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quantopian/qdb | qdb/comm.py | RemoteCommandManager.command_list | python | def command_list(self, tracer, payload):
if not self.payload_check(payload, 'list'):
return self.next_command.tailcall(tracer)
filename = payload.get('file') or tracer.default_file
try:
if tracer.skip_fn(filename):
raise KeyError
if not (payload.get('start') or payload.get('end')):
msg = fmt_msg(
'list',
tracer.get_file(payload['file']),
serial=json.dumps
)
else:
msg = fmt_msg(
'list',
'\n'.join(
tracer.get_file_lines(tracer.canonic(filename))[
int(payload.get('start')):int(payload.get('end'))
]
),
serial=json.dumps
)
except KeyError:
msg = fmt_err_msg(
'list',
'File %s does not exist' % payload['file'],
serial=json.dumps
)
except TypeError:
msg = fmt_err_msg(
'list',
'List slice arguments must be convertable to type int',
serial=json.dumps
)
return self.next_command.tailcall(msg) | List the contents of a file and defer to user control. | https://github.com/quantopian/qdb/blob/c25018d2f0979589a38a07667478cb6022d57ed9/qdb/comm.py#L556-L599 | from __future__ import print_function
from abc import ABCMeta, abstractmethod
import atexit
from bdb import Breakpoint
import errno
from functools import partial
import json
import os
from pprint import pprint
import signal
import socket
from struct import pack, unpack
from textwrap import dedent
from logbook import Logger
from qdb.compat import (
Connection,
PY3,
gevent,
input,
items,
print_,
range,
with_metaclass,
)
from qdb.errors import (
QdbAuthenticationError,
QdbBreakpointReadError,
QdbCommunicationError,
QdbFailedToConnect,
QdbReceivedInvalidData,
QdbUnreachableBreakpoint,
)
from qdb.utils import Timeout, tco
log = Logger('Qdb')
def fmt_msg(event, payload=None, serial=None):
frame = {
'e': event,
'p': payload,
}
return serial(frame) if serial else frame
def fmt_err_msg(error_type, data, serial=None):
return fmt_msg(
'error', {
'type': error_type,
'data': data,
},
serial=serial,
)
def fmt_breakpoint(breakpoint):
return {
'file': breakpoint.file,
'line': breakpoint.line,
'temp': breakpoint.temporary,
'cond': breakpoint.cond,
'func': breakpoint.funcname,
}
class CommandManager(with_metaclass(ABCMeta, object)):
def _fmt_stackframe(self, tracer, stackframe, line):
filename = stackframe.f_code.co_filename
func = stackframe.f_code.co_name
code = tracer.get_line(filename, line)
return {
'file': tracer.canonic(filename),
'line': line,
'func': func,
'code': code,
}
def send_disabled(self):
try:
self.send_event('disabled')
except socket.error:
pass
def send_breakpoints(self):
self.send_event(
'breakpoints',
[fmt_breakpoint(breakpoint) for breakpoint in Breakpoint.bpbynumber
if breakpoint]
)
def send_watchlist(self, tracer):
self.send_event(
'watchlist',
[{'expr': k, 'exc': exc, 'value': val}
for k, (exc, val) in items(tracer.watchlist)],
)
def send_print(self, input_, exc, output):
self.send(fmt_msg(
'print', {
'input': input_,
'exc': exc,
'output': output
},
serial=json.dumps)
)
def send_stack(self, tracer):
stack = []
index = tracer.curindex
skip_fn = tracer.skip_fn
for n, (frame, line) in enumerate(tracer.stack):
if skip_fn(frame.f_code.co_filename):
if n < tracer.curindex:
index -= 1
continue
stack.append(self._fmt_stackframe(tracer, frame, line))
self.send_event(
'stack', {
'index': index,
'stack': stack,
}
)
def send_error(self, error_type, error_data):
self.send(fmt_err_msg(error_type, error_data, serial=json.dumps))
def send_event(self, event, payload=None):
self.send(fmt_msg(event, payload, serial=json.dumps))
@tco
def next_command(self, tracer, msg=None):
if msg:
self.send(msg)
return self.user_next_command(tracer)
@abstractmethod
def send(self, msg):
raise NotImplementedError
@abstractmethod
def user_next_command(self, tracer):
raise NotImplementedError
@abstractmethod
def start(self, tracer, auth_msg=''):
raise NotImplementedError
def stop(self):
self.send_disabled()
self.user_stop()
@abstractmethod
def user_stop(self):
raise NotImplementedError
class NopCommandManager(CommandManager):
def user_next_command(self, tracer):
pass
def send(self, msg):
pass
def start(self, tracer, msg):
pass
def user_stop(self):
pass
class RemoteCommandManager(CommandManager):
def __init__(self):
super(RemoteCommandManager, self).__init__()
if gevent is not None:
import gipc
self._pipe = gipc.pipe
self._start_process = gipc.start_process
else:
import multiprocessing
def _pipe(*args, **kwargs):
a, b = multiprocessing.Pipe(*args, **kwargs)
return Connection(a), Connection(b)
self._pipe = _pipe
def _start_process(*args, **kwargs):
proc = multiprocessing.Process(*args, **kwargs)
proc.start()
return proc
self._start_process = _start_process
self.pipe = None
self.socket = None
self.reader = None
def _socket_connect(self, tracer):
log.info('Connecting to (%s, %d)' % tracer.address)
for n in range(tracer.retry_attempts):
try:
self.socket = socket.create_connection(tracer.address)
break
except socket.error:
log.warn(
'Client %s failed to connect to (%s, %d) on attempt %d...'
% (tracer.uuid, tracer.address[0],
tracer.address[1], n + 1)
)
if self.socket is None:
log.warn(
'Failed to connect to (%s, %d), no longer retying.'
% tracer.address
)
raise QdbFailedToConnect(
tracer.address,
tracer.retry_attempts
)
log.info('Client %s connected to (%s, %d)'
% (tracer.uuid, tracer.address[0],
tracer.address[1]))
def start(self, tracer, auth_msg=''):
self.pipe, child_end = self._pipe()
self._socket_connect(tracer)
self.reader = self._start_process(
target=ServerReader,
args=(child_end, os.getpid(),
self.socket.fileno(),
tracer.pause_signal),
)
with Timeout(5, QdbFailedToConnect(tracer.address,
tracer.retry_attempts)):
while True:
try:
self.pipe.get()
break
except IOError as e:
if e.errno != errno.EAGAIN:
raise
self.send(
fmt_msg(
'start', {
'uuid': tracer.uuid,
'auth': auth_msg,
'local': (0, 0),
},
serial=json.dumps,
)
)
signal.signal(
tracer.pause_signal, partial(self._pause_handler, tracer)
)
atexit.register(self.stop)
def user_stop(self):
if self.reader and self.reader.is_alive():
self.reader.terminate()
self.socket.close()
def fmt_breakpoint_dict(self, tracer, breakpoint):
if 'file' not in breakpoint and tracer.default_file:
breakpoint['file'] = tracer.default_file
if 'file' in breakpoint and 'line' in breakpoint:
breakpoint['filename'] = breakpoint.pop('file')
breakpoint['lineno'] = breakpoint.pop('line')
breakpoint['temporary'] = breakpoint.pop('temp', None)
breakpoint['funcname'] = breakpoint.pop('func', None)
breakpoint.setdefault('cond', None)
return breakpoint
raise QdbBreakpointReadError(breakpoint)
def send(self, msg):
self.socket.sendall(pack('>i', len(msg)))
self.socket.sendall(msg.encode('utf-8'))
def payload_check(self, payload, command):
if payload is None:
self.send_error('payload', '%s: expected payload' % command)
return False
return True
def _pause_handler(self, tracer, signum, stackframe):
if signum == tracer.pause_signal:
tracer.set_step()
def get_events(self):
while self.reader.is_alive():
try:
event = self.pipe.get()
except IOError as i:
if i.errno == errno.EAGAIN:
continue
raise
yield event
def get_commands(self, tracer):
for event in self.get_events():
if event['e'] == 'error':
self.handle_error(event.get('p'))
else:
command = getattr(self, 'command_' + event['e'], None)
if not command:
self.send_error('event', 'Command %s does not exist'
% event['e'])
else:
yield lambda: command(tracer, event.get('p'))
def handle_error(self, payload):
if payload['type'] == 'auth':
raise QdbAuthenticationError(payload['data'])
else:
raise QdbCommunicationError(payload)
def user_next_command(self, tracer, msg=None):
try:
return next(self.get_commands(tracer))()
except StopIteration:
raise QdbCommunicationError('No more commands from server')
def command_step(self, tracer, payload):
tracer.set_step()
def command_return(self, tracer, payload):
tracer.set_return(tracer.curframe)
def command_next(self, tracer, payload):
tracer.set_next(tracer.curframe)
def command_until(self, tracer, payload):
tracer.set_until(tracer.curframe)
def command_continue(self, tracer, payload):
tracer.set_continue()
def command_pprint(self, tracer, payload):
return self.command_eval(tracer, payload, pprint=True)
def command_eval(self, tracer, payload, pprint=False):
if not self.payload_check(payload, 'eval'):
return self.next_command.tailcall(tracer)
tracer.eval_(payload, pprint)
self.send_watchlist(tracer)
return self.next_command.tailcall(tracer)
def command_set_watch(self, tracer, payload):
if not self.payload_check(payload, 'set_watch'):
return self.next_command.tailcall(tracer)
tracer.extend_watchlist(*payload)
self.send_watchlist(tracer)
return self.next_command.tailcall(tracer)
def command_clear_watch(self, tracer, payload):
if not self.payload_check(payload, 'clear_watch'):
return self.next_command.tailcall(tracer)
for w in payload:
tracer.watchlist.pop(w, None)
self.send_watchlist(tracer)
return self.next_command.tailcall(tracer)
def command_set_break(self, tracer, payload):
if not self.payload_check(payload, 'set_break'):
return self.next_command.tailcall(tracer)
try:
breakpoint = self.fmt_breakpoint_dict(tracer, payload)
except QdbBreakpointReadError as b:
err_msg = fmt_err_msg('set_break', str(b), serial=json.dumps)
return self.next_command.tailcall(tracer, err_msg)
err_msg = None
try:
tracer.set_break(**breakpoint)
except QdbUnreachableBreakpoint as u:
err_msg = fmt_err_msg(
'set_breakpoint',
str(u),
serial=json.dumps
)
return self.next_command.tailcall(tracer, err_msg)
def command_clear_break(self, tracer, payload):
if not self.payload_check(payload, 'clear_break'):
return self.next_command.tailcall(tracer)
try:
breakpoint = self.fmt_breakpoint_dict(tracer, payload)
except QdbBreakpointReadError as b:
err_msg = fmt_err_msg('clear_break', str(b), serial=json.dumps)
return self.next_command.tailcall(tracer, err_msg)
tracer.clear_break(**breakpoint)
return self.next_command.tailcall(tracer) | Apache License 2.0 |
dojoteef/dvae | dvae/models/factory.py | ModelFactoryFunction.define_model | python | def define_model(self, graph, reuse=None, **kwargs):
pass | Return a new model. | https://github.com/dojoteef/dvae/blob/93665f17b346a3f42dea7c607e4c5f8365b5895d/dvae/models/factory.py#L91-L93 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from abc import abstractmethod
from abc import abstractproperty
from abc import ABCMeta as AbstractBaseClass
from six import iteritems
from six.moves import xrange
import tensorflow as tf
from dvae.datasets.dataloader import Data
import dvae.utils.graph as graph_utils
import dvae.utils.stats as stats_utils
BATCH_NORM = 'BATCH_NORM'
BATCH_NORM_COLLECTION = {'moving_mean': [BATCH_NORM], 'moving_variance': [BATCH_NORM]}
class ModelFactoryFunction(object):
__metaclass__ = AbstractBaseClass
def __init__(self, dataset, dtype):
self.dtype = dtype
self.dataset = dataset
def batch_norm(self, layer, activation_fn=None, reuse=None):
batch_norm_layer = tf.contrib.layers.batch_norm(
layer, decay=0.999, trainable=False,
is_training=graph_utils.is_training(layer),
scale=True, center=True, activation_fn=activation_fn,
updates_collections=tf.GraphKeys.UPDATE_OPS,
variables_collections=BATCH_NORM_COLLECTION,
scope='batch_norm', reuse=reuse)
return batch_norm_layer
def linear_layer(self, data, shape):
dtype = self.dtype
weights = tf.get_variable(
'weights', dtype=dtype, shape=shape,
regularizer=tf.contrib.layers.l2_regularizer(5e-4),
initializer=tf.contrib.layers.xavier_initializer(uniform=False))
biases = tf.get_variable(
'biases', dtype=dtype, shape=shape[-1:],
regularizer=tf.contrib.layers.l2_regularizer(5e-4),
initializer=tf.zeros_initializer)
return tf.matmul(data, weights) + biases
def linear_layers(self, layer, outputs, activation_fn=None, reuse=None, name='linear'):
layers = []
outputs = layer.get_shape()[-1:].concatenate(outputs)
for idx in xrange(len(outputs) - 1):
with tf.variable_scope(name+str(idx), reuse=reuse):
activate = activation_fn if idx < len(outputs) - 2 else None
layer = self.linear_layer(layer, outputs[idx:idx+2])
if activation_fn:
layer = self.batch_norm(layer, activation_fn=activate, reuse=reuse)
layers.append(layer)
return layers
@abstractmethod | Apache License 2.0 |
speleo3/pymol-psico | psico/electrostatics.py | validate_apbs_exe | python | def validate_apbs_exe(exe):
import os, subprocess
if exe:
exe = cmd.exp_path(exe)
else:
try:
import freemol.apbs
exe = freemol.apbs.get_exe_path()
except:
pass
if not exe:
exe = cmd.exp_path('$SCHRODINGER/utilities/apbs')
if not os.path.exists(exe):
exe = "apbs"
try:
r = subprocess.call([exe, "--version"],
stdout=open(os.devnull, "w"), stderr=subprocess.STDOUT)
if r < 0:
raise CmdException("Broken executable: " + exe)
except OSError:
raise CmdException("Cannot execute: " + exe)
return exe | Get and validate apbs executable.
Raise CmdException if not found or broken. | https://github.com/speleo3/pymol-psico/blob/4e5402b4dca9a509b34a03691f12dc49e93c4973/psico/electrostatics.py#L47-L73 | from __future__ import print_function
from pymol import cmd, CmdException
template_apbs_in = '''
read
mol pqr "{pqrfile}"
end
elec
mg-auto
mol 1
fgcent {fgcent} # fine grid center
cgcent mol 1 # coarse grid center
fglen {fglen}
cglen {cglen}
dime {dime}
lpbe # l=linear, n=non-linear Poisson-Boltzmann equation
bcfl sdh # "Single Debye-Hueckel" boundary condition
pdie 2.0 # protein dielectric
sdie 78.0 # solvent dielectric
chgm spl2 # Cubic B-spline discretization of point charges on grid
srfm smol # smoothed surface for dielectric and ion-accessibility coefficients
swin 0.3
temp 310.0 # temperature
sdens 10.0
calcenergy no
calcforce no
srad {srad} # solvent radius
ion charge +1 conc 0.15 radius 2.0
ion charge -1 conc 0.15 radius 1.8
write pot dx "{mapfile}"
end
quit
''' | BSD 2-Clause Simplified License |
harmon758/harmonbot | Discord/cogs/cryptography.py | Cryptography.encode_blake2b | python | async def encode_blake2b(self, ctx, *, message: str):
digest = crypto_hashes.Hash(crypto_hashes.BLAKE2b(64), backend = openssl_backend)
digest.update(message.encode("UTF-8"))
await ctx.embed_reply(digest.finalize()) | 64-byte digest BLAKE2b | https://github.com/harmon758/harmonbot/blob/def3849beabdaea5e0f9c594dcf6d6d8980782bd/Discord/cogs/cryptography.py#L175-L179 | from discord.ext import commands
import hashlib
import sys
from typing import Optional
import zlib
from cryptography.hazmat.backends.openssl import backend as openssl_backend
from cryptography.hazmat.primitives import hashes as crypto_hashes
import pygost.gost28147
import pygost.gost28147_mac
import pygost.gost34112012
import pygost.gost341194
import pygost.gost3412
from utilities import checks
sys.path.insert(0, "..")
from units.cryptography import (decode_caesar_cipher, encode_caesar_cipher,
decode_morse_code, encode_morse_code,
UnitOutputError)
sys.path.pop(0)
def setup(bot):
bot.add_cog(Cryptography())
class Cryptography(commands.Cog):
async def cog_check(self, ctx):
return await checks.not_forbidden().predicate(ctx)
@commands.group(aliases = ["decrpyt"], invoke_without_command = True, case_insensitive = True)
async def decode(self, ctx):
await ctx.send_help(ctx.command)
@decode.group(name = "caesar", aliases = ["rot"],
invoke_without_command = True, case_insensitive = True)
async def decode_caesar(self, ctx, key: int, *, message: str):
await ctx.embed_reply(decode_caesar_cipher(message, key))
@decode_caesar.command(name = "brute")
async def decode_caesar_brute(self, ctx, *, message: str):
await ctx.embed_reply('\n'.join(f"{key}: {decode_caesar_cipher(message, key)}" for key in range(26)))
@decode.group(name = "gost", aliases = ["гост"],
invoke_without_command = True, case_insensitive = True)
async def decode_gost(self, ctx):
await ctx.send_help(ctx.command)
@decode_gost.group(name = "28147-89", aliases = ["магма", "magma"],
invoke_without_command = True, case_insensitive = True)
async def decode_gost_28147_89(self, ctx):
await ctx.send_help(ctx.command)
@decode_gost_28147_89.command(name = "cbc")
async def decode_gost_28147_89_cbc(self, ctx, key: str, *, data: str):
try:
await ctx.embed_reply(pygost.gost28147.cbc_decrypt(key.encode("UTF-8"), bytearray.fromhex(data)).decode("UTF-8"))
except ValueError as e:
await ctx.embed_reply(f"{ctx.bot.error_emoji} Error: {e}")
@decode_gost_28147_89.command(name = "cfb")
async def decode_gost_28147_89_cfb(self, ctx, key: str, *, data: str):
try:
await ctx.embed_reply(pygost.gost28147.cfb_decrypt(key.encode("UTF-8"), bytearray.fromhex(data)).decode("UTF-8"))
except ValueError as e:
await ctx.embed_reply(f"{ctx.bot.error_emoji} Error: {e}")
@decode_gost_28147_89.command(name = "cnt")
async def decode_gost_28147_89_cnt(self, ctx, key: str, *, data: str):
try:
await ctx.embed_reply(pygost.gost28147.cnt(key.encode("UTF-8"), bytearray.fromhex(data)).decode("UTF-8"))
except ValueError as e:
await ctx.embed_reply(f"{ctx.bot.error_emoji} Error: {e}")
@decode_gost_28147_89.command(name = "ecb")
async def decode_gost_28147_89_ecb(self, ctx, key: str, *, data: str):
try:
await ctx.embed_reply(pygost.gost28147.ecb_decrypt(key.encode("UTF-8"), bytearray.fromhex(data)).decode("UTF-8"))
except ValueError as e:
await ctx.embed_reply(f"{ctx.bot.error_emoji} Error: {e}")
@decode_gost.command(name = "34.12-2015", aliases = ["кузнечик", "kuznyechik"])
async def decode_gost_34_12_2015(self, ctx, key: str, *, data: str):
if len(key) < 32:
return await ctx.embed_reply(f"{ctx.bot.error_emoji} Error: key length must be at least 32")
if len(data) < 16:
return await ctx.embed_reply(f"{ctx.bot.error_emoji} Error: data length must be at least 16")
await ctx.embed_reply(pygost.gost3412.GOST3412Kuznechik(key.encode("UTF-8")).decrypt(bytearray.fromhex(data)).decode("UTF-8"))
@decode.command(name = "morse")
async def decode_morse(self, ctx, *, message: str):
try:
await ctx.embed_reply(decode_morse_code(message))
except UnitOutputError as e:
await ctx.embed_reply(f"{ctx.bot.error_emoji} Error: {e}")
@decode.command(name = "qr")
async def decode_qr(self, ctx, file_url: Optional[str]):
if not file_url:
if ctx.message.attachments:
file_url = ctx.message.attachments[0].url
else:
return await ctx.embed_reply(f"{ctx.bot.error_emoji} Please input a file url or attach an image")
url = f"https://api.qrserver.com/v1/read-qr-code/"
params = {"fileurl": file_url}
async with ctx.bot.aiohttp_session.get(url, params = params) as resp:
if resp.status == 400:
return await ctx.embed_reply(f"{ctx.bot.error_emoji} Error")
data = await resp.json()
if data[0]["symbol"][0]["error"]:
return await ctx.embed_reply(f"{ctx.bot.error_emoji} Error: {data[0]['symbol'][0]['error']}")
decoded = data[0]["symbol"][0]["data"].replace("QR-Code:", "")
if len(decoded) > ctx.bot.EMBED_DESCRIPTION_CHARACTER_LIMIT:
return await ctx.embed_reply(decoded[:ctx.bot.EDCL - 3] + "...",
footer_text = "Decoded message exceeded character limit")
await ctx.embed_reply(decoded)
@decode.command(name = "reverse")
async def decode_reverse(self, ctx, *, message: str):
await ctx.embed_reply(message[::-1])
@commands.group(aliases = ["encrypt"], invoke_without_command = True, case_insensitive = True)
async def encode(self, ctx):
await ctx.send_help(ctx.command)
@encode.command(name = "adler32", aliases = ["adler-32"])
async def encode_adler32(self, ctx, *, message: str):
await ctx.embed_reply(zlib.adler32(message.encode("UTF-8")))
@encode.command(name = "blake2b") | MIT License |
jaegertracing/jaeger-client-python | jaeger_client/thrift_gen/jaeger/Collector.py | Client.submitBatches | python | def submitBatches(self, batches):
self._seqid += 1
future = self._reqs[self._seqid] = concurrent.Future()
self.send_submitBatches(batches)
return future | Parameters:
- batches | https://github.com/jaegertracing/jaeger-client-python/blob/a6c973158bf9b02cd7f5a966ccfd29ab86c44a5b/jaeger_client/thrift_gen/jaeger/Collector.py#L70-L78 | import six
from six.moves import xrange
from thrift.Thrift import TType, TMessageType, TException, TApplicationException
import logging
from .ttypes import *
from thrift.Thrift import TProcessor
from thrift.transport import TTransport
from thrift.protocol import TBinaryProtocol, TProtocol
try:
from thrift.protocol import fastbinary
except:
fastbinary = None
from tornado import gen
from tornado import concurrent
from thrift.transport import TTransport
class Iface(object):
def submitBatches(self, batches):
pass
class Client(Iface):
def __init__(self, transport, iprot_factory, oprot_factory=None):
self._transport = transport
self._iprot_factory = iprot_factory
self._oprot_factory = (oprot_factory if oprot_factory is not None
else iprot_factory)
self._seqid = 0
self._reqs = {}
self._transport.io_loop.spawn_callback(self._start_receiving)
@gen.coroutine
def _start_receiving(self):
while True:
try:
frame = yield self._transport.readFrame()
except TTransport.TTransportException as e:
for future in self._reqs.itervalues():
future.set_exception(e)
self._reqs = {}
return
tr = TTransport.TMemoryBuffer(frame)
iprot = self._iprot_factory.getProtocol(tr)
(fname, mtype, rseqid) = iprot.readMessageBegin()
future = self._reqs.pop(rseqid, None)
if not future:
continue
method = getattr(self, 'recv_' + fname)
try:
result = method(iprot, mtype, rseqid)
except Exception as e:
future.set_exception(e)
else:
future.set_result(result) | Apache License 2.0 |
zhaozhibin/dl-based-intelligent-diagnosis-benchmark | AE_Datasets/R_NA/datasets/XJTU.py | data_load | python | def data_load(filename,label):
fl = pd.read_csv(filename)
fl = fl["Horizontal_vibration_signals"]
fl = fl.values
fl = fl.reshape(-1,1)
data=[]
lab=[]
start,end=0,signal_size
while end<=fl.shape[0]:
data.append(fl[start:end])
lab.append(label)
start +=signal_size
end +=signal_size
return data, lab | This function is mainly used to generate test data and training data.
filename:Data location | https://github.com/zhaozhibin/dl-based-intelligent-diagnosis-benchmark/blob/6dca48f36c2a0bceaad8329089100045fe440bbb/AE_Datasets/R_NA/datasets/XJTU.py#L53-L70 | import os
import pandas as pd
from sklearn.model_selection import train_test_split
from datasets.SequenceDatasets import dataset
from datasets.sequence_aug import *
from tqdm import tqdm
signal_size=1024
label1 = [i for i in range(0,5)]
label2 = [i for i in range(5,10)]
label3 = [i for i in range(10,15)]
def get_files(root, test=False):
WC = os.listdir(root)
datasetname1 = os.listdir(os.path.join(root, WC[0]))
datasetname2 = os.listdir(os.path.join(root, WC[1]))
datasetname3 = os.listdir(os.path.join(root, WC[2]))
data = []
lab =[]
for i in tqdm(range(len(datasetname1))):
files = os.listdir(os.path.join('/tmp',root,WC[0],datasetname1[i]))
for ii in [-4,-3,-2,-1]:
path1 = os.path.join('/tmp',root,WC[0],datasetname1[i],files[ii])
data1, lab1 = data_load(path1,label=label1[i])
data += data1
lab +=lab1
for j in tqdm(range(len(datasetname2))):
files = os.listdir(os.path.join('/tmp',root,WC[1],datasetname2[j]))
for jj in [-4,-3, -2, -1]:
path2 = os.path.join('/tmp',root,WC[1],datasetname2[j],files[jj])
data2, lab2 = data_load(path2,label=label2[j])
data += data2
lab += lab2
for k in tqdm(range(len(datasetname3))):
files = os.listdir(os.path.join('/tmp',root,WC[2],datasetname3[k]))
for kk in [-4,-3, -2, -1]:
path3 = os.path.join('/tmp',root,WC[2],datasetname3[k],files[kk])
data3, lab3 = data_load(path3,label=label3[k])
data += data3
lab += lab3
return [data, lab] | MIT License |
micom-dev/micom | micom/community.py | Community.__init__ | python | def __init__(
self,
taxonomy,
model_db=None,
id=None,
name=None,
rel_threshold=1e-6,
solver=None,
progress=True,
max_exchange=100,
mass=1,
):
super(Community, self).__init__(id, name)
logger.info("building new micom model {}.".format(id))
if not solver:
solver = [
s
for s in ["cplex", "gurobi", "osqp", "glpk"]
if s in cobra.util.solver.solvers
][0]
logger.info("using the %s solver." % solver)
if solver == "glpk":
logger.warning(
"No QP solver found, will use GLPK. A lot of functionality "
"in MICOM will require a QP solver :/"
)
self.solver.configuration.lp_method = "auto"
self.solver.configuration.qp_method = "auto"
self.solver.configuration.presolve = False
self.solver = solver
self._rtol = rel_threshold
self._modification = None
self.mass = mass
self.__db_metrics = None
adjust_solver_config(self.solver)
taxonomy = taxonomy.copy()
if "abundance" not in taxonomy.columns:
taxonomy["abundance"] = 1
taxonomy.abundance /= taxonomy.abundance.sum()
logger.info(
"{} individuals with abundances below threshold".format(
(taxonomy.abundance <= self._rtol).sum()
)
)
taxonomy = taxonomy[taxonomy.abundance > self._rtol]
if not (
isinstance(taxonomy, pd.DataFrame)
and any(taxonomy.columns.isin(["id"] + _ranks))
):
raise ValueError(
"`taxonomy` must be a pandas DataFrame with at least"
" a column `id` or a rank :("
)
if model_db is None and "file" not in taxonomy.columns:
raise ValueError(
"If no model database is specified you need to pass "
"file names for models in a `file` column as well."
)
compressed = False
if model_db is not None:
compressed = model_db.endswith(".qza") or model_db.endswith(".zip")
if compressed:
tdir = TemporaryDirectory(prefix="micom_")
if "file" in taxonomy.columns:
logger.warning(
"The table includes a `file` column even though a model database "
"is used. Will ignore it and use the model database instead."
)
del taxonomy["file"]
if model_db.endswith(".qza"):
manifest = load_qiime_model_db(model_db, tdir.name)
elif model_db.endswith(".zip"):
manifest = load_zip_model_db(model_db, tdir.name)
else:
manifest = load_manifest(model_db)
rank = manifest["summary_rank"][0]
if rank not in taxonomy.columns:
raise ValueError("Missing the column `%s` from the taxonomy." % rank)
if "id" not in taxonomy.columns:
taxonomy["id"] = taxonomy[rank]
keep_cols = [
r
for r in _ranks[0 : (_ranks.index(rank) + 1)]
if r in taxonomy.columns and r in manifest.columns
]
manifest = manifest[keep_cols + ["file"]]
merged = pd.merge(taxonomy, manifest, on=keep_cols)
self.__db_metrics = pd.Series(
{
"found_taxa": merged.shape[0],
"total_taxa": taxonomy.shape[0],
"found_fraction": merged.shape[0] / taxonomy.shape[0],
"found_abundance_fraction": merged.abundance.sum(),
}
)
logger.info(
"Matched %g%% of total abundance in model DB."
% (100.0 * self.__db_metrics[3])
)
if self.__db_metrics["found_abundance_fraction"] < 0.5:
logger.warning(
"Less than 50%% of the abundance could be matched to the "
"model database. Model `%s` may not be representative "
"of the sample" % self.id
)
taxonomy = merged
taxonomy["abundance"] /= taxonomy["abundance"].sum()
if taxonomy.id.str.contains(r"[^A-Za-z0-9_]", regex=True).any():
logger.warning(
"Taxa IDs contain prohibited characters and will be reformatted."
)
taxonomy.id = taxonomy.id.replace(r"[^A-Za-z0-9_\s]+", "_", regex=True)
self.__taxonomy = taxonomy
self.__taxonomy.index = self.__taxonomy.id
obj = Zero
self.taxa = []
index = self.__taxonomy.index
index = track(index, description="Building") if progress else index
for idx in index:
row = self.__taxonomy.loc[idx]
if isinstance(row.file, list):
if len(row.file) > 1:
model = join_models(row.file)
logger.info("joined {} models".format(len(row.file)))
else:
model = load_model(row.file[0])
else:
model = load_model(row.file)
suffix = "__" + idx.replace(" ", "_").strip()
logger.info("converting IDs for {}".format(idx))
external = cobra.medium.find_external_compartment(model)
logger.info(
"Identified %s as the external compartment for %s. "
"If that is wrong you may be in trouble..." % (external, idx)
)
for r in model.reactions:
r.global_id = clean_ids(r.id)
r.id = r.global_id + suffix
r.community_id = idx
r._compartments = None
if "sbo" in r.annotation:
del r.annotation["sbo"]
for m in model.metabolites:
m.global_id = clean_ids(m.id)
m.id = m.global_id + suffix
m.compartment += suffix
m.community_id = idx
logger.info("adding reactions for {} to community".format(idx))
self.add_reactions(model.reactions)
o = self.solver.interface.Objective.clone(
model.objective, model=self.solver
)
obj += o.expression * row.abundance
self.taxa.append(idx)
taxa_obj = self.problem.Constraint(
o.expression, name="objective_" + idx, lb=0.0
)
self.add_cons_vars([taxa_obj])
self.__add_exchanges(
model.reactions,
row,
external_compartment=external,
internal_exchange=max_exchange,
)
self.solver.update()
if compressed:
tdir.cleanup()
com_obj = add_var_from_expression(self, "community_objective", obj, lb=0)
self.objective = self.problem.Objective(com_obj, direction="max") | Create a new community object.
`micom` builds a community from a taxonomy which may simply be a list
of model files in its simplest form. Usually, the taxonomy will contain
additional information such as annotations for the individuals (for
instance phylum, organims or species) and abundances.
The recommended way to build a micom model is to supply a
quantification of taxa (called "taxonomy" here) which specifies the
taxonomic ranks for a taxon and its abundance, and a model database
for a specific rank (for instance "genus"). MICOM will match the
ranks from your taxonomy to the model database and assemble the
community models from that. You will also get information about the
construction process by calling `Community.build_metrics`.
The most customizable way only takes a single table where summarization
and matching to the reference database has already occured. In this
case you will also provide paths to model files for each taxon. This is
the "old" way but may still be applicable if you want to use a custom
database or want full control of matching your data to reference
models.
Notes
-----
`micom` will automatically add exchange fluxes and and a community
objective maximizing the overall growth rate of the community.
Parameters
----------
taxonomy : pandas.DataFrame
The taxonomy used for building the model. Must have at least the
column "id" or a column specifying a taxonomic rank.
If no model database is specified in the next argument
it furthermore requires a column "file" which specifies a filepath
for each model. Valid file extensions are ".pickle", ".xml",
".xml.gz" and ".json". If a model database is specified this must
contain at least a column with the same name as the rank used in
the model database. Thus, for a genus-level database you will need
a column `genus`. Additional taxa ranks can also be specified and
will be used to be more stringent in taxa matching.
Finally, the taxonomy should contain a column `abundance`. It will
be used to quantify each individual in the community. If absent,
MICOM will assume all individuals are present in the same amount.
model_db : str
A pre-built model database. If ending in `.qza` must be a Qiime 2
artifact of type `MetabolicModels[JSON]`. Can also be a folder,
zip (must end in `.zip`) file or None if the taxonomy contains a
column `file`.
id : str, optional
The ID for the community. Should only contain letters and numbers,
otherwise it will be formatted as such.
name : str, optional
The name for the community.
rel_threshold : float < 1, optional
The relative abundance threshold that will be used. Describes the
smallest relative amount of an individual that will be considered
non-zero. All individuals with a smaller relative amount will be
omitted.
solver : str, optional
Which solver to use. Will default to cplex if available which is
better suited for large problems.
progress : bool, optional
Show a progress bar.
max_exchange : positive float, optional
During model constructions exchange reactions are duplicated into
internal and external exchange reactions. This specifies the new
import flux bound for the *internal* exchange reaction. Import
rates for the exchanges between the medium and outside are still
mantained.
mass : positive float, optional
The total mass of the community in gDW. Used to adjust import
fluxes which are assumed to be given as mmol/gDW*h for the
entire community. As a consequence all import fluxes will be
divided by that number.
Attributes
----------
taxa : list
A list of taxa IDs in the community. | https://github.com/micom-dev/micom/blob/b8e3eda8a97ed2fe08e1416711d3a4f1f98bc3a9/micom/community.py#L40-L299 | import re
import pickle
import cobra
import pandas as pd
from optlang.symbolics import Zero
from micom.db import load_zip_model_db, load_manifest
from micom.util import (
load_model,
join_models,
add_var_from_expression,
adjust_solver_config,
clean_ids,
compartment_id,
COMPARTMENT_RE,
)
from micom.logger import logger
from micom.optcom import optcom, solve
from micom.problems import cooperative_tradeoff, knockout_taxa
from micom.qiime_formats import load_qiime_model_db
from rich.progress import track
from tempfile import TemporaryDirectory
_ranks = ["kingdom", "phylum", "class", "order", "family", "genus", "species", "strain"]
cobra.io.sbml.LOGGER.setLevel("ERROR")
cobra.util.solver.logger.setLevel("ERROR")
class Community(cobra.Model): | Apache License 2.0 |
geophysics-ubonn/reda | lib/reda/eis/units.py | get_label | python | def get_label(parameter, ptype, flavor=None, mpl=None):
if flavor is not None:
if flavor not in ('latex', 'mathml'):
raise Exception('flavor not recognized: {}'.format(flavor))
else:
if mpl is None:
raise Exception('either the flavor or mpl must be provided')
rendering = mpl.rcParams['text.usetex']
if rendering:
flavor = 'latex'
else:
flavor = 'mathml'
if parameter not in labels:
raise Exception('parameter not known')
if ptype not in labels[parameter]:
raise Exception('ptype not known')
if flavor not in labels[parameter][ptype]:
raise Exception('flavor not known')
return labels[parameter][ptype][flavor] | Return the label of a given SIP parameter
Parameters
----------
parameter : str
type of parameter, e.g. rmag|rpha|cre|cim
ptype : string
material|meas. Either return the material property (e.g. resistivity)
or the measurement parameter (e.g., impedance)
flavor : string, optional
if set, must be one of latex|mathml. Return a label for latex
processing, or for mathml processing
mpl : matplotlib, optional
if set, infer flavor from mpl.rcParams. Will not be used if flavor is
set
Returns
-------
label : string
the requested label | https://github.com/geophysics-ubonn/reda/blob/5be52ecb184f45f0eabb23451f039fec3d9537c5/lib/reda/eis/units.py#L47-L90 | labels = {
'rmag': {
'material': {
'latex': r'$|\rho|~[\Omega m]$',
'mathml': r'$|\rho| [\Omega m]$',
},
'meas': {
'latex': r'$|Z|~[\Omega]$',
'mathml': r'$|Z| [\Omega]$',
},
},
'rpha': {
'material': {
'latex': r'$-\phi~[mrad]$',
'mathml': r'$-\phi [mrad]$',
},
'meas': {
'latex': r'$-\varphi~[mrad]$',
'mathml': r'$-\varphi [mrad]$',
},
},
'cre': {
'material': {
'latex': r"$\sigma'~[S/m]$",
'mathml': r"$\sigma' [S/m]$",
},
'meas': {
'latex': r"$Y'~[S]$",
'mathml': r"$Y' [S]$",
},
},
'cim': {
'material': {
'latex': r"$\sigma''~[S/m]$",
'mathml': r"$\sigma'' [S/m]$",
},
'meas': {
'latex': r"$Y''~[S]$",
'mathml': r"$Y'' [S]$",
},
},
} | MIT License |
microsoft/univl | modules/until_module.py | PreTrainedModel.from_pretrained | python | def from_pretrained(cls, config, state_dict=None, *inputs, **kwargs):
model = cls(config, *inputs, **kwargs)
if state_dict is None:
return model
model = cls.init_preweight(model, state_dict)
return model | Instantiate a PreTrainedModel from a pre-trained model file or a pytorch state dict.
Download and cache the pre-trained model file if needed. | https://github.com/microsoft/univl/blob/0a7c07f566a3b220731f4abcaa6e1ee59a686596/modules/until_module.py#L166-L177 | import logging
import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
import math
from modules.until_config import PretrainedConfig
logger = logging.getLogger(__name__)
def gelu(x):
return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
def swish(x):
return x * torch.sigmoid(x)
ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish}
class LayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-12):
super(LayerNorm, self).__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.bias = nn.Parameter(torch.zeros(hidden_size))
self.variance_epsilon = eps
def forward(self, x):
u = x.mean(-1, keepdim=True)
s = (x - u).pow(2).mean(-1, keepdim=True)
x = (x - u) / torch.sqrt(s + self.variance_epsilon)
return self.weight * x + self.bias
class PreTrainedModel(nn.Module):
def __init__(self, config, *inputs, **kwargs):
super(PreTrainedModel, self).__init__()
if not isinstance(config, PretrainedConfig):
raise ValueError(
"Parameter config in `{}(config)` should be an instance of class `PretrainedConfig`. "
"To create a model from a Google pretrained model use "
"`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`".format(
self.__class__.__name__, self.__class__.__name__
))
self.config = config
def init_weights(self, module):
if isinstance(module, (nn.Linear, nn.Embedding)):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
elif isinstance(module, LayerNorm):
if 'beta' in dir(module) and 'gamma' in dir(module):
module.beta.data.zero_()
module.gamma.data.fill_(1.0)
else:
module.bias.data.zero_()
module.weight.data.fill_(1.0)
if isinstance(module, nn.Linear) and module.bias is not None:
module.bias.data.zero_()
def resize_token_embeddings(self, new_num_tokens=None):
raise NotImplementedError
@classmethod
def init_preweight(cls, model, state_dict, prefix=None, task_config=None):
old_keys = []
new_keys = []
for key in state_dict.keys():
new_key = None
if 'gamma' in key:
new_key = key.replace('gamma', 'weight')
if 'beta' in key:
new_key = key.replace('beta', 'bias')
if new_key:
old_keys.append(key)
new_keys.append(new_key)
for old_key, new_key in zip(old_keys, new_keys):
state_dict[new_key] = state_dict.pop(old_key)
if prefix is not None:
old_keys = []
new_keys = []
for key in state_dict.keys():
old_keys.append(key)
new_keys.append(prefix + key)
for old_key, new_key in zip(old_keys, new_keys):
state_dict[new_key] = state_dict.pop(old_key)
missing_keys = []
unexpected_keys = []
error_msgs = []
metadata = getattr(state_dict, '_metadata', None)
state_dict = state_dict.copy()
if metadata is not None:
state_dict._metadata = metadata
def load(module, prefix=''):
local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
module._load_from_state_dict(
state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs)
for name, child in module._modules.items():
if child is not None:
load(child, prefix + name + '.')
load(model, prefix='')
if prefix is None and (task_config is None or task_config.local_rank == 0):
logger.info("-" * 20)
if len(missing_keys) > 0:
logger.info("Weights of {} not initialized from pretrained model: {}"
.format(model.__class__.__name__, "\n " + "\n ".join(missing_keys)))
if len(unexpected_keys) > 0:
logger.info("Weights from pretrained model not used in {}: {}"
.format(model.__class__.__name__, "\n " + "\n ".join(unexpected_keys)))
if len(error_msgs) > 0:
logger.error("Weights from pretrained model cause errors in {}: {}"
.format(model.__class__.__name__, "\n " + "\n ".join(error_msgs)))
return model
@property
def dtype(self):
try:
return next(self.parameters()).dtype
except StopIteration:
def find_tensor_attributes(module: nn.Module):
tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)]
return tuples
gen = self._named_members(get_members_fn=find_tensor_attributes)
first_tuple = next(gen)
return first_tuple[1].dtype
@classmethod | MIT License |
genomoncology/related | src/related/validators.py | regex | python | def regex(match_string):
return _RegexValidator(match_string) | A validator that executes each validator passed as arguments. | https://github.com/genomoncology/related/blob/3799cde862b8c9500931706f5f1ce5576028f642/src/related/validators.py#L42-L46 | from attr import attr, attributes
import re
@attributes(repr=False, slots=True)
class _CompositeValidator(object):
validators = attr()
def __call__(self, inst, attr, value):
for validator in self.validators:
validator(inst, attr, value)
def __repr__(self):
return (
"<composite validator for validators {!r}>".format(self.validators)
)
def composite(*validators):
return _CompositeValidator(validators)
@attributes(repr=False, slots=True)
class _RegexValidator(object):
regex = attr()
def __call__(self, inst, attr, value):
if not re.match(self.regex, value):
raise TypeError(
"'{name}' must match {regex!r} (got {value!r}).".format(
name=attr.name, regex=self.regex, value=value), attr,
self.regex, value)
def __repr__(self):
return (
"<regex validator for {!r}>".format(self.regex)
) | MIT License |
iperdance/ipercore | iPERCore/tools/trainers/lwg_trainer.py | LWGTrainer.optimize_D | python | def optimize_D(self, fake_bg, fake_tsf_imgs):
bs, nt, c, h, w = fake_tsf_imgs.shape
fake_tsf_imgs = fake_tsf_imgs.view(bs * nt, c, h, w)
real_tsf_imgs = self._real_tsf.reshape(bs * nt, c, h, w)
tsf_cond = self._input_G_tsf[:, :, -3:].view(bs * nt, -1, h, w)
fake_input_D = torch.cat([fake_tsf_imgs.detach(), tsf_cond], dim=1)
real_input_D = torch.cat([real_tsf_imgs, tsf_cond], dim=1)
real_inputs = {
"x": real_input_D,
"bg_x": None,
"body_rects": self._body_bbox,
"head_rects": self._head_bbox,
"get_avg": True
}
d_real_outs, self._d_real = self.D(real_inputs)
fake_inputs = {
"x": fake_input_D,
"bg_x": None,
"body_rects": self._body_bbox,
"head_rects": self._head_bbox,
"get_avg": True
}
d_fake_outs, self._d_fake = self.D(fake_inputs)
_loss_d_real = self.crt_gan(d_real_outs, 1)
_loss_d_fake = self.crt_gan(d_fake_outs, -1)
return _loss_d_real + _loss_d_fake | Args:
fake_bg (torch.Tensor):
fake_tsf_imgs (torch.Tensor):
Returns: | https://github.com/iperdance/ipercore/blob/1c15b8208a4313c91ce6bf7a97a15fe43cee4a74/iPERCore/tools/trainers/lwg_trainer.py#L791-L832 | import abc
import torch
import torch.nn.functional as F
from collections import OrderedDict
from iPERCore.models.networks import NetworksFactory
from iPERCore.models.networks.criterions import VGGLoss, FaceLoss, LSGANLoss, TVLoss, TemporalSmoothLoss
from iPERCore.tools.utils.filesio.cv_utils import tensor2im
from .base import BaseTrainerModel, FlowCompositionForTrainer
__all__ = ["LWGTrainerABC", "LWGTrainer", "LWGAugBGTrainer", "LWGFrontTrainer"]
class LWGTrainerABC(BaseTrainerModel, abc.ABC):
def __init__(self, opt, device):
super(LWGTrainerABC, self).__init__(opt)
self._name = "LWGTrainerABC"
self.device = device
self._share_bg = self._opt.share_bg
self._ns = self._opt.num_source
self._nt = self._opt.time_step
self._train_opts = self._opt.Train
self._aug_bg = self._train_opts.aug_bg
self._use_gan = self._train_opts.lambda_D_prob > 0
self._create_network()
self._make_optimizer()
self._init_losses()
if self._opt.load_iter > 0:
self.load()
else:
if self._opt.load_path_G != "None":
self.load_params(self.G, self._opt.load_path_G, need_module=False)
if self._opt.load_path_D != "None" and self._use_gan:
self.load_params(self.D, self._opt.load_path_D, need_module=False)
def _create_network(self):
self.flow_comp = FlowCompositionForTrainer(opt=self._opt)
self.flow_comp.eval()
self.G = self._create_generator(cfg=self._opt.neural_render_cfg.Generator)
if self._use_gan:
self.D = self._create_discriminator(cfg=self._opt.neural_render_cfg.Discriminator)
else:
self.D = None
def _create_generator(self, cfg):
gen_name = self._opt.gen_name
return NetworksFactory.get_by_name(gen_name, cfg=cfg, temporal=self._opt.temporal)
def _create_discriminator(self, cfg):
dis_name = self._opt.dis_name
return NetworksFactory.get_by_name(dis_name, cfg=cfg, use_aug_bg=self._aug_bg)
def _make_optimizer(self):
self._current_lr_G = self._train_opts.lr_G
self._current_lr_D = self._train_opts.lr_D
self._optimizer_G = torch.optim.Adam(self.G.parameters(), lr=self._current_lr_G,
betas=(self._train_opts.G_adam_b1, self._train_opts.G_adam_b2))
if self._use_gan:
self._optimizer_D = torch.optim.Adam(self.D.parameters(), lr=self._current_lr_D,
betas=(self._train_opts.D_adam_b1, self._train_opts.D_adam_b2))
def _init_losses(self):
self.crt_l1 = torch.nn.L1Loss()
self.crt_mask = torch.nn.BCELoss()
if self._train_opts.use_vgg != "None":
self.crt_tsf = VGGLoss(vgg_type=self._train_opts.use_vgg,
ckpt_path=self._train_opts.vgg_loss_path, resize=True)
else:
self.crt_tsf = torch.nn.L1Loss()
if self._train_opts.use_face:
self.crt_face = FaceLoss(pretrained_path=self._train_opts.face_loss_path,
factor=self._train_opts.face_factor)
self.crt_gan = LSGANLoss()
self.crt_tv = TVLoss()
self.crt_ts = TemporalSmoothLoss()
self._loss_g_rec = 0.0
self._loss_g_tsf = 0.0
self._loss_g_adv = 0.0
self._loss_g_mask = 0.0
self._loss_g_smooth = 0.0
self._loss_g_face = 0.0
self._d_real = 0.0
self._d_fake = 0.0
def multi_gpu_wrapper(self, f):
self.crt_tsf = self.crt_tsf.to(self.device)
self.flow_comp = self.flow_comp.to(self.device)
self.G = f(self.G.to(self.device))
if self._train_opts.use_face:
self.crt_face = self.crt_face.to(self.device)
if self._use_gan:
self.D = f(self.D.to(self.device))
return self
def gpu_wrapper(self):
self.crt_tsf = self.crt_tsf.to(self.device)
self.flow_comp = self.flow_comp.to(self.device)
self.G = self.G.to(self.device)
if self._train_opts.use_face:
self.crt_face = self.crt_face.to(self.device)
if self._use_gan:
self.D = self.D.to(self.device)
return self
def set_train(self):
self.G.train()
if self._use_gan:
self.D.train()
def set_eval(self):
self.G.eval()
def get_current_errors(self):
loss_g_face = self._loss_g_face if isinstance(self._loss_g_face, float) else self._loss_g_face.item()
loss_g_smooth = self._loss_g_smooth if isinstance(self._loss_g_smooth, float) else self._loss_g_smooth.item()
loss_g_mask = self._loss_g_mask if isinstance(self._loss_g_mask, float) else self._loss_g_mask.item()
loss_g_rec = self._loss_g_rec if isinstance(self._loss_g_rec, float) else self._loss_g_rec.item()
loss_g_tsf = self._loss_g_tsf if isinstance(self._loss_g_tsf, float) else self._loss_g_tsf.item()
loss_g_adv = self._loss_g_adv if isinstance(self._loss_g_adv, float) else self._loss_g_adv.item()
d_real = self._d_real if isinstance(self._d_real, float) else self._d_real.item()
d_fake = self._d_fake if isinstance(self._d_fake, float) else self._d_fake.item()
loss_dict = OrderedDict([("g_rec", loss_g_rec),
("g_tsf", loss_g_tsf),
("g_face", loss_g_face),
("g_adv", loss_g_adv),
("g_mask", loss_g_mask),
("g_mask_smooth", loss_g_smooth),
("d_real", d_real),
("d_fake", d_fake)])
return loss_dict
def get_current_scalars(self):
return OrderedDict([("lr_G", self._current_lr_G), ("lr_D", self._current_lr_D)])
def get_current_visuals(self):
visuals = OrderedDict()
visuals["0_source"] = self._vis_source
visuals["1_uv_img"] = self._vis_uv_img
visuals["2_real_img"] = self._vis_real
visuals["3_fake_src"] = self._vis_fake_src
visuals["4_fake_tsf"] = self._vis_fake_tsf
visuals["5_fake_bg"] = self._vis_fake_bg
visuals["6_fake_mask"] = self._vis_mask
visuals["7_body_mask"] = self._vis_body_mask
return visuals
@torch.no_grad()
def visual_imgs(self, fake_bg, fake_src_imgs, fake_tsf_imgs, fake_masks):
self._vis_fake_bg = tensor2im(fake_bg[0], idx=-1)
self._vis_fake_src = tensor2im(fake_src_imgs[0], idx=-1)
self._vis_fake_tsf = tensor2im(fake_tsf_imgs[0], idx=-1)
self._vis_uv_img = tensor2im(self._uv_img, idx=-1)
self._vis_real = tensor2im(self._real_tsf[0], idx=-1)
self._vis_source = tensor2im(self._real_src[0], idx=-1)
ids = self._opt.num_source - 1
self._vis_mask = tensor2im(fake_masks[0, ids:], idx=-1)
self._vis_body_mask = tensor2im(self._body_mask[0, ids:], idx=-1)
def save(self, label):
if "module" in self.G.__dict__:
self.save_network(self.G.module, "G", label)
if self._use_gan:
self.save_network(self.D.module, "D", label)
else:
self.save_network(self.G, "G", label)
if self._use_gan:
self.save_network(self.D, "D", label)
self.save_optimizer(self._optimizer_G, "G", label)
if self._use_gan:
self.save_optimizer(self._optimizer_D, "D", label)
def load(self):
load_iter = self._opt.load_iter
self.load_network(self.G, "G", load_iter, need_module=False)
if self._use_gan:
self.load_network(self.D, "D", load_iter, need_module=False)
def update_learning_rate(self):
final_lr = self._train_opts.final_lr
lr_decay_G = (self._train_opts.lr_G - final_lr) / self._train_opts.niters_or_epochs_decay
self._current_lr_G -= lr_decay_G
for param_group in self._optimizer_G.param_groups:
param_group["lr"] = self._current_lr_G
print("update G learning rate: %f -> %f" % (self._current_lr_G + lr_decay_G, self._current_lr_G))
if self._use_gan:
lr_decay_D = (self._train_opts.lr_D - final_lr) / self._train_opts.niters_or_epochs_decay
self._current_lr_D -= lr_decay_D
for param_group in self._optimizer_D.param_groups:
param_group["lr"] = self._current_lr_D
print("update D learning rate: %f -> %f" % (self._current_lr_D + lr_decay_D, self._current_lr_D))
def optimize_parameters(self, trainable=True, keep_data_for_visuals=False):
fake_bg, fake_src_imgs, fake_tsf_imgs, fake_masks = self.forward(
keep_data_for_visuals=keep_data_for_visuals)
if trainable:
loss_G = self.optimize_G(fake_bg, fake_src_imgs, fake_tsf_imgs, fake_masks)
self._optimizer_G.zero_grad()
loss_G.backward()
self._optimizer_G.step()
if self._use_gan:
loss_D = self.optimize_D(fake_bg, fake_tsf_imgs)
self._optimizer_D.zero_grad()
loss_D.backward()
self._optimizer_D.step()
@abc.abstractmethod
def forward(self, keep_data_for_visuals):
pass
@abc.abstractmethod
def optimize_G(self, *args, **kwargs):
pass
@abc.abstractmethod
def optimize_D(self, *args, **kwargs):
pass
class LWGAugBGTrainer(LWGTrainerABC):
def __init__(self, opt, device):
super().__init__(opt, device)
self._name = "LWGAugTrainer"
def set_input(self, inputs, device):
with torch.no_grad():
images = inputs["images"].to(device, non_blocking=True)
aug_bg = inputs["bg"].to(device, non_blocking=True)
smpls = inputs["smpls"].to(device, non_blocking=True)
masks = inputs["masks"].to(device, non_blocking=True)
offsets = inputs["offsets"].to(device, non_blocking=True) if "offsets" in inputs else 0
links_ids = inputs["links_ids"].to(device, non_blocking=True) if "links_ids" in inputs else None
ns = self._ns
src_img = images[:, 0:ns].contiguous()
src_smpl = smpls[:, 0:ns].contiguous()
tsf_img = images[:, ns:].contiguous()
tsf_smpl = smpls[:, ns:].contiguous()
src_mask = masks[:, 0:ns].contiguous()
ref_mask = masks[:, ns:].contiguous()
input_G_bg, input_G_src, input_G_tsf, Tst, Ttt, src_mask, tsf_mask, head_bbox, body_bbox, uv_img = self.flow_comp(src_img, tsf_img, src_smpl, tsf_smpl, src_mask=src_mask, ref_mask=ref_mask,
links_ids=links_ids, offsets=offsets, temporal=self._opt.temporal)
self._real_src = src_img
self._real_tsf = tsf_img
self._head_bbox = head_bbox
self._body_bbox = body_bbox
self._body_mask = masks
self._uv_img = uv_img
self._Tst = Tst
self._Ttt = Ttt
self._input_G_src = input_G_src
self._input_G_tsf = input_G_tsf
if not self._share_bg:
input_G_bg_tsf = torch.cat([tsf_img * tsf_mask, tsf_mask], dim=2)
input_G_bg = torch.cat([input_G_bg, input_G_bg_tsf], dim=1)
src_mask = src_mask[:, 0:1]
inpug_G_aug_bg = torch.cat([aug_bg[:, None] * src_mask, src_mask], dim=2)
input_G_bg = torch.cat([input_G_bg, inpug_G_aug_bg], dim=1)
self._real_bg = aug_bg
self._input_G_bg = input_G_bg
def forward(self, keep_data_for_visuals=False):
fake_bg, fake_src_color, fake_src_mask, fake_tsf_color, fake_tsf_mask = self.G(
self._input_G_bg, self._input_G_src, self._input_G_tsf, Tst=self._Tst, Ttt=self._Ttt, only_tsf=False)
if not self._opt.share_bg:
fake_bg_src = fake_bg[:, 0:self._ns]
fake_bg_tsf = fake_bg[:, self._ns:self._ns + self._nt]
else:
fake_bg_src = fake_bg[:, 0:1]
fake_bg_tsf = fake_bg_src
fake_src_imgs = fake_src_mask * fake_bg_src + (1 - fake_src_mask) * fake_src_color
fake_tsf_imgs = fake_tsf_mask * fake_bg_tsf + (1 - fake_tsf_mask) * fake_tsf_color
fake_masks = torch.cat([fake_src_mask, fake_tsf_mask], dim=1)
if keep_data_for_visuals:
self.visual_imgs(fake_bg, fake_src_imgs, fake_tsf_imgs, fake_masks)
return fake_bg, fake_src_imgs, fake_tsf_imgs, fake_masks
def optimize_G(self, fake_bg, fake_src_imgs, fake_tsf_imgs, fake_masks):
ns = fake_src_imgs.shape[1]
bs, nt, c, h, w = fake_tsf_imgs.shape
fake_tsf_imgs = fake_tsf_imgs.view(bs * nt, c, h, w)
real_tsf_imgs = self._real_tsf.view(bs * nt, -1, h, w)
tsf_cond = self._input_G_tsf[:, :, -3:].view(bs * nt, -1, h, w)
fake_input_D = torch.cat([fake_tsf_imgs, tsf_cond], dim=1)
fake_aug_bg = fake_bg[:, -1]
fake_global = torch.cat([fake_aug_bg, self._input_G_bg[:, -1, -1:]], dim=1)
d_inputs = {
"x": fake_input_D,
"bg_x": fake_global,
"body_rects": self._body_bbox,
"head_rects": self._head_bbox,
"get_avg": False
}
d_fake_outs = self.D(d_inputs)
self._loss_g_adv = self.crt_gan(d_fake_outs, 0) * self._train_opts.lambda_D_prob
self._loss_g_rec = (self.crt_l1(fake_src_imgs, self._real_src) +
self.crt_l1(fake_aug_bg, self._real_bg)) / 2 * self._train_opts.lambda_rec
self._loss_g_tsf = self.crt_tsf(fake_tsf_imgs, real_tsf_imgs) * self._train_opts.lambda_tsf
if self._train_opts.use_face:
self._loss_g_face = self.crt_face(
fake_tsf_imgs, real_tsf_imgs,
bbox1=self._head_bbox, bbox2=self._head_bbox) * self._train_opts.lambda_face
fake_masks = fake_masks.view(bs * (ns + nt), 1, h, w)
body_masks = self._body_mask.view(bs * (ns + nt), 1, h, w)
self._loss_g_mask = self.crt_mask(fake_masks, body_masks) * self._train_opts.lambda_mask
self._loss_g_smooth = self.crt_tv(fake_masks) * self._train_opts.lambda_mask_smooth
return self._loss_g_rec + self._loss_g_tsf + self._loss_g_face + self._loss_g_adv + self._loss_g_mask + self._loss_g_smooth
def optimize_D(self, fake_bg, fake_tsf_imgs):
bs, nt, c, h, w = fake_tsf_imgs.shape
fake_tsf_imgs = fake_tsf_imgs.view(bs * nt, c, h, w)
real_tsf_imgs = self._real_tsf.reshape(bs * nt, c, h, w)
tsf_cond = self._input_G_tsf[:, :, -3:].view(bs * nt, -1, h, w)
fake_input_D = torch.cat([fake_tsf_imgs.detach(), tsf_cond], dim=1)
real_input_D = torch.cat([real_tsf_imgs, tsf_cond], dim=1)
fake_aug_bg = fake_bg[:, -1]
fake_bg_x = torch.cat([fake_aug_bg.detach(), self._input_G_bg[:, -1, -1:]], dim=1)
real_bg_x = torch.cat([self._real_bg, self._input_G_bg[:, -1, -1:]], dim=1)
real_inputs = {
"x": real_input_D,
"bg_x": real_bg_x,
"body_rects": self._body_bbox,
"head_rects": self._head_bbox,
"get_avg": True
}
d_real_outs, self._d_real = self.D(real_inputs)
fake_inputs = {
"x": fake_input_D,
"bg_x": fake_bg_x,
"body_rects": self._body_bbox,
"head_rects": self._head_bbox,
"get_avg": True
}
d_fake_outs, self._d_fake = self.D(fake_inputs)
_loss_d_real = self.crt_gan(d_real_outs, 1)
_loss_d_fake = self.crt_gan(d_fake_outs, -1)
return _loss_d_real + _loss_d_fake
class LWGTrainer(LWGTrainerABC):
def __init__(self, opt, device):
super(LWGTrainer, self).__init__(opt, device)
self._name = "LWGTrainer"
def set_input(self, inputs, device):
with torch.no_grad():
images = inputs["images"].to(device, non_blocking=True)
bg = inputs["bg"].to(device, non_blocking=True)
smpls = inputs["smpls"].to(device, non_blocking=True)
masks = inputs["masks"].to(device, non_blocking=True)
offsets = inputs["offsets"].to(device, non_blocking=True)
links_ids = inputs["links_ids"].to(device, non_blocking=True) if "links_ids" in inputs else None
ns = self._ns
src_img = images[:, 0:ns].contiguous()
src_smpl = smpls[:, 0:ns].contiguous()
tsf_img = images[:, ns:].contiguous()
tsf_smpl = smpls[:, ns:].contiguous()
src_mask = masks[:, 0:ns].contiguous()
ref_mask = masks[:, ns:].contiguous()
input_G_bg, input_G_src, input_G_tsf, Tst, Ttt, src_mask, tsf_mask, head_bbox, body_bbox, uv_img = self.flow_comp(src_img, tsf_img, src_smpl, tsf_smpl, src_mask=src_mask, ref_mask=ref_mask,
links_ids=links_ids, offsets=offsets, temporal=self._opt.temporal)
self._real_src = src_img
self._real_tsf = tsf_img
self._head_bbox = head_bbox
self._body_bbox = body_bbox
self._body_mask = masks
self._uv_img = uv_img
self._Tst = Tst
self._Ttt = Ttt
self._input_G_src = input_G_src
self._input_G_tsf = input_G_tsf
if not self._share_bg:
input_G_bg_tsf = torch.cat([tsf_img * tsf_mask, tsf_mask], dim=2)
input_G_bg = torch.cat([input_G_bg, input_G_bg_tsf], dim=1)
self._input_G_bg = input_G_bg
self._real_bg = bg.view(-1, 3, self._opt.image_size, self._opt.image_size)
def forward(self, keep_data_for_visuals=False, return_estimates=False):
input_G_tsf = self._input_G_tsf
fake_bg, fake_src_color, fake_src_mask, fake_tsf_color, fake_tsf_mask = self.G(self._input_G_bg, self._input_G_src, input_G_tsf, Tst=self._Tst, Ttt=self._Ttt, only_tsf=False)
if not self._opt.share_bg:
fake_bg_src = fake_bg[:, 0:self._ns]
fake_bg_tsf = fake_bg[:, self._ns:self._ns + self._nt]
else:
fake_bg_src = fake_bg
fake_bg_tsf = fake_bg
fake_src_imgs = fake_src_mask * fake_bg_src + (1 - fake_src_mask) * fake_src_color
fake_tsf_imgs = fake_tsf_mask * fake_bg_tsf + (1 - fake_tsf_mask) * fake_tsf_color
fake_masks = torch.cat([fake_src_mask, fake_tsf_mask], dim=1)
if keep_data_for_visuals:
self.visual_imgs(fake_bg, fake_src_imgs, fake_tsf_imgs, fake_masks)
return fake_bg, fake_src_imgs, fake_tsf_imgs, fake_masks
def optimize_G(self, fake_bg, fake_src_imgs, fake_tsf_imgs, fake_masks):
ns = fake_src_imgs.shape[1]
bs, nt, c, h, w = fake_tsf_imgs.shape
fake_bg = fake_bg.view(-1, 3, h, w)
fake_tsf_imgs = fake_tsf_imgs.view(bs * nt, c, h, w)
real_tsf_imgs = self._real_tsf.view(bs * nt, -1, h, w)
if self._use_gan:
tsf_cond = self._input_G_tsf[:, :, -3:].view(bs * nt, -1, h, w)
fake_input_D = torch.cat([fake_tsf_imgs, tsf_cond], dim=1)
d_inputs = {
"x": fake_input_D,
"bg_x": None,
"body_rects": self._body_bbox,
"head_rects": self._head_bbox,
"get_avg": False
}
d_fake_outs = self.D(d_inputs)
self._loss_g_adv = self.crt_gan(d_fake_outs, 0) * self._train_opts.lambda_D_prob
self._loss_g_rec = (self.crt_l1(fake_src_imgs, self._real_src) +
self.crt_l1(fake_bg, self._real_bg)) / 2 * self._train_opts.lambda_rec
self._loss_g_tsf = self.crt_tsf(fake_tsf_imgs, real_tsf_imgs) * self._train_opts.lambda_tsf
if self._train_opts.use_face:
self._loss_g_face = self.crt_face(
fake_tsf_imgs, real_tsf_imgs,
bbox1=self._head_bbox, bbox2=self._head_bbox) * self._train_opts.lambda_face
fake_masks = fake_masks.view(bs * (ns + nt), 1, h, w)
body_masks = self._body_mask.view(bs * (ns + nt), 1, h, w)
self._loss_g_mask = self.crt_mask(fake_masks, body_masks) * self._train_opts.lambda_mask
self._loss_g_smooth = self.crt_tv(fake_masks) * self._train_opts.lambda_mask_smooth
return self._loss_g_rec + self._loss_g_tsf + self._loss_g_face + self._loss_g_adv + self._loss_g_mask + self._loss_g_smooth | Apache License 2.0 |
demetoir/allgans | util/Stacker.py | Stacker.max_pooling | python | def max_pooling(self, filter_):
return self.add_layer(max_pooling, filter_) | add max pooling layer | https://github.com/demetoir/allgans/blob/2f972db5e9a65f18aee0695d817f4acc221e54da/util/Stacker.py#L104-L106 | from util.tensor_ops import *
class Stacker:
def __init__(self, start_layer=None, reuse=False, name="stacker"):
self.reuse = reuse
self.layer_count = 1
self.last_layer = start_layer
self.layer_seq = [start_layer]
self.name = name
def add_layer(self, func, *args, **kwargs):
scope_name = self.name + '_layer' + str(self.layer_count)
with tf.variable_scope(scope_name, reuse=self.reuse):
self.last_layer = func(self.last_layer, *args, **kwargs)
self.layer_seq += [self.last_layer]
pass
self.layer_count += 1
return self.last_layer
def bn(self):
return self.add_layer(bn)
def sigmoid(self):
return self.add_layer(sigmoid)
def lrelu(self):
return self.add_layer(lrelu)
def relu(self):
return self.add_layer(relu)
def elu(self):
return self.add_layer(elu)
def linear(self, output_size):
return self.add_layer(linear, output_size)
def conv2d_transpose(self, output_shape, filter_):
return self.add_layer(conv2d_transpose, output_shape, filter_)
def conv2d(self, output_channel, filter_):
return self.add_layer(conv2d, output_channel, filter_)
def conv2d_one_by_one(self, output_channel):
return self.add_layer(conv2d_one_by_one, output_channel)
def upscale_2x(self, output_channel, filter_):
return self.add_layer(upscale_2x, output_channel, filter_)
def upscale_2x_block(self, output_channel, filter_, activate):
return self.add_layer(upscale_2x_block, output_channel, filter_, activate)
def conv_block(self, output_channel, filter_, activate):
return self.add_layer(conv_block, output_channel, filter_, activate)
def avg_pooling(self, filter_):
return self.add_layer(avg_pooling, filter_) | MIT License |
paddlepaddle/paddle | python/paddle/fluid/entry_attr.py | EntryAttr._to_attr | python | def _to_attr(self):
raise NotImplementedError("EntryAttr is base class") | Returns the attributes of this parameter.
Returns:
Parameter attributes(map): The attributes of this parameter. | https://github.com/paddlepaddle/paddle/blob/056b87414880e0520bb4560fc40d5b62db9c5175/python/paddle/fluid/entry_attr.py#L31-L38 | from __future__ import print_function
__all__ = ['ProbabilityEntry', 'CountFilterEntry']
class EntryAttr(object):
def __init__(self):
self._name = None | Apache License 2.0 |
garoe/tf_mvg | mvg_distributions/covariance_representations/covariance_conv.py | PrecisionConvCholFilters.np_off_diag_mask | python | def np_off_diag_mask(self):
assert self.recons_filters_precision.shape[1:3].is_fully_defined()
n = self.recons_filters_precision.shape[1].value
n_width = int(np.sqrt(n))
nb = self.recons_filters_precision.shape[2].value
nf = int(np.sqrt(nb))
kernel = np.zeros((nf, nf), dtype=np.float32)
kernel[nf // 2 + 1:, :] = 1
kernel[nf // 2, nf // 2 + 1:] = 1
kernel_list = np.tile(kernel[np.newaxis, :, :], (n, 1, 1))
cholesky_matrix = np_ops.np_make_matrix_from_kernel_list(kernels=kernel_list, img_size=n_width)
cholesky_matrix = cholesky_matrix.T
return cholesky_matrix | Returns a ndarray of [n,n] that is 1 of the off-diagonal elements in L | https://github.com/garoe/tf_mvg/blob/01bc681a8b3aac5dcf0837d481b963f4968eb777/mvg_distributions/covariance_representations/covariance_conv.py#L567-L585 | import numpy as np
import tensorflow as tf
from mvg_distributions.covariance_representations.covariance_matrix import Covariance, SampleMethod
from mvg_distributions.utils.variable_filter_functions import conv2d_samples_linear_combination_filters
from mvg_distributions.utils.unpooling import unpooling2d_zero_filled
import mvg_distributions.utils.numpy_ops as np_ops
from scipy.sparse.linalg import spsolve_triangular
from scipy.sparse import csc_matrix, csr_matrix, lil_matrix, isspmatrix_csr, SparseEfficiencyWarning
from scipy.sparse import eye as sparse_eye
from scipy.linalg import LinAlgError
from warnings import warn
import os
from tqdm import tqdm
class PrecisionConvFilters(Covariance):
def __init__(self, weights_precision, filters_precision, sample_shape, **kwargs):
super().__init__(**kwargs)
self.sample_shape = sample_shape
if isinstance(self.sample_shape, np.ndarray):
self.sample_shape = tf.TensorShape(self.sample_shape)
if isinstance(self.sample_shape, tf.TensorShape) and self.sample_shape.is_fully_defined():
assert self.sample_shape[3].value == 1, "Only one channel is supported"
num_pixels = self.sample_shape[1] * self.sample_shape[2]
self._matrix_shape = tf.TensorShape([self.sample_shape[0], num_pixels, num_pixels])
else:
num_pixels = self.sample_shape[1] * self.sample_shape[2]
self._matrix_shape = tf.stack([self.sample_shape[0], num_pixels, num_pixels], axis=0)
self.weights_precision = weights_precision
assert self.weights_precision is not None
if filters_precision is None:
self.filters_precision = self._id_filters(self.weights_precision)
else:
self.filters_precision = filters_precision
assert self.filters_precision is not None
self._diag_sqrt_covariance = None
self._diag_sqrt_precision = None
self._recons_filters_precision = None
def _get_epsilon_flat_shape(self, num_samples):
return tf.stack([self.sample_shape[0], num_samples, tf.reduce_prod(self.sample_shape[1:])], axis=0)
def _get_epsilon(self, num_samples, epsilon, seed=None):
epsilon_shape = self._get_epsilon_flat_shape(num_samples)
if epsilon is None:
epsilon = self._build_epsilon(epsilon_shape, seed=seed)
if epsilon.shape.ndims + 1 == epsilon_shape.shape[0].value:
epsilon = tf.expand_dims(epsilon, 1)
return epsilon
def _get_epsilon_img_shape(self, num_samples):
return tf.concat([self.sample_shape[0:1], [num_samples], self.sample_shape[1:]], axis=0)
def _get_epsilon_5_dim(self, num_samples, epsilon, seed=None):
epsilon_shape = self._get_epsilon_img_shape(num_samples)
if epsilon is None:
epsilon = self._build_epsilon(epsilon_shape, seed=seed)
if epsilon.shape.ndims == 2 or epsilon.shape.ndims == 3:
flat_shape = tf.shape(epsilon)
dim_4_5_shape = tf.concat([flat_shape[0:epsilon.shape.ndims - 1], self.sample_shape[1:]], axis=0)
epsilon = tf.reshape(epsilon, dim_4_5_shape)
if epsilon.shape.ndims + 1 == epsilon_shape.shape[0].value:
epsilon = tf.expand_dims(epsilon, 1)
return epsilon
def _flatten_keep_sample_dim(self, inputs):
if inputs.shape.ndims == 3:
return inputs
elif inputs.shape.ndims == 5:
inputs_shape = tf.shape(inputs)
flat_shape = tf.concat([inputs_shape[0:2], [tf.reduce_prod(self.sample_shape[1:])]], axis=0)
return tf.reshape(inputs, shape=flat_shape)
else:
raise ValueError("Invalid number of dimensions {}".format(inputs.shape.ndims))
def x_precision_x(self, x, mean_batch=False, no_gradients=True, **kwargs):
if no_gradients == False:
raise NotImplementedError("")
with tf.name_scope("x_precision_x"):
x_precision = self.whiten_x(x=x, flatten_output=True)
if x_precision.shape.ndims == 2:
x_precision = tf.expand_dims(x_precision, axis=1)
squared_error = tf.multiply(x_precision, x_precision)
squared_error = tf.reduce_sum(squared_error, axis=2)
if squared_error.shape[1].value == 1:
squared_error = tf.squeeze(squared_error, axis=1, name="x_precision_x")
if mean_batch:
squared_error = tf.reduce_mean(squared_error, axis=0, name="mean_x_precision_x")
return squared_error
@staticmethod
def _id_filters(weights_precision):
with tf.name_scope('id_filters'):
num_ch = 1
filter_wh = weights_precision.shape[-1].value
filter_size = int(np.sqrt(filter_wh))
identity_basis = tf.eye(num_rows=filter_wh)
return tf.reshape(identity_basis, (filter_wh, filter_size, filter_size, num_ch, num_ch))
@property
def diag_sqrt_covariance(self):
if self._diag_sqrt_covariance is None:
self._diag_sqrt_covariance = self._build_diag_sqrt_covariance()
return self._diag_sqrt_covariance
def _build_diag_sqrt_covariance(self):
with tf.name_scope("DiagSqrtCovariance"):
return tf.matrix_diag_part(self.sqrt_covariance, name="diag_sqrt_covariance")
@property
def diag_sqrt_precision(self):
if self._diag_sqrt_precision is None:
self._diag_sqrt_precision = self._build_diag_sqrt_precision()
return self._diag_sqrt_precision
def _build_diag_sqrt_precision(self):
with tf.name_scope("DiagSqrtPrecision"):
return self._build_diag_from_weights(name="diag_sqrt_precision")
@property
def recons_filters_precision(self):
if self._recons_filters_precision is None:
self._recons_filters_precision = self._reconstruct_basis(weights=self.weights_precision,
basis=self.filters_precision)
return self._recons_filters_precision
def _build_diag_from_weights(self, name="diag_from_weights"):
weights = self.weights_precision
filters = self.filters_precision
filters.shape.assert_is_fully_defined()
filters_shape = filters.shape.as_list()
center_i = (np.array(filters_shape[1:3]) - 1) // 2
diag_basis = filters[:, center_i[0], center_i[1]]
if weights.shape.is_fully_defined():
w_flat_shape = tf.TensorShape([weights.shape[0], weights.shape[1] * weights.shape[2], weights.shape[3]])
else:
weights_shape = tf.shape(weights)
w_flat_shape = tf.stack([weights_shape[0], weights_shape[1] * weights_shape[2], weights_shape[3]])
weights_flat = tf.reshape(weights, shape=w_flat_shape)
diag_basis = tf.reshape(diag_basis, shape=(1, 1, filters_shape[0]))
diag_covariance = tf.multiply(weights_flat, diag_basis)
return tf.reduce_sum(diag_covariance, axis=2, name=name)
def _build_covariance(self):
with tf.name_scope("Covariance"):
return self._inverse_covariance_or_precision()
def _build_precision(self):
with tf.name_scope("Precision"):
return tf.matmul(self.sqrt_precision, self.sqrt_precision, transpose_b=True, name="precision")
def _build_sqrt_covariance(self):
with tf.name_scope("Covariance_Sqrt"):
return tf.matrix_inverse(self.sqrt_precision, name="covariance_sqrt")
def _build_sqrt_precision(self):
with tf.name_scope("Precision_Sqrt"):
return self._build_matrix_from_basis(weights=self.weights_precision, basis=self.filters_precision,
name="precision_sqrt")
def _reconstruct_basis(self, weights, basis, name="reconstruct_basis"):
with tf.name_scope(name):
basis.shape[3:5].assert_is_fully_defined()
assert basis.shape[3] == 1 and basis.shape[4] == 1, "Only on channel is supported"
basis = tf.squeeze(basis, axis=[3, 4])
if basis.shape.is_fully_defined():
basis_flat_shape = tf.TensorShape([basis.shape[0], basis.shape[1] * basis.shape[2]])
else:
basis_shape = tf.shape(basis)
basis_flat_shape = tf.stack([basis_shape[0], basis_shape[1] * basis_shape[2]])
if weights.shape.is_fully_defined():
weights_shape = weights.shape
weights_flat_shape = tf.TensorShape(
[weights.shape[0], weights.shape[1] * weights.shape[2], weights.shape[3]])
else:
weights_shape = tf.shape(weights)
weights_flat_shape = tf.stack([weights_shape[0], weights_shape[1] * weights_shape[2], weights_shape[3]])
basis_flat = tf.reshape(basis, shape=basis_flat_shape)
basis_flat = tf.expand_dims(basis_flat, axis=0)
basis_flat = tf.tile(basis_flat, multiples=tf.stack([weights_shape[0], 1, 1]))
weights_flat = tf.reshape(weights, shape=weights_flat_shape)
reconstructed_basis = tf.matmul(weights_flat, basis_flat)
output_shape = [weights.shape[0], weights.shape[1] * weights.shape[2], basis.shape[1] * basis.shape[2]]
reconstructed_basis.set_shape(output_shape)
return reconstructed_basis
def _build_matrix_from_basis(self, weights, basis, name=None):
with tf.name_scope(name, default_name="build_matrix_from_basis"):
filters = self.recons_filters_precision
filters_shape, img_h, img_w = self._compute_shapes_for_filter_matrix(basis, weights)
filters = tf.reshape(filters, shape=filters_shape)
filters_shape, filteres_h_half, filters_w_half = self._compute_shapes_for_single_filter(filters)
matrix = list()
k = 0
for i in range(0, img_w):
for j in range(0, img_h):
filter_ij = filters[:, k]
padding_w0 = i - filters_w_half
padding_w1 = img_w - (filters_shape[2].value + padding_w0)
padding_h0 = j - filteres_h_half
padding_h1 = img_h - (filters_shape[3].value + padding_h0)
if padding_w0 < 0:
filter_ij = filter_ij[:, np.abs(padding_w0):filters_shape[2].value, :]
padding_w0 = 0
if padding_w1 < 0:
filter_ij = filter_ij[:, 0:filters_shape[2].value - np.abs(padding_w1), :]
padding_w1 = 0
if padding_h0 < 0:
filter_ij = filter_ij[:, :, np.abs(padding_h0):filters_shape[3].value]
padding_h0 = 0
if padding_h1 < 0:
filter_ij = filter_ij[:, :, 0:filters_shape[3].value - np.abs(padding_h1)]
padding_h1 = 0
padding = tf.constant([[0, 0], [padding_w0, padding_w1], [padding_h0, padding_h1]])
filter_ij = tf.pad(filter_ij, paddings=padding)
filter_ij = tf.layers.flatten(filter_ij)
matrix.append(filter_ij)
k += 1
matrix = tf.stack(matrix, axis=1)
return tf.matrix_transpose(matrix)
def _compute_shapes_for_single_filter(self, filters):
if filters.shape[1:].is_fully_defined():
filters_shape = filters.shape
assert filters_shape[2].value / 2.0 != 0, "Filter width must be an odd number"
assert filters_shape[3].value / 2.0 != 0, "Filter height must be an odd number"
filters_w_half = (filters_shape[2].value - 1) // 2
filteres_h_half = (filters_shape[3].value - 1) // 2
else:
filters_shape = tf.shape(filters)
filters_w_half = (filters_shape[2] - 1) // 2
filteres_h_half = (filters_shape[3] - 1) // 2
return filters_shape, filteres_h_half, filters_w_half
def _compute_shapes_for_filter_matrix(self, basis, weights):
if basis.shape.is_fully_defined() and weights.shape[1:].is_fully_defined():
weights_shape = weights.shape.as_list()
if weights_shape[0] is None:
batch_size = -1
else:
batch_size = weights_shape[0]
filters_shape = [batch_size, weights_shape[1] * weights_shape[2]]
filters_shape = filters_shape + basis.shape[1:3].as_list()
img_w = weights_shape[1]
img_h = weights_shape[2]
else:
weights_shape = tf.shape(weights)
filters_shape = tf.shape(basis)
batch_flat_img_shape = [weights_shape[0], weights_shape[1] * weights_shape[2]]
filters_shape = tf.concat(batch_flat_img_shape + filters_shape[1:3], axis=0)
img_w = weights_shape[1]
img_h = weights_shape[2]
raise NotImplementedError("Only supported for images of known sizes")
return filters_shape, img_h, img_w
def _build_epsilon(self, epsilon_shape, seed=None):
with tf.name_scope("Epsilon"):
return tf.random_normal(shape=epsilon_shape, dtype=self.dtype, seed=seed, name="epsilon")
def _sample_with_net(self, epsilon, filters, weights, name="sample_with_net"):
return conv2d_samples_linear_combination_filters(inputs=epsilon, filters=filters,
alpha=weights, name=name)
def _sample_common(self, num_samples, epsilon, sample_method, return_epsilon, flatten_output, is_covariance):
if is_covariance:
name = "sample_covariance"
else:
name = "whiten_x"
if sample_method == SampleMethod.NET:
weights = self.weights_precision
filters = self.filters_precision
if not is_covariance:
with tf.name_scope(name):
epsilon = self._get_epsilon_5_dim(num_samples, epsilon)
sample = self._sample_with_net(epsilon, filters, weights)
if flatten_output:
epsilon = self._flatten_keep_sample_dim(epsilon)
sample = self._flatten_keep_sample_dim(sample)
epsilon = self._squeeze_sample_dims(epsilon, name='epsilon')
sample = self._squeeze_sample_dims(sample, name='sample')
if return_epsilon:
return sample, epsilon
else:
return sample
else:
sample_method = SampleMethod.CHOLESKY
with tf.name_scope(name):
epsilon = self._get_epsilon_5_dim(num_samples, epsilon)
epsilon_5_dims_shape = tf.shape(epsilon)
epsilon = self._flatten_keep_sample_dim(epsilon)
if is_covariance:
sample = super().sample_covariance(num_samples, epsilon, sample_method=sample_method)
else:
sample = super().whiten_x(num_samples, epsilon, sample_method=sample_method)
if not flatten_output:
epsilon = tf.reshape(epsilon, shape=epsilon_5_dims_shape)
sample = tf.reshape(sample, shape=epsilon_5_dims_shape)
sample = self._squeeze_sample_dims(sample, name='sample')
epsilon = self._squeeze_sample_dims(epsilon, name='epsilon')
if return_epsilon:
return sample, epsilon
else:
return sample
def sample_covariance(self, num_samples=1, epsilon=None, sample_method=None, return_epsilon=False,
flatten_output=False):
if sample_method is None:
sample_method = SampleMethod.NET
return self._sample_common(num_samples, epsilon, sample_method, return_epsilon, flatten_output,
is_covariance=True)
def whiten_x(self, num_samples=1, x=None, sample_method=None, return_epsilon=False,
flatten_output=False):
if sample_method is None:
sample_method = SampleMethod.NET
return self._sample_common(num_samples, x, sample_method, return_epsilon, flatten_output,
is_covariance=False)
class PrecisionConvCholFilters(PrecisionConvFilters):
def __init__(self, weights_precision, filters_precision, sample_shape, **kwargs):
super().__init__(weights_precision=weights_precision, filters_precision=filters_precision,
sample_shape=sample_shape, **kwargs)
self._log_diag_chol_covariance = None
self._log_diag_chol_precision = None
self._diag_covariance = None
self._diag_precision = None
self._recons_filters_precision_aligned = None
self._build_with_covariance = False
self.dtype = weights_precision.dtype
self._t_indices = None
self._l_indices = None
def _build_chol_precision(self):
with tf.name_scope("Precision_Chol"):
return self._build_matrix_from_basis(weights=self.weights_precision, basis=self.filters_precision,
name="precision_chol")
def _build_covariance(self):
with tf.name_scope("Covariance"):
return self._inverse_covariance_or_precision()
def _build_precision(self):
with tf.name_scope("Precision"):
return tf.matmul(self.chol_precision, self.chol_precision, transpose_b=True, name="precision")
def _build_log_det_covariance_with_chol(self):
log_det = 2.0 * tf.reduce_sum(self.log_diag_chol_precision, axis=1)
return tf.negative(log_det, name="log_det_covar")
@property
def log_diag_chol_covariance(self):
if self._log_diag_chol_covariance is None:
self._log_diag_chol_covariance = self._build_log_diag_chol_covariance()
return self._log_diag_chol_covariance
@log_diag_chol_covariance.setter
def log_diag_chol_covariance(self, value):
self._log_diag_chol_covariance = value
def _build_log_diag_chol_covariance(self):
with tf.name_scope("DiagCholCovariance"):
diag_c = tf.matrix_diag_part(self.chol_covariance, name="diag_chol_covariance")
return tf.log(diag_c, name="log_diag_chol_covariance")
@property
def log_diag_chol_precision(self):
if self._log_diag_chol_precision is None:
self._log_diag_chol_precision = self._build_log_diag_chol_precision()
return self._log_diag_chol_precision
@log_diag_chol_precision.setter
def log_diag_chol_precision(self, value):
self._log_diag_chol_precision = value
def _build_log_diag_chol_precision(self):
with tf.name_scope("DiagCholPrecision"):
diag_p = self._build_diag_from_weights(name="diag_chol_precision")
return tf.log(diag_p, name="log_diag_chol_precision")
def _build_diag_sqrt_covariance(self):
with tf.name_scope("DiagSqrtCovariance"):
return tf.matrix_diag_part(self.sqrt_covariance, name="diag_chol_precision")
def _build_diag_sqrt_precision(self):
with tf.name_scope("DiagSqrtPrecision"):
return tf.matrix_diag_part(self.sqrt_precision, name="diag_chol_precision")
def _build_sqrt_covariance(self):
return super(PrecisionConvFilters, self)._build_sqrt_covariance()
def _build_sqrt_precision(self):
return super(PrecisionConvFilters, self)._build_sqrt_precision()
def _conv_filter_for_diag(self, filters_shape):
with tf.name_scope('Cov-Diag-Filter'):
if isinstance(filters_shape, tf.Tensor):
raise NotImplementedError("")
half_size = (filters_shape[2] * filters_shape[3]) // 2 + 1
cov_shape = [filters_shape[2], filters_shape[3], half_size]
cov_filter = np.zeros(shape=cov_shape, dtype=np.float32)
f_width = cov_filter.shape[1]
i = 0
j = 0
for c in range(half_size):
cov_filter[i, j, -(c + 1)] = 1
j += 1
if j == f_width:
j = 0
i += 1
cov_filter = np.expand_dims(cov_filter, axis=3)
cov_filter = tf.constant(cov_filter)
return cov_filter
def _build_diag_part_with_conv(self, filters, basis, weights, name):
filters_shape, img_h, img_w = self._compute_shapes_for_filter_matrix(basis, weights)
with tf.name_scope(name + 'Diag-Part'):
conv_diag_filter = self._conv_filter_for_diag(filters_shape)
half_f_width = (filters_shape[2] * filters_shape[3]) // 2
filters = filters[:, :, half_f_width:]
filters = tf.reshape(filters, (-1, img_w, img_h, half_f_width + 1))
filters_sq = tf.square(filters)
diag_part = tf.nn.conv2d(filters_sq, conv_diag_filter, strides=(1, 1, 1, 1), padding='SAME')
return tf.layers.flatten(diag_part)
def _build_precision_diag_part(self):
return self._build_diag_part_with_conv(filters=self.recons_filters_precision, basis=self.filters_precision,
weights=self.weights_precision, name='Precision')
def _align_filters_per_row(self, filters, basis, weights, name):
filters_shape, img_h, img_w = self._compute_shapes_for_filter_matrix(basis, weights)
with tf.name_scope(name + 'Row-Align'):
conv_filter = self._conv_filter_for_diag(filters_shape)
half_f_width = (filters_shape[2] * filters_shape[3]) // 2
zeroes = filters[:, :, 0:half_f_width]
filters = filters[:, :, half_f_width:]
filters = tf.reshape(filters, (-1, img_w, img_h, half_f_width + 1))
aligned_filters = tf.nn.depthwise_conv2d(filters, conv_filter, strides=(1, 1, 1, 1), padding='SAME')
aligned_filters = tf.reshape(aligned_filters, (-1, img_w * img_h, half_f_width + 1))
aligned_filters = tf.concat([zeroes, aligned_filters], axis=-1)
return aligned_filters
@property
def recons_filters_precision_aligned(self):
if self._recons_filters_precision_aligned is None:
self._recons_filters_precision_aligned = self._align_filters_per_row(filters=self.recons_filters_precision,
basis=self.filters_precision,
weights=self.weights_precision,
name='Aligned-Filters')
return self._recons_filters_precision_aligned | MIT License |
redcokedevelopment/teapot.py | teapot/cogs/music.py | Music.queue | python | async def queue(self, ctx, page: int = 1):
player = self.bot.lavalink.player_manager.get(ctx.guild.id)
if not player.queue:
return await ctx.send('Nothing queued.')
items_per_page = 10
pages = math.ceil(len(player.queue) / items_per_page)
start = (page - 1) * items_per_page
end = start + items_per_page
queue_list = ''
for index, track in enumerate(player.queue[start:end], start=start):
queue_list += f'`{index + 1}.` [**{track.title}**]({track.uri})\n'
embed = discord.Embed(colour=discord.Color.blurple(),
description=f'**{len(player.queue)} tracks**\n\n{queue_list}')
embed.set_footer(text=f'Viewing page {page}/{pages}')
await ctx.send(embed=embed) | Shows the player's queue. | https://github.com/redcokedevelopment/teapot.py/blob/aa4e92d7a1bf6f997051ae3422ba52fc034e317b/teapot/cogs/music.py#L140-L160 | import math
import re
import discord
import lavalink
from discord.ext import commands
import teapot
url_rx = re.compile('https?:\\/\\/(?:www\\.)?.+')
class Music(commands.Cog):
def __init__(self, bot):
self.bot = bot
if not hasattr(bot, 'lavalink'):
bot.lavalink = lavalink.Client(bot.user.id)
bot.lavalink.add_node(teapot.config.lavalink_host(), teapot.config.lavalink_port(),
teapot.config.lavalink_password(), 'zz',
'default')
bot.add_listener(bot.lavalink.voice_update_handler, 'on_socket_response')
bot.lavalink.add_event_hook(self.track_hook)
def cog_unload(self):
self.bot.lavalink._event_hooks.clear()
async def cog_before_invoke(self, ctx):
guild_check = ctx.guild is not None
if guild_check:
await self.ensure_voice(ctx)
return guild_check
async def cog_command_error(self, ctx, error):
if isinstance(error, commands.CommandInvokeError):
await ctx.send(error.original)
async def track_hook(self, event):
if isinstance(event, lavalink.events.QueueEndEvent):
guild_id = int(event.player.guild_id)
await self.connect_to(guild_id, None)
async def connect_to(self, guild_id: int, channel_id: str):
ws = self.bot._connection._get_websocket(guild_id)
await ws.voice_state(str(guild_id), channel_id)
@commands.command(aliases=['p'])
async def play(self, ctx, *, query: str):
player = self.bot.lavalink.player_manager.get(ctx.guild.id)
query = query.strip('<>')
if not url_rx.match(query):
query = f'ytsearch:{query}'
results = await player.node.get_tracks(query)
if not results or not results['tracks']:
return await ctx.send('Nothing found!')
embed = discord.Embed(color=discord.Color.blurple())
if results['loadType'] == 'PLAYLIST_LOADED':
tracks = results['tracks']
for track in tracks:
player.add(requester=ctx.author.id, track=track)
embed.title = 'Playlist Enqueued!'
embed.description = f'{results["playlistInfo"]["name"]} - {len(tracks)} tracks'
else:
track = results['tracks'][0]
embed.title = 'Track Enqueued'
embed.description = f'[{track["info"]["title"]}]({track["info"]["uri"]})'
player.add(requester=ctx.author.id, track=track)
await ctx.send(embed=embed)
if not player.is_playing:
await player.play()
@commands.command()
async def seek(self, ctx, *, seconds: int):
player = self.bot.lavalink.player_manager.get(ctx.guild.id)
track_time = player.position + (seconds * 1000)
await player.seek(track_time)
await ctx.send(f'Moved track to **{lavalink.utils.format_time(track_time)}**')
@commands.command(aliases=['forceskip'])
async def skip(self, ctx):
player = self.bot.lavalink.player_manager.get(ctx.guild.id)
if not player.is_playing:
return await ctx.send('Not playing.')
await player.skip()
await ctx.send('⏭ | Skipped.')
@commands.command()
async def stop(self, ctx):
player = self.bot.lavalink.player_manager.get(ctx.guild.id)
if not player.is_playing:
return await ctx.send('Not playing.')
player.queue.clear()
await player.stop()
await ctx.send('⏹ | Stopped.')
@commands.command(aliases=['np', 'n', 'playing'])
async def now(self, ctx):
player = self.bot.lavalink.player_manager.get(ctx.guild.id)
if not player.current:
return await ctx.send('Nothing playing.')
position = lavalink.utils.format_time(player.position)
if player.current.stream:
duration = '🔴 LIVE'
else:
duration = lavalink.utils.format_time(player.current.duration)
song = f'**[{player.current.title}]({player.current.uri})**\n({position}/{duration})'
embed = discord.Embed(color=discord.Color.blurple(),
title='Now Playing', description=song)
await ctx.send(embed=embed)
@commands.command(aliases=['q']) | MIT License |
kirthevasank/nasbot | nn/nn_comparators.py | _get_conv_filter_size_cost | python | def _get_conv_filter_size_cost(labi, labj, conv_scale):
conv_diff = float(abs(int(labi[-1]) - int(labj[-1])))
return conv_scale * np.sqrt(conv_diff) | Returns the cost for comparing two different convolutional filters. | https://github.com/kirthevasank/nasbot/blob/3c745dc986be30e3721087c8fa768099032a0802/nn/nn_comparators.py#L27-L30 | import numpy as np
from gp.kernel import ExpSumOfDistsKernel, SumOfExpSumOfDistsKernel
from nn import neural_network
from utils.oper_utils import opt_transport
DFLT_TRANSPORT_DIST = 'lp'
DFLT_CONN_COST_FUNC = 'linear'
DFLT_KERN_DIST_POWERS = 1
REPLACE_COST_INF_WITH = 7.65432e5
CONV_RES_RAW_COST_FRAC = 0.9
CNN_STRUCTURAL_PENALTY_GROUPS = ['all', 'conv', 'pool', 'fc']
MLP_STRUCTURAL_PENALTY_GROUPS = ['all', 'rectifier', 'sigmoid']
PATH_LENGTH_TYPES = ['shortest', 'longest', 'rw'] | MIT License |
aws/aws-iot-device-sdk-python | AWSIoTPythonSDK/core/greengrass/discovery/providers.py | DiscoveryInfoProvider.configureCredentials | python | def configureCredentials(self, caPath, certPath, keyPath):
self._ca_path = caPath
self._cert_path = certPath
self._key_path = keyPath | **Description**
Used to configure the credentials for discovery request. Should be called before the discovery request happens.
**Syntax**
.. code:: python
myDiscoveryInfoProvider.configureCredentials("my/ca/path", "my/cert/path", "my/key/path")
**Parameters**
*caPath* - Path to read the root CA file.
*certPath* - Path to read the certificate file.
*keyPath* - Path to read the private key file.
**Returns**
None | https://github.com/aws/aws-iot-device-sdk-python/blob/a67eadfcbf9d229229b18435fb7a109685250854/AWSIoTPythonSDK/core/greengrass/discovery/providers.py#L145-L173 | from AWSIoTPythonSDK.exception.AWSIoTExceptions import DiscoveryInvalidRequestException
from AWSIoTPythonSDK.exception.AWSIoTExceptions import DiscoveryUnauthorizedException
from AWSIoTPythonSDK.exception.AWSIoTExceptions import DiscoveryDataNotFoundException
from AWSIoTPythonSDK.exception.AWSIoTExceptions import DiscoveryThrottlingException
from AWSIoTPythonSDK.exception.AWSIoTExceptions import DiscoveryTimeoutException
from AWSIoTPythonSDK.exception.AWSIoTExceptions import DiscoveryFailure
from AWSIoTPythonSDK.core.greengrass.discovery.models import DiscoveryInfo
from AWSIoTPythonSDK.core.protocol.connection.alpn import SSLContextBuilder
import re
import sys
import ssl
import time
import errno
import logging
import socket
import platform
if platform.system() == 'Windows':
EAGAIN = errno.WSAEWOULDBLOCK
else:
EAGAIN = errno.EAGAIN
class DiscoveryInfoProvider(object):
REQUEST_TYPE_PREFIX = "GET "
PAYLOAD_PREFIX = "/greengrass/discover/thing/"
PAYLOAD_SUFFIX = " HTTP/1.1\r\n"
HOST_PREFIX = "Host: "
HOST_SUFFIX = "\r\n\r\n"
HTTP_PROTOCOL = r"HTTP/1.1 "
CONTENT_LENGTH = r"content-length: "
CONTENT_LENGTH_PATTERN = CONTENT_LENGTH + r"([0-9]+)\r\n"
HTTP_RESPONSE_CODE_PATTERN = HTTP_PROTOCOL + r"([0-9]+) "
HTTP_SC_200 = "200"
HTTP_SC_400 = "400"
HTTP_SC_401 = "401"
HTTP_SC_404 = "404"
HTTP_SC_429 = "429"
LOW_LEVEL_RC_COMPLETE = 0
LOW_LEVEL_RC_TIMEOUT = -1
_logger = logging.getLogger(__name__)
def __init__(self, caPath="", certPath="", keyPath="", host="", port=8443, timeoutSec=120):
self._ca_path = caPath
self._cert_path = certPath
self._key_path = keyPath
self._host = host
self._port = port
self._timeout_sec = timeoutSec
self._expected_exception_map = {
self.HTTP_SC_400 : DiscoveryInvalidRequestException(),
self.HTTP_SC_401 : DiscoveryUnauthorizedException(),
self.HTTP_SC_404 : DiscoveryDataNotFoundException(),
self.HTTP_SC_429 : DiscoveryThrottlingException()
}
def configureEndpoint(self, host, port=8443):
self._host = host
self._port = port | Apache License 2.0 |
santhisenan/sdn_ddos_simulation | ddpg/replay_buffer.py | ReplayBuffer.sample_batch | python | def sample_batch(self, batch_size=32):
_available_batch_length = self._count if self._count < batch_size else batch_size
batch = random.sample(self._buffer, _available_batch_length)
_states = np.array([_experience[0] for _experience in batch])
_actions = np.array([_experience[1] for _experience in batch])
_rewards = np.array([_experience[2] for _experience in batch])
_dones = np.array([_experience[3] for _experience in batch])
_next_states = np.array([_experience[4] for _experience in batch])
return batch | If the number of elements in the replay memory is less than the required
batch_size, then return only those elements present in the memory, else
return 'batch_size' number of elements. | https://github.com/santhisenan/sdn_ddos_simulation/blob/be0f812de4d2e0668f1266a71172948123e3750c/ddpg/replay_buffer.py#L24-L44 | from collections import deque
import random
import numpy as np
class ReplayBuffer(object):
def __init__(self, buffer_size):
self._buffer_size = buffer_size
self._count = 0
self._buffer = deque()
def insert(self, _experience):
if(self._count <= self._buffer_size):
self._buffer.append(_experience)
self._count += 1
else:
self._buffer.popleft()
self._buffer.append(_experience)
def size(self):
return self._count | MIT License |
hyde/hyde | setup.py | find_package_data | python | def find_package_data(
where='.', package='',
exclude=standard_exclude,
exclude_directories=standard_exclude_directories,
only_in_packages=True,
show_ignored=False):
out = {}
stack = [(convert_path(where), '', package, only_in_packages)]
while stack:
where, prefix, package, only_in_packages = stack.pop(0)
for name in os.listdir(where):
fn = os.path.join(where, name)
if os.path.isdir(fn):
bad_name = False
for pattern in exclude_directories:
if fnmatchcase(name, pattern) or fn.lower() == pattern.lower():
bad_name = True
if show_ignored:
msg = "Directory {} ignored by pattern {}"
sys.stderr.write(msg.format(fn, pattern))
break
if bad_name:
continue
if os.path.isfile(os.path.join(fn, '__init__.py')):
if not package:
new_package = name
else:
new_package = package + '.' + name
stack.append((fn, '', new_package, False))
else:
stack.append((fn, prefix + name + '/', package,
only_in_packages))
elif package or not only_in_packages:
bad_name = False
for pattern in exclude:
if fnmatchcase(name, pattern) or fn.lower() == pattern.lower():
bad_name = True
if show_ignored:
msg = "File {} ignored by pattern {}"
sys.stderr.write(msg.format(fn, pattern))
break
if bad_name:
continue
out.setdefault(package, []).append(prefix+name)
return out | Return a dictionary suitable for use in ``package_data``
in a distutils ``setup.py`` file.
The dictionary looks like::
{'package': [files]}
Where ``files`` is a list of all the files in that package that
don't match anything in ``exclude``.
If ``only_in_packages`` is true, then top-level directories that
are not packages won't be included (but directories under packages
will).
Directories matching any pattern in ``exclude_directories`` will
be ignored; by default directories with leading ``.``, ``CVS``,
and ``_darcs`` will be ignored.
If ``show_ignored`` is true, then all the files that aren't
included in package data are shown on stderr (for debugging
purposes).
Note patterns use wildcards, or can be exact paths (including
leading ``./``), and all searching is case-insensitive.
This function is by Ian Bicking. | https://github.com/hyde/hyde/blob/7f415402cc3e007a746eb2b5bc102281fdb415bd/setup.py#L26-L104 | from setuptools import setup, find_packages
from hyde.version import __version__
from distutils.util import convert_path
from fnmatch import fnmatchcase
import os
import sys
PROJECT = 'hyde'
try:
long_description = open('README.rst', 'rt').read()
except IOError:
long_description = ''
standard_exclude = ('*.py', '*.pyc', '*~', '.*', '*.bak', '*.swp*')
standard_exclude_directories = ('.*', 'CVS', '_darcs', './build',
'./dist', 'EGG-INFO', '*.egg-info') | MIT License |
openfun/marsha | src/backend/marsha/core/serializers/video.py | ThumbnailSerializer.get_urls | python | def get_urls(self, obj):
if obj.uploaded_on:
base = f"{settings.AWS_S3_URL_PROTOCOL}://{settings.CLOUDFRONT_DOMAIN}/{obj.video.pk}"
urls = {}
stamp = time_utils.to_timestamp(obj.uploaded_on)
for resolution in settings.VIDEO_RESOLUTIONS:
urls[resolution] = f"{base}/thumbnails/{stamp}_{resolution}.jpg"
return urls
return None | Urls of the thumbnail.
Parameters
----------
obj : Type[models.Thumbnail]
The thumbnail that we want to serialize
Returns
-------
Dict or None
The urls for the thumbnail.
None if the thumbnail is still not uploaded to S3 with success. | https://github.com/openfun/marsha/blob/550be08d7cad91579cd1cc2548ea95751113f15d/src/backend/marsha/core/serializers/video.py#L250-L272 | from datetime import timedelta
from urllib.parse import quote_plus
from django.conf import settings
from django.shortcuts import get_object_or_404
from django.urls import reverse
from django.utils import timezone
from django.utils.text import slugify
from botocore.signers import CloudFrontSigner
from rest_framework import serializers
from rest_framework_simplejwt.models import TokenUser
from ..defaults import IDLE, JITSI, LIVE_CHOICES, LIVE_TYPE_CHOICES, RUNNING, STOPPED
from ..models import LiveRegistration, Playlist, Thumbnail, TimedTextTrack, Video
from ..models.account import ADMINISTRATOR, INSTRUCTOR, LTI_ROLES
from ..utils import cloudfront_utils, time_utils, xmpp_utils
from ..utils.url_utils import build_absolute_uri_behind_proxy
from .base import TimestampField
from .playlist import PlaylistLiteSerializer
class TimedTextTrackSerializer(serializers.ModelSerializer):
class Meta:
model = TimedTextTrack
fields = (
"active_stamp",
"id",
"is_ready_to_show",
"mode",
"language",
"upload_state",
"url",
"source_url",
"video",
)
read_only_fields = (
"id",
"active_stamp",
"is_ready_to_show",
"upload_state",
"url",
"video",
)
active_stamp = TimestampField(
source="uploaded_on", required=False, allow_null=True, read_only=True
)
url = serializers.SerializerMethodField()
source_url = serializers.SerializerMethodField()
video = serializers.PrimaryKeyRelatedField(
read_only=True, pk_field=serializers.CharField()
)
is_ready_to_show = serializers.BooleanField(read_only=True)
def create(self, validated_data):
user = self.context["request"].user
if (
self.initial_data.get("video")
and user.token.get("user")
and user.token["resource_id"] == user.token.get("user", {}).get("id")
):
validated_data["video_id"] = self.initial_data.get("video")
if not validated_data.get("video_id") and isinstance(user, TokenUser):
validated_data["video_id"] = user.id
return super().create(validated_data)
def _sign_url(self, url):
date_less_than = timezone.now() + timedelta(
seconds=settings.CLOUDFRONT_SIGNED_URLS_VALIDITY
)
cloudfront_signer = CloudFrontSigner(
settings.CLOUDFRONT_ACCESS_KEY_ID, cloudfront_utils.rsa_signer
)
return cloudfront_signer.generate_presigned_url(
url, date_less_than=date_less_than
)
def _generate_url(self, obj, object_path, extension=None, content_disposition=None):
base = f"{settings.AWS_S3_URL_PROTOCOL}://{settings.CLOUDFRONT_DOMAIN}/{obj.video.pk}"
stamp = time_utils.to_timestamp(obj.uploaded_on)
mode = f"_{obj.mode}" if obj.mode else ""
url = f"{base}/{object_path}/{stamp}_{obj.language:s}{mode:s}"
if extension:
url = f"{url}.{extension}"
if content_disposition:
url = f"{url}?response-content-disposition={content_disposition}"
return url
def get_source_url(self, obj):
if obj.uploaded_on and obj.extension:
stamp = time_utils.to_timestamp(obj.uploaded_on)
filename = f"{slugify(obj.video.playlist.title)}_{stamp}.{obj.extension}"
url = self._generate_url(
obj,
"timedtext/source",
content_disposition=quote_plus(f"attachment; filename={filename}"),
)
if settings.CLOUDFRONT_SIGNED_URLS_ACTIVE:
url = self._sign_url(url)
return url
return None
def get_url(self, obj):
if obj.uploaded_on:
url = self._generate_url(obj, "timedtext", extension="vtt")
if settings.CLOUDFRONT_SIGNED_URLS_ACTIVE:
url = self._sign_url(url)
return url
return None
class ThumbnailSerializer(serializers.ModelSerializer):
class Meta:
model = Thumbnail
fields = (
"active_stamp",
"id",
"is_ready_to_show",
"upload_state",
"urls",
"video",
)
read_only_fields = (
"active_stamp",
"id",
"is_ready_to_show",
"upload_state",
"urls",
"video",
)
active_stamp = TimestampField(
source="uploaded_on", required=False, allow_null=True, read_only=True
)
video = serializers.PrimaryKeyRelatedField(
read_only=True, pk_field=serializers.CharField()
)
is_ready_to_show = serializers.BooleanField(read_only=True)
urls = serializers.SerializerMethodField()
def create(self, validated_data):
user = self.context["request"].user
if not validated_data.get("video_id") and isinstance(user, TokenUser):
validated_data["video_id"] = user.id
return super().create(validated_data) | MIT License |
compas-dev/compas | src/compas/datastructures/network/matrices.py | network_adjacency_matrix | python | def network_adjacency_matrix(network, rtype='array'):
key_index = network.key_index()
adjacency = [[key_index[nbr] for nbr in network.neighbors(key)] for key in network.nodes()]
return adjacency_matrix(adjacency, rtype=rtype) | Creates a node adjacency matrix from a Network datastructure.
Parameters
----------
network : obj
Network datastructure object to get data from.
rtype : {'array', 'csc', 'csr', 'coo', 'list'}
Format of the result.
Returns
-------
array-like
Constructed adjacency matrix. | https://github.com/compas-dev/compas/blob/d795a8bfe9f21ffa124d09e37e9c0ed2e3520057/src/compas/datastructures/network/matrices.py#L33-L51 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from compas.numerical import adjacency_matrix
from compas.numerical import degree_matrix
from compas.numerical import connectivity_matrix
from compas.numerical import laplacian_matrix
__all__ = [
'network_adjacency_matrix',
'network_degree_matrix',
'network_connectivity_matrix',
'network_laplacian_matrix',
]
def _return_matrix(M, rtype):
if rtype == 'list':
return M.toarray().tolist()
if rtype == 'array':
return M.toarray()
if rtype == 'csr':
return M.tocsr()
if rtype == 'csc':
return M.tocsc()
if rtype == 'coo':
return M.tocoo()
return M | MIT License |
laixintao/pingtop | pingtop/ping.py | checksum | python | def checksum(source_string):
sum = 0
count_to = int((len(source_string) / 2) * 2)
for count in range(0, count_to, 2):
this = source_string[count + 1] * 256 + source_string[count]
sum = sum + this
sum = sum & 0xffffffff
if count_to < len(source_string):
sum = sum + ord(source_string[len(source_string) - 1])
sum = sum & 0xffffffff
sum = (sum >> 16) + (sum & 0xffff)
sum = sum + (sum >> 16)
answer = ~sum
answer = answer & 0xffff
answer = answer >> 8 | (answer << 8 & 0xff00)
return answer | I'm not too confident that this is right but testing seems
to suggest that it gives the same answers as in_cksum in ping.c | https://github.com/laixintao/pingtop/blob/75353119db1af8635fec85a9bf38722e152c426c/pingtop/ping.py#L110-L134 | __version__ = "0.2"
import os
import select
import socket
import struct
import sys
import time
ICMP_ECHO_REQUEST = 8 | MIT License |
zimbra-community/python-zimbra | pythonzimbra/communication.py | Communication.gen_request | python | def gen_request(self, request_type="json", token=None, set_batch=False,
batch_onerror=None):
if request_type == "json":
local_request = RequestJson()
elif request_type == "xml":
local_request = RequestXml()
else:
raise UnknownRequestType()
if token is not None:
local_request.set_auth_token(token)
if set_batch:
local_request.enable_batch(batch_onerror)
return local_request | Convenience method to quickly generate a token
:param request_type: Type of request (defaults to json)
:param token: Authentication token
:param set_batch: Also set this request to batch mode?
:param batch_onerror: Onerror-parameter for batch mode
:return: The request | https://github.com/zimbra-community/python-zimbra/blob/1b4b1e0650bfab52f8df402f217ad5873f01d610/pythonzimbra/communication.py#L59-L89 | from __future__ import unicode_literals
import sys
if sys.version < '3':
import urllib2 as ur
import urllib2 as ue
else:
import urllib.request as ur
import urllib.error as ue
from pythonzimbra.request_json import RequestJson
from pythonzimbra.request_xml import RequestXml
from pythonzimbra.response_xml import ResponseXml
from pythonzimbra.response_json import ResponseJson
from .exceptions.communication import *
class Communication(object):
url = None
""" URL to the zimbra soap interface """
timeout = None
""" Timeout of the request """
context = None
""" SSL Context of the request """
def __init__(self, url, timeout=None, context=None):
self.url = url
self.timeout = timeout
self.context = context
if sys.version < '3' and self.url.startswith("https"):
import tools.urllib2_tls | BSD 2-Clause Simplified License |
partho-maple/coding-interview-gym | leetcode.com/python/457_Circular_Array_Loop.py | Solution.circularArrayLoop | python | def circularArrayLoop(self, nums):
for i in range(len(nums)):
is_forward = nums[i] >= 0
slow, fast = i, i
while True:
slow = self.find_next_index(nums, is_forward, slow)
fast = self.find_next_index(nums, is_forward, fast)
if (fast != -1):
fast = self.find_next_index(nums, is_forward, fast)
if slow == -1 or fast == -1 or slow == fast:
break
if slow != -1 and slow == fast:
return True
return False | :type nums: List[int]
:rtype: bool | https://github.com/partho-maple/coding-interview-gym/blob/f11c78b6e42d1014296fc0f360aa6fc530600493/leetcode.com/python/457_Circular_Array_Loop.py#L2-L26 | class Solution(object): | MIT License |
mpi4jax/mpi4jax | examples/shallow_water.py | get_initial_conditions | python | def get_initial_conditions():
u0_global = 10 * jnp.exp(
-((yy_global - 0.5 * length_y) ** 2) / (0.02 * length_x) ** 2
)
v0_global = jnp.zeros_like(u0_global)
coriolis_global = CORIOLIS_F + yy_global * CORIOLIS_BETA
h_geostrophy = np.cumsum(-dy * u0_global * coriolis_global / GRAVITY, axis=0)
h0_global = (
DEPTH
+ h_geostrophy
- h_geostrophy.mean()
+ 0.2
* np.sin(xx_global / length_x * 10 * np.pi)
* np.cos(yy_global / length_y * 8 * np.pi)
)
h0_local = h0_global[local_slice]
u0_local = u0_global[local_slice]
v0_local = v0_global[local_slice]
token = jax.lax.create_token()
h0_local, token = enforce_boundaries(h0_local, "h", token)
u0_local, token = enforce_boundaries(u0_local, "u", token)
v0_local, token = enforce_boundaries(v0_local, "v", token)
return h0_local, u0_local, v0_local | For the initial conditions, we use a horizontal jet in geostrophic balance. | https://github.com/mpi4jax/mpi4jax/blob/e3ed6f00a5552099f260c6b1f68588917461403b/examples/shallow_water.py#L139-L170 | import os
import sys
import math
import time
import warnings
from contextlib import ExitStack
from collections import namedtuple
from functools import partial
import numpy as np
from mpi4py import MPI
try:
import tqdm
except ImportError:
warnings.warn("Could not import tqdm, can't show progress bar")
HAS_TQDM = False
else:
HAS_TQDM = True
mpi_comm = MPI.COMM_WORLD
mpi_rank = mpi_comm.Get_rank()
mpi_size = mpi_comm.Get_size()
os.environ["CUDA_VISIBLE_DEVICES"] = str(mpi_rank)
import jax
import jax.numpy as jnp
import mpi4jax
supported_nproc = (1, 2, 4, 6, 8, 16)
if mpi_size not in supported_nproc:
raise RuntimeError(
f"Got invalid number of MPI processes: {mpi_size}. "
f"Please choose one of these: {supported_nproc}."
)
nproc_y = min(mpi_size, 2)
nproc_x = mpi_size // nproc_y
proc_idx = np.unravel_index(mpi_rank, (nproc_y, nproc_x))
nx_global = 360 + 2
ny_global = 180 + 2
dx = 5e3
dy = 5e3
assert (nx_global - 2) % nproc_x == 0
assert (ny_global - 2) % nproc_y == 0
nx_local = (nx_global - 2) // nproc_x + 2
ny_local = (ny_global - 2) // nproc_y + 2
x_global, y_global = (
np.arange(-1, nx_global - 1) * dx,
np.arange(-1, ny_global - 1) * dy,
)
yy_global, xx_global = np.meshgrid(y_global, x_global, indexing="ij")
length_x = x_global[-2] - x_global[1]
length_y = y_global[-2] - y_global[1]
local_slice = (
slice((ny_local - 2) * proc_idx[0], (ny_local - 2) * proc_idx[0] + ny_local),
slice((nx_local - 2) * proc_idx[1], (nx_local - 2) * proc_idx[1] + nx_local),
)
x_local = x_global[local_slice[1]]
y_local = y_global[local_slice[0]]
xx_local = xx_global[local_slice]
yy_local = yy_global[local_slice]
GRAVITY = 9.81
DEPTH = 100.0
CORIOLIS_F = 2e-4
CORIOLIS_BETA = 2e-11
CORIOLIS_PARAM = CORIOLIS_F + yy_local * CORIOLIS_BETA
LATERAL_VISCOSITY = 1e-3 * CORIOLIS_F * dx ** 2
DAY_IN_SECONDS = 86_400
PERIODIC_BOUNDARY_X = True
ADAMS_BASHFORTH_A = 1.5 + 0.1
ADAMS_BASHFORTH_B = -(0.5 + 0.1)
PLOT_ETA_RANGE = 10
PLOT_EVERY = 100
MAX_QUIVERS = (25, 50)
dt = 0.125 * min(dx, dy) / np.sqrt(GRAVITY * DEPTH)
@jax.jit | MIT License |
elsonidoq/fito | fito/data_store/base.py | BaseDataStore.get_id | python | def get_id(self, spec):
raise NotImplementedError() | Get's the internal id of a given spec, it should raise KeyError if spec not in self | https://github.com/elsonidoq/fito/blob/e76ab0a9a4eb954b6d88c190cb57d112f94739e1/fito/data_store/base.py#L82-L86 | from fito import config
import warnings
from functools import wraps
from fito import Spec
from fito.data_store.rehash_ui import RehashUI
from fito.operation_runner import FifoCache, OperationRunner
from fito.operations.decorate import as_operation
from fito.specs.base import get_import_path
from fito.specs.fields import NumericField, PrimitiveField
from fito.specs.utils import matching_fields
class BaseDataStore(OperationRunner):
get_cache_size = NumericField(default=0)
verbose = PrimitiveField(default=False, serialize=False)
def __init__(self, *args, **kwargs):
super(BaseDataStore, self).__init__(*args, **kwargs)
if self.get_cache_size > 0:
self.get_cache = FifoCache(self.get_cache_size)
else:
self.get_cache = None
@classmethod
def get_key(cls, spec):
if isinstance(spec, Spec):
return spec.key
else:
assert isinstance(spec, dict)
return Spec._dict2key(spec)
def get(self, spec):
def _get():
try:
return self._get(spec)
except KeyError, e:
if config.interactive_rehash and spec not in RehashUI.ignored_specs:
if self.interactive_rehash(spec):
return self.get(spec)
else:
raise e
else:
raise e
if self.get_cache is None:
return _get()
else:
try:
return self.get_cache[spec]
except KeyError:
res = _get()
self.get_cache.set(spec, res)
return res
def _get(self, spec):
raise NotImplementedError() | MIT License |
dedsecinside/awesome-scripts | APIs/Telegram API/telethon/client/telegrambaseclient.py | TelegramBaseClient._create_exported_sender | python | async def _create_exported_sender(self: 'TelegramClient', dc_id):
dc = await self._get_dc(dc_id)
sender = MTProtoSender(None, loggers=self._log)
await sender.connect(self._connection(
dc.ip_address,
dc.port,
dc.id,
loggers=self._log,
proxy=self._proxy,
local_addr=self._local_addr
))
self._log[__name__].info('Exporting auth for new borrowed sender in %s', dc)
auth = await self(functions.auth.ExportAuthorizationRequest(dc_id))
req = self._init_with(functions.auth.ImportAuthorizationRequest(
id=auth.id, bytes=auth.bytes
))
await sender.send(req)
return sender | Creates a new exported `MTProtoSender` for the given `dc_id` and
returns it. This method should be used by `_borrow_exported_sender`. | https://github.com/dedsecinside/awesome-scripts/blob/856835e5ff5f8a6af2d74bb25800c620feb712e3/APIs/Telegram API/telethon/client/telegrambaseclient.py#L666-L693 | import abc
import re
import asyncio
import collections
import logging
import platform
import time
import typing
from .. import version, helpers, __name__ as __base_name__
from ..crypto import rsa
from ..entitycache import EntityCache
from ..extensions import markdown
from ..network import MTProtoSender, Connection, ConnectionTcpFull, TcpMTProxy
from ..sessions import Session, SQLiteSession, MemorySession
from ..statecache import StateCache
from ..tl import functions, types
from ..tl.alltlobjects import LAYER
DEFAULT_DC_ID = 2
DEFAULT_IPV4_IP = '149.154.167.51'
DEFAULT_IPV6_IP = '2001:67c:4e8:f002::a'
DEFAULT_PORT = 443
if typing.TYPE_CHECKING:
from .telegramclient import TelegramClient
__default_log__ = logging.getLogger(__base_name__)
__default_log__.addHandler(logging.NullHandler())
_DISCONNECT_EXPORTED_AFTER = 60
class _ExportState:
def __init__(self):
self._n = 0
self._zero_ts = 0
self._connected = False
def add_borrow(self):
self._n += 1
self._connected = True
def add_return(self):
self._n -= 1
assert self._n >= 0, 'returned sender more than it was borrowed'
if self._n == 0:
self._zero_ts = time.time()
def should_disconnect(self):
return (self._n == 0
and self._connected
and (time.time() - self._zero_ts) > _DISCONNECT_EXPORTED_AFTER)
def need_connect(self):
return not self._connected
def mark_disconnected(self):
assert self.should_disconnect(), 'marked as disconnected when it was borrowed'
self._connected = False
class TelegramBaseClient(abc.ABC):
__version__ = version.__version__
_config = None
_cdn_config = None
def __init__(
self: 'TelegramClient',
session: 'typing.Union[str, Session]',
api_id: int,
api_hash: str,
*,
connection: 'typing.Type[Connection]' = ConnectionTcpFull,
use_ipv6: bool = False,
proxy: typing.Union[tuple, dict] = None,
local_addr=None,
timeout: int = 10,
request_retries: int = 5,
connection_retries: int =5,
retry_delay: int = 1,
auto_reconnect: bool = True,
sequential_updates: bool = False,
flood_sleep_threshold: int = 60,
raise_last_call_error: bool = False,
device_model: str = None,
system_version: str = None,
app_version: str = None,
lang_code: str = 'en',
system_lang_code: str = 'en',
loop: asyncio.AbstractEventLoop = None,
base_logger: typing.Union[str, logging.Logger] = None):
if not api_id or not api_hash:
raise ValueError(
"Your API ID or Hash cannot be empty or None. "
"Refer to telethon.rtfd.io for more information.")
self._use_ipv6 = use_ipv6
self._loop = asyncio.get_event_loop()
if isinstance(base_logger, str):
base_logger = logging.getLogger(base_logger)
elif not isinstance(base_logger, logging.Logger):
base_logger = __default_log__
class _Loggers(dict):
def __missing__(self, key):
if key.startswith("telethon."):
key = key.split('.', maxsplit=1)[1]
return base_logger.getChild(key)
self._log = _Loggers()
if isinstance(session, str) or session is None:
try:
session = SQLiteSession(session)
except ImportError:
import warnings
warnings.warn(
'The sqlite3 module is not available under this '
'Python installation and no custom session '
'instance was given; using MemorySession.\n'
'You will need to re-login every time unless '
'you use another session storage'
)
session = MemorySession()
elif not isinstance(session, Session):
raise TypeError(
'The given session must be a str or a Session instance.'
)
if (not session.server_address or
(':' in session.server_address) != use_ipv6):
session.set_dc(
DEFAULT_DC_ID,
DEFAULT_IPV6_IP if self._use_ipv6 else DEFAULT_IPV4_IP,
DEFAULT_PORT
)
self.flood_sleep_threshold = flood_sleep_threshold
self.session = session
self._entity_cache = EntityCache()
self.api_id = int(api_id)
self.api_hash = api_hash
if not callable(getattr(self._loop, 'sock_connect', None)):
raise TypeError(
'Event loop of type {} lacks `sock_connect`, which is needed to use proxies.\n\n'
'Change the event loop in use to use proxies:\n'
'# https://github.com/LonamiWebs/Telethon/issues/1337\n'
'import asyncio\n'
'asyncio.set_event_loop(asyncio.SelectorEventLoop())'.format(
self._loop.__class__.__name__
)
)
if local_addr is not None:
if use_ipv6 is False and ':' in local_addr:
raise TypeError(
'A local IPv6 address must only be used with `use_ipv6=True`.'
)
elif use_ipv6 is True and ':' not in local_addr:
raise TypeError(
'`use_ipv6=True` must only be used with a local IPv6 address.'
)
self._raise_last_call_error = raise_last_call_error
self._request_retries = request_retries
self._connection_retries = connection_retries
self._retry_delay = retry_delay or 0
self._proxy = proxy
self._local_addr = local_addr
self._timeout = timeout
self._auto_reconnect = auto_reconnect
assert isinstance(connection, type)
self._connection = connection
init_proxy = None if not issubclass(connection, TcpMTProxy) else types.InputClientProxy(*connection.address_info(proxy))
system = platform.uname()
if system.machine in ('x86_64', 'AMD64'):
default_device_model = 'PC 64bit'
elif system.machine in ('i386','i686','x86'):
default_device_model = 'PC 32bit'
else:
default_device_model = system.machine
default_system_version = re.sub(r'-.+','',system.release)
self._init_with = lambda x: functions.InvokeWithLayerRequest(
LAYER, functions.InitConnectionRequest(
api_id=self.api_id,
device_model=device_model or default_device_model or 'Unknown',
system_version=system_version or default_system_version or '1.0',
app_version=app_version or self.__version__,
lang_code=lang_code,
system_lang_code=system_lang_code,
lang_pack='',
query=x,
proxy=init_proxy
)
)
self._sender = MTProtoSender(
self.session.auth_key,
loggers=self._log,
retries=self._connection_retries,
delay=self._retry_delay,
auto_reconnect=self._auto_reconnect,
connect_timeout=self._timeout,
auth_key_callback=self._auth_key_callback,
update_callback=self._handle_update,
auto_reconnect_callback=self._handle_auto_reconnect
)
self._flood_waited_requests = {}
self._borrowed_senders = {}
self._borrow_sender_lock = asyncio.Lock()
self._updates_handle = None
self._last_request = time.time()
self._channel_pts = {}
if sequential_updates:
self._updates_queue = asyncio.Queue()
self._dispatching_updates_queue = asyncio.Event()
else:
self._updates_queue = set()
self._dispatching_updates_queue = None
self._authorized = None
self._state_cache = StateCache(
self.session.get_update_state(0), self._log)
self._event_builders = []
self._conversations = collections.defaultdict(set)
self._albums = {}
self._parse_mode = markdown
self._phone_code_hash = {}
self._phone = None
self._tos = None
self._self_input_peer = None
self._bot = None
self._megagroup_cache = {}
@property
def loop(self: 'TelegramClient') -> asyncio.AbstractEventLoop:
return self._loop
@property
def disconnected(self: 'TelegramClient') -> asyncio.Future:
return self._sender.disconnected
@property
def flood_sleep_threshold(self):
return self._flood_sleep_threshold
@flood_sleep_threshold.setter
def flood_sleep_threshold(self, value):
self._flood_sleep_threshold = min(value or 0, 24 * 60 * 60)
async def connect(self: 'TelegramClient') -> None:
if not await self._sender.connect(self._connection(
self.session.server_address,
self.session.port,
self.session.dc_id,
loggers=self._log,
proxy=self._proxy,
local_addr=self._local_addr
)):
return
self.session.auth_key = self._sender.auth_key
self.session.save()
await self._sender.send(self._init_with(
functions.help.GetConfigRequest()))
self._updates_handle = self._loop.create_task(self._update_loop())
def is_connected(self: 'TelegramClient') -> bool:
sender = getattr(self, '_sender', None)
return sender and sender.is_connected()
def disconnect(self: 'TelegramClient'):
if self._loop.is_running():
return self._disconnect_coro()
else:
try:
self._loop.run_until_complete(self._disconnect_coro())
except RuntimeError:
pass
async def _disconnect_coro(self: 'TelegramClient'):
await self._disconnect()
async with self._borrow_sender_lock:
for state, sender in self._borrowed_senders.values():
if state.should_disconnect():
await sender.disconnect()
self._borrowed_senders.clear()
if self._dispatching_updates_queue is None and self._updates_queue:
for task in self._updates_queue:
task.cancel()
await asyncio.wait(self._updates_queue)
self._updates_queue.clear()
pts, date = self._state_cache[None]
if pts and date:
self.session.set_update_state(0, types.updates.State(
pts=pts,
qts=0,
date=date,
seq=0,
unread_count=0
))
self.session.close()
async def _disconnect(self: 'TelegramClient'):
await self._sender.disconnect()
await helpers._cancel(self._log[__name__],
updates_handle=self._updates_handle)
async def _switch_dc(self: 'TelegramClient', new_dc):
self._log[__name__].info('Reconnecting to new data center %s', new_dc)
dc = await self._get_dc(new_dc)
self.session.set_dc(dc.id, dc.ip_address, dc.port)
self._sender.auth_key.key = None
self.session.auth_key = None
self.session.save()
await self._disconnect()
return await self.connect()
def _auth_key_callback(self: 'TelegramClient', auth_key):
self.session.auth_key = auth_key
self.session.save()
async def _get_dc(self: 'TelegramClient', dc_id, cdn=False):
cls = self.__class__
if not cls._config:
cls._config = await self(functions.help.GetConfigRequest())
if cdn and not self._cdn_config:
cls._cdn_config = await self(functions.help.GetCdnConfigRequest())
for pk in cls._cdn_config.public_keys:
rsa.add_key(pk.public_key)
return next(
dc for dc in cls._config.dc_options
if dc.id == dc_id
and bool(dc.ipv6) == self._use_ipv6 and bool(dc.cdn) == cdn
) | MIT License |
libcity/bigscity-libcity-datasets | old_backup/nyc_taxi_od.py | convert_to_trajectory | python | def convert_to_trajectory(df):
start = df[['drive_id', 'PULocationID', 'tpep_pickup_datetime']]
end = df[['drive_id', 'DOLocationID', 'tpep_dropoff_datetime']]
start.columns = ['driveid', 'geo_id', 'time_str']
end.columns = ['driveid', 'geo_id', 'time_str']
trajectory_data = pd.concat((start, end), axis=0)
trajectory_data = trajectory_data.loc[trajectory_data['geo_id'].apply(lambda x: not math.isnan(x))]
trajectory_data = convert_time(trajectory_data)
return trajectory_data[['driveid', 'geo_id', 'time', 'timestamp']] | :param df: all data
:return: df['driveid', 'geo_id', 'time', 'timestamp'] | https://github.com/libcity/bigscity-libcity-datasets/blob/9d686af4731d7db821298345734926c0437703e6/old_backup/nyc_taxi_od.py#L95-L107 | import json
import math
import os
from datetime import datetime
import numpy as np
import pandas as pd
old_time_format = '%Y-%m-%d %H:%M:%S'
new_time_format = '%Y-%m-%dT%H:%M:%SZ'
def get_data_url(input_dir_flow, start_year, start_month, end_year, end_month):
pattern = input_dir_flow + "/yellow_tripdata_%d-%02d.csv"
data_url = []
i = start_year
while i <= end_year:
j = start_month if i == start_year else 1
end_j = end_month if i == end_year else 12
while j <= end_j:
data_url.append(pattern % (i, j))
j += 1
i += 1
return data_url
def handle_area_geo(df):
start = df[['PULocationID']]
start.columns = ['a_id']
end = df[['DOLocationID']]
end.columns = ['a_id']
area_data = pd.concat((start, end), axis=0)
area_data['a_id'] = area_data['a_id'].astype('int')
area_data = area_data.sort_values(by='a_id', ascending=True)
area_data = area_data[['a_id']]
area_data = area_data.drop_duplicates()
return area_data
def judge_id(value, dividing_points, equally=True):
if equally:
min_v = dividing_points[0]
interval = dividing_points[1] - dividing_points[0]
idx = int((value - min_v) / interval)
max_id = len(dividing_points) - 2
return min(max_id, idx)
else:
for i, num in enumerate(dividing_points):
if value <= num:
return i - 1
return len(dividing_points)
def get_geo_data(area_geo):
geo_data = pd.DataFrame(
columns=['geo_id', 'type', 'coordinates'])
geo_data['geo_id'] = area_geo['a_id']
geo_data.loc[:, 'type'] = 'state'
geo_data.loc[:, 'coordinates'] = '[[ [] ]]'
return geo_data
def convert_time(df):
df['time'] = df.apply(
lambda x: x['time_str'].replace(' ', 'T') + 'Z',
axis=1)
df['timestamp'] = df.apply(
lambda x: float(datetime.timestamp(
pd.to_datetime(x['time_str'],
utc=True,
format=old_time_format))),
axis=1)
return df | Apache License 2.0 |
ericsson/codechecker | codechecker_common/skiplist_handler.py | SkipListHandler.__call__ | python | def __call__(self, source_file_path: str) -> bool:
return self.should_skip(source_file_path) | Check if the given source should be skipped. | https://github.com/ericsson/codechecker/blob/d2db1b49e8a2b775d41436406b5a2e5d9af76c0f/codechecker_common/skiplist_handler.py#L105-L109 | import fnmatch
import re
import os
from codechecker_common.logger import get_logger
LOG = get_logger('system')
class SkipListHandler:
def __init__(self, skip_file_content=""):
self.__skip = []
if not skip_file_content:
skip_file_content = ""
self.__skip_file_lines = [line.strip() for line
in skip_file_content.splitlines()
if line.strip()]
valid_lines = self.__check_line_format(self.__skip_file_lines)
self.__gen_regex(valid_lines)
def __gen_regex(self, skip_lines):
for skip_line in skip_lines:
norm_skip_path = os.path.normpath(skip_line[1:].strip())
rexpr = re.compile(
fnmatch.translate(norm_skip_path + '*'))
self.__skip.append((skip_line, rexpr))
def __check_line_format(self, skip_lines):
valid_lines = []
for line in skip_lines:
if len(line) < 2 or line[0] not in ['-', '+']:
LOG.warning("Skipping malformed skipfile pattern: %s", line)
continue
valid_lines.append(line)
return valid_lines
@property
def skip_file_lines(self):
return self.__skip_file_lines
def overwrite_skip_content(self, skip_lines):
self.__skip = []
valid_lines = self.__check_line_format(skip_lines)
self.__gen_regex(valid_lines)
def should_skip(self, source):
if not self.__skip:
return False
for line, rexpr in self.__skip:
if rexpr.match(source):
sign = line[0]
return sign == '-'
return False | Apache License 2.0 |
docusign/docusign-python-client | docusign_esign/models/commission_number.py | CommissionNumber.anchor_x_offset | python | def anchor_x_offset(self):
return self._anchor_x_offset | Gets the anchor_x_offset of this CommissionNumber. # noqa: E501
Specifies the X axis location of the tab, in anchorUnits, relative to the anchorString. # noqa: E501
:return: The anchor_x_offset of this CommissionNumber. # noqa: E501
:rtype: str | https://github.com/docusign/docusign-python-client/blob/c6aeafff0d046fa6c10a398be83ba9e24b05d4ea/docusign_esign/models/commission_number.py#L803-L811 | import pprint
import re
import six
from docusign_esign.client.configuration import Configuration
class CommissionNumber(object):
"""
Attributes:
swagger_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
swagger_types = {
'anchor_allow_white_space_in_characters': 'str',
'anchor_allow_white_space_in_characters_metadata': 'PropertyMetadata',
'anchor_case_sensitive': 'str',
'anchor_case_sensitive_metadata': 'PropertyMetadata',
'anchor_horizontal_alignment': 'str',
'anchor_horizontal_alignment_metadata': 'PropertyMetadata',
'anchor_ignore_if_not_present': 'str',
'anchor_ignore_if_not_present_metadata': 'PropertyMetadata',
'anchor_match_whole_word': 'str',
'anchor_match_whole_word_metadata': 'PropertyMetadata',
'anchor_string': 'str',
'anchor_string_metadata': 'PropertyMetadata',
'anchor_tab_processor_version': 'str',
'anchor_tab_processor_version_metadata': 'PropertyMetadata',
'anchor_units': 'str',
'anchor_units_metadata': 'PropertyMetadata',
'anchor_x_offset': 'str',
'anchor_x_offset_metadata': 'PropertyMetadata',
'anchor_y_offset': 'str',
'anchor_y_offset_metadata': 'PropertyMetadata',
'bold': 'str',
'bold_metadata': 'PropertyMetadata',
'conceal_value_on_document': 'str',
'conceal_value_on_document_metadata': 'PropertyMetadata',
'conditional_parent_label': 'str',
'conditional_parent_label_metadata': 'PropertyMetadata',
'conditional_parent_value': 'str',
'conditional_parent_value_metadata': 'PropertyMetadata',
'custom_tab_id': 'str',
'custom_tab_id_metadata': 'PropertyMetadata',
'disable_auto_size': 'str',
'disable_auto_size_metadata': 'PropertyMetadata',
'document_id': 'str',
'document_id_metadata': 'PropertyMetadata',
'error_details': 'ErrorDetails',
'font': 'str',
'font_color': 'str',
'font_color_metadata': 'PropertyMetadata',
'font_metadata': 'PropertyMetadata',
'font_size': 'str',
'font_size_metadata': 'PropertyMetadata',
'form_order': 'str',
'form_order_metadata': 'PropertyMetadata',
'form_page_label': 'str',
'form_page_label_metadata': 'PropertyMetadata',
'form_page_number': 'str',
'form_page_number_metadata': 'PropertyMetadata',
'height': 'str',
'height_metadata': 'PropertyMetadata',
'italic': 'str',
'italic_metadata': 'PropertyMetadata',
'locale_policy': 'LocalePolicyTab',
'locked': 'str',
'locked_metadata': 'PropertyMetadata',
'max_length': 'str',
'max_length_metadata': 'PropertyMetadata',
'merge_field': 'MergeField',
'merge_field_xml': 'str',
'name': 'str',
'name_metadata': 'PropertyMetadata',
'original_value': 'str',
'original_value_metadata': 'PropertyMetadata',
'page_number': 'str',
'page_number_metadata': 'PropertyMetadata',
'recipient_id': 'str',
'recipient_id_guid': 'str',
'recipient_id_guid_metadata': 'PropertyMetadata',
'recipient_id_metadata': 'PropertyMetadata',
'required': 'str',
'required_metadata': 'PropertyMetadata',
'smart_contract_information': 'SmartContractInformation',
'source': 'str',
'status': 'str',
'status_metadata': 'PropertyMetadata',
'tab_group_labels': 'list[str]',
'tab_group_labels_metadata': 'PropertyMetadata',
'tab_id': 'str',
'tab_id_metadata': 'PropertyMetadata',
'tab_label': 'str',
'tab_label_metadata': 'PropertyMetadata',
'tab_order': 'str',
'tab_order_metadata': 'PropertyMetadata',
'tab_type': 'str',
'tab_type_metadata': 'PropertyMetadata',
'template_locked': 'str',
'template_locked_metadata': 'PropertyMetadata',
'template_required': 'str',
'template_required_metadata': 'PropertyMetadata',
'tooltip': 'str',
'tool_tip_metadata': 'PropertyMetadata',
'underline': 'str',
'underline_metadata': 'PropertyMetadata',
'value': 'str',
'value_metadata': 'PropertyMetadata',
'width': 'str',
'width_metadata': 'PropertyMetadata',
'x_position': 'str',
'x_position_metadata': 'PropertyMetadata',
'y_position': 'str',
'y_position_metadata': 'PropertyMetadata'
}
attribute_map = {
'anchor_allow_white_space_in_characters': 'anchorAllowWhiteSpaceInCharacters',
'anchor_allow_white_space_in_characters_metadata': 'anchorAllowWhiteSpaceInCharactersMetadata',
'anchor_case_sensitive': 'anchorCaseSensitive',
'anchor_case_sensitive_metadata': 'anchorCaseSensitiveMetadata',
'anchor_horizontal_alignment': 'anchorHorizontalAlignment',
'anchor_horizontal_alignment_metadata': 'anchorHorizontalAlignmentMetadata',
'anchor_ignore_if_not_present': 'anchorIgnoreIfNotPresent',
'anchor_ignore_if_not_present_metadata': 'anchorIgnoreIfNotPresentMetadata',
'anchor_match_whole_word': 'anchorMatchWholeWord',
'anchor_match_whole_word_metadata': 'anchorMatchWholeWordMetadata',
'anchor_string': 'anchorString',
'anchor_string_metadata': 'anchorStringMetadata',
'anchor_tab_processor_version': 'anchorTabProcessorVersion',
'anchor_tab_processor_version_metadata': 'anchorTabProcessorVersionMetadata',
'anchor_units': 'anchorUnits',
'anchor_units_metadata': 'anchorUnitsMetadata',
'anchor_x_offset': 'anchorXOffset',
'anchor_x_offset_metadata': 'anchorXOffsetMetadata',
'anchor_y_offset': 'anchorYOffset',
'anchor_y_offset_metadata': 'anchorYOffsetMetadata',
'bold': 'bold',
'bold_metadata': 'boldMetadata',
'conceal_value_on_document': 'concealValueOnDocument',
'conceal_value_on_document_metadata': 'concealValueOnDocumentMetadata',
'conditional_parent_label': 'conditionalParentLabel',
'conditional_parent_label_metadata': 'conditionalParentLabelMetadata',
'conditional_parent_value': 'conditionalParentValue',
'conditional_parent_value_metadata': 'conditionalParentValueMetadata',
'custom_tab_id': 'customTabId',
'custom_tab_id_metadata': 'customTabIdMetadata',
'disable_auto_size': 'disableAutoSize',
'disable_auto_size_metadata': 'disableAutoSizeMetadata',
'document_id': 'documentId',
'document_id_metadata': 'documentIdMetadata',
'error_details': 'errorDetails',
'font': 'font',
'font_color': 'fontColor',
'font_color_metadata': 'fontColorMetadata',
'font_metadata': 'fontMetadata',
'font_size': 'fontSize',
'font_size_metadata': 'fontSizeMetadata',
'form_order': 'formOrder',
'form_order_metadata': 'formOrderMetadata',
'form_page_label': 'formPageLabel',
'form_page_label_metadata': 'formPageLabelMetadata',
'form_page_number': 'formPageNumber',
'form_page_number_metadata': 'formPageNumberMetadata',
'height': 'height',
'height_metadata': 'heightMetadata',
'italic': 'italic',
'italic_metadata': 'italicMetadata',
'locale_policy': 'localePolicy',
'locked': 'locked',
'locked_metadata': 'lockedMetadata',
'max_length': 'maxLength',
'max_length_metadata': 'maxLengthMetadata',
'merge_field': 'mergeField',
'merge_field_xml': 'mergeFieldXml',
'name': 'name',
'name_metadata': 'nameMetadata',
'original_value': 'originalValue',
'original_value_metadata': 'originalValueMetadata',
'page_number': 'pageNumber',
'page_number_metadata': 'pageNumberMetadata',
'recipient_id': 'recipientId',
'recipient_id_guid': 'recipientIdGuid',
'recipient_id_guid_metadata': 'recipientIdGuidMetadata',
'recipient_id_metadata': 'recipientIdMetadata',
'required': 'required',
'required_metadata': 'requiredMetadata',
'smart_contract_information': 'smartContractInformation',
'source': 'source',
'status': 'status',
'status_metadata': 'statusMetadata',
'tab_group_labels': 'tabGroupLabels',
'tab_group_labels_metadata': 'tabGroupLabelsMetadata',
'tab_id': 'tabId',
'tab_id_metadata': 'tabIdMetadata',
'tab_label': 'tabLabel',
'tab_label_metadata': 'tabLabelMetadata',
'tab_order': 'tabOrder',
'tab_order_metadata': 'tabOrderMetadata',
'tab_type': 'tabType',
'tab_type_metadata': 'tabTypeMetadata',
'template_locked': 'templateLocked',
'template_locked_metadata': 'templateLockedMetadata',
'template_required': 'templateRequired',
'template_required_metadata': 'templateRequiredMetadata',
'tooltip': 'tooltip',
'tool_tip_metadata': 'toolTipMetadata',
'underline': 'underline',
'underline_metadata': 'underlineMetadata',
'value': 'value',
'value_metadata': 'valueMetadata',
'width': 'width',
'width_metadata': 'widthMetadata',
'x_position': 'xPosition',
'x_position_metadata': 'xPositionMetadata',
'y_position': 'yPosition',
'y_position_metadata': 'yPositionMetadata'
}
def __init__(self, _configuration=None, **kwargs):
if _configuration is None:
_configuration = Configuration()
self._configuration = _configuration
self._anchor_allow_white_space_in_characters = None
self._anchor_allow_white_space_in_characters_metadata = None
self._anchor_case_sensitive = None
self._anchor_case_sensitive_metadata = None
self._anchor_horizontal_alignment = None
self._anchor_horizontal_alignment_metadata = None
self._anchor_ignore_if_not_present = None
self._anchor_ignore_if_not_present_metadata = None
self._anchor_match_whole_word = None
self._anchor_match_whole_word_metadata = None
self._anchor_string = None
self._anchor_string_metadata = None
self._anchor_tab_processor_version = None
self._anchor_tab_processor_version_metadata = None
self._anchor_units = None
self._anchor_units_metadata = None
self._anchor_x_offset = None
self._anchor_x_offset_metadata = None
self._anchor_y_offset = None
self._anchor_y_offset_metadata = None
self._bold = None
self._bold_metadata = None
self._conceal_value_on_document = None
self._conceal_value_on_document_metadata = None
self._conditional_parent_label = None
self._conditional_parent_label_metadata = None
self._conditional_parent_value = None
self._conditional_parent_value_metadata = None
self._custom_tab_id = None
self._custom_tab_id_metadata = None
self._disable_auto_size = None
self._disable_auto_size_metadata = None
self._document_id = None
self._document_id_metadata = None
self._error_details = None
self._font = None
self._font_color = None
self._font_color_metadata = None
self._font_metadata = None
self._font_size = None
self._font_size_metadata = None
self._form_order = None
self._form_order_metadata = None
self._form_page_label = None
self._form_page_label_metadata = None
self._form_page_number = None
self._form_page_number_metadata = None
self._height = None
self._height_metadata = None
self._italic = None
self._italic_metadata = None
self._locale_policy = None
self._locked = None
self._locked_metadata = None
self._max_length = None
self._max_length_metadata = None
self._merge_field = None
self._merge_field_xml = None
self._name = None
self._name_metadata = None
self._original_value = None
self._original_value_metadata = None
self._page_number = None
self._page_number_metadata = None
self._recipient_id = None
self._recipient_id_guid = None
self._recipient_id_guid_metadata = None
self._recipient_id_metadata = None
self._required = None
self._required_metadata = None
self._smart_contract_information = None
self._source = None
self._status = None
self._status_metadata = None
self._tab_group_labels = None
self._tab_group_labels_metadata = None
self._tab_id = None
self._tab_id_metadata = None
self._tab_label = None
self._tab_label_metadata = None
self._tab_order = None
self._tab_order_metadata = None
self._tab_type = None
self._tab_type_metadata = None
self._template_locked = None
self._template_locked_metadata = None
self._template_required = None
self._template_required_metadata = None
self._tooltip = None
self._tool_tip_metadata = None
self._underline = None
self._underline_metadata = None
self._value = None
self._value_metadata = None
self._width = None
self._width_metadata = None
self._x_position = None
self._x_position_metadata = None
self._y_position = None
self._y_position_metadata = None
self.discriminator = None
setattr(self, "_{}".format('anchor_allow_white_space_in_characters'), kwargs.get('anchor_allow_white_space_in_characters', None))
setattr(self, "_{}".format('anchor_allow_white_space_in_characters_metadata'), kwargs.get('anchor_allow_white_space_in_characters_metadata', None))
setattr(self, "_{}".format('anchor_case_sensitive'), kwargs.get('anchor_case_sensitive', None))
setattr(self, "_{}".format('anchor_case_sensitive_metadata'), kwargs.get('anchor_case_sensitive_metadata', None))
setattr(self, "_{}".format('anchor_horizontal_alignment'), kwargs.get('anchor_horizontal_alignment', None))
setattr(self, "_{}".format('anchor_horizontal_alignment_metadata'), kwargs.get('anchor_horizontal_alignment_metadata', None))
setattr(self, "_{}".format('anchor_ignore_if_not_present'), kwargs.get('anchor_ignore_if_not_present', None))
setattr(self, "_{}".format('anchor_ignore_if_not_present_metadata'), kwargs.get('anchor_ignore_if_not_present_metadata', None))
setattr(self, "_{}".format('anchor_match_whole_word'), kwargs.get('anchor_match_whole_word', None))
setattr(self, "_{}".format('anchor_match_whole_word_metadata'), kwargs.get('anchor_match_whole_word_metadata', None))
setattr(self, "_{}".format('anchor_string'), kwargs.get('anchor_string', None))
setattr(self, "_{}".format('anchor_string_metadata'), kwargs.get('anchor_string_metadata', None))
setattr(self, "_{}".format('anchor_tab_processor_version'), kwargs.get('anchor_tab_processor_version', None))
setattr(self, "_{}".format('anchor_tab_processor_version_metadata'), kwargs.get('anchor_tab_processor_version_metadata', None))
setattr(self, "_{}".format('anchor_units'), kwargs.get('anchor_units', None))
setattr(self, "_{}".format('anchor_units_metadata'), kwargs.get('anchor_units_metadata', None))
setattr(self, "_{}".format('anchor_x_offset'), kwargs.get('anchor_x_offset', None))
setattr(self, "_{}".format('anchor_x_offset_metadata'), kwargs.get('anchor_x_offset_metadata', None))
setattr(self, "_{}".format('anchor_y_offset'), kwargs.get('anchor_y_offset', None))
setattr(self, "_{}".format('anchor_y_offset_metadata'), kwargs.get('anchor_y_offset_metadata', None))
setattr(self, "_{}".format('bold'), kwargs.get('bold', None))
setattr(self, "_{}".format('bold_metadata'), kwargs.get('bold_metadata', None))
setattr(self, "_{}".format('conceal_value_on_document'), kwargs.get('conceal_value_on_document', None))
setattr(self, "_{}".format('conceal_value_on_document_metadata'), kwargs.get('conceal_value_on_document_metadata', None))
setattr(self, "_{}".format('conditional_parent_label'), kwargs.get('conditional_parent_label', None))
setattr(self, "_{}".format('conditional_parent_label_metadata'), kwargs.get('conditional_parent_label_metadata', None))
setattr(self, "_{}".format('conditional_parent_value'), kwargs.get('conditional_parent_value', None))
setattr(self, "_{}".format('conditional_parent_value_metadata'), kwargs.get('conditional_parent_value_metadata', None))
setattr(self, "_{}".format('custom_tab_id'), kwargs.get('custom_tab_id', None))
setattr(self, "_{}".format('custom_tab_id_metadata'), kwargs.get('custom_tab_id_metadata', None))
setattr(self, "_{}".format('disable_auto_size'), kwargs.get('disable_auto_size', None))
setattr(self, "_{}".format('disable_auto_size_metadata'), kwargs.get('disable_auto_size_metadata', None))
setattr(self, "_{}".format('document_id'), kwargs.get('document_id', None))
setattr(self, "_{}".format('document_id_metadata'), kwargs.get('document_id_metadata', None))
setattr(self, "_{}".format('error_details'), kwargs.get('error_details', None))
setattr(self, "_{}".format('font'), kwargs.get('font', None))
setattr(self, "_{}".format('font_color'), kwargs.get('font_color', None))
setattr(self, "_{}".format('font_color_metadata'), kwargs.get('font_color_metadata', None))
setattr(self, "_{}".format('font_metadata'), kwargs.get('font_metadata', None))
setattr(self, "_{}".format('font_size'), kwargs.get('font_size', None))
setattr(self, "_{}".format('font_size_metadata'), kwargs.get('font_size_metadata', None))
setattr(self, "_{}".format('form_order'), kwargs.get('form_order', None))
setattr(self, "_{}".format('form_order_metadata'), kwargs.get('form_order_metadata', None))
setattr(self, "_{}".format('form_page_label'), kwargs.get('form_page_label', None))
setattr(self, "_{}".format('form_page_label_metadata'), kwargs.get('form_page_label_metadata', None))
setattr(self, "_{}".format('form_page_number'), kwargs.get('form_page_number', None))
setattr(self, "_{}".format('form_page_number_metadata'), kwargs.get('form_page_number_metadata', None))
setattr(self, "_{}".format('height'), kwargs.get('height', None))
setattr(self, "_{}".format('height_metadata'), kwargs.get('height_metadata', None))
setattr(self, "_{}".format('italic'), kwargs.get('italic', None))
setattr(self, "_{}".format('italic_metadata'), kwargs.get('italic_metadata', None))
setattr(self, "_{}".format('locale_policy'), kwargs.get('locale_policy', None))
setattr(self, "_{}".format('locked'), kwargs.get('locked', None))
setattr(self, "_{}".format('locked_metadata'), kwargs.get('locked_metadata', None))
setattr(self, "_{}".format('max_length'), kwargs.get('max_length', None))
setattr(self, "_{}".format('max_length_metadata'), kwargs.get('max_length_metadata', None))
setattr(self, "_{}".format('merge_field'), kwargs.get('merge_field', None))
setattr(self, "_{}".format('merge_field_xml'), kwargs.get('merge_field_xml', None))
setattr(self, "_{}".format('name'), kwargs.get('name', None))
setattr(self, "_{}".format('name_metadata'), kwargs.get('name_metadata', None))
setattr(self, "_{}".format('original_value'), kwargs.get('original_value', None))
setattr(self, "_{}".format('original_value_metadata'), kwargs.get('original_value_metadata', None))
setattr(self, "_{}".format('page_number'), kwargs.get('page_number', None))
setattr(self, "_{}".format('page_number_metadata'), kwargs.get('page_number_metadata', None))
setattr(self, "_{}".format('recipient_id'), kwargs.get('recipient_id', None))
setattr(self, "_{}".format('recipient_id_guid'), kwargs.get('recipient_id_guid', None))
setattr(self, "_{}".format('recipient_id_guid_metadata'), kwargs.get('recipient_id_guid_metadata', None))
setattr(self, "_{}".format('recipient_id_metadata'), kwargs.get('recipient_id_metadata', None))
setattr(self, "_{}".format('required'), kwargs.get('required', None))
setattr(self, "_{}".format('required_metadata'), kwargs.get('required_metadata', None))
setattr(self, "_{}".format('smart_contract_information'), kwargs.get('smart_contract_information', None))
setattr(self, "_{}".format('source'), kwargs.get('source', None))
setattr(self, "_{}".format('status'), kwargs.get('status', None))
setattr(self, "_{}".format('status_metadata'), kwargs.get('status_metadata', None))
setattr(self, "_{}".format('tab_group_labels'), kwargs.get('tab_group_labels', None))
setattr(self, "_{}".format('tab_group_labels_metadata'), kwargs.get('tab_group_labels_metadata', None))
setattr(self, "_{}".format('tab_id'), kwargs.get('tab_id', None))
setattr(self, "_{}".format('tab_id_metadata'), kwargs.get('tab_id_metadata', None))
setattr(self, "_{}".format('tab_label'), kwargs.get('tab_label', None))
setattr(self, "_{}".format('tab_label_metadata'), kwargs.get('tab_label_metadata', None))
setattr(self, "_{}".format('tab_order'), kwargs.get('tab_order', None))
setattr(self, "_{}".format('tab_order_metadata'), kwargs.get('tab_order_metadata', None))
setattr(self, "_{}".format('tab_type'), kwargs.get('tab_type', None))
setattr(self, "_{}".format('tab_type_metadata'), kwargs.get('tab_type_metadata', None))
setattr(self, "_{}".format('template_locked'), kwargs.get('template_locked', None))
setattr(self, "_{}".format('template_locked_metadata'), kwargs.get('template_locked_metadata', None))
setattr(self, "_{}".format('template_required'), kwargs.get('template_required', None))
setattr(self, "_{}".format('template_required_metadata'), kwargs.get('template_required_metadata', None))
setattr(self, "_{}".format('tooltip'), kwargs.get('tooltip', None))
setattr(self, "_{}".format('tool_tip_metadata'), kwargs.get('tool_tip_metadata', None))
setattr(self, "_{}".format('underline'), kwargs.get('underline', None))
setattr(self, "_{}".format('underline_metadata'), kwargs.get('underline_metadata', None))
setattr(self, "_{}".format('value'), kwargs.get('value', None))
setattr(self, "_{}".format('value_metadata'), kwargs.get('value_metadata', None))
setattr(self, "_{}".format('width'), kwargs.get('width', None))
setattr(self, "_{}".format('width_metadata'), kwargs.get('width_metadata', None))
setattr(self, "_{}".format('x_position'), kwargs.get('x_position', None))
setattr(self, "_{}".format('x_position_metadata'), kwargs.get('x_position_metadata', None))
setattr(self, "_{}".format('y_position'), kwargs.get('y_position', None))
setattr(self, "_{}".format('y_position_metadata'), kwargs.get('y_position_metadata', None))
@property
def anchor_allow_white_space_in_characters(self):
return self._anchor_allow_white_space_in_characters
@anchor_allow_white_space_in_characters.setter
def anchor_allow_white_space_in_characters(self, anchor_allow_white_space_in_characters):
self._anchor_allow_white_space_in_characters = anchor_allow_white_space_in_characters
@property
def anchor_allow_white_space_in_characters_metadata(self):
return self._anchor_allow_white_space_in_characters_metadata
@anchor_allow_white_space_in_characters_metadata.setter
def anchor_allow_white_space_in_characters_metadata(self, anchor_allow_white_space_in_characters_metadata):
self._anchor_allow_white_space_in_characters_metadata = anchor_allow_white_space_in_characters_metadata
@property
def anchor_case_sensitive(self):
return self._anchor_case_sensitive
@anchor_case_sensitive.setter
def anchor_case_sensitive(self, anchor_case_sensitive):
self._anchor_case_sensitive = anchor_case_sensitive
@property
def anchor_case_sensitive_metadata(self):
return self._anchor_case_sensitive_metadata
@anchor_case_sensitive_metadata.setter
def anchor_case_sensitive_metadata(self, anchor_case_sensitive_metadata):
self._anchor_case_sensitive_metadata = anchor_case_sensitive_metadata
@property
def anchor_horizontal_alignment(self):
return self._anchor_horizontal_alignment
@anchor_horizontal_alignment.setter
def anchor_horizontal_alignment(self, anchor_horizontal_alignment):
self._anchor_horizontal_alignment = anchor_horizontal_alignment
@property
def anchor_horizontal_alignment_metadata(self):
return self._anchor_horizontal_alignment_metadata
@anchor_horizontal_alignment_metadata.setter
def anchor_horizontal_alignment_metadata(self, anchor_horizontal_alignment_metadata):
self._anchor_horizontal_alignment_metadata = anchor_horizontal_alignment_metadata
@property
def anchor_ignore_if_not_present(self):
return self._anchor_ignore_if_not_present
@anchor_ignore_if_not_present.setter
def anchor_ignore_if_not_present(self, anchor_ignore_if_not_present):
self._anchor_ignore_if_not_present = anchor_ignore_if_not_present
@property
def anchor_ignore_if_not_present_metadata(self):
return self._anchor_ignore_if_not_present_metadata
@anchor_ignore_if_not_present_metadata.setter
def anchor_ignore_if_not_present_metadata(self, anchor_ignore_if_not_present_metadata):
self._anchor_ignore_if_not_present_metadata = anchor_ignore_if_not_present_metadata
@property
def anchor_match_whole_word(self):
return self._anchor_match_whole_word
@anchor_match_whole_word.setter
def anchor_match_whole_word(self, anchor_match_whole_word):
self._anchor_match_whole_word = anchor_match_whole_word
@property
def anchor_match_whole_word_metadata(self):
return self._anchor_match_whole_word_metadata
@anchor_match_whole_word_metadata.setter
def anchor_match_whole_word_metadata(self, anchor_match_whole_word_metadata):
self._anchor_match_whole_word_metadata = anchor_match_whole_word_metadata
@property
def anchor_string(self):
return self._anchor_string
@anchor_string.setter
def anchor_string(self, anchor_string):
self._anchor_string = anchor_string
@property
def anchor_string_metadata(self):
return self._anchor_string_metadata
@anchor_string_metadata.setter
def anchor_string_metadata(self, anchor_string_metadata):
self._anchor_string_metadata = anchor_string_metadata
@property
def anchor_tab_processor_version(self):
return self._anchor_tab_processor_version
@anchor_tab_processor_version.setter
def anchor_tab_processor_version(self, anchor_tab_processor_version):
self._anchor_tab_processor_version = anchor_tab_processor_version
@property
def anchor_tab_processor_version_metadata(self):
return self._anchor_tab_processor_version_metadata
@anchor_tab_processor_version_metadata.setter
def anchor_tab_processor_version_metadata(self, anchor_tab_processor_version_metadata):
self._anchor_tab_processor_version_metadata = anchor_tab_processor_version_metadata
@property
def anchor_units(self):
return self._anchor_units
@anchor_units.setter
def anchor_units(self, anchor_units):
self._anchor_units = anchor_units
@property
def anchor_units_metadata(self):
return self._anchor_units_metadata
@anchor_units_metadata.setter
def anchor_units_metadata(self, anchor_units_metadata):
self._anchor_units_metadata = anchor_units_metadata
@property | MIT License |
lsapan/docker-swarm-demo | secrets.py | secret | python | def secret(name, strip=True):
with open(secret_path(name), 'r') as f:
val = f.read()
if strip:
val = val.strip()
return val | Returns the value of a secret from the docker container. | https://github.com/lsapan/docker-swarm-demo/blob/f6b905fbbbe4b74d62a4af664f9a677f3cb03b8e/secrets.py#L18-L26 | import os
class SecretNotFoundError(IOError):
pass
def secret_path(name):
secret_path = f'/run/secrets/{name}'
if not os.path.isfile(secret_path):
raise SecretNotFoundError(name)
return secret_path | MIT License |
uvjustin/alarmdotcom | custom_components/alarmdotcom/alarm_control_panel.py | AlarmDotCom._validate_code | python | def _validate_code(self, code):
check = self._code is None or code == self._code
if not check:
_LOGGER.warning("Wrong code entered")
return check | Validate given code. | https://github.com/uvjustin/alarmdotcom/blob/8b7288bfd1c21a5687cd2359753f0d2efceca726/custom_components/alarmdotcom/alarm_control_panel.py#L215-L220 | import logging
import re
from pyalarmdotcomajax import Alarmdotcom, AlarmdotcomADT, AlarmdotcomProtection1
import voluptuous as vol
import homeassistant.components.alarm_control_panel as alarm
try:
from homeassistant.components.alarm_control_panel import AlarmControlPanelEntity
except ImportError:
from homeassistant.components.alarm_control_panel import (
AlarmControlPanel as AlarmControlPanelEntity,
)
from homeassistant.components.alarm_control_panel import PLATFORM_SCHEMA
from homeassistant.components.alarm_control_panel.const import (
SUPPORT_ALARM_ARM_AWAY,
SUPPORT_ALARM_ARM_HOME,
)
from homeassistant.const import (
CONF_CODE,
CONF_NAME,
CONF_PASSWORD,
CONF_USERNAME,
STATE_ALARM_ARMED_AWAY,
STATE_ALARM_ARMED_HOME,
STATE_ALARM_ARMED_NIGHT,
STATE_ALARM_DISARMED,
)
from homeassistant.helpers.aiohttp_client import (
async_create_clientsession,
async_get_clientsession,
)
import homeassistant.helpers.config_validation as cv
_LOGGER = logging.getLogger(__name__)
DEFAULT_NAME = "Alarm.com"
CONF_FORCE_BYPASS = "force_bypass"
CONF_NO_ENTRY_DELAY = "no_entry_delay"
CONF_SILENT_ARMING = "silent_arming"
CONF_ADT = "adt"
CONF_PROTECTION1 = "protection1"
CONF_TWO_FACTOR_COOKIE = "two_factor_cookie"
DOMAIN = "alarmdotcom"
PLATFORM_SCHEMA = PLATFORM_SCHEMA.extend(
{
vol.Required(CONF_PASSWORD): cv.string,
vol.Required(CONF_USERNAME): cv.string,
vol.Optional(CONF_CODE): cv.string,
vol.Optional(CONF_NAME, default=DEFAULT_NAME): cv.string,
vol.Optional(CONF_FORCE_BYPASS, default="false"): cv.string,
vol.Optional(CONF_NO_ENTRY_DELAY, default="false"): cv.string,
vol.Optional(CONF_SILENT_ARMING, default="false"): cv.string,
vol.Optional(CONF_ADT, default=False): cv.boolean,
vol.Optional(CONF_PROTECTION1, default=False): cv.boolean,
vol.Optional(CONF_TWO_FACTOR_COOKIE): cv.string,
}
)
async def async_setup_platform(hass, config, async_add_entities, discovery_info=None):
name = config.get(CONF_NAME)
code = config.get(CONF_CODE)
username = config.get(CONF_USERNAME)
password = config.get(CONF_PASSWORD)
force_bypass = config.get(CONF_FORCE_BYPASS)
no_entry_delay = config.get(CONF_NO_ENTRY_DELAY)
silent_arming = config.get(CONF_SILENT_ARMING)
two_factor_cookie = config.get(CONF_TWO_FACTOR_COOKIE)
use_new_websession = hass.data.get(DOMAIN)
adt_or_protection1 = 0
if config.get(CONF_ADT):
adt_or_protection1 = 1
elif config.get(CONF_PROTECTION1):
adt_or_protection1 = 2
if not use_new_websession:
hass.data[DOMAIN] = True
use_new_websession = False
alarmdotcom = AlarmDotCom(
hass,
name,
code,
username,
password,
force_bypass,
no_entry_delay,
silent_arming,
use_new_websession,
adt_or_protection1,
two_factor_cookie,
)
await alarmdotcom.async_login()
async_add_entities([alarmdotcom])
class AlarmDotCom(AlarmControlPanelEntity):
def __init__(
self,
hass,
name,
code,
username,
password,
force_bypass,
no_entry_delay,
silent_arming,
use_new_websession,
adt_or_protection1,
two_factor_cookie,
):
_LOGGER.debug("Setting up Alarm.com...")
self._name = name
self._code = code if code else None
if use_new_websession:
websession = async_create_clientsession(hass)
_LOGGER.debug("Using new websession.")
else:
websession = async_get_clientsession(hass)
_LOGGER.debug("Using hass websession.")
self._state = None
no_entry_delay = (
"stay" if no_entry_delay.lower() == "home" else no_entry_delay.lower()
)
force_bypass = (
"stay" if force_bypass.lower() == "home" else force_bypass.lower()
)
silent_arming = (
"stay" if silent_arming.lower() == "home" else silent_arming.lower()
)
if adt_or_protection1 == 1:
adc_class = AlarmdotcomADT
elif adt_or_protection1 == 2:
adc_class = AlarmdotcomProtection1
else:
adc_class = Alarmdotcom
self._alarm = adc_class(
username,
password,
websession,
force_bypass,
no_entry_delay,
silent_arming,
two_factor_cookie,
)
async def async_login(self):
await self._alarm.async_login()
async def async_update(self):
await self._alarm.async_update()
return self._alarm.state
@property
def name(self):
return self._name
@property
def code_format(self):
if self._code is None:
return None
if isinstance(self._code, str) and re.search("^\\d+$", self._code):
return alarm.FORMAT_NUMBER
return alarm.FORMAT_TEXT
@property
def state(self):
if self._alarm.state.lower() == "disarmed":
return STATE_ALARM_DISARMED
if self._alarm.state.lower() == "armed stay":
return STATE_ALARM_ARMED_HOME
if self._alarm.state.lower() == "armed away":
return STATE_ALARM_ARMED_AWAY
if self._alarm.state.lower() == "armed night":
return STATE_ALARM_ARMED_NIGHT
return None
@property
def supported_features(self) -> int:
return SUPPORT_ALARM_ARM_HOME | SUPPORT_ALARM_ARM_AWAY
@property
def device_state_attributes(self):
return {"sensor_status": self._alarm.sensor_status}
async def async_alarm_disarm(self, code=None):
if self._validate_code(code):
await self._alarm.async_alarm_disarm()
async def async_alarm_arm_home(self, code=None):
if self._validate_code(code):
await self._alarm.async_alarm_arm_stay()
async def async_alarm_arm_away(self, code=None):
if self._validate_code(code):
await self._alarm.async_alarm_arm_away() | MIT License |
synbiodex/pysbol2 | sbol2/componentdefinition.py | ComponentDefinition.linearize | python | def linearize(self, components=None):
raise NotImplementedError("Not yet implemented") | TODO document
:param components: An optional list of component definitions or URIs.
If None, an empty list of ComponentDefinitions is assumed.
:return: None | https://github.com/synbiodex/pysbol2/blob/127b92d60ecf6f9b6cb8fbf9657bb578bc983090/sbol2/componentdefinition.py#L887-L895 | import os
import posixpath
from typing import Union
from rdflib import URIRef
from .component import Component
from .config import Config, ConfigOptions
from .constants import *
from .toplevel import TopLevel
from .property import OwnedObject, ReferencedObject, URIProperty
from .sbolerror import SBOLError, SBOLErrorCode
from .sequence import Sequence
from .sequenceannotation import SequenceAnnotation
from .sequenceconstraint import SequenceConstraint
class ComponentDefinition(TopLevel):
_types = None
_roles = None
components = None
sequences = None
sequenceAnnotations = None
sequenceConstraints = None
def __init__(self, uri=URIRef("example"),
component_type=URIRef(BIOPAX_DNA),
version=VERSION_STRING,
type_uri=SBOL_COMPONENT_DEFINITION):
super().__init__(type_uri, uri, version)
self.types = URIProperty(self, SBOL_TYPES,
'1', '*', None, component_type)
self.roles = URIProperty(self, SBOL_ROLES,
'0', '*', None)
self.sequences = ReferencedObject(self, SBOL_SEQUENCE_PROPERTY,
SBOL_SEQUENCE, '0', '*', None)
self.sequenceAnnotations = OwnedObject(self, SBOL_SEQUENCE_ANNOTATIONS,
SequenceAnnotation,
'0', '*', None)
self.components = OwnedObject(self, SBOL_COMPONENTS, Component,
'0', '*', None)
self.sequenceConstraints = OwnedObject(self, SBOL_SEQUENCE_CONSTRAINTS,
SequenceConstraint,
'0', '*', None)
self._sequence_cache: Union[Sequence, None] = None
@property
def sequence(self):
seqs = self.sequences
if not seqs:
return None
if self.doc:
seq_uri = seqs[0]
try:
return self.doc.sequences[seq_uri]
except SBOLError as e:
if e.error_code() != SBOLErrorCode.NOT_FOUND_ERROR:
raise
return None
else:
if self._sequence_cache and self._sequence_cache.identity in seqs:
return self._sequence_cache
return None
@sequence.setter
def sequence(self, sequence: Union[Sequence, None]):
if not sequence:
self.sequences = None
self._sequence_cache = None
return
if self.doc:
try:
self.doc.add(sequence)
except SBOLError as e:
if e.error_code() != SBOLErrorCode.DUPLICATE_URI_ERROR:
raise
else:
self._sequence_cache = sequence
self.sequences = [sequence.identity]
def _added_to_document(self, doc):
super()._added_to_document(doc)
if self._sequence_cache:
try:
doc.add(self._sequence_cache)
except SBOLError as e:
if e.error_code() == SBOLErrorCode.SBOL_ERROR_URI_NOT_UNIQUE:
pass
else:
raise
def addType(self, new_type):
val = self.types
val.append(new_type)
self.types = val
def removeType(self, index=0):
val = self.types
del val[index]
self.types = val
def addRole(self, new_role):
val = self.roles
val.append(new_role)
self.roles = val
def removeRole(self, index=0):
val = self.roles
del val[index]
self.roles = val
def assemble(self, component_list, assembly_method=None, doc=None):
if not Config.getOption(ConfigOptions.SBOL_COMPLIANT_URIS):
raise EnvironmentError('Assemble method requires SBOL-compliance enabled')
if not self.doc and not doc:
raise ValueError('Missing doc argument. If the ComponentDefinition does '
'not belong to a Document, a target Document must be '
'specified using the doc keyword argument.')
if doc and self.doc != doc:
raise ValueError('Invalid doc argument. Do not use the doc keyword '
'argument if the ComponentDefinition already belongs '
'to a Document')
doc = doc if doc else self.doc
if isinstance(component_list, list) and all(isinstance(c, ComponentDefinition)
for c in component_list):
for cdef in component_list:
if cdef.doc and cdef.doc is not doc:
raise ValueError('Invalid component_list specified. Assembly '
'subcomponents must belong to the same Document '
'as self.')
elif isinstance(component_list, list) and all(isinstance(c, str)
for c in component_list):
component_identities = component_list[:]
component_list = []
for c_id in component_identities:
if c_id not in doc.componentDefinitions:
raise ValueError('Invalid component_list specified. '
'ComponentDefinition <%s> not found.' % c_id)
cdef = doc.componentDefinitions[c_id]
component_list.append(cdef)
else:
raise TypeError('Invalid component_list specified. Please provide a list '
'of ComponentDefinitions or, alternatively, a list of '
'ComponentDefinition displayIds')
if not self.doc:
doc.addComponentDefinition(self)
for cdef in component_list:
if not cdef.doc:
self.doc.addComponentDefinition(cdef)
if assembly_method:
component_list = assembly_method(component_list)
if not all(type(c) is ComponentDefinition for c in component_list):
raise TypeError('Invalid callback specified for assembly_method. The '
'callback must return a list of ComponentDefinitions')
instance_list = []
for cdef in component_list:
instance_count = 0
component_id = self.persistentIdentity + "/" + cdef.displayId + "_" + str(instance_count) + "/" + self.version
while self.find(component_id):
instance_count += 1
component_id = self.persistentIdentity + "/" + cdef.displayId + "_" + str(instance_count) + "/" + self.version
c = self.components.create(cdef.displayId + "_" + str(instance_count))
c.definition = cdef.identity
instance_list.append(c)
return component_list
def assemblePrimaryStructure(self, primary_structure, assembly_method=None,
doc=None):
primary_structure = self.assemble(primary_structure, assembly_method, doc)
doc = doc if doc else self.doc
if all(isinstance(c, str) for c in primary_structure):
component_identities = primary_structure[:]
primary_structure = []
for c_id in component_identities:
cdef = doc.componentDefinitions[c_id]
primary_structure.append(cdef)
self.types += [SO_LINEAR]
component_map = {}
for c in self.components:
if c.definition not in component_map:
component_map[c.definition] = [c]
else:
component_map[c.definition].append(c)
primary_structure_components = []
for cd in primary_structure:
primary_structure_components.append(component_map[cd.identity].pop())
if len(self.sequenceConstraints):
self.sequenceConstraints.clear()
for upstream, downstream in zip(primary_structure_components[:-1],
primary_structure_components[1:]):
instance_count = 0
constraint_id = 'constraint_%d' % instance_count
while constraint_id in self.sequenceConstraints:
instance_count += 1
constraint_id = 'constraint_%d' % instance_count
sc = self.sequenceConstraints.create(constraint_id)
sc.subject = upstream
sc.object = downstream
sc.restriction = SBOL_RESTRICTION_PRECEDES
def compile(self, assembly_method=None):
if not self.doc:
raise ValueError('Cannot compile <%s>. The ComponentDefinition must belong '
'to a Document in order to compile.' % self.identity)
if self.sequence is None:
sequence_id = self.displayId + '_seq'
compliant_uris = Config.getOption(ConfigOptions.SBOL_COMPLIANT_URIS)
typed_uris = Config.getOption(ConfigOptions.SBOL_TYPED_URIS)
if compliant_uris and typed_uris:
sequence_id = self.displayId
self.sequence = Sequence(sequence_id)
return self.sequence.compile(assembly_method=assembly_method)
def updateSequence(self, composite_sequence=""):
raise NotImplementedError("Not yet implemented")
def getInSequentialOrder(self):
return self.getPrimaryStructureComponents()
def hasUpstreamComponent(self, component):
if len(self.sequenceConstraints) < 1:
raise SBOLError(SBOLErrorCode.SBOL_ERROR_NOT_FOUND,
'Cannot determine upstream Component. '
'Self has no SequenceConstraints')
else:
for sc in self.sequenceConstraints:
if sc.object == component.identity and sc.restriction == SBOL_RESTRICTION_PRECEDES:
return True
return False
def getUpstreamComponent(self, component):
if len(self.sequenceConstraints) < 1:
raise SBOLError(SBOLErrorCode.SBOL_ERROR_NOT_FOUND,
'Cannot get upstream Component. Self '
'has no SequenceConstraints')
else:
upstream_component_id = None
for sc in self.sequenceConstraints:
if sc.object == component.identity and sc.restriction == SBOL_RESTRICTION_PRECEDES:
upstream_component = self.components[sc.subject]
return upstream_component
raise SBOLError(SBOLErrorCode.SBOL_ERROR_END_OF_LIST,
'This component has no upstream '
'component. Use hasUpstreamComponent to catch this error')
def hasDownstreamComponent(self, component):
if len(self.sequenceConstraints) < 1:
raise SBOLError(SBOLErrorCode.SBOL_ERROR_NOT_FOUND,
'Cannot determine upstream Component. '
'Self has no SequenceConstraints')
else:
for sc in self.sequenceConstraints:
if sc.subject == component.identity and sc.restriction == SBOL_RESTRICTION_PRECEDES:
return True
return False
def getDownstreamComponent(self, component):
if len(self.sequenceConstraints) < 1:
raise SBOLError(SBOLErrorCode.SBOL_ERROR_NOT_FOUND,
'Cannot get downstream Component. '
'Self has no SequenceConstraints')
else:
upstream_component_id = None
for sc in self.sequenceConstraints:
if sc.subject == component.identity and sc.restriction == SBOL_RESTRICTION_PRECEDES:
upstream_component = self.components[sc.object]
return upstream_component
raise SBOLError(SBOLErrorCode.SBOL_ERROR_END_OF_LIST,
'This component has no downstream '
'component. Use hasDownstreamComponent to catch this error')
def deleteDownstreamComponent(self, upstream_component):
if not Config.getOption(ConfigOptions.SBOL_COMPLIANT_URIS):
raise ValueError('SBOL-compliant URIs must be enabled to use this method')
if upstream_component.identity not in self.components:
msg = 'Deletion failed. ComponentDefinition %s has no child component %s'
msg = msg % (self.identity, upstream_component.identity)
raise ValueError(msg)
primary_structure = self.getPrimaryStructureComponents()
if upstream_component.identity == primary_structure[-1].identity:
msg = 'Deletion failed. No Components were found downstream of %s'
msg = msg % upstream_component.identity
raise ValueError(
msg)
downstream_component = None
upstream_sequence_constraint = None
downstream_sequence_constraint = None
for c_upstream, c_downstream in zip(primary_structure[:-1],
primary_structure[1:]):
for sc in self.sequenceConstraints:
if (sc.subject == c_upstream.identity and
sc.object == c_downstream.identity and
sc.restriction == SBOL_RESTRICTION_PRECEDES):
upstream_sequence_constraint = downstream_sequence_constraint
downstream_sequence_constraint = sc
if downstream_component:
break
if c_upstream.identity == upstream_component.identity:
downstream_component = c_downstream
if downstream_component:
self.components.remove(downstream_component.identity)
self.sequenceConstraints.remove(downstream_sequence_constraint.identity)
if downstream_sequence_constraint.subject == downstream_component.identity:
upstream_sequence_constraint.object = downstream_sequence_constraint.object
def deleteUpstreamComponent(self, downstream_component):
if not Config.getOption(ConfigOptions.SBOL_COMPLIANT_URIS):
raise ValueError('SBOL-compliant URIs must be enabled to use this method')
if downstream_component.identity not in self.components:
msg = 'Deletion failed. No Components were found upstream of %s'
msg = msg % downstream_component.identity
raise ValueError(msg)
primary_structure = self.getPrimaryStructureComponents()
if downstream_component.identity == primary_structure[0].identity:
msg = 'Deletion failed. Component %s does not have an upstream component'
msg = msg % downstream_component.identity
raise ValueError(msg)
upstream_component = None
upstream_sequence_constraint = None
downstream_sequence_constraint = None
for c_upstream, c_downstream in zip(primary_structure[:-1],
primary_structure[1:]):
for sc in self.sequenceConstraints:
if (sc.subject == c_upstream.identity and
sc.object == c_downstream.identity and
sc.restriction == SBOL_RESTRICTION_PRECEDES):
upstream_sequence_constraint = downstream_sequence_constraint
downstream_sequence_constraint = sc
if c_downstream.identity == downstream_component.identity:
upstream_component = c_upstream
break
if upstream_component:
self.components.remove(upstream_component.identity)
self.sequenceConstraints.remove(downstream_sequence_constraint.identity)
if upstream_sequence_constraint:
upstream_sequence_constraint.object = downstream_sequence_constraint.object
def insertUpstreamComponent(self, downstream, insert):
if not Config.getOption(ConfigOptions.SBOL_COMPLIANT_URIS):
raise ValueError('SBOL-compliant URIs must be enabled to use this method')
if not self.doc:
msg = f'ComponentDefinition {self.identity} does not belong to a Document'
msg += ' Add this ComponentDefinition to a Document before calling'
msg += ' insertUpstreamComponent'
raise ValueError(msg)
if self.doc != insert.doc:
msg = f'Invalid Document for ComponentDefinition {insert.identity}.'
msg += ' Add the insert to the same Document as the calling object.'
raise ValueError(msg)
if not insert.doc:
insert.doc = self.doc
target_constraint = None
for sc in self.sequenceConstraints:
if (sc.object == downstream.identity and
sc.restriction == SBOL_RESTRICTION_PRECEDES):
if target_constraint is not None:
msg = 'SequenceConstraints are ambiguous. The target component'
msg += ' may have more than one downstream component specified'
raise ValueError(msg)
target_constraint = sc
instance_count = 0
component_id = posixpath.join(self.persistentIdentity,
f'{insert.displayId}_{instance_count}',
self.version)
while self.find(component_id):
instance_count += 1
component_id = posixpath.join(self.persistentIdentity,
f'{insert.displayId}_{instance_count}',
self.version)
c_insert = self.components.create(f'{insert.displayId}_{instance_count}')
c_insert.definition = insert.identity
instance_count = 0
sc_id = posixpath.join(self.persistentIdentity,
f'constraint_{instance_count}',
self.version)
while self.find(sc_id):
instance_count += 1
sc_id = posixpath.join(self.persistentIdentity,
f'constraint_{instance_count}',
self.version)
sc_new = self.sequenceConstraints.create(f'constraint_{instance_count}')
sc_new.subject = component_id
sc_new.object = downstream.identity
sc_new.restriction = SBOL_RESTRICTION_PRECEDES
if target_constraint:
target_constraint.object = c_insert.identity
def insertDownstreamComponent(self, upstream, insert):
if not Config.getOption(ConfigOptions.SBOL_COMPLIANT_URIS):
raise ValueError('SBOL-compliant URIs must be enabled to use this method')
if not self.doc:
msg = f'ComponentDefinition {self.identity} does not belong to a Document'
msg += ' Add this ComponentDefinition to a Document before calling'
msg += ' insertUpstreamComponent'
raise ValueError(msg)
if self.doc != insert.doc:
msg = f'Invalid Document for ComponentDefinition {insert.identity}.'
msg += ' Add the insert to the same Document as the calling object.'
raise ValueError(msg)
if not insert.doc:
insert.doc = self.doc
target_constraint = None
for sc in self.sequenceConstraints:
if (sc.subject == upstream.identity and
sc.restriction == SBOL_RESTRICTION_PRECEDES):
if target_constraint is not None:
msg = 'SequenceConstraints are ambiguous. The target component'
msg += ' may have more than one downstream component specified'
raise ValueError(msg)
target_constraint = sc
instance_count = 0
component_id = posixpath.join(self.persistentIdentity,
f'{insert.displayId}_{instance_count}',
self.version)
while self.find(component_id):
instance_count += 1
component_id = posixpath.join(self.persistentIdentity,
f'{insert.displayId}_{instance_count}',
self.version)
c_insert = self.components.create(f'{insert.displayId}_{instance_count}')
c_insert.definition = insert.identity
instance_count = 0
sc_id = posixpath.join(self.persistentIdentity,
f'constraint_{instance_count}',
self.version)
while self.find(sc_id):
instance_count += 1
sc_id = posixpath.join(self.persistentIdentity,
f'constraint_{instance_count}',
self.version)
sc_new = self.sequenceConstraints.create(f'constraint_{instance_count}')
sc_new.subject = upstream.identity
sc_new.object = component_id
sc_new.restriction = SBOL_RESTRICTION_PRECEDES
if target_constraint:
target_constraint.object = c_insert.identity
def getFirstComponent(self):
if len(self.components) < 1:
raise SBOLError(SBOLErrorCode.SBOL_ERROR_NOT_FOUND,
'This ComponentDefinition has no '
'components')
arbitrary_component = self.components[0]
next_component = arbitrary_component
while self.hasUpstreamComponent(next_component):
next_component = self.getUpstreamComponent(next_component)
return next_component
def getLastComponent(self):
if len(self.components) < 1:
raise SBOLError(SBOLErrorCode.SBOL_ERROR_NOT_FOUND,
'This ComponentDefinition has no '
'components')
arbitrary_component = self.components[0]
next_component = arbitrary_component
while self.hasDownstreamComponent(next_component):
next_component = self.getDownstreamComponent(next_component)
return next_component
def applyToComponentHierarchy(self, callback=None, user_data=None):
raise NotImplementedError("Not yet implemented")
def getPrimaryStructureComponents(self):
subcomponents = []
if len(self.components) == 1:
subcomponents.append(self.components[0])
else:
if len(self.sequenceConstraints) != (len(self.components) - 1):
raise ValueError('ComponentDefinition <%s> does not appear to describe'
'a complete primary structure. It appears to be '
'missing SequenceConstraints.' % self.identity)
c_first = self.getFirstComponent()
subcomponents.append(c_first)
c_next = c_first
while self.hasDownstreamComponent(c_next):
c_next = self.getDownstreamComponent(c_next)
subcomponents.append(c_next)
return subcomponents
def getPrimaryStructure(self):
if self.doc is None:
raise SBOLError(SBOLErrorCode.SBOL_ERROR_MISSING_DOCUMENT,
'Cannot get primary structure.'
'Self must belong to a Document.')
component_ids = [c.definition for c in self.getPrimaryStructureComponents()]
return [self.doc.getComponentDefinition(c) for c in component_ids]
def insertDownstream(self, target, component_to_insert):
raise NotImplementedError("Not yet implemented")
def insertUpstream(self, target, component_to_insert):
raise NotImplementedError("Not yet implemented")
def addUpstreamFlank(self, target, elements):
raise NotImplementedError("Not yet implemented")
def addDownstreamFlank(self, target, elements):
raise NotImplementedError("Not yet implemented")
def isRegular(self, msg=None):
raise NotImplementedError("Not yet implemented")
def isComplete(self, msg=None):
raise NotImplementedError("Not yet implemented")
def disassemble(self, range_start=1):
raise NotImplementedError("Not yet implemented") | Apache License 2.0 |
flyteorg/flytekit | flytekit/models/literals.py | RetryStrategy.__init__ | python | def __init__(self, retries):
self._retries = retries | :param int retries: Number of retries to attempt on recoverable failures. If retries is 0, then
only one attempt will be made. | https://github.com/flyteorg/flytekit/blob/6c032035563ae645b0b93558b3fe3362080057ea/flytekit/models/literals.py#L15-L20 | from datetime import datetime as _datetime
import pytz as _pytz
from flyteidl.core import literals_pb2 as _literals_pb2
from google.protobuf.struct_pb2 import Struct
from flytekit.common.exceptions import user as _user_exceptions
from flytekit.models import common as _common
from flytekit.models.core import types as _core_types
from flytekit.models.types import OutputReference as _OutputReference
from flytekit.models.types import SchemaType as _SchemaType
class RetryStrategy(_common.FlyteIdlEntity): | Apache License 2.0 |
practical-data-science/ecommercetools | ecommercetools/utilities/metrics.py | average_tickets_to_resolve | python | def average_tickets_to_resolve(total_tickets, total_resolutions):
return total_tickets / total_resolutions | Returns the average number of tickets required to resolve an issue.
Args:
total_tickets (int): Total chats, emails, or tickets in the period.
total_resolutions (int): Total chats, emails, or tickets resolved in the period.
Returns:
Average number of tickets it takes to resolve an issue. | https://github.com/practical-data-science/ecommercetools/blob/b00175d7775dc4f6ad57b52702a0b2acce3425fc/ecommercetools/utilities/metrics.py#L732-L743 | import math
from datetime import datetime
def tax(gross_revenue, tax_rate=0.2):
return gross_revenue * tax_rate
def net_revenue(gross_revenue, tax_rate=0.2):
total_tax = tax(gross_revenue, tax_rate)
return gross_revenue - total_tax
def aov(total_revenue, total_orders):
return total_revenue / total_orders
def product_cost(gross_revenue, margin, tax_rate=0.2):
revenue_net = net_revenue(gross_revenue, tax_rate)
return revenue_net * margin
def gross_profit(gross_revenue, margin, tax_rate=0.2):
cost_product = product_cost(gross_revenue, margin, tax_rate)
cost_tax = tax(gross_revenue, tax_rate)
return gross_revenue - (cost_product + cost_tax)
def net_profit(gross_revenue, other_costs, margin, tax_rate=0.2):
cost_product = product_cost(gross_revenue, margin, tax_rate)
cost_tax = tax(gross_revenue, tax_rate)
return gross_revenue - (cost_product + cost_tax + other_costs)
def sales_growth_rate(sales_period_1, sales_period_2):
return ((sales_period_2 - sales_period_1) / sales_period_1) * 100
def revenue_per_unit(total_revenue, total_units):
return total_revenue / total_units
def market_share(company_sales, market_sales):
return (company_sales / market_sales) * 100
def retention_rate(customers_repurchasing_current_period,
customers_purchasing_previous_period):
return (customers_repurchasing_current_period / customers_purchasing_previous_period) * 100
def share_of_shelf_index(products_of_brand_x, total_products):
return (products_of_brand_x / total_products) * 100
def product_turnover(units_sold_in_period, average_items_stocked_in_period):
return (units_sold_in_period / average_items_stocked_in_period) * 100
def price_index(price_of_product_x, price_of_product_y):
return (price_of_product_x / price_of_product_y) * 100
def purchase_intention(people_who_declared_interest, total_people):
return (people_who_declared_interest / total_people) * 100
def product_trial_rate(number_of_first_time_purchases, total_purchasers):
return (number_of_first_time_purchases / total_purchasers) * 100
def product_repurchase_rate(number_of_repeat_purchasers, total_purchasers):
return (number_of_repeat_purchasers / total_purchasers) * 100
def product_consumption_rate(total_items, total_orders):
return (total_items / total_orders) * 100
def brand_usage(number_of_brand_purchasers, total_purchasers):
return (number_of_brand_purchasers / total_purchasers) * 100
def brand_penetration_rate(number_of_brand_purchasers, total_purchasers):
return (number_of_brand_purchasers / total_purchasers) * 100
def product_satisfaction(total_reviews, positive_reviews):
return (positive_reviews / total_reviews) * 100
def market_coverage_index(unique_customers_contacted, unique_customers):
return (unique_customers_contacted / unique_customers) * 100
def sales_force_efficiency(number_of_orders_from_visits, number_of_visits):
return (number_of_orders_from_visits / number_of_visits) * 100
def cpm(total_cost, total_recipients):
return (total_cost / total_recipients) * 1000
def cpo(total_cost, total_transactions):
return total_cost / total_transactions
def cpa(total_cost, total_acquisitions):
return total_cost / total_acquisitions
def cpc(total_cost, total_clicks):
return total_cost / total_clicks
def conversion_rate(total_conversions, total_actions):
return (total_conversions / total_actions) * 100
def lin_rodnitsky_ratio(avg_cost_per_conversion_all_queries,
avg_cost_per_conversion_queries_with_one_conversion_or_more):
return avg_cost_per_conversion_all_queries / avg_cost_per_conversion_queries_with_one_conversion_or_more
def romi(total_revenue, total_marketing_costs):
return ((total_revenue - total_marketing_costs) / total_marketing_costs) * 100
def roi(total_revenue, total_marketing_costs, total_other_costs):
total_costs = total_marketing_costs + total_other_costs
return ((total_revenue - total_costs) / total_costs) * 100
def roas(total_revenue, total_marketing_costs):
return total_revenue / total_marketing_costs
def focus_index(average_pages_visited_in_section, total_pages_in_section):
return (average_pages_visited_in_section / total_pages_in_section) * 100
def stickiness(total_visits, total_visit_duration, total_users):
frequency_of_visits = total_visits / total_users
average_visit_duration = total_visit_duration / total_visits
total_reach = total_users / total_visits
return frequency_of_visits * average_visit_duration * total_reach
def sessions_with_product_views(total_sessions, sessions_with_product_views):
return (sessions_with_product_views / total_sessions) * 100
def engagement_rate(followers_who_engaged, total_followers):
return (followers_who_engaged / total_followers) * 100
def dio(average_inventory_cost, cost_of_goods_sold):
return (average_inventory_cost / cost_of_goods_sold) * 365
def safety_stock(max_units_sold_daily, avg_units_sold_daily, max_lead_time, avg_lead_time):
return (max_units_sold_daily * max_lead_time) - (avg_units_sold_daily * avg_lead_time)
def reorder_point(max_units_sold_daily, avg_units_sold_daily, max_lead_time, avg_lead_time, lead_time):
safety = safety_stock(max_units_sold_daily, avg_units_sold_daily, max_lead_time, avg_lead_time)
return (lead_time * avg_units_sold_daily) + safety
def back_order_rate(total_back_orders, total_orders):
return (total_back_orders / total_orders) * 100
def sales_velocity(units_sold_last_12m, number_of_days_in_stock, velocity_days=30):
return (units_sold_last_12m / number_of_days_in_stock) * velocity_days
def accuracy_of_forecast_demand(actual_demand, forecast_demand):
return ((actual_demand - forecast_demand) / actual_demand) * 100
def eoq(demand_in_units, cost_of_ordering, cost_of_carrying):
return math.sqrt(((demand_in_units * cost_of_ordering) * 2) / cost_of_carrying)
def csat(total_responses, positive_responses):
return (positive_responses / total_responses) * 100
def nps(total_promoters, total_detractors, total_respondents):
return ((total_promoters * 100) / total_respondents) - ((total_detractors * 100) / total_respondents)
def ticket_to_order_ratio(total_tickets, total_orders):
return (total_tickets / total_orders) * 100 | MIT License |
rapid7/vm-console-client-python | rapid7vmconsole/models/review.py | Review.to_dict | python | def to_dict(self):
result = {}
for attr, _ in six.iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
if issubclass(Review, dict):
for key, value in self.items():
result[key] = value
return result | Returns the model properties as a dict | https://github.com/rapid7/vm-console-client-python/blob/55e1f573967bce27cc9a2d10c12a949b1142c2b3/rapid7vmconsole/models/review.py#L181-L206 | import pprint
import re
import six
class Review(object):
"""
Attributes:
swagger_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
swagger_types = {
'comment': 'str',
'_date': 'str',
'links': 'list[Link]',
'name': 'str',
'user': 'int'
}
attribute_map = {
'comment': 'comment',
'_date': 'date',
'links': 'links',
'name': 'name',
'user': 'user'
}
def __init__(self, comment=None, _date=None, links=None, name=None, user=None):
self._comment = None
self.__date = None
self._links = None
self._name = None
self._user = None
self.discriminator = None
if comment is not None:
self.comment = comment
if _date is not None:
self._date = _date
if links is not None:
self.links = links
if name is not None:
self.name = name
if user is not None:
self.user = user
@property
def comment(self):
return self._comment
@comment.setter
def comment(self, comment):
self._comment = comment
@property
def _date(self):
return self.__date
@_date.setter
def _date(self, _date):
self.__date = _date
@property
def links(self):
return self._links
@links.setter
def links(self, links):
self._links = links
@property
def name(self):
return self._name
@name.setter
def name(self, name):
self._name = name
@property
def user(self):
return self._user
@user.setter
def user(self, user):
self._user = user | MIT License |
googlecloudplatform/tensorflow-recommendation-wals | airflow/plugins/gae_admin_plugin.py | AppEngineAdminHook.get_version_identifiers | python | def get_version_identifiers(self, project_id, service_id):
request = self._gaeadmin.apps().services().versions().list(appsId=project_id,
servicesId=service_id)
versions = []
while request is not None:
versions_doc = request.execute()
versions.extend([v['id'] for v in versions_doc['versions']])
request = self._gaeadmin.apps().services().versions().list_next(request,
versions_doc)
return versions | Get list of versions of a service on App Engine Engine.
Args:
project_id: project id
service_id: service id
Returns:
the list of version identifiers if successful and raises an error otherwise. | https://github.com/googlecloudplatform/tensorflow-recommendation-wals/blob/2116cd21e4cc77b7380cccc9fee6f2ed606119db/airflow/plugins/gae_admin_plugin.py#L121-L140 | from airflow.contrib.hooks.gcp_api_base_hook import GoogleCloudBaseHook
from airflow.exceptions import AirflowException
from airflow.models import BaseOperator
from airflow.plugins_manager import AirflowPlugin
from airflow.utils.decorators import apply_defaults
from apiclient.discovery import build
from datetime import datetime
from googleapiclient import errors
import logging
from oauth2client.client import GoogleCredentials
import time
class AppEngineAdminHook(GoogleCloudBaseHook):
def __init__(self, gcp_conn_id='google_cloud_default', delegate_to=None):
super(AppEngineAdminHook, self).__init__(gcp_conn_id, delegate_to)
self._gaeadmin = self.get_ae_conn()
self._svcadmin = self.get_svc_conn()
def get_ae_conn(self):
credentials = GoogleCredentials.get_application_default()
return build('appengine', 'v1', credentials=credentials)
def get_svc_conn(self):
credentials = GoogleCredentials.get_application_default()
return build('servicemanagement', 'v1', credentials=credentials)
def create_version(self, project_id, service_id, version_spec):
create_request = self._gaeadmin.apps().services().versions().create(
appsId=project_id, servicesId=service_id, body=version_spec)
response = create_request.execute()
op_name = response['name'].split('/')[-1]
return self._wait_for_operation_done(project_id, op_name)
def migrate_traffic(self, project_id, service_id, new_version):
split_config = {'split': {'allocations': {new_version: '1'}}}
migrate_request = self._gaeadmin.apps().services().patch(
appsId=project_id, servicesId=service_id, updateMask='split',
body=split_config)
response = migrate_request.execute()
op_name = response['name'].split('/')[-1]
return self._wait_for_operation_done(project_id, op_name)
def get_endpoint_config(self, service_id):
resource = self._svcadmin.services().rollouts()
list_request = resource.list(serviceName=service_id)
response = list_request.execute()
config_id = response['rollouts'][0]['rolloutId']
return config_id
def get_version(self, project_id, service_id, version):
resource = self._gaeadmin.apps().services().versions()
get_request = resource.get(appsId=project_id, servicesId=service_id,
versionsId=version, view='FULL')
response = get_request.execute()
return response | Apache License 2.0 |
ebagdasa/federated_adaptation | utils/text_load.py | Corpus.tokenize_train | python | def tokenize_train(self, path):
files = os.listdir(path)
per_participant_ids = list()
per_participant_ids_test = list()
per_participant_different_words = list()
per_participant_voc_size = list()
for file in tqdm(files[:self.authors_no]):
if 'checkpoint' in file:
continue
new_path=f'{path}/{file}'
with open(new_path, 'r') as f:
diff_word = 0
tokens = 0
word_list = list()
for line in f:
words = get_word_list(line, self.dictionary)
tokens += len(words)
wordidx = [self.dictionary.word2idx[x] for x in words]
diff_word += sum([i not in word_list for i in wordidx])
word_list.extend(wordidx)
ids = torch.LongTensor(word_list)
ids_test = torch.LongTensor(word_list[len(word_list)//100*(100-self.local_test_perc):])
if len(ids)>=10:
per_participant_ids.append(ids)
per_participant_ids_test.append(ids_test)
per_participant_different_words.append(diff_word)
per_participant_voc_size.append(tokens)
return per_participant_ids, per_participant_ids_test, per_participant_different_words, per_participant_voc_size | We return a list of ids per each participant.
:param path:
:return: | https://github.com/ebagdasa/federated_adaptation/blob/0c0ae97445fddc635c427ae100854bc70a00a11c/utils/text_load.py#L49-L82 | import os
import torch
import json
import re
from tqdm import tqdm
import random
filter_symbols = re.compile('[a-zA-Z]*')
class Dictionary(object):
def __init__(self):
self.word2idx = {}
self.idx2word = []
def add_word(self, word):
raise ValueError("Please don't call this method, so we won't break the dictionary :) ")
def __len__(self):
return len(self.idx2word)
def get_word_list(line, dictionary):
splitted_words = json.loads(line.lower()).split()
words = ['<bos>']
for word in splitted_words:
word = filter_symbols.search(word)[0]
if len(word)>1:
if dictionary.word2idx.get(word, False):
words.append(word)
else:
words.append('<unk>')
words.append('<eos>')
return words
class Corpus(object):
def __init__(self, params, dictionary):
repopath = params['repo_path']
self.path = f'{repopath}/data'
authors_no = params['number_of_total_participants']
self.local_test_perc = params['local_test_perc']
self.dictionary = dictionary
self.no_tokens = len(self.dictionary)
self.authors_no = authors_no
self.auxiliary = self.tokenize_aux(os.path.join(self.path, 'test_data.json'))
self.train, self.test, self.diff_words, self.voc_size = self.tokenize_train(f'{self.path}/shard_by_author') | MIT License |
laszukdawid/pyemd | PyEMD/EEMD.py | EEMD.eemd | python | def eemd(self, S: np.ndarray, T: Optional[np.ndarray] = None, max_imf: int = -1) -> np.ndarray:
if T is None:
T = get_timeline(len(S), S.dtype)
scale = self.noise_width*np.abs(np.max(S)-np.min(S))
self._S = S
self._T = T
self._N = len(S)
self._scale = scale
self.max_imf = max_imf
if self.parallel:
pool = Pool(processes=self.processes)
all_IMFs = pool.map(self._trial_update, range(self.trials))
pool.close()
else:
all_IMFs = map(self._trial_update, range(self.trials))
self._all_imfs = defaultdict(list)
for (imfs, trend) in all_IMFs:
if trend is not None:
self._all_imfs[-1].append(trend)
for imf_num, imf in enumerate(imfs):
self._all_imfs[imf_num].append(imf)
self._all_imfs = dict(self._all_imfs)
if -1 in self._all_imfs:
self._all_imfs[len(self._all_imfs)] = self._all_imfs.pop(-1)
for imf_num in self._all_imfs.keys():
self._all_imfs[imf_num] = np.array(self._all_imfs[imf_num])
self.E_IMF = self.ensemble_mean()
self.residue = S - np.sum(self.E_IMF, axis=0)
return self.E_IMF | Performs EEMD on provided signal.
For a large number of iterations defined by `trials` attr
the method performs :py:meth:`emd` on a signal with added white noise.
Parameters
----------
S : numpy array,
Input signal on which EEMD is performed.
T : numpy array or None, (default: None)
If none passed samples are numerated.
max_imf : int, (default: -1)
Defines up to how many IMFs each decomposition should
be performed. By default (negative value) it decomposes
all IMFs.
Returns
-------
eIMF : numpy array
Set of ensemble IMFs produced from input signal. In general,
these do not have to be, and most likely will not be, same as IMFs
produced using EMD. | https://github.com/laszukdawid/pyemd/blob/3d8ec292cd2ba8cba327d3e0ad576366a8ead6ff/PyEMD/EEMD.py#L141-L212 | from __future__ import print_function
import logging
import numpy as np
from collections import defaultdict
from multiprocessing import Pool
from typing import Dict, List, Optional, Sequence, Tuple, Union
from PyEMD.utils import get_timeline
class EEMD:
logger = logging.getLogger(__name__)
noise_kinds_all = ["normal", "uniform"]
def __init__(self, trials: int = 100, noise_width: float = 0.05, ext_EMD = None, parallel: bool = False, **kwargs):
self.trials = trials
self.noise_width = noise_width
self.separate_trends = bool(kwargs.get('separate_trends', False))
self.random = np.random.RandomState()
self.noise_kind = kwargs.get('noise_kind', 'normal')
self.parallel = parallel
self.processes = kwargs.get('processes')
if self.processes is not None and not self.parallel:
self.logger.warning("Passed value for process has no effect when `parallel` is False.")
if ext_EMD is None:
from PyEMD import EMD
self.EMD = EMD(**kwargs)
else:
self.EMD = ext_EMD
self.E_IMF = None
self.residue = None
self._all_imfs = {}
def __call__(self, S: np.ndarray, T: Optional[np.ndarray] = None, max_imf: int = -1) -> np.ndarray:
return self.eemd(S, T=T, max_imf=max_imf)
def __getstate__(self) -> Dict:
self_dict = self.__dict__.copy()
if 'pool' in self_dict:
del self_dict['pool']
return self_dict
def generate_noise(self, scale: float, size: Union[int, Sequence[int]]) -> np.ndarray:
if self.noise_kind == "normal":
noise = self.random.normal(loc=0, scale=scale, size=size)
elif self.noise_kind == "uniform":
noise = self.random.uniform(low=-scale/2, high=scale/2, size=size)
else:
raise ValueError("Unsupported noise kind. Please assigned `noise_kind` to be one of these: {0}".format(
str(self.noise_kinds_all)))
return noise
def noise_seed(self, seed: int) -> None:
self.random.seed(seed) | Apache License 2.0 |
biolink/kgx | kgx/utils/kgx_utils.py | expand | python | def expand(
curie: str, prefix_maps: Optional[List[dict]] = None, fallback: bool = True
) -> str:
default_curie_maps = [
get_jsonld_context("monarch_context"),
get_jsonld_context("obo_context"),
]
if prefix_maps:
uri = expand_uri(curie, prefix_maps)
if uri == curie and fallback:
uri = expand_uri(curie, default_curie_maps)
else:
uri = expand_uri(curie, default_curie_maps)
return uri | Expand a given CURIE to an URI, based on mappings from `prefix_map`.
This method will return the CURIE as the IRI if there is no mapping found.
Parameters
----------
curie: str
A CURIE
prefix_maps: Optional[List[dict]]
A list of prefix maps to use for mapping
fallback: bool
Determines whether to fallback to default prefix mappings, as determined
by `prefixcommons.curie_util`, when CURIE prefix is not found in `prefix_maps`.
Returns
-------
str
A URI corresponding to the CURIE | https://github.com/biolink/kgx/blob/247d113d5b593f078afce1951c63eee2a8cc1248/kgx/utils/kgx_utils.py#L264-L299 | import importlib
import re
import time
import uuid
from enum import Enum
from typing import List, Dict, Set, Optional, Any, Union
import stringcase
from linkml_runtime.linkml_model.meta import (
TypeDefinitionName,
ElementName,
SlotDefinition,
ClassDefinition,
TypeDefinition,
Element,
)
from bmt import Toolkit
from cachetools import LRUCache
import pandas as pd
import numpy as np
from prefixcommons.curie_util import contract_uri
from prefixcommons.curie_util import expand_uri
from kgx.config import get_logger, get_jsonld_context, get_biolink_model_schema
from kgx.graph.base_graph import BaseGraph
curie_lookup_service = None
cache = None
log = get_logger()
DEFAULT_NODE_CATEGORY = "biolink:NamedThing"
DEFAULT_EDGE_PREDICATE = "biolink:related_to"
CORE_NODE_PROPERTIES = {"id", "name"}
CORE_EDGE_PROPERTIES = {"id", "subject", "predicate", "object", "type"}
LIST_DELIMITER = "|"
class GraphEntityType(Enum):
GRAPH = "graph"
NODE = "node"
EDGE = "edge"
provenance_slot_types = {
"knowledge_source": list,
"primary_knowledge_source": str,
"original_knowledge_source": str,
"aggregator_knowledge_source": list,
"supporting_data_source": list,
"provided_by": list,
}
column_types = {
"publications": list,
"qualifiers": list,
"category": list,
"synonym": list,
"same_as": list,
"negated": bool,
"xrefs": list,
}
column_types.update(provenance_slot_types)
knowledge_provenance_properties = set(provenance_slot_types.keys())
extension_types = {"csv": ",", "tsv": "\t", "csv:neo4j": ",", "tsv:neo4j": "\t"}
archive_read_mode = {"tar": "r", "tar.gz": "r:gz", "tar.bz2": "r:bz2"}
archive_write_mode = {"tar": "w", "tar.gz": "w:gz", "tar.bz2": "w:bz2"}
archive_format = {
"r": "tar",
"r:gz": "tar.gz",
"r:bz2": "tar.bz2",
"w": "tar",
"w:gz": "tar.gz",
"w:bz2": "tar.bz2",
}
is_provenance_property_multivalued = {
"knowledge_source": True,
"primary_knowledge_source": False,
"original_knowledge_source": False,
"aggregator_knowledge_source": True,
"supporting_data_source": True,
"provided_by": True,
}
is_property_multivalued = {
"id": False,
"subject": False,
"object": False,
"predicate": False,
"description": False,
"synonym": True,
"in_taxon": False,
"same_as": True,
"name": False,
"has_evidence": False,
"category": True,
"publications": True,
"type": False,
"relation": False,
}
is_property_multivalued.update(is_provenance_property_multivalued)
def camelcase_to_sentencecase(s: str) -> str:
return stringcase.sentencecase(s).lower()
def snakecase_to_sentencecase(s: str) -> str:
return stringcase.sentencecase(s).lower()
def sentencecase_to_snakecase(s: str) -> str:
return stringcase.snakecase(s).lower()
def sentencecase_to_camelcase(s: str) -> str:
return stringcase.pascalcase(stringcase.snakecase(s))
def format_biolink_category(s: str) -> str:
if re.match("biolink:.+", s):
return s
else:
formatted = sentencecase_to_camelcase(s)
return f"biolink:{formatted}"
def format_biolink_slots(s: str) -> str:
if re.match("biolink:.+", s):
return s
else:
formatted = sentencecase_to_snakecase(s)
return f"biolink:{formatted}"
def contract(
uri: str, prefix_maps: Optional[List[Dict]] = None, fallback: bool = True
) -> str:
curie = uri
default_curie_maps = [
get_jsonld_context("monarch_context"),
get_jsonld_context("obo_context"),
]
if prefix_maps:
curie_list = contract_uri(uri, prefix_maps)
if len(curie_list) == 0:
if fallback:
curie_list = contract_uri(uri, default_curie_maps)
if curie_list:
curie = curie_list[0]
else:
curie = curie_list[0]
else:
curie_list = contract_uri(uri, default_curie_maps)
if len(curie_list) > 0:
curie = curie_list[0]
return curie | BSD 3-Clause New or Revised License |
nok/sklearn-porter | sklearn_porter/utils/Shell.py | Shell._run | python | def _run(method, cmd, cwd=None, shell=True, universal_newlines=True,
stderr=STDOUT):
if not cmd:
error_msg = 'Passed empty text or list'
raise AttributeError(error_msg)
if isinstance(cmd, six.string_types):
cmd = str(cmd)
if shell:
if isinstance(cmd, list):
cmd = ' '.join(cmd)
else:
if isinstance(cmd, str):
cmd = cmd.strip().split()
out = method(cmd, shell=shell, cwd=cwd, stderr=stderr,
universal_newlines=universal_newlines)
if isinstance(out, bytes):
out = out.decode('utf-8')
return str(out).strip() | Internal wrapper for `call` amd `check_output` | https://github.com/nok/sklearn-porter/blob/8658c6567e28c570d96ab2e858c510f84b1d94dc/sklearn_porter/utils/Shell.py#L13-L31 | import six
from subprocess import call
from subprocess import check_output
from subprocess import STDOUT
class Shell(object):
@staticmethod | MIT License |
chuckus/chromewhip | chromewhip/protocol/emulation.py | Emulation.setDefaultBackgroundColorOverride | python | def setDefaultBackgroundColorOverride(cls,
color: Optional['DOM.RGBA'] = None,
):
return (
cls.build_send_payload("setDefaultBackgroundColorOverride", {
"color": color,
}),
None
) | Sets or clears an override of the default background color of the frame. This override is used
if the content does not specify one.
:param color: RGBA of the default background color. If not specified, any existing override will be
cleared.
:type color: DOM.RGBA | https://github.com/chuckus/chromewhip/blob/7249f64f96df3c6ca0859a3da06ce7ddcebbfded/chromewhip/protocol/emulation.py#L112-L126 | import logging
from typing import Any, Optional, Union
from chromewhip.helpers import PayloadMixin, BaseEvent, ChromeTypeBase
log = logging.getLogger(__name__)
from chromewhip.protocol import dom as DOM
from chromewhip.protocol import page as Page
from chromewhip.protocol import runtime as Runtime
class ScreenOrientation(ChromeTypeBase):
def __init__(self,
type: Union['str'],
angle: Union['int'],
):
self.type = type
self.angle = angle
VirtualTimePolicy = str
class Emulation(PayloadMixin):
@classmethod
def canEmulate(cls):
return (
cls.build_send_payload("canEmulate", {
}),
cls.convert_payload({
"result": {
"class": bool,
"optional": False
},
})
)
@classmethod
def clearDeviceMetricsOverride(cls):
return (
cls.build_send_payload("clearDeviceMetricsOverride", {
}),
None
)
@classmethod
def clearGeolocationOverride(cls):
return (
cls.build_send_payload("clearGeolocationOverride", {
}),
None
)
@classmethod
def resetPageScaleFactor(cls):
return (
cls.build_send_payload("resetPageScaleFactor", {
}),
None
)
@classmethod
def setFocusEmulationEnabled(cls,
enabled: Union['bool'],
):
return (
cls.build_send_payload("setFocusEmulationEnabled", {
"enabled": enabled,
}),
None
)
@classmethod
def setCPUThrottlingRate(cls,
rate: Union['float'],
):
return (
cls.build_send_payload("setCPUThrottlingRate", {
"rate": rate,
}),
None
)
@classmethod | MIT License |
facelessuser/subclrschm | subclrschm/lib/gui/custom_statusbar.py | CustomStatusBar.__init__ | python | def __init__(self, parent, name, fields=None):
field_array = [-1] if not fields else fields[:]
super(CustomStatusBar, self).__init__(
parent,
id=wx.ID_ANY,
style=wx.STB_DEFAULT_STYLE,
name=name
)
self.sb_setup(field_array) | Init the CustomStatusBar object. | https://github.com/facelessuser/subclrschm/blob/52cf5bc39bac6e3dd6d44061cd0c005cdc9a41d1/subclrschm/lib/gui/custom_statusbar.py#L259-L269 | from __future__ import unicode_literals
from collections import OrderedDict
import wx
import wx.lib.agw.supertooltip
from .. import util
if wx.VERSION > (2, 9, 4):
def monkey_patch():
import inspect
import re
target_line = re.compile(r'([ ]{8})(maxWidth = max\(bmpWidth\+\(textWidth\+self._spacing\*3\), maxWidth\)\n)')
tt_source = inspect.getsourcelines(wx.lib.agw.supertooltip.ToolTipWindowBase.OnPaint)[0]
count = 0
found = False
for line in tt_source:
if not found:
m = target_line.match(line)
if m:
tt_source[count] = m.group(0)
found = True
count += 1
continue
tt_source[count] = line[4:]
count += 1
exec(''.join(tt_source))
wx.lib.agw.supertooltip.ToolTipWindowBase.OnPaint = locals()['OnPaint']
monkey_patch()
class ContextMenu(wx.Menu):
def __init__(self, parent, menu, pos):
wx.Menu.__init__(self)
self._callbacks = {}
for i in menu:
menuid = wx.NewId()
item = wx.MenuItem(self, menuid, i[0])
self._callbacks[menuid] = i[1]
self.Append(item)
self.Bind(wx.EVT_MENU, self.on_callback, item)
parent.PopupMenu(self, pos)
def on_callback(self, event):
menuid = event.GetId()
self._callbacks[menuid](event)
event.Skip()
class ToolTip(wx.lib.agw.supertooltip.SuperToolTip):
def __init__(self, target, message, header="", style="Office 2007 Blue", start_delay=.1):
super(ToolTip, self).__init__(message, header=header)
self.SetTarget(target)
self.ApplyStyle(style)
self.SetStartDelay(start_delay)
target.tooltip = self
def hide(self):
if self._superToolTip:
self._superToolTip.Destroy()
class TimedStatusExtension(object):
def set_timed_status(self, text, index=0):
if self.text_timer[index].IsRunning():
self.text_timer[index].Stop()
else:
self.saved_text = self.GetStatusText(index)
self.SetStatusText(text, index)
self.text_timer[index].Start(5000, oneShot=True)
def sb_time_setup(self, field_count):
self.field_count = field_count
self.saved_text = [""] * field_count
self.text_timer = [wx.Timer(self)] * field_count
count = 0
for x in self.text_timer:
self.Bind(wx.EVT_TIMER, lambda event, index=count: self.clear_text(event, index), self.text_timer[count])
count += 1
def clear_text(self, event, index):
self.SetStatusText(self.saved_text, index)
def set_status(self, text, index=0):
if self.text_timer[index].IsRunning():
self.text_timer[index].Stop()
self.SetStatusText(text, index)
class IconTrayExtension(object):
fields = [-1]
def sb_tray_setup(self):
self.SetFieldsCount(len(self.fields) + 1)
self.SetStatusText('', 0)
self.SetStatusWidths(self.fields + [1])
self.sb_icons = OrderedDict()
self.Bind(wx.EVT_SIZE, self.on_sb_size)
def remove_icon(self, name):
if name in self.sb_icons:
self.hide_tooltip(name)
self.sb_icons[name].Destroy()
del self.sb_icons[name]
self.place_icons(resize=True)
def hide_tooltip(self, name):
if self.sb_icons[name].tooltip:
self.sb_icons[name].tooltip.hide()
def set_icon(
self, name, icon, msg=None, context=None,
click_right=None, click_left=None,
dclick_right=None, dclick_left=None
):
if name in self.sb_icons:
self.hide_tooltip(name)
self.sb_icons[name].Destroy()
bmp = wx.StaticBitmap(self)
bmp.SetBitmap(label=icon)
self.sb_icons[name] = bmp
if msg is not None:
ToolTip(self.sb_icons[name], msg)
if click_left is not None:
self.sb_icons[name].Bind(wx.EVT_LEFT_DOWN, click_left)
if context is not None:
self.sb_icons[name].Bind(wx.EVT_RIGHT_DOWN, lambda e: self.show_menu(name, context))
elif click_right is not None:
self.sb_icons[name].Bind(wx.EVT_RIGHT_DOWN, click_right)
if dclick_left is not None:
self.sb_icons[name].Bind(wx.EVT_LEFT_DCLICK, dclick_left)
if dclick_right is not None:
self.sb_icons[name].Bind(wx.EVT_RIGHT_DCLICK, dclick_right)
self.place_icons(resize=True)
def show_menu(self, name, context):
self.hide_tooltip(name)
ContextMenu(self, context, self.sb_icons[name].GetPosition())
def place_icons(self, resize=False):
x_offset = 0
if resize:
platform = util.platform()
if platform in "osx":
self.SetStatusWidths([-1, len(self.sb_icons) * 20 + 10])
elif platform == "windows":
if len(self.sb_icons):
self.SetStatusWidths([-1, (len(self.sb_icons) - 1) * 20 + 1])
else:
self.SetStatusWidths([-1, len(self.sb_icons) * 20 + 1])
else:
self.SetStatusWidths([-1, len(self.sb_icons) * 20 + 1])
rect = self.GetFieldRect(len(self.fields))
for v in self.sb_icons.values():
v.SetPosition((rect.x + x_offset, rect.y))
v.Hide()
v.Show()
x_offset += 20
def on_sb_size(self, event):
event.Skip()
self.place_icons()
class CustomStatusExtension(IconTrayExtension, TimedStatusExtension):
def sb_setup(self, fields):
self.fields = fields
self.sb_tray_setup()
self.sb_time_setup(len(self.fields))
class CustomStatusBar(wx.StatusBar, CustomStatusExtension): | MIT License |
nlesc/yeap16-ai-3d-printing | deepy3d/util.py | get_closest_factors | python | def get_closest_factors(number):
a = int(np.sqrt(number))
while number % a != 0:
a -= 1
b = number/a
if a == 1 or b == 1:
a, b = get_closest_factors(number + 1)
return a, b | Find the 2 factors of a number that are closest together. | https://github.com/nlesc/yeap16-ai-3d-printing/blob/4f15c1851d819290dc7a922c9470a76ff458945c/deepy3d/util.py#L40-L53 | import numpy as np
def block_index(num_blocks, len_list):
if num_blocks < 1:
ValueError('Number of blocks must be larger or equal to 1')
if len_list < num_blocks:
ValueError('Length of list must be larger than number of blocks.')
lin_list = np.linspace(0, num_blocks, len_list, endpoint=False)
return np.floor(lin_list).astype('uint8')
def limit_mem():
K.get_session().close()
cfg = K.tf.ConfigProto()
cfg.gpu_options.allow_growth = True
K.set_session(K.tf.Session(config=cfg)) | Apache License 2.0 |
tonyfischetti/sake | sakelib/build.py | write_shas_to_shastore | python | def write_shas_to_shastore(sha_dict):
if sys.version_info[0] < 3:
fn_open = open
else:
fn_open = io.open
with fn_open(".shastore", "w") as fh:
fh.write("---\n")
fh.write('sake version: {}\n'.format(constants.VERSION))
if sha_dict:
fh.write(yaml.dump(sha_dict))
fh.write("...") | Writes a sha1 dictionary stored in memory to
the .shastore file | https://github.com/tonyfischetti/sake/blob/818f1b1ad97a0d7bcf2c9e0082affb2865b25f26/sakelib/build.py#L116-L130 | from __future__ import unicode_literals
from __future__ import print_function
import glob
import hashlib
import io
import locale
from multiprocessing import Pool
import networkx as nx
import os.path
import shlex
from subprocess import Popen, PIPE
import sys
import yaml
from . import acts
from . import constants
ERROR_FN = sys.stderr.write
def check_shastore_version(from_store, settings):
sprint = settings["sprint"]
error = settings["error"]
sprint("checking .shastore version for potential incompatibilities",
level="verbose")
if not from_store or 'sake version' not in from_store:
errmes = ["Since you've used this project last, a new version of ",
"sake was installed that introduced backwards incompatible",
" changes. Run 'sake clean', and rebuild before continuing\n"]
errmes = " ".join(errmes)
error(errmes)
sys.exit(1)
def get_sha(a_file, settings=None):
if settings:
error = settings["error"]
else:
error = ERROR_FN
try:
BLOCKSIZE = 65536
hasher = hashlib.sha1()
with io.open(a_file, "rb") as fh:
buf = fh.read(BLOCKSIZE)
while len(buf) > 0:
hasher.update(buf)
buf = fh.read(BLOCKSIZE)
the_hash = hasher.hexdigest()
except IOError:
errmes = "File '{}' could not be read! Exiting!".format(a_file)
error(errmes)
sys.exit(1)
except:
errmes = "Unspecified error returning sha1 hash. Exiting!"
error(errmes)
sys.exit(1)
return the_hash | MIT License |
flow-dev/robustvideomatting | inference.py | auto_downsample_ratio | python | def auto_downsample_ratio(h, w):
return min(512 / max(h, w), 1) | Automatically find a downsample ratio so that the largest side of the resolution be 512px. | https://github.com/flow-dev/robustvideomatting/blob/b8848d58188fcc1e56edc7c8636aabdae1971284/inference.py#L154-L158 | import torch
import os
from torch.utils.data import DataLoader
from torchvision import transforms
from typing import Optional, Tuple
from tqdm.auto import tqdm
from inference_utils import VideoReader, VideoWriter, ImageSequenceReader, ImageSequenceWriter
def convert_video(model,
input_source: str,
input_resize: Optional[Tuple[int, int]] = None,
downsample_ratio: Optional[float] = None,
output_type: str = 'video',
output_composition: Optional[str] = None,
output_alpha: Optional[str] = None,
output_foreground: Optional[str] = None,
output_video_mbps: Optional[float] = None,
seq_chunk: int = 1,
num_workers: int = 0,
progress: bool = True,
device: Optional[str] = None,
dtype: Optional[torch.dtype] = None):
assert downsample_ratio is None or (downsample_ratio > 0 and downsample_ratio <= 1), 'Downsample ratio must be between 0 (exclusive) and 1 (inclusive).'
assert any([output_composition, output_alpha, output_foreground]), 'Must provide at least one output.'
assert output_type in ['video', 'png_sequence'], 'Only support "video" and "png_sequence" output modes.'
assert seq_chunk >= 1, 'Sequence chunk must be >= 1'
assert num_workers >= 0, 'Number of workers must be >= 0'
assert output_video_mbps == None or output_type == 'video', 'Mbps is not available for png_sequence output.'
if input_resize is not None:
transform = transforms.Compose([
transforms.Resize(input_resize[::-1]),
transforms.ToTensor()
])
else:
transform = transforms.ToTensor()
if os.path.isfile(input_source):
source = VideoReader(input_source, transform)
else:
source = ImageSequenceReader(input_source, transform)
reader = DataLoader(source, batch_size=seq_chunk, pin_memory=True, num_workers=num_workers)
if output_type == 'video':
frame_rate = source.frame_rate if isinstance(source, VideoReader) else 30
output_video_mbps = 1 if output_video_mbps is None else output_video_mbps
if output_composition is not None:
writer_com = VideoWriter(
path=output_composition,
frame_rate=frame_rate,
bit_rate=int(output_video_mbps * 1000000))
if output_alpha is not None:
writer_pha = VideoWriter(
path=output_alpha,
frame_rate=frame_rate,
bit_rate=int(output_video_mbps * 1000000))
if output_foreground is not None:
writer_fgr = VideoWriter(
path=output_foreground,
frame_rate=frame_rate,
bit_rate=int(output_video_mbps * 1000000))
else:
if output_composition is not None:
writer_com = ImageSequenceWriter(output_composition, 'png')
if output_alpha is not None:
writer_pha = ImageSequenceWriter(output_alpha, 'png')
if output_foreground is not None:
writer_fgr = ImageSequenceWriter(output_foreground, 'png')
model = model.eval()
if device is None or dtype is None:
param = next(model.parameters())
dtype = param.dtype
device = param.device
if (output_composition is not None) and (output_type == 'video'):
bgr = torch.tensor([120, 255, 155], device=device, dtype=dtype).div(255).view(1, 1, 3, 1, 1)
try:
with torch.no_grad():
bar = tqdm(total=len(source), disable=not progress, dynamic_ncols=True)
rec = [None] * 4
for src in reader:
if downsample_ratio is None:
downsample_ratio = auto_downsample_ratio(*src.shape[2:])
src = src.to(device, dtype, non_blocking=True).unsqueeze(0)
fgr, pha, *rec = model(src, *rec, downsample_ratio)
if output_foreground is not None:
writer_fgr.write(fgr[0])
if output_alpha is not None:
writer_pha.write(pha[0])
if output_composition is not None:
if output_type == 'video':
com = fgr * pha + bgr * (1 - pha)
else:
fgr = fgr * pha.gt(0)
com = torch.cat([fgr, pha], dim=-3)
writer_com.write(com[0])
bar.update(src.size(1))
finally:
if output_composition is not None:
writer_com.close()
if output_alpha is not None:
writer_pha.close()
if output_foreground is not None:
writer_fgr.close() | Apache License 2.0 |
avast/retdec-regression-tests-framework | tests/parsers/c_parser/stmts/statement_tests.py | StatementTests.get_return_stmt | python | def get_return_stmt(self, code):
func = self.insert_into_function_body(code)
return func.return_stmts[0] | Returns the first return stmt in the given code. | https://github.com/avast/retdec-regression-tests-framework/blob/a8d024475bf76cd6acdee3c9df3a3d38a2ec63df/tests/parsers/c_parser/stmts/statement_tests.py#L52-L55 | from unittest import mock
from regression_tests.parsers.c_parser.stmts.break_stmt import BreakStmt
from regression_tests.parsers.c_parser.stmts.continue_stmt import ContinueStmt
from regression_tests.parsers.c_parser.stmts.do_while_loop import DoWhileLoop
from regression_tests.parsers.c_parser.stmts.empty_stmt import EmptyStmt
from regression_tests.parsers.c_parser.stmts.for_loop import ForLoop
from regression_tests.parsers.c_parser.stmts.goto_stmt import GotoStmt
from regression_tests.parsers.c_parser.stmts.if_stmt import IfStmt
from regression_tests.parsers.c_parser.stmts.return_stmt import ReturnStmt
from regression_tests.parsers.c_parser.stmts.statement import Statement
from regression_tests.parsers.c_parser.stmts.switch_stmt import SwitchStmt
from regression_tests.parsers.c_parser.stmts.while_loop import WhileLoop
from tests.parsers.c_parser import WithModuleTests
class StatementTests(WithModuleTests):
def insert_into_function_body(self, code):
return self.get_func("""
void func() {
%s
}
""" % code, 'func')
def get_for_loop(self, code):
func = self.insert_into_function_body(code)
return func.for_loops[0]
def get_while_loop(self, code):
func = self.insert_into_function_body(code)
return func.while_loops[0]
def get_do_while_loop(self, code):
func = self.insert_into_function_body(code)
return func.do_while_loops[0]
def get_if_stmt(self, code):
func = self.insert_into_function_body(code)
return func.if_stmts[0] | MIT License |
openshift/kuryr-kubernetes | kuryr_kubernetes/controller/drivers/base.py | PodSubnetsDriver.get_subnets | python | def get_subnets(self, pod, project_id):
raise NotImplementedError() | Get subnets for Pod.
:param pod: dict containing Kubernetes Pod object
:param project_id: OpenStack project ID
:return: dict containing the mapping 'subnet_id' -> 'network' for all
the subnets we want to create ports on, where 'network' is an
`os_vif.network.Network` object containing a single
`os_vif.subnet.Subnet` object corresponding to the 'subnet_id' | https://github.com/openshift/kuryr-kubernetes/blob/7b2e7f83b91fa711d1a506c451be8f1143cdcd86/kuryr_kubernetes/controller/drivers/base.py#L148-L158 | import abc
from kuryr.lib._i18n import _
from stevedore import driver as stv_driver
from kuryr_kubernetes import config
_DRIVER_NAMESPACE_BASE = 'kuryr_kubernetes.controller.drivers'
_DRIVER_MANAGERS = {}
_MULTI_VIF_DRIVERS = []
class DriverBase(object):
@classmethod
def get_instance(cls, specific_driver=None, scope='default'):
alias = cls.ALIAS
if specific_driver:
driver_key = '{}:{}:{}'.format(alias, specific_driver, scope)
else:
driver_key = '{}:_from_cfg:{}'.format(alias, scope)
try:
manager = _DRIVER_MANAGERS[driver_key]
except KeyError:
driver_name = (specific_driver or
config.CONF.kubernetes[alias + '_driver'])
manager = stv_driver.DriverManager(
namespace="%s.%s" % (_DRIVER_NAMESPACE_BASE, alias),
name=driver_name,
invoke_on_load=True)
_DRIVER_MANAGERS[driver_key] = manager
driver = manager.driver
if not isinstance(driver, cls):
raise TypeError(_("Invalid %(alias)r driver type: %(driver)s, "
"must be a subclass of %(type)s") % {
'alias': alias,
'driver': driver.__class__.__name__,
'type': cls})
return driver
def __str__(self):
return self.__class__.__name__
class PodProjectDriver(DriverBase, metaclass=abc.ABCMeta):
ALIAS = 'pod_project'
@abc.abstractmethod
def get_project(self, pod):
raise NotImplementedError()
class ServiceProjectDriver(DriverBase, metaclass=abc.ABCMeta):
ALIAS = 'service_project'
@abc.abstractmethod
def get_project(self, service):
raise NotImplementedError()
class NamespaceProjectDriver(DriverBase, metaclass=abc.ABCMeta):
ALIAS = 'namespace_project'
@abc.abstractmethod
def get_project(self, namespace):
raise NotImplementedError()
class PodSubnetsDriver(DriverBase, metaclass=abc.ABCMeta):
ALIAS = 'pod_subnets'
@abc.abstractmethod | Apache License 2.0 |
kcyu2014/eval-nas | search_policies/cnn/random_policy/nasbench_weight_sharing_policy.py | NasBenchWeightSharingPolicy.run | python | def run(self):
train_queue, valid_queue, test_queue, criterion = self.initialize_run()
args = self.args
model, optimizer, scheduler = self.initialize_model()
fitness_dict = {}
self.optimizer = optimizer
self.scheduler = scheduler
logging.info(">> Begin the search with supernet method :".format(args.supernet_train_method))
for epoch in range(args.epochs):
scheduler.step()
lr = scheduler.get_lr()[0]
train_acc, train_obj = self.train_fn(train_queue, valid_queue, model, criterion, optimizer, lr)
self.logging_fn(train_acc, train_obj, epoch, 'Train', display_dict={'lr': lr})
valid_acc, valid_obj = self.validate_model(model, valid_queue, self.model_spec_id, self.model_spec)
self.logging_fn(valid_acc, valid_obj, epoch, 'Valid')
if not self.check_should_save(epoch):
continue
self.save_duplicate_arch_pool('valid', epoch)
fitness_dict = self.evaluate(epoch, test_queue, fitnesses_dict=fitness_dict, train_queue=train_queue)
utils.save_checkpoint(model, optimizer, self.running_stats, self.exp_dir)
self.save_results(epoch, rank_details=True)
ep_k = [k for k in self.ranking_per_epoch.keys()][-1]
best_id = self.ranking_per_epoch[ep_k][-1][1].geno_id
return best_id, self.search_space.nasbench_model_specs[best_id] | Procedure of training. This run describes the entire training procedure.
:return: | https://github.com/kcyu2014/eval-nas/blob/385376a3ef96336b54ee7e696af1d02b97aa5c32/search_policies/cnn/random_policy/nasbench_weight_sharing_policy.py#L141-L177 | import os
import gc
import logging
import operator
import IPython
import shutil
import numpy as np
import torch
from functools import partial
from collections import namedtuple, OrderedDict, deque
import utils
from search_policies.cnn.cnn_general_search_policies import CNNSearchPolicy
from search_policies.cnn.enas_policy.enas_micro.data.data import RepeatedDataLoader
from search_policies.cnn.search_space.nas_bench.nasbench_search_space import NASbenchSearchSpace, NasBenchSearchSpaceLinear, NasBenchSearchSpaceSubsample, NasBenchSearchSpaceICLRInfluenceWS
from search_policies.cnn import model as model_module
import search_policies.cnn.procedures as procedure_ops
from search_policies.cnn.search_space.nas_bench.util import change_model_spec
from search_policies.cnn.utils import AverageMeter
from visualization.process_data import tensorboard_summarize_list
Rank = namedtuple('Rank', 'valid_acc valid_obj geno_id gt_rank')
class NasBenchWeightSharingPolicy(CNNSearchPolicy):
trained_model_spec_ids = []
eval_result = OrderedDict()
model_spec = None
model_spec_id = None
@property
def nasbench_model_specs(self):
return self.search_space.nasbench_hashs
@property
def nasbench_hashs(self):
return self.search_space.nasbench_hashs
@property
def evaluate_model_spec_ids(self):
return self.search_space.evaluate_model_spec_ids
def evaluate_model_spec_id_pool(self):
return self.search_space.evaluate_model_spec_id_pool()
def model_spec_by_id(self, mid):
return self.search_space.nasbench_model_specs[mid]
def random_sampler(self, model, architect, args):
rand_spec_id, rand_spec = self.search_space.random_topology()
self.model_spec_id = rand_spec_id
self.model_spec = rand_spec
new_model = change_model_spec(model, rand_spec)
self.trained_model_spec_ids.append(rand_spec_id)
return new_model
def op_sampler(self, model, architect, args):
spec = self.model_spec
ops = spec.ops
avail_ops = self.search_space.available_ops
try:
op_vs_choice = np.tile(np.arange(len(avail_ops)), (len(ops)-2, 1))
op_vs_choice = np.apply_along_axis(np.random.permutation, 1, op_vs_choice).transpose()
for i in range(len(avail_ops)):
new_ops = [avail_ops[ind] for ind in op_vs_choice[i]]
spec.ops = ['input',] + new_ops + ['output']
yield change_model_spec(model, spec)
except ValueError as e:
logging.warning(f'Op sampler: received exception {e}, return the original model without any op sampling.')
yield model
def __init__(self, args, full_dataset=False):
super(NasBenchWeightSharingPolicy, self).__init__(
args=args,
sub_dir_path='{}_SEED_{}'.format(args.supernet_train_method, args.seed)
)
self.args = args
if args.search_space == 'nasbench':
self.model_fn = model_module.NasBenchNetSearch
self.search_space = NASbenchSearchSpace(args, full_dataset=full_dataset)
elif args.search_space == 'nasbench_linear':
self.model_fn = model_module.NasBenchNetSearch
self.search_space = NasBenchSearchSpaceLinear(args)
elif args.search_space == 'nasbench_subspace':
self.model_fn = model_module.NasBenchNetSearch
self.search_space = NasBenchSearchSpaceSubsample(args)
elif args.search_space == 'nasbench_iclr_wsinfluence':
self.model_fn = model_module.NasBenchNetSearch
self.search_space = NasBenchSearchSpaceICLRInfluenceWS(args)
else:
raise NotImplementedError("Other search space not supported at this moment.")
self.counter = 0 | MIT License |
virgesmith/ukcensusapi | ukcensusapi/Nomisweb.py | _get_api_key | python | def _get_api_key(cache_dir):
filename = cache_dir / "NOMIS_API_KEY"
if os.path.isfile(str(filename)):
with open(str(filename), "r") as file:
content = file.readlines()
return None if len(content) == 0 else content[0].replace("\n","")
return os.environ.get("NOMIS_API_KEY") | Look for key in file NOMIS_API_KEY in cache dir, falling back to env var | https://github.com/virgesmith/ukcensusapi/blob/b78a753375665d1aa05c0d30813d7e533834d015/ukcensusapi/Nomisweb.py#L21-L31 | import os
import json
import hashlib
import warnings
from pathlib import Path
from collections import OrderedDict
from urllib import request
from urllib.error import HTTPError
from urllib.error import URLError
from urllib.parse import urlencode
from socket import timeout
import pandas as pd
import ukcensusapi.utils as utils | MIT License |
clericpy/torequests | torequests/dummy.py | Loop.wait_all_tasks_done | python | def wait_all_tasks_done(self,
timeout=NotSet,
delay: float = 0.5,
interval: float = 0.1):
timeout = self._timeout if timeout is NotSet else timeout
timeout = timeout or float("inf")
start_time = time_time()
time_sleep(delay)
while 1:
if not self.todo_tasks:
return self.all_tasks
if time_time() - start_time > timeout:
return self.done_tasks
time_sleep(interval) | Block, only be used while loop running in a single non-main thread. Not SMART! | https://github.com/clericpy/torequests/blob/e57ce331aa850db45c198dc90b9d01e437384b61/torequests/dummy.py#L302-L316 | from asyncio import (Future, Queue, Task, TimeoutError, as_completed, gather,
get_event_loop, iscoroutine, new_event_loop, sleep, wait,
wait_for)
from asyncio.futures import _chain_future
from concurrent.futures import ALL_COMPLETED
from functools import wraps
from time import sleep as time_sleep
from time import time as time_time
from typing import (Callable, Coroutine, Dict, List, Optional, Sequence, Set,
Union)
from urllib.parse import urlparse
from aiohttp import BasicAuth, ClientError, ClientSession, ClientTimeout
from ._py3_patch import (NewResponse, NotSet, _ensure_can_be_await,
_exhaust_simple_coro, _py36_all_task_patch, logger)
from .exceptions import FailureException, ValidationError
from .frequency_controller.async_tools import AsyncFrequency as Frequency
from .main import Error, NewFuture, Pool, ProcessPool
__all__ = "NewTask Loop Asyncme coros Requests Workshop".split(" ")
class NewTask(Task):
_PENDING = "PENDING"
_CANCELLED = "CANCELLED"
_FINISHED = "FINISHED"
_RESPONSE_ARGS = ("encoding", "request_encoding", "content")
def __init__(self,
coro,
*,
loop=None,
callback: Union[Callable, Sequence] = None,
extra_args=None):
assert iscoroutine(coro), repr(coro)
super().__init__(coro, loop=loop)
self._callback_result = NotSet
self.extra_args = extra_args or ()
self.task_start_time = time_time()
self.task_end_time = 0.0
self.task_cost_time = 0.0
if callback:
if not isinstance(callback, (list, tuple, set)):
callback = [callback]
self.add_done_callback(self.set_task_time)
for fn in callback:
self.add_done_callback(self.wrap_callback(fn))
@staticmethod
def wrap_callback(function: Callable) -> Callable:
@wraps(function)
def wrapped(task):
task._callback_result = function(task)
return task._callback_result
return wrapped
@staticmethod
def set_task_time(task: 'NewTask'):
task.task_end_time = time_time()
task.task_cost_time = task.task_end_time - task.task_start_time
@property
def _done_callbacks(self):
return self._callbacks
@property
def cx(self):
return self.callback_result
@property
def callback_result(self):
if self._state == self._PENDING:
self._loop.run_until_complete(self)
if self._callback_result is NotSet:
result = self.result()
else:
result = self._callback_result
return result
@property
def x(self):
if self._state == self._PENDING:
self._loop.run_until_complete(self)
return self.result()
def __getattr__(self, name):
return getattr(self.x, name)
def __setattr__(self, name, value):
if name in self._RESPONSE_ARGS:
self.x.__setattr__(name, value)
else:
object.__setattr__(self, name, value)
class Loop:
def __init__(self,
n: int = None,
interval: float = 0,
timeout: Optional[float] = None,
default_callback: Optional[Callable] = None,
loop=None,
**kwargs):
self._loop = loop
self.default_callback = default_callback
self.async_running = False
self._timeout = timeout
self.frequency = Frequency(n, interval)
@property
def loop(self):
if self._loop is None:
try:
self._loop = get_event_loop()
except RuntimeError:
self._loop = new_event_loop()
elif self._loop.is_closed():
self._loop = new_event_loop()
return self._loop
def _wrap_coro_function_with_frequency(self, coro_func):
@wraps(coro_func)
async def new_coro_func(*args, **kwargs):
if self.frequency:
async with self.frequency:
result = await coro_func(*args, **kwargs)
return result
else:
result = await coro_func(*args, **kwargs)
return result
return new_coro_func
def run_in_executor(self, executor=None, func=None, *args):
return self.loop.run_in_executor(executor, func, *args)
def run_in_thread_pool(self, pool_size=None, func=None, *args):
executor = Pool(pool_size)
return self.loop.run_in_executor(executor, func, *args)
def run_in_process_pool(self, pool_size=None, func=None, *args):
executor = ProcessPool(pool_size)
return self.loop.run_in_executor(executor, func, *args)
def run_coroutine_threadsafe(self, coro, loop=None, callback=None):
if not iscoroutine(coro):
raise TypeError("A await in coroutines. object is required")
loop = loop or self.loop
future = NewFuture(callback=callback)
def callback_func():
try:
_chain_future(NewTask(coro, loop=loop), future)
except Exception as exc:
if future.set_running_or_notify_cancel():
future.set_exception(exc)
raise
loop.call_soon_threadsafe(callback_func)
return future
def apply(self,
coro_function: Callable,
args: Optional[Sequence] = None,
kwargs: Optional[dict] = None,
callback: Optional[Callable] = None):
args = args or ()
kwargs = kwargs or {}
coro = self._wrap_coro_function_with_frequency(coro_function)(*args,
**kwargs)
return self.submit(coro, callback=callback)
def submit(self, coro, callback: Optional[Callable] = None):
callback = callback or self.default_callback
if self.async_running:
return self.run_coroutine_threadsafe(coro, callback=callback)
else:
return NewTask(coro, loop=self.loop, callback=callback)
def submitter(self, f: Callable) -> Callable:
f = self._wrap_coro_function_with_frequency(f)
@wraps(f)
def wrapped(*args, **kwargs):
return self.submit(f(*args, **kwargs))
return wrapped
@property
def x(self):
return self.run()
async def wait(self, fs, timeout=None, return_when=ALL_COMPLETED):
if fs:
return await wait(fs, timeout=timeout, return_when=return_when)
@property
def todo_tasks(self) -> List[Task]:
tasks = [
task for task in self.all_tasks if task._state == NewTask._PENDING
]
return tasks
@property
def done_tasks(self) -> List[Task]:
tasks = [
task for task in self.all_tasks if task._state != NewTask._PENDING
]
return tasks
def run(self, tasks: List[Task] = None, timeout=NotSet):
timeout = self._timeout if timeout is NotSet else timeout
if self.async_running or self.loop.is_running():
return self.wait_all_tasks_done(timeout)
else:
tasks = [task for task in tasks or self.todo_tasks]
return self.loop.run_until_complete(
self.wait(tasks, timeout=timeout)) | MIT License |
ying-wen/malib_deprecated | malib/utils/tf_utils.py | soft_variables_update | python | def soft_variables_update(
source_variables, target_variables, tau=1.0, sort_variables_by_name=False, name=None
):
if tau < 0 or tau > 1:
raise ValueError("Input `tau` should be in [0, 1].")
updates = []
op_name = "soft_variables_update"
if name is not None:
op_name = "{}_{}".format(name, op_name)
if tau == 0.0 or not source_variables or not target_variables:
return tf.no_op(name=op_name)
if sort_variables_by_name:
source_variables = sorted(source_variables, key=lambda x: x.name)
target_variables = sorted(target_variables, key=lambda x: x.name)
for (v_s, v_t) in zip(source_variables, target_variables):
v_t.shape.assert_is_compatible_with(v_s.shape)
if tau == 1.0:
update = v_t.assign(v_s)
else:
update = v_t.assign((1 - tau) * v_t + tau * v_s)
updates.append(update)
return tf.group(*updates, name=op_name) | Performs a soft/hard update of variables from the source to the target.
For each variable v_t in target variables and its corresponding variable v_s
in source variables, a soft update is:
v_t = (1 - tau) * v_t + tau * v_s
When tau is 1.0 (the default), then it does a hard update:
v_t = v_s
Args:
source_variables: list of source variables.
target_variables: list of target variables.
tau: A float scalar in [0, 1]. When tau is 1.0 (the default), we do a hard
update.
sort_variables_by_name: A bool, when True would sort the variables by name
before doing the update.
name: A string, name.
Returns:
An operation that updates target variables from source variables.
Raises:
ValueError: if tau is not in [0, 1]. | https://github.com/ying-wen/malib_deprecated/blob/875338b81c4d87064ad31201f461ef742db05f25/malib/utils/tf_utils.py#L7-L48 | import tensorflow as tf
EPS = 1e-6 | MIT License |
dedsecinside/awesome-scripts | Machine Learning & AI/Linear_Regression_with_Gradient_Descent.py | r2_alpha | python | def r2_alpha(r2, alphas):
plt.plot(alphas, r2)
plt.title('R^2 vs. Learning Rate')
print(max(r2))
print(np.linspace(0.001,1,10)[r2.index(max(r2))]) | Plot r2 alpha
Args:
r2: (todo): write your description
alphas: (array): write your description | https://github.com/dedsecinside/awesome-scripts/blob/856835e5ff5f8a6af2d74bb25800c620feb712e3/Machine Learning & AI/Linear_Regression_with_Gradient_Descent.py#L150-L165 | import random
import matplotlib.pyplot as plt
import numpy as np
def sse(n,a0,a1,x,y):
s=0
mean=np.mean(y)
for i in range(n):
s+=(a0+a1*x[i]-mean)**2
return s/(2*n)
def cost(n,a0,a1,x,y,ch,p=2):
s=0;
if ch=='sum-of-squares':
for i in range(n):
s+=(a0+a1*x[i]-y[i])**2
return s/(2*n)
if ch=='l-p norm':
for i in range(n):
s+=abs(a0+a1*x[i]-y[i])**p
return s**(1/p)
def dela0(n,a0,a1,x,y):
s=0;
for i in range(n):
s+=(a0+a1*x[i]-y[i])
return s/n
def dela1(n,a0,a1,x,y):
s=0;
for i in range(n):
s+=(a0+a1*x[i]-y[i])*x[i]
return s/n
def predict(x, y, alphas=np.linspace(0.001,1,10), it=1000):
mx=max(x)
my=max(y)
for i in range(len(x)):
x[i]/=mx
y[i]/=my
coeff, r2, cos = grad_desc(x, y, alphas, it)
coeff, x, y = rescale(x, y, coeff, mx, my)
r2_alpha(r2, alphas)
plot_predict(x, y, coeff, r2, cos, it)
a0=coeff[r2.index(max(r2))][0]
a1=coeff[r2.index(max(r2))][1]
pred=a0+x*a1
return pred
def grad_desc(x, y, alphas, it):
r2=[]
coeff=[]
for alpha in alphas:
cos=[]
a0=random.random()
a1=random.random()
for i in range(it):
temp0=a0-alpha*dela0(len(x),a0,a1,x,y)
temp1=a1-alpha*dela1(len(x),a0,a1,x,y)
a0=temp0
a1=temp1
cos.append(cost(len(x),a0,a1,x,y,ch='sum-of-squares'))
r2.append(1-(cos[-1]/(cos[-1]+sse(len(x),a0,a1,x,y))))
coeff.append([a0,a1])
return coeff, r2, cos | MIT License |
ing-bank/skorecard | skorecard/features_bucket_mapping.py | merge_features_bucket_mapping | python | def merge_features_bucket_mapping(a: FeaturesBucketMapping, b: FeaturesBucketMapping) -> FeaturesBucketMapping:
assert isinstance(a, FeaturesBucketMapping)
assert isinstance(b, FeaturesBucketMapping)
cols_in_both = [col for col in a.columns if col in b.columns]
cols_in_a = [col for col in a.columns if col not in b.columns]
cols_in_b = [col for col in b.columns if col not in a.columns]
features_bucket_mapping = FeaturesBucketMapping()
for col in cols_in_both:
c = merge_bucket_mapping(a.get(col), b.get(col))
features_bucket_mapping.append(c)
for col in cols_in_a:
features_bucket_mapping.append(a.get(col))
for col in cols_in_b:
features_bucket_mapping.append(b.get(col))
return features_bucket_mapping | Merge two sets of sequentual FeatureBucketMapping.
If there are unique features, we'll add them as-in. | https://github.com/ing-bank/skorecard/blob/8ab8d38db9385aab049a7a8bef4d5f235d3f46ce/skorecard/features_bucket_mapping.py#L161-L186 | import yaml
import dataclasses
from skorecard.bucket_mapping import BucketMapping, merge_bucket_mapping
class FeaturesBucketMapping:
def __init__(self, maps=[]):
self.maps = {}
if isinstance(maps, list):
for bucketmap in maps:
self.append(bucketmap)
if isinstance(maps, dict):
for _, bucketmap in maps.items():
if not isinstance(bucketmap, BucketMapping):
bucketmap = BucketMapping(**bucketmap)
self.append(bucketmap)
def __repr__(self):
class_name = self.__class__.__name__
maps = list(self.maps.values())
return f"{class_name}({maps})"
def __len__(self):
return len(self.maps)
def __eq__(self, other):
return self.maps == other.maps
def __getitem__(self, key):
return self.maps[key]
def __setitem__(self, key, value):
self.maps[key] = value
def get(self, col: str):
return self.maps[col]
def append(self, bucketmap: BucketMapping) -> None:
assert isinstance(bucketmap, BucketMapping)
self.maps[bucketmap.feature_name] = bucketmap
def load_yml(self) -> None:
raise NotImplementedError("todo")
def save_yml(self, file) -> None:
if isinstance(file, str):
file = open(file, "w")
yaml.safe_dump(self.as_dict(), file)
def load_dict(self, obj):
assert isinstance(obj, dict)
self.maps = {}
for feature, bucketmap in obj.items():
self.append(BucketMapping(**bucketmap))
def as_dict(self):
return {k: dataclasses.asdict(v) for k, v in self.maps.items()}
@property
def columns(self):
return list(self.as_dict().keys()) | MIT License |
awslabs/aws-data-api | vendor/tornado/template.py | BaseLoader.__init__ | python | def __init__(self, autoescape=_DEFAULT_AUTOESCAPE, namespace=None,
whitespace=None):
self.autoescape = autoescape
self.namespace = namespace or {}
self.whitespace = whitespace
self.templates = {}
self.lock = threading.RLock() | Construct a template loader.
:arg str autoescape: The name of a function in the template
namespace, such as "xhtml_escape", or ``None`` to disable
autoescaping by default.
:arg dict namespace: A dictionary to be added to the default template
namespace, or ``None``.
:arg str whitespace: A string specifying default behavior for
whitespace in templates; see `filter_whitespace` for options.
Default is "single" for files ending in ".html" and ".js" and
"all" for other files.
.. versionchanged:: 4.3
Added ``whitespace`` parameter. | https://github.com/awslabs/aws-data-api/blob/81f6ad1fd89935fcec600ced2b404f37d87254fe/vendor/tornado/template.py#L385-L411 | from __future__ import absolute_import, division, print_function
import datetime
import linecache
import os.path
import posixpath
import re
import threading
from tornado import escape
from tornado.log import app_log
from tornado.util import ObjectDict, exec_in, unicode_type, PY3
if PY3:
from io import StringIO
else:
from cStringIO import StringIO
_DEFAULT_AUTOESCAPE = "xhtml_escape"
_UNSET = object()
def filter_whitespace(mode, text):
if mode == 'all':
return text
elif mode == 'single':
text = re.sub(r"([\t ]+)", " ", text)
text = re.sub(r"(\s*\n\s*)", "\n", text)
return text
elif mode == 'oneline':
return re.sub(r"(\s+)", " ", text)
else:
raise Exception("invalid whitespace mode %s" % mode)
class Template(object):
def __init__(self, template_string, name="<string>", loader=None,
compress_whitespace=_UNSET, autoescape=_UNSET,
whitespace=None):
self.name = escape.native_str(name)
if compress_whitespace is not _UNSET:
if whitespace is not None:
raise Exception("cannot set both whitespace and compress_whitespace")
whitespace = "single" if compress_whitespace else "all"
if whitespace is None:
if loader and loader.whitespace:
whitespace = loader.whitespace
else:
if name.endswith(".html") or name.endswith(".js"):
whitespace = "single"
else:
whitespace = "all"
filter_whitespace(whitespace, '')
if autoescape is not _UNSET:
self.autoescape = autoescape
elif loader:
self.autoescape = loader.autoescape
else:
self.autoescape = _DEFAULT_AUTOESCAPE
self.namespace = loader.namespace if loader else {}
reader = _TemplateReader(name, escape.native_str(template_string),
whitespace)
self.file = _File(self, _parse(reader, self))
self.code = self._generate_python(loader)
self.loader = loader
try:
self.compiled = compile(
escape.to_unicode(self.code),
"%s.generated.py" % self.name.replace('.', '_'),
"exec", dont_inherit=True)
except Exception:
formatted_code = _format_code(self.code).rstrip()
app_log.error("%s code:\n%s", self.name, formatted_code)
raise
def generate(self, **kwargs):
namespace = {
"escape": escape.xhtml_escape,
"xhtml_escape": escape.xhtml_escape,
"url_escape": escape.url_escape,
"json_encode": escape.json_encode,
"squeeze": escape.squeeze,
"linkify": escape.linkify,
"datetime": datetime,
"_tt_utf8": escape.utf8,
"_tt_string_types": (unicode_type, bytes),
"__name__": self.name.replace('.', '_'),
"__loader__": ObjectDict(get_source=lambda name: self.code),
}
namespace.update(self.namespace)
namespace.update(kwargs)
exec_in(self.compiled, namespace)
execute = namespace["_tt_execute"]
linecache.clearcache()
return execute()
def _generate_python(self, loader):
buffer = StringIO()
try:
named_blocks = {}
ancestors = self._get_ancestors(loader)
ancestors.reverse()
for ancestor in ancestors:
ancestor.find_named_blocks(loader, named_blocks)
writer = _CodeWriter(buffer, named_blocks, loader,
ancestors[0].template)
ancestors[0].generate(writer)
return buffer.getvalue()
finally:
buffer.close()
def _get_ancestors(self, loader):
ancestors = [self.file]
for chunk in self.file.body.chunks:
if isinstance(chunk, _ExtendsBlock):
if not loader:
raise ParseError("{% extends %} block found, but no "
"template loader")
template = loader.load(chunk.name, self.name)
ancestors.extend(template._get_ancestors(loader))
return ancestors
class BaseLoader(object): | Apache License 2.0 |
purestorage-openconnect/py-pure-client | pypureclient/flasharray/FA_2_1/models/volume_response.py | VolumeResponse.__init__ | python | def __init__(
self,
items=None,
):
if items is not None:
self.items = items | Keyword args:
items (list[Volume]): Returns a list of all items after filtering. The values are displayed for each name where meaningful. If `total_only=true`, the `items` list will be empty. | https://github.com/purestorage-openconnect/py-pure-client/blob/2d9fdef0b73321cea9613e7d1eb881b42845099b/pypureclient/flasharray/FA_2_1/models/volume_response.py#L43-L52 | import pprint
import re
import six
import typing
from ....properties import Property
if typing.TYPE_CHECKING:
from pypureclient.flasharray.FA_2_1 import models
class VolumeResponse(object):
swagger_types = {
'items': 'list[Volume]'
}
attribute_map = {
'items': 'items'
}
required_args = {
} | BSD 2-Clause Simplified License |
docusign/docusign-python-client | docusign_esign/models/login_account.py | LoginAccount.is_default | python | def is_default(self):
return self._is_default | Gets the is_default of this LoginAccount. # noqa: E501
This value is true if this is the default account for the user, otherwise false is returned. # noqa: E501
:return: The is_default of this LoginAccount. # noqa: E501
:rtype: str | https://github.com/docusign/docusign-python-client/blob/c6aeafff0d046fa6c10a398be83ba9e24b05d4ea/docusign_esign/models/login_account.py#L187-L195 | import pprint
import re
import six
from docusign_esign.client.configuration import Configuration
class LoginAccount(object):
"""
Attributes:
swagger_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
swagger_types = {
'account_id': 'str',
'account_id_guid': 'str',
'base_url': 'str',
'email': 'str',
'is_default': 'str',
'login_account_settings': 'list[NameValue]',
'login_user_settings': 'list[NameValue]',
'name': 'str',
'site_description': 'str',
'user_id': 'str',
'user_name': 'str'
}
attribute_map = {
'account_id': 'accountId',
'account_id_guid': 'accountIdGuid',
'base_url': 'baseUrl',
'email': 'email',
'is_default': 'isDefault',
'login_account_settings': 'loginAccountSettings',
'login_user_settings': 'loginUserSettings',
'name': 'name',
'site_description': 'siteDescription',
'user_id': 'userId',
'user_name': 'userName'
}
def __init__(self, _configuration=None, **kwargs):
if _configuration is None:
_configuration = Configuration()
self._configuration = _configuration
self._account_id = None
self._account_id_guid = None
self._base_url = None
self._email = None
self._is_default = None
self._login_account_settings = None
self._login_user_settings = None
self._name = None
self._site_description = None
self._user_id = None
self._user_name = None
self.discriminator = None
setattr(self, "_{}".format('account_id'), kwargs.get('account_id', None))
setattr(self, "_{}".format('account_id_guid'), kwargs.get('account_id_guid', None))
setattr(self, "_{}".format('base_url'), kwargs.get('base_url', None))
setattr(self, "_{}".format('email'), kwargs.get('email', None))
setattr(self, "_{}".format('is_default'), kwargs.get('is_default', None))
setattr(self, "_{}".format('login_account_settings'), kwargs.get('login_account_settings', None))
setattr(self, "_{}".format('login_user_settings'), kwargs.get('login_user_settings', None))
setattr(self, "_{}".format('name'), kwargs.get('name', None))
setattr(self, "_{}".format('site_description'), kwargs.get('site_description', None))
setattr(self, "_{}".format('user_id'), kwargs.get('user_id', None))
setattr(self, "_{}".format('user_name'), kwargs.get('user_name', None))
@property
def account_id(self):
return self._account_id
@account_id.setter
def account_id(self, account_id):
self._account_id = account_id
@property
def account_id_guid(self):
return self._account_id_guid
@account_id_guid.setter
def account_id_guid(self, account_id_guid):
self._account_id_guid = account_id_guid
@property
def base_url(self):
return self._base_url
@base_url.setter
def base_url(self, base_url):
self._base_url = base_url
@property
def email(self):
return self._email
@email.setter
def email(self, email):
self._email = email
@property | MIT License |
rikonor/vanguard-api | seleniumapis/browser/browser.py | Browser.find_element_by_any | python | def find_element_by_any(self, search_term):
return self.find_element_by_id(search_term) or self.find_element_by_name(search_term) | Find an element by a search term
Try ID, then fallback to Name | https://github.com/rikonor/vanguard-api/blob/5462b2327cacad68bedb945dc323d534ebbdfeee/seleniumapis/browser/browser.py#L37-L43 | from selenium import webdriver
from selenium.webdriver.common.desired_capabilities import DesiredCapabilities
from selenium.common.exceptions import *
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
class Browser(object):
def __init__(self):
self.remote = "http://selenium:4444/wd/hub"
self.browser_type = DesiredCapabilities.CHROME
self.browser = None
def start(self):
self.driver = webdriver.Remote(
command_executor=self.remote,
desired_capabilities=self.browser_type)
def get(self, path):
self.driver.get(path)
def close(self):
self.driver.close()
@property
def inputs(self):
return []
@property
def title(self):
return self.driver.title
def contains_text(self, text):
return text in self.driver.page_source | MIT License |
tamasgal/km3pipe | km3pipe/io/daq.py | DAQPump.seek_to_frame | python | def seek_to_frame(self, index):
pointer_position = self.frame_positions[index]
self.blob_file.seek(pointer_position, 0) | Move file pointer to the frame with given index. | https://github.com/tamasgal/km3pipe/blob/c10fa39f72f3a8e384025712344378012fae5115/km3pipe/io/daq.py#L231-L234 | from collections import namedtuple
from io import BytesIO
import json
import math
import struct
from struct import unpack
import time
import pprint
from urllib.request import urlopen, URLError
import numpy as np
from thepipe import Module, Blob
from km3pipe.dataclasses import Table
from km3pipe.sys import ignored
from km3pipe.logger import get_logger, get_printer
__author__ = "Tamas Gal"
__copyright__ = "Copyright 2016, Tamas Gal and the KM3NeT collaboration."
__credits__ = []
__license__ = "MIT"
__maintainer__ = "Tamas Gal"
__email__ = "tgal@km3net.de"
__status__ = "Development"
log = get_logger(__name__)
DATA_TYPES = {
101: "DAQSuperFrame",
201: "DAQSummaryFrame",
1001: "DAQTimeslice",
1002: "DAQTimeslice",
1003: "DAQTimeslice",
1004: "DAQTimeslice",
1005: "DAQTimeslice",
2001: "DAQSummaryslice",
10001: "DAQEvent",
}
MINIMAL_RATE_HZ = 2.0e3
MAXIMAL_RATE_HZ = 2.0e6
class TimesliceParser(Module):
def configure(self):
self.legacy = self.get("legacy", default=False)
def _get_raw_data(self, blob):
if "CHPrefix" in blob:
if not str(blob["CHPrefix"].tag).startswith("IO_TS"):
log.info("Not an IO_TS* blob")
return blob
return BytesIO(blob["CHData"])
if "FileIO" in blob:
return blob["FileIO"]
if "RawBytes" in blob:
return BytesIO(blob["RawBytes"])
def process(self, blob):
data = self._get_raw_data(blob)
if data is None:
return blob
try:
ts_info, ts_frameinfos, ts_hits = self._parse_timeslice(data)
except struct.error:
log.error("Could not parse Timeslice")
log.error(blob.keys())
else:
blob["TSHits"] = ts_hits
blob["TimesliceInfo"] = ts_info
blob["TimesliceFrameInfos"] = ts_frameinfos
return blob
def _parse_timeslice(self, data):
tsl_size, datatype = unpack("<ii", data.read(8))
if not self.legacy:
version = unpack("<h", data.read(2))[0]
if version != 1:
raise ValueError(
"Unsupported DAQTimeslice version ({}) or legacy DAQ. "
"Make sure Jpp v13+ is used or pass 'legacy=True' "
"to {}.".format(version, self.__class__.__name__)
)
det_id, run, sqnr = unpack("<iii", data.read(12))
timestamp, ns_ticks, n_frames = unpack("<iii", data.read(12))
ts_info = Table.from_template(
{
"frame_index": sqnr,
"slice_id": 0,
"timestamp": timestamp,
"nanoseconds": ns_ticks * 16,
"n_frames": n_frames,
},
"TimesliceInfo",
)
ts_frameinfos = {}
_dom_ids = []
_channel_ids = []
_times = []
_tots = []
for _ in range(n_frames):
frame_size, datatype = unpack("<ii", data.read(8))
det_id, run, sqnr = unpack("<iii", data.read(12))
timestamp, ns_ticks, dom_id = unpack("<iii", data.read(12))
dataqueue_status = unpack("<i", data.read(4))[0]
dom_status = unpack("<iiii", data.read(4 * 4))
n_hits = unpack("<i", data.read(4))[0]
ts_frameinfos[dom_id] = Table.from_template(
{
"det_id": det_id,
"run_id": run,
"sqnr": sqnr,
"timestamp": timestamp,
"nanoseconds": ns_ticks * 16,
"dom_id": dom_id,
"dataqueue_status": dataqueue_status,
"dom_status": dom_status,
"n_hits": n_hits,
},
"TimesliceFrameInfo",
)
for j in range(n_hits):
hit = unpack("!BlB", data.read(6))
_dom_ids.append(dom_id)
_channel_ids.append(hit[0])
_times.append(hit[1])
_tots.append(hit[2])
ts_hits = Table(
{
"channel_id": np.array(_channel_ids),
"dom_id": np.array(_dom_ids),
"time": np.array(_times),
"tot": np.array(_tots),
},
name="TimesliceHits",
h5loc="/timeslice_hits",
split_h5=True,
)
return ts_info, ts_frameinfos, ts_hits
class RePump(Module):
def configure(self):
self.filename = self.require("filename")
self.fobj = open(self.filename, "rb")
def process(self, blob):
try:
length, data_type = unpack("<ii", self.fobj.read(8))
self.fobj.seek(-8, 1)
except struct.error:
raise StopIteration
data = self.fobj.read(length)
blob["RawBytes"] = data
return blob
def finish(self):
self.fobj.close()
class DAQPump(Module):
def configure(self):
self.filename = self.require("filename")
self.legacy = self.get("legacy", default=False)
self.frame_positions = []
self.index = 0
self.blob_file = self.open_file(self.filename)
self.determine_frame_positions()
def next_blob(self):
blob_file = self.blob_file
try:
preamble = DAQPreamble(file_obj=blob_file)
except struct.error:
raise StopIteration
try:
data_type = DATA_TYPES[preamble.data_type]
except KeyError:
log.error("Unknown datatype: {0}".format(preamble.data_type))
data_type = "Unknown"
blob = Blob()
blob[data_type] = None
blob["DAQPreamble"] = preamble
if data_type == "DAQSummaryslice":
daq_frame = DAQSummaryslice(blob_file, legacy=self.legacy)
blob[data_type] = daq_frame
blob["DAQHeader"] = daq_frame.header
elif data_type == "DAQEvent":
daq_frame = DAQEvent(blob_file, legacy=self.legacy)
blob[data_type] = daq_frame
blob["DAQHeader"] = daq_frame.header
else:
log.warning(
"Skipping DAQ frame with data type code '{0}'.".format(
preamble.data_type
)
)
blob_file.seek(preamble.length - DAQPreamble.size, 1)
return blob | MIT License |
vitrioil/speech-separation | src/loader/data.py | Signal.augment_audio | python | def augment_audio(self, augmenter: Callable, *args, **kwargs):
self.audio = augmenter(self.audio, *args, **kwargs) | Change the audio via the augmenter method. | https://github.com/vitrioil/speech-separation/blob/65a532d36cf0725d622f18ef058cf5a537c01070/src/loader/data.py#L65-L69 | import os
import cv2
import librosa
import numpy as np
from pathlib import Path
from typing import Callable, Tuple, List
EMBED_DIR = [Path("../data/train/embed")]
SPEC_DIR = [Path("../data/train/spec")]
def get_frames(video):
frame_count = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
frame_width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
buffer_video = np.empty((frame_count, frame_height, frame_width, 3), np.dtype('uint8'))
frame = 0
ret = True
while (frame < frame_count and ret):
ret, f = video.read()
buffer_video[frame] = cv2.cvtColor(f, cv2.COLOR_BGR2RGB)
frame += 1
video.release()
return buffer_video
class Signal:
def __init__(self, video_path: str, audio_path: str, audio_ext=".mp3", sr=16_000, video_start_length=0, load_spec=True):
self.video_path = Path(video_path)
self.audio_path = Path(audio_path)
self.video_start_length = video_start_length
self.embed_path = None
self.embed_saved = False
self.embed = None
self.load_spec = load_spec
self._is_spec = False
self.spec_path = Path(*self.audio_path.parts[:-2], "spec", self.audio_path.stem + ".npy")
self._load(sr=sr)
self._check_video_embed()
self._convert_video()
def _load(self, sr: int):
self.audio = None
if self.load_spec and self.spec_path.is_file():
self.audio = np.load(self.spec_path)
self._is_spec = True
if self.audio is None:
self.audio, sr = librosa.load(self.audio_path.as_posix(), sr=sr)
self._is_spec = False
self.video = cv2.VideoCapture(self.video_path.as_posix()) | MIT License |
jakecover/distest | distest/TestInterface/_reply.py | assert_reply_contains | python | async def assert_reply_contains(self, contents, substring):
response = await self.wait_for_reply(contents)
return await self.assert_message_contains(response, substring) | Send a message and wait for a response. If the response does not contain
the given substring, fail the test.
:param str contents: The content of the trigger message. (A command)
:param str substring: The string to test against.
:returns: The reply.
:rtype: discord.Message
:raises: ResponseDidNotMatchError | https://github.com/jakecover/distest/blob/8810c884546a37a67881ddf3fbeed03b6eccebe5/distest/TestInterface/_reply.py#L22-L33 | from asyncio import sleep
from inspect import signature, _ParameterKind
from typing import Dict
from discord import Embed, Message
async def assert_reply_equals(self, contents, matches):
response = await self.wait_for_reply(contents)
return await self.assert_message_equals(response, matches) | MIT License |
thingsboard/python_tb_rest_client | tb_rest_client/models/models_pe/report_config.py | ReportConfig.to_dict | python | def to_dict(self):
result = {}
for attr, _ in six.iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
if issubclass(ReportConfig, dict):
for key, value in self.items():
result[key] = value
return result | Returns the model properties as a dict | https://github.com/thingsboard/python_tb_rest_client/blob/87c6a3703974fc8a86e4c72c444168ee2b758ecb/tb_rest_client/models/models_pe/report_config.py#L300-L325 | import pprint
import re
import six
class ReportConfig(object):
swagger_types = {
'base_url': 'str',
'dashboard_id': 'str',
'name_pattern': 'str',
'state': 'str',
'timewindow': 'str',
'timezone': 'str',
'type': 'str',
'use_current_user_credentials': 'bool',
'use_dashboard_timewindow': 'bool',
'user_id': 'str'
}
attribute_map = {
'base_url': 'baseUrl',
'dashboard_id': 'dashboardId',
'name_pattern': 'namePattern',
'state': 'state',
'timewindow': 'timewindow',
'timezone': 'timezone',
'type': 'type',
'use_current_user_credentials': 'useCurrentUserCredentials',
'use_dashboard_timewindow': 'useDashboardTimewindow',
'user_id': 'userId'
}
def __init__(self, base_url=None, dashboard_id=None, name_pattern=None, state=None, timewindow=None, timezone=None, type=None, use_current_user_credentials=None, use_dashboard_timewindow=None, user_id=None):
self._base_url = None
self._dashboard_id = None
self._name_pattern = None
self._state = None
self._timewindow = None
self._timezone = None
self._type = None
self._use_current_user_credentials = None
self._use_dashboard_timewindow = None
self._user_id = None
self.discriminator = None
if base_url is not None:
self.base_url = base_url
if dashboard_id is not None:
self.dashboard_id = dashboard_id
if name_pattern is not None:
self.name_pattern = name_pattern
if state is not None:
self.state = state
if timewindow is not None:
self.timewindow = timewindow
if timezone is not None:
self.timezone = timezone
if type is not None:
self.type = type
if use_current_user_credentials is not None:
self.use_current_user_credentials = use_current_user_credentials
if use_dashboard_timewindow is not None:
self.use_dashboard_timewindow = use_dashboard_timewindow
if user_id is not None:
self.user_id = user_id
@property
def base_url(self):
return self._base_url
@base_url.setter
def base_url(self, base_url):
self._base_url = base_url
@property
def dashboard_id(self):
return self._dashboard_id
@dashboard_id.setter
def dashboard_id(self, dashboard_id):
self._dashboard_id = dashboard_id
@property
def name_pattern(self):
return self._name_pattern
@name_pattern.setter
def name_pattern(self, name_pattern):
self._name_pattern = name_pattern
@property
def state(self):
return self._state
@state.setter
def state(self, state):
self._state = state
@property
def timewindow(self):
return self._timewindow
@timewindow.setter
def timewindow(self, timewindow):
self._timewindow = timewindow
@property
def timezone(self):
return self._timezone
@timezone.setter
def timezone(self, timezone):
self._timezone = timezone
@property
def type(self):
return self._type
@type.setter
def type(self, type):
self._type = type
@property
def use_current_user_credentials(self):
return self._use_current_user_credentials
@use_current_user_credentials.setter
def use_current_user_credentials(self, use_current_user_credentials):
self._use_current_user_credentials = use_current_user_credentials
@property
def use_dashboard_timewindow(self):
return self._use_dashboard_timewindow
@use_dashboard_timewindow.setter
def use_dashboard_timewindow(self, use_dashboard_timewindow):
self._use_dashboard_timewindow = use_dashboard_timewindow
@property
def user_id(self):
return self._user_id
@user_id.setter
def user_id(self, user_id):
self._user_id = user_id | Apache License 2.0 |
xilinx/pyxir | python/pyxir/contrib/dpuv1/dpuv1_op_support.py | mean_op_support | python | def mean_op_support(X, bXs, tXs):
axes = X.attrs['axes']
keepdims = X.attrs['keepdims']
return len(axes) == 2 and keepdims | Check whether we can execute the provided Mean operator
on the dpuv1 target | https://github.com/xilinx/pyxir/blob/bef661d6d77adcdbd2cf4163f2cf3a1d31d40406/python/pyxir/contrib/dpuv1/dpuv1_op_support.py#L212-L220 | import math
import pyxir
import logging
logger = logging.getLogger('pyxir')
@pyxir.register_op_support_check('dpuv1', 'BatchNorm')
def batchnorm_op_support(X, bXs, tXs):
axis = X.attrs['axis']
channels = X.shapes[axis]
return channels >= 1 and channels <= 4096
@pyxir.register_op_support_check('dpuv1', 'BiasAdd')
def biasadd_op_support(X, bXs, tXs):
axis = X.attrs['axis']
channels = X.shapes[axis]
return channels >= 1 and channels <= 4096
@pyxir.register_op_support_check('dpuv1', 'Cast')
def cast_op_support(X, bXs, tXs):
dtype = X.attrs['dtype']
return dtype == 'float32'
@pyxir.register_op_support_check('dpuv1', 'Concat')
def concat_op_support(X, bXs, tXs):
axis = X.attrs['axis']
channels = X.shapes[axis]
return channels >= 1 and channels <= 4096
@pyxir.register_op_support_check('dpuv1', 'Convolution')
def conv2d_op_support(X, bXs, tXs):
data_layout = X.attrs['data_layout']
kernel_h, kernel_w = X.attrs['kernel_size']
stride_h, stride_w = X.attrs['strides']
dilation_h, dilation_w = X.attrs['dilation']
padding_h, padding_w = X.attrs['padding'][data_layout.index('H')], X.attrs['padding'][data_layout.index('H')]
padding_h_top, padding_h_bot = padding_h
padding_w_left, padding_w_right = padding_w
ch_in, ch_out = X.attrs['channels']
groups = X.attrs['groups']
return groups == 1 and kernel_h >= 1 and kernel_h <= 15 and kernel_w >= 1 and kernel_w <= 15 and stride_h in [1, 2, 4, 8] and stride_w in [1, 2, 4, 8] and ch_in >= 1 and ch_in <= 4096 and ch_out >= 1 and ch_out <= 4096 and dilation_h in [1, 2, 4] and dilation_w in [1, 2, 4]
@pyxir.register_op_support_check('dpuv1', 'Conv2DTranspose')
def conv2d_transpose_op_support(X, bXs, tXs):
data_layout = X.attrs['data_layout']
kernel_h, kernel_w = X.attrs['kernel_size']
stride_h, stride_w = X.attrs['strides']
dilation_h, dilation_w = X.attrs['dilation']
padding_h, padding_w = X.attrs['padding'][data_layout.index('H')], X.attrs['padding'][data_layout.index('W')]
padding_h_top, padding_h_bot = padding_h
padding_w_left, padding_w_right = padding_w
padding = X.attrs['padding']
ch_in, ch_out = X.attrs['channels']
groups = X.attrs['groups']
return groups == 1 and kernel_h >= 1 and kernel_h <= 15 and kernel_w >= 1 and kernel_w <= 15 and stride_h in [1, 2, 4, 8] and stride_w in [1, 2, 4, 8] and ch_in >= 1 and ch_in <= 4096 and ch_out >= 1 and ch_out <= 4096 and dilation_h in [1, 2, 4] and dilation_w in [1, 2, 4]
@pyxir.register_op_support_check('dpuv1', 'DPU')
def dpu_op_support(X, bXs, tXs):
return True
@pyxir.register_op_support_check('dpuv1', 'Eltwise')
def eltwise_op_support(X, bXs, tXs):
return True
@pyxir.register_op_support_check('dpuv1', 'Pad')
def pooling_op_support(X, bXs, tXs):
padding = X.attrs['padding']
return True
@pyxir.register_op_support_check('dpuv1', 'Pooling')
def pooling_op_support(X, bXs, tXs):
data_layout = X.attrs['data_layout']
kernel_h, kernel_w = X.attrs['kernel_size']
stride_h, stride_w = X.attrs['strides']
padding_h, padding_w = X.attrs['padding'][data_layout.index('H')], X.attrs['padding'][data_layout.index('H')]
padding_h_top, padding_h_bot = padding_h
padding_w_left, padding_w_right = padding_w
channels = X.shapes[data_layout.index('C')]
return kernel_h >= 1 and kernel_h <= 15 and kernel_w >= 1 and kernel_w <= 15 and stride_h in [1, 2, 4, 8] and stride_w in [1, 2, 4, 8] and channels >= 1 and channels <= 4096
@pyxir.register_op_support_check('dpuv1', 'Mean') | Apache License 2.0 |
kriaga/health-checker | HealthChecker/venv/Lib/site-packages/nltk/data.py | normalize_resource_name | python | def normalize_resource_name(resource_name, allow_relative=True, relative_path=None):
is_dir = bool(re.search(r'[\\/.]$', resource_name)) or resource_name.endswith(os.path.sep)
if sys.platform.startswith('win'):
resource_name = resource_name.lstrip('/')
else:
resource_name = re.sub(r'^/+', '/', resource_name)
if allow_relative:
resource_name = os.path.normpath(resource_name)
else:
if relative_path is None:
relative_path = os.curdir
resource_name = os.path.abspath(
os.path.join(relative_path, resource_name))
resource_name = resource_name.replace('\\', '/').replace(os.path.sep, '/')
if sys.platform.startswith('win') and os.path.isabs(resource_name):
resource_name = '/' + resource_name
if is_dir and not resource_name.endswith('/'):
resource_name += '/'
return resource_name | :type resource_name: str or unicode
:param resource_name: The name of the resource to search for.
Resource names are posix-style relative path names, such as
``corpora/brown``. Directory names will automatically
be converted to a platform-appropriate path separator.
Directory trailing slashes are preserved
>>> windows = sys.platform.startswith('win')
>>> normalize_resource_name('.', True)
'./'
>>> normalize_resource_name('./', True)
'./'
>>> windows or normalize_resource_name('dir/file', False, '/') == '/dir/file'
True
>>> not windows or normalize_resource_name('C:/file', False, '/') == '/C:/file'
True
>>> windows or normalize_resource_name('/dir/file', False, '/') == '/dir/file'
True
>>> windows or normalize_resource_name('../dir/file', False, '/') == '/dir/file'
True
>>> not windows or normalize_resource_name('/dir/file', True, '/') == 'dir/file'
True
>>> windows or normalize_resource_name('/dir/file', True, '/') == '/dir/file'
True | https://github.com/kriaga/health-checker/blob/3d9ce933f131bcbb897103b0f509cc45393cae4a/HealthChecker/venv/Lib/site-packages/nltk/data.py#L210-L254 | from __future__ import print_function, unicode_literals
from __future__ import division
from abc import ABCMeta, abstractmethod
from six import add_metaclass
import functools
import textwrap
import io
import os
import re
import sys
import zipfile
import codecs
from gzip import GzipFile, READ as GZ_READ, WRITE as GZ_WRITE
try:
textwrap_indent = functools.partial(textwrap.indent, prefix=' ')
except AttributeError:
textwrap_fill = functools.partial(textwrap.fill,
initial_indent=' ',
subsequent_indent=' ',
replace_whitespace=False)
def textwrap_indent(text):
return '\n'.join(textwrap_fill(line) for line in text.splitlines())
try:
from zlib import Z_SYNC_FLUSH as FLUSH
except ImportError:
from zlib import Z_FINISH as FLUSH
try:
import cPickle as pickle
except ImportError:
import pickle
from six import string_types, text_type
from six.moves.urllib.request import urlopen, url2pathname
import nltk
from nltk.compat import py3_data, add_py3_data, BytesIO
path = []
_paths_from_env = os.environ.get('NLTK_DATA', str('')).split(os.pathsep)
path += [d for d in _paths_from_env if d]
if 'APPENGINE_RUNTIME' not in os.environ and os.path.expanduser('~/') != '~/':
path.append(os.path.expanduser(str('~/nltk_data')))
if sys.platform.startswith('win'):
path += [
str(r'C:\nltk_data'), str(r'D:\nltk_data'), str(r'E:\nltk_data'),
os.path.join(sys.prefix, str('nltk_data')),
os.path.join(sys.prefix, str('share'), str('nltk_data')),
os.path.join(sys.prefix, str('lib'), str('nltk_data')),
os.path.join(
os.environ.get(str('APPDATA'), str('C:\\')), str('nltk_data'))
]
else:
path += [
str('/usr/share/nltk_data'),
str('/usr/local/share/nltk_data'),
str('/usr/lib/nltk_data'),
str('/usr/local/lib/nltk_data'),
os.path.join(sys.prefix, str('nltk_data')),
os.path.join(sys.prefix, str('share'), str('nltk_data')),
os.path.join(sys.prefix, str('lib'), str('nltk_data'))
]
def gzip_open_unicode(filename, mode="rb", compresslevel=9, encoding='utf-8',
fileobj=None, errors=None, newline=None):
if fileobj is None:
fileobj = GzipFile(filename, mode, compresslevel, fileobj)
return io.TextIOWrapper(fileobj, encoding, errors, newline)
def split_resource_url(resource_url):
protocol, path_ = resource_url.split(':', 1)
if protocol == 'nltk':
pass
elif protocol == 'file':
if path_.startswith('/'):
path_ = '/' + path_.lstrip('/')
else:
path_ = re.sub(r'^/{0,2}', '', path_)
return protocol, path_
def normalize_resource_url(resource_url):
try:
protocol, name = split_resource_url(resource_url)
except ValueError:
protocol = 'nltk'
name = resource_url
if protocol == 'nltk' and os.path.isabs(name):
protocol = 'file://'
name = normalize_resource_name(name, False, None)
elif protocol == 'file':
protocol = 'file://'
name = normalize_resource_name(name, False, None)
elif protocol == 'nltk':
protocol = 'nltk:'
name = normalize_resource_name(name, True)
else:
protocol += '://'
return ''.join([protocol, name]) | MIT License |
bigmlcom/bigmler | bigmler/resourcesapi/batch_anomaly_scores.py | create_batch_anomaly_score | python | def create_batch_anomaly_score(anomaly, test_dataset,
batch_anomaly_score_args, args,
api=None, session_file=None,
path=None, log=None):
if api is None:
api = bigml.api.BigML()
message = dated("Creating batch anomaly score.\n")
log_message(message, log_file=session_file, console=args.verbosity)
batch_anomaly_score = api.create_batch_anomaly_score(
anomaly, test_dataset, batch_anomaly_score_args, retries=None)
log_created_resources(
"batch_anomaly_score", path,
bigml.api.get_batch_anomaly_score_id(batch_anomaly_score),
mode='a')
batch_anomaly_score_id = check_resource_error(
batch_anomaly_score, "Failed to create batch prediction: ")
try:
batch_anomaly_score = check_resource(batch_anomaly_score,
api.get_batch_anomaly_score,
raise_on_error=True)
except Exception as exception:
sys.exit("Failed to get a finished batch anomaly score: %s"
% str(exception))
message = dated("Batch anomaly score created: %s\n"
% get_url(batch_anomaly_score))
log_message(message, log_file=session_file, console=args.verbosity)
log_message("%s\n" % batch_anomaly_score_id, log_file=log)
if args.reports:
report(args.reports, path, batch_anomaly_score)
return batch_anomaly_score | Creates remote batch anomaly score | https://github.com/bigmlcom/bigmler/blob/91973ca1e752954302bf26bb22aa6874dc34ce69/bigmler/resourcesapi/batch_anomaly_scores.py#L74-L106 | import sys
import bigml.api
from bigmler.utils import (dated, get_url, log_message, check_resource,
check_resource_error, log_created_resources)
from bigmler.reports import report
from bigmler.resourcesapi.common import set_basic_batch_args, map_fields, update_json_args
from bigmler.resourcesapi.common import FULL_FORMAT
def set_batch_anomaly_score_args(args, fields=None,
dataset_fields=None):
batch_anomaly_score_args = set_basic_batch_args(args, args.name)
if args.fields_map_ and fields is not None:
if dataset_fields is None:
dataset_fields = fields
batch_anomaly_score_args.update({
"fields_map": map_fields(args.fields_map_,
fields, dataset_fields)})
if args.prediction_info == FULL_FORMAT:
batch_anomaly_score_args.update(all_fields=True)
if args.prediction_fields:
batch_anomaly_score_args.update(all_fields=False)
prediction_fields = []
for field in args.prediction_fields.split(args.args_separator):
field = field.strip()
if not field in dataset_fields.fields:
try:
field = dataset_fields.field_id(field)
except ValueError as exc:
sys.exit(exc)
prediction_fields.append(field)
batch_anomaly_score_args.update(output_fields=prediction_fields)
if 'batch_anomaly_score' in args.json_args:
update_json_args(
batch_anomaly_score_args,
args.json_args.get('batch_anomaly_score'),
fields)
return batch_anomaly_score_args | Apache License 2.0 |
loudnate/openaps-predict | openapscontrib/predict/predict.py | ceil_datetime_at_minute_interval | python | def ceil_datetime_at_minute_interval(timestamp, minute):
nsecs = timestamp.minute * 60 + timestamp.second + timestamp.microsecond * 1e-6
seconds = minute * 60
delta = (nsecs // seconds) * seconds + seconds - nsecs
if delta < seconds:
return timestamp + datetime.timedelta(seconds=delta)
else:
return timestamp | From http://stackoverflow.com/questions/13071384/python-ceil-a-datetime-to-next-quarter-of-an-hour
:param timestamp:
:type timestamp: datetime.datetime
:param minute:
:type minute: int
:return:
:rtype: datetime.datetime | https://github.com/loudnate/openaps-predict/blob/a2da82148318d86b0935a1fca596477d84dd8247/openapscontrib/predict/predict.py#L50-L71 | from collections import defaultdict
import datetime
from dateutil.parser import parse
from functools32 import lru_cache
import math
from numpy import arange
from scipy.stats import linregress
from models import Unit
class Schedule(object):
def __init__(self, entries):
self.entries = entries
@lru_cache()
def at(self, time):
result = {}
for entry in self.entries:
if parse(entry['start']).time() > time:
break
result = entry
return result
def floor_datetime_at_minute_interval(timestamp, minute):
return timestamp - datetime.timedelta(
minutes=timestamp.minute % minute,
seconds=timestamp.second,
microseconds=timestamp.microsecond
) | MIT License |
edx-unsupported/edx-load-tests | loadtests/student_notes/locustfile.py | BaseNotesTask._create_many_notes | python | def _create_many_notes(self, num_notes):
for _ in xrange(num_notes):
self._create_note() | Create many notes within the course for the current user. | https://github.com/edx-unsupported/edx-load-tests/blob/1a6dc891d2fb72575f354521988a531489f30032/loadtests/student_notes/locustfile.py#L202-L207 | import os
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
from contextlib import contextmanager
from copy import copy
import json
from locust import HttpLocust, task, TaskSet
import logging
import random
from helpers import settings
settings.init(__name__, required_data=[
'courses',
'NOTES_HOST',
'NUM_NOTES',
'NUM_WORDS',
'NUM_TAGS',
'NUM_SEARCH_TERMS',
'LOCUST_TASK_SET',
'LOCUST_MIN_WAIT',
'LOCUST_MAX_WAIT',
])
from helpers.mixins import EnrollmentTaskSetMixin
from helpers.edx_app import EdxAppTasks
HIGHLIGHT_TAG = 'span'
HIGHLIGHT_CLASS = 'note-highlight'
DATA_DIRECTORY = os.path.join(os.path.dirname(__file__), 'notes_data/')
with open(os.path.join(DATA_DIRECTORY, 'basic_words.txt')) as f:
NOTES_TEXT = [word for line in f for word in line.split()]
log = logging.getLogger(__name__)
def pick_some(sequence, num_items):
return random.sample(sequence, random.randint(1, num_items))
class BaseNotesTask(EdxAppTasks, EnrollmentTaskSetMixin):
def __init__(self, *args, **kwargs):
super(BaseNotesTask, self).__init__(*args, **kwargs)
self._notes = {}
self.api_client = copy(self.client)
self.api_client.auth = None
def on_start(self):
self.auto_auth()
self.enroll(self.course_id)
@property
def annotator_auth_token(self):
return self.client.get(
'/courses/{course_id}/edxnotes/token/'.format(course_id=self.course_id),
headers={'content-type': 'text/plain'},
).content
@contextmanager
def get_posted_student_note(self, warning_message):
try:
yield self._notes[random.choice(self._notes.keys())]
except IndexError:
log.debug(warning_message)
yield self._create_note()
def _request_from_notes_service(self, method, path, params_or_body=None, **kwargs):
method = method.lower()
kwargs.update({
'headers': {
'x-annotator-auth-token': self.annotator_auth_token,
'X-CSRFToken': self.client.cookies.get('csrftoken', ''),
'content-type': 'application/json',
},
})
if params_or_body is not None:
if method == 'get':
kwargs.update({'params': params_or_body})
elif method in ['post', 'patch', 'put', 'delete']:
kwargs.update({'data': json.dumps(params_or_body)})
return getattr(self.api_client, method)(
settings.data['NOTES_HOST'] + path,
**kwargs
)
def get(self, path, params=None, **kwargs):
return self._request_from_notes_service('get', path, params, **kwargs)
def post(self, path, body=None, **kwargs):
return self._request_from_notes_service('post', path, body, **kwargs)
def put(self, path, body=None, **kwargs):
return self._request_from_notes_service('put', path, body, **kwargs)
def delete(self, path, params=None, **kwargs):
return self._request_from_notes_service('delete', path, params, **kwargs)
def _create_note(self):
data = {
'user': self._anonymous_user_id,
'course_id': self.course_id,
'text': ' '.join(pick_some(
NOTES_TEXT,
settings.data['NUM_WORDS'],
)),
'tags': pick_some(
NOTES_TEXT,
settings.data['NUM_TAGS'],
),
'quote': ' '.join(pick_some(NOTES_TEXT, 5)),
'usage_id': self.course_data.html_usage_id,
'ranges': [
{
'start': '/div[1]/p[1]',
'end': '/div[1]/p[1]',
'startOffset': 0,
'endOffset': 6
}
],
}
note = json.loads(self.post('/api/v1/annotations/', data).content)
self._notes[note['id']] = note
return note | Apache License 2.0 |
bobotig/thermalprinter | thermalprinter/thermalprinter.py | ThermalPrinter.upside_down | python | def upside_down(self, state=False):
state = bool(state)
if state is not self._upside_down:
self._upside_down = state
self.send_command(Command.ESC, 123, int(state)) | Turns on/off upside-down printing mode. | https://github.com/bobotig/thermalprinter/blob/4cf697049d6c4fbad31ba8ea6842e0a4bc1b35ad/thermalprinter/thermalprinter.py#L624-L630 | from atexit import register
from time import sleep
from serial import Serial
from .constants import (BarCodePosition, CharSet, Chinese, CodePage,
CodePageConverted, Command)
from .exceptions import (ThermalPrinterValueError,
ThermalPrinterCommunicationError)
from .validate import (validate_barcode, validate_barcode_position,
validate_charset, validate_chinese_format,
validate_codepage)
class ThermalPrinter(Serial):
__lines = 0
__feeds = 0
def __init__(self, port='/dev/ttyAMA0', baudrate=19200, **kwargs):
self.heat_time = int(kwargs.get('heat_time', 80))
if not 0 <= self.heat_time <= 255:
raise ThermalPrinterValueError(
'heat_time should be between 0 and 255 (default: 80).')
self.heat_interval = int(kwargs.get('heat_interval', 12))
if not 0 <= self.heat_interval <= 255:
raise ThermalPrinterValueError(
'heat_interval should be between 0 and 255 (default: 12).')
self.most_heated_point = int(kwargs.get('most_heated_point', 3))
if not 0 <= self.most_heated_point <= 255:
raise ThermalPrinterValueError(
'most_heated_point should be between 0 and 255 (default: 3).')
self._baudrate = baudrate
self._byte_time = 11.0 / float(self._baudrate)
self._dot_feed_time = 0.0025
self._dot_print_time = 0.033
super().__init__(port=port, baudrate=self._baudrate)
sleep(0.5)
register(self._on_exit)
self.send_command(Command.ESC, 55, self.most_heated_point,
self.heat_time, self.heat_interval)
self.reset()
def __enter__(self):
return self
def _on_exit(self):
self.close()
def __repr__(self):
settings = (
'heat_interval=' + str(self.heat_interval),
'heat_time=' + str(self.heat_time),
'most_heated_point=' + str(self.most_heated_point),
)
states = []
for var in vars(self):
if not var.startswith('_'):
continue
try:
attr = getattr(self, var[1:])
except AttributeError:
continue
else:
if not callable(attr):
continue
states.append('{}={}'.format(var[1:], getattr(self, var)))
return '{name}<id=0x{id:x}, {settings}>({states})'.format(
name=type(self).__name__,
id=id(self),
settings=', '.join(sorted(settings)),
states=', '.join(sorted(states)))
@property
def is_online(self):
return self.__is_online
@property
def is_sleeping(self):
return self.__is_sleeping
@property
def lines(self):
return self.__lines
@property
def feeds(self):
return self.__feeds
@property
def max_column(self):
return self.__max_column
def out(self, data, line_feed=True, **kwargs):
if data is None:
return
for style, value in kwargs.items():
try:
getattr(self, style)(value)
except TypeError:
pass
self.write(self.to_bytes(data))
if line_feed:
self.write(b'\n')
self.__lines += 1
if self._size != 'S':
self.__lines += 1
sleep(2 * self._dot_feed_time * self._char_height)
for style, value in kwargs.items():
try:
getattr(self, style)()
except TypeError:
pass
def send_command(self, *args):
for data in args:
if isinstance(data, Command):
data = data.value
self.write(bytes([data]))
sleep(len(args) * self._byte_time)
def to_bytes(self, data):
if isinstance(data, (bool, int, float, complex)):
data = str(data)
elif isinstance(data, bytes):
return data
elif isinstance(data, bytearray):
return bytes(data)
elif isinstance(data, memoryview):
return data.tobytes()
encoding = 'utf-8' if self._chinese else self._codepage.name
try:
return bytes(data, encoding, errors='replace')
except LookupError:
encoding = CodePageConverted[self._codepage.name].value
return bytes(data, encoding, errors='replace')
def barcode(self, data, barcode_type):
validate_barcode(data, barcode_type)
code = barcode_type.value[0]
self.send_command(Command.GS, 107, code, len(data))
for char in list(data):
char = bytes([ord(char)])
self.write(char)
sleep(
(self._barcode_height + self._line_spacing) * self._dot_print_time)
self.__lines += int(self._barcode_height / self._line_spacing) + 1
def barcode_height(self, height=162):
if not isinstance(height, int) or not 1 <= height <= 255:
raise ThermalPrinterValueError(
'height should be between 1 and 255 (default: 162).')
if height != self._barcode_height:
self._barcode_height = height
self.send_command(Command.GS, 104, height)
def barcode_left_margin(self, margin=0):
if not isinstance(margin, int) or not 0 <= margin <= 255:
raise ThermalPrinterValueError(
'margin should be between 0 and 255 (default: 0).')
if margin != self._barcode_left_margin:
self._barcode_left_margin = margin
self.send_command(Command.GS, 120, margin)
def barcode_position(self, position=BarCodePosition.HIDDEN):
validate_barcode_position(position)
if position is not self._barcode_position:
self._barcode_position = position
self.send_command(Command.GS, 72, position.value)
def barcode_width(self, width=3):
if not isinstance(width, int) or not 2 <= width <= 6:
raise ThermalPrinterValueError(
'width should be between 2 and 6 (default: 3).')
if width != self._barcode_width:
self._barcode_width = width
self.send_command(Command.GS, 119, width)
def bold(self, state=False):
state = bool(state)
if state is not self._bold:
self._bold = state
self.send_command(Command.ESC, 69, int(state))
def charset(self, charset=CharSet.USA):
validate_charset(charset)
if charset is not self._charset:
self._charset = charset
self.send_command(Command.ESC, 82, charset.value)
def char_spacing(self, spacing=0):
if not isinstance(spacing, int) or not 0 <= spacing <= 255:
raise ThermalPrinterValueError(
'spacing should be between 0 and 255 (default: 0).')
if spacing != self._char_spacing:
self._char_spacing = spacing
self.send_command(Command.ESC, 32, spacing)
def chinese(self, state=False):
state = bool(state)
if state is not self._chinese:
self._chinese = state
self.send_command(Command.FS, 38 if state else 46)
def chinese_format(self, fmt=Chinese.GBK):
validate_chinese_format(fmt)
if fmt is not self._chinese_format:
self._chinese_format = fmt
self.send_command(Command.ESC, 57, fmt.value)
def codepage(self, codepage=CodePage.CP437):
validate_codepage(codepage)
if not self._chinese and codepage is not self._codepage:
self._codepage = codepage
value, _ = codepage.value
self.send_command(Command.ESC, 116, value)
sleep(0.05)
def double_height(self, state=False):
state = bool(state)
if state is not self._double_height:
self._double_height = state
self._char_height = 48 if state else 24
self.send_command(Command.ESC, 33, 16 if state else 0)
def double_width(self, state=False):
state = bool(state)
if state is not self._double_width:
self._double_width = state
self.__max_column = 16 if state else 32
self.send_command(Command.ESC, 14 if state else 20, 1)
def feed(self, number=1):
if not isinstance(number, int) or not 0 <= number <= 255:
raise ThermalPrinterValueError(
'number should be between 0 and 255 (default: 1).')
self.send_command(Command.ESC, 100, number)
sleep(number * self._dot_feed_time * self._char_height)
self.__feeds += number
def flush(self, clear=False):
self.send_command(Command.ESC, 64)
self.reset_output_buffer()
sleep(0.05)
if clear:
self.reset_input_buffer()
def image(self, image):
if not hasattr(image, 'im'):
raise ThermalPrinterValueError('image should be a PIL Image.')
if image.mode != '1':
image = image.convert('1')
width = min(image.size[0], 384)
height = image.size[1]
row_bytes = int((width + 7) / 8)
row_bytes_clipped = min(row_bytes, 48)
bitmap = bytearray(row_bytes * height)
pixels = image.load()
for col in range(height):
offset = col * row_bytes
row = 0
for pad in range(row_bytes):
sum_ = 0
bit = 128
while bit > 0:
if row >= width:
break
if pixels[row, col] == 0:
sum_ |= bit
row += 1
bit >>= 1
bitmap[offset + pad] = sum_
idx = 0
for row_start in range(0, height, 255):
chunk_height = min(height - row_start, 255)
self.send_command(Command.DC2, 42, chunk_height,
row_bytes_clipped)
for _ in range(chunk_height):
for _ in range(row_bytes_clipped):
self.write(bytes([bitmap[idx]]))
idx += 1
sleep(row_bytes_clipped * self._byte_time)
idx += row_bytes - row_bytes_clipped
self.__lines += height // self._line_spacing + 1
def inverse(self, state=False):
state = bool(state)
if state is not self._inverse:
self._inverse = state
self.send_command(Command.GS, 66, int(state))
def justify(self, value='L'):
if not isinstance(value, str) or value not in 'LCRlcr':
err = 'value should be one of L (left, default), C (center)'
err += ' or R (right).'
raise ThermalPrinterValueError(err)
value = value.upper()
if value != self._justify:
self._justify = value
if value == 'C':
pos = 1
elif value == 'R':
pos = 2
else:
pos = 0
self.send_command(Command.ESC, 97, pos)
def left_margin(self, margin=0):
if not isinstance(margin, int) or not 0 <= margin <= 47:
raise ThermalPrinterValueError(
'margin should be between 0 and 47 (default: 0).')
if margin != self._left_margin:
self._left_margin = margin
self.send_command(Command.ESC, 66, margin)
def line_spacing(self, spacing=30):
if not isinstance(spacing, int) or not 0 <= spacing <= 255:
raise ThermalPrinterValueError(
'spacing should be between 0 and 255 (default: 30).')
if spacing != self._line_spacing:
self._line_spacing = spacing
self.send_command(Command.ESC, 51, spacing)
def offline(self):
if self.is_online:
self.__is_online = False
self.send_command(Command.ESC, 61, 0)
def online(self):
if not self.is_online:
self.__is_online = True
self.send_command(Command.ESC, 61, 1)
def reset(self):
self.send_command(Command.ESC, 64)
self.__max_column = 32
self.__is_online = True
self.__is_sleeping = False
self._barcode_height = 162
self._barcode_left_margin = 0
self._barcode_position = BarCodePosition.HIDDEN
self._barcode_width = 3
self._bold = False
self._charset = CharSet.USA
self._char_spacing = 0
self._char_height = 24
self._chinese = False
self._chinese_format = Chinese.GBK
self._codepage = CodePage.CP437
self._double_height = False
self._double_width = False
self._inverse = False
self._justify = 'L'
self._left_margin = 0
self._line_spacing = 30
self._rotate = False
self._size = 'S'
self._strike = False
self._underline = 0
self._upside_down = False
def rotate(self, state=False):
state = bool(state)
if state is not self._rotate:
self._rotate = state
self.send_command(Command.ESC, 86, int(state))
def size(self, value='S'):
if not isinstance(value, str) or value not in 'SMLsml':
err = 'value should be one of S (small, default), M (medium)'
err += ' or L (large).'
raise ThermalPrinterValueError(err)
value = value.upper()
if value != self._size:
self._size = value
if value == 'L':
size, self._char_height, self.__max_column = 0x11, 48, 16
elif value == 'M':
size, self._char_height, self.__max_column = 0x01, 48, 32
else:
size, self._char_height, self.__max_column = 0x00, 24, 32
self.send_command(Command.GS, 33, size)
def sleep(self, seconds=1):
if self.is_sleeping:
return
if not isinstance(seconds, int) or seconds < 0:
raise ThermalPrinterValueError(
'seconds should be null or positive (default: 0).')
if seconds:
self.__is_sleeping = True
self.send_command(Command.ESC, 56, seconds, seconds >> 8)
def status(self, raise_on_error=True):
ret = {'movement': True, 'paper': True,
'temp': True, 'voltage': True}
self.send_command(Command.ESC, 118, 0)
sleep(0.05)
if self.in_waiting:
stat = ord(self.read(1))
ret['movement'] = stat & 0b00000001 == 1
ret['paper'] = stat & 0b00000100 == 0
ret['voltage'] = stat & 0b00001000 == 0
ret['temp'] = stat & 0b01000000 == 0
elif raise_on_error:
raise ThermalPrinterCommunicationError()
return ret
def strike(self, state=False):
state = bool(state)
if state is not self._strike:
self._strike = state
self.send_command(Command.ESC, 71, int(state))
def test(self):
self.send_command(Command.DC2, 84)
sleep(self._dot_print_time * 24 * 26
+ self._dot_feed_time * (8 * 26 + 32))
def underline(self, weight=0):
if not isinstance(weight, int) or not 0 <= weight <= 2:
raise ThermalPrinterValueError(
'weight should be between 0 and 2 (default: 0).')
if weight != self._underline:
self._underline = weight
self.send_command(Command.ESC, 45, weight) | MIT License |
flask-admin/flask-admin | flask_admin/contrib/appengine/form.py | AdminModelConverter.convert_GeoPtProperty | python | def convert_GeoPtProperty(self, model, prop, kwargs):
return GeoPtPropertyField(**kwargs) | Returns a form field for a ``ndb.GeoPtProperty``. | https://github.com/flask-admin/flask-admin/blob/e39f786374ce0e60db93f583efacb4672de0025c/flask_admin/contrib/appengine/form.py#L8-L10 | from wtforms_appengine.ndb import ModelConverter
from .fields import GeoPtPropertyField
from flask_admin.model.form import converts
class AdminModelConverter(ModelConverter):
@converts('GeoPt') | BSD 3-Clause New or Revised License |
chrklemm/sesmg | program_files/create_objects.py | Sinks.create_sink | python | def create_sink(self, de: dict, timeseries_args: dict):
self.nodes_sinks.append(
solph.Sink(label=de['label'],
inputs={
self.busd[de['input']]:
solph.Flow(**timeseries_args)})) | Creates an oemof sink with fixed or unfixed timeseries.
:param de: dictionary containing all information for the
creation of an oemof sink. At least the
following key-value-pairs have to be included:
- 'label'
- 'input'
:type de: dict
:param timeseries_args: dictionary rather containing the
'fix-attribute' or the 'min-' and
'max-attribute' of a sink
:type timeseries_args: dict
Christian Klemm - christian.klemm@fh-muenster.de | https://github.com/chrklemm/sesmg/blob/382ffd600b98d3cc6df53abed0cb3526187cb1cf/program_files/create_objects.py#L693-L718 | from oemof import solph
import logging
import os
import pandas as pd
from feedinlib import *
import demandlib.bdew as bdew
import datetime
import numpy
def buses(nodes_data: dict, nodes: list) -> dict:
busd = {}
for i, b in nodes_data['buses'].iterrows():
if b['active']:
bus = solph.Bus(label=b['label'])
nodes.append(bus)
busd[b['label']] = bus
logging.info(' ' + 'Bus created: ' + b['label'])
if b['excess']:
inputs = {
busd[b['label']]:
solph.Flow(variable_costs=b['excess costs'],
emission_factor=b[
'excess constraint costs'])}
nodes.append(
solph.Sink(
label=b['label'] + '_excess',
inputs=inputs))
if b['shortage']:
outputs = {
busd[b['label']]:
solph.Flow(
variable_costs=b['shortage costs'],
emission_factor=b[
'shortage constraint costs'])}
nodes.append(
solph.Source(
label=b['label'] + '_shortage',
outputs=outputs))
return busd
class Sources:
def create_source(self, so: dict, timeseries_args: dict, output=None):
if output is None:
output = self.busd[so['output']]
if str(so['input']) in ['0', 'None', 'none', 'nan']:
minimum = so['min. investment capacity']
maximum = so['max. investment capacity']
existing = so['existing capacity']
else:
minimum = so['min. investment capacity'] * so['Conversion Factor']
maximum = so['max. investment capacity'] * so['Conversion Factor']
existing = so['existing capacity'] * so['Conversion Factor']
self.nodes_sources.append(
solph.Source(
label=so['label'],
outputs={output: solph.Flow(
investment=solph.Investment(
ep_costs=so[
'periodical costs'],
periodical_constraint_costs=so[
'periodical constraint costs'],
minimum=minimum,
maximum=maximum,
existing=existing,
nonconvex=True if
so['non-convex investment'] == 1
else False,
offset=so[
'fix investment costs']),
**timeseries_args,
variable_costs=so['variable costs'],
emission_factor=so[
'variable constraint costs']
)}
))
def commodity_source(self, so: dict):
self.create_source(so, {'min': 0, 'max': 1}, self.busd[so['output']])
logging.info(' ' + 'Commodity Source created: ' + so['label'])
def timeseries_source(self, so: dict, time_series):
if so['fixed'] == 1:
args = {'fix': time_series[so['label'] + '.fix'].tolist()}
elif so['fixed'] == 0:
args = {'min': time_series[so['label'] + '.min'].tolist(),
'max': time_series[so['label'] + '.max'].tolist()}
else:
raise SystemError(so['label'] + " Error in fixed attribute")
self.create_source(so, args, self.busd[so['output']])
logging.info(' ' + 'Timeseries Source created: ' + so['label'])
def pv_source(self, so: dict, my_weather_pandas_dataframe):
parameter_set = {
'azimuth': so['Azimuth'],
'tilt': so['Surface Tilt'],
'module_name': so['Modul Model'],
'inverter_name': so['Inverter Model'],
'albedo': so['Albedo']}
pv_module = powerplants.Photovoltaic(**parameter_set)
my_weather_pandas_dataframe['ghi'] = (my_weather_pandas_dataframe.dirhi
+ my_weather_pandas_dataframe.dhi)
name_dc = {'temperature': 'temp_air', 'windspeed': 'v_wind'}
my_weather_pandas_dataframe.rename(columns=name_dc)
feedin = pv_module.feedin(
weather=my_weather_pandas_dataframe,
location=(so['Latitude'], so['Longitude']),
scaling='peak_power')
for i in range(len(feedin)):
if feedin[i] < 0:
feedin[i] = 0
if feedin[i] > 1:
feedin[i] = 1
feedin = feedin.fillna(0)
if so['fixed'] == 1:
args = {'fix': feedin}
elif so['fixed'] == 0:
args = {'min': 0, 'max': feedin}
else:
raise SystemError(so['label'] + " Error in fixed attribute")
self.create_source(so, args, self.busd[so['output']])
logging.info(' ' + 'Source created: ' + so['label'])
def windpower_source(self, so: dict, weather_df_wind):
turbine_data = {
'turbine_type': so['Turbine Model'],
'hub_height': so['Hub Height']}
wind_turbine = WindPowerPlant(**turbine_data)
data_height = {'pressure': 0, 'temperature': 2, 'wind_speed': 10,
'roughness_length': 0}
weather_df_wind = weather_df_wind[['windspeed', 'temperature', 'z0', 'pressure']]
weather_df_wind.columns = [['wind_speed', 'temperature', 'roughness_length', 'pressure'],
[data_height['wind_speed'], data_height['temperature'],
data_height['roughness_length'], data_height['pressure']]]
feedin_wind_scaled = wind_turbine.feedin(
weather=weather_df_wind, scaling='nominal_power')
if so['fixed'] == 1:
args = {'fix': feedin_wind_scaled}
elif so['fixed'] == 0:
args = {'min': 0, 'max': feedin_wind_scaled}
else:
raise SystemError(so['label'] + " Error in fixed attribute")
self.create_source(so, args, self.busd[so['output']])
logging.info(' ' + 'Source created: ' + so['label'])
def solar_heat_source(self, so, data):
from oemof.thermal.solar_thermal_collector import flat_plate_precalc
from oemof.thermal.concentrating_solar_power import csp_precalc
import numpy
col_bus = solph.Bus(label=so['label'] + '_bus')
self.nodes_sources.append(col_bus)
self.busd[so['label'] + '_bus'] = col_bus
output = col_bus
data.index.name = 'Datum'
data = data.asfreq('h')
data['ghi'] = (data["dirhi"] + data["dhi"])
if so['technology'] == 'solar_thermal_flat_plate':
precalc_results = flat_plate_precalc(
lat=so['Latitude'],
long=so['Longitude'],
collector_tilt=so['Surface Tilt'],
collector_azimuth=so['Azimuth'],
eta_0=so['ETA 0'],
a_1=so['A1'],
a_2=so['A2'],
temp_collector_inlet=
so['Temperature Inlet'],
delta_temp_n=
so['Temperature Difference'],
irradiance_global=(data['ghi']),
irradiance_diffuse=(data['dhi']),
temp_amb=data['temperature'])
collectors_heat = precalc_results.collectors_heat/1000
irradiance = precalc_results.col_ira/1000
elif so['technology'] == 'concentrated_solar_power':
precalc_results = csp_precalc(
lat=so['Latitude'],
long=so['Longitude'],
collector_tilt=so['Surface Tilt'],
collector_azimuth=so['Azimuth'],
cleanliness = so['Cleanliness'],
a_1=so['A1'],
a_2 = so['A2'],
eta_0=so['ETA 0'],
c_1 = so['C1'],
c_2 = so['C2'],
temp_collector_inlet = so['Temperature Inlet'],
temp_collector_outlet = so['Temperature Inlet']
+ so['Temperature Difference'],
temp_amb=data['temperature'],
E_dir_hor=data['dirhi'])
collectors_heat = precalc_results.collector_heat/1000
irradiance = precalc_results.collector_irradiance/1000
if so['fixed'] == 1:
args = {'fix': collectors_heat}
elif so['fixed'] == 0:
args = {'min': 0, 'max': collectors_heat}
else:
raise SystemError(so['label'] + " Error in fixed attribute")
self.create_source(so, args, output)
self.nodes_sources.append(solph.Transformer(
label=so['label'] + '_collector',
inputs={self.busd[so['label'] + '_bus']:
solph.Flow(variable_costs=0),
self.busd[so['input']]: solph.Flow(variable_costs=0)},
outputs={self.busd[so['output']]: solph.Flow(variable_costs=0)},
conversion_factors={
self.busd[so['label'] + '_bus']: 1,
self.busd[so['input']]:
so['Electric Consumption'] *
(1 - so['Peripheral Losses']),
self.busd[so['output']]:
1 - so['Peripheral Losses']
}))
logging.info(' ' + 'Source created: ' + so['label']
+ ", Max Heat power output per year and m²: {:2.2f}".
format(numpy.sum(collectors_heat)) + ' kWh/(m²a)'
+ ", Irradiance on collector per year and m²: "
"{:2.2f}".format(numpy.sum(irradiance)) + ' kWh/(m²a)')
def __init__(self, nodes_data: dict, nodes: list, busd: dict,
time_series, weather_data):
self.nodes_sources = []
self.busd = busd.copy()
for i, so in nodes_data['sources'].iterrows():
if so['active']:
if so['technology'] == 'other':
self.commodity_source(so)
elif so['technology'] == 'photovoltaic':
self.pv_source(so, weather_data)
elif so['technology'] == 'windpower':
self.windpower_source(so, weather_data)
elif so['technology'] == 'timeseries':
self.timeseries_source(so, time_series)
elif so['technology'] in ['solar_thermal_flat_plate',
'concentrated_solar_power']:
self.solar_heat_source(so, weather_data)
for i in range(len(self.nodes_sources)):
nodes.append(self.nodes_sources[i])
class Sinks:
busd = None
nodes_sinks = [] | MIT License |
facebookresearch/nevergrad | nevergrad/parametrization/core.py | as_parameter | python | def as_parameter(param: tp.Any) -> Parameter:
if isinstance(param, Parameter):
return param
else:
return Constant(param) | Returns a Parameter from anything:
either the input if it is already a parameter, or a Constant if not
This is convenient for iterating over Parameter and other objects alike | https://github.com/facebookresearch/nevergrad/blob/1981997603e361b1fd5b5e2aeb8173c4eae6aef0/nevergrad/parametrization/core.py#L469-L477 | import uuid
import warnings
import numpy as np
import nevergrad.common.typing as tp
from nevergrad.common import errors
from . import utils
from ._layering import ValueProperty as ValueProperty
from ._layering import Layered as Layered
from ._layering import Level as Level
P = tp.TypeVar("P", bound="Parameter")
class Parameter(Layered):
_LAYER_LEVEL = Level.ROOT
value: ValueProperty[tp.Any, tp.Any] = ValueProperty()
def __init__(self) -> None:
super().__init__()
self._subobjects = utils.Subobjects(
self, base=Parameter, attribute="__dict__"
)
self.parents_uids: tp.List[str] = []
self.heritage: tp.Dict[tp.Hashable, tp.Any] = {"lineage": self.uid}
self.loss: tp.Optional[float] = None
self._losses: tp.Optional[np.ndarray] = None
self._dimension: tp.Optional[int] = None
self._random_state: tp.Optional[np.random.RandomState] = None
self._generation = 0
self._constraint_checkers: tp.List[tp.Callable[[tp.Any], tp.Union[bool, float]]] = []
self._name: tp.Optional[str] = None
self._frozen = False
self._meta: tp.Dict[tp.Hashable, tp.Any] = {}
self.function = utils.FunctionInfo()
@property
def descriptors(self) -> utils.DeprecatedDescriptors:
return utils.DeprecatedDescriptors(self)
@property
def losses(self) -> np.ndarray:
if self._losses is not None:
return self._losses
if self.loss is not None:
return np.array([self.loss], dtype=float)
raise RuntimeError("No loss was provided")
@property
def args(self) -> tp.Tuple[tp.Any, ...]:
return (self.value,)
@property
def kwargs(self) -> tp.Dict[str, tp.Any]:
return {}
@property
def dimension(self) -> int:
if self._dimension is None:
try:
self._dimension = self.get_standardized_data(reference=self).size
except errors.UnsupportedParameterOperationError:
self._dimension = 0
return self._dimension
def mutate(self) -> None:
self._check_frozen()
self._subobjects.apply("mutate")
self._layers[-1]._layered_mutate()
def _layered_mutate(self) -> None:
self.set_standardized_data(self.random_state.normal(size=self.dimension))
def sample(self: P) -> P:
self.random_state
child = self._layers[-1]._layered_sample()
if not isinstance(child, Parameter) and not isinstance(child, type(self)):
raise errors.NevergradRuntimeError("Unexpected sample return type")
child._set_parenthood(None)
return child
def recombine(self: P, *others: P) -> None:
if not others:
return
self._check_frozen()
assert all(isinstance(o, self.__class__) for o in others)
self._subobjects.apply("recombine", *others)
self._layers[-1]._layered_recombine(*others)
def get_standardized_data(self: P, *, reference: P) -> np.ndarray:
assert reference is None or isinstance(
reference, self.__class__
), f"Expected {type(self)} but got {type(reference)} as reference"
return self._internal_get_standardized_data(self if reference is None else reference)
def _internal_get_standardized_data(self: P, reference: P) -> np.ndarray:
raise errors.UnsupportedParameterOperationError(
f"Export to standardized data space is not implemented for {self.name}"
)
def set_standardized_data(self: P, data: tp.ArrayLike, *, reference: tp.Optional[P] = None) -> P:
sent_reference = self if reference is None else reference
assert isinstance(
sent_reference, self.__class__
), f"Expected {type(self)} but got {type(sent_reference)} as reference"
self._check_frozen()
del self.value
self._internal_set_standardized_data(np.array(data, copy=False), reference=sent_reference)
return self
def _internal_set_standardized_data(
self: P, data: np.ndarray, reference: P
) -> None:
if data.size:
raise errors.UnsupportedParameterOperationError(
f"Import from standardized data space is not implemented for {self.name}"
)
@property
def generation(self) -> int:
return self._generation
def get_value_hash(self) -> tp.Hashable:
val = self.value
if isinstance(val, (str, bytes, float, int)):
return val
elif isinstance(val, np.ndarray):
return val.tobytes()
else:
raise errors.UnsupportedParameterOperationError(
f"Value hash is not supported for object {self.name}"
)
def __repr__(self) -> str:
strings = [self.name]
if not callable(self.value):
strings.append(str(self.value))
return ":".join(strings)
def satisfies_constraints(self) -> bool:
inside = self._subobjects.apply("satisfies_constraints")
if not all(inside.values()):
return False
if not self._constraint_checkers:
return True
val = self.value
return all(utils.float_penalty(func(val)) <= 0 for func in self._constraint_checkers)
def register_cheap_constraint(
self,
func: tp.Union[tp.Callable[[tp.Any], bool], tp.Callable[[tp.Any], float]],
as_layer: bool = False,
) -> None:
if getattr(func, "__name__", "not lambda") == "<lambda>":
warnings.warn("Lambda as constraint is not advised because it may not be picklable.")
if not as_layer:
self._constraint_checkers.append(func)
else:
from nevergrad.ops.constraints import Constraint
import nevergrad as ng
compat_func = (
func
if not isinstance(self, ng.p.Instrumentation)
else utils._ConstraintCompatibilityFunction(func)
)
self.add_layer(Constraint(compat_func))
@property
def random_state(self) -> np.random.RandomState:
if self._random_state is None:
seed = np.random.randint(2 ** 32, dtype=np.uint32)
self._set_random_state(np.random.RandomState(seed))
assert self._random_state is not None
return self._random_state
@random_state.setter
def random_state(self, random_state: tp.Optional[np.random.RandomState]) -> None:
self._set_random_state(random_state)
def _set_random_state(self, random_state: tp.Optional[np.random.RandomState]) -> None:
self._random_state = random_state
self._subobjects.apply("_set_random_state", random_state)
def spawn_child(self: P, new_value: tp.Optional[tp.Any] = None) -> P:
self.random_state
child = self.copy()
child._set_parenthood(self)
if new_value is not None:
child.value = new_value
return child
def copy(self: P) -> P:
child = super().copy()
child.uid = uuid.uuid4().hex
child._frozen = False
child._subobjects = self._subobjects.new(child)
child._meta = {}
child.parents_uids = list(self.parents_uids)
child.heritage = dict(self.heritage)
child.loss = None
child._losses = None
child._constraint_checkers = list(self._constraint_checkers)
if self is not self._layers[0]:
raise errors.NevergradRuntimeError("Something has gone horribly wrong with the layers")
attribute = self._subobjects.attribute
container = getattr(child, attribute)
if attribute != "__dict__":
container = dict(container) if isinstance(container, dict) else list(container)
setattr(child, attribute, container)
for key, val in self._subobjects.items():
container[key] = val.copy()
del child.value
return child
def _set_parenthood(self, parent: tp.Optional["Parameter"]) -> None:
if parent is None:
self._generation = 0
self.heritage = dict(lineage=self.uid)
self.parents_uids = []
else:
self._generation = parent.generation + 1
self.parents_uids = [parent.uid]
self._subobjects.apply("_set_parenthood", parent)
def freeze(self) -> None:
self._frozen = True
self._subobjects.apply("freeze")
def _check_frozen(self) -> None:
if self._frozen and not isinstance(
self, Constant
):
raise RuntimeError(
f"Cannot modify frozen Parameter {self.name}, please spawn a child and modify it instead"
"(optimizers freeze the parametrization and all asked and told candidates to avoid border effects)"
)
self.random_state
self._subobjects.apply("_check_frozen")
class Constant(Parameter):
def __init__(self, value: tp.Any) -> None:
super().__init__()
if isinstance(value, Parameter) and not isinstance(self, MultiobjectiveReference):
raise TypeError("Only non-parameters can be wrapped in a Constant")
self._value = value
def _get_name(self) -> str:
return str(self._value)
def get_value_hash(self) -> tp.Hashable:
try:
return super().get_value_hash()
except errors.UnsupportedParameterOperationError:
return "#non-hashable-constant#"
def _layered_get_value(self) -> tp.Any:
return self._value
def _layered_set_value(self, value: tp.Any) -> None:
different = False
if isinstance(value, np.ndarray):
if not np.equal(value, self._value).all():
different = True
elif not (value == self._value or value is self._value):
different = True
if different:
raise ValueError(
f'Constant value can only be updated to the same value (in this case "{self._value}")'
)
def _layered_sample(self: P) -> P:
return self
def get_standardized_data(
self: P, *, reference: tp.Optional[P] = None
) -> np.ndarray:
return np.array([])
def spawn_child(self: P, new_value: tp.Optional[tp.Any] = None) -> P:
if new_value is not None:
self.value = new_value
return self
def recombine(self: P, *others: P) -> None:
pass
def mutate(self) -> None:
pass | MIT License |
cainmagi/mdnt | data/deprecated/h5py.py | H5SupSaver.dump | python | def dump(self, keyword, data):
if self.f is None:
raise OSError('Should not dump data before opening a file.')
self.f.create_dataset(keyword, data=data, **self.__kwargs)
if self.logver > 0:
print('Dump {0} into the file. The data shape is {1}.'.format(keyword, data.shape)) | Dump the dataset with a keyword into the file.
Arguments:
keyword: the keyword of the dumped dataset.
data: dataset, should be a numpy array. | https://github.com/cainmagi/mdnt/blob/4affd8a83698ce6786c04dddacdcf7415f8c5f90/data/deprecated/h5py.py#L73-L84 | import h5py
import numpy as np
import tensorflow as tf
import os
REMOVE_DEPRECATION = False
def depcatedInfo():
try:
raise DeprecationWarning('This library has been deprecated.')
except Exception as e:
if not REMOVE_DEPRECATION:
raise e
class H5SupSaver:
def __init__(self, fileName):
self.f = None
depcatedInfo()
self.logver = 0
self.__kwargs = dict()
self.open(fileName)
self.config(dtype='f')
def config(self, **kwargs):
logver = kwargs.pop('logver', None)
if logver is not None:
self.logver = logver
self.__kwargs.update(kwargs)
if self.logver > 0:
print('Current configuration is:', self.__kwargs) | MIT License |
tektoncd/experimental | sdk/python/tekton_pipeline/models/v1beta1_pipeline_list.py | V1beta1PipelineList.api_version | python | def api_version(self, api_version):
self._api_version = api_version | Sets the api_version of this V1beta1PipelineList.
APIVersion defines the versioned schema of this representation of an object. Servers should convert recognized schemas to the latest internal value, and may reject unrecognized values. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#resources # noqa: E501
:param api_version: The api_version of this V1beta1PipelineList. # noqa: E501
:type: str | https://github.com/tektoncd/experimental/blob/0ba4e7a2b9d45ed4accaecbb34dac006d665796a/sdk/python/tekton_pipeline/models/v1beta1_pipeline_list.py#L95-L104 | import pprint
import re
import six
from tekton_pipeline.configuration import Configuration
class V1beta1PipelineList(object):
"""
Attributes:
openapi_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
openapi_types = {
'api_version': 'str',
'items': 'list[V1beta1Pipeline]',
'kind': 'str',
'metadata': 'V1ListMeta'
}
attribute_map = {
'api_version': 'apiVersion',
'items': 'items',
'kind': 'kind',
'metadata': 'metadata'
}
def __init__(self, api_version=None, items=None, kind=None, metadata=None, local_vars_configuration=None):
if local_vars_configuration is None:
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._api_version = None
self._items = None
self._kind = None
self._metadata = None
self.discriminator = None
if api_version is not None:
self.api_version = api_version
self.items = items
if kind is not None:
self.kind = kind
if metadata is not None:
self.metadata = metadata
@property
def api_version(self):
return self._api_version
@api_version.setter | Apache License 2.0 |
qkaiser/cottontail | cottontail/rabbitmq_management.py | RabbitMQManagementClient.get_request | python | def get_request(self, path):
response = requests.get(
"{}://{}:{}/api/{}".format(self._scheme, self._host, self._port, path),
auth=(self._username, self._password),
verify=False,
timeout=5
)
if response.status_code == 200:
return response.json()
elif response.status_code == 401 or response.status_code == 403:
raise UnauthorizedAccessException(
"Authorization error: can't access /api/{}".format(path))
elif response.status_code == 404:
return None
else:
raise Exception("An error occured") | Wrapper for GET requests to the API.
Args:
path (str): REST path appended to /api
Returns:
HTTP response JSON object.
Raises:
UnauthorizedException | https://github.com/qkaiser/cottontail/blob/b7f5222959cf6229ef33d7369e5e8881e6181727/cottontail/rabbitmq_management.py#L60-L87 | try:
from urllib.parse import quote
except ImportError:
from urllib import quote
import requests
from requests.packages.urllib3.exceptions import InsecureRequestWarning
requests.packages.urllib3.disable_warnings(InsecureRequestWarning)
class UnauthorizedAccessException(Exception):
pass
class RabbitMQManagementClient(object):
def __init__(self, host, port=15672, username="guest", password="guest", ssl=False):
self._host = host
self._port = port
self._username = username
self._password = password
self._scheme = "https" if ssl else "http" | BSD 3-Clause New or Revised License |
nsls-ii/pyxrf | pyxrf/core/tests/test_quant_analysis.py | _create_file_with_ref_standards | python | def _create_file_with_ref_standards(*, wd):
sd = _standard_data_sample[0]
file_path = os.path.join(wd, ".pyxrf", "quantitative_standards.yaml")
standard_data = []
for n in range(2):
sd_copy = copy.deepcopy(sd)
sd_copy["serial"] += f"{n}"
sd_copy["name"] = f"Test reference standard #{sd_copy['serial']}"
d = sd_copy["compounds"]
total_density = 0
for c in d:
d[c] += np.random.rand() * 2 - 1
total_density += d[c]
sd_copy["density"] = total_density
standard_data.append(sd_copy)
save_xrf_standard_yaml_file(file_path=file_path, standard_data=standard_data)
img_list = []
eline_lists = [("Ni_K", "Ga_K", "S_K"), ("S_K", "Cr_K", "Ni_K")]
for elist in eline_lists:
img_list.append(gen_xrf_map_dict(elines=elist))
return file_path, img_list | r"""
Create a file with user-defined standards based on ``_standard_data_sample[0]``.
The file contains the descriptions of 2 standards with identical sets of elements/compounds
with slightly different densities.
The created file is placed at the standard default location ``<wd>/.pyxrf/quantiative_standards.yaml``.
Working directory (typically ``~``) is set to temporary directory for using with PyTest.
It is expected that the dataset will be processed using incident energy value 12.0 keV. | https://github.com/nsls-ii/pyxrf/blob/0aa4e175f541edfaa8f71daf54b54a07e4ab2b04/pyxrf/core/tests/test_quant_analysis.py#L727-L767 | import os
import pytest
import jsonschema
import copy
import numpy as np
import numpy.testing as npt
import time as ttime
import re
from pyxrf.core.utils import convert_time_from_nexus_string
from pyxrf.core.xrf_utils import validate_element_str, generate_eline_list, split_compound_mass
from pyxrf.core.quant_analysis import (
save_xrf_standard_yaml_file,
load_xrf_standard_yaml_file,
_xrf_standard_schema,
load_included_xrf_standard_yaml_file,
compute_standard_element_densities,
_xrf_quant_fluor_schema,
save_xrf_quant_fluor_json_file,
load_xrf_quant_fluor_json_file,
get_quant_fluor_data_dict,
fill_quant_fluor_data_dict,
prune_quant_fluor_data_dict,
set_quant_fluor_data_dict_optional,
set_quant_fluor_data_dict_time,
ParamQuantEstimation,
ParamQuantitativeAnalysis,
)
_standard_data_sample = [
{
"name": "Test Micromatter 411570",
"serial": "411570",
"description": "InSx 22.4 (In=16.0 S=6.4) / SrF2 20.6 / Cr 19.8 / Ni 19.2 / GaAs 19.1 (Ga=8.3 As=10.8)",
"compounds": {"In": 16.0, "S": 6.4, "SrF2": 20.6, "Cr": 19.8, "Ni": 19.2, "Ga": 8.3, "As": 10.8},
"density": 101.1,
},
{
"name": "Test Micromatter 411640",
"serial": "411640",
"description": "CeF3 21.1 / Au 20.6",
"compounds": {"CeF3": 21.1, "Au": 20.6}
},
]
@pytest.mark.parametrize("standard_data", [
_standard_data_sample,
[]
])
def test_save_xrf_standard_yaml_file1(tmp_path, standard_data):
yaml_path = ["yaml", "param", "file"]
file_name = "standard.yaml"
yaml_path = os.path.join(tmp_path, *yaml_path, file_name)
save_xrf_standard_yaml_file(yaml_path, standard_data)
data_loaded = load_xrf_standard_yaml_file(yaml_path)
assert data_loaded == standard_data, "Loaded data is not equal to the original data"
def test_save_xrf_standard_yaml_file2(tmp_path):
yaml_path = ["yaml", "param", "file"]
file_name = "standard.yaml"
yaml_path = os.path.join(tmp_path, *yaml_path, file_name)
standard_data = []
save_xrf_standard_yaml_file(yaml_path, standard_data)
with pytest.raises(IOError, match=f"File '{re.escape(yaml_path)}' already exists"):
save_xrf_standard_yaml_file(yaml_path, standard_data)
save_xrf_standard_yaml_file(yaml_path, standard_data, overwrite_existing=True)
def test_save_xrf_standard_yaml_file3(tmp_path):
yaml_path = ["yaml", "param", "file"]
file_name = "standard.yaml"
yaml_path = os.path.join(tmp_path, *yaml_path, file_name)
standard_data = copy.deepcopy(_standard_data_sample)
standard_data[0]["density"] = 50.0
save_xrf_standard_yaml_file(yaml_path, standard_data)
with pytest.raises(RuntimeError, match="Sum of areal densities does not match total density"):
load_xrf_standard_yaml_file(yaml_path)
def test_load_xrf_standard_yaml_file1(tmp_path):
file_name = "standard.yaml"
yaml_path = os.path.join(tmp_path, file_name)
with pytest.raises(IOError, match=f"File '{re.escape(yaml_path)}' does not exist"):
load_xrf_standard_yaml_file(yaml_path)
yaml_path = ["yaml", "param", "file"]
yaml_path = os.path.join(tmp_path, *yaml_path, file_name)
with pytest.raises(IOError, match=f"File '{re.escape(yaml_path)}' does not exist"):
load_xrf_standard_yaml_file(yaml_path)
def test_load_xrf_standard_yaml_file2(tmp_path):
yaml_path = ["yaml", "param", "file"]
file_name = "standard.yaml"
yaml_path = os.path.join(tmp_path, *yaml_path, file_name)
standard_data = copy.deepcopy(_standard_data_sample)
for v in standard_data:
v["name"] = 50.36
save_xrf_standard_yaml_file(yaml_path, standard_data)
with pytest.raises(jsonschema.ValidationError):
load_xrf_standard_yaml_file(yaml_path)
load_xrf_standard_yaml_file(yaml_path, schema=None)
schema = copy.deepcopy(_xrf_standard_schema)
schema["properties"]["name"] = {"type": ["string", "number"]}
standard_data_out = load_xrf_standard_yaml_file(yaml_path, schema=schema)
assert standard_data_out == standard_data, "Loaded data is different from the original"
def test_load_included_xrf_standard_yaml_file():
data = load_included_xrf_standard_yaml_file()
assert len(data) > 1, "Standard YAML file can not be read"
def test_compute_standard_element_densities():
standard_data = _standard_data_sample
for data in standard_data:
if "density" in data:
total_density = data["density"]
else:
total_density = np.sum(list(data["compounds"].values()))
element_densities = compute_standard_element_densities(data["compounds"])
assert all(
[validate_element_str(_) for _ in element_densities.keys()]
), f"Some of the elements in the list {list(element_densities.keys())} have invalid representation"
npt.assert_almost_equal(
np.sum(list(element_densities.values())),
total_density,
err_msg="The sum of element densities and the total sum don't match",
)
_xrf_standard_fluor_sample = {
"name": "Hypothetical sample #41157",
"serial": "41157",
"description": "Name of hypothetical sample",
"element_lines": {
"In": {"density": 16.0, "fluorescence": 1.5453452},
"S_K": {"density": 6.4, "fluorescence": 2.0344345},
"Sr_L": {"density": 20.6, "fluorescence": 0.93452365},
"Au_M": {"density": 10.4, "fluorescence": 0.734234},
"Cr_Ka": {"density": 19.8, "fluorescence": 0.7435234},
"Ni_Kb": {"density": 19.2, "fluorescence": 0.7435234},
"Ga_Ka1": {"density": 8.3, "fluorescence": 0.7435234},
"Mg_Ka2": {"density": 9.6, "fluorescence": None},
},
"incident_energy": 10.5,
"detector_channel": "sum",
"scaler_name": "i0",
"distance_to_sample": 50.6,
"creation_time_local": "2020-01-10T11:50:39+00:00",
"source_scan_id": 92276,
"source_scan_uid": "f07e3065-ab92-4b20-a702-ef61ed164dbc",
}
def _get_data_and_json_path(tmp_path):
json_path = ["json", "param", "file"]
file_name = "standard.yaml"
json_path = os.path.join(tmp_path, *json_path, file_name)
data = _xrf_standard_fluor_sample
return data, json_path
def test_save_xrf_quant_fluor_json_file1(tmp_path):
data, json_path = _get_data_and_json_path(tmp_path)
save_xrf_quant_fluor_json_file(json_path, data)
data_loaded = load_xrf_quant_fluor_json_file(json_path)
assert data_loaded == data, "Loaded data is not equal to the original data"
def test_save_xrf_quant_fluor_json_file2(tmp_path):
data, json_path = _get_data_and_json_path(tmp_path)
save_xrf_quant_fluor_json_file(json_path, data)
with pytest.raises(IOError, match=f"File '{re.escape(json_path)}' already exists"):
save_xrf_quant_fluor_json_file(json_path, data)
save_xrf_quant_fluor_json_file(json_path, data, overwrite_existing=True)
def test_save_xrf_quant_fluor_json_file3(tmp_path):
data, json_path = _get_data_and_json_path(tmp_path)
data = copy.deepcopy(data)
data["incident_energy"] = "incident_energy"
with pytest.raises(jsonschema.ValidationError):
save_xrf_quant_fluor_json_file(json_path, data)
def test_save_xrf_quant_fluor_json_file4(tmp_path):
data, json_path = _get_data_and_json_path(tmp_path)
data = copy.deepcopy(data)
data["detector_channel"] = None
data["scaler_name"] = None
data["distance_to_sample"] = None
save_xrf_quant_fluor_json_file(json_path, data)
data_loaded = load_xrf_quant_fluor_json_file(json_path)
assert data_loaded == data, "Loaded data is not equal to the original data"
def test_load_xrf_quant_fluor_json_file1(tmp_path):
_, json_path = _get_data_and_json_path(tmp_path)
with pytest.raises(IOError, match=f"File '{re.escape(json_path)}' does not exist"):
load_xrf_quant_fluor_json_file(json_path)
def test_load_xrf_quant_fluor_json_file2(tmp_path):
data, json_path = _get_data_and_json_path(tmp_path)
save_xrf_quant_fluor_json_file(json_path, data)
schema = copy.deepcopy(_xrf_quant_fluor_schema)
schema["properties"]["scaler_name"] = {"type": "number"}
with pytest.raises(jsonschema.ValidationError):
load_xrf_quant_fluor_json_file(json_path, schema=schema)
def test_get_quant_fluor_data_dict():
for standard_data in _standard_data_sample:
quant_fluor_data_dict = get_quant_fluor_data_dict(standard_data, incident_energy=12.0)
jsonschema.validate(instance=quant_fluor_data_dict, schema=_xrf_quant_fluor_schema)
assert quant_fluor_data_dict["name"] == standard_data["name"], "Dictionary element 'name' is incorrect"
assert (
quant_fluor_data_dict["serial"] == standard_data["serial"]
), "Dictionary element 'serial' is incorrect"
assert (
quant_fluor_data_dict["description"] == standard_data["description"]
), "Dictionary element 'description' is incorrect"
eline_set = set()
mass_sum_expected = 0
for cmpd, mass in standard_data["compounds"].items():
em_dict = split_compound_mass(cmpd, mass)
for el, ms in em_dict.items():
elines = generate_eline_list([el], incident_energy=12.0)
n_elines = len(elines)
if n_elines:
mass_sum_expected += n_elines * ms
eline_set.update(elines)
eline_out_list = list(quant_fluor_data_dict["element_lines"].keys())
assert len(eline_out_list) == len(eline_set), "The number of emission lines is not as expected"
assert (
set(eline_out_list) == eline_set
), "Generated object contains emission lines that are different from expected"
mass_sum = sum([_["density"] for _ in quant_fluor_data_dict["element_lines"].values()])
assert (
mass_sum == mass_sum_expected
), "The total mass (density) of the components is different from expected"
def gen_xrf_map_dict(nx=10, ny=5, elines=["S_K", "Au_M", "Fe_K"]):
img = {}
for e in elines:
map = np.random.rand(ny, nx) * np.random.rand() * 10
img[e] = map
map_sclr = np.ones(shape=(ny, nx), dtype=float) * np.random.rand() * 2
img["sclr"] = map_sclr
return img
@pytest.mark.parametrize("map_dims", [
{"nx": 10, "ny": 5},
{"nx": 1, "ny": 5},
{"nx": 2, "ny": 5},
{"nx": 3, "ny": 5},
{"nx": 10, "ny": 1},
{"nx": 10, "ny": 2},
{"nx": 10, "ny": 3},
])
def test_fill_quant_fluor_data_dict(map_dims):
fluor_standard = copy.deepcopy(_xrf_standard_fluor_sample)
nx = map_dims["nx"]
ny = map_dims["ny"]
img = gen_xrf_map_dict(nx=nx, ny=ny)
if nx < 3:
nx_min, nx_max = 0, nx
else:
nx_min, nx_max = 1, -1
if ny < 3:
ny_min, ny_max = 0, nx
else:
ny_min, ny_max = 1, -1
map_S_K_fluor = np.mean(img["S_K"][ny_min:ny_max, nx_min:nx_max])
map_Au_M_fluor = np.mean(img["Au_M"][ny_min:ny_max, nx_min:nx_max])
v_sclr = np.mean(img["sclr"][ny_min:ny_max, nx_min:nx_max])
fill_quant_fluor_data_dict(fluor_standard, xrf_map_dict=img, scaler_name="sclr")
npt.assert_almost_equal(
fluor_standard["element_lines"]["S_K"]["fluorescence"],
map_S_K_fluor / v_sclr,
err_msg="Fluorescence of 'S_K' is estimated incorrectly",
)
npt.assert_almost_equal(
fluor_standard["element_lines"]["Au_M"]["fluorescence"],
map_Au_M_fluor / v_sclr,
err_msg="Fluorescence of 'Au_M' is estimated incorrectly",
)
for eline, param in fluor_standard["element_lines"].items():
assert (eline in img) or (
param["fluorescence"] is None
), f"Fluorescence line {eline} is not present in the dataset and it was not reset to None"
fill_quant_fluor_data_dict(fluor_standard, xrf_map_dict=img, scaler_name="abc")
npt.assert_almost_equal(
fluor_standard["element_lines"]["S_K"]["fluorescence"],
map_S_K_fluor,
err_msg="Fluorescence of 'S_K' is estimated incorrectly",
)
fill_quant_fluor_data_dict(fluor_standard, xrf_map_dict=img, scaler_name=None)
npt.assert_almost_equal(
fluor_standard["element_lines"]["S_K"]["fluorescence"],
map_S_K_fluor,
err_msg="Fluorescence of 'S_K' is estimated incorrectly",
)
def test_prune_quant_fluor_data_dict():
fluor_standard = copy.deepcopy(_xrf_standard_fluor_sample)
elines_not_none = []
for eline, info in fluor_standard["element_lines"].items():
set_to_none = np.random.rand() < 0.5
if set_to_none:
info["fluorescence"] = None
if info["fluorescence"] is not None:
elines_not_none.append(eline)
fluor_standard_pruned = prune_quant_fluor_data_dict(fluor_standard)
for eline, info in fluor_standard_pruned["element_lines"].items():
assert eline in elines_not_none, f"Emission line {eline} should have been removed from the dictionary"
assert info["fluorescence"] is not None, f"Emission line {eline} has 'fluorescence' set to None"
def test_set_quant_fluor_data_dict_optional_1():
fluor_standard = copy.deepcopy(_xrf_standard_fluor_sample)
scan_id = 45378
scan_uid = "abc-12345"
set_quant_fluor_data_dict_optional(fluor_standard, scan_id=scan_id, scan_uid=scan_uid)
assert fluor_standard["source_scan_id"] == scan_id, "Scan ID is set incorrectly"
assert fluor_standard["source_scan_uid"] == scan_uid, "Scan UID is set incorrectly"
t = fluor_standard["creation_time_local"]
assert t is not None, "Time is not set"
t = convert_time_from_nexus_string(t)
t = ttime.mktime(t)
t_current = ttime.mktime(ttime.localtime())
assert abs(t_current - t) < 5.0, "Time is set incorrectly"
scan_id2 = 45346
scan_id2_str = f"{scan_id2}"
set_quant_fluor_data_dict_optional(fluor_standard, scan_id=scan_id2_str)
assert fluor_standard["source_scan_id"] == scan_id2, "Scan ID is set incorrectly"
def test_set_quant_fluor_data_dict_optional_2():
fluor_standard = copy.deepcopy(_xrf_standard_fluor_sample)
with pytest.raises(RuntimeError, match="Parameter 'scan_id' must be integer or a string representing integer"):
set_quant_fluor_data_dict_optional(fluor_standard, scan_id="abc_34g")
with pytest.raises(RuntimeError, match="Parameter 'scan_id' must be integer or a string representing integer"):
set_quant_fluor_data_dict_optional(fluor_standard, scan_id=[1, 5, 14])
with pytest.raises(RuntimeError, match="Parameter 'scan_uid' must be a string representing scan UID"):
set_quant_fluor_data_dict_optional(fluor_standard, scan_uid=[1, 5, 14])
def test_set_quant_fluor_data_dict_time():
fluor_standard = copy.deepcopy(_xrf_standard_fluor_sample)
set_quant_fluor_data_dict_time(fluor_standard)
t = fluor_standard["creation_time_local"]
assert t is not None, "Time is not set"
t = convert_time_from_nexus_string(t)
t = ttime.mktime(t)
t_current = ttime.mktime(ttime.localtime())
assert abs(t_current - t) < 5.0, "Time is set incorrectly"
def test_ParamQuantEstimation_1(tmp_path):
home_dir = tmp_path
config_dir = ".pyxrf"
standards_fln = "quantitative_standards.yaml"
pqe = ParamQuantEstimation(home_dir=home_dir)
file_path = os.path.join(home_dir, config_dir, standards_fln)
assert os.path.isfile(
file_path
), f"Empty file for user-defined reference standards '{file_path}' was not created"
pqe.load_standards()
assert pqe.standards_built_in is not None, "Failed to load built-in standards"
assert pqe.standards_custom is not None, "Failed to load user-defined standards"
pqe.clear_standards()
assert pqe.standards_built_in is None, "Failed to clear loaded built-in standards"
assert pqe.standards_custom is None, "Failed to clear loaded user-defined standards"
def test_ParamQuantEstimation_2(tmp_path):
standard_data = _standard_data_sample
home_dir = tmp_path
config_dir = ".pyxrf"
standards_fln = "quantitative_standards.yaml"
file_path = os.path.join(home_dir, config_dir, standards_fln)
save_xrf_standard_yaml_file(file_path, standard_data)
pqe = ParamQuantEstimation(home_dir=home_dir)
pqe.load_standards()
assert pqe.standards_built_in is not None, "Failed to load built-in standards"
assert len(pqe.standards_built_in) > 0, "The number of loaded built-in standards is ZERO"
assert pqe.standards_custom is not None, "Failed to load user-defined standards"
assert len(pqe.standards_custom) > 0, "The number of loaded user-defined standards is ZERO"
for st in pqe.standards_custom:
serial = st["serial"]
assert pqe._find_standard_custom(st), f"Standard {serial} was not found in user-defined list"
assert not pqe._find_standard_built_in(st), f"Standard {serial} was found in built-in list"
assert pqe.find_standard(st), f"Standard {serial} was not found"
assert pqe.find_standard(st["name"], key="name"), f"Failed to find standard {serial} by name"
assert pqe.find_standard(st["serial"], key="serial"), f"Failed to find standard {serial} by serial number"
assert pqe.is_standard_custom(st), f"Standard {serial} was not identified as user-defined"
pqe.set_selected_standard(st)
assert pqe.standard_selected == st, f"Can't select standard {serial}"
for st in pqe.standards_built_in:
serial = st["serial"]
assert not pqe._find_standard_custom(st), f"Standard {serial} was found in user-defined list"
assert pqe._find_standard_built_in(st), f"Standard {serial} was not found in built-in list"
assert pqe.find_standard(st), f"Standard {serial} was not found"
assert pqe.find_standard(st["name"], key="name"), f"Failed to find standard {serial} by name"
assert pqe.find_standard(st["serial"], key="serial"), f"Failed to find standard {serial} by serial number"
assert not pqe.is_standard_custom(st), f"Standard {serial} was identified as user-defined"
pqe.set_selected_standard(st)
assert pqe.standard_selected == st, f"Can't select standard {serial}"
st = {"serial": "09876", "name": "Some name"}
st_selected = pqe.set_selected_standard(st)
assert st_selected == pqe.standard_selected, "Return value of 'set_selected_standard' is incorrect"
assert st_selected == pqe.standards_custom[0], "Incorrect standard is selected"
pqe.set_selected_standard()
assert st_selected == pqe.standards_custom[0], "Incorrect standard is selected"
pqe.standards_custom = None
pqe.set_selected_standard(st)
assert pqe.standard_selected == pqe.standards_built_in[0], "Incorrect standard is selected"
pqe.standards_built_in = None
pqe.set_selected_standard(st)
assert pqe.standard_selected is None, "Incorrect standard is selected"
def test_ParamQuantEstimation_3(tmp_path):
standard_data = _standard_data_sample
home_dir = tmp_path
config_dir = ".pyxrf"
standards_fln = "quantitative_standards.yaml"
file_path = os.path.join(home_dir, config_dir, standards_fln)
save_xrf_standard_yaml_file(file_path, standard_data)
pqe = ParamQuantEstimation(home_dir=home_dir)
pqe.load_standards()
incident_energy = 12.0
img = gen_xrf_map_dict()
pqe.set_selected_standard()
pqe.gen_fluorescence_data_dict(incident_energy=incident_energy)
scaler_name = "sclr"
pqe.fill_fluorescence_data_dict(xrf_map_dict=img, scaler_name=scaler_name)
qfdd = get_quant_fluor_data_dict(standard_data[0], incident_energy)
fill_quant_fluor_data_dict(qfdd, xrf_map_dict=img, scaler_name=scaler_name)
assert (
pqe.fluorescence_data_dict == qfdd
), "The filled fluorescence data dictionary does not match the expected"
pview, msg_warnings = pqe.get_fluorescence_data_dict_text_preview()
assert len(msg_warnings), "Warning is not found in preview"
pview, msg_warnings = pqe.get_fluorescence_data_dict_text_preview(enable_warnings=False)
assert not len(msg_warnings), "Warnings are disabled, but still generated"
pqe.set_detector_channel_in_data_dict(detector_channel="sum")
assert pqe.fluorescence_data_dict["detector_channel"] == "sum", "Detector channel was not set correctly"
distance_to_sample = 2.5
pqe.set_distance_to_sample_in_data_dict(distance_to_sample=distance_to_sample)
assert (
pqe.fluorescence_data_dict["distance_to_sample"] == distance_to_sample
), "Distance-to-sample was not set correctly"
scan_id = 65476
scan_uid = "abcdef-12345678"
qfdd = copy.deepcopy(pqe.fluorescence_data_dict)
pqe.set_optional_parameters(scan_id=scan_id, scan_uid=scan_uid)
set_quant_fluor_data_dict_optional(qfdd, scan_id=scan_id, scan_uid=scan_uid)
qfdd["creation_time_local"] = pqe.fluorescence_data_dict["creation_time_local"]
assert pqe.fluorescence_data_dict == qfdd, "Optional parameters are not set correctly"
pview, msg_warnings = pqe.get_fluorescence_data_dict_text_preview()
assert not len(msg_warnings), "Preview is expected to contain no warnings"
fln_suggested = pqe.get_suggested_json_fln()
assert (
f"_{pqe.fluorescence_data_dict['serial']}." in fln_suggested
), f"Serial of the reference is not found in the suggested file name {fln_suggested}"
file_path = os.path.join(tmp_path, fln_suggested)
pqe.save_fluorescence_data_dict(file_path=file_path)
qfdd = load_xrf_quant_fluor_json_file(file_path=file_path)
assert qfdd == prune_quant_fluor_data_dict(
pqe.fluorescence_data_dict
), "Error occurred while saving and loading calibration data dictionary. Dictionaries don't match" | BSD 3-Clause New or Revised License |
pyconll/pyconll | pyconll/tree/_treebuilder.py | TreeBuilder._assert_initialization_status | python | def _assert_initialization_status(self) -> None:
if self.root is None:
raise ValueError(
'The TreeBuilder has not created a root for the Tree yet') | Asserts the initialization invariant on the root of this builder.
Raises:
ValueError: If the TreeBuilder's root has not been initialized. | https://github.com/pyconll/pyconll/blob/a69b1bfb884aab7b449e19a7cc8850dcf7e985c0/pyconll/tree/_treebuilder.py#L203-L212 | from typing import Any, Generic, TypeVar
from pyconll.tree.tree import Tree
T = TypeVar('T')
class TreeBuilder(Generic[T]):
def __init__(self) -> None:
self.root: Any = None
self.current: Any = None
self.constructed: bool = False
def create_root(self, data: T) -> None:
self.root = Tree(data)
self.current = self.root
def move_to_parent(self) -> None:
self._assert_initialization_status()
if self.current is self.root:
raise ValueError('Currently at root. Cannot move to parent')
self.current = self.current.parent
def move_to_child(self, i: int) -> None:
self._assert_initialization_status()
try:
self.current = self.current[i]
except IndexError as e:
raise IndexError(
'{}-th child is out of range. There are {} children on this node'
.format(i, len(self.current))) from e
def move_to_root(self) -> None:
self._assert_initialization_status()
self.current = self.root
def set_data(self, data: T) -> None:
self._assert_initialization_status()
self._copy_if_necessary()
self.current._data = data
def remove_child(self, i: int) -> None:
self._assert_initialization_status()
self._copy_if_necessary()
try:
del self.current._children[i]
except IndexError as e:
raise IndexError(
'{}-th child is out of range. There are {} children on this node'
.format(i, len(self.current))) from e
def add_child(self, data: T, move: bool = False) -> None:
self._assert_initialization_status()
self._copy_if_necessary()
child: Tree[T] = Tree(data)
child._parent = self.current
self.current._children.append(child)
if move:
l = len(self.current)
self.move_to_child(l - 1)
def build(self) -> Tree[T]:
self._assert_initialization_status()
self.constructed = True
return self.root
def _copy_if_necessary(self) -> None:
if self.constructed:
self._copy()
self.constructed = False
def _copy(self) -> None:
new_root: Tree[T] = Tree(self.root.data)
new_current = None
if self.current is self.root:
new_current = new_root
queue = [(new_root, self.root._children)]
while queue:
new_parent, children = queue.pop()
new_children = []
for child in children:
new_child: Tree[T] = Tree(child.data)
new_child._parent = new_parent
new_children.append(new_child)
queue.append((new_child, child._children))
if self.current is child:
new_current = new_child
new_parent._children = new_children
self.root = new_root
self.current = new_current | MIT License |
merll/docker-map | dockermap/map/client.py | MappingDockerClient.restart | python | def restart(self, container, instances=None, map_name=None, **kwargs):
return self.run_actions('restart', container, instances=instances, map_name=map_name, **kwargs) | Restarts instances for a container configuration.
:param container: Container name.
:type container: unicode | str
:param instances: Instance names to stop. If not specified, will restart all instances as specified in the
configuration (or just one default instance).
:type instances: collections.Iterable[unicode | str | NoneType]
:param map_name: Container map name. Optional - if not provided the default map is used.
:type map_name: unicode | str
:param kwargs: Additional kwargs. If multiple actions are resulting from this, they will only be applied to
the main container restart.
:return: Return values of restarted containers.
:rtype: list[dockermap.map.runner.ActionOutput] | https://github.com/merll/docker-map/blob/54e325595fc0b6b9d154dacc790a222f957895da/dockermap/map/client.py#L284-L300 | from __future__ import unicode_literals
import logging
import sys
from ..exceptions import PartialResultsError
from .action import simple, script, update
from .config.client import ClientConfiguration
from .config.main import ContainerMap
from .config.utils import get_map_config_ids
from .exceptions import ActionException, ActionRunnerException
from .policy.base import BasePolicy
from .runner.base import DockerClientRunner
from .state.base import (SingleStateGenerator, DependencyStateGenerator, DependentStateGenerator,
ImageDependencyStateGenerator)
from .state.update import UpdateStateGenerator
log = logging.getLogger(__name__)
def _set_forced_update_ids(kwargs, maps, default_map_name, default_instances):
value = kwargs.pop('force_update', None)
if not value:
return
config_ids = get_map_config_ids(value, maps, default_map_name, default_instances)
if config_ids:
kwargs['force_update'] = set(config_ids)
class MappingDockerClient(object):
configuration_class = ClientConfiguration
policy_class = BasePolicy
generators = {
'create': (DependencyStateGenerator, simple.CreateActionGenerator),
'start': (DependencyStateGenerator, simple.StartActionGenerator),
'restart': (SingleStateGenerator, simple.RestartActionGenerator),
'stop': (DependentStateGenerator, simple.StopActionGenerator),
'remove': (DependentStateGenerator, simple.RemoveActionGenerator),
'startup': (DependencyStateGenerator, simple.StartupActionGenerator),
'shutdown': (DependentStateGenerator, simple.ShutdownActionGenerator),
'update': (UpdateStateGenerator, update.UpdateActionGenerator),
'script': (DependencyStateGenerator, script.ScriptActionGenerator),
'signal': (SingleStateGenerator, simple.SignalActionGenerator),
'pull_images': (ImageDependencyStateGenerator, simple.ImagePullActionGenerator),
}
runner_class = DockerClientRunner
def __init__(self, container_maps=None, docker_client=None, clients=None,
map_defaults=None, option_defaults=None):
if container_maps:
if isinstance(container_maps, ContainerMap):
self._default_map = container_maps.name
self._maps = {container_maps.name: container_maps}
elif isinstance(container_maps, (list, tuple)):
self._default_map = None
self._maps = {c_map.name: c_map for c_map in container_maps}
elif isinstance(container_maps, dict):
self._default_map = None
self._maps = container_maps
else:
raise ValueError("Unexpected type of 'container_maps' argument: {0}".format(type(container_maps).__name__))
else:
self._default_map = None
self._maps = {}
if clients and isinstance(clients, (list, tuple)):
self._clients = dict(clients)
else:
self._clients = clients or {}
if docker_client is not None:
if isinstance(docker_client, ClientConfiguration):
default_client = docker_client
else:
default_client = self.configuration_class.from_client(docker_client)
self._clients[self.policy_class.default_client_name] = default_client
self._map_defaults = map_defaults or {}
self._option_defaults = option_defaults or {}
self._policy = None
def get_policy(self):
if not self._policy:
self._policy = self.policy_class(self._maps, self._clients,
self._map_defaults, self._option_defaults)
return self._policy
def get_state_generator(self, action_name, policy, kwargs):
state_generator_cls = self.generators[action_name][0]
state_generator = state_generator_cls(policy, kwargs)
return state_generator
def get_action_generator(self, action_name, policy, kwargs):
action_generator_cls = self.generators[action_name][1]
action_generator = action_generator_cls(policy, kwargs)
return action_generator
def get_runner(self, policy, kwargs):
return self.runner_class(policy, kwargs)
def get_states(self, action_name, config_name, instances=None, map_name=None, **kwargs):
policy = self.get_policy()
_set_forced_update_ids(kwargs, policy.container_maps, map_name or self._default_map, instances)
state_generator = self.get_state_generator(action_name, policy, kwargs)
log.debug("Remaining kwargs passed to client actions: %s", kwargs)
config_ids = get_map_config_ids(config_name, policy.container_maps, map_name or self._default_map,
instances)
log.debug("Generating states for configurations: %s", config_ids)
return state_generator.get_states(config_ids)
def get_actions(self, action_name, config_name, instances=None, map_name=None, **kwargs):
policy = self.get_policy()
action_generator = self.get_action_generator(action_name, policy, kwargs)
for state in self.get_states(action_name, config_name, instances=instances, map_name=map_name, **kwargs):
log.debug("Evaluating state: %s.", state)
actions = action_generator.get_state_actions(state, **kwargs)
if actions:
log.debug("Running actions: %s", actions)
yield actions
else:
log.debug("No actions returned.")
def run_actions(self, action_name, config_name, instances=None, map_name=None, **kwargs):
policy = self.get_policy()
results = []
runner = self.get_runner(policy, kwargs)
for action_list in self.get_actions(action_name, config_name, instances, map_name, **kwargs):
try:
for res in runner.run_actions(action_list):
results.append(res)
except ActionException as ae:
raise ActionRunnerException.from_action_exception(ae, results)
except:
exc_info = sys.exc_info()
raise PartialResultsError(exc_info, results)
return results
def create(self, container, instances=None, map_name=None, **kwargs):
return self.run_actions('create', container, instances=instances, map_name=map_name, **kwargs)
def start(self, container, instances=None, map_name=None, **kwargs):
return self.run_actions('start', container, instances=instances, map_name=map_name, **kwargs) | MIT License |
pyannote/pyannote-database | pyannote/database/protocol/speaker_recognition.py | SpeakerRecognitionProtocol.train | python | def train(self, yield_name=False):
generator = self.trn_iter()
for name, item in generator:
if yield_name:
yield name, self.preprocess(item)
else:
yield self.preprocess(item) | Iterate over the training set
This will yield dictionaries with the followings keys:
* database: str
unique database identifier
* uri: str
unique recording identifier
* channel: int
index of resource channel to use
* speaker: str
unique speaker identifier
as well as keys coming from the provided preprocessors.
Usage
-----
>>> for item in protocol.train():
... uri = item['uri']
... channel = item['channel']
... speaker = item['speaker'] | https://github.com/pyannote/pyannote-database/blob/7391b48e70f087dd963776d37257321bed1e313a/pyannote/database/protocol/speaker_recognition.py#L95-L125 | from .protocol import Protocol
class SpeakerRecognitionProtocol(Protocol):
def trn_iter(self):
raise NotImplementedError(
"Custom speaker recognition protocol " 'should implement "trn_iter".'
)
def trn_enroll_iter(self):
raise NotImplementedError(
"Custom speaker recognition protocol " 'should implement "trn_enroll_iter".'
)
def trn_test_iter(self):
raise NotImplementedError(
"Custom speaker recognition protocol " 'should implement "trn_test_iter".'
)
def trn_keys(self):
raise NotImplementedError(
"Custom speaker recognition protocol " 'should implement "trn_keys".'
)
def dev_enroll_iter(self):
raise NotImplementedError(
"Custom speaker recognition protocol " 'should implement "dev_enroll_iter".'
)
def dev_test_iter(self):
raise NotImplementedError(
"Custom speaker recognition protocol " 'should implement "dev_test_iter".'
)
def dev_keys(self):
raise NotImplementedError(
"Custom speaker recognition protocol " 'should implement "dev_keys".'
)
def tst_enroll_iter(self):
raise NotImplementedError(
"Custom speaker recognition protocol " 'should implement "tst_enroll_iter".'
)
def tst_test_iter(self):
raise NotImplementedError(
"Custom speaker recognition protocol " 'should implement "tst_test_iter".'
)
def tst_keys(self):
raise NotImplementedError(
"Custom speaker recognition protocol " 'should implement tst_keys".'
) | MIT License |
chaffelson/whoville | whoville/cloudbreak/models/rds_config_response.py | RDSConfigResponse.cluster_names | python | def cluster_names(self, cluster_names):
self._cluster_names = cluster_names | Sets the cluster_names of this RDSConfigResponse.
list of clusters which use config
:param cluster_names: The cluster_names of this RDSConfigResponse.
:type: list[str] | https://github.com/chaffelson/whoville/blob/f71fda629c9fd50d0a482120165ea5abcc754522/whoville/cloudbreak/models/rds_config_response.py#L284-L293 | from pprint import pformat
from six import iteritems
import re
class RDSConfigResponse(object):
"""
Attributes:
swagger_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
swagger_types = {
'name': 'str',
'connection_url': 'str',
'type': 'str',
'connector_jar_url': 'str',
'id': 'int',
'creation_date': 'int',
'public_in_account': 'bool',
'cluster_names': 'list[str]',
'stack_version': 'str',
'database_engine': 'str',
'connection_driver': 'str',
'database_engine_display_name': 'str',
'workspace': 'WorkspaceResourceResponse'
}
attribute_map = {
'name': 'name',
'connection_url': 'connectionURL',
'type': 'type',
'connector_jar_url': 'connectorJarUrl',
'id': 'id',
'creation_date': 'creationDate',
'public_in_account': 'publicInAccount',
'cluster_names': 'clusterNames',
'stack_version': 'stackVersion',
'database_engine': 'databaseEngine',
'connection_driver': 'connectionDriver',
'database_engine_display_name': 'databaseEngineDisplayName',
'workspace': 'workspace'
}
def __init__(self, name=None, connection_url=None, type=None, connector_jar_url=None, id=None, creation_date=None, public_in_account=False, cluster_names=None, stack_version=None, database_engine=None, connection_driver=None, database_engine_display_name=None, workspace=None):
self._name = None
self._connection_url = None
self._type = None
self._connector_jar_url = None
self._id = None
self._creation_date = None
self._public_in_account = None
self._cluster_names = None
self._stack_version = None
self._database_engine = None
self._connection_driver = None
self._database_engine_display_name = None
self._workspace = None
self.name = name
self.connection_url = connection_url
self.type = type
if connector_jar_url is not None:
self.connector_jar_url = connector_jar_url
if id is not None:
self.id = id
if creation_date is not None:
self.creation_date = creation_date
if public_in_account is not None:
self.public_in_account = public_in_account
if cluster_names is not None:
self.cluster_names = cluster_names
if stack_version is not None:
self.stack_version = stack_version
self.database_engine = database_engine
self.connection_driver = connection_driver
self.database_engine_display_name = database_engine_display_name
if workspace is not None:
self.workspace = workspace
@property
def name(self):
return self._name
@name.setter
def name(self, name):
if name is None:
raise ValueError("Invalid value for `name`, must not be `None`")
self._name = name
@property
def connection_url(self):
return self._connection_url
@connection_url.setter
def connection_url(self, connection_url):
if connection_url is None:
raise ValueError("Invalid value for `connection_url`, must not be `None`")
self._connection_url = connection_url
@property
def type(self):
return self._type
@type.setter
def type(self, type):
if type is None:
raise ValueError("Invalid value for `type`, must not be `None`")
self._type = type
@property
def connector_jar_url(self):
return self._connector_jar_url
@connector_jar_url.setter
def connector_jar_url(self, connector_jar_url):
self._connector_jar_url = connector_jar_url
@property
def id(self):
return self._id
@id.setter
def id(self, id):
self._id = id
@property
def creation_date(self):
return self._creation_date
@creation_date.setter
def creation_date(self, creation_date):
self._creation_date = creation_date
@property
def public_in_account(self):
return self._public_in_account
@public_in_account.setter
def public_in_account(self, public_in_account):
self._public_in_account = public_in_account
@property
def cluster_names(self):
return self._cluster_names
@cluster_names.setter | Apache License 2.0 |
smartelect/smartelect | civil_registry/tests/factories.py | CitizenFactory._setup_next_sequence | python | def _setup_next_sequence(cls):
return 1 | Set up an initial sequence value for Sequence attributes.
Returns:
int: the first available ID to use for instances of this factory.
Note: If we don't override this, then DjangoModelFactory bases
the initial value on the max PK of the corresponding model,
which in my case is absolutely huge and breaks the way we come
up with a valid national ID, above. | https://github.com/smartelect/smartelect/blob/d6d35f2fa8f60e756ad5247f8f0a5f05830e92f8/civil_registry/tests/factories.py#L52-L63 | from datetime import date
import random
import string
import factory
import factory.fuzzy
from civil_registry.models import Citizen, TempCitizen
from libya_elections.constants import MALE
def get_nid(stub):
from register.tests.factories import get_unused_gender_appropriate_national_id
return get_unused_gender_appropriate_national_id(stub)
def get_unused_civil_registry_id(stub):
civil_registry_id = random.randint(1, 999999999)
while Citizen.objects.filter(pk=civil_registry_id).exists():
civil_registry_id = random.randint(1, 999999999)
return civil_registry_id
class CitizenFactory(factory.DjangoModelFactory):
class Meta:
model = Citizen
civil_registry_id = factory.LazyAttribute(get_unused_civil_registry_id)
national_id = factory.LazyAttribute(get_nid)
birth_date = date(1913, 2, 3)
gender = MALE
fbr_number = factory.fuzzy.FuzzyText(prefix='se', chars=string.digits)
first_name = factory.fuzzy.FuzzyText()
father_name = factory.fuzzy.FuzzyText()
grandfather_name = factory.fuzzy.FuzzyText()
mother_name = factory.fuzzy.FuzzyText()
family_name = factory.fuzzy.FuzzyText()
missing = None
@classmethod | Apache License 2.0 |
http-apis/hydra-python-agent | hydra_agent/tests/test_redis.py | Tests.collection_endpoints | python | def collection_endpoints(self):
print("testing collection endpoints with db=0 ...")
query = ('GRAPH.QUERY','apigraph', "MATCH (p:collection) RETURN p")
redis_db = redis.StrictRedis(host='localhost', port=6379, db=0)
redis_reply = [[[b'p.id', b'p.operations', b'p.type'], [b'vocab:EntryPoint/HttpApiLogCollection', b"['GET', 'PUT']", b'HttpApiLogCollection'], [b'vocab:EntryPoint/AnomalyCollection', b"['GET', 'PUT']", b'AnomalyCollection'], [b'vocab:EntryPoint/CommandCollection', b"['GET', 'PUT']", b'CommandCollection'], [b'vocab:EntryPoint/ControllerLogCollection', b"['GET', 'PUT']", b'ControllerLogCollection'], [b'vocab:EntryPoint/DatastreamCollection', b"['GET', 'PUT']", b'DatastreamCollection'], [b'vocab:EntryPoint/MessageCollection', b"['GET', 'PUT']", b'MessageCollection'], [b'vocab:EntryPoint/DroneLogCollection', b"['GET', 'PUT']", b'DroneLogCollection'], [b'vocab:EntryPoint/DroneCollection', b"['GET', 'PUT']", b'DroneCollection']], [b'Query internal execution time: 0.089501 milliseconds']]
redis_db_execute_command_query = MagicMock(return_value = redis_reply)
property_list = redis_reply[0][0]
if (b"p.id" in property_list and
b"p.operations" in property_list and
b"p.type" in property_list):
return True
else:
return False | Test for testing the data stored in collection endpoints
`redis_reply` is data which will get from redis_db_0 on `query` execution. | https://github.com/http-apis/hydra-python-agent/blob/e2bcd51f3cbf0700cd44e5e392e3c21af2cbd2a3/hydra_agent/tests/test_redis.py#L26-L43 | import unittest
import redis
from unittest.mock import MagicMock
class Tests:
def entry_point(self):
print("testing entrypoint with db=0 ...")
query = ('GRAPH.QUERY','apigraph', "MATCH (p:id) RETURN p")
redis_db = redis.StrictRedis(host='localhost', port=6379, db=0)
redis_reply = [[[b'p.url', b'p.id', b'p.supportedOperation'], [b'http://localhost:8080/api', b'vocab:Entrypoint', b'GET']], [b'Query internal execution time: 0.071272 milliseconds']]
redis_db_execute_command_query = MagicMock(return_value = redis_reply)
property_list = redis_reply[0][0]
if (b"p.id" in property_list and
b"p.url" in property_list and
b"p.supportedOperation" in property_list):
return True
else:
return False | MIT License |
ollo69/ha_tuya_custom | custom_components/tuya_custom/__init__.py | TuyaDevice.object_id | python | def object_id(self):
return self._tuya.object_id() | Return Tuya device id. | https://github.com/ollo69/ha_tuya_custom/blob/66b8722afc9c771318b8c67865907e5ac0aac602/custom_components/tuya_custom/__init__.py#L380-L382 | import asyncio
from datetime import timedelta
import logging
from .tuyaha.tuyaapi import (
DEFAULTREGION,
TuyaApi,
TuyaAPIException,
TuyaAPIRateLimitException,
TuyaFrequentlyInvokeException,
TuyaNetException,
TuyaServerException,
)
import voluptuous as vol
from homeassistant.config_entries import SOURCE_IMPORT, ConfigEntry
from homeassistant.const import (
CONF_PASSWORD,
CONF_PLATFORM,
CONF_REGION,
CONF_USERNAME,
)
from homeassistant.core import HomeAssistant, callback
from homeassistant.exceptions import ConfigEntryNotReady
import homeassistant.helpers.config_validation as cv
from homeassistant.helpers.dispatcher import (
async_dispatcher_connect,
async_dispatcher_send,
)
from homeassistant.helpers.entity import Entity
from homeassistant.helpers.event import async_track_time_interval
from .const import (
CONF_COUNTRYCODE,
CONF_DISCOVERY_INTERVAL,
CONF_QUERY_DEVICE,
CONF_QUERY_INTERVAL,
DEFAULT_DISCOVERY_INTERVAL,
DEFAULT_QUERY_INTERVAL,
DOMAIN,
SIGNAL_CONFIG_ENTITY,
SIGNAL_DELETE_ENTITY,
SIGNAL_UPDATE_ENTITY,
TUYA_DATA,
TUYA_DEVICES_CONF,
TUYA_DISCOVERY_NEW,
TUYA_PLATFORMS,
TUYA_TYPE_NOT_QUERY,
)
_LOGGER = logging.getLogger(__name__)
ATTR_TUYA_DEV_ID = "tuya_device_id"
ENTRY_IS_SETUP = "tuya_entry_is_setup"
SERVICE_FORCE_UPDATE = "force_update"
SERVICE_PULL_DEVICES = "pull_devices"
TUYA_TYPE_TO_HA = {
"climate": "climate",
"cover": "cover",
"fan": "fan",
"light": "light",
"scene": "scene",
"switch": "switch",
}
TUYA_TRACKER = "tuya_tracker"
TUYA_DEVICE_CONF_SCHEMA = {
vol.Optional(TUYA_DEVICES_CONF): vol.All(
cv.ensure_list,
[
vol.Schema(
{
vol.Required("device_name"): cv.string,
vol.Optional("unit_of_measurement"): cv.temperature_unit,
vol.Optional("temp_divider", default=0): cv.positive_int,
vol.Optional("curr_temp_divider", default=0): cv.positive_int,
vol.Optional("ext_temp_sensor"): cv.string,
vol.Optional("support_color"): cv.boolean,
vol.Optional("brightness_range_mode", default=0): cv.positive_int,
vol.Optional("min_kelvin"): cv.positive_int,
vol.Optional("max_kelvin"): cv.positive_int,
vol.Optional("max_tuya_temp"): cv.positive_int,
}
)
],
)
}
CONFIG_SCHEMA = vol.Schema(
vol.All(
cv.deprecated(DOMAIN),
{
DOMAIN: vol.Schema(
{
vol.Optional(CONF_USERNAME): cv.string,
vol.Optional(CONF_COUNTRYCODE): cv.string,
vol.Optional(CONF_PASSWORD): cv.string,
vol.Optional(CONF_PLATFORM, default="tuya"): cv.string,
}
).extend(TUYA_DEVICE_CONF_SCHEMA)
},
),
extra=vol.ALLOW_EXTRA,
)
def _update_discovery_interval(hass, interval):
tuya = hass.data[DOMAIN].get(TUYA_DATA)
if not tuya:
return
try:
tuya.discovery_interval = interval
_LOGGER.info("Tuya discovery device poll interval set to %s seconds", interval)
except ValueError as ex:
_LOGGER.warning(ex)
def _update_query_interval(hass, interval):
tuya = hass.data[DOMAIN].get(TUYA_DATA)
if not tuya:
return
try:
tuya.query_interval = interval
_LOGGER.info("Tuya query device poll interval set to %s seconds", interval)
except ValueError as ex:
_LOGGER.warning(ex)
async def async_setup(hass, config):
conf = config.get(DOMAIN)
if conf is not None:
devices_config = conf.pop(TUYA_DEVICES_CONF, {})
if devices_config:
_LOGGER.warning(
"Options from configuration.yaml is not supported anymore. Use integration options"
)
user = conf.get(CONF_USERNAME)
pwd = conf.get(CONF_PASSWORD)
country = conf.get(CONF_COUNTRYCODE)
if user and pwd and country:
hass.async_create_task(
hass.config_entries.flow.async_init(
DOMAIN, context={"source": SOURCE_IMPORT}, data=conf
)
)
return True
async def async_setup_entry(hass: HomeAssistant, entry: ConfigEntry):
tuya = TuyaApi()
username = entry.data[CONF_USERNAME]
password = entry.data[CONF_PASSWORD]
country_code = entry.data[CONF_COUNTRYCODE]
platform = entry.data[CONF_PLATFORM]
region = entry.data.get(CONF_REGION, DEFAULTREGION)
try:
await hass.async_add_executor_job(
tuya.init, username, password, country_code, platform, region
)
except (
TuyaNetException,
TuyaServerException,
TuyaFrequentlyInvokeException,
) as exc:
raise ConfigEntryNotReady() from exc
except TuyaAPIRateLimitException as exc:
_LOGGER.error("Tuya login rate limited")
raise ConfigEntryNotReady() from exc
except TuyaAPIException as exc:
_LOGGER.error(
"Connection error during integration setup. Error: %s", exc,
)
return False
hass.data[DOMAIN] = {
TUYA_DATA: tuya,
TUYA_DEVICES_CONF: entry.options.copy(),
TUYA_TRACKER: None,
ENTRY_IS_SETUP: set(),
"entities": {},
"pending": {},
"listener": entry.add_update_listener(update_listener),
}
_update_discovery_interval(
hass, entry.options.get(CONF_DISCOVERY_INTERVAL, DEFAULT_DISCOVERY_INTERVAL)
)
_update_query_interval(
hass, entry.options.get(CONF_QUERY_INTERVAL, DEFAULT_QUERY_INTERVAL)
)
async def async_load_devices(device_list):
device_type_list = {}
for device in device_list:
dev_type = device.device_type()
if (
dev_type in TUYA_TYPE_TO_HA
and device.object_id() not in hass.data[DOMAIN]["entities"]
):
ha_type = TUYA_TYPE_TO_HA[dev_type]
if ha_type not in device_type_list:
device_type_list[ha_type] = []
device_type_list[ha_type].append(device.object_id())
hass.data[DOMAIN]["entities"][device.object_id()] = None
for ha_type, dev_ids in device_type_list.items():
config_entries_key = f"{ha_type}.tuya"
if config_entries_key not in hass.data[DOMAIN][ENTRY_IS_SETUP]:
hass.data[DOMAIN]["pending"][ha_type] = dev_ids
hass.async_create_task(
hass.config_entries.async_forward_entry_setup(entry, ha_type)
)
hass.data[DOMAIN][ENTRY_IS_SETUP].add(config_entries_key)
else:
async_dispatcher_send(hass, TUYA_DISCOVERY_NEW.format(ha_type), dev_ids)
await async_load_devices(tuya.get_all_devices())
def _get_updated_devices():
try:
tuya.poll_devices_update()
except TuyaFrequentlyInvokeException as exc:
_LOGGER.error(exc)
return tuya.get_all_devices()
async def async_poll_devices_update(event_time):
_LOGGER.debug("Pull devices from Tuya")
device_list = await hass.async_add_executor_job(_get_updated_devices)
await async_load_devices(device_list)
newlist_ids = []
for device in device_list:
newlist_ids.append(device.object_id())
for dev_id in list(hass.data[DOMAIN]["entities"]):
if dev_id not in newlist_ids:
async_dispatcher_send(hass, SIGNAL_DELETE_ENTITY, dev_id)
hass.data[DOMAIN]["entities"].pop(dev_id)
hass.data[DOMAIN][TUYA_TRACKER] = async_track_time_interval(
hass, async_poll_devices_update, timedelta(minutes=2)
)
hass.services.async_register(
DOMAIN, SERVICE_PULL_DEVICES, async_poll_devices_update
)
async def async_force_update(call):
async_dispatcher_send(hass, SIGNAL_UPDATE_ENTITY)
hass.services.async_register(DOMAIN, SERVICE_FORCE_UPDATE, async_force_update)
return True
async def async_unload_entry(hass: HomeAssistant, entry: ConfigEntry):
unload_ok = all(
await asyncio.gather(
*[
hass.config_entries.async_forward_entry_unload(
entry, component.split(".", 1)[0]
)
for component in hass.data[DOMAIN][ENTRY_IS_SETUP]
]
)
)
if unload_ok:
hass.data[DOMAIN]["listener"]()
hass.data[DOMAIN][TUYA_TRACKER]()
hass.services.async_remove(DOMAIN, SERVICE_FORCE_UPDATE)
hass.services.async_remove(DOMAIN, SERVICE_PULL_DEVICES)
hass.data.pop(DOMAIN)
return unload_ok
async def update_listener(hass: HomeAssistant, entry: ConfigEntry):
hass.data[DOMAIN][TUYA_DEVICES_CONF] = entry.options.copy()
_update_discovery_interval(
hass, entry.options.get(CONF_DISCOVERY_INTERVAL, DEFAULT_DISCOVERY_INTERVAL)
)
_update_query_interval(
hass, entry.options.get(CONF_QUERY_INTERVAL, DEFAULT_QUERY_INTERVAL)
)
async_dispatcher_send(hass, SIGNAL_CONFIG_ENTITY)
async def cleanup_device_registry(hass: HomeAssistant, device_id):
device_registry = await hass.helpers.device_registry.async_get_registry()
entity_registry = await hass.helpers.entity_registry.async_get_registry()
if device_id and not hass.helpers.entity_registry.async_entries_for_device(
entity_registry, device_id, include_disabled_entities=True
):
device_registry.async_remove_device(device_id)
class TuyaDevice(Entity):
_dev_can_query_count = 0
def __init__(self, tuya, platform):
self._tuya = tuya
self._tuya_platform = platform
def _device_can_query(self):
dev_type = self._tuya.device_type()
return dev_type not in TUYA_TYPE_NOT_QUERY
def _inc_device_count(self):
if not self._device_can_query():
return
TuyaDevice._dev_can_query_count += 1
def _dec_device_count(self):
if not self._device_can_query():
return
TuyaDevice._dev_can_query_count -= 1
def _get_device_config(self):
devices_config = self.hass.data[DOMAIN].get(TUYA_DEVICES_CONF)
if not devices_config:
return {}
dev_conf = devices_config.get(self.object_id, {})
if dev_conf:
_LOGGER.debug(
"Configuration for deviceID %s: %s", self.object_id, str(dev_conf)
)
return dev_conf
async def async_added_to_hass(self):
self.hass.data[DOMAIN]["entities"][self.object_id] = self.entity_id
self.async_on_remove(
async_dispatcher_connect(
self.hass, SIGNAL_DELETE_ENTITY, self._delete_callback
)
)
self.async_on_remove(
async_dispatcher_connect(
self.hass, SIGNAL_UPDATE_ENTITY, self._update_callback
)
)
self._inc_device_count()
async def async_will_remove_from_hass(self):
self._dec_device_count()
@property | Apache License 2.0 |
erigones/esdc-ce | api/serializers.py | BaseSerializer.get_field_key | python | def get_field_key(self, field_name):
return field_name | Return the key that should be used for a given field. | https://github.com/erigones/esdc-ce/blob/f83a62d0d430e3c8f9aac23d958583b0efce4312/api/serializers.py#L356-L360 | from __future__ import unicode_literals
import copy
import datetime
import inspect
import types
from collections import OrderedDict
from decimal import Decimal
from django.apps import apps
from django.core.paginator import Page
from django.db import models
from django.forms import widgets
from django.utils import six
from django.utils.functional import cached_property
from django.utils.encoding import force_text, python_2_unicode_compatible
from django.utils.translation import ugettext_lazy as _
from django.core.exceptions import ValidationError, ObjectDoesNotExist as DjangoObjectDoesNotExist
from django.contrib.contenttypes.fields import GenericForeignKey
from api.relations import *
from api.fields import *
from api.fields import is_simple_callable, get_component
def _resolve_model(obj):
if isinstance(obj, six.string_types) and len(obj.split('.')) == 2:
app_name, model_name = obj.split('.')
return apps.get_model(app_name, model_name)
elif inspect.isclass(obj) and issubclass(obj, models.Model):
return obj
else:
raise ValueError("{0} is not a Django model".format(obj))
def pretty_name(name):
if not name:
return ''
return name.replace('_', ' ').capitalize()
def field_value(source, value):
for component in source.split('.'):
value = get_component(value, component)
if value is None:
break
return value
@python_2_unicode_compatible
class ErrorList(list):
def __str__(self):
return str([force_text(i) for i in self])
def __repr__(self):
return repr([force_text(i) for i in self])
def __iter__(self):
return (force_text(i) for i in list.__iter__(self))
class RelationsList(list):
_deleted = []
class APIValidationError(ValidationError):
@property
def api_errors(self):
if hasattr(self, 'message'):
return ErrorList([self.message])
return self.messages
class ObjectDoesNotExist(ValidationError):
def __init__(self, value, field_name='name', **kwargs):
message = _('Object with %(field)s=%(value)s does not exist.') % {'field': field_name, 'value': value}
super(ObjectDoesNotExist, self).__init__(message, **kwargs)
class NoPermissionToModify(ValidationError):
def __init__(self, **kwargs):
message = _('You don\'t have permission to modify this attribute.')
super(NoPermissionToModify, self).__init__(message, **kwargs)
class NestedValidationError(ValidationError):
def __init__(self, message):
if isinstance(message, dict):
self._messages = [message]
else:
self._messages = message
@property
def messages(self):
return self._messages
class DictWithMetadata(dict):
def __getstate__(self):
return dict(self)
class SortedDictWithMetadata(OrderedDict):
def __reduce__(self):
return self.__class__, (OrderedDict(self), )
def __getstate__(self):
return OrderedDict(self).__dict__
def _is_protected_type(obj):
return isinstance(obj, six.string_types + six.integer_types + (
types.NoneType,
datetime.datetime, datetime.date, datetime.time,
float, Decimal,
))
def _get_declared_fields(bases, attrs):
fields = [(field_name, attrs.pop(field_name))
for field_name, obj in list(six.iteritems(attrs))
if isinstance(obj, Field)]
fields.sort(key=lambda x: x[1].creation_counter)
for base in bases[::-1]:
if hasattr(base, 'base_fields'):
fields = list(base.base_fields.items()) + fields
return OrderedDict(fields)
class SerializerMetaclass(type):
def __new__(mcs, name, bases, attrs):
attrs['base_fields'] = _get_declared_fields(bases, attrs)
return super(SerializerMetaclass, mcs).__new__(mcs, name, bases, attrs)
class SerializerOptions(object):
def __init__(self, meta):
self.depth = getattr(meta, 'depth', 0)
self.fields = getattr(meta, 'fields', ())
self.exclude = getattr(meta, 'exclude', ())
class BaseSerializer(WritableField):
class Meta(object):
pass
_options_class = SerializerOptions
_dict_class = SortedDictWithMetadata
def __init__(self, instance=None, data=None, files=None, context=None, partial=False, many=False,
allow_add_remove=False, **kwargs):
super(BaseSerializer, self).__init__(**kwargs)
self.opts = self._options_class(self.Meta)
self.parent = None
self.root = None
self.partial = partial
self.many = many
self.allow_add_remove = allow_add_remove
self.context = context or {}
self.init_data = data
self.init_files = files
self.object = instance
self._data = None
self._files = None
self._errors = None
if many and instance is not None and not hasattr(instance, '__iter__'):
raise ValueError('instance should be a queryset or other iterable with many=True')
if allow_add_remove and not many:
raise ValueError('allow_add_remove should only be used for bulk updates, but you have not set many=True')
@cached_property
def fields(self):
return self.get_fields()
def get_default_fields(self):
return {}
def get_fields(self):
ret = OrderedDict()
base_fields = copy.deepcopy(self.base_fields)
for key, field in base_fields.items():
ret[key] = field
default_fields = self.get_default_fields()
for key, val in default_fields.items():
if key not in ret:
ret[key] = val
if self.opts.fields:
assert isinstance(self.opts.fields, (list, tuple)), '`fields` must be a list or tuple'
new = OrderedDict()
for key in self.opts.fields:
new[key] = ret[key]
ret = new
if self.opts.exclude:
assert isinstance(self.opts.exclude, (list, tuple)), '`exclude` must be a list or tuple'
for key in self.opts.exclude:
ret.pop(key, None)
for key, field in ret.items():
field.initialize(parent=self, field_name=key)
return ret | Apache License 2.0 |
hathornetwork/hathor-core | hathor/stratum/stratum.py | StratumProtocol.create_job_tx | python | def create_job_tx(self, jobid: UUID) -> BaseTransaction:
if self.mine_txs and self.factory.tx_queue:
funds_hash = self.factory.tx_queue[0]
tx = self.factory.mining_tx_pool[funds_hash]
tx.timestamp = self.factory.get_current_timestamp()
tx.parents = self.manager.get_new_tx_parents(tx.timestamp)
self.log.debug('prepared tx for mining', tx=tx)
return tx
peer_id = self.manager.my_peer.id
assert peer_id is not None
assert self.miner_id is not None
data = '{}-{}-{}'.format(peer_id[:32], self.miner_id.hex, jobid.hex).encode()[:settings.BLOCK_DATA_MAX_SIZE]
block = self.manager.generate_mining_block(data=data, address=self.miner_address,
merge_mined=self.merged_mining)
self.log.debug('prepared block for mining', block=block)
return block | Creates a BaseTransaction for the designated miner job.
:return: created BaseTransaction
:rtype: BaseTransaction | https://github.com/hathornetwork/hathor-core/blob/b8bd2428b9fab4f53dfc4d92de230ffae48fbf46/hathor/stratum/stratum.py#L640-L667 | from abc import ABC, abstractmethod
from hashlib import sha256
from itertools import count
from json import JSONDecodeError
from math import log
from multiprocessing import Process, Queue as MQueue
from multiprocessing.sharedctypes import Array, Value
from os import cpu_count
from string import hexdigits
from time import sleep
from typing import TYPE_CHECKING, Any, Callable, Dict, Iterator, List, NamedTuple, Optional, Set, Tuple, Union, cast
from uuid import UUID, uuid4
from structlog import get_logger
from twisted.internet import reactor, task
from twisted.internet.defer import Deferred
from twisted.internet.interfaces import IAddress, IDelayedCall, IReactorCore, IReactorTCP
from twisted.internet.protocol import Factory
from twisted.protocols.basic import LineReceiver
from twisted.python.failure import Failure
from hathor.conf import HathorSettings
from hathor.crypto.util import decode_address
from hathor.exception import InvalidNewTransaction
from hathor.pubsub import EventArguments, HathorEvents
from hathor.transaction import BaseTransaction, BitcoinAuxPow, Block, MergeMinedBlock, Transaction, sum_weights
from hathor.transaction.exceptions import PowError, ScriptError, TxValidationError
from hathor.util import json_dumpb, json_loadb
from hathor.wallet.exceptions import InvalidAddress
if TYPE_CHECKING:
from multiprocessing.sharedctypes import _Array, _Value
from hathor.manager import HathorManager
logger = get_logger()
settings = HathorSettings()
def valid_uuid(uuid: Any) -> bool:
return isinstance(uuid, str) and len(uuid) == 32 and all(c in hexdigits for c in uuid)
def valid_uuid_or_none(uuid: Any) -> bool:
return uuid is None or valid_uuid(uuid)
UNRECOVERABLE_ERROR_CODE_MAX = -32600
PARSE_ERROR = {'code': -32700, 'message': 'Parse error'}
INTERNAL_ERROR = {'code': -32603, 'message': 'Internal error'}
INVALID_PARAMS = {'code': -32602, 'message': 'Invalid params'}
METHOD_NOT_FOUND = {'code': -32601, 'message': 'Method not found'}
INVALID_REQUEST = {'code': -32600, 'message': 'Invalid Request'}
NODE_SYNCING = {'code': 10, 'message': 'Node syncing'}
INVALID_ADDRESS = {'code': 22, 'message': 'Address to send mined funds is invalid'}
INVALID_SOLUTION = {'code': 30, 'message': 'Invalid solution'}
STALE_JOB = {'code': 31, 'message': 'Stale job submitted'}
JOB_NOT_FOUND = {'code': 32, 'message': 'Job not found'}
PROPAGATION_FAILED = {'code': 33, 'message': 'Solution propagation failed'}
DUPLICATE_SOLUTION = {'code': 34, 'message': 'Solution already submitted'}
class ServerJob:
id: UUID
created: int
submitted: Optional[int]
miner: UUID
tx: BaseTransaction
weight: float
timeoutTask: IDelayedCall
def __init__(self, jobid: UUID, created: int, miner: UUID, tx: BaseTransaction, weight: float):
self.id = jobid
self.created = created
self.miner = miner
self.tx = tx
self.submitted = None
self.weight = weight
class MinerJob(NamedTuple):
data: '_Array' = Array('B', 2048)
data_size: '_Value' = Value('I')
job_id: '_Array' = Array('B', 16)
nonce_size: '_Value' = Value('I')
weight: '_Value' = Value('d')
def update_job(self, params: Dict[str, Any]) -> bool:
try:
data = bytes.fromhex(params['data'])
data_size: int = len(data)
self.data[:data_size] = data
self.data_size.value = data_size
self.job_id[:] = bytes.fromhex(params['job_id'])
self.nonce_size.value = int(params['nonce_size'])
self.weight.value = float(params['weight'])
except KeyError:
return False
return True
class MinerSubmit(NamedTuple):
job_id: str
nonce: str = ''
aux_pow: str = ''
class MinerStatistics(NamedTuple):
address: str
blocks_found: int
completed_jobs: int
connection_start_time: int
estimated_hash_rate: float
miner_id: str
class JSONRPC(LineReceiver, ABC):
delimiter = b'\n'
use_ok = True
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.log = logger.new()
def lineReceived(self, line: bytes) -> None:
self.log.debug('line received', line=line)
try:
data = json_loadb(line)
except JSONDecodeError:
return self.send_error(PARSE_ERROR, data={'message': repr(line)})
assert isinstance(data, dict)
msgid = data.get('id')
if 'method' in data:
return self.handle_request(data['method'], data.get('params'), msgid)
elif 'result' in data and 'error' in data:
if data['result'] and data['error'] is None:
return self.handle_result(data['result'], msgid)
elif data['error'] and data['result'] is None:
return self.handle_error(data['error'], data.get('data'), msgid)
elif 'result' in data:
return self.handle_result(data['result'], msgid)
elif 'error' in data:
return self.handle_error(data['error'], data.get('data'), msgid)
return self.send_error(
INVALID_REQUEST, data={
'message': data,
'error': 'Could not identify message as request, result or error.'
})
@abstractmethod
def handle_request(self, method: str, params: Optional[Union[List, Dict]], msgid: Optional[str]) -> None:
raise NotImplementedError
@abstractmethod
def handle_result(self, result: Any, msgid: Optional[str]) -> None:
raise NotImplementedError
@abstractmethod
def handle_error(self, error: Dict, data: Any, msgid: Optional[str]) -> None:
raise NotImplementedError
def send_request(self, method: str, params: Optional[Union[List, Dict]], msgid: Union[str, int, None] = None,
ok: Optional[bool] = None) -> None:
data: Dict[str, Any] = {'method': method, 'params': params}
self.log.debug('send request', method=method, params=params)
data['id'] = msgid
if ok is True:
data['result'] = 'ok' if self.use_ok else True
self.send_json(data)
def send_result(self, result: Any, msgid: Optional[str]) -> None:
data = {'result': result, 'error': None}
if msgid is not None:
data['id'] = msgid
self.log.debug('send result', data=data)
return self.send_json(data)
def send_error(self, error: Dict, msgid: Optional[str] = None, data: Any = None) -> None:
message = {'error': error, 'data': data}
if msgid is not None:
message['id'] = msgid
self.log.info('send_error', error=error, data=data)
self.send_json(message)
if error['code'] <= UNRECOVERABLE_ERROR_CODE_MAX and self.transport is not None:
self.transport.loseConnection()
def send_json(self, json: Dict) -> None:
try:
message = json_dumpb(json)
self.log.debug('send line', line=message)
self.sendLine(message)
except TypeError:
self.log.error('failed to encode', json=json)
class StratumProtocol(JSONRPC):
JOBS_HISTORY = 100
AVERAGE_JOB_TIME = 5
BLOCK_MAXIMUM_JOB_TIME = 15
TX_MAXIMUM_JOB_TIME = 1
address: IAddress
current_job: Optional[ServerJob]
jobs: Dict[UUID, ServerJob]
job_ids: List[UUID]
factory: 'StratumFactory'
manager: 'HathorManager'
miner_id: Optional[UUID]
miner_address: Optional[bytes]
estimated_hash_rate: float
completed_jobs: int
connection_start_time: int
blocks_found: int
merged_mining: bool
def __init__(self, factory: 'StratumFactory', manager: 'HathorManager', address: IAddress,
id_generator: Optional[Callable[[], Iterator[Union[str, int]]]] = lambda: count()):
self.log = logger.new(address=address)
self.factory = factory
self.manager = manager
self.address = address
self.current_job = None
self.jobs = {}
self.miner_id = None
self.miner_address = None
self.job_ids = []
self.mine_txs = settings.STRATUM_MINE_TXS_DEFAULT
self.estimated_hash_rate = 0.0
self.completed_jobs = 0
self.connection_start_time = 0
self.blocks_found = 0
self._iter_id = id_generator and id_generator() or None
self.subscribed = False
def _next_id(self):
if self._iter_id:
return str(next(self._iter_id))
def connectionMade(self) -> None:
self.miner_id = uuid4()
self.connection_start_time = self.factory.get_current_timestamp()
self.log = self.log.bind(miner_id=self.miner_id, conn_at=self.connection_start_time, address=self.address)
self.log.debug('new connection')
def connectionLost(self, reason: Failure = None) -> None:
if self.subscribed:
self.log.info('miner disconnected')
assert self.miner_id is not None
self.factory.miner_protocols.pop(self.miner_id, None)
def handle_request(self, method: str, params: Optional[Union[List, Dict]], msgid: Optional[str]) -> None:
self.log.debug('handle request', msgid=msgid, method=method, params=params)
if method in ['mining.subscribe', 'subscribe', 'mining.submit', 'submit']:
if not self.manager.can_start_mining():
return self.send_error(NODE_SYNCING, msgid)
if method in ['mining.subscribe', 'subscribe']:
params = cast(Dict, params)
return self.handle_subscribe(params, msgid)
if method in ['mining.submit', 'submit']:
params = cast(Dict, params)
return self.handle_submit(params, msgid)
self.send_error(METHOD_NOT_FOUND, msgid, data={'method': method, 'supported_methods': ['submit', 'subscribe']})
def handle_result(self, result: Any, msgid: Optional[str]) -> None:
self.log.debug('handle result', msgid=msgid, result=result)
def handle_error(self, error: Dict, data: Any, msgid: Optional[str]) -> None:
self.log.error('handle error', msgid=msgid, error=error)
def handle_subscribe(self, params: Dict, msgid: Optional[str]) -> None:
assert self.miner_id is not None
self.log.debug('handle subscribe', msgid=msgid, params=params)
if params and 'address' in params and params['address'] is not None:
try:
address = params['address']
self.miner_address = decode_address(address)
self.log.debug('miner with address', id=self.miner_id, address=address)
except InvalidAddress:
self.send_error(INVALID_ADDRESS, msgid)
self.transport.loseConnection()
return
if params and 'mine_txs' in params:
self.mine_txs = params['mine_txs']
if params and 'merged_mining' in params:
self.merged_mining = params['merged_mining']
else:
self.merged_mining = False
if params and params.get('mine_txs') and params.get('merged_mining'):
err = INVALID_PARAMS.copy()
err['message'] = 'Cannot set both merged_mining=True and mine_txs=True'
return self.send_error(err, msgid)
if self.merged_mining:
self.log.debug('merged_mining=True implies mine_txs=False')
self.mine_txs = False
self.factory.miner_protocols[self.miner_id] = self
self.log.info('miner subscribed', address=self.miner_address, mine_txs=self.mine_txs,
merged_mining=self.merged_mining)
self.send_result('ok', msgid)
self.subscribed = True
self.job_request()
def handle_submit(self, params: Dict, msgid: Optional[str]) -> None:
from hathor.merged_mining.bitcoin import sha256d_hash
self.log.debug('handle submit', msgid=msgid, params=params)
if 'job_id' not in params or 'nonce' not in params:
return self.send_error(INVALID_PARAMS, msgid, {'params': params, 'required': ['job_id', 'nonce']})
if not valid_uuid(params['job_id']):
return self.send_error(INVALID_PARAMS, msgid, {
'job_id': params['job_id'],
'message': 'job_id is invalid uuid4'
})
job_id = UUID(params['job_id'])
job = self.jobs.get(job_id)
if job is None:
return self.send_error(JOB_NOT_FOUND, msgid, {
'current_job': self.current_job and self.current_job.id.hex,
'job_id': job_id.hex
})
if job is not self.current_job or job.submitted is not None:
return self.send_error(STALE_JOB, msgid, {
'current_job': self.current_job and self.current_job.id.hex,
'job_id': job_id.hex
})
tx = job.tx.clone()
block_base = tx.get_header_without_nonce()
block_base_hash = sha256d_hash(block_base)
if params.get('aux_pow'):
assert isinstance(tx, MergeMinedBlock), 'expected MergeMinedBlock got ' + type(tx).__name__
tx.aux_pow = BitcoinAuxPow.from_bytes(bytes.fromhex(params['aux_pow']))
tx.nonce = 0
else:
tx.nonce = int(params['nonce'], 16)
tx.update_hash()
assert tx.hash is not None
self.log.debug('share received', block=tx, block_base=block_base.hex(), block_base_hash=block_base_hash.hex())
try:
tx.verify_pow(job.weight)
except PowError:
self.log.error('bad share, discard', job_weight=job.weight, tx=tx)
return self.send_error(INVALID_SOLUTION, msgid, {
'hash': tx.hash.hex(),
'target': int(tx.get_target()).to_bytes(32, 'big').hex()
})
job.submitted = self.factory.get_current_timestamp()
self.completed_jobs += 1
self.send_result('ok', msgid)
self.manager.reactor.callLater(0, self.job_request)
try:
tx.verify_pow()
except PowError:
self.log.info('high hash, keep mining', tx=tx)
return
else:
self.log.info('low hash, new block candidate', tx=tx)
if isinstance(tx, Block):
try:
self.manager.submit_block(tx, fails_silently=False)
self.blocks_found += 1
except (InvalidNewTransaction, TxValidationError) as e:
self.log.warn('block propagation failed', block=tx, error=e)
else:
self.log.info('new block found', block=tx)
elif isinstance(tx, Transaction):
self.log.info('transaction mined', tx=tx)
funds_hash = tx.get_funds_hash()
if funds_hash in self.factory.mining_tx_pool:
self.factory.mined_txs[funds_hash] = tx
del self.factory.mining_tx_pool[funds_hash]
if funds_hash in self.factory.tx_queue:
self.factory.tx_queue.remove(funds_hash)
if funds_hash in self.factory.deferreds_tx:
d = self.factory.deferreds_tx.pop(funds_hash)
d.callback(tx)
else:
assert False, 'tx should either be a Block or Transaction'
def job_request(self) -> None:
try:
job = self.create_job()
except (ValueError, ScriptError) as e:
self.send_error(INVALID_PARAMS, data={
'message': str(e)
})
else:
if job:
job_data = {
'data': job.tx.get_header_without_nonce().hex(),
'job_id': job.id.hex,
'nonce_size': job.tx.SERIALIZATION_NONCE_SIZE,
'weight': float(job.weight),
}
if job.tx.is_block:
assert isinstance(job.tx, Block)
job_data['parent_hash'] = job.tx.get_block_parent_hash().hex()
self.send_request('job', job_data, self._next_id())
def create_job(self) -> ServerJob:
assert self.miner_id is not None
self.cancel_current_job_timeout()
jobid = uuid4()
tx = self.create_job_tx(jobid)
job = ServerJob(jobid, self.factory.get_current_timestamp(), self.miner_id, tx, 0.0)
self.current_job = job
self.jobs[job.id] = job
self.job_ids.append(job.id)
share_weight = self.calculate_share_weight()
job.weight = min(share_weight, tx.weight)
def jobTimeout(job: ServerJob, protocol: StratumProtocol) -> None:
if job is protocol.current_job and job.submitted is None:
if self.miner_id in self.factory.miner_protocols:
protocol.job_request()
timeout = self.BLOCK_MAXIMUM_JOB_TIME if tx.is_block else self.TX_MAXIMUM_JOB_TIME
job.timeoutTask = self.manager.reactor.callLater(timeout, jobTimeout, job, self)
if len(self.job_ids) > self.JOBS_HISTORY:
del self.jobs[self.job_ids.pop(0)]
return job | Apache License 2.0 |
jasonmcintosh/rabbitmq-zabbix | scripts/rabbitmq/api.py | RabbitMQAPI.check_aliveness | python | def check_aliveness(self):
return self.call_api('aliveness-test/%2f')['status'] | Check the aliveness status of a given vhost. | https://github.com/jasonmcintosh/rabbitmq-zabbix/blob/8ecadfdd2cab6154eb7ab73ce4e7bb39b21b61c1/scripts/rabbitmq/api.py#L209-L211 | from __future__ import unicode_literals
import io
import json
import optparse
import socket
import urllib2
import subprocess
import os
import logging
class RabbitMQAPI(object):
def __init__(self, user_name='guest', password='guest', host_name='',
port=15672, conf='/etc/zabbix/zabbix_agentd.conf', senderhostname=None, protocol='http'):
self.user_name = user_name
self.password = password
self.host_name = host_name or socket.gethostname()
self.port = port
self.conf = conf or '/etc/zabbix/zabbix_agentd.conf'
self.senderhostname = senderhostname or socket.gethostname()
self.protocol = protocol or 'http'
def call_api(self, path):
url = '{0}://{1}:{2}/api/{3}'.format(self.protocol, self.host_name, self.port, path)
password_mgr = urllib2.HTTPPasswordMgrWithDefaultRealm()
password_mgr.add_password(None, url, self.user_name, self.password)
handler = urllib2.HTTPBasicAuthHandler(password_mgr)
logging.debug('Issue a rabbit API call to get data on ' + path + " against " + self.host_name)
logging.debug('Full URL:' + url)
return json.loads(urllib2.build_opener(handler).open(url).read())
def list_queues(self, filters=None):
queues = []
if not filters:
filters = [{}]
for queue in self.call_api('queues'):
logging.debug("Discovered queue " + queue['name'] + ", checking to see if it's filtered...")
for _filter in filters:
check = [(x, y) for x, y in queue.items() if x in _filter]
shared_items = set(_filter.items()).intersection(check)
if len(shared_items) == len(_filter):
element = {'{#NODENAME}': queue['node'],
'{#VHOSTNAME}': queue['vhost'],
'{#QUEUENAME}': queue['name']}
queues.append(element)
logging.debug('Discovered queue '+queue['vhost']+'/'+queue['name'])
break
return queues
def list_shovels(self, filters=None):
shovels = []
if not filters:
filters = [{}]
try:
for shovel in self.call_api('shovels'):
logging.debug("Discovered shovel " + shovel['name'] + ", checking to see if it's filtered...")
for _filter in filters:
check = [(x, y) for x, y in shovel.items() if x in _filter]
shared_items = set(_filter.items()).intersection(check)
if len(shared_items) == len(_filter):
element = {'{#VHOSTNAME}': shovel['vhost'],
'{#SHOVELNAME}': shovel['name']}
shovels.append(element)
logging.debug('Discovered shovel '+shovel['vhost']+'/'+shovel['name'])
break
return shovels
except urllib2.HTTPError as err:
if err.code == 404:
return shovels
else:
raise err
def list_nodes(self):
nodes = []
for node in self.call_api('nodes'):
name = node['name'].split('@')[1]
element = {'{#NODENAME}': name,
'{#NODETYPE}': node['type']}
nodes.append(element)
logging.debug('Discovered nodes '+name+'/'+node['type'])
return nodes
def check_queue(self, filters=None):
return_code = 0
if not filters:
filters = [{}]
buffer = io.StringIO()
try:
for queue in self.call_api('queues'):
success = False
logging.debug("Filtering out by " + str(filters))
for _filter in filters:
check = [(x, y) for x, y in queue.items() if x in _filter]
shared_items = set(_filter.items()).intersection(check)
if len(shared_items) == len(_filter):
success = True
break
if success:
self._prepare_data(queue, buffer)
except urllib2.HTTPError as err:
if err.code == 404:
buffer.close()
return return_code
else:
raise err
return_code = self._send_data(buffer)
buffer.close()
return return_code
def check_shovel(self, filters=None):
return_code = 0
if not filters:
filters = [{}]
buffer = io.StringIO()
try:
for shovel in self.call_api('shovels'):
success = False
logging.debug("Filtering out by " + str(filters))
for _filter in filters:
check = [(x, y) for x, y in shovel.items() if x in _filter]
shared_items = set(_filter.items()).intersection(check)
if len(shared_items) == len(_filter):
success = True
break
if success:
key = '"rabbitmq.shovels[{0},shovel_{1},{2}]"'
key = key.format(shovel['vhost'], 'state', shovel['name'])
value = shovel.get('state', 0)
logging.debug("SENDER_DATA: - %s %s" % (key,value))
buffer.write("- %s %s\n" % (key, value))
except urllib2.HTTPError as err:
if err.code == 404:
buffer.close()
return return_code
else:
raise err
return_code = self._send_data(buffer)
buffer.close()
return return_code
def _prepare_data(self, queue, file):
for item in ['memory', 'messages', 'messages_unacknowledged',
'consumers']:
key = '"rabbitmq.queues[{0},queue_{1},{2}]"'
key = key.format(queue['vhost'], item, queue['name'])
value = queue.get(item, 0)
logging.debug("SENDER_DATA: - %s %s" % (key,value))
file.write("- %s %s\n" % (key, value))
for item in ['deliver_get', 'publish', 'ack']:
key = '"rabbitmq.queues[{0},queue_message_stats_{1},{2}]"'
key = key.format(queue['vhost'], item, queue['name'])
value = queue.get('message_stats', {}).get(item, 0)
logging.debug("SENDER_DATA: - %s %s" % (key,value))
file.write("- %s %s\n" % (key, value))
def _send_data(self, file):
args = 'zabbix_sender -vv -c {0} -i -'
if self.senderhostname:
args = args + " -s '%s' " % self.senderhostname
return_code = 0
process = subprocess.Popen(args.format(self.conf),
shell=True,
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
out, err = process.communicate(input=file.getvalue())
logging.debug("Finished sending data")
return_code = process.wait()
logging.info("Found return code of " + str(return_code))
if return_code == 1:
logging.error(out)
logging.error(err)
else:
logging.debug(err)
logging.debug(out)
return return_code | Apache License 2.0 |
bennylope/django-organizations | src/organizations/utils.py | model_field_attr | python | def model_field_attr(model, model_field, attr):
fields = dict([(field.name, field) for field in model._meta.fields])
return getattr(fields[model_field], attr) | Returns the specified attribute for the specified field on the model class. | https://github.com/bennylope/django-organizations/blob/55808ad7e4b23ef4612c9226cadce46d514c9a79/src/organizations/utils.py#L89-L94 | from itertools import chain
def default_org_model():
from organizations.models import Organization
return Organization
def model_field_names(model):
return list(
set(
chain.from_iterable(
(field.name, field.attname)
if hasattr(field, "attname")
else (field.name,)
for field in model._meta.get_fields()
if not (field.many_to_one and field.related_model is None)
)
)
)
def create_organization(
user,
name,
slug=None,
is_active=None,
org_defaults=None,
org_user_defaults=None,
**kwargs
):
org_model = (
kwargs.pop("model", None)
or kwargs.pop("org_model", None)
or default_org_model()
)
kwargs.pop("org_user_model", None)
org_owner_model = org_model.owner.related.related_model
org_user_model = org_model.organization_users.rel.related_model
if org_defaults is None:
org_defaults = {}
if org_user_defaults is None:
if "is_admin" in model_field_names(org_user_model):
org_user_defaults = {"is_admin": True}
else:
org_user_defaults = {}
if slug is not None:
org_defaults.update({"slug": slug})
if is_active is not None:
org_defaults.update({"is_active": is_active})
org_defaults.update({"name": name})
organization = org_model.objects.create(**org_defaults)
org_user_defaults.update({"organization": organization, "user": user})
new_user = org_user_model.objects.create(**org_user_defaults)
org_owner_model.objects.create(
organization=organization, organization_user=new_user
)
return organization | BSD 2-Clause Simplified License |
westpa/westpa | lib/west_tools/westtools/wipi.py | __get_data_for_iteration__.successful_trajectories | python | def successful_trajectories(self):
state_changes = np.where(self.raw['states'][:,:-1] != self.raw['states'][:,1:])
walkers = state_changes[0]
new_states = state_changes[1] + 1
old_states = state_changes[1]
walker = {}
for z, (i, j) in enumerate(zip(old_states, new_states)):
istate = self.raw['states'][walkers[z], i]
jstate = self.raw['states'][walkers[z], j]
try:
walker[istate,jstate].append(walkers[z])
except:
walker[istate,jstate] = [walkers[z]]
walker = WIPIDataset(raw=walker, key=None)
return walker | Returns which trajectories are successful. | https://github.com/westpa/westpa/blob/cda177c5dea2cee571d71c4b04fcc625dc5f689c/lib/west_tools/westtools/wipi.py#L427-L450 | import numpy as np
import os, sys
import scipy.sparse as sp
from westtools import Plotter
import itertools
class WIPIDataset(object):
def __init__(self, raw, key):
self.__dict__ = {}
self.raw = raw
self.name = key
def __repr__(self):
if isinstance(self.__dict__['raw'], dict):
return repr(self.__dir__())
else:
return repr(self.raw)
def __getitem__(self, value):
if not isinstance(value, str):
return self.__dict__['raw'][value]
if value in list(self.__dict__['raw'].keys()):
return self.__dict__['raw'][value]
elif value in list(self.__dict__.keys()):
return self.__dict__[value]
def __setitem__(self, key, value):
self.__dict__[key] = value
def __getattr__(self, value):
if value in dir(self.__dict__['raw']):
return getattr(self.__dict__['raw'], value)
else:
return self.__getitem__(value)
def __setattr__(self, key, value):
self.__dict__[key] = value
def __dir__(self):
dict_keys = list(self.__dict__.keys())
remove = ['raw', 'name', '__dict__', 'plotter']
for i in remove:
try:
dict_keys.remove(str(i))
except:
pass
if isinstance(self.__dict__['raw'], dict):
return sorted(set(list(self.raw.keys()) + dict_keys))
else:
return sorted(set(dict_keys))
def keys(self):
print(self.__dir__())
def __add__(self, other):
return self.__dict__['raw'] + other
def __radd__(self, other):
return other + self.__dict__['raw']
def __sub__(self, other):
return self.__dict__['raw'] - other
def __rsub__(self, other):
return other - self.__dict__['raw']
def __mul__(self, other):
return self.__dict__['raw'] * other
def __rmul__(self, other):
return other * self.__dict__['raw']
def __truediv__(self, other):
return self.__dict__['raw'] / other
def __floordiv__(self, other):
return self.__dict__['raw'] // other
def __rtruediv__(self, other):
return other / self.__dict__['raw']
def __mod__(self, other):
return self.__dict__['raw'] % other
def __pow__(self, other):
return self.__dict__['raw'] ** other
def __lshift__(self, other):
return self.__dict__['raw'] << other
def __rshift__(self, other):
return self.__dict__['raw'] >> other
def __and__(self, other):
return self.__dict__['raw'] & other
def __eq__(self, other):
return self.__dict__['raw'] == other
def __ne__(self, other):
return self.__dict__['raw'] != other
def __lt__(self, other):
return self.__dict__['raw'] < other
def __gt__(self, other):
return self.__dict__['raw'] > other
def __le__(self, other):
return self.__dict__['raw'] <= other
def __ge__(self, other):
return self.__dict__['raw'] >= other
def __xor__(self, other):
return self.__dict__['raw'] ^ other
def __or__(self, other):
return self.__dict__['raw'] | other
class KineticsIteration(object):
def __init__(self, kin_h5file, index, assign, iteration=-1):
self.__dict__ = {}
self.h5file = kin_h5file
_2D_h5keys = [ 'conditional_flux_evolution', 'rate_evolution' ]
_1D_h5keys = [ 'state_pop_evolution', 'color_prob_evolution', 'target_flux_evolution' ]
for key in _2D_h5keys:
try:
self.__dict__[key] = self.__2D_with_error__(key, index, assign)
except:
self.__dict__[key] = None
for key in _1D_h5keys:
try:
self.__dict__[key] = self.__1D_with_error__(key, index, assign)
except:
self.__dict__[key] = None
try:
self.__dict__['total_fluxes'] = WIPIDataset(raw=np.array(self.h5file['total_fluxes']), key='total_fluxes')
except:
pass
def __repr__(self):
return repr(self.__dir__())
def __getitem__(self, value):
if value in list(self.__dict__.keys()):
return self.__dict__[value]
def __setitem__(self, key, value):
self.__dict__[key] = value
def __getattr__(self, value):
if value in list(self.__dict__.keys()):
return self.__dict__[value]
def __setattr__(self, key, value):
self.__dict__[key] = value
def __dir__(self):
dict_keys = list(self.__dict__.keys())
remove = [ 'h5file', '__dict__']
for i in remove:
try:
dict_keys.remove(str(i))
except:
pass
return sorted(set(dict_keys))
def keys(self):
print(self.__dir__())
class __custom_dataset__(object):
def __init__(self, raw, assign, key):
self.__dict__ = {}
self.raw = raw
self.name = key
self.assign = assign
self.nstates = assign.attrs['nstates']
self.dim = len(raw.shape)
def __repr__(self):
return repr(self.__dir__())
def __getitem__(self, value):
if value in self.__dict__['raw'].dtype.names:
return self.__dict__['raw'][value]
elif value in list(self.__dict__.keys()):
return self.__dict__[value]
def __setitem__(self, key, value):
self.__dict__[key] = value
def __getattr__(self, value):
if value in self.__dict__['raw'].dtype.names:
return self.__dict__['raw'][value]
elif value in list(self.__dict__.keys()):
return self.__dict__[value]
def __setattr__(self, key, value):
self.__dict__[key] = value
def __dir__(self):
dict_keys = list(self.__dict__.keys())
remove = ['assign', 'dim', 'nstates', 'plotter', '__dict__']
for i in remove:
try:
dict_keys.remove(str(i))
except:
pass
return sorted(set(list(self.raw.dtype.names) + dict_keys))
def keys(self):
print(self.__dir__())
def _repr_pretty_(self, p, cycle):
if self.dim == 1:
return self._1D_repr_pretty_(p, cycle)
if self.dim == 2:
return self._2D_repr_pretty_(p, cycle)
def _1D_repr_pretty_(self, p, cycle):
maxlabellen = max(list(map(len,self.assign['state_labels'])))
p.text('')
p.text('{name} data:\n'.format(name=self.name))
for istate in range(self.nstates):
p.text('{:{maxlabellen}s}: mean={:21.15e} CI=({:21.15e}, {:21.15e}) * tau^-1\n'
.format(self.assign['state_labels'][istate],
self.raw['expected'][istate],
self.raw['ci_lbound'][istate],
self.raw['ci_ubound'][istate],
maxlabellen=maxlabellen))
p.text('To access data, index via the following names:\n')
p.text(str(self.__dir__()))
return " "
def _2D_repr_pretty_(self, p, cycle):
maxlabellen = max(list(map(len,self.assign['state_labels'])))
p.text('')
p.text('{name} data:\n'.format(name=self.name))
for istate in range(self.nstates):
for jstate in range(self.nstates):
if istate == jstate: continue
p.text('{:{maxlabellen}s} -> {:{maxlabellen}s}: mean={:21.15e} CI=({:21.15e}, {:21.15e}) * tau^-1\n'
.format(self.assign['state_labels'][istate], self.assign['state_labels'][jstate],
self.raw['expected'][istate, jstate],
self.raw['ci_lbound'][istate, jstate],
self.raw['ci_ubound'][istate, jstate],
maxlabellen=maxlabellen))
p.text('To access data, index via the following names:\n')
p.text(str(self.__dir__()))
return " "
def __2D_with_error__(self, h5key, index, assign):
self.step_iter = (self.h5file[h5key]['iter_stop'][0] - self.h5file[h5key]['iter_start'][0])[1,0]
value = ((index-self.h5file.attrs['iter_start']) // self.step_iter)
if value < 0:
value = 0
raw = self.h5file[h5key][value, :, :]
error = (raw['ci_ubound'] - raw['ci_lbound']) / (2*raw['expected'])
expected = raw['expected']
raw = self.__custom_dataset__(raw, assign, h5key)
raw.error = error
raw.plotter = Plotter(self.h5file, h5key, iteration=value, interface='text')
raw.plot = raw.plotter.plot
return raw
def __1D_with_error__(self, h5key, index, assign):
self.step_iter = (self.h5file[h5key]['iter_stop'][0] - self.h5file[h5key]['iter_start'][0])[1]
value = ((index-self.h5file.attrs['iter_start']) // self.step_iter)
if value < 0:
value = 0
raw = self.h5file[h5key][value, :]
error = (raw['ci_ubound'] - raw['ci_lbound']) / (2*raw['expected'])
expected = raw['expected']
raw = self.__custom_dataset__(raw, assign, h5key)
raw.error = error
raw.plotter = Plotter(self.h5file, h5key, iteration=value, interface='text')
raw.plot = raw.plotter.plot
return raw
class __get_data_for_iteration__(object):
def __init__(self, parent, value, seg_ids = None):
self.__dict__ = {}
iter_group = parent.data_reader.get_iter_group(value)
self.parent = parent
current = {}
current['iteration'] = value
if seg_ids is None:
seg_ids = range(0, iter_group['seg_index']['weight'].shape[0])
current['weights'] = iter_group['seg_index']['weight'][seg_ids]
current['pcoord'] = iter_group['pcoord'][...][seg_ids, :, :]
try:
current['auxdata'] = {}
for key in list(iter_group['auxdata'].keys()):
current['auxdata'][key] = iter_group['auxdata'][key][...][seg_ids, :]
except:
pass
current['parents'] = iter_group['seg_index']['parent_id'][seg_ids]
current['summary'] = parent.data_reader.data_manager.get_iter_summary(int(value))
current['seg_id'] = np.array(list(range(0, iter_group['seg_index'].shape[0])))[seg_ids]
current['walkers'] = current['summary']['n_particles']
current['states'] = parent.assign['trajlabels'][value-1, :current['walkers'], :][seg_ids]
current['bins'] = parent.assign['assignments'][value-1, :current['walkers'], :][seg_ids]
nbins = parent.assign['state_map'].shape[0]
nstates = parent.assign['state_labels'].shape[0] + 1
current['direct'] = KineticsIteration(parent.direct, value, parent.assign, value)
evolution_datasets = [ 'rate_evolution', 'conditional_flux_evolution', 'state_pop_evolution', 'color_prob_evolution' , 'total_fluxes', 'target_flux_evolution']
try:
current['reweight'] = KineticsIteration(parent.reweight, value, parent.assign, value)
matrix = parent.reweight['iterations/iter_{:08d}'.format(value)]
current['instant_matrix'] = sp.coo_matrix((matrix['flux'][...], (matrix['rows'][...], matrix['cols'][...])), shape=((nbins-1)*2, (nbins-1)*2)).todense()
reweighting = True
except:
current['reweight'] = parent.reweight['rate_evolution']
current['instant_matrix'] = parent.reweight['bin_populations']
current['matrix'] = parent.reweight['bin_populations']
reweighting = False
if reweighting:
for key in evolution_datasets:
current[key] = WIPIDataset(raw={ 'direct': current['direct'][key], 'reweight': current['reweight'][key] }, key='a')
else:
for key in evolution_datasets:
current[key] = WIPIDataset(raw={ 'direct': current['direct'][key] }, key='direct')
self.raw = current
def __repr__(self):
return repr(self.__dir__())
def keys(self):
return list(self.__dict__['raw'].keys())
def __setitem__(self, key, value):
self.__dict__[key] = value
def __getattr__(self, value):
if value in list(self.__dict__['raw'].keys()):
return self.__dict__['raw'][value]
elif value in list(self.__dict__.keys()):
return self.__dict__[value]
def __setattr__(self, key, value):
self.__dict__[key] = value
def __dir__(self):
dict_keys = list(self.__dict__.keys())
dict_keys += ['maxweight', 'minweight', 'walkers', 'aggregate_walkers', 'successful_trajectories']
remove = ['__dict__']
for i in remove:
try:
dict_keys.remove(str(i))
except:
pass
return sorted(set(list(self.__dict__['raw'].keys()) + dict_keys))
@property
def maxweight(self):
walker = np.where(self.raw['weights'] == np.max(self.raw['weights']))[0][0]
return self.__getitem__(walker)
@property
def minweight(self):
walker = np.where(self.raw['weights'] == np.min(self.raw['weights']))[0][0]
return self.__getitem__(walker)
@property | MIT License |
hszhao/pointweb | lib/pointops/functions/pointops.py | pairwise_distances | python | def pairwise_distances(x, y=None):
x_norm = (x ** 2).sum(1).view(-1, 1)
if y is not None:
y_t = torch.transpose(y, 0, 1)
y_norm = (y ** 2).sum(1).view(1, -1)
else:
y_t = torch.transpose(x, 0, 1)
y_norm = x_norm.view(1, -1)
dist = x_norm + y_norm - 2.0 * torch.mm(x, y_t)
import numpy as np
return torch.clamp(dist, 0.0, np.inf) | Input: x is a Nxd matrix
y is an optional Mxd matirx
Output: dist is a NxM matrix where dist[i,j] is the square norm between x[i,:] and y[j,:]
if y is not given then use 'y=x'.
i.e. dist[i,j] = ||x[i,:]-y[j,:]||^2 | https://github.com/hszhao/pointweb/blob/f31fe05616c3c068f6c1870170a3caaf1f7d8abb/lib/pointops/functions/pointops.py#L346-L363 | from typing import Tuple
import torch
from torch.autograd import Function
import torch.nn as nn
import pointops_cuda
class FurthestSampling(Function):
@staticmethod
def forward(ctx, xyz, m):
assert xyz.is_contiguous()
b, n, _ = xyz.size()
idx = torch.cuda.IntTensor(b, m)
temp = torch.cuda.FloatTensor(b, n).fill_(1e10)
pointops_cuda.furthestsampling_cuda(b, n, m, xyz, temp, idx)
return idx
@staticmethod
def backward(xyz, a=None):
return None, None
furthestsampling = FurthestSampling.apply
class Gathering(Function):
@staticmethod
def forward(ctx, features, idx):
assert features.is_contiguous()
assert idx.is_contiguous()
b, c, n = features.size()
m = idx.size(1)
output = torch.cuda.FloatTensor(b, c, m)
pointops_cuda.gathering_forward_cuda(b, c, n, m, features, idx, output)
ctx.for_backwards = (idx, c, n)
return output
@staticmethod
def backward(ctx, grad_out):
idx, c, n = ctx.for_backwards
b, m = idx.size()
grad_features = torch.cuda.FloatTensor(b, c, n).zero_()
grad_out_data = grad_out.data.contiguous()
pointops_cuda.gathering_backward_cuda(b, c, n, m, grad_out_data, idx, grad_features.data)
return grad_features, None
gathering = Gathering.apply
class NearestNeighbor(Function):
@staticmethod
def forward(ctx, unknown: torch.Tensor, known: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
assert unknown.is_contiguous()
assert known.is_contiguous()
b, n, _ = unknown.size()
m = known.size(1)
dist2 = torch.cuda.FloatTensor(b, n, 3)
idx = torch.cuda.IntTensor(b, n, 3)
pointops_cuda.nearestneighbor_cuda(b, n, m, unknown, known, dist2, idx)
return torch.sqrt(dist2), idx
@staticmethod
def backward(ctx, a=None, b=None):
return None, None
nearestneighbor = NearestNeighbor.apply
class Interpolation(Function):
@staticmethod
def forward(ctx, features: torch.Tensor, idx: torch.Tensor, weight: torch.Tensor) -> torch.Tensor:
assert features.is_contiguous()
assert idx.is_contiguous()
assert weight.is_contiguous()
b, c, m = features.size()
n = idx.size(1)
ctx.interpolation_for_backward = (idx, weight, m)
output = torch.cuda.FloatTensor(b, c, n)
pointops_cuda.interpolation_forward_cuda(b, c, m, n, features, idx, weight, output)
return output
@staticmethod
def backward(ctx, grad_out: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
idx, weight, m = ctx.interpolation_for_backward
b, c, n = grad_out.size()
grad_features = torch.cuda.FloatTensor(b, c, m).zero_()
grad_out_data = grad_out.data.contiguous()
pointops_cuda.interpolation_backward_cuda(b, c, n, m, grad_out_data, idx, weight, grad_features.data)
return grad_features, None, None
interpolation = Interpolation.apply
class Grouping(Function):
@staticmethod
def forward(ctx, features: torch.Tensor, idx: torch.Tensor) -> torch.Tensor:
assert features.is_contiguous()
assert idx.is_contiguous()
b, c, n = features.size()
_, m, nsample = idx.size()
output = torch.cuda.FloatTensor(b, c, m, nsample)
pointops_cuda.grouping_forward_cuda(b, c, n, m, nsample, features, idx, output)
ctx.for_backwards = (idx, n)
return output
@staticmethod
def backward(ctx, grad_out: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
idx, n = ctx.for_backwards
b, c, m, nsample = grad_out.size()
grad_features = torch.cuda.FloatTensor(b, c, n).zero_()
grad_out_data = grad_out.data.contiguous()
pointops_cuda.grouping_backward_cuda(b, c, n, m, nsample, grad_out_data, idx, grad_features.data)
return grad_features, None
grouping = Grouping.apply
class GroupingInt(Function):
@staticmethod
def forward(ctx, features: torch.Tensor, idx: torch.Tensor) -> torch.Tensor:
assert features.is_contiguous()
assert idx.is_contiguous()
b, c, n = features.size()
_, m, nsample = idx.size()
output = torch.cuda.LongTensor(b, c, m, nsample)
pointops_cuda.grouping_int_forward_cuda(b, c, n, m, nsample, features, idx, output)
return output
@staticmethod
def backward(ctx, a=None):
return None, None
grouping_int = GroupingInt.apply
class BallQuery(Function):
@staticmethod
def forward(ctx, radius: float, nsample: int, xyz: torch.Tensor, new_xyz: torch.Tensor) -> torch.Tensor:
assert xyz.is_contiguous()
assert new_xyz.is_contiguous()
b, n, _ = xyz.size()
m = new_xyz.size(1)
idx = torch.cuda.IntTensor(b, m, nsample).zero_()
pointops_cuda.ballquery_cuda(b, n, m, radius, nsample, new_xyz, xyz, idx)
return idx
@staticmethod
def backward(ctx, a=None):
return None, None, None, None
ballquery = BallQuery.apply
class FeatureDistribute(Function):
@staticmethod
def forward(ctx, max_xyz: torch.Tensor, xyz: torch.Tensor) -> torch.Tensor:
assert max_xyz.is_contiguous()
assert xyz.is_contiguous()
b, n, _ = max_xyz.size()
m = xyz.size(1)
distribute_idx = torch.cuda.IntTensor(b, m).zero_()
pointops_cuda.featuredistribute_cuda(b, n, m, max_xyz, xyz, distribute_idx)
return distribute_idx
@staticmethod
def backward(ctx, a=None):
return None, None
featuredistribute = FeatureDistribute.apply
class FeatureGather(Function):
@staticmethod
def forward(ctx, max_feature: torch.Tensor, distribute_idx: torch.Tensor) -> torch.Tensor:
assert max_feature.is_contiguous()
assert distribute_idx.is_contiguous()
b, c, n = max_feature.size()
m = distribute_idx.size(1)
distribute_feature = torch.cuda.FloatTensor(b, c, m).zero_()
pointops_cuda.featuregather_forward_cuda(b, n, m, c, max_feature, distribute_idx, distribute_feature)
ctx.for_backwards = (distribute_idx, n)
return distribute_feature
@staticmethod
def backward(ctx, grad_distribute_feature: torch.Tensor):
distribute_idx, n = ctx.for_backwards
b, c, m = grad_distribute_feature.size()
grad_max_feature = torch.cuda.FloatTensor(b, c, n).zero_()
grad_distribute_feature_data = grad_distribute_feature.data.contiguous()
pointops_cuda.featuregather_backward_cuda(b, n, m, c, grad_distribute_feature_data, distribute_idx, grad_max_feature.data)
return grad_max_feature, None
featuregather = FeatureGather.apply
class LabelStatBallRange(Function):
@staticmethod
def forward(ctx, radius: float, xyz: torch.Tensor, new_xyz: torch.Tensor, label_stat: torch.Tensor) -> torch.Tensor:
assert xyz.is_contiguous()
assert new_xyz.is_contiguous()
assert label_stat.is_contiguous()
b, n, nclass = label_stat.size()
m = new_xyz.size(1)
new_label_stat = torch.cuda.IntTensor(b, m, nclass).zero_()
pointops_cuda.labelstat_ballrange_cuda(b, n, m, radius, nclass, new_xyz, xyz, label_stat, new_label_stat)
return new_label_stat
@staticmethod
def backward(ctx, a=None):
return None, None, None, None
labelstat_ballrange = LabelStatBallRange.apply
class LabelStatIdx(Function):
@staticmethod
def forward(ctx, nsample: int, label_stat: torch.Tensor, idx: torch.Tensor) -> torch.Tensor:
assert label_stat.is_contiguous()
assert idx.is_contiguous()
b, n, nclass = label_stat.size()
m = idx.size(1)
new_label_stat = torch.cuda.IntTensor(b, m, nclass).zero_()
pointops_cuda.labelstat_idx_cuda(b, n, m, nsample, nclass, label_stat, idx, new_label_stat)
return new_label_stat
@staticmethod
def backward(ctx, a=None):
return None, None, None
labelstat_idx = LabelStatIdx.apply
class LabelStatAndBallQuery(Function):
@staticmethod
def forward(ctx, radius: float, nsample: int, xyz: torch.Tensor, new_xyz: torch.Tensor, label_stat: torch.Tensor):
assert xyz.is_contiguous()
assert new_xyz.is_contiguous()
assert label_stat.is_contiguous()
b, n, nclass = label_stat.size()
m = new_xyz.size(1)
new_label_stat = torch.cuda.IntTensor(b, m, nclass).zero_()
idx = torch.cuda.IntTensor(b, m, nsample).zero_()
pointops_cuda.labelstat_and_ballquery_cuda(b, n, m, radius, nsample, nclass, new_xyz, xyz, label_stat, idx, new_label_stat)
return new_label_stat, idx
@staticmethod
def backward(ctx, a=None, b=None):
return None, None, None, None, None
labelstat_and_ballquery = LabelStatAndBallQuery.apply | MIT License |
probcomp/bayeslite | src/core.py | bayesdb_table_has_column | python | def bayesdb_table_has_column(bdb, table, name):
bayesdb_table_guarantee_columns(bdb, table)
sql = 'SELECT COUNT(*) FROM bayesdb_column WHERE tabname = ? AND name = ?'
return cursor_value(bdb.sql_execute(sql, (table, name))) | True if the table named `table` has a column named `name`.
`bdb` must have a table named `table`. If you're not sure, call
:func:`bayesdb_has_table` first.
WARNING: This may modify the database by populating the
``bayesdb_column`` table if it has not yet been populated. | https://github.com/probcomp/bayeslite/blob/211e5eb3821a464a2fffeb9d35e3097e1b7a99ba/src/core.py#L82-L93 | from bayeslite.exception import BQLError
from bayeslite.sqlite3_util import sqlite3_quote_name
from bayeslite.util import casefold
from bayeslite.util import cursor_value
def bayesdb_has_table(bdb, name):
qt = sqlite3_quote_name(name)
cursor = bdb.sql_execute('PRAGMA table_info(%s)' % (qt,))
try:
cursor.next()
except StopIteration:
return False
else:
return True
def bayesdb_table_column_names(bdb, table):
bayesdb_table_guarantee_columns(bdb, table)
sql = '''
SELECT name FROM bayesdb_column WHERE tabname = ?
ORDER BY colno ASC
'''
return [str(row[0]) for row in bdb.sql_execute(sql, (table,))] | Apache License 2.0 |
zagaran/mongolia | mongolia/database_collection.py | DatabaseCollection.__init__ | python | def __init__(self, path=None, objtype=None, query=None, sort_by=ID_KEY, ascending=True,
page=0, page_size=None, read_only=False, projection=None, field=None,
**kwargs):
if objtype:
self.OBJTYPE = objtype
if path:
self.PATH = path
if not query:
query = kwargs
if field:
if field == ID_KEY:
projection = [ID_KEY]
else:
projection = {field: True, ID_KEY: False}
if projection:
read_only = True
results = self.db().find(query, projection=projection)
if isinstance(sort_by, list):
results = results.sort(sort_by)
elif sort_by:
results = results.sort(sort_by, ASCENDING if ascending else DESCENDING)
if page_size:
results.limit(page_size).skip(page_size * page)
if field:
for result in results:
if field in result:
self.append(result[field])
elif read_only:
for result in results:
self.append(result)
else:
for result in results:
self.append(self.OBJTYPE(path=self.PATH, _new_object=result)) | Loads a list of DatabaseObjects from path matching query. If nothing
matches the query (possibly because there is nothing in the specified
mongo collection), the created DatabaseCollection will be an empty
list and have bool(returned object) == False
NOTE: The path and objtype parameters to this function are to allow
use of the DatabaseCollection class directly. However, this class is
intended for subclassing and children of it should override either the
OBJTYPE or PATH attribute rather than passing them as parameters here.
WARNING: if you are attempting to grab a particularly large set of
results (such as an entire collection), your system may run out of
memory. In the event of large collections, the number of returned
results can be reduced either by filtering the results with the query
parameter or by using pagination via the page_size and page parameters.
If all you need to do is iterate over the collection, use the `iterator`
classmethod.
@param path: the path of the database to query, in the form
"database.colletion"; pass None to use the value of the
PATH property of the object or, if that is none, the
PATH property of OBJTYPE
@param objtype: the object type to use for these DatabaseObjects;
pass None to use the OBJTYPE property of the class
@param query: a dictionary specifying key-value pairs that the result
must match. If query is None, use kwargs in it's place
@param sort_by: a key to use for the sort order of the results; ID_KEY
by default. Can also be a list of pairs [(key, direction), ...], as
in pymongo's sort function. If set to None, no sort operation is
applied.
@param ascending: whether to sort the results in ascending order of
the sort_by key (if True) or descending order (if False). Ignored if
sort_by is a list or None.
@param page: the page number of results to return if pagination is
being uses; note that if page_size is None, this parameter is
ignored; page is 0-indexed
@param page_size: returns only a single page of results, this
defining the number of results in a page; see also the page
parameter; if this is None or 0, paging is disabled
@param read_only: returns the contents as python dictionaries rather than
DatabaseObjects. This is not read_only in the sense that the
returned objects are immutable, but in the sense that they have no
attached .save() methods, so there is no way to write modifications
to them back to the database.
@param projection: specifies fields to return in a projection query. May
be a list of field names, in which case the ID_KEY field is always
returned whether or not listed. To prevent ID_KEY from being
included, use a dict instead (see pymongo documentation for
projection queries). Specifying this field implies read_only.
@param field: returns simply the indicated field for each object
rather than the entire object. For example, if ID_KEY is passed in
for this parameter, only the ID's of the collection are returned,
not the entire contents. This behaves similarly to read_only
in that the returned objects cannot be saved to the database if
they are updated. Objects that do not have the indicated field
are omitted from results. Do not combine with the projection parameter.
@param **kwargs: used as query parameters if query is None
@raise Exception: if path, self.PATH, and self.OBJTYPE.PATH are all
None; the database path must be defined in at least one of these | https://github.com/zagaran/mongolia/blob/18d921017123b9edb7b70b01d8cfb3eb491b4cb6/mongolia/database_collection.py#L67-L161 | import json
from pymongo import ASCENDING, DESCENDING
from mongolia.constants import ID_KEY, GT
from mongolia.database_object import DatabaseObject
from mongolia.json_codecs import MongoliaJSONEncoder
class DatabaseCollection(list):
OBJTYPE = DatabaseObject
PATH = None | MIT License |
diofant/diofant | diofant/simplify/radsimp.py | rcollect | python | def rcollect(expr, *vars):
if expr.is_Atom or not expr.has(*vars):
return expr
else:
expr = expr.__class__(*[rcollect(arg, *vars) for arg in expr.args])
if expr.is_Add:
return collect(expr, vars)
else:
return expr | Recursively collect sums in an expression.
Examples
========
>>> expr = (x**2*y + x*y + x + y)/(x + y)
>>> rcollect(expr, y)
(x + y*(x**2 + x + 1))/(x + y)
See Also
========
collect, collect_const, collect_sqrt | https://github.com/diofant/diofant/blob/05c50552b0e0533f1dbf2ec05e65b6c45b7e2c11/diofant/simplify/radsimp.py#L387-L412 | from collections import defaultdict
from ..core import (Add, Derivative, I, Integer, Mul, Pow, Rational,
expand_mul, expand_power_base, gcd_terms, symbols)
from ..core.compatibility import iterable
from ..core.exprtools import Factors
from ..core.function import _mexpand
from ..core.mul import _keep_coeff, _unevaluated_Mul
from ..core.sympify import sympify
from ..functions import log, sqrt
from ..polys import gcd
from ..utilities import default_sort_key, ordered
from .sqrtdenest import sqrtdenest
def collect(expr, syms, func=None, evaluate=True, exact=False, distribute_order_term=True):
def make_expression(terms):
product = []
for term, rat, sym, deriv in terms:
if deriv is not None:
var, order = deriv
while order > 0:
term, order = Derivative(term, var), order - 1
if sym is None:
if rat == 1:
product.append(term)
else:
product.append(Pow(term, rat))
else:
product.append(Pow(term, rat*sym))
return Mul(*product)
def parse_derivative(deriv):
expr, sym, order = deriv.expr, deriv.variables[0], 1
for s in deriv.variables[1:]:
if s == sym:
order += 1
else:
raise NotImplementedError(
'Improve MV Derivative support in collect')
while isinstance(expr, Derivative):
s0 = expr.variables[0]
for s in expr.variables:
if s != s0:
raise NotImplementedError(
'Improve MV Derivative support in collect')
if s0 == sym:
expr, order = expr.expr, order + len(expr.variables)
else:
break
return expr, (sym, Rational(order))
def parse_term(expr):
rat_expo, sym_expo = Integer(1), None
sexpr, deriv = expr, None
if expr.is_Pow:
if isinstance(expr.base, Derivative):
sexpr, deriv = parse_derivative(expr.base)
else:
sexpr = expr.base
if expr.exp.is_Number:
rat_expo = expr.exp
else:
coeff, tail = expr.exp.as_coeff_Mul()
if coeff.is_Number:
rat_expo, sym_expo = coeff, tail
else:
sym_expo = expr.exp
elif isinstance(expr, Derivative):
sexpr, deriv = parse_derivative(expr)
return sexpr, rat_expo, sym_expo, deriv
def parse_expression(terms, pattern):
pattern = Mul.make_args(pattern)
if len(terms) >= len(pattern):
pattern = [parse_term(elem) for elem in pattern]
terms = terms[:]
elems, common_expo, has_deriv = [], None, False
for elem, e_rat, e_sym, e_ord in pattern:
if elem.is_Number and e_rat == 1 and e_sym is None:
continue
for j in range(len(terms)):
if terms[j] is None:
continue
term, t_rat, t_sym, t_ord = terms[j]
if t_ord is not None:
has_deriv = True
if (term.match(elem) is not None and
(t_sym == e_sym or t_sym is not None and
e_sym is not None and
t_sym.match(e_sym) is not None)):
if exact is False:
expo = t_rat / e_rat
if common_expo is None:
common_expo = expo
else:
if common_expo != expo:
common_expo = 1
else:
if e_rat != t_rat or e_ord != t_ord:
continue
elems.append(terms[j])
terms[j] = None
break
else:
return
return [_f for _f in terms if _f], elems, common_expo, has_deriv
if evaluate:
if expr.is_Mul:
return expr.func(*[
collect(term, syms, func, True, exact, distribute_order_term)
for term in expr.args])
elif expr.is_Pow:
b = collect(
expr.base, syms, func, True, exact, distribute_order_term)
return Pow(b, expr.exp)
if iterable(syms):
syms = [expand_power_base(i, deep=False) for i in syms]
else:
syms = [expand_power_base(syms, deep=False)]
expr = sympify(expr)
order_term = None
if distribute_order_term:
order_term = expr.getO()
if order_term is not None:
if order_term.has(*syms):
order_term = None
else:
expr = expr.removeO()
summa = [expand_power_base(i, deep=False) for i in Add.make_args(expr)]
collected, disliked = defaultdict(list), Integer(0)
for product in summa:
terms = [parse_term(i) for i in Mul.make_args(product)]
for symbol in syms:
result = parse_expression(terms, symbol)
if result is not None:
terms, elems, common_expo, has_deriv = result
if not has_deriv:
index = 1
for elem in elems:
e = elem[1]
if elem[2] is not None:
e *= elem[2]
index *= Pow(elem[0], e)
else:
index = make_expression(elems)
terms = expand_power_base(make_expression(terms), deep=False)
index = expand_power_base(index, deep=False)
collected[index].append(terms)
break
else:
disliked += product
collected = {k: Add(*v) for k, v in collected.items()}
if disliked != 0:
collected[Integer(1)] = disliked
if order_term is not None:
for key, val in collected.items():
collected[key] = val + order_term
if func is not None:
collected = {key: func(val) for key, val in collected.items()}
if evaluate:
return Add(*[key*val for key, val in collected.items()])
else:
return collected | BSD 3-Clause New or Revised License |
cityofsantamonica/mds-provider | mds/db/loaders.py | Records.load | python | def load(self, source, **kwargs):
if isinstance(source, dict):
source = [source]
df = pd.DataFrame.from_records(source)
super().load(df, **kwargs) | Load data from one or more MDS Provider records.
Parameters:
source: dict, list
One or more dicts of type record_type.
record_type: str
The type of MDS data.
table: str
The name of the database table to insert this data into.
engine: sqlalchemy.engine.Engine
The engine used for connections to the database backend.
Additional keyword arguments are passed-through to DataFrameLoader.load(). | https://github.com/cityofsantamonica/mds-provider/blob/02abcb227c35cdfe78a39e35b3157f7c2916c028/mds/db/loaders.py#L193-L216 | import string
import pandas as pd
from ..db import sql
from ..fake import util
from ..files import DataFile
from ..schemas import STATUS_CHANGES, TRIPS, EVENTS, VEHICLES, Schema
from ..versions import UnexpectedVersionError, Version
class DataFrame():
def load(self, source, **kwargs):
record_type = kwargs.pop("record_type")
table = kwargs.pop("table")
engine = kwargs.pop("engine")
version = Version(kwargs.get("version", Version.mds_lower()))
version.raise_if_unsupported()
before_load = kwargs.get("before_load")
stage_first = kwargs.get("stage_first")
on_conflict_update = kwargs.get("on_conflict_update")
if before_load is not None:
transform = before_load(source, version)
source = source if transform is None else transform
if not stage_first:
source.to_sql(table, engine, if_exists="append", index=False)
return
factor = stage_first if isinstance(stage_first, int) else 1
temp = f"{table}_tmp_{util.random_string(factor, chars=string.ascii_lowercase)}"
source.to_sql(temp, engine, if_exists="replace", index=False)
with engine.begin() as conn:
if record_type in [STATUS_CHANGES, EVENTS]:
query = sql.insert_status_changes_from(temp,
table,
version=version,
on_conflict_update=on_conflict_update)
elif record_type == TRIPS:
query = sql.insert_trips_from(temp,
table,
version=version,
on_conflict_update=on_conflict_update)
elif record_type == VEHICLES:
query = sql.insert_vehicles_from(temp,
table,
version=version,
on_conflict_update=on_conflict_update)
if query is not None:
conn.execute(query)
conn.execute(f"DROP TABLE {temp}")
@classmethod
def can_load(cls, source):
return isinstance(source, pd.DataFrame)
class File(DataFrame):
def load(self, source, **kwargs):
record_type = kwargs.get("record_type")
version = Version(kwargs.get("version"))
_version, df = DataFile(record_type, source).load_dataframe()
if version and _version != version:
raise UnexpectedVersionError(_version, version)
return super().load(df, **kwargs)
@classmethod
def can_load(cls, source):
try:
return DataFile(source).file_sources
except:
return False
class Records(DataFrame): | MIT License |
google/cauliflowervest | cauliflowervest/server/handlers/maintenance.py | _update_schema | python | def _update_schema(model, cursor=None, num_updated=0):
query = model.all()
if cursor:
query.with_cursor(cursor)
updated = 0
for p in query.fetch(limit=_BATCH_SIZE):
_reinsert_entity(model, p.key())
updated += 1
if updated > 0:
num_updated += updated
logging.info(
'Put %d %s entities to Datastore for a total of %d',
updated, model.ESCROW_TYPE_NAME, num_updated)
deferred.defer(
_update_schema, model, cursor=query.cursor(),
num_updated=num_updated, _queue=_QUEUE_NAME, _countdown=20)
else:
logging.info(
'UpdateSchema complete for %s with %d updates!', model.ESCROW_TYPE_NAME,
num_updated) | Add tag field. | https://github.com/google/cauliflowervest/blob/d3f52501ebed8b9a392350c8e177bbc602a6a09d/cauliflowervest/server/handlers/maintenance.py#L41-L63 | import httplib
import logging
from google.appengine.api import users
from google.appengine.ext import db
from google.appengine.ext import deferred
from cauliflowervest import settings as base_settings
from cauliflowervest.server.handlers import base_handler
from cauliflowervest.server.models import base
from cauliflowervest.server.models import util
_BATCH_SIZE = 20
_QUEUE_NAME = 'serial'
@db.transactional()
def _reinsert_entity(model, entity_key):
entity = model.get(entity_key)
entity.tag = getattr(entity, 'tag', 'default')
super(base.BasePassphrase, entity).put() | Apache License 2.0 |
jmchilton/galaxy-central | galaxy/datatypes/sniff.py | is_bed | python | def is_bed(headers, skip=0):
try:
if not headers:
return False
for hdr in headers[skip:]:
try:
map(int, [ hdr[1], hdr[2] ])
except:
return False
return True
except:
return False | Checks for 'bedness'
>>> fname = get_test_fname('test_tab.bed')
>>> headers = get_headers(fname, sep='\\t')
>>> is_bed(headers)
True
>>> fname = get_test_fname('interval.bed')
>>> headers = get_headers(fname, sep='\\t')
>>> is_bed(headers)
False | https://github.com/jmchilton/galaxy-central/blob/31e2fd3a32b06ddfba06ae5b044efdce1d93f08c/galaxy/datatypes/sniff.py#L221-L246 | import logging, sys, os, csv, tempfile, shutil, re
log = logging.getLogger(__name__)
def get_test_fname(fname):
path, name = os.path.split(__file__)
full_path = os.path.join(path, 'test', fname)
return full_path
def stream_to_file(stream):
fd, temp_name = tempfile.mkstemp()
while 1:
chunk = stream.read(1048576)
if not chunk:
break
os.write(fd, chunk)
os.close(fd)
return temp_name
def convert_newlines(fname):
fd, temp_name = tempfile.mkstemp()
os.close(fd)
shutil.copyfile(fname, temp_name)
fp = open(fname, "wt")
for line in file(temp_name, "U"):
line = line.strip() + '\n'
fp.write(line)
fp.close()
os.remove(temp_name)
def sep2tabs(fname, patt="\\s+"):
fd, temp_name = tempfile.mkstemp()
os.close(fd)
shutil.copyfile(fname, temp_name)
regexp = re.compile(patt)
fp = open(fname, 'wt')
for line in file(temp_name):
line = line.strip()
elems = regexp.split(line)
fp.write('\t'.join(elems) + '\n')
fp.close()
os.remove(temp_name)
def get_headers(fname, sep, count=30):
headers = []
for idx, line in enumerate(file(fname)):
line = line.strip()
if idx == count:
break
headers.append( line.split(sep) )
return headers
def is_column_based(fname, sep='\t'):
headers = get_headers(fname, sep=sep)
if not headers:
return False
count = len(headers[0])
if count < 2:
return False
for hdr in headers:
if len(hdr) != count:
return False
return True
def is_fasta(headers):
try:
return len(headers) > 1 and headers[0][0] and headers[0][0][0] == ">"
except:
return False
def is_gff(headers):
try:
return len(headers) > 2 and headers[0][1] and headers[0][1].startswith('gff-version')
except:
return False
def is_maf(headers):
try:
return len(headers) > 1 and headers[0][0] and headers[0][0] == "##maf"
except:
return False
def is_lav(headers):
try:
return len(headers) > 1 and headers[0][0] and headers[0][0].startswith('#:lav')
except:
return False
def is_axt(headers):
try:
return (len(headers) >= 4) and (headers[0][7] == '-' or headers[0][7] == '+') and (headers[3] == []) and (len(headers[0])==9 or len(headers[0])==10)
except:
return False
def is_wiggle(headers):
try:
for idx, hdr in enumerate(headers):
if hdr and hdr[0] == "track":
return True
if idx > 10:
break
return False
except:
return False | MIT License |
smartbgp/yabgp | yabgp/message/open.py | Open.construct | python | def construct(self, my_capability):
capas = b''
if 'afi_safi' in my_capability:
capas += Capability(capa_code=1, capa_length=4).construct(my_capability)
if my_capability.get('cisco_route_refresh'):
capas += Capability(capa_code=128, capa_length=0).construct(my_capability)
if my_capability.get('route_refresh'):
capas += Capability(capa_code=2, capa_length=0).construct(my_capability)
if self.asn > 65535:
capas += Capability(capa_code=65, capa_length=4, capa_value=self.asn).construct(my_capability)
self.asn = 23456
else:
if my_capability.get('four_bytes_as'):
capas += Capability(capa_code=65, capa_length=4, capa_value=self.asn).construct(my_capability)
if my_capability.get('add_path'):
capas += Capability(capa_code=69, capa_length=4, capa_value=my_capability['add_path']).construct()
if my_capability.get('enhanced_route_refresh'):
capas += Capability(capa_code=70, capa_length=0).construct()
open_header = struct.pack('!BHHIB', self.version, self.asn, self.hold_time,
self.bgp_id, len(capas))
message = open_header + capas
return self.construct_header(message) | Construct a BGP Open message | https://github.com/smartbgp/yabgp/blob/f073633a813899cd9b413bc28ea2f7737deee141/yabgp/message/open.py#L232-L265 | import struct
import netaddr
from yabgp.common import exception as excp
from yabgp.common import constants as bgp_cons
class Open(object):
def __init__(self, version=None, asn=None, hold_time=None,
bgp_id=None, opt_para_len=None, opt_paras=None):
self.version = version
self.asn = asn
self.hold_time = hold_time
self.bgp_id = bgp_id
self.opt_para_len = opt_para_len
self.opt_paras = opt_paras
self.capa_dict = {}
def parse(self, message):
try:
self.version, self.asn, self.hold_time, self.bgp_id, self.opt_para_len = struct.unpack('!BHHIB', message[:10])
except:
raise excp.MessageHeaderError(
sub_error=bgp_cons.ERR_MSG_HDR_BAD_MSG_LEN,
data=message[:10])
self.bgp_id = str(netaddr.IPAddress(self.bgp_id))
if self.version != 4:
raise excp.OpenMessageError(
sub_error=bgp_cons.ERR_MSG_OPEN_UNSUP_VERSION,
data=self.version)
if isinstance(self.asn, float):
self.asn = str(self.asn).split('.')
self.asn = 65536 * (int(self.asn[0])) + int(self.asn[1])
if self.asn == 0:
raise excp.OpenMessageError(
sub_error=bgp_cons.ERR_MSG_OPEN_BAD_PEER_AS,
data=self.asn)
if self.bgp_id == 0:
raise excp.OpenMessageError(
sub_error=bgp_cons.ERR_MSG_OPEN_BAD_BGP_ID,
data=self.bgp_id)
if self.opt_para_len:
self.opt_paras = message[10:]
while self.opt_paras:
opt_para_type, opt_para_length = struct.unpack('!BB', self.opt_paras[:2])
if opt_para_type != 2:
raise excp.OpenMessageError(
sub_error=bgp_cons.ERR_MSG_OPEN_UNSUP_OPT_PARAM,
data=message[10:])
capabilities = self.opt_paras[2:opt_para_length + 2]
while capabilities:
capability = Capability()
capability.parse(capabilities)
if capability.capa_code == capability.FOUR_BYTES_ASN:
asn = struct.unpack('!I', capability.capa_value)[0]
self.asn = asn
self.capa_dict['four_bytes_as'] = True
elif capability.capa_code == capability.MULTIPROTOCOL_EXTENSIONS:
if 'afi_safi' not in self.capa_dict:
self.capa_dict['afi_safi'] = []
afi, res, safi = struct.unpack('!HBB', capability.capa_value)
self.capa_dict['afi_safi'].append((afi, safi))
elif capability.capa_code == capability.ROUTE_REFRESH:
self.capa_dict['route_refresh'] = True
elif capability.capa_code == capability.CISCO_ROUTE_REFRESH:
self.capa_dict['cisco_route_refresh'] = True
elif capability.capa_code == capability.GRACEFUL_RESTART:
self.capa_dict['graceful_restart'] = True
elif capability.capa_code == capability.CISCO_MULTISESSION_BGP:
self.capa_dict['cisco_multi_session'] = True
elif capability.capa_code == capability.ENHANCED_ROUTE_REFRESH:
self.capa_dict['enhanced_route_refresh'] = True
elif capability.capa_code == capability.ADD_PATH:
if 'add_path' not in self.capa_dict:
self.capa_dict['add_path'] = []
while len(capability.capa_value) % 4 == 0 and capability.capa_value:
afi, safi, send_rev = struct.unpack('!HBB', capability.capa_value[:4])
self.capa_dict['add_path'].append(
{
'afi_safi': bgp_cons.AFI_SAFI_DICT[(afi, safi)],
'send/receive': bgp_cons.ADD_PATH_ACT_DICT[send_rev]
}
)
capability.capa_value = capability.capa_value[4:]
elif capability.capa_code == capability.LLGR:
self.capa_dict['LLGR'] = []
while len(capability.capa_value) >= 7:
afi, safi, flag = struct.unpack('!HBB', capability.capa_value[:4])
time = struct.unpack('!I', b'\x00' + capability.capa_value[4:7])[0]
self.capa_dict['LLGR'].append({
'afi_safi': [afi, safi],
'time': time
})
capability.capa_value = capability.capa_value[7:]
elif capability.capa_code == capability.EXTENDED_NEXT_HOP:
self.capa_dict['ext_nexthop'] = []
while len(capability.capa_value) > 0:
afi, safi, nexthop = struct.unpack('!HHH', capability.capa_value[:6])
capability.capa_value = capability.capa_value[6:]
self.capa_dict['ext_nexthop'].append({
"afi_safi": [afi, safi],
"nexthop_afi": nexthop
})
else:
self.capa_dict[str(capability.capa_code)] = repr(capability.capa_value)
capabilities = capabilities[2 + capability.capa_length:]
self.opt_paras = self.opt_paras[opt_para_length + 2:]
return {
'version': self.version,
'asn': self.asn,
'hold_time': self.hold_time,
'bgp_id': self.bgp_id,
'capabilities': self.capa_dict
}
@staticmethod
def construct_header(msg):
return b'\xff'*16 + struct.pack('!HB',
len(msg) + 19,
1) + msg | Apache License 2.0 |
pbattaglia/scenesim | scenesim/physics/bulletbase.py | BulletBase.add_ghostnode | python | def add_ghostnode(node):
name = "%s-ghost" % node.getName()
ghost = NodePath(BulletGhostNode(name))
ghost.reparentTo(node)
return ghost | Adds a child ghostnode to a node as a workaround for the
ghost-static node collision detection problem. | https://github.com/pbattaglia/scenesim/blob/2633c63bc5cb97ea99017b2e25fc9b4f66d72605/scenesim/physics/bulletbase.py#L582-L588 | from collections import Iterable
from contextlib import contextmanager
from functools import update_wrapper
from itertools import combinations, izip
from math import isnan, sqrt
from warnings import warn
import numpy as np
from panda3d.bullet import (BulletBaseCharacterControllerNode, BulletBodyNode,
BulletConstraint, BulletDebugNode,
BulletGenericConstraint, BulletGhostNode,
BulletVehicle, BulletWorld)
from panda3d.core import (BitMask32, NodePath, Point3,
PythonCallbackObject, Quat, TransformState,
Vec3)
from pdb import set_trace as BP
nan = float("nan")
class BulletBaseError(Exception):
pass
class DeactivationEnabledWarning(UserWarning):
pass
class CollisionMonitor(object):
def __init__(self, world):
self.world = world
self.reset()
self.use_callback = False
def push_notifiers(self, bodies):
if self.use_callback:
self.notifies = {body: body.notifiesCollisions()
for body in bodies}
for body in bodies:
body.notifyCollisions(True)
self.world.setContactAddedCallback(PythonCallbackObject(self))
else:
self.bodies = bodies
self.reset()
def pop_notifiers(self):
if self.use_callback:
self.world.clearContactAddedCallback()
for body, notify in self.notifies.iteritems():
body.notifyCollisions(notify)
else:
pass
def reset(self):
self._N = 0
def __nonzero__(self):
return self._N > 0
def __iadd__(self, x):
self._N += x
def __gt__(self, x):
return self._N > x
def __ge__(self, x):
return self._N >= x
def __lt__(self, x):
return self._N < x
def __le__(self, x):
return self._N <= x
def __call__(self, data):
self.__iadd__(1)
def detect18(self):
if not self.use_callback:
def detect(a, b):
return self.world.contactTestPair(a, b).getNumContacts() > 0
n = sum(detect(a, b) for a, b in combinations(self.bodies, 2))
self += n
class JointManager(dict):
def __init__(self, *args, **kwargs):
try:
self.bbase = kwargs.pop("bbase")
except KeyError:
pass
super(JointManager, self).__init__(*args, **kwargs)
def __setitem__(self, key, val):
if not isinstance(val, BulletConstraint):
raise TypeError("Bad type: %s" % type(val))
try:
self.bbase.attach(val)
except AttributeError:
BP()
pass
super(self.__class__, self).__setitem__(key, val)
def __delitem__(self, key):
try:
self.bbase.remove(self[key])
except AttributeError:
BP()
pass
super(self.__class__, self).__delitem__(key)
def pop(self, key):
joint = super(JointManager, self).pop(key)
try:
self.bbase.remove(joint)
except AttributeError:
pass
return joint
@staticmethod
def make_fixed(np0, np1, type_=BulletGenericConstraint, cfm=0.01, erp=.99):
t0 = np0.getTop()
t1 = np1.getTop()
p0 = np0.getPos(t0)
p1 = np1.getPos(t1)
q0 = np0.getQuat(t0)
q1 = np1.getQuat(t1)
pivot = Point3((p0 + p1) / 2.)
disp = Point3((p1 - p0) / 2.)
pivot_np = t0.attachNewNode("pivot-node")
pivot_np.setPos(pivot)
s = Vec3(1, 1, 1)
q = Quat.identQuat()
q0i = Quat(q0)
q1i = Quat(q1)
q0i.invertInPlace()
q1i.invertInPlace()
q0i *= q
q1i *= q
ts0 = TransformState.makePosQuatScale(disp, q0i, s)
ts1 = TransformState.makePosQuatScale(-disp, q1i, s)
pivot_np.removeNode()
joint = type_(np0.node(), np1.node(), ts0, ts1, False)
for ax in xrange(4):
joint.setAngularLimit(ax, 0, 0)
joint.setLinearLimit(ax, 0, 0)
joint.setParam(type_.CPErp, erp)
joint.setParam(type_.CPCfm, cfm)
joint.setDebugDrawSize(2)
return joint
class BulletBase(object):
ghost_bit = BitMask32.bit(1)
static_bit = BitMask32.bit(2)
dynamic_bit = ghost_bit | static_bit
bw_types = (BulletBaseCharacterControllerNode, BulletBodyNode,
BulletConstraint, BulletVehicle)
def __init__(self):
self.world = None
self.sim_par = {"size": 1. / 100, "n_subs": 10, "size_sub": 1. / 1000}
self.axis_constraint_fac = Vec3(1, 1, 1)
self.axis_constraint_disp = Vec3(nan, nan, nan)
self._destructables = ()
def init(self):
self.world = BulletWorld()
def destroy(self):
for key in self._destructables:
getattr(self, key).destroy()
def setup_debug(self):
debug_node = BulletDebugNode('Debug')
debug_node.showWireframe(True)
debug_node.showConstraints(True)
debug_node.showBoundingBoxes(True)
debug_node.showNormals(True)
self.world.setDebugNode(debug_node)
return debug_node
@property
def bodies(self):
bodies = (self.world.getRigidBodies() + self.world.getSoftBodies() +
self.world.getGhosts())
return bodies
def _constrain_axis(self, body):
for axis, (f, d) in enumerate(zip(self.axis_constraint_fac,
self.axis_constraint_disp)):
if not f and not isnan(d):
nodep = NodePath(body)
pos = nodep.getPos()
pos[axis] = d
nodep.setPos(pos)
try:
body.setLinearFactor(self.axis_constraint_fac)
s = sum(self.axis_constraint_fac)
if s == 3.:
v = self.axis_constraint_fac
elif s == 2.:
v = -self.axis_constraint_fac + 1
else:
v = Vec3.zero()
body.setAngularFactor(v)
except AttributeError:
pass
def set_axis_constraint(self, axis, on, disp=None):
self.axis_constraint_fac[axis] = int(on)
self.axis_constraint_disp[axis] = disp if disp is not None else nan
for body in self.bodies:
self._constrain_axis(body)
def attach(self, objs, suppress_deact_warn=False):
if not self.world:
raise BulletBaseError("No BulletWorld initialized.")
if not isinstance(objs, Iterable):
objs = [objs]
elif isinstance(objs, dict):
objs = objs.itervalues()
bw_objs = []
for obj in objs:
if isinstance(obj, NodePath):
obj = obj.node()
if isinstance(obj, self.bw_types):
bw_objs.append(obj)
bw_objs = set(bw_objs) - set(self.bodies)
for obj in bw_objs:
de = getattr(obj, "isDeactivationEnabled", lambda: True)()
if not suppress_deact_warn and de:
msg = "Deactivation is enabled on object: %s" % obj
warn(msg, DeactivationEnabledWarning)
self._constrain_axis(obj)
try:
self.world.attach(obj)
except AttributeError:
DeprecationWarning("Upgrade to Panda3d 1.9.")
for attr in ("attachRigidBody", "attachConstraint",
"attachGhost"):
attach = getattr(self.world, attr)
try:
attach(obj)
except TypeError:
pass
else:
break
def remove(self, objs):
if not self.world:
raise BulletBaseError("No BulletWorld initialized.")
if not isinstance(objs, Iterable):
objs = [objs]
elif isinstance(objs, dict):
objs = objs.itervalues()
bw_objs = []
for obj in objs:
if isinstance(obj, NodePath):
obj = obj.node()
if isinstance(obj, self.bw_types):
bw_objs.append(obj)
for obj in bw_objs:
try:
self.world.remove(obj)
except AttributeError:
DeprecationWarning("Upgrade to Panda3d 1.9.")
for attr in ("removeRigidBody", "removeConstraint",
"removeGhost"):
remove = getattr(self.world, attr)
try:
remove(obj)
except TypeError:
pass
else:
break
def remove_all(self):
objs = (self.world.getCharacters() + self.world.getConstraints() +
self.world.getGhosts() + self.world.getRigidBodies() +
self.world.getSoftBodies() + self.world.getVehicles())
self.remove(objs)
@property
def gravity(self):
return self.world.getGravity()
@gravity.setter
def gravity(self, gravity):
self.world.setGravity(Vec3(*gravity))
def step_fast(self):
self.world.doPhysics(self.sim_par["size"], self.sim_par["n_subs"],
self.sim_par["size_sub"])
def step(self, *args, **kwargs):
dt = args[0] if len(args) > 0 else self.sim_par["size"]
n_subs = args[1] if len(args) > 1 else self.sim_par["n_subs"]
size_sub = args[2] if len(args) > 2 else self.sim_par["size_sub"]
force = kwargs.get("force", None)
if force:
bodies, vecpos, dur = force
dt0 = np.clip(dur, 0., dt)
n_subs0 = int(np.ceil(n_subs * dt0 / dt))
dt1 = dt - dt0
n_subs1 = n_subs - n_subs0 + 1
for body in bodies:
body.applyForce(Vec3(*vecpos[0]), Point3(*vecpos[1]))
self.world.doPhysics(dt0, n_subs0, size_sub)
for body in bodies:
body.clearForces()
else:
dt1 = dt
n_subs1 = n_subs
self.world.doPhysics(dt1, n_subs1, size_sub)
@staticmethod
def attenuate_velocities(bodies, linvelfac=0., angvelfac=0.):
for body in bodies:
linvel0 = body.getLinearVelocity()
angvel0 = body.getAngularVelocity()
if linvel0.normalize():
linvel = linvel0 * linvelfac
body.setLinearVelocity(linvel)
if angvel0.normalize():
angvel = angvel0 * angvelfac
body.setAngularVelocity(angvel)
def repel(self, n_steps=1000, thresh=10, step_size=0.01):
@contextmanager
def repel_context(world):
def change_contact_thresh(bodies, thresh=0.001):
if isinstance(thresh, Iterable):
it = izip(bodies, thresh)
else:
it = ((body, thresh) for body in bodies)
thresh0 = []
for body, th in it:
thresh0.append(body.getContactProcessingThreshold())
body.setContactProcessingThreshold(th)
return thresh0
def rescale_masses(bodies):
masses, inertias = zip(*[(body.getMass(), body.getInertia())
for body in bodies])
volumefac = 1.
for body, mass, inertia in zip(bodies, masses, inertias):
if mass > 0.:
it = inertia / mass * 12
h = sqrt((it[0] - it[1] + it[2]) / 2)
w = sqrt(it[2] - h ** 2)
d = sqrt(it[1] - w ** 2)
volume = h * w * d
body.setMass(volume * volumefac)
return masses
bodies = world.getRigidBodies()
gravity = world.getGravity()
world.setGravity(Vec3.zero())
delta = -0.001
cp_thresh = change_contact_thresh(bodies, thresh=delta)
masses = rescale_masses(bodies)
deactivations = [b.isDeactivationEnabled() for b in bodies]
for body in bodies:
body.setDeactivationEnabled(False)
self.attenuate_velocities(bodies)
collisions = CollisionMonitor(world)
collisions.push_notifiers(bodies)
yield bodies, collisions
collisions.pop_notifiers()
self.attenuate_velocities(bodies)
change_contact_thresh(bodies, thresh=cp_thresh)
for body, mass in zip(bodies, masses):
body.setMass(mass)
world.setGravity(gravity)
for body, d in zip(bodies, deactivations):
body.setDeactivationEnabled(d)
with repel_context(self.world) as (bodies, collisions):
done_count = 0
for istep in xrange(n_steps):
self.world.doPhysics(step_size, 1, step_size)
collisions.detect18()
if collisions:
done_count = 0
else:
done_count += 1
if any(body.getMass() > 0. and not body.isActive()
for body in bodies):
BP()
if done_count >= thresh:
break
linvelfac = bool(collisions) * 0.001
angvelfac = bool(collisions) * 0.001
self.attenuate_velocities(bodies, linvelfac, angvelfac)
collisions.reset()
return istep
@classmethod
def add_collide_mask(cls, func0):
def func(*args, **kwargs):
node = func0(*args, **kwargs)
if isinstance(node, BulletGhostNode):
bit = cls.ghost_bit
elif node.getMass() == 0.:
bit = cls.static_bit
else:
bit = cls.dynamic_bit
node.setCollideMask(bit)
return node
return update_wrapper(func0, func)
@staticmethod | MIT License |
onshape-public/onshape-clients | python/onshape_client/oas/models/bt_microversion_info.py | BTMicroversionInfo.openapi_types | python | def openapi_types():
return {
"microversion": (str,),
} | This must be a class method so a model may have properties that are
of type self, this ensures that we don't create a cyclic import
Returns
openapi_types (dict): The key is attribute name
and the value is attribute type. | https://github.com/onshape-public/onshape-clients/blob/20843a00c628e516e7219e17a23ec4ef2bf9f16f/python/onshape_client/oas/models/bt_microversion_info.py#L66-L77 | from __future__ import absolute_import
import re
import sys
import six
import nulltype
from onshape_client.oas.model_utils import (
ModelComposed,
ModelNormal,
ModelSimple,
date,
datetime,
file_type,
int,
none_type,
str,
validate_get_composed_info,
)
class BTMicroversionInfo(ModelNormal):
allowed_values = {}
validations = {}
additional_properties_type = None
@staticmethod | MIT License |
decred/tinydecred | decred/decred/dcr/blockchain.py | LocalNode.header | python | def header(self, blockHash):
try:
return self.headerDB[blockHash]
except database.NoValueError:
header = self.rpc.getBlockHeader(blockHash, verbose=False)
self.headerDB[header.cachedHash()] = header
return header | Get the header, from the headerDB if possible, otherwise fetch from RPC.
Args:
blockHash (ByteArray): The block header hash.
Returns:
BlockHeader: The block header. | https://github.com/decred/tinydecred/blob/f7f7d9f7da8d49d9ae9a72e5579b07a3b8572267/decred/decred/dcr/blockchain.py#L66-L81 | import time
from decred.dcr import addrlib, rpc
from decred.dcr.wire.msgblock import BlockHeader
from decred.util import database, helpers
from decred.util.encode import ByteArray
log = helpers.getLogger("blockchain")
class LocalNode:
def __init__(self, netParams, dbPath, url, user, pw, certPath=None):
self.netParams = netParams
self.db = database.KeyValueDatabase(dbPath)
self.mainchainDB = self.db.child(
"mainchain", datatypes=("INTEGER", "BLOB"), blobber=ByteArray
)
self.headerDB = self.db.child(
"headers", blobber=BlockHeader, keyBlobber=ByteArray
)
self.socketURL = helpers.makeWebsocketURL(url, "ws")
self.rpc = None
def connect():
self.close()
self.rpc = rpc.WebsocketClient(self.socketURL, user, pw, cert=certPath)
self.connect = connect
connect()
def close(self):
if self.rpc:
self.rpc.close()
def connected(self):
return self.rpc and not self.rpc.closed | ISC License |
muneebalam/scrapenhl2 | scrapenhl2/plot/app/player_page.py | generate_table | python | def generate_table(dataframe):
return html.Table(
[html.Tr([html.Th(col) for col in dataframe.columns])] +
[html.Tr([html.Td(dataframe.iloc[i][col]) for col in dataframe.columns]) for i in range(len(dataframe))]) | Transforms a pandas dataframe into an HTML table | https://github.com/muneebalam/scrapenhl2/blob/a9867f03d002773da852fc150f2976adc2ba8c25/scrapenhl2/plot/app/player_page.py#L24-L31 | import pandas as pd
import datetime
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
import plotly.graph_objs as go
import scrapenhl2.scrape.schedules as schedules
import scrapenhl2.scrape.players as players
import scrapenhl2.plot.rolling_cf_gf as rolling_cf_gf
import scrapenhl2.plot.rolling_boxcars as rolling_boxcars
import scrapenhl2.plot.visualization_helper as vhelper
import scrapenhl2.scrape.team_info as team_info | MIT License |
seldonio/alibi | alibi/explainers/cfrl_tabular.py | CounterfactualRLTabular._diversity | python | def _diversity(self,
X: np.ndarray,
Y_t: np.ndarray,
C: Optional[List[Dict[str, List[Union[str, float]]]]],
num_samples: int = 1,
batch_size: int = 100,
patience: int = 1000,
tolerance: float = 1e-3) -> Explanation:
if C is None:
raise ValueError("A diverse set of counterfactual can not be generated if a `None` conditioning is "
"used during training. Use the `explain` method to generate a counterfactual. The "
"generation process is deterministic in its core. If conditioning is used during training "
"a diverse set of counterfactual can be generated by restricting each feature condition "
"to a subset to remain feasible.")
if X.shape[0] != 1:
raise ValueError("Only a single input instance can be passed.")
if Y_t.shape[0] != 1:
raise ValueError("Only a single label can be passed.")
if (C is not None) and len(C) > 1:
raise ValueError("At most, one condition can be passed.")
X_repeated = np.tile(X, (batch_size, 1))
Y_t = np.tile(np.atleast_2d(Y_t), (batch_size, 1))
X_cf_buff = None
for i in tqdm(count()):
if i == patience:
break
if (X_cf_buff is not None) and (X_cf_buff.shape[0] >= num_samples):
break
C_vec = get_conditional_vector(X=X_repeated,
condition=C[0] if len(C) else {},
preprocessor=self.params["encoder_preprocessor"],
feature_names=self.params["feature_names"],
category_map=self.params["category_map"],
stats=self.params["stats"],
immutable_features=self.params["immutable_features"],
diverse=True)
results = self._compute_counterfactual(X=X_repeated, Y_t=Y_t, C=C_vec)
X_cf, Y_m_cf, Y_t = results["X_cf"], results["Y_m_cf"], results["Y_t"]
X_cf = X_cf[Y_t == Y_m_cf]
if X_cf.shape[0] == 0:
continue
_, indices = np.unique(np.floor(X_cf / tolerance).astype(int), return_index=True, axis=0)
if X_cf_buff is None:
X_cf_buff = X_cf[indices]
else:
X_cf_buff = np.concatenate([X_cf_buff, X_cf[indices]], axis=0)
_, indices = np.unique(np.floor(X_cf_buff / tolerance).astype(int), return_index=True, axis=0)
X_cf_buff = X_cf_buff[indices]
X_cf = X_cf_buff[:num_samples] if (X_cf_buff is not None) else np.array([])
Y_m_cf = self.params["predictor"](X_cf) if X_cf.shape[0] != 0 else np.array([])
if self._is_classification(pred=Y_m_cf):
Y_m_cf = np.argmax(Y_m_cf, axis=1)
Y_m = self.params["predictor"](X)
if self._is_classification(Y_m):
Y_m = np.argmax(Y_m, axis=1)
if self._is_classification(Y_t):
Y_t = np.argmax(Y_t, axis=1)
return self._build_explanation(X=X, Y_m=Y_m, X_cf=X_cf, Y_m_cf=Y_m_cf, Y_t=Y_t, C=C) | Generates a set of diverse counterfactuals given a single instance, target and conditioning.
Parameters
----------
X
Input instance.
Y_t
Target label.
C
List of conditional dictionaries. If `None`, it means that no conditioning was used during training
(i.e. the `conditional_func` returns `None`).
num_samples
Number of counterfactual samples to be generated.
batch_size
Batch size used at inference.
num_samples
Number of diversity samples to be generated. Considered only if `diversity=True`.
batch_size
Batch size to use when generating counterfactuals.
patience
Maximum number of iterations to perform diversity search stops. If -1, the search stops only if
the desired number of samples has been found.
tolerance
Tolerance to distinguish two counterfactual instances.
Returns
-------
Explanation object containing the diverse counterfactuals. | https://github.com/seldonio/alibi/blob/ef757b9579f85ef2e3dfc7088211969616ee3fdb/alibi/explainers/cfrl_tabular.py#L377-L497 | from alibi.api.interfaces import Explainer, Explanation
from alibi.utils.frameworks import has_pytorch, has_tensorflow
from alibi.explainers.cfrl_base import CounterfactualRL, Postprocessing, _PARAM_TYPES
from alibi.explainers.backends.cfrl_tabular import sample, get_conditional_vector, get_statistics
import numpy as np
from tqdm import tqdm
from itertools import count
from functools import partial
from typing import Tuple, List, Dict, Callable, Union, Optional, TYPE_CHECKING
if TYPE_CHECKING:
import torch
import tensorflow
if has_pytorch:
from alibi.explainers.backends.pytorch import cfrl_tabular as pytorch_tabular_backend
if has_tensorflow:
from alibi.explainers.backends.tensorflow import cfrl_tabular as tensorflow_tabular_backend
class SampleTabularPostprocessing(Postprocessing):
def __init__(self, category_map: Dict[int, List[str]], stats: Dict[int, Dict[str, float]]):
super().__init__()
self.category_map = category_map
self.stats = stats
def __call__(self, X_cf: List[np.ndarray], X: np.ndarray, C: Optional[np.ndarray]) -> List[np.ndarray]:
return sample(X_hat_split=X_cf,
X_ohe=X,
C=C,
stats=self.stats,
category_map=self.category_map)
class ConcatTabularPostprocessing(Postprocessing):
def __call__(self, X_cf: List[np.ndarray], X: np.ndarray, C: Optional[np.ndarray]) -> np.ndarray:
return np.concatenate(X_cf, axis=1)
_PARAM_TYPES["complex"] += ["conditional_vector", "stats"]
class CounterfactualRLTabular(CounterfactualRL):
def __init__(self,
predictor: Callable[[np.ndarray], np.ndarray],
encoder: 'Union[tensorflow.keras.Model, torch.nn.Module]',
decoder: 'Union[tensorflow.keras.Model, torch.nn.Module]',
encoder_preprocessor: Callable,
decoder_inv_preprocessor: Callable,
coeff_sparsity: float,
coeff_consistency: float,
feature_names: List[str],
category_map: Dict[int, List[str]],
immutable_features: Optional[List[str]] = None,
ranges: Optional[Dict[str, Tuple[int, int]]] = None,
weight_num: float = 1.0,
weight_cat: float = 1.0,
latent_dim: Optional[int] = None,
backend: str = "tensorflow",
seed: int = 0,
**kwargs):
super().__init__(encoder=encoder, decoder=decoder, latent_dim=latent_dim, predictor=predictor,
coeff_sparsity=coeff_sparsity, coeff_consistency=coeff_consistency, backend=backend,
seed=seed, **kwargs)
self.params["encoder_preprocessor"] = encoder_preprocessor
self.params["decoder_inv_preprocessor"] = decoder_inv_preprocessor
self.params["category_map"] = category_map
self.params["feature_names"] = feature_names
self.params["ranges"] = ranges if (ranges is not None) else dict()
self.params["immutable_features"] = immutable_features if (immutable_features is not None) else list()
self.params["weight_num"] = weight_num
self.params["weight_cat"] = weight_cat
if "sparsity_loss" not in kwargs:
self.params["sparsity_loss"] = partial(self.backend.sparsity_loss,
category_map=self.params["category_map"],
weight_num=weight_num,
weight_cat=weight_cat)
if "consistency_loss" not in kwargs:
self.params["consistency_loss"] = self.backend.consistency_loss
if "conditional_func" not in kwargs:
self.params["conditional_func"] = partial(self.backend.generate_condition,
feature_names=self.params["feature_names"],
category_map=self.params["category_map"],
ranges=self.params["ranges"],
immutable_features=self.params["immutable_features"])
if "conditional_vector" not in kwargs:
self.params["conditional_vector"] = partial(get_conditional_vector,
preprocessor=self.params["encoder_preprocessor"],
feature_names=self.params["feature_names"],
category_map=self.params["category_map"],
ranges=self.params["ranges"],
immutable_features=self.params["immutable_features"])
self.meta["params"].update(CounterfactualRLTabular._serialize_params(self.params))
def _select_backend(self, backend, **kwargs):
return tensorflow_tabular_backend if backend == "tensorflow" else pytorch_tabular_backend
def _validate_input(self, X: np.ndarray):
if len(X.shape) != 2:
raise ValueError(f"The input should be a 2D array. Found {len(X.shape)}D instead.")
if X.shape[1] != len(self.params["feature_names"]):
raise ValueError(f"Unexpected number of features. The expected number "
f"is {len(self.params['feature_names'])}, but the input has {X.shape[1]} features.")
return X
def fit(self, X: np.ndarray) -> 'Explainer':
self.params["stats"] = get_statistics(X=X,
preprocessor=self.params["encoder_preprocessor"],
category_map=self.params["category_map"])
self.params["postprocessing_funcs"] = [
SampleTabularPostprocessing(stats=self.params["stats"], category_map=self.params["category_map"]),
ConcatTabularPostprocessing(),
]
self.meta["params"].update(CounterfactualRLTabular._serialize_params(self.params))
self._validate_input(X)
return super().fit(X)
def explain(self,
X: np.ndarray,
Y_t: np.ndarray = None,
C: Optional[List[Dict[str, List[Union[str, float]]]]] = None,
batch_size: int = 100,
diversity: bool = False,
num_samples: int = 1,
patience: int = 1000,
tolerance: float = 1e-3) -> Explanation:
self._validate_input(X)
self._validate_target(Y_t)
if diversity:
return self._diversity(X=X,
Y_t=Y_t,
C=C,
num_samples=num_samples,
batch_size=batch_size,
patience=patience,
tolerance=tolerance)
X_zeros = np.zeros((1, X.shape[1]))
C_zeros = self.params["conditional_func"](X_zeros)
if C is None:
if C_zeros is not None:
raise ValueError("A `None` conditioning is not a valid input when training with conditioning. "
"If no feature conditioning is desired for the given input, `C` is expected to be an "
"empty list. A `None` conditioning is valid only when no conditioning was used "
"during training (i.e. `conditional_func` returns `None`).")
return super().explain(X=X, Y_t=Y_t, C=C, batch_size=batch_size)
elif C_zeros is None:
raise ValueError("Conditioning different than `None` is not a valid input when training without "
"conditioning. If feature conditioning is desired, consider defining an appropriate "
"`conditional_func` that does not return `None`.")
if len(C) == 0:
C = [dict()]
if len(C) != 1 and len(C) != X.shape[0]:
raise ValueError("The number of conditions should be 1 or equals the number of samples in x.")
if len(C) == 1:
C_vec = self.params["conditional_vector"](X=X,
condition=C[0],
stats=self.params["stats"])
else:
C_vecs = []
for i in range(len(C)):
C_vecs.append(self.params["conditional_vector"](X=np.atleast_2d(X[i]),
condition=C[i],
stats=self.params["stats"]))
C_vec = np.concatenate(C_vecs, axis=0)
explanation = super().explain(X=X, Y_t=Y_t, C=C_vec, batch_size=batch_size)
explanation.data.update({"condition": C})
return explanation | Apache License 2.0 |