(self):
# Dispatch to DatetimeIndex to ensure identical
# Series/Index behavior
if (isinstance(other, datetime.date) and
not isinstance(other, datetime.datetime)):
# https://github.com/pandas-dev/pandas/issues/21152
# Compatibility for difference between Series comparison w/
# datetime and date
msg = (
"Comparing Series of datetimes with 'datetime.date'. "
"Currently, the 'datetime.date' is coerced to a "
"datetime. In the future pandas will not coerce, "
"and {future}. "
"To retain the current behavior, "
"convert the 'datetime.date' to a datetime with "
"'pd.Timestamp'."
)
if op in {operator.lt, operator.le, operator.gt, operator.ge}:
future = "a TypeError will be raised"
else:
future = (
"'the values will not compare equal to the "
"'datetime.date'"
)
msg = '\n'.join(textwrap.wrap(msg.format(future=future)))
warnings.warn(msg, FutureWarning, stacklevel=2)
other = pd.Timestamp(other)
res_values = dispatch_to_index_op(op, self, other,
pd.DatetimeIndex)
return self._constructor(res_values, index=self.index,
name=res_name)
elif is_timedelta64_dtype(self):
res_values = dispatch_to_index_op(op, self, other,
pd.TimedeltaIndex)
return self._constructor(res_values, index=self.index,
name=res_name)
elif (is_extension_array_dtype(self) or
(is_extension_array_dtype(other) and not is_scalar(other))):
# Note: the `not is_scalar(other)` condition rules out
# e.g. other == "category"
return dispatch_to_extension_op(op, self, other)
elif isinstance(other, ABCSeries):
# By this point we have checked that self._indexed_same(other)
res_values = na_op(self.values, other.values)
# rename is needed in case res_name is None and res_values.name
# is not.
return self._constructor(res_values, index=self.index,
name=res_name).rename(res_name)
elif isinstance(other, (np.ndarray, pd.Index)):
# do not check length of zerodim array
# as it will broadcast
if other.ndim != 0 and len(self) != len(other):
raise ValueError('Lengths must match to compare')
res_values = na_op(self.values, np.asarray(other))
result = self._constructor(res_values, index=self.index)
# rename is needed in case res_name is None and self.name
# is not.
return result.__finalize__(self).rename(res_name)
elif is_scalar(other) and isna(other):
# numpy does not like comparisons vs None
if op is operator.ne:
res_values = np.ones(len(self), dtype=bool)
else:
res_values = np.zeros(len(self), dtype=bool)
return self._constructor(res_values, index=self.index,
name=res_name, dtype='bool')
else:
values = self.get_values()
with np.errstate(all='ignore'):
res = na_op(values, other)
if is_scalar(res):
raise TypeError('Could not compare {typ} type with Series'
.format(typ=type(other)))
# always return a full value series here
res_values = com.values_from_object(res)
return self._constructor(res_values, index=self.index,
name=res_name, dtype='bool')
wrapper.__name__ = op_name
return wrapper
def _bool_method_SERIES(cls, op, special):
"""
Wrapper function for Series arithmetic operations, to avoid
code duplication.
"""
op_name = _get_op_name(op, special)
def na_op(x, y):
try:
result = op(x, y)
except TypeError:
assert not isinstance(y, (list, ABCSeries, ABCIndexClass))
if isinstance(y, np.ndarray):
# bool-bool dtype operations should be OK, should not get here
assert not (is_bool_dtype(x) and is_bool_dtype(y))
x = ensure_object(x)
y = ensure_object(y)
result = libops.vec_binop(x, y, op)
else:
# let null fall thru
assert lib.is_scalar(y)
if not isna(y):
y = bool(y)
try:
result = libops.scalar_binop(x, y, op)
except (TypeError, ValueError, AttributeError,
OverflowError, NotImplementedError):
raise TypeError("cannot compare a dtyped [{dtype}] array "
"with a scalar of type [{typ}]"
.format(dtype=x.dtype,
typ=type(y).__name__))
return result
fill_int = lambda x: x.fillna(0)
fill_bool = lambda x: x.fillna(False).astype(bool)
def wrapper(self, other):
is_self_int_dtype = is_integer_dtype(self.dtype)
self, other = _align_method_SERIES(self, other, align_asobject=True)
res_name = get_op_result_name(self, other)
if isinstance(other, ABCDataFrame):
# Defer to DataFrame implementation; fail early
return NotImplemented
elif isinstance(other, (ABCSeries, ABCIndexClass)):
is_other_int_dtype = is_integer_dtype(other.dtype)
other = fill_int(other) if is_other_int_dtype else fill_bool(other)
ovalues = other.values
finalizer = lambda x: x
else:
# scalars, list, tuple, np.array
is_other_int_dtype = is_integer_dtype(np.asarray(other))
if is_list_like(other) and not isinstance(other, np.ndarray):
# TODO: Can we do this before the is_integer_dtype check?
# could the is_integer_dtype check be checking the wrong
# thing? e.g. other = [[0, 1], [2, 3], [4, 5]]?
other = construct_1d_object_array_from_listlike(other)
ovalues = other
finalizer = lambda x: x.__finalize__(self)
# For int vs int `^`, `|`, `&` are bitwise operators and return
# integer dtypes. Otherwise these are boolean ops
filler = (fill_int if is_self_int_dtype and is_other_int_dtype
else fill_bool)
res_values = na_op(self.values, ovalues)
unfilled = self._constructor(res_values,
index=self.index, name=res_name)
filled = filler(unfilled)
return finalizer(filled)
wrapper.__name__ = op_name
return wrapper
def _flex_method_SERIES(cls, op, special):
name = _get_op_name(op, special)
doc = _make_flex_doc(name, 'series')
@Appender(doc)
def flex_wrapper(self, other, level=None, fill_value=None, axis=0):
# validate axis
if axis is not None:
self._get_axis_number(axis)
if isinstance(other, ABCSeries):
return self._binop(other, op, level=level, fill_value=fill_value)
elif isinstance(other, (np.ndarray, list, tuple)):
if len(other) != len(self):
raise ValueError('Lengths must be equal')
other = self._constructor(other, self.index)
return self._binop(other, op, level=level, fill_value=fill_value)
else:
if fill_value is not None:
self = self.fillna(fill_value)
return self._constructor(op(self, other),
self.index).__finalize__(self)
flex_wrapper.__name__ = name
return flex_wrapper
# -----------------------------------------------------------------------------
# DataFrame
def _combine_series_frame(self, other, func, fill_value=None, axis=None,
level=None):
"""
Apply binary operator `func` to self, other using alignment and fill
conventions determined by the fill_value, axis, and level kwargs.
Parameters
----------
self : DataFrame
other : Series
func : binary operator
fill_value : object, default None
axis : {0, 1, 'columns', 'index', None}, default None
level : int or None, default None
Returns
-------
result : DataFrame
"""
if fill_value is not None:
raise NotImplementedError("fill_value {fill} not supported."
.format(fill=fill_value))
if axis is not None:
axis = self._get_axis_number(axis)
if axis == 0:
return self._combine_match_index(other, func, level=level)
else:
return self._combine_match_columns(other, func, level=level)
else:
if not len(other):
return self * np.nan
if not len(self):
# Ambiguous case, use _series so works with DataFrame
return self._constructor(data=self._series, index=self.index,
columns=self.columns)
# default axis is columns
return self._combine_match_columns(other, func, level=level)
def _align_method_FRAME(left, right, axis):
""" convert rhs to meet lhs dims if input is list, tuple or np.ndarray """
def to_series(right):
msg = ('Unable to coerce to Series, length must be {req_len}: '
'given {given_len}')
if axis is not None and left._get_axis_name(axis) == 'index':
if len(left.index) != len(right):
raise ValueError(msg.format(req_len=len(left.index),
given_len=len(right)))
right = left._constructor_sliced(right, index=left.index)
else:
if len(left.columns) != len(right):
raise ValueError(msg.format(req_len=len(left.columns),
given_len=len(right)))
right = left._constructor_sliced(right, index=left.columns)
return right
if isinstance(right, np.ndarray):
if right.ndim == 1:
right = to_series(right)
elif right.ndim == 2:
if right.shape == left.shape:
right = left._constructor(right, index=left.index,
columns=left.columns)
elif right.shape[0] == left.shape[0] and right.shape[1] == 1:
# Broadcast across columns
right = np.broadcast_to(right, left.shape)
right = left._constructor(right,
index=left.index,
columns=left.columns)
elif right.shape[1] == left.shape[1] and right.shape[0] == 1:
# Broadcast along rows
right = to_series(right[0, :])
else:
raise ValueError("Unable to coerce to DataFrame, shape "
"must be {req_shape}: given {given_shape}"
.format(req_shape=left.shape,
given_shape=right.shape))
elif right.ndim > 2:
raise ValueError('Unable to coerce to Series/DataFrame, dim '
'must be <= 2: {dim}'.format(dim=right.shape))
elif (is_list_like(right) and
not isinstance(right, (ABCSeries, ABCDataFrame))):
# GH17901
right = to_series(right)
return right
def _arith_method_FRAME(cls, op, special):
str_rep = _get_opstr(op, cls)
op_name = _get_op_name(op, special)
eval_kwargs = _gen_eval_kwargs(op_name)
fill_zeros = _gen_fill_zeros(op_name)
default_axis = _get_frame_op_default_axis(op_name)
def na_op(x, y):
import pandas.core.computation.expressions as expressions
try:
result = expressions.evaluate(op, str_rep, x, y, **eval_kwargs)
except TypeError:
result = masked_arith_op(x, y, op)
result = missing.fill_zeros(result, x, y, op_name, fill_zeros)
return result
if op_name in _op_descriptions:
# i.e. include "add" but not "__add__"
doc = _make_flex_doc(op_name, 'dataframe')
else:
doc = _arith_doc_FRAME % op_name
@Appender(doc)
def f(self, other, axis=default_axis, level=None, fill_value=None):
other = _align_method_FRAME(self, other, axis)
if isinstance(other, ABCDataFrame):
# Another DataFrame
pass_op = op if should_series_dispatch(self, other, op) else na_op
return self._combine_frame(other, pass_op, fill_value, level)
elif isinstance(other, ABCSeries):
# For these values of `axis`, we end up dispatching to Series op,
# so do not want the masked op.
pass_op = op if axis in [0, "columns", None] else na_op
return _combine_series_frame(self, other, pass_op,
fill_value=fill_value, axis=axis,
level=level)
else:
if fill_value is not None:
self = self.fillna(fill_value)
assert np.ndim(other) == 0
return self._combine_const(other, op)
f.__name__ = op_name
return f
def _flex_comp_method_FRAME(cls, op, special):
str_rep = _get_opstr(op, cls)
op_name = _get_op_name(op, special)
default_axis = _get_frame_op_default_axis(op_name)
def na_op(x, y):
try:
with np.errstate(invalid='ignore'):
result = op(x, y)
except TypeError:
result = mask_cmp_op(x, y, op)
return result
doc = _flex_comp_doc_FRAME.format(op_name=op_name,
desc=_op_descriptions[op_name]['desc'])
@Appender(doc)
def f(self, other, axis=default_axis, level=None):
other = _align_method_FRAME(self, other, axis)
if isinstance(other, ABCDataFrame):
# Another DataFrame
if not self._indexed_same(other):
self, other = self.align(other, 'outer',
level=level, copy=False)
return dispatch_to_series(self, other, na_op, str_rep)
elif isinstance(other, ABCSeries):
return _combine_series_frame(self, other, na_op,
fill_value=None, axis=axis,
level=level)
else:
assert np.ndim(other) == 0, other
return self._combine_const(other, na_op)
f.__name__ = op_name
return f
def _comp_method_FRAME(cls, func, special):
str_rep = _get_opstr(func, cls)
op_name = _get_op_name(func, special)
@Appender('Wrapper for comparison method {name}'.format(name=op_name))
def f(self, other):
other = _align_method_FRAME(self, other, axis=None)
if isinstance(other, ABCDataFrame):
# Another DataFrame
if not self._indexed_same(other):
raise ValueError('Can only compare identically-labeled '
'DataFrame objects')
return dispatch_to_series(self, other, func, str_rep)
elif isinstance(other, ABCSeries):
return _combine_series_frame(self, other, func,
fill_value=None, axis=None,
level=None)
else:
# straight boolean comparisons we want to allow all columns
# (regardless of dtype to pass thru) See #4537 for discussion.
res = self._combine_const(other, func)
return res.fillna(True).astype(bool)
f.__name__ = op_name
return f
# -----------------------------------------------------------------------------
# Sparse
def _cast_sparse_series_op(left, right, opname):
"""
For SparseSeries operation, coerce to float64 if the result is expected
to have NaN or inf values
Parameters
----------
left : SparseArray
right : SparseArray
opname : str
Returns
-------
left : SparseArray
right : SparseArray
"""
from pandas.core.sparse.api import SparseDtype
opname = opname.strip('_')
# TODO: This should be moved to the array?
if is_integer_dtype(left) and is_integer_dtype(right):
# series coerces to float64 if result should have NaN/inf
if opname in ('floordiv', 'mod') and (right.to_dense() == 0).any():
left = left.astype(SparseDtype(np.float64, left.fill_value))
right = right.astype(SparseDtype(np.float64, right.fill_value))
elif opname in ('rfloordiv', 'rmod') and (left.to_dense() == 0).any():
left = left.astype(SparseDtype(np.float64, left.fill_value))
right = right.astype(SparseDtype(np.float64, right.fill_value))
return left, right
def _arith_method_SPARSE_SERIES(cls, op, special):
"""
Wrapper function for Series arithmetic operations, to avoid
code duplication.
"""
op_name = _get_op_name(op, special)
def wrapper(self, other):
if isinstance(other, ABCDataFrame):
return NotImplemented
elif isinstance(other, ABCSeries):
if not isinstance(other, ABCSparseSeries):
other = other.to_sparse(fill_value=self.fill_value)
return _sparse_series_op(self, other, op, op_name)
elif is_scalar(other):
with np.errstate(all='ignore'):
new_values = op(self.values, other)
return self._constructor(new_values,
index=self.index,
name=self.name)
else: # pragma: no cover
raise TypeError('operation with {other} not supported'
.format(other=type(other)))
wrapper.__name__ = op_name
return wrapper
def _sparse_series_op(left, right, op, name):
left, right = left.align(right, join='outer', copy=False)
new_index = left.index
new_name = get_op_result_name(left, right)
from pandas.core.arrays.sparse import _sparse_array_op
lvalues, rvalues = _cast_sparse_series_op(left.values, right.values, name)
result = _sparse_array_op(lvalues, rvalues, op, name)
return left._constructor(result, index=new_index, name=new_name)
def _arith_method_SPARSE_ARRAY(cls, op, special):
"""
Wrapper function for Series arithmetic operations, to avoid
code duplication.
"""
op_name = _get_op_name(op, special)
def wrapper(self, other):
from pandas.core.arrays.sparse.array import (
SparseArray, _sparse_array_op, _wrap_result, _get_fill)
if isinstance(other, np.ndarray):
if len(self) != len(other):
raise AssertionError("length mismatch: {self} vs. {other}"
.format(self=len(self), other=len(other)))
if not isinstance(other, SparseArray):
dtype = getattr(other, 'dtype', None)
other = SparseArray(other, fill_value=self.fill_value,
dtype=dtype)
return _sparse_array_op(self, other, op, op_name)
elif is_scalar(other):
with np.errstate(all='ignore'):
fill = op(_get_fill(self), np.asarray(other))
result = op(self.sp_values, other)
return _wrap_result(op_name, result, self.sp_index, fill)
else: # pragma: no cover
raise TypeError('operation with {other} not supported'
.format(other=type(other)))
wrapper.__name__ = op_name
return wrapper
|
from collections import OrderedDict
import numpy as np
from numpy import nan
from numpy.random import randn
import pytest
import pandas as pd
from pandas import DataFrame, Index, MultiIndex, Series
from pandas.core.reshape.concat import concat
from pandas.core.reshape.merge import merge
import pandas.util.testing as tm
@pytest.fixture
def left():
"""left dataframe (not multi-indexed) for multi-index join tests"""
# a little relevant example with NAs
key1 = ['bar', 'bar', 'bar', 'foo', 'foo', 'baz', 'baz', 'qux',
'qux', 'snap']
key2 = ['two', 'one', 'three', 'one', 'two', 'one', 'two', 'two',
'three', 'one']
data = np.random.randn(len(key1))
return DataFrame({'key1': key1, 'key2': key2, 'data': data})
@pytest.fixture
def right():
"""right dataframe (multi-indexed) for multi-index join tests"""
index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'],
['one', 'two', 'three']],
codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
[0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
names=['key1', 'key2'])
return DataFrame(np.random.randn(10, 3), index=index,
columns=['j_one', 'j_two', 'j_three'])
@pytest.fixture
def left_multi():
return (
DataFrame(
dict(Origin=['A', 'A', 'B', 'B', 'C'],
Destination=['A', 'B', 'A', 'C', 'A'],
Period=['AM', 'AM', 'IP', 'AM', 'OP'],
TripPurp=['hbw', 'nhb', 'hbo', 'nhb', 'hbw'],
Trips=[1987, 3647, 2470, 4296, 4444]),
columns=['Origin', 'Destination', 'Period',
'TripPurp', 'Trips'])
.set_index(['Origin', 'Destination', 'Period', 'TripPurp']))
@pytest.fixture
def right_multi():
return (
DataFrame(
dict(Origin=['A', 'A', 'B', 'B', 'C', 'C', 'E'],
Destination=['A', 'B', 'A', 'B', 'A', 'B', 'F'],
Period=['AM', 'AM', 'IP', 'AM', 'OP', 'IP', 'AM'],
LinkType=['a', 'b', 'c', 'b', 'a', 'b', 'a'],
Distance=[100, 80, 90, 80, 75, 35, 55]),
columns=['Origin', 'Destination', 'Period',
'LinkType', 'Distance'])
.set_index(['Origin', 'Destination', 'Period', 'LinkType']))
@pytest.fixture
def on_cols_multi():
return ['Origin', 'Destination', 'Period']
@pytest.fixture
def idx_cols_multi():
return ['Origin', 'Destination', 'Period', 'TripPurp', 'LinkType']
class TestMergeMulti:
def setup_method(self):
self.index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'],
['one', 'two', 'three']],
codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
[0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
names=['first', 'second'])
self.to_join = DataFrame(np.random.randn(10, 3), index=self.index,
columns=['j_one', 'j_two', 'j_three'])
# a little relevant example with NAs
key1 = ['bar', 'bar', 'bar', 'foo', 'foo', 'baz', 'baz', 'qux',
'qux', 'snap']
key2 = ['two', 'one', 'three', 'one', 'two', 'one', 'two', 'two',
'three', 'one']
data = np.random.randn(len(key1))
self.data = DataFrame({'key1': key1, 'key2': key2,
'data': data})
def test_merge_on_multikey(self, left, right, join_type):
on_cols = ['key1', 'key2']
result = (left.join(right, on=on_cols, how=join_type)
.reset_index(drop=True))
expected = pd.merge(left, right.reset_index(),
on=on_cols, how=join_type)
tm.assert_frame_equal(result, expected)
result = (left.join(right, on=on_cols, how=join_type, sort=True)
.reset_index(drop=True))
expected = pd.merge(left, right.reset_index(),
on=on_cols, how=join_type, sort=True)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("sort", [False, True])
def test_left_join_multi_index(self, left, right, sort):
icols = ['1st', '2nd', '3rd']
def bind_cols(df):
iord = lambda a: 0 if a != a else ord(a)
f = lambda ts: ts.map(iord) - ord('a')
return (f(df['1st']) + f(df['3rd']) * 1e2 +
df['2nd'].fillna(0) * 1e4)
def run_asserts(left, right, sort):
res = left.join(right, on=icols, how='left', sort=sort)
assert len(left) < len(res) + 1
assert not res['4th'].isna().any()
assert not res['5th'].isna().any()
tm.assert_series_equal(
res['4th'], - res['5th'], check_names=False)
result = bind_cols(res.iloc[:, :-2])
tm.assert_series_equal(res['4th'], result, check_names=False)
assert result.name is None
if sort:
tm.assert_frame_equal(
res, res.sort_values(icols, kind='mergesort'))
out = merge(left, right.reset_index(), on=icols,
sort=sort, how='left')
res.index = np.arange(len(res))
tm.assert_frame_equal(out, res)
lc = list(map(chr, np.arange(ord('a'), ord('z') + 1)))
left = DataFrame(np.random.choice(lc, (5000, 2)),
columns=['1st', '3rd'])
left.insert(1, '2nd', np.random.randint(0, 1000, len(left)))
i = np.random.permutation(len(left))
right = left.iloc[i].copy()
left['4th'] = bind_cols(left)
right['5th'] = - bind_cols(right)
right.set_index(icols, inplace=True)
run_asserts(left, right, sort)
# inject some nulls
left.loc[1::23, '1st'] = np.nan
left.loc[2::37, '2nd'] = np.nan
left.loc[3::43, '3rd'] = np.nan
left['4th'] = bind_cols(left)
i = np.random.permutation(len(left))
right = left.iloc[i, :-1]
right['5th'] = - bind_cols(right)
right.set_index(icols, inplace=True)
run_asserts(left, right, sort)
@pytest.mark.parametrize("sort", [False, True])
def test_merge_right_vs_left(self, left, right, sort):
# compare left vs right merge with multikey
on_cols = ['key1', 'key2']
merged_left_right = left.merge(right,
left_on=on_cols, right_index=True,
how='left', sort=sort)
merge_right_left = right.merge(left,
right_on=on_cols, left_index=True,
how='right', sort=sort)
# Reorder columns
merge_right_left = merge_right_left[merged_left_right.columns]
tm.assert_frame_equal(merged_left_right, merge_right_left)
def test_compress_group_combinations(self):
# ~ 40000000 possible unique groups
key1 = tm.rands_array(10, 10000)
key1 = np.tile(key1, 2)
key2 = key1[::-1]
df = DataFrame({'key1': key1, 'key2': key2,
'value1': np.random.randn(20000)})
df2 = DataFrame({'key1': key1[::2], 'key2': key2[::2],
'value2': np.random.randn(10000)})
# just to hit the label compression code path
merge(df, df2, how='outer')
def test_left_join_index_preserve_order(self):
on_cols = ['k1', 'k2']
left = DataFrame({'k1': [0, 1, 2] * 8,
'k2': ['foo', 'bar'] * 12,
'v': np.array(np.arange(24), dtype=np.int64)})
index = MultiIndex.from_tuples([(2, 'bar'), (1, 'foo')])
right = DataFrame({'v2': [5, 7]}, index=index)
result = left.join(right, on=on_cols)
expected = left.copy()
expected['v2'] = np.nan
expected.loc[(expected.k1 == 2) & (expected.k2 == 'bar'), 'v2'] = 5
expected.loc[(expected.k1 == 1) & (expected.k2 == 'foo'), 'v2'] = 7
tm.assert_frame_equal(result, expected)
result.sort_values(on_cols, kind='mergesort', inplace=True)
expected = left.join(right, on=on_cols, sort=True)
tm.assert_frame_equal(result, expected)
# test join with multi dtypes blocks
left = DataFrame({'k1': [0, 1, 2] * 8,
'k2': ['foo', 'bar'] * 12,
'k3': np.array([0, 1, 2] * 8, dtype=np.float32),
'v': np.array(np.arange(24), dtype=np.int32)})
index = MultiIndex.from_tuples([(2, 'bar'), (1, 'foo')])
right = DataFrame({'v2': [5, 7]}, index=index)
result = left.join(right, on=on_cols)
expected = left.copy()
expected['v2'] = np.nan
expected.loc[(expected.k1 == 2) & (expected.k2 == 'bar'), 'v2'] = 5
expected.loc[(expected.k1 == 1) & (expected.k2 == 'foo'), 'v2'] = 7
tm.assert_frame_equal(result, expected)
result = result.sort_values(on_cols, kind='mergesort')
expected = left.join(right, on=on_cols, sort=True)
tm.assert_frame_equal(result, expected)
def test_left_join_index_multi_match_multiindex(self):
left = DataFrame([
['X', 'Y', 'C', 'a'],
['W', 'Y', 'C', 'e'],
['V', 'Q', 'A', 'h'],
['V', 'R', 'D', 'i'],
['X', 'Y', 'D', 'b'],
['X', 'Y', 'A', 'c'],
['W', 'Q', 'B', 'f'],
['W', 'R', 'C', 'g'],
['V', 'Y', 'C', 'j'],
['X', 'Y', 'B', 'd']],
columns=['cola', 'colb', 'colc', 'tag'],
index=[3, 2, 0, 1, 7, 6, 4, 5, 9, 8])
right = (DataFrame([
['W', 'R', 'C', 0],
['W', 'Q', 'B', 3],
['W', 'Q', 'B', 8],
['X', 'Y', 'A', 1],
['X', 'Y', 'A', 4],
['X', 'Y', 'B', 5],
['X', 'Y', 'C', 6],
['X', 'Y', 'C', 9],
['X', 'Q', 'C', -6],
['X', 'R', 'C', -9],
['V', 'Y', 'C', 7],
['V', 'R', 'D', 2],
['V', 'R', 'D', -1],
['V', 'Q', 'A', -3]],
columns=['col1', 'col2', 'col3', 'val'])
.set_index(['col1', 'col2', 'col3']))
result = left.join(right, on=['cola', 'colb', 'colc'], how='left')
expected = DataFrame([
['X', 'Y', 'C', 'a', 6],
['X', 'Y', 'C', 'a', 9],
['W', 'Y', 'C', 'e', nan],
['V', 'Q', 'A', 'h', -3],
['V', 'R', 'D', 'i', 2],
['V', 'R', 'D', 'i', -1],
['X', 'Y', 'D', 'b', nan],
['X', 'Y', 'A', 'c', 1],
['X', 'Y', 'A', 'c', 4],
['W', 'Q', 'B', 'f', 3],
['W', 'Q', 'B', 'f', 8],
['W', 'R', 'C', 'g', 0],
['V', 'Y', 'C', 'j', 7],
['X', 'Y', 'B', 'd', 5]],
columns=['cola', 'colb', 'colc', 'tag', 'val'],
index=[3, 3, 2, 0, 1, 1, 7, 6, 6, 4, 4, 5, 9, 8])
tm.assert_frame_equal(result, expected)
result = left.join(right, on=['cola', 'colb', 'colc'],
how='left', sort=True)
expected = expected.sort_values(['cola', 'colb', 'colc'],
kind='mergesort')
tm.assert_frame_equal(result, expected)
def test_left_join_index_multi_match(self):
left = DataFrame([
['c', 0],
['b', 1],
['a', 2],
['b', 3]],
columns=['tag', 'val'],
index=[2, 0, 1, 3])
right = (DataFrame([
['a', 'v'],
['c', 'w'],
['c', 'x'],
['d', 'y'],
['a', 'z'],
['c', 'r'],
['e', 'q'],
['c', 's']],
columns=['tag', 'char'])
.set_index('tag'))
result = left.join(right, on='tag', how='left')
expected = DataFrame([
['c', 0, 'w'],
['c', 0, 'x'],
['c', 0, 'r'],
['c', 0, 's'],
['b', 1, nan],
['a', 2, 'v'],
['a', 2, 'z'],
['b', 3, nan]],
columns=['tag', 'val', 'char'],
index=[2, 2, 2, 2, 0, 1, 1, 3])
tm.assert_frame_equal(result, expected)
result = left.join(right, on='tag', how='left', sort=True)
expected2 = expected.sort_values('tag', kind='mergesort')
tm.assert_frame_equal(result, expected2)
# GH7331 - maintain left frame order in left merge
result = merge(left, right.reset_index(), how='left', on='tag')
expected.index = np.arange(len(expected))
tm.assert_frame_equal(result, expected)
def test_left_merge_na_buglet(self):
left = DataFrame({'id': list('abcde'), 'v1': randn(5),
'v2': randn(5), 'dummy': list('abcde'),
'v3': randn(5)},
columns=['id', 'v1', 'v2', 'dummy', 'v3'])
right = DataFrame({'id': ['a', 'b', np.nan, np.nan, np.nan],
'sv3': [1.234, 5.678, np.nan, np.nan, np.nan]})
result = merge(left, right, on='id', how='left')
rdf = right.drop(['id'], axis=1)
expected = left.join(rdf)
tm.assert_frame_equal(result, expected)
def test_merge_na_keys(self):
data = [[1950, "A", 1.5],
[1950, "B", 1.5],
[1955, "B", 1.5],
[1960, "B", np.nan],
[1970, "B", 4.],
[1950, "C", 4.],
[1960, "C", np.nan],
[1965, "C", 3.],
[1970, "C", 4.]]
frame = DataFrame(data, columns=["year", "panel", "data"])
other_data = [[1960, 'A', np.nan],
[1970, 'A', np.nan],
[1955, 'A', np.nan],
[1965, 'A', np.nan],
[1965, 'B', np.nan],
[1955, 'C', np.nan]]
other = DataFrame(other_data, columns=['year', 'panel', 'data'])
result = frame.merge(other, how='outer')
expected = frame.fillna(-999).merge(other.fillna(-999), how='outer')
expected = expected.replace(-999, np.nan)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("klass", [None, np.asarray, Series, Index])
def test_merge_datetime_index(self, klass):
# see gh-19038
df = DataFrame([1, 2, 3],
["2016-01-01", "2017-01-01", "2018-01-01"],
columns=["a"])
df.index = pd.to_datetime(df.index)
on_vector = df.index.year
if klass is not None:
on_vector = klass(on_vector)
expected = DataFrame(
OrderedDict([
("a", [1, 2, 3]),
("key_1", [2016, 2017, 2018]),
])
)
result = df.merge(df, on=["a", on_vector], how="inner")
tm.assert_frame_equal(result, expected)
expected = DataFrame(
OrderedDict([
("key_0", [2016, 2017, 2018]),
("a_x", [1, 2, 3]),
("a_y", [1, 2, 3]),
])
)
result = df.merge(df, on=[df.index.year], how="inner")
tm.assert_frame_equal(result, expected)
def test_join_multi_levels(self):
# GH 3662
# merge multi-levels
household = (
DataFrame(
dict(household_id=[1, 2, 3],
male=[0, 1, 0],
wealth=[196087.3, 316478.7, 294750]),
columns=['household_id', 'male', 'wealth'])
.set_index('household_id'))
portfolio = (
DataFrame(
dict(household_id=[1, 2, 2, 3, 3, 3, 4],
asset_id=["nl0000301109", "nl0000289783", "gb00b03mlx29",
"gb00b03mlx29", "lu0197800237", "nl0000289965",
np.nan],
name=["ABN Amro", "Robeco", "Royal Dutch Shell",
"Royal Dutch Shell",
"AAB Eastern Europe Equity Fund",
"Postbank BioTech Fonds", np.nan],
share=[1.0, 0.4, 0.6, 0.15, 0.6, 0.25, 1.0]),
columns=['household_id', 'asset_id', 'name', 'share'])
.set_index(['household_id', 'asset_id']))
result = household.join(portfolio, how='inner')
expected = (
DataFrame(
dict(male=[0, 1, 1, 0, 0, 0],
wealth=[196087.3, 316478.7, 316478.7,
294750.0, 294750.0, 294750.0],
name=['ABN Amro', 'Robeco', 'Royal Dutch Shell',
'Royal Dutch Shell',
'AAB Eastern Europe Equity Fund',
'Postbank BioTech Fonds'],
share=[1.00, 0.40, 0.60, 0.15, 0.60, 0.25],
household_id=[1, 2, 2, 3, 3, 3],
asset_id=['nl0000301109', 'nl0000289783', 'gb00b03mlx29',
'gb00b03mlx29', 'lu0197800237',
'nl0000289965']))
.set_index(['household_id', 'asset_id'])
.reindex(columns=['male', 'wealth', 'name', 'share']))
tm.assert_frame_equal(result, expected)
# equivalency
result = (merge(household.reset_index(), portfolio.reset_index(),
on=['household_id'], how='inner')
.set_index(['household_id', 'asset_id']))
tm.assert_frame_equal(result, expected)
result = household.join(portfolio, how='outer')
expected = (concat([
expected,
(DataFrame(
dict(share=[1.00]),
index=MultiIndex.from_tuples(
[(4, np.nan)],
names=['household_id', 'asset_id'])))
], axis=0, sort=True).reindex(columns=expected.columns))
tm.assert_frame_equal(result, expected)
# invalid cases
household.index.name = 'foo'
with pytest.raises(ValueError):
household.join(portfolio, how='inner')
portfolio2 = portfolio.copy()
portfolio2.index.set_names(['household_id', 'foo'])
with pytest.raises(ValueError):
portfolio2.join(portfolio, how='inner')
def test_join_multi_levels2(self):
# some more advanced merges
# GH6360
household = (
DataFrame(
dict(household_id=[1, 2, 2, 3, 3, 3, 4],
asset_id=["nl0000301109", "nl0000301109", "gb00b03mlx29",
"gb00b03mlx29", "lu0197800237", "nl0000289965",
np.nan],
share=[1.0, 0.4, 0.6, 0.15, 0.6, 0.25, 1.0]),
columns=['household_id', 'asset_id', 'share'])
.set_index(['household_id', 'asset_id']))
log_return = DataFrame(dict(
asset_id=["gb00b03mlx29", "gb00b03mlx29",
"gb00b03mlx29", "lu0197800237", "lu0197800237"],
t=[233, 234, 235, 180, 181],
log_return=[.09604978, -.06524096, .03532373, .03025441, .036997]
)).set_index(["asset_id", "t"])
expected = (
DataFrame(dict(
household_id=[2, 2, 2, 3, 3, 3, 3, 3],
asset_id=["gb00b03mlx29", "gb00b03mlx29",
"gb00b03mlx29", "gb00b03mlx29",
"gb00b03mlx29", "gb00b03mlx29",
"lu0197800237", "lu0197800237"],
t=[233, 234, 235, 233, 234, 235, 180, 181],
share=[0.6, 0.6, 0.6, 0.15, 0.15, 0.15, 0.6, 0.6],
log_return=[.09604978, -.06524096, .03532373,
.09604978, -.06524096, .03532373,
.03025441, .036997]
))
.set_index(["household_id", "asset_id", "t"])
.reindex(columns=['share', 'log_return']))
# this is the equivalency
result = (merge(household.reset_index(), log_return.reset_index(),
on=['asset_id'], how='inner')
.set_index(['household_id', 'asset_id', 't']))
tm.assert_frame_equal(result, expected)
expected = (
DataFrame(dict(
household_id=[1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 4],
asset_id=["nl0000301109", "nl0000301109", "gb00b03mlx29",
"gb00b03mlx29", "gb00b03mlx29",
"gb00b03mlx29", "gb00b03mlx29", "gb00b03mlx29",
"lu0197800237", "lu0197800237",
"nl0000289965", None],
t=[None, None, 233, 234, 235, 233, 234,
235, 180, 181, None, None],
share=[1.0, 0.4, 0.6, 0.6, 0.6, 0.15,
0.15, 0.15, 0.6, 0.6, 0.25, 1.0],
log_return=[None, None, .09604978, -.06524096, .03532373,
.09604978, -.06524096, .03532373,
.03025441, .036997, None, None]
))
.set_index(["household_id", "asset_id", "t"])
.reindex(columns=['share', 'log_return']))
result = (merge(household.reset_index(), log_return.reset_index(),
on=['asset_id'], how='outer')
.set_index(['household_id', 'asset_id', 't']))
tm.assert_frame_equal(result, expected)
class TestJoinMultiMulti:
def test_join_multi_multi(self, left_multi, right_multi, join_type,
on_cols_multi, idx_cols_multi):
# Multi-index join tests
expected = (pd.merge(left_multi.reset_index(),
right_multi.reset_index(),
how=join_type, on=on_cols_multi).
set_index(idx_cols_multi).sort_index())
result = left_multi.join(right_multi, how=join_type).sort_index()
tm.assert_frame_equal(result, expected)
def test_join_multi_empty_frames(self, left_multi, right_multi, join_type,
on_cols_multi, idx_cols_multi):
left_multi = left_multi.drop(columns=left_multi.columns)
right_multi = right_multi.drop(columns=right_multi.columns)
expected = (pd.merge(left_multi.reset_index(),
right_multi.reset_index(),
how=join_type, on=on_cols_multi)
.set_index(idx_cols_multi).sort_index())
result = left_multi.join(right_multi, how=join_type).sort_index()
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("box", [None, np.asarray, Series, Index])
def test_merge_datetime_index(self, box):
# see gh-19038
df = DataFrame([1, 2, 3],
["2016-01-01", "2017-01-01", "2018-01-01"],
columns=["a"])
df.index = pd.to_datetime(df.index)
on_vector = df.index.year
if box is not None:
on_vector = box(on_vector)
expected = DataFrame(
OrderedDict([
("a", [1, 2, 3]),
("key_1", [2016, 2017, 2018]),
])
)
result = df.merge(df, on=["a", on_vector], how="inner")
tm.assert_frame_equal(result, expected)
expected = DataFrame(
OrderedDict([
("key_0", [2016, 2017, 2018]),
("a_x", [1, 2, 3]),
("a_y", [1, 2, 3]),
])
)
result = df.merge(df, on=[df.index.year], how="inner")
tm.assert_frame_equal(result, expected)
def test_single_common_level(self):
index_left = pd.MultiIndex.from_tuples([('K0', 'X0'), ('K0', 'X1'),
('K1', 'X2')],
names=['key', 'X'])
left = pd.DataFrame({'A': ['A0', 'A1', 'A2'],
'B': ['B0', 'B1', 'B2']},
index=index_left)
index_right = pd.MultiIndex.from_tuples([('K0', 'Y0'), ('K1', 'Y1'),
('K2', 'Y2'), ('K2', 'Y3')],
names=['key', 'Y'])
right = pd.DataFrame({'C': ['C0', 'C1', 'C2', 'C3'],
'D': ['D0', 'D1', 'D2', 'D3']},
index=index_right)
result = left.join(right)
expected = (pd.merge(left.reset_index(), right.reset_index(),
on=['key'], how='inner')
.set_index(['key', 'X', 'Y']))
tm.assert_frame_equal(result, expected)
|
|
pandas/tests/reshape/merge/test_multi.py
|
|
import py
import sys, inspect
from compiler import parse, ast, pycodegen
from _pytest.assertion.util import format_explanation, BuiltinAssertionError
passthroughex = py.builtin._sysex
class Failure:
def __init__(self, node):
self.exc, self.value, self.tb = sys.exc_info()
self.node = node
class View(object):
"""View base class.
If C is a subclass of View, then C(x) creates a proxy object around
the object x. The actual class of the proxy is not C in general,
but a *subclass* of C determined by the rules below. To avoid confusion
we call view class the class of the proxy (a subclass of C, so of View)
and object class the class of x.
Attributes and methods not found in the proxy are automatically read on x.
Other operations like setting attributes are performed on the proxy, as
determined by its view class. The object x is available from the proxy
as its __obj__ attribute.
The view class selection is determined by the __view__ tuples and the
optional __viewkey__ method. By default, the selected view class is the
most specific subclass of C whose __view__ mentions the class of x.
If no such subclass is found, the search proceeds with the parent
object classes. For example, C(True) will first look for a subclass
of C with __view__ = (..., bool, ...) and only if it doesn't find any
look for one with __view__ = (..., int, ...), and then ..., object,...
If everything fails the class C itself is considered to be the default.
Alternatively, the view class selection can be driven by another aspect
of the object x, instead of the class of x, by overriding __viewkey__.
See last example at the end of this module.
"""
_viewcache = {}
__view__ = ()
def __new__(rootclass, obj, *args, **kwds):
self = object.__new__(rootclass)
self.__obj__ = obj
self.__rootclass__ = rootclass
key = self.__viewkey__()
try:
self.__class__ = self._viewcache[key]
except KeyError:
self.__class__ = self._selectsubclass(key)
return self
def __getattr__(self, attr):
# attributes not found in the normal hierarchy rooted on View
# are looked up in the object's real class
return getattr(self.__obj__, attr)
def __viewkey__(self):
return self.__obj__.__class__
def __matchkey__(self, key, subclasses):
if inspect.isclass(key):
keys = inspect.getmro(key)
else:
keys = [key]
for key in keys:
result = [C for C in subclasses if key in C.__view__]
if result:
return result
return []
def _selectsubclass(self, key):
subclasses = list(enumsubclasses(self.__rootclass__))
for C in subclasses:
if not isinstance(C.__view__, tuple):
C.__view__ = (C.__view__,)
choices = self.__matchkey__(key, subclasses)
if not choices:
return self.__rootclass__
elif len(choices) == 1:
return choices[0]
else:
# combine the multiple choices
return type('?', tuple(choices), {})
def __repr__(self):
return '%s(%r)' % (self.__rootclass__.__name__, self.__obj__)
def enumsubclasses(cls):
for subcls in cls.__subclasses__():
for subsubclass in enumsubclasses(subcls):
yield subsubclass
yield cls
class Interpretable(View):
"""A parse tree node with a few extra methods."""
explanation = None
def is_builtin(self, frame):
return False
def eval(self, frame):
# fall-back for unknown expression nodes
try:
expr = ast.Expression(self.__obj__)
expr.filename = '<eval>'
self.__obj__.filename = '<eval>'
co = pycodegen.ExpressionCodeGenerator(expr).getCode()
result = frame.eval(co)
except passthroughex:
raise
except:
raise Failure(self)
self.result = result
self.explanation = self.explanation or frame.repr(self.result)
def run(self, frame):
# fall-back for unknown statement nodes
try:
expr = ast.Module(None, ast.Stmt([self.__obj__]))
expr.filename = '<run>'
co = pycodegen.ModuleCodeGenerator(expr).getCode()
frame.exec_(co)
except passthroughex:
raise
except:
raise Failure(self)
def nice_explanation(self):
return format_explanation(self.explanation)
class Name(Interpretable):
__view__ = ast.Name
def is_local(self, frame):
source = '%r in locals() is not globals()' % self.name
try:
return frame.is_true(frame.eval(source))
except passthroughex:
raise
except:
return False
def is_global(self, frame):
source = '%r in globals()' % self.name
try:
return frame.is_true(frame.eval(source))
except passthroughex:
raise
except:
return False
def is_builtin(self, frame):
source = '%r not in locals() and %r not in globals()' % (
self.name, self.name)
try:
return frame.is_true(frame.eval(source))
except passthroughex:
raise
except:
return False
def eval(self, frame):
super(Name, self).eval(frame)
if not self.is_local(frame):
self.explanation = self.name
class Compare(Interpretable):
__view__ = ast.Compare
def eval(self, frame):
expr = Interpretable(self.expr)
expr.eval(frame)
for operation, expr2 in self.ops:
if hasattr(self, 'result'):
# shortcutting in chained expressions
if not frame.is_true(self.result):
break
expr2 = Interpretable(expr2)
expr2.eval(frame)
self.explanation = "%s %s %s" % (
expr.explanation, operation, expr2.explanation)
source = "__exprinfo_left %s __exprinfo_right" % operation
try:
self.result = frame.eval(source,
__exprinfo_left=expr.result,
__exprinfo_right=expr2.result)
except passthroughex:
raise
except:
raise Failure(self)
expr = expr2
class And(Interpretable):
__view__ = ast.And
def eval(self, frame):
explanations = []
for expr in self.nodes:
expr = Interpretable(expr)
expr.eval(frame)
explanations.append(expr.explanation)
self.result = expr.result
if not frame.is_true(expr.result):
break
self.explanation = '(' + ' and '.join(explanations) + ')'
class Or(Interpretable):
__view__ = ast.Or
def eval(self, frame):
explanations = []
for expr in self.nodes:
expr = Interpretable(expr)
expr.eval(frame)
explanations.append(expr.explanation)
self.result = expr.result
if frame.is_true(expr.result):
break
self.explanation = '(' + ' or '.join(explanations) + ')'
# == Unary operations ==
keepalive = []
for astclass, astpattern in {
ast.Not : 'not __exprinfo_expr',
ast.Invert : '(~__exprinfo_expr)',
}.items():
class UnaryArith(Interpretable):
__view__ = astclass
def eval(self, frame, astpattern=astpattern):
expr = Interpretable(self.expr)
expr.eval(frame)
self.explanation = astpattern.replace('__exprinfo_expr',
expr.explanation)
try:
self.result = frame.eval(astpattern,
__exprinfo_expr=expr.result)
except passthroughex:
raise
except:
raise Failure(self)
keepalive.append(UnaryArith)
# == Binary operations ==
for astclass, astpattern in {
ast.Add : '(__exprinfo_left + __exprinfo_right)',
ast.Sub : '(__exprinfo_left - __exprinfo_right)',
ast.Mul : '(__exprinfo_left * __exprinfo_right)',
ast.Div : '(__exprinfo_left / __exprinfo_right)',
ast.Mod : '(__exprinfo_left % __exprinfo_right)',
ast.Power : '(__exprinfo_left ** __exprinfo_right)',
}.items():
class BinaryArith(Interpretable):
__view__ = astclass
def eval(self, frame, astpattern=astpattern):
left = Interpretable(self.left)
left.eval(frame)
right = Interpretable(self.right)
right.eval(frame)
self.explanation = (astpattern
.replace('__exprinfo_left', left .explanation)
.replace('__exprinfo_right', right.explanation))
try:
self.result = frame.eval(astpattern,
__exprinfo_left=left.result,
__exprinfo_right=right.result)
except passthroughex:
raise
except:
raise Failure(self)
keepalive.append(BinaryArith)
class CallFunc(Interpretable):
__view__ = ast.CallFunc
def is_bool(self, frame):
source = 'isinstance(__exprinfo_value, bool)'
try:
return frame.is_true(frame.eval(source,
__exprinfo_value=self.result))
except passthroughex:
raise
except:
return False
def eval(self, frame):
node = Interpretable(self.node)
node.eval(frame)
explanations = []
vars = {'__exprinfo_fn': node.result}
source = '__exprinfo_fn('
for a in self.args:
if isinstance(a, ast.Keyword):
keyword = a.name
a = a.expr
else:
keyword = None
a = Interpretable(a)
a.eval(frame)
argname = '__exprinfo_%d' % len(vars)
vars[argname] = a.result
if keyword is None:
source += argname + ','
explanations.append(a.explanation)
else:
source += '%s=%s,' % (keyword, argname)
explanations.append('%s=%s' % (keyword, a.explanation))
if self.star_args:
star_args = Interpretable(self.star_args)
star_args.eval(frame)
argname = '__exprinfo_star'
vars[argname] = star_args.result
source += '*' + argname + ','
explanations.append('*' + star_args.explanation)
if self.dstar_args:
dstar_args = Interpretable(self.dstar_args)
dstar_args.eval(frame)
argname = '__exprinfo_kwds'
vars[argname] = dstar_args.result
source += '**' + argname + ','
explanations.append('**' + dstar_args.explanation)
self.explanation = "%s(%s)" % (
node.explanation, ', '.join(explanations))
if source.endswith(','):
source = source[:-1]
source += ')'
try:
self.result = frame.eval(source, **vars)
except passthroughex:
raise
except:
raise Failure(self)
if not node.is_builtin(frame) or not self.is_bool(frame):
r = frame.repr(self.result)
self.explanation = '%s\n{%s = %s\n}' % (r, r, self.explanation)
class Getattr(Interpretable):
__view__ = ast.Getattr
def eval(self, frame):
expr = Interpretable(self.expr)
expr.eval(frame)
source = '__exprinfo_expr.%s' % self.attrname
try:
self.result = frame.eval(source, __exprinfo_expr=expr.result)
except passthroughex:
raise
except:
raise Failure(self)
self.explanation = '%s.%s' % (expr.explanation, self.attrname)
# if the attribute comes from the instance, its value is interesting
source = ('hasattr(__exprinfo_expr, "__dict__") and '
'%r in __exprinfo_expr.__dict__' % self.attrname)
try:
from_instance = frame.is_true(
frame.eval(source, __exprinfo_expr=expr.result))
except passthroughex:
raise
except:
from_instance = True
if from_instance:
r = frame.repr(self.result)
self.explanation = '%s\n{%s = %s\n}' % (r, r, self.explanation)
# == Re-interpretation of full statements ==
class Assert(Interpretable):
__view__ = ast.Assert
def run(self, frame):
test = Interpretable(self.test)
test.eval(frame)
# print the result as 'assert <explanation>'
self.result = test.result
self.explanation = 'assert ' + test.explanation
if not frame.is_true(test.result):
try:
raise BuiltinAssertionError
except passthroughex:
raise
except:
raise Failure(self)
class Assign(Interpretable):
__view__ = ast.Assign
def run(self, frame):
expr = Interpretable(self.expr)
expr.eval(frame)
self.result = expr.result
self.explanation = '... = ' + expr.explanation
# fall-back-run the rest of the assignment
ass = ast.Assign(self.nodes, ast.Name('__exprinfo_expr'))
mod = ast.Module(None, ast.Stmt([ass]))
mod.filename = '<run>'
co = pycodegen.ModuleCodeGenerator(mod).getCode()
try:
frame.exec_(co, __exprinfo_expr=expr.result)
except passthroughex:
raise
except:
raise Failure(self)
class Discard(Interpretable):
__view__ = ast.Discard
def run(self, frame):
expr = Interpretable(self.expr)
expr.eval(frame)
self.result = expr.result
self.explanation = expr.explanation
class Stmt(Interpretable):
__view__ = ast.Stmt
def run(self, frame):
for stmt in self.nodes:
stmt = Interpretable(stmt)
stmt.run(frame)
def report_failure(e):
explanation = e.node.nice_explanation()
if explanation:
explanation = ", in: " + explanation
else:
explanation = ""
sys.stdout.write("%s: %s%s\n" % (e.exc.__name__, e.value, explanation))
def check(s, frame=None):
if frame is None:
frame = sys._getframe(1)
frame = py.code.Frame(frame)
expr = parse(s, 'eval')
assert isinstance(expr, ast.Expression)
node = Interpretable(expr.node)
try:
node.eval(frame)
except passthroughex:
raise
except Failure:
e = sys.exc_info()[1]
report_failure(e)
else:
if not frame.is_true(node.result):
sys.stderr.write("assertion failed: %s\n" % node.nice_explanation())
###########################################################
# API / Entry points
# #########################################################
def interpret(source, frame, should_fail=False):
module = Interpretable(parse(source, 'exec').node)
#print "got module", module
if isinstance(frame, py.std.types.FrameType):
frame = py.code.Frame(frame)
try:
module.run(frame)
except Failure:
e = sys.exc_info()[1]
return getfailure(e)
except passthroughex:
raise
except:
import traceback
traceback.print_exc()
if should_fail:
return ("(assertion failed, but when it was re-run for "
"printing intermediate values, it did not fail. Suggestions: "
"compute assert expression before the assert or use --assert=plain)")
else:
return None
def getmsg(excinfo):
if isinstance(excinfo, tuple):
excinfo = py.code.ExceptionInfo(excinfo)
#frame, line = gettbline(tb)
#frame = py.code.Frame(frame)
#return interpret(line, frame)
tb = excinfo.traceback[-1]
source = str(tb.statement).strip()
x = interpret(source, tb.frame, should_fail=True)
if not isinstance(x, str):
raise TypeError("interpret returned non-string %r" % (x,))
return x
def getfailure(e):
explanation = e.node.nice_explanation()
if str(e.value):
lines = explanation.split('\n')
lines[0] += " << %s" % (e.value,)
explanation = '\n'.join(lines)
text = "%s: %s" % (e.exc.__name__, explanation)
if text.startswith('AssertionError: assert '):
text = text[16:]
return text
def run(s, frame=None):
if frame is None:
frame = sys._getframe(1)
frame = py.code.Frame(frame)
module = Interpretable(parse(s, 'exec').node)
try:
module.run(frame)
except Failure:
e = sys.exc_info()[1]
report_failure(e)
if __name__ == '__main__':
# example:
def f():
return 5
def g():
return 3
def h(x):
return 'never'
check("f() * g() == 5")
check("not f()")
check("not (f() and g() or 0)")
check("f() == g()")
i = 4
check("i == f()")
check("len(f()) == 0")
check("isinstance(2+3+4, float)")
run("x = i")
check("x == 5")
run("assert not f(), 'oops'")
run("a, b, c = 1, 2")
run("a, b, c = f()")
check("max([f(),g()]) == 4")
check("'hello'[g()] == 'h'")
run("'guk%d' % h(f())")
|
"PYTEST_DONT_REWRITE"
import pytest, py
from _pytest.assertion import util
def exvalue():
return py.std.sys.exc_info()[1]
def f():
return 2
def test_not_being_rewritten():
assert "@py_builtins" not in globals()
def test_assert():
try:
assert f() == 3
except AssertionError:
e = exvalue()
s = str(e)
assert s.startswith('assert 2 == 3\n')
def test_assert_with_explicit_message():
try:
assert f() == 3, "hello"
except AssertionError:
e = exvalue()
assert e.msg == 'hello'
def test_assert_within_finally():
excinfo = py.test.raises(ZeroDivisionError, """
try:
1/0
finally:
i = 42
""")
s = excinfo.exconly()
assert py.std.re.search("division.+by zero", s) is not None
#def g():
# A.f()
#excinfo = getexcinfo(TypeError, g)
#msg = getmsg(excinfo)
#assert msg.find("must be called with A") != -1
def test_assert_multiline_1():
try:
assert (f() ==
3)
except AssertionError:
e = exvalue()
s = str(e)
assert s.startswith('assert 2 == 3\n')
def test_assert_multiline_2():
try:
assert (f() == (4,
3)[-1])
except AssertionError:
e = exvalue()
s = str(e)
assert s.startswith('assert 2 ==')
def test_in():
try:
assert "hi" in [1, 2]
except AssertionError:
e = exvalue()
s = str(e)
assert s.startswith("assert 'hi' in")
def test_is():
try:
assert 1 is 2
except AssertionError:
e = exvalue()
s = str(e)
assert s.startswith("assert 1 is 2")
@py.test.mark.skipif("sys.version_info < (2,6)")
def test_attrib():
class Foo(object):
b = 1
i = Foo()
try:
assert i.b == 2
except AssertionError:
e = exvalue()
s = str(e)
assert s.startswith("assert 1 == 2")
@py.test.mark.skipif("sys.version_info < (2,6)")
def test_attrib_inst():
class Foo(object):
b = 1
try:
assert Foo().b == 2
except AssertionError:
e = exvalue()
s = str(e)
assert s.startswith("assert 1 == 2")
def test_len():
l = list(range(42))
try:
assert len(l) == 100
except AssertionError:
e = exvalue()
s = str(e)
assert s.startswith("assert 42 == 100")
assert "where 42 = len([" in s
def test_assert_non_string_message():
class A:
def __str__(self):
return "hello"
try:
assert 0 == 1, A()
except AssertionError:
e = exvalue()
assert e.msg == "hello"
def test_assert_keyword_arg():
def f(x=3):
return False
try:
assert f(x=5)
except AssertionError:
e = exvalue()
assert "x=5" in e.msg
# These tests should both fail, but should fail nicely...
class WeirdRepr:
def __repr__(self):
return '<WeirdRepr\nsecond line>'
def bug_test_assert_repr():
v = WeirdRepr()
try:
assert v == 1
except AssertionError:
e = exvalue()
assert e.msg.find('WeirdRepr') != -1
assert e.msg.find('second line') != -1
assert 0
def test_assert_non_string():
try:
assert 0, ['list']
except AssertionError:
e = exvalue()
assert e.msg.find("list") != -1
def test_assert_implicit_multiline():
try:
x = [1,2,3]
assert x != [1,
2, 3]
except AssertionError:
e = exvalue()
assert e.msg.find('assert [1, 2, 3] !=') != -1
def test_assert_with_brokenrepr_arg():
class BrokenRepr:
def __repr__(self): 0 / 0
e = AssertionError(BrokenRepr())
if e.msg.find("broken __repr__") == -1:
py.test.fail("broken __repr__ not handle correctly")
def test_multiple_statements_per_line():
try:
a = 1; assert a == 2
except AssertionError:
e = exvalue()
assert "assert 1 == 2" in e.msg
def test_power():
try:
assert 2**3 == 7
except AssertionError:
e = exvalue()
assert "assert (2 ** 3) == 7" in e.msg
class TestView:
def setup_class(cls):
cls.View = pytest.importorskip("_pytest.assertion.oldinterpret").View
def test_class_dispatch(self):
### Use a custom class hierarchy with existing instances
class Picklable(self.View):
pass
class Simple(Picklable):
__view__ = object
def pickle(self):
return repr(self.__obj__)
class Seq(Picklable):
__view__ = list, tuple, dict
def pickle(self):
return ';'.join(
[Picklable(item).pickle() for item in self.__obj__])
class Dict(Seq):
__view__ = dict
def pickle(self):
return Seq.pickle(self) + '!' + Seq(self.values()).pickle()
assert Picklable(123).pickle() == '123'
assert Picklable([1,[2,3],4]).pickle() == '1;2;3;4'
assert Picklable({1:2}).pickle() == '1!2'
def test_viewtype_class_hierarchy(self):
# Use a custom class hierarchy based on attributes of existing instances
class Operation:
"Existing class that I don't want to change."
def __init__(self, opname, *args):
self.opname = opname
self.args = args
existing = [Operation('+', 4, 5),
Operation('getitem', '', 'join'),
Operation('setattr', 'x', 'y', 3),
Operation('-', 12, 1)]
class PyOp(self.View):
def __viewkey__(self):
return self.opname
def generate(self):
return '%s(%s)' % (self.opname, ', '.join(map(repr, self.args)))
class PyBinaryOp(PyOp):
__view__ = ('+', '-', '*', '/')
def generate(self):
return '%s %s %s' % (self.args[0], self.opname, self.args[1])
codelines = [PyOp(op).generate() for op in existing]
assert codelines == ["4 + 5", "getitem('', 'join')",
"setattr('x', 'y', 3)", "12 - 1"]
@py.test.mark.skipif("sys.version_info < (2,6)")
def test_assert_customizable_reprcompare(monkeypatch):
monkeypatch.setattr(util, '_reprcompare', lambda *args: 'hello')
try:
assert 3 == 4
except AssertionError:
e = exvalue()
s = str(e)
assert "hello" in s
def test_assert_long_source_1():
try:
assert len == [
(None, ['somet text', 'more text']),
]
except AssertionError:
e = exvalue()
s = str(e)
assert 're-run' not in s
assert 'somet text' in s
def test_assert_long_source_2():
try:
assert(len == [
(None, ['somet text', 'more text']),
])
except AssertionError:
e = exvalue()
s = str(e)
assert 're-run' not in s
assert 'somet text' in s
def test_assert_raise_alias(testdir):
testdir.makepyfile("""
"PYTEST_DONT_REWRITE"
import sys
EX = AssertionError
def test_hello():
raise EX("hello"
"multi"
"line")
""")
result = testdir.runpytest()
result.stdout.fnmatch_lines([
"*def test_hello*",
"*raise EX*",
"*1 failed*",
])
@pytest.mark.skipif("sys.version_info < (2,5)")
def test_assert_raise_subclass():
class SomeEx(AssertionError):
def __init__(self, *args):
super(SomeEx, self).__init__()
try:
raise SomeEx("hello")
except AssertionError:
s = str(exvalue())
assert 're-run' not in s
assert 'could not determine' in s
def test_assert_raises_in_nonzero_of_object_pytest_issue10():
class A(object):
def __nonzero__(self):
raise ValueError(42)
def __lt__(self, other):
return A()
def __repr__(self):
return "<MY42 object>"
def myany(x):
return True
try:
assert not(myany(A() < 0))
except AssertionError:
e = exvalue()
s = str(e)
assert "<MY42 object> < 0" in s
@py.test.mark.skipif("sys.version_info >= (2,6)")
def test_oldinterpret_importation():
# we had a cyclic import there
# requires pytest on sys.path
res = py.std.subprocess.call([
py.std.sys.executable, '-c', str(py.code.Source("""
try:
from _pytest.assertion.newinterpret import interpret
except ImportError:
from _pytest.assertion.oldinterpret import interpret
"""))
])
assert res == 0
|
|
testing/test_assertinterpret.py
|
_pytest/assertion/oldinterpret.py
|
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Astronomical and physics constants in SI units. See :mod:`astropy.constants`
for a complete listing of constants defined in Astropy.
"""
import math
from .constant import Constant, EMConstant
# PHYSICAL CONSTANTS
# https://en.wikipedia.org/wiki/2019_redefinition_of_SI_base_units
class CODATA2018(Constant):
default_reference = 'CODATA 2018'
_registry = {}
_has_incompatible_units = set()
class EMCODATA2018(CODATA2018, EMConstant):
_registry = CODATA2018._registry
h = CODATA2018('h', "Planck constant", 6.62607015e-34,
'J s', 0.0, system='si')
hbar = CODATA2018('hbar', "Reduced Planck constant", h.value / (2 * math.pi),
'J s', 0.0, system='si')
k_B = CODATA2018('k_B', "Boltzmann constant", 1.380649e-23,
'J / (K)', 0.0, system='si')
c = CODATA2018('c', "Speed of light in vacuum", 299792458.,
'm / (s)', 0.0, system='si')
G = CODATA2018('G', "Gravitational constant", 6.67430e-11,
'm3 / (kg s2)', 0.00015e-11, system='si')
g0 = CODATA2018('g0', "Standard acceleration of gravity", 9.80665,
'm / s2', 0.0, system='si')
m_p = CODATA2018('m_p', "Proton mass", 1.67262192369e-27,
'kg', 0.00000000051e-27, system='si')
m_n = CODATA2018('m_n', "Neutron mass", 1.67492749804e-27,
'kg', 0.00000000095e-27, system='si')
m_e = CODATA2018('m_e', "Electron mass", 9.1093837015e-31,
'kg', 0.0000000028e-31, system='si')
u = CODATA2018('u', "Atomic mass", 1.66053906660e-27,
'kg', 0.00000000050e-27, system='si')
sigma_sb = CODATA2018(
'sigma_sb', "Stefan-Boltzmann constant",
2 * math.pi ** 5 * k_B.value ** 4 / (15 * h.value ** 3 * c.value ** 2),
'W / (K4 m2)', 0.0, system='si')
e = EMCODATA2018('e', 'Electron charge', 1.602176634e-19,
'C', 0.0, system='si')
eps0 = EMCODATA2018('eps0', 'Vacuum electric permittivity', 8.8541878128e-12,
'F/m', 0.0000000013e-12, system='si')
N_A = CODATA2018('N_A', "Avogadro's number", 6.02214076e23,
'1 / (mol)', 0.0, system='si')
R = CODATA2018('R', "Gas constant", k_B.value * N_A.value,
'J / (K mol)', 0.0, system='si')
Ryd = CODATA2018('Ryd', 'Rydberg constant', 10973731.568160,
'1 / (m)', 0.000021, system='si')
a0 = CODATA2018('a0', "Bohr radius", 5.29177210903e-11,
'm', 0.00000000080e-11, system='si')
muB = CODATA2018('muB', "Bohr magneton", 9.2740100783e-24,
'J/T', 0.0000000028e-24, system='si')
alpha = CODATA2018('alpha', "Fine-structure constant", 7.2973525693e-3,
'', 0.0000000011e-3, system='si')
atm = CODATA2018('atm', "Standard atmosphere", 101325,
'Pa', 0.0, system='si')
mu0 = CODATA2018('mu0', "Vacuum magnetic permeability", 1.25663706212e-6,
'N/A2', 0.00000000019e-6, system='si')
sigma_T = CODATA2018('sigma_T', "Thomson scattering cross-section",
6.6524587321e-29, 'm2', 0.0000000060e-29,
system='si')
# Formula taken from NIST wall chart.
# The numerical factor is from a numerical solution to the equation for the
# maximum. See https://en.wikipedia.org/wiki/Wien%27s_displacement_law
b_wien = CODATA2018('b_wien', 'Wien wavelength displacement law constant',
h.value * c.value / (k_B.value * 4.965114231744276), 'm K',
0.0, system='si')
# CGS constants.
# Only constants that cannot be converted directly from S.I. are defined here.
# Because both e and c are exact, these are also exact by definition.
e_esu = EMCODATA2018(e.abbrev, e.name, e.value * c.value * 10.0,
'statC', 0.0, system='esu')
e_emu = EMCODATA2018(e.abbrev, e.name, e.value / 10, 'abC',
0.0, system='emu')
e_gauss = EMCODATA2018(e.abbrev, e.name, e.value * c.value * 10.0,
'Fr', 0.0, system='gauss')
|
# Licensed under a 3-clause BSD style license - see LICENSE.rst
import pytest
from numpy.testing import assert_equal, assert_allclose
from astropy import units as u
from astropy.time import Time, TimeDelta
from astropy.utils.data import get_pkg_data_filename
from astropy.timeseries.periodograms import BoxLeastSquares, LombScargle
from astropy.timeseries.binned import BinnedTimeSeries
from astropy.tests.helper import assert_quantity_allclose
CSV_FILE = get_pkg_data_filename('data/binned.csv')
def test_empty_initialization():
ts = BinnedTimeSeries()
ts['time_bin_start'] = Time([1, 2, 3], format='mjd')
def test_empty_initialization_invalid():
# Make sure things crash when the first column added is not a time column
ts = BinnedTimeSeries()
with pytest.raises(ValueError) as exc:
ts['flux'] = [1, 2, 3]
assert exc.value.args[0] == ("BinnedTimeSeries object is invalid - expected "
"'time_bin_start' as the first column but found 'flux'")
def test_initialization_time_bin_invalid():
# Make sure things crash when time_bin_* is passed incorrectly.
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(data=[[1, 4, 3]])
assert exc.value.args[0] == ("'time_bin_start' has not been specified")
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(time_bin_start='2016-03-22T12:30:31', data=[[1, 4, 3]])
assert exc.value.args[0] == ("Either 'time_bin_size' or 'time_bin_end' should be specified")
def test_initialization_time_bin_both():
# Make sure things crash when time_bin_* is passed twice.
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(data={"time_bin_start": ["2016-03-22T12:30:31"]},
time_bin_start="2016-03-22T12:30:31")
assert exc.value.args[0] == ("'time_bin_start' has been given both in the table "
"and as a keyword argument")
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(data={"time_bin_size": ["2016-03-22T12:30:31"]},
time_bin_size=[1]*u.s)
assert exc.value.args[0] == ("'time_bin_size' has been given both in the table "
"and as a keyword argument")
def test_initialization_time_bin_size():
# Make sure things crash when time_bin_size has no units
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(data={"time": ["2016-03-22T12:30:31"]},
time_bin_start="2016-03-22T12:30:31",
time_bin_size=1)
assert exc.value.args[0] == ("'time_bin_size' should be a Quantity or a TimeDelta")
# TimeDelta for time_bin_size
ts = BinnedTimeSeries(data={"time": ["2016-03-22T12:30:31"]},
time_bin_start="2016-03-22T12:30:31",
time_bin_size=TimeDelta(1))
assert isinstance(ts.time_bin_size, u.quantity.Quantity)
def test_initialization_time_bin_start_scalar():
# Make sure things crash when time_bin_start is a scalar with no time_bin_size
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(data={"time": ["2016-03-22T12:30:31"]},
time_bin_start=Time(1, format='mjd'),
time_bin_end=Time(1, format='mjd'))
assert exc.value.args[0] == ("'time_bin_start' is scalar, so 'time_bin_size' is required")
def test_initialization_n_bins():
# Make sure things crash with incorrect n_bins
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(data={"time": ["2016-03-22T12:30:31"]},
time_bin_start=Time(1, format='mjd'),
time_bin_size=1*u.s,
time_bin_end=Time(1, format='mjd'),
n_bins=10)
assert exc.value.args[0] == ("'n_bins' has been given and it is not the "
"same length as the input data.")
def test_initialization_non_scalar_time():
# Make sure things crash with incorrect size of time_bin_start
with pytest.raises(ValueError) as exc:
BinnedTimeSeries(data={"time": ["2016-03-22T12:30:31"]},
time_bin_start=["2016-03-22T12:30:31", "2016-03-22T12:30:32"],
time_bin_size=1*u.s,
time_bin_end=Time(1, format='mjd'))
assert exc.value.args[0] == ("Length of 'time_bin_start' (2) should match table length (1)")
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(data={"time": ["2016-03-22T12:30:31"]},
time_bin_start=["2016-03-22T12:30:31"],
time_bin_size=None,
time_bin_end=None)
assert exc.value.args[0] == ("Either 'time_bin_size' or 'time_bin_end' should be specified")
def test_even_contiguous():
# Initialize a ``BinnedTimeSeries`` with even contiguous bins by specifying
# the bin width:
ts = BinnedTimeSeries(time_bin_start='2016-03-22T12:30:31',
time_bin_size=3 * u.s, data=[[1, 4, 3]])
assert_equal(ts.time_bin_start.isot, ['2016-03-22T12:30:31.000',
'2016-03-22T12:30:34.000',
'2016-03-22T12:30:37.000'])
assert_equal(ts.time_bin_center.isot, ['2016-03-22T12:30:32.500',
'2016-03-22T12:30:35.500',
'2016-03-22T12:30:38.500'])
assert_equal(ts.time_bin_end.isot, ['2016-03-22T12:30:34.000',
'2016-03-22T12:30:37.000',
'2016-03-22T12:30:40.000'])
def test_uneven_contiguous():
# Initialize a ``BinnedTimeSeries`` with uneven contiguous bins by giving an
# end time:
ts = BinnedTimeSeries(time_bin_start=['2016-03-22T12:30:31',
'2016-03-22T12:30:32',
'2016-03-22T12:30:40'],
time_bin_end='2016-03-22T12:30:55',
data=[[1, 4, 3]])
assert_equal(ts.time_bin_start.isot, ['2016-03-22T12:30:31.000',
'2016-03-22T12:30:32.000',
'2016-03-22T12:30:40.000'])
assert_equal(ts.time_bin_center.isot, ['2016-03-22T12:30:31.500',
'2016-03-22T12:30:36.000',
'2016-03-22T12:30:47.500'])
assert_equal(ts.time_bin_end.isot, ['2016-03-22T12:30:32.000',
'2016-03-22T12:30:40.000',
'2016-03-22T12:30:55.000'])
def test_uneven_non_contiguous():
# Initialize a ``BinnedTimeSeries`` with uneven non-contiguous bins with
# lists of start times, bin sizes and data:
ts = BinnedTimeSeries(time_bin_start=['2016-03-22T12:30:31',
'2016-03-22T12:30:38',
'2016-03-22T12:34:40'],
time_bin_size=[5, 100, 2]*u.s,
data=[[1, 4, 3]])
assert_equal(ts.time_bin_start.isot, ['2016-03-22T12:30:31.000',
'2016-03-22T12:30:38.000',
'2016-03-22T12:34:40.000'])
assert_equal(ts.time_bin_center.isot, ['2016-03-22T12:30:33.500',
'2016-03-22T12:31:28.000',
'2016-03-22T12:34:41.000'])
assert_equal(ts.time_bin_end.isot, ['2016-03-22T12:30:36.000',
'2016-03-22T12:32:18.000',
'2016-03-22T12:34:42.000'])
def test_uneven_non_contiguous_full():
# Initialize a ``BinnedTimeSeries`` with uneven non-contiguous bins by
# specifying the start and end times for the bins:
ts = BinnedTimeSeries(time_bin_start=['2016-03-22T12:30:31',
'2016-03-22T12:30:33',
'2016-03-22T12:30:40'],
time_bin_end=['2016-03-22T12:30:32',
'2016-03-22T12:30:35',
'2016-03-22T12:30:41'],
data=[[1, 4, 3]])
assert_equal(ts.time_bin_start.isot, ['2016-03-22T12:30:31.000',
'2016-03-22T12:30:33.000',
'2016-03-22T12:30:40.000'])
assert_equal(ts.time_bin_center.isot, ['2016-03-22T12:30:31.500',
'2016-03-22T12:30:34.000',
'2016-03-22T12:30:40.500'])
assert_equal(ts.time_bin_end.isot, ['2016-03-22T12:30:32.000',
'2016-03-22T12:30:35.000',
'2016-03-22T12:30:41.000'])
def test_read_empty():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, format='csv')
assert exc.value.args[0] == '``time_bin_start_column`` should be provided since the default Table readers are being used.'
def test_read_no_size_end():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start', format='csv')
assert exc.value.args[0] == 'Either `time_bin_end_column` or `time_bin_size_column` should be provided.'
def test_read_both_extra_bins():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start', time_bin_end_column='END', time_bin_size_column='bin_size', format='csv')
assert exc.value.args[0] == "Cannot specify both `time_bin_end_column` and `time_bin_size_column`."
def test_read_size_no_unit():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start', time_bin_size_column='bin_size', format='csv')
assert exc.value.args[0] == "The bin size unit should be specified as an astropy Unit using ``time_bin_size_unit``."
def test_read_start_time_missing():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='abc', time_bin_size_column='bin_size', time_bin_size_unit=u.second, format='csv')
assert exc.value.args[0] == "Bin start time column 'abc' not found in the input data."
def test_read_end_time_missing():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start', time_bin_end_column="missing", format='csv')
assert exc.value.args[0] == "Bin end time column 'missing' not found in the input data."
def test_read_size_missing():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start', time_bin_size_column="missing", time_bin_size_unit=u.second, format='csv')
assert exc.value.args[0] == "Bin size column 'missing' not found in the input data."
def test_read_time_unit_missing():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start', time_bin_size_column="bin_size", format='csv')
assert exc.value.args[0] == "The bin size unit should be specified as an astropy Unit using ``time_bin_size_unit``."
def test_read():
timeseries = BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start',
time_bin_end_column='time_end', format='csv')
assert timeseries.colnames == ['time_bin_start', 'time_bin_size', 'bin_size', 'A', 'B', 'C', 'D', 'E', 'F']
assert len(timeseries) == 10
assert timeseries['B'].sum() == 1151.54
timeseries = BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start',
time_bin_size_column='bin_size',
time_bin_size_unit=u.second, format='csv')
assert timeseries.colnames == ['time_bin_start', 'time_bin_size', 'time_end', 'A', 'B', 'C', 'D', 'E', 'F']
assert len(timeseries) == 10
assert timeseries['B'].sum() == 1151.54
@pytest.mark.parametrize('cls', [BoxLeastSquares, LombScargle])
def test_periodogram(cls):
# Note that we don't need to check the actual results from the periodogram
# classes here since these are tested extensively in
# astropy.timeseries.periodograms.
ts = BinnedTimeSeries(time_bin_start='2016-03-22T12:30:31',
time_bin_size=3 * u.s, data=[[1, 4, 3], [3, 4, 3]], names=['a', 'b'])
p1 = cls.from_timeseries(ts, 'a')
assert isinstance(p1, cls)
assert_allclose(p1.t.jd, ts.time_bin_center.jd)
assert_equal(p1.y, ts['a'])
assert p1.dy is None
p2 = cls.from_timeseries(ts, 'a', uncertainty='b')
assert_quantity_allclose(p2.dy, ts['b'])
p3 = cls.from_timeseries(ts, 'a', uncertainty=0.1)
assert_allclose(p3.dy, 0.1)
|
|
astropy/timeseries/tests/test_binned.py
|
astropy/constants/codata2018.py
|
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
This package contains functions for reading and writing HDF5 tables that are
not meant to be used directly, but instead are available as readers/writers in
`astropy.table`. See :ref:`table_io` for more details.
"""
import os
import warnings
import numpy as np
# NOTE: Do not import anything from astropy.table here.
# https://github.com/astropy/astropy/issues/6604
from astropy.utils.exceptions import AstropyUserWarning, AstropyDeprecationWarning
HDF5_SIGNATURE = b'\x89HDF\r\n\x1a\n'
META_KEY = '__table_column_meta__'
__all__ = ['read_table_hdf5', 'write_table_hdf5']
def meta_path(path):
return path + '.' + META_KEY
def _find_all_structured_arrays(handle):
"""
Find all structured arrays in an HDF5 file
"""
import h5py
structured_arrays = []
def append_structured_arrays(name, obj):
if isinstance(obj, h5py.Dataset) and obj.dtype.kind == 'V':
structured_arrays.append(name)
handle.visititems(append_structured_arrays)
return structured_arrays
def is_hdf5(origin, filepath, fileobj, *args, **kwargs):
if fileobj is not None:
loc = fileobj.tell()
try:
signature = fileobj.read(8)
finally:
fileobj.seek(loc)
return signature == HDF5_SIGNATURE
elif filepath is not None:
return filepath.endswith(('.hdf5', '.h5'))
try:
import h5py
except ImportError:
return False
else:
return isinstance(args[0], (h5py.File, h5py.Group, h5py.Dataset))
def read_table_hdf5(input, path=None, character_as_bytes=True):
"""
Read a Table object from an HDF5 file
This requires `h5py <http://www.h5py.org/>`_ to be installed. If more than one
table is present in the HDF5 file or group, the first table is read in and
a warning is displayed.
Parameters
----------
input : str or :class:`h5py:File` or :class:`h5py:Group` or
:class:`h5py:Dataset` If a string, the filename to read the table from.
If an h5py object, either the file or the group object to read the
table from.
path : str
The path from which to read the table inside the HDF5 file.
This should be relative to the input file or group.
character_as_bytes: boolean
If `True` then Table columns are left as bytes.
If `False` then Table columns are converted to unicode.
"""
try:
import h5py
except ImportError:
raise Exception("h5py is required to read and write HDF5 files")
# This function is iterative, and only gets to writing the file when
# the input is an hdf5 Group. Moreover, the input variable is changed in
# place.
# Here, we save its value to be used at the end when the conditions are
# right.
input_save = input
if isinstance(input, (h5py.File, h5py.Group)):
# If a path was specified, follow the path
if path is not None:
try:
input = input[path]
except (KeyError, ValueError):
raise OSError(f"Path {path} does not exist")
# `input` is now either a group or a dataset. If it is a group, we
# will search for all structured arrays inside the group, and if there
# is one we can proceed otherwise an error is raised. If it is a
# dataset, we just proceed with the reading.
if isinstance(input, h5py.Group):
# Find all structured arrays in group
arrays = _find_all_structured_arrays(input)
if len(arrays) == 0:
raise ValueError("no table found in HDF5 group {}".
format(path))
elif len(arrays) > 0:
path = arrays[0] if path is None else path + '/' + arrays[0]
if len(arrays) > 1:
warnings.warn("path= was not specified but multiple tables"
" are present, reading in first available"
" table (path={})".format(path),
AstropyUserWarning)
return read_table_hdf5(input, path=path)
elif not isinstance(input, h5py.Dataset):
# If a file object was passed, then we need to extract the filename
# because h5py cannot properly read in file objects.
if hasattr(input, 'read'):
try:
input = input.name
except AttributeError:
raise TypeError("h5py can only open regular files")
# Open the file for reading, and recursively call read_table_hdf5 with
# the file object and the path.
f = h5py.File(input, 'r')
try:
return read_table_hdf5(f, path=path, character_as_bytes=character_as_bytes)
finally:
f.close()
# If we are here, `input` should be a Dataset object, which we can now
# convert to a Table.
# Create a Table object
from astropy.table import Table, meta, serialize
table = Table(np.array(input))
# Read the meta-data from the file. For back-compatibility, we can read
# the old file format where the serialized metadata were saved in the
# attributes of the HDF5 dataset.
# In the new format, instead, metadata are stored in a new dataset in the
# same file. This is introduced in Astropy 3.0
old_version_meta = META_KEY in input.attrs
new_version_meta = path is not None and meta_path(path) in input_save
if old_version_meta or new_version_meta:
if new_version_meta:
header = meta.get_header_from_yaml(
h.decode('utf-8') for h in input_save[meta_path(path)])
elif old_version_meta:
header = meta.get_header_from_yaml(
h.decode('utf-8') for h in input.attrs[META_KEY])
if 'meta' in list(header.keys()):
table.meta = header['meta']
header_cols = dict((x['name'], x) for x in header['datatype'])
for col in table.columns.values():
for attr in ('description', 'format', 'unit', 'meta'):
if attr in header_cols[col.name]:
setattr(col, attr, header_cols[col.name][attr])
# Construct new table with mixins, using tbl.meta['__serialized_columns__']
# as guidance.
table = serialize._construct_mixins_from_columns(table)
else:
# Read the meta-data from the file
table.meta.update(input.attrs)
if not character_as_bytes:
table.convert_bytestring_to_unicode()
return table
def _encode_mixins(tbl):
"""Encode a Table ``tbl`` that may have mixin columns to a Table with only
astropy Columns + appropriate meta-data to allow subsequent decoding.
"""
from astropy.table import serialize
from astropy.table.table import has_info_class
from astropy import units as u
from astropy.utils.data_info import MixinInfo, serialize_context_as
# If PyYAML is not available then check to see if there are any mixin cols
# that *require* YAML serialization. HDF5 already has support for
# Quantity, so if those are the only mixins the proceed without doing the
# YAML bit, for backward compatibility (i.e. not requiring YAML to write
# Quantity).
try:
import yaml
except ImportError:
for col in tbl.itercols():
if (has_info_class(col, MixinInfo) and
col.__class__ is not u.Quantity):
raise TypeError("cannot write type {} column '{}' "
"to HDF5 without PyYAML installed."
.format(col.__class__.__name__, col.info.name))
# Convert the table to one with no mixins, only Column objects. This adds
# meta data which is extracted with meta.get_yaml_from_table.
with serialize_context_as('hdf5'):
encode_tbl = serialize.represent_mixins_as_columns(tbl)
return encode_tbl
def write_table_hdf5(table, output, path=None, compression=False,
append=False, overwrite=False, serialize_meta=False):
"""
Write a Table object to an HDF5 file
This requires `h5py <http://www.h5py.org/>`_ to be installed.
Parameters
----------
table : `~astropy.table.Table`
Data table that is to be written to file.
output : str or :class:`h5py:File` or :class:`h5py:Group`
If a string, the filename to write the table to. If an h5py object,
either the file or the group object to write the table to.
path : str
The path to which to write the table inside the HDF5 file.
This should be relative to the input file or group.
If not specified, defaults to ``__astropy_table__``.
compression : bool or str or int
Whether to compress the table inside the HDF5 file. If set to `True`,
``'gzip'`` compression is used. If a string is specified, it should be
one of ``'gzip'``, ``'szip'``, or ``'lzf'``. If an integer is
specified (in the range 0-9), ``'gzip'`` compression is used, and the
integer denotes the compression level.
append : bool
Whether to append the table to an existing HDF5 file.
overwrite : bool
Whether to overwrite any existing file without warning.
If ``append=True`` and ``overwrite=True`` then only the dataset will be
replaced; the file/group will not be overwritten.
"""
from astropy.table import meta
try:
import h5py
except ImportError:
raise Exception("h5py is required to read and write HDF5 files")
if path is None:
# table is just an arbitrary, hardcoded string here.
path = '__astropy_table__'
elif path.endswith('/'):
raise ValueError("table path should end with table name, not /")
if '/' in path:
group, name = path.rsplit('/', 1)
else:
group, name = None, path
if isinstance(output, (h5py.File, h5py.Group)):
if len(list(output.keys())) > 0 and name == '__astropy_table__':
raise ValueError("table path should always be set via the "
"path= argument when writing to existing "
"files")
elif name == '__astropy_table__':
warnings.warn("table path was not set via the path= argument; "
"using default path {}".format(path))
if group:
try:
output_group = output[group]
except (KeyError, ValueError):
output_group = output.create_group(group)
else:
output_group = output
elif isinstance(output, str):
if os.path.exists(output) and not append:
if overwrite and not append:
os.remove(output)
else:
raise OSError(f"File exists: {output}")
# Open the file for appending or writing
f = h5py.File(output, 'a' if append else 'w')
# Recursively call the write function
try:
return write_table_hdf5(table, f, path=path,
compression=compression, append=append,
overwrite=overwrite,
serialize_meta=serialize_meta)
finally:
f.close()
else:
raise TypeError('output should be a string or an h5py File or '
'Group object')
# Check whether table already exists
if name in output_group:
if append and overwrite:
# Delete only the dataset itself
del output_group[name]
else:
raise OSError(f"Table {path} already exists")
# Encode any mixin columns as plain columns + appropriate metadata
table = _encode_mixins(table)
# Table with numpy unicode strings can't be written in HDF5 so
# to write such a table a copy of table is made containing columns as
# bytestrings. Now this copy of the table can be written in HDF5.
if any(col.info.dtype.kind == 'U' for col in table.itercols()):
table = table.copy(copy_data=False)
table.convert_unicode_to_bytestring()
# Warn if information will be lost when serialize_meta=False. This is
# hardcoded to the set difference between column info attributes and what
# HDF5 can store natively (name, dtype) with no meta.
if serialize_meta is False:
for col in table.itercols():
for attr in ('unit', 'format', 'description', 'meta'):
if getattr(col.info, attr, None) not in (None, {}):
warnings.warn("table contains column(s) with defined 'unit', 'format',"
" 'description', or 'meta' info attributes. These will"
" be dropped since serialize_meta=False.",
AstropyUserWarning)
# Write the table to the file
if compression:
if compression is True:
compression = 'gzip'
dset = output_group.create_dataset(name, data=table.as_array(),
compression=compression)
else:
dset = output_group.create_dataset(name, data=table.as_array())
if serialize_meta:
header_yaml = meta.get_yaml_from_table(table)
header_encoded = [h.encode('utf-8') for h in header_yaml]
output_group.create_dataset(meta_path(name),
data=header_encoded)
else:
# Write the Table meta dict key:value pairs to the file as HDF5
# attributes. This works only for a limited set of scalar data types
# like numbers, strings, etc., but not any complex types. This path
# also ignores column meta like unit or format.
for key in table.meta:
val = table.meta[key]
try:
dset.attrs[key] = val
except TypeError:
warnings.warn("Attribute `{}` of type {} cannot be written to "
"HDF5 files - skipping. (Consider specifying "
"serialize_meta=True to write all meta data)".format(key, type(val)),
AstropyUserWarning)
def register_hdf5():
"""
Register HDF5 with Unified I/O.
"""
from astropy.io import registry as io_registry
from astropy.table import Table
io_registry.register_reader('hdf5', Table, read_table_hdf5)
io_registry.register_writer('hdf5', Table, write_table_hdf5)
io_registry.register_identifier('hdf5', Table, is_hdf5)
|
# Licensed under a 3-clause BSD style license - see LICENSE.rst
import pytest
from numpy.testing import assert_equal, assert_allclose
from astropy import units as u
from astropy.time import Time, TimeDelta
from astropy.utils.data import get_pkg_data_filename
from astropy.timeseries.periodograms import BoxLeastSquares, LombScargle
from astropy.timeseries.binned import BinnedTimeSeries
from astropy.tests.helper import assert_quantity_allclose
CSV_FILE = get_pkg_data_filename('data/binned.csv')
def test_empty_initialization():
ts = BinnedTimeSeries()
ts['time_bin_start'] = Time([1, 2, 3], format='mjd')
def test_empty_initialization_invalid():
# Make sure things crash when the first column added is not a time column
ts = BinnedTimeSeries()
with pytest.raises(ValueError) as exc:
ts['flux'] = [1, 2, 3]
assert exc.value.args[0] == ("BinnedTimeSeries object is invalid - expected "
"'time_bin_start' as the first column but found 'flux'")
def test_initialization_time_bin_invalid():
# Make sure things crash when time_bin_* is passed incorrectly.
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(data=[[1, 4, 3]])
assert exc.value.args[0] == ("'time_bin_start' has not been specified")
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(time_bin_start='2016-03-22T12:30:31', data=[[1, 4, 3]])
assert exc.value.args[0] == ("Either 'time_bin_size' or 'time_bin_end' should be specified")
def test_initialization_time_bin_both():
# Make sure things crash when time_bin_* is passed twice.
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(data={"time_bin_start": ["2016-03-22T12:30:31"]},
time_bin_start="2016-03-22T12:30:31")
assert exc.value.args[0] == ("'time_bin_start' has been given both in the table "
"and as a keyword argument")
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(data={"time_bin_size": ["2016-03-22T12:30:31"]},
time_bin_size=[1]*u.s)
assert exc.value.args[0] == ("'time_bin_size' has been given both in the table "
"and as a keyword argument")
def test_initialization_time_bin_size():
# Make sure things crash when time_bin_size has no units
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(data={"time": ["2016-03-22T12:30:31"]},
time_bin_start="2016-03-22T12:30:31",
time_bin_size=1)
assert exc.value.args[0] == ("'time_bin_size' should be a Quantity or a TimeDelta")
# TimeDelta for time_bin_size
ts = BinnedTimeSeries(data={"time": ["2016-03-22T12:30:31"]},
time_bin_start="2016-03-22T12:30:31",
time_bin_size=TimeDelta(1))
assert isinstance(ts.time_bin_size, u.quantity.Quantity)
def test_initialization_time_bin_start_scalar():
# Make sure things crash when time_bin_start is a scalar with no time_bin_size
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(data={"time": ["2016-03-22T12:30:31"]},
time_bin_start=Time(1, format='mjd'),
time_bin_end=Time(1, format='mjd'))
assert exc.value.args[0] == ("'time_bin_start' is scalar, so 'time_bin_size' is required")
def test_initialization_n_bins():
# Make sure things crash with incorrect n_bins
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(data={"time": ["2016-03-22T12:30:31"]},
time_bin_start=Time(1, format='mjd'),
time_bin_size=1*u.s,
time_bin_end=Time(1, format='mjd'),
n_bins=10)
assert exc.value.args[0] == ("'n_bins' has been given and it is not the "
"same length as the input data.")
def test_initialization_non_scalar_time():
# Make sure things crash with incorrect size of time_bin_start
with pytest.raises(ValueError) as exc:
BinnedTimeSeries(data={"time": ["2016-03-22T12:30:31"]},
time_bin_start=["2016-03-22T12:30:31", "2016-03-22T12:30:32"],
time_bin_size=1*u.s,
time_bin_end=Time(1, format='mjd'))
assert exc.value.args[0] == ("Length of 'time_bin_start' (2) should match table length (1)")
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(data={"time": ["2016-03-22T12:30:31"]},
time_bin_start=["2016-03-22T12:30:31"],
time_bin_size=None,
time_bin_end=None)
assert exc.value.args[0] == ("Either 'time_bin_size' or 'time_bin_end' should be specified")
def test_even_contiguous():
# Initialize a ``BinnedTimeSeries`` with even contiguous bins by specifying
# the bin width:
ts = BinnedTimeSeries(time_bin_start='2016-03-22T12:30:31',
time_bin_size=3 * u.s, data=[[1, 4, 3]])
assert_equal(ts.time_bin_start.isot, ['2016-03-22T12:30:31.000',
'2016-03-22T12:30:34.000',
'2016-03-22T12:30:37.000'])
assert_equal(ts.time_bin_center.isot, ['2016-03-22T12:30:32.500',
'2016-03-22T12:30:35.500',
'2016-03-22T12:30:38.500'])
assert_equal(ts.time_bin_end.isot, ['2016-03-22T12:30:34.000',
'2016-03-22T12:30:37.000',
'2016-03-22T12:30:40.000'])
def test_uneven_contiguous():
# Initialize a ``BinnedTimeSeries`` with uneven contiguous bins by giving an
# end time:
ts = BinnedTimeSeries(time_bin_start=['2016-03-22T12:30:31',
'2016-03-22T12:30:32',
'2016-03-22T12:30:40'],
time_bin_end='2016-03-22T12:30:55',
data=[[1, 4, 3]])
assert_equal(ts.time_bin_start.isot, ['2016-03-22T12:30:31.000',
'2016-03-22T12:30:32.000',
'2016-03-22T12:30:40.000'])
assert_equal(ts.time_bin_center.isot, ['2016-03-22T12:30:31.500',
'2016-03-22T12:30:36.000',
'2016-03-22T12:30:47.500'])
assert_equal(ts.time_bin_end.isot, ['2016-03-22T12:30:32.000',
'2016-03-22T12:30:40.000',
'2016-03-22T12:30:55.000'])
def test_uneven_non_contiguous():
# Initialize a ``BinnedTimeSeries`` with uneven non-contiguous bins with
# lists of start times, bin sizes and data:
ts = BinnedTimeSeries(time_bin_start=['2016-03-22T12:30:31',
'2016-03-22T12:30:38',
'2016-03-22T12:34:40'],
time_bin_size=[5, 100, 2]*u.s,
data=[[1, 4, 3]])
assert_equal(ts.time_bin_start.isot, ['2016-03-22T12:30:31.000',
'2016-03-22T12:30:38.000',
'2016-03-22T12:34:40.000'])
assert_equal(ts.time_bin_center.isot, ['2016-03-22T12:30:33.500',
'2016-03-22T12:31:28.000',
'2016-03-22T12:34:41.000'])
assert_equal(ts.time_bin_end.isot, ['2016-03-22T12:30:36.000',
'2016-03-22T12:32:18.000',
'2016-03-22T12:34:42.000'])
def test_uneven_non_contiguous_full():
# Initialize a ``BinnedTimeSeries`` with uneven non-contiguous bins by
# specifying the start and end times for the bins:
ts = BinnedTimeSeries(time_bin_start=['2016-03-22T12:30:31',
'2016-03-22T12:30:33',
'2016-03-22T12:30:40'],
time_bin_end=['2016-03-22T12:30:32',
'2016-03-22T12:30:35',
'2016-03-22T12:30:41'],
data=[[1, 4, 3]])
assert_equal(ts.time_bin_start.isot, ['2016-03-22T12:30:31.000',
'2016-03-22T12:30:33.000',
'2016-03-22T12:30:40.000'])
assert_equal(ts.time_bin_center.isot, ['2016-03-22T12:30:31.500',
'2016-03-22T12:30:34.000',
'2016-03-22T12:30:40.500'])
assert_equal(ts.time_bin_end.isot, ['2016-03-22T12:30:32.000',
'2016-03-22T12:30:35.000',
'2016-03-22T12:30:41.000'])
def test_read_empty():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, format='csv')
assert exc.value.args[0] == '``time_bin_start_column`` should be provided since the default Table readers are being used.'
def test_read_no_size_end():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start', format='csv')
assert exc.value.args[0] == 'Either `time_bin_end_column` or `time_bin_size_column` should be provided.'
def test_read_both_extra_bins():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start', time_bin_end_column='END', time_bin_size_column='bin_size', format='csv')
assert exc.value.args[0] == "Cannot specify both `time_bin_end_column` and `time_bin_size_column`."
def test_read_size_no_unit():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start', time_bin_size_column='bin_size', format='csv')
assert exc.value.args[0] == "The bin size unit should be specified as an astropy Unit using ``time_bin_size_unit``."
def test_read_start_time_missing():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='abc', time_bin_size_column='bin_size', time_bin_size_unit=u.second, format='csv')
assert exc.value.args[0] == "Bin start time column 'abc' not found in the input data."
def test_read_end_time_missing():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start', time_bin_end_column="missing", format='csv')
assert exc.value.args[0] == "Bin end time column 'missing' not found in the input data."
def test_read_size_missing():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start', time_bin_size_column="missing", time_bin_size_unit=u.second, format='csv')
assert exc.value.args[0] == "Bin size column 'missing' not found in the input data."
def test_read_time_unit_missing():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start', time_bin_size_column="bin_size", format='csv')
assert exc.value.args[0] == "The bin size unit should be specified as an astropy Unit using ``time_bin_size_unit``."
def test_read():
timeseries = BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start',
time_bin_end_column='time_end', format='csv')
assert timeseries.colnames == ['time_bin_start', 'time_bin_size', 'bin_size', 'A', 'B', 'C', 'D', 'E', 'F']
assert len(timeseries) == 10
assert timeseries['B'].sum() == 1151.54
timeseries = BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start',
time_bin_size_column='bin_size',
time_bin_size_unit=u.second, format='csv')
assert timeseries.colnames == ['time_bin_start', 'time_bin_size', 'time_end', 'A', 'B', 'C', 'D', 'E', 'F']
assert len(timeseries) == 10
assert timeseries['B'].sum() == 1151.54
@pytest.mark.parametrize('cls', [BoxLeastSquares, LombScargle])
def test_periodogram(cls):
# Note that we don't need to check the actual results from the periodogram
# classes here since these are tested extensively in
# astropy.timeseries.periodograms.
ts = BinnedTimeSeries(time_bin_start='2016-03-22T12:30:31',
time_bin_size=3 * u.s, data=[[1, 4, 3], [3, 4, 3]], names=['a', 'b'])
p1 = cls.from_timeseries(ts, 'a')
assert isinstance(p1, cls)
assert_allclose(p1.t.jd, ts.time_bin_center.jd)
assert_equal(p1.y, ts['a'])
assert p1.dy is None
p2 = cls.from_timeseries(ts, 'a', uncertainty='b')
assert_quantity_allclose(p2.dy, ts['b'])
p3 = cls.from_timeseries(ts, 'a', uncertainty=0.1)
assert_allclose(p3.dy, 0.1)
|
|
astropy/timeseries/tests/test_binned.py
|
|
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
This subpackage provides a framework for representing models and
performing model evaluation and fitting. It supports 1D and 2D models
and fitting with parameter constraints. It has some predefined models
and fitting routines.
"""
from . import fitting
from . import models
from .core import *
from .parameters import *
from .separable import *
|
# Licensed under a 3-clause BSD style license - see LICENSE.rst
import pytest
from numpy.testing import assert_equal, assert_allclose
from astropy import units as u
from astropy.time import Time, TimeDelta
from astropy.utils.data import get_pkg_data_filename
from astropy.timeseries.periodograms import BoxLeastSquares, LombScargle
from astropy.timeseries.binned import BinnedTimeSeries
from astropy.tests.helper import assert_quantity_allclose
CSV_FILE = get_pkg_data_filename('data/binned.csv')
def test_empty_initialization():
ts = BinnedTimeSeries()
ts['time_bin_start'] = Time([1, 2, 3], format='mjd')
def test_empty_initialization_invalid():
# Make sure things crash when the first column added is not a time column
ts = BinnedTimeSeries()
with pytest.raises(ValueError) as exc:
ts['flux'] = [1, 2, 3]
assert exc.value.args[0] == ("BinnedTimeSeries object is invalid - expected "
"'time_bin_start' as the first column but found 'flux'")
def test_initialization_time_bin_invalid():
# Make sure things crash when time_bin_* is passed incorrectly.
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(data=[[1, 4, 3]])
assert exc.value.args[0] == ("'time_bin_start' has not been specified")
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(time_bin_start='2016-03-22T12:30:31', data=[[1, 4, 3]])
assert exc.value.args[0] == ("Either 'time_bin_size' or 'time_bin_end' should be specified")
def test_initialization_time_bin_both():
# Make sure things crash when time_bin_* is passed twice.
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(data={"time_bin_start": ["2016-03-22T12:30:31"]},
time_bin_start="2016-03-22T12:30:31")
assert exc.value.args[0] == ("'time_bin_start' has been given both in the table "
"and as a keyword argument")
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(data={"time_bin_size": ["2016-03-22T12:30:31"]},
time_bin_size=[1]*u.s)
assert exc.value.args[0] == ("'time_bin_size' has been given both in the table "
"and as a keyword argument")
def test_initialization_time_bin_size():
# Make sure things crash when time_bin_size has no units
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(data={"time": ["2016-03-22T12:30:31"]},
time_bin_start="2016-03-22T12:30:31",
time_bin_size=1)
assert exc.value.args[0] == ("'time_bin_size' should be a Quantity or a TimeDelta")
# TimeDelta for time_bin_size
ts = BinnedTimeSeries(data={"time": ["2016-03-22T12:30:31"]},
time_bin_start="2016-03-22T12:30:31",
time_bin_size=TimeDelta(1))
assert isinstance(ts.time_bin_size, u.quantity.Quantity)
def test_initialization_time_bin_start_scalar():
# Make sure things crash when time_bin_start is a scalar with no time_bin_size
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(data={"time": ["2016-03-22T12:30:31"]},
time_bin_start=Time(1, format='mjd'),
time_bin_end=Time(1, format='mjd'))
assert exc.value.args[0] == ("'time_bin_start' is scalar, so 'time_bin_size' is required")
def test_initialization_n_bins():
# Make sure things crash with incorrect n_bins
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(data={"time": ["2016-03-22T12:30:31"]},
time_bin_start=Time(1, format='mjd'),
time_bin_size=1*u.s,
time_bin_end=Time(1, format='mjd'),
n_bins=10)
assert exc.value.args[0] == ("'n_bins' has been given and it is not the "
"same length as the input data.")
def test_initialization_non_scalar_time():
# Make sure things crash with incorrect size of time_bin_start
with pytest.raises(ValueError) as exc:
BinnedTimeSeries(data={"time": ["2016-03-22T12:30:31"]},
time_bin_start=["2016-03-22T12:30:31", "2016-03-22T12:30:32"],
time_bin_size=1*u.s,
time_bin_end=Time(1, format='mjd'))
assert exc.value.args[0] == ("Length of 'time_bin_start' (2) should match table length (1)")
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(data={"time": ["2016-03-22T12:30:31"]},
time_bin_start=["2016-03-22T12:30:31"],
time_bin_size=None,
time_bin_end=None)
assert exc.value.args[0] == ("Either 'time_bin_size' or 'time_bin_end' should be specified")
def test_even_contiguous():
# Initialize a ``BinnedTimeSeries`` with even contiguous bins by specifying
# the bin width:
ts = BinnedTimeSeries(time_bin_start='2016-03-22T12:30:31',
time_bin_size=3 * u.s, data=[[1, 4, 3]])
assert_equal(ts.time_bin_start.isot, ['2016-03-22T12:30:31.000',
'2016-03-22T12:30:34.000',
'2016-03-22T12:30:37.000'])
assert_equal(ts.time_bin_center.isot, ['2016-03-22T12:30:32.500',
'2016-03-22T12:30:35.500',
'2016-03-22T12:30:38.500'])
assert_equal(ts.time_bin_end.isot, ['2016-03-22T12:30:34.000',
'2016-03-22T12:30:37.000',
'2016-03-22T12:30:40.000'])
def test_uneven_contiguous():
# Initialize a ``BinnedTimeSeries`` with uneven contiguous bins by giving an
# end time:
ts = BinnedTimeSeries(time_bin_start=['2016-03-22T12:30:31',
'2016-03-22T12:30:32',
'2016-03-22T12:30:40'],
time_bin_end='2016-03-22T12:30:55',
data=[[1, 4, 3]])
assert_equal(ts.time_bin_start.isot, ['2016-03-22T12:30:31.000',
'2016-03-22T12:30:32.000',
'2016-03-22T12:30:40.000'])
assert_equal(ts.time_bin_center.isot, ['2016-03-22T12:30:31.500',
'2016-03-22T12:30:36.000',
'2016-03-22T12:30:47.500'])
assert_equal(ts.time_bin_end.isot, ['2016-03-22T12:30:32.000',
'2016-03-22T12:30:40.000',
'2016-03-22T12:30:55.000'])
def test_uneven_non_contiguous():
# Initialize a ``BinnedTimeSeries`` with uneven non-contiguous bins with
# lists of start times, bin sizes and data:
ts = BinnedTimeSeries(time_bin_start=['2016-03-22T12:30:31',
'2016-03-22T12:30:38',
'2016-03-22T12:34:40'],
time_bin_size=[5, 100, 2]*u.s,
data=[[1, 4, 3]])
assert_equal(ts.time_bin_start.isot, ['2016-03-22T12:30:31.000',
'2016-03-22T12:30:38.000',
'2016-03-22T12:34:40.000'])
assert_equal(ts.time_bin_center.isot, ['2016-03-22T12:30:33.500',
'2016-03-22T12:31:28.000',
'2016-03-22T12:34:41.000'])
assert_equal(ts.time_bin_end.isot, ['2016-03-22T12:30:36.000',
'2016-03-22T12:32:18.000',
'2016-03-22T12:34:42.000'])
def test_uneven_non_contiguous_full():
# Initialize a ``BinnedTimeSeries`` with uneven non-contiguous bins by
# specifying the start and end times for the bins:
ts = BinnedTimeSeries(time_bin_start=['2016-03-22T12:30:31',
'2016-03-22T12:30:33',
'2016-03-22T12:30:40'],
time_bin_end=['2016-03-22T12:30:32',
'2016-03-22T12:30:35',
'2016-03-22T12:30:41'],
data=[[1, 4, 3]])
assert_equal(ts.time_bin_start.isot, ['2016-03-22T12:30:31.000',
'2016-03-22T12:30:33.000',
'2016-03-22T12:30:40.000'])
assert_equal(ts.time_bin_center.isot, ['2016-03-22T12:30:31.500',
'2016-03-22T12:30:34.000',
'2016-03-22T12:30:40.500'])
assert_equal(ts.time_bin_end.isot, ['2016-03-22T12:30:32.000',
'2016-03-22T12:30:35.000',
'2016-03-22T12:30:41.000'])
def test_read_empty():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, format='csv')
assert exc.value.args[0] == '``time_bin_start_column`` should be provided since the default Table readers are being used.'
def test_read_no_size_end():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start', format='csv')
assert exc.value.args[0] == 'Either `time_bin_end_column` or `time_bin_size_column` should be provided.'
def test_read_both_extra_bins():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start', time_bin_end_column='END', time_bin_size_column='bin_size', format='csv')
assert exc.value.args[0] == "Cannot specify both `time_bin_end_column` and `time_bin_size_column`."
def test_read_size_no_unit():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start', time_bin_size_column='bin_size', format='csv')
assert exc.value.args[0] == "The bin size unit should be specified as an astropy Unit using ``time_bin_size_unit``."
def test_read_start_time_missing():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='abc', time_bin_size_column='bin_size', time_bin_size_unit=u.second, format='csv')
assert exc.value.args[0] == "Bin start time column 'abc' not found in the input data."
def test_read_end_time_missing():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start', time_bin_end_column="missing", format='csv')
assert exc.value.args[0] == "Bin end time column 'missing' not found in the input data."
def test_read_size_missing():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start', time_bin_size_column="missing", time_bin_size_unit=u.second, format='csv')
assert exc.value.args[0] == "Bin size column 'missing' not found in the input data."
def test_read_time_unit_missing():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start', time_bin_size_column="bin_size", format='csv')
assert exc.value.args[0] == "The bin size unit should be specified as an astropy Unit using ``time_bin_size_unit``."
def test_read():
timeseries = BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start',
time_bin_end_column='time_end', format='csv')
assert timeseries.colnames == ['time_bin_start', 'time_bin_size', 'bin_size', 'A', 'B', 'C', 'D', 'E', 'F']
assert len(timeseries) == 10
assert timeseries['B'].sum() == 1151.54
timeseries = BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start',
time_bin_size_column='bin_size',
time_bin_size_unit=u.second, format='csv')
assert timeseries.colnames == ['time_bin_start', 'time_bin_size', 'time_end', 'A', 'B', 'C', 'D', 'E', 'F']
assert len(timeseries) == 10
assert timeseries['B'].sum() == 1151.54
@pytest.mark.parametrize('cls', [BoxLeastSquares, LombScargle])
def test_periodogram(cls):
# Note that we don't need to check the actual results from the periodogram
# classes here since these are tested extensively in
# astropy.timeseries.periodograms.
ts = BinnedTimeSeries(time_bin_start='2016-03-22T12:30:31',
time_bin_size=3 * u.s, data=[[1, 4, 3], [3, 4, 3]], names=['a', 'b'])
p1 = cls.from_timeseries(ts, 'a')
assert isinstance(p1, cls)
assert_allclose(p1.t.jd, ts.time_bin_center.jd)
assert_equal(p1.y, ts['a'])
assert p1.dy is None
p2 = cls.from_timeseries(ts, 'a', uncertainty='b')
assert_quantity_allclose(p2.dy, ts['b'])
p3 = cls.from_timeseries(ts, 'a', uncertainty=0.1)
assert_allclose(p3.dy, 0.1)
|
|
astropy/timeseries/tests/test_binned.py
|
astropy/modeling/__init__.py
|
# -*- coding: utf-8 -*-
# Licensed under a 3-clause BSD style license - see LICENSE.rst
from astropy.utils.decorators import format_doc
from astropy.coordinates.baseframe import frame_transform_graph, base_doc
from astropy.coordinates.attributes import TimeAttribute
from astropy.coordinates.transformations import DynamicMatrixTransform
from astropy.coordinates import earth_orientation as earth
from .baseradec import BaseRADecFrame, doc_components
from .utils import EQUINOX_J2000
__all__ = ['FK5']
doc_footer = """
Other parameters
----------------
equinox : `~astropy.time.Time`
The equinox of this frame.
"""
@format_doc(base_doc, components=doc_components, footer=doc_footer)
class FK5(BaseRADecFrame):
"""
A coordinate or frame in the FK5 system.
Note that this is a barycentric version of FK5 - that is, the origin for
this frame is the Solar System Barycenter, *not* the Earth geocenter.
The frame attributes are listed under **Other Parameters**.
"""
equinox = TimeAttribute(default=EQUINOX_J2000)
@staticmethod
def _precession_matrix(oldequinox, newequinox):
"""
Compute and return the precession matrix for FK5 based on Capitaine et
al. 2003/IAU2006. Used inside some of the transformation functions.
Parameters
----------
oldequinox : `~astropy.time.Time`
The equinox to precess from.
newequinox : `~astropy.time.Time`
The equinox to precess to.
Returns
-------
newcoord : array
The precession matrix to transform to the new equinox
"""
return earth.precession_matrix_Capitaine(oldequinox, newequinox)
# This is the "self-transform". Defined at module level because the decorator
# needs a reference to the FK5 class
@frame_transform_graph.transform(DynamicMatrixTransform, FK5, FK5)
def fk5_to_fk5(fk5coord1, fk5frame2):
return fk5coord1._precession_matrix(fk5coord1.equinox, fk5frame2.equinox)
|
# Licensed under a 3-clause BSD style license - see LICENSE.rst
import pytest
from numpy.testing import assert_equal, assert_allclose
from astropy import units as u
from astropy.time import Time, TimeDelta
from astropy.utils.data import get_pkg_data_filename
from astropy.timeseries.periodograms import BoxLeastSquares, LombScargle
from astropy.timeseries.binned import BinnedTimeSeries
from astropy.tests.helper import assert_quantity_allclose
CSV_FILE = get_pkg_data_filename('data/binned.csv')
def test_empty_initialization():
ts = BinnedTimeSeries()
ts['time_bin_start'] = Time([1, 2, 3], format='mjd')
def test_empty_initialization_invalid():
# Make sure things crash when the first column added is not a time column
ts = BinnedTimeSeries()
with pytest.raises(ValueError) as exc:
ts['flux'] = [1, 2, 3]
assert exc.value.args[0] == ("BinnedTimeSeries object is invalid - expected "
"'time_bin_start' as the first column but found 'flux'")
def test_initialization_time_bin_invalid():
# Make sure things crash when time_bin_* is passed incorrectly.
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(data=[[1, 4, 3]])
assert exc.value.args[0] == ("'time_bin_start' has not been specified")
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(time_bin_start='2016-03-22T12:30:31', data=[[1, 4, 3]])
assert exc.value.args[0] == ("Either 'time_bin_size' or 'time_bin_end' should be specified")
def test_initialization_time_bin_both():
# Make sure things crash when time_bin_* is passed twice.
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(data={"time_bin_start": ["2016-03-22T12:30:31"]},
time_bin_start="2016-03-22T12:30:31")
assert exc.value.args[0] == ("'time_bin_start' has been given both in the table "
"and as a keyword argument")
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(data={"time_bin_size": ["2016-03-22T12:30:31"]},
time_bin_size=[1]*u.s)
assert exc.value.args[0] == ("'time_bin_size' has been given both in the table "
"and as a keyword argument")
def test_initialization_time_bin_size():
# Make sure things crash when time_bin_size has no units
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(data={"time": ["2016-03-22T12:30:31"]},
time_bin_start="2016-03-22T12:30:31",
time_bin_size=1)
assert exc.value.args[0] == ("'time_bin_size' should be a Quantity or a TimeDelta")
# TimeDelta for time_bin_size
ts = BinnedTimeSeries(data={"time": ["2016-03-22T12:30:31"]},
time_bin_start="2016-03-22T12:30:31",
time_bin_size=TimeDelta(1))
assert isinstance(ts.time_bin_size, u.quantity.Quantity)
def test_initialization_time_bin_start_scalar():
# Make sure things crash when time_bin_start is a scalar with no time_bin_size
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(data={"time": ["2016-03-22T12:30:31"]},
time_bin_start=Time(1, format='mjd'),
time_bin_end=Time(1, format='mjd'))
assert exc.value.args[0] == ("'time_bin_start' is scalar, so 'time_bin_size' is required")
def test_initialization_n_bins():
# Make sure things crash with incorrect n_bins
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(data={"time": ["2016-03-22T12:30:31"]},
time_bin_start=Time(1, format='mjd'),
time_bin_size=1*u.s,
time_bin_end=Time(1, format='mjd'),
n_bins=10)
assert exc.value.args[0] == ("'n_bins' has been given and it is not the "
"same length as the input data.")
def test_initialization_non_scalar_time():
# Make sure things crash with incorrect size of time_bin_start
with pytest.raises(ValueError) as exc:
BinnedTimeSeries(data={"time": ["2016-03-22T12:30:31"]},
time_bin_start=["2016-03-22T12:30:31", "2016-03-22T12:30:32"],
time_bin_size=1*u.s,
time_bin_end=Time(1, format='mjd'))
assert exc.value.args[0] == ("Length of 'time_bin_start' (2) should match table length (1)")
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(data={"time": ["2016-03-22T12:30:31"]},
time_bin_start=["2016-03-22T12:30:31"],
time_bin_size=None,
time_bin_end=None)
assert exc.value.args[0] == ("Either 'time_bin_size' or 'time_bin_end' should be specified")
def test_even_contiguous():
# Initialize a ``BinnedTimeSeries`` with even contiguous bins by specifying
# the bin width:
ts = BinnedTimeSeries(time_bin_start='2016-03-22T12:30:31',
time_bin_size=3 * u.s, data=[[1, 4, 3]])
assert_equal(ts.time_bin_start.isot, ['2016-03-22T12:30:31.000',
'2016-03-22T12:30:34.000',
'2016-03-22T12:30:37.000'])
assert_equal(ts.time_bin_center.isot, ['2016-03-22T12:30:32.500',
'2016-03-22T12:30:35.500',
'2016-03-22T12:30:38.500'])
assert_equal(ts.time_bin_end.isot, ['2016-03-22T12:30:34.000',
'2016-03-22T12:30:37.000',
'2016-03-22T12:30:40.000'])
def test_uneven_contiguous():
# Initialize a ``BinnedTimeSeries`` with uneven contiguous bins by giving an
# end time:
ts = BinnedTimeSeries(time_bin_start=['2016-03-22T12:30:31',
'2016-03-22T12:30:32',
'2016-03-22T12:30:40'],
time_bin_end='2016-03-22T12:30:55',
data=[[1, 4, 3]])
assert_equal(ts.time_bin_start.isot, ['2016-03-22T12:30:31.000',
'2016-03-22T12:30:32.000',
'2016-03-22T12:30:40.000'])
assert_equal(ts.time_bin_center.isot, ['2016-03-22T12:30:31.500',
'2016-03-22T12:30:36.000',
'2016-03-22T12:30:47.500'])
assert_equal(ts.time_bin_end.isot, ['2016-03-22T12:30:32.000',
'2016-03-22T12:30:40.000',
'2016-03-22T12:30:55.000'])
def test_uneven_non_contiguous():
# Initialize a ``BinnedTimeSeries`` with uneven non-contiguous bins with
# lists of start times, bin sizes and data:
ts = BinnedTimeSeries(time_bin_start=['2016-03-22T12:30:31',
'2016-03-22T12:30:38',
'2016-03-22T12:34:40'],
time_bin_size=[5, 100, 2]*u.s,
data=[[1, 4, 3]])
assert_equal(ts.time_bin_start.isot, ['2016-03-22T12:30:31.000',
'2016-03-22T12:30:38.000',
'2016-03-22T12:34:40.000'])
assert_equal(ts.time_bin_center.isot, ['2016-03-22T12:30:33.500',
'2016-03-22T12:31:28.000',
'2016-03-22T12:34:41.000'])
assert_equal(ts.time_bin_end.isot, ['2016-03-22T12:30:36.000',
'2016-03-22T12:32:18.000',
'2016-03-22T12:34:42.000'])
def test_uneven_non_contiguous_full():
# Initialize a ``BinnedTimeSeries`` with uneven non-contiguous bins by
# specifying the start and end times for the bins:
ts = BinnedTimeSeries(time_bin_start=['2016-03-22T12:30:31',
'2016-03-22T12:30:33',
'2016-03-22T12:30:40'],
time_bin_end=['2016-03-22T12:30:32',
'2016-03-22T12:30:35',
'2016-03-22T12:30:41'],
data=[[1, 4, 3]])
assert_equal(ts.time_bin_start.isot, ['2016-03-22T12:30:31.000',
'2016-03-22T12:30:33.000',
'2016-03-22T12:30:40.000'])
assert_equal(ts.time_bin_center.isot, ['2016-03-22T12:30:31.500',
'2016-03-22T12:30:34.000',
'2016-03-22T12:30:40.500'])
assert_equal(ts.time_bin_end.isot, ['2016-03-22T12:30:32.000',
'2016-03-22T12:30:35.000',
'2016-03-22T12:30:41.000'])
def test_read_empty():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, format='csv')
assert exc.value.args[0] == '``time_bin_start_column`` should be provided since the default Table readers are being used.'
def test_read_no_size_end():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start', format='csv')
assert exc.value.args[0] == 'Either `time_bin_end_column` or `time_bin_size_column` should be provided.'
def test_read_both_extra_bins():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start', time_bin_end_column='END', time_bin_size_column='bin_size', format='csv')
assert exc.value.args[0] == "Cannot specify both `time_bin_end_column` and `time_bin_size_column`."
def test_read_size_no_unit():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start', time_bin_size_column='bin_size', format='csv')
assert exc.value.args[0] == "The bin size unit should be specified as an astropy Unit using ``time_bin_size_unit``."
def test_read_start_time_missing():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='abc', time_bin_size_column='bin_size', time_bin_size_unit=u.second, format='csv')
assert exc.value.args[0] == "Bin start time column 'abc' not found in the input data."
def test_read_end_time_missing():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start', time_bin_end_column="missing", format='csv')
assert exc.value.args[0] == "Bin end time column 'missing' not found in the input data."
def test_read_size_missing():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start', time_bin_size_column="missing", time_bin_size_unit=u.second, format='csv')
assert exc.value.args[0] == "Bin size column 'missing' not found in the input data."
def test_read_time_unit_missing():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start', time_bin_size_column="bin_size", format='csv')
assert exc.value.args[0] == "The bin size unit should be specified as an astropy Unit using ``time_bin_size_unit``."
def test_read():
timeseries = BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start',
time_bin_end_column='time_end', format='csv')
assert timeseries.colnames == ['time_bin_start', 'time_bin_size', 'bin_size', 'A', 'B', 'C', 'D', 'E', 'F']
assert len(timeseries) == 10
assert timeseries['B'].sum() == 1151.54
timeseries = BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start',
time_bin_size_column='bin_size',
time_bin_size_unit=u.second, format='csv')
assert timeseries.colnames == ['time_bin_start', 'time_bin_size', 'time_end', 'A', 'B', 'C', 'D', 'E', 'F']
assert len(timeseries) == 10
assert timeseries['B'].sum() == 1151.54
@pytest.mark.parametrize('cls', [BoxLeastSquares, LombScargle])
def test_periodogram(cls):
# Note that we don't need to check the actual results from the periodogram
# classes here since these are tested extensively in
# astropy.timeseries.periodograms.
ts = BinnedTimeSeries(time_bin_start='2016-03-22T12:30:31',
time_bin_size=3 * u.s, data=[[1, 4, 3], [3, 4, 3]], names=['a', 'b'])
p1 = cls.from_timeseries(ts, 'a')
assert isinstance(p1, cls)
assert_allclose(p1.t.jd, ts.time_bin_center.jd)
assert_equal(p1.y, ts['a'])
assert p1.dy is None
p2 = cls.from_timeseries(ts, 'a', uncertainty='b')
assert_quantity_allclose(p2.dy, ts['b'])
p3 = cls.from_timeseries(ts, 'a', uncertainty=0.1)
assert_allclose(p3.dy, 0.1)
|
|
astropy/timeseries/tests/test_binned.py
|
astropy/coordinates/builtin_frames/fk5.py
|
# Licensed under a 3-clause BSD style license - see PYFITS.rst
import collections
import copy
import itertools
import re
import warnings
from .card import Card, _pad, KEYWORD_LENGTH, UNDEFINED
from .file import _File
from .util import (encode_ascii, decode_ascii, fileobj_closed,
fileobj_is_binary, path_like)
from ._utils import parse_header
from astropy.utils import isiterable
from astropy.utils.exceptions import AstropyUserWarning
from astropy.utils.decorators import deprecated_renamed_argument
BLOCK_SIZE = 2880 # the FITS block size
# This regular expression can match a *valid* END card which just consists of
# the string 'END' followed by all spaces, or an *invalid* end card which
# consists of END, followed by any character that is *not* a valid character
# for a valid FITS keyword (that is, this is not a keyword like 'ENDER' which
# starts with 'END' but is not 'END'), followed by any arbitrary bytes. An
# invalid end card may also consist of just 'END' with no trailing bytes.
HEADER_END_RE = re.compile(encode_ascii(
r'(?:(?P<valid>END {77}) *)|(?P<invalid>END$|END {0,76}[^A-Z0-9_-])'))
# According to the FITS standard the only characters that may appear in a
# header record are the restricted ASCII chars from 0x20 through 0x7E.
VALID_HEADER_CHARS = set(map(chr, range(0x20, 0x7F)))
END_CARD = 'END' + ' ' * 77
__doctest_skip__ = ['Header', 'Header.comments', 'Header.fromtextfile',
'Header.totextfile', 'Header.set', 'Header.update']
class Header:
"""
FITS header class. This class exposes both a dict-like interface and a
list-like interface to FITS headers.
The header may be indexed by keyword and, like a dict, the associated value
will be returned. When the header contains cards with duplicate keywords,
only the value of the first card with the given keyword will be returned.
It is also possible to use a 2-tuple as the index in the form (keyword,
n)--this returns the n-th value with that keyword, in the case where there
are duplicate keywords.
For example::
>>> header['NAXIS']
0
>>> header[('FOO', 1)] # Return the value of the second FOO keyword
'foo'
The header may also be indexed by card number::
>>> header[0] # Return the value of the first card in the header
'T'
Commentary keywords such as HISTORY and COMMENT are special cases: When
indexing the Header object with either 'HISTORY' or 'COMMENT' a list of all
the HISTORY/COMMENT values is returned::
>>> header['HISTORY']
This is the first history entry in this header.
This is the second history entry in this header.
...
See the Astropy documentation for more details on working with headers.
"""
def __init__(self, cards=[], copy=False):
"""
Construct a `Header` from an iterable and/or text file.
Parameters
----------
cards : A list of `Card` objects, optional
The cards to initialize the header with. Also allowed are other
`Header` (or `dict`-like) objects.
.. versionchanged:: 1.2
Allowed ``cards`` to be a `dict`-like object.
copy : bool, optional
If ``True`` copies the ``cards`` if they were another `Header`
instance.
Default is ``False``.
.. versionadded:: 1.3
"""
self.clear()
if isinstance(cards, Header):
if copy:
cards = cards.copy()
cards = cards.cards
elif isinstance(cards, dict):
cards = cards.items()
for card in cards:
self.append(card, end=True)
self._modified = False
def __len__(self):
return len(self._cards)
def __iter__(self):
for card in self._cards:
yield card.keyword
def __contains__(self, keyword):
if keyword in self._keyword_indices or keyword in self._rvkc_indices:
# For the most common case (single, standard form keyword lookup)
# this will work and is an O(1) check. If it fails that doesn't
# guarantee absence, just that we have to perform the full set of
# checks in self._cardindex
return True
try:
self._cardindex(keyword)
except (KeyError, IndexError):
return False
return True
def __getitem__(self, key):
if isinstance(key, slice):
return self.__class__([copy.copy(c) for c in self._cards[key]])
elif self._haswildcard(key):
return self.__class__([copy.copy(self._cards[idx])
for idx in self._wildcardmatch(key)])
elif (isinstance(key, str) and
key.upper() in Card._commentary_keywords):
key = key.upper()
# Special case for commentary cards
return _HeaderCommentaryCards(self, key)
if isinstance(key, tuple):
keyword = key[0]
else:
keyword = key
card = self._cards[self._cardindex(key)]
if card.field_specifier is not None and keyword == card.rawkeyword:
# This is RVKC; if only the top-level keyword was specified return
# the raw value, not the parsed out float value
return card.rawvalue
value = card.value
if value == UNDEFINED:
return None
return value
def __setitem__(self, key, value):
if self._set_slice(key, value, self):
return
if isinstance(value, tuple):
if not (0 < len(value) <= 2):
raise ValueError(
'A Header item may be set with either a scalar value, '
'a 1-tuple containing a scalar value, or a 2-tuple '
'containing a scalar value and comment string.')
if len(value) == 1:
value, comment = value[0], None
if value is None:
value = UNDEFINED
elif len(value) == 2:
value, comment = value
if value is None:
value = UNDEFINED
if comment is None:
comment = ''
else:
comment = None
card = None
if isinstance(key, int):
card = self._cards[key]
elif isinstance(key, tuple):
card = self._cards[self._cardindex(key)]
if value is None:
value = UNDEFINED
if card:
card.value = value
if comment is not None:
card.comment = comment
if card._modified:
self._modified = True
else:
# If we get an IndexError that should be raised; we don't allow
# assignment to non-existing indices
self._update((key, value, comment))
def __delitem__(self, key):
if isinstance(key, slice) or self._haswildcard(key):
# This is very inefficient but it's not a commonly used feature.
# If someone out there complains that they make heavy use of slice
# deletions and it's too slow, well, we can worry about it then
# [the solution is not too complicated--it would be wait 'til all
# the cards are deleted before updating _keyword_indices rather
# than updating it once for each card that gets deleted]
if isinstance(key, slice):
indices = range(*key.indices(len(self)))
# If the slice step is backwards we want to reverse it, because
# it will be reversed in a few lines...
if key.step and key.step < 0:
indices = reversed(indices)
else:
indices = self._wildcardmatch(key)
for idx in reversed(indices):
del self[idx]
return
elif isinstance(key, str):
# delete ALL cards with the same keyword name
key = Card.normalize_keyword(key)
indices = self._keyword_indices
if key not in self._keyword_indices:
indices = self._rvkc_indices
if key not in indices:
# if keyword is not present raise KeyError.
# To delete keyword without caring if they were present,
# Header.remove(Keyword) can be used with optional argument ignore_missing as True
raise KeyError(f"Keyword '{key}' not found.")
for idx in reversed(indices[key]):
# Have to copy the indices list since it will be modified below
del self[idx]
return
idx = self._cardindex(key)
card = self._cards[idx]
keyword = card.keyword
del self._cards[idx]
keyword = Card.normalize_keyword(keyword)
indices = self._keyword_indices[keyword]
indices.remove(idx)
if not indices:
del self._keyword_indices[keyword]
# Also update RVKC indices if necessary :/
if card.field_specifier is not None:
indices = self._rvkc_indices[card.rawkeyword]
indices.remove(idx)
if not indices:
del self._rvkc_indices[card.rawkeyword]
# We also need to update all other indices
self._updateindices(idx, increment=False)
self._modified = True
def __repr__(self):
return self.tostring(sep='\n', endcard=False, padding=False)
def __str__(self):
return self.tostring()
def __eq__(self, other):
"""
Two Headers are equal only if they have the exact same string
representation.
"""
return str(self) == str(other)
def __add__(self, other):
temp = self.copy(strip=False)
temp.extend(other)
return temp
def __iadd__(self, other):
self.extend(other)
return self
def _ipython_key_completions_(self):
return self.__iter__()
@property
def cards(self):
"""
The underlying physical cards that make up this Header; it can be
looked at, but it should not be modified directly.
"""
return _CardAccessor(self)
@property
def comments(self):
"""
View the comments associated with each keyword, if any.
For example, to see the comment on the NAXIS keyword:
>>> header.comments['NAXIS']
number of data axes
Comments can also be updated through this interface:
>>> header.comments['NAXIS'] = 'Number of data axes'
"""
return _HeaderComments(self)
@property
def _modified(self):
"""
Whether or not the header has been modified; this is a property so that
it can also check each card for modifications--cards may have been
modified directly without the header containing it otherwise knowing.
"""
modified_cards = any(c._modified for c in self._cards)
if modified_cards:
# If any cards were modified then by definition the header was
# modified
self.__dict__['_modified'] = True
return self.__dict__['_modified']
@_modified.setter
def _modified(self, val):
self.__dict__['_modified'] = val
@classmethod
def fromstring(cls, data, sep=''):
"""
Creates an HDU header from a byte string containing the entire header
data.
Parameters
----------
data : str or bytes
String or bytes containing the entire header. In the case of bytes
they will be decoded using latin-1 (only plain ASCII characters are
allowed in FITS headers but latin-1 allows us to retain any invalid
bytes that might appear in malformatted FITS files).
sep : str, optional
The string separating cards from each other, such as a newline. By
default there is no card separator (as is the case in a raw FITS
file). In general this is only used in cases where a header was
printed as text (e.g. with newlines after each card) and you want
to create a new `Header` from it by copy/pasting.
Examples
--------
>>> from astropy.io.fits import Header
>>> hdr = Header({'SIMPLE': True})
>>> Header.fromstring(hdr.tostring()) == hdr
True
If you want to create a `Header` from printed text it's not necessary
to have the exact binary structure as it would appear in a FITS file,
with the full 80 byte card length. Rather, each "card" can end in a
newline and does not have to be padded out to a full card length as
long as it "looks like" a FITS header:
>>> hdr = Header.fromstring(\"\"\"\\
... SIMPLE = T / conforms to FITS standard
... BITPIX = 8 / array data type
... NAXIS = 0 / number of array dimensions
... EXTEND = T
... \"\"\", sep='\\n')
>>> hdr['SIMPLE']
True
>>> hdr['BITPIX']
8
>>> len(hdr)
4
Returns
-------
header
A new `Header` instance.
"""
cards = []
# If the card separator contains characters that may validly appear in
# a card, the only way to unambiguously distinguish between cards is to
# require that they be Card.length long. However, if the separator
# contains non-valid characters (namely \n) the cards may be split
# immediately at the separator
require_full_cardlength = set(sep).issubset(VALID_HEADER_CHARS)
if isinstance(data, bytes):
# FITS supports only ASCII, but decode as latin1 and just take all
# bytes for now; if it results in mojibake due to e.g. UTF-8
# encoded data in a FITS header that's OK because it shouldn't be
# there in the first place--accepting it here still gives us the
# opportunity to display warnings later during validation
CONTINUE = b'CONTINUE'
END = b'END'
end_card = END_CARD.encode('ascii')
sep = sep.encode('latin1')
empty = b''
else:
CONTINUE = 'CONTINUE'
END = 'END'
end_card = END_CARD
empty = ''
# Split the header into individual cards
idx = 0
image = []
while idx < len(data):
if require_full_cardlength:
end_idx = idx + Card.length
else:
try:
end_idx = data.index(sep, idx)
except ValueError:
end_idx = len(data)
next_image = data[idx:end_idx]
idx = end_idx + len(sep)
if image:
if next_image[:8] == CONTINUE:
image.append(next_image)
continue
cards.append(Card.fromstring(empty.join(image)))
if require_full_cardlength:
if next_image == end_card:
image = []
break
else:
if next_image.split(sep)[0].rstrip() == END:
image = []
break
image = [next_image]
# Add the last image that was found before the end, if any
if image:
cards.append(Card.fromstring(empty.join(image)))
return cls._fromcards(cards)
@classmethod
def fromfile(cls, fileobj, sep='', endcard=True, padding=True):
"""
Similar to :meth:`Header.fromstring`, but reads the header string from
a given file-like object or filename.
Parameters
----------
fileobj : str, file-like
A filename or an open file-like object from which a FITS header is
to be read. For open file handles the file pointer must be at the
beginning of the header.
sep : str, optional
The string separating cards from each other, such as a newline. By
default there is no card separator (as is the case in a raw FITS
file).
endcard : bool, optional
If True (the default) the header must end with an END card in order
to be considered valid. If an END card is not found an
`OSError` is raised.
padding : bool, optional
If True (the default) the header will be required to be padded out
to a multiple of 2880, the FITS header block size. Otherwise any
padding, or lack thereof, is ignored.
Returns
-------
header
A new `Header` instance.
"""
close_file = False
if isinstance(fileobj, path_like):
# If sep is non-empty we are trying to read a header printed to a
# text file, so open in text mode by default to support newline
# handling; if a binary-mode file object is passed in, the user is
# then on their own w.r.t. newline handling.
#
# Otherwise assume we are reading from an actual FITS file and open
# in binary mode.
if sep:
fileobj = open(fileobj, 'r', encoding='latin1')
else:
fileobj = open(fileobj, 'rb')
close_file = True
try:
is_binary = fileobj_is_binary(fileobj)
def block_iter(nbytes):
while True:
data = fileobj.read(nbytes)
if data:
yield data
else:
break
return cls._from_blocks(block_iter, is_binary, sep, endcard,
padding)[1]
finally:
if close_file:
fileobj.close()
@classmethod
def _fromcards(cls, cards):
header = cls()
for idx, card in enumerate(cards):
header._cards.append(card)
keyword = Card.normalize_keyword(card.keyword)
header._keyword_indices[keyword].append(idx)
if card.field_specifier is not None:
header._rvkc_indices[card.rawkeyword].append(idx)
header._modified = False
return header
@classmethod
def _from_blocks(cls, block_iter, is_binary, sep, endcard, padding):
"""
The meat of `Header.fromfile`; in a separate method so that
`Header.fromfile` itself is just responsible for wrapping file
handling. Also used by `_BaseHDU.fromstring`.
``block_iter`` should be a callable which, given a block size n
(typically 2880 bytes as used by the FITS standard) returns an iterator
of byte strings of that block size.
``is_binary`` specifies whether the returned blocks are bytes or text
Returns both the entire header *string*, and the `Header` object
returned by Header.fromstring on that string.
"""
actual_block_size = _block_size(sep)
clen = Card.length + len(sep)
blocks = block_iter(actual_block_size)
# Read the first header block.
try:
block = next(blocks)
except StopIteration:
raise EOFError()
if not is_binary:
# TODO: There needs to be error handling at *this* level for
# non-ASCII characters; maybe at this stage decoding latin-1 might
# be safer
block = encode_ascii(block)
read_blocks = []
is_eof = False
end_found = False
# continue reading header blocks until END card or EOF is reached
while True:
# find the END card
end_found, block = cls._find_end_card(block, clen)
read_blocks.append(decode_ascii(block))
if end_found:
break
try:
block = next(blocks)
except StopIteration:
is_eof = True
break
if not block:
is_eof = True
break
if not is_binary:
block = encode_ascii(block)
if not end_found and is_eof and endcard:
# TODO: Pass this error to validation framework as an ERROR,
# rather than raising an exception
raise OSError('Header missing END card.')
header_str = ''.join(read_blocks)
_check_padding(header_str, actual_block_size, is_eof,
check_block_size=padding)
return header_str, cls.fromstring(header_str, sep=sep)
@classmethod
def _find_end_card(cls, block, card_len):
"""
Utility method to search a header block for the END card and handle
invalid END cards.
This method can also returned a modified copy of the input header block
in case an invalid end card needs to be sanitized.
"""
for mo in HEADER_END_RE.finditer(block):
# Ensure the END card was found, and it started on the
# boundary of a new card (see ticket #142)
if mo.start() % card_len != 0:
continue
# This must be the last header block, otherwise the
# file is malformatted
if mo.group('invalid'):
offset = mo.start()
trailing = block[offset + 3:offset + card_len - 3].rstrip()
if trailing:
trailing = repr(trailing).lstrip('ub')
# TODO: Pass this warning up to the validation framework
warnings.warn(
'Unexpected bytes trailing END keyword: {}; these '
'bytes will be replaced with spaces on write.'.format(
trailing), AstropyUserWarning)
else:
# TODO: Pass this warning up to the validation framework
warnings.warn(
'Missing padding to end of the FITS block after the '
'END keyword; additional spaces will be appended to '
'the file upon writing to pad out to {} '
'bytes.'.format(BLOCK_SIZE), AstropyUserWarning)
# Sanitize out invalid END card now that the appropriate
# warnings have been issued
block = (block[:offset] + encode_ascii(END_CARD) +
block[offset + len(END_CARD):])
return True, block
return False, block
def tostring(self, sep='', endcard=True, padding=True):
r"""
Returns a string representation of the header.
By default this uses no separator between cards, adds the END card, and
pads the string with spaces to the next multiple of 2880 bytes. That
is, it returns the header exactly as it would appear in a FITS file.
Parameters
----------
sep : str, optional
The character or string with which to separate cards. By default
there is no separator, but one could use ``'\\n'``, for example, to
separate each card with a new line
endcard : bool, optional
If True (default) adds the END card to the end of the header
string
padding : bool, optional
If True (default) pads the string with spaces out to the next
multiple of 2880 characters
Returns
-------
s : str
A string representing a FITS header.
"""
lines = []
for card in self._cards:
s = str(card)
# Cards with CONTINUE cards may be longer than 80 chars; so break
# them into multiple lines
while s:
lines.append(s[:Card.length])
s = s[Card.length:]
s = sep.join(lines)
if endcard:
s += sep + _pad('END')
if padding:
s += ' ' * _pad_length(len(s))
return s
@deprecated_renamed_argument('clobber', 'overwrite', '2.0')
def tofile(self, fileobj, sep='', endcard=True, padding=True,
overwrite=False):
r"""
Writes the header to file or file-like object.
By default this writes the header exactly as it would be written to a
FITS file, with the END card included and padding to the next multiple
of 2880 bytes. However, aspects of this may be controlled.
Parameters
----------
fileobj : str, file, optional
Either the pathname of a file, or an open file handle or file-like
object
sep : str, optional
The character or string with which to separate cards. By default
there is no separator, but one could use ``'\\n'``, for example, to
separate each card with a new line
endcard : bool, optional
If `True` (default) adds the END card to the end of the header
string
padding : bool, optional
If `True` (default) pads the string with spaces out to the next
multiple of 2880 characters
overwrite : bool, optional
If ``True``, overwrite the output file if it exists. Raises an
``OSError`` if ``False`` and the output file exists. Default is
``False``.
.. versionchanged:: 1.3
``overwrite`` replaces the deprecated ``clobber`` argument.
"""
close_file = fileobj_closed(fileobj)
if not isinstance(fileobj, _File):
fileobj = _File(fileobj, mode='ostream', overwrite=overwrite)
try:
blocks = self.tostring(sep=sep, endcard=endcard, padding=padding)
actual_block_size = _block_size(sep)
if padding and len(blocks) % actual_block_size != 0:
raise OSError(
'Header size ({}) is not a multiple of block '
'size ({}).'.format(
len(blocks) - actual_block_size + BLOCK_SIZE,
BLOCK_SIZE))
if not fileobj.simulateonly:
fileobj.flush()
fileobj.write(blocks.encode('ascii'))
fileobj.flush()
finally:
if close_file:
fileobj.close()
@classmethod
def fromtextfile(cls, fileobj, endcard=False):
"""
Read a header from a simple text file or file-like object.
Equivalent to::
>>> Header.fromfile(fileobj, sep='\\n', endcard=False,
... padding=False)
See Also
--------
fromfile
"""
return cls.fromfile(fileobj, sep='\n', endcard=endcard, padding=False)
@deprecated_renamed_argument('clobber', 'overwrite', '2.0')
def totextfile(self, fileobj, endcard=False, overwrite=False):
"""
Write the header as text to a file or a file-like object.
Equivalent to::
>>> Header.tofile(fileobj, sep='\\n', endcard=False,
... padding=False, overwrite=overwrite)
.. versionchanged:: 1.3
``overwrite`` replaces the deprecated ``clobber`` argument.
See Also
--------
tofile
"""
self.tofile(fileobj, sep='\n', endcard=endcard, padding=False,
overwrite=overwrite)
def clear(self):
"""
Remove all cards from the header.
"""
self._cards = []
self._keyword_indices = collections.defaultdict(list)
self._rvkc_indices = collections.defaultdict(list)
def copy(self, strip=False):
"""
Make a copy of the :class:`Header`.
.. versionchanged:: 1.3
`copy.copy` and `copy.deepcopy` on a `Header` will call this
method.
Parameters
----------
strip : bool, optional
If `True`, strip any headers that are specific to one of the
standard HDU types, so that this header can be used in a different
HDU.
Returns
-------
header
A new :class:`Header` instance.
"""
tmp = self.__class__((copy.copy(card) for card in self._cards))
if strip:
tmp._strip()
return tmp
def __copy__(self):
return self.copy()
def __deepcopy__(self, *args, **kwargs):
return self.copy()
@classmethod
def fromkeys(cls, iterable, value=None):
"""
Similar to :meth:`dict.fromkeys`--creates a new `Header` from an
iterable of keywords and an optional default value.
This method is not likely to be particularly useful for creating real
world FITS headers, but it is useful for testing.
Parameters
----------
iterable
Any iterable that returns strings representing FITS keywords.
value : optional
A default value to assign to each keyword; must be a valid type for
FITS keywords.
Returns
-------
header
A new `Header` instance.
"""
d = cls()
if not isinstance(value, tuple):
value = (value,)
for key in iterable:
d.append((key,) + value)
return d
def get(self, key, default=None):
"""
Similar to :meth:`dict.get`--returns the value associated with keyword
in the header, or a default value if the keyword is not found.
Parameters
----------
key : str
A keyword that may or may not be in the header.
default : optional
A default value to return if the keyword is not found in the
header.
Returns
-------
value
The value associated with the given keyword, or the default value
if the keyword is not in the header.
"""
try:
return self[key]
except (KeyError, IndexError):
return default
def set(self, keyword, value=None, comment=None, before=None, after=None):
"""
Set the value and/or comment and/or position of a specified keyword.
If the keyword does not already exist in the header, a new keyword is
created in the specified position, or appended to the end of the header
if no position is specified.
This method is similar to :meth:`Header.update` prior to Astropy v0.1.
.. note::
It should be noted that ``header.set(keyword, value)`` and
``header.set(keyword, value, comment)`` are equivalent to
``header[keyword] = value`` and
``header[keyword] = (value, comment)`` respectively.
New keywords can also be inserted relative to existing keywords
using, for example::
>>> header.insert('NAXIS1', ('NAXIS', 2, 'Number of axes'))
to insert before an existing keyword, or::
>>> header.insert('NAXIS', ('NAXIS1', 4096), after=True)
to insert after an existing keyword.
The only advantage of using :meth:`Header.set` is that it
easily replaces the old usage of :meth:`Header.update` both
conceptually and in terms of function signature.
Parameters
----------
keyword : str
A header keyword
value : str, optional
The value to set for the given keyword; if None the existing value
is kept, but '' may be used to set a blank value
comment : str, optional
The comment to set for the given keyword; if None the existing
comment is kept, but ``''`` may be used to set a blank comment
before : str, int, optional
Name of the keyword, or index of the `Card` before which this card
should be located in the header. The argument ``before`` takes
precedence over ``after`` if both specified.
after : str, int, optional
Name of the keyword, or index of the `Card` after which this card
should be located in the header.
"""
# Create a temporary card that looks like the one being set; if the
# temporary card turns out to be a RVKC this will make it easier to
# deal with the idiosyncrasies thereof
# Don't try to make a temporary card though if they keyword looks like
# it might be a HIERARCH card or is otherwise invalid--this step is
# only for validating RVKCs.
if (len(keyword) <= KEYWORD_LENGTH and
Card._keywd_FSC_RE.match(keyword) and
keyword not in self._keyword_indices):
new_card = Card(keyword, value, comment)
new_keyword = new_card.keyword
else:
new_keyword = keyword
if (new_keyword not in Card._commentary_keywords and
new_keyword in self):
if comment is None:
comment = self.comments[keyword]
if value is None:
value = self[keyword]
self[keyword] = (value, comment)
if before is not None or after is not None:
card = self._cards[self._cardindex(keyword)]
self._relativeinsert(card, before=before, after=after,
replace=True)
elif before is not None or after is not None:
self._relativeinsert((keyword, value, comment), before=before,
after=after)
else:
self[keyword] = (value, comment)
def items(self):
"""Like :meth:`dict.items`."""
for card in self._cards:
yield (card.keyword, card.value)
def keys(self):
"""
Like :meth:`dict.keys`--iterating directly over the `Header`
instance has the same behavior.
"""
for card in self._cards:
yield card.keyword
def values(self):
"""Like :meth:`dict.values`."""
for card in self._cards:
yield card.value
def pop(self, *args):
"""
Works like :meth:`list.pop` if no arguments or an index argument are
supplied; otherwise works like :meth:`dict.pop`.
"""
if len(args) > 2:
raise TypeError('Header.pop expected at most 2 arguments, got '
'{}'.format(len(args)))
if len(args) == 0:
key = -1
else:
key = args[0]
try:
value = self[key]
except (KeyError, IndexError):
if len(args) == 2:
return args[1]
raise
del self[key]
return value
def popitem(self):
"""Similar to :meth:`dict.popitem`."""
try:
k, v = next(self.items())
except StopIteration:
raise KeyError('Header is empty')
del self[k]
return k, v
def setdefault(self, key, default=None):
"""Similar to :meth:`dict.setdefault`."""
try:
return self[key]
except (KeyError, IndexError):
self[key] = default
return default
def update(self, *args, **kwargs):
"""
Update the Header with new keyword values, updating the values of
existing keywords and appending new keywords otherwise; similar to
`dict.update`.
`update` accepts either a dict-like object or an iterable. In the
former case the keys must be header keywords and the values may be
either scalar values or (value, comment) tuples. In the case of an
iterable the items must be (keyword, value) tuples or (keyword, value,
comment) tuples.
Arbitrary arguments are also accepted, in which case the update() is
called again with the kwargs dict as its only argument. That is,
::
>>> header.update(NAXIS1=100, NAXIS2=100)
is equivalent to::
header.update({'NAXIS1': 100, 'NAXIS2': 100})
.. warning::
As this method works similarly to `dict.update` it is very
different from the ``Header.update()`` method in Astropy v0.1.
Use of the old API was
**deprecated** for a long time and is now removed. Most uses of the
old API can be replaced as follows:
* Replace ::
header.update(keyword, value)
with ::
header[keyword] = value
* Replace ::
header.update(keyword, value, comment=comment)
with ::
header[keyword] = (value, comment)
* Replace ::
header.update(keyword, value, before=before_keyword)
with ::
header.insert(before_keyword, (keyword, value))
* Replace ::
header.update(keyword, value, after=after_keyword)
with ::
header.insert(after_keyword, (keyword, value),
after=True)
See also :meth:`Header.set` which is a new method that provides an
interface similar to the old ``Header.update()`` and may help make
transition a little easier.
"""
if args:
other = args[0]
else:
other = None
def update_from_dict(k, v):
if not isinstance(v, tuple):
card = Card(k, v)
elif 0 < len(v) <= 2:
card = Card(*((k,) + v))
else:
raise ValueError(
'Header update value for key %r is invalid; the '
'value must be either a scalar, a 1-tuple '
'containing the scalar value, or a 2-tuple '
'containing the value and a comment string.' % k)
self._update(card)
if other is None:
pass
elif isinstance(other, Header):
for card in other.cards:
self._update(card)
elif hasattr(other, 'items'):
for k, v in other.items():
update_from_dict(k, v)
elif hasattr(other, 'keys'):
for k in other.keys():
update_from_dict(k, other[k])
else:
for idx, card in enumerate(other):
if isinstance(card, Card):
self._update(card)
elif isinstance(card, tuple) and (1 < len(card) <= 3):
self._update(Card(*card))
else:
raise ValueError(
'Header update sequence item #{} is invalid; '
'the item must either be a 2-tuple containing '
'a keyword and value, or a 3-tuple containing '
'a keyword, value, and comment string.'.format(idx))
if kwargs:
self.update(kwargs)
def append(self, card=None, useblanks=True, bottom=False, end=False):
"""
Appends a new keyword+value card to the end of the Header, similar
to `list.append`.
By default if the last cards in the Header have commentary keywords,
this will append the new keyword before the commentary (unless the new
keyword is also commentary).
Also differs from `list.append` in that it can be called with no
arguments: In this case a blank card is appended to the end of the
Header. In the case all the keyword arguments are ignored.
Parameters
----------
card : str, tuple
A keyword or a (keyword, value, [comment]) tuple representing a
single header card; the comment is optional in which case a
2-tuple may be used
useblanks : bool, optional
If there are blank cards at the end of the Header, replace the
first blank card so that the total number of cards in the Header
does not increase. Otherwise preserve the number of blank cards.
bottom : bool, optional
If True, instead of appending after the last non-commentary card,
append after the last non-blank card.
end : bool, optional
If True, ignore the useblanks and bottom options, and append at the
very end of the Header.
"""
if isinstance(card, str):
card = Card(card)
elif isinstance(card, tuple):
card = Card(*card)
elif card is None:
card = Card()
elif not isinstance(card, Card):
raise ValueError(
'The value appended to a Header must be either a keyword or '
'(keyword, value, [comment]) tuple; got: {!r}'.format(card))
if not end and card.is_blank:
# Blank cards should always just be appended to the end
end = True
if end:
self._cards.append(card)
idx = len(self._cards) - 1
else:
idx = len(self._cards) - 1
while idx >= 0 and self._cards[idx].is_blank:
idx -= 1
if not bottom and card.keyword not in Card._commentary_keywords:
while (idx >= 0 and
self._cards[idx].keyword in Card._commentary_keywords):
idx -= 1
idx += 1
self._cards.insert(idx, card)
self._updateindices(idx)
keyword = Card.normalize_keyword(card.keyword)
self._keyword_indices[keyword].append(idx)
if card.field_specifier is not None:
self._rvkc_indices[card.rawkeyword].append(idx)
if not end:
# If the appended card was a commentary card, and it was appended
# before existing cards with the same keyword, the indices for
# cards with that keyword may have changed
if not bottom and card.keyword in Card._commentary_keywords:
self._keyword_indices[keyword].sort()
# Finally, if useblanks, delete a blank cards from the end
if useblanks and self._countblanks():
# Don't do this unless there is at least one blanks at the end
# of the header; we need to convert the card to its string
# image to see how long it is. In the vast majority of cases
# this will just be 80 (Card.length) but it may be longer for
# CONTINUE cards
self._useblanks(len(str(card)) // Card.length)
self._modified = True
def extend(self, cards, strip=True, unique=False, update=False,
update_first=False, useblanks=True, bottom=False, end=False):
"""
Appends multiple keyword+value cards to the end of the header, similar
to `list.extend`.
Parameters
----------
cards : iterable
An iterable of (keyword, value, [comment]) tuples; see
`Header.append`.
strip : bool, optional
Remove any keywords that have meaning only to specific types of
HDUs, so that only more general keywords are added from extension
Header or Card list (default: `True`).
unique : bool, optional
If `True`, ensures that no duplicate keywords are appended;
keywords already in this header are simply discarded. The
exception is commentary keywords (COMMENT, HISTORY, etc.): they are
only treated as duplicates if their values match.
update : bool, optional
If `True`, update the current header with the values and comments
from duplicate keywords in the input header. This supersedes the
``unique`` argument. Commentary keywords are treated the same as
if ``unique=True``.
update_first : bool, optional
If the first keyword in the header is 'SIMPLE', and the first
keyword in the input header is 'XTENSION', the 'SIMPLE' keyword is
replaced by the 'XTENSION' keyword. Likewise if the first keyword
in the header is 'XTENSION' and the first keyword in the input
header is 'SIMPLE', the 'XTENSION' keyword is replaced by the
'SIMPLE' keyword. This behavior is otherwise dumb as to whether or
not the resulting header is a valid primary or extension header.
This is mostly provided to support backwards compatibility with the
old ``Header.fromTxtFile`` method, and only applies if
``update=True``.
useblanks, bottom, end : bool, optional
These arguments are passed to :meth:`Header.append` while appending
new cards to the header.
"""
temp = self.__class__(cards)
if strip:
temp._strip()
if len(self):
first = self._cards[0].keyword
else:
first = None
# We don't immediately modify the header, because first we need to sift
# out any duplicates in the new header prior to adding them to the
# existing header, but while *allowing* duplicates from the header
# being extended from (see ticket #156)
extend_cards = []
for idx, card in enumerate(temp.cards):
keyword = card.keyword
if keyword not in Card._commentary_keywords:
if unique and not update and keyword in self:
continue
elif update:
if idx == 0 and update_first:
# Dumbly update the first keyword to either SIMPLE or
# XTENSION as the case may be, as was in the case in
# Header.fromTxtFile
if ((keyword == 'SIMPLE' and first == 'XTENSION') or
(keyword == 'XTENSION' and first == 'SIMPLE')):
del self[0]
self.insert(0, card)
else:
self[keyword] = (card.value, card.comment)
elif keyword in self:
self[keyword] = (card.value, card.comment)
else:
extend_cards.append(card)
else:
extend_cards.append(card)
else:
if (unique or update) and keyword in self:
if card.is_blank:
extend_cards.append(card)
continue
for value in self[keyword]:
if value == card.value:
break
else:
extend_cards.append(card)
else:
extend_cards.append(card)
for card in extend_cards:
self.append(card, useblanks=useblanks, bottom=bottom, end=end)
def count(self, keyword):
"""
Returns the count of the given keyword in the header, similar to
`list.count` if the Header object is treated as a list of keywords.
Parameters
----------
keyword : str
The keyword to count instances of in the header
"""
keyword = Card.normalize_keyword(keyword)
# We have to look before we leap, since otherwise _keyword_indices,
# being a defaultdict, will create an entry for the nonexistent keyword
if keyword not in self._keyword_indices:
raise KeyError(f"Keyword {keyword!r} not found.")
return len(self._keyword_indices[keyword])
def index(self, keyword, start=None, stop=None):
"""
Returns the index if the first instance of the given keyword in the
header, similar to `list.index` if the Header object is treated as a
list of keywords.
Parameters
----------
keyword : str
The keyword to look up in the list of all keywords in the header
start : int, optional
The lower bound for the index
stop : int, optional
The upper bound for the index
"""
if start is None:
start = 0
if stop is None:
stop = len(self._cards)
if stop < start:
step = -1
else:
step = 1
norm_keyword = Card.normalize_keyword(keyword)
for idx in range(start, stop, step):
if self._cards[idx].keyword.upper() == norm_keyword:
return idx
else:
raise ValueError('The keyword {!r} is not in the '
' header.'.format(keyword))
def insert(self, key, card, useblanks=True, after=False):
"""
Inserts a new keyword+value card into the Header at a given location,
similar to `list.insert`.
Parameters
----------
key : int, str, or tuple
The index into the list of header keywords before which the
new keyword should be inserted, or the name of a keyword before
which the new keyword should be inserted. Can also accept a
(keyword, index) tuple for inserting around duplicate keywords.
card : str, tuple
A keyword or a (keyword, value, [comment]) tuple; see
`Header.append`
useblanks : bool, optional
If there are blank cards at the end of the Header, replace the
first blank card so that the total number of cards in the Header
does not increase. Otherwise preserve the number of blank cards.
after : bool, optional
If set to `True`, insert *after* the specified index or keyword,
rather than before it. Defaults to `False`.
"""
if not isinstance(key, int):
# Don't pass through ints to _cardindex because it will not take
# kindly to indices outside the existing number of cards in the
# header, which insert needs to be able to support (for example
# when inserting into empty headers)
idx = self._cardindex(key)
else:
idx = key
if after:
if idx == -1:
idx = len(self._cards)
else:
idx += 1
if idx >= len(self._cards):
# This is just an append (Though it must be an append absolutely to
# the bottom, ignoring blanks, etc.--the point of the insert method
# is that you get exactly what you asked for with no surprises)
self.append(card, end=True)
return
if isinstance(card, str):
card = Card(card)
elif isinstance(card, tuple):
card = Card(*card)
elif not isinstance(card, Card):
raise ValueError(
'The value inserted into a Header must be either a keyword or '
'(keyword, value, [comment]) tuple; got: {!r}'.format(card))
self._cards.insert(idx, card)
keyword = card.keyword
# If idx was < 0, determine the actual index according to the rules
# used by list.insert()
if idx < 0:
idx += len(self._cards) - 1
if idx < 0:
idx = 0
# All the keyword indices above the insertion point must be updated
self._updateindices(idx)
keyword = Card.normalize_keyword(keyword)
self._keyword_indices[keyword].append(idx)
count = len(self._keyword_indices[keyword])
if count > 1:
# There were already keywords with this same name
if keyword not in Card._commentary_keywords:
warnings.warn(
'A {!r} keyword already exists in this header. Inserting '
'duplicate keyword.'.format(keyword), AstropyUserWarning)
self._keyword_indices[keyword].sort()
if card.field_specifier is not None:
# Update the index of RVKC as well
rvkc_indices = self._rvkc_indices[card.rawkeyword]
rvkc_indices.append(idx)
rvkc_indices.sort()
if useblanks:
self._useblanks(len(str(card)) // Card.length)
self._modified = True
def remove(self, keyword, ignore_missing=False, remove_all=False):
"""
Removes the first instance of the given keyword from the header similar
to `list.remove` if the Header object is treated as a list of keywords.
Parameters
----------
keyword : str
The keyword of which to remove the first instance in the header.
ignore_missing : bool, optional
When True, ignores missing keywords. Otherwise, if the keyword
is not present in the header a KeyError is raised.
remove_all : bool, optional
When True, all instances of keyword will be removed.
Otherwise only the first instance of the given keyword is removed.
"""
keyword = Card.normalize_keyword(keyword)
if keyword in self._keyword_indices:
del self[self._keyword_indices[keyword][0]]
if remove_all:
while keyword in self._keyword_indices:
del self[self._keyword_indices[keyword][0]]
elif not ignore_missing:
raise KeyError(f"Keyword '{keyword}' not found.")
def rename_keyword(self, oldkeyword, newkeyword, force=False):
"""
Rename a card's keyword in the header.
Parameters
----------
oldkeyword : str or int
Old keyword or card index
newkeyword : str
New keyword
force : bool, optional
When `True`, if the new keyword already exists in the header, force
the creation of a duplicate keyword. Otherwise a
`ValueError` is raised.
"""
oldkeyword = Card.normalize_keyword(oldkeyword)
newkeyword = Card.normalize_keyword(newkeyword)
if newkeyword == 'CONTINUE':
raise ValueError('Can not rename to CONTINUE')
if (newkeyword in Card._commentary_keywords or
oldkeyword in Card._commentary_keywords):
if not (newkeyword in Card._commentary_keywords and
oldkeyword in Card._commentary_keywords):
raise ValueError('Regular and commentary keys can not be '
'renamed to each other.')
elif not force and newkeyword in self:
raise ValueError('Intended keyword {} already exists in header.'
.format(newkeyword))
idx = self.index(oldkeyword)
card = self._cards[idx]
del self[idx]
self.insert(idx, (newkeyword, card.value, card.comment))
def add_history(self, value, before=None, after=None):
"""
Add a ``HISTORY`` card.
Parameters
----------
value : str
History text to be added.
before : str or int, optional
Same as in `Header.update`
after : str or int, optional
Same as in `Header.update`
"""
self._add_commentary('HISTORY', value, before=before, after=after)
def add_comment(self, value, before=None, after=None):
"""
Add a ``COMMENT`` card.
Parameters
----------
value : str
Text to be added.
before : str or int, optional
Same as in `Header.update`
after : str or int, optional
Same as in `Header.update`
"""
self._add_commentary('COMMENT', value, before=before, after=after)
def add_blank(self, value='', before=None, after=None):
"""
Add a blank card.
Parameters
----------
value : str, optional
Text to be added.
before : str or int, optional
Same as in `Header.update`
after : str or int, optional
Same as in `Header.update`
"""
self._add_commentary('', value, before=before, after=after)
def _update(self, card):
"""
The real update code. If keyword already exists, its value and/or
comment will be updated. Otherwise a new card will be appended.
This will not create a duplicate keyword except in the case of
commentary cards. The only other way to force creation of a duplicate
is to use the insert(), append(), or extend() methods.
"""
keyword, value, comment = card
# Lookups for existing/known keywords are case-insensitive
keyword = keyword.upper()
if keyword.startswith('HIERARCH '):
keyword = keyword[9:]
if (keyword not in Card._commentary_keywords and
keyword in self._keyword_indices):
# Easy; just update the value/comment
idx = self._keyword_indices[keyword][0]
existing_card = self._cards[idx]
existing_card.value = value
if comment is not None:
# '' should be used to explicitly blank a comment
existing_card.comment = comment
if existing_card._modified:
self._modified = True
elif keyword in Card._commentary_keywords:
cards = self._splitcommentary(keyword, value)
if keyword in self._keyword_indices:
# Append after the last keyword of the same type
idx = self.index(keyword, start=len(self) - 1, stop=-1)
isblank = not (keyword or value or comment)
for c in reversed(cards):
self.insert(idx + 1, c, useblanks=(not isblank))
else:
for c in cards:
self.append(c, bottom=True)
else:
# A new keyword! self.append() will handle updating _modified
self.append(card)
def _cardindex(self, key):
"""Returns an index into the ._cards list given a valid lookup key."""
# This used to just set key = (key, 0) and then go on to act as if the
# user passed in a tuple, but it's much more common to just be given a
# string as the key, so optimize more for that case
if isinstance(key, str):
keyword = key
n = 0
elif isinstance(key, int):
# If < 0, determine the actual index
if key < 0:
key += len(self._cards)
if key < 0 or key >= len(self._cards):
raise IndexError('Header index out of range.')
return key
elif isinstance(key, slice):
return key
elif isinstance(key, tuple):
if (len(key) != 2 or not isinstance(key[0], str) or
not isinstance(key[1], int)):
raise ValueError(
'Tuple indices must be 2-tuples consisting of a '
'keyword string and an integer index.')
keyword, n = key
else:
raise ValueError(
'Header indices must be either a string, a 2-tuple, or '
'an integer.')
keyword = Card.normalize_keyword(keyword)
# Returns the index into _cards for the n-th card with the given
# keyword (where n is 0-based)
indices = self._keyword_indices.get(keyword, None)
if keyword and not indices:
if len(keyword) > KEYWORD_LENGTH or '.' in keyword:
raise KeyError(f"Keyword {keyword!r} not found.")
else:
# Maybe it's a RVKC?
indices = self._rvkc_indices.get(keyword, None)
if not indices:
raise KeyError(f"Keyword {keyword!r} not found.")
try:
return indices[n]
except IndexError:
raise IndexError('There are only {} {!r} cards in the '
'header.'.format(len(indices), keyword))
def _keyword_from_index(self, idx):
"""
Given an integer index, return the (keyword, repeat) tuple that index
refers to. For most keywords the repeat will always be zero, but it
may be greater than zero for keywords that are duplicated (especially
commentary keywords).
In a sense this is the inverse of self.index, except that it also
supports duplicates.
"""
if idx < 0:
idx += len(self._cards)
keyword = self._cards[idx].keyword
keyword = Card.normalize_keyword(keyword)
repeat = self._keyword_indices[keyword].index(idx)
return keyword, repeat
def _relativeinsert(self, card, before=None, after=None, replace=False):
"""
Inserts a new card before or after an existing card; used to
implement support for the legacy before/after keyword arguments to
Header.update().
If replace=True, move an existing card with the same keyword.
"""
if before is None:
insertionkey = after
else:
insertionkey = before
def get_insertion_idx():
if not (isinstance(insertionkey, int) and
insertionkey >= len(self._cards)):
idx = self._cardindex(insertionkey)
else:
idx = insertionkey
if before is None:
idx += 1
return idx
if replace:
# The card presumably already exists somewhere in the header.
# Check whether or not we actually have to move it; if it does need
# to be moved we just delete it and then it will be reinserted
# below
old_idx = self._cardindex(card.keyword)
insertion_idx = get_insertion_idx()
if (insertion_idx >= len(self._cards) and
old_idx == len(self._cards) - 1):
# The card would be appended to the end, but it's already at
# the end
return
if before is not None:
if old_idx == insertion_idx - 1:
return
elif after is not None and old_idx == insertion_idx:
return
del self[old_idx]
# Even if replace=True, the insertion idx may have changed since the
# old card was deleted
idx = get_insertion_idx()
if card[0] in Card._commentary_keywords:
cards = reversed(self._splitcommentary(card[0], card[1]))
else:
cards = [card]
for c in cards:
self.insert(idx, c)
def _updateindices(self, idx, increment=True):
"""
For all cards with index above idx, increment or decrement its index
value in the keyword_indices dict.
"""
if idx > len(self._cards):
# Save us some effort
return
increment = 1 if increment else -1
for index_sets in (self._keyword_indices, self._rvkc_indices):
for indices in index_sets.values():
for jdx, keyword_index in enumerate(indices):
if keyword_index >= idx:
indices[jdx] += increment
def _countblanks(self):
"""Returns the number of blank cards at the end of the Header."""
for idx in range(1, len(self._cards)):
if not self._cards[-idx].is_blank:
return idx - 1
return 0
def _useblanks(self, count):
for _ in range(count):
if self._cards[-1].is_blank:
del self[-1]
else:
break
def _haswildcard(self, keyword):
"""Return `True` if the input keyword contains a wildcard pattern."""
return (isinstance(keyword, str) and
(keyword.endswith('...') or '*' in keyword or '?' in keyword))
def _wildcardmatch(self, pattern):
"""
Returns a list of indices of the cards matching the given wildcard
pattern.
* '*' matches 0 or more characters
* '?' matches a single character
* '...' matches 0 or more of any non-whitespace character
"""
pattern = pattern.replace('*', r'.*').replace('?', r'.')
pattern = pattern.replace('...', r'\S*') + '$'
pattern_re = re.compile(pattern, re.I)
return [idx for idx, card in enumerate(self._cards)
if pattern_re.match(card.keyword)]
def _set_slice(self, key, value, target):
"""
Used to implement Header.__setitem__ and CardAccessor.__setitem__.
"""
if isinstance(key, slice) or self._haswildcard(key):
if isinstance(key, slice):
indices = range(*key.indices(len(target)))
else:
indices = self._wildcardmatch(key)
if isinstance(value, str) or not isiterable(value):
value = itertools.repeat(value, len(indices))
for idx, val in zip(indices, value):
target[idx] = val
return True
return False
def _splitcommentary(self, keyword, value):
"""
Given a commentary keyword and value, returns a list of the one or more
cards needed to represent the full value. This is primarily used to
create the multiple commentary cards needed to represent a long value
that won't fit into a single commentary card.
"""
# The maximum value in each card can be the maximum card length minus
# the maximum key length (which can include spaces if they key length
# less than 8
maxlen = Card.length - KEYWORD_LENGTH
valuestr = str(value)
if len(valuestr) <= maxlen:
# The value can fit in a single card
cards = [Card(keyword, value)]
else:
# The value must be split across multiple consecutive commentary
# cards
idx = 0
cards = []
while idx < len(valuestr):
cards.append(Card(keyword, valuestr[idx:idx + maxlen]))
idx += maxlen
return cards
def _strip(self):
"""
Strip cards specific to a certain kind of header.
Strip cards like ``SIMPLE``, ``BITPIX``, etc. so the rest of
the header can be used to reconstruct another kind of header.
"""
# TODO: Previously this only deleted some cards specific to an HDU if
# _hdutype matched that type. But it seemed simple enough to just
# delete all desired cards anyways, and just ignore the KeyErrors if
# they don't exist.
# However, it might be desirable to make this extendable somehow--have
# a way for HDU classes to specify some headers that are specific only
# to that type, and should be removed otherwise.
if 'NAXIS' in self:
naxis = self['NAXIS']
else:
naxis = 0
if 'TFIELDS' in self:
tfields = self['TFIELDS']
else:
tfields = 0
for idx in range(naxis):
try:
del self['NAXIS' + str(idx + 1)]
except KeyError:
pass
for name in ('TFORM', 'TSCAL', 'TZERO', 'TNULL', 'TTYPE',
'TUNIT', 'TDISP', 'TDIM', 'THEAP', 'TBCOL'):
for idx in range(tfields):
try:
del self[name + str(idx + 1)]
except KeyError:
pass
for name in ('SIMPLE', 'XTENSION', 'BITPIX', 'NAXIS', 'EXTEND',
'PCOUNT', 'GCOUNT', 'GROUPS', 'BSCALE', 'BZERO',
'TFIELDS'):
try:
del self[name]
except KeyError:
pass
def _add_commentary(self, key, value, before=None, after=None):
"""
Add a commentary card.
If ``before`` and ``after`` are `None`, add to the last occurrence
of cards of the same name (except blank card). If there is no
card (or blank card), append at the end.
"""
if before is not None or after is not None:
self._relativeinsert((key, value), before=before,
after=after)
else:
self[key] = value
collections.abc.MutableSequence.register(Header)
collections.abc.MutableMapping.register(Header)
class _DelayedHeader:
"""
Descriptor used to create the Header object from the header string that
was stored in HDU._header_str when parsing the file.
"""
def __get__(self, obj, owner=None):
try:
return obj.__dict__['_header']
except KeyError:
if obj._header_str is not None:
hdr = Header.fromstring(obj._header_str)
obj._header_str = None
else:
raise AttributeError("'{}' object has no attribute '_header'"
.format(obj.__class__.__name__))
obj.__dict__['_header'] = hdr
return hdr
def __set__(self, obj, val):
obj.__dict__['_header'] = val
def __delete__(self, obj):
del obj.__dict__['_header']
class _BasicHeaderCards:
"""
This class allows to access cards with the _BasicHeader.cards attribute.
This is needed because during the HDU class detection, some HDUs uses
the .cards interface. Cards cannot be modified here as the _BasicHeader
object will be deleted once the HDU object is created.
"""
def __init__(self, header):
self.header = header
def __getitem__(self, key):
# .cards is a list of cards, so key here is an integer.
# get the keyword name from its index.
key = self.header._keys[key]
# then we get the card from the _BasicHeader._cards list, or parse it
# if needed.
try:
return self.header._cards[key]
except KeyError:
cardstr = self.header._raw_cards[key]
card = Card.fromstring(cardstr)
self.header._cards[key] = card
return card
class _BasicHeader(collections.abc.Mapping):
"""This class provides a fast header parsing, without all the additional
features of the Header class. Here only standard keywords are parsed, no
support for CONTINUE, HIERARCH, COMMENT, HISTORY, or rvkc.
The raw card images are stored and parsed only if needed. The idea is that
to create the HDU objects, only a small subset of standard cards is needed.
Once a card is parsed, which is deferred to the Card class, the Card object
is kept in a cache. This is useful because a small subset of cards is used
a lot in the HDU creation process (NAXIS, XTENSION, ...).
"""
def __init__(self, cards):
# dict of (keywords, card images)
self._raw_cards = cards
self._keys = list(cards.keys())
# dict of (keyword, Card object) storing the parsed cards
self._cards = {}
# the _BasicHeaderCards object allows to access Card objects from
# keyword indices
self.cards = _BasicHeaderCards(self)
self._modified = False
def __getitem__(self, key):
if isinstance(key, int):
key = self._keys[key]
try:
return self._cards[key].value
except KeyError:
# parse the Card and store it
cardstr = self._raw_cards[key]
self._cards[key] = card = Card.fromstring(cardstr)
return card.value
def __len__(self):
return len(self._raw_cards)
def __iter__(self):
return iter(self._raw_cards)
def index(self, keyword):
return self._keys.index(keyword)
@classmethod
def fromfile(cls, fileobj):
"""The main method to parse a FITS header from a file. The parsing is
done with the parse_header function implemented in Cython."""
close_file = False
if isinstance(fileobj, str):
fileobj = open(fileobj, 'rb')
close_file = True
try:
header_str, cards = parse_header(fileobj)
_check_padding(header_str, BLOCK_SIZE, False)
return header_str, cls(cards)
finally:
if close_file:
fileobj.close()
class _CardAccessor:
"""
This is a generic class for wrapping a Header in such a way that you can
use the header's slice/filtering capabilities to return a subset of cards
and do something with them.
This is sort of the opposite notion of the old CardList class--whereas
Header used to use CardList to get lists of cards, this uses Header to get
lists of cards.
"""
# TODO: Consider giving this dict/list methods like Header itself
def __init__(self, header):
self._header = header
def __repr__(self):
return '\n'.join(repr(c) for c in self._header._cards)
def __len__(self):
return len(self._header._cards)
def __iter__(self):
return iter(self._header._cards)
def __eq__(self, other):
# If the `other` item is a scalar we will still treat it as equal if
# this _CardAccessor only contains one item
if not isiterable(other) or isinstance(other, str):
if len(self) == 1:
other = [other]
else:
return False
for a, b in itertools.zip_longest(self, other):
if a != b:
return False
else:
return True
def __ne__(self, other):
return not (self == other)
def __getitem__(self, item):
if isinstance(item, slice) or self._header._haswildcard(item):
return self.__class__(self._header[item])
idx = self._header._cardindex(item)
return self._header._cards[idx]
def _setslice(self, item, value):
"""
Helper for implementing __setitem__ on _CardAccessor subclasses; slices
should always be handled in this same way.
"""
if isinstance(item, slice) or self._header._haswildcard(item):
if isinstance(item, slice):
indices = range(*item.indices(len(self)))
else:
indices = self._header._wildcardmatch(item)
if isinstance(value, str) or not isiterable(value):
value = itertools.repeat(value, len(indices))
for idx, val in zip(indices, value):
self[idx] = val
return True
return False
collections.abc.Mapping.register(_CardAccessor)
collections.abc.Sequence.register(_CardAccessor)
class _HeaderComments(_CardAccessor):
"""
A class used internally by the Header class for the Header.comments
attribute access.
This object can be used to display all the keyword comments in the Header,
or look up the comments on specific keywords. It allows all the same forms
of keyword lookup as the Header class itself, but returns comments instead
of values.
"""
def __iter__(self):
for card in self._header._cards:
yield card.comment
def __repr__(self):
"""Returns a simple list of all keywords and their comments."""
keyword_length = KEYWORD_LENGTH
for card in self._header._cards:
keyword_length = max(keyword_length, len(card.keyword))
return '\n'.join('{:>{len}} {}'.format(c.keyword, c.comment,
len=keyword_length)
for c in self._header._cards)
def __getitem__(self, item):
"""
Slices and filter strings return a new _HeaderComments containing the
returned cards. Otherwise the comment of a single card is returned.
"""
item = super().__getitem__(item)
if isinstance(item, _HeaderComments):
# The item key was a slice
return item
return item.comment
def __setitem__(self, item, comment):
"""
Set/update the comment on specified card or cards.
Slice/filter updates work similarly to how Header.__setitem__ works.
"""
if self._header._set_slice(item, comment, self):
return
# In this case, key/index errors should be raised; don't update
# comments of nonexistent cards
idx = self._header._cardindex(item)
value = self._header[idx]
self._header[idx] = (value, comment)
class _HeaderCommentaryCards(_CardAccessor):
"""
This is used to return a list-like sequence over all the values in the
header for a given commentary keyword, such as HISTORY.
"""
def __init__(self, header, keyword=''):
super().__init__(header)
self._keyword = keyword
self._count = self._header.count(self._keyword)
self._indices = slice(self._count).indices(self._count)
# __len__ and __iter__ need to be overridden from the base class due to the
# different approach this class has to take for slicing
def __len__(self):
return len(range(*self._indices))
def __iter__(self):
for idx in range(*self._indices):
yield self._header[(self._keyword, idx)]
def __repr__(self):
return '\n'.join(self)
def __getitem__(self, idx):
if isinstance(idx, slice):
n = self.__class__(self._header, self._keyword)
n._indices = idx.indices(self._count)
return n
elif not isinstance(idx, int):
raise ValueError(f'{self._keyword} index must be an integer')
idx = list(range(*self._indices))[idx]
return self._header[(self._keyword, idx)]
def __setitem__(self, item, value):
"""
Set the value of a specified commentary card or cards.
Slice/filter updates work similarly to how Header.__setitem__ works.
"""
if self._header._set_slice(item, value, self):
return
# In this case, key/index errors should be raised; don't update
# comments of nonexistent cards
self._header[(self._keyword, item)] = value
def _block_size(sep):
"""
Determine the size of a FITS header block if a non-blank separator is used
between cards.
"""
return BLOCK_SIZE + (len(sep) * (BLOCK_SIZE // Card.length - 1))
def _pad_length(stringlen):
"""Bytes needed to pad the input stringlen to the next FITS block."""
return (BLOCK_SIZE - (stringlen % BLOCK_SIZE)) % BLOCK_SIZE
def _check_padding(header_str, block_size, is_eof, check_block_size=True):
# Strip any zero-padding (see ticket #106)
if header_str and header_str[-1] == '\0':
if is_eof and header_str.strip('\0') == '':
# TODO: Pass this warning to validation framework
warnings.warn(
'Unexpected extra padding at the end of the file. This '
'padding may not be preserved when saving changes.',
AstropyUserWarning)
raise EOFError()
else:
# Replace the illegal null bytes with spaces as required by
# the FITS standard, and issue a nasty warning
# TODO: Pass this warning to validation framework
warnings.warn(
'Header block contains null bytes instead of spaces for '
'padding, and is not FITS-compliant. Nulls may be '
'replaced with spaces upon writing.', AstropyUserWarning)
header_str.replace('\0', ' ')
if check_block_size and (len(header_str) % block_size) != 0:
# This error message ignores the length of the separator for
# now, but maybe it shouldn't?
actual_len = len(header_str) - block_size + BLOCK_SIZE
# TODO: Pass this error to validation framework
raise ValueError('Header size is not multiple of {}: {}'
.format(BLOCK_SIZE, actual_len))
|
# Licensed under a 3-clause BSD style license - see LICENSE.rst
import pytest
from numpy.testing import assert_equal, assert_allclose
from astropy import units as u
from astropy.time import Time, TimeDelta
from astropy.utils.data import get_pkg_data_filename
from astropy.timeseries.periodograms import BoxLeastSquares, LombScargle
from astropy.timeseries.binned import BinnedTimeSeries
from astropy.tests.helper import assert_quantity_allclose
CSV_FILE = get_pkg_data_filename('data/binned.csv')
def test_empty_initialization():
ts = BinnedTimeSeries()
ts['time_bin_start'] = Time([1, 2, 3], format='mjd')
def test_empty_initialization_invalid():
# Make sure things crash when the first column added is not a time column
ts = BinnedTimeSeries()
with pytest.raises(ValueError) as exc:
ts['flux'] = [1, 2, 3]
assert exc.value.args[0] == ("BinnedTimeSeries object is invalid - expected "
"'time_bin_start' as the first column but found 'flux'")
def test_initialization_time_bin_invalid():
# Make sure things crash when time_bin_* is passed incorrectly.
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(data=[[1, 4, 3]])
assert exc.value.args[0] == ("'time_bin_start' has not been specified")
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(time_bin_start='2016-03-22T12:30:31', data=[[1, 4, 3]])
assert exc.value.args[0] == ("Either 'time_bin_size' or 'time_bin_end' should be specified")
def test_initialization_time_bin_both():
# Make sure things crash when time_bin_* is passed twice.
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(data={"time_bin_start": ["2016-03-22T12:30:31"]},
time_bin_start="2016-03-22T12:30:31")
assert exc.value.args[0] == ("'time_bin_start' has been given both in the table "
"and as a keyword argument")
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(data={"time_bin_size": ["2016-03-22T12:30:31"]},
time_bin_size=[1]*u.s)
assert exc.value.args[0] == ("'time_bin_size' has been given both in the table "
"and as a keyword argument")
def test_initialization_time_bin_size():
# Make sure things crash when time_bin_size has no units
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(data={"time": ["2016-03-22T12:30:31"]},
time_bin_start="2016-03-22T12:30:31",
time_bin_size=1)
assert exc.value.args[0] == ("'time_bin_size' should be a Quantity or a TimeDelta")
# TimeDelta for time_bin_size
ts = BinnedTimeSeries(data={"time": ["2016-03-22T12:30:31"]},
time_bin_start="2016-03-22T12:30:31",
time_bin_size=TimeDelta(1))
assert isinstance(ts.time_bin_size, u.quantity.Quantity)
def test_initialization_time_bin_start_scalar():
# Make sure things crash when time_bin_start is a scalar with no time_bin_size
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(data={"time": ["2016-03-22T12:30:31"]},
time_bin_start=Time(1, format='mjd'),
time_bin_end=Time(1, format='mjd'))
assert exc.value.args[0] == ("'time_bin_start' is scalar, so 'time_bin_size' is required")
def test_initialization_n_bins():
# Make sure things crash with incorrect n_bins
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(data={"time": ["2016-03-22T12:30:31"]},
time_bin_start=Time(1, format='mjd'),
time_bin_size=1*u.s,
time_bin_end=Time(1, format='mjd'),
n_bins=10)
assert exc.value.args[0] == ("'n_bins' has been given and it is not the "
"same length as the input data.")
def test_initialization_non_scalar_time():
# Make sure things crash with incorrect size of time_bin_start
with pytest.raises(ValueError) as exc:
BinnedTimeSeries(data={"time": ["2016-03-22T12:30:31"]},
time_bin_start=["2016-03-22T12:30:31", "2016-03-22T12:30:32"],
time_bin_size=1*u.s,
time_bin_end=Time(1, format='mjd'))
assert exc.value.args[0] == ("Length of 'time_bin_start' (2) should match table length (1)")
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(data={"time": ["2016-03-22T12:30:31"]},
time_bin_start=["2016-03-22T12:30:31"],
time_bin_size=None,
time_bin_end=None)
assert exc.value.args[0] == ("Either 'time_bin_size' or 'time_bin_end' should be specified")
def test_even_contiguous():
# Initialize a ``BinnedTimeSeries`` with even contiguous bins by specifying
# the bin width:
ts = BinnedTimeSeries(time_bin_start='2016-03-22T12:30:31',
time_bin_size=3 * u.s, data=[[1, 4, 3]])
assert_equal(ts.time_bin_start.isot, ['2016-03-22T12:30:31.000',
'2016-03-22T12:30:34.000',
'2016-03-22T12:30:37.000'])
assert_equal(ts.time_bin_center.isot, ['2016-03-22T12:30:32.500',
'2016-03-22T12:30:35.500',
'2016-03-22T12:30:38.500'])
assert_equal(ts.time_bin_end.isot, ['2016-03-22T12:30:34.000',
'2016-03-22T12:30:37.000',
'2016-03-22T12:30:40.000'])
def test_uneven_contiguous():
# Initialize a ``BinnedTimeSeries`` with uneven contiguous bins by giving an
# end time:
ts = BinnedTimeSeries(time_bin_start=['2016-03-22T12:30:31',
'2016-03-22T12:30:32',
'2016-03-22T12:30:40'],
time_bin_end='2016-03-22T12:30:55',
data=[[1, 4, 3]])
assert_equal(ts.time_bin_start.isot, ['2016-03-22T12:30:31.000',
'2016-03-22T12:30:32.000',
'2016-03-22T12:30:40.000'])
assert_equal(ts.time_bin_center.isot, ['2016-03-22T12:30:31.500',
'2016-03-22T12:30:36.000',
'2016-03-22T12:30:47.500'])
assert_equal(ts.time_bin_end.isot, ['2016-03-22T12:30:32.000',
'2016-03-22T12:30:40.000',
'2016-03-22T12:30:55.000'])
def test_uneven_non_contiguous():
# Initialize a ``BinnedTimeSeries`` with uneven non-contiguous bins with
# lists of start times, bin sizes and data:
ts = BinnedTimeSeries(time_bin_start=['2016-03-22T12:30:31',
'2016-03-22T12:30:38',
'2016-03-22T12:34:40'],
time_bin_size=[5, 100, 2]*u.s,
data=[[1, 4, 3]])
assert_equal(ts.time_bin_start.isot, ['2016-03-22T12:30:31.000',
'2016-03-22T12:30:38.000',
'2016-03-22T12:34:40.000'])
assert_equal(ts.time_bin_center.isot, ['2016-03-22T12:30:33.500',
'2016-03-22T12:31:28.000',
'2016-03-22T12:34:41.000'])
assert_equal(ts.time_bin_end.isot, ['2016-03-22T12:30:36.000',
'2016-03-22T12:32:18.000',
'2016-03-22T12:34:42.000'])
def test_uneven_non_contiguous_full():
# Initialize a ``BinnedTimeSeries`` with uneven non-contiguous bins by
# specifying the start and end times for the bins:
ts = BinnedTimeSeries(time_bin_start=['2016-03-22T12:30:31',
'2016-03-22T12:30:33',
'2016-03-22T12:30:40'],
time_bin_end=['2016-03-22T12:30:32',
'2016-03-22T12:30:35',
'2016-03-22T12:30:41'],
data=[[1, 4, 3]])
assert_equal(ts.time_bin_start.isot, ['2016-03-22T12:30:31.000',
'2016-03-22T12:30:33.000',
'2016-03-22T12:30:40.000'])
assert_equal(ts.time_bin_center.isot, ['2016-03-22T12:30:31.500',
'2016-03-22T12:30:34.000',
'2016-03-22T12:30:40.500'])
assert_equal(ts.time_bin_end.isot, ['2016-03-22T12:30:32.000',
'2016-03-22T12:30:35.000',
'2016-03-22T12:30:41.000'])
def test_read_empty():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, format='csv')
assert exc.value.args[0] == '``time_bin_start_column`` should be provided since the default Table readers are being used.'
def test_read_no_size_end():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start', format='csv')
assert exc.value.args[0] == 'Either `time_bin_end_column` or `time_bin_size_column` should be provided.'
def test_read_both_extra_bins():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start', time_bin_end_column='END', time_bin_size_column='bin_size', format='csv')
assert exc.value.args[0] == "Cannot specify both `time_bin_end_column` and `time_bin_size_column`."
def test_read_size_no_unit():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start', time_bin_size_column='bin_size', format='csv')
assert exc.value.args[0] == "The bin size unit should be specified as an astropy Unit using ``time_bin_size_unit``."
def test_read_start_time_missing():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='abc', time_bin_size_column='bin_size', time_bin_size_unit=u.second, format='csv')
assert exc.value.args[0] == "Bin start time column 'abc' not found in the input data."
def test_read_end_time_missing():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start', time_bin_end_column="missing", format='csv')
assert exc.value.args[0] == "Bin end time column 'missing' not found in the input data."
def test_read_size_missing():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start', time_bin_size_column="missing", time_bin_size_unit=u.second, format='csv')
assert exc.value.args[0] == "Bin size column 'missing' not found in the input data."
def test_read_time_unit_missing():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start', time_bin_size_column="bin_size", format='csv')
assert exc.value.args[0] == "The bin size unit should be specified as an astropy Unit using ``time_bin_size_unit``."
def test_read():
timeseries = BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start',
time_bin_end_column='time_end', format='csv')
assert timeseries.colnames == ['time_bin_start', 'time_bin_size', 'bin_size', 'A', 'B', 'C', 'D', 'E', 'F']
assert len(timeseries) == 10
assert timeseries['B'].sum() == 1151.54
timeseries = BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start',
time_bin_size_column='bin_size',
time_bin_size_unit=u.second, format='csv')
assert timeseries.colnames == ['time_bin_start', 'time_bin_size', 'time_end', 'A', 'B', 'C', 'D', 'E', 'F']
assert len(timeseries) == 10
assert timeseries['B'].sum() == 1151.54
@pytest.mark.parametrize('cls', [BoxLeastSquares, LombScargle])
def test_periodogram(cls):
# Note that we don't need to check the actual results from the periodogram
# classes here since these are tested extensively in
# astropy.timeseries.periodograms.
ts = BinnedTimeSeries(time_bin_start='2016-03-22T12:30:31',
time_bin_size=3 * u.s, data=[[1, 4, 3], [3, 4, 3]], names=['a', 'b'])
p1 = cls.from_timeseries(ts, 'a')
assert isinstance(p1, cls)
assert_allclose(p1.t.jd, ts.time_bin_center.jd)
assert_equal(p1.y, ts['a'])
assert p1.dy is None
p2 = cls.from_timeseries(ts, 'a', uncertainty='b')
assert_quantity_allclose(p2.dy, ts['b'])
p3 = cls.from_timeseries(ts, 'a', uncertainty=0.1)
assert_allclose(p3.dy, 0.1)
|
|
astropy/timeseries/tests/test_binned.py
|
astropy/io/fits/header.py
|
# Licensed under a 3-clause BSD style license - see LICENSE.rst
import os
from os.path import join
from distutils.core import Extension
from distutils import log
from astropy_helpers import setup_helpers, utils
from astropy_helpers.version_helpers import get_pkg_version_module
wcs_setup_package = utils.import_file(join('astropy', 'wcs', 'setup_package.py'))
MODELING_ROOT = os.path.relpath(os.path.dirname(__file__))
MODELING_SRC = join(MODELING_ROOT, 'src')
SRC_FILES = [join(MODELING_SRC, 'projections.c.templ'),
__file__]
GEN_FILES = [join(MODELING_SRC, 'projections.c')]
# This defines the set of projection functions that we want to wrap.
# The key is the projection name, and the value is the number of
# parameters.
# (These are in the order that the appear in the WCS coordinate
# systems paper).
projections = {
'azp': 2,
'szp': 3,
'tan': 0,
'stg': 0,
'sin': 2,
'arc': 0,
'zea': 0,
'air': 1,
'cyp': 2,
'cea': 1,
'mer': 0,
'sfl': 0,
'par': 0,
'mol': 0,
'ait': 0,
'cop': 2,
'coe': 2,
'cod': 2,
'coo': 2,
'bon': 1,
'pco': 0,
'tsc': 0,
'csc': 0,
'qsc': 0,
'hpx': 2,
'xph': 0,
}
def pre_build_py_hook(cmd_obj):
preprocess_source()
def pre_build_ext_hook(cmd_obj):
preprocess_source()
def pre_sdist_hook(cmd_obj):
preprocess_source()
def preprocess_source():
# TODO: Move this to setup_helpers
# Generating the wcslib wrappers should only be done if needed. This also
# ensures that it is not done for any release tarball since those will
# include core.py and core.c.
if all(os.path.exists(filename) for filename in GEN_FILES):
# Determine modification times
src_mtime = max(os.path.getmtime(filename) for filename in SRC_FILES)
gen_mtime = min(os.path.getmtime(filename) for filename in GEN_FILES)
version = get_pkg_version_module('astropy')
if gen_mtime > src_mtime:
# If generated source is recent enough, don't update
return
elif version.release:
# or, if we're on a release, issue a warning, but go ahead and use
# the wrappers anyway
log.warn('WARNING: The autogenerated wrappers in '
'astropy.modeling._projections seem to be older '
'than the source templates used to create '
'them. Because this is a release version we will '
'use them anyway, but this might be a sign of '
'some sort of version mismatch or other '
'tampering. Or it might just mean you moved '
'some files around or otherwise accidentally '
'changed timestamps.')
return
# otherwise rebuild the autogenerated files
# If jinja2 isn't present, then print a warning and use existing files
try:
import jinja2 # pylint: disable=W0611
except ImportError:
log.warn("WARNING: jinja2 could not be imported, so the existing "
"modeling _projections.c file will be used")
return
from jinja2 import Environment, FileSystemLoader
# Prepare the jinja2 templating environment
env = Environment(loader=FileSystemLoader(MODELING_SRC))
c_in = env.get_template('projections.c.templ')
c_out = c_in.render(projections=projections)
with open(join(MODELING_SRC, 'projections.c'), 'w') as fd:
fd.write(c_out)
def get_extensions():
wcslib_files = [ # List of wcslib files to compile
'prj.c',
'wcserr.c',
'wcsprintf.c',
'wcsutil.c'
]
wcslib_config_paths = [
join(MODELING_SRC, 'wcsconfig.h')
]
cfg = setup_helpers.DistutilsExtensionArgs()
wcs_setup_package.get_wcslib_cfg(cfg, wcslib_files, wcslib_config_paths)
cfg['include_dirs'].append(MODELING_SRC)
astropy_files = [ # List of astropy.modeling files to compile
'projections.c'
]
cfg['sources'].extend(join(MODELING_SRC, x) for x in astropy_files)
cfg['sources'] = [str(x) for x in cfg['sources']]
cfg = dict((str(key), val) for key, val in cfg.items())
return [Extension('astropy.modeling._projections', **cfg)]
|
# Licensed under a 3-clause BSD style license - see LICENSE.rst
import pytest
from numpy.testing import assert_equal, assert_allclose
from astropy import units as u
from astropy.time import Time, TimeDelta
from astropy.utils.data import get_pkg_data_filename
from astropy.timeseries.periodograms import BoxLeastSquares, LombScargle
from astropy.timeseries.binned import BinnedTimeSeries
from astropy.tests.helper import assert_quantity_allclose
CSV_FILE = get_pkg_data_filename('data/binned.csv')
def test_empty_initialization():
ts = BinnedTimeSeries()
ts['time_bin_start'] = Time([1, 2, 3], format='mjd')
def test_empty_initialization_invalid():
# Make sure things crash when the first column added is not a time column
ts = BinnedTimeSeries()
with pytest.raises(ValueError) as exc:
ts['flux'] = [1, 2, 3]
assert exc.value.args[0] == ("BinnedTimeSeries object is invalid - expected "
"'time_bin_start' as the first column but found 'flux'")
def test_initialization_time_bin_invalid():
# Make sure things crash when time_bin_* is passed incorrectly.
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(data=[[1, 4, 3]])
assert exc.value.args[0] == ("'time_bin_start' has not been specified")
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(time_bin_start='2016-03-22T12:30:31', data=[[1, 4, 3]])
assert exc.value.args[0] == ("Either 'time_bin_size' or 'time_bin_end' should be specified")
def test_initialization_time_bin_both():
# Make sure things crash when time_bin_* is passed twice.
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(data={"time_bin_start": ["2016-03-22T12:30:31"]},
time_bin_start="2016-03-22T12:30:31")
assert exc.value.args[0] == ("'time_bin_start' has been given both in the table "
"and as a keyword argument")
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(data={"time_bin_size": ["2016-03-22T12:30:31"]},
time_bin_size=[1]*u.s)
assert exc.value.args[0] == ("'time_bin_size' has been given both in the table "
"and as a keyword argument")
def test_initialization_time_bin_size():
# Make sure things crash when time_bin_size has no units
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(data={"time": ["2016-03-22T12:30:31"]},
time_bin_start="2016-03-22T12:30:31",
time_bin_size=1)
assert exc.value.args[0] == ("'time_bin_size' should be a Quantity or a TimeDelta")
# TimeDelta for time_bin_size
ts = BinnedTimeSeries(data={"time": ["2016-03-22T12:30:31"]},
time_bin_start="2016-03-22T12:30:31",
time_bin_size=TimeDelta(1))
assert isinstance(ts.time_bin_size, u.quantity.Quantity)
def test_initialization_time_bin_start_scalar():
# Make sure things crash when time_bin_start is a scalar with no time_bin_size
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(data={"time": ["2016-03-22T12:30:31"]},
time_bin_start=Time(1, format='mjd'),
time_bin_end=Time(1, format='mjd'))
assert exc.value.args[0] == ("'time_bin_start' is scalar, so 'time_bin_size' is required")
def test_initialization_n_bins():
# Make sure things crash with incorrect n_bins
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(data={"time": ["2016-03-22T12:30:31"]},
time_bin_start=Time(1, format='mjd'),
time_bin_size=1*u.s,
time_bin_end=Time(1, format='mjd'),
n_bins=10)
assert exc.value.args[0] == ("'n_bins' has been given and it is not the "
"same length as the input data.")
def test_initialization_non_scalar_time():
# Make sure things crash with incorrect size of time_bin_start
with pytest.raises(ValueError) as exc:
BinnedTimeSeries(data={"time": ["2016-03-22T12:30:31"]},
time_bin_start=["2016-03-22T12:30:31", "2016-03-22T12:30:32"],
time_bin_size=1*u.s,
time_bin_end=Time(1, format='mjd'))
assert exc.value.args[0] == ("Length of 'time_bin_start' (2) should match table length (1)")
with pytest.raises(TypeError) as exc:
BinnedTimeSeries(data={"time": ["2016-03-22T12:30:31"]},
time_bin_start=["2016-03-22T12:30:31"],
time_bin_size=None,
time_bin_end=None)
assert exc.value.args[0] == ("Either 'time_bin_size' or 'time_bin_end' should be specified")
def test_even_contiguous():
# Initialize a ``BinnedTimeSeries`` with even contiguous bins by specifying
# the bin width:
ts = BinnedTimeSeries(time_bin_start='2016-03-22T12:30:31',
time_bin_size=3 * u.s, data=[[1, 4, 3]])
assert_equal(ts.time_bin_start.isot, ['2016-03-22T12:30:31.000',
'2016-03-22T12:30:34.000',
'2016-03-22T12:30:37.000'])
assert_equal(ts.time_bin_center.isot, ['2016-03-22T12:30:32.500',
'2016-03-22T12:30:35.500',
'2016-03-22T12:30:38.500'])
assert_equal(ts.time_bin_end.isot, ['2016-03-22T12:30:34.000',
'2016-03-22T12:30:37.000',
'2016-03-22T12:30:40.000'])
def test_uneven_contiguous():
# Initialize a ``BinnedTimeSeries`` with uneven contiguous bins by giving an
# end time:
ts = BinnedTimeSeries(time_bin_start=['2016-03-22T12:30:31',
'2016-03-22T12:30:32',
'2016-03-22T12:30:40'],
time_bin_end='2016-03-22T12:30:55',
data=[[1, 4, 3]])
assert_equal(ts.time_bin_start.isot, ['2016-03-22T12:30:31.000',
'2016-03-22T12:30:32.000',
'2016-03-22T12:30:40.000'])
assert_equal(ts.time_bin_center.isot, ['2016-03-22T12:30:31.500',
'2016-03-22T12:30:36.000',
'2016-03-22T12:30:47.500'])
assert_equal(ts.time_bin_end.isot, ['2016-03-22T12:30:32.000',
'2016-03-22T12:30:40.000',
'2016-03-22T12:30:55.000'])
def test_uneven_non_contiguous():
# Initialize a ``BinnedTimeSeries`` with uneven non-contiguous bins with
# lists of start times, bin sizes and data:
ts = BinnedTimeSeries(time_bin_start=['2016-03-22T12:30:31',
'2016-03-22T12:30:38',
'2016-03-22T12:34:40'],
time_bin_size=[5, 100, 2]*u.s,
data=[[1, 4, 3]])
assert_equal(ts.time_bin_start.isot, ['2016-03-22T12:30:31.000',
'2016-03-22T12:30:38.000',
'2016-03-22T12:34:40.000'])
assert_equal(ts.time_bin_center.isot, ['2016-03-22T12:30:33.500',
'2016-03-22T12:31:28.000',
'2016-03-22T12:34:41.000'])
assert_equal(ts.time_bin_end.isot, ['2016-03-22T12:30:36.000',
'2016-03-22T12:32:18.000',
'2016-03-22T12:34:42.000'])
def test_uneven_non_contiguous_full():
# Initialize a ``BinnedTimeSeries`` with uneven non-contiguous bins by
# specifying the start and end times for the bins:
ts = BinnedTimeSeries(time_bin_start=['2016-03-22T12:30:31',
'2016-03-22T12:30:33',
'2016-03-22T12:30:40'],
time_bin_end=['2016-03-22T12:30:32',
'2016-03-22T12:30:35',
'2016-03-22T12:30:41'],
data=[[1, 4, 3]])
assert_equal(ts.time_bin_start.isot, ['2016-03-22T12:30:31.000',
'2016-03-22T12:30:33.000',
'2016-03-22T12:30:40.000'])
assert_equal(ts.time_bin_center.isot, ['2016-03-22T12:30:31.500',
'2016-03-22T12:30:34.000',
'2016-03-22T12:30:40.500'])
assert_equal(ts.time_bin_end.isot, ['2016-03-22T12:30:32.000',
'2016-03-22T12:30:35.000',
'2016-03-22T12:30:41.000'])
def test_read_empty():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, format='csv')
assert exc.value.args[0] == '``time_bin_start_column`` should be provided since the default Table readers are being used.'
def test_read_no_size_end():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start', format='csv')
assert exc.value.args[0] == 'Either `time_bin_end_column` or `time_bin_size_column` should be provided.'
def test_read_both_extra_bins():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start', time_bin_end_column='END', time_bin_size_column='bin_size', format='csv')
assert exc.value.args[0] == "Cannot specify both `time_bin_end_column` and `time_bin_size_column`."
def test_read_size_no_unit():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start', time_bin_size_column='bin_size', format='csv')
assert exc.value.args[0] == "The bin size unit should be specified as an astropy Unit using ``time_bin_size_unit``."
def test_read_start_time_missing():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='abc', time_bin_size_column='bin_size', time_bin_size_unit=u.second, format='csv')
assert exc.value.args[0] == "Bin start time column 'abc' not found in the input data."
def test_read_end_time_missing():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start', time_bin_end_column="missing", format='csv')
assert exc.value.args[0] == "Bin end time column 'missing' not found in the input data."
def test_read_size_missing():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start', time_bin_size_column="missing", time_bin_size_unit=u.second, format='csv')
assert exc.value.args[0] == "Bin size column 'missing' not found in the input data."
def test_read_time_unit_missing():
with pytest.raises(ValueError) as exc:
BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start', time_bin_size_column="bin_size", format='csv')
assert exc.value.args[0] == "The bin size unit should be specified as an astropy Unit using ``time_bin_size_unit``."
def test_read():
timeseries = BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start',
time_bin_end_column='time_end', format='csv')
assert timeseries.colnames == ['time_bin_start', 'time_bin_size', 'bin_size', 'A', 'B', 'C', 'D', 'E', 'F']
assert len(timeseries) == 10
assert timeseries['B'].sum() == 1151.54
timeseries = BinnedTimeSeries.read(CSV_FILE, time_bin_start_column='time_start',
time_bin_size_column='bin_size',
time_bin_size_unit=u.second, format='csv')
assert timeseries.colnames == ['time_bin_start', 'time_bin_size', 'time_end', 'A', 'B', 'C', 'D', 'E', 'F']
assert len(timeseries) == 10
assert timeseries['B'].sum() == 1151.54
@pytest.mark.parametrize('cls', [BoxLeastSquares, LombScargle])
def test_periodogram(cls):
# Note that we don't need to check the actual results from the periodogram
# classes here since these are tested extensively in
# astropy.timeseries.periodograms.
ts = BinnedTimeSeries(time_bin_start='2016-03-22T12:30:31',
time_bin_size=3 * u.s, data=[[1, 4, 3], [3, 4, 3]], names=['a', 'b'])
p1 = cls.from_timeseries(ts, 'a')
assert isinstance(p1, cls)
assert_allclose(p1.t.jd, ts.time_bin_center.jd)
assert_equal(p1.y, ts['a'])
assert p1.dy is None
p2 = cls.from_timeseries(ts, 'a', uncertainty='b')
assert_quantity_allclose(p2.dy, ts['b'])
p3 = cls.from_timeseries(ts, 'a', uncertainty=0.1)
assert_allclose(p3.dy, 0.1)
|
|
astropy/timeseries/tests/test_binned.py
|
astropy/modeling/setup_package.py
|
"""Base async HTTP client implementation."""
import sys
from vine import Thenable, promise, maybe_promise
from kombu.exceptions import HttpError
from kombu.utils.compat import coro
from kombu.utils.encoding import bytes_to_str
from kombu.utils.functional import maybe_list, memoize
try: # pragma: no cover
from http.client import responses
except ImportError:
from httplib import responses # noqa
__all__ = ('Headers', 'Response', 'Request')
PYPY = hasattr(sys, 'pypy_version_info')
@memoize(maxsize=1000)
def normalize_header(key):
return '-'.join(p.capitalize() for p in key.split('-'))
class Headers(dict):
"""Represents a mapping of HTTP headers."""
# TODO: This is just a regular dict and will not perform normalization
# when looking up keys etc.
#: Set when all of the headers have been read.
complete = False
#: Internal attribute used to keep track of continuation lines.
_prev_key = None
@Thenable.register
class Request:
"""A HTTP Request.
Arguments:
url (str): The URL to request.
method (str): The HTTP method to use (defaults to ``GET``).
Keyword Arguments:
headers (Dict, ~kombu.asynchronous.http.Headers): Optional headers for
this request
body (str): Optional body for this request.
connect_timeout (float): Connection timeout in float seconds
Default is 30.0.
timeout (float): Time in float seconds before the request times out
Default is 30.0.
follow_redirects (bool): Specify if the client should follow redirects
Enabled by default.
max_redirects (int): Maximum number of redirects (default 6).
use_gzip (bool): Allow the server to use gzip compression.
Enabled by default.
validate_cert (bool): Set to true if the server certificate should be
verified when performing ``https://`` requests.
Enabled by default.
auth_username (str): Username for HTTP authentication.
auth_password (str): Password for HTTP authentication.
auth_mode (str): Type of HTTP authentication (``basic`` or ``digest``).
user_agent (str): Custom user agent for this request.
network_interace (str): Network interface to use for this request.
on_ready (Callable): Callback to be called when the response has been
received. Must accept single ``response`` argument.
on_stream (Callable): Optional callback to be called every time body
content has been read from the socket. If specified then the
response body and buffer attributes will not be available.
on_timeout (callable): Optional callback to be called if the request
times out.
on_header (Callable): Optional callback to be called for every header
line received from the server. The signature
is ``(headers, line)`` and note that if you want
``response.headers`` to be populated then your callback needs to
also call ``client.on_header(headers, line)``.
on_prepare (Callable): Optional callback that is implementation
specific (e.g. curl client will pass the ``curl`` instance to
this callback).
proxy_host (str): Optional proxy host. Note that a ``proxy_port`` must
also be provided or a :exc:`ValueError` will be raised.
proxy_username (str): Optional username to use when logging in
to the proxy.
proxy_password (str): Optional password to use when authenticating
with the proxy server.
ca_certs (str): Custom CA certificates file to use.
client_key (str): Optional filename for client SSL key.
client_cert (str): Optional filename for client SSL certificate.
"""
body = user_agent = network_interface = \
auth_username = auth_password = auth_mode = \
proxy_host = proxy_port = proxy_username = proxy_password = \
ca_certs = client_key = client_cert = None
connect_timeout = 30.0
request_timeout = 30.0
follow_redirects = True
max_redirects = 6
use_gzip = True
validate_cert = True
if not PYPY: # pragma: no cover
__slots__ = ('url', 'method', 'on_ready', 'on_timeout', 'on_stream',
'on_prepare', 'on_header', 'headers',
'__weakref__', '__dict__')
def __init__(self, url, method='GET', on_ready=None, on_timeout=None,
on_stream=None, on_prepare=None, on_header=None,
headers=None, **kwargs):
self.url = url
self.method = method or self.method
self.on_ready = maybe_promise(on_ready) or promise()
self.on_timeout = maybe_promise(on_timeout)
self.on_stream = maybe_promise(on_stream)
self.on_prepare = maybe_promise(on_prepare)
self.on_header = maybe_promise(on_header)
if kwargs:
for k, v in kwargs.items():
setattr(self, k, v)
if not isinstance(headers, Headers):
headers = Headers(headers or {})
self.headers = headers
def then(self, callback, errback=None):
self.on_ready.then(callback, errback)
def __repr__(self):
return '<Request: {0.method} {0.url} {0.body}>'.format(self)
class Response:
"""HTTP Response.
Arguments:
request (~kombu.asynchronous.http.Request): See :attr:`request`.
code (int): See :attr:`code`.
headers (~kombu.asynchronous.http.Headers): See :attr:`headers`.
buffer (bytes): See :attr:`buffer`
effective_url (str): See :attr:`effective_url`.
status (str): See :attr:`status`.
Attributes:
request (~kombu.asynchronous.http.Request): object used to
get this response.
code (int): HTTP response code (e.g. 200, 404, or 500).
headers (~kombu.asynchronous.http.Headers): HTTP headers
for this response.
buffer (bytes): Socket read buffer.
effective_url (str): The destination url for this request after
following redirects.
error (Exception): Error instance if the request resulted in
a HTTP error code.
status (str): Human equivalent of :attr:`code`,
e.g. ``OK``, `Not found`, or 'Internal Server Error'.
"""
if not PYPY: # pragma: no cover
__slots__ = ('request', 'code', 'headers', 'buffer', 'effective_url',
'error', 'status', '_body', '__weakref__')
def __init__(self, request, code, headers=None, buffer=None,
effective_url=None, error=None, status=None):
self.request = request
self.code = code
self.headers = headers if headers is not None else Headers()
self.buffer = buffer
self.effective_url = effective_url or request.url
self._body = None
self.status = status or responses.get(self.code, 'Unknown')
self.error = error
if self.error is None and (self.code < 200 or self.code > 299):
self.error = HttpError(self.code, self.status, self)
def raise_for_error(self):
"""Raise if the request resulted in an HTTP error code.
Raises:
:class:`~kombu.exceptions.HttpError`
"""
if self.error:
raise self.error
@property
def body(self):
"""The full contents of the response body.
Note:
Accessing this propery will evaluate the buffer
and subsequent accesses will be cached.
"""
if self._body is None:
if self.buffer is not None:
self._body = self.buffer.getvalue()
return self._body
# these are for compatibility with Requests
@property
def status_code(self):
return self.code
@property
def content(self):
return self.body
@coro
def header_parser(keyt=normalize_header):
while 1:
(line, headers) = yield
if line.startswith('HTTP/'):
continue
elif not line:
headers.complete = True
continue
elif line[0].isspace():
pkey = headers._prev_key
headers[pkey] = ' '.join([headers.get(pkey) or '', line.lstrip()])
else:
key, value = line.split(':', 1)
key = headers._prev_key = keyt(key)
headers[key] = value.strip()
class BaseClient:
Headers = Headers
Request = Request
Response = Response
def __init__(self, hub, **kwargs):
self.hub = hub
self._header_parser = header_parser()
def perform(self, request, **kwargs):
for req in maybe_list(request) or []:
if not isinstance(req, self.Request):
req = self.Request(req, **kwargs)
self.add_request(req)
def add_request(self, request):
raise NotImplementedError('must implement add_request')
def close(self):
pass
def on_header(self, headers, line):
try:
self._header_parser.send((bytes_to_str(line), headers))
except StopIteration:
self._header_parser = header_parser()
def __enter__(self):
return self
def __exit__(self, *exc_info):
self.close()
|
import tempfile
import pytest
import t.skip
from kombu import Connection, Exchange, Queue, Consumer, Producer
@t.skip.if_win32
class test_FilesystemTransport:
def setup(self):
self.channels = set()
try:
data_folder_in = tempfile.mkdtemp()
data_folder_out = tempfile.mkdtemp()
except Exception:
pytest.skip('filesystem transport: cannot create tempfiles')
self.c = Connection(transport='filesystem',
transport_options={
'data_folder_in': data_folder_in,
'data_folder_out': data_folder_out,
})
self.channels.add(self.c.default_channel)
self.p = Connection(transport='filesystem',
transport_options={
'data_folder_in': data_folder_out,
'data_folder_out': data_folder_in,
})
self.channels.add(self.p.default_channel)
self.e = Exchange('test_transport_filesystem')
self.q = Queue('test_transport_filesystem',
exchange=self.e,
routing_key='test_transport_filesystem')
self.q2 = Queue('test_transport_filesystem2',
exchange=self.e,
routing_key='test_transport_filesystem2')
def teardown(self):
# make sure we don't attempt to restore messages at shutdown.
for channel in self.channels:
try:
channel._qos._dirty.clear()
except AttributeError:
pass
try:
channel._qos._delivered.clear()
except AttributeError:
pass
def _add_channel(self, channel):
self.channels.add(channel)
return channel
def test_produce_consume_noack(self):
producer = Producer(self._add_channel(self.p.channel()), self.e)
consumer = Consumer(self._add_channel(self.c.channel()), self.q,
no_ack=True)
for i in range(10):
producer.publish({'foo': i},
routing_key='test_transport_filesystem')
_received = []
def callback(message_data, message):
_received.append(message)
consumer.register_callback(callback)
consumer.consume()
while 1:
if len(_received) == 10:
break
self.c.drain_events()
assert len(_received) == 10
def test_produce_consume(self):
producer_channel = self._add_channel(self.p.channel())
consumer_channel = self._add_channel(self.c.channel())
producer = Producer(producer_channel, self.e)
consumer1 = Consumer(consumer_channel, self.q)
consumer2 = Consumer(consumer_channel, self.q2)
self.q2(consumer_channel).declare()
for i in range(10):
producer.publish({'foo': i},
routing_key='test_transport_filesystem')
for i in range(10):
producer.publish({'foo': i},
routing_key='test_transport_filesystem2')
_received1 = []
_received2 = []
def callback1(message_data, message):
_received1.append(message)
message.ack()
def callback2(message_data, message):
_received2.append(message)
message.ack()
consumer1.register_callback(callback1)
consumer2.register_callback(callback2)
consumer1.consume()
consumer2.consume()
while 1:
if len(_received1) + len(_received2) == 20:
break
self.c.drain_events()
assert len(_received1) + len(_received2) == 20
# compression
producer.publish({'compressed': True},
routing_key='test_transport_filesystem',
compression='zlib')
m = self.q(consumer_channel).get()
assert m.payload == {'compressed': True}
# queue.delete
for i in range(10):
producer.publish({'foo': i},
routing_key='test_transport_filesystem')
assert self.q(consumer_channel).get()
self.q(consumer_channel).delete()
self.q(consumer_channel).declare()
assert self.q(consumer_channel).get() is None
# queue.purge
for i in range(10):
producer.publish({'foo': i},
routing_key='test_transport_filesystem2')
assert self.q2(consumer_channel).get()
self.q2(consumer_channel).purge()
assert self.q2(consumer_channel).get() is None
|
|
t/unit/transport/test_filesystem.py
|
kombu/asynchronous/http/base.py
|
from warnings import catch_warnings
import numpy as np
from pandas.core.dtypes import generic as gt
import pandas as pd
import pandas._testing as tm
class TestABCClasses:
tuples = [[1, 2, 2], ["red", "blue", "red"]]
multi_index = pd.MultiIndex.from_arrays(tuples, names=("number", "color"))
datetime_index = pd.to_datetime(["2000/1/1", "2010/1/1"])
timedelta_index = pd.to_timedelta(np.arange(5), unit="s")
period_index = pd.period_range("2000/1/1", "2010/1/1/", freq="M")
categorical = pd.Categorical([1, 2, 3], categories=[2, 3, 1])
categorical_df = pd.DataFrame({"values": [1, 2, 3]}, index=categorical)
df = pd.DataFrame({"names": ["a", "b", "c"]}, index=multi_index)
sparse_array = pd.arrays.SparseArray(np.random.randn(10))
datetime_array = pd.core.arrays.DatetimeArray(datetime_index)
timedelta_array = pd.core.arrays.TimedeltaArray(timedelta_index)
def test_abc_types(self):
assert isinstance(pd.Index(["a", "b", "c"]), gt.ABCIndex)
assert isinstance(pd.Int64Index([1, 2, 3]), gt.ABCInt64Index)
assert isinstance(pd.UInt64Index([1, 2, 3]), gt.ABCUInt64Index)
assert isinstance(pd.Float64Index([1, 2, 3]), gt.ABCFloat64Index)
assert isinstance(self.multi_index, gt.ABCMultiIndex)
assert isinstance(self.datetime_index, gt.ABCDatetimeIndex)
assert isinstance(self.timedelta_index, gt.ABCTimedeltaIndex)
assert isinstance(self.period_index, gt.ABCPeriodIndex)
assert isinstance(self.categorical_df.index, gt.ABCCategoricalIndex)
assert isinstance(pd.Index(["a", "b", "c"]), gt.ABCIndexClass)
assert isinstance(pd.Int64Index([1, 2, 3]), gt.ABCIndexClass)
assert isinstance(pd.Series([1, 2, 3]), gt.ABCSeries)
assert isinstance(self.df, gt.ABCDataFrame)
assert isinstance(self.sparse_array, gt.ABCExtensionArray)
assert isinstance(self.categorical, gt.ABCCategorical)
assert isinstance(self.datetime_array, gt.ABCDatetimeArray)
assert not isinstance(self.datetime_index, gt.ABCDatetimeArray)
assert isinstance(self.timedelta_array, gt.ABCTimedeltaArray)
assert not isinstance(self.timedelta_index, gt.ABCTimedeltaArray)
def test_setattr_warnings():
# GH7175 - GOTCHA: You can't use dot notation to add a column...
d = {
"one": pd.Series([1.0, 2.0, 3.0], index=["a", "b", "c"]),
"two": pd.Series([1.0, 2.0, 3.0, 4.0], index=["a", "b", "c", "d"]),
}
df = pd.DataFrame(d)
with catch_warnings(record=True) as w:
# successfully add new column
# this should not raise a warning
df["three"] = df.two + 1
assert len(w) == 0
assert df.three.sum() > df.two.sum()
with catch_warnings(record=True) as w:
# successfully modify column in place
# this should not raise a warning
df.one += 1
assert len(w) == 0
assert df.one.iloc[0] == 2
with catch_warnings(record=True) as w:
# successfully add an attribute to a series
# this should not raise a warning
df.two.not_an_index = [1, 2]
assert len(w) == 0
with tm.assert_produces_warning(UserWarning):
# warn when setting column to nonexistent name
df.four = df.two + 2
assert df.four.sum() > df.two.sum()
|
"""
Tests that work on both the Python and C engines but do not have a
specific classification into the other test modules.
"""
import codecs
import csv
from datetime import datetime
from io import StringIO
import os
import platform
from urllib.error import URLError
import numpy as np
import pytest
from pandas._libs.tslib import Timestamp
from pandas.errors import DtypeWarning, EmptyDataError, ParserError
import pandas.util._test_decorators as td
from pandas import DataFrame, Index, MultiIndex, Series, compat, concat
import pandas._testing as tm
from pandas.io.parsers import CParserWrapper, TextFileReader, TextParser
def test_override_set_noconvert_columns():
# see gh-17351
#
# Usecols needs to be sorted in _set_noconvert_columns based
# on the test_usecols_with_parse_dates test from test_usecols.py
class MyTextFileReader(TextFileReader):
def __init__(self):
self._currow = 0
self.squeeze = False
class MyCParserWrapper(CParserWrapper):
def _set_noconvert_columns(self):
if self.usecols_dtype == "integer":
# self.usecols is a set, which is documented as unordered
# but in practice, a CPython set of integers is sorted.
# In other implementations this assumption does not hold.
# The following code simulates a different order, which
# before GH 17351 would cause the wrong columns to be
# converted via the parse_dates parameter
self.usecols = list(self.usecols)
self.usecols.reverse()
return CParserWrapper._set_noconvert_columns(self)
data = """a,b,c,d,e
0,1,20140101,0900,4
0,1,20140102,1000,4"""
parse_dates = [[1, 2]]
cols = {
"a": [0, 0],
"c_d": [Timestamp("2014-01-01 09:00:00"), Timestamp("2014-01-02 10:00:00")],
}
expected = DataFrame(cols, columns=["c_d", "a"])
parser = MyTextFileReader()
parser.options = {
"usecols": [0, 2, 3],
"parse_dates": parse_dates,
"delimiter": ",",
}
parser._engine = MyCParserWrapper(StringIO(data), **parser.options)
result = parser.read()
tm.assert_frame_equal(result, expected)
def test_empty_decimal_marker(all_parsers):
data = """A|B|C
1|2,334|5
10|13|10.
"""
# Parsers support only length-1 decimals
msg = "Only length-1 decimal markers supported"
parser = all_parsers
with pytest.raises(ValueError, match=msg):
parser.read_csv(StringIO(data), decimal="")
def test_bad_stream_exception(all_parsers, csv_dir_path):
# see gh-13652
#
# This test validates that both the Python engine and C engine will
# raise UnicodeDecodeError instead of C engine raising ParserError
# and swallowing the exception that caused read to fail.
path = os.path.join(csv_dir_path, "sauron.SHIFT_JIS.csv")
codec = codecs.lookup("utf-8")
utf8 = codecs.lookup("utf-8")
parser = all_parsers
msg = "'utf-8' codec can't decode byte"
# Stream must be binary UTF8.
with open(path, "rb") as handle, codecs.StreamRecoder(
handle, utf8.encode, utf8.decode, codec.streamreader, codec.streamwriter
) as stream:
with pytest.raises(UnicodeDecodeError, match=msg):
parser.read_csv(stream)
def test_read_csv_local(all_parsers, csv1):
prefix = "file:///" if compat.is_platform_windows() else "file://"
parser = all_parsers
fname = prefix + str(os.path.abspath(csv1))
result = parser.read_csv(fname, index_col=0, parse_dates=True)
expected = DataFrame(
[
[0.980269, 3.685731, -0.364216805298, -1.159738],
[1.047916, -0.041232, -0.16181208307, 0.212549],
[0.498581, 0.731168, -0.537677223318, 1.346270],
[1.120202, 1.567621, 0.00364077397681, 0.675253],
[-0.487094, 0.571455, -1.6116394093, 0.103469],
[0.836649, 0.246462, 0.588542635376, 1.062782],
[-0.157161, 1.340307, 1.1957779562, -1.097007],
],
columns=["A", "B", "C", "D"],
index=Index(
[
datetime(2000, 1, 3),
datetime(2000, 1, 4),
datetime(2000, 1, 5),
datetime(2000, 1, 6),
datetime(2000, 1, 7),
datetime(2000, 1, 10),
datetime(2000, 1, 11),
],
name="index",
),
)
tm.assert_frame_equal(result, expected)
def test_1000_sep(all_parsers):
parser = all_parsers
data = """A|B|C
1|2,334|5
10|13|10.
"""
expected = DataFrame({"A": [1, 10], "B": [2334, 13], "C": [5, 10.0]})
result = parser.read_csv(StringIO(data), sep="|", thousands=",")
tm.assert_frame_equal(result, expected)
def test_squeeze(all_parsers):
data = """\
a,1
b,2
c,3
"""
parser = all_parsers
index = Index(["a", "b", "c"], name=0)
expected = Series([1, 2, 3], name=1, index=index)
result = parser.read_csv(StringIO(data), index_col=0, header=None, squeeze=True)
tm.assert_series_equal(result, expected)
# see gh-8217
#
# Series should not be a view.
assert not result._is_view
def test_malformed(all_parsers):
# see gh-6607
parser = all_parsers
data = """ignore
A,B,C
1,2,3 # comment
1,2,3,4,5
2,3,4
"""
msg = "Expected 3 fields in line 4, saw 5"
with pytest.raises(ParserError, match=msg):
parser.read_csv(StringIO(data), header=1, comment="#")
@pytest.mark.parametrize("nrows", [5, 3, None])
def test_malformed_chunks(all_parsers, nrows):
data = """ignore
A,B,C
skip
1,2,3
3,5,10 # comment
1,2,3,4,5
2,3,4
"""
parser = all_parsers
msg = "Expected 3 fields in line 6, saw 5"
reader = parser.read_csv(
StringIO(data), header=1, comment="#", iterator=True, chunksize=1, skiprows=[2]
)
with pytest.raises(ParserError, match=msg):
reader.read(nrows)
def test_unnamed_columns(all_parsers):
data = """A,B,C,,
1,2,3,4,5
6,7,8,9,10
11,12,13,14,15
"""
parser = all_parsers
expected = DataFrame(
[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15]],
dtype=np.int64,
columns=["A", "B", "C", "Unnamed: 3", "Unnamed: 4"],
)
result = parser.read_csv(StringIO(data))
tm.assert_frame_equal(result, expected)
def test_csv_mixed_type(all_parsers):
data = """A,B,C
a,1,2
b,3,4
c,4,5
"""
parser = all_parsers
expected = DataFrame({"A": ["a", "b", "c"], "B": [1, 3, 4], "C": [2, 4, 5]})
result = parser.read_csv(StringIO(data))
tm.assert_frame_equal(result, expected)
def test_read_csv_low_memory_no_rows_with_index(all_parsers):
# see gh-21141
parser = all_parsers
if not parser.low_memory:
pytest.skip("This is a low-memory specific test")
data = """A,B,C
1,1,1,2
2,2,3,4
3,3,4,5
"""
result = parser.read_csv(StringIO(data), low_memory=True, index_col=0, nrows=0)
expected = DataFrame(columns=["A", "B", "C"])
tm.assert_frame_equal(result, expected)
def test_read_csv_dataframe(all_parsers, csv1):
parser = all_parsers
result = parser.read_csv(csv1, index_col=0, parse_dates=True)
expected = DataFrame(
[
[0.980269, 3.685731, -0.364216805298, -1.159738],
[1.047916, -0.041232, -0.16181208307, 0.212549],
[0.498581, 0.731168, -0.537677223318, 1.346270],
[1.120202, 1.567621, 0.00364077397681, 0.675253],
[-0.487094, 0.571455, -1.6116394093, 0.103469],
[0.836649, 0.246462, 0.588542635376, 1.062782],
[-0.157161, 1.340307, 1.1957779562, -1.097007],
],
columns=["A", "B", "C", "D"],
index=Index(
[
datetime(2000, 1, 3),
datetime(2000, 1, 4),
datetime(2000, 1, 5),
datetime(2000, 1, 6),
datetime(2000, 1, 7),
datetime(2000, 1, 10),
datetime(2000, 1, 11),
],
name="index",
),
)
tm.assert_frame_equal(result, expected)
def test_read_csv_no_index_name(all_parsers, csv_dir_path):
parser = all_parsers
csv2 = os.path.join(csv_dir_path, "test2.csv")
result = parser.read_csv(csv2, index_col=0, parse_dates=True)
expected = DataFrame(
[
[0.980269, 3.685731, -0.364216805298, -1.159738, "foo"],
[1.047916, -0.041232, -0.16181208307, 0.212549, "bar"],
[0.498581, 0.731168, -0.537677223318, 1.346270, "baz"],
[1.120202, 1.567621, 0.00364077397681, 0.675253, "qux"],
[-0.487094, 0.571455, -1.6116394093, 0.103469, "foo2"],
],
columns=["A", "B", "C", "D", "E"],
index=Index(
[
datetime(2000, 1, 3),
datetime(2000, 1, 4),
datetime(2000, 1, 5),
datetime(2000, 1, 6),
datetime(2000, 1, 7),
]
),
)
tm.assert_frame_equal(result, expected)
def test_read_csv_wrong_num_columns(all_parsers):
# Too few columns.
data = """A,B,C,D,E,F
1,2,3,4,5,6
6,7,8,9,10,11,12
11,12,13,14,15,16
"""
parser = all_parsers
msg = "Expected 6 fields in line 3, saw 7"
with pytest.raises(ParserError, match=msg):
parser.read_csv(StringIO(data))
def test_read_duplicate_index_explicit(all_parsers):
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo,12,13,14,15
bar,12,13,14,15
"""
parser = all_parsers
result = parser.read_csv(StringIO(data), index_col=0)
expected = DataFrame(
[
[2, 3, 4, 5],
[7, 8, 9, 10],
[12, 13, 14, 15],
[12, 13, 14, 15],
[12, 13, 14, 15],
[12, 13, 14, 15],
],
columns=["A", "B", "C", "D"],
index=Index(["foo", "bar", "baz", "qux", "foo", "bar"], name="index"),
)
tm.assert_frame_equal(result, expected)
def test_read_duplicate_index_implicit(all_parsers):
data = """A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo,12,13,14,15
bar,12,13,14,15
"""
parser = all_parsers
result = parser.read_csv(StringIO(data))
expected = DataFrame(
[
[2, 3, 4, 5],
[7, 8, 9, 10],
[12, 13, 14, 15],
[12, 13, 14, 15],
[12, 13, 14, 15],
[12, 13, 14, 15],
],
columns=["A", "B", "C", "D"],
index=Index(["foo", "bar", "baz", "qux", "foo", "bar"]),
)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"data,kwargs,expected",
[
(
"A,B\nTrue,1\nFalse,2\nTrue,3",
dict(),
DataFrame([[True, 1], [False, 2], [True, 3]], columns=["A", "B"]),
),
(
"A,B\nYES,1\nno,2\nyes,3\nNo,3\nYes,3",
dict(true_values=["yes", "Yes", "YES"], false_values=["no", "NO", "No"]),
DataFrame(
[[True, 1], [False, 2], [True, 3], [False, 3], [True, 3]],
columns=["A", "B"],
),
),
(
"A,B\nTRUE,1\nFALSE,2\nTRUE,3",
dict(),
DataFrame([[True, 1], [False, 2], [True, 3]], columns=["A", "B"]),
),
(
"A,B\nfoo,bar\nbar,foo",
dict(true_values=["foo"], false_values=["bar"]),
DataFrame([[True, False], [False, True]], columns=["A", "B"]),
),
],
)
def test_parse_bool(all_parsers, data, kwargs, expected):
parser = all_parsers
result = parser.read_csv(StringIO(data), **kwargs)
tm.assert_frame_equal(result, expected)
def test_int_conversion(all_parsers):
data = """A,B
1.0,1
2.0,2
3.0,3
"""
parser = all_parsers
result = parser.read_csv(StringIO(data))
expected = DataFrame([[1.0, 1], [2.0, 2], [3.0, 3]], columns=["A", "B"])
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("nrows", [3, 3.0])
def test_read_nrows(all_parsers, nrows):
# see gh-10476
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
"""
expected = DataFrame(
[["foo", 2, 3, 4, 5], ["bar", 7, 8, 9, 10], ["baz", 12, 13, 14, 15]],
columns=["index", "A", "B", "C", "D"],
)
parser = all_parsers
result = parser.read_csv(StringIO(data), nrows=nrows)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("nrows", [1.2, "foo", -1])
def test_read_nrows_bad(all_parsers, nrows):
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
"""
msg = r"'nrows' must be an integer >=0"
parser = all_parsers
with pytest.raises(ValueError, match=msg):
parser.read_csv(StringIO(data), nrows=nrows)
@pytest.mark.parametrize("index_col", [0, "index"])
def test_read_chunksize_with_index(all_parsers, index_col):
parser = all_parsers
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
"""
reader = parser.read_csv(StringIO(data), index_col=0, chunksize=2)
expected = DataFrame(
[
["foo", 2, 3, 4, 5],
["bar", 7, 8, 9, 10],
["baz", 12, 13, 14, 15],
["qux", 12, 13, 14, 15],
["foo2", 12, 13, 14, 15],
["bar2", 12, 13, 14, 15],
],
columns=["index", "A", "B", "C", "D"],
)
expected = expected.set_index("index")
chunks = list(reader)
tm.assert_frame_equal(chunks[0], expected[:2])
tm.assert_frame_equal(chunks[1], expected[2:4])
tm.assert_frame_equal(chunks[2], expected[4:])
@pytest.mark.parametrize("chunksize", [1.3, "foo", 0])
def test_read_chunksize_bad(all_parsers, chunksize):
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
"""
parser = all_parsers
msg = r"'chunksize' must be an integer >=1"
with pytest.raises(ValueError, match=msg):
parser.read_csv(StringIO(data), chunksize=chunksize)
@pytest.mark.parametrize("chunksize", [2, 8])
def test_read_chunksize_and_nrows(all_parsers, chunksize):
# see gh-15755
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
"""
parser = all_parsers
kwargs = dict(index_col=0, nrows=5)
reader = parser.read_csv(StringIO(data), chunksize=chunksize, **kwargs)
expected = parser.read_csv(StringIO(data), **kwargs)
tm.assert_frame_equal(concat(reader), expected)
def test_read_chunksize_and_nrows_changing_size(all_parsers):
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
"""
parser = all_parsers
kwargs = dict(index_col=0, nrows=5)
reader = parser.read_csv(StringIO(data), chunksize=8, **kwargs)
expected = parser.read_csv(StringIO(data), **kwargs)
tm.assert_frame_equal(reader.get_chunk(size=2), expected.iloc[:2])
tm.assert_frame_equal(reader.get_chunk(size=4), expected.iloc[2:5])
with pytest.raises(StopIteration, match=""):
reader.get_chunk(size=3)
def test_get_chunk_passed_chunksize(all_parsers):
parser = all_parsers
data = """A,B,C
1,2,3
4,5,6
7,8,9
1,2,3"""
reader = parser.read_csv(StringIO(data), chunksize=2)
result = reader.get_chunk()
expected = DataFrame([[1, 2, 3], [4, 5, 6]], columns=["A", "B", "C"])
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("kwargs", [dict(), dict(index_col=0)])
def test_read_chunksize_compat(all_parsers, kwargs):
# see gh-12185
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
"""
parser = all_parsers
reader = parser.read_csv(StringIO(data), chunksize=2, **kwargs)
result = parser.read_csv(StringIO(data), **kwargs)
tm.assert_frame_equal(concat(reader), result)
def test_read_chunksize_jagged_names(all_parsers):
# see gh-23509
parser = all_parsers
data = "\n".join(["0"] * 7 + [",".join(["0"] * 10)])
expected = DataFrame([[0] + [np.nan] * 9] * 7 + [[0] * 10])
reader = parser.read_csv(StringIO(data), names=range(10), chunksize=4)
result = concat(reader)
tm.assert_frame_equal(result, expected)
def test_read_data_list(all_parsers):
parser = all_parsers
kwargs = dict(index_col=0)
data = "A,B,C\nfoo,1,2,3\nbar,4,5,6"
data_list = [["A", "B", "C"], ["foo", "1", "2", "3"], ["bar", "4", "5", "6"]]
expected = parser.read_csv(StringIO(data), **kwargs)
parser = TextParser(data_list, chunksize=2, **kwargs)
result = parser.read()
tm.assert_frame_equal(result, expected)
def test_iterator(all_parsers):
# see gh-6607
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
"""
parser = all_parsers
kwargs = dict(index_col=0)
expected = parser.read_csv(StringIO(data), **kwargs)
reader = parser.read_csv(StringIO(data), iterator=True, **kwargs)
first_chunk = reader.read(3)
tm.assert_frame_equal(first_chunk, expected[:3])
last_chunk = reader.read(5)
tm.assert_frame_equal(last_chunk, expected[3:])
def test_iterator2(all_parsers):
parser = all_parsers
data = """A,B,C
foo,1,2,3
bar,4,5,6
baz,7,8,9
"""
reader = parser.read_csv(StringIO(data), iterator=True)
result = list(reader)
expected = DataFrame(
[[1, 2, 3], [4, 5, 6], [7, 8, 9]],
index=["foo", "bar", "baz"],
columns=["A", "B", "C"],
)
tm.assert_frame_equal(result[0], expected)
def test_reader_list(all_parsers):
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
"""
parser = all_parsers
kwargs = dict(index_col=0)
lines = list(csv.reader(StringIO(data)))
reader = TextParser(lines, chunksize=2, **kwargs)
expected = parser.read_csv(StringIO(data), **kwargs)
chunks = list(reader)
tm.assert_frame_equal(chunks[0], expected[:2])
tm.assert_frame_equal(chunks[1], expected[2:4])
tm.assert_frame_equal(chunks[2], expected[4:])
def test_reader_list_skiprows(all_parsers):
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
"""
parser = all_parsers
kwargs = dict(index_col=0)
lines = list(csv.reader(StringIO(data)))
reader = TextParser(lines, chunksize=2, skiprows=[1], **kwargs)
expected = parser.read_csv(StringIO(data), **kwargs)
chunks = list(reader)
tm.assert_frame_equal(chunks[0], expected[1:3])
def test_iterator_stop_on_chunksize(all_parsers):
# gh-3967: stopping iteration when chunksize is specified
parser = all_parsers
data = """A,B,C
foo,1,2,3
bar,4,5,6
baz,7,8,9
"""
reader = parser.read_csv(StringIO(data), chunksize=1)
result = list(reader)
assert len(result) == 3
expected = DataFrame(
[[1, 2, 3], [4, 5, 6], [7, 8, 9]],
index=["foo", "bar", "baz"],
columns=["A", "B", "C"],
)
tm.assert_frame_equal(concat(result), expected)
@pytest.mark.parametrize(
"kwargs", [dict(iterator=True, chunksize=1), dict(iterator=True), dict(chunksize=1)]
)
def test_iterator_skipfooter_errors(all_parsers, kwargs):
msg = "'skipfooter' not supported for 'iteration'"
parser = all_parsers
data = "a\n1\n2"
with pytest.raises(ValueError, match=msg):
parser.read_csv(StringIO(data), skipfooter=1, **kwargs)
def test_nrows_skipfooter_errors(all_parsers):
msg = "'skipfooter' not supported with 'nrows'"
data = "a\n1\n2\n3\n4\n5\n6"
parser = all_parsers
with pytest.raises(ValueError, match=msg):
parser.read_csv(StringIO(data), skipfooter=1, nrows=5)
@pytest.mark.parametrize(
"data,kwargs,expected",
[
(
"""foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
""",
dict(index_col=0, names=["index", "A", "B", "C", "D"]),
DataFrame(
[
[2, 3, 4, 5],
[7, 8, 9, 10],
[12, 13, 14, 15],
[12, 13, 14, 15],
[12, 13, 14, 15],
[12, 13, 14, 15],
],
index=Index(["foo", "bar", "baz", "qux", "foo2", "bar2"], name="index"),
columns=["A", "B", "C", "D"],
),
),
(
"""foo,one,2,3,4,5
foo,two,7,8,9,10
foo,three,12,13,14,15
bar,one,12,13,14,15
bar,two,12,13,14,15
""",
dict(index_col=[0, 1], names=["index1", "index2", "A", "B", "C", "D"]),
DataFrame(
[
[2, 3, 4, 5],
[7, 8, 9, 10],
[12, 13, 14, 15],
[12, 13, 14, 15],
[12, 13, 14, 15],
],
index=MultiIndex.from_tuples(
[
("foo", "one"),
("foo", "two"),
("foo", "three"),
("bar", "one"),
("bar", "two"),
],
names=["index1", "index2"],
),
columns=["A", "B", "C", "D"],
),
),
],
)
def test_pass_names_with_index(all_parsers, data, kwargs, expected):
parser = all_parsers
result = parser.read_csv(StringIO(data), **kwargs)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("index_col", [[0, 1], [1, 0]])
def test_multi_index_no_level_names(all_parsers, index_col):
data = """index1,index2,A,B,C,D
foo,one,2,3,4,5
foo,two,7,8,9,10
foo,three,12,13,14,15
bar,one,12,13,14,15
bar,two,12,13,14,15
"""
headless_data = "\n".join(data.split("\n")[1:])
names = ["A", "B", "C", "D"]
parser = all_parsers
result = parser.read_csv(
StringIO(headless_data), index_col=index_col, header=None, names=names
)
expected = parser.read_csv(StringIO(data), index_col=index_col)
# No index names in headless data.
expected.index.names = [None] * 2
tm.assert_frame_equal(result, expected)
def test_multi_index_no_level_names_implicit(all_parsers):
parser = all_parsers
data = """A,B,C,D
foo,one,2,3,4,5
foo,two,7,8,9,10
foo,three,12,13,14,15
bar,one,12,13,14,15
bar,two,12,13,14,15
"""
result = parser.read_csv(StringIO(data))
expected = DataFrame(
[
[2, 3, 4, 5],
[7, 8, 9, 10],
[12, 13, 14, 15],
[12, 13, 14, 15],
[12, 13, 14, 15],
],
columns=["A", "B", "C", "D"],
index=MultiIndex.from_tuples(
[
("foo", "one"),
("foo", "two"),
("foo", "three"),
("bar", "one"),
("bar", "two"),
]
),
)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"data,expected,header",
[
("a,b", DataFrame(columns=["a", "b"]), [0]),
(
"a,b\nc,d",
DataFrame(columns=MultiIndex.from_tuples([("a", "c"), ("b", "d")])),
[0, 1],
),
],
)
@pytest.mark.parametrize("round_trip", [True, False])
def test_multi_index_blank_df(all_parsers, data, expected, header, round_trip):
# see gh-14545
parser = all_parsers
data = expected.to_csv(index=False) if round_trip else data
result = parser.read_csv(StringIO(data), header=header)
tm.assert_frame_equal(result, expected)
def test_no_unnamed_index(all_parsers):
parser = all_parsers
data = """ id c0 c1 c2
0 1 0 a b
1 2 0 c d
2 2 2 e f
"""
result = parser.read_csv(StringIO(data), sep=" ")
expected = DataFrame(
[[0, 1, 0, "a", "b"], [1, 2, 0, "c", "d"], [2, 2, 2, "e", "f"]],
columns=["Unnamed: 0", "id", "c0", "c1", "c2"],
)
tm.assert_frame_equal(result, expected)
def test_read_csv_parse_simple_list(all_parsers):
parser = all_parsers
data = """foo
bar baz
qux foo
foo
bar"""
result = parser.read_csv(StringIO(data), header=None)
expected = DataFrame(["foo", "bar baz", "qux foo", "foo", "bar"])
tm.assert_frame_equal(result, expected)
@tm.network
def test_url(all_parsers, csv_dir_path):
# TODO: FTP testing
parser = all_parsers
kwargs = dict(sep="\t")
url = (
"https://raw.github.com/pandas-dev/pandas/master/"
"pandas/tests/io/parser/data/salaries.csv"
)
url_result = parser.read_csv(url, **kwargs)
local_path = os.path.join(csv_dir_path, "salaries.csv")
local_result = parser.read_csv(local_path, **kwargs)
tm.assert_frame_equal(url_result, local_result)
@pytest.mark.slow
def test_local_file(all_parsers, csv_dir_path):
parser = all_parsers
kwargs = dict(sep="\t")
local_path = os.path.join(csv_dir_path, "salaries.csv")
local_result = parser.read_csv(local_path, **kwargs)
url = "file://localhost/" + local_path
try:
url_result = parser.read_csv(url, **kwargs)
tm.assert_frame_equal(url_result, local_result)
except URLError:
# Fails on some systems.
pytest.skip("Failing on: " + " ".join(platform.uname()))
def test_path_path_lib(all_parsers):
parser = all_parsers
df = tm.makeDataFrame()
result = tm.round_trip_pathlib(df.to_csv, lambda p: parser.read_csv(p, index_col=0))
tm.assert_frame_equal(df, result)
def test_path_local_path(all_parsers):
parser = all_parsers
df = tm.makeDataFrame()
result = tm.round_trip_localpath(
df.to_csv, lambda p: parser.read_csv(p, index_col=0)
)
tm.assert_frame_equal(df, result)
def test_nonexistent_path(all_parsers):
# gh-2428: pls no segfault
# gh-14086: raise more helpful FileNotFoundError
# GH#29233 "File foo" instead of "File b'foo'"
parser = all_parsers
path = f"{tm.rands(10)}.csv"
msg = r"\[Errno 2\]"
with pytest.raises(FileNotFoundError, match=msg) as e:
parser.read_csv(path)
assert path == e.value.filename
@td.skip_if_windows # os.chmod does not work in windows
def test_no_permission(all_parsers):
# GH 23784
parser = all_parsers
msg = r"\[Errno 13\]"
with tm.ensure_clean() as path:
os.chmod(path, 0) # make file unreadable
# verify that this process cannot open the file (not running as sudo)
try:
with open(path):
pass
pytest.skip("Running as sudo.")
except PermissionError:
pass
with pytest.raises(PermissionError, match=msg) as e:
parser.read_csv(path)
assert path == e.value.filename
def test_missing_trailing_delimiters(all_parsers):
parser = all_parsers
data = """A,B,C,D
1,2,3,4
1,3,3,
1,4,5"""
result = parser.read_csv(StringIO(data))
expected = DataFrame(
[[1, 2, 3, 4], [1, 3, 3, np.nan], [1, 4, 5, np.nan]],
columns=["A", "B", "C", "D"],
)
tm.assert_frame_equal(result, expected)
def test_skip_initial_space(all_parsers):
data = (
'"09-Apr-2012", "01:10:18.300", 2456026.548822908, 12849, '
"1.00361, 1.12551, 330.65659, 0355626618.16711, 73.48821, "
"314.11625, 1917.09447, 179.71425, 80.000, 240.000, -350, "
"70.06056, 344.98370, 1, 1, -0.689265, -0.692787, "
"0.212036, 14.7674, 41.605, -9999.0, -9999.0, "
"-9999.0, -9999.0, -9999.0, -9999.0, 000, 012, 128"
)
parser = all_parsers
result = parser.read_csv(
StringIO(data),
names=list(range(33)),
header=None,
na_values=["-9999.0"],
skipinitialspace=True,
)
expected = DataFrame(
[
[
"09-Apr-2012",
"01:10:18.300",
2456026.548822908,
12849,
1.00361,
1.12551,
330.65659,
355626618.16711,
73.48821,
314.11625,
1917.09447,
179.71425,
80.0,
240.0,
-350,
70.06056,
344.9837,
1,
1,
-0.689265,
-0.692787,
0.212036,
14.7674,
41.605,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
0,
12,
128,
]
]
)
tm.assert_frame_equal(result, expected)
def test_trailing_delimiters(all_parsers):
# see gh-2442
data = """A,B,C
1,2,3,
4,5,6,
7,8,9,"""
parser = all_parsers
result = parser.read_csv(StringIO(data), index_col=False)
expected = DataFrame({"A": [1, 4, 7], "B": [2, 5, 8], "C": [3, 6, 9]})
tm.assert_frame_equal(result, expected)
def test_escapechar(all_parsers):
# https://stackoverflow.com/questions/13824840/feature-request-for-
# pandas-read-csv
data = '''SEARCH_TERM,ACTUAL_URL
"bra tv bord","http://www.ikea.com/se/sv/catalog/categories/departments/living_room/10475/?se%7cps%7cnonbranded%7cvardagsrum%7cgoogle%7ctv_bord"
"tv p\xc3\xa5 hjul","http://www.ikea.com/se/sv/catalog/categories/departments/living_room/10475/?se%7cps%7cnonbranded%7cvardagsrum%7cgoogle%7ctv_bord"
"SLAGBORD, \\"Bergslagen\\", IKEA:s 1700-tals series","http://www.ikea.com/se/sv/catalog/categories/departments/living_room/10475/?se%7cps%7cnonbranded%7cvardagsrum%7cgoogle%7ctv_bord"''' # noqa
parser = all_parsers
result = parser.read_csv(
StringIO(data), escapechar="\\", quotechar='"', encoding="utf-8"
)
assert result["SEARCH_TERM"][2] == 'SLAGBORD, "Bergslagen", IKEA:s 1700-tals series'
tm.assert_index_equal(result.columns, Index(["SEARCH_TERM", "ACTUAL_URL"]))
def test_int64_min_issues(all_parsers):
# see gh-2599
parser = all_parsers
data = "A,B\n0,0\n0,"
result = parser.read_csv(StringIO(data))
expected = DataFrame({"A": [0, 0], "B": [0, np.nan]})
tm.assert_frame_equal(result, expected)
def test_parse_integers_above_fp_precision(all_parsers):
data = """Numbers
17007000002000191
17007000002000191
17007000002000191
17007000002000191
17007000002000192
17007000002000192
17007000002000192
17007000002000192
17007000002000192
17007000002000194"""
parser = all_parsers
result = parser.read_csv(StringIO(data))
expected = DataFrame(
{
"Numbers": [
17007000002000191,
17007000002000191,
17007000002000191,
17007000002000191,
17007000002000192,
17007000002000192,
17007000002000192,
17007000002000192,
17007000002000192,
17007000002000194,
]
}
)
tm.assert_frame_equal(result, expected)
def test_chunks_have_consistent_numerical_type(all_parsers):
parser = all_parsers
integers = [str(i) for i in range(499999)]
data = "a\n" + "\n".join(integers + ["1.0", "2.0"] + integers)
# Coercions should work without warnings.
with tm.assert_produces_warning(None):
result = parser.read_csv(StringIO(data))
assert type(result.a[0]) is np.float64
assert result.a.dtype == np.float
def test_warn_if_chunks_have_mismatched_type(all_parsers):
warning_type = None
parser = all_parsers
integers = [str(i) for i in range(499999)]
data = "a\n" + "\n".join(integers + ["a", "b"] + integers)
# see gh-3866: if chunks are different types and can't
# be coerced using numerical types, then issue warning.
if parser.engine == "c" and parser.low_memory:
warning_type = DtypeWarning
with tm.assert_produces_warning(warning_type):
df = parser.read_csv(StringIO(data))
assert df.a.dtype == np.object
@pytest.mark.parametrize("sep", [" ", r"\s+"])
def test_integer_overflow_bug(all_parsers, sep):
# see gh-2601
data = "65248E10 11\n55555E55 22\n"
parser = all_parsers
result = parser.read_csv(StringIO(data), header=None, sep=sep)
expected = DataFrame([[6.5248e14, 11], [5.5555e59, 22]])
tm.assert_frame_equal(result, expected)
def test_catch_too_many_names(all_parsers):
# see gh-5156
data = """\
1,2,3
4,,6
7,8,9
10,11,12\n"""
parser = all_parsers
msg = (
"Too many columns specified: expected 4 and found 3"
if parser.engine == "c"
else "Number of passed names did not match "
"number of header fields in the file"
)
with pytest.raises(ValueError, match=msg):
parser.read_csv(StringIO(data), header=0, names=["a", "b", "c", "d"])
def test_ignore_leading_whitespace(all_parsers):
# see gh-3374, gh-6607
parser = all_parsers
data = " a b c\n 1 2 3\n 4 5 6\n 7 8 9"
result = parser.read_csv(StringIO(data), sep=r"\s+")
expected = DataFrame({"a": [1, 4, 7], "b": [2, 5, 8], "c": [3, 6, 9]})
tm.assert_frame_equal(result, expected)
def test_chunk_begins_with_newline_whitespace(all_parsers):
# see gh-10022
parser = all_parsers
data = "\n hello\nworld\n"
result = parser.read_csv(StringIO(data), header=None)
expected = DataFrame([" hello", "world"])
tm.assert_frame_equal(result, expected)
def test_empty_with_index(all_parsers):
# see gh-10184
data = "x,y"
parser = all_parsers
result = parser.read_csv(StringIO(data), index_col=0)
expected = DataFrame(columns=["y"], index=Index([], name="x"))
tm.assert_frame_equal(result, expected)
def test_empty_with_multi_index(all_parsers):
# see gh-10467
data = "x,y,z"
parser = all_parsers
result = parser.read_csv(StringIO(data), index_col=["x", "y"])
expected = DataFrame(
columns=["z"], index=MultiIndex.from_arrays([[]] * 2, names=["x", "y"])
)
tm.assert_frame_equal(result, expected)
def test_empty_with_reversed_multi_index(all_parsers):
data = "x,y,z"
parser = all_parsers
result = parser.read_csv(StringIO(data), index_col=[1, 0])
expected = DataFrame(
columns=["z"], index=MultiIndex.from_arrays([[]] * 2, names=["y", "x"])
)
tm.assert_frame_equal(result, expected)
def test_float_parser(all_parsers):
# see gh-9565
parser = all_parsers
data = "45e-1,4.5,45.,inf,-inf"
result = parser.read_csv(StringIO(data), header=None)
expected = DataFrame([[float(s) for s in data.split(",")]])
tm.assert_frame_equal(result, expected)
def test_scientific_no_exponent(all_parsers):
# see gh-12215
df = DataFrame.from_dict({"w": ["2e"], "x": ["3E"], "y": ["42e"], "z": ["632E"]})
data = df.to_csv(index=False)
parser = all_parsers
for precision in parser.float_precision_choices:
df_roundtrip = parser.read_csv(StringIO(data), float_precision=precision)
tm.assert_frame_equal(df_roundtrip, df)
@pytest.mark.parametrize("conv", [None, np.int64, np.uint64])
def test_int64_overflow(all_parsers, conv):
data = """ID
00013007854817840016671868
00013007854817840016749251
00013007854817840016754630
00013007854817840016781876
00013007854817840017028824
00013007854817840017963235
00013007854817840018860166"""
parser = all_parsers
if conv is None:
# 13007854817840016671868 > UINT64_MAX, so this
# will overflow and return object as the dtype.
result = parser.read_csv(StringIO(data))
expected = DataFrame(
[
"00013007854817840016671868",
"00013007854817840016749251",
"00013007854817840016754630",
"00013007854817840016781876",
"00013007854817840017028824",
"00013007854817840017963235",
"00013007854817840018860166",
],
columns=["ID"],
)
tm.assert_frame_equal(result, expected)
else:
# 13007854817840016671868 > UINT64_MAX, so attempts
# to cast to either int64 or uint64 will result in
# an OverflowError being raised.
msg = (
"(Python int too large to convert to C long)|"
"(long too big to convert)|"
"(int too big to convert)"
)
with pytest.raises(OverflowError, match=msg):
parser.read_csv(StringIO(data), converters={"ID": conv})
@pytest.mark.parametrize(
"val", [np.iinfo(np.uint64).max, np.iinfo(np.int64).max, np.iinfo(np.int64).min]
)
def test_int64_uint64_range(all_parsers, val):
# These numbers fall right inside the int64-uint64
# range, so they should be parsed as string.
parser = all_parsers
result = parser.read_csv(StringIO(str(val)), header=None)
expected = DataFrame([val])
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"val", [np.iinfo(np.uint64).max + 1, np.iinfo(np.int64).min - 1]
)
def test_outside_int64_uint64_range(all_parsers, val):
# These numbers fall just outside the int64-uint64
# range, so they should be parsed as string.
parser = all_parsers
result = parser.read_csv(StringIO(str(val)), header=None)
expected = DataFrame([str(val)])
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("exp_data", [[str(-1), str(2 ** 63)], [str(2 ** 63), str(-1)]])
def test_numeric_range_too_wide(all_parsers, exp_data):
# No numerical dtype can hold both negative and uint64
# values, so they should be cast as string.
parser = all_parsers
data = "\n".join(exp_data)
expected = DataFrame(exp_data)
result = parser.read_csv(StringIO(data), header=None)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("iterator", [True, False])
def test_empty_with_nrows_chunksize(all_parsers, iterator):
# see gh-9535
parser = all_parsers
expected = DataFrame(columns=["foo", "bar"])
nrows = 10
data = StringIO("foo,bar\n")
if iterator:
result = next(iter(parser.read_csv(data, chunksize=nrows)))
else:
result = parser.read_csv(data, nrows=nrows)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"data,kwargs,expected,msg",
[
# gh-10728: WHITESPACE_LINE
(
"a,b,c\n4,5,6\n ",
dict(),
DataFrame([[4, 5, 6]], columns=["a", "b", "c"]),
None,
),
# gh-10548: EAT_LINE_COMMENT
(
"a,b,c\n4,5,6\n#comment",
dict(comment="#"),
DataFrame([[4, 5, 6]], columns=["a", "b", "c"]),
None,
),
# EAT_CRNL_NOP
(
"a,b,c\n4,5,6\n\r",
dict(),
DataFrame([[4, 5, 6]], columns=["a", "b", "c"]),
None,
),
# EAT_COMMENT
(
"a,b,c\n4,5,6#comment",
dict(comment="#"),
DataFrame([[4, 5, 6]], columns=["a", "b", "c"]),
None,
),
# SKIP_LINE
(
"a,b,c\n4,5,6\nskipme",
dict(skiprows=[2]),
DataFrame([[4, 5, 6]], columns=["a", "b", "c"]),
None,
),
# EAT_LINE_COMMENT
(
"a,b,c\n4,5,6\n#comment",
dict(comment="#", skip_blank_lines=False),
DataFrame([[4, 5, 6]], columns=["a", "b", "c"]),
None,
),
# IN_FIELD
(
"a,b,c\n4,5,6\n ",
dict(skip_blank_lines=False),
DataFrame([["4", 5, 6], [" ", None, None]], columns=["a", "b", "c"]),
None,
),
# EAT_CRNL
(
"a,b,c\n4,5,6\n\r",
dict(skip_blank_lines=False),
DataFrame([[4, 5, 6], [None, None, None]], columns=["a", "b", "c"]),
None,
),
# ESCAPED_CHAR
(
"a,b,c\n4,5,6\n\\",
dict(escapechar="\\"),
None,
"(EOF following escape character)|(unexpected end of data)",
),
# ESCAPE_IN_QUOTED_FIELD
(
'a,b,c\n4,5,6\n"\\',
dict(escapechar="\\"),
None,
"(EOF inside string starting at row 2)|(unexpected end of data)",
),
# IN_QUOTED_FIELD
(
'a,b,c\n4,5,6\n"',
dict(escapechar="\\"),
None,
"(EOF inside string starting at row 2)|(unexpected end of data)",
),
],
ids=[
"whitespace-line",
"eat-line-comment",
"eat-crnl-nop",
"eat-comment",
"skip-line",
"eat-line-comment",
"in-field",
"eat-crnl",
"escaped-char",
"escape-in-quoted-field",
"in-quoted-field",
],
)
def test_eof_states(all_parsers, data, kwargs, expected, msg):
# see gh-10728, gh-10548
parser = all_parsers
if expected is None:
with pytest.raises(ParserError, match=msg):
parser.read_csv(StringIO(data), **kwargs)
else:
result = parser.read_csv(StringIO(data), **kwargs)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("usecols", [None, [0, 1], ["a", "b"]])
def test_uneven_lines_with_usecols(all_parsers, usecols):
# see gh-12203
parser = all_parsers
data = r"""a,b,c
0,1,2
3,4,5,6,7
8,9,10"""
if usecols is None:
# Make sure that an error is still raised
# when the "usecols" parameter is not provided.
msg = r"Expected \d+ fields in line \d+, saw \d+"
with pytest.raises(ParserError, match=msg):
parser.read_csv(StringIO(data))
else:
expected = DataFrame({"a": [0, 3, 8], "b": [1, 4, 9]})
result = parser.read_csv(StringIO(data), usecols=usecols)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"data,kwargs,expected",
[
# First, check to see that the response of parser when faced with no
# provided columns raises the correct error, with or without usecols.
("", dict(), None),
("", dict(usecols=["X"]), None),
(
",,",
dict(names=["Dummy", "X", "Dummy_2"], usecols=["X"]),
DataFrame(columns=["X"], index=[0], dtype=np.float64),
),
(
"",
dict(names=["Dummy", "X", "Dummy_2"], usecols=["X"]),
DataFrame(columns=["X"]),
),
],
)
def test_read_empty_with_usecols(all_parsers, data, kwargs, expected):
# see gh-12493
parser = all_parsers
if expected is None:
msg = "No columns to parse from file"
with pytest.raises(EmptyDataError, match=msg):
parser.read_csv(StringIO(data), **kwargs)
else:
result = parser.read_csv(StringIO(data), **kwargs)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"kwargs,expected",
[
# gh-8661, gh-8679: this should ignore six lines, including
# lines with trailing whitespace and blank lines.
(
dict(
header=None,
delim_whitespace=True,
skiprows=[0, 1, 2, 3, 5, 6],
skip_blank_lines=True,
),
DataFrame([[1.0, 2.0, 4.0], [5.1, np.nan, 10.0]]),
),
# gh-8983: test skipping set of rows after a row with trailing spaces.
(
dict(
delim_whitespace=True, skiprows=[1, 2, 3, 5, 6], skip_blank_lines=True
),
DataFrame({"A": [1.0, 5.1], "B": [2.0, np.nan], "C": [4.0, 10]}),
),
],
)
def test_trailing_spaces(all_parsers, kwargs, expected):
data = "A B C \nrandom line with trailing spaces \nskip\n1,2,3\n1,2.,4.\nrandom line with trailing tabs\t\t\t\n \n5.1,NaN,10.0\n" # noqa
parser = all_parsers
result = parser.read_csv(StringIO(data.replace(",", " ")), **kwargs)
tm.assert_frame_equal(result, expected)
def test_raise_on_sep_with_delim_whitespace(all_parsers):
# see gh-6607
data = "a b c\n1 2 3"
parser = all_parsers
with pytest.raises(ValueError, match="you can only specify one"):
parser.read_csv(StringIO(data), sep=r"\s", delim_whitespace=True)
@pytest.mark.parametrize("delim_whitespace", [True, False])
def test_single_char_leading_whitespace(all_parsers, delim_whitespace):
# see gh-9710
parser = all_parsers
data = """\
MyColumn
a
b
a
b\n"""
expected = DataFrame({"MyColumn": list("abab")})
result = parser.read_csv(
StringIO(data), skipinitialspace=True, delim_whitespace=delim_whitespace
)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"sep,skip_blank_lines,exp_data",
[
(",", True, [[1.0, 2.0, 4.0], [5.0, np.nan, 10.0], [-70.0, 0.4, 1.0]]),
(r"\s+", True, [[1.0, 2.0, 4.0], [5.0, np.nan, 10.0], [-70.0, 0.4, 1.0]]),
(
",",
False,
[
[1.0, 2.0, 4.0],
[np.nan, np.nan, np.nan],
[np.nan, np.nan, np.nan],
[5.0, np.nan, 10.0],
[np.nan, np.nan, np.nan],
[-70.0, 0.4, 1.0],
],
),
],
)
def test_empty_lines(all_parsers, sep, skip_blank_lines, exp_data):
parser = all_parsers
data = """\
A,B,C
1,2.,4.
5.,NaN,10.0
-70,.4,1
"""
if sep == r"\s+":
data = data.replace(",", " ")
result = parser.read_csv(StringIO(data), sep=sep, skip_blank_lines=skip_blank_lines)
expected = DataFrame(exp_data, columns=["A", "B", "C"])
tm.assert_frame_equal(result, expected)
def test_whitespace_lines(all_parsers):
parser = all_parsers
data = """
\t \t\t
\t
A,B,C
\t 1,2.,4.
5.,NaN,10.0
"""
expected = DataFrame([[1, 2.0, 4.0], [5.0, np.nan, 10.0]], columns=["A", "B", "C"])
result = parser.read_csv(StringIO(data))
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"data,expected",
[
(
""" A B C D
a 1 2 3 4
b 1 2 3 4
c 1 2 3 4
""",
DataFrame(
[[1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4]],
columns=["A", "B", "C", "D"],
index=["a", "b", "c"],
),
),
(
" a b c\n1 2 3 \n4 5 6\n 7 8 9",
DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], columns=["a", "b", "c"]),
),
],
)
def test_whitespace_regex_separator(all_parsers, data, expected):
# see gh-6607
parser = all_parsers
result = parser.read_csv(StringIO(data), sep=r"\s+")
tm.assert_frame_equal(result, expected)
def test_verbose_read(all_parsers, capsys):
parser = all_parsers
data = """a,b,c,d
one,1,2,3
one,1,2,3
,1,2,3
one,1,2,3
,1,2,3
,1,2,3
one,1,2,3
two,1,2,3"""
# Engines are verbose in different ways.
parser.read_csv(StringIO(data), verbose=True)
captured = capsys.readouterr()
if parser.engine == "c":
assert "Tokenization took:" in captured.out
assert "Parser memory cleanup took:" in captured.out
else: # Python engine
assert captured.out == "Filled 3 NA values in column a\n"
def test_verbose_read2(all_parsers, capsys):
parser = all_parsers
data = """a,b,c,d
one,1,2,3
two,1,2,3
three,1,2,3
four,1,2,3
five,1,2,3
,1,2,3
seven,1,2,3
eight,1,2,3"""
parser.read_csv(StringIO(data), verbose=True, index_col=0)
captured = capsys.readouterr()
# Engines are verbose in different ways.
if parser.engine == "c":
assert "Tokenization took:" in captured.out
assert "Parser memory cleanup took:" in captured.out
else: # Python engine
assert captured.out == "Filled 1 NA values in column a\n"
def test_iteration_open_handle(all_parsers):
parser = all_parsers
kwargs = dict(squeeze=True, header=None)
with tm.ensure_clean() as path:
with open(path, "w") as f:
f.write("AAA\nBBB\nCCC\nDDD\nEEE\nFFF\nGGG")
with open(path, "r") as f:
for line in f:
if "CCC" in line:
break
result = parser.read_csv(f, **kwargs)
expected = Series(["DDD", "EEE", "FFF", "GGG"], name=0)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"data,thousands,decimal",
[
(
"""A|B|C
1|2,334.01|5
10|13|10.
""",
",",
".",
),
(
"""A|B|C
1|2.334,01|5
10|13|10,
""",
".",
",",
),
],
)
def test_1000_sep_with_decimal(all_parsers, data, thousands, decimal):
parser = all_parsers
expected = DataFrame({"A": [1, 10], "B": [2334.01, 13], "C": [5, 10.0]})
result = parser.read_csv(
StringIO(data), sep="|", thousands=thousands, decimal=decimal
)
tm.assert_frame_equal(result, expected)
def test_euro_decimal_format(all_parsers):
parser = all_parsers
data = """Id;Number1;Number2;Text1;Text2;Number3
1;1521,1541;187101,9543;ABC;poi;4,738797819
2;121,12;14897,76;DEF;uyt;0,377320872
3;878,158;108013,434;GHI;rez;2,735694704"""
result = parser.read_csv(StringIO(data), sep=";", decimal=",")
expected = DataFrame(
[
[1, 1521.1541, 187101.9543, "ABC", "poi", 4.738797819],
[2, 121.12, 14897.76, "DEF", "uyt", 0.377320872],
[3, 878.158, 108013.434, "GHI", "rez", 2.735694704],
],
columns=["Id", "Number1", "Number2", "Text1", "Text2", "Number3"],
)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("na_filter", [True, False])
def test_inf_parsing(all_parsers, na_filter):
parser = all_parsers
data = """\
,A
a,inf
b,-inf
c,+Inf
d,-Inf
e,INF
f,-INF
g,+INf
h,-INf
i,inF
j,-inF"""
expected = DataFrame(
{"A": [float("inf"), float("-inf")] * 5},
index=["a", "b", "c", "d", "e", "f", "g", "h", "i", "j"],
)
result = parser.read_csv(StringIO(data), index_col=0, na_filter=na_filter)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("na_filter", [True, False])
def test_infinity_parsing(all_parsers, na_filter):
parser = all_parsers
data = """\
,A
a,Infinity
b,-Infinity
c,+Infinity
"""
expected = DataFrame(
{"A": [float("infinity"), float("-infinity"), float("+infinity")]},
index=["a", "b", "c"],
)
result = parser.read_csv(StringIO(data), index_col=0, na_filter=na_filter)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("nrows", [0, 1, 2, 3, 4, 5])
def test_raise_on_no_columns(all_parsers, nrows):
parser = all_parsers
data = "\n" * nrows
msg = "No columns to parse from file"
with pytest.raises(EmptyDataError, match=msg):
parser.read_csv(StringIO(data))
def test_memory_map(all_parsers, csv_dir_path):
mmap_file = os.path.join(csv_dir_path, "test_mmap.csv")
parser = all_parsers
expected = DataFrame(
{"a": [1, 2, 3], "b": ["one", "two", "three"], "c": ["I", "II", "III"]}
)
result = parser.read_csv(mmap_file, memory_map=True)
tm.assert_frame_equal(result, expected)
def test_null_byte_char(all_parsers):
# see gh-2741
data = "\x00,foo"
names = ["a", "b"]
parser = all_parsers
if parser.engine == "c":
expected = DataFrame([[np.nan, "foo"]], columns=names)
out = parser.read_csv(StringIO(data), names=names)
tm.assert_frame_equal(out, expected)
else:
msg = "NULL byte detected"
with pytest.raises(ParserError, match=msg):
parser.read_csv(StringIO(data), names=names)
def test_temporary_file(all_parsers):
# see gh-13398
parser = all_parsers
data = "0 0"
with tm.ensure_clean(mode="w+", return_filelike=True) as new_file:
new_file.write(data)
new_file.flush()
new_file.seek(0)
result = parser.read_csv(new_file, sep=r"\s+", header=None)
expected = DataFrame([[0, 0]])
tm.assert_frame_equal(result, expected)
def test_internal_eof_byte(all_parsers):
# see gh-5500
parser = all_parsers
data = "a,b\n1\x1a,2"
expected = DataFrame([["1\x1a", 2]], columns=["a", "b"])
result = parser.read_csv(StringIO(data))
tm.assert_frame_equal(result, expected)
def test_internal_eof_byte_to_file(all_parsers):
# see gh-16559
parser = all_parsers
data = b'c1,c2\r\n"test \x1a test", test\r\n'
expected = DataFrame([["test \x1a test", " test"]], columns=["c1", "c2"])
path = f"__{tm.rands(10)}__.csv"
with tm.ensure_clean(path) as path:
with open(path, "wb") as f:
f.write(data)
result = parser.read_csv(path)
tm.assert_frame_equal(result, expected)
def test_sub_character(all_parsers, csv_dir_path):
# see gh-16893
filename = os.path.join(csv_dir_path, "sub_char.csv")
expected = DataFrame([[1, 2, 3]], columns=["a", "\x1ab", "c"])
parser = all_parsers
result = parser.read_csv(filename)
tm.assert_frame_equal(result, expected)
def test_file_handle_string_io(all_parsers):
# gh-14418
#
# Don't close user provided file handles.
parser = all_parsers
data = "a,b\n1,2"
fh = StringIO(data)
parser.read_csv(fh)
assert not fh.closed
def test_file_handles_with_open(all_parsers, csv1):
# gh-14418
#
# Don't close user provided file handles.
parser = all_parsers
for mode in ["r", "rb"]:
with open(csv1, mode) as f:
parser.read_csv(f)
assert not f.closed
def test_invalid_file_buffer_class(all_parsers):
# see gh-15337
class InvalidBuffer:
pass
parser = all_parsers
msg = "Invalid file path or buffer object type"
with pytest.raises(ValueError, match=msg):
parser.read_csv(InvalidBuffer())
def test_invalid_file_buffer_mock(all_parsers):
# see gh-15337
parser = all_parsers
msg = "Invalid file path or buffer object type"
class Foo:
pass
with pytest.raises(ValueError, match=msg):
parser.read_csv(Foo())
def test_valid_file_buffer_seems_invalid(all_parsers):
# gh-16135: we want to ensure that "tell" and "seek"
# aren't actually being used when we call `read_csv`
#
# Thus, while the object may look "invalid" (these
# methods are attributes of the `StringIO` class),
# it is still a valid file-object for our purposes.
class NoSeekTellBuffer(StringIO):
def tell(self):
raise AttributeError("No tell method")
def seek(self, pos, whence=0):
raise AttributeError("No seek method")
data = "a\n1"
parser = all_parsers
expected = DataFrame({"a": [1]})
result = parser.read_csv(NoSeekTellBuffer(data))
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"kwargs",
[dict(), dict(error_bad_lines=True)], # Default is True. # Explicitly pass in.
)
@pytest.mark.parametrize(
"warn_kwargs", [dict(), dict(warn_bad_lines=True), dict(warn_bad_lines=False)]
)
def test_error_bad_lines(all_parsers, kwargs, warn_kwargs):
# see gh-15925
parser = all_parsers
kwargs.update(**warn_kwargs)
data = "a\n1\n1,2,3\n4\n5,6,7"
msg = "Expected 1 fields in line 3, saw 3"
with pytest.raises(ParserError, match=msg):
parser.read_csv(StringIO(data), **kwargs)
def test_warn_bad_lines(all_parsers, capsys):
# see gh-15925
parser = all_parsers
data = "a\n1\n1,2,3\n4\n5,6,7"
expected = DataFrame({"a": [1, 4]})
result = parser.read_csv(StringIO(data), error_bad_lines=False, warn_bad_lines=True)
tm.assert_frame_equal(result, expected)
captured = capsys.readouterr()
assert "Skipping line 3" in captured.err
assert "Skipping line 5" in captured.err
def test_suppress_error_output(all_parsers, capsys):
# see gh-15925
parser = all_parsers
data = "a\n1\n1,2,3\n4\n5,6,7"
expected = DataFrame({"a": [1, 4]})
result = parser.read_csv(
StringIO(data), error_bad_lines=False, warn_bad_lines=False
)
tm.assert_frame_equal(result, expected)
captured = capsys.readouterr()
assert captured.err == ""
@pytest.mark.parametrize("filename", ["sé-es-vé.csv", "ru-sй.csv", "中文文件名.csv"])
def test_filename_with_special_chars(all_parsers, filename):
# see gh-15086.
parser = all_parsers
df = DataFrame({"a": [1, 2, 3]})
with tm.ensure_clean(filename) as path:
df.to_csv(path, index=False)
result = parser.read_csv(path)
tm.assert_frame_equal(result, df)
def test_read_csv_memory_growth_chunksize(all_parsers):
# see gh-24805
#
# Let's just make sure that we don't crash
# as we iteratively process all chunks.
parser = all_parsers
with tm.ensure_clean() as path:
with open(path, "w") as f:
for i in range(1000):
f.write(str(i) + "\n")
result = parser.read_csv(path, chunksize=20)
for _ in result:
pass
def test_read_csv_raises_on_header_prefix(all_parsers):
# gh-27394
parser = all_parsers
msg = "Argument prefix must be None if argument header is not None"
s = StringIO("0,1\n2,3")
with pytest.raises(ValueError, match=msg):
parser.read_csv(s, header=0, prefix="_X")
def test_read_table_equivalency_to_read_csv(all_parsers):
# see gh-21948
# As of 0.25.0, read_table is undeprecated
parser = all_parsers
data = "a\tb\n1\t2\n3\t4"
expected = parser.read_csv(StringIO(data), sep="\t")
result = parser.read_table(StringIO(data))
tm.assert_frame_equal(result, expected)
def test_first_row_bom(all_parsers):
# see gh-26545
parser = all_parsers
data = '''\ufeff"Head1" "Head2" "Head3"'''
result = parser.read_csv(StringIO(data), delimiter="\t")
expected = DataFrame(columns=["Head1", "Head2", "Head3"])
tm.assert_frame_equal(result, expected)
def test_integer_precision(all_parsers):
# Gh 7072
s = """1,1;0;0;0;1;1;3844;3844;3844;1;1;1;1;1;1;0;0;1;1;0;0,,,4321583677327450765
5,1;0;0;0;1;1;843;843;843;1;1;1;1;1;1;0;0;1;1;0;0,64.0,;,4321113141090630389"""
parser = all_parsers
result = parser.read_csv(StringIO(s), header=None)[4]
expected = Series([4321583677327450765, 4321113141090630389], name=4)
tm.assert_series_equal(result, expected)
def test_file_descriptor_leak(all_parsers):
# GH 31488
parser = all_parsers
with tm.ensure_clean() as path:
def test():
with pytest.raises(EmptyDataError, match="No columns to parse from file"):
parser.read_csv(path)
td.check_file_leaks(test)()
@pytest.mark.parametrize("nrows", range(1, 6))
def test_blank_lines_between_header_and_data_rows(all_parsers, nrows):
# GH 28071
ref = DataFrame(
[[np.nan, np.nan], [np.nan, np.nan], [1, 2], [np.nan, np.nan], [3, 4]],
columns=list("ab"),
)
csv = "\nheader\n\na,b\n\n\n1,2\n\n3,4"
parser = all_parsers
df = parser.read_csv(StringIO(csv), header=3, nrows=nrows, skip_blank_lines=False)
tm.assert_frame_equal(df, ref[:nrows])
def test_no_header_two_extra_columns(all_parsers):
# GH 26218
column_names = ["one", "two", "three"]
ref = DataFrame([["foo", "bar", "baz"]], columns=column_names)
stream = StringIO("foo,bar,baz,bam,blah")
parser = all_parsers
df = parser.read_csv(stream, header=None, names=column_names, index_col=False)
tm.assert_frame_equal(df, ref)
|
|
pandas/tests/io/parser/test_common.py
|
pandas/tests/dtypes/test_generic.py
|
import pandas as pd
import pandas._testing as tm
class TestUnaryOps:
def test_invert(self):
a = pd.array([True, False, None], dtype="boolean")
expected = pd.array([False, True, None], dtype="boolean")
tm.assert_extension_array_equal(~a, expected)
expected = pd.Series(expected, index=["a", "b", "c"], name="name")
result = ~pd.Series(a, index=["a", "b", "c"], name="name")
tm.assert_series_equal(result, expected)
df = pd.DataFrame({"A": a, "B": [True, False, False]}, index=["a", "b", "c"])
result = ~df
expected = pd.DataFrame(
{"A": expected, "B": [False, True, True]}, index=["a", "b", "c"]
)
tm.assert_frame_equal(result, expected)
|
"""
Tests that work on both the Python and C engines but do not have a
specific classification into the other test modules.
"""
import codecs
import csv
from datetime import datetime
from io import StringIO
import os
import platform
from urllib.error import URLError
import numpy as np
import pytest
from pandas._libs.tslib import Timestamp
from pandas.errors import DtypeWarning, EmptyDataError, ParserError
import pandas.util._test_decorators as td
from pandas import DataFrame, Index, MultiIndex, Series, compat, concat
import pandas._testing as tm
from pandas.io.parsers import CParserWrapper, TextFileReader, TextParser
def test_override_set_noconvert_columns():
# see gh-17351
#
# Usecols needs to be sorted in _set_noconvert_columns based
# on the test_usecols_with_parse_dates test from test_usecols.py
class MyTextFileReader(TextFileReader):
def __init__(self):
self._currow = 0
self.squeeze = False
class MyCParserWrapper(CParserWrapper):
def _set_noconvert_columns(self):
if self.usecols_dtype == "integer":
# self.usecols is a set, which is documented as unordered
# but in practice, a CPython set of integers is sorted.
# In other implementations this assumption does not hold.
# The following code simulates a different order, which
# before GH 17351 would cause the wrong columns to be
# converted via the parse_dates parameter
self.usecols = list(self.usecols)
self.usecols.reverse()
return CParserWrapper._set_noconvert_columns(self)
data = """a,b,c,d,e
0,1,20140101,0900,4
0,1,20140102,1000,4"""
parse_dates = [[1, 2]]
cols = {
"a": [0, 0],
"c_d": [Timestamp("2014-01-01 09:00:00"), Timestamp("2014-01-02 10:00:00")],
}
expected = DataFrame(cols, columns=["c_d", "a"])
parser = MyTextFileReader()
parser.options = {
"usecols": [0, 2, 3],
"parse_dates": parse_dates,
"delimiter": ",",
}
parser._engine = MyCParserWrapper(StringIO(data), **parser.options)
result = parser.read()
tm.assert_frame_equal(result, expected)
def test_empty_decimal_marker(all_parsers):
data = """A|B|C
1|2,334|5
10|13|10.
"""
# Parsers support only length-1 decimals
msg = "Only length-1 decimal markers supported"
parser = all_parsers
with pytest.raises(ValueError, match=msg):
parser.read_csv(StringIO(data), decimal="")
def test_bad_stream_exception(all_parsers, csv_dir_path):
# see gh-13652
#
# This test validates that both the Python engine and C engine will
# raise UnicodeDecodeError instead of C engine raising ParserError
# and swallowing the exception that caused read to fail.
path = os.path.join(csv_dir_path, "sauron.SHIFT_JIS.csv")
codec = codecs.lookup("utf-8")
utf8 = codecs.lookup("utf-8")
parser = all_parsers
msg = "'utf-8' codec can't decode byte"
# Stream must be binary UTF8.
with open(path, "rb") as handle, codecs.StreamRecoder(
handle, utf8.encode, utf8.decode, codec.streamreader, codec.streamwriter
) as stream:
with pytest.raises(UnicodeDecodeError, match=msg):
parser.read_csv(stream)
def test_read_csv_local(all_parsers, csv1):
prefix = "file:///" if compat.is_platform_windows() else "file://"
parser = all_parsers
fname = prefix + str(os.path.abspath(csv1))
result = parser.read_csv(fname, index_col=0, parse_dates=True)
expected = DataFrame(
[
[0.980269, 3.685731, -0.364216805298, -1.159738],
[1.047916, -0.041232, -0.16181208307, 0.212549],
[0.498581, 0.731168, -0.537677223318, 1.346270],
[1.120202, 1.567621, 0.00364077397681, 0.675253],
[-0.487094, 0.571455, -1.6116394093, 0.103469],
[0.836649, 0.246462, 0.588542635376, 1.062782],
[-0.157161, 1.340307, 1.1957779562, -1.097007],
],
columns=["A", "B", "C", "D"],
index=Index(
[
datetime(2000, 1, 3),
datetime(2000, 1, 4),
datetime(2000, 1, 5),
datetime(2000, 1, 6),
datetime(2000, 1, 7),
datetime(2000, 1, 10),
datetime(2000, 1, 11),
],
name="index",
),
)
tm.assert_frame_equal(result, expected)
def test_1000_sep(all_parsers):
parser = all_parsers
data = """A|B|C
1|2,334|5
10|13|10.
"""
expected = DataFrame({"A": [1, 10], "B": [2334, 13], "C": [5, 10.0]})
result = parser.read_csv(StringIO(data), sep="|", thousands=",")
tm.assert_frame_equal(result, expected)
def test_squeeze(all_parsers):
data = """\
a,1
b,2
c,3
"""
parser = all_parsers
index = Index(["a", "b", "c"], name=0)
expected = Series([1, 2, 3], name=1, index=index)
result = parser.read_csv(StringIO(data), index_col=0, header=None, squeeze=True)
tm.assert_series_equal(result, expected)
# see gh-8217
#
# Series should not be a view.
assert not result._is_view
def test_malformed(all_parsers):
# see gh-6607
parser = all_parsers
data = """ignore
A,B,C
1,2,3 # comment
1,2,3,4,5
2,3,4
"""
msg = "Expected 3 fields in line 4, saw 5"
with pytest.raises(ParserError, match=msg):
parser.read_csv(StringIO(data), header=1, comment="#")
@pytest.mark.parametrize("nrows", [5, 3, None])
def test_malformed_chunks(all_parsers, nrows):
data = """ignore
A,B,C
skip
1,2,3
3,5,10 # comment
1,2,3,4,5
2,3,4
"""
parser = all_parsers
msg = "Expected 3 fields in line 6, saw 5"
reader = parser.read_csv(
StringIO(data), header=1, comment="#", iterator=True, chunksize=1, skiprows=[2]
)
with pytest.raises(ParserError, match=msg):
reader.read(nrows)
def test_unnamed_columns(all_parsers):
data = """A,B,C,,
1,2,3,4,5
6,7,8,9,10
11,12,13,14,15
"""
parser = all_parsers
expected = DataFrame(
[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15]],
dtype=np.int64,
columns=["A", "B", "C", "Unnamed: 3", "Unnamed: 4"],
)
result = parser.read_csv(StringIO(data))
tm.assert_frame_equal(result, expected)
def test_csv_mixed_type(all_parsers):
data = """A,B,C
a,1,2
b,3,4
c,4,5
"""
parser = all_parsers
expected = DataFrame({"A": ["a", "b", "c"], "B": [1, 3, 4], "C": [2, 4, 5]})
result = parser.read_csv(StringIO(data))
tm.assert_frame_equal(result, expected)
def test_read_csv_low_memory_no_rows_with_index(all_parsers):
# see gh-21141
parser = all_parsers
if not parser.low_memory:
pytest.skip("This is a low-memory specific test")
data = """A,B,C
1,1,1,2
2,2,3,4
3,3,4,5
"""
result = parser.read_csv(StringIO(data), low_memory=True, index_col=0, nrows=0)
expected = DataFrame(columns=["A", "B", "C"])
tm.assert_frame_equal(result, expected)
def test_read_csv_dataframe(all_parsers, csv1):
parser = all_parsers
result = parser.read_csv(csv1, index_col=0, parse_dates=True)
expected = DataFrame(
[
[0.980269, 3.685731, -0.364216805298, -1.159738],
[1.047916, -0.041232, -0.16181208307, 0.212549],
[0.498581, 0.731168, -0.537677223318, 1.346270],
[1.120202, 1.567621, 0.00364077397681, 0.675253],
[-0.487094, 0.571455, -1.6116394093, 0.103469],
[0.836649, 0.246462, 0.588542635376, 1.062782],
[-0.157161, 1.340307, 1.1957779562, -1.097007],
],
columns=["A", "B", "C", "D"],
index=Index(
[
datetime(2000, 1, 3),
datetime(2000, 1, 4),
datetime(2000, 1, 5),
datetime(2000, 1, 6),
datetime(2000, 1, 7),
datetime(2000, 1, 10),
datetime(2000, 1, 11),
],
name="index",
),
)
tm.assert_frame_equal(result, expected)
def test_read_csv_no_index_name(all_parsers, csv_dir_path):
parser = all_parsers
csv2 = os.path.join(csv_dir_path, "test2.csv")
result = parser.read_csv(csv2, index_col=0, parse_dates=True)
expected = DataFrame(
[
[0.980269, 3.685731, -0.364216805298, -1.159738, "foo"],
[1.047916, -0.041232, -0.16181208307, 0.212549, "bar"],
[0.498581, 0.731168, -0.537677223318, 1.346270, "baz"],
[1.120202, 1.567621, 0.00364077397681, 0.675253, "qux"],
[-0.487094, 0.571455, -1.6116394093, 0.103469, "foo2"],
],
columns=["A", "B", "C", "D", "E"],
index=Index(
[
datetime(2000, 1, 3),
datetime(2000, 1, 4),
datetime(2000, 1, 5),
datetime(2000, 1, 6),
datetime(2000, 1, 7),
]
),
)
tm.assert_frame_equal(result, expected)
def test_read_csv_wrong_num_columns(all_parsers):
# Too few columns.
data = """A,B,C,D,E,F
1,2,3,4,5,6
6,7,8,9,10,11,12
11,12,13,14,15,16
"""
parser = all_parsers
msg = "Expected 6 fields in line 3, saw 7"
with pytest.raises(ParserError, match=msg):
parser.read_csv(StringIO(data))
def test_read_duplicate_index_explicit(all_parsers):
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo,12,13,14,15
bar,12,13,14,15
"""
parser = all_parsers
result = parser.read_csv(StringIO(data), index_col=0)
expected = DataFrame(
[
[2, 3, 4, 5],
[7, 8, 9, 10],
[12, 13, 14, 15],
[12, 13, 14, 15],
[12, 13, 14, 15],
[12, 13, 14, 15],
],
columns=["A", "B", "C", "D"],
index=Index(["foo", "bar", "baz", "qux", "foo", "bar"], name="index"),
)
tm.assert_frame_equal(result, expected)
def test_read_duplicate_index_implicit(all_parsers):
data = """A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo,12,13,14,15
bar,12,13,14,15
"""
parser = all_parsers
result = parser.read_csv(StringIO(data))
expected = DataFrame(
[
[2, 3, 4, 5],
[7, 8, 9, 10],
[12, 13, 14, 15],
[12, 13, 14, 15],
[12, 13, 14, 15],
[12, 13, 14, 15],
],
columns=["A", "B", "C", "D"],
index=Index(["foo", "bar", "baz", "qux", "foo", "bar"]),
)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"data,kwargs,expected",
[
(
"A,B\nTrue,1\nFalse,2\nTrue,3",
dict(),
DataFrame([[True, 1], [False, 2], [True, 3]], columns=["A", "B"]),
),
(
"A,B\nYES,1\nno,2\nyes,3\nNo,3\nYes,3",
dict(true_values=["yes", "Yes", "YES"], false_values=["no", "NO", "No"]),
DataFrame(
[[True, 1], [False, 2], [True, 3], [False, 3], [True, 3]],
columns=["A", "B"],
),
),
(
"A,B\nTRUE,1\nFALSE,2\nTRUE,3",
dict(),
DataFrame([[True, 1], [False, 2], [True, 3]], columns=["A", "B"]),
),
(
"A,B\nfoo,bar\nbar,foo",
dict(true_values=["foo"], false_values=["bar"]),
DataFrame([[True, False], [False, True]], columns=["A", "B"]),
),
],
)
def test_parse_bool(all_parsers, data, kwargs, expected):
parser = all_parsers
result = parser.read_csv(StringIO(data), **kwargs)
tm.assert_frame_equal(result, expected)
def test_int_conversion(all_parsers):
data = """A,B
1.0,1
2.0,2
3.0,3
"""
parser = all_parsers
result = parser.read_csv(StringIO(data))
expected = DataFrame([[1.0, 1], [2.0, 2], [3.0, 3]], columns=["A", "B"])
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("nrows", [3, 3.0])
def test_read_nrows(all_parsers, nrows):
# see gh-10476
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
"""
expected = DataFrame(
[["foo", 2, 3, 4, 5], ["bar", 7, 8, 9, 10], ["baz", 12, 13, 14, 15]],
columns=["index", "A", "B", "C", "D"],
)
parser = all_parsers
result = parser.read_csv(StringIO(data), nrows=nrows)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("nrows", [1.2, "foo", -1])
def test_read_nrows_bad(all_parsers, nrows):
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
"""
msg = r"'nrows' must be an integer >=0"
parser = all_parsers
with pytest.raises(ValueError, match=msg):
parser.read_csv(StringIO(data), nrows=nrows)
@pytest.mark.parametrize("index_col", [0, "index"])
def test_read_chunksize_with_index(all_parsers, index_col):
parser = all_parsers
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
"""
reader = parser.read_csv(StringIO(data), index_col=0, chunksize=2)
expected = DataFrame(
[
["foo", 2, 3, 4, 5],
["bar", 7, 8, 9, 10],
["baz", 12, 13, 14, 15],
["qux", 12, 13, 14, 15],
["foo2", 12, 13, 14, 15],
["bar2", 12, 13, 14, 15],
],
columns=["index", "A", "B", "C", "D"],
)
expected = expected.set_index("index")
chunks = list(reader)
tm.assert_frame_equal(chunks[0], expected[:2])
tm.assert_frame_equal(chunks[1], expected[2:4])
tm.assert_frame_equal(chunks[2], expected[4:])
@pytest.mark.parametrize("chunksize", [1.3, "foo", 0])
def test_read_chunksize_bad(all_parsers, chunksize):
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
"""
parser = all_parsers
msg = r"'chunksize' must be an integer >=1"
with pytest.raises(ValueError, match=msg):
parser.read_csv(StringIO(data), chunksize=chunksize)
@pytest.mark.parametrize("chunksize", [2, 8])
def test_read_chunksize_and_nrows(all_parsers, chunksize):
# see gh-15755
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
"""
parser = all_parsers
kwargs = dict(index_col=0, nrows=5)
reader = parser.read_csv(StringIO(data), chunksize=chunksize, **kwargs)
expected = parser.read_csv(StringIO(data), **kwargs)
tm.assert_frame_equal(concat(reader), expected)
def test_read_chunksize_and_nrows_changing_size(all_parsers):
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
"""
parser = all_parsers
kwargs = dict(index_col=0, nrows=5)
reader = parser.read_csv(StringIO(data), chunksize=8, **kwargs)
expected = parser.read_csv(StringIO(data), **kwargs)
tm.assert_frame_equal(reader.get_chunk(size=2), expected.iloc[:2])
tm.assert_frame_equal(reader.get_chunk(size=4), expected.iloc[2:5])
with pytest.raises(StopIteration, match=""):
reader.get_chunk(size=3)
def test_get_chunk_passed_chunksize(all_parsers):
parser = all_parsers
data = """A,B,C
1,2,3
4,5,6
7,8,9
1,2,3"""
reader = parser.read_csv(StringIO(data), chunksize=2)
result = reader.get_chunk()
expected = DataFrame([[1, 2, 3], [4, 5, 6]], columns=["A", "B", "C"])
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("kwargs", [dict(), dict(index_col=0)])
def test_read_chunksize_compat(all_parsers, kwargs):
# see gh-12185
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
"""
parser = all_parsers
reader = parser.read_csv(StringIO(data), chunksize=2, **kwargs)
result = parser.read_csv(StringIO(data), **kwargs)
tm.assert_frame_equal(concat(reader), result)
def test_read_chunksize_jagged_names(all_parsers):
# see gh-23509
parser = all_parsers
data = "\n".join(["0"] * 7 + [",".join(["0"] * 10)])
expected = DataFrame([[0] + [np.nan] * 9] * 7 + [[0] * 10])
reader = parser.read_csv(StringIO(data), names=range(10), chunksize=4)
result = concat(reader)
tm.assert_frame_equal(result, expected)
def test_read_data_list(all_parsers):
parser = all_parsers
kwargs = dict(index_col=0)
data = "A,B,C\nfoo,1,2,3\nbar,4,5,6"
data_list = [["A", "B", "C"], ["foo", "1", "2", "3"], ["bar", "4", "5", "6"]]
expected = parser.read_csv(StringIO(data), **kwargs)
parser = TextParser(data_list, chunksize=2, **kwargs)
result = parser.read()
tm.assert_frame_equal(result, expected)
def test_iterator(all_parsers):
# see gh-6607
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
"""
parser = all_parsers
kwargs = dict(index_col=0)
expected = parser.read_csv(StringIO(data), **kwargs)
reader = parser.read_csv(StringIO(data), iterator=True, **kwargs)
first_chunk = reader.read(3)
tm.assert_frame_equal(first_chunk, expected[:3])
last_chunk = reader.read(5)
tm.assert_frame_equal(last_chunk, expected[3:])
def test_iterator2(all_parsers):
parser = all_parsers
data = """A,B,C
foo,1,2,3
bar,4,5,6
baz,7,8,9
"""
reader = parser.read_csv(StringIO(data), iterator=True)
result = list(reader)
expected = DataFrame(
[[1, 2, 3], [4, 5, 6], [7, 8, 9]],
index=["foo", "bar", "baz"],
columns=["A", "B", "C"],
)
tm.assert_frame_equal(result[0], expected)
def test_reader_list(all_parsers):
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
"""
parser = all_parsers
kwargs = dict(index_col=0)
lines = list(csv.reader(StringIO(data)))
reader = TextParser(lines, chunksize=2, **kwargs)
expected = parser.read_csv(StringIO(data), **kwargs)
chunks = list(reader)
tm.assert_frame_equal(chunks[0], expected[:2])
tm.assert_frame_equal(chunks[1], expected[2:4])
tm.assert_frame_equal(chunks[2], expected[4:])
def test_reader_list_skiprows(all_parsers):
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
"""
parser = all_parsers
kwargs = dict(index_col=0)
lines = list(csv.reader(StringIO(data)))
reader = TextParser(lines, chunksize=2, skiprows=[1], **kwargs)
expected = parser.read_csv(StringIO(data), **kwargs)
chunks = list(reader)
tm.assert_frame_equal(chunks[0], expected[1:3])
def test_iterator_stop_on_chunksize(all_parsers):
# gh-3967: stopping iteration when chunksize is specified
parser = all_parsers
data = """A,B,C
foo,1,2,3
bar,4,5,6
baz,7,8,9
"""
reader = parser.read_csv(StringIO(data), chunksize=1)
result = list(reader)
assert len(result) == 3
expected = DataFrame(
[[1, 2, 3], [4, 5, 6], [7, 8, 9]],
index=["foo", "bar", "baz"],
columns=["A", "B", "C"],
)
tm.assert_frame_equal(concat(result), expected)
@pytest.mark.parametrize(
"kwargs", [dict(iterator=True, chunksize=1), dict(iterator=True), dict(chunksize=1)]
)
def test_iterator_skipfooter_errors(all_parsers, kwargs):
msg = "'skipfooter' not supported for 'iteration'"
parser = all_parsers
data = "a\n1\n2"
with pytest.raises(ValueError, match=msg):
parser.read_csv(StringIO(data), skipfooter=1, **kwargs)
def test_nrows_skipfooter_errors(all_parsers):
msg = "'skipfooter' not supported with 'nrows'"
data = "a\n1\n2\n3\n4\n5\n6"
parser = all_parsers
with pytest.raises(ValueError, match=msg):
parser.read_csv(StringIO(data), skipfooter=1, nrows=5)
@pytest.mark.parametrize(
"data,kwargs,expected",
[
(
"""foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
""",
dict(index_col=0, names=["index", "A", "B", "C", "D"]),
DataFrame(
[
[2, 3, 4, 5],
[7, 8, 9, 10],
[12, 13, 14, 15],
[12, 13, 14, 15],
[12, 13, 14, 15],
[12, 13, 14, 15],
],
index=Index(["foo", "bar", "baz", "qux", "foo2", "bar2"], name="index"),
columns=["A", "B", "C", "D"],
),
),
(
"""foo,one,2,3,4,5
foo,two,7,8,9,10
foo,three,12,13,14,15
bar,one,12,13,14,15
bar,two,12,13,14,15
""",
dict(index_col=[0, 1], names=["index1", "index2", "A", "B", "C", "D"]),
DataFrame(
[
[2, 3, 4, 5],
[7, 8, 9, 10],
[12, 13, 14, 15],
[12, 13, 14, 15],
[12, 13, 14, 15],
],
index=MultiIndex.from_tuples(
[
("foo", "one"),
("foo", "two"),
("foo", "three"),
("bar", "one"),
("bar", "two"),
],
names=["index1", "index2"],
),
columns=["A", "B", "C", "D"],
),
),
],
)
def test_pass_names_with_index(all_parsers, data, kwargs, expected):
parser = all_parsers
result = parser.read_csv(StringIO(data), **kwargs)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("index_col", [[0, 1], [1, 0]])
def test_multi_index_no_level_names(all_parsers, index_col):
data = """index1,index2,A,B,C,D
foo,one,2,3,4,5
foo,two,7,8,9,10
foo,three,12,13,14,15
bar,one,12,13,14,15
bar,two,12,13,14,15
"""
headless_data = "\n".join(data.split("\n")[1:])
names = ["A", "B", "C", "D"]
parser = all_parsers
result = parser.read_csv(
StringIO(headless_data), index_col=index_col, header=None, names=names
)
expected = parser.read_csv(StringIO(data), index_col=index_col)
# No index names in headless data.
expected.index.names = [None] * 2
tm.assert_frame_equal(result, expected)
def test_multi_index_no_level_names_implicit(all_parsers):
parser = all_parsers
data = """A,B,C,D
foo,one,2,3,4,5
foo,two,7,8,9,10
foo,three,12,13,14,15
bar,one,12,13,14,15
bar,two,12,13,14,15
"""
result = parser.read_csv(StringIO(data))
expected = DataFrame(
[
[2, 3, 4, 5],
[7, 8, 9, 10],
[12, 13, 14, 15],
[12, 13, 14, 15],
[12, 13, 14, 15],
],
columns=["A", "B", "C", "D"],
index=MultiIndex.from_tuples(
[
("foo", "one"),
("foo", "two"),
("foo", "three"),
("bar", "one"),
("bar", "two"),
]
),
)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"data,expected,header",
[
("a,b", DataFrame(columns=["a", "b"]), [0]),
(
"a,b\nc,d",
DataFrame(columns=MultiIndex.from_tuples([("a", "c"), ("b", "d")])),
[0, 1],
),
],
)
@pytest.mark.parametrize("round_trip", [True, False])
def test_multi_index_blank_df(all_parsers, data, expected, header, round_trip):
# see gh-14545
parser = all_parsers
data = expected.to_csv(index=False) if round_trip else data
result = parser.read_csv(StringIO(data), header=header)
tm.assert_frame_equal(result, expected)
def test_no_unnamed_index(all_parsers):
parser = all_parsers
data = """ id c0 c1 c2
0 1 0 a b
1 2 0 c d
2 2 2 e f
"""
result = parser.read_csv(StringIO(data), sep=" ")
expected = DataFrame(
[[0, 1, 0, "a", "b"], [1, 2, 0, "c", "d"], [2, 2, 2, "e", "f"]],
columns=["Unnamed: 0", "id", "c0", "c1", "c2"],
)
tm.assert_frame_equal(result, expected)
def test_read_csv_parse_simple_list(all_parsers):
parser = all_parsers
data = """foo
bar baz
qux foo
foo
bar"""
result = parser.read_csv(StringIO(data), header=None)
expected = DataFrame(["foo", "bar baz", "qux foo", "foo", "bar"])
tm.assert_frame_equal(result, expected)
@tm.network
def test_url(all_parsers, csv_dir_path):
# TODO: FTP testing
parser = all_parsers
kwargs = dict(sep="\t")
url = (
"https://raw.github.com/pandas-dev/pandas/master/"
"pandas/tests/io/parser/data/salaries.csv"
)
url_result = parser.read_csv(url, **kwargs)
local_path = os.path.join(csv_dir_path, "salaries.csv")
local_result = parser.read_csv(local_path, **kwargs)
tm.assert_frame_equal(url_result, local_result)
@pytest.mark.slow
def test_local_file(all_parsers, csv_dir_path):
parser = all_parsers
kwargs = dict(sep="\t")
local_path = os.path.join(csv_dir_path, "salaries.csv")
local_result = parser.read_csv(local_path, **kwargs)
url = "file://localhost/" + local_path
try:
url_result = parser.read_csv(url, **kwargs)
tm.assert_frame_equal(url_result, local_result)
except URLError:
# Fails on some systems.
pytest.skip("Failing on: " + " ".join(platform.uname()))
def test_path_path_lib(all_parsers):
parser = all_parsers
df = tm.makeDataFrame()
result = tm.round_trip_pathlib(df.to_csv, lambda p: parser.read_csv(p, index_col=0))
tm.assert_frame_equal(df, result)
def test_path_local_path(all_parsers):
parser = all_parsers
df = tm.makeDataFrame()
result = tm.round_trip_localpath(
df.to_csv, lambda p: parser.read_csv(p, index_col=0)
)
tm.assert_frame_equal(df, result)
def test_nonexistent_path(all_parsers):
# gh-2428: pls no segfault
# gh-14086: raise more helpful FileNotFoundError
# GH#29233 "File foo" instead of "File b'foo'"
parser = all_parsers
path = f"{tm.rands(10)}.csv"
msg = r"\[Errno 2\]"
with pytest.raises(FileNotFoundError, match=msg) as e:
parser.read_csv(path)
assert path == e.value.filename
@td.skip_if_windows # os.chmod does not work in windows
def test_no_permission(all_parsers):
# GH 23784
parser = all_parsers
msg = r"\[Errno 13\]"
with tm.ensure_clean() as path:
os.chmod(path, 0) # make file unreadable
# verify that this process cannot open the file (not running as sudo)
try:
with open(path):
pass
pytest.skip("Running as sudo.")
except PermissionError:
pass
with pytest.raises(PermissionError, match=msg) as e:
parser.read_csv(path)
assert path == e.value.filename
def test_missing_trailing_delimiters(all_parsers):
parser = all_parsers
data = """A,B,C,D
1,2,3,4
1,3,3,
1,4,5"""
result = parser.read_csv(StringIO(data))
expected = DataFrame(
[[1, 2, 3, 4], [1, 3, 3, np.nan], [1, 4, 5, np.nan]],
columns=["A", "B", "C", "D"],
)
tm.assert_frame_equal(result, expected)
def test_skip_initial_space(all_parsers):
data = (
'"09-Apr-2012", "01:10:18.300", 2456026.548822908, 12849, '
"1.00361, 1.12551, 330.65659, 0355626618.16711, 73.48821, "
"314.11625, 1917.09447, 179.71425, 80.000, 240.000, -350, "
"70.06056, 344.98370, 1, 1, -0.689265, -0.692787, "
"0.212036, 14.7674, 41.605, -9999.0, -9999.0, "
"-9999.0, -9999.0, -9999.0, -9999.0, 000, 012, 128"
)
parser = all_parsers
result = parser.read_csv(
StringIO(data),
names=list(range(33)),
header=None,
na_values=["-9999.0"],
skipinitialspace=True,
)
expected = DataFrame(
[
[
"09-Apr-2012",
"01:10:18.300",
2456026.548822908,
12849,
1.00361,
1.12551,
330.65659,
355626618.16711,
73.48821,
314.11625,
1917.09447,
179.71425,
80.0,
240.0,
-350,
70.06056,
344.9837,
1,
1,
-0.689265,
-0.692787,
0.212036,
14.7674,
41.605,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
0,
12,
128,
]
]
)
tm.assert_frame_equal(result, expected)
def test_trailing_delimiters(all_parsers):
# see gh-2442
data = """A,B,C
1,2,3,
4,5,6,
7,8,9,"""
parser = all_parsers
result = parser.read_csv(StringIO(data), index_col=False)
expected = DataFrame({"A": [1, 4, 7], "B": [2, 5, 8], "C": [3, 6, 9]})
tm.assert_frame_equal(result, expected)
def test_escapechar(all_parsers):
# https://stackoverflow.com/questions/13824840/feature-request-for-
# pandas-read-csv
data = '''SEARCH_TERM,ACTUAL_URL
"bra tv bord","http://www.ikea.com/se/sv/catalog/categories/departments/living_room/10475/?se%7cps%7cnonbranded%7cvardagsrum%7cgoogle%7ctv_bord"
"tv p\xc3\xa5 hjul","http://www.ikea.com/se/sv/catalog/categories/departments/living_room/10475/?se%7cps%7cnonbranded%7cvardagsrum%7cgoogle%7ctv_bord"
"SLAGBORD, \\"Bergslagen\\", IKEA:s 1700-tals series","http://www.ikea.com/se/sv/catalog/categories/departments/living_room/10475/?se%7cps%7cnonbranded%7cvardagsrum%7cgoogle%7ctv_bord"''' # noqa
parser = all_parsers
result = parser.read_csv(
StringIO(data), escapechar="\\", quotechar='"', encoding="utf-8"
)
assert result["SEARCH_TERM"][2] == 'SLAGBORD, "Bergslagen", IKEA:s 1700-tals series'
tm.assert_index_equal(result.columns, Index(["SEARCH_TERM", "ACTUAL_URL"]))
def test_int64_min_issues(all_parsers):
# see gh-2599
parser = all_parsers
data = "A,B\n0,0\n0,"
result = parser.read_csv(StringIO(data))
expected = DataFrame({"A": [0, 0], "B": [0, np.nan]})
tm.assert_frame_equal(result, expected)
def test_parse_integers_above_fp_precision(all_parsers):
data = """Numbers
17007000002000191
17007000002000191
17007000002000191
17007000002000191
17007000002000192
17007000002000192
17007000002000192
17007000002000192
17007000002000192
17007000002000194"""
parser = all_parsers
result = parser.read_csv(StringIO(data))
expected = DataFrame(
{
"Numbers": [
17007000002000191,
17007000002000191,
17007000002000191,
17007000002000191,
17007000002000192,
17007000002000192,
17007000002000192,
17007000002000192,
17007000002000192,
17007000002000194,
]
}
)
tm.assert_frame_equal(result, expected)
def test_chunks_have_consistent_numerical_type(all_parsers):
parser = all_parsers
integers = [str(i) for i in range(499999)]
data = "a\n" + "\n".join(integers + ["1.0", "2.0"] + integers)
# Coercions should work without warnings.
with tm.assert_produces_warning(None):
result = parser.read_csv(StringIO(data))
assert type(result.a[0]) is np.float64
assert result.a.dtype == np.float
def test_warn_if_chunks_have_mismatched_type(all_parsers):
warning_type = None
parser = all_parsers
integers = [str(i) for i in range(499999)]
data = "a\n" + "\n".join(integers + ["a", "b"] + integers)
# see gh-3866: if chunks are different types and can't
# be coerced using numerical types, then issue warning.
if parser.engine == "c" and parser.low_memory:
warning_type = DtypeWarning
with tm.assert_produces_warning(warning_type):
df = parser.read_csv(StringIO(data))
assert df.a.dtype == np.object
@pytest.mark.parametrize("sep", [" ", r"\s+"])
def test_integer_overflow_bug(all_parsers, sep):
# see gh-2601
data = "65248E10 11\n55555E55 22\n"
parser = all_parsers
result = parser.read_csv(StringIO(data), header=None, sep=sep)
expected = DataFrame([[6.5248e14, 11], [5.5555e59, 22]])
tm.assert_frame_equal(result, expected)
def test_catch_too_many_names(all_parsers):
# see gh-5156
data = """\
1,2,3
4,,6
7,8,9
10,11,12\n"""
parser = all_parsers
msg = (
"Too many columns specified: expected 4 and found 3"
if parser.engine == "c"
else "Number of passed names did not match "
"number of header fields in the file"
)
with pytest.raises(ValueError, match=msg):
parser.read_csv(StringIO(data), header=0, names=["a", "b", "c", "d"])
def test_ignore_leading_whitespace(all_parsers):
# see gh-3374, gh-6607
parser = all_parsers
data = " a b c\n 1 2 3\n 4 5 6\n 7 8 9"
result = parser.read_csv(StringIO(data), sep=r"\s+")
expected = DataFrame({"a": [1, 4, 7], "b": [2, 5, 8], "c": [3, 6, 9]})
tm.assert_frame_equal(result, expected)
def test_chunk_begins_with_newline_whitespace(all_parsers):
# see gh-10022
parser = all_parsers
data = "\n hello\nworld\n"
result = parser.read_csv(StringIO(data), header=None)
expected = DataFrame([" hello", "world"])
tm.assert_frame_equal(result, expected)
def test_empty_with_index(all_parsers):
# see gh-10184
data = "x,y"
parser = all_parsers
result = parser.read_csv(StringIO(data), index_col=0)
expected = DataFrame(columns=["y"], index=Index([], name="x"))
tm.assert_frame_equal(result, expected)
def test_empty_with_multi_index(all_parsers):
# see gh-10467
data = "x,y,z"
parser = all_parsers
result = parser.read_csv(StringIO(data), index_col=["x", "y"])
expected = DataFrame(
columns=["z"], index=MultiIndex.from_arrays([[]] * 2, names=["x", "y"])
)
tm.assert_frame_equal(result, expected)
def test_empty_with_reversed_multi_index(all_parsers):
data = "x,y,z"
parser = all_parsers
result = parser.read_csv(StringIO(data), index_col=[1, 0])
expected = DataFrame(
columns=["z"], index=MultiIndex.from_arrays([[]] * 2, names=["y", "x"])
)
tm.assert_frame_equal(result, expected)
def test_float_parser(all_parsers):
# see gh-9565
parser = all_parsers
data = "45e-1,4.5,45.,inf,-inf"
result = parser.read_csv(StringIO(data), header=None)
expected = DataFrame([[float(s) for s in data.split(",")]])
tm.assert_frame_equal(result, expected)
def test_scientific_no_exponent(all_parsers):
# see gh-12215
df = DataFrame.from_dict({"w": ["2e"], "x": ["3E"], "y": ["42e"], "z": ["632E"]})
data = df.to_csv(index=False)
parser = all_parsers
for precision in parser.float_precision_choices:
df_roundtrip = parser.read_csv(StringIO(data), float_precision=precision)
tm.assert_frame_equal(df_roundtrip, df)
@pytest.mark.parametrize("conv", [None, np.int64, np.uint64])
def test_int64_overflow(all_parsers, conv):
data = """ID
00013007854817840016671868
00013007854817840016749251
00013007854817840016754630
00013007854817840016781876
00013007854817840017028824
00013007854817840017963235
00013007854817840018860166"""
parser = all_parsers
if conv is None:
# 13007854817840016671868 > UINT64_MAX, so this
# will overflow and return object as the dtype.
result = parser.read_csv(StringIO(data))
expected = DataFrame(
[
"00013007854817840016671868",
"00013007854817840016749251",
"00013007854817840016754630",
"00013007854817840016781876",
"00013007854817840017028824",
"00013007854817840017963235",
"00013007854817840018860166",
],
columns=["ID"],
)
tm.assert_frame_equal(result, expected)
else:
# 13007854817840016671868 > UINT64_MAX, so attempts
# to cast to either int64 or uint64 will result in
# an OverflowError being raised.
msg = (
"(Python int too large to convert to C long)|"
"(long too big to convert)|"
"(int too big to convert)"
)
with pytest.raises(OverflowError, match=msg):
parser.read_csv(StringIO(data), converters={"ID": conv})
@pytest.mark.parametrize(
"val", [np.iinfo(np.uint64).max, np.iinfo(np.int64).max, np.iinfo(np.int64).min]
)
def test_int64_uint64_range(all_parsers, val):
# These numbers fall right inside the int64-uint64
# range, so they should be parsed as string.
parser = all_parsers
result = parser.read_csv(StringIO(str(val)), header=None)
expected = DataFrame([val])
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"val", [np.iinfo(np.uint64).max + 1, np.iinfo(np.int64).min - 1]
)
def test_outside_int64_uint64_range(all_parsers, val):
# These numbers fall just outside the int64-uint64
# range, so they should be parsed as string.
parser = all_parsers
result = parser.read_csv(StringIO(str(val)), header=None)
expected = DataFrame([str(val)])
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("exp_data", [[str(-1), str(2 ** 63)], [str(2 ** 63), str(-1)]])
def test_numeric_range_too_wide(all_parsers, exp_data):
# No numerical dtype can hold both negative and uint64
# values, so they should be cast as string.
parser = all_parsers
data = "\n".join(exp_data)
expected = DataFrame(exp_data)
result = parser.read_csv(StringIO(data), header=None)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("iterator", [True, False])
def test_empty_with_nrows_chunksize(all_parsers, iterator):
# see gh-9535
parser = all_parsers
expected = DataFrame(columns=["foo", "bar"])
nrows = 10
data = StringIO("foo,bar\n")
if iterator:
result = next(iter(parser.read_csv(data, chunksize=nrows)))
else:
result = parser.read_csv(data, nrows=nrows)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"data,kwargs,expected,msg",
[
# gh-10728: WHITESPACE_LINE
(
"a,b,c\n4,5,6\n ",
dict(),
DataFrame([[4, 5, 6]], columns=["a", "b", "c"]),
None,
),
# gh-10548: EAT_LINE_COMMENT
(
"a,b,c\n4,5,6\n#comment",
dict(comment="#"),
DataFrame([[4, 5, 6]], columns=["a", "b", "c"]),
None,
),
# EAT_CRNL_NOP
(
"a,b,c\n4,5,6\n\r",
dict(),
DataFrame([[4, 5, 6]], columns=["a", "b", "c"]),
None,
),
# EAT_COMMENT
(
"a,b,c\n4,5,6#comment",
dict(comment="#"),
DataFrame([[4, 5, 6]], columns=["a", "b", "c"]),
None,
),
# SKIP_LINE
(
"a,b,c\n4,5,6\nskipme",
dict(skiprows=[2]),
DataFrame([[4, 5, 6]], columns=["a", "b", "c"]),
None,
),
# EAT_LINE_COMMENT
(
"a,b,c\n4,5,6\n#comment",
dict(comment="#", skip_blank_lines=False),
DataFrame([[4, 5, 6]], columns=["a", "b", "c"]),
None,
),
# IN_FIELD
(
"a,b,c\n4,5,6\n ",
dict(skip_blank_lines=False),
DataFrame([["4", 5, 6], [" ", None, None]], columns=["a", "b", "c"]),
None,
),
# EAT_CRNL
(
"a,b,c\n4,5,6\n\r",
dict(skip_blank_lines=False),
DataFrame([[4, 5, 6], [None, None, None]], columns=["a", "b", "c"]),
None,
),
# ESCAPED_CHAR
(
"a,b,c\n4,5,6\n\\",
dict(escapechar="\\"),
None,
"(EOF following escape character)|(unexpected end of data)",
),
# ESCAPE_IN_QUOTED_FIELD
(
'a,b,c\n4,5,6\n"\\',
dict(escapechar="\\"),
None,
"(EOF inside string starting at row 2)|(unexpected end of data)",
),
# IN_QUOTED_FIELD
(
'a,b,c\n4,5,6\n"',
dict(escapechar="\\"),
None,
"(EOF inside string starting at row 2)|(unexpected end of data)",
),
],
ids=[
"whitespace-line",
"eat-line-comment",
"eat-crnl-nop",
"eat-comment",
"skip-line",
"eat-line-comment",
"in-field",
"eat-crnl",
"escaped-char",
"escape-in-quoted-field",
"in-quoted-field",
],
)
def test_eof_states(all_parsers, data, kwargs, expected, msg):
# see gh-10728, gh-10548
parser = all_parsers
if expected is None:
with pytest.raises(ParserError, match=msg):
parser.read_csv(StringIO(data), **kwargs)
else:
result = parser.read_csv(StringIO(data), **kwargs)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("usecols", [None, [0, 1], ["a", "b"]])
def test_uneven_lines_with_usecols(all_parsers, usecols):
# see gh-12203
parser = all_parsers
data = r"""a,b,c
0,1,2
3,4,5,6,7
8,9,10"""
if usecols is None:
# Make sure that an error is still raised
# when the "usecols" parameter is not provided.
msg = r"Expected \d+ fields in line \d+, saw \d+"
with pytest.raises(ParserError, match=msg):
parser.read_csv(StringIO(data))
else:
expected = DataFrame({"a": [0, 3, 8], "b": [1, 4, 9]})
result = parser.read_csv(StringIO(data), usecols=usecols)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"data,kwargs,expected",
[
# First, check to see that the response of parser when faced with no
# provided columns raises the correct error, with or without usecols.
("", dict(), None),
("", dict(usecols=["X"]), None),
(
",,",
dict(names=["Dummy", "X", "Dummy_2"], usecols=["X"]),
DataFrame(columns=["X"], index=[0], dtype=np.float64),
),
(
"",
dict(names=["Dummy", "X", "Dummy_2"], usecols=["X"]),
DataFrame(columns=["X"]),
),
],
)
def test_read_empty_with_usecols(all_parsers, data, kwargs, expected):
# see gh-12493
parser = all_parsers
if expected is None:
msg = "No columns to parse from file"
with pytest.raises(EmptyDataError, match=msg):
parser.read_csv(StringIO(data), **kwargs)
else:
result = parser.read_csv(StringIO(data), **kwargs)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"kwargs,expected",
[
# gh-8661, gh-8679: this should ignore six lines, including
# lines with trailing whitespace and blank lines.
(
dict(
header=None,
delim_whitespace=True,
skiprows=[0, 1, 2, 3, 5, 6],
skip_blank_lines=True,
),
DataFrame([[1.0, 2.0, 4.0], [5.1, np.nan, 10.0]]),
),
# gh-8983: test skipping set of rows after a row with trailing spaces.
(
dict(
delim_whitespace=True, skiprows=[1, 2, 3, 5, 6], skip_blank_lines=True
),
DataFrame({"A": [1.0, 5.1], "B": [2.0, np.nan], "C": [4.0, 10]}),
),
],
)
def test_trailing_spaces(all_parsers, kwargs, expected):
data = "A B C \nrandom line with trailing spaces \nskip\n1,2,3\n1,2.,4.\nrandom line with trailing tabs\t\t\t\n \n5.1,NaN,10.0\n" # noqa
parser = all_parsers
result = parser.read_csv(StringIO(data.replace(",", " ")), **kwargs)
tm.assert_frame_equal(result, expected)
def test_raise_on_sep_with_delim_whitespace(all_parsers):
# see gh-6607
data = "a b c\n1 2 3"
parser = all_parsers
with pytest.raises(ValueError, match="you can only specify one"):
parser.read_csv(StringIO(data), sep=r"\s", delim_whitespace=True)
@pytest.mark.parametrize("delim_whitespace", [True, False])
def test_single_char_leading_whitespace(all_parsers, delim_whitespace):
# see gh-9710
parser = all_parsers
data = """\
MyColumn
a
b
a
b\n"""
expected = DataFrame({"MyColumn": list("abab")})
result = parser.read_csv(
StringIO(data), skipinitialspace=True, delim_whitespace=delim_whitespace
)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"sep,skip_blank_lines,exp_data",
[
(",", True, [[1.0, 2.0, 4.0], [5.0, np.nan, 10.0], [-70.0, 0.4, 1.0]]),
(r"\s+", True, [[1.0, 2.0, 4.0], [5.0, np.nan, 10.0], [-70.0, 0.4, 1.0]]),
(
",",
False,
[
[1.0, 2.0, 4.0],
[np.nan, np.nan, np.nan],
[np.nan, np.nan, np.nan],
[5.0, np.nan, 10.0],
[np.nan, np.nan, np.nan],
[-70.0, 0.4, 1.0],
],
),
],
)
def test_empty_lines(all_parsers, sep, skip_blank_lines, exp_data):
parser = all_parsers
data = """\
A,B,C
1,2.,4.
5.,NaN,10.0
-70,.4,1
"""
if sep == r"\s+":
data = data.replace(",", " ")
result = parser.read_csv(StringIO(data), sep=sep, skip_blank_lines=skip_blank_lines)
expected = DataFrame(exp_data, columns=["A", "B", "C"])
tm.assert_frame_equal(result, expected)
def test_whitespace_lines(all_parsers):
parser = all_parsers
data = """
\t \t\t
\t
A,B,C
\t 1,2.,4.
5.,NaN,10.0
"""
expected = DataFrame([[1, 2.0, 4.0], [5.0, np.nan, 10.0]], columns=["A", "B", "C"])
result = parser.read_csv(StringIO(data))
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"data,expected",
[
(
""" A B C D
a 1 2 3 4
b 1 2 3 4
c 1 2 3 4
""",
DataFrame(
[[1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4]],
columns=["A", "B", "C", "D"],
index=["a", "b", "c"],
),
),
(
" a b c\n1 2 3 \n4 5 6\n 7 8 9",
DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], columns=["a", "b", "c"]),
),
],
)
def test_whitespace_regex_separator(all_parsers, data, expected):
# see gh-6607
parser = all_parsers
result = parser.read_csv(StringIO(data), sep=r"\s+")
tm.assert_frame_equal(result, expected)
def test_verbose_read(all_parsers, capsys):
parser = all_parsers
data = """a,b,c,d
one,1,2,3
one,1,2,3
,1,2,3
one,1,2,3
,1,2,3
,1,2,3
one,1,2,3
two,1,2,3"""
# Engines are verbose in different ways.
parser.read_csv(StringIO(data), verbose=True)
captured = capsys.readouterr()
if parser.engine == "c":
assert "Tokenization took:" in captured.out
assert "Parser memory cleanup took:" in captured.out
else: # Python engine
assert captured.out == "Filled 3 NA values in column a\n"
def test_verbose_read2(all_parsers, capsys):
parser = all_parsers
data = """a,b,c,d
one,1,2,3
two,1,2,3
three,1,2,3
four,1,2,3
five,1,2,3
,1,2,3
seven,1,2,3
eight,1,2,3"""
parser.read_csv(StringIO(data), verbose=True, index_col=0)
captured = capsys.readouterr()
# Engines are verbose in different ways.
if parser.engine == "c":
assert "Tokenization took:" in captured.out
assert "Parser memory cleanup took:" in captured.out
else: # Python engine
assert captured.out == "Filled 1 NA values in column a\n"
def test_iteration_open_handle(all_parsers):
parser = all_parsers
kwargs = dict(squeeze=True, header=None)
with tm.ensure_clean() as path:
with open(path, "w") as f:
f.write("AAA\nBBB\nCCC\nDDD\nEEE\nFFF\nGGG")
with open(path, "r") as f:
for line in f:
if "CCC" in line:
break
result = parser.read_csv(f, **kwargs)
expected = Series(["DDD", "EEE", "FFF", "GGG"], name=0)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"data,thousands,decimal",
[
(
"""A|B|C
1|2,334.01|5
10|13|10.
""",
",",
".",
),
(
"""A|B|C
1|2.334,01|5
10|13|10,
""",
".",
",",
),
],
)
def test_1000_sep_with_decimal(all_parsers, data, thousands, decimal):
parser = all_parsers
expected = DataFrame({"A": [1, 10], "B": [2334.01, 13], "C": [5, 10.0]})
result = parser.read_csv(
StringIO(data), sep="|", thousands=thousands, decimal=decimal
)
tm.assert_frame_equal(result, expected)
def test_euro_decimal_format(all_parsers):
parser = all_parsers
data = """Id;Number1;Number2;Text1;Text2;Number3
1;1521,1541;187101,9543;ABC;poi;4,738797819
2;121,12;14897,76;DEF;uyt;0,377320872
3;878,158;108013,434;GHI;rez;2,735694704"""
result = parser.read_csv(StringIO(data), sep=";", decimal=",")
expected = DataFrame(
[
[1, 1521.1541, 187101.9543, "ABC", "poi", 4.738797819],
[2, 121.12, 14897.76, "DEF", "uyt", 0.377320872],
[3, 878.158, 108013.434, "GHI", "rez", 2.735694704],
],
columns=["Id", "Number1", "Number2", "Text1", "Text2", "Number3"],
)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("na_filter", [True, False])
def test_inf_parsing(all_parsers, na_filter):
parser = all_parsers
data = """\
,A
a,inf
b,-inf
c,+Inf
d,-Inf
e,INF
f,-INF
g,+INf
h,-INf
i,inF
j,-inF"""
expected = DataFrame(
{"A": [float("inf"), float("-inf")] * 5},
index=["a", "b", "c", "d", "e", "f", "g", "h", "i", "j"],
)
result = parser.read_csv(StringIO(data), index_col=0, na_filter=na_filter)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("na_filter", [True, False])
def test_infinity_parsing(all_parsers, na_filter):
parser = all_parsers
data = """\
,A
a,Infinity
b,-Infinity
c,+Infinity
"""
expected = DataFrame(
{"A": [float("infinity"), float("-infinity"), float("+infinity")]},
index=["a", "b", "c"],
)
result = parser.read_csv(StringIO(data), index_col=0, na_filter=na_filter)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("nrows", [0, 1, 2, 3, 4, 5])
def test_raise_on_no_columns(all_parsers, nrows):
parser = all_parsers
data = "\n" * nrows
msg = "No columns to parse from file"
with pytest.raises(EmptyDataError, match=msg):
parser.read_csv(StringIO(data))
def test_memory_map(all_parsers, csv_dir_path):
mmap_file = os.path.join(csv_dir_path, "test_mmap.csv")
parser = all_parsers
expected = DataFrame(
{"a": [1, 2, 3], "b": ["one", "two", "three"], "c": ["I", "II", "III"]}
)
result = parser.read_csv(mmap_file, memory_map=True)
tm.assert_frame_equal(result, expected)
def test_null_byte_char(all_parsers):
# see gh-2741
data = "\x00,foo"
names = ["a", "b"]
parser = all_parsers
if parser.engine == "c":
expected = DataFrame([[np.nan, "foo"]], columns=names)
out = parser.read_csv(StringIO(data), names=names)
tm.assert_frame_equal(out, expected)
else:
msg = "NULL byte detected"
with pytest.raises(ParserError, match=msg):
parser.read_csv(StringIO(data), names=names)
def test_temporary_file(all_parsers):
# see gh-13398
parser = all_parsers
data = "0 0"
with tm.ensure_clean(mode="w+", return_filelike=True) as new_file:
new_file.write(data)
new_file.flush()
new_file.seek(0)
result = parser.read_csv(new_file, sep=r"\s+", header=None)
expected = DataFrame([[0, 0]])
tm.assert_frame_equal(result, expected)
def test_internal_eof_byte(all_parsers):
# see gh-5500
parser = all_parsers
data = "a,b\n1\x1a,2"
expected = DataFrame([["1\x1a", 2]], columns=["a", "b"])
result = parser.read_csv(StringIO(data))
tm.assert_frame_equal(result, expected)
def test_internal_eof_byte_to_file(all_parsers):
# see gh-16559
parser = all_parsers
data = b'c1,c2\r\n"test \x1a test", test\r\n'
expected = DataFrame([["test \x1a test", " test"]], columns=["c1", "c2"])
path = f"__{tm.rands(10)}__.csv"
with tm.ensure_clean(path) as path:
with open(path, "wb") as f:
f.write(data)
result = parser.read_csv(path)
tm.assert_frame_equal(result, expected)
def test_sub_character(all_parsers, csv_dir_path):
# see gh-16893
filename = os.path.join(csv_dir_path, "sub_char.csv")
expected = DataFrame([[1, 2, 3]], columns=["a", "\x1ab", "c"])
parser = all_parsers
result = parser.read_csv(filename)
tm.assert_frame_equal(result, expected)
def test_file_handle_string_io(all_parsers):
# gh-14418
#
# Don't close user provided file handles.
parser = all_parsers
data = "a,b\n1,2"
fh = StringIO(data)
parser.read_csv(fh)
assert not fh.closed
def test_file_handles_with_open(all_parsers, csv1):
# gh-14418
#
# Don't close user provided file handles.
parser = all_parsers
for mode in ["r", "rb"]:
with open(csv1, mode) as f:
parser.read_csv(f)
assert not f.closed
def test_invalid_file_buffer_class(all_parsers):
# see gh-15337
class InvalidBuffer:
pass
parser = all_parsers
msg = "Invalid file path or buffer object type"
with pytest.raises(ValueError, match=msg):
parser.read_csv(InvalidBuffer())
def test_invalid_file_buffer_mock(all_parsers):
# see gh-15337
parser = all_parsers
msg = "Invalid file path or buffer object type"
class Foo:
pass
with pytest.raises(ValueError, match=msg):
parser.read_csv(Foo())
def test_valid_file_buffer_seems_invalid(all_parsers):
# gh-16135: we want to ensure that "tell" and "seek"
# aren't actually being used when we call `read_csv`
#
# Thus, while the object may look "invalid" (these
# methods are attributes of the `StringIO` class),
# it is still a valid file-object for our purposes.
class NoSeekTellBuffer(StringIO):
def tell(self):
raise AttributeError("No tell method")
def seek(self, pos, whence=0):
raise AttributeError("No seek method")
data = "a\n1"
parser = all_parsers
expected = DataFrame({"a": [1]})
result = parser.read_csv(NoSeekTellBuffer(data))
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"kwargs",
[dict(), dict(error_bad_lines=True)], # Default is True. # Explicitly pass in.
)
@pytest.mark.parametrize(
"warn_kwargs", [dict(), dict(warn_bad_lines=True), dict(warn_bad_lines=False)]
)
def test_error_bad_lines(all_parsers, kwargs, warn_kwargs):
# see gh-15925
parser = all_parsers
kwargs.update(**warn_kwargs)
data = "a\n1\n1,2,3\n4\n5,6,7"
msg = "Expected 1 fields in line 3, saw 3"
with pytest.raises(ParserError, match=msg):
parser.read_csv(StringIO(data), **kwargs)
def test_warn_bad_lines(all_parsers, capsys):
# see gh-15925
parser = all_parsers
data = "a\n1\n1,2,3\n4\n5,6,7"
expected = DataFrame({"a": [1, 4]})
result = parser.read_csv(StringIO(data), error_bad_lines=False, warn_bad_lines=True)
tm.assert_frame_equal(result, expected)
captured = capsys.readouterr()
assert "Skipping line 3" in captured.err
assert "Skipping line 5" in captured.err
def test_suppress_error_output(all_parsers, capsys):
# see gh-15925
parser = all_parsers
data = "a\n1\n1,2,3\n4\n5,6,7"
expected = DataFrame({"a": [1, 4]})
result = parser.read_csv(
StringIO(data), error_bad_lines=False, warn_bad_lines=False
)
tm.assert_frame_equal(result, expected)
captured = capsys.readouterr()
assert captured.err == ""
@pytest.mark.parametrize("filename", ["sé-es-vé.csv", "ru-sй.csv", "中文文件名.csv"])
def test_filename_with_special_chars(all_parsers, filename):
# see gh-15086.
parser = all_parsers
df = DataFrame({"a": [1, 2, 3]})
with tm.ensure_clean(filename) as path:
df.to_csv(path, index=False)
result = parser.read_csv(path)
tm.assert_frame_equal(result, df)
def test_read_csv_memory_growth_chunksize(all_parsers):
# see gh-24805
#
# Let's just make sure that we don't crash
# as we iteratively process all chunks.
parser = all_parsers
with tm.ensure_clean() as path:
with open(path, "w") as f:
for i in range(1000):
f.write(str(i) + "\n")
result = parser.read_csv(path, chunksize=20)
for _ in result:
pass
def test_read_csv_raises_on_header_prefix(all_parsers):
# gh-27394
parser = all_parsers
msg = "Argument prefix must be None if argument header is not None"
s = StringIO("0,1\n2,3")
with pytest.raises(ValueError, match=msg):
parser.read_csv(s, header=0, prefix="_X")
def test_read_table_equivalency_to_read_csv(all_parsers):
# see gh-21948
# As of 0.25.0, read_table is undeprecated
parser = all_parsers
data = "a\tb\n1\t2\n3\t4"
expected = parser.read_csv(StringIO(data), sep="\t")
result = parser.read_table(StringIO(data))
tm.assert_frame_equal(result, expected)
def test_first_row_bom(all_parsers):
# see gh-26545
parser = all_parsers
data = '''\ufeff"Head1" "Head2" "Head3"'''
result = parser.read_csv(StringIO(data), delimiter="\t")
expected = DataFrame(columns=["Head1", "Head2", "Head3"])
tm.assert_frame_equal(result, expected)
def test_integer_precision(all_parsers):
# Gh 7072
s = """1,1;0;0;0;1;1;3844;3844;3844;1;1;1;1;1;1;0;0;1;1;0;0,,,4321583677327450765
5,1;0;0;0;1;1;843;843;843;1;1;1;1;1;1;0;0;1;1;0;0,64.0,;,4321113141090630389"""
parser = all_parsers
result = parser.read_csv(StringIO(s), header=None)[4]
expected = Series([4321583677327450765, 4321113141090630389], name=4)
tm.assert_series_equal(result, expected)
def test_file_descriptor_leak(all_parsers):
# GH 31488
parser = all_parsers
with tm.ensure_clean() as path:
def test():
with pytest.raises(EmptyDataError, match="No columns to parse from file"):
parser.read_csv(path)
td.check_file_leaks(test)()
@pytest.mark.parametrize("nrows", range(1, 6))
def test_blank_lines_between_header_and_data_rows(all_parsers, nrows):
# GH 28071
ref = DataFrame(
[[np.nan, np.nan], [np.nan, np.nan], [1, 2], [np.nan, np.nan], [3, 4]],
columns=list("ab"),
)
csv = "\nheader\n\na,b\n\n\n1,2\n\n3,4"
parser = all_parsers
df = parser.read_csv(StringIO(csv), header=3, nrows=nrows, skip_blank_lines=False)
tm.assert_frame_equal(df, ref[:nrows])
def test_no_header_two_extra_columns(all_parsers):
# GH 26218
column_names = ["one", "two", "three"]
ref = DataFrame([["foo", "bar", "baz"]], columns=column_names)
stream = StringIO("foo,bar,baz,bam,blah")
parser = all_parsers
df = parser.read_csv(stream, header=None, names=column_names, index_col=False)
tm.assert_frame_equal(df, ref)
|
|
pandas/tests/io/parser/test_common.py
|
pandas/tests/arrays/boolean/test_ops.py
|
from contextlib import contextmanager
from pandas.plotting._core import _get_plot_backend
def table(ax, data, rowLabels=None, colLabels=None, **kwargs):
"""
Helper function to convert DataFrame and Series to matplotlib.table.
Parameters
----------
ax : Matplotlib axes object
data : DataFrame or Series
Data for table contents.
**kwargs
Keyword arguments to be passed to matplotlib.table.table.
If `rowLabels` or `colLabels` is not specified, data index or column
name will be used.
Returns
-------
matplotlib table object
"""
plot_backend = _get_plot_backend("matplotlib")
return plot_backend.table(
ax=ax, data=data, rowLabels=None, colLabels=None, **kwargs
)
def register():
"""
Register pandas formatters and converters with matplotlib.
This function modifies the global ``matplotlib.units.registry``
dictionary. pandas adds custom converters for
* pd.Timestamp
* pd.Period
* np.datetime64
* datetime.datetime
* datetime.date
* datetime.time
See Also
--------
deregister_matplotlib_converters : Remove pandas formatters and converters.
"""
plot_backend = _get_plot_backend("matplotlib")
plot_backend.register()
def deregister():
"""
Remove pandas formatters and converters.
Removes the custom converters added by :func:`register`. This
attempts to set the state of the registry back to the state before
pandas registered its own units. Converters for pandas' own types like
Timestamp and Period are removed completely. Converters for types
pandas overwrites, like ``datetime.datetime``, are restored to their
original value.
See Also
--------
register_matplotlib_converters : Register pandas formatters and converters
with matplotlib.
"""
plot_backend = _get_plot_backend("matplotlib")
plot_backend.deregister()
def scatter_matrix(
frame,
alpha=0.5,
figsize=None,
ax=None,
grid=False,
diagonal="hist",
marker=".",
density_kwds=None,
hist_kwds=None,
range_padding=0.05,
**kwargs,
):
"""
Draw a matrix of scatter plots.
Parameters
----------
frame : DataFrame
alpha : float, optional
Amount of transparency applied.
figsize : (float,float), optional
A tuple (width, height) in inches.
ax : Matplotlib axis object, optional
grid : bool, optional
Setting this to True will show the grid.
diagonal : {'hist', 'kde'}
Pick between 'kde' and 'hist' for either Kernel Density Estimation or
Histogram plot in the diagonal.
marker : str, optional
Matplotlib marker type, default '.'.
density_kwds : keywords
Keyword arguments to be passed to kernel density estimate plot.
hist_kwds : keywords
Keyword arguments to be passed to hist function.
range_padding : float, default 0.05
Relative extension of axis range in x and y with respect to
(x_max - x_min) or (y_max - y_min).
**kwargs
Keyword arguments to be passed to scatter function.
Returns
-------
numpy.ndarray
A matrix of scatter plots.
Examples
--------
.. plot::
:context: close-figs
>>> df = pd.DataFrame(np.random.randn(1000, 4), columns=['A','B','C','D'])
>>> pd.plotting.scatter_matrix(df, alpha=0.2)
"""
plot_backend = _get_plot_backend("matplotlib")
return plot_backend.scatter_matrix(
frame=frame,
alpha=alpha,
figsize=figsize,
ax=ax,
grid=grid,
diagonal=diagonal,
marker=marker,
density_kwds=density_kwds,
hist_kwds=hist_kwds,
range_padding=range_padding,
**kwargs,
)
def radviz(frame, class_column, ax=None, color=None, colormap=None, **kwds):
"""
Plot a multidimensional dataset in 2D.
Each Series in the DataFrame is represented as a evenly distributed
slice on a circle. Each data point is rendered in the circle according to
the value on each Series. Highly correlated `Series` in the `DataFrame`
are placed closer on the unit circle.
RadViz allow to project a N-dimensional data set into a 2D space where the
influence of each dimension can be interpreted as a balance between the
influence of all dimensions.
More info available at the `original article
<https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.135.889>`_
describing RadViz.
Parameters
----------
frame : `DataFrame`
pandas object holding the data.
class_column : str
Column name containing the name of the data point category.
ax : :class:`matplotlib.axes.Axes`, optional
A plot instance to which to add the information.
color : list[str] or tuple[str], optional
Assign a color to each category. Example: ['blue', 'green'].
colormap : str or :class:`matplotlib.colors.Colormap`, default None
Colormap to select colors from. If string, load colormap with that
name from matplotlib.
**kwds
Options to pass to matplotlib scatter plotting method.
Returns
-------
class:`matplotlib.axes.Axes`
See Also
--------
plotting.andrews_curves : Plot clustering visualization.
Examples
--------
.. plot::
:context: close-figs
>>> df = pd.DataFrame(
... {
... 'SepalLength': [6.5, 7.7, 5.1, 5.8, 7.6, 5.0, 5.4, 4.6, 6.7, 4.6],
... 'SepalWidth': [3.0, 3.8, 3.8, 2.7, 3.0, 2.3, 3.0, 3.2, 3.3, 3.6],
... 'PetalLength': [5.5, 6.7, 1.9, 5.1, 6.6, 3.3, 4.5, 1.4, 5.7, 1.0],
... 'PetalWidth': [1.8, 2.2, 0.4, 1.9, 2.1, 1.0, 1.5, 0.2, 2.1, 0.2],
... 'Category': [
... 'virginica',
... 'virginica',
... 'setosa',
... 'virginica',
... 'virginica',
... 'versicolor',
... 'versicolor',
... 'setosa',
... 'virginica',
... 'setosa'
... ]
... }
... )
>>> pd.plotting.radviz(df, 'Category')
"""
plot_backend = _get_plot_backend("matplotlib")
return plot_backend.radviz(
frame=frame,
class_column=class_column,
ax=ax,
color=color,
colormap=colormap,
**kwds,
)
def andrews_curves(
frame, class_column, ax=None, samples=200, color=None, colormap=None, **kwargs
):
"""
Generate a matplotlib plot of Andrews curves, for visualising clusters of
multivariate data.
Andrews curves have the functional form:
f(t) = x_1/sqrt(2) + x_2 sin(t) + x_3 cos(t) +
x_4 sin(2t) + x_5 cos(2t) + ...
Where x coefficients correspond to the values of each dimension and t is
linearly spaced between -pi and +pi. Each row of frame then corresponds to
a single curve.
Parameters
----------
frame : DataFrame
Data to be plotted, preferably normalized to (0.0, 1.0).
class_column : Name of the column containing class names
ax : matplotlib axes object, default None
samples : Number of points to plot in each curve
color : list or tuple, optional
Colors to use for the different classes.
colormap : str or matplotlib colormap object, default None
Colormap to select colors from. If string, load colormap with that name
from matplotlib.
**kwargs
Options to pass to matplotlib plotting method.
Returns
-------
class:`matplotlip.axis.Axes`
Examples
--------
.. plot::
:context: close-figs
>>> df = pd.read_csv(
... 'https://raw.github.com/pandas-dev/'
... 'pandas/master/pandas/tests/io/data/csv/iris.csv'
... )
>>> pd.plotting.andrews_curves(df, 'Name')
"""
plot_backend = _get_plot_backend("matplotlib")
return plot_backend.andrews_curves(
frame=frame,
class_column=class_column,
ax=ax,
samples=samples,
color=color,
colormap=colormap,
**kwargs,
)
def bootstrap_plot(series, fig=None, size=50, samples=500, **kwds):
"""
Bootstrap plot on mean, median and mid-range statistics.
The bootstrap plot is used to estimate the uncertainty of a statistic
by relaying on random sampling with replacement [1]_. This function will
generate bootstrapping plots for mean, median and mid-range statistics
for the given number of samples of the given size.
.. [1] "Bootstrapping (statistics)" in \
https://en.wikipedia.org/wiki/Bootstrapping_%28statistics%29
Parameters
----------
series : pandas.Series
pandas Series from where to get the samplings for the bootstrapping.
fig : matplotlib.figure.Figure, default None
If given, it will use the `fig` reference for plotting instead of
creating a new one with default parameters.
size : int, default 50
Number of data points to consider during each sampling. It must be
greater or equal than the length of the `series`.
samples : int, default 500
Number of times the bootstrap procedure is performed.
**kwds
Options to pass to matplotlib plotting method.
Returns
-------
matplotlib.figure.Figure
Matplotlib figure.
See Also
--------
DataFrame.plot : Basic plotting for DataFrame objects.
Series.plot : Basic plotting for Series objects.
Examples
--------
This example draws a basic bootstap plot for a Series.
.. plot::
:context: close-figs
>>> s = pd.Series(np.random.uniform(size=100))
>>> pd.plotting.bootstrap_plot(s)
"""
plot_backend = _get_plot_backend("matplotlib")
return plot_backend.bootstrap_plot(
series=series, fig=fig, size=size, samples=samples, **kwds
)
def parallel_coordinates(
frame,
class_column,
cols=None,
ax=None,
color=None,
use_columns=False,
xticks=None,
colormap=None,
axvlines=True,
axvlines_kwds=None,
sort_labels=False,
**kwargs,
):
"""
Parallel coordinates plotting.
Parameters
----------
frame : DataFrame
class_column : str
Column name containing class names.
cols : list, optional
A list of column names to use.
ax : matplotlib.axis, optional
Matplotlib axis object.
color : list or tuple, optional
Colors to use for the different classes.
use_columns : bool, optional
If true, columns will be used as xticks.
xticks : list or tuple, optional
A list of values to use for xticks.
colormap : str or matplotlib colormap, default None
Colormap to use for line colors.
axvlines : bool, optional
If true, vertical lines will be added at each xtick.
axvlines_kwds : keywords, optional
Options to be passed to axvline method for vertical lines.
sort_labels : bool, default False
Sort class_column labels, useful when assigning colors.
**kwargs
Options to pass to matplotlib plotting method.
Returns
-------
class:`matplotlib.axis.Axes`
Examples
--------
.. plot::
:context: close-figs
>>> df = pd.read_csv(
... 'https://raw.github.com/pandas-dev/'
... 'pandas/master/pandas/tests/io/data/csv/iris.csv'
... )
>>> pd.plotting.parallel_coordinates(
... df, 'Name', color=('#556270', '#4ECDC4', '#C7F464')
... )
"""
plot_backend = _get_plot_backend("matplotlib")
return plot_backend.parallel_coordinates(
frame=frame,
class_column=class_column,
cols=cols,
ax=ax,
color=color,
use_columns=use_columns,
xticks=xticks,
colormap=colormap,
axvlines=axvlines,
axvlines_kwds=axvlines_kwds,
sort_labels=sort_labels,
**kwargs,
)
def lag_plot(series, lag=1, ax=None, **kwds):
"""
Lag plot for time series.
Parameters
----------
series : Time series
lag : lag of the scatter plot, default 1
ax : Matplotlib axis object, optional
**kwds
Matplotlib scatter method keyword arguments.
Returns
-------
class:`matplotlib.axis.Axes`
Examples
--------
Lag plots are most commonly used to look for patterns in time series data.
Given the following time series
.. plot::
:context: close-figs
>>> np.random.seed(5)
>>> x = np.cumsum(np.random.normal(loc=1, scale=5, size=50))
>>> s = pd.Series(x)
>>> s.plot()
A lag plot with ``lag=1`` returns
.. plot::
:context: close-figs
>>> pd.plotting.lag_plot(s, lag=1)
"""
plot_backend = _get_plot_backend("matplotlib")
return plot_backend.lag_plot(series=series, lag=lag, ax=ax, **kwds)
def autocorrelation_plot(series, ax=None, **kwargs):
"""
Autocorrelation plot for time series.
Parameters
----------
series : Time series
ax : Matplotlib axis object, optional
**kwargs
Options to pass to matplotlib plotting method.
Returns
-------
class:`matplotlib.axis.Axes`
Examples
--------
The horizontal lines in the plot correspond to 95% and 99% confidence bands.
The dashed line is 99% confidence band.
.. plot::
:context: close-figs
>>> spacing = np.linspace(-9 * np.pi, 9 * np.pi, num=1000)
>>> s = pd.Series(0.7 * np.random.rand(1000) + 0.3 * np.sin(spacing))
>>> pd.plotting.autocorrelation_plot(s)
"""
plot_backend = _get_plot_backend("matplotlib")
return plot_backend.autocorrelation_plot(series=series, ax=ax, **kwargs)
class _Options(dict):
"""
Stores pandas plotting options.
Allows for parameter aliasing so you can just use parameter names that are
the same as the plot function parameters, but is stored in a canonical
format that makes it easy to breakdown into groups later.
"""
# alias so the names are same as plotting method parameter names
_ALIASES = {"x_compat": "xaxis.compat"}
_DEFAULT_KEYS = ["xaxis.compat"]
def __init__(self, deprecated=False):
self._deprecated = deprecated
super().__setitem__("xaxis.compat", False)
def __getitem__(self, key):
key = self._get_canonical_key(key)
if key not in self:
raise ValueError(f"{key} is not a valid pandas plotting option")
return super().__getitem__(key)
def __setitem__(self, key, value):
key = self._get_canonical_key(key)
return super().__setitem__(key, value)
def __delitem__(self, key):
key = self._get_canonical_key(key)
if key in self._DEFAULT_KEYS:
raise ValueError(f"Cannot remove default parameter {key}")
return super().__delitem__(key)
def __contains__(self, key) -> bool:
key = self._get_canonical_key(key)
return super().__contains__(key)
def reset(self):
"""
Reset the option store to its initial state
Returns
-------
None
"""
self.__init__()
def _get_canonical_key(self, key):
return self._ALIASES.get(key, key)
@contextmanager
def use(self, key, value):
"""
Temporarily set a parameter value using the with statement.
Aliasing allowed.
"""
old_value = self[key]
try:
self[key] = value
yield self
finally:
self[key] = old_value
plot_params = _Options()
|
"""
Tests that work on both the Python and C engines but do not have a
specific classification into the other test modules.
"""
import codecs
import csv
from datetime import datetime
from io import StringIO
import os
import platform
from urllib.error import URLError
import numpy as np
import pytest
from pandas._libs.tslib import Timestamp
from pandas.errors import DtypeWarning, EmptyDataError, ParserError
import pandas.util._test_decorators as td
from pandas import DataFrame, Index, MultiIndex, Series, compat, concat
import pandas._testing as tm
from pandas.io.parsers import CParserWrapper, TextFileReader, TextParser
def test_override_set_noconvert_columns():
# see gh-17351
#
# Usecols needs to be sorted in _set_noconvert_columns based
# on the test_usecols_with_parse_dates test from test_usecols.py
class MyTextFileReader(TextFileReader):
def __init__(self):
self._currow = 0
self.squeeze = False
class MyCParserWrapper(CParserWrapper):
def _set_noconvert_columns(self):
if self.usecols_dtype == "integer":
# self.usecols is a set, which is documented as unordered
# but in practice, a CPython set of integers is sorted.
# In other implementations this assumption does not hold.
# The following code simulates a different order, which
# before GH 17351 would cause the wrong columns to be
# converted via the parse_dates parameter
self.usecols = list(self.usecols)
self.usecols.reverse()
return CParserWrapper._set_noconvert_columns(self)
data = """a,b,c,d,e
0,1,20140101,0900,4
0,1,20140102,1000,4"""
parse_dates = [[1, 2]]
cols = {
"a": [0, 0],
"c_d": [Timestamp("2014-01-01 09:00:00"), Timestamp("2014-01-02 10:00:00")],
}
expected = DataFrame(cols, columns=["c_d", "a"])
parser = MyTextFileReader()
parser.options = {
"usecols": [0, 2, 3],
"parse_dates": parse_dates,
"delimiter": ",",
}
parser._engine = MyCParserWrapper(StringIO(data), **parser.options)
result = parser.read()
tm.assert_frame_equal(result, expected)
def test_empty_decimal_marker(all_parsers):
data = """A|B|C
1|2,334|5
10|13|10.
"""
# Parsers support only length-1 decimals
msg = "Only length-1 decimal markers supported"
parser = all_parsers
with pytest.raises(ValueError, match=msg):
parser.read_csv(StringIO(data), decimal="")
def test_bad_stream_exception(all_parsers, csv_dir_path):
# see gh-13652
#
# This test validates that both the Python engine and C engine will
# raise UnicodeDecodeError instead of C engine raising ParserError
# and swallowing the exception that caused read to fail.
path = os.path.join(csv_dir_path, "sauron.SHIFT_JIS.csv")
codec = codecs.lookup("utf-8")
utf8 = codecs.lookup("utf-8")
parser = all_parsers
msg = "'utf-8' codec can't decode byte"
# Stream must be binary UTF8.
with open(path, "rb") as handle, codecs.StreamRecoder(
handle, utf8.encode, utf8.decode, codec.streamreader, codec.streamwriter
) as stream:
with pytest.raises(UnicodeDecodeError, match=msg):
parser.read_csv(stream)
def test_read_csv_local(all_parsers, csv1):
prefix = "file:///" if compat.is_platform_windows() else "file://"
parser = all_parsers
fname = prefix + str(os.path.abspath(csv1))
result = parser.read_csv(fname, index_col=0, parse_dates=True)
expected = DataFrame(
[
[0.980269, 3.685731, -0.364216805298, -1.159738],
[1.047916, -0.041232, -0.16181208307, 0.212549],
[0.498581, 0.731168, -0.537677223318, 1.346270],
[1.120202, 1.567621, 0.00364077397681, 0.675253],
[-0.487094, 0.571455, -1.6116394093, 0.103469],
[0.836649, 0.246462, 0.588542635376, 1.062782],
[-0.157161, 1.340307, 1.1957779562, -1.097007],
],
columns=["A", "B", "C", "D"],
index=Index(
[
datetime(2000, 1, 3),
datetime(2000, 1, 4),
datetime(2000, 1, 5),
datetime(2000, 1, 6),
datetime(2000, 1, 7),
datetime(2000, 1, 10),
datetime(2000, 1, 11),
],
name="index",
),
)
tm.assert_frame_equal(result, expected)
def test_1000_sep(all_parsers):
parser = all_parsers
data = """A|B|C
1|2,334|5
10|13|10.
"""
expected = DataFrame({"A": [1, 10], "B": [2334, 13], "C": [5, 10.0]})
result = parser.read_csv(StringIO(data), sep="|", thousands=",")
tm.assert_frame_equal(result, expected)
def test_squeeze(all_parsers):
data = """\
a,1
b,2
c,3
"""
parser = all_parsers
index = Index(["a", "b", "c"], name=0)
expected = Series([1, 2, 3], name=1, index=index)
result = parser.read_csv(StringIO(data), index_col=0, header=None, squeeze=True)
tm.assert_series_equal(result, expected)
# see gh-8217
#
# Series should not be a view.
assert not result._is_view
def test_malformed(all_parsers):
# see gh-6607
parser = all_parsers
data = """ignore
A,B,C
1,2,3 # comment
1,2,3,4,5
2,3,4
"""
msg = "Expected 3 fields in line 4, saw 5"
with pytest.raises(ParserError, match=msg):
parser.read_csv(StringIO(data), header=1, comment="#")
@pytest.mark.parametrize("nrows", [5, 3, None])
def test_malformed_chunks(all_parsers, nrows):
data = """ignore
A,B,C
skip
1,2,3
3,5,10 # comment
1,2,3,4,5
2,3,4
"""
parser = all_parsers
msg = "Expected 3 fields in line 6, saw 5"
reader = parser.read_csv(
StringIO(data), header=1, comment="#", iterator=True, chunksize=1, skiprows=[2]
)
with pytest.raises(ParserError, match=msg):
reader.read(nrows)
def test_unnamed_columns(all_parsers):
data = """A,B,C,,
1,2,3,4,5
6,7,8,9,10
11,12,13,14,15
"""
parser = all_parsers
expected = DataFrame(
[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15]],
dtype=np.int64,
columns=["A", "B", "C", "Unnamed: 3", "Unnamed: 4"],
)
result = parser.read_csv(StringIO(data))
tm.assert_frame_equal(result, expected)
def test_csv_mixed_type(all_parsers):
data = """A,B,C
a,1,2
b,3,4
c,4,5
"""
parser = all_parsers
expected = DataFrame({"A": ["a", "b", "c"], "B": [1, 3, 4], "C": [2, 4, 5]})
result = parser.read_csv(StringIO(data))
tm.assert_frame_equal(result, expected)
def test_read_csv_low_memory_no_rows_with_index(all_parsers):
# see gh-21141
parser = all_parsers
if not parser.low_memory:
pytest.skip("This is a low-memory specific test")
data = """A,B,C
1,1,1,2
2,2,3,4
3,3,4,5
"""
result = parser.read_csv(StringIO(data), low_memory=True, index_col=0, nrows=0)
expected = DataFrame(columns=["A", "B", "C"])
tm.assert_frame_equal(result, expected)
def test_read_csv_dataframe(all_parsers, csv1):
parser = all_parsers
result = parser.read_csv(csv1, index_col=0, parse_dates=True)
expected = DataFrame(
[
[0.980269, 3.685731, -0.364216805298, -1.159738],
[1.047916, -0.041232, -0.16181208307, 0.212549],
[0.498581, 0.731168, -0.537677223318, 1.346270],
[1.120202, 1.567621, 0.00364077397681, 0.675253],
[-0.487094, 0.571455, -1.6116394093, 0.103469],
[0.836649, 0.246462, 0.588542635376, 1.062782],
[-0.157161, 1.340307, 1.1957779562, -1.097007],
],
columns=["A", "B", "C", "D"],
index=Index(
[
datetime(2000, 1, 3),
datetime(2000, 1, 4),
datetime(2000, 1, 5),
datetime(2000, 1, 6),
datetime(2000, 1, 7),
datetime(2000, 1, 10),
datetime(2000, 1, 11),
],
name="index",
),
)
tm.assert_frame_equal(result, expected)
def test_read_csv_no_index_name(all_parsers, csv_dir_path):
parser = all_parsers
csv2 = os.path.join(csv_dir_path, "test2.csv")
result = parser.read_csv(csv2, index_col=0, parse_dates=True)
expected = DataFrame(
[
[0.980269, 3.685731, -0.364216805298, -1.159738, "foo"],
[1.047916, -0.041232, -0.16181208307, 0.212549, "bar"],
[0.498581, 0.731168, -0.537677223318, 1.346270, "baz"],
[1.120202, 1.567621, 0.00364077397681, 0.675253, "qux"],
[-0.487094, 0.571455, -1.6116394093, 0.103469, "foo2"],
],
columns=["A", "B", "C", "D", "E"],
index=Index(
[
datetime(2000, 1, 3),
datetime(2000, 1, 4),
datetime(2000, 1, 5),
datetime(2000, 1, 6),
datetime(2000, 1, 7),
]
),
)
tm.assert_frame_equal(result, expected)
def test_read_csv_wrong_num_columns(all_parsers):
# Too few columns.
data = """A,B,C,D,E,F
1,2,3,4,5,6
6,7,8,9,10,11,12
11,12,13,14,15,16
"""
parser = all_parsers
msg = "Expected 6 fields in line 3, saw 7"
with pytest.raises(ParserError, match=msg):
parser.read_csv(StringIO(data))
def test_read_duplicate_index_explicit(all_parsers):
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo,12,13,14,15
bar,12,13,14,15
"""
parser = all_parsers
result = parser.read_csv(StringIO(data), index_col=0)
expected = DataFrame(
[
[2, 3, 4, 5],
[7, 8, 9, 10],
[12, 13, 14, 15],
[12, 13, 14, 15],
[12, 13, 14, 15],
[12, 13, 14, 15],
],
columns=["A", "B", "C", "D"],
index=Index(["foo", "bar", "baz", "qux", "foo", "bar"], name="index"),
)
tm.assert_frame_equal(result, expected)
def test_read_duplicate_index_implicit(all_parsers):
data = """A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo,12,13,14,15
bar,12,13,14,15
"""
parser = all_parsers
result = parser.read_csv(StringIO(data))
expected = DataFrame(
[
[2, 3, 4, 5],
[7, 8, 9, 10],
[12, 13, 14, 15],
[12, 13, 14, 15],
[12, 13, 14, 15],
[12, 13, 14, 15],
],
columns=["A", "B", "C", "D"],
index=Index(["foo", "bar", "baz", "qux", "foo", "bar"]),
)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"data,kwargs,expected",
[
(
"A,B\nTrue,1\nFalse,2\nTrue,3",
dict(),
DataFrame([[True, 1], [False, 2], [True, 3]], columns=["A", "B"]),
),
(
"A,B\nYES,1\nno,2\nyes,3\nNo,3\nYes,3",
dict(true_values=["yes", "Yes", "YES"], false_values=["no", "NO", "No"]),
DataFrame(
[[True, 1], [False, 2], [True, 3], [False, 3], [True, 3]],
columns=["A", "B"],
),
),
(
"A,B\nTRUE,1\nFALSE,2\nTRUE,3",
dict(),
DataFrame([[True, 1], [False, 2], [True, 3]], columns=["A", "B"]),
),
(
"A,B\nfoo,bar\nbar,foo",
dict(true_values=["foo"], false_values=["bar"]),
DataFrame([[True, False], [False, True]], columns=["A", "B"]),
),
],
)
def test_parse_bool(all_parsers, data, kwargs, expected):
parser = all_parsers
result = parser.read_csv(StringIO(data), **kwargs)
tm.assert_frame_equal(result, expected)
def test_int_conversion(all_parsers):
data = """A,B
1.0,1
2.0,2
3.0,3
"""
parser = all_parsers
result = parser.read_csv(StringIO(data))
expected = DataFrame([[1.0, 1], [2.0, 2], [3.0, 3]], columns=["A", "B"])
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("nrows", [3, 3.0])
def test_read_nrows(all_parsers, nrows):
# see gh-10476
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
"""
expected = DataFrame(
[["foo", 2, 3, 4, 5], ["bar", 7, 8, 9, 10], ["baz", 12, 13, 14, 15]],
columns=["index", "A", "B", "C", "D"],
)
parser = all_parsers
result = parser.read_csv(StringIO(data), nrows=nrows)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("nrows", [1.2, "foo", -1])
def test_read_nrows_bad(all_parsers, nrows):
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
"""
msg = r"'nrows' must be an integer >=0"
parser = all_parsers
with pytest.raises(ValueError, match=msg):
parser.read_csv(StringIO(data), nrows=nrows)
@pytest.mark.parametrize("index_col", [0, "index"])
def test_read_chunksize_with_index(all_parsers, index_col):
parser = all_parsers
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
"""
reader = parser.read_csv(StringIO(data), index_col=0, chunksize=2)
expected = DataFrame(
[
["foo", 2, 3, 4, 5],
["bar", 7, 8, 9, 10],
["baz", 12, 13, 14, 15],
["qux", 12, 13, 14, 15],
["foo2", 12, 13, 14, 15],
["bar2", 12, 13, 14, 15],
],
columns=["index", "A", "B", "C", "D"],
)
expected = expected.set_index("index")
chunks = list(reader)
tm.assert_frame_equal(chunks[0], expected[:2])
tm.assert_frame_equal(chunks[1], expected[2:4])
tm.assert_frame_equal(chunks[2], expected[4:])
@pytest.mark.parametrize("chunksize", [1.3, "foo", 0])
def test_read_chunksize_bad(all_parsers, chunksize):
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
"""
parser = all_parsers
msg = r"'chunksize' must be an integer >=1"
with pytest.raises(ValueError, match=msg):
parser.read_csv(StringIO(data), chunksize=chunksize)
@pytest.mark.parametrize("chunksize", [2, 8])
def test_read_chunksize_and_nrows(all_parsers, chunksize):
# see gh-15755
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
"""
parser = all_parsers
kwargs = dict(index_col=0, nrows=5)
reader = parser.read_csv(StringIO(data), chunksize=chunksize, **kwargs)
expected = parser.read_csv(StringIO(data), **kwargs)
tm.assert_frame_equal(concat(reader), expected)
def test_read_chunksize_and_nrows_changing_size(all_parsers):
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
"""
parser = all_parsers
kwargs = dict(index_col=0, nrows=5)
reader = parser.read_csv(StringIO(data), chunksize=8, **kwargs)
expected = parser.read_csv(StringIO(data), **kwargs)
tm.assert_frame_equal(reader.get_chunk(size=2), expected.iloc[:2])
tm.assert_frame_equal(reader.get_chunk(size=4), expected.iloc[2:5])
with pytest.raises(StopIteration, match=""):
reader.get_chunk(size=3)
def test_get_chunk_passed_chunksize(all_parsers):
parser = all_parsers
data = """A,B,C
1,2,3
4,5,6
7,8,9
1,2,3"""
reader = parser.read_csv(StringIO(data), chunksize=2)
result = reader.get_chunk()
expected = DataFrame([[1, 2, 3], [4, 5, 6]], columns=["A", "B", "C"])
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("kwargs", [dict(), dict(index_col=0)])
def test_read_chunksize_compat(all_parsers, kwargs):
# see gh-12185
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
"""
parser = all_parsers
reader = parser.read_csv(StringIO(data), chunksize=2, **kwargs)
result = parser.read_csv(StringIO(data), **kwargs)
tm.assert_frame_equal(concat(reader), result)
def test_read_chunksize_jagged_names(all_parsers):
# see gh-23509
parser = all_parsers
data = "\n".join(["0"] * 7 + [",".join(["0"] * 10)])
expected = DataFrame([[0] + [np.nan] * 9] * 7 + [[0] * 10])
reader = parser.read_csv(StringIO(data), names=range(10), chunksize=4)
result = concat(reader)
tm.assert_frame_equal(result, expected)
def test_read_data_list(all_parsers):
parser = all_parsers
kwargs = dict(index_col=0)
data = "A,B,C\nfoo,1,2,3\nbar,4,5,6"
data_list = [["A", "B", "C"], ["foo", "1", "2", "3"], ["bar", "4", "5", "6"]]
expected = parser.read_csv(StringIO(data), **kwargs)
parser = TextParser(data_list, chunksize=2, **kwargs)
result = parser.read()
tm.assert_frame_equal(result, expected)
def test_iterator(all_parsers):
# see gh-6607
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
"""
parser = all_parsers
kwargs = dict(index_col=0)
expected = parser.read_csv(StringIO(data), **kwargs)
reader = parser.read_csv(StringIO(data), iterator=True, **kwargs)
first_chunk = reader.read(3)
tm.assert_frame_equal(first_chunk, expected[:3])
last_chunk = reader.read(5)
tm.assert_frame_equal(last_chunk, expected[3:])
def test_iterator2(all_parsers):
parser = all_parsers
data = """A,B,C
foo,1,2,3
bar,4,5,6
baz,7,8,9
"""
reader = parser.read_csv(StringIO(data), iterator=True)
result = list(reader)
expected = DataFrame(
[[1, 2, 3], [4, 5, 6], [7, 8, 9]],
index=["foo", "bar", "baz"],
columns=["A", "B", "C"],
)
tm.assert_frame_equal(result[0], expected)
def test_reader_list(all_parsers):
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
"""
parser = all_parsers
kwargs = dict(index_col=0)
lines = list(csv.reader(StringIO(data)))
reader = TextParser(lines, chunksize=2, **kwargs)
expected = parser.read_csv(StringIO(data), **kwargs)
chunks = list(reader)
tm.assert_frame_equal(chunks[0], expected[:2])
tm.assert_frame_equal(chunks[1], expected[2:4])
tm.assert_frame_equal(chunks[2], expected[4:])
def test_reader_list_skiprows(all_parsers):
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
"""
parser = all_parsers
kwargs = dict(index_col=0)
lines = list(csv.reader(StringIO(data)))
reader = TextParser(lines, chunksize=2, skiprows=[1], **kwargs)
expected = parser.read_csv(StringIO(data), **kwargs)
chunks = list(reader)
tm.assert_frame_equal(chunks[0], expected[1:3])
def test_iterator_stop_on_chunksize(all_parsers):
# gh-3967: stopping iteration when chunksize is specified
parser = all_parsers
data = """A,B,C
foo,1,2,3
bar,4,5,6
baz,7,8,9
"""
reader = parser.read_csv(StringIO(data), chunksize=1)
result = list(reader)
assert len(result) == 3
expected = DataFrame(
[[1, 2, 3], [4, 5, 6], [7, 8, 9]],
index=["foo", "bar", "baz"],
columns=["A", "B", "C"],
)
tm.assert_frame_equal(concat(result), expected)
@pytest.mark.parametrize(
"kwargs", [dict(iterator=True, chunksize=1), dict(iterator=True), dict(chunksize=1)]
)
def test_iterator_skipfooter_errors(all_parsers, kwargs):
msg = "'skipfooter' not supported for 'iteration'"
parser = all_parsers
data = "a\n1\n2"
with pytest.raises(ValueError, match=msg):
parser.read_csv(StringIO(data), skipfooter=1, **kwargs)
def test_nrows_skipfooter_errors(all_parsers):
msg = "'skipfooter' not supported with 'nrows'"
data = "a\n1\n2\n3\n4\n5\n6"
parser = all_parsers
with pytest.raises(ValueError, match=msg):
parser.read_csv(StringIO(data), skipfooter=1, nrows=5)
@pytest.mark.parametrize(
"data,kwargs,expected",
[
(
"""foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
""",
dict(index_col=0, names=["index", "A", "B", "C", "D"]),
DataFrame(
[
[2, 3, 4, 5],
[7, 8, 9, 10],
[12, 13, 14, 15],
[12, 13, 14, 15],
[12, 13, 14, 15],
[12, 13, 14, 15],
],
index=Index(["foo", "bar", "baz", "qux", "foo2", "bar2"], name="index"),
columns=["A", "B", "C", "D"],
),
),
(
"""foo,one,2,3,4,5
foo,two,7,8,9,10
foo,three,12,13,14,15
bar,one,12,13,14,15
bar,two,12,13,14,15
""",
dict(index_col=[0, 1], names=["index1", "index2", "A", "B", "C", "D"]),
DataFrame(
[
[2, 3, 4, 5],
[7, 8, 9, 10],
[12, 13, 14, 15],
[12, 13, 14, 15],
[12, 13, 14, 15],
],
index=MultiIndex.from_tuples(
[
("foo", "one"),
("foo", "two"),
("foo", "three"),
("bar", "one"),
("bar", "two"),
],
names=["index1", "index2"],
),
columns=["A", "B", "C", "D"],
),
),
],
)
def test_pass_names_with_index(all_parsers, data, kwargs, expected):
parser = all_parsers
result = parser.read_csv(StringIO(data), **kwargs)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("index_col", [[0, 1], [1, 0]])
def test_multi_index_no_level_names(all_parsers, index_col):
data = """index1,index2,A,B,C,D
foo,one,2,3,4,5
foo,two,7,8,9,10
foo,three,12,13,14,15
bar,one,12,13,14,15
bar,two,12,13,14,15
"""
headless_data = "\n".join(data.split("\n")[1:])
names = ["A", "B", "C", "D"]
parser = all_parsers
result = parser.read_csv(
StringIO(headless_data), index_col=index_col, header=None, names=names
)
expected = parser.read_csv(StringIO(data), index_col=index_col)
# No index names in headless data.
expected.index.names = [None] * 2
tm.assert_frame_equal(result, expected)
def test_multi_index_no_level_names_implicit(all_parsers):
parser = all_parsers
data = """A,B,C,D
foo,one,2,3,4,5
foo,two,7,8,9,10
foo,three,12,13,14,15
bar,one,12,13,14,15
bar,two,12,13,14,15
"""
result = parser.read_csv(StringIO(data))
expected = DataFrame(
[
[2, 3, 4, 5],
[7, 8, 9, 10],
[12, 13, 14, 15],
[12, 13, 14, 15],
[12, 13, 14, 15],
],
columns=["A", "B", "C", "D"],
index=MultiIndex.from_tuples(
[
("foo", "one"),
("foo", "two"),
("foo", "three"),
("bar", "one"),
("bar", "two"),
]
),
)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"data,expected,header",
[
("a,b", DataFrame(columns=["a", "b"]), [0]),
(
"a,b\nc,d",
DataFrame(columns=MultiIndex.from_tuples([("a", "c"), ("b", "d")])),
[0, 1],
),
],
)
@pytest.mark.parametrize("round_trip", [True, False])
def test_multi_index_blank_df(all_parsers, data, expected, header, round_trip):
# see gh-14545
parser = all_parsers
data = expected.to_csv(index=False) if round_trip else data
result = parser.read_csv(StringIO(data), header=header)
tm.assert_frame_equal(result, expected)
def test_no_unnamed_index(all_parsers):
parser = all_parsers
data = """ id c0 c1 c2
0 1 0 a b
1 2 0 c d
2 2 2 e f
"""
result = parser.read_csv(StringIO(data), sep=" ")
expected = DataFrame(
[[0, 1, 0, "a", "b"], [1, 2, 0, "c", "d"], [2, 2, 2, "e", "f"]],
columns=["Unnamed: 0", "id", "c0", "c1", "c2"],
)
tm.assert_frame_equal(result, expected)
def test_read_csv_parse_simple_list(all_parsers):
parser = all_parsers
data = """foo
bar baz
qux foo
foo
bar"""
result = parser.read_csv(StringIO(data), header=None)
expected = DataFrame(["foo", "bar baz", "qux foo", "foo", "bar"])
tm.assert_frame_equal(result, expected)
@tm.network
def test_url(all_parsers, csv_dir_path):
# TODO: FTP testing
parser = all_parsers
kwargs = dict(sep="\t")
url = (
"https://raw.github.com/pandas-dev/pandas/master/"
"pandas/tests/io/parser/data/salaries.csv"
)
url_result = parser.read_csv(url, **kwargs)
local_path = os.path.join(csv_dir_path, "salaries.csv")
local_result = parser.read_csv(local_path, **kwargs)
tm.assert_frame_equal(url_result, local_result)
@pytest.mark.slow
def test_local_file(all_parsers, csv_dir_path):
parser = all_parsers
kwargs = dict(sep="\t")
local_path = os.path.join(csv_dir_path, "salaries.csv")
local_result = parser.read_csv(local_path, **kwargs)
url = "file://localhost/" + local_path
try:
url_result = parser.read_csv(url, **kwargs)
tm.assert_frame_equal(url_result, local_result)
except URLError:
# Fails on some systems.
pytest.skip("Failing on: " + " ".join(platform.uname()))
def test_path_path_lib(all_parsers):
parser = all_parsers
df = tm.makeDataFrame()
result = tm.round_trip_pathlib(df.to_csv, lambda p: parser.read_csv(p, index_col=0))
tm.assert_frame_equal(df, result)
def test_path_local_path(all_parsers):
parser = all_parsers
df = tm.makeDataFrame()
result = tm.round_trip_localpath(
df.to_csv, lambda p: parser.read_csv(p, index_col=0)
)
tm.assert_frame_equal(df, result)
def test_nonexistent_path(all_parsers):
# gh-2428: pls no segfault
# gh-14086: raise more helpful FileNotFoundError
# GH#29233 "File foo" instead of "File b'foo'"
parser = all_parsers
path = f"{tm.rands(10)}.csv"
msg = r"\[Errno 2\]"
with pytest.raises(FileNotFoundError, match=msg) as e:
parser.read_csv(path)
assert path == e.value.filename
@td.skip_if_windows # os.chmod does not work in windows
def test_no_permission(all_parsers):
# GH 23784
parser = all_parsers
msg = r"\[Errno 13\]"
with tm.ensure_clean() as path:
os.chmod(path, 0) # make file unreadable
# verify that this process cannot open the file (not running as sudo)
try:
with open(path):
pass
pytest.skip("Running as sudo.")
except PermissionError:
pass
with pytest.raises(PermissionError, match=msg) as e:
parser.read_csv(path)
assert path == e.value.filename
def test_missing_trailing_delimiters(all_parsers):
parser = all_parsers
data = """A,B,C,D
1,2,3,4
1,3,3,
1,4,5"""
result = parser.read_csv(StringIO(data))
expected = DataFrame(
[[1, 2, 3, 4], [1, 3, 3, np.nan], [1, 4, 5, np.nan]],
columns=["A", "B", "C", "D"],
)
tm.assert_frame_equal(result, expected)
def test_skip_initial_space(all_parsers):
data = (
'"09-Apr-2012", "01:10:18.300", 2456026.548822908, 12849, '
"1.00361, 1.12551, 330.65659, 0355626618.16711, 73.48821, "
"314.11625, 1917.09447, 179.71425, 80.000, 240.000, -350, "
"70.06056, 344.98370, 1, 1, -0.689265, -0.692787, "
"0.212036, 14.7674, 41.605, -9999.0, -9999.0, "
"-9999.0, -9999.0, -9999.0, -9999.0, 000, 012, 128"
)
parser = all_parsers
result = parser.read_csv(
StringIO(data),
names=list(range(33)),
header=None,
na_values=["-9999.0"],
skipinitialspace=True,
)
expected = DataFrame(
[
[
"09-Apr-2012",
"01:10:18.300",
2456026.548822908,
12849,
1.00361,
1.12551,
330.65659,
355626618.16711,
73.48821,
314.11625,
1917.09447,
179.71425,
80.0,
240.0,
-350,
70.06056,
344.9837,
1,
1,
-0.689265,
-0.692787,
0.212036,
14.7674,
41.605,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
0,
12,
128,
]
]
)
tm.assert_frame_equal(result, expected)
def test_trailing_delimiters(all_parsers):
# see gh-2442
data = """A,B,C
1,2,3,
4,5,6,
7,8,9,"""
parser = all_parsers
result = parser.read_csv(StringIO(data), index_col=False)
expected = DataFrame({"A": [1, 4, 7], "B": [2, 5, 8], "C": [3, 6, 9]})
tm.assert_frame_equal(result, expected)
def test_escapechar(all_parsers):
# https://stackoverflow.com/questions/13824840/feature-request-for-
# pandas-read-csv
data = '''SEARCH_TERM,ACTUAL_URL
"bra tv bord","http://www.ikea.com/se/sv/catalog/categories/departments/living_room/10475/?se%7cps%7cnonbranded%7cvardagsrum%7cgoogle%7ctv_bord"
"tv p\xc3\xa5 hjul","http://www.ikea.com/se/sv/catalog/categories/departments/living_room/10475/?se%7cps%7cnonbranded%7cvardagsrum%7cgoogle%7ctv_bord"
"SLAGBORD, \\"Bergslagen\\", IKEA:s 1700-tals series","http://www.ikea.com/se/sv/catalog/categories/departments/living_room/10475/?se%7cps%7cnonbranded%7cvardagsrum%7cgoogle%7ctv_bord"''' # noqa
parser = all_parsers
result = parser.read_csv(
StringIO(data), escapechar="\\", quotechar='"', encoding="utf-8"
)
assert result["SEARCH_TERM"][2] == 'SLAGBORD, "Bergslagen", IKEA:s 1700-tals series'
tm.assert_index_equal(result.columns, Index(["SEARCH_TERM", "ACTUAL_URL"]))
def test_int64_min_issues(all_parsers):
# see gh-2599
parser = all_parsers
data = "A,B\n0,0\n0,"
result = parser.read_csv(StringIO(data))
expected = DataFrame({"A": [0, 0], "B": [0, np.nan]})
tm.assert_frame_equal(result, expected)
def test_parse_integers_above_fp_precision(all_parsers):
data = """Numbers
17007000002000191
17007000002000191
17007000002000191
17007000002000191
17007000002000192
17007000002000192
17007000002000192
17007000002000192
17007000002000192
17007000002000194"""
parser = all_parsers
result = parser.read_csv(StringIO(data))
expected = DataFrame(
{
"Numbers": [
17007000002000191,
17007000002000191,
17007000002000191,
17007000002000191,
17007000002000192,
17007000002000192,
17007000002000192,
17007000002000192,
17007000002000192,
17007000002000194,
]
}
)
tm.assert_frame_equal(result, expected)
def test_chunks_have_consistent_numerical_type(all_parsers):
parser = all_parsers
integers = [str(i) for i in range(499999)]
data = "a\n" + "\n".join(integers + ["1.0", "2.0"] + integers)
# Coercions should work without warnings.
with tm.assert_produces_warning(None):
result = parser.read_csv(StringIO(data))
assert type(result.a[0]) is np.float64
assert result.a.dtype == np.float
def test_warn_if_chunks_have_mismatched_type(all_parsers):
warning_type = None
parser = all_parsers
integers = [str(i) for i in range(499999)]
data = "a\n" + "\n".join(integers + ["a", "b"] + integers)
# see gh-3866: if chunks are different types and can't
# be coerced using numerical types, then issue warning.
if parser.engine == "c" and parser.low_memory:
warning_type = DtypeWarning
with tm.assert_produces_warning(warning_type):
df = parser.read_csv(StringIO(data))
assert df.a.dtype == np.object
@pytest.mark.parametrize("sep", [" ", r"\s+"])
def test_integer_overflow_bug(all_parsers, sep):
# see gh-2601
data = "65248E10 11\n55555E55 22\n"
parser = all_parsers
result = parser.read_csv(StringIO(data), header=None, sep=sep)
expected = DataFrame([[6.5248e14, 11], [5.5555e59, 22]])
tm.assert_frame_equal(result, expected)
def test_catch_too_many_names(all_parsers):
# see gh-5156
data = """\
1,2,3
4,,6
7,8,9
10,11,12\n"""
parser = all_parsers
msg = (
"Too many columns specified: expected 4 and found 3"
if parser.engine == "c"
else "Number of passed names did not match "
"number of header fields in the file"
)
with pytest.raises(ValueError, match=msg):
parser.read_csv(StringIO(data), header=0, names=["a", "b", "c", "d"])
def test_ignore_leading_whitespace(all_parsers):
# see gh-3374, gh-6607
parser = all_parsers
data = " a b c\n 1 2 3\n 4 5 6\n 7 8 9"
result = parser.read_csv(StringIO(data), sep=r"\s+")
expected = DataFrame({"a": [1, 4, 7], "b": [2, 5, 8], "c": [3, 6, 9]})
tm.assert_frame_equal(result, expected)
def test_chunk_begins_with_newline_whitespace(all_parsers):
# see gh-10022
parser = all_parsers
data = "\n hello\nworld\n"
result = parser.read_csv(StringIO(data), header=None)
expected = DataFrame([" hello", "world"])
tm.assert_frame_equal(result, expected)
def test_empty_with_index(all_parsers):
# see gh-10184
data = "x,y"
parser = all_parsers
result = parser.read_csv(StringIO(data), index_col=0)
expected = DataFrame(columns=["y"], index=Index([], name="x"))
tm.assert_frame_equal(result, expected)
def test_empty_with_multi_index(all_parsers):
# see gh-10467
data = "x,y,z"
parser = all_parsers
result = parser.read_csv(StringIO(data), index_col=["x", "y"])
expected = DataFrame(
columns=["z"], index=MultiIndex.from_arrays([[]] * 2, names=["x", "y"])
)
tm.assert_frame_equal(result, expected)
def test_empty_with_reversed_multi_index(all_parsers):
data = "x,y,z"
parser = all_parsers
result = parser.read_csv(StringIO(data), index_col=[1, 0])
expected = DataFrame(
columns=["z"], index=MultiIndex.from_arrays([[]] * 2, names=["y", "x"])
)
tm.assert_frame_equal(result, expected)
def test_float_parser(all_parsers):
# see gh-9565
parser = all_parsers
data = "45e-1,4.5,45.,inf,-inf"
result = parser.read_csv(StringIO(data), header=None)
expected = DataFrame([[float(s) for s in data.split(",")]])
tm.assert_frame_equal(result, expected)
def test_scientific_no_exponent(all_parsers):
# see gh-12215
df = DataFrame.from_dict({"w": ["2e"], "x": ["3E"], "y": ["42e"], "z": ["632E"]})
data = df.to_csv(index=False)
parser = all_parsers
for precision in parser.float_precision_choices:
df_roundtrip = parser.read_csv(StringIO(data), float_precision=precision)
tm.assert_frame_equal(df_roundtrip, df)
@pytest.mark.parametrize("conv", [None, np.int64, np.uint64])
def test_int64_overflow(all_parsers, conv):
data = """ID
00013007854817840016671868
00013007854817840016749251
00013007854817840016754630
00013007854817840016781876
00013007854817840017028824
00013007854817840017963235
00013007854817840018860166"""
parser = all_parsers
if conv is None:
# 13007854817840016671868 > UINT64_MAX, so this
# will overflow and return object as the dtype.
result = parser.read_csv(StringIO(data))
expected = DataFrame(
[
"00013007854817840016671868",
"00013007854817840016749251",
"00013007854817840016754630",
"00013007854817840016781876",
"00013007854817840017028824",
"00013007854817840017963235",
"00013007854817840018860166",
],
columns=["ID"],
)
tm.assert_frame_equal(result, expected)
else:
# 13007854817840016671868 > UINT64_MAX, so attempts
# to cast to either int64 or uint64 will result in
# an OverflowError being raised.
msg = (
"(Python int too large to convert to C long)|"
"(long too big to convert)|"
"(int too big to convert)"
)
with pytest.raises(OverflowError, match=msg):
parser.read_csv(StringIO(data), converters={"ID": conv})
@pytest.mark.parametrize(
"val", [np.iinfo(np.uint64).max, np.iinfo(np.int64).max, np.iinfo(np.int64).min]
)
def test_int64_uint64_range(all_parsers, val):
# These numbers fall right inside the int64-uint64
# range, so they should be parsed as string.
parser = all_parsers
result = parser.read_csv(StringIO(str(val)), header=None)
expected = DataFrame([val])
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"val", [np.iinfo(np.uint64).max + 1, np.iinfo(np.int64).min - 1]
)
def test_outside_int64_uint64_range(all_parsers, val):
# These numbers fall just outside the int64-uint64
# range, so they should be parsed as string.
parser = all_parsers
result = parser.read_csv(StringIO(str(val)), header=None)
expected = DataFrame([str(val)])
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("exp_data", [[str(-1), str(2 ** 63)], [str(2 ** 63), str(-1)]])
def test_numeric_range_too_wide(all_parsers, exp_data):
# No numerical dtype can hold both negative and uint64
# values, so they should be cast as string.
parser = all_parsers
data = "\n".join(exp_data)
expected = DataFrame(exp_data)
result = parser.read_csv(StringIO(data), header=None)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("iterator", [True, False])
def test_empty_with_nrows_chunksize(all_parsers, iterator):
# see gh-9535
parser = all_parsers
expected = DataFrame(columns=["foo", "bar"])
nrows = 10
data = StringIO("foo,bar\n")
if iterator:
result = next(iter(parser.read_csv(data, chunksize=nrows)))
else:
result = parser.read_csv(data, nrows=nrows)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"data,kwargs,expected,msg",
[
# gh-10728: WHITESPACE_LINE
(
"a,b,c\n4,5,6\n ",
dict(),
DataFrame([[4, 5, 6]], columns=["a", "b", "c"]),
None,
),
# gh-10548: EAT_LINE_COMMENT
(
"a,b,c\n4,5,6\n#comment",
dict(comment="#"),
DataFrame([[4, 5, 6]], columns=["a", "b", "c"]),
None,
),
# EAT_CRNL_NOP
(
"a,b,c\n4,5,6\n\r",
dict(),
DataFrame([[4, 5, 6]], columns=["a", "b", "c"]),
None,
),
# EAT_COMMENT
(
"a,b,c\n4,5,6#comment",
dict(comment="#"),
DataFrame([[4, 5, 6]], columns=["a", "b", "c"]),
None,
),
# SKIP_LINE
(
"a,b,c\n4,5,6\nskipme",
dict(skiprows=[2]),
DataFrame([[4, 5, 6]], columns=["a", "b", "c"]),
None,
),
# EAT_LINE_COMMENT
(
"a,b,c\n4,5,6\n#comment",
dict(comment="#", skip_blank_lines=False),
DataFrame([[4, 5, 6]], columns=["a", "b", "c"]),
None,
),
# IN_FIELD
(
"a,b,c\n4,5,6\n ",
dict(skip_blank_lines=False),
DataFrame([["4", 5, 6], [" ", None, None]], columns=["a", "b", "c"]),
None,
),
# EAT_CRNL
(
"a,b,c\n4,5,6\n\r",
dict(skip_blank_lines=False),
DataFrame([[4, 5, 6], [None, None, None]], columns=["a", "b", "c"]),
None,
),
# ESCAPED_CHAR
(
"a,b,c\n4,5,6\n\\",
dict(escapechar="\\"),
None,
"(EOF following escape character)|(unexpected end of data)",
),
# ESCAPE_IN_QUOTED_FIELD
(
'a,b,c\n4,5,6\n"\\',
dict(escapechar="\\"),
None,
"(EOF inside string starting at row 2)|(unexpected end of data)",
),
# IN_QUOTED_FIELD
(
'a,b,c\n4,5,6\n"',
dict(escapechar="\\"),
None,
"(EOF inside string starting at row 2)|(unexpected end of data)",
),
],
ids=[
"whitespace-line",
"eat-line-comment",
"eat-crnl-nop",
"eat-comment",
"skip-line",
"eat-line-comment",
"in-field",
"eat-crnl",
"escaped-char",
"escape-in-quoted-field",
"in-quoted-field",
],
)
def test_eof_states(all_parsers, data, kwargs, expected, msg):
# see gh-10728, gh-10548
parser = all_parsers
if expected is None:
with pytest.raises(ParserError, match=msg):
parser.read_csv(StringIO(data), **kwargs)
else:
result = parser.read_csv(StringIO(data), **kwargs)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("usecols", [None, [0, 1], ["a", "b"]])
def test_uneven_lines_with_usecols(all_parsers, usecols):
# see gh-12203
parser = all_parsers
data = r"""a,b,c
0,1,2
3,4,5,6,7
8,9,10"""
if usecols is None:
# Make sure that an error is still raised
# when the "usecols" parameter is not provided.
msg = r"Expected \d+ fields in line \d+, saw \d+"
with pytest.raises(ParserError, match=msg):
parser.read_csv(StringIO(data))
else:
expected = DataFrame({"a": [0, 3, 8], "b": [1, 4, 9]})
result = parser.read_csv(StringIO(data), usecols=usecols)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"data,kwargs,expected",
[
# First, check to see that the response of parser when faced with no
# provided columns raises the correct error, with or without usecols.
("", dict(), None),
("", dict(usecols=["X"]), None),
(
",,",
dict(names=["Dummy", "X", "Dummy_2"], usecols=["X"]),
DataFrame(columns=["X"], index=[0], dtype=np.float64),
),
(
"",
dict(names=["Dummy", "X", "Dummy_2"], usecols=["X"]),
DataFrame(columns=["X"]),
),
],
)
def test_read_empty_with_usecols(all_parsers, data, kwargs, expected):
# see gh-12493
parser = all_parsers
if expected is None:
msg = "No columns to parse from file"
with pytest.raises(EmptyDataError, match=msg):
parser.read_csv(StringIO(data), **kwargs)
else:
result = parser.read_csv(StringIO(data), **kwargs)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"kwargs,expected",
[
# gh-8661, gh-8679: this should ignore six lines, including
# lines with trailing whitespace and blank lines.
(
dict(
header=None,
delim_whitespace=True,
skiprows=[0, 1, 2, 3, 5, 6],
skip_blank_lines=True,
),
DataFrame([[1.0, 2.0, 4.0], [5.1, np.nan, 10.0]]),
),
# gh-8983: test skipping set of rows after a row with trailing spaces.
(
dict(
delim_whitespace=True, skiprows=[1, 2, 3, 5, 6], skip_blank_lines=True
),
DataFrame({"A": [1.0, 5.1], "B": [2.0, np.nan], "C": [4.0, 10]}),
),
],
)
def test_trailing_spaces(all_parsers, kwargs, expected):
data = "A B C \nrandom line with trailing spaces \nskip\n1,2,3\n1,2.,4.\nrandom line with trailing tabs\t\t\t\n \n5.1,NaN,10.0\n" # noqa
parser = all_parsers
result = parser.read_csv(StringIO(data.replace(",", " ")), **kwargs)
tm.assert_frame_equal(result, expected)
def test_raise_on_sep_with_delim_whitespace(all_parsers):
# see gh-6607
data = "a b c\n1 2 3"
parser = all_parsers
with pytest.raises(ValueError, match="you can only specify one"):
parser.read_csv(StringIO(data), sep=r"\s", delim_whitespace=True)
@pytest.mark.parametrize("delim_whitespace", [True, False])
def test_single_char_leading_whitespace(all_parsers, delim_whitespace):
# see gh-9710
parser = all_parsers
data = """\
MyColumn
a
b
a
b\n"""
expected = DataFrame({"MyColumn": list("abab")})
result = parser.read_csv(
StringIO(data), skipinitialspace=True, delim_whitespace=delim_whitespace
)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"sep,skip_blank_lines,exp_data",
[
(",", True, [[1.0, 2.0, 4.0], [5.0, np.nan, 10.0], [-70.0, 0.4, 1.0]]),
(r"\s+", True, [[1.0, 2.0, 4.0], [5.0, np.nan, 10.0], [-70.0, 0.4, 1.0]]),
(
",",
False,
[
[1.0, 2.0, 4.0],
[np.nan, np.nan, np.nan],
[np.nan, np.nan, np.nan],
[5.0, np.nan, 10.0],
[np.nan, np.nan, np.nan],
[-70.0, 0.4, 1.0],
],
),
],
)
def test_empty_lines(all_parsers, sep, skip_blank_lines, exp_data):
parser = all_parsers
data = """\
A,B,C
1,2.,4.
5.,NaN,10.0
-70,.4,1
"""
if sep == r"\s+":
data = data.replace(",", " ")
result = parser.read_csv(StringIO(data), sep=sep, skip_blank_lines=skip_blank_lines)
expected = DataFrame(exp_data, columns=["A", "B", "C"])
tm.assert_frame_equal(result, expected)
def test_whitespace_lines(all_parsers):
parser = all_parsers
data = """
\t \t\t
\t
A,B,C
\t 1,2.,4.
5.,NaN,10.0
"""
expected = DataFrame([[1, 2.0, 4.0], [5.0, np.nan, 10.0]], columns=["A", "B", "C"])
result = parser.read_csv(StringIO(data))
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"data,expected",
[
(
""" A B C D
a 1 2 3 4
b 1 2 3 4
c 1 2 3 4
""",
DataFrame(
[[1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4]],
columns=["A", "B", "C", "D"],
index=["a", "b", "c"],
),
),
(
" a b c\n1 2 3 \n4 5 6\n 7 8 9",
DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], columns=["a", "b", "c"]),
),
],
)
def test_whitespace_regex_separator(all_parsers, data, expected):
# see gh-6607
parser = all_parsers
result = parser.read_csv(StringIO(data), sep=r"\s+")
tm.assert_frame_equal(result, expected)
def test_verbose_read(all_parsers, capsys):
parser = all_parsers
data = """a,b,c,d
one,1,2,3
one,1,2,3
,1,2,3
one,1,2,3
,1,2,3
,1,2,3
one,1,2,3
two,1,2,3"""
# Engines are verbose in different ways.
parser.read_csv(StringIO(data), verbose=True)
captured = capsys.readouterr()
if parser.engine == "c":
assert "Tokenization took:" in captured.out
assert "Parser memory cleanup took:" in captured.out
else: # Python engine
assert captured.out == "Filled 3 NA values in column a\n"
def test_verbose_read2(all_parsers, capsys):
parser = all_parsers
data = """a,b,c,d
one,1,2,3
two,1,2,3
three,1,2,3
four,1,2,3
five,1,2,3
,1,2,3
seven,1,2,3
eight,1,2,3"""
parser.read_csv(StringIO(data), verbose=True, index_col=0)
captured = capsys.readouterr()
# Engines are verbose in different ways.
if parser.engine == "c":
assert "Tokenization took:" in captured.out
assert "Parser memory cleanup took:" in captured.out
else: # Python engine
assert captured.out == "Filled 1 NA values in column a\n"
def test_iteration_open_handle(all_parsers):
parser = all_parsers
kwargs = dict(squeeze=True, header=None)
with tm.ensure_clean() as path:
with open(path, "w") as f:
f.write("AAA\nBBB\nCCC\nDDD\nEEE\nFFF\nGGG")
with open(path, "r") as f:
for line in f:
if "CCC" in line:
break
result = parser.read_csv(f, **kwargs)
expected = Series(["DDD", "EEE", "FFF", "GGG"], name=0)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"data,thousands,decimal",
[
(
"""A|B|C
1|2,334.01|5
10|13|10.
""",
",",
".",
),
(
"""A|B|C
1|2.334,01|5
10|13|10,
""",
".",
",",
),
],
)
def test_1000_sep_with_decimal(all_parsers, data, thousands, decimal):
parser = all_parsers
expected = DataFrame({"A": [1, 10], "B": [2334.01, 13], "C": [5, 10.0]})
result = parser.read_csv(
StringIO(data), sep="|", thousands=thousands, decimal=decimal
)
tm.assert_frame_equal(result, expected)
def test_euro_decimal_format(all_parsers):
parser = all_parsers
data = """Id;Number1;Number2;Text1;Text2;Number3
1;1521,1541;187101,9543;ABC;poi;4,738797819
2;121,12;14897,76;DEF;uyt;0,377320872
3;878,158;108013,434;GHI;rez;2,735694704"""
result = parser.read_csv(StringIO(data), sep=";", decimal=",")
expected = DataFrame(
[
[1, 1521.1541, 187101.9543, "ABC", "poi", 4.738797819],
[2, 121.12, 14897.76, "DEF", "uyt", 0.377320872],
[3, 878.158, 108013.434, "GHI", "rez", 2.735694704],
],
columns=["Id", "Number1", "Number2", "Text1", "Text2", "Number3"],
)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("na_filter", [True, False])
def test_inf_parsing(all_parsers, na_filter):
parser = all_parsers
data = """\
,A
a,inf
b,-inf
c,+Inf
d,-Inf
e,INF
f,-INF
g,+INf
h,-INf
i,inF
j,-inF"""
expected = DataFrame(
{"A": [float("inf"), float("-inf")] * 5},
index=["a", "b", "c", "d", "e", "f", "g", "h", "i", "j"],
)
result = parser.read_csv(StringIO(data), index_col=0, na_filter=na_filter)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("na_filter", [True, False])
def test_infinity_parsing(all_parsers, na_filter):
parser = all_parsers
data = """\
,A
a,Infinity
b,-Infinity
c,+Infinity
"""
expected = DataFrame(
{"A": [float("infinity"), float("-infinity"), float("+infinity")]},
index=["a", "b", "c"],
)
result = parser.read_csv(StringIO(data), index_col=0, na_filter=na_filter)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("nrows", [0, 1, 2, 3, 4, 5])
def test_raise_on_no_columns(all_parsers, nrows):
parser = all_parsers
data = "\n" * nrows
msg = "No columns to parse from file"
with pytest.raises(EmptyDataError, match=msg):
parser.read_csv(StringIO(data))
def test_memory_map(all_parsers, csv_dir_path):
mmap_file = os.path.join(csv_dir_path, "test_mmap.csv")
parser = all_parsers
expected = DataFrame(
{"a": [1, 2, 3], "b": ["one", "two", "three"], "c": ["I", "II", "III"]}
)
result = parser.read_csv(mmap_file, memory_map=True)
tm.assert_frame_equal(result, expected)
def test_null_byte_char(all_parsers):
# see gh-2741
data = "\x00,foo"
names = ["a", "b"]
parser = all_parsers
if parser.engine == "c":
expected = DataFrame([[np.nan, "foo"]], columns=names)
out = parser.read_csv(StringIO(data), names=names)
tm.assert_frame_equal(out, expected)
else:
msg = "NULL byte detected"
with pytest.raises(ParserError, match=msg):
parser.read_csv(StringIO(data), names=names)
def test_temporary_file(all_parsers):
# see gh-13398
parser = all_parsers
data = "0 0"
with tm.ensure_clean(mode="w+", return_filelike=True) as new_file:
new_file.write(data)
new_file.flush()
new_file.seek(0)
result = parser.read_csv(new_file, sep=r"\s+", header=None)
expected = DataFrame([[0, 0]])
tm.assert_frame_equal(result, expected)
def test_internal_eof_byte(all_parsers):
# see gh-5500
parser = all_parsers
data = "a,b\n1\x1a,2"
expected = DataFrame([["1\x1a", 2]], columns=["a", "b"])
result = parser.read_csv(StringIO(data))
tm.assert_frame_equal(result, expected)
def test_internal_eof_byte_to_file(all_parsers):
# see gh-16559
parser = all_parsers
data = b'c1,c2\r\n"test \x1a test", test\r\n'
expected = DataFrame([["test \x1a test", " test"]], columns=["c1", "c2"])
path = f"__{tm.rands(10)}__.csv"
with tm.ensure_clean(path) as path:
with open(path, "wb") as f:
f.write(data)
result = parser.read_csv(path)
tm.assert_frame_equal(result, expected)
def test_sub_character(all_parsers, csv_dir_path):
# see gh-16893
filename = os.path.join(csv_dir_path, "sub_char.csv")
expected = DataFrame([[1, 2, 3]], columns=["a", "\x1ab", "c"])
parser = all_parsers
result = parser.read_csv(filename)
tm.assert_frame_equal(result, expected)
def test_file_handle_string_io(all_parsers):
# gh-14418
#
# Don't close user provided file handles.
parser = all_parsers
data = "a,b\n1,2"
fh = StringIO(data)
parser.read_csv(fh)
assert not fh.closed
def test_file_handles_with_open(all_parsers, csv1):
# gh-14418
#
# Don't close user provided file handles.
parser = all_parsers
for mode in ["r", "rb"]:
with open(csv1, mode) as f:
parser.read_csv(f)
assert not f.closed
def test_invalid_file_buffer_class(all_parsers):
# see gh-15337
class InvalidBuffer:
pass
parser = all_parsers
msg = "Invalid file path or buffer object type"
with pytest.raises(ValueError, match=msg):
parser.read_csv(InvalidBuffer())
def test_invalid_file_buffer_mock(all_parsers):
# see gh-15337
parser = all_parsers
msg = "Invalid file path or buffer object type"
class Foo:
pass
with pytest.raises(ValueError, match=msg):
parser.read_csv(Foo())
def test_valid_file_buffer_seems_invalid(all_parsers):
# gh-16135: we want to ensure that "tell" and "seek"
# aren't actually being used when we call `read_csv`
#
# Thus, while the object may look "invalid" (these
# methods are attributes of the `StringIO` class),
# it is still a valid file-object for our purposes.
class NoSeekTellBuffer(StringIO):
def tell(self):
raise AttributeError("No tell method")
def seek(self, pos, whence=0):
raise AttributeError("No seek method")
data = "a\n1"
parser = all_parsers
expected = DataFrame({"a": [1]})
result = parser.read_csv(NoSeekTellBuffer(data))
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"kwargs",
[dict(), dict(error_bad_lines=True)], # Default is True. # Explicitly pass in.
)
@pytest.mark.parametrize(
"warn_kwargs", [dict(), dict(warn_bad_lines=True), dict(warn_bad_lines=False)]
)
def test_error_bad_lines(all_parsers, kwargs, warn_kwargs):
# see gh-15925
parser = all_parsers
kwargs.update(**warn_kwargs)
data = "a\n1\n1,2,3\n4\n5,6,7"
msg = "Expected 1 fields in line 3, saw 3"
with pytest.raises(ParserError, match=msg):
parser.read_csv(StringIO(data), **kwargs)
def test_warn_bad_lines(all_parsers, capsys):
# see gh-15925
parser = all_parsers
data = "a\n1\n1,2,3\n4\n5,6,7"
expected = DataFrame({"a": [1, 4]})
result = parser.read_csv(StringIO(data), error_bad_lines=False, warn_bad_lines=True)
tm.assert_frame_equal(result, expected)
captured = capsys.readouterr()
assert "Skipping line 3" in captured.err
assert "Skipping line 5" in captured.err
def test_suppress_error_output(all_parsers, capsys):
# see gh-15925
parser = all_parsers
data = "a\n1\n1,2,3\n4\n5,6,7"
expected = DataFrame({"a": [1, 4]})
result = parser.read_csv(
StringIO(data), error_bad_lines=False, warn_bad_lines=False
)
tm.assert_frame_equal(result, expected)
captured = capsys.readouterr()
assert captured.err == ""
@pytest.mark.parametrize("filename", ["sé-es-vé.csv", "ru-sй.csv", "中文文件名.csv"])
def test_filename_with_special_chars(all_parsers, filename):
# see gh-15086.
parser = all_parsers
df = DataFrame({"a": [1, 2, 3]})
with tm.ensure_clean(filename) as path:
df.to_csv(path, index=False)
result = parser.read_csv(path)
tm.assert_frame_equal(result, df)
def test_read_csv_memory_growth_chunksize(all_parsers):
# see gh-24805
#
# Let's just make sure that we don't crash
# as we iteratively process all chunks.
parser = all_parsers
with tm.ensure_clean() as path:
with open(path, "w") as f:
for i in range(1000):
f.write(str(i) + "\n")
result = parser.read_csv(path, chunksize=20)
for _ in result:
pass
def test_read_csv_raises_on_header_prefix(all_parsers):
# gh-27394
parser = all_parsers
msg = "Argument prefix must be None if argument header is not None"
s = StringIO("0,1\n2,3")
with pytest.raises(ValueError, match=msg):
parser.read_csv(s, header=0, prefix="_X")
def test_read_table_equivalency_to_read_csv(all_parsers):
# see gh-21948
# As of 0.25.0, read_table is undeprecated
parser = all_parsers
data = "a\tb\n1\t2\n3\t4"
expected = parser.read_csv(StringIO(data), sep="\t")
result = parser.read_table(StringIO(data))
tm.assert_frame_equal(result, expected)
def test_first_row_bom(all_parsers):
# see gh-26545
parser = all_parsers
data = '''\ufeff"Head1" "Head2" "Head3"'''
result = parser.read_csv(StringIO(data), delimiter="\t")
expected = DataFrame(columns=["Head1", "Head2", "Head3"])
tm.assert_frame_equal(result, expected)
def test_integer_precision(all_parsers):
# Gh 7072
s = """1,1;0;0;0;1;1;3844;3844;3844;1;1;1;1;1;1;0;0;1;1;0;0,,,4321583677327450765
5,1;0;0;0;1;1;843;843;843;1;1;1;1;1;1;0;0;1;1;0;0,64.0,;,4321113141090630389"""
parser = all_parsers
result = parser.read_csv(StringIO(s), header=None)[4]
expected = Series([4321583677327450765, 4321113141090630389], name=4)
tm.assert_series_equal(result, expected)
def test_file_descriptor_leak(all_parsers):
# GH 31488
parser = all_parsers
with tm.ensure_clean() as path:
def test():
with pytest.raises(EmptyDataError, match="No columns to parse from file"):
parser.read_csv(path)
td.check_file_leaks(test)()
@pytest.mark.parametrize("nrows", range(1, 6))
def test_blank_lines_between_header_and_data_rows(all_parsers, nrows):
# GH 28071
ref = DataFrame(
[[np.nan, np.nan], [np.nan, np.nan], [1, 2], [np.nan, np.nan], [3, 4]],
columns=list("ab"),
)
csv = "\nheader\n\na,b\n\n\n1,2\n\n3,4"
parser = all_parsers
df = parser.read_csv(StringIO(csv), header=3, nrows=nrows, skip_blank_lines=False)
tm.assert_frame_equal(df, ref[:nrows])
def test_no_header_two_extra_columns(all_parsers):
# GH 26218
column_names = ["one", "two", "three"]
ref = DataFrame([["foo", "bar", "baz"]], columns=column_names)
stream = StringIO("foo,bar,baz,bam,blah")
parser = all_parsers
df = parser.read_csv(stream, header=None, names=column_names, index_col=False)
tm.assert_frame_equal(df, ref)
|
|
pandas/tests/io/parser/test_common.py
|
|
"""
Helper functions to generate range-like data for DatetimeArray
(and possibly TimedeltaArray/PeriodArray)
"""
from typing import Union
import numpy as np
from pandas._libs.tslibs import OutOfBoundsDatetime, Timedelta, Timestamp
from pandas.tseries.offsets import DateOffset
def generate_regular_range(
start: Union[Timestamp, Timedelta],
end: Union[Timestamp, Timedelta],
periods: int,
freq: DateOffset,
):
"""
Generate a range of dates or timestamps with the spans between dates
described by the given `freq` DateOffset.
Parameters
----------
start : Timedelta, Timestamp or None
First point of produced date range.
end : Timedelta, Timestamp or None
Last point of produced date range.
periods : int
Number of periods in produced date range.
freq : Tick
Describes space between dates in produced date range.
Returns
-------
ndarray[np.int64] Representing nanoseconds.
"""
start = start.value if start is not None else None
end = end.value if end is not None else None
stride = freq.nanos
if periods is None:
b = start
# cannot just use e = Timestamp(end) + 1 because arange breaks when
# stride is too large, see GH10887
e = b + (end - b) // stride * stride + stride // 2 + 1
elif start is not None:
b = start
e = _generate_range_overflow_safe(b, periods, stride, side="start")
elif end is not None:
e = end + stride
b = _generate_range_overflow_safe(e, periods, stride, side="end")
else:
raise ValueError(
"at least 'start' or 'end' should be specified if a 'period' is given."
)
with np.errstate(over="raise"):
# If the range is sufficiently large, np.arange may overflow
# and incorrectly return an empty array if not caught.
try:
values = np.arange(b, e, stride, dtype=np.int64)
except FloatingPointError:
xdr = [b]
while xdr[-1] != e:
xdr.append(xdr[-1] + stride)
values = np.array(xdr[:-1], dtype=np.int64)
return values
def _generate_range_overflow_safe(
endpoint: int, periods: int, stride: int, side: str = "start"
) -> int:
"""
Calculate the second endpoint for passing to np.arange, checking
to avoid an integer overflow. Catch OverflowError and re-raise
as OutOfBoundsDatetime.
Parameters
----------
endpoint : int
nanosecond timestamp of the known endpoint of the desired range
periods : int
number of periods in the desired range
stride : int
nanoseconds between periods in the desired range
side : {'start', 'end'}
which end of the range `endpoint` refers to
Returns
-------
other_end : int
Raises
------
OutOfBoundsDatetime
"""
# GH#14187 raise instead of incorrectly wrapping around
assert side in ["start", "end"]
i64max = np.uint64(np.iinfo(np.int64).max)
msg = f"Cannot generate range with {side}={endpoint} and periods={periods}"
with np.errstate(over="raise"):
# if periods * strides cannot be multiplied within the *uint64* bounds,
# we cannot salvage the operation by recursing, so raise
try:
addend = np.uint64(periods) * np.uint64(np.abs(stride))
except FloatingPointError as err:
raise OutOfBoundsDatetime(msg) from err
if np.abs(addend) <= i64max:
# relatively easy case without casting concerns
return _generate_range_overflow_safe_signed(endpoint, periods, stride, side)
elif (endpoint > 0 and side == "start" and stride > 0) or (
endpoint < 0 and side == "end" and stride > 0
):
# no chance of not-overflowing
raise OutOfBoundsDatetime(msg)
elif side == "end" and endpoint > i64max and endpoint - stride <= i64max:
# in _generate_regular_range we added `stride` thereby overflowing
# the bounds. Adjust to fix this.
return _generate_range_overflow_safe(
endpoint - stride, periods - 1, stride, side
)
# split into smaller pieces
mid_periods = periods // 2
remaining = periods - mid_periods
assert 0 < remaining < periods, (remaining, periods, endpoint, stride)
midpoint = _generate_range_overflow_safe(endpoint, mid_periods, stride, side)
return _generate_range_overflow_safe(midpoint, remaining, stride, side)
def _generate_range_overflow_safe_signed(
endpoint: int, periods: int, stride: int, side: str
) -> int:
"""
A special case for _generate_range_overflow_safe where `periods * stride`
can be calculated without overflowing int64 bounds.
"""
assert side in ["start", "end"]
if side == "end":
stride *= -1
with np.errstate(over="raise"):
addend = np.int64(periods) * np.int64(stride)
try:
# easy case with no overflows
return np.int64(endpoint) + addend
except (FloatingPointError, OverflowError):
# with endpoint negative and addend positive we risk
# FloatingPointError; with reversed signed we risk OverflowError
pass
# if stride and endpoint had opposite signs, then endpoint + addend
# should never overflow. so they must have the same signs
assert (stride > 0 and endpoint >= 0) or (stride < 0 and endpoint <= 0)
if stride > 0:
# watch out for very special case in which we just slightly
# exceed implementation bounds, but when passing the result to
# np.arange will get a result slightly within the bounds
result = np.uint64(endpoint) + np.uint64(addend)
i64max = np.uint64(np.iinfo(np.int64).max)
assert result > i64max
if result <= i64max + np.uint64(stride):
return result
raise OutOfBoundsDatetime(
f"Cannot generate range with {side}={endpoint} and periods={periods}"
)
|
"""
Tests that work on both the Python and C engines but do not have a
specific classification into the other test modules.
"""
import codecs
import csv
from datetime import datetime
from io import StringIO
import os
import platform
from urllib.error import URLError
import numpy as np
import pytest
from pandas._libs.tslib import Timestamp
from pandas.errors import DtypeWarning, EmptyDataError, ParserError
import pandas.util._test_decorators as td
from pandas import DataFrame, Index, MultiIndex, Series, compat, concat
import pandas._testing as tm
from pandas.io.parsers import CParserWrapper, TextFileReader, TextParser
def test_override_set_noconvert_columns():
# see gh-17351
#
# Usecols needs to be sorted in _set_noconvert_columns based
# on the test_usecols_with_parse_dates test from test_usecols.py
class MyTextFileReader(TextFileReader):
def __init__(self):
self._currow = 0
self.squeeze = False
class MyCParserWrapper(CParserWrapper):
def _set_noconvert_columns(self):
if self.usecols_dtype == "integer":
# self.usecols is a set, which is documented as unordered
# but in practice, a CPython set of integers is sorted.
# In other implementations this assumption does not hold.
# The following code simulates a different order, which
# before GH 17351 would cause the wrong columns to be
# converted via the parse_dates parameter
self.usecols = list(self.usecols)
self.usecols.reverse()
return CParserWrapper._set_noconvert_columns(self)
data = """a,b,c,d,e
0,1,20140101,0900,4
0,1,20140102,1000,4"""
parse_dates = [[1, 2]]
cols = {
"a": [0, 0],
"c_d": [Timestamp("2014-01-01 09:00:00"), Timestamp("2014-01-02 10:00:00")],
}
expected = DataFrame(cols, columns=["c_d", "a"])
parser = MyTextFileReader()
parser.options = {
"usecols": [0, 2, 3],
"parse_dates": parse_dates,
"delimiter": ",",
}
parser._engine = MyCParserWrapper(StringIO(data), **parser.options)
result = parser.read()
tm.assert_frame_equal(result, expected)
def test_empty_decimal_marker(all_parsers):
data = """A|B|C
1|2,334|5
10|13|10.
"""
# Parsers support only length-1 decimals
msg = "Only length-1 decimal markers supported"
parser = all_parsers
with pytest.raises(ValueError, match=msg):
parser.read_csv(StringIO(data), decimal="")
def test_bad_stream_exception(all_parsers, csv_dir_path):
# see gh-13652
#
# This test validates that both the Python engine and C engine will
# raise UnicodeDecodeError instead of C engine raising ParserError
# and swallowing the exception that caused read to fail.
path = os.path.join(csv_dir_path, "sauron.SHIFT_JIS.csv")
codec = codecs.lookup("utf-8")
utf8 = codecs.lookup("utf-8")
parser = all_parsers
msg = "'utf-8' codec can't decode byte"
# Stream must be binary UTF8.
with open(path, "rb") as handle, codecs.StreamRecoder(
handle, utf8.encode, utf8.decode, codec.streamreader, codec.streamwriter
) as stream:
with pytest.raises(UnicodeDecodeError, match=msg):
parser.read_csv(stream)
def test_read_csv_local(all_parsers, csv1):
prefix = "file:///" if compat.is_platform_windows() else "file://"
parser = all_parsers
fname = prefix + str(os.path.abspath(csv1))
result = parser.read_csv(fname, index_col=0, parse_dates=True)
expected = DataFrame(
[
[0.980269, 3.685731, -0.364216805298, -1.159738],
[1.047916, -0.041232, -0.16181208307, 0.212549],
[0.498581, 0.731168, -0.537677223318, 1.346270],
[1.120202, 1.567621, 0.00364077397681, 0.675253],
[-0.487094, 0.571455, -1.6116394093, 0.103469],
[0.836649, 0.246462, 0.588542635376, 1.062782],
[-0.157161, 1.340307, 1.1957779562, -1.097007],
],
columns=["A", "B", "C", "D"],
index=Index(
[
datetime(2000, 1, 3),
datetime(2000, 1, 4),
datetime(2000, 1, 5),
datetime(2000, 1, 6),
datetime(2000, 1, 7),
datetime(2000, 1, 10),
datetime(2000, 1, 11),
],
name="index",
),
)
tm.assert_frame_equal(result, expected)
def test_1000_sep(all_parsers):
parser = all_parsers
data = """A|B|C
1|2,334|5
10|13|10.
"""
expected = DataFrame({"A": [1, 10], "B": [2334, 13], "C": [5, 10.0]})
result = parser.read_csv(StringIO(data), sep="|", thousands=",")
tm.assert_frame_equal(result, expected)
def test_squeeze(all_parsers):
data = """\
a,1
b,2
c,3
"""
parser = all_parsers
index = Index(["a", "b", "c"], name=0)
expected = Series([1, 2, 3], name=1, index=index)
result = parser.read_csv(StringIO(data), index_col=0, header=None, squeeze=True)
tm.assert_series_equal(result, expected)
# see gh-8217
#
# Series should not be a view.
assert not result._is_view
def test_malformed(all_parsers):
# see gh-6607
parser = all_parsers
data = """ignore
A,B,C
1,2,3 # comment
1,2,3,4,5
2,3,4
"""
msg = "Expected 3 fields in line 4, saw 5"
with pytest.raises(ParserError, match=msg):
parser.read_csv(StringIO(data), header=1, comment="#")
@pytest.mark.parametrize("nrows", [5, 3, None])
def test_malformed_chunks(all_parsers, nrows):
data = """ignore
A,B,C
skip
1,2,3
3,5,10 # comment
1,2,3,4,5
2,3,4
"""
parser = all_parsers
msg = "Expected 3 fields in line 6, saw 5"
reader = parser.read_csv(
StringIO(data), header=1, comment="#", iterator=True, chunksize=1, skiprows=[2]
)
with pytest.raises(ParserError, match=msg):
reader.read(nrows)
def test_unnamed_columns(all_parsers):
data = """A,B,C,,
1,2,3,4,5
6,7,8,9,10
11,12,13,14,15
"""
parser = all_parsers
expected = DataFrame(
[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15]],
dtype=np.int64,
columns=["A", "B", "C", "Unnamed: 3", "Unnamed: 4"],
)
result = parser.read_csv(StringIO(data))
tm.assert_frame_equal(result, expected)
def test_csv_mixed_type(all_parsers):
data = """A,B,C
a,1,2
b,3,4
c,4,5
"""
parser = all_parsers
expected = DataFrame({"A": ["a", "b", "c"], "B": [1, 3, 4], "C": [2, 4, 5]})
result = parser.read_csv(StringIO(data))
tm.assert_frame_equal(result, expected)
def test_read_csv_low_memory_no_rows_with_index(all_parsers):
# see gh-21141
parser = all_parsers
if not parser.low_memory:
pytest.skip("This is a low-memory specific test")
data = """A,B,C
1,1,1,2
2,2,3,4
3,3,4,5
"""
result = parser.read_csv(StringIO(data), low_memory=True, index_col=0, nrows=0)
expected = DataFrame(columns=["A", "B", "C"])
tm.assert_frame_equal(result, expected)
def test_read_csv_dataframe(all_parsers, csv1):
parser = all_parsers
result = parser.read_csv(csv1, index_col=0, parse_dates=True)
expected = DataFrame(
[
[0.980269, 3.685731, -0.364216805298, -1.159738],
[1.047916, -0.041232, -0.16181208307, 0.212549],
[0.498581, 0.731168, -0.537677223318, 1.346270],
[1.120202, 1.567621, 0.00364077397681, 0.675253],
[-0.487094, 0.571455, -1.6116394093, 0.103469],
[0.836649, 0.246462, 0.588542635376, 1.062782],
[-0.157161, 1.340307, 1.1957779562, -1.097007],
],
columns=["A", "B", "C", "D"],
index=Index(
[
datetime(2000, 1, 3),
datetime(2000, 1, 4),
datetime(2000, 1, 5),
datetime(2000, 1, 6),
datetime(2000, 1, 7),
datetime(2000, 1, 10),
datetime(2000, 1, 11),
],
name="index",
),
)
tm.assert_frame_equal(result, expected)
def test_read_csv_no_index_name(all_parsers, csv_dir_path):
parser = all_parsers
csv2 = os.path.join(csv_dir_path, "test2.csv")
result = parser.read_csv(csv2, index_col=0, parse_dates=True)
expected = DataFrame(
[
[0.980269, 3.685731, -0.364216805298, -1.159738, "foo"],
[1.047916, -0.041232, -0.16181208307, 0.212549, "bar"],
[0.498581, 0.731168, -0.537677223318, 1.346270, "baz"],
[1.120202, 1.567621, 0.00364077397681, 0.675253, "qux"],
[-0.487094, 0.571455, -1.6116394093, 0.103469, "foo2"],
],
columns=["A", "B", "C", "D", "E"],
index=Index(
[
datetime(2000, 1, 3),
datetime(2000, 1, 4),
datetime(2000, 1, 5),
datetime(2000, 1, 6),
datetime(2000, 1, 7),
]
),
)
tm.assert_frame_equal(result, expected)
def test_read_csv_wrong_num_columns(all_parsers):
# Too few columns.
data = """A,B,C,D,E,F
1,2,3,4,5,6
6,7,8,9,10,11,12
11,12,13,14,15,16
"""
parser = all_parsers
msg = "Expected 6 fields in line 3, saw 7"
with pytest.raises(ParserError, match=msg):
parser.read_csv(StringIO(data))
def test_read_duplicate_index_explicit(all_parsers):
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo,12,13,14,15
bar,12,13,14,15
"""
parser = all_parsers
result = parser.read_csv(StringIO(data), index_col=0)
expected = DataFrame(
[
[2, 3, 4, 5],
[7, 8, 9, 10],
[12, 13, 14, 15],
[12, 13, 14, 15],
[12, 13, 14, 15],
[12, 13, 14, 15],
],
columns=["A", "B", "C", "D"],
index=Index(["foo", "bar", "baz", "qux", "foo", "bar"], name="index"),
)
tm.assert_frame_equal(result, expected)
def test_read_duplicate_index_implicit(all_parsers):
data = """A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo,12,13,14,15
bar,12,13,14,15
"""
parser = all_parsers
result = parser.read_csv(StringIO(data))
expected = DataFrame(
[
[2, 3, 4, 5],
[7, 8, 9, 10],
[12, 13, 14, 15],
[12, 13, 14, 15],
[12, 13, 14, 15],
[12, 13, 14, 15],
],
columns=["A", "B", "C", "D"],
index=Index(["foo", "bar", "baz", "qux", "foo", "bar"]),
)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"data,kwargs,expected",
[
(
"A,B\nTrue,1\nFalse,2\nTrue,3",
dict(),
DataFrame([[True, 1], [False, 2], [True, 3]], columns=["A", "B"]),
),
(
"A,B\nYES,1\nno,2\nyes,3\nNo,3\nYes,3",
dict(true_values=["yes", "Yes", "YES"], false_values=["no", "NO", "No"]),
DataFrame(
[[True, 1], [False, 2], [True, 3], [False, 3], [True, 3]],
columns=["A", "B"],
),
),
(
"A,B\nTRUE,1\nFALSE,2\nTRUE,3",
dict(),
DataFrame([[True, 1], [False, 2], [True, 3]], columns=["A", "B"]),
),
(
"A,B\nfoo,bar\nbar,foo",
dict(true_values=["foo"], false_values=["bar"]),
DataFrame([[True, False], [False, True]], columns=["A", "B"]),
),
],
)
def test_parse_bool(all_parsers, data, kwargs, expected):
parser = all_parsers
result = parser.read_csv(StringIO(data), **kwargs)
tm.assert_frame_equal(result, expected)
def test_int_conversion(all_parsers):
data = """A,B
1.0,1
2.0,2
3.0,3
"""
parser = all_parsers
result = parser.read_csv(StringIO(data))
expected = DataFrame([[1.0, 1], [2.0, 2], [3.0, 3]], columns=["A", "B"])
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("nrows", [3, 3.0])
def test_read_nrows(all_parsers, nrows):
# see gh-10476
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
"""
expected = DataFrame(
[["foo", 2, 3, 4, 5], ["bar", 7, 8, 9, 10], ["baz", 12, 13, 14, 15]],
columns=["index", "A", "B", "C", "D"],
)
parser = all_parsers
result = parser.read_csv(StringIO(data), nrows=nrows)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("nrows", [1.2, "foo", -1])
def test_read_nrows_bad(all_parsers, nrows):
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
"""
msg = r"'nrows' must be an integer >=0"
parser = all_parsers
with pytest.raises(ValueError, match=msg):
parser.read_csv(StringIO(data), nrows=nrows)
@pytest.mark.parametrize("index_col", [0, "index"])
def test_read_chunksize_with_index(all_parsers, index_col):
parser = all_parsers
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
"""
reader = parser.read_csv(StringIO(data), index_col=0, chunksize=2)
expected = DataFrame(
[
["foo", 2, 3, 4, 5],
["bar", 7, 8, 9, 10],
["baz", 12, 13, 14, 15],
["qux", 12, 13, 14, 15],
["foo2", 12, 13, 14, 15],
["bar2", 12, 13, 14, 15],
],
columns=["index", "A", "B", "C", "D"],
)
expected = expected.set_index("index")
chunks = list(reader)
tm.assert_frame_equal(chunks[0], expected[:2])
tm.assert_frame_equal(chunks[1], expected[2:4])
tm.assert_frame_equal(chunks[2], expected[4:])
@pytest.mark.parametrize("chunksize", [1.3, "foo", 0])
def test_read_chunksize_bad(all_parsers, chunksize):
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
"""
parser = all_parsers
msg = r"'chunksize' must be an integer >=1"
with pytest.raises(ValueError, match=msg):
parser.read_csv(StringIO(data), chunksize=chunksize)
@pytest.mark.parametrize("chunksize", [2, 8])
def test_read_chunksize_and_nrows(all_parsers, chunksize):
# see gh-15755
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
"""
parser = all_parsers
kwargs = dict(index_col=0, nrows=5)
reader = parser.read_csv(StringIO(data), chunksize=chunksize, **kwargs)
expected = parser.read_csv(StringIO(data), **kwargs)
tm.assert_frame_equal(concat(reader), expected)
def test_read_chunksize_and_nrows_changing_size(all_parsers):
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
"""
parser = all_parsers
kwargs = dict(index_col=0, nrows=5)
reader = parser.read_csv(StringIO(data), chunksize=8, **kwargs)
expected = parser.read_csv(StringIO(data), **kwargs)
tm.assert_frame_equal(reader.get_chunk(size=2), expected.iloc[:2])
tm.assert_frame_equal(reader.get_chunk(size=4), expected.iloc[2:5])
with pytest.raises(StopIteration, match=""):
reader.get_chunk(size=3)
def test_get_chunk_passed_chunksize(all_parsers):
parser = all_parsers
data = """A,B,C
1,2,3
4,5,6
7,8,9
1,2,3"""
reader = parser.read_csv(StringIO(data), chunksize=2)
result = reader.get_chunk()
expected = DataFrame([[1, 2, 3], [4, 5, 6]], columns=["A", "B", "C"])
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("kwargs", [dict(), dict(index_col=0)])
def test_read_chunksize_compat(all_parsers, kwargs):
# see gh-12185
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
"""
parser = all_parsers
reader = parser.read_csv(StringIO(data), chunksize=2, **kwargs)
result = parser.read_csv(StringIO(data), **kwargs)
tm.assert_frame_equal(concat(reader), result)
def test_read_chunksize_jagged_names(all_parsers):
# see gh-23509
parser = all_parsers
data = "\n".join(["0"] * 7 + [",".join(["0"] * 10)])
expected = DataFrame([[0] + [np.nan] * 9] * 7 + [[0] * 10])
reader = parser.read_csv(StringIO(data), names=range(10), chunksize=4)
result = concat(reader)
tm.assert_frame_equal(result, expected)
def test_read_data_list(all_parsers):
parser = all_parsers
kwargs = dict(index_col=0)
data = "A,B,C\nfoo,1,2,3\nbar,4,5,6"
data_list = [["A", "B", "C"], ["foo", "1", "2", "3"], ["bar", "4", "5", "6"]]
expected = parser.read_csv(StringIO(data), **kwargs)
parser = TextParser(data_list, chunksize=2, **kwargs)
result = parser.read()
tm.assert_frame_equal(result, expected)
def test_iterator(all_parsers):
# see gh-6607
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
"""
parser = all_parsers
kwargs = dict(index_col=0)
expected = parser.read_csv(StringIO(data), **kwargs)
reader = parser.read_csv(StringIO(data), iterator=True, **kwargs)
first_chunk = reader.read(3)
tm.assert_frame_equal(first_chunk, expected[:3])
last_chunk = reader.read(5)
tm.assert_frame_equal(last_chunk, expected[3:])
def test_iterator2(all_parsers):
parser = all_parsers
data = """A,B,C
foo,1,2,3
bar,4,5,6
baz,7,8,9
"""
reader = parser.read_csv(StringIO(data), iterator=True)
result = list(reader)
expected = DataFrame(
[[1, 2, 3], [4, 5, 6], [7, 8, 9]],
index=["foo", "bar", "baz"],
columns=["A", "B", "C"],
)
tm.assert_frame_equal(result[0], expected)
def test_reader_list(all_parsers):
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
"""
parser = all_parsers
kwargs = dict(index_col=0)
lines = list(csv.reader(StringIO(data)))
reader = TextParser(lines, chunksize=2, **kwargs)
expected = parser.read_csv(StringIO(data), **kwargs)
chunks = list(reader)
tm.assert_frame_equal(chunks[0], expected[:2])
tm.assert_frame_equal(chunks[1], expected[2:4])
tm.assert_frame_equal(chunks[2], expected[4:])
def test_reader_list_skiprows(all_parsers):
data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
"""
parser = all_parsers
kwargs = dict(index_col=0)
lines = list(csv.reader(StringIO(data)))
reader = TextParser(lines, chunksize=2, skiprows=[1], **kwargs)
expected = parser.read_csv(StringIO(data), **kwargs)
chunks = list(reader)
tm.assert_frame_equal(chunks[0], expected[1:3])
def test_iterator_stop_on_chunksize(all_parsers):
# gh-3967: stopping iteration when chunksize is specified
parser = all_parsers
data = """A,B,C
foo,1,2,3
bar,4,5,6
baz,7,8,9
"""
reader = parser.read_csv(StringIO(data), chunksize=1)
result = list(reader)
assert len(result) == 3
expected = DataFrame(
[[1, 2, 3], [4, 5, 6], [7, 8, 9]],
index=["foo", "bar", "baz"],
columns=["A", "B", "C"],
)
tm.assert_frame_equal(concat(result), expected)
@pytest.mark.parametrize(
"kwargs", [dict(iterator=True, chunksize=1), dict(iterator=True), dict(chunksize=1)]
)
def test_iterator_skipfooter_errors(all_parsers, kwargs):
msg = "'skipfooter' not supported for 'iteration'"
parser = all_parsers
data = "a\n1\n2"
with pytest.raises(ValueError, match=msg):
parser.read_csv(StringIO(data), skipfooter=1, **kwargs)
def test_nrows_skipfooter_errors(all_parsers):
msg = "'skipfooter' not supported with 'nrows'"
data = "a\n1\n2\n3\n4\n5\n6"
parser = all_parsers
with pytest.raises(ValueError, match=msg):
parser.read_csv(StringIO(data), skipfooter=1, nrows=5)
@pytest.mark.parametrize(
"data,kwargs,expected",
[
(
"""foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15
foo2,12,13,14,15
bar2,12,13,14,15
""",
dict(index_col=0, names=["index", "A", "B", "C", "D"]),
DataFrame(
[
[2, 3, 4, 5],
[7, 8, 9, 10],
[12, 13, 14, 15],
[12, 13, 14, 15],
[12, 13, 14, 15],
[12, 13, 14, 15],
],
index=Index(["foo", "bar", "baz", "qux", "foo2", "bar2"], name="index"),
columns=["A", "B", "C", "D"],
),
),
(
"""foo,one,2,3,4,5
foo,two,7,8,9,10
foo,three,12,13,14,15
bar,one,12,13,14,15
bar,two,12,13,14,15
""",
dict(index_col=[0, 1], names=["index1", "index2", "A", "B", "C", "D"]),
DataFrame(
[
[2, 3, 4, 5],
[7, 8, 9, 10],
[12, 13, 14, 15],
[12, 13, 14, 15],
[12, 13, 14, 15],
],
index=MultiIndex.from_tuples(
[
("foo", "one"),
("foo", "two"),
("foo", "three"),
("bar", "one"),
("bar", "two"),
],
names=["index1", "index2"],
),
columns=["A", "B", "C", "D"],
),
),
],
)
def test_pass_names_with_index(all_parsers, data, kwargs, expected):
parser = all_parsers
result = parser.read_csv(StringIO(data), **kwargs)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("index_col", [[0, 1], [1, 0]])
def test_multi_index_no_level_names(all_parsers, index_col):
data = """index1,index2,A,B,C,D
foo,one,2,3,4,5
foo,two,7,8,9,10
foo,three,12,13,14,15
bar,one,12,13,14,15
bar,two,12,13,14,15
"""
headless_data = "\n".join(data.split("\n")[1:])
names = ["A", "B", "C", "D"]
parser = all_parsers
result = parser.read_csv(
StringIO(headless_data), index_col=index_col, header=None, names=names
)
expected = parser.read_csv(StringIO(data), index_col=index_col)
# No index names in headless data.
expected.index.names = [None] * 2
tm.assert_frame_equal(result, expected)
def test_multi_index_no_level_names_implicit(all_parsers):
parser = all_parsers
data = """A,B,C,D
foo,one,2,3,4,5
foo,two,7,8,9,10
foo,three,12,13,14,15
bar,one,12,13,14,15
bar,two,12,13,14,15
"""
result = parser.read_csv(StringIO(data))
expected = DataFrame(
[
[2, 3, 4, 5],
[7, 8, 9, 10],
[12, 13, 14, 15],
[12, 13, 14, 15],
[12, 13, 14, 15],
],
columns=["A", "B", "C", "D"],
index=MultiIndex.from_tuples(
[
("foo", "one"),
("foo", "two"),
("foo", "three"),
("bar", "one"),
("bar", "two"),
]
),
)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"data,expected,header",
[
("a,b", DataFrame(columns=["a", "b"]), [0]),
(
"a,b\nc,d",
DataFrame(columns=MultiIndex.from_tuples([("a", "c"), ("b", "d")])),
[0, 1],
),
],
)
@pytest.mark.parametrize("round_trip", [True, False])
def test_multi_index_blank_df(all_parsers, data, expected, header, round_trip):
# see gh-14545
parser = all_parsers
data = expected.to_csv(index=False) if round_trip else data
result = parser.read_csv(StringIO(data), header=header)
tm.assert_frame_equal(result, expected)
def test_no_unnamed_index(all_parsers):
parser = all_parsers
data = """ id c0 c1 c2
0 1 0 a b
1 2 0 c d
2 2 2 e f
"""
result = parser.read_csv(StringIO(data), sep=" ")
expected = DataFrame(
[[0, 1, 0, "a", "b"], [1, 2, 0, "c", "d"], [2, 2, 2, "e", "f"]],
columns=["Unnamed: 0", "id", "c0", "c1", "c2"],
)
tm.assert_frame_equal(result, expected)
def test_read_csv_parse_simple_list(all_parsers):
parser = all_parsers
data = """foo
bar baz
qux foo
foo
bar"""
result = parser.read_csv(StringIO(data), header=None)
expected = DataFrame(["foo", "bar baz", "qux foo", "foo", "bar"])
tm.assert_frame_equal(result, expected)
@tm.network
def test_url(all_parsers, csv_dir_path):
# TODO: FTP testing
parser = all_parsers
kwargs = dict(sep="\t")
url = (
"https://raw.github.com/pandas-dev/pandas/master/"
"pandas/tests/io/parser/data/salaries.csv"
)
url_result = parser.read_csv(url, **kwargs)
local_path = os.path.join(csv_dir_path, "salaries.csv")
local_result = parser.read_csv(local_path, **kwargs)
tm.assert_frame_equal(url_result, local_result)
@pytest.mark.slow
def test_local_file(all_parsers, csv_dir_path):
parser = all_parsers
kwargs = dict(sep="\t")
local_path = os.path.join(csv_dir_path, "salaries.csv")
local_result = parser.read_csv(local_path, **kwargs)
url = "file://localhost/" + local_path
try:
url_result = parser.read_csv(url, **kwargs)
tm.assert_frame_equal(url_result, local_result)
except URLError:
# Fails on some systems.
pytest.skip("Failing on: " + " ".join(platform.uname()))
def test_path_path_lib(all_parsers):
parser = all_parsers
df = tm.makeDataFrame()
result = tm.round_trip_pathlib(df.to_csv, lambda p: parser.read_csv(p, index_col=0))
tm.assert_frame_equal(df, result)
def test_path_local_path(all_parsers):
parser = all_parsers
df = tm.makeDataFrame()
result = tm.round_trip_localpath(
df.to_csv, lambda p: parser.read_csv(p, index_col=0)
)
tm.assert_frame_equal(df, result)
def test_nonexistent_path(all_parsers):
# gh-2428: pls no segfault
# gh-14086: raise more helpful FileNotFoundError
# GH#29233 "File foo" instead of "File b'foo'"
parser = all_parsers
path = f"{tm.rands(10)}.csv"
msg = r"\[Errno 2\]"
with pytest.raises(FileNotFoundError, match=msg) as e:
parser.read_csv(path)
assert path == e.value.filename
@td.skip_if_windows # os.chmod does not work in windows
def test_no_permission(all_parsers):
# GH 23784
parser = all_parsers
msg = r"\[Errno 13\]"
with tm.ensure_clean() as path:
os.chmod(path, 0) # make file unreadable
# verify that this process cannot open the file (not running as sudo)
try:
with open(path):
pass
pytest.skip("Running as sudo.")
except PermissionError:
pass
with pytest.raises(PermissionError, match=msg) as e:
parser.read_csv(path)
assert path == e.value.filename
def test_missing_trailing_delimiters(all_parsers):
parser = all_parsers
data = """A,B,C,D
1,2,3,4
1,3,3,
1,4,5"""
result = parser.read_csv(StringIO(data))
expected = DataFrame(
[[1, 2, 3, 4], [1, 3, 3, np.nan], [1, 4, 5, np.nan]],
columns=["A", "B", "C", "D"],
)
tm.assert_frame_equal(result, expected)
def test_skip_initial_space(all_parsers):
data = (
'"09-Apr-2012", "01:10:18.300", 2456026.548822908, 12849, '
"1.00361, 1.12551, 330.65659, 0355626618.16711, 73.48821, "
"314.11625, 1917.09447, 179.71425, 80.000, 240.000, -350, "
"70.06056, 344.98370, 1, 1, -0.689265, -0.692787, "
"0.212036, 14.7674, 41.605, -9999.0, -9999.0, "
"-9999.0, -9999.0, -9999.0, -9999.0, 000, 012, 128"
)
parser = all_parsers
result = parser.read_csv(
StringIO(data),
names=list(range(33)),
header=None,
na_values=["-9999.0"],
skipinitialspace=True,
)
expected = DataFrame(
[
[
"09-Apr-2012",
"01:10:18.300",
2456026.548822908,
12849,
1.00361,
1.12551,
330.65659,
355626618.16711,
73.48821,
314.11625,
1917.09447,
179.71425,
80.0,
240.0,
-350,
70.06056,
344.9837,
1,
1,
-0.689265,
-0.692787,
0.212036,
14.7674,
41.605,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
0,
12,
128,
]
]
)
tm.assert_frame_equal(result, expected)
def test_trailing_delimiters(all_parsers):
# see gh-2442
data = """A,B,C
1,2,3,
4,5,6,
7,8,9,"""
parser = all_parsers
result = parser.read_csv(StringIO(data), index_col=False)
expected = DataFrame({"A": [1, 4, 7], "B": [2, 5, 8], "C": [3, 6, 9]})
tm.assert_frame_equal(result, expected)
def test_escapechar(all_parsers):
# https://stackoverflow.com/questions/13824840/feature-request-for-
# pandas-read-csv
data = '''SEARCH_TERM,ACTUAL_URL
"bra tv bord","http://www.ikea.com/se/sv/catalog/categories/departments/living_room/10475/?se%7cps%7cnonbranded%7cvardagsrum%7cgoogle%7ctv_bord"
"tv p\xc3\xa5 hjul","http://www.ikea.com/se/sv/catalog/categories/departments/living_room/10475/?se%7cps%7cnonbranded%7cvardagsrum%7cgoogle%7ctv_bord"
"SLAGBORD, \\"Bergslagen\\", IKEA:s 1700-tals series","http://www.ikea.com/se/sv/catalog/categories/departments/living_room/10475/?se%7cps%7cnonbranded%7cvardagsrum%7cgoogle%7ctv_bord"''' # noqa
parser = all_parsers
result = parser.read_csv(
StringIO(data), escapechar="\\", quotechar='"', encoding="utf-8"
)
assert result["SEARCH_TERM"][2] == 'SLAGBORD, "Bergslagen", IKEA:s 1700-tals series'
tm.assert_index_equal(result.columns, Index(["SEARCH_TERM", "ACTUAL_URL"]))
def test_int64_min_issues(all_parsers):
# see gh-2599
parser = all_parsers
data = "A,B\n0,0\n0,"
result = parser.read_csv(StringIO(data))
expected = DataFrame({"A": [0, 0], "B": [0, np.nan]})
tm.assert_frame_equal(result, expected)
def test_parse_integers_above_fp_precision(all_parsers):
data = """Numbers
17007000002000191
17007000002000191
17007000002000191
17007000002000191
17007000002000192
17007000002000192
17007000002000192
17007000002000192
17007000002000192
17007000002000194"""
parser = all_parsers
result = parser.read_csv(StringIO(data))
expected = DataFrame(
{
"Numbers": [
17007000002000191,
17007000002000191,
17007000002000191,
17007000002000191,
17007000002000192,
17007000002000192,
17007000002000192,
17007000002000192,
17007000002000192,
17007000002000194,
]
}
)
tm.assert_frame_equal(result, expected)
def test_chunks_have_consistent_numerical_type(all_parsers):
parser = all_parsers
integers = [str(i) for i in range(499999)]
data = "a\n" + "\n".join(integers + ["1.0", "2.0"] + integers)
# Coercions should work without warnings.
with tm.assert_produces_warning(None):
result = parser.read_csv(StringIO(data))
assert type(result.a[0]) is np.float64
assert result.a.dtype == np.float
def test_warn_if_chunks_have_mismatched_type(all_parsers):
warning_type = None
parser = all_parsers
integers = [str(i) for i in range(499999)]
data = "a\n" + "\n".join(integers + ["a", "b"] + integers)
# see gh-3866: if chunks are different types and can't
# be coerced using numerical types, then issue warning.
if parser.engine == "c" and parser.low_memory:
warning_type = DtypeWarning
with tm.assert_produces_warning(warning_type):
df = parser.read_csv(StringIO(data))
assert df.a.dtype == np.object
@pytest.mark.parametrize("sep", [" ", r"\s+"])
def test_integer_overflow_bug(all_parsers, sep):
# see gh-2601
data = "65248E10 11\n55555E55 22\n"
parser = all_parsers
result = parser.read_csv(StringIO(data), header=None, sep=sep)
expected = DataFrame([[6.5248e14, 11], [5.5555e59, 22]])
tm.assert_frame_equal(result, expected)
def test_catch_too_many_names(all_parsers):
# see gh-5156
data = """\
1,2,3
4,,6
7,8,9
10,11,12\n"""
parser = all_parsers
msg = (
"Too many columns specified: expected 4 and found 3"
if parser.engine == "c"
else "Number of passed names did not match "
"number of header fields in the file"
)
with pytest.raises(ValueError, match=msg):
parser.read_csv(StringIO(data), header=0, names=["a", "b", "c", "d"])
def test_ignore_leading_whitespace(all_parsers):
# see gh-3374, gh-6607
parser = all_parsers
data = " a b c\n 1 2 3\n 4 5 6\n 7 8 9"
result = parser.read_csv(StringIO(data), sep=r"\s+")
expected = DataFrame({"a": [1, 4, 7], "b": [2, 5, 8], "c": [3, 6, 9]})
tm.assert_frame_equal(result, expected)
def test_chunk_begins_with_newline_whitespace(all_parsers):
# see gh-10022
parser = all_parsers
data = "\n hello\nworld\n"
result = parser.read_csv(StringIO(data), header=None)
expected = DataFrame([" hello", "world"])
tm.assert_frame_equal(result, expected)
def test_empty_with_index(all_parsers):
# see gh-10184
data = "x,y"
parser = all_parsers
result = parser.read_csv(StringIO(data), index_col=0)
expected = DataFrame(columns=["y"], index=Index([], name="x"))
tm.assert_frame_equal(result, expected)
def test_empty_with_multi_index(all_parsers):
# see gh-10467
data = "x,y,z"
parser = all_parsers
result = parser.read_csv(StringIO(data), index_col=["x", "y"])
expected = DataFrame(
columns=["z"], index=MultiIndex.from_arrays([[]] * 2, names=["x", "y"])
)
tm.assert_frame_equal(result, expected)
def test_empty_with_reversed_multi_index(all_parsers):
data = "x,y,z"
parser = all_parsers
result = parser.read_csv(StringIO(data), index_col=[1, 0])
expected = DataFrame(
columns=["z"], index=MultiIndex.from_arrays([[]] * 2, names=["y", "x"])
)
tm.assert_frame_equal(result, expected)
def test_float_parser(all_parsers):
# see gh-9565
parser = all_parsers
data = "45e-1,4.5,45.,inf,-inf"
result = parser.read_csv(StringIO(data), header=None)
expected = DataFrame([[float(s) for s in data.split(",")]])
tm.assert_frame_equal(result, expected)
def test_scientific_no_exponent(all_parsers):
# see gh-12215
df = DataFrame.from_dict({"w": ["2e"], "x": ["3E"], "y": ["42e"], "z": ["632E"]})
data = df.to_csv(index=False)
parser = all_parsers
for precision in parser.float_precision_choices:
df_roundtrip = parser.read_csv(StringIO(data), float_precision=precision)
tm.assert_frame_equal(df_roundtrip, df)
@pytest.mark.parametrize("conv", [None, np.int64, np.uint64])
def test_int64_overflow(all_parsers, conv):
data = """ID
00013007854817840016671868
00013007854817840016749251
00013007854817840016754630
00013007854817840016781876
00013007854817840017028824
00013007854817840017963235
00013007854817840018860166"""
parser = all_parsers
if conv is None:
# 13007854817840016671868 > UINT64_MAX, so this
# will overflow and return object as the dtype.
result = parser.read_csv(StringIO(data))
expected = DataFrame(
[
"00013007854817840016671868",
"00013007854817840016749251",
"00013007854817840016754630",
"00013007854817840016781876",
"00013007854817840017028824",
"00013007854817840017963235",
"00013007854817840018860166",
],
columns=["ID"],
)
tm.assert_frame_equal(result, expected)
else:
# 13007854817840016671868 > UINT64_MAX, so attempts
# to cast to either int64 or uint64 will result in
# an OverflowError being raised.
msg = (
"(Python int too large to convert to C long)|"
"(long too big to convert)|"
"(int too big to convert)"
)
with pytest.raises(OverflowError, match=msg):
parser.read_csv(StringIO(data), converters={"ID": conv})
@pytest.mark.parametrize(
"val", [np.iinfo(np.uint64).max, np.iinfo(np.int64).max, np.iinfo(np.int64).min]
)
def test_int64_uint64_range(all_parsers, val):
# These numbers fall right inside the int64-uint64
# range, so they should be parsed as string.
parser = all_parsers
result = parser.read_csv(StringIO(str(val)), header=None)
expected = DataFrame([val])
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"val", [np.iinfo(np.uint64).max + 1, np.iinfo(np.int64).min - 1]
)
def test_outside_int64_uint64_range(all_parsers, val):
# These numbers fall just outside the int64-uint64
# range, so they should be parsed as string.
parser = all_parsers
result = parser.read_csv(StringIO(str(val)), header=None)
expected = DataFrame([str(val)])
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("exp_data", [[str(-1), str(2 ** 63)], [str(2 ** 63), str(-1)]])
def test_numeric_range_too_wide(all_parsers, exp_data):
# No numerical dtype can hold both negative and uint64
# values, so they should be cast as string.
parser = all_parsers
data = "\n".join(exp_data)
expected = DataFrame(exp_data)
result = parser.read_csv(StringIO(data), header=None)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("iterator", [True, False])
def test_empty_with_nrows_chunksize(all_parsers, iterator):
# see gh-9535
parser = all_parsers
expected = DataFrame(columns=["foo", "bar"])
nrows = 10
data = StringIO("foo,bar\n")
if iterator:
result = next(iter(parser.read_csv(data, chunksize=nrows)))
else:
result = parser.read_csv(data, nrows=nrows)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"data,kwargs,expected,msg",
[
# gh-10728: WHITESPACE_LINE
(
"a,b,c\n4,5,6\n ",
dict(),
DataFrame([[4, 5, 6]], columns=["a", "b", "c"]),
None,
),
# gh-10548: EAT_LINE_COMMENT
(
"a,b,c\n4,5,6\n#comment",
dict(comment="#"),
DataFrame([[4, 5, 6]], columns=["a", "b", "c"]),
None,
),
# EAT_CRNL_NOP
(
"a,b,c\n4,5,6\n\r",
dict(),
DataFrame([[4, 5, 6]], columns=["a", "b", "c"]),
None,
),
# EAT_COMMENT
(
"a,b,c\n4,5,6#comment",
dict(comment="#"),
DataFrame([[4, 5, 6]], columns=["a", "b", "c"]),
None,
),
# SKIP_LINE
(
"a,b,c\n4,5,6\nskipme",
dict(skiprows=[2]),
DataFrame([[4, 5, 6]], columns=["a", "b", "c"]),
None,
),
# EAT_LINE_COMMENT
(
"a,b,c\n4,5,6\n#comment",
dict(comment="#", skip_blank_lines=False),
DataFrame([[4, 5, 6]], columns=["a", "b", "c"]),
None,
),
# IN_FIELD
(
"a,b,c\n4,5,6\n ",
dict(skip_blank_lines=False),
DataFrame([["4", 5, 6], [" ", None, None]], columns=["a", "b", "c"]),
None,
),
# EAT_CRNL
(
"a,b,c\n4,5,6\n\r",
dict(skip_blank_lines=False),
DataFrame([[4, 5, 6], [None, None, None]], columns=["a", "b", "c"]),
None,
),
# ESCAPED_CHAR
(
"a,b,c\n4,5,6\n\\",
dict(escapechar="\\"),
None,
"(EOF following escape character)|(unexpected end of data)",
),
# ESCAPE_IN_QUOTED_FIELD
(
'a,b,c\n4,5,6\n"\\',
dict(escapechar="\\"),
None,
"(EOF inside string starting at row 2)|(unexpected end of data)",
),
# IN_QUOTED_FIELD
(
'a,b,c\n4,5,6\n"',
dict(escapechar="\\"),
None,
"(EOF inside string starting at row 2)|(unexpected end of data)",
),
],
ids=[
"whitespace-line",
"eat-line-comment",
"eat-crnl-nop",
"eat-comment",
"skip-line",
"eat-line-comment",
"in-field",
"eat-crnl",
"escaped-char",
"escape-in-quoted-field",
"in-quoted-field",
],
)
def test_eof_states(all_parsers, data, kwargs, expected, msg):
# see gh-10728, gh-10548
parser = all_parsers
if expected is None:
with pytest.raises(ParserError, match=msg):
parser.read_csv(StringIO(data), **kwargs)
else:
result = parser.read_csv(StringIO(data), **kwargs)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("usecols", [None, [0, 1], ["a", "b"]])
def test_uneven_lines_with_usecols(all_parsers, usecols):
# see gh-12203
parser = all_parsers
data = r"""a,b,c
0,1,2
3,4,5,6,7
8,9,10"""
if usecols is None:
# Make sure that an error is still raised
# when the "usecols" parameter is not provided.
msg = r"Expected \d+ fields in line \d+, saw \d+"
with pytest.raises(ParserError, match=msg):
parser.read_csv(StringIO(data))
else:
expected = DataFrame({"a": [0, 3, 8], "b": [1, 4, 9]})
result = parser.read_csv(StringIO(data), usecols=usecols)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"data,kwargs,expected",
[
# First, check to see that the response of parser when faced with no
# provided columns raises the correct error, with or without usecols.
("", dict(), None),
("", dict(usecols=["X"]), None),
(
",,",
dict(names=["Dummy", "X", "Dummy_2"], usecols=["X"]),
DataFrame(columns=["X"], index=[0], dtype=np.float64),
),
(
"",
dict(names=["Dummy", "X", "Dummy_2"], usecols=["X"]),
DataFrame(columns=["X"]),
),
],
)
def test_read_empty_with_usecols(all_parsers, data, kwargs, expected):
# see gh-12493
parser = all_parsers
if expected is None:
msg = "No columns to parse from file"
with pytest.raises(EmptyDataError, match=msg):
parser.read_csv(StringIO(data), **kwargs)
else:
result = parser.read_csv(StringIO(data), **kwargs)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"kwargs,expected",
[
# gh-8661, gh-8679: this should ignore six lines, including
# lines with trailing whitespace and blank lines.
(
dict(
header=None,
delim_whitespace=True,
skiprows=[0, 1, 2, 3, 5, 6],
skip_blank_lines=True,
),
DataFrame([[1.0, 2.0, 4.0], [5.1, np.nan, 10.0]]),
),
# gh-8983: test skipping set of rows after a row with trailing spaces.
(
dict(
delim_whitespace=True, skiprows=[1, 2, 3, 5, 6], skip_blank_lines=True
),
DataFrame({"A": [1.0, 5.1], "B": [2.0, np.nan], "C": [4.0, 10]}),
),
],
)
def test_trailing_spaces(all_parsers, kwargs, expected):
data = "A B C \nrandom line with trailing spaces \nskip\n1,2,3\n1,2.,4.\nrandom line with trailing tabs\t\t\t\n \n5.1,NaN,10.0\n" # noqa
parser = all_parsers
result = parser.read_csv(StringIO(data.replace(",", " ")), **kwargs)
tm.assert_frame_equal(result, expected)
def test_raise_on_sep_with_delim_whitespace(all_parsers):
# see gh-6607
data = "a b c\n1 2 3"
parser = all_parsers
with pytest.raises(ValueError, match="you can only specify one"):
parser.read_csv(StringIO(data), sep=r"\s", delim_whitespace=True)
@pytest.mark.parametrize("delim_whitespace", [True, False])
def test_single_char_leading_whitespace(all_parsers, delim_whitespace):
# see gh-9710
parser = all_parsers
data = """\
MyColumn
a
b
a
b\n"""
expected = DataFrame({"MyColumn": list("abab")})
result = parser.read_csv(
StringIO(data), skipinitialspace=True, delim_whitespace=delim_whitespace
)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"sep,skip_blank_lines,exp_data",
[
(",", True, [[1.0, 2.0, 4.0], [5.0, np.nan, 10.0], [-70.0, 0.4, 1.0]]),
(r"\s+", True, [[1.0, 2.0, 4.0], [5.0, np.nan, 10.0], [-70.0, 0.4, 1.0]]),
(
",",
False,
[
[1.0, 2.0, 4.0],
[np.nan, np.nan, np.nan],
[np.nan, np.nan, np.nan],
[5.0, np.nan, 10.0],
[np.nan, np.nan, np.nan],
[-70.0, 0.4, 1.0],
],
),
],
)
def test_empty_lines(all_parsers, sep, skip_blank_lines, exp_data):
parser = all_parsers
data = """\
A,B,C
1,2.,4.
5.,NaN,10.0
-70,.4,1
"""
if sep == r"\s+":
data = data.replace(",", " ")
result = parser.read_csv(StringIO(data), sep=sep, skip_blank_lines=skip_blank_lines)
expected = DataFrame(exp_data, columns=["A", "B", "C"])
tm.assert_frame_equal(result, expected)
def test_whitespace_lines(all_parsers):
parser = all_parsers
data = """
\t \t\t
\t
A,B,C
\t 1,2.,4.
5.,NaN,10.0
"""
expected = DataFrame([[1, 2.0, 4.0], [5.0, np.nan, 10.0]], columns=["A", "B", "C"])
result = parser.read_csv(StringIO(data))
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"data,expected",
[
(
""" A B C D
a 1 2 3 4
b 1 2 3 4
c 1 2 3 4
""",
DataFrame(
[[1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4]],
columns=["A", "B", "C", "D"],
index=["a", "b", "c"],
),
),
(
" a b c\n1 2 3 \n4 5 6\n 7 8 9",
DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], columns=["a", "b", "c"]),
),
],
)
def test_whitespace_regex_separator(all_parsers, data, expected):
# see gh-6607
parser = all_parsers
result = parser.read_csv(StringIO(data), sep=r"\s+")
tm.assert_frame_equal(result, expected)
def test_verbose_read(all_parsers, capsys):
parser = all_parsers
data = """a,b,c,d
one,1,2,3
one,1,2,3
,1,2,3
one,1,2,3
,1,2,3
,1,2,3
one,1,2,3
two,1,2,3"""
# Engines are verbose in different ways.
parser.read_csv(StringIO(data), verbose=True)
captured = capsys.readouterr()
if parser.engine == "c":
assert "Tokenization took:" in captured.out
assert "Parser memory cleanup took:" in captured.out
else: # Python engine
assert captured.out == "Filled 3 NA values in column a\n"
def test_verbose_read2(all_parsers, capsys):
parser = all_parsers
data = """a,b,c,d
one,1,2,3
two,1,2,3
three,1,2,3
four,1,2,3
five,1,2,3
,1,2,3
seven,1,2,3
eight,1,2,3"""
parser.read_csv(StringIO(data), verbose=True, index_col=0)
captured = capsys.readouterr()
# Engines are verbose in different ways.
if parser.engine == "c":
assert "Tokenization took:" in captured.out
assert "Parser memory cleanup took:" in captured.out
else: # Python engine
assert captured.out == "Filled 1 NA values in column a\n"
def test_iteration_open_handle(all_parsers):
parser = all_parsers
kwargs = dict(squeeze=True, header=None)
with tm.ensure_clean() as path:
with open(path, "w") as f:
f.write("AAA\nBBB\nCCC\nDDD\nEEE\nFFF\nGGG")
with open(path, "r") as f:
for line in f:
if "CCC" in line:
break
result = parser.read_csv(f, **kwargs)
expected = Series(["DDD", "EEE", "FFF", "GGG"], name=0)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"data,thousands,decimal",
[
(
"""A|B|C
1|2,334.01|5
10|13|10.
""",
",",
".",
),
(
"""A|B|C
1|2.334,01|5
10|13|10,
""",
".",
",",
),
],
)
def test_1000_sep_with_decimal(all_parsers, data, thousands, decimal):
parser = all_parsers
expected = DataFrame({"A": [1, 10], "B": [2334.01, 13], "C": [5, 10.0]})
result = parser.read_csv(
StringIO(data), sep="|", thousands=thousands, decimal=decimal
)
tm.assert_frame_equal(result, expected)
def test_euro_decimal_format(all_parsers):
parser = all_parsers
data = """Id;Number1;Number2;Text1;Text2;Number3
1;1521,1541;187101,9543;ABC;poi;4,738797819
2;121,12;14897,76;DEF;uyt;0,377320872
3;878,158;108013,434;GHI;rez;2,735694704"""
result = parser.read_csv(StringIO(data), sep=";", decimal=",")
expected = DataFrame(
[
[1, 1521.1541, 187101.9543, "ABC", "poi", 4.738797819],
[2, 121.12, 14897.76, "DEF", "uyt", 0.377320872],
[3, 878.158, 108013.434, "GHI", "rez", 2.735694704],
],
columns=["Id", "Number1", "Number2", "Text1", "Text2", "Number3"],
)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("na_filter", [True, False])
def test_inf_parsing(all_parsers, na_filter):
parser = all_parsers
data = """\
,A
a,inf
b,-inf
c,+Inf
d,-Inf
e,INF
f,-INF
g,+INf
h,-INf
i,inF
j,-inF"""
expected = DataFrame(
{"A": [float("inf"), float("-inf")] * 5},
index=["a", "b", "c", "d", "e", "f", "g", "h", "i", "j"],
)
result = parser.read_csv(StringIO(data), index_col=0, na_filter=na_filter)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("na_filter", [True, False])
def test_infinity_parsing(all_parsers, na_filter):
parser = all_parsers
data = """\
,A
a,Infinity
b,-Infinity
c,+Infinity
"""
expected = DataFrame(
{"A": [float("infinity"), float("-infinity"), float("+infinity")]},
index=["a", "b", "c"],
)
result = parser.read_csv(StringIO(data), index_col=0, na_filter=na_filter)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("nrows", [0, 1, 2, 3, 4, 5])
def test_raise_on_no_columns(all_parsers, nrows):
parser = all_parsers
data = "\n" * nrows
msg = "No columns to parse from file"
with pytest.raises(EmptyDataError, match=msg):
parser.read_csv(StringIO(data))
def test_memory_map(all_parsers, csv_dir_path):
mmap_file = os.path.join(csv_dir_path, "test_mmap.csv")
parser = all_parsers
expected = DataFrame(
{"a": [1, 2, 3], "b": ["one", "two", "three"], "c": ["I", "II", "III"]}
)
result = parser.read_csv(mmap_file, memory_map=True)
tm.assert_frame_equal(result, expected)
def test_null_byte_char(all_parsers):
# see gh-2741
data = "\x00,foo"
names = ["a", "b"]
parser = all_parsers
if parser.engine == "c":
expected = DataFrame([[np.nan, "foo"]], columns=names)
out = parser.read_csv(StringIO(data), names=names)
tm.assert_frame_equal(out, expected)
else:
msg = "NULL byte detected"
with pytest.raises(ParserError, match=msg):
parser.read_csv(StringIO(data), names=names)
def test_temporary_file(all_parsers):
# see gh-13398
parser = all_parsers
data = "0 0"
with tm.ensure_clean(mode="w+", return_filelike=True) as new_file:
new_file.write(data)
new_file.flush()
new_file.seek(0)
result = parser.read_csv(new_file, sep=r"\s+", header=None)
expected = DataFrame([[0, 0]])
tm.assert_frame_equal(result, expected)
def test_internal_eof_byte(all_parsers):
# see gh-5500
parser = all_parsers
data = "a,b\n1\x1a,2"
expected = DataFrame([["1\x1a", 2]], columns=["a", "b"])
result = parser.read_csv(StringIO(data))
tm.assert_frame_equal(result, expected)
def test_internal_eof_byte_to_file(all_parsers):
# see gh-16559
parser = all_parsers
data = b'c1,c2\r\n"test \x1a test", test\r\n'
expected = DataFrame([["test \x1a test", " test"]], columns=["c1", "c2"])
path = f"__{tm.rands(10)}__.csv"
with tm.ensure_clean(path) as path:
with open(path, "wb") as f:
f.write(data)
result = parser.read_csv(path)
tm.assert_frame_equal(result, expected)
def test_sub_character(all_parsers, csv_dir_path):
# see gh-16893
filename = os.path.join(csv_dir_path, "sub_char.csv")
expected = DataFrame([[1, 2, 3]], columns=["a", "\x1ab", "c"])
parser = all_parsers
result = parser.read_csv(filename)
tm.assert_frame_equal(result, expected)
def test_file_handle_string_io(all_parsers):
# gh-14418
#
# Don't close user provided file handles.
parser = all_parsers
data = "a,b\n1,2"
fh = StringIO(data)
parser.read_csv(fh)
assert not fh.closed
def test_file_handles_with_open(all_parsers, csv1):
# gh-14418
#
# Don't close user provided file handles.
parser = all_parsers
for mode in ["r", "rb"]:
with open(csv1, mode) as f:
parser.read_csv(f)
assert not f.closed
def test_invalid_file_buffer_class(all_parsers):
# see gh-15337
class InvalidBuffer:
pass
parser = all_parsers
msg = "Invalid file path or buffer object type"
with pytest.raises(ValueError, match=msg):
parser.read_csv(InvalidBuffer())
def test_invalid_file_buffer_mock(all_parsers):
# see gh-15337
parser = all_parsers
msg = "Invalid file path or buffer object type"
class Foo:
pass
with pytest.raises(ValueError, match=msg):
parser.read_csv(Foo())
def test_valid_file_buffer_seems_invalid(all_parsers):
# gh-16135: we want to ensure that "tell" and "seek"
# aren't actually being used when we call `read_csv`
#
# Thus, while the object may look "invalid" (these
# methods are attributes of the `StringIO` class),
# it is still a valid file-object for our purposes.
class NoSeekTellBuffer(StringIO):
def tell(self):
raise AttributeError("No tell method")
def seek(self, pos, whence=0):
raise AttributeError("No seek method")
data = "a\n1"
parser = all_parsers
expected = DataFrame({"a": [1]})
result = parser.read_csv(NoSeekTellBuffer(data))
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"kwargs",
[dict(), dict(error_bad_lines=True)], # Default is True. # Explicitly pass in.
)
@pytest.mark.parametrize(
"warn_kwargs", [dict(), dict(warn_bad_lines=True), dict(warn_bad_lines=False)]
)
def test_error_bad_lines(all_parsers, kwargs, warn_kwargs):
# see gh-15925
parser = all_parsers
kwargs.update(**warn_kwargs)
data = "a\n1\n1,2,3\n4\n5,6,7"
msg = "Expected 1 fields in line 3, saw 3"
with pytest.raises(ParserError, match=msg):
parser.read_csv(StringIO(data), **kwargs)
def test_warn_bad_lines(all_parsers, capsys):
# see gh-15925
parser = all_parsers
data = "a\n1\n1,2,3\n4\n5,6,7"
expected = DataFrame({"a": [1, 4]})
result = parser.read_csv(StringIO(data), error_bad_lines=False, warn_bad_lines=True)
tm.assert_frame_equal(result, expected)
captured = capsys.readouterr()
assert "Skipping line 3" in captured.err
assert "Skipping line 5" in captured.err
def test_suppress_error_output(all_parsers, capsys):
# see gh-15925
parser = all_parsers
data = "a\n1\n1,2,3\n4\n5,6,7"
expected = DataFrame({"a": [1, 4]})
result = parser.read_csv(
StringIO(data), error_bad_lines=False, warn_bad_lines=False
)
tm.assert_frame_equal(result, expected)
captured = capsys.readouterr()
assert captured.err == ""
@pytest.mark.parametrize("filename", ["sé-es-vé.csv", "ru-sй.csv", "中文文件名.csv"])
def test_filename_with_special_chars(all_parsers, filename):
# see gh-15086.
parser = all_parsers
df = DataFrame({"a": [1, 2, 3]})
with tm.ensure_clean(filename) as path:
df.to_csv(path, index=False)
result = parser.read_csv(path)
tm.assert_frame_equal(result, df)
def test_read_csv_memory_growth_chunksize(all_parsers):
# see gh-24805
#
# Let's just make sure that we don't crash
# as we iteratively process all chunks.
parser = all_parsers
with tm.ensure_clean() as path:
with open(path, "w") as f:
for i in range(1000):
f.write(str(i) + "\n")
result = parser.read_csv(path, chunksize=20)
for _ in result:
pass
def test_read_csv_raises_on_header_prefix(all_parsers):
# gh-27394
parser = all_parsers
msg = "Argument prefix must be None if argument header is not None"
s = StringIO("0,1\n2,3")
with pytest.raises(ValueError, match=msg):
parser.read_csv(s, header=0, prefix="_X")
def test_read_table_equivalency_to_read_csv(all_parsers):
# see gh-21948
# As of 0.25.0, read_table is undeprecated
parser = all_parsers
data = "a\tb\n1\t2\n3\t4"
expected = parser.read_csv(StringIO(data), sep="\t")
result = parser.read_table(StringIO(data))
tm.assert_frame_equal(result, expected)
def test_first_row_bom(all_parsers):
# see gh-26545
parser = all_parsers
data = '''\ufeff"Head1" "Head2" "Head3"'''
result = parser.read_csv(StringIO(data), delimiter="\t")
expected = DataFrame(columns=["Head1", "Head2", "Head3"])
tm.assert_frame_equal(result, expected)
def test_integer_precision(all_parsers):
# Gh 7072
s = """1,1;0;0;0;1;1;3844;3844;3844;1;1;1;1;1;1;0;0;1;1;0;0,,,4321583677327450765
5,1;0;0;0;1;1;843;843;843;1;1;1;1;1;1;0;0;1;1;0;0,64.0,;,4321113141090630389"""
parser = all_parsers
result = parser.read_csv(StringIO(s), header=None)[4]
expected = Series([4321583677327450765, 4321113141090630389], name=4)
tm.assert_series_equal(result, expected)
def test_file_descriptor_leak(all_parsers):
# GH 31488
parser = all_parsers
with tm.ensure_clean() as path:
def test():
with pytest.raises(EmptyDataError, match="No columns to parse from file"):
parser.read_csv(path)
td.check_file_leaks(test)()
@pytest.mark.parametrize("nrows", range(1, 6))
def test_blank_lines_between_header_and_data_rows(all_parsers, nrows):
# GH 28071
ref = DataFrame(
[[np.nan, np.nan], [np.nan, np.nan], [1, 2], [np.nan, np.nan], [3, 4]],
columns=list("ab"),
)
csv = "\nheader\n\na,b\n\n\n1,2\n\n3,4"
parser = all_parsers
df = parser.read_csv(StringIO(csv), header=3, nrows=nrows, skip_blank_lines=False)
tm.assert_frame_equal(df, ref[:nrows])
def test_no_header_two_extra_columns(all_parsers):
# GH 26218
column_names = ["one", "two", "three"]
ref = DataFrame([["foo", "bar", "baz"]], columns=column_names)
stream = StringIO("foo,bar,baz,bam,blah")
parser = all_parsers
df = parser.read_csv(stream, header=None, names=column_names, index_col=False)
tm.assert_frame_equal(df, ref)
|
|
pandas/tests/io/parser/test_common.py
|
pandas/core/arrays/_ranges.py
|
from __future__ import print_function
from argparse import ArgumentParser
import os
from ruamel import yaml
from binstar_client.utils import get_server_api
from binstar_client.errors import NotFound
from conda.version import VersionOrder
__all__ = ['PackageCopier']
class PackageCopier(object):
def __init__(self, source, destination, input_packages, token=''):
"""
Parameters
----------
source : ``str``
Name of source conda channel.
destination : ``str``
Name of destination conda channel.
input_package : ``dict``
Dictionary in which keys are package names and values are either
a string version number (e.g. ``'1.0.1'``) or ``None``, which
indicates the latest version on the source channel should be
copied. This dictionary should contain the packages that
potentially need to be copied.
token : ``str``, optional
Token for conda API. Needed for the actual copy operation.
"""
self.source = source
self.destination = destination
self.input_packages = input_packages
self.api = get_server_api(token)
self.to_copy = self._package_versions_to_copy()
def _package_versions_to_copy(self):
"""
Determine which version of each package in packages
should be copied from conda channel source to channel
destination.
Returns
-------
``dict``
Dictionary whose keys are the packages that actually need to be
copied and whose values are the version to be copied.
"""
packages = self.input_packages
copy_versions = {}
for p, version in packages.items():
copy_builds = []
need_to_copy = False
# This will end up True if the version exists on both src and dest
# and triggers a comparison of file names. Technically, it could
# be omitted, but seems more likely to be clear to future me.
check_builds = False
cf = self.api.package(self.source, p)
cf_version = VersionOrder(cf['latest_version'])
if version is not None:
pinned_version = VersionOrder(version)
else:
pinned_version = None
if pinned_version is not None:
if str(pinned_version) not in cf['versions']:
error_message = ('Version {} of package {} not '
'found on source channel {}.')
err = error_message.format(pinned_version, p,
self.source)
raise RuntimeError(err)
try:
ap = self.api.package(self.destination, p)
except NotFound:
need_to_copy = True
ap_version = None
else:
ap_version = VersionOrder(ap['latest_version'])
if pinned_version is None:
if cf_version > ap_version:
need_to_copy = True
elif cf_version == ap_version:
check_builds = True
else:
if str(pinned_version) not in ap['versions']:
need_to_copy = True
else:
check_builds = True
if check_builds:
# If we get here it means that the same version is on both
# source and destination so we need to check the individual
# builds.
check_version = pinned_version or cf_version
copy_builds = \
self._check_for_missing_builds(cf,
ap,
check_version)
need_to_copy = len(copy_builds) > 0
if need_to_copy:
copy_versions[p] = (str(cf_version), copy_builds)
return copy_versions
def _check_for_missing_builds(self, source, dest, version):
"""
For two packages that have the same version, see if there are any
files on the source that are not on the destination.
source and dest are both conda channels, and version
should be a string.
"""
def files_for_version(channel, version):
files = [f['basename'] for f in channel['files']
if VersionOrder(version) == VersionOrder(f['version'])]
return files
source_files = files_for_version(source, version)
destination_files = files_for_version(dest, version)
need_to_copy = [src for src in source_files
if src not in destination_files]
return need_to_copy
def copy_packages(self):
"""
Actually do the copying of the packages.
"""
for p, v in self.to_copy.items():
version, buildnames = v
if not buildnames:
# Copy all of the builds for this version
self.api.copy(self.source, p, version, to_owner=self.destination)
else:
for build in buildnames:
self.api.copy(self.source, p, version,
basename=build,
to_owner=self.destination)
def main(arguments=None):
parser = ArgumentParser('Simple script for copying packages '
'from one conda owner to another')
parser.add_argument('packages_yaml',
help=('Packages to copy, as a yaml dictionary. '
'Keys are package names, values are version, '
'or None for the latest version from '
'the source.'))
parser.add_argument('--source', default='conda-forge',
help='Source conda channel owner.')
parser.add_argument('--token', default='',
help=('anaconda.org API token. May set '
'environmental variable BINSTAR_TOKEN '
'instead.'))
parser.add_argument('destination_channel',
help=('Destination conda channel owner.'))
if arguments is None:
args = parser.parse_args()
else:
args = parser.parse_args(arguments)
source = args.source
dest = args.destination_channel
package_file = args.packages_yaml
token = args.token
with open(package_file) as f:
packages = yaml.load(f)
# No token on command line, try the environment...
if not token:
token = os.getenv('BINSTAR_TOKEN')
# Still no token, so raise an error
if not token:
raise RuntimeError('Set an anaconda.org API token before running')
pc = PackageCopier(source, dest, packages, token=token)
pc.copy_packages()
if __name__ == '__main__':
main()
|
import pytest
from os import getenv
from binstar_client.utils import get_server_api
from binstar_client.errors import NotFound
from ..copy_packages import PackageCopier
SOURCE = 'conda-forge'
DEST = 'astropy-channel-copy-test'
# Destination channel contains only the packages:
# wcsaxes
# only versions 0.7 and 0.8, but not the latest on conda-forge,
# which is 0.9.
# sep
# only version 0.5.2, copied from channel mwcraig,
# which contains only that version.
def test_package_not_on_source():
# Package does not exist on source channel
# Expected outcome: NotFound
packages = {'asudifjqeiroufnver': None}
with pytest.raises(NotFound):
PackageCopier(SOURCE, DEST, packages)
# Whether or not version exists on destination channel:
def test_version_not_in_source():
# Package version is pinned and...
# ...pinned version is not in source channel
# Expected outcome: RuntimeError and specific message
packages = {'wcsaxes': '0.0.0'}
with pytest.raises(RuntimeError):
PackageCopier(SOURCE, DEST, packages)
# Package version is pinned and...
def test_version_pinned_not_in_destination():
# ...pinned version is not in destination channel
# Expected outcome: copy
packages = {'wcsaxes': '0.9'}
pc = PackageCopier(SOURCE, DEST, packages)
assert 'wcsaxes' in pc.to_copy
def test_version_pinned_in_destination():
# ...pinned version is in destination channel
# Expected outcome: No copy
packages = {'wcsaxes': '0.8'}
pc = PackageCopier(SOURCE, DEST, packages)
assert 'wcsaxes' not in pc.to_copy
# Package version is not pinned and...
def test_version_not_pinned_not_in_destination():
# ...destination channel is not up to date
# Expected outcome: copy
packages = {'wcsaxes': None}
pc = PackageCopier(SOURCE, DEST, packages)
assert 'wcsaxes' in pc.to_copy
def test_version_not_pinned_no_update_needed():
# ...destination is up to date
# Expected outcome: no copy
packages = {'sep': None}
pc = PackageCopier('mwcraig', DEST, packages)
assert 'sep' not in pc.to_copy
token = getenv('COPY_TEST_BINSTAR_TOKEN')
@pytest.mark.skipif(token is None,
reason='binstar token not set')
def test_package_copying():
api = get_server_api(token)
packages = {'wcsaxes': None}
pc = PackageCopier(SOURCE, DEST, packages, token=token)
# Make sure v0.9 has not accidentally ended up in the channel.
dest_wcs = api.package(DEST, 'wcsaxes')
assert "0.9" not in dest_wcs['versions']
# Copy 0.9 to the channel.
pc.copy_packages()
# Make sure it is really there.
dest_wcs = api.package(DEST, 'wcsaxes')
assert "0.9" in dest_wcs['versions']
# Remove it...
api.remove_release(DEST, 'wcsaxes', "0.9")
# ...and make sure it is really gone.
dest_wcs = api.package(DEST, 'wcsaxes')
assert "0.9" not in dest_wcs['versions']
|
astropy/conda-build-tools
|
extruder/tests/test_channel_copy.py
|
extruder/copy_packages.py
|
from pandas.util._decorators import cache_readonly
import pandas.util.testing as tm
import pandas as pd
_ts = tm.makeTimeSeries()
class TestData(object):
@cache_readonly
def ts(self):
ts = _ts.copy()
ts.name = 'ts'
return ts
@cache_readonly
def series(self):
series = tm.makeStringSeries()
series.name = 'series'
return series
@cache_readonly
def objSeries(self):
objSeries = tm.makeObjectSeries()
objSeries.name = 'objects'
return objSeries
@cache_readonly
def empty(self):
return pd.Series([], index=[])
|
from datetime import timedelta
import pytest
import numpy as np
import pandas as pd
import pandas.util.testing as tm
from pandas import TimedeltaIndex, timedelta_range, compat, Index, Timedelta
class TestTimedeltaIndex(object):
_multiprocess_can_split_ = True
def test_insert(self):
idx = TimedeltaIndex(['4day', '1day', '2day'], name='idx')
result = idx.insert(2, timedelta(days=5))
exp = TimedeltaIndex(['4day', '1day', '5day', '2day'], name='idx')
tm.assert_index_equal(result, exp)
# insertion of non-datetime should coerce to object index
result = idx.insert(1, 'inserted')
expected = Index([Timedelta('4day'), 'inserted', Timedelta('1day'),
Timedelta('2day')], name='idx')
assert not isinstance(result, TimedeltaIndex)
tm.assert_index_equal(result, expected)
assert result.name == expected.name
idx = timedelta_range('1day 00:00:01', periods=3, freq='s', name='idx')
# preserve freq
expected_0 = TimedeltaIndex(['1day', '1day 00:00:01', '1day 00:00:02',
'1day 00:00:03'],
name='idx', freq='s')
expected_3 = TimedeltaIndex(['1day 00:00:01', '1day 00:00:02',
'1day 00:00:03', '1day 00:00:04'],
name='idx', freq='s')
# reset freq to None
expected_1_nofreq = TimedeltaIndex(['1day 00:00:01', '1day 00:00:01',
'1day 00:00:02', '1day 00:00:03'],
name='idx', freq=None)
expected_3_nofreq = TimedeltaIndex(['1day 00:00:01', '1day 00:00:02',
'1day 00:00:03', '1day 00:00:05'],
name='idx', freq=None)
cases = [(0, Timedelta('1day'), expected_0),
(-3, Timedelta('1day'), expected_0),
(3, Timedelta('1day 00:00:04'), expected_3),
(1, Timedelta('1day 00:00:01'), expected_1_nofreq),
(3, Timedelta('1day 00:00:05'), expected_3_nofreq)]
for n, d, expected in cases:
result = idx.insert(n, d)
tm.assert_index_equal(result, expected)
assert result.name == expected.name
assert result.freq == expected.freq
# GH 18295 (test missing)
expected = TimedeltaIndex(['1day', pd.NaT, '2day', '3day'])
for na in (np.nan, pd.NaT, None):
result = timedelta_range('1day', '3day').insert(1, na)
tm.assert_index_equal(result, expected)
def test_delete(self):
idx = timedelta_range(start='1 Days', periods=5, freq='D', name='idx')
# prserve freq
expected_0 = timedelta_range(start='2 Days', periods=4, freq='D',
name='idx')
expected_4 = timedelta_range(start='1 Days', periods=4, freq='D',
name='idx')
# reset freq to None
expected_1 = TimedeltaIndex(
['1 day', '3 day', '4 day', '5 day'], freq=None, name='idx')
cases = {0: expected_0,
-5: expected_0,
-1: expected_4,
4: expected_4,
1: expected_1}
for n, expected in compat.iteritems(cases):
result = idx.delete(n)
tm.assert_index_equal(result, expected)
assert result.name == expected.name
assert result.freq == expected.freq
with pytest.raises((IndexError, ValueError)):
# either depeidnig on numpy version
result = idx.delete(5)
def test_delete_slice(self):
idx = timedelta_range(start='1 days', periods=10, freq='D', name='idx')
# prserve freq
expected_0_2 = timedelta_range(start='4 days', periods=7, freq='D',
name='idx')
expected_7_9 = timedelta_range(start='1 days', periods=7, freq='D',
name='idx')
# reset freq to None
expected_3_5 = TimedeltaIndex(['1 d', '2 d', '3 d',
'7 d', '8 d', '9 d', '10d'],
freq=None, name='idx')
cases = {(0, 1, 2): expected_0_2,
(7, 8, 9): expected_7_9,
(3, 4, 5): expected_3_5}
for n, expected in compat.iteritems(cases):
result = idx.delete(n)
tm.assert_index_equal(result, expected)
assert result.name == expected.name
assert result.freq == expected.freq
result = idx.delete(slice(n[0], n[-1] + 1))
tm.assert_index_equal(result, expected)
assert result.name == expected.name
assert result.freq == expected.freq
def test_getitem(self):
idx1 = timedelta_range('1 day', '31 day', freq='D', name='idx')
for idx in [idx1]:
result = idx[0]
assert result == Timedelta('1 day')
result = idx[0:5]
expected = timedelta_range('1 day', '5 day', freq='D',
name='idx')
tm.assert_index_equal(result, expected)
assert result.freq == expected.freq
result = idx[0:10:2]
expected = timedelta_range('1 day', '9 day', freq='2D',
name='idx')
tm.assert_index_equal(result, expected)
assert result.freq == expected.freq
result = idx[-20:-5:3]
expected = timedelta_range('12 day', '24 day', freq='3D',
name='idx')
tm.assert_index_equal(result, expected)
assert result.freq == expected.freq
result = idx[4::-1]
expected = TimedeltaIndex(['5 day', '4 day', '3 day',
'2 day', '1 day'],
freq='-1D', name='idx')
tm.assert_index_equal(result, expected)
assert result.freq == expected.freq
def test_take(self):
# GH 10295
idx1 = timedelta_range('1 day', '31 day', freq='D', name='idx')
for idx in [idx1]:
result = idx.take([0])
assert result == Timedelta('1 day')
result = idx.take([-1])
assert result == Timedelta('31 day')
result = idx.take([0, 1, 2])
expected = timedelta_range('1 day', '3 day', freq='D',
name='idx')
tm.assert_index_equal(result, expected)
assert result.freq == expected.freq
result = idx.take([0, 2, 4])
expected = timedelta_range('1 day', '5 day', freq='2D',
name='idx')
tm.assert_index_equal(result, expected)
assert result.freq == expected.freq
result = idx.take([7, 4, 1])
expected = timedelta_range('8 day', '2 day', freq='-3D',
name='idx')
tm.assert_index_equal(result, expected)
assert result.freq == expected.freq
result = idx.take([3, 2, 5])
expected = TimedeltaIndex(['4 day', '3 day', '6 day'], name='idx')
tm.assert_index_equal(result, expected)
assert result.freq is None
result = idx.take([-3, 2, 5])
expected = TimedeltaIndex(['29 day', '3 day', '6 day'], name='idx')
tm.assert_index_equal(result, expected)
assert result.freq is None
def test_take_invalid_kwargs(self):
idx = timedelta_range('1 day', '31 day', freq='D', name='idx')
indices = [1, 6, 5, 9, 10, 13, 15, 3]
msg = r"take\(\) got an unexpected keyword argument 'foo'"
tm.assert_raises_regex(TypeError, msg, idx.take,
indices, foo=2)
msg = "the 'out' parameter is not supported"
tm.assert_raises_regex(ValueError, msg, idx.take,
indices, out=indices)
msg = "the 'mode' parameter is not supported"
tm.assert_raises_regex(ValueError, msg, idx.take,
indices, mode='clip')
# TODO: This method came from test_timedelta; de-dup with version above
def test_take2(self):
tds = ['1day 02:00:00', '1 day 04:00:00', '1 day 10:00:00']
idx = TimedeltaIndex(start='1d', end='2d', freq='H', name='idx')
expected = TimedeltaIndex(tds, freq=None, name='idx')
taken1 = idx.take([2, 4, 10])
taken2 = idx[[2, 4, 10]]
for taken in [taken1, taken2]:
tm.assert_index_equal(taken, expected)
assert isinstance(taken, TimedeltaIndex)
assert taken.freq is None
assert taken.name == expected.name
def test_take_fill_value(self):
# GH 12631
idx = TimedeltaIndex(['1 days', '2 days', '3 days'],
name='xxx')
result = idx.take(np.array([1, 0, -1]))
expected = TimedeltaIndex(['2 days', '1 days', '3 days'],
name='xxx')
tm.assert_index_equal(result, expected)
# fill_value
result = idx.take(np.array([1, 0, -1]), fill_value=True)
expected = TimedeltaIndex(['2 days', '1 days', 'NaT'],
name='xxx')
tm.assert_index_equal(result, expected)
# allow_fill=False
result = idx.take(np.array([1, 0, -1]), allow_fill=False,
fill_value=True)
expected = TimedeltaIndex(['2 days', '1 days', '3 days'],
name='xxx')
tm.assert_index_equal(result, expected)
msg = ('When allow_fill=True and fill_value is not None, '
'all indices must be >= -1')
with tm.assert_raises_regex(ValueError, msg):
idx.take(np.array([1, 0, -2]), fill_value=True)
with tm.assert_raises_regex(ValueError, msg):
idx.take(np.array([1, 0, -5]), fill_value=True)
with pytest.raises(IndexError):
idx.take(np.array([1, -5]))
def test_get_loc(self):
idx = pd.to_timedelta(['0 days', '1 days', '2 days'])
for method in [None, 'pad', 'backfill', 'nearest']:
assert idx.get_loc(idx[1], method) == 1
assert idx.get_loc(idx[1].to_pytimedelta(), method) == 1
assert idx.get_loc(str(idx[1]), method) == 1
assert idx.get_loc(idx[1], 'pad',
tolerance=Timedelta(0)) == 1
assert idx.get_loc(idx[1], 'pad',
tolerance=np.timedelta64(0, 's')) == 1
assert idx.get_loc(idx[1], 'pad',
tolerance=timedelta(0)) == 1
with tm.assert_raises_regex(ValueError,
'unit abbreviation w/o a number'):
idx.get_loc(idx[1], method='nearest', tolerance='foo')
with pytest.raises(
ValueError,
match='tolerance size must match'):
idx.get_loc(idx[1], method='nearest',
tolerance=[Timedelta(0).to_timedelta64(),
Timedelta(0).to_timedelta64()])
for method, loc in [('pad', 1), ('backfill', 2), ('nearest', 1)]:
assert idx.get_loc('1 day 1 hour', method) == loc
# GH 16909
assert idx.get_loc(idx[1].to_timedelta64()) == 1
# GH 16896
assert idx.get_loc('0 days') == 0
def test_get_loc_nat(self):
tidx = TimedeltaIndex(['1 days 01:00:00', 'NaT', '2 days 01:00:00'])
assert tidx.get_loc(pd.NaT) == 1
assert tidx.get_loc(None) == 1
assert tidx.get_loc(float('nan')) == 1
assert tidx.get_loc(np.nan) == 1
def test_get_indexer(self):
idx = pd.to_timedelta(['0 days', '1 days', '2 days'])
tm.assert_numpy_array_equal(idx.get_indexer(idx),
np.array([0, 1, 2], dtype=np.intp))
target = pd.to_timedelta(['-1 hour', '12 hours', '1 day 1 hour'])
tm.assert_numpy_array_equal(idx.get_indexer(target, 'pad'),
np.array([-1, 0, 1], dtype=np.intp))
tm.assert_numpy_array_equal(idx.get_indexer(target, 'backfill'),
np.array([0, 1, 2], dtype=np.intp))
tm.assert_numpy_array_equal(idx.get_indexer(target, 'nearest'),
np.array([0, 1, 1], dtype=np.intp))
res = idx.get_indexer(target, 'nearest',
tolerance=Timedelta('1 hour'))
tm.assert_numpy_array_equal(res, np.array([0, -1, 1], dtype=np.intp))
|
|
pandas/tests/indexes/timedeltas/test_indexing.py
|
pandas/tests/series/common.py
|
"""
Support for Yamaha Receivers.
For more details about this platform, please refer to the documentation at
https://home-assistant.io/components/media_player.yamaha/
"""
import logging
import voluptuous as vol
from homeassistant.components.media_player import (
SUPPORT_TURN_OFF, SUPPORT_TURN_ON, SUPPORT_VOLUME_MUTE, SUPPORT_VOLUME_SET,
SUPPORT_SELECT_SOURCE, SUPPORT_PLAY_MEDIA, SUPPORT_PAUSE, SUPPORT_STOP,
SUPPORT_NEXT_TRACK, SUPPORT_PREVIOUS_TRACK, SUPPORT_PLAY,
MEDIA_TYPE_MUSIC,
MediaPlayerDevice, PLATFORM_SCHEMA)
from homeassistant.const import (CONF_NAME, CONF_HOST, STATE_OFF, STATE_ON,
STATE_PLAYING, STATE_IDLE)
import homeassistant.helpers.config_validation as cv
REQUIREMENTS = ['rxv==0.4.0']
_LOGGER = logging.getLogger(__name__)
SUPPORT_YAMAHA = SUPPORT_VOLUME_SET | SUPPORT_VOLUME_MUTE | \
SUPPORT_TURN_ON | SUPPORT_TURN_OFF | SUPPORT_SELECT_SOURCE | SUPPORT_PLAY
CONF_SOURCE_NAMES = 'source_names'
CONF_SOURCE_IGNORE = 'source_ignore'
CONF_ZONE_IGNORE = 'zone_ignore'
DEFAULT_NAME = 'Yamaha Receiver'
KNOWN = 'yamaha_known_receivers'
PLATFORM_SCHEMA = PLATFORM_SCHEMA.extend({
vol.Optional(CONF_NAME, default=DEFAULT_NAME): cv.string,
vol.Optional(CONF_HOST): cv.string,
vol.Optional(CONF_SOURCE_IGNORE, default=[]):
vol.All(cv.ensure_list, [cv.string]),
vol.Optional(CONF_ZONE_IGNORE, default=[]):
vol.All(cv.ensure_list, [cv.string]),
vol.Optional(CONF_SOURCE_NAMES, default={}): {cv.string: cv.string},
})
def setup_platform(hass, config, add_devices, discovery_info=None):
"""Set up the Yamaha platform."""
import rxv
# keep track of configured receivers so that we don't end up
# discovering a receiver dynamically that we have static config
# for.
if hass.data.get(KNOWN, None) is None:
hass.data[KNOWN] = set()
name = config.get(CONF_NAME)
host = config.get(CONF_HOST)
source_ignore = config.get(CONF_SOURCE_IGNORE)
source_names = config.get(CONF_SOURCE_NAMES)
zone_ignore = config.get(CONF_ZONE_IGNORE)
if discovery_info is not None:
name = discovery_info.get('name')
model = discovery_info.get('model_name')
ctrl_url = discovery_info.get('control_url')
desc_url = discovery_info.get('description_url')
if ctrl_url in hass.data[KNOWN]:
_LOGGER.info("%s already manually configured", ctrl_url)
return
receivers = rxv.RXV(
ctrl_url,
model_name=model,
friendly_name=name,
unit_desc_url=desc_url).zone_controllers()
_LOGGER.info("Receivers: %s", receivers)
# when we are dynamically discovered config is empty
zone_ignore = []
elif host is None:
receivers = []
for recv in rxv.find():
receivers.extend(recv.zone_controllers())
else:
ctrl_url = "http://{}:80/YamahaRemoteControl/ctrl".format(host)
receivers = rxv.RXV(ctrl_url, name).zone_controllers()
for receiver in receivers:
if receiver.zone not in zone_ignore:
hass.data[KNOWN].add(receiver.ctrl_url)
add_devices([
YamahaDevice(name, receiver, source_ignore, source_names)])
class YamahaDevice(MediaPlayerDevice):
"""Representation of a Yamaha device."""
def __init__(self, name, receiver, source_ignore, source_names):
"""Initialize the Yamaha Receiver."""
self._receiver = receiver
self._muted = False
self._volume = 0
self._pwstate = STATE_OFF
self._current_source = None
self._source_list = None
self._source_ignore = source_ignore or []
self._source_names = source_names or {}
self._reverse_mapping = None
self._playback_support = None
self._is_playback_supported = False
self._play_status = None
self.update()
self._name = name
self._zone = receiver.zone
def update(self):
"""Get the latest details from the device."""
self._play_status = self._receiver.play_status()
if self._receiver.on:
if self._play_status is None:
self._pwstate = STATE_ON
elif self._play_status.playing:
self._pwstate = STATE_PLAYING
else:
self._pwstate = STATE_IDLE
else:
self._pwstate = STATE_OFF
self._muted = self._receiver.mute
self._volume = (self._receiver.volume / 100) + 1
if self.source_list is None:
self.build_source_list()
current_source = self._receiver.input
self._current_source = self._source_names.get(
current_source, current_source)
self._playback_support = self._receiver.get_playback_support()
self._is_playback_supported = self._receiver.is_playback_supported(
self._current_source)
def build_source_list(self):
"""Build the source list."""
self._reverse_mapping = {alias: source for source, alias in
self._source_names.items()}
self._source_list = sorted(
self._source_names.get(source, source) for source in
self._receiver.inputs()
if source not in self._source_ignore)
@property
def name(self):
"""Return the name of the device."""
name = self._name
if self._zone != "Main_Zone":
# Zone will be one of Main_Zone, Zone_2, Zone_3
name += " " + self._zone.replace('_', ' ')
return name
@property
def state(self):
"""Return the state of the device."""
return self._pwstate
@property
def volume_level(self):
"""Volume level of the media player (0..1)."""
return self._volume
@property
def is_volume_muted(self):
"""Boolean if volume is currently muted."""
return self._muted
@property
def source(self):
"""Return the current input source."""
return self._current_source
@property
def source_list(self):
"""List of available input sources."""
return self._source_list
@property
def supported_features(self):
"""Flag media player features that are supported."""
supported_features = SUPPORT_YAMAHA
supports = self._playback_support
mapping = {'play': (SUPPORT_PLAY | SUPPORT_PLAY_MEDIA),
'pause': SUPPORT_PAUSE,
'stop': SUPPORT_STOP,
'skip_f': SUPPORT_NEXT_TRACK,
'skip_r': SUPPORT_PREVIOUS_TRACK}
for attr, feature in mapping.items():
if getattr(supports, attr, False):
supported_features |= feature
return supported_features
def turn_off(self):
"""Turn off media player."""
self._receiver.on = False
def set_volume_level(self, volume):
"""Set volume level, range 0..1."""
receiver_vol = 100 - (volume * 100)
negative_receiver_vol = -receiver_vol
self._receiver.volume = negative_receiver_vol
def mute_volume(self, mute):
"""Mute (true) or unmute (false) media player."""
self._receiver.mute = mute
def turn_on(self):
"""Turn the media player on."""
self._receiver.on = True
self._volume = (self._receiver.volume / 100) + 1
def media_play(self):
"""Send play commmand."""
self._call_playback_function(self._receiver.play, "play")
def media_pause(self):
"""Send pause command."""
self._call_playback_function(self._receiver.pause, "pause")
def media_stop(self):
"""Send stop command."""
self._call_playback_function(self._receiver.stop, "stop")
def media_previous_track(self):
"""Send previous track command."""
self._call_playback_function(self._receiver.previous, "previous track")
def media_next_track(self):
"""Send next track command."""
self._call_playback_function(self._receiver.next, "next track")
def _call_playback_function(self, function, function_text):
import rxv
try:
function()
except rxv.exceptions.ResponseException:
_LOGGER.warning(
"Failed to execute %s on %s", function_text, self._name)
def select_source(self, source):
"""Select input source."""
self._receiver.input = self._reverse_mapping.get(source, source)
def play_media(self, media_type, media_id, **kwargs):
"""Play media from an ID.
This exposes a pass through for various input sources in the
Yamaha to direct play certain kinds of media. media_type is
treated as the input type that we are setting, and media id is
specific to it.
"""
if media_type == "NET RADIO":
self._receiver.net_radio(media_id)
@property
def media_artist(self):
"""Artist of current playing media."""
if self._play_status is not None:
return self._play_status.artist
@property
def media_album_name(self):
"""Album of current playing media."""
if self._play_status is not None:
return self._play_status.album
@property
def media_content_type(self):
"""Content type of current playing media."""
# Loose assumption that if playback is supported, we are playing music
if self._is_playback_supported:
return MEDIA_TYPE_MUSIC
return None
@property
def media_title(self):
"""Artist of current playing media."""
if self._play_status is not None:
song = self._play_status.song
station = self._play_status.station
# If both song and station is available, print both, otherwise
# just the one we have.
if song and station:
return '{}: {}'.format(station, song)
else:
return song or station
|
"""The tests for the TTS component."""
import ctypes
import os
import shutil
from unittest.mock import patch, PropertyMock
import pytest
import requests
import homeassistant.components.http as http
import homeassistant.components.tts as tts
from homeassistant.components.tts.demo import DemoProvider
from homeassistant.components.media_player import (
SERVICE_PLAY_MEDIA, MEDIA_TYPE_MUSIC, ATTR_MEDIA_CONTENT_ID,
ATTR_MEDIA_CONTENT_TYPE, DOMAIN as DOMAIN_MP)
from homeassistant.setup import setup_component
from tests.common import (
get_test_home_assistant, get_test_instance_port, assert_setup_component,
mock_service)
@pytest.fixture(autouse=True)
def mutagen_mock():
"""Mock writing tags."""
with patch('homeassistant.components.tts.SpeechManager.write_tags',
side_effect=lambda *args: args[1]):
yield
class TestTTS(object):
"""Test the Google speech component."""
def setup_method(self):
"""Setup things to be run when tests are started."""
self.hass = get_test_home_assistant()
self.demo_provider = DemoProvider('en')
self.default_tts_cache = self.hass.config.path(tts.DEFAULT_CACHE_DIR)
setup_component(
self.hass, http.DOMAIN,
{http.DOMAIN: {http.CONF_SERVER_PORT: get_test_instance_port()}})
def teardown_method(self):
"""Stop everything that was started."""
if os.path.isdir(self.default_tts_cache):
shutil.rmtree(self.default_tts_cache)
self.hass.stop()
def test_setup_component_demo(self):
"""Setup the demo platform with defaults."""
config = {
tts.DOMAIN: {
'platform': 'demo',
}
}
with assert_setup_component(1, tts.DOMAIN):
setup_component(self.hass, tts.DOMAIN, config)
assert self.hass.services.has_service(tts.DOMAIN, 'demo_say')
assert self.hass.services.has_service(tts.DOMAIN, 'clear_cache')
@patch('os.mkdir', side_effect=OSError(2, "No access"))
def test_setup_component_demo_no_access_cache_folder(self, mock_mkdir):
"""Setup the demo platform with defaults."""
config = {
tts.DOMAIN: {
'platform': 'demo',
}
}
assert not setup_component(self.hass, tts.DOMAIN, config)
assert not self.hass.services.has_service(tts.DOMAIN, 'demo_say')
assert not self.hass.services.has_service(tts.DOMAIN, 'clear_cache')
def test_setup_component_and_test_service(self):
"""Setup the demo platform and call service."""
calls = mock_service(self.hass, DOMAIN_MP, SERVICE_PLAY_MEDIA)
config = {
tts.DOMAIN: {
'platform': 'demo',
}
}
with assert_setup_component(1, tts.DOMAIN):
setup_component(self.hass, tts.DOMAIN, config)
self.hass.services.call(tts.DOMAIN, 'demo_say', {
tts.ATTR_MESSAGE: "I person is on front of your door.",
})
self.hass.block_till_done()
assert len(calls) == 1
assert calls[0].data[ATTR_MEDIA_CONTENT_TYPE] == MEDIA_TYPE_MUSIC
assert calls[0].data[ATTR_MEDIA_CONTENT_ID].find(
"/api/tts_proxy/265944c108cbb00b2a621be5930513e03a0bb2cd"
"_en_-_demo.mp3") \
!= -1
assert os.path.isfile(os.path.join(
self.default_tts_cache,
"265944c108cbb00b2a621be5930513e03a0bb2cd_en_-_demo.mp3"))
def test_setup_component_and_test_service_with_config_language(self):
"""Setup the demo platform and call service."""
calls = mock_service(self.hass, DOMAIN_MP, SERVICE_PLAY_MEDIA)
config = {
tts.DOMAIN: {
'platform': 'demo',
'language': 'de'
}
}
with assert_setup_component(1, tts.DOMAIN):
setup_component(self.hass, tts.DOMAIN, config)
self.hass.services.call(tts.DOMAIN, 'demo_say', {
tts.ATTR_MESSAGE: "I person is on front of your door.",
})
self.hass.block_till_done()
assert len(calls) == 1
assert calls[0].data[ATTR_MEDIA_CONTENT_TYPE] == MEDIA_TYPE_MUSIC
assert calls[0].data[ATTR_MEDIA_CONTENT_ID].find(
"/api/tts_proxy/265944c108cbb00b2a621be5930513e03a0bb2cd"
"_de_-_demo.mp3") \
!= -1
assert os.path.isfile(os.path.join(
self.default_tts_cache,
"265944c108cbb00b2a621be5930513e03a0bb2cd_de_-_demo.mp3"))
def test_setup_component_and_test_service_with_wrong_conf_language(self):
"""Setup the demo platform and call service with wrong config."""
config = {
tts.DOMAIN: {
'platform': 'demo',
'language': 'ru'
}
}
with assert_setup_component(0, tts.DOMAIN):
setup_component(self.hass, tts.DOMAIN, config)
def test_setup_component_and_test_service_with_service_language(self):
"""Setup the demo platform and call service."""
calls = mock_service(self.hass, DOMAIN_MP, SERVICE_PLAY_MEDIA)
config = {
tts.DOMAIN: {
'platform': 'demo',
}
}
with assert_setup_component(1, tts.DOMAIN):
setup_component(self.hass, tts.DOMAIN, config)
self.hass.services.call(tts.DOMAIN, 'demo_say', {
tts.ATTR_MESSAGE: "I person is on front of your door.",
tts.ATTR_LANGUAGE: "de",
})
self.hass.block_till_done()
assert len(calls) == 1
assert calls[0].data[ATTR_MEDIA_CONTENT_TYPE] == MEDIA_TYPE_MUSIC
assert calls[0].data[ATTR_MEDIA_CONTENT_ID].find(
"/api/tts_proxy/265944c108cbb00b2a621be5930513e03a0bb2cd"
"_de_-_demo.mp3") \
!= -1
assert os.path.isfile(os.path.join(
self.default_tts_cache,
"265944c108cbb00b2a621be5930513e03a0bb2cd_de_-_demo.mp3"))
def test_setup_component_test_service_with_wrong_service_language(self):
"""Setup the demo platform and call service."""
calls = mock_service(self.hass, DOMAIN_MP, SERVICE_PLAY_MEDIA)
config = {
tts.DOMAIN: {
'platform': 'demo',
}
}
with assert_setup_component(1, tts.DOMAIN):
setup_component(self.hass, tts.DOMAIN, config)
self.hass.services.call(tts.DOMAIN, 'demo_say', {
tts.ATTR_MESSAGE: "I person is on front of your door.",
tts.ATTR_LANGUAGE: "lang",
})
self.hass.block_till_done()
assert len(calls) == 0
assert not os.path.isfile(os.path.join(
self.default_tts_cache,
"265944c108cbb00b2a621be5930513e03a0bb2cd_lang_-_demo.mp3"))
def test_setup_component_and_test_service_with_service_options(self):
"""Setup the demo platform and call service with options."""
calls = mock_service(self.hass, DOMAIN_MP, SERVICE_PLAY_MEDIA)
config = {
tts.DOMAIN: {
'platform': 'demo',
}
}
with assert_setup_component(1, tts.DOMAIN):
setup_component(self.hass, tts.DOMAIN, config)
self.hass.services.call(tts.DOMAIN, 'demo_say', {
tts.ATTR_MESSAGE: "I person is on front of your door.",
tts.ATTR_LANGUAGE: "de",
tts.ATTR_OPTIONS: {
'voice': 'alex'
}
})
self.hass.block_till_done()
opt_hash = ctypes.c_size_t(hash(frozenset({'voice': 'alex'}))).value
assert len(calls) == 1
assert calls[0].data[ATTR_MEDIA_CONTENT_TYPE] == MEDIA_TYPE_MUSIC
assert calls[0].data[ATTR_MEDIA_CONTENT_ID].find(
"/api/tts_proxy/265944c108cbb00b2a621be5930513e03a0bb2cd"
"_de_{0}_demo.mp3".format(opt_hash)) \
!= -1
assert os.path.isfile(os.path.join(
self.default_tts_cache,
"265944c108cbb00b2a621be5930513e03a0bb2cd_de_{0}_demo.mp3".format(
opt_hash)))
@patch('homeassistant.components.tts.demo.DemoProvider.default_options',
new_callable=PropertyMock(return_value={'voice': 'alex'}))
def test_setup_component_and_test_with_service_options_def(self, def_mock):
"""Setup the demo platform and call service with default options."""
calls = mock_service(self.hass, DOMAIN_MP, SERVICE_PLAY_MEDIA)
config = {
tts.DOMAIN: {
'platform': 'demo',
}
}
with assert_setup_component(1, tts.DOMAIN):
setup_component(self.hass, tts.DOMAIN, config)
self.hass.services.call(tts.DOMAIN, 'demo_say', {
tts.ATTR_MESSAGE: "I person is on front of your door.",
tts.ATTR_LANGUAGE: "de",
})
self.hass.block_till_done()
opt_hash = ctypes.c_size_t(hash(frozenset({'voice': 'alex'}))).value
assert len(calls) == 1
assert calls[0].data[ATTR_MEDIA_CONTENT_TYPE] == MEDIA_TYPE_MUSIC
assert calls[0].data[ATTR_MEDIA_CONTENT_ID].find(
"/api/tts_proxy/265944c108cbb00b2a621be5930513e03a0bb2cd"
"_de_{0}_demo.mp3".format(opt_hash)) \
!= -1
assert os.path.isfile(os.path.join(
self.default_tts_cache,
"265944c108cbb00b2a621be5930513e03a0bb2cd_de_{0}_demo.mp3".format(
opt_hash)))
def test_setup_component_and_test_service_with_service_options_wrong(self):
"""Setup the demo platform and call service with wrong options."""
calls = mock_service(self.hass, DOMAIN_MP, SERVICE_PLAY_MEDIA)
config = {
tts.DOMAIN: {
'platform': 'demo',
}
}
with assert_setup_component(1, tts.DOMAIN):
setup_component(self.hass, tts.DOMAIN, config)
self.hass.services.call(tts.DOMAIN, 'demo_say', {
tts.ATTR_MESSAGE: "I person is on front of your door.",
tts.ATTR_LANGUAGE: "de",
tts.ATTR_OPTIONS: {
'speed': 1
}
})
self.hass.block_till_done()
opt_hash = ctypes.c_size_t(hash(frozenset({'speed': 1}))).value
assert len(calls) == 0
assert not os.path.isfile(os.path.join(
self.default_tts_cache,
"265944c108cbb00b2a621be5930513e03a0bb2cd_de_{0}_demo.mp3".format(
opt_hash)))
def test_setup_component_and_test_service_clear_cache(self):
"""Setup the demo platform and call service clear cache."""
calls = mock_service(self.hass, DOMAIN_MP, SERVICE_PLAY_MEDIA)
config = {
tts.DOMAIN: {
'platform': 'demo',
}
}
with assert_setup_component(1, tts.DOMAIN):
setup_component(self.hass, tts.DOMAIN, config)
self.hass.services.call(tts.DOMAIN, 'demo_say', {
tts.ATTR_MESSAGE: "I person is on front of your door.",
})
self.hass.block_till_done()
assert len(calls) == 1
assert os.path.isfile(os.path.join(
self.default_tts_cache,
"265944c108cbb00b2a621be5930513e03a0bb2cd_en_-_demo.mp3"))
self.hass.services.call(tts.DOMAIN, tts.SERVICE_CLEAR_CACHE, {})
self.hass.block_till_done()
assert not os.path.isfile(os.path.join(
self.default_tts_cache,
"265944c108cbb00b2a621be5930513e03a0bb2cd_en_-_demo.mp3"))
def test_setup_component_and_test_service_with_receive_voice(self):
"""Setup the demo platform and call service and receive voice."""
calls = mock_service(self.hass, DOMAIN_MP, SERVICE_PLAY_MEDIA)
config = {
tts.DOMAIN: {
'platform': 'demo',
}
}
with assert_setup_component(1, tts.DOMAIN):
setup_component(self.hass, tts.DOMAIN, config)
self.hass.start()
self.hass.services.call(tts.DOMAIN, 'demo_say', {
tts.ATTR_MESSAGE: "I person is on front of your door.",
})
self.hass.block_till_done()
assert len(calls) == 1
req = requests.get(calls[0].data[ATTR_MEDIA_CONTENT_ID])
_, demo_data = self.demo_provider.get_tts_audio("bla", 'en')
demo_data = tts.SpeechManager.write_tags(
"265944c108cbb00b2a621be5930513e03a0bb2cd_en_-_demo.mp3",
demo_data, self.demo_provider,
"I person is on front of your door.", 'en', None)
assert req.status_code == 200
assert req.content == demo_data
def test_setup_component_and_test_service_with_receive_voice_german(self):
"""Setup the demo platform and call service and receive voice."""
calls = mock_service(self.hass, DOMAIN_MP, SERVICE_PLAY_MEDIA)
config = {
tts.DOMAIN: {
'platform': 'demo',
'language': 'de',
}
}
with assert_setup_component(1, tts.DOMAIN):
setup_component(self.hass, tts.DOMAIN, config)
self.hass.start()
self.hass.services.call(tts.DOMAIN, 'demo_say', {
tts.ATTR_MESSAGE: "I person is on front of your door.",
})
self.hass.block_till_done()
assert len(calls) == 1
req = requests.get(calls[0].data[ATTR_MEDIA_CONTENT_ID])
_, demo_data = self.demo_provider.get_tts_audio("bla", "de")
demo_data = tts.SpeechManager.write_tags(
"265944c108cbb00b2a621be5930513e03a0bb2cd_de_-_demo.mp3",
demo_data, self.demo_provider,
"I person is on front of your door.", 'de', None)
assert req.status_code == 200
assert req.content == demo_data
def test_setup_component_and_web_view_wrong_file(self):
"""Setup the demo platform and receive wrong file from web."""
config = {
tts.DOMAIN: {
'platform': 'demo',
}
}
with assert_setup_component(1, tts.DOMAIN):
setup_component(self.hass, tts.DOMAIN, config)
self.hass.start()
url = ("{}/api/tts_proxy/265944c108cbb00b2a621be5930513e03a0bb2cd"
"_en_-_demo.mp3").format(self.hass.config.api.base_url)
req = requests.get(url)
assert req.status_code == 404
def test_setup_component_and_web_view_wrong_filename(self):
"""Setup the demo platform and receive wrong filename from web."""
config = {
tts.DOMAIN: {
'platform': 'demo',
}
}
with assert_setup_component(1, tts.DOMAIN):
setup_component(self.hass, tts.DOMAIN, config)
self.hass.start()
url = ("{}/api/tts_proxy/265944dsk32c1b2a621be5930510bb2cd"
"_en_-_demo.mp3").format(self.hass.config.api.base_url)
req = requests.get(url)
assert req.status_code == 404
def test_setup_component_test_without_cache(self):
"""Setup demo platform without cache."""
calls = mock_service(self.hass, DOMAIN_MP, SERVICE_PLAY_MEDIA)
config = {
tts.DOMAIN: {
'platform': 'demo',
'cache': False,
}
}
with assert_setup_component(1, tts.DOMAIN):
setup_component(self.hass, tts.DOMAIN, config)
self.hass.services.call(tts.DOMAIN, 'demo_say', {
tts.ATTR_MESSAGE: "I person is on front of your door.",
})
self.hass.block_till_done()
assert len(calls) == 1
assert not os.path.isfile(os.path.join(
self.default_tts_cache,
"265944c108cbb00b2a621be5930513e03a0bb2cd_en_-_demo.mp3"))
def test_setup_component_test_with_cache_call_service_without_cache(self):
"""Setup demo platform with cache and call service without cache."""
calls = mock_service(self.hass, DOMAIN_MP, SERVICE_PLAY_MEDIA)
config = {
tts.DOMAIN: {
'platform': 'demo',
'cache': True,
}
}
with assert_setup_component(1, tts.DOMAIN):
setup_component(self.hass, tts.DOMAIN, config)
self.hass.services.call(tts.DOMAIN, 'demo_say', {
tts.ATTR_MESSAGE: "I person is on front of your door.",
tts.ATTR_CACHE: False,
})
self.hass.block_till_done()
assert len(calls) == 1
assert not os.path.isfile(os.path.join(
self.default_tts_cache,
"265944c108cbb00b2a621be5930513e03a0bb2cd_en_-_demo.mp3"))
def test_setup_component_test_with_cache_dir(self):
"""Setup demo platform with cache and call service without cache."""
calls = mock_service(self.hass, DOMAIN_MP, SERVICE_PLAY_MEDIA)
_, demo_data = self.demo_provider.get_tts_audio("bla", 'en')
cache_file = os.path.join(
self.default_tts_cache,
"265944c108cbb00b2a621be5930513e03a0bb2cd_en_-_demo.mp3")
os.mkdir(self.default_tts_cache)
with open(cache_file, "wb") as voice_file:
voice_file.write(demo_data)
config = {
tts.DOMAIN: {
'platform': 'demo',
'cache': True,
}
}
with assert_setup_component(1, tts.DOMAIN):
setup_component(self.hass, tts.DOMAIN, config)
with patch('homeassistant.components.tts.demo.DemoProvider.'
'get_tts_audio', return_value=(None, None)):
self.hass.services.call(tts.DOMAIN, 'demo_say', {
tts.ATTR_MESSAGE: "I person is on front of your door.",
})
self.hass.block_till_done()
assert len(calls) == 1
assert calls[0].data[ATTR_MEDIA_CONTENT_ID].find(
"/api/tts_proxy/265944c108cbb00b2a621be5930513e03a0bb2cd"
"_en_-_demo.mp3") \
!= -1
@patch('homeassistant.components.tts.demo.DemoProvider.get_tts_audio',
return_value=(None, None))
def test_setup_component_test_with_error_on_get_tts(self, tts_mock):
"""Setup demo platform with wrong get_tts_audio."""
calls = mock_service(self.hass, DOMAIN_MP, SERVICE_PLAY_MEDIA)
config = {
tts.DOMAIN: {
'platform': 'demo'
}
}
with assert_setup_component(1, tts.DOMAIN):
setup_component(self.hass, tts.DOMAIN, config)
self.hass.services.call(tts.DOMAIN, 'demo_say', {
tts.ATTR_MESSAGE: "I person is on front of your door.",
})
self.hass.block_till_done()
assert len(calls) == 0
def test_setup_component_load_cache_retrieve_without_mem_cache(self):
"""Setup component and load cache and get without mem cache."""
_, demo_data = self.demo_provider.get_tts_audio("bla", 'en')
cache_file = os.path.join(
self.default_tts_cache,
"265944c108cbb00b2a621be5930513e03a0bb2cd_en_-_demo.mp3")
os.mkdir(self.default_tts_cache)
with open(cache_file, "wb") as voice_file:
voice_file.write(demo_data)
config = {
tts.DOMAIN: {
'platform': 'demo',
'cache': True,
}
}
with assert_setup_component(1, tts.DOMAIN):
setup_component(self.hass, tts.DOMAIN, config)
self.hass.start()
url = ("{}/api/tts_proxy/265944c108cbb00b2a621be5930513e03a0bb2cd"
"_en_-_demo.mp3").format(self.hass.config.api.base_url)
req = requests.get(url)
assert req.status_code == 200
assert req.content == demo_data
|
happyleavesaoc/home-assistant
|
tests/components/tts/test_init.py
|
homeassistant/components/media_player/yamaha.py
|
import hashlib
import json
import os
import sqlalchemy as sa
from sqlalchemy.ext.declarative import declarative_base
from sacred.dependencies import get_digest
from sacred.serializer import restore
Base = declarative_base()
class Source(Base):
__tablename__ = "source"
@classmethod
def get_or_create(cls, filename, md5sum, session, basedir):
instance = (
session.query(cls).filter_by(filename=filename, md5sum=md5sum).first()
)
if instance:
return instance
full_path = os.path.join(basedir, filename)
md5sum_ = get_digest(full_path)
assert md5sum_ == md5sum, "found md5 mismatch for {}: {} != {}".format(
filename, md5sum, md5sum_
)
with open(full_path, "r") as f:
return cls(filename=filename, md5sum=md5sum, content=f.read())
source_id = sa.Column(sa.Integer, primary_key=True)
filename = sa.Column(sa.String(256))
md5sum = sa.Column(sa.String(32))
content = sa.Column(sa.Text)
def to_json(self):
return {"filename": self.filename, "md5sum": self.md5sum}
class Repository(Base):
__tablename__ = "repository"
@classmethod
def get_or_create(cls, url, commit, dirty, session):
instance = (
session.query(cls).filter_by(url=url, commit=commit, dirty=dirty).first()
)
if instance:
return instance
return cls(url=url, commit=commit, dirty=dirty)
repository_id = sa.Column(sa.Integer, primary_key=True)
url = sa.Column(sa.String(2048))
commit = sa.Column(sa.String(40))
dirty = sa.Column(sa.Boolean)
def to_json(self):
return {"url": self.url, "commit": self.commit, "dirty": self.dirty}
class Dependency(Base):
__tablename__ = "dependency"
@classmethod
def get_or_create(cls, dep, session):
name, _, version = dep.partition("==")
instance = session.query(cls).filter_by(name=name, version=version).first()
if instance:
return instance
return cls(name=name, version=version)
dependency_id = sa.Column(sa.Integer, primary_key=True)
name = sa.Column(sa.String(32))
version = sa.Column(sa.String(16))
def to_json(self):
return "{}=={}".format(self.name, self.version)
class Artifact(Base):
__tablename__ = "artifact"
@classmethod
def create(cls, name, filename):
with open(filename, "rb") as f:
return cls(filename=name, content=f.read())
artifact_id = sa.Column(sa.Integer, primary_key=True)
filename = sa.Column(sa.String(64))
content = sa.Column(sa.LargeBinary)
run_id = sa.Column(sa.String(24), sa.ForeignKey("run.run_id"))
run = sa.orm.relationship("Run", backref=sa.orm.backref("artifacts"))
def to_json(self):
return {"_id": self.artifact_id, "filename": self.filename}
class Resource(Base):
__tablename__ = "resource"
@classmethod
def get_or_create(cls, filename, session):
md5sum = get_digest(filename)
instance = (
session.query(cls).filter_by(filename=filename, md5sum=md5sum).first()
)
if instance:
return instance
with open(filename, "rb") as f:
return cls(filename=filename, md5sum=md5sum, content=f.read())
resource_id = sa.Column(sa.Integer, primary_key=True)
filename = sa.Column(sa.String(256))
md5sum = sa.Column(sa.String(32))
content = sa.Column(sa.LargeBinary)
def to_json(self):
return {"filename": self.filename, "md5sum": self.md5sum}
class Host(Base):
__tablename__ = "host"
@classmethod
def get_or_create(cls, host_info, session):
h = dict(
hostname=host_info["hostname"],
cpu=host_info["cpu"],
os=host_info["os"][0],
os_info=host_info["os"][1],
python_version=host_info["python_version"],
)
return session.query(cls).filter_by(**h).first() or cls(**h)
host_id = sa.Column(sa.Integer, primary_key=True)
cpu = sa.Column(sa.String(64))
hostname = sa.Column(sa.String(64))
os = sa.Column(sa.String(16))
os_info = sa.Column(sa.String(64))
python_version = sa.Column(sa.String(16))
def to_json(self):
return {
"cpu": self.cpu,
"hostname": self.hostname,
"os": [self.os, self.os_info],
"python_version": self.python_version,
}
experiment_source_association = sa.Table(
"experiments_sources",
Base.metadata,
sa.Column("experiment_id", sa.Integer, sa.ForeignKey("experiment.experiment_id")),
sa.Column("source_id", sa.Integer, sa.ForeignKey("source.source_id")),
)
experiment_repository_association = sa.Table(
"experiments_repositories",
Base.metadata,
sa.Column("experiment_id", sa.Integer, sa.ForeignKey("experiment.experiment_id")),
sa.Column("repository_id", sa.Integer, sa.ForeignKey("repository.repository_id")),
)
experiment_dependency_association = sa.Table(
"experiments_dependencies",
Base.metadata,
sa.Column("experiment_id", sa.Integer, sa.ForeignKey("experiment.experiment_id")),
sa.Column("dependency_id", sa.Integer, sa.ForeignKey("dependency.dependency_id")),
)
class Experiment(Base):
__tablename__ = "experiment"
@classmethod
def get_or_create(cls, ex_info, session):
name = ex_info["name"]
# Compute a MD5sum of the ex_info to determine its uniqueness
h = hashlib.md5()
h.update(json.dumps(ex_info).encode())
md5 = h.hexdigest()
instance = session.query(cls).filter_by(name=name, md5sum=md5).first()
if instance:
return instance
dependencies = [
Dependency.get_or_create(d, session) for d in ex_info["dependencies"]
]
sources = [
Source.get_or_create(s, md5sum, session, ex_info["base_dir"])
for s, md5sum in ex_info["sources"]
]
repositories = set()
for r in ex_info["repositories"]:
repository = Repository.get_or_create(
r["url"], r["commit"], r["dirty"], session
)
session.add(repository)
repositories.add(repository)
repositories = list(repositories)
return cls(
name=name,
dependencies=dependencies,
sources=sources,
repositories=repositories,
md5sum=md5,
base_dir=ex_info["base_dir"],
)
experiment_id = sa.Column(sa.Integer, primary_key=True)
name = sa.Column(sa.String(32))
md5sum = sa.Column(sa.String(32))
base_dir = sa.Column(sa.String(64))
sources = sa.orm.relationship(
"Source", secondary=experiment_source_association, backref="experiments"
)
repositories = sa.orm.relationship(
"Repository", secondary=experiment_repository_association, backref="experiments"
)
dependencies = sa.orm.relationship(
"Dependency", secondary=experiment_dependency_association, backref="experiments"
)
def to_json(self):
return {
"name": self.name,
"base_dir": self.base_dir,
"sources": [s.to_json() for s in self.sources],
"repositories": [r.to_json() for r in self.repositories],
"dependencies": [d.to_json() for d in self.dependencies],
}
run_resource_association = sa.Table(
"runs_resources",
Base.metadata,
sa.Column("run_id", sa.String(24), sa.ForeignKey("run.run_id")),
sa.Column("resource_id", sa.Integer, sa.ForeignKey("resource.resource_id")),
)
class Run(Base):
__tablename__ = "run"
id = sa.Column(sa.Integer, primary_key=True)
run_id = sa.Column(sa.String(24), unique=True)
command = sa.Column(sa.String(64))
# times
start_time = sa.Column(sa.DateTime)
heartbeat = sa.Column(sa.DateTime)
stop_time = sa.Column(sa.DateTime)
queue_time = sa.Column(sa.DateTime)
# meta info
priority = sa.Column(sa.Float)
comment = sa.Column(sa.Text)
fail_trace = sa.Column(sa.Text)
# Captured out
# TODO: move to separate table?
captured_out = sa.Column(sa.Text)
# Configuration & info
# TODO: switch type to json if possible
config = sa.Column(sa.Text)
info = sa.Column(sa.Text)
status = sa.Column(
sa.Enum(
"RUNNING",
"COMPLETED",
"INTERRUPTED",
"TIMEOUT",
"FAILED",
name="status_enum",
)
)
host_id = sa.Column(sa.Integer, sa.ForeignKey("host.host_id"))
host = sa.orm.relationship("Host", backref=sa.orm.backref("runs"))
experiment_id = sa.Column(sa.Integer, sa.ForeignKey("experiment.experiment_id"))
experiment = sa.orm.relationship("Experiment", backref=sa.orm.backref("runs"))
# artifacts = backref
resources = sa.orm.relationship(
"Resource", secondary=run_resource_association, backref="runs"
)
result = sa.Column(sa.Float)
def to_json(self):
return {
"_id": self.run_id,
"command": self.command,
"start_time": self.start_time,
"heartbeat": self.heartbeat,
"stop_time": self.stop_time,
"queue_time": self.queue_time,
"status": self.status,
"result": self.result,
"meta": {"comment": self.comment, "priority": self.priority},
"resources": [r.to_json() for r in self.resources],
"artifacts": [a.to_json() for a in self.artifacts],
"host": self.host.to_json(),
"experiment": self.experiment.to_json(),
"config": restore(json.loads(self.config)),
"captured_out": self.captured_out,
"fail_trace": self.fail_trace,
}
|
#!/usr/bin/env python
# coding=utf-8
from sacred import Ingredient
"""Global Docstring"""
from mock import patch
import pytest
import sys
from sacred import cli_option
from sacred import host_info_gatherer
from sacred.experiment import Experiment
from sacred.utils import apply_backspaces_and_linefeeds, ConfigAddedError, SacredError
@pytest.fixture
def ex():
return Experiment("ator3000")
def test_main(ex):
@ex.main
def foo():
pass
assert "foo" in ex.commands
assert ex.commands["foo"] == foo
assert ex.default_command == "foo"
def test_automain_imported(ex):
main_called = [False]
with patch.object(sys, "argv", ["test.py"]):
@ex.automain
def foo():
main_called[0] = True
assert "foo" in ex.commands
assert ex.commands["foo"] == foo
assert ex.default_command == "foo"
assert main_called[0] is False
def test_automain_script_runs_main(ex):
global __name__
oldname = __name__
main_called = [False]
try:
__name__ = "__main__"
with patch.object(sys, "argv", ["test.py"]):
@ex.automain
def foo():
main_called[0] = True
assert "foo" in ex.commands
assert ex.commands["foo"] == foo
assert ex.default_command == "foo"
assert main_called[0] is True
finally:
__name__ = oldname
def test_fails_on_unused_config_updates(ex):
@ex.config
def cfg():
a = 1
c = 3
@ex.main
def foo(a, b=2):
return a + b
# normal config updates work
assert ex.run(config_updates={"a": 3}).result == 5
# not in config but used works
assert ex.run(config_updates={"b": 8}).result == 9
# unused but in config updates work
assert ex.run(config_updates={"c": 9}).result == 3
# unused config updates raise
with pytest.raises(ConfigAddedError):
ex.run(config_updates={"d": 3})
def test_fails_on_nested_unused_config_updates(ex):
@ex.config
def cfg():
a = {"b": 1}
d = {"e": 3}
@ex.main
def foo(a):
return a["b"]
# normal config updates work
assert ex.run(config_updates={"a": {"b": 2}}).result == 2
# not in config but parent is works
assert ex.run(config_updates={"a": {"c": 5}}).result == 1
# unused but in config works
assert ex.run(config_updates={"d": {"e": 7}}).result == 1
# unused nested config updates raise
with pytest.raises(ConfigAddedError):
ex.run(config_updates={"d": {"f": 3}})
def test_considers_captured_functions_for_fail_on_unused_config(ex):
@ex.config
def cfg():
a = 1
@ex.capture
def transmogrify(a, b=0):
return a + b
@ex.main
def foo():
return transmogrify()
assert ex.run(config_updates={"a": 7}).result == 7
assert ex.run(config_updates={"b": 3}).result == 4
with pytest.raises(ConfigAddedError):
ex.run(config_updates={"c": 3})
def test_considers_prefix_for_fail_on_unused_config(ex):
@ex.config
def cfg():
a = {"b": 1}
@ex.capture(prefix="a")
def transmogrify(b):
return b
@ex.main
def foo():
return transmogrify()
assert ex.run(config_updates={"a": {"b": 3}}).result == 3
with pytest.raises(ConfigAddedError):
ex.run(config_updates={"b": 5})
with pytest.raises(ConfigAddedError):
ex.run(config_updates={"a": {"c": 5}})
def test_non_existing_prefix_is_treated_as_empty_dict(ex):
@ex.capture(prefix="nonexisting")
def transmogrify(b=10):
return b
@ex.main
def foo():
return transmogrify()
assert ex.run().result == 10
def test_using_a_named_config(ex):
@ex.config
def cfg():
a = 1
@ex.named_config
def ncfg_first():
a = 10
@ex.named_config
def ncfg_second(a):
a = a * 2
@ex.main
def run(a):
return a
assert ex.run().result == 1
assert ex.run(named_configs=["ncfg_first"]).result == 10
assert ex.run(named_configs=["ncfg_first", "ncfg_second"]).result == 20
with pytest.raises(KeyError, match=r".*not in preset for ConfigScope"):
ex.run(named_configs=["ncfg_second", "ncfg_first"])
def test_empty_dict_named_config(ex):
@ex.named_config
def ncfg():
empty_dict = {}
nested_empty_dict = {"k1": {"k2": {}}}
@ex.automain
def main(empty_dict=1, nested_empty_dict=2):
return empty_dict, nested_empty_dict
assert ex.run().result == (1, 2)
assert ex.run(named_configs=["ncfg"]).result == ({}, {"k1": {"k2": {}}})
def test_empty_dict_config_updates(ex):
@ex.config
def cfg():
a = 1
@ex.config
def default():
a = {"b": 1}
@ex.main
def main():
pass
r = ex.run()
assert r.config["a"]["b"] == 1
def test_named_config_and_ingredient():
ing = Ingredient("foo")
@ing.config
def cfg():
a = 10
ex = Experiment(ingredients=[ing])
@ex.config
def default():
b = 20
@ex.named_config
def named():
b = 30
@ex.main
def main():
pass
r = ex.run(named_configs=["named"])
assert r.config["b"] == 30
assert r.config["foo"] == {"a": 10}
def test_captured_out_filter(ex, capsys):
@ex.main
def run_print_mock_progress():
sys.stdout.write("progress 0")
sys.stdout.flush()
for i in range(10):
sys.stdout.write("\b")
sys.stdout.write("{}".format(i))
sys.stdout.flush()
ex.captured_out_filter = apply_backspaces_and_linefeeds
# disable logging and set capture mode to python
options = {"--loglevel": "CRITICAL", "--capture": "sys"}
with capsys.disabled():
assert ex.run(options=options).captured_out == "progress 9"
def test_adding_option_hooks(ex):
@ex.option_hook
def hook(options):
pass
@ex.option_hook
def hook2(options):
pass
assert hook in ex.option_hooks
assert hook2 in ex.option_hooks
def test_option_hooks_without_options_arg_raises(ex):
with pytest.raises(KeyError):
@ex.option_hook
def invalid_hook(wrong_arg_name):
pass
def test_config_hook_updates_config(ex):
@ex.config
def cfg():
a = "hello"
@ex.config_hook
def hook(config, command_name, logger):
config.update({"a": "me"})
return config
@ex.main
def foo():
pass
r = ex.run()
assert r.config["a"] == "me"
def test_info_kwarg_updates_info(ex):
"""Tests that the info kwarg of Experiment.create_run is used to update Run.info"""
@ex.automain
def foo():
pass
run = ex.run(info={"bar": "baz"})
assert "bar" in run.info
def test_info_kwargs_default_behavior(ex):
"""Tests the default behavior of Experiment.create_run when the info kwarg is not specified."""
@ex.automain
def foo(_run):
_run.info["bar"] = "baz"
run = ex.run()
assert "bar" in run.info
def test_fails_on_config_write(ex):
@ex.config
def cfg():
a = "hello"
nested_dict = {"dict": {"dict": 1234, "list": [1, 2, 3, 4]}}
nested_list = [{"a": 42}, (1, 2, 3, 4), [1, 2, 3, 4]]
nested_tuple = ({"a": 42}, (1, 2, 3, 4), [1, 2, 3, 4])
@ex.main
def main(_config, nested_dict, nested_list, nested_tuple):
raises_list = pytest.raises(
SacredError, match="The configuration is read-only in a captured function!"
)
raises_dict = pytest.raises(
SacredError, match="The configuration is read-only in a captured function!"
)
print("in main")
# Test for ReadOnlyDict
with raises_dict:
_config["a"] = "world!"
with raises_dict:
nested_dict["dict"] = "world!"
with raises_dict:
nested_dict["list"] = "world!"
with raises_dict:
nested_dict.clear()
with raises_dict:
nested_dict.update({"a": "world"})
# Test ReadOnlyList
with raises_list:
nested_dict["dict"]["list"][0] = 1
with raises_list:
nested_list[0] = "world!"
with raises_list:
nested_dict.clear()
# Test nested tuple
with raises_dict:
nested_tuple[0]["a"] = "world!"
with raises_list:
nested_tuple[2][0] = 123
ex.run()
def test_add_config_dict_chain(ex):
@ex.config
def config1():
"""This is my demo configuration"""
dictnest_cap = {"key_1": "value_1", "key_2": "value_2"}
@ex.config
def config2():
"""This is my demo configuration"""
dictnest_cap = {"key_2": "update_value_2", "key_3": "value3", "key_4": "value4"}
adict = {"dictnest_dict": {"key_1": "value_1", "key_2": "value_2"}}
ex.add_config(adict)
bdict = {
"dictnest_dict": {
"key_2": "update_value_2",
"key_3": "value3",
"key_4": "value4",
}
}
ex.add_config(bdict)
@ex.automain
def run():
pass
final_config = ex.run().config
assert final_config["dictnest_cap"] == {
"key_1": "value_1",
"key_2": "update_value_2",
"key_3": "value3",
"key_4": "value4",
}
assert final_config["dictnest_cap"] == final_config["dictnest_dict"]
def test_additional_gatherers():
@host_info_gatherer("hello")
def get_hello():
return "hello world"
experiment = Experiment("ator3000", additional_host_info=[get_hello])
@experiment.main
def foo():
pass
experiment.run()
assert experiment.current_run.host_info["hello"] == "hello world"
@pytest.mark.parametrize("command_line_option", ["-w", "--warning"])
def test_additional_cli_options_flag(command_line_option):
executed = [False]
@cli_option("-w", "--warning", is_flag=True)
def dummy_option(args, run):
executed[0] = True
experiment = Experiment("ator3000", additional_cli_options=[dummy_option])
@experiment.main
def foo():
pass
experiment.run_commandline([__file__, command_line_option])
assert executed[0]
@pytest.mark.parametrize("command_line_option", ["-w", "--warning"])
def test_additional_cli_options(command_line_option):
executed = [False]
@cli_option("-w", "--warning")
def dummy_option(args, run):
executed[0] = args
experiment = Experiment("ator3000", additional_cli_options=[dummy_option])
@experiment.main
def foo():
pass
experiment.run_commandline([__file__, command_line_option, "10"])
assert executed[0] == "10"
|
|
|
sacred/observers/sql_bases.py
|
# This file helps to compute a version number in source trees obtained from
# git-archive tarball (such as those provided by githubs download-from-tag
# feature). Distribution tarballs (built by setup.py sdist) and build
# directories (produced by setup.py build) will contain a much shorter file
# that just contains the computed version number.
# This file is released into the public domain. Generated by
# versioneer-0.15 (https://github.com/warner/python-versioneer)
import errno
import os
import re
import subprocess
import sys
from pandas.compat import PY3
def get_keywords():
# these strings will be replaced by git during git-archive.
# setup.py/versioneer.py will grep for the variable names, so they must
# each be defined on a line of their own. _version.py will just call
# get_keywords().
git_refnames = "$Format:%d$"
git_full = "$Format:%H$"
keywords = {"refnames": git_refnames, "full": git_full}
return keywords
class VersioneerConfig(object):
pass
def get_config():
# these strings are filled in when 'setup.py versioneer' creates
# _version.py
cfg = VersioneerConfig()
cfg.VCS = "git"
cfg.style = "pep440"
cfg.tag_prefix = "v"
cfg.parentdir_prefix = "pandas-"
cfg.versionfile_source = "pandas/_version.py"
cfg.verbose = False
return cfg
class NotThisMethod(Exception):
pass
LONG_VERSION_PY = {}
HANDLERS = {}
def register_vcs_handler(vcs, method): # decorator
def decorate(f):
if vcs not in HANDLERS:
HANDLERS[vcs] = {}
HANDLERS[vcs][method] = f
return f
return decorate
def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False):
assert isinstance(commands, list)
p = None
for c in commands:
try:
dispcmd = str([c] + args)
# remember shell=False, so use git.cmd on windows, not just git
p = subprocess.Popen([c] + args, cwd=cwd, stdout=subprocess.PIPE,
stderr=(subprocess.PIPE if hide_stderr
else None))
break
except EnvironmentError:
e = sys.exc_info()[1]
if e.errno == errno.ENOENT:
continue
if verbose:
print("unable to run {dispcmd}".format(dispcmd=dispcmd))
print(e)
return None
else:
if verbose:
print("unable to find command, tried %s" % (commands,))
return None
stdout = p.communicate()[0].strip()
if PY3:
stdout = stdout.decode()
if p.returncode != 0:
if verbose:
print("unable to run {dispcmd} (error)".format(dispcmd=dispcmd))
return None
return stdout
def versions_from_parentdir(parentdir_prefix, root, verbose):
# Source tarballs conventionally unpack into a directory that includes
# both the project name and a version string.
dirname = os.path.basename(root)
if not dirname.startswith(parentdir_prefix):
if verbose:
print("guessing rootdir is '{root}', but '{dirname}' "
"doesn't start with prefix '{parentdir_prefix}'".format(
root=root, dirname=dirname,
parentdir_prefix=parentdir_prefix))
raise NotThisMethod("rootdir doesn't start with parentdir_prefix")
return {"version": dirname[len(parentdir_prefix):],
"full-revisionid": None,
"dirty": False, "error": None}
@register_vcs_handler("git", "get_keywords")
def git_get_keywords(versionfile_abs):
# the code embedded in _version.py can just fetch the value of these
# keywords. When used from setup.py, we don't want to import _version.py,
# so we do it with a regexp instead. This function is not used from
# _version.py.
keywords = {}
try:
f = open(versionfile_abs, "r")
for line in f.readlines():
if line.strip().startswith("git_refnames ="):
mo = re.search(r'=\s*"(.*)"', line)
if mo:
keywords["refnames"] = mo.group(1)
if line.strip().startswith("git_full ="):
mo = re.search(r'=\s*"(.*)"', line)
if mo:
keywords["full"] = mo.group(1)
f.close()
except EnvironmentError:
pass
return keywords
@register_vcs_handler("git", "keywords")
def git_versions_from_keywords(keywords, tag_prefix, verbose):
if not keywords:
raise NotThisMethod("no keywords at all, weird")
refnames = keywords["refnames"].strip()
if refnames.startswith("$Format"):
if verbose:
print("keywords are unexpanded, not using")
raise NotThisMethod("unexpanded keywords, not a git-archive tarball")
refs = {r.strip() for r in refnames.strip("()").split(",")}
# starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of
# just "foo-1.0". If we see a "tag: " prefix, prefer those.
TAG = "tag: "
tags = {r[len(TAG):] for r in refs if r.startswith(TAG)}
if not tags:
# Either we're using git < 1.8.3, or there really are no tags. We use
# a heuristic: assume all version tags have a digit. The old git %d
# expansion behaves like git log --decorate=short and strips out the
# refs/heads/ and refs/tags/ prefixes that would let us distinguish
# between branches and tags. By ignoring refnames without digits, we
# filter out many common branch names like "release" and
# "stabilization", as well as "HEAD" and "master".
tags = {r for r in refs if re.search(r'\d', r)}
if verbose:
print("discarding '{}', no digits".format(",".join(refs - tags)))
if verbose:
print("likely tags: {}".format(",".join(sorted(tags))))
for ref in sorted(tags):
# sorting will prefer e.g. "2.0" over "2.0rc1"
if ref.startswith(tag_prefix):
r = ref[len(tag_prefix):]
if verbose:
print("picking {r}".format(r=r))
return {"version": r,
"full-revisionid": keywords["full"].strip(),
"dirty": False, "error": None
}
# no suitable tags, so version is "0+unknown", but full hex is still there
if verbose:
print("no suitable tags, using unknown + full revision id")
return {"version": "0+unknown",
"full-revisionid": keywords["full"].strip(),
"dirty": False, "error": "no suitable tags"}
@register_vcs_handler("git", "pieces_from_vcs")
def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command):
# this runs 'git' from the root of the source tree. This only gets called
# if the git-archive 'subst' keywords were *not* expanded, and
# _version.py hasn't already been rewritten with a short version string,
# meaning we're inside a checked out source tree.
if not os.path.exists(os.path.join(root, ".git")):
if verbose:
print("no .git in {root}".format(root=root))
raise NotThisMethod("no .git directory")
GITS = ["git"]
if sys.platform == "win32":
GITS = ["git.cmd", "git.exe"]
# if there is a tag, this yields TAG-NUM-gHEX[-dirty]
# if there are no tags, this yields HEX[-dirty] (no NUM)
describe_out = run_command(GITS, ["describe", "--tags", "--dirty",
"--always", "--long"],
cwd=root)
# --long was added in git-1.5.5
if describe_out is None:
raise NotThisMethod("'git describe' failed")
describe_out = describe_out.strip()
full_out = run_command(GITS, ["rev-parse", "HEAD"], cwd=root)
if full_out is None:
raise NotThisMethod("'git rev-parse' failed")
full_out = full_out.strip()
pieces = {}
pieces["long"] = full_out
pieces["short"] = full_out[:7] # maybe improved later
pieces["error"] = None
# parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]
# TAG might have hyphens.
git_describe = describe_out
# look for -dirty suffix
dirty = git_describe.endswith("-dirty")
pieces["dirty"] = dirty
if dirty:
git_describe = git_describe[:git_describe.rindex("-dirty")]
# now we have TAG-NUM-gHEX or HEX
if "-" in git_describe:
# TAG-NUM-gHEX
mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe)
if not mo:
# unparseable. Maybe git-describe is misbehaving?
pieces["error"] = ("unable to parse git-describe output: "
"'{describe_out}'".format(
describe_out=describe_out))
return pieces
# tag
full_tag = mo.group(1)
if not full_tag.startswith(tag_prefix):
if verbose:
fmt = "tag '{full_tag}' doesn't start with prefix " \
"'{tag_prefix}'"
print(fmt.format(full_tag=full_tag, tag_prefix=tag_prefix))
pieces["error"] = ("tag '{full_tag}' doesn't start with "
"prefix '{tag_prefix}'".format(
full_tag, tag_prefix))
return pieces
pieces["closest-tag"] = full_tag[len(tag_prefix):]
# distance: number of commits since tag
pieces["distance"] = int(mo.group(2))
# commit: short hex revision ID
pieces["short"] = mo.group(3)
else:
# HEX: no tags
pieces["closest-tag"] = None
count_out = run_command(GITS, ["rev-list", "HEAD", "--count"],
cwd=root)
pieces["distance"] = int(count_out) # total number of commits
return pieces
def plus_or_dot(pieces):
if "+" in pieces.get("closest-tag", ""):
return "."
return "+"
def render_pep440(pieces):
# now build up version string, with post-release "local version
# identifier". Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you
# get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty
# exceptions:
# 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"] or pieces["dirty"]:
rendered += plus_or_dot(pieces)
rendered += "{:d}.g{}".format(pieces["distance"], pieces["short"])
if pieces["dirty"]:
rendered += ".dirty"
else:
# exception #1
rendered = "0+untagged.{:d}.g{}".format(pieces["distance"],
pieces["short"])
if pieces["dirty"]:
rendered += ".dirty"
return rendered
def render_pep440_pre(pieces):
# TAG[.post.devDISTANCE] . No -dirty
# exceptions:
# 1: no tags. 0.post.devDISTANCE
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"]:
rendered += ".post.dev%d" % pieces["distance"]
else:
# exception #1
rendered = "0.post.dev%d" % pieces["distance"]
return rendered
def render_pep440_post(pieces):
# TAG[.postDISTANCE[.dev0]+gHEX] . The ".dev0" means dirty. Note that
# .dev0 sorts backwards (a dirty tree will appear "older" than the
# corresponding clean one), but you shouldn't be releasing software with
# -dirty anyways.
# exceptions:
# 1: no tags. 0.postDISTANCE[.dev0]
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"] or pieces["dirty"]:
rendered += ".post{:d}".format(pieces["distance"])
if pieces["dirty"]:
rendered += ".dev0"
rendered += plus_or_dot(pieces)
rendered += "g{}".format(pieces["short"])
else:
# exception #1
rendered = "0.post%d" % pieces["distance"]
if pieces["dirty"]:
rendered += ".dev0"
rendered += "+g{}".format(pieces["short"])
return rendered
def render_pep440_old(pieces):
# TAG[.postDISTANCE[.dev0]] . The ".dev0" means dirty.
# exceptions:
# 1: no tags. 0.postDISTANCE[.dev0]
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"] or pieces["dirty"]:
rendered += ".post%d" % pieces["distance"]
if pieces["dirty"]:
rendered += ".dev0"
else:
# exception #1
rendered = "0.post%d" % pieces["distance"]
if pieces["dirty"]:
rendered += ".dev0"
return rendered
def render_git_describe(pieces):
# TAG[-DISTANCE-gHEX][-dirty], like 'git describe --tags --dirty
# --always'
# exceptions:
# 1: no tags. HEX[-dirty] (note: no 'g' prefix)
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"]:
rendered += "-{:d}-g{}".format(pieces["distance"], pieces["short"])
else:
# exception #1
rendered = pieces["short"]
if pieces["dirty"]:
rendered += "-dirty"
return rendered
def render_git_describe_long(pieces):
# TAG-DISTANCE-gHEX[-dirty], like 'git describe --tags --dirty
# --always -long'. The distance/hash is unconditional.
# exceptions:
# 1: no tags. HEX[-dirty] (note: no 'g' prefix)
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
rendered += "-{:d}-g{}".format(pieces["distance"], pieces["short"])
else:
# exception #1
rendered = pieces["short"]
if pieces["dirty"]:
rendered += "-dirty"
return rendered
def render(pieces, style):
if pieces["error"]:
return {"version": "unknown",
"full-revisionid": pieces.get("long"),
"dirty": None,
"error": pieces["error"]}
if not style or style == "default":
style = "pep440" # the default
if style == "pep440":
rendered = render_pep440(pieces)
elif style == "pep440-pre":
rendered = render_pep440_pre(pieces)
elif style == "pep440-post":
rendered = render_pep440_post(pieces)
elif style == "pep440-old":
rendered = render_pep440_old(pieces)
elif style == "git-describe":
rendered = render_git_describe(pieces)
elif style == "git-describe-long":
rendered = render_git_describe_long(pieces)
else:
raise ValueError("unknown style '{style}'".format(style=style))
return {"version": rendered, "full-revisionid": pieces["long"],
"dirty": pieces["dirty"], "error": None}
def get_versions():
# I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have
# __file__, we can work backwards from there to the root. Some
# py2exe/bbfreeze/non-CPython implementations don't do __file__, in which
# case we can only use expanded keywords.
cfg = get_config()
verbose = cfg.verbose
try:
return git_versions_from_keywords(get_keywords(), cfg.tag_prefix,
verbose)
except NotThisMethod:
pass
try:
root = os.path.realpath(__file__)
# versionfile_source is the relative path from the top of the source
# tree (where the .git directory might live) to this file. Invert
# this to find the root from __file__.
for i in cfg.versionfile_source.split('/'):
root = os.path.dirname(root)
except NameError:
return {"version": "0+unknown", "full-revisionid": None,
"dirty": None,
"error": "unable to find root of source tree"}
try:
pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose)
return render(pieces, cfg.style)
except NotThisMethod:
pass
try:
if cfg.parentdir_prefix:
return versions_from_parentdir(cfg.parentdir_prefix, root, verbose)
except NotThisMethod:
pass
return {"version": "0+unknown", "full-revisionid": None,
"dirty": None,
"error": "unable to compute version"}
|
# -*- coding: utf-8 -*-
"""
test .agg behavior / note that .apply is tested generally in test_groupby.py
"""
import pytest
import numpy as np
import pandas as pd
from pandas import concat, DataFrame, Index, MultiIndex, Series
from pandas.core.groupby.grouper import Grouping
from pandas.core.base import SpecificationError
from pandas.compat import OrderedDict
import pandas.util.testing as tm
def test_agg_regression1(tsframe):
grouped = tsframe.groupby([lambda x: x.year, lambda x: x.month])
result = grouped.agg(np.mean)
expected = grouped.mean()
tm.assert_frame_equal(result, expected)
def test_agg_must_agg(df):
grouped = df.groupby('A')['C']
msg = "Must produce aggregated value"
with pytest.raises(Exception, match=msg):
grouped.agg(lambda x: x.describe())
with pytest.raises(Exception, match=msg):
grouped.agg(lambda x: x.index[:2])
def test_agg_ser_multi_key(df):
# TODO(wesm): unused
ser = df.C # noqa
f = lambda x: x.sum()
results = df.C.groupby([df.A, df.B]).aggregate(f)
expected = df.groupby(['A', 'B']).sum()['C']
tm.assert_series_equal(results, expected)
def test_groupby_aggregation_mixed_dtype():
# GH 6212
expected = DataFrame({
'v1': [5, 5, 7, np.nan, 3, 3, 4, 1],
'v2': [55, 55, 77, np.nan, 33, 33, 44, 11]},
index=MultiIndex.from_tuples([(1, 95), (1, 99), (2, 95), (2, 99),
('big', 'damp'),
('blue', 'dry'),
('red', 'red'), ('red', 'wet')],
names=['by1', 'by2']))
df = DataFrame({
'v1': [1, 3, 5, 7, 8, 3, 5, np.nan, 4, 5, 7, 9],
'v2': [11, 33, 55, 77, 88, 33, 55, np.nan, 44, 55, 77, 99],
'by1': ["red", "blue", 1, 2, np.nan, "big", 1, 2, "red", 1, np.nan,
12],
'by2': ["wet", "dry", 99, 95, np.nan, "damp", 95, 99, "red", 99,
np.nan, np.nan]
})
g = df.groupby(['by1', 'by2'])
result = g[['v1', 'v2']].mean()
tm.assert_frame_equal(result, expected)
def test_agg_apply_corner(ts, tsframe):
# nothing to group, all NA
grouped = ts.groupby(ts * np.nan)
assert ts.dtype == np.float64
# groupby float64 values results in Float64Index
exp = Series([], dtype=np.float64,
index=pd.Index([], dtype=np.float64))
tm.assert_series_equal(grouped.sum(), exp)
tm.assert_series_equal(grouped.agg(np.sum), exp)
tm.assert_series_equal(grouped.apply(np.sum), exp,
check_index_type=False)
# DataFrame
grouped = tsframe.groupby(tsframe['A'] * np.nan)
exp_df = DataFrame(columns=tsframe.columns, dtype=float,
index=pd.Index([], dtype=np.float64))
tm.assert_frame_equal(grouped.sum(), exp_df, check_names=False)
tm.assert_frame_equal(grouped.agg(np.sum), exp_df, check_names=False)
tm.assert_frame_equal(grouped.apply(np.sum), exp_df.iloc[:, :0],
check_names=False)
def test_agg_grouping_is_list_tuple(ts):
df = tm.makeTimeDataFrame()
grouped = df.groupby(lambda x: x.year)
grouper = grouped.grouper.groupings[0].grouper
grouped.grouper.groupings[0] = Grouping(ts.index, list(grouper))
result = grouped.agg(np.mean)
expected = grouped.mean()
tm.assert_frame_equal(result, expected)
grouped.grouper.groupings[0] = Grouping(ts.index, tuple(grouper))
result = grouped.agg(np.mean)
expected = grouped.mean()
tm.assert_frame_equal(result, expected)
def test_agg_python_multiindex(mframe):
grouped = mframe.groupby(['A', 'B'])
result = grouped.agg(np.mean)
expected = grouped.mean()
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize('groupbyfunc', [
lambda x: x.weekday(),
[lambda x: x.month, lambda x: x.weekday()],
])
def test_aggregate_str_func(tsframe, groupbyfunc):
grouped = tsframe.groupby(groupbyfunc)
# single series
result = grouped['A'].agg('std')
expected = grouped['A'].std()
tm.assert_series_equal(result, expected)
# group frame by function name
result = grouped.aggregate('var')
expected = grouped.var()
tm.assert_frame_equal(result, expected)
# group frame by function dict
result = grouped.agg(OrderedDict([['A', 'var'],
['B', 'std'],
['C', 'mean'],
['D', 'sem']]))
expected = DataFrame(OrderedDict([['A', grouped['A'].var()],
['B', grouped['B'].std()],
['C', grouped['C'].mean()],
['D', grouped['D'].sem()]]))
tm.assert_frame_equal(result, expected)
def test_aggregate_item_by_item(df):
grouped = df.groupby('A')
aggfun = lambda ser: ser.size
result = grouped.agg(aggfun)
foo = (df.A == 'foo').sum()
bar = (df.A == 'bar').sum()
K = len(result.columns)
# GH5782
# odd comparisons can result here, so cast to make easy
exp = pd.Series(np.array([foo] * K), index=list('BCD'),
dtype=np.float64, name='foo')
tm.assert_series_equal(result.xs('foo'), exp)
exp = pd.Series(np.array([bar] * K), index=list('BCD'),
dtype=np.float64, name='bar')
tm.assert_almost_equal(result.xs('bar'), exp)
def aggfun(ser):
return ser.size
result = DataFrame().groupby(df.A).agg(aggfun)
assert isinstance(result, DataFrame)
assert len(result) == 0
def test_wrap_agg_out(three_group):
grouped = three_group.groupby(['A', 'B'])
def func(ser):
if ser.dtype == np.object:
raise TypeError
else:
return ser.sum()
result = grouped.aggregate(func)
exp_grouped = three_group.loc[:, three_group.columns != 'C']
expected = exp_grouped.groupby(['A', 'B']).aggregate(func)
tm.assert_frame_equal(result, expected)
def test_agg_multiple_functions_maintain_order(df):
# GH #610
funcs = [('mean', np.mean), ('max', np.max), ('min', np.min)]
result = df.groupby('A')['C'].agg(funcs)
exp_cols = Index(['mean', 'max', 'min'])
tm.assert_index_equal(result.columns, exp_cols)
def test_multiple_functions_tuples_and_non_tuples(df):
# #1359
funcs = [('foo', 'mean'), 'std']
ex_funcs = [('foo', 'mean'), ('std', 'std')]
result = df.groupby('A')['C'].agg(funcs)
expected = df.groupby('A')['C'].agg(ex_funcs)
tm.assert_frame_equal(result, expected)
result = df.groupby('A').agg(funcs)
expected = df.groupby('A').agg(ex_funcs)
tm.assert_frame_equal(result, expected)
def test_agg_multiple_functions_too_many_lambdas(df):
grouped = df.groupby('A')
funcs = ['mean', lambda x: x.mean(), lambda x: x.std()]
msg = 'Function names must be unique, found multiple named <lambda>'
with pytest.raises(SpecificationError, match=msg):
grouped.agg(funcs)
def test_more_flexible_frame_multi_function(df):
grouped = df.groupby('A')
exmean = grouped.agg(OrderedDict([['C', np.mean], ['D', np.mean]]))
exstd = grouped.agg(OrderedDict([['C', np.std], ['D', np.std]]))
expected = concat([exmean, exstd], keys=['mean', 'std'], axis=1)
expected = expected.swaplevel(0, 1, axis=1).sort_index(level=0, axis=1)
d = OrderedDict([['C', [np.mean, np.std]], ['D', [np.mean, np.std]]])
result = grouped.aggregate(d)
tm.assert_frame_equal(result, expected)
# be careful
result = grouped.aggregate(OrderedDict([['C', np.mean],
['D', [np.mean, np.std]]]))
expected = grouped.aggregate(OrderedDict([['C', np.mean],
['D', [np.mean, np.std]]]))
tm.assert_frame_equal(result, expected)
def foo(x):
return np.mean(x)
def bar(x):
return np.std(x, ddof=1)
# this uses column selection & renaming
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
d = OrderedDict([['C', np.mean],
['D', OrderedDict([['foo', np.mean],
['bar', np.std]])]])
result = grouped.aggregate(d)
d = OrderedDict([['C', [np.mean]], ['D', [foo, bar]]])
expected = grouped.aggregate(d)
tm.assert_frame_equal(result, expected)
def test_multi_function_flexible_mix(df):
# GH #1268
grouped = df.groupby('A')
# Expected
d = OrderedDict([['C', OrderedDict([['foo', 'mean'], ['bar', 'std']])],
['D', {'sum': 'sum'}]])
# this uses column selection & renaming
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
expected = grouped.aggregate(d)
# Test 1
d = OrderedDict([['C', OrderedDict([['foo', 'mean'], ['bar', 'std']])],
['D', 'sum']])
# this uses column selection & renaming
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
result = grouped.aggregate(d)
tm.assert_frame_equal(result, expected)
# Test 2
d = OrderedDict([['C', OrderedDict([['foo', 'mean'], ['bar', 'std']])],
['D', ['sum']]])
# this uses column selection & renaming
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
result = grouped.aggregate(d)
tm.assert_frame_equal(result, expected)
|
|
pandas/tests/groupby/aggregate/test_aggregate.py
|
|
"""
Read SAS7BDAT files
Based on code written by Jared Hobbs:
https://bitbucket.org/jaredhobbs/sas7bdat
See also:
https://github.com/BioStatMatt/sas7bdat
Partial documentation of the file format:
https://cran.r-project.org/web/packages/sas7bdat/vignettes/sas7bdat.pdf
Reference for binary data compression:
http://collaboration.cmc.ec.gc.ca/science/rpn/biblio/ddj/Website/articles/CUJ/1992/9210/ross/ross.htm
"""
from datetime import datetime
import struct
import numpy as np
from pandas.errors import EmptyDataError
import pandas as pd
from pandas import compat
from pandas.io.common import BaseIterator, get_filepath_or_buffer
from pandas.io.sas._sas import Parser
import pandas.io.sas.sas_constants as const
class _subheader_pointer(object):
pass
class _column(object):
pass
# SAS7BDAT represents a SAS data file in SAS7BDAT format.
class SAS7BDATReader(BaseIterator):
"""
Read SAS files in SAS7BDAT format.
Parameters
----------
path_or_buf : path name or buffer
Name of SAS file or file-like object pointing to SAS file
contents.
index : column identifier, defaults to None
Column to use as index.
convert_dates : boolean, defaults to True
Attempt to convert dates to Pandas datetime values. Note that
some rarely used SAS date formats may be unsupported.
blank_missing : boolean, defaults to True
Convert empty strings to missing values (SAS uses blanks to
indicate missing character variables).
chunksize : int, defaults to None
Return SAS7BDATReader object for iterations, returns chunks
with given number of lines.
encoding : string, defaults to None
String encoding.
convert_text : bool, defaults to True
If False, text variables are left as raw bytes.
convert_header_text : bool, defaults to True
If False, header text, including column names, are left as raw
bytes.
"""
def __init__(self, path_or_buf, index=None, convert_dates=True,
blank_missing=True, chunksize=None, encoding=None,
convert_text=True, convert_header_text=True):
self.index = index
self.convert_dates = convert_dates
self.blank_missing = blank_missing
self.chunksize = chunksize
self.encoding = encoding
self.convert_text = convert_text
self.convert_header_text = convert_header_text
self.default_encoding = "latin-1"
self.compression = ""
self.column_names_strings = []
self.column_names = []
self.column_formats = []
self.columns = []
self._current_page_data_subheader_pointers = []
self._cached_page = None
self._column_data_lengths = []
self._column_data_offsets = []
self._column_types = []
self._current_row_in_file_index = 0
self._current_row_on_page_index = 0
self._current_row_in_file_index = 0
self._path_or_buf, _, _, _ = get_filepath_or_buffer(path_or_buf)
if isinstance(self._path_or_buf, compat.string_types):
self._path_or_buf = open(self._path_or_buf, 'rb')
self.handle = self._path_or_buf
self._get_properties()
self._parse_metadata()
def column_data_lengths(self):
"""Return a numpy int64 array of the column data lengths"""
return np.asarray(self._column_data_lengths, dtype=np.int64)
def column_data_offsets(self):
"""Return a numpy int64 array of the column offsets"""
return np.asarray(self._column_data_offsets, dtype=np.int64)
def column_types(self):
"""Returns a numpy character array of the column types:
s (string) or d (double)"""
return np.asarray(self._column_types, dtype=np.dtype('S1'))
def close(self):
try:
self.handle.close()
except AttributeError:
pass
def _get_properties(self):
# Check magic number
self._path_or_buf.seek(0)
self._cached_page = self._path_or_buf.read(288)
if self._cached_page[0:len(const.magic)] != const.magic:
self.close()
raise ValueError("magic number mismatch (not a SAS file?)")
# Get alignment information
align1, align2 = 0, 0
buf = self._read_bytes(const.align_1_offset, const.align_1_length)
if buf == const.u64_byte_checker_value:
align2 = const.align_2_value
self.U64 = True
self._int_length = 8
self._page_bit_offset = const.page_bit_offset_x64
self._subheader_pointer_length = const.subheader_pointer_length_x64
else:
self.U64 = False
self._page_bit_offset = const.page_bit_offset_x86
self._subheader_pointer_length = const.subheader_pointer_length_x86
self._int_length = 4
buf = self._read_bytes(const.align_2_offset, const.align_2_length)
if buf == const.align_1_checker_value:
align1 = const.align_2_value
total_align = align1 + align2
# Get endianness information
buf = self._read_bytes(const.endianness_offset,
const.endianness_length)
if buf == b'\x01':
self.byte_order = "<"
else:
self.byte_order = ">"
# Get encoding information
buf = self._read_bytes(const.encoding_offset, const.encoding_length)[0]
if buf in const.encoding_names:
self.file_encoding = const.encoding_names[buf]
else:
self.file_encoding = "unknown (code=%s)" % str(buf)
# Get platform information
buf = self._read_bytes(const.platform_offset, const.platform_length)
if buf == b'1':
self.platform = "unix"
elif buf == b'2':
self.platform = "windows"
else:
self.platform = "unknown"
buf = self._read_bytes(const.dataset_offset, const.dataset_length)
self.name = buf.rstrip(b'\x00 ')
if self.convert_header_text:
self.name = self.name.decode(
self.encoding or self.default_encoding)
buf = self._read_bytes(const.file_type_offset, const.file_type_length)
self.file_type = buf.rstrip(b'\x00 ')
if self.convert_header_text:
self.file_type = self.file_type.decode(
self.encoding or self.default_encoding)
# Timestamp is epoch 01/01/1960
epoch = datetime(1960, 1, 1)
x = self._read_float(const.date_created_offset + align1,
const.date_created_length)
self.date_created = epoch + pd.to_timedelta(x, unit='s')
x = self._read_float(const.date_modified_offset + align1,
const.date_modified_length)
self.date_modified = epoch + pd.to_timedelta(x, unit='s')
self.header_length = self._read_int(const.header_size_offset + align1,
const.header_size_length)
# Read the rest of the header into cached_page.
buf = self._path_or_buf.read(self.header_length - 288)
self._cached_page += buf
if len(self._cached_page) != self.header_length:
self.close()
raise ValueError("The SAS7BDAT file appears to be truncated.")
self._page_length = self._read_int(const.page_size_offset + align1,
const.page_size_length)
self._page_count = self._read_int(const.page_count_offset + align1,
const.page_count_length)
buf = self._read_bytes(const.sas_release_offset + total_align,
const.sas_release_length)
self.sas_release = buf.rstrip(b'\x00 ')
if self.convert_header_text:
self.sas_release = self.sas_release.decode(
self.encoding or self.default_encoding)
buf = self._read_bytes(const.sas_server_type_offset + total_align,
const.sas_server_type_length)
self.server_type = buf.rstrip(b'\x00 ')
if self.convert_header_text:
self.server_type = self.server_type.decode(
self.encoding or self.default_encoding)
buf = self._read_bytes(const.os_version_number_offset + total_align,
const.os_version_number_length)
self.os_version = buf.rstrip(b'\x00 ')
if self.convert_header_text:
self.os_version = self.os_version.decode(
self.encoding or self.default_encoding)
buf = self._read_bytes(const.os_name_offset + total_align,
const.os_name_length)
buf = buf.rstrip(b'\x00 ')
if len(buf) > 0:
self.os_name = buf.decode(self.encoding or self.default_encoding)
else:
buf = self._read_bytes(const.os_maker_offset + total_align,
const.os_maker_length)
self.os_name = buf.rstrip(b'\x00 ')
if self.convert_header_text:
self.os_name = self.os_name.decode(
self.encoding or self.default_encoding)
def __next__(self):
da = self.read(nrows=self.chunksize or 1)
if da is None:
raise StopIteration
return da
# Read a single float of the given width (4 or 8).
def _read_float(self, offset, width):
if width not in (4, 8):
self.close()
raise ValueError("invalid float width")
buf = self._read_bytes(offset, width)
fd = "f" if width == 4 else "d"
return struct.unpack(self.byte_order + fd, buf)[0]
# Read a single signed integer of the given width (1, 2, 4 or 8).
def _read_int(self, offset, width):
if width not in (1, 2, 4, 8):
self.close()
raise ValueError("invalid int width")
buf = self._read_bytes(offset, width)
it = {1: "b", 2: "h", 4: "l", 8: "q"}[width]
iv = struct.unpack(self.byte_order + it, buf)[0]
return iv
def _read_bytes(self, offset, length):
if self._cached_page is None:
self._path_or_buf.seek(offset)
buf = self._path_or_buf.read(length)
if len(buf) < length:
self.close()
msg = "Unable to read {:d} bytes from file position {:d}."
raise ValueError(msg.format(length, offset))
return buf
else:
if offset + length > len(self._cached_page):
self.close()
raise ValueError("The cached page is too small.")
return self._cached_page[offset:offset + length]
def _parse_metadata(self):
done = False
while not done:
self._cached_page = self._path_or_buf.read(self._page_length)
if len(self._cached_page) <= 0:
break
if len(self._cached_page) != self._page_length:
self.close()
raise ValueError(
"Failed to read a meta data page from the SAS file.")
done = self._process_page_meta()
def _process_page_meta(self):
self._read_page_header()
pt = [const.page_meta_type, const.page_amd_type] + const.page_mix_types
if self._current_page_type in pt:
self._process_page_metadata()
is_data_page = self._current_page_type & const.page_data_type
is_mix_page = self._current_page_type in const.page_mix_types
return (is_data_page or is_mix_page
or self._current_page_data_subheader_pointers != [])
def _read_page_header(self):
bit_offset = self._page_bit_offset
tx = const.page_type_offset + bit_offset
self._current_page_type = self._read_int(tx, const.page_type_length)
tx = const.block_count_offset + bit_offset
self._current_page_block_count = self._read_int(
tx, const.block_count_length)
tx = const.subheader_count_offset + bit_offset
self._current_page_subheaders_count = (
self._read_int(tx, const.subheader_count_length))
def _process_page_metadata(self):
bit_offset = self._page_bit_offset
for i in range(self._current_page_subheaders_count):
pointer = self._process_subheader_pointers(
const.subheader_pointers_offset + bit_offset, i)
if pointer.length == 0:
continue
if pointer.compression == const.truncated_subheader_id:
continue
subheader_signature = self._read_subheader_signature(
pointer.offset)
subheader_index = (
self._get_subheader_index(subheader_signature,
pointer.compression, pointer.ptype))
self._process_subheader(subheader_index, pointer)
def _get_subheader_index(self, signature, compression, ptype):
index = const.subheader_signature_to_index.get(signature)
if index is None:
f1 = ((compression == const.compressed_subheader_id) or
(compression == 0))
f2 = (ptype == const.compressed_subheader_type)
if (self.compression != "") and f1 and f2:
index = const.SASIndex.data_subheader_index
else:
self.close()
raise ValueError("Unknown subheader signature")
return index
def _process_subheader_pointers(self, offset, subheader_pointer_index):
subheader_pointer_length = self._subheader_pointer_length
total_offset = (offset +
subheader_pointer_length * subheader_pointer_index)
subheader_offset = self._read_int(total_offset, self._int_length)
total_offset += self._int_length
subheader_length = self._read_int(total_offset, self._int_length)
total_offset += self._int_length
subheader_compression = self._read_int(total_offset, 1)
total_offset += 1
subheader_type = self._read_int(total_offset, 1)
x = _subheader_pointer()
x.offset = subheader_offset
x.length = subheader_length
x.compression = subheader_compression
x.ptype = subheader_type
return x
def _read_subheader_signature(self, offset):
subheader_signature = self._read_bytes(offset, self._int_length)
return subheader_signature
def _process_subheader(self, subheader_index, pointer):
offset = pointer.offset
length = pointer.length
if subheader_index == const.SASIndex.row_size_index:
processor = self._process_rowsize_subheader
elif subheader_index == const.SASIndex.column_size_index:
processor = self._process_columnsize_subheader
elif subheader_index == const.SASIndex.column_text_index:
processor = self._process_columntext_subheader
elif subheader_index == const.SASIndex.column_name_index:
processor = self._process_columnname_subheader
elif subheader_index == const.SASIndex.column_attributes_index:
processor = self._process_columnattributes_subheader
elif subheader_index == const.SASIndex.format_and_label_index:
processor = self._process_format_subheader
elif subheader_index == const.SASIndex.column_list_index:
processor = self._process_columnlist_subheader
elif subheader_index == const.SASIndex.subheader_counts_index:
processor = self._process_subheader_counts
elif subheader_index == const.SASIndex.data_subheader_index:
self._current_page_data_subheader_pointers.append(pointer)
return
else:
raise ValueError("unknown subheader index")
processor(offset, length)
def _process_rowsize_subheader(self, offset, length):
int_len = self._int_length
lcs_offset = offset
lcp_offset = offset
if self.U64:
lcs_offset += 682
lcp_offset += 706
else:
lcs_offset += 354
lcp_offset += 378
self.row_length = self._read_int(
offset + const.row_length_offset_multiplier * int_len, int_len)
self.row_count = self._read_int(
offset + const.row_count_offset_multiplier * int_len, int_len)
self.col_count_p1 = self._read_int(
offset + const.col_count_p1_multiplier * int_len, int_len)
self.col_count_p2 = self._read_int(
offset + const.col_count_p2_multiplier * int_len, int_len)
mx = const.row_count_on_mix_page_offset_multiplier * int_len
self._mix_page_row_count = self._read_int(offset + mx, int_len)
self._lcs = self._read_int(lcs_offset, 2)
self._lcp = self._read_int(lcp_offset, 2)
def _process_columnsize_subheader(self, offset, length):
int_len = self._int_length
offset += int_len
self.column_count = self._read_int(offset, int_len)
if (self.col_count_p1 + self.col_count_p2 !=
self.column_count):
print("Warning: column count mismatch (%d + %d != %d)\n",
self.col_count_p1, self.col_count_p2, self.column_count)
# Unknown purpose
def _process_subheader_counts(self, offset, length):
pass
def _process_columntext_subheader(self, offset, length):
offset += self._int_length
text_block_size = self._read_int(offset, const.text_block_size_length)
buf = self._read_bytes(offset, text_block_size)
cname_raw = buf[0:text_block_size].rstrip(b"\x00 ")
cname = cname_raw
if self.convert_header_text:
cname = cname.decode(self.encoding or self.default_encoding)
self.column_names_strings.append(cname)
if len(self.column_names_strings) == 1:
compression_literal = ""
for cl in const.compression_literals:
if cl in cname_raw:
compression_literal = cl
self.compression = compression_literal
offset -= self._int_length
offset1 = offset + 16
if self.U64:
offset1 += 4
buf = self._read_bytes(offset1, self._lcp)
compression_literal = buf.rstrip(b"\x00")
if compression_literal == "":
self._lcs = 0
offset1 = offset + 32
if self.U64:
offset1 += 4
buf = self._read_bytes(offset1, self._lcp)
self.creator_proc = buf[0:self._lcp]
elif compression_literal == const.rle_compression:
offset1 = offset + 40
if self.U64:
offset1 += 4
buf = self._read_bytes(offset1, self._lcp)
self.creator_proc = buf[0:self._lcp]
elif self._lcs > 0:
self._lcp = 0
offset1 = offset + 16
if self.U64:
offset1 += 4
buf = self._read_bytes(offset1, self._lcs)
self.creator_proc = buf[0:self._lcp]
if self.convert_header_text:
if hasattr(self, "creator_proc"):
self.creator_proc = self.creator_proc.decode(
self.encoding or self.default_encoding)
def _process_columnname_subheader(self, offset, length):
int_len = self._int_length
offset += int_len
column_name_pointers_count = (length - 2 * int_len - 12) // 8
for i in range(column_name_pointers_count):
text_subheader = offset + const.column_name_pointer_length * \
(i + 1) + const.column_name_text_subheader_offset
col_name_offset = offset + const.column_name_pointer_length * \
(i + 1) + const.column_name_offset_offset
col_name_length = offset + const.column_name_pointer_length * \
(i + 1) + const.column_name_length_offset
idx = self._read_int(
text_subheader, const.column_name_text_subheader_length)
col_offset = self._read_int(
col_name_offset, const.column_name_offset_length)
col_len = self._read_int(
col_name_length, const.column_name_length_length)
name_str = self.column_names_strings[idx]
self.column_names.append(name_str[col_offset:col_offset + col_len])
def _process_columnattributes_subheader(self, offset, length):
int_len = self._int_length
column_attributes_vectors_count = (
length - 2 * int_len - 12) // (int_len + 8)
for i in range(column_attributes_vectors_count):
col_data_offset = (offset + int_len +
const.column_data_offset_offset +
i * (int_len + 8))
col_data_len = (offset + 2 * int_len +
const.column_data_length_offset +
i * (int_len + 8))
col_types = (offset + 2 * int_len +
const.column_type_offset + i * (int_len + 8))
x = self._read_int(col_data_offset, int_len)
self._column_data_offsets.append(x)
x = self._read_int(col_data_len, const.column_data_length_length)
self._column_data_lengths.append(x)
x = self._read_int(col_types, const.column_type_length)
self._column_types.append(b'd' if x == 1 else b's')
def _process_columnlist_subheader(self, offset, length):
# unknown purpose
pass
def _process_format_subheader(self, offset, length):
int_len = self._int_length
text_subheader_format = (
offset +
const.column_format_text_subheader_index_offset +
3 * int_len)
col_format_offset = (offset +
const.column_format_offset_offset +
3 * int_len)
col_format_len = (offset +
const.column_format_length_offset +
3 * int_len)
text_subheader_label = (
offset +
const.column_label_text_subheader_index_offset +
3 * int_len)
col_label_offset = (offset +
const.column_label_offset_offset +
3 * int_len)
col_label_len = offset + const.column_label_length_offset + 3 * int_len
x = self._read_int(text_subheader_format,
const.column_format_text_subheader_index_length)
format_idx = min(x, len(self.column_names_strings) - 1)
format_start = self._read_int(
col_format_offset, const.column_format_offset_length)
format_len = self._read_int(
col_format_len, const.column_format_length_length)
label_idx = self._read_int(
text_subheader_label,
const.column_label_text_subheader_index_length)
label_idx = min(label_idx, len(self.column_names_strings) - 1)
label_start = self._read_int(
col_label_offset, const.column_label_offset_length)
label_len = self._read_int(col_label_len,
const.column_label_length_length)
label_names = self.column_names_strings[label_idx]
column_label = label_names[label_start: label_start + label_len]
format_names = self.column_names_strings[format_idx]
column_format = format_names[format_start: format_start + format_len]
current_column_number = len(self.columns)
col = _column()
col.col_id = current_column_number
col.name = self.column_names[current_column_number]
col.label = column_label
col.format = column_format
col.ctype = self._column_types[current_column_number]
col.length = self._column_data_lengths[current_column_number]
self.column_formats.append(column_format)
self.columns.append(col)
def read(self, nrows=None):
if (nrows is None) and (self.chunksize is not None):
nrows = self.chunksize
elif nrows is None:
nrows = self.row_count
if len(self._column_types) == 0:
self.close()
raise EmptyDataError("No columns to parse from file")
if self._current_row_in_file_index >= self.row_count:
return None
m = self.row_count - self._current_row_in_file_index
if nrows > m:
nrows = m
nd = self._column_types.count(b'd')
ns = self._column_types.count(b's')
self._string_chunk = np.empty((ns, nrows), dtype=np.object)
self._byte_chunk = np.zeros((nd, 8 * nrows), dtype=np.uint8)
self._current_row_in_chunk_index = 0
p = Parser(self)
p.read(nrows)
rslt = self._chunk_to_dataframe()
if self.index is not None:
rslt = rslt.set_index(self.index)
return rslt
def _read_next_page(self):
self._current_page_data_subheader_pointers = []
self._cached_page = self._path_or_buf.read(self._page_length)
if len(self._cached_page) <= 0:
return True
elif len(self._cached_page) != self._page_length:
self.close()
msg = ("failed to read complete page from file "
"(read {:d} of {:d} bytes)")
raise ValueError(msg.format(len(self._cached_page),
self._page_length))
self._read_page_header()
page_type = self._current_page_type
if page_type == const.page_meta_type:
self._process_page_metadata()
is_data_page = page_type & const.page_data_type
pt = [const.page_meta_type] + const.page_mix_types
if not is_data_page and self._current_page_type not in pt:
return self._read_next_page()
return False
def _chunk_to_dataframe(self):
n = self._current_row_in_chunk_index
m = self._current_row_in_file_index
ix = range(m - n, m)
rslt = pd.DataFrame(index=ix)
js, jb = 0, 0
for j in range(self.column_count):
name = self.column_names[j]
if self._column_types[j] == b'd':
rslt[name] = self._byte_chunk[jb, :].view(
dtype=self.byte_order + 'd')
rslt[name] = np.asarray(rslt[name], dtype=np.float64)
if self.convert_dates:
unit = None
if self.column_formats[j] in const.sas_date_formats:
unit = 'd'
elif self.column_formats[j] in const.sas_datetime_formats:
unit = 's'
if unit:
rslt[name] = pd.to_datetime(rslt[name], unit=unit,
origin="1960-01-01")
jb += 1
elif self._column_types[j] == b's':
rslt[name] = self._string_chunk[js, :]
if self.convert_text and (self.encoding is not None):
rslt[name] = rslt[name].str.decode(
self.encoding or self.default_encoding)
if self.blank_missing:
ii = rslt[name].str.len() == 0
rslt.loc[ii, name] = np.nan
js += 1
else:
self.close()
raise ValueError("unknown column type %s" %
self._column_types[j])
return rslt
|
# -*- coding: utf-8 -*-
"""
test .agg behavior / note that .apply is tested generally in test_groupby.py
"""
import pytest
import numpy as np
import pandas as pd
from pandas import concat, DataFrame, Index, MultiIndex, Series
from pandas.core.groupby.grouper import Grouping
from pandas.core.base import SpecificationError
from pandas.compat import OrderedDict
import pandas.util.testing as tm
def test_agg_regression1(tsframe):
grouped = tsframe.groupby([lambda x: x.year, lambda x: x.month])
result = grouped.agg(np.mean)
expected = grouped.mean()
tm.assert_frame_equal(result, expected)
def test_agg_must_agg(df):
grouped = df.groupby('A')['C']
msg = "Must produce aggregated value"
with pytest.raises(Exception, match=msg):
grouped.agg(lambda x: x.describe())
with pytest.raises(Exception, match=msg):
grouped.agg(lambda x: x.index[:2])
def test_agg_ser_multi_key(df):
# TODO(wesm): unused
ser = df.C # noqa
f = lambda x: x.sum()
results = df.C.groupby([df.A, df.B]).aggregate(f)
expected = df.groupby(['A', 'B']).sum()['C']
tm.assert_series_equal(results, expected)
def test_groupby_aggregation_mixed_dtype():
# GH 6212
expected = DataFrame({
'v1': [5, 5, 7, np.nan, 3, 3, 4, 1],
'v2': [55, 55, 77, np.nan, 33, 33, 44, 11]},
index=MultiIndex.from_tuples([(1, 95), (1, 99), (2, 95), (2, 99),
('big', 'damp'),
('blue', 'dry'),
('red', 'red'), ('red', 'wet')],
names=['by1', 'by2']))
df = DataFrame({
'v1': [1, 3, 5, 7, 8, 3, 5, np.nan, 4, 5, 7, 9],
'v2': [11, 33, 55, 77, 88, 33, 55, np.nan, 44, 55, 77, 99],
'by1': ["red", "blue", 1, 2, np.nan, "big", 1, 2, "red", 1, np.nan,
12],
'by2': ["wet", "dry", 99, 95, np.nan, "damp", 95, 99, "red", 99,
np.nan, np.nan]
})
g = df.groupby(['by1', 'by2'])
result = g[['v1', 'v2']].mean()
tm.assert_frame_equal(result, expected)
def test_agg_apply_corner(ts, tsframe):
# nothing to group, all NA
grouped = ts.groupby(ts * np.nan)
assert ts.dtype == np.float64
# groupby float64 values results in Float64Index
exp = Series([], dtype=np.float64,
index=pd.Index([], dtype=np.float64))
tm.assert_series_equal(grouped.sum(), exp)
tm.assert_series_equal(grouped.agg(np.sum), exp)
tm.assert_series_equal(grouped.apply(np.sum), exp,
check_index_type=False)
# DataFrame
grouped = tsframe.groupby(tsframe['A'] * np.nan)
exp_df = DataFrame(columns=tsframe.columns, dtype=float,
index=pd.Index([], dtype=np.float64))
tm.assert_frame_equal(grouped.sum(), exp_df, check_names=False)
tm.assert_frame_equal(grouped.agg(np.sum), exp_df, check_names=False)
tm.assert_frame_equal(grouped.apply(np.sum), exp_df.iloc[:, :0],
check_names=False)
def test_agg_grouping_is_list_tuple(ts):
df = tm.makeTimeDataFrame()
grouped = df.groupby(lambda x: x.year)
grouper = grouped.grouper.groupings[0].grouper
grouped.grouper.groupings[0] = Grouping(ts.index, list(grouper))
result = grouped.agg(np.mean)
expected = grouped.mean()
tm.assert_frame_equal(result, expected)
grouped.grouper.groupings[0] = Grouping(ts.index, tuple(grouper))
result = grouped.agg(np.mean)
expected = grouped.mean()
tm.assert_frame_equal(result, expected)
def test_agg_python_multiindex(mframe):
grouped = mframe.groupby(['A', 'B'])
result = grouped.agg(np.mean)
expected = grouped.mean()
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize('groupbyfunc', [
lambda x: x.weekday(),
[lambda x: x.month, lambda x: x.weekday()],
])
def test_aggregate_str_func(tsframe, groupbyfunc):
grouped = tsframe.groupby(groupbyfunc)
# single series
result = grouped['A'].agg('std')
expected = grouped['A'].std()
tm.assert_series_equal(result, expected)
# group frame by function name
result = grouped.aggregate('var')
expected = grouped.var()
tm.assert_frame_equal(result, expected)
# group frame by function dict
result = grouped.agg(OrderedDict([['A', 'var'],
['B', 'std'],
['C', 'mean'],
['D', 'sem']]))
expected = DataFrame(OrderedDict([['A', grouped['A'].var()],
['B', grouped['B'].std()],
['C', grouped['C'].mean()],
['D', grouped['D'].sem()]]))
tm.assert_frame_equal(result, expected)
def test_aggregate_item_by_item(df):
grouped = df.groupby('A')
aggfun = lambda ser: ser.size
result = grouped.agg(aggfun)
foo = (df.A == 'foo').sum()
bar = (df.A == 'bar').sum()
K = len(result.columns)
# GH5782
# odd comparisons can result here, so cast to make easy
exp = pd.Series(np.array([foo] * K), index=list('BCD'),
dtype=np.float64, name='foo')
tm.assert_series_equal(result.xs('foo'), exp)
exp = pd.Series(np.array([bar] * K), index=list('BCD'),
dtype=np.float64, name='bar')
tm.assert_almost_equal(result.xs('bar'), exp)
def aggfun(ser):
return ser.size
result = DataFrame().groupby(df.A).agg(aggfun)
assert isinstance(result, DataFrame)
assert len(result) == 0
def test_wrap_agg_out(three_group):
grouped = three_group.groupby(['A', 'B'])
def func(ser):
if ser.dtype == np.object:
raise TypeError
else:
return ser.sum()
result = grouped.aggregate(func)
exp_grouped = three_group.loc[:, three_group.columns != 'C']
expected = exp_grouped.groupby(['A', 'B']).aggregate(func)
tm.assert_frame_equal(result, expected)
def test_agg_multiple_functions_maintain_order(df):
# GH #610
funcs = [('mean', np.mean), ('max', np.max), ('min', np.min)]
result = df.groupby('A')['C'].agg(funcs)
exp_cols = Index(['mean', 'max', 'min'])
tm.assert_index_equal(result.columns, exp_cols)
def test_multiple_functions_tuples_and_non_tuples(df):
# #1359
funcs = [('foo', 'mean'), 'std']
ex_funcs = [('foo', 'mean'), ('std', 'std')]
result = df.groupby('A')['C'].agg(funcs)
expected = df.groupby('A')['C'].agg(ex_funcs)
tm.assert_frame_equal(result, expected)
result = df.groupby('A').agg(funcs)
expected = df.groupby('A').agg(ex_funcs)
tm.assert_frame_equal(result, expected)
def test_agg_multiple_functions_too_many_lambdas(df):
grouped = df.groupby('A')
funcs = ['mean', lambda x: x.mean(), lambda x: x.std()]
msg = 'Function names must be unique, found multiple named <lambda>'
with pytest.raises(SpecificationError, match=msg):
grouped.agg(funcs)
def test_more_flexible_frame_multi_function(df):
grouped = df.groupby('A')
exmean = grouped.agg(OrderedDict([['C', np.mean], ['D', np.mean]]))
exstd = grouped.agg(OrderedDict([['C', np.std], ['D', np.std]]))
expected = concat([exmean, exstd], keys=['mean', 'std'], axis=1)
expected = expected.swaplevel(0, 1, axis=1).sort_index(level=0, axis=1)
d = OrderedDict([['C', [np.mean, np.std]], ['D', [np.mean, np.std]]])
result = grouped.aggregate(d)
tm.assert_frame_equal(result, expected)
# be careful
result = grouped.aggregate(OrderedDict([['C', np.mean],
['D', [np.mean, np.std]]]))
expected = grouped.aggregate(OrderedDict([['C', np.mean],
['D', [np.mean, np.std]]]))
tm.assert_frame_equal(result, expected)
def foo(x):
return np.mean(x)
def bar(x):
return np.std(x, ddof=1)
# this uses column selection & renaming
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
d = OrderedDict([['C', np.mean],
['D', OrderedDict([['foo', np.mean],
['bar', np.std]])]])
result = grouped.aggregate(d)
d = OrderedDict([['C', [np.mean]], ['D', [foo, bar]]])
expected = grouped.aggregate(d)
tm.assert_frame_equal(result, expected)
def test_multi_function_flexible_mix(df):
# GH #1268
grouped = df.groupby('A')
# Expected
d = OrderedDict([['C', OrderedDict([['foo', 'mean'], ['bar', 'std']])],
['D', {'sum': 'sum'}]])
# this uses column selection & renaming
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
expected = grouped.aggregate(d)
# Test 1
d = OrderedDict([['C', OrderedDict([['foo', 'mean'], ['bar', 'std']])],
['D', 'sum']])
# this uses column selection & renaming
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
result = grouped.aggregate(d)
tm.assert_frame_equal(result, expected)
# Test 2
d = OrderedDict([['C', OrderedDict([['foo', 'mean'], ['bar', 'std']])],
['D', ['sum']]])
# this uses column selection & renaming
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
result = grouped.aggregate(d)
tm.assert_frame_equal(result, expected)
|
|
pandas/tests/groupby/aggregate/test_aggregate.py
|
pandas/io/sas/sas7bdat.py
|
from .dual_structured_quad import (
DualRectilinearGraph,
DualStructuredQuadGraph,
DualUniformRectilinearGraph,
)
from .structured_quad import (
RectilinearGraph,
StructuredQuadGraph,
UniformRectilinearGraph,
)
__all__ = [
"StructuredQuadGraph",
"RectilinearGraph",
"UniformRectilinearGraph",
"DualUniformRectilinearGraph",
"DualRectilinearGraph",
"DualStructuredQuadGraph",
]
|
import numpy as np
import pytest
from landlab import HexModelGrid
def test_perimeter_nodes():
"""Test perimeter nodes of a hex grid."""
grid = HexModelGrid((3, 4), node_layout="rect")
assert np.all(grid.perimeter_nodes == [3, 7, 11, 10, 9, 8, 4, 0, 1, 2])
def test_right_edge_nodes():
"""Test right edge nodes of a hex grid."""
grid = HexModelGrid((3, 4), node_layout="rect")
assert np.all(grid.nodes_at_right_edge == [3, 7, 11])
def test_top_edge_nodes():
"""Test top edge nodes of a hex grid."""
grid = HexModelGrid((3, 4), node_layout="rect")
assert np.all(grid.nodes_at_top_edge == [8, 9, 10, 11])
def test_left_edge_nodes():
"""Test left edge nodes of a hex grid."""
grid = HexModelGrid((3, 4), node_layout="rect")
assert np.all(grid.nodes_at_left_edge == [0, 4, 8])
def test_bottom_edge_nodes():
"""Test bottom edge nodes of a hex grid."""
grid = HexModelGrid((3, 4), node_layout="rect")
assert np.all(grid.nodes_at_bottom_edge == [0, 1, 2, 3])
def test_edges_are_readonly(edge_name):
grid = HexModelGrid((3, 4), node_layout="rect")
assert not grid.perimeter_nodes.flags["WRITEABLE"]
with pytest.raises(ValueError):
getattr(grid, "nodes_at_" + edge_name)[0] = 999
def test_edges_are_cached(edge_name):
grid = HexModelGrid((3, 4), node_layout="rect")
x = grid.perimeter_nodes
assert grid.perimeter_nodes is x
x = getattr(grid, "nodes_at_" + edge_name)
assert getattr(grid, "nodes_at_" + edge_name) is x
|
|
tests/grid/test_hex_grid/test_edges.py
|
landlab/graph/structured_quad/__init__.py
|
#!/usr/bin/env python
"""
.. codeauthor:: D Litwin, G Tucker
.. sectionauthor:: D Litwin, G Tucker
"""
from .dupuit_percolator import GroundwaterDupuitPercolator
__all__ = ["GroundwaterDupuitPercolator"]
|
import numpy as np
import pytest
from landlab import HexModelGrid
def test_perimeter_nodes():
"""Test perimeter nodes of a hex grid."""
grid = HexModelGrid((3, 4), node_layout="rect")
assert np.all(grid.perimeter_nodes == [3, 7, 11, 10, 9, 8, 4, 0, 1, 2])
def test_right_edge_nodes():
"""Test right edge nodes of a hex grid."""
grid = HexModelGrid((3, 4), node_layout="rect")
assert np.all(grid.nodes_at_right_edge == [3, 7, 11])
def test_top_edge_nodes():
"""Test top edge nodes of a hex grid."""
grid = HexModelGrid((3, 4), node_layout="rect")
assert np.all(grid.nodes_at_top_edge == [8, 9, 10, 11])
def test_left_edge_nodes():
"""Test left edge nodes of a hex grid."""
grid = HexModelGrid((3, 4), node_layout="rect")
assert np.all(grid.nodes_at_left_edge == [0, 4, 8])
def test_bottom_edge_nodes():
"""Test bottom edge nodes of a hex grid."""
grid = HexModelGrid((3, 4), node_layout="rect")
assert np.all(grid.nodes_at_bottom_edge == [0, 1, 2, 3])
def test_edges_are_readonly(edge_name):
grid = HexModelGrid((3, 4), node_layout="rect")
assert not grid.perimeter_nodes.flags["WRITEABLE"]
with pytest.raises(ValueError):
getattr(grid, "nodes_at_" + edge_name)[0] = 999
def test_edges_are_cached(edge_name):
grid = HexModelGrid((3, 4), node_layout="rect")
x = grid.perimeter_nodes
assert grid.perimeter_nodes is x
x = getattr(grid, "nodes_at_" + edge_name)
assert getattr(grid, "nodes_at_" + edge_name) is x
|
|
tests/grid/test_hex_grid/test_edges.py
|
landlab/components/groundwater/__init__.py
|
from .drainage_density import DrainageDensity
__all__ = ["DrainageDensity"]
|
import numpy as np
import pytest
from landlab import HexModelGrid
def test_perimeter_nodes():
"""Test perimeter nodes of a hex grid."""
grid = HexModelGrid((3, 4), node_layout="rect")
assert np.all(grid.perimeter_nodes == [3, 7, 11, 10, 9, 8, 4, 0, 1, 2])
def test_right_edge_nodes():
"""Test right edge nodes of a hex grid."""
grid = HexModelGrid((3, 4), node_layout="rect")
assert np.all(grid.nodes_at_right_edge == [3, 7, 11])
def test_top_edge_nodes():
"""Test top edge nodes of a hex grid."""
grid = HexModelGrid((3, 4), node_layout="rect")
assert np.all(grid.nodes_at_top_edge == [8, 9, 10, 11])
def test_left_edge_nodes():
"""Test left edge nodes of a hex grid."""
grid = HexModelGrid((3, 4), node_layout="rect")
assert np.all(grid.nodes_at_left_edge == [0, 4, 8])
def test_bottom_edge_nodes():
"""Test bottom edge nodes of a hex grid."""
grid = HexModelGrid((3, 4), node_layout="rect")
assert np.all(grid.nodes_at_bottom_edge == [0, 1, 2, 3])
def test_edges_are_readonly(edge_name):
grid = HexModelGrid((3, 4), node_layout="rect")
assert not grid.perimeter_nodes.flags["WRITEABLE"]
with pytest.raises(ValueError):
getattr(grid, "nodes_at_" + edge_name)[0] = 999
def test_edges_are_cached(edge_name):
grid = HexModelGrid((3, 4), node_layout="rect")
x = grid.perimeter_nodes
assert grid.perimeter_nodes is x
x = getattr(grid, "nodes_at_" + edge_name)
assert getattr(grid, "nodes_at_" + edge_name) is x
|
|
tests/grid/test_hex_grid/test_edges.py
|
landlab/components/drainage_density/__init__.py
|
# -*- coding: utf-8 -*-
# added new list_tbl definition
from functools import partial
from navmazing import NavigateToAttribute, NavigateToSibling
from cfme.common import SummaryMixin, Taggable
from cfme.fixtures import pytest_selenium as sel
from cfme.web_ui import CheckboxTable, toolbar as tb, paginator, InfoBlock, match_location
from utils.appliance import Navigatable
from utils.appliance.implementations.ui import CFMENavigateStep, navigator, navigate_to
from . import pol_btn, mon_btn
list_tbl = CheckboxTable(table_locator="//div[@id='list_grid']//table")
match_page = partial(match_location, controller='container_node', title='Nodes')
class Node(Taggable, SummaryMixin, Navigatable):
def __init__(self, name, provider, appliance=None):
self.name = name
self.provider = provider
Navigatable.__init__(self, appliance=appliance)
def load_details(self, refresh=False):
navigate_to(self, 'Details')
if refresh:
tb.refresh()
def get_detail(self, *ident):
""" Gets details from the details infoblock
Args:
*ident: Table name and Key name, e.g. "Relationships", "Images"
Returns: A string representing the contents of the summary's value.
"""
self.load_details(refresh=False)
return InfoBlock.text(*ident)
@navigator.register(Node, 'All')
class All(CFMENavigateStep):
prerequisite = NavigateToAttribute('appliance.server', 'LoggedIn')
def am_i_here(self):
return match_page(summary='Nodes')
def step(self):
from cfme.web_ui.menu import nav
nav._nav_to_fn('Compute', 'Containers', 'Container Nodes')(None)
def resetter(self):
# Reset view and selection
tb.select("List View")
sel.check(paginator.check_all())
sel.uncheck(paginator.check_all())
@navigator.register(Node, 'Details')
class Details(CFMENavigateStep):
prerequisite = NavigateToSibling('All')
def am_i_here(self):
return match_page(summary='{} (Summary)'.format(self.obj.name))
def step(self):
# Assuming default list view from prerequisite
list_tbl.click_row_by_cells({'Name': self.obj.name, 'Provider': self.obj.provider.name})
@navigator.register(Node, 'EditTags')
class EditTags(CFMENavigateStep):
prerequisite = NavigateToSibling('Details')
def am_i_here(self):
match_page(summary='Tag Assignment')
def step(self):
pol_btn('Edit Tags')
@navigator.register(Node, 'ManagePolicies')
class ManagePolicies(CFMENavigateStep):
prerequisite = NavigateToSibling('Details')
def am_i_here(self):
match_page(summary='Select Policy Profiles')
def step(self):
pol_btn('Manage Policies')
@navigator.register(Node, 'Utilization')
class Utilization(CFMENavigateStep):
prerequisite = NavigateToSibling('Details')
def am_i_here(self):
match_page(summary='{} Capacity & Utilization'.format(self.obj.name))
def step(self):
mon_btn('Utilization')
|
# -*- coding: utf-8 -*-
import datetime
import fauxfactory
import pytest
from cfme.rest import dialog as _dialog
from cfme.rest import services as _services
from cfme.rest import service_catalogs as _service_catalogs
from cfme.rest import service_templates as _service_templates
from cfme import test_requirements
from utils.providers import setup_a_provider as _setup_a_provider
from utils.wait import wait_for
from utils import error, version
pytestmark = [test_requirements.service,
pytest.mark.tier(2)]
@pytest.fixture(scope="module")
def a_provider():
return _setup_a_provider("infra")
@pytest.fixture(scope="function")
def dialog():
return _dialog()
@pytest.fixture(scope="function")
def service_catalogs(request, rest_api):
return _service_catalogs(request, rest_api)
@pytest.fixture(scope="function")
def services(request, rest_api, a_provider, dialog, service_catalogs):
return _services(request, rest_api, a_provider, dialog, service_catalogs)
@pytest.fixture(scope='function')
def service_templates(request, rest_api, dialog):
return _service_templates(request, rest_api, dialog)
class TestServiceRESTAPI(object):
def test_edit_service(self, rest_api, services):
"""Tests editing a service.
Prerequisities:
* An appliance with ``/api`` available.
Steps:
* POST /api/services (method ``edit``) with the ``name``
* Check if the service with ``new_name`` exists
Metadata:
test_flag: rest
"""
ser = services[0]
new_name = fauxfactory.gen_alphanumeric()
ser.action.edit(name=new_name)
wait_for(
lambda: rest_api.collections.services.find_by(name=new_name),
num_sec=180,
delay=10,
)
def test_edit_multiple_services(self, rest_api, services):
"""Tests editing multiple service catalogs at time.
Prerequisities:
* An appliance with ``/api`` available.
Steps:
* POST /api/services (method ``edit``) with the list of dictionaries used to edit
* Check if the services with ``new_name`` each exists
Metadata:
test_flag: rest
"""
new_names = []
services_data_edited = []
for ser in services:
new_name = fauxfactory.gen_alphanumeric()
new_names.append(new_name)
services_data_edited.append({
"href": ser.href,
"name": new_name,
})
rest_api.collections.services.action.edit(*services_data_edited)
for new_name in new_names:
wait_for(
lambda: rest_api.collections.service_templates.find_by(name=new_name),
num_sec=180,
delay=10,
)
def test_delete_service(self, rest_api, services):
service = rest_api.collections.services[0]
service.action.delete()
with error.expected("ActiveRecord::RecordNotFound"):
service.action.delete()
def test_delete_services(self, rest_api, services):
rest_api.collections.services.action.delete(*services)
with error.expected("ActiveRecord::RecordNotFound"):
rest_api.collections.services.action.delete(*services)
def test_retire_service_now(self, rest_api, services):
"""Test retiring a service
Prerequisities:
* An appliance with ``/api`` available.
Steps:
* Retrieve list of entities using GET /api/services , pick the first one
* POST /api/service/<id> (method ``retire``)
Metadata:
test_flag: rest
"""
assert "retire" in rest_api.collections.services.action.all
retire_service = services[0]
retire_service.action.retire()
wait_for(
lambda: not rest_api.collections.services.find_by(name=retire_service.name),
num_sec=600,
delay=10,
)
def test_retire_service_future(self, rest_api, services):
"""Test retiring a service
Prerequisities:
* An appliance with ``/api`` available.
Steps:
* Retrieve list of entities using GET /api/services , pick the first one
* POST /api/service/<id> (method ``retire``) with the ``retire_date``
Metadata:
test_flag: rest
"""
assert "retire" in rest_api.collections.services.action.all
retire_service = services[0]
date = (datetime.datetime.now() + datetime.timedelta(days=5)).strftime('%m/%d/%y')
future = {
"date": date,
"warn": "4",
}
date_before = retire_service.updated_at
retire_service.action.retire(future)
def _finished():
retire_service.reload()
if retire_service.updated_at > date_before:
return True
return False
wait_for(_finished, num_sec=600, delay=5, message="REST automation_request finishes")
@pytest.mark.uncollectif(lambda: version.current_version() < '5.5')
def test_set_service_owner(self, rest_api, services):
if "set_ownership" not in rest_api.collections.services.action.all:
pytest.skip("Set owner action for service is not implemented in this version")
service = services[0]
user = rest_api.collections.users.get(userid='admin')
data = {
"owner": {"href": user.href}
}
service.action.set_ownership(data)
service.reload()
assert hasattr(service, "evm_owner")
assert service.evm_owner.userid == user.userid
@pytest.mark.uncollectif(lambda: version.current_version() < '5.5')
def test_set_services_owner(self, rest_api, services):
if "set_ownership" not in rest_api.collections.services.action.all:
pytest.skip("Set owner action for service is not implemented in this version")
data = []
user = rest_api.collections.users.get(userid='admin')
for service in services:
tmp_data = {
"href": service.href,
"owner": {"href": user.href}
}
data.append(tmp_data)
rest_api.collections.services.action.set_ownership(*data)
for service in services:
service.reload()
assert hasattr(service, "evm_owner")
assert service.evm_owner.userid == user.userid
class TestServiceDialogsRESTAPI(object):
@pytest.mark.uncollectif(lambda: version.current_version() < '5.7')
@pytest.mark.parametrize("method", ["post", "delete"])
def test_delete_service_dialog(self, rest_api, dialog, method):
service_dialog = rest_api.collections.service_dialogs.find_by(label=dialog.label)[0]
service_dialog.action.delete(force_method=method)
with error.expected("ActiveRecord::RecordNotFound"):
service_dialog.action.delete()
@pytest.mark.uncollectif(lambda: version.current_version() < '5.7')
def test_delete_service_dialogs(self, rest_api, dialog):
service_dialog = rest_api.collections.service_dialogs.find_by(label=dialog.label)[0]
rest_api.collections.service_dialogs.action.delete(service_dialog)
with error.expected("ActiveRecord::RecordNotFound"):
rest_api.collections.service_dialogs.action.delete(service_dialog)
class TestServiceTemplateRESTAPI(object):
def test_edit_service_template(self, rest_api, service_templates):
"""Tests editing a service template.
Prerequisities:
* An appliance with ``/api`` available.
Steps:
* POST /api/service_templates (method ``edit``) with the ``name``
* Check if the service_template with ``new_name`` exists
Metadata:
test_flag: rest
"""
scl = rest_api.collections.service_templates[0]
new_name = fauxfactory.gen_alphanumeric()
scl.action.edit(name=new_name)
wait_for(
lambda: rest_api.collections.service_catalogs.find_by(name=new_name),
num_sec=180,
delay=10,
)
def test_delete_service_templates(self, rest_api, service_templates):
rest_api.collections.service_templates.action.delete(*service_templates)
with error.expected("ActiveRecord::RecordNotFound"):
rest_api.collections.service_templates.action.delete(*service_templates)
def test_delete_service_template(self, rest_api, service_templates):
s_tpl = rest_api.collections.service_templates[0]
s_tpl.action.delete()
with error.expected("ActiveRecord::RecordNotFound"):
s_tpl.action.delete()
@pytest.mark.uncollectif(lambda: version.current_version() < '5.5')
def test_assign_unassign_service_template_to_service_catalog(self, rest_api, service_catalogs,
service_templates):
"""Tests assigning and unassigning the service templates to service catalog.
Prerequisities:
* An appliance with ``/api`` available.
Steps:
* POST /api/service_catalogs/<id>/service_templates (method ``assign``)
with the list of dictionaries service templates list
* Check if the service_templates were assigned to the service catalog
* POST /api/service_catalogs/<id>/service_templates (method ``unassign``)
with the list of dictionaries service templates list
* Check if the service_templates were unassigned to the service catalog
Metadata:
test_flag: rest
"""
scl = service_catalogs[0]
stpl = service_templates[0]
scl.service_templates.action.assign(stpl)
scl.reload()
assert stpl.id in [st.id for st in scl.service_templates.all]
scl.service_templates.action.unassign(stpl)
scl.reload()
assert stpl.id not in [st.id for st in scl.service_templates.all]
def test_edit_multiple_service_templates(self, rest_api, service_templates):
"""Tests editing multiple service catalogs at time.
Prerequisities:
* An appliance with ``/api`` available.
Steps:
* POST /api/service_templates (method ``edit``)
with the list of dictionaries used to edit
* Check if the service_templates with ``new_name`` each exists
Metadata:
test_flag: rest
"""
new_names = []
service_tpls_data_edited = []
for tpl in service_templates:
new_name = fauxfactory.gen_alphanumeric()
new_names.append(new_name)
service_tpls_data_edited.append({
"href": tpl.href,
"name": new_name,
})
rest_api.collections.service_templates.action.edit(*service_tpls_data_edited)
for new_name in new_names:
wait_for(
lambda: rest_api.collections.service_templates.find_by(name=new_name),
num_sec=180,
delay=10,
)
|
|
cfme/tests/services/test_rest_services.py
|
|
# vim: ft=python fileencoding=utf-8 sts=4 sw=4 et:
# Copyright 2015-2020 Florian Bruhin (The Compiler) <mail@qutebrowser.org>
#
# This file is part of qutebrowser.
#
# qutebrowser is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# qutebrowser is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with qutebrowser. If not, see <http://www.gnu.org/licenses/>.
"""Tools related to error printing/displaying."""
from PyQt5.QtWidgets import QMessageBox
from qutebrowser.utils import log, utils
def _get_name(exc: BaseException) -> str:
"""Get a suitable exception name as a string."""
prefixes = ['qutebrowser', 'builtins']
name = utils.qualname(exc.__class__)
for prefix in prefixes:
if name.startswith(prefix):
name = name[len(prefix) + 1:]
break
return name
def handle_fatal_exc(exc: BaseException,
title: str, *,
no_err_windows: bool,
pre_text: str = '',
post_text: str = '') -> None:
"""Handle a fatal "expected" exception by displaying an error box.
If --no-err-windows is given as argument, the text is logged to the error
logger instead.
Args:
exc: The Exception object being handled.
no_err_windows: Show text in log instead of error window.
title: The title to be used for the error message.
pre_text: The text to be displayed before the exception text.
post_text: The text to be displayed after the exception text.
"""
if no_err_windows:
lines = [
"Handling fatal {} with --no-err-windows!".format(_get_name(exc)),
"",
"title: {}".format(title),
"pre_text: {}".format(pre_text),
"post_text: {}".format(post_text),
"exception text: {}".format(str(exc) or 'none'),
]
log.misc.exception('\n'.join(lines))
else:
if pre_text:
msg_text = '{}: {}'.format(pre_text, exc)
else:
msg_text = str(exc)
if post_text:
msg_text += '\n\n{}'.format(post_text)
msgbox = QMessageBox(QMessageBox.Critical, title, msg_text)
msgbox.exec_()
|
# vim: ft=python fileencoding=utf-8 sts=4 sw=4 et:
# Copyright 2015-2020 Florian Bruhin (The Compiler) <mail@qutebrowser.org>
#
# This file is part of qutebrowser.
#
# qutebrowser is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# qutebrowser is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with qutebrowser. If not, see <http://www.gnu.org/licenses/>.
import pytest_bdd as bdd
from qutebrowser.utils import qtutils
bdd.scenarios('qutescheme.feature')
@bdd.then(bdd.parsers.parse("the {kind} request should be blocked"))
def request_blocked(request, quteproc, kind):
blocking_set_msg = (
"Blocking malicious request from qute://settings/set?* to "
"qute://settings/set?*")
blocking_csrf_msg = (
"Blocking malicious request from "
"http://localhost:*/data/misc/qutescheme_csrf.html to "
"qute://settings/set?*")
blocking_js_msg = (
"[http://localhost:*/data/misc/qutescheme_csrf.html:0] Not allowed to "
"load local resource: qute://settings/set?*"
)
unsafe_redirect_msg = "Load error: ERR_UNSAFE_REDIRECT"
blocked_request_msg = "Load error: ERR_BLOCKED_BY_CLIENT"
webkit_error_invalid = (
"Error while loading qute://settings/set?*: Invalid qute://settings "
"request")
webkit_error_unsupported = (
"Error while loading qute://settings/set?*: Unsupported request type")
if request.config.webengine and qtutils.version_check('5.12'):
# On Qt 5.12, we mark qute:// as a local scheme, causing most requests
# being blocked by Chromium internally (logging to the JS console).
expected_messages = {
'img': [blocking_js_msg],
'link': [blocking_js_msg],
'redirect': [blocking_set_msg, blocked_request_msg],
'form': [blocking_js_msg],
}
if qtutils.version_check('5.15', compiled=False):
# On Qt 5.15, Chromium blocks the redirect as ERR_UNSAFE_REDIRECT
# instead.
expected_messages['redirect'] = [unsafe_redirect_msg]
elif request.config.webengine:
expected_messages = {
'img': [blocking_csrf_msg],
'link': [blocking_set_msg, blocked_request_msg],
'redirect': [blocking_set_msg, blocked_request_msg],
'form': [blocking_set_msg, blocked_request_msg],
}
else: # QtWebKit
expected_messages = {
'img': [blocking_csrf_msg],
'link': [blocking_csrf_msg, webkit_error_invalid],
'redirect': [blocking_csrf_msg, webkit_error_invalid],
'form': [webkit_error_unsupported],
}
for pattern in expected_messages[kind]:
msg = quteproc.wait_for(message=pattern)
msg.expected = True
|
|
tests/end2end/features/test_qutescheme_bdd.py
|
qutebrowser/utils/error.py
|
# Copyright 2019, Google LLC All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from google.api_core import gapic_v1
from google.cloud.pubsub_v1.publisher._sequencer import base
from google.pubsub_v1 import types as gapic_types
class UnorderedSequencer(base.Sequencer):
""" Sequences messages into batches for one topic without any ordering.
Public methods are NOT thread-safe.
"""
def __init__(self, client, topic):
self._client = client
self._topic = topic
self._current_batch = None
self._stopped = False
def is_finished(self):
""" Whether the sequencer is finished and should be cleaned up.
Returns:
bool: Whether the sequencer is finished and should be cleaned up.
"""
# TODO: Implement. Not implementing yet because of possible performance
# impact due to extra locking required. This does mean that
# UnorderedSequencers don't get cleaned up, but this is the same as
# previously existing behavior.
return False
def stop(self):
""" Stop the sequencer.
Subsequent publishes will fail.
Raises:
RuntimeError:
If called after stop() has already been called.
"""
if self._stopped:
raise RuntimeError("Unordered sequencer already stopped.")
self.commit()
self._stopped = True
def commit(self):
""" Commit the batch.
Raises:
RuntimeError:
If called after stop() has already been called.
"""
if self._stopped:
raise RuntimeError("Unordered sequencer already stopped.")
if self._current_batch:
self._current_batch.commit()
# At this point, we lose track of the old batch, but we don't
# care since we just committed it.
# Setting this to None guarantees the next publish() creates a new
# batch.
self._current_batch = None
def unpause(self):
""" Not relevant for this class. """
raise NotImplementedError
def _create_batch(
self,
commit_retry=gapic_v1.method.DEFAULT,
commit_timeout: gapic_types.TimeoutType = gapic_v1.method.DEFAULT,
):
""" Create a new batch using the client's batch class and other stored
settings.
Args:
commit_retry (Optional[google.api_core.retry.Retry]):
The retry settings to apply when publishing the batch.
commit_timeout (:class:`~.pubsub_v1.types.TimeoutType`):
The timeout to apply when publishing the batch.
"""
return self._client._batch_class(
client=self._client,
topic=self._topic,
settings=self._client.batch_settings,
batch_done_callback=None,
commit_when_full=True,
commit_retry=commit_retry,
commit_timeout=commit_timeout,
)
def publish(
self,
message,
retry=gapic_v1.method.DEFAULT,
timeout: gapic_types.TimeoutType = gapic_v1.method.DEFAULT,
):
""" Batch message into existing or new batch.
Args:
message (~.pubsub_v1.types.PubsubMessage):
The Pub/Sub message.
retry (Optional[google.api_core.retry.Retry]):
The retry settings to apply when publishing the message.
timeout (:class:`~.pubsub_v1.types.TimeoutType`):
The timeout to apply when publishing the message.
Returns:
~google.api_core.future.Future: An object conforming to
the :class:`~concurrent.futures.Future` interface. The future tracks
the publishing status of the message.
Raises:
RuntimeError:
If called after stop() has already been called.
pubsub_v1.publisher.exceptions.MessageTooLargeError: If publishing
the ``message`` would exceed the max size limit on the backend.
"""
if self._stopped:
raise RuntimeError("Unordered sequencer already stopped.")
if not self._current_batch:
newbatch = self._create_batch(commit_retry=retry, commit_timeout=timeout)
self._current_batch = newbatch
batch = self._current_batch
future = None
while future is None:
# Might throw MessageTooLargeError
future = batch.publish(message)
# batch is full, triggering commit_when_full
if future is None:
batch = self._create_batch(commit_retry=retry, commit_timeout=timeout)
# At this point, we lose track of the old batch, but we don't
# care since it's already committed (because it was full.)
self._current_batch = batch
return future
# Used only for testing.
def _set_batch(self, batch):
self._current_batch = batch
|
# Copyright 2017, Google LLC All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import datetime
import threading
import time
import mock
import pytest
import google.api_core.exceptions
from google.api_core import gapic_v1
from google.auth import credentials
from google.cloud.pubsub_v1 import publisher
from google.cloud.pubsub_v1 import types
from google.cloud.pubsub_v1.publisher import exceptions
from google.cloud.pubsub_v1.publisher._batch.base import BatchStatus
from google.cloud.pubsub_v1.publisher._batch.base import BatchCancellationReason
from google.cloud.pubsub_v1.publisher._batch import thread
from google.cloud.pubsub_v1.publisher._batch.thread import Batch
from google.pubsub_v1 import types as gapic_types
def create_client():
creds = mock.Mock(spec=credentials.Credentials)
return publisher.Client(credentials=creds)
def create_batch(
topic="topic_name",
batch_done_callback=None,
commit_when_full=True,
commit_retry=gapic_v1.method.DEFAULT,
commit_timeout: gapic_types.TimeoutType = gapic_v1.method.DEFAULT,
**batch_settings
):
"""Return a batch object suitable for testing.
Args:
topic (str): Topic name.
batch_done_callback (Callable[bool]): A callable that is called when
the batch is done, either with a success or a failure flag.
commit_when_full (bool): Whether to commit the batch when the batch
has reached byte-size or number-of-messages limits.
commit_retry (Optional[google.api_core.retry.Retry]): The retry settings
for the batch commit call.
commit_timeout (:class:`~.pubsub_v1.types.TimeoutType`):
The timeout to apply to the batch commit call.
batch_settings (Mapping[str, str]): Arguments passed on to the
:class:``~.pubsub_v1.types.BatchSettings`` constructor.
Returns:
~.pubsub_v1.publisher.batch.thread.Batch: A batch object.
"""
client = create_client()
settings = types.BatchSettings(**batch_settings)
return Batch(
client,
topic,
settings,
batch_done_callback=batch_done_callback,
commit_when_full=commit_when_full,
commit_retry=commit_retry,
commit_timeout=commit_timeout,
)
@mock.patch.object(threading, "Lock")
def test_make_lock(Lock):
lock = Batch.make_lock()
assert lock is Lock.return_value
Lock.assert_called_once_with()
def test_client():
client = create_client()
settings = types.BatchSettings()
batch = Batch(client, "topic_name", settings)
assert batch.client is client
def test_commit():
batch = create_batch()
with mock.patch.object(
Batch, "_start_commit_thread", autospec=True
) as _start_commit_thread:
batch.commit()
_start_commit_thread.assert_called_once()
# The batch's status needs to be something other than "accepting messages",
# since the commit started.
assert batch.status != BatchStatus.ACCEPTING_MESSAGES
assert batch.status == BatchStatus.STARTING
def test_commit_no_op():
batch = create_batch()
batch._status = BatchStatus.IN_PROGRESS
with mock.patch.object(threading, "Thread", autospec=True) as Thread:
batch.commit()
# Make sure a thread was not created.
Thread.assert_not_called()
# Check that batch status is unchanged.
assert batch.status == BatchStatus.IN_PROGRESS
def test_blocking__commit():
batch = create_batch()
futures = (
batch.publish({"data": b"This is my message."}),
batch.publish({"data": b"This is another message."}),
)
# Set up the underlying API publish method to return a PublishResponse.
publish_response = gapic_types.PublishResponse(message_ids=["a", "b"])
patch = mock.patch.object(
type(batch.client.api), "publish", return_value=publish_response
)
with patch as publish:
batch._commit()
# Establish that the underlying API call was made with expected
# arguments.
publish.assert_called_once_with(
topic="topic_name",
messages=[
gapic_types.PubsubMessage(data=b"This is my message."),
gapic_types.PubsubMessage(data=b"This is another message."),
],
retry=gapic_v1.method.DEFAULT,
timeout=gapic_v1.method.DEFAULT,
)
# Establish that all of the futures are done, and that they have the
# expected values.
assert futures[0].done()
assert futures[0].result() == "a"
assert futures[1].done()
assert futures[1].result() == "b"
def test_blocking__commit_custom_retry():
batch = create_batch(commit_retry=mock.sentinel.custom_retry)
batch.publish({"data": b"This is my message."})
# Set up the underlying API publish method to return a PublishResponse.
publish_response = gapic_types.PublishResponse(message_ids=["a"])
patch = mock.patch.object(
type(batch.client.api), "publish", return_value=publish_response
)
with patch as publish:
batch._commit()
# Establish that the underlying API call was made with expected
# arguments.
publish.assert_called_once_with(
topic="topic_name",
messages=[gapic_types.PubsubMessage(data=b"This is my message.")],
retry=mock.sentinel.custom_retry,
timeout=gapic_v1.method.DEFAULT,
)
def test_blocking__commit_custom_timeout():
batch = create_batch(commit_timeout=mock.sentinel.custom_timeout)
batch.publish({"data": b"This is my message."})
# Set up the underlying API publish method to return a PublishResponse.
publish_response = gapic_types.PublishResponse(message_ids=["a"])
patch = mock.patch.object(
type(batch.client.api), "publish", return_value=publish_response
)
with patch as publish:
batch._commit()
# Establish that the underlying API call was made with expected
# arguments.
publish.assert_called_once_with(
topic="topic_name",
messages=[gapic_types.PubsubMessage(data=b"This is my message.")],
retry=gapic_v1.method.DEFAULT,
timeout=mock.sentinel.custom_timeout,
)
def test_client_api_publish_not_blocking_additional_publish_calls():
batch = create_batch(max_messages=1)
api_publish_called = threading.Event()
def api_publish_delay(topic="", messages=(), retry=None, timeout=None):
api_publish_called.set()
time.sleep(1.0)
message_ids = [str(i) for i in range(len(messages))]
return gapic_types.PublishResponse(message_ids=message_ids)
api_publish_patch = mock.patch.object(
type(batch.client.api), "publish", side_effect=api_publish_delay
)
with api_publish_patch:
batch.publish({"data": b"first message"})
start = datetime.datetime.now()
event_set = api_publish_called.wait(timeout=1.0)
if not event_set: # pragma: NO COVER
pytest.fail("API publish was not called in time")
batch.publish({"data": b"second message"})
end = datetime.datetime.now()
# While a batch commit in progress, waiting for the API publish call to
# complete should not unnecessariliy delay other calls to batch.publish().
assert (end - start).total_seconds() < 1.0
@mock.patch.object(thread, "_LOGGER")
def test_blocking__commit_starting(_LOGGER):
batch = create_batch()
batch._status = BatchStatus.STARTING
batch._commit()
assert batch._status == BatchStatus.SUCCESS
_LOGGER.debug.assert_called_once_with("No messages to publish, exiting commit")
@mock.patch.object(thread, "_LOGGER")
def test_blocking__commit_already_started(_LOGGER):
batch = create_batch()
batch._status = BatchStatus.IN_PROGRESS
batch._commit()
assert batch._status == BatchStatus.IN_PROGRESS
_LOGGER.debug.assert_called_once_with(
"Batch is already in progress or has been cancelled, exiting commit"
)
def test_blocking__commit_no_messages():
batch = create_batch()
with mock.patch.object(type(batch.client.api), "publish") as publish:
batch._commit()
assert publish.call_count == 0
def test_blocking__commit_wrong_messageid_length():
batch = create_batch()
futures = (
batch.publish({"data": b"blah blah blah"}),
batch.publish({"data": b"blah blah blah blah"}),
)
# Set up a PublishResponse that only returns one message ID.
publish_response = gapic_types.PublishResponse(message_ids=["a"])
patch = mock.patch.object(
type(batch.client.api), "publish", return_value=publish_response
)
with patch:
batch._commit()
for future in futures:
assert future.done()
assert isinstance(future.exception(), exceptions.PublishError)
def test_block__commmit_api_error():
batch = create_batch()
futures = (
batch.publish({"data": b"blah blah blah"}),
batch.publish({"data": b"blah blah blah blah"}),
)
# Make the API throw an error when publishing.
error = google.api_core.exceptions.InternalServerError("uh oh")
patch = mock.patch.object(type(batch.client.api), "publish", side_effect=error)
with patch:
batch._commit()
for future in futures:
assert future.done()
assert future.exception() == error
def test_block__commmit_retry_error():
batch = create_batch()
futures = (
batch.publish({"data": b"blah blah blah"}),
batch.publish({"data": b"blah blah blah blah"}),
)
# Make the API throw an error when publishing.
error = google.api_core.exceptions.RetryError("uh oh", None)
patch = mock.patch.object(type(batch.client.api), "publish", side_effect=error)
with patch:
batch._commit()
for future in futures:
assert future.done()
assert future.exception() == error
def test_publish_updating_batch_size():
batch = create_batch(topic="topic_foo")
messages = (
gapic_types.PubsubMessage(data=b"foobarbaz"),
gapic_types.PubsubMessage(data=b"spameggs"),
gapic_types.PubsubMessage(data=b"1335020400"),
)
# Publish each of the messages, which should save them to the batch.
futures = [batch.publish(message) for message in messages]
# There should be three messages on the batch, and three futures.
assert len(batch.messages) == 3
assert batch._futures == futures
# The size should have been incremented by the sum of the size
# contributions of each message to the PublishRequest.
base_request_size = gapic_types.PublishRequest(topic="topic_foo")._pb.ByteSize()
expected_request_size = base_request_size + sum(
gapic_types.PublishRequest(messages=[msg])._pb.ByteSize() for msg in messages
)
assert batch.size == expected_request_size
assert batch.size > 0 # I do not always trust protobuf.
def test_publish():
batch = create_batch()
message = gapic_types.PubsubMessage()
future = batch.publish(message)
assert len(batch.messages) == 1
assert batch._futures == [future]
def test_publish_max_messages_zero():
batch = create_batch(topic="topic_foo", max_messages=0)
message = gapic_types.PubsubMessage(data=b"foobarbaz")
with mock.patch.object(batch, "commit") as commit:
future = batch.publish(message)
assert future is not None
assert len(batch.messages) == 1
assert batch._futures == [future]
commit.assert_called_once()
def test_publish_max_messages_enforced():
batch = create_batch(topic="topic_foo", max_messages=1)
message = gapic_types.PubsubMessage(data=b"foobarbaz")
message2 = gapic_types.PubsubMessage(data=b"foobarbaz2")
future = batch.publish(message)
future2 = batch.publish(message2)
assert future is not None
assert future2 is None
assert len(batch.messages) == 1
assert len(batch._futures) == 1
def test_publish_max_bytes_enforced():
batch = create_batch(topic="topic_foo", max_bytes=15)
message = gapic_types.PubsubMessage(data=b"foobarbaz")
message2 = gapic_types.PubsubMessage(data=b"foobarbaz2")
future = batch.publish(message)
future2 = batch.publish(message2)
assert future is not None
assert future2 is None
assert len(batch.messages) == 1
assert len(batch._futures) == 1
def test_publish_exceed_max_messages():
max_messages = 4
batch = create_batch(max_messages=max_messages)
messages = (
gapic_types.PubsubMessage(data=b"foobarbaz"),
gapic_types.PubsubMessage(data=b"spameggs"),
gapic_types.PubsubMessage(data=b"1335020400"),
)
# Publish each of the messages, which should save them to the batch.
with mock.patch.object(batch, "commit") as commit:
futures = [batch.publish(message) for message in messages]
assert batch._futures == futures
assert len(futures) == max_messages - 1
# Commit should not yet have been called.
assert commit.call_count == 0
# When a fourth message is published, commit should be called.
# No future will be returned in this case.
future = batch.publish(gapic_types.PubsubMessage(data=b"last one"))
commit.assert_called_once_with()
assert future is None
assert batch._futures == futures
@mock.patch.object(thread, "_SERVER_PUBLISH_MAX_BYTES", 1000)
def test_publish_single_message_size_exceeds_server_size_limit():
batch = create_batch(
topic="topic_foo",
max_messages=1000,
max_bytes=1000 * 1000, # way larger than (mocked) server side limit
)
big_message = gapic_types.PubsubMessage(data=b"x" * 984)
request_size = gapic_types.PublishRequest(
topic="topic_foo", messages=[big_message]
)._pb.ByteSize()
assert request_size == 1001 # sanity check, just above the (mocked) server limit
with pytest.raises(exceptions.MessageTooLargeError):
batch.publish(big_message)
@mock.patch.object(thread, "_SERVER_PUBLISH_MAX_BYTES", 1000)
def test_publish_total_messages_size_exceeds_server_size_limit():
batch = create_batch(topic="topic_foo", max_messages=10, max_bytes=1500)
messages = (
gapic_types.PubsubMessage(data=b"x" * 500),
gapic_types.PubsubMessage(data=b"x" * 600),
)
# Sanity check - request size is still below BatchSettings.max_bytes,
# but it exceeds the server-side size limit.
request_size = gapic_types.PublishRequest(
topic="topic_foo", messages=messages
)._pb.ByteSize()
assert 1000 < request_size < 1500
with mock.patch.object(batch, "commit") as fake_commit:
batch.publish(messages[0])
batch.publish(messages[1])
# The server side limit should kick in and cause a commit.
fake_commit.assert_called_once()
def test_publish_dict():
batch = create_batch()
future = batch.publish({"data": b"foobarbaz", "attributes": {"spam": "eggs"}})
# There should be one message on the batch.
expected_message = gapic_types.PubsubMessage(
data=b"foobarbaz", attributes={"spam": "eggs"}
)
assert batch.messages == [expected_message]
assert batch._futures == [future]
def test_cancel():
batch = create_batch()
futures = (
batch.publish({"data": b"This is my message."}),
batch.publish({"data": b"This is another message."}),
)
batch.cancel(BatchCancellationReason.PRIOR_ORDERED_MESSAGE_FAILED)
# Assert all futures are cancelled with an error.
for future in futures:
exc = future.exception()
assert type(exc) is RuntimeError
assert exc.args[0] == BatchCancellationReason.PRIOR_ORDERED_MESSAGE_FAILED.value
def test_do_not_commit_when_full_when_flag_is_off():
max_messages = 4
# Set commit_when_full flag to False
batch = create_batch(max_messages=max_messages, commit_when_full=False)
messages = (
gapic_types.PubsubMessage(data=b"foobarbaz"),
gapic_types.PubsubMessage(data=b"spameggs"),
gapic_types.PubsubMessage(data=b"1335020400"),
)
with mock.patch.object(batch, "commit") as commit:
# Publish 3 messages.
futures = [batch.publish(message) for message in messages]
assert len(futures) == 3
# When a fourth message is published, commit should not be called.
future = batch.publish(gapic_types.PubsubMessage(data=b"last one"))
assert commit.call_count == 0
assert future is None
class BatchDoneCallbackTracker(object):
def __init__(self):
self.called = False
self.success = None
def __call__(self, success):
self.called = True
self.success = success
def test_batch_done_callback_called_on_success():
batch_done_callback_tracker = BatchDoneCallbackTracker()
batch = create_batch(batch_done_callback=batch_done_callback_tracker)
# Ensure messages exist.
message = gapic_types.PubsubMessage(data=b"foobarbaz")
batch.publish(message)
# One response for one published message.
publish_response = gapic_types.PublishResponse(message_ids=["a"])
with mock.patch.object(
type(batch.client.api), "publish", return_value=publish_response
):
batch._commit()
assert batch_done_callback_tracker.called
assert batch_done_callback_tracker.success
def test_batch_done_callback_called_on_publish_failure():
batch_done_callback_tracker = BatchDoneCallbackTracker()
batch = create_batch(batch_done_callback=batch_done_callback_tracker)
# Ensure messages exist.
message = gapic_types.PubsubMessage(data=b"foobarbaz")
batch.publish(message)
# One response for one published message.
publish_response = gapic_types.PublishResponse(message_ids=["a"])
# Induce publish error.
error = google.api_core.exceptions.InternalServerError("uh oh")
with mock.patch.object(
type(batch.client.api),
"publish",
return_value=publish_response,
side_effect=error,
):
batch._commit()
assert batch_done_callback_tracker.called
assert not batch_done_callback_tracker.success
def test_batch_done_callback_called_on_publish_response_invalid():
batch_done_callback_tracker = BatchDoneCallbackTracker()
batch = create_batch(batch_done_callback=batch_done_callback_tracker)
# Ensure messages exist.
message = gapic_types.PubsubMessage(data=b"foobarbaz")
batch.publish(message)
# No message ids returned in successful publish response -> invalid.
publish_response = gapic_types.PublishResponse(message_ids=[])
with mock.patch.object(
type(batch.client.api), "publish", return_value=publish_response
):
batch._commit()
assert batch_done_callback_tracker.called
assert not batch_done_callback_tracker.success
|
|
tests/unit/pubsub_v1/publisher/batch/test_thread.py
|
google/cloud/pubsub_v1/publisher/_sequencer/unordered_sequencer.py
|
# -*- coding: utf-8 -*-
from branca.element import MacroElement
from folium.elements import JSCSSMixin
from folium.utilities import parse_options
from jinja2 import Template
class MousePosition(JSCSSMixin, MacroElement):
"""Add a field that shows the coordinates of the mouse position.
Uses the Leaflet plugin by Ardhi Lukianto under MIT license.
https://github.com/ardhi/Leaflet.MousePosition
Parameters
----------
position : str, default 'bottomright'
The standard Control position parameter for the widget.
separator : str, default ' : '
Character used to separate latitude and longitude values.
empty_string : str, default 'Unavailable'
Initial text to display.
lng_first : bool, default False
Whether to put the longitude first or not.
Set as True to display longitude before latitude.
num_digits : int, default '5'
Number of decimal places included in the displayed
longitude and latitude decimal degree values.
prefix : str, default ''
A string to be prepended to the coordinates.
lat_formatter : str, default None
Custom Javascript function to format the latitude value.
lng_formatter : str, default None
Custom Javascript function to format the longitude value.
Examples
--------
>>> fmtr = "function(num) {return L.Util.formatNum(num, 3) + ' º ';};"
>>> MousePosition(position='topright', separator=' | ', prefix="Mouse:",
... lat_formatter=fmtr, lng_formatter=fmtr)
"""
_template = Template("""
{% macro script(this, kwargs) %}
var {{ this.get_name() }} = new L.Control.MousePosition(
{{ this.options|tojson }}
);
{{ this.get_name() }}.options["latFormatter"] =
{{ this.lat_formatter }};
{{ this.get_name() }}.options["lngFormatter"] =
{{ this.lng_formatter }};
{{ this._parent.get_name() }}.addControl({{ this.get_name() }});
{% endmacro %}
""")
default_js = [
('Control_MousePosition_js',
'https://cdn.jsdelivr.net/gh/ardhi/Leaflet.MousePosition/src/L.Control.MousePosition.min.js')
]
default_css = [
('Control_MousePosition_css',
'https://cdn.jsdelivr.net/gh/ardhi/Leaflet.MousePosition/src/L.Control.MousePosition.min.css')
]
def __init__(self, position='bottomright', separator=' : ',
empty_string='Unavailable', lng_first=False, num_digits=5,
prefix='', lat_formatter=None, lng_formatter=None, **kwargs):
super(MousePosition, self).__init__()
self._name = 'MousePosition'
self.options = parse_options(
position=position,
separator=separator,
empty_string=empty_string,
lng_first=lng_first,
num_digits=num_digits,
prefix=prefix,
**kwargs
)
self.lat_formatter = lat_formatter or 'undefined'
self.lng_formatter = lng_formatter or 'undefined'
|
# -*- coding: utf-8 -*-
"""
Test HeatMap
------------
"""
import folium
from folium.plugins import HeatMap
from folium.utilities import normalize
from jinja2 import Template
import numpy as np
import pytest
def test_heat_map():
np.random.seed(3141592)
data = (np.random.normal(size=(100, 2)) * np.array([[1, 1]]) +
np.array([[48, 5]]))
m = folium.Map([48., 5.], tiles='stamentoner', zoom_start=6)
hm = HeatMap(data)
m.add_child(hm)
m._repr_html_()
out = normalize(m._parent.render())
# We verify that the script import is present.
script = '<script src="https://cdn.jsdelivr.net/gh/python-visualization/folium@master/folium/templates/leaflet_heat.min.js"></script>' # noqa
assert script in out
# We verify that the script part is correct.
tmpl = Template("""
var {{this.get_name()}} = L.heatLayer(
{{this.data}},
{
minOpacity: {{this.min_opacity}},
maxZoom: {{this.max_zoom}},
radius: {{this.radius}},
blur: {{this.blur}},
gradient: {{this.gradient}}
})
.addTo({{this._parent.get_name()}});
""")
assert tmpl.render(this=hm)
bounds = m.get_bounds()
np.testing.assert_allclose(
bounds,
[[46.218566840847025, 3.0302801394447734],
[50.75345011431167, 7.132453997672826]])
def test_heatmap_data():
data = HeatMap(np.array([[3, 4, 1], [5, 6, 1], [7, 8, 0.5]])).data
assert isinstance(data, list)
assert len(data) == 3
for i in range(len(data)):
assert isinstance(data[i], list)
assert len(data[i]) == 3
def test_heat_map_exception():
with pytest.raises(ValueError):
HeatMap(np.array([[4, 5, 1], [3, 6, np.nan]]))
with pytest.raises(Exception):
HeatMap(np.array([3, 4, 5]))
|
|
tests/plugins/test_heat_map.py
|
folium/plugins/mouse_position.py
|
"""Support for N26 switches."""
import logging
from homeassistant.components.switch import SwitchDevice
from . import DEFAULT_SCAN_INTERVAL, DOMAIN
from .const import CARD_STATE_ACTIVE, CARD_STATE_BLOCKED, DATA
_LOGGER = logging.getLogger(__name__)
SCAN_INTERVAL = DEFAULT_SCAN_INTERVAL
def setup_platform(hass, config, add_entities, discovery_info=None):
"""Set up the N26 switch platform."""
if discovery_info is None:
return
api_list = hass.data[DOMAIN][DATA]
switch_entities = []
for api_data in api_list:
for card in api_data.cards:
switch_entities.append(N26CardSwitch(api_data, card))
add_entities(switch_entities)
class N26CardSwitch(SwitchDevice):
"""Representation of a N26 card block/unblock switch."""
def __init__(self, api_data, card: dict):
"""Initialize the N26 card block/unblock switch."""
self._data = api_data
self._card = card
@property
def unique_id(self):
"""Return the unique ID of the entity."""
return self._card["id"]
@property
def name(self) -> str:
"""Friendly name of the sensor."""
return "card_{}".format(self._card["id"])
@property
def is_on(self):
"""Return true if switch is on."""
return self._card["status"] == CARD_STATE_ACTIVE
def turn_on(self, **kwargs):
"""Block the card."""
self._data.api.unblock_card(self._card["id"])
self._card["status"] = CARD_STATE_ACTIVE
def turn_off(self, **kwargs):
"""Unblock the card."""
self._data.api.block_card(self._card["id"])
self._card["status"] = CARD_STATE_BLOCKED
def update(self):
"""Update the switch state."""
self._data.update_cards()
self._card = self._data.card(self._card["id"], self._card)
|
"""The tests for the demo water_heater component."""
import unittest
import pytest
import voluptuous as vol
from homeassistant.components import water_heater
from homeassistant.setup import setup_component
from homeassistant.util.unit_system import IMPERIAL_SYSTEM
from tests.common import get_test_home_assistant
from tests.components.water_heater import common
ENTITY_WATER_HEATER = "water_heater.demo_water_heater"
ENTITY_WATER_HEATER_CELSIUS = "water_heater.demo_water_heater_celsius"
class TestDemowater_heater(unittest.TestCase):
"""Test the demo water_heater."""
def setUp(self): # pylint: disable=invalid-name
"""Set up things to be run when tests are started."""
self.hass = get_test_home_assistant()
self.hass.config.units = IMPERIAL_SYSTEM
assert setup_component(
self.hass, water_heater.DOMAIN, {"water_heater": {"platform": "demo"}}
)
def tearDown(self): # pylint: disable=invalid-name
"""Stop down everything that was started."""
self.hass.stop()
def test_setup_params(self):
"""Test the initial parameters."""
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert 119 == state.attributes.get("temperature")
assert "off" == state.attributes.get("away_mode")
assert "eco" == state.attributes.get("operation_mode")
def test_default_setup_params(self):
"""Test the setup with default parameters."""
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert 110 == state.attributes.get("min_temp")
assert 140 == state.attributes.get("max_temp")
def test_set_only_target_temp_bad_attr(self):
"""Test setting the target temperature without required attribute."""
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert 119 == state.attributes.get("temperature")
with pytest.raises(vol.Invalid):
common.set_temperature(self.hass, None, ENTITY_WATER_HEATER)
self.hass.block_till_done()
assert 119 == state.attributes.get("temperature")
def test_set_only_target_temp(self):
"""Test the setting of the target temperature."""
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert 119 == state.attributes.get("temperature")
common.set_temperature(self.hass, 110, ENTITY_WATER_HEATER)
self.hass.block_till_done()
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert 110 == state.attributes.get("temperature")
def test_set_operation_bad_attr_and_state(self):
"""Test setting operation mode without required attribute.
Also check the state.
"""
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert "eco" == state.attributes.get("operation_mode")
assert "eco" == state.state
with pytest.raises(vol.Invalid):
common.set_operation_mode(self.hass, None, ENTITY_WATER_HEATER)
self.hass.block_till_done()
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert "eco" == state.attributes.get("operation_mode")
assert "eco" == state.state
def test_set_operation(self):
"""Test setting of new operation mode."""
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert "eco" == state.attributes.get("operation_mode")
assert "eco" == state.state
common.set_operation_mode(self.hass, "electric", ENTITY_WATER_HEATER)
self.hass.block_till_done()
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert "electric" == state.attributes.get("operation_mode")
assert "electric" == state.state
def test_set_away_mode_bad_attr(self):
"""Test setting the away mode without required attribute."""
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert "off" == state.attributes.get("away_mode")
with pytest.raises(vol.Invalid):
common.set_away_mode(self.hass, None, ENTITY_WATER_HEATER)
self.hass.block_till_done()
assert "off" == state.attributes.get("away_mode")
def test_set_away_mode_on(self):
"""Test setting the away mode on/true."""
common.set_away_mode(self.hass, True, ENTITY_WATER_HEATER)
self.hass.block_till_done()
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert "on" == state.attributes.get("away_mode")
def test_set_away_mode_off(self):
"""Test setting the away mode off/false."""
common.set_away_mode(self.hass, False, ENTITY_WATER_HEATER_CELSIUS)
self.hass.block_till_done()
state = self.hass.states.get(ENTITY_WATER_HEATER_CELSIUS)
assert "off" == state.attributes.get("away_mode")
def test_set_only_target_temp_with_convert(self):
"""Test the setting of the target temperature."""
state = self.hass.states.get(ENTITY_WATER_HEATER_CELSIUS)
assert 113 == state.attributes.get("temperature")
common.set_temperature(self.hass, 114, ENTITY_WATER_HEATER_CELSIUS)
self.hass.block_till_done()
state = self.hass.states.get(ENTITY_WATER_HEATER_CELSIUS)
assert 114 == state.attributes.get("temperature")
|
|
tests/components/demo/test_water_heater.py
|
homeassistant/components/n26/switch.py
|
"""Support for the Dyson 360 eye vacuum cleaner robot."""
import logging
from libpurecool.const import Dyson360EyeMode, PowerMode
from libpurecool.dyson_360_eye import Dyson360Eye
from homeassistant.components.vacuum import (
SUPPORT_BATTERY,
SUPPORT_FAN_SPEED,
SUPPORT_PAUSE,
SUPPORT_RETURN_HOME,
SUPPORT_STATUS,
SUPPORT_STOP,
SUPPORT_TURN_OFF,
SUPPORT_TURN_ON,
VacuumDevice,
)
from homeassistant.helpers.icon import icon_for_battery_level
from . import DYSON_DEVICES
_LOGGER = logging.getLogger(__name__)
ATTR_CLEAN_ID = "clean_id"
ATTR_FULL_CLEAN_TYPE = "full_clean_type"
ATTR_POSITION = "position"
DYSON_360_EYE_DEVICES = "dyson_360_eye_devices"
SUPPORT_DYSON = (
SUPPORT_TURN_ON
| SUPPORT_TURN_OFF
| SUPPORT_PAUSE
| SUPPORT_RETURN_HOME
| SUPPORT_FAN_SPEED
| SUPPORT_STATUS
| SUPPORT_BATTERY
| SUPPORT_STOP
)
def setup_platform(hass, config, add_entities, discovery_info=None):
"""Set up the Dyson 360 Eye robot vacuum platform."""
_LOGGER.debug("Creating new Dyson 360 Eye robot vacuum")
if DYSON_360_EYE_DEVICES not in hass.data:
hass.data[DYSON_360_EYE_DEVICES] = []
# Get Dyson Devices from parent component
for device in [d for d in hass.data[DYSON_DEVICES] if isinstance(d, Dyson360Eye)]:
dyson_entity = Dyson360EyeDevice(device)
hass.data[DYSON_360_EYE_DEVICES].append(dyson_entity)
add_entities(hass.data[DYSON_360_EYE_DEVICES])
return True
class Dyson360EyeDevice(VacuumDevice):
"""Dyson 360 Eye robot vacuum device."""
def __init__(self, device):
"""Dyson 360 Eye robot vacuum device."""
_LOGGER.debug("Creating device %s", device.name)
self._device = device
async def async_added_to_hass(self):
"""Call when entity is added to hass."""
self.hass.async_add_job(self._device.add_message_listener, self.on_message)
def on_message(self, message):
"""Handle a new messages that was received from the vacuum."""
_LOGGER.debug("Message received for %s device: %s", self.name, message)
self.schedule_update_ha_state()
@property
def should_poll(self) -> bool:
"""Return True if entity has to be polled for state.
False if entity pushes its state to HA.
"""
return False
@property
def name(self):
"""Return the name of the device."""
return self._device.name
@property
def status(self):
"""Return the status of the vacuum cleaner."""
dyson_labels = {
Dyson360EyeMode.INACTIVE_CHARGING: "Stopped - Charging",
Dyson360EyeMode.INACTIVE_CHARGED: "Stopped - Charged",
Dyson360EyeMode.FULL_CLEAN_PAUSED: "Paused",
Dyson360EyeMode.FULL_CLEAN_RUNNING: "Cleaning",
Dyson360EyeMode.FULL_CLEAN_ABORTED: "Returning home",
Dyson360EyeMode.FULL_CLEAN_INITIATED: "Start cleaning",
Dyson360EyeMode.FAULT_USER_RECOVERABLE: "Error - device blocked",
Dyson360EyeMode.FAULT_REPLACE_ON_DOCK: "Error - Replace device on dock",
Dyson360EyeMode.FULL_CLEAN_FINISHED: "Finished",
Dyson360EyeMode.FULL_CLEAN_NEEDS_CHARGE: "Need charging",
}
return dyson_labels.get(self._device.state.state, self._device.state.state)
@property
def battery_level(self):
"""Return the battery level of the vacuum cleaner."""
return self._device.state.battery_level
@property
def fan_speed(self):
"""Return the fan speed of the vacuum cleaner."""
speed_labels = {PowerMode.MAX: "Max", PowerMode.QUIET: "Quiet"}
return speed_labels[self._device.state.power_mode]
@property
def fan_speed_list(self):
"""Get the list of available fan speed steps of the vacuum cleaner."""
return ["Quiet", "Max"]
@property
def device_state_attributes(self):
"""Return the specific state attributes of this vacuum cleaner."""
return {ATTR_POSITION: str(self._device.state.position)}
@property
def is_on(self) -> bool:
"""Return True if entity is on."""
return self._device.state.state in [
Dyson360EyeMode.FULL_CLEAN_INITIATED,
Dyson360EyeMode.FULL_CLEAN_ABORTED,
Dyson360EyeMode.FULL_CLEAN_RUNNING,
]
@property
def available(self) -> bool:
"""Return True if entity is available."""
return True
@property
def supported_features(self):
"""Flag vacuum cleaner robot features that are supported."""
return SUPPORT_DYSON
@property
def battery_icon(self):
"""Return the battery icon for the vacuum cleaner."""
charging = self._device.state.state in [Dyson360EyeMode.INACTIVE_CHARGING]
return icon_for_battery_level(
battery_level=self.battery_level, charging=charging
)
def turn_on(self, **kwargs):
"""Turn the vacuum on."""
_LOGGER.debug("Turn on device %s", self.name)
if self._device.state.state in [Dyson360EyeMode.FULL_CLEAN_PAUSED]:
self._device.resume()
else:
self._device.start()
def turn_off(self, **kwargs):
"""Turn the vacuum off and return to home."""
_LOGGER.debug("Turn off device %s", self.name)
self._device.pause()
def stop(self, **kwargs):
"""Stop the vacuum cleaner."""
_LOGGER.debug("Stop device %s", self.name)
self._device.pause()
def set_fan_speed(self, fan_speed, **kwargs):
"""Set fan speed."""
_LOGGER.debug("Set fan speed %s on device %s", fan_speed, self.name)
power_modes = {"Quiet": PowerMode.QUIET, "Max": PowerMode.MAX}
self._device.set_power_mode(power_modes[fan_speed])
def start_pause(self, **kwargs):
"""Start, pause or resume the cleaning task."""
if self._device.state.state in [Dyson360EyeMode.FULL_CLEAN_PAUSED]:
_LOGGER.debug("Resume device %s", self.name)
self._device.resume()
elif self._device.state.state in [
Dyson360EyeMode.INACTIVE_CHARGED,
Dyson360EyeMode.INACTIVE_CHARGING,
]:
_LOGGER.debug("Start device %s", self.name)
self._device.start()
else:
_LOGGER.debug("Pause device %s", self.name)
self._device.pause()
def return_to_base(self, **kwargs):
"""Set the vacuum cleaner to return to the dock."""
_LOGGER.debug("Return to base device %s", self.name)
self._device.abort()
|
"""The tests for the demo water_heater component."""
import unittest
import pytest
import voluptuous as vol
from homeassistant.components import water_heater
from homeassistant.setup import setup_component
from homeassistant.util.unit_system import IMPERIAL_SYSTEM
from tests.common import get_test_home_assistant
from tests.components.water_heater import common
ENTITY_WATER_HEATER = "water_heater.demo_water_heater"
ENTITY_WATER_HEATER_CELSIUS = "water_heater.demo_water_heater_celsius"
class TestDemowater_heater(unittest.TestCase):
"""Test the demo water_heater."""
def setUp(self): # pylint: disable=invalid-name
"""Set up things to be run when tests are started."""
self.hass = get_test_home_assistant()
self.hass.config.units = IMPERIAL_SYSTEM
assert setup_component(
self.hass, water_heater.DOMAIN, {"water_heater": {"platform": "demo"}}
)
def tearDown(self): # pylint: disable=invalid-name
"""Stop down everything that was started."""
self.hass.stop()
def test_setup_params(self):
"""Test the initial parameters."""
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert 119 == state.attributes.get("temperature")
assert "off" == state.attributes.get("away_mode")
assert "eco" == state.attributes.get("operation_mode")
def test_default_setup_params(self):
"""Test the setup with default parameters."""
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert 110 == state.attributes.get("min_temp")
assert 140 == state.attributes.get("max_temp")
def test_set_only_target_temp_bad_attr(self):
"""Test setting the target temperature without required attribute."""
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert 119 == state.attributes.get("temperature")
with pytest.raises(vol.Invalid):
common.set_temperature(self.hass, None, ENTITY_WATER_HEATER)
self.hass.block_till_done()
assert 119 == state.attributes.get("temperature")
def test_set_only_target_temp(self):
"""Test the setting of the target temperature."""
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert 119 == state.attributes.get("temperature")
common.set_temperature(self.hass, 110, ENTITY_WATER_HEATER)
self.hass.block_till_done()
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert 110 == state.attributes.get("temperature")
def test_set_operation_bad_attr_and_state(self):
"""Test setting operation mode without required attribute.
Also check the state.
"""
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert "eco" == state.attributes.get("operation_mode")
assert "eco" == state.state
with pytest.raises(vol.Invalid):
common.set_operation_mode(self.hass, None, ENTITY_WATER_HEATER)
self.hass.block_till_done()
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert "eco" == state.attributes.get("operation_mode")
assert "eco" == state.state
def test_set_operation(self):
"""Test setting of new operation mode."""
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert "eco" == state.attributes.get("operation_mode")
assert "eco" == state.state
common.set_operation_mode(self.hass, "electric", ENTITY_WATER_HEATER)
self.hass.block_till_done()
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert "electric" == state.attributes.get("operation_mode")
assert "electric" == state.state
def test_set_away_mode_bad_attr(self):
"""Test setting the away mode without required attribute."""
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert "off" == state.attributes.get("away_mode")
with pytest.raises(vol.Invalid):
common.set_away_mode(self.hass, None, ENTITY_WATER_HEATER)
self.hass.block_till_done()
assert "off" == state.attributes.get("away_mode")
def test_set_away_mode_on(self):
"""Test setting the away mode on/true."""
common.set_away_mode(self.hass, True, ENTITY_WATER_HEATER)
self.hass.block_till_done()
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert "on" == state.attributes.get("away_mode")
def test_set_away_mode_off(self):
"""Test setting the away mode off/false."""
common.set_away_mode(self.hass, False, ENTITY_WATER_HEATER_CELSIUS)
self.hass.block_till_done()
state = self.hass.states.get(ENTITY_WATER_HEATER_CELSIUS)
assert "off" == state.attributes.get("away_mode")
def test_set_only_target_temp_with_convert(self):
"""Test the setting of the target temperature."""
state = self.hass.states.get(ENTITY_WATER_HEATER_CELSIUS)
assert 113 == state.attributes.get("temperature")
common.set_temperature(self.hass, 114, ENTITY_WATER_HEATER_CELSIUS)
self.hass.block_till_done()
state = self.hass.states.get(ENTITY_WATER_HEATER_CELSIUS)
assert 114 == state.attributes.get("temperature")
|
|
tests/components/demo/test_water_heater.py
|
homeassistant/components/dyson/vacuum.py
|
"""Support for monitoring an SABnzbd NZB client."""
from datetime import timedelta
import logging
from pysabnzbd import SabnzbdApi, SabnzbdApiException
import voluptuous as vol
from homeassistant.components.discovery import SERVICE_SABNZBD
from homeassistant.const import (
CONF_API_KEY,
CONF_HOST,
CONF_NAME,
CONF_PATH,
CONF_PORT,
CONF_SENSORS,
CONF_SSL,
)
from homeassistant.core import callback
from homeassistant.helpers import discovery
from homeassistant.helpers.aiohttp_client import async_get_clientsession
import homeassistant.helpers.config_validation as cv
from homeassistant.helpers.dispatcher import async_dispatcher_send
from homeassistant.helpers.event import async_track_time_interval
from homeassistant.util.json import load_json, save_json
_LOGGER = logging.getLogger(__name__)
DOMAIN = "sabnzbd"
DATA_SABNZBD = "sabznbd"
_CONFIGURING = {}
ATTR_SPEED = "speed"
BASE_URL_FORMAT = "{}://{}:{}/"
CONFIG_FILE = "sabnzbd.conf"
DEFAULT_HOST = "localhost"
DEFAULT_NAME = "SABnzbd"
DEFAULT_PORT = 8080
DEFAULT_SPEED_LIMIT = "100"
DEFAULT_SSL = False
UPDATE_INTERVAL = timedelta(seconds=30)
SERVICE_PAUSE = "pause"
SERVICE_RESUME = "resume"
SERVICE_SET_SPEED = "set_speed"
SIGNAL_SABNZBD_UPDATED = "sabnzbd_updated"
SENSOR_TYPES = {
"current_status": ["Status", None, "status"],
"speed": ["Speed", "MB/s", "kbpersec"],
"queue_size": ["Queue", "MB", "mb"],
"queue_remaining": ["Left", "MB", "mbleft"],
"disk_size": ["Disk", "GB", "diskspacetotal1"],
"disk_free": ["Disk Free", "GB", "diskspace1"],
"queue_count": ["Queue Count", None, "noofslots_total"],
"day_size": ["Daily Total", "GB", "day_size"],
"week_size": ["Weekly Total", "GB", "week_size"],
"month_size": ["Monthly Total", "GB", "month_size"],
"total_size": ["Total", "GB", "total_size"],
}
SPEED_LIMIT_SCHEMA = vol.Schema(
{vol.Optional(ATTR_SPEED, default=DEFAULT_SPEED_LIMIT): cv.string}
)
CONFIG_SCHEMA = vol.Schema(
{
DOMAIN: vol.Schema(
{
vol.Required(CONF_API_KEY): cv.string,
vol.Optional(CONF_HOST, default=DEFAULT_HOST): cv.string,
vol.Optional(CONF_PATH): cv.string,
vol.Optional(CONF_NAME, default=DEFAULT_NAME): cv.string,
vol.Optional(CONF_PORT, default=DEFAULT_PORT): cv.port,
vol.Optional(CONF_SENSORS): vol.All(
cv.ensure_list, [vol.In(SENSOR_TYPES)]
),
vol.Optional(CONF_SSL, default=DEFAULT_SSL): cv.boolean,
}
)
},
extra=vol.ALLOW_EXTRA,
)
async def async_check_sabnzbd(sab_api):
"""Check if we can reach SABnzbd."""
try:
await sab_api.check_available()
return True
except SabnzbdApiException:
_LOGGER.error("Connection to SABnzbd API failed")
return False
async def async_configure_sabnzbd(
hass, config, use_ssl, name=DEFAULT_NAME, api_key=None
):
"""Try to configure Sabnzbd and request api key if configuration fails."""
host = config[CONF_HOST]
port = config[CONF_PORT]
web_root = config.get(CONF_PATH)
uri_scheme = "https" if use_ssl else "http"
base_url = BASE_URL_FORMAT.format(uri_scheme, host, port)
if api_key is None:
conf = await hass.async_add_job(load_json, hass.config.path(CONFIG_FILE))
api_key = conf.get(base_url, {}).get(CONF_API_KEY, "")
sab_api = SabnzbdApi(
base_url, api_key, web_root=web_root, session=async_get_clientsession(hass)
)
if await async_check_sabnzbd(sab_api):
async_setup_sabnzbd(hass, sab_api, config, name)
else:
async_request_configuration(hass, config, base_url, web_root)
async def async_setup(hass, config):
"""Set up the SABnzbd component."""
async def sabnzbd_discovered(service, info):
"""Handle service discovery."""
ssl = info.get("properties", {}).get("https", "0") == "1"
await async_configure_sabnzbd(hass, info, ssl)
discovery.async_listen(hass, SERVICE_SABNZBD, sabnzbd_discovered)
conf = config.get(DOMAIN)
if conf is not None:
use_ssl = conf.get(CONF_SSL)
name = conf.get(CONF_NAME)
api_key = conf.get(CONF_API_KEY)
await async_configure_sabnzbd(hass, conf, use_ssl, name, api_key)
return True
@callback
def async_setup_sabnzbd(hass, sab_api, config, name):
"""Set up SABnzbd sensors and services."""
sab_api_data = SabnzbdApiData(sab_api, name, config.get(CONF_SENSORS, {}))
if config.get(CONF_SENSORS):
hass.data[DATA_SABNZBD] = sab_api_data
hass.async_create_task(
discovery.async_load_platform(hass, "sensor", DOMAIN, {}, config)
)
async def async_service_handler(service):
"""Handle service calls."""
if service.service == SERVICE_PAUSE:
await sab_api_data.async_pause_queue()
elif service.service == SERVICE_RESUME:
await sab_api_data.async_resume_queue()
elif service.service == SERVICE_SET_SPEED:
speed = service.data.get(ATTR_SPEED)
await sab_api_data.async_set_queue_speed(speed)
hass.services.async_register(
DOMAIN, SERVICE_PAUSE, async_service_handler, schema=vol.Schema({})
)
hass.services.async_register(
DOMAIN, SERVICE_RESUME, async_service_handler, schema=vol.Schema({})
)
hass.services.async_register(
DOMAIN, SERVICE_SET_SPEED, async_service_handler, schema=SPEED_LIMIT_SCHEMA
)
async def async_update_sabnzbd(now):
"""Refresh SABnzbd queue data."""
try:
await sab_api.refresh_data()
async_dispatcher_send(hass, SIGNAL_SABNZBD_UPDATED, None)
except SabnzbdApiException as err:
_LOGGER.error(err)
async_track_time_interval(hass, async_update_sabnzbd, UPDATE_INTERVAL)
@callback
def async_request_configuration(hass, config, host, web_root):
"""Request configuration steps from the user."""
configurator = hass.components.configurator
# We got an error if this method is called while we are configuring
if host in _CONFIGURING:
configurator.async_notify_errors(
_CONFIGURING[host], "Failed to register, please try again."
)
return
async def async_configuration_callback(data):
"""Handle configuration changes."""
api_key = data.get(CONF_API_KEY)
sab_api = SabnzbdApi(
host, api_key, web_root=web_root, session=async_get_clientsession(hass)
)
if not await async_check_sabnzbd(sab_api):
return
def success():
"""Signal successful setup."""
conf = load_json(hass.config.path(CONFIG_FILE))
conf[host] = {CONF_API_KEY: api_key}
save_json(hass.config.path(CONFIG_FILE), conf)
req_config = _CONFIGURING.pop(host)
configurator.request_done(req_config)
hass.async_add_job(success)
async_setup_sabnzbd(hass, sab_api, config, config.get(CONF_NAME, DEFAULT_NAME))
_CONFIGURING[host] = configurator.async_request_config(
DEFAULT_NAME,
async_configuration_callback,
description="Enter the API Key",
submit_caption="Confirm",
fields=[{"id": CONF_API_KEY, "name": "API Key", "type": ""}],
)
class SabnzbdApiData:
"""Class for storing/refreshing sabnzbd api queue data."""
def __init__(self, sab_api, name, sensors):
"""Initialize component."""
self.sab_api = sab_api
self.name = name
self.sensors = sensors
async def async_pause_queue(self):
"""Pause Sabnzbd queue."""
try:
return await self.sab_api.pause_queue()
except SabnzbdApiException as err:
_LOGGER.error(err)
return False
async def async_resume_queue(self):
"""Resume Sabnzbd queue."""
try:
return await self.sab_api.resume_queue()
except SabnzbdApiException as err:
_LOGGER.error(err)
return False
async def async_set_queue_speed(self, limit):
"""Set speed limit for the Sabnzbd queue."""
try:
return await self.sab_api.set_speed_limit(limit)
except SabnzbdApiException as err:
_LOGGER.error(err)
return False
def get_queue_field(self, field):
"""Return the value for the given field from the Sabnzbd queue."""
return self.sab_api.queue.get(field)
|
"""The tests for the demo water_heater component."""
import unittest
import pytest
import voluptuous as vol
from homeassistant.components import water_heater
from homeassistant.setup import setup_component
from homeassistant.util.unit_system import IMPERIAL_SYSTEM
from tests.common import get_test_home_assistant
from tests.components.water_heater import common
ENTITY_WATER_HEATER = "water_heater.demo_water_heater"
ENTITY_WATER_HEATER_CELSIUS = "water_heater.demo_water_heater_celsius"
class TestDemowater_heater(unittest.TestCase):
"""Test the demo water_heater."""
def setUp(self): # pylint: disable=invalid-name
"""Set up things to be run when tests are started."""
self.hass = get_test_home_assistant()
self.hass.config.units = IMPERIAL_SYSTEM
assert setup_component(
self.hass, water_heater.DOMAIN, {"water_heater": {"platform": "demo"}}
)
def tearDown(self): # pylint: disable=invalid-name
"""Stop down everything that was started."""
self.hass.stop()
def test_setup_params(self):
"""Test the initial parameters."""
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert 119 == state.attributes.get("temperature")
assert "off" == state.attributes.get("away_mode")
assert "eco" == state.attributes.get("operation_mode")
def test_default_setup_params(self):
"""Test the setup with default parameters."""
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert 110 == state.attributes.get("min_temp")
assert 140 == state.attributes.get("max_temp")
def test_set_only_target_temp_bad_attr(self):
"""Test setting the target temperature without required attribute."""
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert 119 == state.attributes.get("temperature")
with pytest.raises(vol.Invalid):
common.set_temperature(self.hass, None, ENTITY_WATER_HEATER)
self.hass.block_till_done()
assert 119 == state.attributes.get("temperature")
def test_set_only_target_temp(self):
"""Test the setting of the target temperature."""
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert 119 == state.attributes.get("temperature")
common.set_temperature(self.hass, 110, ENTITY_WATER_HEATER)
self.hass.block_till_done()
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert 110 == state.attributes.get("temperature")
def test_set_operation_bad_attr_and_state(self):
"""Test setting operation mode without required attribute.
Also check the state.
"""
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert "eco" == state.attributes.get("operation_mode")
assert "eco" == state.state
with pytest.raises(vol.Invalid):
common.set_operation_mode(self.hass, None, ENTITY_WATER_HEATER)
self.hass.block_till_done()
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert "eco" == state.attributes.get("operation_mode")
assert "eco" == state.state
def test_set_operation(self):
"""Test setting of new operation mode."""
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert "eco" == state.attributes.get("operation_mode")
assert "eco" == state.state
common.set_operation_mode(self.hass, "electric", ENTITY_WATER_HEATER)
self.hass.block_till_done()
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert "electric" == state.attributes.get("operation_mode")
assert "electric" == state.state
def test_set_away_mode_bad_attr(self):
"""Test setting the away mode without required attribute."""
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert "off" == state.attributes.get("away_mode")
with pytest.raises(vol.Invalid):
common.set_away_mode(self.hass, None, ENTITY_WATER_HEATER)
self.hass.block_till_done()
assert "off" == state.attributes.get("away_mode")
def test_set_away_mode_on(self):
"""Test setting the away mode on/true."""
common.set_away_mode(self.hass, True, ENTITY_WATER_HEATER)
self.hass.block_till_done()
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert "on" == state.attributes.get("away_mode")
def test_set_away_mode_off(self):
"""Test setting the away mode off/false."""
common.set_away_mode(self.hass, False, ENTITY_WATER_HEATER_CELSIUS)
self.hass.block_till_done()
state = self.hass.states.get(ENTITY_WATER_HEATER_CELSIUS)
assert "off" == state.attributes.get("away_mode")
def test_set_only_target_temp_with_convert(self):
"""Test the setting of the target temperature."""
state = self.hass.states.get(ENTITY_WATER_HEATER_CELSIUS)
assert 113 == state.attributes.get("temperature")
common.set_temperature(self.hass, 114, ENTITY_WATER_HEATER_CELSIUS)
self.hass.block_till_done()
state = self.hass.states.get(ENTITY_WATER_HEATER_CELSIUS)
assert 114 == state.attributes.get("temperature")
|
|
tests/components/demo/test_water_heater.py
|
homeassistant/components/sabnzbd/__init__.py
|
"""Support for Satel Integra devices."""
import collections
import logging
from satel_integra.satel_integra import AsyncSatel
import voluptuous as vol
from homeassistant.const import CONF_HOST, CONF_PORT, EVENT_HOMEASSISTANT_STOP
from homeassistant.core import callback
from homeassistant.helpers import config_validation as cv
from homeassistant.helpers.discovery import async_load_platform
from homeassistant.helpers.dispatcher import async_dispatcher_send
DEFAULT_ALARM_NAME = "satel_integra"
DEFAULT_PORT = 7094
DEFAULT_CONF_ARM_HOME_MODE = 1
DEFAULT_DEVICE_PARTITION = 1
DEFAULT_ZONE_TYPE = "motion"
_LOGGER = logging.getLogger(__name__)
DOMAIN = "satel_integra"
DATA_SATEL = "satel_integra"
CONF_DEVICE_CODE = "code"
CONF_DEVICE_PARTITIONS = "partitions"
CONF_ARM_HOME_MODE = "arm_home_mode"
CONF_ZONE_NAME = "name"
CONF_ZONE_TYPE = "type"
CONF_ZONES = "zones"
CONF_OUTPUTS = "outputs"
CONF_SWITCHABLE_OUTPUTS = "switchable_outputs"
ZONES = "zones"
SIGNAL_PANEL_MESSAGE = "satel_integra.panel_message"
SIGNAL_PANEL_ARM_AWAY = "satel_integra.panel_arm_away"
SIGNAL_PANEL_ARM_HOME = "satel_integra.panel_arm_home"
SIGNAL_PANEL_DISARM = "satel_integra.panel_disarm"
SIGNAL_ZONES_UPDATED = "satel_integra.zones_updated"
SIGNAL_OUTPUTS_UPDATED = "satel_integra.outputs_updated"
ZONE_SCHEMA = vol.Schema(
{
vol.Required(CONF_ZONE_NAME): cv.string,
vol.Optional(CONF_ZONE_TYPE, default=DEFAULT_ZONE_TYPE): cv.string,
}
)
EDITABLE_OUTPUT_SCHEMA = vol.Schema({vol.Required(CONF_ZONE_NAME): cv.string})
PARTITION_SCHEMA = vol.Schema(
{
vol.Required(CONF_ZONE_NAME): cv.string,
vol.Optional(CONF_ARM_HOME_MODE, default=DEFAULT_CONF_ARM_HOME_MODE): vol.In(
[1, 2, 3]
),
}
)
def is_alarm_code_necessary(value):
"""Check if alarm code must be configured."""
if value.get(CONF_SWITCHABLE_OUTPUTS) and CONF_DEVICE_CODE not in value:
raise vol.Invalid(
"You need to specify alarm " " code to use switchable_outputs"
)
return value
CONFIG_SCHEMA = vol.Schema(
{
DOMAIN: vol.All(
{
vol.Required(CONF_HOST): cv.string,
vol.Optional(CONF_PORT, default=DEFAULT_PORT): cv.port,
vol.Optional(CONF_DEVICE_CODE): cv.string,
vol.Optional(CONF_DEVICE_PARTITIONS, default={}): {
vol.Coerce(int): PARTITION_SCHEMA
},
vol.Optional(CONF_ZONES, default={}): {vol.Coerce(int): ZONE_SCHEMA},
vol.Optional(CONF_OUTPUTS, default={}): {vol.Coerce(int): ZONE_SCHEMA},
vol.Optional(CONF_SWITCHABLE_OUTPUTS, default={}): {
vol.Coerce(int): EDITABLE_OUTPUT_SCHEMA
},
},
is_alarm_code_necessary,
)
},
extra=vol.ALLOW_EXTRA,
)
async def async_setup(hass, config):
"""Set up the Satel Integra component."""
conf = config.get(DOMAIN)
zones = conf.get(CONF_ZONES)
outputs = conf.get(CONF_OUTPUTS)
switchable_outputs = conf.get(CONF_SWITCHABLE_OUTPUTS)
host = conf.get(CONF_HOST)
port = conf.get(CONF_PORT)
partitions = conf.get(CONF_DEVICE_PARTITIONS)
monitored_outputs = collections.OrderedDict(
list(outputs.items()) + list(switchable_outputs.items())
)
controller = AsyncSatel(host, port, hass.loop, zones, monitored_outputs, partitions)
hass.data[DATA_SATEL] = controller
result = await controller.connect()
if not result:
return False
async def _close():
controller.close()
hass.bus.async_listen_once(EVENT_HOMEASSISTANT_STOP, _close())
_LOGGER.debug("Arm home config: %s, mode: %s ", conf, conf.get(CONF_ARM_HOME_MODE))
hass.async_create_task(
async_load_platform(hass, "alarm_control_panel", DOMAIN, conf, config)
)
hass.async_create_task(
async_load_platform(
hass,
"binary_sensor",
DOMAIN,
{CONF_ZONES: zones, CONF_OUTPUTS: outputs},
config,
)
)
hass.async_create_task(
async_load_platform(
hass,
"switch",
DOMAIN,
{
CONF_SWITCHABLE_OUTPUTS: switchable_outputs,
CONF_DEVICE_CODE: conf.get(CONF_DEVICE_CODE),
},
config,
)
)
@callback
def alarm_status_update_callback():
"""Send status update received from alarm to home assistant."""
_LOGGER.debug("Sending request to update panel state")
async_dispatcher_send(hass, SIGNAL_PANEL_MESSAGE)
@callback
def zones_update_callback(status):
"""Update zone objects as per notification from the alarm."""
_LOGGER.debug("Zones callback, status: %s", status)
async_dispatcher_send(hass, SIGNAL_ZONES_UPDATED, status[ZONES])
@callback
def outputs_update_callback(status):
"""Update zone objects as per notification from the alarm."""
_LOGGER.debug("Outputs updated callback , status: %s", status)
async_dispatcher_send(hass, SIGNAL_OUTPUTS_UPDATED, status["outputs"])
# Create a task instead of adding a tracking job, since this task will
# run until the connection to satel_integra is closed.
hass.loop.create_task(controller.keep_alive())
hass.loop.create_task(
controller.monitor_status(
alarm_status_update_callback, zones_update_callback, outputs_update_callback
)
)
return True
|
"""The tests for the demo water_heater component."""
import unittest
import pytest
import voluptuous as vol
from homeassistant.components import water_heater
from homeassistant.setup import setup_component
from homeassistant.util.unit_system import IMPERIAL_SYSTEM
from tests.common import get_test_home_assistant
from tests.components.water_heater import common
ENTITY_WATER_HEATER = "water_heater.demo_water_heater"
ENTITY_WATER_HEATER_CELSIUS = "water_heater.demo_water_heater_celsius"
class TestDemowater_heater(unittest.TestCase):
"""Test the demo water_heater."""
def setUp(self): # pylint: disable=invalid-name
"""Set up things to be run when tests are started."""
self.hass = get_test_home_assistant()
self.hass.config.units = IMPERIAL_SYSTEM
assert setup_component(
self.hass, water_heater.DOMAIN, {"water_heater": {"platform": "demo"}}
)
def tearDown(self): # pylint: disable=invalid-name
"""Stop down everything that was started."""
self.hass.stop()
def test_setup_params(self):
"""Test the initial parameters."""
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert 119 == state.attributes.get("temperature")
assert "off" == state.attributes.get("away_mode")
assert "eco" == state.attributes.get("operation_mode")
def test_default_setup_params(self):
"""Test the setup with default parameters."""
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert 110 == state.attributes.get("min_temp")
assert 140 == state.attributes.get("max_temp")
def test_set_only_target_temp_bad_attr(self):
"""Test setting the target temperature without required attribute."""
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert 119 == state.attributes.get("temperature")
with pytest.raises(vol.Invalid):
common.set_temperature(self.hass, None, ENTITY_WATER_HEATER)
self.hass.block_till_done()
assert 119 == state.attributes.get("temperature")
def test_set_only_target_temp(self):
"""Test the setting of the target temperature."""
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert 119 == state.attributes.get("temperature")
common.set_temperature(self.hass, 110, ENTITY_WATER_HEATER)
self.hass.block_till_done()
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert 110 == state.attributes.get("temperature")
def test_set_operation_bad_attr_and_state(self):
"""Test setting operation mode without required attribute.
Also check the state.
"""
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert "eco" == state.attributes.get("operation_mode")
assert "eco" == state.state
with pytest.raises(vol.Invalid):
common.set_operation_mode(self.hass, None, ENTITY_WATER_HEATER)
self.hass.block_till_done()
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert "eco" == state.attributes.get("operation_mode")
assert "eco" == state.state
def test_set_operation(self):
"""Test setting of new operation mode."""
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert "eco" == state.attributes.get("operation_mode")
assert "eco" == state.state
common.set_operation_mode(self.hass, "electric", ENTITY_WATER_HEATER)
self.hass.block_till_done()
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert "electric" == state.attributes.get("operation_mode")
assert "electric" == state.state
def test_set_away_mode_bad_attr(self):
"""Test setting the away mode without required attribute."""
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert "off" == state.attributes.get("away_mode")
with pytest.raises(vol.Invalid):
common.set_away_mode(self.hass, None, ENTITY_WATER_HEATER)
self.hass.block_till_done()
assert "off" == state.attributes.get("away_mode")
def test_set_away_mode_on(self):
"""Test setting the away mode on/true."""
common.set_away_mode(self.hass, True, ENTITY_WATER_HEATER)
self.hass.block_till_done()
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert "on" == state.attributes.get("away_mode")
def test_set_away_mode_off(self):
"""Test setting the away mode off/false."""
common.set_away_mode(self.hass, False, ENTITY_WATER_HEATER_CELSIUS)
self.hass.block_till_done()
state = self.hass.states.get(ENTITY_WATER_HEATER_CELSIUS)
assert "off" == state.attributes.get("away_mode")
def test_set_only_target_temp_with_convert(self):
"""Test the setting of the target temperature."""
state = self.hass.states.get(ENTITY_WATER_HEATER_CELSIUS)
assert 113 == state.attributes.get("temperature")
common.set_temperature(self.hass, 114, ENTITY_WATER_HEATER_CELSIUS)
self.hass.block_till_done()
state = self.hass.states.get(ENTITY_WATER_HEATER_CELSIUS)
assert 114 == state.attributes.get("temperature")
|
|
tests/components/demo/test_water_heater.py
|
homeassistant/components/satel_integra/__init__.py
|
"""Mail (SMTP) notification service."""
from email.mime.application import MIMEApplication
from email.mime.image import MIMEImage
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
import email.utils
import logging
import os
import smtplib
import voluptuous as vol
from homeassistant.components.notify import (
ATTR_DATA,
ATTR_TITLE,
ATTR_TITLE_DEFAULT,
PLATFORM_SCHEMA,
BaseNotificationService,
)
from homeassistant.const import (
CONF_PASSWORD,
CONF_PORT,
CONF_RECIPIENT,
CONF_SENDER,
CONF_TIMEOUT,
CONF_USERNAME,
)
import homeassistant.helpers.config_validation as cv
import homeassistant.util.dt as dt_util
_LOGGER = logging.getLogger(__name__)
ATTR_IMAGES = "images" # optional embedded image file attachments
ATTR_HTML = "html"
CONF_ENCRYPTION = "encryption"
CONF_DEBUG = "debug"
CONF_SERVER = "server"
CONF_SENDER_NAME = "sender_name"
DEFAULT_HOST = "localhost"
DEFAULT_PORT = 587
DEFAULT_TIMEOUT = 5
DEFAULT_DEBUG = False
DEFAULT_ENCRYPTION = "starttls"
ENCRYPTION_OPTIONS = ["tls", "starttls", "none"]
# pylint: disable=no-value-for-parameter
PLATFORM_SCHEMA = PLATFORM_SCHEMA.extend(
{
vol.Required(CONF_RECIPIENT): vol.All(cv.ensure_list, [vol.Email()]),
vol.Required(CONF_SENDER): vol.Email(),
vol.Optional(CONF_SERVER, default=DEFAULT_HOST): cv.string,
vol.Optional(CONF_PORT, default=DEFAULT_PORT): cv.port,
vol.Optional(CONF_TIMEOUT, default=DEFAULT_TIMEOUT): cv.positive_int,
vol.Optional(CONF_ENCRYPTION, default=DEFAULT_ENCRYPTION): vol.In(
ENCRYPTION_OPTIONS
),
vol.Optional(CONF_USERNAME): cv.string,
vol.Optional(CONF_PASSWORD): cv.string,
vol.Optional(CONF_SENDER_NAME): cv.string,
vol.Optional(CONF_DEBUG, default=DEFAULT_DEBUG): cv.boolean,
}
)
def get_service(hass, config, discovery_info=None):
"""Get the mail notification service."""
mail_service = MailNotificationService(
config.get(CONF_SERVER),
config.get(CONF_PORT),
config.get(CONF_TIMEOUT),
config.get(CONF_SENDER),
config.get(CONF_ENCRYPTION),
config.get(CONF_USERNAME),
config.get(CONF_PASSWORD),
config.get(CONF_RECIPIENT),
config.get(CONF_SENDER_NAME),
config.get(CONF_DEBUG),
)
if mail_service.connection_is_valid():
return mail_service
return None
class MailNotificationService(BaseNotificationService):
"""Implement the notification service for E-mail messages."""
def __init__(
self,
server,
port,
timeout,
sender,
encryption,
username,
password,
recipients,
sender_name,
debug,
):
"""Initialize the SMTP service."""
self._server = server
self._port = port
self._timeout = timeout
self._sender = sender
self.encryption = encryption
self.username = username
self.password = password
self.recipients = recipients
self._sender_name = sender_name
self.debug = debug
self.tries = 2
def connect(self):
"""Connect/authenticate to SMTP Server."""
if self.encryption == "tls":
mail = smtplib.SMTP_SSL(self._server, self._port, timeout=self._timeout)
else:
mail = smtplib.SMTP(self._server, self._port, timeout=self._timeout)
mail.set_debuglevel(self.debug)
mail.ehlo_or_helo_if_needed()
if self.encryption == "starttls":
mail.starttls()
mail.ehlo()
if self.username and self.password:
mail.login(self.username, self.password)
return mail
def connection_is_valid(self):
"""Check for valid config, verify connectivity."""
server = None
try:
server = self.connect()
except (smtplib.socket.gaierror, ConnectionRefusedError):
_LOGGER.exception(
"SMTP server not found or refused connection (%s:%s). "
"Please check the IP address, hostname, and availability of your SMTP server.",
self._server,
self._port,
)
except smtplib.SMTPAuthenticationError:
_LOGGER.exception(
"Login not possible. "
"Please check your setting and/or your credentials"
)
return False
finally:
if server:
server.quit()
return True
def send_message(self, message="", **kwargs):
"""
Build and send a message to a user.
Will send plain text normally, or will build a multipart HTML message
with inline image attachments if images config is defined, or will
build a multipart HTML if html config is defined.
"""
subject = kwargs.get(ATTR_TITLE, ATTR_TITLE_DEFAULT)
data = kwargs.get(ATTR_DATA)
if data:
if ATTR_HTML in data:
msg = _build_html_msg(
message, data[ATTR_HTML], images=data.get(ATTR_IMAGES, [])
)
else:
msg = _build_multipart_msg(message, images=data.get(ATTR_IMAGES, []))
else:
msg = _build_text_msg(message)
msg["Subject"] = subject
msg["To"] = ",".join(self.recipients)
if self._sender_name:
msg["From"] = f"{self._sender_name} <{self._sender}>"
else:
msg["From"] = self._sender
msg["X-Mailer"] = "HomeAssistant"
msg["Date"] = email.utils.format_datetime(dt_util.now())
msg["Message-Id"] = email.utils.make_msgid()
return self._send_email(msg)
def _send_email(self, msg):
"""Send the message."""
mail = self.connect()
for _ in range(self.tries):
try:
mail.sendmail(self._sender, self.recipients, msg.as_string())
break
except smtplib.SMTPServerDisconnected:
_LOGGER.warning(
"SMTPServerDisconnected sending mail: retrying connection"
)
mail.quit()
mail = self.connect()
except smtplib.SMTPException:
_LOGGER.warning("SMTPException sending mail: retrying connection")
mail.quit()
mail = self.connect()
mail.quit()
def _build_text_msg(message):
"""Build plaintext email."""
_LOGGER.debug("Building plain text email")
return MIMEText(message)
def _build_multipart_msg(message, images):
"""Build Multipart message with in-line images."""
_LOGGER.debug("Building multipart email with embedded attachment(s)")
msg = MIMEMultipart("related")
msg_alt = MIMEMultipart("alternative")
msg.attach(msg_alt)
body_txt = MIMEText(message)
msg_alt.attach(body_txt)
body_text = [f"<p>{message}</p><br>"]
for atch_num, atch_name in enumerate(images):
cid = f"image{atch_num}"
body_text.append(f'<img src="cid:{cid}"><br>')
try:
with open(atch_name, "rb") as attachment_file:
file_bytes = attachment_file.read()
try:
attachment = MIMEImage(file_bytes)
msg.attach(attachment)
attachment.add_header("Content-ID", f"<{cid}>")
except TypeError:
_LOGGER.warning(
"Attachment %s has an unknown MIME type. "
"Falling back to file",
atch_name,
)
attachment = MIMEApplication(file_bytes, Name=atch_name)
attachment["Content-Disposition"] = (
"attachment; " 'filename="%s"' % atch_name
)
msg.attach(attachment)
except FileNotFoundError:
_LOGGER.warning("Attachment %s not found. Skipping", atch_name)
body_html = MIMEText("".join(body_text), "html")
msg_alt.attach(body_html)
return msg
def _build_html_msg(text, html, images):
"""Build Multipart message with in-line images and rich HTML (UTF-8)."""
_LOGGER.debug("Building HTML rich email")
msg = MIMEMultipart("related")
alternative = MIMEMultipart("alternative")
alternative.attach(MIMEText(text, _charset="utf-8"))
alternative.attach(MIMEText(html, ATTR_HTML, _charset="utf-8"))
msg.attach(alternative)
for atch_num, atch_name in enumerate(images):
name = os.path.basename(atch_name)
try:
with open(atch_name, "rb") as attachment_file:
attachment = MIMEImage(attachment_file.read(), filename=name)
msg.attach(attachment)
attachment.add_header("Content-ID", f"<{name}>")
except FileNotFoundError:
_LOGGER.warning(
"Attachment %s [#%s] not found. Skipping", atch_name, atch_num
)
return msg
|
"""The tests for the demo water_heater component."""
import unittest
import pytest
import voluptuous as vol
from homeassistant.components import water_heater
from homeassistant.setup import setup_component
from homeassistant.util.unit_system import IMPERIAL_SYSTEM
from tests.common import get_test_home_assistant
from tests.components.water_heater import common
ENTITY_WATER_HEATER = "water_heater.demo_water_heater"
ENTITY_WATER_HEATER_CELSIUS = "water_heater.demo_water_heater_celsius"
class TestDemowater_heater(unittest.TestCase):
"""Test the demo water_heater."""
def setUp(self): # pylint: disable=invalid-name
"""Set up things to be run when tests are started."""
self.hass = get_test_home_assistant()
self.hass.config.units = IMPERIAL_SYSTEM
assert setup_component(
self.hass, water_heater.DOMAIN, {"water_heater": {"platform": "demo"}}
)
def tearDown(self): # pylint: disable=invalid-name
"""Stop down everything that was started."""
self.hass.stop()
def test_setup_params(self):
"""Test the initial parameters."""
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert 119 == state.attributes.get("temperature")
assert "off" == state.attributes.get("away_mode")
assert "eco" == state.attributes.get("operation_mode")
def test_default_setup_params(self):
"""Test the setup with default parameters."""
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert 110 == state.attributes.get("min_temp")
assert 140 == state.attributes.get("max_temp")
def test_set_only_target_temp_bad_attr(self):
"""Test setting the target temperature without required attribute."""
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert 119 == state.attributes.get("temperature")
with pytest.raises(vol.Invalid):
common.set_temperature(self.hass, None, ENTITY_WATER_HEATER)
self.hass.block_till_done()
assert 119 == state.attributes.get("temperature")
def test_set_only_target_temp(self):
"""Test the setting of the target temperature."""
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert 119 == state.attributes.get("temperature")
common.set_temperature(self.hass, 110, ENTITY_WATER_HEATER)
self.hass.block_till_done()
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert 110 == state.attributes.get("temperature")
def test_set_operation_bad_attr_and_state(self):
"""Test setting operation mode without required attribute.
Also check the state.
"""
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert "eco" == state.attributes.get("operation_mode")
assert "eco" == state.state
with pytest.raises(vol.Invalid):
common.set_operation_mode(self.hass, None, ENTITY_WATER_HEATER)
self.hass.block_till_done()
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert "eco" == state.attributes.get("operation_mode")
assert "eco" == state.state
def test_set_operation(self):
"""Test setting of new operation mode."""
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert "eco" == state.attributes.get("operation_mode")
assert "eco" == state.state
common.set_operation_mode(self.hass, "electric", ENTITY_WATER_HEATER)
self.hass.block_till_done()
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert "electric" == state.attributes.get("operation_mode")
assert "electric" == state.state
def test_set_away_mode_bad_attr(self):
"""Test setting the away mode without required attribute."""
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert "off" == state.attributes.get("away_mode")
with pytest.raises(vol.Invalid):
common.set_away_mode(self.hass, None, ENTITY_WATER_HEATER)
self.hass.block_till_done()
assert "off" == state.attributes.get("away_mode")
def test_set_away_mode_on(self):
"""Test setting the away mode on/true."""
common.set_away_mode(self.hass, True, ENTITY_WATER_HEATER)
self.hass.block_till_done()
state = self.hass.states.get(ENTITY_WATER_HEATER)
assert "on" == state.attributes.get("away_mode")
def test_set_away_mode_off(self):
"""Test setting the away mode off/false."""
common.set_away_mode(self.hass, False, ENTITY_WATER_HEATER_CELSIUS)
self.hass.block_till_done()
state = self.hass.states.get(ENTITY_WATER_HEATER_CELSIUS)
assert "off" == state.attributes.get("away_mode")
def test_set_only_target_temp_with_convert(self):
"""Test the setting of the target temperature."""
state = self.hass.states.get(ENTITY_WATER_HEATER_CELSIUS)
assert 113 == state.attributes.get("temperature")
common.set_temperature(self.hass, 114, ENTITY_WATER_HEATER_CELSIUS)
self.hass.block_till_done()
state = self.hass.states.get(ENTITY_WATER_HEATER_CELSIUS)
assert 114 == state.attributes.get("temperature")
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tests/components/demo/test_water_heater.py
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homeassistant/components/smtp/notify.py
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