content
stringlengths 1
1.04M
| input_ids
sequencelengths 1
774k
| ratio_char_token
float64 0.38
22.9
| token_count
int64 1
774k
|
|---|---|---|---|
if __name__ == '__main__':
print("This is program is being run by itself")
else:
print("i am being imported from another module")
from mymodule import sayhi,version
sayhi()
print("Verison",version)
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#!/usr/bin/env python
# -*- coding:utf-8 -*-
import numpy as np
import rmgpy.constants as constants
from rmgpy.statmech import Conformer, IdealGasTranslation, LinearRotor, NonlinearRotor, HarmonicOscillator
from rmgpy.qm.qmdata import QMData
from rmgpy.qm.symmetry import PointGroupCalculator
from .qchem import QChem
from .molpro import Molpro
from .mol import atomic_symbol_dict, geo_to_rmg_mol, get_bac_correction
# Experimental heats of formation of atoms (kcal/mol)
h0expt = {'H': 51.63,
'C': 169.98,
'N': 112.53,
'O': 58.99}
h298corr = {'H': 1.01,
'C': 0.25,
'N': 1.04,
'O': 1.04}
# Spin-orbit corrections for neutral atoms
atom_socs = {'H': 0.0,
'C': 0.000135,
'N': 0.0,
'O': 0.000355}
# Atomic reference energies at 0K in Hartree
atom_energies = {
'ccsd(t)-f12a/cc-pvdz-f12': {
'H': -0.499811124128,
'N': -54.525946786123,
'O': -74.994643838203,
'C': -37.787831744881,
},
'ccsd(t)-f12b/cc-pvdz-f12': {
'H': -0.499811124128,
'N': -54.522814689877,
'O': -74.989919455883,
'C': -37.785040449664,
},
'ccsd(t)-f12a/cc-pvtz-f12': {
'H': -0.499946213253,
'N': -54.529590447091,
'O': -75.003545717458,
'C': -37.789552049511,
},
'ccsd(t)-f12b/cc-pvtz-f12': {
'H': -0.499946213253,
'N': -54.527721253368,
'O': -75.000516530163,
'C': -37.787925879006,
},
'ccsd(t)-f12a/cc-pvqz-f12': {
'H': -0.499994558326,
'N': -54.530194782830,
'O': -75.005192195863,
'C': -37.789729174726,
},
'ccsd(t)-f12b/cc-pvqz-f12': {
'H': -0.499994558326,
'N': -54.529107245074,
'O': -75.003414816890,
'C': -37.788775207449,
},
'ccsd(t)-f12a/aug-cc-pv5z': {
'H': -0.499994816870,
'N': -54.529731561126,
'O': -75.004562049197,
'C': -37.789360554007,
},
'ccsd(t)-f12b/aug-cc-pv5z': {
'H': -0.499994816870,
'N': -54.528933245046,
'O': -75.003291308092,
'C': -37.788641170961,
},
'b3lyp/6-31g(2df,p)': {
'H': -0.500273,
'N': -54.583861,
'O': -75.064579,
'C': -37.846772,
}
}
freq_scale_factors = {
'b3lyp/6-31g(2df,p)': 0.965,
'wb97x-d3/def2-tzvp': 0.975,
}
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] | 1.658856 | 1,451 |
from kokki import Package
Package("pip",
provider = "kokki.providers.package.easy_install.EasyInstallProvider"
) | [
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from django.dispatch import Signal
prefs_save = Signal(providing_args=['app', 'updated_prefs'])
"""Issued when dynamic preferences models are saved."""
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] | 3.422222 | 45 |
# Generated by Django 3.2.3 on 2021-06-13 05:29
import ckeditor.fields
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
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import time
import asyncio
import aiopg
import uvloop
import asyncpg
asyncio.set_event_loop_policy(uvloop.EventLoopPolicy())
dsn = 'dbname=rssant user=rssant password=rssant host=127.0.0.1'
loop = asyncio.get_event_loop()
loop.run_until_complete(run_aiopg())
loop = asyncio.get_event_loop()
loop.run_until_complete(run_asyncpg())
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] | 2.648438 | 128 |
from matplotlib import use
# use("Qt5Agg")
# use('TkAgg')
from mpl_toolkits.mplot3d import Axes3D # Not explicitly used, but necessary
from matplotlib.transforms import Affine2D # Not explicitly used, but necessary
import mpl_toolkits.axisartist.floating_axes as floating_axes # Not explicitly used, but necessary
import numpy as np
from scipy import stats
from matplotlib import pyplot as plt
from matplotlib.pyplot import *
from matplotlib import animation
from matplotlib import cm
import matplotlib.colors as mc
from scipy.spatial.transform import Rotation
import pylab as pl
import pickle
import os
import platform
import time
import datetime
from BPs import *
from ComputationalTools import *
from utils import Get_models
os.environ['KMP_DUPLICATE_LIB_OK']='True'
plt.rcParams['savefig.facecolor'] = "0.8"
if platform.system() == "Darwin":
print("Using MacOS.")
plt.rcParams['animation.ffmpeg_path'] = "/usr/local/bin/ffmpeg"
elif platform.system() == "Linux":
print("Using Linux.")
plt.rcParams['animation.ffmpeg_path'] = "/usr/bin/ffmpeg"
else:
print("Using Windows.")
plt.rcParams['animation.ffmpeg_path'] = 'C:/Users/juyil/ffmpeg/bin/ffmpeg.exe'
data_folder = "Data"
figures_folder = "Figures"
""" Calculate gaussian kde estimate for a dataset """
""" Create a python generator for a pickle file """
""" Record positio of panels of viz """
fig_size = 6
grid_span = 6
span_figure_r = 3
span_figure_c = 6
#%%
if __name__ == "__main__":
pass | [
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] | 2.723894 | 565 |
"""setuptools module for uniscripts.
"""
# Always prefer setuptools over distutils
from setuptools import setup, find_packages
# To use a consistent encoding
from codecs import open
from os import path
here = path.abspath(path.dirname(__file__))
# adapted from https://coderwall.com/p/qawuyq/use-markdown-readme-s-in-python-modules
try:
import pypandoc
long_description = pypandoc.convert(path.join(here, 'README.md'), 'rst', format='markdown_github')
except (IOError, ImportError):
with open(path.join(here, 'README.md')) as f:
long_description = f.read()
setup(
name='uniscripts',
# PEP440
version='1.0.5',
description='query Unicode script metadata',
long_description=long_description,
url='https://github.com/leoboiko/uniscripts',
# Author details
author='Leonardo Boiko',
author_email='leoboiko@namakajiri.net',
# Choose your license
license='CC0 1.0 Universal (CC0 1.0) Public Domain Dedication',
# https://pypi.python.org/pypi?%3Aaction=list_classifiers
classifiers=[
# 3 - Alpha
# 4 - Beta
# 5 - Production/Stable
'Development Status :: 4 - Beta',
'Intended Audience :: Developers',
'Topic :: Software Development :: Internationalization',
'Topic :: Software Development :: Localization',
'Topic :: Text Processing',
'Topic :: Text Processing :: Linguistic',
'License :: CC0 1.0 Universal (CC0 1.0) Public Domain Dedication',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.2', # probably?
'Programming Language :: Python :: 3.3', # I hope...
'Programming Language :: Python :: 3.4', # actually tested here
],
keywords='unicode script scripts uax24 hiragana katakana kanji han',
# packages=find_packages(exclude=['contrib', 'docs', 'tests*', 'update']),
packages=['uniscripts'],
# cf. https://packaging.python.org/en/latest/requirements.html
install_requires=[],
extras_require={},
package_data={},
data_files=[],
entry_points={},
)
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220,
220,
2721,
62,
47911,
41888,
4357,
628,
220,
220,
220,
33849,
62,
46115,
34758,
5512,
628,
220,
220,
220,
5301,
62,
7890,
34758,
5512,
628,
220,
220,
220,
1366,
62,
16624,
41888,
4357,
628,
220,
220,
220,
5726,
62,
13033,
34758,
5512,
198,
8,
198
] | 2.667503 | 797 |
import os
import sys
import numpy as np
| [
11748,
28686,
198,
11748,
25064,
198,
11748,
299,
32152,
355,
45941,
628
] | 3.416667 | 12 |
from pymongo import MongoClient
client= MongoClient('localhost', 27017)
db=client.db_cek
if __name__=='__main__':
main() | [
6738,
279,
4948,
25162,
1330,
42591,
11792,
198,
198,
16366,
28,
42591,
11792,
10786,
36750,
3256,
2681,
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8,
198,
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28,
16366,
13,
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62,
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74,
198,
198,
361,
11593,
3672,
834,
855,
6,
834,
12417,
834,
10354,
198,
220,
220,
220,
1388,
3419
] | 2.73913 | 46 |
import numpy as np
# Element-wise multiplication of three vectors
| [
11748,
299,
32152,
355,
45941,
628,
198,
2,
11703,
12,
3083,
48473,
286,
1115,
30104,
198
] | 4.25 | 16 |
import requests
from django.shortcuts import render
from django.contrib.auth.decorators import login_required
from .models import Book
# following line has to match the settings-file you are using
from django.conf import settings
def sync_zotero(request):
""" renders a simple template with a button to trigger sync_zotero_action function """
return render(request, 'bib/synczotero.html')
@login_required
def sync_zotero_action(request):
""" fetches the last n items form zoter and syncs it with the bib entries in defc-db"""
root = "https://api.zotero.org/users/"
params = "{}/collections/{}/items/top?v=3&key={}".format(
settings.Z_USER_ID, settings.Z_COLLECTION, settings.Z_API_KEY)
url = root + params + "&sort=dateModified&limit=25"
books_before = len(Book.objects.all())
try:
r = requests.get(url)
error = "No errors from ZoteroAPI"
except:
error = "aa! errors! The API didn´t response with a proper json-file"
response = r.json()
failed = []
saved = []
for x in response:
try:
x["data"]["creators"][0]
try:
x["data"]["creators"][0]["name"]
name = x["data"]["creators"][0]["name"]
except:
firstname = x["data"]["creators"][0]["firstName"]
lastname = x["data"]["creators"][0]["lastName"]
name = "{}, {}".format(lastname, firstname)
except:
name = "no name provided"
NewBook = Book(
zoterokey=x["data"]["key"], item_type=x["data"]["itemType"],
author=name,
title=x["data"]["title"],
short_title=x["data"]["shortTitle"]
)
try:
NewBook.save()
saved.append(x["data"])
except:
failed(x['data'])
books_after = len(Book.objects.all())
context = {}
context["error"] = error
context["saved"] = saved
context["failed"] = failed
context["books_before"] = [books_before]
context["books_after"] = [books_after]
return render(request, 'bib/synczotero_action.html', context)
| [
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27261,
89,
313,
3529,
62,
2673,
13,
6494,
3256,
4732,
8,
198
] | 2.264489 | 949 |
import os
import subprocess
file = "some_file.txt"
with open(file, 'w') as opened_file:
opened_file.write('Hola!')
subprocess.run(["cat", file])
if os.path.exists(file):
os.remove(file)
assert not os.path.exists(file)
| [
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] | 2.48913 | 92 |
TASK_STAGES = (
('n', 'Not Started'),
('i', 'In Progress'),
('r', 'In Review'),
('d', 'Done'),
)
| [
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] | 1.821918 | 73 |
from urllib.parse import urlencode
import uuid
from formtools.wizard.storage.base import BaseStorage
from formtools.wizard.storage.session import SessionStorage
import requests
from django.conf import settings
from django.contrib.sessions.exceptions import SuspiciousSession
from django.core.cache import cache
from django.shortcuts import Http404
CACHE_KEY_USER = 'wizard-user-cache-key'
# unusual character that is unlikely to be included in each product label
PRODUCT_DELIMITER = 'µ'
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6,
628,
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628,
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] | 3.424658 | 146 |
import discord
import aiohttp
from bs4 import BeautifulSoup
from discord.ext import commands
from helpers import utilityfunctions as util
| [
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] | 4.147059 | 34 |
#!/usr/bin/env python
# Copyright (c) 2018, 2019 Diamond Key Security, NFP
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
# - Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
#
# - Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
#
# - Neither the name of the NORDUnet nor the names of its contributors may
# be used to endorse or promote products derived from this software
# without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS
# IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
# TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
# HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
# TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
# LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import struct
import threading
from uuid import UUID
# import classes from the original cryptech.muxd
# cryptech_muxd has been renamed to cryptech/muxd.py
from hsm_tools.cryptech.muxd import logger
from hsm_tools.hsm import CrypTechDeviceState
from settings import HSMSettings
from hsm_tools.cryptech.cryptech.libhal import ContextManagedUnpacker, xdrlib
from hsm_tools.rpc_action import RPCAction
from hsm_tools.cryptech_port import DKS_RPCFunc, DKS_HALKeyType,\
DKS_HALKeyFlag, DKS_HALError
from hsm_tools.threadsafevar import ThreadSafeVariable
from rpc_builder import KeyMatchDetails, RPCpkey_open, RPCKeygen_result
from load_distribution import LoadDistribution
def rpc_get_int(msg, location):
"Get an int from a location in an RPC message"
return struct.unpack(">L", msg[location:location+4])[0]
def rpc_set_int(msg, data, location):
"Set an int from a location in an RPC message"
return msg[:location] + struct.pack(">L", data) + msg[location+4:]
class KeyHandleDetails:
"""Information on the key that a handle points to"""
class MuxSession:
"""Simple class for defining the state of a
connection to the load balancer"""
class RPCPreprocessor:
"""Able to load balance between multiple rpcs"""
def choose_rpc(self):
"""Simple Heuristic for selecting an alpha RPC channel to use"""
DEVICE_USES_BEFORE_NEXT = 2
device_count = len(self.rpc_list)
with(self.choose_any_thread_lock):
# first try to evenly distribute
self.next_any_device_uses += 1
if(self.next_any_device_uses > DEVICE_USES_BEFORE_NEXT):
self.next_any_device_uses = 0
self.next_any_device += 1
if(self.next_any_device >= device_count):
self.next_any_device = 0
# make sure this has the smallest weight
# If only one process is using the HSM, next_rpc
# will probably be ok, but if multiple processes
# are using the HSM, it's possible that the call
# may try to use a device that's busy
# initialize to weight of device
device_weight = self.get_cryptech_device_weight(self.next_any_device)
for device_index in xrange(device_count):
# if we find a device with a lower weight, use it
if (self.next_any_device != device_index):
new_device_weight = self.get_cryptech_device_weight(device_index)
if (new_device_weight < device_weight):
device_weight = new_device_weight
self.next_any_device = device_index
# reset uses
self.next_any_device_uses = 0
return self.next_any_device
@property
def handle_set_rpc(self, code, client, unpacker, session):
"""Special DKS RPC to set the RPC to use for all calls"""
logger.info("RPC code received %s, handle 0x%x",
DKS_RPCFunc.RPC_FUNC_SET_RPC_DEVICE, client)
# get the serial to switch to
rpc_index = unpacker.unpack_uint()
response = xdrlib.Packer()
response.pack_uint(code)
response.pack_uint(client)
if (session.from_ethernet):
# the RPC can not be explicitly set from an outside
# ethernet connection
response.pack_uint(DKS_HALError.HAL_ERROR_FORBIDDEN)
elif (rpc_index < len(self.rpc_list)):
# set the rpc to use for this session
session.rpc_index = rpc_index
response.pack_uint(DKS_HALError.HAL_OK)
else:
response.pack_uint(DKS_HALError.HAL_ERROR_BAD_ARGUMENTS)
unencoded_response = response.get_buffer()
return RPCAction(unencoded_response, None, None)
def handle_enable_cache_keygen(self, code, client, unpacker, session):
"""Special DKS RPC to enable caching of generated keys"""
logger.info("RPC code received %s, handle 0x%x",
DKS_RPCFunc.RPC_FUNC_ENABLE_CACHE_KEYGEN.name, client)
response = xdrlib.Packer()
response.pack_uint(code)
response.pack_uint(client)
if (session.from_ethernet):
# keygen caching can not be explicitly set from
# an ethernet connection
response.pack_uint(DKS_HALError.HAL_ERROR_FORBIDDEN)
else:
response.pack_uint(DKS_HALError.HAL_OK)
unencoded_response = response.get_buffer()
session.cache_generated_keys = True
print('caching enabled')
return RPCAction(unencoded_response, None, None)
def handle_disable_cache_keygen(self, code, client, unpacker, session):
"""Special DKS RPC to disable caching of generated keys"""
logger.info("RPC code received %s, handle 0x%x",
DKS_RPCFunc.RPC_FUNC_DISABLE_CACHE_KEYGEN.name, client)
response = xdrlib.Packer()
response.pack_uint(code)
response.pack_uint(client)
if (session.from_ethernet):
# keygen caching can not be explicitly set from
# an ethernet connection
response.pack_uint(DKS_HALError.HAL_ERROR_FORBIDDEN)
else:
response.pack_uint(DKS_HALError.HAL_OK)
unencoded_response = response.get_buffer()
session.cache_generated_keys = False
print('caching disabled')
return RPCAction(unencoded_response, None, None)
def handle_use_incoming_device_uuids(self, code, client, unpacker, session):
"""Special DKS RPC to enable using incoming device uuids"""
logger.info("RPC code received %s, handle 0x%x",
DKS_RPCFunc.RPC_FUNC_USE_INCOMING_DEVICE_UUIDS.name, client)
response = xdrlib.Packer()
response.pack_uint(code)
response.pack_uint(client)
if (session.from_ethernet):
# using device uuids can not be set fom
# an ethernet connection
response.pack_uint(DKS_HALError.HAL_ERROR_FORBIDDEN)
else:
response.pack_uint(DKS_HALError.HAL_OK)
unencoded_response = response.get_buffer()
session.incoming_uuids_are_device_uuids = True
print('accepting incoming device uuids')
return RPCAction(unencoded_response, None, None)
def handle_use_incoming_master_uuids(self, code, client, unpacker, session):
"""Special DKS RPC to enable using incoming master uuids"""
logger.info("RPC code received %s, handle 0x%x",
DKS_RPCFunc.RPC_FUNC_USE_INCOMING_MASTER_UUIDS.name, client)
response = xdrlib.Packer()
response.pack_uint(code)
response.pack_uint(client)
if (session.from_ethernet):
# using device uuids can not be set fom
# an ethernet connection
response.pack_uint(DKS_HALError.HAL_ERROR_FORBIDDEN)
else:
response.pack_uint(DKS_HALError.HAL_OK)
unencoded_response = response.get_buffer()
session.incoming_uuids_are_device_uuids = False
print('accepting incoming master uuids')
return RPCAction(unencoded_response, None, None)
def handle_rpc_any(self, code, client, unpacker, session):
"""Can run on any available alpha because this is not alpha specific"""
rpc_index = session.rpc_index if(session.rpc_index >= 0) else self.choose_rpc()
logger.info("any rpc sent to %i", rpc_index)
return RPCAction(None, [self.rpc_list[rpc_index]], None)
def handle_rpc_all(self, code, client, unpacker, session):
"""Must run on all alphas to either to keep PINs synchronized
or because we don't know which alpha we'll need later"""
# if the rpc_index has been set for the session, always use it
if(session.rpc_index >= 0):
return RPCAction(None, [self.rpc_list[session.rpc_index]], None)
rpc_list = self.make_all_rpc_list()
return RPCAction(None, rpc_list, self.callback_rpc_all)
def handle_rpc_starthash(self, code, client, unpacker, session):
"""This is the begining of a hash operation. Any RPC can be used."""
# select an RPC to use for this hashing operation
session.cur_hashing_index = session.rpc_index if(session.rpc_index >= 0) else self.choose_rpc()
logger.info("hashing on RPC: %i", session.cur_hashing_index)
return RPCAction(None, [self.rpc_list[session.cur_hashing_index]], self.callback_rpc_starthash)
def handle_rpc_hash(self, code, client, unpacker, session):
"""Once a hash has started, we have to continue with it the same RPC"""
# get the handle of the hash operation
handle = unpacker.unpack_uint()
# this handle must be a key
if(handle not in session.hash_rpcs):
logger.info("handle_rpc_hash: handle not in session.hash_rpcs")
return self.create_error_response(code, client, DKS_HALError.HAL_ERROR_BAD_ARGUMENTS)
return RPCAction(None, [self.rpc_list[session.hash_rpcs[handle]]], None)
def handle_rpc_endhash(self, code, client, unpacker, session):
"""we've finished a hash operation"""
# get the handle of the hash operation
handle = unpacker.unpack_uint()
# this handle must be a key
if(handle not in session.hash_rpcs):
logger.info("handle_rpc_hash: handle not in session.hash_rpcs")
return self.create_error_response(code, client, DKS_HALError.HAL_ERROR_BAD_ARGUMENTS)
rpc_index = session.hash_rpcs[handle]
# the handle no longer needs to be in the dictionary
del session.hash_rpcs.pop[handle]
return RPCAction(None, [self.rpc_list[rpc_index]], None)
def handle_rpc_usecurrent(self, code, client, unpacker, session):
"""The manually selected RPC must be used"""
rpc_index = session.rpc_index
if(rpc_index < 0):
rpc_index = session.key_op_data.rpc_index
return RPCAction(None, [self.rpc_list[rpc_index]], None)
def handle_rpc_pkey(self, code, client, unpacker, session):
"""use handle to select RPC"""
# get the handle of the hash operation
handle = unpacker.unpack_uint()
# this handle must be a key
if(handle not in session.key_rpcs):
logger.info("handle_rpc_pkey: handle not in session.key_rpcs")
return self.create_error_response(code, client, DKS_HALError.HAL_ERROR_BAD_ARGUMENTS)
rpc_index = session.key_rpcs[handle].rpc_index
device_uuid = session.key_rpcs[handle].uuid
# logger.info("Using pkey handle:%i RPC:%i", handle, rpc_index)
session.key_op_data = KeyOperationData(rpc_index, handle, device_uuid)
if (code == DKS_RPCFunc.RPC_FUNC_PKEY_DELETE or
code == DKS_RPCFunc.RPC_FUNC_PKEY_CLOSE):
return RPCAction(None, [self.rpc_list[rpc_index]], self.callback_rpc_close_deletekey)
else:
return RPCAction(None, [self.rpc_list[rpc_index]], None)
def handle_rpc_pkeyload(self, code, client, unpacker, session):
"""use manually selected RPC and get returned uuid and handle"""
# if the session rpc_index has not be set, this must getting the public key
# rpc_index = session.rpc_index
# if(rpc_index < 0):
# rpc_index = self.choose_rpc() #session.key_op_data.rpc_index
# select an RPC to use for this hashing operation
session.key_op_data.rpc_index = session.rpc_index if(session.rpc_index >= 0) else self.choose_rpc()
logger.info("session.rpc_index == %i session.key_op_data.rpc_index == %i",
session.rpc_index, session.key_op_data.rpc_index)
# consume pkcs11 session id
unpacker.unpack_uint()
# consume der
unpacker.unpack_bytes()
# get flags
session.flags = unpacker.unpack_uint()
if hasattr(session, 'pkey_type'):
# treat as the public version of the last privte key generated as this is the standard usage
if(session.pkey_type == DKS_HALKeyType.HAL_KEY_TYPE_RSA_PRIVATE and
session.flags & DKS_HALKeyFlag.HAL_KEY_FLAG_PUBLIC):
session.pkey_type = DKS_HALKeyType.HAL_KEY_TYPE_RSA_PUBLIC
elif(session.pkey_type == DKS_HALKeyType.HAL_KEY_TYPE_EC_PRIVATE and
session.flags & DKS_HALKeyFlag.HAL_KEY_FLAG_PUBLIC):
session.pkey_type = DKS_HALKeyType.HAL_KEY_TYPE_EC_PUBLIC
elif(session.pkey_type == DKS_HALKeyType.HAL_KEY_TYPE_HASHSIG_PRIVATE and
session.flags & DKS_HALKeyFlag.HAL_KEY_FLAG_PUBLIC):
session.pkey_type = DKS_HALKeyType.HAL_KEY_TYPE_HASHSIG_PUBLIC
else:
session.pkey_type = DKS_HALKeyType.HAL_KEY_TYPE_NONE
session.curve = 0
# inform the load balancer that we are doing an expensive key operation
self.update_device_weight(session.key_op_data.rpc_index, self.pkey_gen_weight)
return RPCAction(None, [self.rpc_list[session.key_op_data.rpc_index]], self.callback_rpc_keygen)
def handle_rpc_pkeyimport(self, code, client, unpacker, session):
"""use manually selected RPC and get returned uuid and handle"""
# if the session rpc_index has not be set, this must getting the public key
# rpc_index = session.rpc_index
# if(rpc_index < 0):
# rpc_index = self.choose_rpc() #session.key_op_data.rpc_index
# select an RPC to use for this hashing operation
session.key_op_data.rpc_index = session.rpc_index if(session.rpc_index >= 0) else self.choose_rpc()
logger.info("session.rpc_index == %i session.key_op_data.rpc_index == %i",
session.rpc_index, session.key_op_data.rpc_index)
# consume pkcs11 session id
unpacker.unpack_uint()
# consume kekek
unpacker.unpack_uint()
# consume pkcs8
unpacker.unpack_bytes()
# consume kek
unpacker.unpack_bytes()
# get flags
session.flags = unpacker.unpack_uint()
if hasattr(session, 'pkey_type'):
# treat as the public version of the last privte key generated as this is the standard usage
if(session.pkey_type == DKS_HALKeyType.HAL_KEY_TYPE_RSA_PRIVATE and
session.flags & DKS_HALKeyFlag.HAL_KEY_FLAG_PUBLIC):
session.pkey_type = DKS_HALKeyType.HAL_KEY_TYPE_RSA_PUBLIC
elif(session.pkey_type == DKS_HALKeyType.HAL_KEY_TYPE_EC_PRIVATE and
session.flags & DKS_HALKeyFlag.HAL_KEY_FLAG_PUBLIC):
session.pkey_type = DKS_HALKeyType.HAL_KEY_TYPE_EC_PUBLIC
elif(session.pkey_type == DKS_HALKeyType.HAL_KEY_TYPE_HASHSIG_PRIVATE and
session.flags & DKS_HALKeyFlag.HAL_KEY_FLAG_PUBLIC):
session.pkey_type = DKS_HALKeyType.HAL_KEY_TYPE_HASHSIG_PUBLIC
else:
session.pkey_type = DKS_HALKeyType.HAL_KEY_TYPE_NONE
session.curve = 0
# inform the load balancer that we are doing an expensive key operation
self.update_device_weight(session.key_op_data.rpc_index, self.pkey_gen_weight)
return RPCAction(None, [self.rpc_list[session.key_op_data.rpc_index]], self.callback_rpc_keygen)
def handle_rpc_keygen(self, code, client, unpacker, session):
"""A key has been generated. Returns uuid and handle"""
# consume pkcs11 session id
unpacker.unpack_uint()
# save the key settings
if (code == DKS_RPCFunc.RPC_FUNC_PKEY_GENERATE_RSA):
session.pkey_type = DKS_HALKeyType.HAL_KEY_TYPE_RSA_PRIVATE
# consume keylen
unpacker.unpack_uint()
# get the exponent because we need the size
exponent = unpacker.unpack_bytes()
# get the location of the flags so we can change if needed
exp_len = len(exponent)
exp_padding = (4 - exp_len % 4) % 4
flag_location = 20 + exp_len + exp_padding
elif (code == DKS_RPCFunc.RPC_FUNC_PKEY_GENERATE_EC):
session.pkey_type = DKS_HALKeyType.HAL_KEY_TYPE_EC_PRIVATE
# get the location of the flags so we can change if needed
flag_location = 16
elif (code == DKS_RPCFunc.RPC_FUNC_PKEY_GENERATE_HASHSIG):
session.pkey_type = DKS_HALKeyType.HAL_KEY_TYPE_HASHSIG_PRIVATE
# get the location of the flags so we can change if needed
flag_location = 24
# get the flags
session.flags = rpc_get_int(session.current_request, flag_location)
# check to see if the rpc has been setup to allow exportable private keys
if ((session.enable_exportable_private_keys == True) and
(session.flags & DKS_HALKeyFlag.HAL_KEY_FLAG_USAGE_KEYENCIPHERMENT) == 0 and
(session.flags & DKS_HALKeyFlag.HAL_KEY_FLAG_USAGE_DATAENCIPHERMENT) == 0):
new_flag = session.flags | DKS_HALKeyFlag.HAL_KEY_FLAG_EXPORTABLE
session.current_request = rpc_set_int(session.current_request, new_flag, flag_location)
# sanity check. Make sure we get back what we just set
session.flags = rpc_get_int(session.current_request, flag_location)
logger.info("Key Gen Flags: 0x%X"%session.flags)
# select an RPC to use for this hashing operation
session.key_op_data.rpc_index = session.rpc_index if(session.rpc_index >= 0) else self.choose_rpc()
logger.info("session.rpc_index == %i session.key_op_data.rpc_index == %i",
session.rpc_index, session.key_op_data.rpc_index)
# inform the load balancer that we are doing an expensive key operation
self.update_device_weight(session.key_op_data.rpc_index, self.pkey_gen_weight)
return RPCAction(None, [self.rpc_list[session.key_op_data.rpc_index]], self.callback_rpc_keygen)
def handle_rpc_pkeymatch(self, code, client, unpacker, session):
"""match on all rpcs and then combine results
incoming UUIDs are master table UUIDs"""
# if the rpc_index has been set for the session, always use it
if(session.incoming_uuids_are_device_uuids):
if(session.rpc_index >= 0):
return RPCAction(None, [self.rpc_list[session.rpc_index]], None)
else:
logger.info("handle_rpc_pkeymatch: using device uuid, but device not set")
return self.create_error_response(code, client, DKS_HALError.HAL_ERROR_IMPOSSIBLE)
session.keymatch_details = KeyMatchDetails()
# unpack and store key match attributes
session.keymatch_details.unpack(unpacker)
logger.info("pkey_match: result_max = %i, uuid = %s",
session.keymatch_details.result_max, session.keymatch_details.uuid)
# if uuid is none, search RPC 0
# else search starting with the RPC that the uuid is on
if(session.keymatch_details.uuid == KeyMatchDetails.none_uuid):
if(session.rpc_index >= 0):
session.keymatch_details.rpc_index = session.rpc_index
else:
session.keymatch_details.rpc_index = 0
else:
# need to convert master_uuid to device_uuid
if(session.rpc_index >= 0):
device_list = session.cache.get_alphas(session.keymatch_details.uuid)
if (session.rpc_index not in device_list):
logger.info("handle_rpc_pkeyopen: session.rpc_index not in device_list")
return self.create_error_response(code, client, DKS_HALError.HAL_ERROR_KEY_NOT_FOUND)
session.keymatch_details.rpc_index = session.rpc_index
# need to update the command with the new UUID
session.keymatch_details.uuid = device_list[session.rpc_index]
else:
# find the rpc that this is on
device_to_search = session.cache.get_alpha_lowest_index(session.keymatch_details.uuid)
if(device_to_search is None):
return self.create_error_response(code, client, DKS_HALError.HAL_ERROR_RPC_TRANSPORT)
session.keymatch_details.rpc_index = device_to_search[0]
# need to update the command with the new UUID
session.keymatch_details.uuid = device_to_search[1]
session.current_request = session.keymatch_details.repack(code, client)
# make sure the rpc_index was set
if(hasattr(session.keymatch_details, 'rpc_index') == False):
return self.create_error_response(code, client, DKS_HALError.HAL_ERROR_RPC_TRANSPORT)
return RPCAction(None, [self.rpc_list[session.keymatch_details.rpc_index]], self.callback_rpc_pkeymatch)
def create_function_table(self):
"""Use a table to quickly select the method to handle each RPC request"""
self.function_table[DKS_RPCFunc.RPC_FUNC_GET_VERSION] = self.handle_rpc_any
self.function_table[DKS_RPCFunc.RPC_FUNC_GET_RANDOM] = self.handle_rpc_any
self.function_table[DKS_RPCFunc.RPC_FUNC_SET_PIN] = self.handle_rpc_all
self.function_table[DKS_RPCFunc.RPC_FUNC_LOGIN] = self.handle_rpc_all
self.function_table[DKS_RPCFunc.RPC_FUNC_LOGOUT] = self.handle_rpc_all
self.function_table[DKS_RPCFunc.RPC_FUNC_LOGOUT_ALL] = self.handle_rpc_all
self.function_table[DKS_RPCFunc.RPC_FUNC_IS_LOGGED_IN] = self.handle_rpc_all
self.function_table[DKS_RPCFunc.RPC_FUNC_HASH_GET_DIGEST_LEN] = self.handle_rpc_any
self.function_table[DKS_RPCFunc.RPC_FUNC_HASH_GET_DIGEST_ALGORITHM_ID] = self.handle_rpc_any
self.function_table[DKS_RPCFunc.RPC_FUNC_HASH_GET_ALGORITHM] = self.handle_rpc_hash
self.function_table[DKS_RPCFunc.RPC_FUNC_HASH_INITIALIZE] = self.handle_rpc_starthash
self.function_table[DKS_RPCFunc.RPC_FUNC_HASH_UPDATE] = self.handle_rpc_hash
self.function_table[DKS_RPCFunc.RPC_FUNC_HASH_FINALIZE] = self.handle_rpc_endhash
self.function_table[DKS_RPCFunc.RPC_FUNC_PKEY_LOAD] = self.handle_rpc_pkeyload
self.function_table[DKS_RPCFunc.RPC_FUNC_PKEY_OPEN] = self.handle_rpc_pkeyopen
self.function_table[DKS_RPCFunc.RPC_FUNC_PKEY_GENERATE_RSA] = self.handle_rpc_keygen
self.function_table[DKS_RPCFunc.RPC_FUNC_PKEY_GENERATE_EC] = self.handle_rpc_keygen
self.function_table[DKS_RPCFunc.RPC_FUNC_PKEY_CLOSE] = self.handle_rpc_pkey
self.function_table[DKS_RPCFunc.RPC_FUNC_PKEY_DELETE] = self.handle_rpc_pkey
self.function_table[DKS_RPCFunc.RPC_FUNC_PKEY_GET_KEY_TYPE] = self.handle_rpc_pkey
self.function_table[DKS_RPCFunc.RPC_FUNC_PKEY_GET_KEY_CURVE] = self.handle_rpc_pkey
self.function_table[DKS_RPCFunc.RPC_FUNC_PKEY_GET_KEY_FLAGS] = self.handle_rpc_pkey
self.function_table[DKS_RPCFunc.RPC_FUNC_PKEY_GET_PUBLIC_KEY_LEN] = self.handle_rpc_pkey
self.function_table[DKS_RPCFunc.RPC_FUNC_PKEY_GET_PUBLIC_KEY] = self.handle_rpc_pkey
self.function_table[DKS_RPCFunc.RPC_FUNC_PKEY_SIGN] = self.handle_rpc_pkey
self.function_table[DKS_RPCFunc.RPC_FUNC_PKEY_VERIFY] = self.handle_rpc_pkey
self.function_table[DKS_RPCFunc.RPC_FUNC_PKEY_MATCH] = self.handle_rpc_pkeymatch
self.function_table[DKS_RPCFunc.RPC_FUNC_PKEY_SET_ATTRIBUTES] = self.handle_rpc_pkey
self.function_table[DKS_RPCFunc.RPC_FUNC_PKEY_GET_ATTRIBUTES] = self.handle_rpc_pkey
self.function_table[DKS_RPCFunc.RPC_FUNC_PKEY_EXPORT] = self.handle_rpc_pkeyexport
self.function_table[DKS_RPCFunc.RPC_FUNC_PKEY_IMPORT] = self.handle_rpc_pkeyimport
self.function_table[DKS_RPCFunc.RPC_FUNC_PKEY_GENERATE_HASHSIG] = self.handle_rpc_keygen
self.function_table[DKS_RPCFunc.RPC_FUNC_GET_HSM_STATE] = self.handle_rpc_getdevice_state
self.function_table[DKS_RPCFunc.RPC_FUNC_GET_IP] = self.handle_rpc_getdevice_ip
self.function_table[DKS_RPCFunc.RPC_FUNC_SET_RPC_DEVICE] = self.handle_set_rpc
self.function_table[DKS_RPCFunc.RPC_FUNC_ENABLE_CACHE_KEYGEN] = self.handle_enable_cache_keygen
self.function_table[DKS_RPCFunc.RPC_FUNC_DISABLE_CACHE_KEYGEN] = self.handle_disable_cache_keygen
self.function_table[DKS_RPCFunc.RPC_FUNC_CHECK_TAMPER] = self.handle_rpc_usecurrent
self.function_table[DKS_RPCFunc.RPC_FUNC_USE_INCOMING_DEVICE_UUIDS] = self.handle_use_incoming_device_uuids
self.function_table[DKS_RPCFunc.RPC_FUNC_USE_INCOMING_MASTER_UUIDS] = self.handle_use_incoming_master_uuids
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62,
20373,
62,
25216,
62,
49,
4090,
62,
5105,
32936,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
7,
29891,
13,
79,
2539,
62,
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6624,
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27015,
62,
39,
1847,
9218,
6030,
13,
39,
1847,
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62,
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62,
4805,
3824,
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290,
198,
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220,
220,
220,
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220,
220,
220,
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6246,
13,
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1222,
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27015,
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39,
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9218,
34227,
13,
39,
1847,
62,
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62,
38948,
62,
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32936,
2599,
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220,
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220,
220,
220,
220,
220,
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13,
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13,
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198,
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220,
220,
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13,
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62,
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1921,
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3528,
62,
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3824,
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290,
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220,
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220,
220,
220,
220,
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220,
220,
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6246,
13,
33152,
1222,
360,
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62,
39,
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13,
39,
1847,
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62,
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62,
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32936,
2599,
198,
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220,
220,
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13,
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62,
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62,
39,
1921,
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3528,
62,
5105,
32936,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6246,
13,
79,
2539,
62,
4906,
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360,
27015,
62,
39,
1847,
9218,
6030,
13,
39,
1847,
62,
20373,
62,
25216,
62,
45,
11651,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6246,
13,
22019,
303,
796,
657,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
4175,
262,
3440,
3652,
8250,
326,
356,
389,
1804,
281,
5789,
1994,
4905,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
19119,
62,
25202,
62,
6551,
7,
29891,
13,
2539,
62,
404,
62,
7890,
13,
81,
14751,
62,
9630,
11,
2116,
13,
79,
2539,
62,
5235,
62,
6551,
8,
628,
220,
220,
220,
220,
220,
220,
220,
1441,
39400,
12502,
7,
14202,
11,
685,
944,
13,
81,
14751,
62,
4868,
58,
29891,
13,
2539,
62,
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62,
7890,
13,
81,
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62,
9630,
60,
4357,
2116,
13,
47423,
62,
81,
14751,
62,
2539,
5235,
8,
628,
220,
220,
220,
825,
5412,
62,
81,
14751,
62,
79,
2539,
11748,
7,
944,
11,
2438,
11,
5456,
11,
555,
8002,
263,
11,
6246,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
1904,
14500,
6163,
39400,
290,
651,
4504,
334,
27112,
290,
5412,
37811,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
611,
262,
6246,
374,
14751,
62,
9630,
468,
407,
307,
900,
11,
428,
1276,
1972,
262,
1171,
1994,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
374,
14751,
62,
9630,
796,
6246,
13,
81,
14751,
62,
9630,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
611,
7,
81,
14751,
62,
9630,
1279,
657,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
220,
220,
220,
220,
374,
14751,
62,
9630,
796,
2116,
13,
6679,
577,
62,
81,
14751,
3419,
1303,
29891,
13,
2539,
62,
404,
62,
7890,
13,
81,
14751,
62,
9630,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
2922,
281,
39400,
284,
779,
329,
428,
49544,
4905,
198,
220,
220,
220,
220,
220,
220,
220,
6246,
13,
2539,
62,
404,
62,
7890,
13,
81,
14751,
62,
9630,
796,
6246,
13,
81,
14751,
62,
9630,
611,
7,
29891,
13,
81,
14751,
62,
9630,
18189,
657,
8,
2073,
2116,
13,
6679,
577,
62,
81,
14751,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
49706,
13,
10951,
7203,
29891,
13,
81,
14751,
62,
9630,
6624,
4064,
72,
220,
6246,
13,
2539,
62,
404,
62,
7890,
13,
81,
14751,
62,
9630,
6624,
4064,
72,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6246,
13,
81,
14751,
62,
9630,
11,
6246,
13,
2539,
62,
404,
62,
7890,
13,
81,
14751,
62,
9630,
8,
628,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
15000,
279,
74,
6359,
1157,
6246,
4686,
198,
220,
220,
220,
220,
220,
220,
220,
555,
8002,
263,
13,
403,
8002,
62,
28611,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
15000,
885,
365,
74,
198,
220,
220,
220,
220,
220,
220,
220,
555,
8002,
263,
13,
403,
8002,
62,
28611,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
15000,
279,
74,
6359,
23,
198,
220,
220,
220,
220,
220,
220,
220,
555,
8002,
263,
13,
403,
8002,
62,
33661,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
15000,
885,
74,
198,
220,
220,
220,
220,
220,
220,
220,
555,
8002,
263,
13,
403,
8002,
62,
33661,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
9701,
198,
220,
220,
220,
220,
220,
220,
220,
6246,
13,
33152,
796,
555,
8002,
263,
13,
403,
8002,
62,
28611,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
611,
468,
35226,
7,
29891,
11,
705,
79,
2539,
62,
4906,
6,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
2190,
355,
262,
1171,
2196,
286,
262,
938,
1953,
660,
1994,
7560,
355,
428,
318,
262,
3210,
8748,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
7,
29891,
13,
79,
2539,
62,
4906,
6624,
360,
27015,
62,
39,
1847,
9218,
6030,
13,
39,
1847,
62,
20373,
62,
25216,
62,
49,
4090,
62,
4805,
3824,
6158,
290,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6246,
13,
33152,
1222,
360,
27015,
62,
39,
1847,
9218,
34227,
13,
39,
1847,
62,
20373,
62,
38948,
62,
5105,
32936,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6246,
13,
79,
2539,
62,
4906,
796,
360,
27015,
62,
39,
1847,
9218,
6030,
13,
39,
1847,
62,
20373,
62,
25216,
62,
49,
4090,
62,
5105,
32936,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
7,
29891,
13,
79,
2539,
62,
4906,
6624,
360,
27015,
62,
39,
1847,
9218,
6030,
13,
39,
1847,
62,
20373,
62,
25216,
62,
2943,
62,
4805,
3824,
6158,
290,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6246,
13,
33152,
1222,
360,
27015,
62,
39,
1847,
9218,
34227,
13,
39,
1847,
62,
20373,
62,
38948,
62,
5105,
32936,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6246,
13,
79,
2539,
62,
4906,
796,
360,
27015,
62,
39,
1847,
9218,
6030,
13,
39,
1847,
62,
20373,
62,
25216,
62,
2943,
62,
5105,
32936,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
7,
29891,
13,
79,
2539,
62,
4906,
6624,
360,
27015,
62,
39,
1847,
9218,
6030,
13,
39,
1847,
62,
20373,
62,
25216,
62,
39,
1921,
7998,
3528,
62,
4805,
3824,
6158,
290,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6246,
13,
33152,
1222,
360,
27015,
62,
39,
1847,
9218,
34227,
13,
39,
1847,
62,
20373,
62,
38948,
62,
5105,
32936,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6246,
13,
79,
2539,
62,
4906,
796,
360,
27015,
62,
39,
1847,
9218,
6030,
13,
39,
1847,
62,
20373,
62,
25216,
62,
39,
1921,
7998,
3528,
62,
5105,
32936,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6246,
13,
79,
2539,
62,
4906,
796,
360,
27015,
62,
39,
1847,
9218,
6030,
13,
39,
1847,
62,
20373,
62,
25216,
62,
45,
11651,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6246,
13,
22019,
303,
796,
657,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
4175,
262,
3440,
3652,
8250,
326,
356,
389,
1804,
281,
5789,
1994,
4905,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
19119,
62,
25202,
62,
6551,
7,
29891,
13,
2539,
62,
404,
62,
7890,
13,
81,
14751,
62,
9630,
11,
2116,
13,
79,
2539,
62,
5235,
62,
6551,
8,
628,
220,
220,
220,
220,
220,
220,
220,
1441,
39400,
12502,
7,
14202,
11,
685,
944,
13,
81,
14751,
62,
4868,
58,
29891,
13,
2539,
62,
404,
62,
7890,
13,
81,
14751,
62,
9630,
60,
4357,
2116,
13,
47423,
62,
81,
14751,
62,
2539,
5235,
8,
628,
198,
220,
220,
220,
825,
5412,
62,
81,
14751,
62,
2539,
5235,
7,
944,
11,
2438,
11,
5456,
11,
555,
8002,
263,
11,
6246,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
32,
1994,
468,
587,
7560,
13,
16409,
334,
27112,
290,
5412,
37811,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
15000,
279,
74,
6359,
1157,
6246,
4686,
198,
220,
220,
220,
220,
220,
220,
220,
555,
8002,
263,
13,
403,
8002,
62,
28611,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
3613,
262,
1994,
6460,
198,
220,
220,
220,
220,
220,
220,
220,
611,
357,
8189,
6624,
360,
27015,
62,
49,
5662,
37,
19524,
13,
49,
5662,
62,
42296,
34,
62,
47,
20373,
62,
35353,
1137,
6158,
62,
49,
4090,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6246,
13,
79,
2539,
62,
4906,
796,
360,
27015,
62,
39,
1847,
9218,
6030,
13,
39,
1847,
62,
20373,
62,
25216,
62,
49,
4090,
62,
4805,
3824,
6158,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
15000,
1994,
11925,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
555,
8002,
263,
13,
403,
8002,
62,
28611,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
262,
28622,
780,
356,
761,
262,
2546,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
28622,
796,
555,
8002,
263,
13,
403,
8002,
62,
33661,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
262,
4067,
286,
262,
9701,
523,
356,
460,
1487,
611,
2622,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1033,
62,
11925,
796,
18896,
7,
11201,
3471,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1033,
62,
39231,
796,
357,
19,
532,
1033,
62,
11925,
4064,
604,
8,
4064,
604,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6056,
62,
24886,
796,
1160,
1343,
1033,
62,
11925,
1343,
1033,
62,
39231,
628,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
357,
8189,
6624,
360,
27015,
62,
49,
5662,
37,
19524,
13,
49,
5662,
62,
42296,
34,
62,
47,
20373,
62,
35353,
1137,
6158,
62,
2943,
2599,
198,
220,
220,
220,
220,
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62,
1904,
62,
259,
4976,
62,
9866,
62,
12303,
2340,
198
] | 2.235051 | 11,840 |
import math
import pickle
import torch
import torch.nn.functional as F
from torch import nn
from torch_sparse import SparseTensor
from estimation.sparse_to_dense import scipy_to_dense
from models.GCNII_DenseLayer import GCNIIConv_arxiv
from models.GCNII_layer import GCNIIdenseConv
from estimation.generate_edge_index_and_weight import compute_dege_index_and_weight
from estimation.graph_model import BNPGraphModel
from models.initial_graph import initial_graph
| [
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] | 3.176871 | 147 |
from gtts import gTTS
from pydub import AudioSegment
AudioSegment.converter = "C:\\ffmpeg\\bin\\ffmpeg.exe"
AudioSegment.ffmpeg = "C:\\ffmpeg\\bin\\ffmpeg.exe"
AudioSegment.ffprobe ="C:\\ffmpeg\\bin\\ffprobe.exe"
from pydub.playback import play
import playsound
tts = gTTS(text='Test Hatsune Miku', lang='en', slow=True)
tts.save("input.mp3")
sound = AudioSegment.from_file('input.mp3', format="mp3")
# Customize
octaves = 0.2
new_sample_rate = int(sound.frame_rate * (2.0 ** octaves))
hipitch_sound = sound._spawn(sound.raw_data, overrides={'frame_rate': new_sample_rate})
hipitch_sound = hipitch_sound.set_frame_rate(44100)
play(hipitch_sound)
hipitch_sound.export("output.mp3", format="mp3")
playsound.playsound("output.mp3", True)
| [
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13,
26024,
633,
7203,
22915,
13,
3149,
18,
1600,
6407,
8,
198
] | 2.568966 | 290 |
from datetime import datetime
from time import sleep
import requests
from mycroft import MycroftSkill, intent_handler
from mycroft.audio import wait_while_speaking
from mycroft.util.format import nice_duration, TimeResolution
API_ENDPOINT = "https://example.com/api"
API_KEY = "XXXXXXXXXXXXXXXXX"
MINUTES = 60 # seconds
HOURS = 60 * MINUTES
| [
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] | 3.317308 | 104 |
#!/usr/bin/env python3
from sys import argv
with open(argv[1]) as f:
for line in f:
length, numbers = line.rstrip("\n").split(";")
duplicates = {}
for n in numbers.split(","):
if n in duplicates:
print(n)
break
duplicates[n] = True
| [
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220,
220,
220,
220,
220,
14184,
16856,
58,
77,
60,
796,
6407,
198
] | 1.921687 | 166 |
#Print multiples of 10 for numbers in a given range
for num in range(5):
if num > 0:
print(num * 10) | [
198,
2,
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286,
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8
] | 2.815789 | 38 |
from SpaceUnitTest import SpaceUnitTest
if __name__ == '__main__':
unittest.main()
| [
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] | 2.83871 | 31 |
import torch
from detectron2.layers import cat
from bodyhands.utils.extend_utils_boxes import pairwise_ioa
from detectron2.structures import Boxes | [
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"""
mpltex
======
"""
golden_ratio = (5**.5-1.0)/2.0
packages = ['amsmath', 'amssymb', 'amsfonts', 'amsbsy', 'bm']
usepackages = [r'\usepackage{{{0}}}'.format(pkg) for pkg in packages]
def get_rcParams(fig_width_pt = 232.0, scale = 1.0, dpi = 600):
"""TODO: write a description
Parameters
----------
fig_width_pt: float (232.0)
Width of the figure in points.
Use LaTeX \showthe\linewidth to figure out what number to use.
232.0 for two-column IEEEtran articles
YYYY for MIT/WHOI thesis figures
"""
fig_width = pt_to_in(fig_width_pt) # width in inches
fig_height = fig_width * golden_ratio # height in inches
fig_size = [fig_width * scale, fig_height * scale]
params = { 'axes.labelsize': 10,
'text.fontsize': 8,
'legend.fontsize': 8,
'xtick.labelsize': 8,
'ytick.labelsize': 8,
'font.family': 'serif',
'font.serif': 'Times',
'text.usetex': True,
'text.latex.preamble': usepackages,
'figure.figsize': fig_size,
'figure.dpi': dpi}
return params
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] | 2.0067 | 597 |
#!/usr/bin/env python3
from constraint import *
import pyexcel as pe
from time import time
# create the problem object
problem = Problem(RecursiveBacktrackingSolver())
# import the Garam grid
grid = pe.get_array(file_name='csp_grid.ods')
n_lines, n_cols = len(grid), len(grid[0])
# display the Garam grid nicely
print('Initial grid:')
print()
for i in range(n_lines):
for j in range(n_cols):
if grid[i][j] == '':
print(' ', end='')
else:
print(grid[i][j], end='')
print()
# add the variables to the problem
for i in range(n_lines):
for j in range(n_cols):
if grid[i][j] == '?':
domain = range(0, 10)
elif type(grid[i][j]) == int:
domain = [grid[i][j]]
problem.addVariable((i, j), domain)
# create the "coor grid" (grid whose cases are tuples containing the cases coordinates)
grid_coor = [[(i, j) for j in range(n_cols)] for i in range(n_lines)]
# set the first case of each constraint
h_constraints_origins = [[0, 0], [0, 8],
[2, 4],
[5, 0], [5, 8],
[9, 0], [9, 8],
[11, 4],
[14, 0], [14, 8]]
v_constraints_origins = [[0, 0], [9, 0],
[5, 2],
[0, 4], [9, 4],
[0, 8], [9, 8],
[5, 10],
[0, 12], [9, 12]]
# get the horizontal constraints
h_constraints = []
for origin in h_constraints_origins:
i_origin, j_origin = origin
constraint = grid_coor[i_origin][j_origin:j_origin+5]
h_constraints.append(constraint)
# get the vertical constraints
v_constraints = []
for k in range(len(v_constraints_origins)):
i_origin, j_origin = v_constraints_origins[k]
if k == 2 or k == 7:
nb_cases = 5
else:
nb_cases = 6
constraint = [line[j_origin] for line in grid_coor[i_origin:i_origin+nb_cases]]
v_constraints.append(constraint)
# add the constraints to the problem
constraints = h_constraints + v_constraints
for constraint in constraints:
# get the operation type (+, - or *)
i, j = constraint[1]
op = grid[i][j]
if len(constraint) == 5:
if op == '+':
constraint_function = lambda a, b, c: a+b == c
elif op == '-':
constraint_function = lambda a, b, c: a-b == c
elif op == '*':
constraint_function = lambda a, b, c: a*b == c
problem.addConstraint(constraint_function, (constraint[0], constraint[2], constraint[4]))
print('{}{}{}={}'.format(grid[constraint[0][0]][constraint[0][1]],
op,
grid[constraint[2][0]][constraint[2][1]],
grid[constraint[4][0]][constraint[4][1]]))
elif len(constraint) == 6:
if op == '+':
constraint_function = lambda a, b, c, d: a+b == c*10+d
elif op == '-':
constraint_function = lambda a, b, c, d: a-b == c*10+d
elif op == '*':
constraint_function = lambda a, b, c, d: a*b == c*10+d
problem.addConstraint(constraint_function, (constraint[0], constraint[2], constraint[4], constraint[5]))
print('{}{}{}={}{}'.format(grid[constraint[0][0]][constraint[0][1]],
op,
grid[constraint[2][0]][constraint[2][1]],
grid[constraint[4][0]][constraint[4][1]],
grid[constraint[5][0]][constraint[5][1]]))
print()
print('Solving the problem...')
# solve the problem
start = time()
solution = problem.getSolution()
end = time()
print('Elapsed time: {:.0f} s'.format(end-start))
# display the solution
print()
print('Solved grid:')
print()
for i in range(n_lines):
for j in range(n_cols):
if grid[i][j] == '?' or type(grid[i][j]) == int:
print(solution[(i, j)], end='')
elif grid[i][j] == '':
print(' ', end='')
elif type(grid[i][j]) == str:
print(grid[i][j], end='')
print()
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28,
7061,
8,
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220,
220,
220,
3601,
3419,
198
] | 1.94993 | 2,137 |
# data = list(range(1, 46))
#
# import random
#
#
# def random_pop(data):
# number = random.randint(0, len(data) - 1)
# return data.pop(number)
#
#
# for i in range(1, 7):
# print(random_pop(data))
##
# import webbrowser
#
# url = "https://coastwatch.pfeg.noaa.gov/erddap/griddap/erdMH1chlamday.geotif?chlorophyll%5B(2021-11-16T00:00:00Z)%5D%5B(-57.97917):(-66.68751)%5D%5B(-63.64583):(-54.9375)%5D&.draw=surface&.vars=longitude%7Clatitude%7Cchlorophyll&.colorBar=%7C%7C%7C%7C%7C&.bgColor=0xffccccff"
#
# # urllib.request.urlretrieve(imgURL, "E:/test/image1.tif")
#
# webbrowser.open(url)
#
# import webbrowser
#
# url = "https://www.naver.com"
#
# webbrowser.open(url)
## 날짜반복
from datetime import date, timedelta
## 시작날짜와 종료날짜 설정
start_date = date(2021, 1,1)
end_date = date(2021,12,31)
##
for single_date in daterange(start_date, end_date):
a = (single_date.strftime("%Y-%m-%d"))
print(a) | [
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12,
4,
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12,
4,
67,
48774,
198,
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4798,
7,
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8
] | 1.942553 | 470 |
from .base import *
DEBUG = False
WAGTAILSEARCH_BACKENDS = {
'default': {
'BACKEND': 'wagtail.wagtailsearch.backends.elasticsearch.ElasticSearch',
'INDEX': 'wagtaildemo'
}
}
CACHES = {
'default': {
'BACKEND': 'redis_cache.cache.RedisCache',
'LOCATION': '127.0.0.1:6379',
'KEY_PREFIX': 'wagtaildemo',
'OPTIONS': {
'CLIENT_CLASS': 'redis_cache.client.DefaultClient',
}
}
}
# Use the cached template loader
TEMPLATE_LOADERS = (
('django.template.loaders.cached.Loader', (
'django.template.loaders.filesystem.Loader',
'django.template.loaders.app_directories.Loader',
)),
)
try:
from .local import *
except ImportError:
pass
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8,
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25,
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764,
12001,
1330,
1635,
198,
16341,
17267,
12331,
25,
198,
197,
6603,
198
] | 2.135838 | 346 |
from sklearn import tree
from sklearn.datasets import load_iris
from sklearn.externals.six import StringIO
import pydot
from IPython.display import Image
if __name__ == '__main__':
iris = load_iris()
print predictByDTC(iris.data, iris.target, iris.data[0:,])
| [
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] | 2.67619 | 105 |
import minimalmodbus
import serial
| [
11748,
10926,
4666,
10885,
198,
11748,
11389,
198
] | 4.375 | 8 |
import os
import sys
from configparser import ConfigParser
configr = ConfigParser()
from subprocess import Popen as pop
pp = os.getcwd()
config = ConfigParser()
val = []
path = pp
para = path + "/det.ini"
print(para)
val2 = []
config.read(para)
ad = config['installation']['installation']
print(pp)
if ad == "False":
pas = input("pleae enter your password for root permission:")
ll = "echo " + pas + " |" + " sudo -S -k apt-get install flite"
if ad == "False":
print("starting")
os.system(ll)
config['installation'] = {
"installation" : "true"
}
with open(para, 'w') as f:
config.write(f)
print("done")
#say("a quick brown fox jumps over the lazy dog")
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7,
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64,
11,
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86,
11537,
355,
277,
25,
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220,
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8,
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220,
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3601,
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2,
16706,
7203,
64,
2068,
7586,
21831,
18045,
625,
262,
16931,
3290,
4943,
198
] | 2.700389 | 257 |
# Generated by Django 3.2.9 on 2021-12-05 08:52
from django.db import migrations, models
| [
2,
2980,
515,
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513,
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17,
13,
24,
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] | 2.84375 | 32 |
text = " <option>Álvaro Obregón</option>\n" +" <option>Azcapotzalco</option>\n" +" <option>Benito Juárez</option>\n" +" <option>Coyoacán</option>\n" +" <option>Cuajimalpa de Morelos</option>\n" +" <option>Cuauhtémoc</option>\n" +" <option>Gustavo A. Madero</option>\n" +" <option>Iztacalco</option>\n" +" <option>Iztapalapa</option>\n" +" <option>Magdalena Contreras</option>\n" +" <option>Miguel Hidalgo</option>\n" +" <option>Milpa Alta</option>\n" +" <option>Tláhuac</option>\n" +" <option>Tlalpan</option> \n" +" <option>Venustiano Carranza</option>\n" +" <option>Xochimilco</option> "
text = text.replace("<option>", "").replace("</option>", "").replace(" ", "")
items = text.split("\n")
res = ""
for item in items:
res += "<option value=\""+item+"\">" +item+ "</option>\n"
print(res)
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220,
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220,
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220,
220,
220,
220,
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220,
220,
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13,
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59,
77,
4943,
198,
411,
796,
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198,
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2378,
287,
3709,
25,
198,
220,
220,
220,
581,
15853,
33490,
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1988,
17553,
1,
10,
9186,
10,
1,
38214,
1,
1343,
9186,
10,
366,
3556,
18076,
29,
59,
77,
1,
628,
198,
4798,
7,
411,
8,
198
] | 1.677177 | 666 |
# tk01.py
import tkinter as tk
main()
| [
2,
256,
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13,
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198,
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256,
74,
3849,
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256,
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198,
12417,
3419,
198
] | 2.052632 | 19 |
#
# Author: Frantisek Kolacek <work@kolacek.it
# Version: 1.0
#
import logging
import sqlite3
from datetime import datetime
from .exception import StashDatabaseException
| [
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"""
Handle only one request one time
not good for developer
Flask is a small and lightweight Python web framework that provides useful tools and features that make creating web applications in Python easier.
It gives developers flexibility and is a more accessible framework for new developers since you can build a
web application quickly using only a single Python file
Flask is a web development framework created in Python language.
This Framework is based on the robust foundation of Jinja2 templates engine and Werkzeug comprehensive WSGI web application library.
"""
# An object of Flask class is our WSGI application
from flask import Flask
# Flask constructor takes the name of current
# module (__name__) as argument
app = Flask(__name__)
# route() function of the Flask class is a
# decorator, tells the application which URL
# should call the associated function
@app.route("/")
@app.route("/subash")
#not a good way to implement below code in flask
app.add_url_rule("/hello", "hello", hello_world1)
if __name__ == "__main__":
app.run(host="0.0.0.0", port=2224) # runs the application
# app.run(host="0.0.0.0", port=2224, debug=True) # DEBUG MODE | [
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28,
1828,
1731,
11,
14257,
28,
17821,
8,
1303,
16959,
337,
16820
] | 3.790323 | 310 |
from cerulean import Terminal
| [
6738,
26074,
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272,
1330,
24523,
628,
628,
198
] | 3.777778 | 9 |
# Copyright 2014-2016 Ivan Kravets <me@ikravets.com>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import re
import sys
from imp import load_source
from multiprocessing import cpu_count
from os.path import isdir, isfile, join
import click
from platformio import app, exception, util
from platformio.app import get_state_item, set_state_item
from platformio.pkgmanager import PackageManager
PLATFORM_PACKAGES = {
"framework-arduinoavr": [
("Arduino Wiring-based Framework (AVR Core, 1.6)",
"http://arduino.cc/en/Reference/HomePage")
],
"framework-arduinosam": [
("Arduino Wiring-based Framework (SAM Core, 1.6)",
"http://arduino.cc/en/Reference/HomePage")
],
"framework-arduinoteensy": [
("Arduino Wiring-based Framework",
"http://arduino.cc/en/Reference/HomePage")
],
"framework-arduinomsp430": [
("Arduino Wiring-based Framework (MSP430 Core)",
"http://arduino.cc/en/Reference/HomePage")
],
"framework-arduinoespressif": [
("Arduino Wiring-based Framework (ESP8266 Core)",
"https://github.com/esp8266/Arduino")
],
"framework-arduinonordicnrf51": [
("Arduino Wiring-based Framework (RFduino Core)",
"https://github.com/RFduino/RFduino")
],
"framework-energiamsp430": [
("Energia Wiring-based Framework (MSP430 Core)",
"http://energia.nu/reference/")
],
"framework-energiativa": [
("Energia Wiring-based Framework (LM4F Core)",
"http://energia.nu/reference/")
],
"framework-cmsis": [
("Vendor-independent hardware abstraction layer for the Cortex-M "
"processor series",
"http://www.arm.com/products/processors/"
"cortex-m/cortex-microcontroller-software-interface-standard.php")
],
"framework-spl": [
("Standard Peripheral Library for STM32 MCUs",
"http://www.st.com"
"/web/catalog/tools/FM147/CL1794/SC961/SS1743/PF257890")
],
"framework-libopencm3": [
("libOpenCM3 Framework", "http://www.libopencm3.org/")
],
"framework-mbed": [
("mbed Framework", "http://mbed.org")
],
"framework-wiringpi": [
("GPIO Interface library for the Raspberry Pi", "http://wiringpi.com")
],
"sdk-esp8266": [
("ESP8266 SDK", "http://bbs.espressif.com")
],
"ldscripts": [
("Linker Scripts",
"https://sourceware.org/binutils/docs/ld/Scripts.html")
],
"toolchain-atmelavr": [
("avr-gcc", "https://gcc.gnu.org/wiki/avr-gcc"),
("GDB", "http://www.gnu.org/software/gdb/"),
("AVaRICE", "http://avarice.sourceforge.net/"),
("SimulAVR", "http://www.nongnu.org/simulavr/")
],
"toolchain-gccarmnoneeabi": [
("gcc-arm-embedded", "https://launchpad.net/gcc-arm-embedded"),
("GDB", "http://www.gnu.org/software/gdb/")
],
"toolchain-gccarmlinuxgnueabi": [
("GCC for Linux ARM GNU EABI", "https://gcc.gnu.org"),
("GDB", "http://www.gnu.org/software/gdb/")
],
"toolchain-gccmingw32": [
("MinGW", "http://www.mingw.org")
],
"toolchain-gcclinux32": [
("GCC for Linux i686", "https://gcc.gnu.org")
],
"toolchain-gcclinux64": [
("GCC for Linux x86_64", "https://gcc.gnu.org")
],
"toolchain-xtensa": [
("xtensa-gcc", "https://github.com/jcmvbkbc/gcc-xtensa"),
("GDB", "http://www.gnu.org/software/gdb/")
],
"toolchain-timsp430": [
("msp-gcc", "http://sourceforge.net/projects/mspgcc/"),
("GDB", "http://www.gnu.org/software/gdb/")
],
"tool-scons": [
("SCons software construction tool", "http://www.scons.org")
],
"tool-avrdude": [
("AVRDUDE", "http://www.nongnu.org/avrdude/")
],
"tool-micronucleus": [
("Micronucleus", "https://github.com/micronucleus/micronucleus")
],
"tool-bossac": [
("BOSSA CLI", "https://sourceforge.net/projects/b-o-s-s-a/")
],
"tool-openocd": [
("OpenOCD", "http://openocd.org")
],
"tool-stlink": [
("ST-Link", "https://github.com/texane/stlink")
],
"tool-teensy": [
("Teensy Loader", "https://www.pjrc.com/teensy/loader.html")
],
"tool-lm4flash": [
("Flash Programmer", "http://www.ti.com/tool/lmflashprogrammer")
],
"tool-mspdebug": [
("MSPDebug", "http://mspdebug.sourceforge.net/")
],
"tool-esptool": [
("esptool-ck", "https://github.com/igrr/esptool-ck")
],
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("rfdloader", "https://github.com/RFduino/RFduino")
],
"tool-mkspiffs": [
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] | 2.252448 | 2,349 |
# -*- coding: utf-8 -*-
{%- set app = pillar.kedb.server %}
from os.path import join, dirname, abspath, normpath
DATABASES = {
'default': {
{%- if app.database.engine == 'mysql' %}
'ENGINE': 'django.db.backends.mysql',
'PORT': '3306',
'OPTIONS': {'init_command': 'SET storage_engine=INNODB,character_set_connection=utf8,collation_connection=utf8_unicode_ci', },
{% else %}
'ENGINE': 'django.db.backends.postgresql_psycopg2',
'PORT': '5432',
{%- endif %}
'HOST': '{{ app.database.host }}',
'NAME': '{{ app.database.name }}',
'PASSWORD': '{{ app.database.password }}',
'USER': '{{ app.database.user }}'
}
}
CACHES = {
'default': {
'BACKEND': 'django.core.cache.backends.memcached.MemcachedCache',
'LOCATION': '{{ app.cache.host }}:11211',
'TIMEOUT': 120,
'KEY_PREFIX': '{{ app.cache.prefix }}'
}
}
EMAIL_HOST = '{{ app.mail.host }}',
EMAIL_HOST_USER = '{{ app.mail.user }}',
EMAIL_HOST_PASSWORD = '{{ app.mail.password }}'
{%- if pillar.linux is defined %}
TIME_ZONE = '{{ pillar.linux.system.timezone }}'
{%- else %}
TIME_ZONE = '{{ pillar.system.timezone }}'
{%- endif %}
SECRET_KEY = '{{ app.secret_key }}'
{%- if app.logger_handler is defined %}
LOCAL_INSTALLED_APPS = (
'raven.contrib.django.raven_compat',
)
{%- else %}
LOGGING = {
'version': 1,
# When set to True this will disable all logging except
# for loggers specified in this configuration dictionary. Note that
# if nothing is specified here and disable_existing_loggers is True,
# django.db.backends will still log unless it is disabled explicitly.
'disable_existing_loggers': False,
'root': {
'level': 'WARNING',
'handlers': ['sentry'],
},
'formatters': {
'verbose': {
'format': '%(asctime)s %(process)d %(levelname)s %(name)s '
'%(message)s'
},
},
'handlers': {
'sentry': {
'level': 'ERROR',
'class': 'raven.contrib.django.raven_compat.handlers.SentryHandler',
},
'file': {
'level': 'DEBUG',
'class': 'logging.FileHandler',
'filename': '/var/log/kedb/django.log',
'formatter': 'verbose',
},
},
}
{%- endif %}
RAVEN_CONFIG = {
{% if app.logger_handler is defined %}
'dsn': '{{ app.logger_handler.dsn }}',
{% endif %}
}
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] | 2.142733 | 1,149 |
#!/usr/bin/env python3
import re, sys
import getopt, pickle
import time, datetime
import urllib.request
import smtplib
from email.mime.text import MIMEText
import getpass
# import browser_cookie3
# Create email with subject and body, then sends via gmail address provided
# Gets and caches your gmail address
# Gets your gmail password (does not cache)
# Strip off script name in arg list
if __name__ == "__main__":
main(sys.argv[1:])
| [
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21015,
18,
198,
198,
11748,
302,
11,
25064,
198,
11748,
651,
8738,
11,
2298,
293,
198,
11748,
640,
11,
4818,
8079,
198,
198,
11748,
2956,
297,
571,
13,
25927,
198,
11748,
895,
83,
489,
571,
198,
6738,
3053,
13,
76,
524,
13,
5239,
1330,
337,
3955,
2767,
2302,
198,
11748,
651,
6603,
198,
2,
1330,
6444,
62,
44453,
18,
628,
198,
2,
13610,
3053,
351,
2426,
290,
1767,
11,
788,
12800,
2884,
308,
4529,
2209,
2810,
628,
198,
2,
29620,
290,
50177,
534,
308,
4529,
2209,
628,
198,
2,
29620,
534,
308,
4529,
9206,
357,
22437,
407,
12940,
8,
628,
198,
2,
18508,
572,
4226,
1438,
287,
1822,
1351,
198,
361,
11593,
3672,
834,
6624,
366,
834,
12417,
834,
1298,
198,
197,
12417,
7,
17597,
13,
853,
85,
58,
16,
25,
12962,
198
] | 3.185714 | 140 |
import threading
import logging
LOCK = threading.Lock()
logger = logging.getLogger("CERTBOT")
handler = logging.StreamHandler()
logger.addHandler(handler)
logger.setLevel(logging.INFO)
| [
11748,
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] | 3.1 | 60 |
from .WtBtAnalyst import WtBtAnalyst
from .WtCtaOptimizer import WtCtaOptimizer
__all__ = ["WtBtAnalyst","WtCtaOptimizer"] | [
6738,
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#!/usr/bin/env python3
from collections import defaultdict
from copy import copy
from strips import *
from golog_program import *
from domains.math1 import S
s = S(0)
p = Sequence(
Choose(
Exec(S.incr()),
Exec(S.double())
),
Choose(
Exec(S.incr()),
Exec(S.double())
),
Test(lambda s: s.n == 1)
)
debug(p, s)
| [
2,
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8393,
7,
50,
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23352,
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220,
220,
220,
220,
220,
220,
220,
10612,
198,
220,
220,
220,
220,
220,
220,
220,
6208,
7,
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264,
25,
264,
13,
77,
6624,
352,
8,
198,
8,
198,
198,
24442,
7,
79,
11,
264,
8,
198
] | 1.930233 | 215 |
# Copyright (c) 2019 Works Applications Co., Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import unittest
import sudachipy
import sudachipy.dictionarylib as dictionarylib
if __name__ == '__main__':
unittest.main()
| [
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198,
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28686,
198,
11748,
555,
715,
395,
198,
198,
11748,
424,
67,
620,
541,
88,
198,
11748,
424,
67,
620,
541,
88,
13,
67,
14188,
8019,
355,
22155,
8019,
628,
198,
198,
361,
11593,
3672,
834,
6624,
705,
834,
12417,
834,
10354,
198,
220,
220,
220,
555,
715,
395,
13,
12417,
3419,
198
] | 3.535885 | 209 |
"""Wrappers for rlpyt algorithms that inject reward from a custom reward model
at execution time.
TODO: need to figure out exactly how I'm going to do this for algorithms other
than PG. Some notes:
- For PG algorithms (PPO + A2C) it's easy to override the reward used at
training time by subclassing & overriding the process_returns(samples)
method. This won't work for algos with replay buffers!
- Not sure what to do for DQN. Prioritised DQN is a pain (and probably not
possible to do efficiently anyway, so I may as well skip it). Probably my
best bet is to override the loss() function to use actual reward. That will
also be annoying b/c by default the algorithm uses a "return_" thing
calculated by the replay buffer; to avoid that, I'll have to touch most parts
of the loss() method (so say goodbye to forward-compat with future versions
of rlpyt…).
- Also not yet sure how to customise reward evaluation in samplers; perhaps I
shouldn't be doing that at all, and should instead write my own eval code?
I'll definitely need my own code if I want to display both eval_score and the
learnt reward."""
from collections import namedtuple
import warnings
from rlpyt.algos.pg.a2c import A2C
from rlpyt.algos.pg.ppo import PPO
from rlpyt.utils.tensor import infer_leading_dims, restore_leading_dims
import torch
from mtil.utils.misc import RunningMeanVariance, tree_map
# ################# #
# For PG algorithms #
# ################# #
class RewardEvaluatorMT:
"""Batching, multi-task reward evaluator which can optionally standardise
reward values."""
# ################ #
# For DQN variants #
# ################ #
# (TODO: going to try policy gradient first & then move to DQN if it seems more
# efficient)
| [
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17,
66,
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83,
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83,
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1330,
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62,
12294,
62,
67,
12078,
11,
11169,
62,
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62,
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28034,
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6738,
45079,
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5308,
272,
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590,
11,
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62,
8899,
198,
198,
2,
1303,
14468,
1303,
198,
2,
1114,
23842,
16113,
1303,
198,
2,
1303,
14468,
1303,
628,
198,
4871,
32307,
36,
2100,
84,
1352,
13752,
25,
198,
220,
220,
220,
37227,
33,
19775,
11,
5021,
12,
35943,
6721,
5418,
84,
1352,
543,
460,
42976,
3210,
786,
198,
220,
220,
220,
6721,
3815,
526,
15931,
628,
628,
198,
198,
2,
1303,
7804,
4242,
21017,
1303,
198,
2,
1114,
360,
48,
45,
17670,
1303,
198,
2,
1303,
7804,
4242,
21017,
1303,
198,
198,
2,
357,
51,
3727,
46,
25,
1016,
284,
1949,
2450,
31312,
717,
1222,
788,
1445,
284,
360,
48,
45,
611,
340,
2331,
517,
198,
2,
6942,
8,
198
] | 3.492032 | 502 |
import pytest
from unittest.mock import patch
from ..alarm_time import diff_between_weekdays, get_next_weekday, next_time
# time.localtime(126000) 35*60*60
# time.struct_time(tm_year=1970, tm_mon=1, tm_mday=2,
# tm_hour=12, tm_min=0, tm_sec=0,
# tm_wday=4, tm_yday=2, tm_isdst=0)
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62,
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88,
820,
28,
17,
11,
256,
76,
62,
9409,
301,
28,
15,
8,
628,
628
] | 1.893491 | 169 |
# Approach 1
# reverseList([1, 2, 3, 4, 5, 6], 0, 5) = [6 5 4 3 2 1]
# Approach 2
# reverseList([1, 2, 3, 4, 5, 6], 0, 5) = [6 5 4 3 2 1]
| [
2,
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8,
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21,
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604,
513,
362,
352,
60,
198
] | 2 | 71 |
import requests
import json
import time
import datetime
import random
import jwt
from requests.models import Response
| [
11748,
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198,
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474,
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198,
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7007,
13,
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1330,
18261,
628
] | 4.407407 | 27 |
from django.db import models
from django.contrib.auth.models import User
from article.models import ArticlePost
# django-ckeditor
# from ckeditor.fields import RichTextField
from ckeditor_uploader.fields import RichTextUploadingField
# django-mptt
from mptt.models import MPTTModel, TreeForeignKey
# 博文的评论
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198
] | 3.019608 | 102 |
import torch
import cv2
import numpy as np
import os
from models.cspnet import CSPNet_p3p4p5
from utils.keras_weights_loader import load_keras_weights
from utils.utils import *
if __name__ == '__main__':
device = 'cuda:0'
weights_path = 'net_e121_l0.hdf5'
out_path = 'output/valresults/city/h/off/121'
input_dim = [1024, 2048]
if not os.path.exists(out_path):
os.makedirs(out_path)
res_file = os.path.join(out_path, 'val_det.txt')
model = CSPNet_p3p4p5()
load_keras_weights(model, weights_path)
model.to(device).eval()
f = open('data/citypersons/val.txt', 'r')
files = f.readlines();
num_imgs = len(files)
res_all = []
for i in range(0, num_imgs):
l = files[i]
print(l)
img = cv2.imread('data/citypersons/leftImg8bit/val/' + l.strip())
x = format_img(img)
with torch.no_grad():
x = torch.from_numpy(x).to(device)
x = x.permute(0, 3, 1, 2)
x_cls, x_reg, x_off = model(x)
Y = [x_cls.detach().cpu().numpy(), x_reg.detach().cpu().numpy(), x_off.detach().cpu().numpy()]
boxes = parse_det_offset(Y, input_dim, score=0.1, down=4)
if len(boxes)>0:
f_res = np.repeat(i + 1, len(boxes), axis=0).reshape((-1, 1))
boxes[:, [2, 3]] -= boxes[:, [0, 1]]
res_all += np.concatenate((f_res, boxes), axis=-1).tolist()
np.savetxt(res_file, np.array(res_all), fmt='%6f')
f.close()
exit(0)
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19836,
3419,
198,
220,
220,
220,
8420,
7,
15,
8,
628
] | 2.013514 | 740 |
DEFAULT_BITS = 16
| [
7206,
38865,
62,
26094,
50,
796,
1467,
198
] | 2.25 | 8 |
#!/usr/bin/python
| [
2,
48443,
14629,
14,
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29412,
628,
198
] | 2.222222 | 9 |
#Escreva um programa que pergunte a quantidade de Km percorridos por um carro alugado e a quantidade de dias pelos quais ele foi alugado. Calcule o preço a pagar, sabendo que o carro custa R$60 por dia e R$0,15 por Km rodado.
dias = int(input('Por quantos dias o carro foi alugado? '))
km = float(input('Quantos quilometros você andou com ele? '))
total = km * 0.15 + dias * 60
print('=' * 80)
print('A diaria do carro é de R$60 e temos um adicional de R$0.15 por km.')
print('=' * 80)
print('Você tem uma divida de R${:.2f}\nDiarias R${}\nKm R${:.2f}'.format(total, dias * 60, km * 0.15))
print('=' * 80)
| [
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4008,
198,
4798,
10786,
11639,
1635,
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198
] | 2.405512 | 254 |
s='Some text with spaces'
x=s.split()
{i:x.count(i) for i in x} | [
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6,
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] | 2.172414 | 29 |
import os
from sphinx_scylladb_theme import override_rst_epilog, override_smv_latest_version
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] | 2.648649 | 37 |
import unittest
from httmock import HTTMock
from myria.connection import MyriaConnection
from myria.test.mock import *
from myria.udf import MyriaFunction, MyriaPostgresFunction, myria_function
from raco.backends.myria.connection import FunctionTypes
from raco.myrial.parser import Parser
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from sport_activities_features.weather_identification import WeatherIdentification
from sport_activities_features.tcx_manipulation import TCXFile
#read TCX file
tcx_file = TCXFile()
tcx_data = tcx_file.read_one_file("path_to_the_file")
#configure visual crossing api key
visual_crossing_api_key = "API_KEY" # https://www.visualcrossing.com/weather-api
#return weather objects
weather = WeatherIdentification(tcx_data['positions'], tcx_data['timestamps'], visual_crossing_api_key)
weatherlist = weather.get_weather() | [
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] | 3.120482 | 166 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import os.path as op
import shutil as su
from datetime import datetime
import numpy as np
import pandas as pd
import xarray as xr
from scipy.io import loadmat
from .geo import gc_distance
# AUX. FUNCTIONs
def geo_distance_azimuth(lat_matrix, lon_matrix, lat_point, lon_point):
'''
Returns geodesic distance and azimuth between lat,lon matrix and lat,lon
point in degrees
'''
arcl = np.zeros(lat_matrix.shape) * np.nan
azi = np.zeros(lat_matrix.shape) * np.nan
sh1, sh2 = lat_matrix.shape
for i in range(sh1):
for j in range(sh2):
arcl[i,j], azi[i,j] = gc_distance(
lat_point, lon_point, lat_matrix[i][j], lon_matrix[i][j]
)
return arcl, azi
# SWAN INPUT/OUTPUT STAT LIBRARY
class SwanIO(object):
'SWAN numerical model input/output'
def make_project(self):
'makes swan project folder and subfolders'
if not op.isdir(self.proj.p_main): os.makedirs(self.proj.p_main)
if not op.isdir(self.proj.p_cases): os.makedirs(self.proj.p_cases)
class SwanIO_STAT(SwanIO):
'SWAN numerical model input/output - STATIONARY cases'
def make_input(self, p_file, id_run, ws, bnd):
'''
Writes input.swn file from waves sea state for stationary execution
p_file - input.swn file path
ws - wave sea state (hs, per, dr, spr)
bnd - wave sea state active boundaries
more info: http://swanmodel.sourceforge.net/online_doc/swanuse/node23.html
'''
# TODO: check readinp idla
# .swn file parameters
sea_level = self.proj.params['sea_level']
jonswap_gamma = self.proj.params['jonswap_gamma']
coords_spherical = self.proj.params['coords_spherical']
waves_period = self.proj.params['waves_period']
# main mesh
mm = self.proj.mesh_main
# .swn text file
t = "PROJ '{0}' '{1}'\n$\n".format(self.proj.name, id_run)
t += 'MODE STAT\n'
# spherical coordinates (mercator) switch
if coords_spherical != None:
t += 'COORDINATES SPHER {0}\n'.format(coords_spherical)
# sea level
t += 'SET level={0} NAUTICAL\n$\n'.format(sea_level)
# computational grid
t += 'CGRID REGULAR {0} {1} {2} {3} {4} {5} {6} CIRCLE 72 0.0345 1.00 34\n$\n'.format(
mm.cg['xpc'], mm.cg['ypc'], mm.cg['alpc'], mm.cg['xlenc'],
mm.cg['ylenc'], mm.cg['mxc']-1, mm.cg['myc']-1)
# bathymetry
t += 'INPGRID BOTTOM REGULAR {0} {1} {2} {3} {4} {5} {6}\n'.format(
mm.dg['xpc'], mm.dg['ypc'], mm.dg['alpc'], mm.dg['mxc'],
mm.dg['myc'], mm.dg['dxinp'], mm.dg['dyinp'])
t += "READINP BOTTOM 1 '{0}' {1} 0 FREE\n$\n".format(
mm.depth_fn, mm.dg_idla)
# waves boundary conditions
t += 'BOUND SHAPespec JONswap {0} {1} DSPR DEGR\n'.format(
jonswap_gamma, waves_period)
for ic in bnd:
t += "BOUN SIDE {0} CONstant PAR {1:.3f} {2:.3f} {3:.3f} {4:.3f}\n".format(
ic, ws.hs, ws.per, ws.dir, ws.spr)
t += "$\n"
# numerics
t += 'OFF QUAD\n'
# t += 'PROP BSBT\n'
# t += 'WCAP\n'
t += 'BREA\n'
t += 'FRICTION JONSWAP\n$\n'
# optional nested mesh
r_ns = [self.proj.run_nest1, self.proj.run_nest2, self.proj.run_nest3]
m_ns = [self.proj.mesh_nest1, self.proj.mesh_nest2, self.proj.mesh_nest3]
nout_0 = ['nest1', 'nest2', 'nest3']
nout_1 = ['bounds_nest1.dat', 'bounds_nest2.dat', 'bounds_nest3.dat']
for r_n, m_n, n0, n1 in zip(r_ns, m_ns, nout_0, nout_1):
if r_n:
t += "NGRID '{0}' {1} {2} {3} {4} {5} {6} {7}\n".format(
n0, m_n.cg['xpc'], m_n.cg['ypc'], m_n.cg['alpc'],
m_n.cg['xlenc'], m_n.cg['ylenc'],
np.int32(m_n.cg['xlenc']/mm.cg['dxinp']),
np.int32(m_n.cg['ylenc']/mm.cg['dyinp'])
)
t += "NESTOUT '{0}' '{1}'\n".format(n0, n1)
# output
t += "BLOCK 'COMPGRID' NOHEAD '{0}' LAY 3 HSIGN TM02 DIR TPS DSPR\n$\n".format(
mm.output_fn,
)
# compute
t += 'TEST 1,0\n'
t += 'COMPUTE \n'
t += 'STOP\n$\n'
# write file:
with open(p_file, 'w') as f:
f.write(t)
# log
fmt2 = ' 7.2f'
print(
'SWAN CASE: {1} ---> hs {2:{0}}, per {3:{0}}, dir {4:{0}}, spr {5:{0}}'.format(
fmt2, id_run, ws.hs, ws.per, ws.dir, ws.spr
)
)
def make_input_nested(self, p_file, id_run):
'''
Writes input_nested.swn file from waves sea state for stationary execution
p_file - input_nestedN.swn file path
'''
# TODO check myc-1, mxc -1
# .swn file parameters
sea_level = self.proj.params['sea_level']
coords_spherical = self.proj.params['coords_spherical']
nested_bounds = self.proj.params['nested_bounds']
# SWAN nested Computacional grid
mn1 = self.proj.mesh_nest1
# .swn text file
t = "PROJ '{0}' '{1}'\n$\n".format(self.proj.name, id_run)
t += 'MODE STAT\n'
# spherical coordinates (mercator) switch
if coords_spherical != None:
t += 'COORDINATES SPHER {0}\n'.format(coords_spherical)
t += 'SET level={0} NAUTICAL\n$\n'.format(sea_level)
# computational grid
t += 'CGRID REGULAR {0} {1} {2} {3} {4} {5} {6} CIRCLE 72 0.03558410 1.00 35\n$\n'.format(
mn1.cg['xpc'], mn1.cg['ypc'], mn1.cg['alpc'], mn1.cg['xlenc'],
mn1.cg['ylenc'], mn1.cg['mxc']-1, mn1.cg['myc']-1)
# bathymetry
t += 'INPGRID BOTTOM REGULAR {0} {1} {2} {3} {4} {5} {6}\n'.format(
mn1.dg['xpc'], mn1.dg['ypc'], mn1.dg['alpc'], mn1.dg['mxc']-1,
mn1.dg['myc']-1, mn1.dg['dxinp'], mn1.dg['dyinp'])
t += "READINP BOTTOM 1 '{0}' {1} 0 FREE\n$\n".format(
mn1.depth_fn, mn1.dg_idla)
# Boundary Conditions
t += "BOUN NEST '{0}' {1}\n".format('bounds_nest1.dat', nested_bounds)
# wind file
t += "$\n"
# numerics
t += 'OFF QUAD\n'
# t += 'GEN1\n'
# t += 'PROP BSBT\n'
# t += 'WCAP\n'
t += 'BREA\n'
t += 'FRICTION JONSWAP\n$\n'
# output
t += "BLOCK 'COMPGRID' NOHEAD '{0}' LAY 3 HSIGN TM02 DIR TPS DSPR\n$\n".format(
mn1.output_fn,
)
# compute
t += 'TEST 1,0\n'
t += 'COMPUTE \n'
t += 'STOP\n$\n'
# write file:
with open(p_file, 'w') as f:
f.write(t)
def build_case(self, case_id, waves_ss, bnd=['N', 'E', 'W', 'S']):
'''
Build SWAN STAT case input files for given wave sea state (hs, per, dir, spr)
ix_case - SWAN case index (int)
waves_ss - wave sea state (hs, per, dir, spr)
bnd - wave sea state active boundaries
'''
# SWAN case path
p_case = op.join(self.proj.p_cases, case_id)
# make execution dir
if not op.isdir(p_case): os.makedirs(p_case)
# make depth file for main mesh
self.proj.mesh_main.export_dat(p_case)
# make input.swn file
self.make_input(op.join(p_case, 'input.swn'), case_id, waves_ss, bnd)
# optional nested mesh depth and input files
r_ns = [self.proj.run_nest1, self.proj.run_nest2, self.proj.run_nest3]
m_ns = [self.proj.mesh_nest1, self.proj.mesh_nest2, self.proj.mesh_nest3]
i_ns = ['input_nest1.swn', 'input_nest2.swn', 'input_nest3.swn']
for r_n, m_n, i_n in zip(r_ns, m_ns, i_ns):
if r_n:
m_n.export_dat(p_case)
self.make_input_nested(op.join(p_case, i_n), case_id)
def output_case(self, p_case, mesh):
'read .mat output file from stationary and returns xarray.Dataset'
# extract output from selected mesh
p_mat = op.join(p_case, mesh.output_fn)
xds_out = self.outmat2xr(p_mat)
# set X and Y values
X, Y = mesh.get_XY()
xds_out = xds_out.assign_coords(X=X)
xds_out = xds_out.assign_coords(Y=Y)
# rename to longitude latitude in spherical coords cases
coords_spherical = self.proj.params['coords_spherical']
if coords_spherical != None:
xds_out = xds_out.rename({'X':'lon', 'Y':'lat'})
return xds_out
class SwanIO_NONSTAT(SwanIO):
'SWAN numerical model input/output - NON STATIONARY cases'
def make_out_points(self, p_file):
'Generates desired output-points coordinates file'
# define and save output points
x_out = self.proj.x_out
y_out = self.proj.y_out
if not x_out or not y_out:
return
else:
points = np.vstack((x_out,y_out)).T
np.savetxt(p_file, points, fmt='%.2f')
def make_wave_files(self, p_case, waves_event, time, bnd):
'Generate event wave files (swan compatible)'
# wave variables
hs = waves_event.hs.values[:]
per = waves_event.per.values[:]
direc = waves_event.dir.values[:]
spr = waves_event.spr.values[:]
# csv file
num_data = len(time)
data = np.zeros((num_data, 5))
data[:, 0] = time
data[:, 1] = hs
data[:, 2] = per
data[:, 3] = direc
data[:, 4] = spr
# Copy file for all boundaries
save = op.join(p_case, 'series_waves.dat')
np.savetxt(save, data, header='TPAR', comments='', fmt='%8.4f %2.3f %2.3f %3.2f %3.1f')
for i in bnd:
su.copyfile(save, op.join(p_case, 'series_waves_{0}.dat'.format(i)))
def make_wind_files(self, p_case, waves_event):
'''
Generate event wind mesh files (swan compatible)
uses wave_event U10 and V10 values at the entire SWAN comp. grid
'''
# wind variables
u10 = waves_event.U10.values[:]
v10 = waves_event.V10.values[:]
# main mesh
mm = self.proj.mesh_main
# each time needs 2D (mesh) wind files (U,V)
mxc = mm.cg['mxc'] # number mesh x
myc = mm.cg['myc'] # number mesh y
txt = ''
for c, (u, v) in enumerate(zip(u10,v10)):
# single point wind -> entire SWAN comp.grid wind
aux = np.ones((mxc, myc))
# TODO: wind has to be rotated if alpc != 0
# csv file
u_2d = aux * u
v_2d = aux * v
u_v_stack = np.vstack((u_2d, v_2d))
save = op.join(p_case, 'wind_{0:06}.dat'.format(c))
np.savetxt(save, u_v_stack, fmt='%.2f')
# wind list file
txt += 'wind_{0:06}.dat\n'.format(c)
# winds file path
save = op.join(p_case, 'series_wind.dat')
with open(save, 'w') as f:
f.write(txt)
def make_vortex_files(self, p_case, storm_track):
'''
Generate event wind mesh files (swan compatible)
uses wave_event storm path data over SWAN computational grid
needs SPHERICAL COORDINATES
'''
# parameters
RE = 6378.135 # Earth radius
# wind variables
storm_move = storm_track.move.values[:]
storm_vf = storm_track.vf.values[:]
storm_lon = storm_track.lon.values[:]
storm_lat = storm_track.lat.values[:]
storm_pn = storm_track.pn.values[:]
storm_p0 = storm_track.p0.values[:]
times = storm_track.index[:]
# main mesh
mm = self.proj.mesh_main
# comp. grid for generating vortex wind files
mxc = mm.cg['mxc'] # number mesh x
myc = mm.cg['myc'] # number mesh y
# comp. grid lat, lon limits
lon0 = mm.cg['xpc']
lat0 = mm.cg['ypc']
lon1 = mm.cg['xpc'] + mm.cg['xlenc']
lat1 = mm.cg['ypc'] + mm.cg['ylenc']
cg_lon = np.linspace(lon0, lon1, mxc)
cg_lat = np.linspace(lat0, lat1, myc)
mg_lon, mg_lat = np.meshgrid(cg_lon, cg_lat)
# wind output holder
hld_W = np.zeros((len(cg_lat), len(cg_lon), len(storm_move)))
hld_D = np.zeros((len(cg_lat), len(cg_lon), len(storm_move)))
# each time needs 2D (mesh) wind files (U,V)
txt = ''
for c, (lo, la, p0, pn, move, vf) in enumerate(zip(
storm_lon, storm_lat, storm_p0, storm_pn, storm_move, storm_vf)):
# get distance and angle between points
arcl, beta = geo_distance_azimuth(mg_lat, mg_lon, la, lo)
r = arcl * np.pi / 180.0 * RE
if p0 < 900: p0 = 900 # fix p0
# Silva et al. 2010
RC = 0.4785 * p0 - 413.01
# TODO usar otro radio ciclostrofico?
# Hydromet Rankin-Vortex model (eq. 76)
pr = p0 + (pn - p0) * np.exp(-2*RC/r)
py, px = np.gradient(pr)
ang = np.arctan2(py, px) + np.sign(la) * np.pi/2.0
# Wind model
w = 0.2618 # velocidad angular Earth (rad/h)
f = 2 * w * np.sin(la*np.pi/180) # coriolis
ur = 21.8 * np.sqrt(pn-p0) - 0.5 * f * RC # wind max grad (km/h)
fv = np.zeros(mg_lon.shape)
s1 = r/RC < 1 # eq. (9) Rodo (2009)
fv[s1] = 1 - 0.971 * np.exp(-6.826 * np.power(r[s1]/RC, 4.798))
s2 = r/RC >=1 # eq. (10) Rodo (2009)
nc = (f*RC)/ur
A = -0.99 * (1.066-np.exp(-1.936*nc))
B = -0.357 * (1.4456-np.exp(-5.2388*nc))
fv[s2] = np.exp(A*np.power(np.log(r[s2]/RC),3) * \
np.exp(B*np.log(r[s2]/RC)))
abnaut = move + beta
ab = np.remainder(-abnaut+270, 360) *np.pi/180 # nautical to cartesian
W = 0.986 * (fv*ur + 0.5*vf * np.cos(ab-np.pi/2))
W[W<0] = 0
# TODO: wind has to be rotated if alpc != 0
# csv file
u_2d = W * np.cos(ang) / 3.6 # km/h --> m/s
v_2d = W * np.sin(ang) / 3.6 # km/h --> m/s
u_v_stack = np.vstack((u_2d, v_2d))
save = op.join(p_case, 'wind_{0:06}.dat'.format(c))
np.savetxt(save, u_v_stack, fmt='%.2f')
# wind list file
txt += 'wind_{0:06}.dat\n'.format(c)
# hold wind data (m/s)
hld_W[:,:,c] = W / 3.6 # km/h --> m/s
hld_D[:,:,c] = 270 - np.rad2deg(ang) # direction (º clock. rel. north)
# winds file path
save = op.join(p_case, 'series_wind.dat')
with open(save, 'w') as f:
f.write(txt)
# aux. save vortex wind fields
p_vortex = op.join(p_case, 'vortex_wind.nc')
xds_vortex = xr.Dataset(
{
'W': (('lat','lon','time'), hld_W, {'units':'m/s'}),
'Dir': (('lat','lon','time'), hld_D, {'units':'º'})
},
coords={
'Y' : cg_lat,
'X' : cg_lon,
'time' : times,
}
)
xds_vortex.attrs['xlabel'] = 'Longitude (º)'
xds_vortex.attrs['ylabel'] = 'Latitude (º)'
xds_vortex.to_netcdf(p_vortex)
def make_level_files(self, p_case, wave_event):
'Generate event level mesh files (swan compatible)'
# parse pandas time index to swan iso format
swan_iso_fmt = '%Y%m%d.%H%M'
time = pd.to_datetime(wave_event.index).strftime(swan_iso_fmt).values[:]
# level variables
zeta = wave_event.level.values[:]
tide = wave_event.tide.values[:]
# main mesh
mm = self.proj.mesh_main
# each time needs 2D (mesh) level
mxc = mm.cg['mxc'] # number mesh x
myc = mm.cg['myc'] # number mesh y
txt = ''
for c, (z, t) in enumerate(zip(zeta, tide)):
# single point level -> entire SWAN comp.grid level
aux = np.ones((mxc, myc)).T
# csv file
l = z + t # total level
l_2d = aux * l
save = op.join(p_case, 'level_{0:06}.dat'.format(c))
np.savetxt(save, l_2d, fmt='%.2f')
# level list file
txt += 'level_{0:06}.dat\n'.format(c)
# waves file path
save = op.join(p_case, 'series_level.dat')
with open(save, 'w') as f:
f.write(txt)
def make_input(self, p_file, id_run, time, make_waves=True,
make_winds=True, wvs_bnd=['N', 'E', 'W', 'S']):
'''
Writes input.swn file from waves event for non-stationary execution
p_file - input.swn file path
time - event time at swan iso format
make_waves - activates waves input files generation (at waves_bnd)
make_winds - activates wind input files generation
more info: http://swanmodel.sourceforge.net/online_doc/swanuse/node23.html
'''
# event time (swan iso format)
t0_iso = time[0]
t1_iso = time[-1]
# .swn file parameters
sea_level = self.proj.params['sea_level']
jonswap_gamma = self.proj.params['jonswap_gamma']
cdcap = self.proj.params['cdcap']
maxerr = self.proj.params['maxerr']
coords_spherical = self.proj.params['coords_spherical']
waves_period = self.proj.params['waves_period']
# main mesh
mm = self.proj.mesh_main
# output points
x_out = self.proj.x_out
y_out = self.proj.y_out
# computational data
dt_comp = 5 # time step (minutes)
# .swn text file
t = "PROJ '{0}' '{1}'\n$\n".format(self.proj.name, id_run)
t += 'MODE NONSTAT\n'
# spherical coordinates (mercator) swich
if coords_spherical:
t += 'COORDINATES SPHER CCM\n'
# cdcap
cdcap_str = ''
if cdcap: cdcap_str = 'cdcap={0}'.format(cdcap)
# max error (caution)
maxerr_str = ''
if maxerr: maxerr_str = 'maxerr={0}'.format(maxerr)
# set level and cdcap (if available)
t += 'SET level={0} {1} {2} NAUTICAL\n$\n'.format(
sea_level, cdcap_str, maxerr_str
)
# computational grid
t += 'CGRID REGULAR {0} {1} {2} {3} {4} {5} {6} CIRCLE 72 0.0345 1.00 34\n$\n'.format(
mm.cg['xpc'], mm.cg['ypc'], mm.cg['alpc'], mm.cg['xlenc'],
mm.cg['ylenc'], mm.cg['mxc']-1, mm.cg['myc']-1)
# bathymetry
t += 'INPGRID BOTTOM REGULAR {0} {1} {2} {3} {4} {5} {6}\n'.format(
mm.dg['xpc'], mm.dg['ypc'], mm.dg['alpc'], mm.dg['mxc'],
mm.dg['myc'], mm.dg['dxinp'], mm.dg['dyinp'])
t += "READINP BOTTOM 1 '{0}' {1} 0 FREE\n$\n".format(
mm.depth_fn, mm.dg_idla)
# wind
t += 'INPGRID WIND REGULAR {0} {1} {2} {3} {4} {5} {6} NONSTAT {7} 1 HR {8}\n'.format(
mm.cg['xpc'], mm.cg['ypc'], mm.cg['alpc'], mm.cg['mxc']-1,
mm.cg['myc']-1, mm.cg['dxinp'], mm.cg['dyinp'], t0_iso, t1_iso)
t += "READINP WIND 1. SERIES '{0}' 3 0 FREE\n$\n".format('series_wind.dat')
# level
t += 'INPGRID WLEV REGULAR {0} {1} {2} {3} {4} {5} {6} NONSTAT {7} 1 HR {8}\n'.format(
mm.cg['xpc'], mm.cg['ypc'], mm.cg['alpc'], mm.cg['mxc']-1,
mm.cg['myc']-1, mm.cg['dxinp'], mm.cg['dyinp'], t0_iso, t1_iso)
t += "READINP WLEV 1. SERIES '{0}' 3 0 FREE\n$\n".format('series_level.dat')
# waves boundary conditions
if make_waves:
t += 'BOUND SHAPespec JONswap {0} {1} DSPR DEGR\n'.format(
jonswap_gamma, waves_period)
for ic in wvs_bnd:
t += "BOUN SIDE {0} CONstant FILE 'series_waves_{0}.dat'\n".format(ic)
# numerics & physics
t += 'WIND DRAG WU\n'
t += 'GEN3 ST6 5.7E-7 8.0E-6 4.0 4.0 UP HWANG VECTAU TRUE10\n'
t += 'SSWELL\n'
t += 'QUAD iquad=8\n'
t += 'WCAP\n'
t += 'PROP BSBT\n'
if not coords_spherical:
t += 'SETUP\n' # not compatible with spherical
t += 'BREA\n'
t += 'FRICTION JONSWAP\n$\n'
t += 'TRIADS\n'
t += 'DIFFRAC\n'
# output
t += "BLOCK 'COMPGRID' NOHEAD '{0}' LAY 3 HSIGN TM02 DIR TPS DSPR OUT {1} 1.0 HR\n$\n".format(
mm.output_fn, t0_iso)
# output points
if not x_out or not y_out:
pass
else:
t += "POINTS 'outpts' FILE 'points_out.dat'\n"
t += "TABLE 'outpts' NOHEAD 'table_outpts.dat' DEP HS HSWELL DIR RTP TM02 DSPR WIND WATLEV OUT {0} {1} MIN\n$\n".format(t0_iso, dt_comp)
# compute
t += 'TEST 1,0\n'
t += 'COMPUTE NONSTAT {0} {1} MIN {2}\n'.format(t0_iso, dt_comp, t1_iso)
t += 'STOP\n$\n'
# write file:
with open(p_file, 'w') as f:
f.write(t)
def build_case(self, case_id, waves_event, storm_track=None,
make_waves=True, make_winds=True, waves_bnd=['N', 'E', 'W', 'S']):
'''
Build SWAN NONSTAT case input files for given wave dataset
case_id - SWAN case index (int)
waves_event - waves event time series (pandas.Dataframe)
also contains level, tide and wind (not storm track) variables
[n x 8] (hs, per, dir, spr, U10, V10, level, tide)
storm_track - None / storm track time series (pandas.Dataframe)
storm_track generated winds have priority over waves_event winds
[n x 6] (move, vf, lon, lat, pn, p0)
'''
# SWAN case path
p_case = op.join(self.proj.p_cases, case_id)
# make execution dir
if not op.isdir(p_case): os.makedirs(p_case)
# make depth file for main mesh
self.proj.mesh_main.export_dat(p_case)
# make output points file
self.make_out_points(op.join(p_case, 'points_out.dat'))
# parse pandas time index to swan iso format
swan_iso_fmt = '%Y%m%d.%H%M'
time_swan = pd.to_datetime(waves_event.index).strftime(swan_iso_fmt).values[:]
# make wave files
if make_waves:
self.make_wave_files(p_case, waves_event, time_swan, waves_bnd)
# make wind files
# TODO: vortex model, if active, will override wind files
if make_winds:
self.make_wind_files(p_case, waves_event)
# vortex model for storm tracks
if isinstance(storm_track, pd.DataFrame):
self.make_vortex_files(p_case, storm_track)
# make water level files
self.make_level_files(p_case, waves_event)
# make input.swn file
self.make_input(
op.join(p_case, 'input.swn'), case_id, time_swan,
make_waves = make_waves, make_winds = make_winds,
)
# TODO: add optional nested mesh depth and input files
def output_case(self, p_case, mesh):
'read .mat output file from non-stationary and returns xarray.Dataset'
# extract output from selected mesh
p_mat = op.join(p_case, mesh.output_fn)
xds_out = self.outmat2xr(p_mat)
# set X and Y values
X, Y = mesh.get_XY()
xds_out = xds_out.assign_coords(X=X)
xds_out = xds_out.assign_coords(Y=Y)
# rename to longitude latitude in spherical coords cases
coords_spherical = self.proj.params['coords_spherical']
if coords_spherical != None:
xds_out = xds_out.rename({'X':'lon', 'Y':'lat'})
return xds_out
def get_t0_dt(self, p_input):
'gets output points time_ini and delta_time (min) from SWAN input.swn file'
# read input.swn and file data
with open(p_input, 'r') as fR:
ls = fR.readlines()
lx = [x for x in ls if x.startswith('TABLE')][0].split(' ')
t0_str = lx[-3] # start date
dt_min = lx[-2] # dt (minutes)
swan_iso_fmt = '%Y%m%d.%H%M'
t0 = datetime.strptime(t0_str, swan_iso_fmt)
return t0, dt_min
def output_points(self, p_case):
'read table_outpts.dat output file and returns xarray.Dataset'
p_dat = op.join(p_case, 'table_outpts.dat')
# variable names
names = ['DEP', 'HS', 'HSWELL', 'DIR', 'RTP', 'TM02', 'DSPR', 'WIND',
'WATLEV', 'OUT' ]
x_out = self.proj.x_out
y_out = self.proj.y_out
# points are mixed at output file
np_pts = np.genfromtxt(p_dat)
n_rows = np_pts.shape[0]
# number of points
n_pts = len(x_out)
l_xds_pts = []
for i in range(n_pts):
ix_p = np.arange(i, n_rows, n_pts)
np_pti = np_pts[ix_p, :]
xds_pti = xr.Dataset({}) #, coords='time')
for c, n in enumerate(names):
xds_pti[n] = (('time'), np_pti[:,c])
l_xds_pts.append(xds_pti)
xds_out = xr.concat(l_xds_pts, dim='point')
# add point x and y
xds_out['x_point'] = (('point'), x_out)
xds_out['y_point'] = (('point'), y_out)
# add times dim values
t0, dt_min = self.get_t0_dt(op.join(p_case, 'input.swn'))
time_out = pd.date_range(t0, periods=len(xds_out.time), freq='{0}min'.format(dt_min))
xds_out = xds_out.assign_coords(time=time_out)
return xds_out
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] | 1.845631 | 13,837 |
import numpy as np
import matplotlib.pyplot as plt
k = 100
iters = 1000
episodes = 50
eps_01_rewards = np.zeros(iters)
eps_1_rewards = np.zeros(iters)
eps_decay_rewards = np.zeros(iters)
ucb_rewards = np.zeros(iters)
for i in range(episodes):
random_test_list = [x for x in np.random.normal(0, 1, k)]
eps_01 = greedy_bandit(k, 0.01, iters, random_test_list)
eps_1 = greedy_bandit(k, 0.1, iters, random_test_list)
eps_decay = e_decay_bandit(k, 0.3, 0.1, iters, random_test_list)
ucb = ucb_bandit(k, 2, iters, random_test_list)
eps_01.iterate()
eps_1.iterate()
eps_decay.decay_iterate()
ucb.ucb_iterate()
eps_01_rewards += (eps_01.reward - eps_01_rewards) / (i + 1)
eps_1_rewards += (eps_1.reward - eps_1_rewards) / (i + 1)
eps_decay_rewards += (eps_decay.reward - eps_decay_rewards) / (i + 1)
ucb_rewards += (ucb.reward - ucb_rewards) / (i + 1)
plt.figure(figsize=(12, 8))
plt.plot(eps_01_rewards, label="epsilon=0.01")
plt.plot(eps_1_rewards, label="epsilon=0.1")
plt.plot(eps_decay_rewards, label="e_decay")
plt.plot(ucb_rewards, label="upper confidence bound")
plt.legend()
plt.xlabel("Iterations")
plt.ylabel("Average Reward")
plt.title("Avg Rewards after " + str(episodes) + " Episodes")
plt.show()
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] | 2.120805 | 596 |
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import itertools
import json
import os
import random
from pathlib import Path
from typing import Dict, List, Optional, Union
import numpy as np
import torch
import torch.distributed as dist
import torch.utils.data as pt_data
import webdataset as wd
from omegaconf import DictConfig, ListConfig, OmegaConf
from pytorch_lightning import Trainer
from pytorch_lightning.utilities import rank_zero_only
from sacrebleu import corpus_bleu
from nemo.collections.common.data import ConcatDataset
from nemo.collections.common.losses import NLLLoss, SmoothedCrossEntropyLoss
from nemo.collections.common.metrics import GlobalAverageLossMetric
from nemo.collections.common.parts import transformer_weights_init
from nemo.collections.common.tokenizers.bytelevel_tokenizers import ByteLevelProcessor
from nemo.collections.common.tokenizers.chinese_tokenizers import ChineseProcessor
from nemo.collections.common.tokenizers.en_ja_tokenizers import EnJaProcessor, JaMecabProcessor
from nemo.collections.common.tokenizers.indic_tokenizers import IndicProcessor
from nemo.collections.common.tokenizers.moses_tokenizers import MosesProcessor
from nemo.collections.nlp.data import TarredTranslationDataset, TranslationDataset
from nemo.collections.nlp.models.enc_dec_nlp_model import EncDecNLPModel
from nemo.collections.nlp.models.machine_translation.mt_enc_dec_config import MTEncDecModelConfig
from nemo.collections.nlp.modules.common import TokenClassifier
from nemo.collections.nlp.modules.common.lm_utils import get_transformer
from nemo.collections.nlp.modules.common.tokenizer_utils import get_nmt_tokenizer
from nemo.collections.nlp.modules.common.transformer import BeamSearchSequenceGenerator, TopKSequenceGenerator
from nemo.core.classes import Exportable
from nemo.core.classes.common import PretrainedModelInfo, typecheck
from nemo.utils import logging, model_utils, timers
__all__ = ['MTEncDecModel']
class MTEncDecModel(EncDecNLPModel, Exportable):
"""
Encoder-decoder machine translation model.
"""
@classmethod
def _validate_encoder_decoder_hidden_size(self):
"""
Validate encoder and decoder hidden sizes, and enforce same size.
Can be overridden by child classes to support encoder/decoder different
hidden_size.
"""
if self.encoder.hidden_size != self.decoder.hidden_size:
raise ValueError(
f"Class does not support encoder.hidden_size ({self.encoder.hidden_size}) != decoder.hidden_size ({self.decoder.hidden_size}). Please use bottleneck architecture instead (i.e., model.encoder.arch = 'seq2seq' in conf/aayn_bottleneck.yaml)"
)
@classmethod
@typecheck()
def training_step(self, batch, batch_idx):
"""
Lightning calls this inside the training loop with the data from the training dataloader
passed in as `batch`.
"""
# forward pass
for i in range(len(batch)):
if batch[i].ndim == 3:
# Dataset returns already batched data and the first dimension of size 1 added by DataLoader
# is excess.
batch[i] = batch[i].squeeze(dim=0)
src_ids, src_mask, tgt_ids, tgt_mask, labels = batch
log_probs = self(src_ids, src_mask, tgt_ids, tgt_mask)
train_loss = self.loss_fn(log_probs=log_probs, labels=labels)
tensorboard_logs = {
'train_loss': train_loss,
'lr': self._optimizer.param_groups[0]['lr'],
}
return {'loss': train_loss, 'log': tensorboard_logs}
@rank_zero_only
def validation_step(self, batch, batch_idx, dataloader_idx=0):
"""
Lightning calls this inside the validation loop with the data from the validation dataloader
passed in as `batch`.
"""
return self.eval_step(batch, batch_idx, 'val', dataloader_idx)
def validation_epoch_end(self, outputs):
"""
Called at the end of validation to aggregate outputs.
:param outputs: list of individual outputs of each validation step.
"""
self.eval_epoch_end(outputs, 'val', self.global_rank)
@classmethod
@classmethod
@classmethod
@classmethod
@classmethod
@classmethod
def setup_pre_and_post_processing_utils(
cls, source_lang, target_lang, encoder_tokenizer_library, decoder_tokenizer_library
):
"""
Creates source and target processor objects for input and output pre/post-processing.
"""
source_processor, target_processor = None, None
if encoder_tokenizer_library == 'byte-level':
source_processor = ByteLevelProcessor()
elif (source_lang == 'en' and target_lang == 'ja') or (source_lang == 'ja' and target_lang == 'en'):
source_processor = EnJaProcessor(source_lang)
elif source_lang == 'ja-mecab':
source_processor = JaMecabProcessor()
elif source_lang == 'zh':
source_processor = ChineseProcessor()
elif source_lang == 'hi':
source_processor = IndicProcessor(source_lang)
elif source_lang == 'ignore':
source_processor = None
elif source_lang is not None and source_lang not in ['ja', 'zh', 'hi']:
source_processor = MosesProcessor(source_lang)
if decoder_tokenizer_library == 'byte-level':
target_processor = ByteLevelProcessor()
elif (source_lang == 'en' and target_lang == 'ja') or (source_lang == 'ja' and target_lang == 'en'):
target_processor = EnJaProcessor(target_lang)
elif target_lang == 'ja-mecab':
target_processor = JaMecabProcessor()
elif target_lang == 'zh':
target_processor = ChineseProcessor()
elif target_lang == 'hi':
target_processor = IndicProcessor(target_lang)
elif target_lang == 'ignore':
target_processor = None
elif target_lang is not None and target_lang not in ['ja', 'zh', 'hi']:
target_processor = MosesProcessor(target_lang)
return source_processor, target_processor
@classmethod
@torch.no_grad()
def batch_translate(
self, src: torch.LongTensor, src_mask: torch.LongTensor, return_beam_scores: bool = False, cache={}
):
"""
Translates a minibatch of inputs from source language to target language.
Args:
src: minibatch of inputs in the src language (batch x seq_len)
src_mask: mask tensor indicating elements to be ignored (batch x seq_len)
Returns:
translations: a list strings containing detokenized translations
inputs: a list of string containing detokenized inputs
"""
mode = self.training
timer = cache.get("timer", None)
try:
self.eval()
if timer is not None:
timer.start("encoder")
src_hiddens = self.encoder(input_ids=src, encoder_mask=src_mask)
if timer is not None:
timer.stop("encoder")
timer.start("sampler")
best_translations = self.beam_search(
encoder_hidden_states=src_hiddens, encoder_input_mask=src_mask, return_beam_scores=return_beam_scores
)
if timer is not None:
timer.stop("sampler")
if return_beam_scores:
all_translations, scores, best_translations = best_translations
scores = scores.view(-1)
all_translations = MTEncDecModel.ids_to_postprocessed_text(
all_translations, self.decoder_tokenizer, self.target_processor, filter_beam_ids=True
)
best_translations = MTEncDecModel.ids_to_postprocessed_text(
best_translations, self.decoder_tokenizer, self.target_processor, filter_beam_ids=True
)
inputs = MTEncDecModel.ids_to_postprocessed_text(
src, self.encoder_tokenizer, self.source_processor, filter_beam_ids=False
)
finally:
self.train(mode=mode)
if return_beam_scores:
return inputs, all_translations, scores.data.cpu().numpy().tolist(), best_translations
return inputs, best_translations
@classmethod
@torch.no_grad()
def translate(
self,
text: List[str],
source_lang: str = None,
target_lang: str = None,
return_beam_scores: bool = False,
log_timing: bool = False,
) -> List[str]:
"""
Translates list of sentences from source language to target language.
Should be regular text, this method performs its own tokenization/de-tokenization
Args:
text: list of strings to translate
source_lang: if not "ignore", corresponding MosesTokenizer and MosesPunctNormalizer will be run
target_lang: if not "ignore", corresponding MosesDecokenizer will be run
return_beam_scores: if True, returns a list of translations and their corresponding beam scores.
log_timing: if True, prints timing information.
Returns:
list of translated strings
"""
# __TODO__: This will reset both source and target processors even if you want to reset just one.
if source_lang is not None or target_lang is not None:
self.source_processor, self.target_processor = MTEncDecModel.setup_pre_and_post_processing_utils(
source_lang, target_lang, self.encoder_tokenizer_library, self.decoder_tokenizer_library
)
mode = self.training
prepend_ids = []
if self.multilingual:
if source_lang is None or target_lang is None:
raise ValueError("Expect source_lang and target_lang to infer for multilingual model.")
src_symbol = self.encoder_tokenizer.token_to_id('<' + source_lang + '>')
tgt_symbol = self.encoder_tokenizer.token_to_id('<' + target_lang + '>')
if src_symbol in self.multilingual_ids:
prepend_ids = [src_symbol]
elif tgt_symbol in self.multilingual_ids:
prepend_ids = [tgt_symbol]
if log_timing:
timer = timers.NamedTimer()
else:
timer = None
cache = {
"timer": timer,
}
try:
self.eval()
src, src_mask = MTEncDecModel.prepare_inference_batch(
text=text,
prepend_ids=prepend_ids,
target=False,
source_processor=self.source_processor,
target_processor=self.target_processor,
encoder_tokenizer=self.encoder_tokenizer,
decoder_tokenizer=self.decoder_tokenizer,
device=self.device,
)
if return_beam_scores:
_, all_translations, scores, best_translations = self.batch_translate(
src, src_mask, return_beam_scores=True, cache=cache,
)
return_val = all_translations, scores, best_translations
else:
_, best_translations = self.batch_translate(src, src_mask, return_beam_scores=False, cache=cache)
return_val = best_translations
finally:
self.train(mode=mode)
if log_timing:
timing = timer.export()
timing["mean_src_length"] = src_mask.sum().cpu().item() / src_mask.shape[0]
tgt, tgt_mask = self.prepare_inference_batch(
text=best_translations,
prepend_ids=prepend_ids,
target=True,
source_processor=self.source_processor,
target_processor=self.target_processor,
encoder_tokenizer=self.encoder_tokenizer,
decoder_tokenizer=self.decoder_tokenizer,
device=self.device,
)
timing["mean_tgt_length"] = tgt_mask.sum().cpu().item() / tgt_mask.shape[0]
if type(return_val) is tuple:
return_val = return_val + (timing,)
else:
return_val = (return_val, timing)
return return_val
def itn_translate_tn(
self,
text: List[str],
source_lang: str = None,
target_lang: str = None,
return_beam_scores: bool = False,
log_timing: bool = False,
inverse_normalizer=None,
normalizer=None,
) -> List[str]:
"""
Calls the translate() method with the option of running ITN (inverse text-normalization) on the input adn TN (text-normalization) on the output.
Pipeline : ITN -> translate -> TN
NOTE: ITN and TN objects must be initialized with the right languages.
Args:
text: list of strings to translate
source_lang: if not "ignore", corresponding MosesTokenizer and MosesPunctNormalizer will be run
target_lang: if not "ignore", corresponding MosesDecokenizer will be run
return_beam_scores: if True, returns a list of translations and their corresponding beam scores.
log_timing: if True, prints timing information.
inverse_normalizer: instance of nemo_text_processing.inverse_text_normalization.inverse_normalize.InverseNormalizer
normalizer: instance of nemo_text_processing.text_normalization.normalize.Normalizer
Returns:
list of translated strings
"""
if inverse_normalizer is not None:
text = [inverse_normalizer.normalize(example) for example in text]
translations = self.translate(text, source_lang, target_lang, return_beam_scores, log_timing)
if normalizer is not None:
translations = [normalizer.normalize(example) for example in translations]
return translations
# EncDecRNNTModel is exported in 2 parts
@classmethod
def list_available_models(cls) -> Optional[Dict[str, str]]:
"""
This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud.
Returns:
List of available pre-trained models.
"""
result = []
model = PretrainedModelInfo(
pretrained_model_name="nmt_en_de_transformer12x2",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_en_de_transformer12x2/versions/1.0.0rc1/files/nmt_en_de_transformer12x2.nemo",
description="En->De translation model. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:nmt_en_de_transformer12x2",
)
result.append(model)
model = PretrainedModelInfo(
pretrained_model_name="nmt_de_en_transformer12x2",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_de_en_transformer12x2/versions/1.0.0rc1/files/nmt_de_en_transformer12x2.nemo",
description="De->En translation model. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:nmt_de_en_transformer12x2",
)
result.append(model)
model = PretrainedModelInfo(
pretrained_model_name="nmt_en_es_transformer12x2",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_en_es_transformer12x2/versions/1.0.0rc1/files/nmt_en_es_transformer12x2.nemo",
description="En->Es translation model. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:nmt_en_es_transformer12x2",
)
result.append(model)
model = PretrainedModelInfo(
pretrained_model_name="nmt_es_en_transformer12x2",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_es_en_transformer12x2/versions/1.0.0rc1/files/nmt_es_en_transformer12x2.nemo",
description="Es->En translation model. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:nmt_es_en_transformer12x2",
)
result.append(model)
model = PretrainedModelInfo(
pretrained_model_name="nmt_en_fr_transformer12x2",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_en_fr_transformer12x2/versions/1.0.0rc1/files/nmt_en_fr_transformer12x2.nemo",
description="En->Fr translation model. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:nmt_en_fr_transformer12x2",
)
result.append(model)
model = PretrainedModelInfo(
pretrained_model_name="nmt_fr_en_transformer12x2",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_fr_en_transformer12x2/versions/1.0.0rc1/files/nmt_fr_en_transformer12x2.nemo",
description="Fr->En translation model. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:nmt_fr_en_transformer12x2",
)
result.append(model)
model = PretrainedModelInfo(
pretrained_model_name="nmt_en_ru_transformer6x6",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_en_ru_transformer6x6/versions/1.0.0rc1/files/nmt_en_ru_transformer6x6.nemo",
description="En->Ru translation model. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:nmt_en_ru_transformer6x6",
)
result.append(model)
model = PretrainedModelInfo(
pretrained_model_name="nmt_ru_en_transformer6x6",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_ru_en_transformer6x6/versions/1.0.0rc1/files/nmt_ru_en_transformer6x6.nemo",
description="Ru->En translation model. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:nmt_ru_en_transformer6x6",
)
result.append(model)
model = PretrainedModelInfo(
pretrained_model_name="nmt_zh_en_transformer6x6",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_zh_en_transformer6x6/versions/1.0.0rc1/files/nmt_zh_en_transformer6x6.nemo",
description="Zh->En translation model. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:nmt_zh_en_transformer6x6",
)
result.append(model)
model = PretrainedModelInfo(
pretrained_model_name="nmt_en_zh_transformer6x6",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_en_zh_transformer6x6/versions/1.0.0rc1/files/nmt_en_zh_transformer6x6.nemo",
description="En->Zh translation model. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:nmt_en_zh_transformer6x6",
)
result.append(model)
# English <-> Hindi models
model = PretrainedModelInfo(
pretrained_model_name="nmt_hi_en_transformer12x2",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_hi_en_transformer12x2/versions/v1.0.0/files/nmt_hi_en_transformer12x2.nemo",
description="Hi->En translation model. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:nmt_hi_en_transformer12x2",
)
result.append(model)
model = PretrainedModelInfo(
pretrained_model_name="nmt_en_hi_transformer12x2",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_en_hi_transformer12x2/versions/v1.0.0/files/nmt_en_hi_transformer12x2.nemo",
description="En->Hi translation model. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:nmt_en_hi_transformer12x2",
)
result.append(model)
# De/Fr/Es -> English models
model = PretrainedModelInfo(
pretrained_model_name="mnmt_deesfr_en_transformer12x2",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/mnmt_deesfr_en_transformer12x2/versions/1.2.0/files/mnmt_deesfr_en_transformer12x2.nemo",
description="De/Es/Fr->En multilingual many-one translation model. The model has 12 encoder and 2 decoder layers with hidden dim 1,024. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:mnmt_deesfr_en_transformer12x2",
)
result.append(model)
model = PretrainedModelInfo(
pretrained_model_name="mnmt_deesfr_en_transformer24x6",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/mnmt_deesfr_en_transformer24x6/versions/1.2.0/files/mnmt_deesfr_en_transformer24x6.nemo",
description="De/Es/Fr->En multilingual many-one translation model. The model has 24 encoder and 6 decoder layers with hidden dim 1,024. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:mnmt_deesfr_en_transformer24x6",
)
result.append(model)
model = PretrainedModelInfo(
pretrained_model_name="mnmt_deesfr_en_transformer6x6",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/mnmt_deesfr_en_transformer6x6/versions/1.2.0/files/mnmt_deesfr_en_transformer6x6.nemo",
description="De/Es/Fr->En multilingual many-one translation model. The model has 6 encoder and 6 decoder layers with hidden dim 1,024. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:mnmt_deesfr_en_transformer6x6",
)
result.append(model)
# English -> De/Fr/Es models
model = PretrainedModelInfo(
pretrained_model_name="mnmt_en_deesfr_transformer12x2",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/mnmt_en_deesfr_transformer12x2/versions/1.2.0/files/mnmt_en_deesfr_transformer12x2.nemo",
description="En->De/Es/Fr multilingual one-many translation model. The model has 12 encoder and 2 decoder layers with hidden dim 1,024. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:mnmt_en_deesfr_transformer12x2",
)
result.append(model)
model = PretrainedModelInfo(
pretrained_model_name="mnmt_en_deesfr_transformer24x6",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/mnmt_en_deesfr_transformer24x6/versions/1.2.0/files/mnmt_en_deesfr_transformer24x6.nemo",
description="En->De/Es/Fr multilingual one-many translation model. The model has 24 encoder and 6 decoder layers with hidden dim 1,024. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:mnmt_en_deesfr_transformer24x6",
)
result.append(model)
model = PretrainedModelInfo(
pretrained_model_name="mnmt_en_deesfr_transformer6x6",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/mnmt_en_deesfr_transformer6x6/versions/1.2.0/files/mnmt_en_deesfr_transformer6x6.nemo",
description="En->De/Es/Fr multilingual one-many translation model. The model has 6 encoder and 6 decoder layers with hidden dim 1,024. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:mnmt_en_deesfr_transformer6x6",
)
result.append(model)
model = PretrainedModelInfo(
pretrained_model_name="mnmt_en_deesfr_transformerbase",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/mnmt_en_deesfr_transformerbase/versions/1.2.0/files/mnmt_en_deesfr_transformerbase.nemo",
description="En->De/Es/Fr multilingual one-many translation model. The model has 6 encoder and 6 decoder layers with hidden dim 512. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:mnmt_en_deesfr_transformerbase",
)
result.append(model)
# 24x6 models
model = PretrainedModelInfo(
pretrained_model_name="nmt_en_de_transformer24x6",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_en_de_transformer24x6/versions/1.5/files/en_de_24x6.nemo",
description="En->De translation model. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:nmt_en_de_transformer24x6",
)
result.append(model)
model = PretrainedModelInfo(
pretrained_model_name="nmt_de_en_transformer24x6",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_de_en_transformer24x6/versions/1.5/files/de_en_24x6.nemo",
description="De->En translation model. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:nmt_de_en_transformer24x6",
)
result.append(model)
model = PretrainedModelInfo(
pretrained_model_name="nmt_en_es_transformer24x6",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_en_es_transformer24x6/versions/1.5/files/en_es_24x6.nemo",
description="En->Es translation model. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:nmt_en_es_transformer24x6",
)
result.append(model)
model = PretrainedModelInfo(
pretrained_model_name="nmt_es_en_transformer24x6",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_es_en_transformer24x6/versions/1.5/files/es_en_24x6.nemo",
description="Es->En translation model. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:nmt_es_en_transformer24x6",
)
result.append(model)
model = PretrainedModelInfo(
pretrained_model_name="nmt_en_fr_transformer24x6",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_en_fr_transformer24x6/versions/1.5/files/en_fr_24x6.nemo",
description="En->Fr translation model. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:nmt_en_fr_transformer24x6",
)
result.append(model)
model = PretrainedModelInfo(
pretrained_model_name="nmt_fr_en_transformer24x6",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_fr_en_transformer24x6/versions/1.5/files/fr_en_24x6.nemo",
description="Fr->En translation model. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:nmt_fr_en_transformer24x6",
)
result.append(model)
model = PretrainedModelInfo(
pretrained_model_name="nmt_en_ru_transformer24x6",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_en_ru_transformer24x6/versions/1.5/files/en_ru_24x6.nemo",
description="En->Ru translation model. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:nmt_en_ru_transformer24x6",
)
result.append(model)
model = PretrainedModelInfo(
pretrained_model_name="nmt_ru_en_transformer24x6",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_ru_en_transformer24x6/versions/1.5/files/ru_en_24x6.nemo",
description="Ru->En translation model. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:nmt_ru_en_transformer24x6",
)
result.append(model)
model = PretrainedModelInfo(
pretrained_model_name="nmt_en_zh_transformer24x6",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_en_zh_transformer24x6/versions/1.5/files/en_zh_24x6.nemo",
description="En->Zh translation model. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:nmt_en_zh_transformer24x6",
)
result.append(model)
model = PretrainedModelInfo(
pretrained_model_name="nmt_zh_en_transformer24x6",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_zh_en_transformer24x6/versions/1.5/files/zh_en_24x6.nemo",
description="Zh->En translation model. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:nmt_zh_en_transformer24x6",
)
result.append(model)
return result
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6,
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220,
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1288,
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2073,
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19781,
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366,
45016,
1298,
19781,
11,
198,
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] | 2.297566 | 12,367 |
# pylint: disable=unused-argument,unused-variable
from datetime import datetime
import pytest
from xoto3.utils.oncall_default import NotSafeToDefaultError, OnCallDefault
utcnow = OnCallDefault(datetime.utcnow)
def test_disallow_positional_without_default():
"""A positional-possible argument without a default could have a
positional argument provided after it and then we'd be unable to tell
for sure whether it had been provided intentionally.
"""
with pytest.raises(NotSafeToDefaultError):
@utcnow.apply_to("when")
GeorgeKwargs = OnCallDefault(lambda: dict(b=2, c=3))
| [
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] | 3.10101 | 198 |
import os
import sys
import argparse
import numpy as np
import pandas as pd
from pyntcloud.io import write_ply
if __name__ == '__main__':
main()
| [
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] | 2.884615 | 52 |
"""
Test installation of the Quotient offering, as well as testing
signup with different combinations of selected benefactor factories
"""
from time import time
from twisted.trial.unittest import TestCase
from twisted.python.reflect import qual
from axiom.scripts import axiomatic
from axiom.store import Store
from axiom import userbase
from axiom.test.util import getPristineStore
from xmantissa import offering, signup
from xmantissa.plugins.free_signup import freeTicket
from xmantissa.product import Product
from xquotient import exmess
from xquotient.compose import Composer
from xquotient.inbox import Inbox
class InstallationTestCase(TestCase):
"""
Tests to ensure we can at least get as far as installing the
application and signing up. We don't really care whether the
right stuff was installed.
"""
def createSignupAndSignup(self, powerups):
"""
Signup via a newly-created signup, using a unique email address.
@return: substore, which will be endowed with C{product}
"""
product = Product(store=self.store, types=[qual(p) for (name, desc, p) in powerups])
qsignup = self.signupConfig.createSignup(
u'admin@localhost',
freeTicket.itemClass,
{'prefixURL': u'signup'},
product,
u'', u'')
booth = qsignup.booth
localpart = unicode(str(time()), 'ascii')
ticket = booth.createTicket(
booth, localpart + '@localhost', product)
ticket.claim()
return self.loginSystem.accountByAddress(
localpart, u'localhost').avatars.open()
def testBasic(self):
"""
Test signup with the top-most Quotient powerup
"""
self.createSignupAndSignup([(None, None, Inbox)])
def testCompose(self):
"""
Test signup with the compose benefactor (which
depends on the top-most Quotient benefactor)
"""
self.createSignupAndSignup([(None, None, Composer)])
def testAll(self):
"""
Test signup with all benefactors
"""
self.createSignupAndSignup(
getQuotientOffering().installablePowerups)
def testDefaultMessageDisplayPrefs(self):
"""
On signup, users' preferred message format should be HTML.
"""
ss = self.createSignupAndSignup(
getQuotientOffering().installablePowerups)
self.assertEqual(ss.findUnique(
exmess.MessageDisplayPreferenceCollection).preferredFormat, u"text/html")
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18186,
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1,
5239,
14,
6494,
4943,
198
] | 2.47081 | 1,062 |
import json
import numpy as np
| [
11748,
33918,
198,
11748,
299,
32152,
355,
45941,
628
] | 3.555556 | 9 |
# -*- coding: utf-8 -*-
from __future__ import absolute_import, division, print_function
from builtins import object, zip
from future.utils import PY2, as_native_str, string_types
import datetime
import io
import os
import re
import sys
from uuid import uuid4
import dateutil
import pytz
import requests
from dateutil.parser import parse
from blackfynn import log
from blackfynn.extensions import numpy as np
from blackfynn.extensions import pandas as pd
from blackfynn.extensions import require_extension
from blackfynn.utils import get_data_type, infer_epoch, usecs_to_datetime, value_as_type
try: # Python 3
from inspect import getfullargspec
except ImportError: # Python 2
from inspect import getargspec as getfullargspec
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Helpers
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
def get_package_class(data):
"""
Determines package type and returns appropriate class.
"""
content = data.get("content", data)
if "packageType" not in content:
p = Dataset
else:
ptype = content["packageType"].lower()
if ptype == "collection":
p = Collection
elif ptype == "timeseries":
p = TimeSeries
elif ptype == "dataset":
p = Dataset
else:
p = DataPackage
return p
def _flatten_file_args(files):
"""
Flatten file arguments so that upload methods can be called either as
dataset.upload(file1, file2)
or as
dataset.upload([file1, file2])
"""
if len(files) == 1 and not isinstance(files[0], string_types):
# single argument - is it iterable and not a string?
try:
files = list(files[0])
except Exception:
pass
return files
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Basics
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
class Property(object):
"""
Property of a blackfynn object.
Args:
key (str): the key of the property
value (str,number): the value of the property
fixed (bool): if true, the value cannot be changed after the property is created
hidden (bool): if true, the value is hidden on the platform
category (str): the category of the property, default: "Blackfynn"
data_type (str): one of 'string', 'integer', 'double', 'date', 'user'
"""
_data_types = ["string", "integer", "double", "date", "user", "boolean"]
def as_dict(self):
"""
Representation of instance as dictionary, used when calling API.
"""
return {
"key": self.key,
"value": str(self.value), # value needs to be string :-(
"dataType": self.data_type,
"fixed": self.fixed,
"hidden": self.hidden,
"category": self.category,
}
@classmethod
def from_dict(cls, data, category="Blackfynn"):
"""
Create an instance from dictionary, used when handling API response.
"""
return cls(
key=data["key"],
value=data["value"],
category=category,
fixed=data["fixed"],
hidden=data["hidden"],
data_type=data["dataType"],
)
@as_native_str()
class BaseNode(object):
"""
Base class to serve all objects
"""
_api = None
_object_key = "content"
@classmethod
@property
def exists(self):
"""
Whether or not the instance of this object exists on the platform.
"""
return self.id is not None
class BaseDataNode(BaseNode):
"""
Base class to serve all "data" node-types on platform, e.g. Packages and Collections.
"""
_type_name = "packageType"
def add_properties(self, *entries):
"""
Add properties to object.
Args:
entries (list): list of Property objects to add to this object
"""
self._set_properties(*entries)
# update on platform
if self.exists:
self.update_properties()
def insert_property(
self,
key,
value,
fixed=False,
hidden=False,
category="Blackfynn",
data_type=None,
):
"""
Add property to object using simplified interface.
Args:
key (str): the key of the property
value (str,number): the value of the property
fixed (bool): if true, the value cannot be changed after the property is created
hidden (bool): if true, the value is hidden on the platform
category (str): the category of the property, default: "Blackfynn"
data_type (str): one of 'string', 'integer', 'double', 'date', 'user'
Note:
This method is being depreciated in favor of ``set_property()`` method (see below).
"""
return self.set_property(
key=key,
value=value,
fixed=fixed,
hidden=hidden,
category=category,
data_type=data_type,
)
def set_property(
self,
key,
value,
fixed=False,
hidden=False,
category="Blackfynn",
data_type=None,
):
"""
Add property to object using simplified interface.
Args:
key (str): the key of the property
value (str,number): the value of the property
fixed (bool): if true, the value cannot be changed after the property is created
hidden (bool): if true, the value is hidden on the platform
category (str): the category of the property, default: "Blackfynn"
data_type (str): one of 'string', 'integer', 'double', 'date', 'user'
"""
self._set_properties(
Property(
key=key,
value=value,
fixed=fixed,
hidden=hidden,
category=category,
data_type=data_type,
)
)
# update on platform, if possible
if self.exists:
self.update_properties()
@property
def properties(self):
"""
Returns a list of properties attached to object.
"""
props = []
for category in self._properties.values():
props.extend(category.values())
return props
def get_property(self, key, category="Blackfynn"):
"""
Returns a single property for the provided key, if available
Args:
key (str): key of the desired property
category (str, optional): category of property
Returns:
object of type ``Property``
Example::
pkg.set_property('quality', 85.0)
pkg.get_property('quality')
"""
return self._properties[category].get(key, None)
def remove_property(self, key, category="Blackfynn"):
"""
Removes property of key ``key`` and category ``category`` from the object.
Args:
key (str): key of property to remove
category (str, optional): category of property to remove
"""
if key in self._properties[category]:
# remove by setting blank
self._properties[category][key].value = ""
# update remotely
self.update_properties()
# get rid of it locally
self._properties[category].pop(key)
def update(self, **kwargs):
"""
Updates object on the platform (with any local changes) and syncs
local instance with API response object.
Exmple::
pkg = bf.get('N:package:1234-1234-1234-1234')
pkg.name = "New name"
pkg.update()
"""
self._check_exists()
r = self._api.core.update(self, **kwargs)
_update_self(self, r)
def delete(self):
"""
Delete object from platform.
"""
self._check_exists()
r = self._api.core.delete(self)
self.id = None
@classmethod
class BaseCollection(BaseDataNode):
"""
Base class used for both ``Dataset`` and ``Collection``.
"""
def add(self, *items):
"""
Add items to the Collection/Dataset.
"""
self._check_exists()
for item in items:
# initialize if need be
if self._items is None:
self._items = []
if isinstance(self, Dataset):
item.parent = None
item.dataset = self.id
elif hasattr(self, "dataset"):
item.parent = self.id
item.dataset = self.dataset
# create, if not already created
new_item = self._api.core.create(item)
item.__dict__.update(new_item.__dict__)
# add item
self._items.append(item)
def remove(self, *items):
"""
Removes items, where items can be an object or the object's ID (string).
"""
self._check_exists()
for item in items:
if item not in self._items:
raise Exception("Cannot remove item, not in collection:{}".format(item))
self._api.data.delete(*items)
# remove locally
for item in items:
self._items.remove(item)
@property
def items(self):
"""
Get all items inside Dataset/Collection (i.e. non-nested items).
Note:
You can also iterate over items inside a Dataset/Colleciton without using ``.items``::
for item in my_dataset:
print("item name = ", item.name)
"""
self._check_exists()
if self._items is None:
new_self = self._get_method(self)
new_items = new_self._items
self._items = new_items if new_items is not None else []
return self._items
@property
def print_tree(self, indent=0):
"""
Prints a tree of **all** items inside object.
"""
self._check_exists()
print(u"{}{}".format(" " * indent, self))
for item in self.items:
if isinstance(item, BaseCollection):
item.print_tree(indent=indent + 2)
else:
print(u"{}{}".format(" " * (indent + 2), item))
def get_items_by_name(self, name):
"""
Get an item inside of object by name (if match is found).
Args:
name (str): the name of the item
Returns:
list of matches
Note:
This only works for **first-level** items, meaning it must exist directly inside the current object;
nested items will not be returned.
"""
self._check_exists()
# note: non-hierarchical
return [x for x in self.items if x.name == name]
def upload(self, *files, **kwargs):
"""
Upload files into current object.
Args:
files: list of local files to upload. If the Blackfynn CLI Agent is
installed you can also upload a directory. See :ref:`agent` for
more information.
Keyword Args:
display_progress (boolean): If ``True``, a progress bar will be
shown to track upload progress. Defaults to ``False``.
use_agent (boolean): If ``True``, and a compatible version of the
Agent is installed, uploads will be performed by the
Blackfynn CLI Agent. This allows large file upload in excess
of 1 hour. Defaults to ``True``.
recursive (boolean): If ``True``, the nested folder structure of
the uploaded directory will be preversed. This can only be used
with the Blackfynn CLI Agent. Defaults to ``False``.
Example::
my_collection.upload('/path/to/file1.nii.gz', '/path/to/file2.pdf')
"""
self._check_exists()
files = _flatten_file_args(files)
return self._api.io.upload_files(self, files, append=False, **kwargs)
def create_collection(self, name):
"""
Create a new collection within the current object. Collections can be created within
datasets and within other collections.
Args:
name (str): The name of the to-be-created collection
Returns:
The created ``Collection`` object.
Example::
from blackfynn import Blackfynn()
bf = Blackfynn()
ds = bf.get_dataset('my_dataset')
# create collection in dataset
col1 = ds.create_collection('my_collection')
# create collection in collection
col2 = col1.create_collection('another_collection')
"""
c = Collection(name)
self.add(c)
return c
# sequence-like method
# sequence-like method
# sequence-like method
# sequence-like method
def __contains__(self, item):
"""
Tests if item is in the collection, where item can be either
an object's ID (string) or an object's instance.
"""
self._check_exists()
if isinstance(item, string_types):
some_id = self._api.data._get_id(item)
item_ids = [x.id for x in self.items]
contains = some_id in item_ids
elif self._items is None:
return False
else:
return item in self._items
return contains
@classmethod
@as_native_str()
class DataPackage(BaseDataNode):
"""
DataPackage is the core data object representation on the platform.
Args:
name (str): The name of the data package
package_type (str): The package type, e.g. 'TimeSeries', 'MRI', etc.
Note:
``package_type`` must be a supported package type. See our data type
registry for supported values.
"""
@property
def sources(self):
"""
Returns the sources of a DataPackage. Sources are the raw, unmodified
files (if they exist) that contains the package's data.
"""
self._check_exists()
return self._api.packages.get_sources(self)
@property
def files(self):
"""
Returns the files of a DataPackage. Files are the possibly modified
source files (e.g. converted to a different format), but they could also
be the source files themselves.
"""
self._check_exists()
return self._api.packages.get_files(self)
@property
def view(self):
"""
Returns the object(s) used to view the package. This is typically a set of
file objects, that may be the DataPackage's sources or files, but could also be
a unique object specific for the viewer.
"""
self._check_exists()
return self._api.packages.get_view(self)
def process(self):
"""
Process a data package that has successfully uploaded it's source
files but has not yet been processed by the Blackfynn ETL.
"""
self._check_exists()
return self._api.packages.process(self)
def relate_to(self, *records):
"""
Relate current ``DataPackage`` to one or more ``Record`` objects.
Args:
records (list of Records): Records to relate to data package
Returns:
``Relationship`` that defines the link
Example:
Relate package to a single record::
eeg.relate_to(participant_123)
Relate package to multiple records::
# relate to explicit list of records
eeg.relate_to(
participant_001
participant_002,
participant_003,
)
# relate to all participants
eeg.relate_to(participants.get_all())
Note:
The created relationship will be of the form ``DataPackage`` --(``belongs_to``)--> ``Record``.
"""
self._check_exists()
if isinstance(records, Record):
records = [records]
assert all(
[isinstance(r, Record) for r in records]
), "all records must be object of type Record"
# auto-create relationship type
relationships = self._api.concepts.relationships.get_all(self.dataset)
if "belongs_to" not in relationships:
r = RelationshipType(
dataset_id=self.dataset, name="belongs_to", description="belongs_to"
)
self._api.concepts.relationships.create(self.dataset, r)
return [
self._api.concepts.proxies.create(
self.dataset, self.id, "belongs_to", r, {}
)
for r in records
]
@classmethod
@classmethod
@as_native_str()
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Files
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
class File(BaseDataNode):
"""
File node on the Blackfynn platform. Points to some S3 location.
Args:
name (str): Name of the file (without extension)
s3_key (str): S3 key of file
s3_bucket (str): S3 bucket of file
file_type (str): Type of file, e.g. 'MPEG', 'PDF'
size (long): Size of file
Note:
``file_type`` must be a supported file type. See our file type registry
for a list of supported file types.
"""
_type_name = "fileType"
@property
def url(self):
"""
The presigned-URL of the file.
"""
self._check_exists()
return self._api.packages.get_presigned_url_for_file(self.pkg_id, self.id)
def download(self, destination):
"""
Download the file.
Args:
destination (str): path for downloading; can be absolute file path,
prefix or destination directory.
"""
if self.type == "DirectoryViewerData":
raise NotImplementedError(
"Downloading S3 directories is currently not supported"
)
if os.path.isdir(destination):
# destination dir
f_local = os.path.join(destination, os.path.basename(self.s3_key))
if "." not in os.path.basename(destination):
# destination dir + prefix
f_local = destination + "_" + os.path.basename(self.s3_key)
else:
# exact location
f_local = destination
r = requests.get(self.url, stream=True)
with io.open(f_local, "wb") as f:
for chunk in r.iter_content(chunk_size=1024):
if chunk:
f.write(chunk)
# set local path
self.local_path = f_local
return f_local
@as_native_str()
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Time series
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
class TimeSeries(DataPackage):
"""
Represents a timeseries package on the platform. TimeSeries packages
contain channels, which contain time-dependent data sampled at some
frequency.
Args:
name: The name of the timeseries package
"""
@property
def start(self):
"""
The start time of time series data (over all channels)
"""
self._check_exists()
return sorted([x.start for x in self.channels])[0]
@property
def end(self):
"""
The end time (in usecs) of time series data (over all channels)
"""
self._check_exists()
return sorted([x.end for x in self.channels])[-1]
def limits(self):
"""
Returns time limit tuple (start, end) of package.
"""
channels = self.channels
start = sorted([x.start for x in channels])[0]
end = sorted([x.end for x in channels])[-1]
return start, end
def segments(self, start=None, stop=None, gap_factor=2):
"""
Returns list of contiguous data segments available for package. Segments are
assesssed for all channels, and the union of segments is returned.
Args:
start (int, datetime, optional):
Return segments starting after this time
(default earliest start of any channel)
stop (int, datetime, optional):
Return segments starting before this time
(default latest end time of any channel)
gap_factor (int, optional):
Gaps are computed by ``sampling_rate * gap_factor`` (default 2)
Returns:
List of tuples, where each tuple represents the (start, stop) of contiguous data.
"""
# flattenened list of segments across all channels
channel_segments = [
segment
for channel in self.channels
for segment in channel.segments(
start=start, stop=stop, gap_factor=gap_factor
)
]
# union all segments
union_segments = []
for begin, end in sorted(channel_segments):
if union_segments and union_segments[-1][1] >= begin - 1:
new_segment = (union_segments[-1][0], max(union_segments[-1][1], end))
union_segments.pop()
union_segments.append(new_segment)
else:
union_segments.append((begin, end))
return union_segments
@property
def channels(self):
"""
Returns list of Channel objects associated with package.
Note:
This is a dynamically generated property, so every call will make an API request.
Suggested usage::
channels = ts.channels
for ch in channels:
print(ch)
This will be much slower, as the API request is being made each time.::
for ch in ts.channels:
print(ch)
"""
self._check_exists()
# always dynamically return channel list
return self._api.timeseries.get_channels(self)
def get_channel(self, channel):
"""
Get channel by ID.
Args:
channel (str): ID of channel
"""
self._check_exists()
return self._api.timeseries.get_channel(self, channel)
def add_channels(self, *channels):
"""
Add channels to TimeSeries package.
Args:
channels: list of Channel objects.
"""
self._check_exists()
for channel in channels:
ch = self._api.timeseries.create_channel(self, channel)
channel.__dict__.update(ch.__dict__)
def remove_channels(self, *channels):
"""
Remove channels from TimeSeries package.
Args:
channels: list of Channel objects or IDs
"""
self._check_exists()
for channel in channels:
if isinstance(channel, TimeSeriesChannel):
self._api.timeseries.delete_channel(channel)
channel.id = None
channel._pkg = None
else:
self._api.timeseries.delete_channel_by_id(self.id, channel)
# ~~~~~~~~~~~~~~~~~~
# Data
# ~~~~~~~~~~~~~~~~~~
def get_data(
self, start=None, end=None, length=None, channels=None, use_cache=True
):
"""
Get timeseries data between ``start`` and ``end`` or ``start`` and ``start + length``
on specified channels (default all channels).
Args:
start (optional): start time of data (usecs or datetime object)
end (optional): end time of data (usecs or datetime object)
length (optional): length of data to retrieve, e.g. '1s', '5s', '10m', '1h'
channels (optional): list of channel objects or IDs, default all channels.
Note:
Data requests will be automatically chunked and combined into a single Pandas
DataFrame. However, you must be sure you request only a span of data that
will properly fit in memory.
See ``get_data_iter`` for an iterator approach to timeseries data retrieval.
Example:
Get 5 seconds of data from start over all channels::
data = ts.get_data(length='5s')
Get data betwen 12345 and 56789 (representing usecs since Epoch)::
data = ts.get_data(start=12345, end=56789)
Get first 10 seconds for the first two channels::
data = ts.get_data(length='10s', channels=ts.channels[:2])
"""
self._check_exists()
return self._api.timeseries.get_ts_data(
self,
start=start,
end=end,
length=length,
channels=channels,
use_cache=use_cache,
)
def get_data_iter(
self,
channels=None,
start=None,
end=None,
length=None,
chunk_size=None,
use_cache=True,
):
"""
Returns iterator over the data. Must specify **either ``end`` OR ``length``**, not both.
Args:
channels (optional): channels to retrieve data for (default: all)
start: start time of data (default: earliest time available).
end: end time of data (default: latest time avialable).
length: some time length, e.g. '1s', '5m', '1h' or number of usecs
chunk: some time length, e.g. '1s', '5m', '1h' or number of usecs
Returns:
iterator of Pandas Series, each the size of ``chunk_size``.
"""
self._check_exists()
return self._api.timeseries.get_ts_data_iter(
self,
channels=channels,
start=start,
end=end,
length=length,
chunk_size=chunk_size,
use_cache=use_cache,
)
def write_annotation_file(self, file, layer_names=None):
"""
Writes all layers to a csv .bfannot file
Args:
file : path to .bfannot output file. Appends extension if necessary
layer_names (optional): List of layer names to write
"""
return self._api.timeseries.write_annotation_file(self, file, layer_names)
def append_annotation_file(self, file):
"""
Processes .bfannot file and adds to timeseries package.
Args:
file : path to .bfannot file
"""
self._check_exists()
return self._api.timeseries.process_annotation_file(self, file)
def append_files(self, *files, **kwargs):
"""
Append files to this timeseries package.
Args:
files: list of local files to upload.
Keyword Args:
display_progress (boolean): If ``True``, a progress bar will be
shown to track upload progress. Defaults to ``False``.
use_agent (boolean): If ``True``, and a compatible version of the
Agent is installed, uploads will be performed by the
Blackfynn CLI Agent. This allows large file upload in excess
of 1 hour. Defaults to ``True``.
"""
self._check_exists()
files = _flatten_file_args(files)
return self._api.io.upload_files(self, files, append=True, **kwargs)
# ~~~~~~~~~~~~~~~~~~
# Annotations
# ~~~~~~~~~~~~~~~~~~
@property
def layers(self):
"""
List of annotation layers attached to TimeSeries package.
"""
self._check_exists()
# always dynamically return annotation layers
return self._api.timeseries.get_annotation_layers(self)
def get_layer(self, id_or_name):
"""
Get annotation layer by ID or name.
Args:
id_or_name: layer ID or name
"""
self._check_exists()
layers = self.layers
matches = [x for x in layers if x.id == id_or_name]
if len(matches) == 0:
matches = [x for x in layers if x.name == id_or_name]
if len(matches) == 0:
raise Exception("No layers match criteria.")
if len(matches) > 1:
raise Exception("More than one layer matched criteria")
return matches[0]
def add_layer(self, layer, description=None):
"""
Args:
layer: TimeSeriesAnnotationLayer object or name of annotation layer
description (str, optional): description of layer
"""
self._check_exists()
return self._api.timeseries.create_annotation_layer(
self, layer=layer, description=description
)
def add_annotations(self, layer, annotations):
"""
Args:
layer: either TimeSeriesAnnotationLayer object or name of annotation layer.
Note that non existing layers will be created.
annotations: TimeSeriesAnnotation object(s)
Returns:
list of TimeSeriesAnnotation objects
"""
self._check_exists()
cur_layer = self._api.timeseries.create_annotation_layer(
self, layer=layer, description=None
)
return self._api.timeseries.create_annotations(
layer=cur_layer, annotations=annotations
)
def insert_annotation(
self,
layer,
annotation,
start=None,
end=None,
channel_ids=None,
annotation_description=None,
):
"""
Insert annotations using a more direct interface, without the need for layer/annotation objects.
Args:
layer: str of new/existing layer or annotation layer object
annotation: str of annotation event
start (optional): start of annotation
end (optional): end of annotation
channels_ids (optional): list of channel IDs to apply annotation
annotation_description (optional): description of annotation
Example:
To add annotation on layer "my-events" across all channels::
ts.insert_annotation('my-events', 'my annotation event')
To add annotation to first channel::
ts.insert_annotation('my-events', 'first channel event', channel_ids=ts.channels[0])
"""
self._check_exists()
cur_layer = self._api.timeseries.create_annotation_layer(
self, layer=layer, description=None
)
return self._api.timeseries.create_annotation(
layer=cur_layer,
annotation=annotation,
start=start,
end=end,
channel_ids=channel_ids,
description=annotation_description,
)
def delete_layer(self, layer):
"""
Delete annotation layer.
Args:
layer: annotation layer object
"""
self._check_exists()
return self._api.timeseries.delete_annotation_layer(layer)
def annotation_counts(self, start, end, layers, period, channels=None):
"""
Get annotation counts between ``start`` and ``end``.
Args:
start (datetime or microseconds) : The starting time of the range to query
end (datetime or microseconds) : The ending time of the the range to query
layers ([TimeSeriesLayer]) : List of layers for which to count annotations
period (string) : The length of time to group the counts.
Formatted as a string - e.g. '1s', '5m', '3h'
channels ([TimeSeriesChannel]) : List of channel (if omitted, all channels will be used)
"""
self._check_exists()
return self._api.timeseries.query_annotation_counts(
ts=self,
layers=layers,
channels=channels,
start=start,
end=end,
period=period,
)
@as_native_str()
class TimeSeriesChannel(BaseDataNode):
"""
TimeSeriesChannel represents a single source of time series data. (e.g. electrode)
Args:
name (str): Name of channel
rate (float): Rate of the channel (Hz)
start (optional): Absolute start time of all data (datetime obj)
end (optional): Absolute end time of all data (datetime obj)
unit (str, optional): Unit of measurement
channel_type (str, optional): One of 'continuous' or 'event'
source_type (str, optional): The source of data, e.g. "EEG"
group (str, optional): The channel group, default: "default"
"""
@property
def start(self):
"""
The start time of channel data (microseconds since Epoch)
"""
return self._start
@start.setter
@property
@property
def end(self):
"""
The end time (in usecs) of channel data (microseconds since Epoch)
"""
return self._end
@end.setter
@property
def segments(self, start=None, stop=None, gap_factor=2):
"""
Return list of contiguous segments of valid data for channel.
Args:
start (long, datetime, optional):
Return segments starting after this time (default start of channel)
stop (long, datetime, optional):
Return segments starting before this time (default end of channel)
gap_factor (int, optional):
Gaps are computed by ``sampling_period * gap_factor`` (default 2)
Returns:
List of tuples, where each tuple represents the (start, stop) of contiguous data.
"""
start = self.start if start is None else start
stop = self.end if stop is None else stop
return self._api.timeseries.get_segments(
self._pkg, self, start=start, stop=stop, gap_factor=gap_factor
)
@property
def get_data(self, start=None, end=None, length=None, use_cache=True):
"""
Get channel data between ``start`` and ``end`` or ``start`` and ``start + length``
Args:
start (optional): start time of data (usecs or datetime object)
end (optional): end time of data (usecs or datetime object)
length (optional): length of data to retrieve, e.g. '1s', '5s', '10m', '1h'
use_cache (optional): whether to use locally cached data
Returns:
Pandas Series containing requested data for channel.
Note:
Data requests will be automatically chunked and combined into a single Pandas
Series. However, you must be sure you request only a span of data that
will properly fit in memory.
See ``get_data_iter`` for an iterator approach to timeseries data retrieval.
Example:
Get 5 seconds of data from start over all channels::
data = channel.get_data(length='5s')
Get data betwen 12345 and 56789 (representing usecs since Epoch)::
data = channel.get_data(start=12345, end=56789)
"""
return self._api.timeseries.get_ts_data(
ts=self._pkg,
start=start,
end=end,
length=length,
channels=[self],
use_cache=use_cache,
)
def get_data_iter(
self, start=None, end=None, length=None, chunk_size=None, use_cache=True
):
"""
Returns iterator over the data. Must specify **either ``end`` OR ``length``**, not both.
Args:
start (optional): start time of data (default: earliest time available).
end (optional): end time of data (default: latest time avialable).
length (optional): some time length, e.g. '1s', '5m', '1h' or number of usecs
chunk_size (optional): some time length, e.g. '1s', '5m', '1h' or number of usecs
use_cache (optional): whether to use locally cached data
Returns:
Iterator of Pandas Series, each the size of ``chunk_size``.
"""
return self._api.timeseries.get_ts_data_iter(
ts=self._pkg,
start=start,
end=end,
length=length,
channels=[self],
chunk_size=chunk_size,
use_cache=use_cache,
)
@as_native_str()
class TimeSeriesAnnotationLayer(BaseNode):
"""
Annotation layer containing one or more annotations. Layers are used
to separate annotations into logically distinct groups when applied
to the same data package.
Args:
name: Name of the layer
time_series_id: The TimeSeries ID which the layer applies
description: Description of the layer
"""
_object_key = None
def iter_annotations(self, window_size=10, channels=None):
"""
Iterate over annotations according to some window size (seconds).
Args:
window_size (float): Number of seconds in window
channels: List of channel objects or IDs
Yields:
List of annotations found in current window.
"""
self._check_exists()
ts = self._api.core.get(self.time_series_id)
return self._api.timeseries.iter_annotations(
ts=ts, layer=self, channels=channels, window_size=window_size
)
def add_annotations(self, annotations):
"""
Add annotations to layer.
Args:
annotations (str): List of annotation objects to add.
"""
self._check_exists()
return self._api.timeseries.create_annotations(
layer=self, annotations=annotations
)
def insert_annotation(
self, annotation, start=None, end=None, channel_ids=None, description=None
):
"""
Add annotations; proxy for ``add_annotations``.
Args:
annotation (str): Annotation string
start: Start time (usecs or datetime)
end: End time (usecs or datetime)
channel_ids: list of channel IDs
Returns:
The created annotation object.
"""
self._check_exists()
return self._api.timeseries.create_annotation(
layer=self,
annotation=annotation,
start=start,
end=end,
channel_ids=channel_ids,
description=description,
)
def annotations(self, start=None, end=None, channels=None):
"""
Get annotations between ``start`` and ``end`` over ``channels`` (all channels by default).
Args:
start: Start time
end: End time
channels: List of channel objects or IDs
"""
self._check_exists()
ts = self._api.core.get(self.time_series_id)
return self._api.timeseries.get_annotations(
ts=ts, layer=self, channels=channels, start=start, end=end
)
def annotation_counts(self, start, end, period, channels=None):
"""
The number of annotations between ``start`` and ``end`` over selected
channels (all by default).
Args:
start (datetime or microseconds) : The starting time of the range to query
end (datetime or microseconds) : The ending time of the the range to query
period (string) : The length of time to group the counts.
Formatted as a string - e.g. '1s', '5m', '3h'
channels ([TimeSeriesChannel]) : List of channel (if omitted, all channels will be used)
"""
self._check_exists()
ts = self._api.core.get(self.time_series_id)
return self._api.timeseries.query_annotation_counts(
ts=ts, layers=[self], channels=channels, start=start, end=end, period=period
)
def delete(self):
"""
Delete annotation layer.
"""
self._check_exists()
return self._api.timeseries.delete_annotation_layer(self)
@as_native_str()
class TimeSeriesAnnotation(BaseNode):
"""
Annotation is an event on one or more channels in a dataset
Args:
label (str): The label for the annotation
channel_ids: List of channel IDs that annotation applies
start: Start time
end: End time
name: Name of annotation
layer_id: Layer ID for annoation (all annotations exist on a layer)
time_series_id: TimeSeries package ID
description: Description of annotation
"""
_object_key = None
@as_native_str()
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# User
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Organizations
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Datasets
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Collections
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# PublishInfo
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# UserStubDTO
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# DatasetStatusStub
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# StatusLogEntry
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# StatusLogResponse
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Collaborators
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Models & Relationships
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Model Helpers
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Python 2
if PY2:
python_to_blackfynn_type_map = {
str: "string",
unicode: "string",
int: "long",
long: "long",
float: "double",
bool: "boolean",
datetime.date: "date",
datetime.datetime: "date",
}
blackfynn_to_python_type_map = {
"string": unicode,
"long": int,
"double": float,
"boolean": bool,
"date": datetime.datetime,
}
# Python 3
else:
python_to_blackfynn_type_map = {
str: "string",
int: "long",
float: "double",
bool: "boolean",
datetime.date: "date",
datetime.datetime: "date",
}
blackfynn_to_python_type_map = {
"string": str,
"long": int,
"double": float,
"boolean": bool,
"date": datetime.datetime,
}
valid_python_types = tuple(python_to_blackfynn_type_map.keys())
class ModelPropertyType(object):
"""
Representation of model property types in the platform.
"""
@property
@staticmethod
def _build_from(data):
"""
Construct a ``ModelPropertyType`` from any data source. This is responsible
for dispatching construction to subclasses for special cases such as
enumerated and array types.
"""
if isinstance(data, ModelPropertyType):
return data
elif isinstance(data, dict) and (
data["type"].lower() == "array" or "items" in data
):
return ModelPropertyEnumType.from_dict(data)
return ModelPropertyType.from_dict(data)
@classmethod
def _decode_value(self, value):
"""
Decode a model value received from the Blackfynn API into the Python
representation mandated by this `ModelPropertyType`.
"""
if value is None:
return None
elif self.data_type == bool:
if isinstance(value, bool):
return value
elif isinstance(value, str) or isinstance(value, unicode):
if value.lower() == "false":
return False
elif value.lower() == "true":
return True
else:
return bool(value)
else:
return bool(value)
elif self.data_type in (datetime.date, datetime.datetime):
if isinstance(value, (datetime.date, datetime.datetime)):
return value
else:
return dateutil.parser.parse(value)
return self.data_type(value)
def _encode_value(self, value):
"""
Encode a Python value into something that can be sent to the Blackfynn API.
"""
if value is None:
return None
elif isinstance(value, (datetime.date, datetime.datetime)):
if value.tzinfo is None or value.tzinfo.utcoffset(value) is None:
value = pytz.utc.localize(value)
v = value.isoformat()
# isoformat() does not include microseconds if microseconds is
# 0, but we always need microseconds in the formatted string
if not value.microsecond:
v = "{}.000000{}".format(v[:-6], v[-6:])
return v
return self.data_type(value)
@as_native_str()
class ModelPropertyEnumType(ModelPropertyType):
"""
A special case of a ``ModelPropertyType`` that contains enumerated values
and arrays of values.
This can take one of several forms:
* If ``enum`` is a list of objects, then the values of this property may
only be one of the given values.
* If ``multi_select`` is ``True``, then values of this property may be lists
of objects.
* If ``enum`` is a list of objects *and* ``multi_select`` is ``True``, then
values of this property must be lists of items in ``enum``.
"""
def _decode_value(self, value):
"""
Decode a model value received from the Blackfynn API into the Python
representation mandated by this ``ModelPropertyType``.
"""
if value is None:
return None
self._assert_value_in_enum(value)
if self.multi_select:
return [super(ModelPropertyEnumType, self)._decode_value(v) for v in value]
return super(ModelPropertyEnumType, self)._decode_value(value)
def _encode_value(self, value):
"""
Encode a Python value into something that can be sent to the Blackfynn API.
"""
if value is None:
return None
self._assert_value_in_enum(value)
if self.multi_select:
return [super(ModelPropertyEnumType, self)._encode_value(v) for v in value]
return super(ModelPropertyEnumType, self)._encode_value(value)
def _assert_value_in_enum(self, value):
"""Check that values are in the enumerated type."""
if self.enum and self.multi_select:
for v in value:
if v not in self.enum:
raise Exception(
"Value '{}' is not a member of {}".format(v, self.enum)
)
elif self.enum and value not in self.enum:
raise Exception("Value '{}' is not a member of {}".format(value, self.enum))
@classmethod
@as_native_str()
class BaseModelProperty(object):
"""
Fallback values for property fields are resolved as follows:
(1) A property is `required` if it is the title of the model
(2) A property is `default` if it is `required`. `default` is deprecated and
`required` is now the source of truth.
(3) If the `display_name` for a property is not provided use `name` instead
The default values of both `required` and `default` can be on be overridden
by passing in explicit
"""
@classmethod
@classmethod
@property
@type.setter
@property
@property
@property
@property
@as_native_str()
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Models
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
class Model(BaseModelNode):
"""
Representation of a Model in the knowledge graph.
"""
_object_key = ""
_property_cls = ModelProperty
def update(self):
"""
Updates the details of the ``Model`` on the platform.
Example::
mouse.update()
Note:
Currently, you can only append new properties to a ``Model``.
"""
self._check_exists()
_update_self(self, self._api.concepts.update(self.dataset_id, self))
def delete(self):
"""
Deletes a model from the platform. Must not have any instances.
"""
return self._api.concepts.delete(self.dataset_id, self)
def get_all(self, limit=100, offset=0):
"""
Retrieves all records of the model from the platform.
Returns:
List of ``Record``
Example::
mice = mouse.get_all()
"""
return self._api.concepts.instances.get_all(
self.dataset_id, self, limit=limit, offset=offset
)
def get(self, id):
"""
Retrieves a record of the model by id from the platform.
Args:
id: the Blackfynn id of the model
Returns:
A single ``Record``
Example::
mouse_001 = mouse.get(123456789)
"""
return self._api.concepts.instances.get(self.dataset_id, id, self)
def query(self):
"""
Run a query with this model as the join target.
"""
return self._api.concepts.query.new(self, self.dataset_id)
def get_connected(self):
"""Retrieves all connected models
Connected is defined as model that can be reached by following
outgoing relationships starting at the current model
Args:
id: The Blackfynn id of the "root" model
Returns:
A list of models connected to the given model
Example::
connected_models = mouse.get_connected()
"""
return self._api.concepts.get_connected(self.dataset_id, self.id)
def create_record(self, values=dict()):
"""
Creates a record of the model on the platform.
Args:
values (dict, optional): values for properties defined in the `Model` schema
Returns:
The newly created ``Record``
Example::
mouse_002 = mouse.create_record({"id": 2, "weight": 2.2})
"""
self._check_exists()
data_keys = set(values.keys())
schema_keys = set(self.schema.keys())
assert (
len(data_keys & schema_keys) > 0
), "An instance of {} must include values for at least one of its properties: {}".format(
self.type, schema_keys
)
self._validate_values_against_schema(values)
values = [
dict(name=k, value=v, dataType=self.schema.get(k)._type)
for k, v in values.items()
]
ci = Record(dataset_id=self.dataset_id, type=self.type, values=values)
ci = self._api.concepts.instances.create(self.dataset_id, ci)
return ci
def create_records(self, values_list):
"""
Creates multiple records of the model on the platform.
Args:
values_list (list): array of dictionaries corresponding to record values.
Returns:
List of newly created ``Record`` objects.
Example::
mouse.create_records([
{ 'id': 311, 'weight': 1.9 },
{ 'id': 312, 'weight': 2.1 },
{ 'id': 313, 'weight': 1.8 },
{ 'id': 314, 'weight': 2.3 },
{ 'id': 315, 'weight': 2.0 }
])
"""
self._check_exists()
schema_keys = set(self.schema.keys())
for values in values_list:
data_keys = set(values.keys())
assert (
len(data_keys & schema_keys) > 0
), "An instance of {} must include values for at least one of its propertes: {}".format(
self.type, schema_keys
)
self._validate_values_against_schema(values)
ci_list = [
Record(
dataset_id=self.dataset_id,
type=self.type,
values=[
dict(name=k, value=v, dataType=self.schema.get(k)._type)
for k, v in values.items()
],
)
for values in values_list
]
return self._api.concepts.instances.create_many(self.dataset_id, self, *ci_list)
def delete_records(self, *records):
"""
Deletes one or more records of a concept from the platform.
Args:
*records: instances and/or ids of records to delete
Returns:
``None``
Logs the list of records that failed to delete.
Example::
mouse.delete(mouse_002, 123456789, mouse_003.id)
"""
result = self._api.concepts.delete_instances(self.dataset_id, self, *records)
for error in result["errors"]:
self._logger.error(
"Failed to delete instance {} with error: {}".format(error[0], error[1])
)
def get_related(self):
"""
Returns a list of related model types and counts of those
relationships.
"Related" indicates that the model could be connected to the current
model via some relationship, i.e. ``B`` is "related to" ``A`` if there
exist ``A -[relationship]-> B``. Note that the directionality
matters. If ``B`` is the queried model, ``A`` would not appear in the
list of "related" models.
Returns:
List of ``Model`` objects related via a defined relationship
Example::
related_models = mouse.get_related()
"""
return self._api.concepts.get_related(self.dataset_id, self)
@as_native_str()
class Record(BaseRecord):
"""
Represents a record of a ``Model``.
Includes its neighbors, relationships, and links.
"""
_object_key = ""
_value_cls = ModelValue
def get_related(self, model=None, group=False):
"""
Returns all related records.
Args:
model (str, Model, optional): Return only related records of this type
group (bool, optional): If true, group results by model type (dict)
Returns:
List of ``Record`` objects. If ``group`` is ``True``, then the result
is a dictionary of ``RecordSet`` objects keyed by model names.
Example:
Get all connected records of type ``disease`` with relationship ``has``::
mouse_001.get_related('disease', 'has')
Get all connected records::
mouse_001.get_related()
"""
if model is None:
# return all connected records
related_by_model = self._api.concepts.instances.get_all_related(
self.dataset_id, self
)
if group:
return related_by_model
else:
if len(related_by_model) == 1:
# try to retain RecordSet type
return list(related_by_model.values())[0]
# mixed return types, cannot keep RecordSets
related = []
for model_name, model_related in related_by_model.items():
related.extend(model_related)
return related
else:
return self._api.concepts.instances.get_all_related_of_type(
self.dataset_id, self, model
)
def get_files(self):
"""
All files related to the current record.
Returns:
List of data objects i.e. ``DataPackage``
Example::
mouse_001.get_files()
"""
return self._api.concepts.files(self.dataset_id, self.type, self)
def relate_to(
self, destinations, relationship_type="related_to", values=None, direction="to"
):
"""
Relate record to one or more ``Record`` or ``DataPackage`` objects.
Args:
destinations (list of Record or DataPackage):
A list containing the ``Record`` or ``DataPackage`` objects to relate to current record
relationship_type (RelationshipType, str, optional):
Type of relationship to create
values (list of dictionaries, optional):
A list of dictionaries corresponding to relationship values
direction (str, optional):
Relationship direction. Valid values are ``'to'`` and ``'from'``
Returns:
List of created ``Relationship`` objects.
.. note::
Destinations must all be of type ``DataPackage`` or ``Record``; you cannot mix destination types.
Example:
Relate to a single ``Record``, define relationship type::
mouse_001.relate_to(lab_009, 'located_at')
Relate to multiple ``DataPackage`` objects::
mouse_001.relate_to([eeg, mri1, mri2])
"""
self._check_exists()
# accept object or list
if isinstance(destinations, (Record, DataPackage)):
destinations = [destinations]
if isinstance(destinations, Collection):
destinations = destinations.items
if not destinations:
return None
# default values
if values is None:
values = [dict() for _ in destinations] if values is None else values
else:
values = [dict(name=k, value=v) for val in values for k, v in val.items()]
assert len(destinations) == len(
values
), "Length of values must match length of destinations"
# check type
if not (
all([isinstance(d, DataPackage) for d in destinations])
or all([isinstance(d, Record) for d in destinations])
):
raise Exception(
"All destinations must be of object type Record or DataPackage"
)
# auto-create relationship type
if isinstance(relationship_type, string_types):
relationships_types = self._api.concepts.relationships.get_all(
self.dataset_id
)
if relationship_type not in relationships_types:
r = RelationshipType(
dataset_id=self.dataset_id,
name=relationship_type,
description=relationship_type,
source=self.model.id,
destination=destinations[0].model.id,
)
relationship_type = self._api.concepts.relationships.create(
self.dataset_id, r
)
else:
relationship_type = relationships_types[relationship_type]
# relationships (to packages)
if isinstance(destinations[0], DataPackage):
# if linking packages, link one at a time
result = [
self._api.concepts.relationships.instances.link(
self.dataset_id, relationship_type, self, d, values=v
)
for d, v in zip(destinations, values)
]
return RelationshipSet(relationship_type, result)
# relationships (to records)
if direction == "to":
relationships = [
Relationship(
type=relationship_type.type,
dataset_id=self.dataset_id,
source=self,
destination=d.id,
values=v,
)
for d, v in zip(destinations, values)
]
elif direction == "from":
relationships = [
Relationship(
type=relationship_type.type,
dataset_id=self.dataset_id,
source=d.id,
destination=self,
values=v,
)
for d, v in zip(destinations, values)
]
else:
raise Exception('Direction must be value "to" or "from"')
# use batch endpoint to create relationships
return self._api.concepts.relationships.instances.create_many(
self.dataset_id, relationship_type, *relationships
)
def get_linked_values(self):
"""
Get all link values attached to this Record.
"""
return self._api.concepts.instances.get_linked_values(
self.dataset_id, self.model, self
)
def get_linked_value(self, link):
"""
Get a link value by name or id.
"""
all_links = self.get_linked_values()
# First assume link is a link value id:
for l in all_links:
if link == l.id:
return l
# Then assume link is a linked property name:
try:
prop_id = self.model.get_linked_property(link).id
except:
raise Exception(
"No link found with a name or ID matching '{}'".format(link)
)
else:
for l in all_links:
if prop_id == l.type.id:
return l
raise Exception("No link found with a name or ID matching '{}'".format(link))
def add_linked_value(self, target, link):
"""
Attach a linked property value to the Record.
target: the id or Record object of the target record
link: the id or LinkedModelProperty object of the link type
"""
model = self.model
if isinstance(target, Record):
target = target.id
if isinstance(link, LinkedModelProperty):
link_id = link.id
elif isinstance(link, string_types):
link_id = model.get_linked_property(link).id
payload = dict(
name=model.type,
displayName=model.display_name,
schemaLinkedPropertyId=link_id,
to=target,
)
return self._api.concepts.instances.create_link(
self.dataset_id, self.model, self, payload
)
def delete_linked_value(self, link_name):
"""
Delete a link by name or id.
"""
link = self.get_linked_value(link_name)
self._api.concepts.instances.remove_link(
self.dataset_id, self.model, self, link
)
@property
def model(self):
"""
The ``Model`` of the current record.
Returns:
A single ``Model``.
"""
return self._api.concepts.get(self.dataset_id, self.type)
def update(self):
"""
Updates the values of the record on the platform (after modification).
Example::
mouse_001.set('name', 'Mickey')
mouse_001.update()
"""
self._check_exists()
_update_self(self, self._api.concepts.instances.update(self.dataset_id, self))
def delete(self):
"""
Deletes the instance from the platform.
Example::
mouse_001.delete()
"""
return self._api.concepts.instances.delete(self.dataset_id, self)
@as_native_str()
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Query Result
#
# Returned per "row" result of a query
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Relationships
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
class RelationshipType(BaseModelNode):
"""
Model for defining a relationships.
"""
_object_key = ""
_property_cls = RelationshipProperty
# TODO: delete when update is supported, handled in super-class
# TODO: delete when update is supported, handled in super-class
def get_all(self):
"""
Retrieves all relationships of this type from the platform.
Returns:
List of ``Relationship``
Example::
belongs_to_relationships = belongs_to.get_all()
"""
return self._api.concepts.relationships.instances.get_all(self.dataset_id, self)
def get(self, id):
"""
Retrieves a relationship by id from the platform.
Args:
id (int): the id of the instance
Returns:
A single ``Relationship``
Example::
mouse_001 = mouse.get(123456789)
"""
return self._api.concepts.relationships.instances.get(self.dataset_id, id, self)
def relate(self, source, destination, values=dict()):
"""
Relates a ``Record`` to another ``Record`` or ``DataPackage`` using current relationship.
Args:
source (Record, DataPackage): record or data package the relationship orginates from
destination (Record, DataPackage): record or data package the relationship points to
values (dict, optional): values for properties defined in the relationship's schema
Returns:
The newly created ``Relationship``
Example:
Create a relationship between a ``Record`` and a ``DataPackage``::
from_relationship.relate(mouse_001, eeg)
Create a relationship (with values) between a ``Record`` and a ``DataPackage``::
from_relationship.relate(mouse_001, eeg, {"date": datetime.datetime(1991, 02, 26, 07, 0)})
"""
self._check_exists()
self._validate_values_against_schema(values)
return self._api.concepts.relationships.instances.link(
self.dataset_id, self, source, destination, values
)
def create(self, items):
"""
Create multiple relationships between records using current relationship type.
Args:
items (list): List of relationships to be created.
Each relationship should be either a dictionary or tuple.
If relationships are dictionaries, they are required to have
``from``/``to`` or ``source``/``destination`` keys.
There is an optional ``values`` key which can be used
to attach metadata to the relationship;
``values`` should be a dictionary with key/value pairs.
If relationships are tuples, they must be in the form
``(source, dest)``.
Returns:
Array of newly created ``Relationships`` objects
Example:
Create multiple relationships (dictionary format)::
diagnosed_with.create([
{ 'from': participant_001, 'to': parkinsons},
{ 'from': participant_321, 'to': als}
])
Create multiple relationships (tuple format)::
diagnosed_with.create([
(participant_001, parkinsons),
(participant_321, als)
])
"""
self._check_exists()
# handle non-array
if isinstance(items, (dict, tuple)):
items = [items]
relations = []
for value in items:
# get source, destination, and values
if isinstance(value, tuple):
src, dest = value
vals = {}
elif isinstance(value, dict):
src = value.get("from", value.get("source"))
dest = value.get("to", value.get("destination"))
vals = value.get("values", {})
else:
raise Exception(
"Expected relationship as tuple or dictionary, found {}".format(
type(value)
)
)
# Check sources and destinations
if not isinstance(src, (Record, DataPackage, string_types)):
raise Exception(
"source must be object of type Record, DataPackage, or UUID"
)
if not isinstance(dest, (Record, DataPackage, string_types)):
raise Exception(
"destination must be object of type Record, DataPackage, or UUID"
)
# create local relationship object
relations.append(
Relationship(
dataset_id=self.dataset_id,
type=self.type,
source=src,
destination=dest,
values=[
dict(name=k, value=v, dataType=self.schema.get(k).type)
for k, v in vals.items()
],
)
)
return self._api.concepts.relationships.instances.create_many(
self.dataset_id, self, *relations
)
@as_native_str()
class Relationship(BaseRecord):
"""
A single instance of a ``RelationshipType``.
"""
_object_key = ""
def relationship(self):
"""
Retrieves the relationship definition of this instance from the platform
Returns:
A single ``RelationshipType``.
"""
return self._api.concepts.relationships.get(self.dataset_id, self.type)
# TODO: delete when update is supported, handled in super-class
def delete(self):
"""
Deletes the instance from the platform.
Example::
mouse_001_eeg_link.delete()
"""
return self._api.concepts.relationships.instances.delete(self.dataset_id, self)
@classmethod
@as_native_str()
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Proxies
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Model/Relation Instance Sets
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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220,
220,
611,
2116,
13,
1069,
1023,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
19119,
62,
48310,
3419,
628,
220,
220,
220,
825,
7550,
62,
26745,
7,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
11,
198,
220,
220,
220,
220,
220,
220,
220,
1994,
11,
198,
220,
220,
220,
220,
220,
220,
220,
1988,
11,
198,
220,
220,
220,
220,
220,
220,
220,
5969,
28,
25101,
11,
198,
220,
220,
220,
220,
220,
220,
220,
7104,
28,
25101,
11,
198,
220,
220,
220,
220,
220,
220,
220,
6536,
2625,
9915,
69,
2047,
77,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
1366,
62,
4906,
28,
14202,
11,
198,
220,
220,
220,
15179,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
3060,
3119,
284,
2134,
1262,
27009,
7071,
13,
628,
220,
220,
220,
220,
220,
220,
220,
943,
14542,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1994,
357,
2536,
2599,
262,
1994,
286,
262,
3119,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
357,
2536,
11,
17618,
2599,
262,
1988,
286,
262,
3119,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5969,
357,
30388,
2599,
611,
2081,
11,
262,
1988,
2314,
307,
3421,
706,
262,
3119,
318,
2727,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7104,
357,
30388,
2599,
611,
2081,
11,
262,
1988,
318,
7104,
319,
262,
3859,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6536,
357,
2536,
2599,
262,
6536,
286,
262,
3119,
11,
4277,
25,
366,
9915,
69,
2047,
77,
1,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1366,
62,
4906,
357,
2536,
2599,
530,
286,
705,
8841,
3256,
705,
41433,
3256,
705,
23352,
3256,
705,
4475,
3256,
705,
7220,
6,
628,
220,
220,
220,
220,
220,
220,
220,
5740,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
770,
2446,
318,
852,
1207,
29102,
515,
287,
2661,
286,
7559,
2617,
62,
26745,
3419,
15506,
2446,
357,
3826,
2174,
737,
628,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13,
2617,
62,
26745,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1994,
28,
2539,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
28,
8367,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5969,
28,
34021,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7104,
28,
30342,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6536,
28,
22872,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1366,
62,
4906,
28,
7890,
62,
4906,
11,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
628,
220,
220,
220,
825,
900,
62,
26745,
7,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
11,
198,
220,
220,
220,
220,
220,
220,
220,
1994,
11,
198,
220,
220,
220,
220,
220,
220,
220,
1988,
11,
198,
220,
220,
220,
220,
220,
220,
220,
5969,
28,
25101,
11,
198,
220,
220,
220,
220,
220,
220,
220,
7104,
28,
25101,
11,
198,
220,
220,
220,
220,
220,
220,
220,
6536,
2625,
9915,
69,
2047,
77,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
1366,
62,
4906,
28,
14202,
11,
198,
220,
220,
220,
15179,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
3060,
3119,
284,
2134,
1262,
27009,
7071,
13,
628,
220,
220,
220,
220,
220,
220,
220,
943,
14542,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1994,
357,
2536,
2599,
262,
1994,
286,
262,
3119,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
357,
2536,
11,
17618,
2599,
262,
1988,
286,
262,
3119,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5969,
357,
30388,
2599,
611,
2081,
11,
262,
1988,
2314,
307,
3421,
706,
262,
3119,
318,
2727,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7104,
357,
30388,
2599,
611,
2081,
11,
262,
1988,
318,
7104,
319,
262,
3859,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6536,
357,
2536,
2599,
262,
6536,
286,
262,
3119,
11,
4277,
25,
366,
9915,
69,
2047,
77,
1,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1366,
62,
4906,
357,
2536,
2599,
530,
286,
705,
8841,
3256,
705,
41433,
3256,
705,
23352,
3256,
705,
4475,
3256,
705,
7220,
6,
628,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
2617,
62,
48310,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
14161,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1994,
28,
2539,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
28,
8367,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5969,
28,
34021,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7104,
28,
30342,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6536,
28,
22872,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1366,
62,
4906,
28,
7890,
62,
4906,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
4296,
319,
3859,
11,
611,
1744,
198,
220,
220,
220,
220,
220,
220,
220,
611,
2116,
13,
1069,
1023,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
19119,
62,
48310,
3419,
628,
220,
220,
220,
2488,
26745,
198,
220,
220,
220,
825,
6608,
7,
944,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
16409,
257,
1351,
286,
6608,
7223,
284,
2134,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
25744,
796,
17635,
198,
220,
220,
220,
220,
220,
220,
220,
329,
6536,
287,
2116,
13557,
48310,
13,
27160,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
25744,
13,
2302,
437,
7,
22872,
13,
27160,
28955,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
25744,
628,
220,
220,
220,
825,
651,
62,
26745,
7,
944,
11,
1994,
11,
6536,
2625,
9915,
69,
2047,
77,
1,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
16409,
257,
2060,
3119,
329,
262,
2810,
1994,
11,
611,
1695,
628,
220,
220,
220,
220,
220,
220,
220,
943,
14542,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1994,
357,
2536,
2599,
1994,
286,
262,
10348,
3119,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6536,
357,
2536,
11,
11902,
2599,
6536,
286,
3119,
628,
220,
220,
220,
220,
220,
220,
220,
16409,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2134,
286,
2099,
7559,
21746,
15506,
628,
220,
220,
220,
220,
220,
220,
220,
17934,
3712,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
279,
10025,
13,
2617,
62,
26745,
10786,
13237,
3256,
7600,
13,
15,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
279,
10025,
13,
1136,
62,
26745,
10786,
13237,
11537,
628,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13557,
48310,
58,
22872,
4083,
1136,
7,
2539,
11,
6045,
8,
628,
220,
220,
220,
825,
4781,
62,
26745,
7,
944,
11,
1994,
11,
6536,
2625,
9915,
69,
2047,
77,
1,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
3982,
5241,
3119,
286,
1994,
7559,
2539,
15506,
290,
6536,
7559,
22872,
15506,
422,
262,
2134,
13,
628,
220,
220,
220,
220,
220,
220,
220,
943,
14542,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1994,
357,
2536,
2599,
1994,
286,
3119,
284,
4781,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6536,
357,
2536,
11,
11902,
2599,
6536,
286,
3119,
284,
4781,
628,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
611,
1994,
287,
2116,
13557,
48310,
58,
22872,
5974,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
4781,
416,
4634,
9178,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
48310,
58,
22872,
7131,
2539,
4083,
8367,
796,
13538,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
4296,
19863,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
19119,
62,
48310,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
5755,
286,
340,
15726,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
48310,
58,
22872,
4083,
12924,
7,
2539,
8,
628,
220,
220,
220,
825,
4296,
7,
944,
11,
12429,
46265,
22046,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
28090,
2134,
319,
262,
3859,
357,
4480,
597,
1957,
2458,
8,
290,
6171,
6359,
198,
220,
220,
220,
220,
220,
220,
220,
1957,
4554,
351,
7824,
2882,
2134,
13,
628,
220,
220,
220,
220,
220,
220,
220,
1475,
76,
1154,
3712,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
279,
10025,
796,
275,
69,
13,
1136,
10786,
45,
25,
26495,
25,
1065,
2682,
12,
1065,
2682,
12,
1065,
2682,
12,
1065,
2682,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
279,
10025,
13,
3672,
796,
366,
3791,
1438,
1,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
279,
10025,
13,
19119,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
9122,
62,
1069,
1023,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
374,
796,
2116,
13557,
15042,
13,
7295,
13,
19119,
7,
944,
11,
12429,
46265,
22046,
8,
198,
220,
220,
220,
220,
220,
220,
220,
4808,
19119,
62,
944,
7,
944,
11,
374,
8,
628,
220,
220,
220,
825,
12233,
7,
944,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
23520,
2134,
422,
3859,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
9122,
62,
1069,
1023,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
374,
796,
2116,
13557,
15042,
13,
7295,
13,
33678,
7,
944,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
312,
796,
6045,
628,
220,
220,
220,
2488,
4871,
24396,
628,
198,
4871,
7308,
36307,
7,
14881,
6601,
19667,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
7308,
1398,
973,
329,
1111,
7559,
27354,
292,
316,
15506,
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37227,
628,
220,
220,
220,
2488,
26745,
628,
220,
220,
220,
2488,
12708,
24396,
198,
220,
220,
220,
825,
4808,
11249,
62,
6738,
7,
7890,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
28407,
257,
7559,
17633,
21746,
6030,
15506,
422,
597,
1366,
2723,
13,
770,
318,
4497,
198,
220,
220,
220,
220,
220,
220,
220,
329,
4596,
19775,
5103,
284,
850,
37724,
329,
2041,
2663,
884,
355,
198,
220,
220,
220,
220,
220,
220,
220,
27056,
515,
290,
7177,
3858,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
611,
318,
39098,
7,
7890,
11,
9104,
21746,
6030,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
1366,
628,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
318,
39098,
7,
7890,
11,
8633,
8,
290,
357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1366,
14692,
4906,
1,
4083,
21037,
3419,
6624,
366,
18747,
1,
393,
366,
23814,
1,
287,
1366,
198,
220,
220,
220,
220,
220,
220,
220,
15179,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
9104,
21746,
4834,
388,
6030,
13,
6738,
62,
11600,
7,
7890,
8,
628,
220,
220,
220,
220,
220,
220,
220,
1441,
9104,
21746,
6030,
13,
6738,
62,
11600,
7,
7890,
8,
628,
220,
220,
220,
2488,
4871,
24396,
628,
220,
220,
220,
825,
4808,
12501,
1098,
62,
8367,
7,
944,
11,
1988,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
4280,
1098,
257,
2746,
1988,
2722,
422,
262,
2619,
69,
2047,
77,
7824,
656,
262,
11361,
198,
220,
220,
220,
220,
220,
220,
220,
10552,
28853,
416,
428,
4600,
17633,
21746,
6030,
44646,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
611,
1988,
318,
6045,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
6045,
628,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
2116,
13,
7890,
62,
4906,
6624,
20512,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
318,
39098,
7,
8367,
11,
20512,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
1988,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
318,
39098,
7,
8367,
11,
965,
8,
393,
318,
39098,
7,
8367,
11,
28000,
1098,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
1988,
13,
21037,
3419,
6624,
366,
9562,
1298,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
10352,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
1988,
13,
21037,
3419,
6624,
366,
7942,
1298,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
6407,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
20512,
7,
8367,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
20512,
7,
8367,
8,
628,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
2116,
13,
7890,
62,
4906,
287,
357,
19608,
8079,
13,
4475,
11,
4818,
8079,
13,
19608,
8079,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
318,
39098,
7,
8367,
11,
357,
19608,
8079,
13,
4475,
11,
4818,
8079,
13,
19608,
8079,
8,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
1988,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
3128,
22602,
13,
48610,
13,
29572,
7,
8367,
8,
628,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13,
7890,
62,
4906,
7,
8367,
8,
628,
220,
220,
220,
825,
4808,
268,
8189,
62,
8367,
7,
944,
11,
1988,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
2039,
8189,
257,
11361,
1988,
656,
1223,
326,
460,
307,
1908,
284,
262,
2619,
69,
2047,
77,
7824,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
611,
1988,
318,
6045,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
6045,
628,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
318,
39098,
7,
8367,
11,
357,
19608,
8079,
13,
4475,
11,
4818,
8079,
13,
19608,
8079,
8,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
1988,
13,
22877,
10951,
318,
6045,
393,
1988,
13,
22877,
10951,
13,
315,
1073,
487,
2617,
7,
8367,
8,
318,
6045,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
12972,
22877,
13,
315,
66,
13,
12001,
1096,
7,
8367,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
410,
796,
1988,
13,
26786,
18982,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
47279,
18982,
3419,
857,
407,
2291,
4580,
43012,
611,
4580,
43012,
318,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
657,
11,
475,
356,
1464,
761,
4580,
43012,
287,
262,
39559,
4731,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
407,
1988,
13,
24055,
12227,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
410,
796,
45144,
27422,
10535,
90,
92,
1911,
18982,
7,
85,
58,
21912,
21,
4357,
410,
58,
12,
21,
25,
12962,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
410,
628,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13,
7890,
62,
4906,
7,
8367,
8,
628,
220,
220,
220,
2488,
292,
62,
30191,
62,
2536,
3419,
628,
198,
4871,
9104,
21746,
4834,
388,
6030,
7,
17633,
21746,
6030,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
317,
2041,
1339,
286,
257,
7559,
17633,
21746,
6030,
15506,
326,
4909,
27056,
515,
3815,
198,
220,
220,
220,
290,
26515,
286,
3815,
13,
628,
220,
220,
220,
770,
460,
1011,
530,
286,
1811,
5107,
25,
628,
220,
220,
220,
220,
220,
1635,
1002,
7559,
44709,
15506,
318,
257,
1351,
286,
5563,
11,
788,
262,
3815,
286,
428,
3119,
743,
198,
220,
220,
220,
220,
220,
220,
220,
691,
307,
530,
286,
262,
1813,
3815,
13,
198,
220,
220,
220,
220,
220,
1635,
1002,
7559,
41684,
62,
19738,
15506,
318,
7559,
17821,
15506,
11,
788,
3815,
286,
428,
3119,
743,
307,
8341,
198,
220,
220,
220,
220,
220,
220,
220,
286,
5563,
13,
198,
220,
220,
220,
220,
220,
1635,
1002,
7559,
44709,
15506,
318,
257,
1351,
286,
5563,
1635,
392,
9,
7559,
41684,
62,
19738,
15506,
318,
7559,
17821,
15506,
11,
788,
198,
220,
220,
220,
220,
220,
220,
220,
3815,
286,
428,
3119,
1276,
307,
8341,
286,
3709,
287,
7559,
44709,
15506,
13,
198,
220,
220,
220,
37227,
628,
220,
220,
220,
825,
4808,
12501,
1098,
62,
8367,
7,
944,
11,
1988,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
4280,
1098,
257,
2746,
1988,
2722,
422,
262,
2619,
69,
2047,
77,
7824,
656,
262,
11361,
198,
220,
220,
220,
220,
220,
220,
220,
10552,
28853,
416,
428,
7559,
17633,
21746,
6030,
15506,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
611,
1988,
318,
6045,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
6045,
628,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
30493,
62,
8367,
62,
259,
62,
44709,
7,
8367,
8,
628,
220,
220,
220,
220,
220,
220,
220,
611,
2116,
13,
41684,
62,
19738,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
685,
16668,
7,
17633,
21746,
4834,
388,
6030,
11,
2116,
737,
62,
12501,
1098,
62,
8367,
7,
85,
8,
329,
410,
287,
1988,
60,
628,
220,
220,
220,
220,
220,
220,
220,
1441,
2208,
7,
17633,
21746,
4834,
388,
6030,
11,
2116,
737,
62,
12501,
1098,
62,
8367,
7,
8367,
8,
628,
220,
220,
220,
825,
4808,
268,
8189,
62,
8367,
7,
944,
11,
1988,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
2039,
8189,
257,
11361,
1988,
656,
1223,
326,
460,
307,
1908,
284,
262,
2619,
69,
2047,
77,
7824,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
611,
1988,
318,
6045,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
6045,
628,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
30493,
62,
8367,
62,
259,
62,
44709,
7,
8367,
8,
628,
220,
220,
220,
220,
220,
220,
220,
611,
2116,
13,
41684,
62,
19738,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
685,
16668,
7,
17633,
21746,
4834,
388,
6030,
11,
2116,
737,
62,
268,
8189,
62,
8367,
7,
85,
8,
329,
410,
287,
1988,
60,
628,
220,
220,
220,
220,
220,
220,
220,
1441,
2208,
7,
17633,
21746,
4834,
388,
6030,
11,
2116,
737,
62,
268,
8189,
62,
8367,
7,
8367,
8,
628,
220,
220,
220,
825,
4808,
30493,
62,
8367,
62,
259,
62,
44709,
7,
944,
11,
1988,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
9787,
326,
3815,
389,
287,
262,
27056,
515,
2099,
526,
15931,
198,
220,
220,
220,
220,
220,
220,
220,
611,
2116,
13,
44709,
290,
2116,
13,
41684,
62,
19738,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
410,
287,
1988,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
410,
407,
287,
2116,
13,
44709,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5298,
35528,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
11395,
705,
90,
92,
6,
318,
407,
257,
2888,
286,
23884,
1911,
18982,
7,
85,
11,
2116,
13,
44709,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
628,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
2116,
13,
44709,
290,
1988,
407,
287,
2116,
13,
44709,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5298,
35528,
7203,
11395,
705,
90,
92,
6,
318,
407,
257,
2888,
286,
23884,
1911,
18982,
7,
8367,
11,
2116,
13,
44709,
4008,
628,
220,
220,
220,
2488,
4871,
24396,
628,
220,
220,
220,
2488,
292,
62,
30191,
62,
2536,
3419,
628,
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220,
220,
220,
16409,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7343,
286,
7559,
47117,
1056,
15506,
628,
220,
220,
220,
220,
220,
220,
220,
17934,
3712,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
14448,
62,
1462,
62,
39468,
5748,
796,
14448,
62,
1462,
13,
1136,
62,
439,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13557,
15042,
13,
43169,
82,
13,
39468,
5748,
13,
8625,
1817,
13,
1136,
62,
439,
7,
944,
13,
19608,
292,
316,
62,
312,
11,
2116,
8,
628,
220,
220,
220,
825,
651,
7,
944,
11,
4686,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
4990,
5034,
1158,
257,
2776,
416,
4686,
422,
262,
3859,
13,
628,
220,
220,
220,
220,
220,
220,
220,
943,
14542,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4686,
357,
600,
2599,
262,
4686,
286,
262,
4554,
628,
220,
220,
220,
220,
220,
220,
220,
16409,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
317,
2060,
7559,
47117,
1056,
15506,
628,
220,
220,
220,
220,
220,
220,
220,
17934,
3712,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
10211,
62,
8298,
796,
10211,
13,
1136,
7,
10163,
2231,
3134,
4531,
8,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13557,
15042,
13,
43169,
82,
13,
39468,
5748,
13,
8625,
1817,
13,
1136,
7,
944,
13,
19608,
292,
316,
62,
312,
11,
4686,
11,
2116,
8,
628,
220,
220,
220,
825,
15124,
7,
944,
11,
2723,
11,
10965,
11,
3815,
28,
11600,
3419,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
4718,
689,
257,
7559,
23739,
15506,
284,
1194,
7559,
23739,
15506,
393,
7559,
6601,
27813,
15506,
1262,
1459,
2776,
13,
628,
220,
220,
220,
220,
220,
220,
220,
943,
14542,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2723,
357,
23739,
11,
6060,
27813,
2599,
220,
220,
220,
220,
220,
1700,
393,
1366,
5301,
262,
2776,
393,
1655,
689,
422,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
10965,
357,
23739,
11,
6060,
27813,
2599,
1700,
393,
1366,
5301,
262,
2776,
2173,
284,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3815,
357,
11600,
11,
11902,
2599,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3815,
329,
6608,
5447,
287,
262,
2776,
338,
32815,
628,
220,
220,
220,
220,
220,
220,
220,
16409,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
383,
8308,
2727,
7559,
47117,
1056,
15506,
628,
220,
220,
220,
220,
220,
220,
220,
17934,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
13610,
257,
2776,
1022,
257,
7559,
23739,
15506,
290,
257,
7559,
6601,
27813,
15506,
3712,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
422,
62,
39468,
1056,
13,
2411,
378,
7,
35888,
62,
8298,
11,
304,
1533,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
13610,
257,
2776,
357,
4480,
3815,
8,
1022,
257,
7559,
23739,
15506,
290,
257,
7559,
6601,
27813,
15506,
3712,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
422,
62,
39468,
1056,
13,
2411,
378,
7,
35888,
62,
8298,
11,
304,
1533,
11,
19779,
4475,
1298,
4818,
8079,
13,
19608,
8079,
7,
24529,
11,
7816,
11,
2608,
11,
8753,
11,
657,
8,
30072,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
9122,
62,
1069,
1023,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
12102,
378,
62,
27160,
62,
32826,
62,
15952,
2611,
7,
27160,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13557,
15042,
13,
43169,
82,
13,
39468,
5748,
13,
8625,
1817,
13,
8726,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
19608,
292,
316,
62,
312,
11,
2116,
11,
2723,
11,
10965,
11,
3815,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
628,
220,
220,
220,
825,
2251,
7,
944,
11,
3709,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
13610,
3294,
6958,
1022,
4406,
1262,
1459,
2776,
2099,
13,
628,
220,
220,
220,
220,
220,
220,
220,
943,
14542,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3709,
357,
4868,
2599,
7343,
286,
6958,
284,
307,
2727,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5501,
2776,
815,
307,
2035,
257,
22155,
393,
46545,
13,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1002,
6958,
389,
48589,
3166,
11,
484,
389,
2672,
284,
423,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7559,
6738,
15506,
14,
15506,
1462,
15506,
393,
7559,
10459,
15506,
14,
15506,
16520,
1883,
15506,
8251,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1318,
318,
281,
11902,
7559,
27160,
15506,
1994,
543,
460,
307,
973,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
284,
10199,
20150,
284,
262,
2776,
26,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7559,
27160,
15506,
815,
307,
257,
22155,
351,
1994,
14,
8367,
14729,
13,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1002,
6958,
389,
12777,
2374,
11,
484,
1276,
307,
287,
262,
1296,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
11592,
10459,
11,
2244,
8,
15506,
13,
628,
220,
220,
220,
220,
220,
220,
220,
16409,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
15690,
286,
8308,
2727,
7559,
47117,
5748,
15506,
5563,
628,
220,
220,
220,
220,
220,
220,
220,
17934,
25,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
13610,
3294,
6958,
357,
67,
14188,
5794,
2599,
25,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
14641,
62,
4480,
13,
17953,
26933,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1391,
705,
6738,
10354,
18399,
62,
8298,
11,
705,
1462,
10354,
3952,
1040,
684,
5512,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1391,
705,
6738,
10354,
18399,
62,
36453,
11,
705,
1462,
10354,
435,
82,
92,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
33761,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
13610,
3294,
6958,
357,
83,
29291,
5794,
2599,
25,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
14641,
62,
4480,
13,
17953,
26933,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
48013,
415,
62,
8298,
11,
3952,
1040,
684,
828,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
48013,
415,
62,
36453,
11,
435,
82,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
33761,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
9122,
62,
1069,
1023,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
5412,
1729,
12,
18747,
198,
220,
220,
220,
220,
220,
220,
220,
611,
318,
39098,
7,
23814,
11,
357,
11600,
11,
46545,
8,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3709,
796,
685,
23814,
60,
628,
220,
220,
220,
220,
220,
220,
220,
2316,
796,
17635,
198,
220,
220,
220,
220,
220,
220,
220,
329,
1988,
287,
3709,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
2723,
11,
10965,
11,
290,
3815,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
318,
39098,
7,
8367,
11,
46545,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
12351,
11,
2244,
796,
1988,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
410,
874,
796,
23884,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
318,
39098,
7,
8367,
11,
8633,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
12351,
796,
1988,
13,
1136,
7203,
6738,
1600,
1988,
13,
1136,
7203,
10459,
48774,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2244,
796,
1988,
13,
1136,
7203,
1462,
1600,
1988,
13,
1136,
7203,
16520,
1883,
48774,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
410,
874,
796,
1988,
13,
1136,
7203,
27160,
1600,
23884,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5298,
35528,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
3109,
7254,
2776,
355,
46545,
393,
22155,
11,
1043,
23884,
1911,
18982,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
7,
8367,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
6822,
4237,
290,
23982,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
407,
318,
39098,
7,
10677,
11,
357,
23739,
11,
6060,
27813,
11,
4731,
62,
19199,
8,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5298,
35528,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
10459,
1276,
307,
2134,
286,
2099,
13266,
11,
6060,
27813,
11,
393,
471,
27586,
1,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
407,
318,
39098,
7,
16520,
11,
357,
23739,
11,
6060,
27813,
11,
4731,
62,
19199,
8,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5298,
35528,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
16520,
1883,
1276,
307,
2134,
286,
2099,
13266,
11,
6060,
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] | 2.263841 | 31,212 |
from fastapi import status
from fastapi.testclient import TestClient
from api import db
from api.models import Card
def test_get_legacy_card(client: TestClient, session: db.Session):
"""Must be able to read JSON for a legacy card"""
# This is handled by a migration normally (legacy cards can't normally be created by this API)
card = (
session.query(Card)
.filter(Card.stub == "example-phoenixborn", Card.is_legacy == True)
.first()
)
card.json["release"]["is_legacy"] = True
card.json["is_legacy"] = True
db.flag_modified(card, "json")
session.commit()
response = client.get("/v2/cards/example-phoenixborn", params={"show_legacy": True})
assert response.status_code == status.HTTP_200_OK
assert response.json()["is_legacy"] == True, response.json()
def test_get_card(client: TestClient, session: db.Session):
"""Must be able to read non-Legacy JSON"""
response = client.get("/v2/cards/example-phoenixborn")
assert response.status_code == status.HTTP_200_OK
assert "is_legacy" not in response.json(), response.json()
def test_get_nonexistent_card(client: TestClient, session: db.Session):
"""Must throw an error when the card doesn't exist"""
response = client.get("/v2/cards/no-luck")
assert response.status_code == status.HTTP_404_NOT_FOUND, response.json()
def test_get_details_no_card(client: TestClient, session: db.Session):
"""Must fail properly when requesting non-existent card"""
response = client.get("/v2/cards/no-luck/details")
assert response.status_code == status.HTTP_404_NOT_FOUND, response.json()
def test_get_details_root_phoenixborn(client: TestClient, session: db.Session):
"""Must properly find root Phoenixborn cards"""
response = client.get("/v2/cards/example-conjured-alteration/details")
assert response.status_code == status.HTTP_200_OK
def test_get_details_root_non_phoenixborn(client: TestClient, session: db.Session):
"""Must properly find root non-Phoenixborn cards"""
response = client.get("/v2/cards/summon-example-conjuration/details")
assert response.status_code == status.HTTP_200_OK
def test_get_details_root_phoenixborn_conjurations(
client: TestClient, session: db.Session
):
"""Must properly find conjurations when looking up Phoenixborn unique"""
response = client.get("/v2/cards/example-ally/details")
assert response.status_code == status.HTTP_200_OK
def test_get_details_non_phoenixborn_conjuration(
client: TestClient, session: db.Session
):
"""Must properly find root summons for non-Phoenixborn conjurations"""
response = client.get("/v2/cards/example-conjuration/details")
assert response.status_code == status.HTTP_200_OK
def test_get_details_phoenixborn(client: TestClient, session: db.Session):
"""Must properly find connected cards when looking up Phoenixborn"""
response = client.get("/v2/cards/example-phoenixborn/details")
assert response.status_code == status.HTTP_200_OK
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] | 3.020979 | 1,001 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# This file is part of CbM (https://github.com/ec-jrc/cbm).
# Author : Gilbert Voican, Konstantinos Anastasakis
# Credits : GTCAP Team
# Copyright : 2021 European Commission, Joint Research Centre
# License : 3-Clause BSD
import os
import psycopg2
# from psycopg2 import Error
import subprocess
from osgeo import gdal
from osgeo import ogr
from osgeo import osr
from rasterstats import zonal_stats
from fiona import open as fopen
import fiona
from yaml import load, FullLoader
from os.path import dirname, abspath, join, normpath
from pathlib import Path
from cbm.utils import config
from cbm.datas import db
import geopandas as gpd
path_foi_func = normpath(join(dirname(abspath(__file__)), 'foi_db_func'))
def main(reference_data, raster_classif_file, yaml_file, pre_min_het,
pre_max_het, area_threshold, db_str='main'):
"""FOI assessment is based on spatial analysis of a “thematic” raster
produced in advance.
The thematic raster can be the result of a any image/raster processing
method yielding a class label for each pixel - crop classification, behavior
analysis of land phenomenon, gridded data on soil, slope, humidity, etc.
The starting point was the idea that inside of an homgeneous parcel we
should have only one type of pixel.
For example if the thematic raster is the result of a crop classification,
inside a parcel we should have only one type of pixels that represent the
respective crop
If the thematic raster is the result of a behaviour analysis, all the pixels
inside a parcel should behave in the same way during a period of time.
The FOI assessment is based on the analysis made on the presence and
distribution of different types of pixels inside the FOI.
Args:
reference_data (str): Spatial data to be tested -
parcels that will be checked for heterogeneity and cardinality
The parcels poligons in .shp file format or
database table name without .shp ending.
raster_classif_file (str): Thematic raster - classification raster, or
raster from other source that will be used for testing
heterogeneity and cardinality
yaml_file: YAML file that holds the classes of thematic raster file.
can be also a simple list of values in the notebook corespondence
between pixel values and names for the classes
pre_min_het: Minimum thresholds for heterogeneity checks.
pre_max_het: Maximum thresholds for heterogeneity checks.
area_threshold: Minimum area for clusters selection.
Returns:
bool: True if successful, False otherwise.
"""
# database connection string
if type(db_str) is str:
db_connection = f"PG:{db.conn_str(db_str)}"
elif type(db_str) is list:
db_connection = "PG:host={} port={} dbname={} user={} password={}".format(
*db_str)
# ogr2ogr options
geom_field_name = "GEOMETRY_NAME=wkb_geometry"
overwrite_option = "-OVERWRITE"
geom_type = "MULTIPOLYGON"
output_format = "PostgreSQL"
# Path for storing the processed data - final spatial data that will be
# exported after database processing
processed_data = normpath(join('foi', 'processed_data'))
os.makedirs(processed_data, exist_ok=True)
# Path for storing the final output data
output_data = normpath(join('foi', 'output_data'))
os.makedirs(output_data, exist_ok=True)
reference_data_name = os.path.splitext(os.path.basename(reference_data))[0]
try:
with open(f"{config.get_value(['paths','temp'])}tb_prefix", 'r') as f:
reference_data_table = f.read()
except Exception:
reference_data_table = reference_data_name
# Vector file resulted from the raster stats pixel count
# pixelcount_output = f'{output_data}pixel_count_{reference_data_table}.shp'
pixelcount_output = f'{processed_data}/{reference_data_name}_pixelcount.shp'
# Vector file resulted from raster to vector process (polygonize)
polygonize_output = f'{processed_data}/{reference_data_name}_polygonize.shp'
# Name of the table to be created in the database - import of the pixel
# count into the database
pixelcount_table = f"{reference_data_name}_pixelcount"
# Name of the table to be created in the database - import of the
# polygonize result into the database
polygonize_table = f"{reference_data_name}_polygonize"
# Name and path of the files resulted from the analysis
heterogeneity_output = f'{output_data}/{reference_data_name}_foih_v1.shp'
cardinality_output = f'{output_data}/{reference_data_name}_foic_v1.shp'
cardinality_output_clusters = f'{output_data}/{reference_data_name}_foic_clusters_v1.shp'
sql = "SELECT * FROM " + reference_data_table + ";"
try:
ps_connection = db_conn()
ps_connection.autocommit = True
cursor = ps_connection.cursor()
gpd_data = gpd.read_postgis(
sql=sql, con=ps_connection, geom_col='wkb_geometry')
except (Exception, psycopg2.DatabaseError) as error:
print("Error while connecting to PostgreSQL", error)
finally:
# closing database connection.
if(ps_connection):
cursor.close()
ps_connection.close()
# print("PostgreSQL connection is closed")
temp_reference_data = f'foi/{reference_data_name}_temp.shp'
gpd_data.to_file(temp_reference_data)
shape = fiona.open(temp_reference_data)
spatialRef = shape.crs["init"]
# print("Vector EPSG: ", spatialRef)
# Import reference data shapefile to database.
# Overwrite option is needed, otherwise the import will append new
# values to the ones existing in the table
subprocess.call(["ogr2ogr", overwrite_option, "-nlt", geom_type, "-lco",
geom_field_name, "-a_srs", spatialRef, "-nln",
reference_data_table, "-f", "PostgreSQL", db_connection,
reference_data])
# Reading the values from yaml file
conf = load(open(yaml_file, 'r').read(), Loader=FullLoader)
category_map = conf['category_map']
rst_fields = list(category_map.values())
# Counting the number of pixels for each parcel. The fields with names of
# the classes from yaml file will be added,
# and updated with the number of pixels from each category
with fopen(temp_reference_data, 'r') as input:
spatialRef = input.crs["init"]
schema = input.schema
for i in rst_fields:
schema['properties'][i] = 'int:5'
rst_attribs = dict.fromkeys(rst_fields, 0)
with fopen(pixelcount_output, 'w', 'ESRI Shapefile', schema) as output:
for i, vct_feat in enumerate(input):
vct_val_dict = dict(vct_feat['properties'])
rst_val_dict = zonal_stats(
vct_feat, raster_classif_file,
categorical=True, copy_properties=True,
category_map=category_map, nodata=-999)[0]
vct_val_dict.update(rst_attribs)
for lu in rst_val_dict:
vct_val_dict[lu] = rst_val_dict.get(lu)
for atrib in vct_val_dict:
vct_feat['properties'][atrib] = vct_val_dict.get(atrib)
output.write(vct_feat)
print("Finished pixel calculation!")
# Import resulted shapefile, with the number of pixels for each class to
# database. Overwrite option is needed, otherwise the
# import will append new values to the ones existing in the table
subprocess.call(["ogr2ogr", overwrite_option, "-nlt", geom_type, "-a_srs",
spatialRef, "-nln", pixelcount_table, "-f", "PostgreSQL",
db_connection, pixelcount_output])
# Number of classes from the thematic raster
num_classes = len(category_map)
# Minimum and maximum thresholds for heterogeneity checks. In this example,
# any parcel
# with percentage of pixels for one class between 30 and 70 from the total,
# will be considered heterogenous.
# min_heterogeneity_threshold = 30
# max_heterogeneity_threshold = 70
min_heterogeneity_threshold = pre_min_het
max_heterogeneity_threshold = pre_max_het
# Calling the PostgreSQL function wich checks the heterogeneity.
# The function calculates the percentages and sets an attribute
# "foi_h" to 1 when the percentage of pixels is between thresholds
try:
ps_connection = db_conn()
ps_connection.autocommit = True
cursor = ps_connection.cursor()
# call stored procedure
cursor.callproc('public.check_heterogeneity', (
pixelcount_table, num_classes, min_heterogeneity_threshold,
max_heterogeneity_threshold))
print("Running function to check heterogeneity")
except (Exception, psycopg2.DatabaseError) as error:
print("Error while connecting to PostgreSQL", error)
finally:
# closing database connection.
if(ps_connection):
cursor.close()
ps_connection.close()
print("PostgreSQL connection is closed")
print("Heterogeneity assessment function finished")
# Export processed data - heterogeneity, to shapefile
subprocess.call(["ogr2ogr", "-f", "ESRI Shapefile",
heterogeneity_output, db_connection, pixelcount_table])
print("Heterogeneity analysis output downloaded")
# Polygonize the thematic raster. The process takes into account only
# one band (in this case - first band). Can be used with 8 connected
# pixels or with 4 connected pixels.
connectedness = '-8'
sourceRaster = gdal.Open(raster_classif_file)
band = sourceRaster.GetRasterBand(1)
srs = osr.SpatialReference(wkt=sourceRaster.GetProjection())
dst_layername = polygonize_output
drv = ogr.GetDriverByName("ESRI Shapefile")
dst_ds = drv.CreateDataSource(dst_layername)
dst_layer = dst_ds.CreateLayer(dst_layername, srs=srs)
fd = ogr.FieldDefn("DN", ogr.OFTInteger)
dst_layer.CreateField(fd)
dst_field = dst_layer.GetLayerDefn().GetFieldIndex("DN")
gdal.Polygonize(band, None, dst_layer, dst_field,
[connectedness], callback=None)
dst_ds.Destroy()
# Import polygonize result to database
subprocess.call(["ogr2ogr", overwrite_option, "-nlt", geom_type, "-lco",
geom_field_name, "-nln", polygonize_table, "-f",
output_format, db_connection, polygonize_output])
# Names of the tables to be created in the database during the processing
processed_clusters = polygonize_table + "_clusters"
processed_cardinality = polygonize_table + "_cardin"
# Spatial data to be tested - parcels that will be checked for cardinality
# (I think we should use the same data as for heterogeneity)
# reference_table = 'reference_data'
# Minimum area for clusters selection - only clusters bigger that the
# threshold will be counted
# area_threshold = 2000
# Calling the PostgreSQL function wich checks the cardinality. The function
# fixes the geometry for the spatial data resulted from polygnize, clips
# the polygonize result with the parcels that needs to be checked,
# calculates the area of the clusters inside each parcel, selects the
# clusters that are more than one type, each of them bigger that the
# threshold, in each parcel.
# The function creates two new tables: one with the clusters that matches
# the conditions, the other with data to be tested and a new column
# "foi_c" wich is 1 if the parcel has more that two types of clusters
# with the area bigger than the thershold
# TO DO: put the unique identifier as function param
try:
ps_connection = db_conn()
ps_connection.autocommit = True
cursor = ps_connection.cursor()
# call stored procedure
# cursor.callproc('public.check_cardinality', (
# polygonize_table, reference_data_table, area_threshold))
cursor.execute(
"CALL public.check_cardinality_procedure( %s, %s, %s, %s); ",
(polygonize_table, reference_data_table, area_threshold, 10000))
print("Running function to check cardinality")
except (Exception, psycopg2.DatabaseError) as error:
print("Error while connecting to PostgreSQL", error)
finally:
# closing database connection.
if(ps_connection):
cursor.close()
ps_connection.close()
print("PostgreSQL connection is closed")
# Export processed data - clusters, to shapefile
subprocess.call(["ogr2ogr", "-f", "ESRI Shapefile",
cardinality_output_clusters, db_connection,
processed_clusters])
print("Cardinality assessment function finished")
# Export processed data - data to be tested with "foi_c" flag, to shapefile
subprocess.call(["ogr2ogr", "-f", "ESRI Shapefile",
cardinality_output, db_connection, processed_cardinality])
print("Cardinality analysis output downloaded")
filelist_temp = [f for f in os.listdir(
'foi') if f.startswith(Path(temp_reference_data).stem)]
for f in filelist_temp:
os.remove(os.path.join('foi', f))
if __name__ == "__main__":
import sys
main(sys.argv)
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1303,
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266,
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13,
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62,
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796,
20613,
62,
37043,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
26692,
62,
38659,
13,
2306,
420,
2002,
270,
796,
6407,
628,
220,
220,
220,
220,
220,
220,
220,
23493,
796,
26692,
62,
38659,
13,
66,
21471,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
869,
8574,
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198,
220,
220,
220,
220,
220,
220,
220,
23493,
13,
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36942,
10786,
11377,
13,
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62,
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37477,
3256,
357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
17465,
9127,
62,
11487,
11,
997,
62,
37724,
11,
949,
62,
43332,
37477,
62,
400,
10126,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3509,
62,
43332,
37477,
62,
400,
10126,
4008,
628,
220,
220,
220,
220,
220,
220,
220,
3601,
7203,
28768,
2163,
284,
2198,
49763,
4943,
628,
220,
220,
220,
2845,
357,
16922,
11,
17331,
22163,
70,
17,
13,
38105,
12331,
8,
355,
4049,
25,
198,
220,
220,
220,
220,
220,
220,
220,
3601,
7203,
12331,
981,
14320,
284,
2947,
47701,
1600,
4049,
8,
628,
220,
220,
220,
3443,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
9605,
6831,
4637,
13,
198,
220,
220,
220,
220,
220,
220,
220,
611,
7,
862,
62,
38659,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
23493,
13,
19836,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
26692,
62,
38659,
13,
19836,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3601,
7203,
6307,
47701,
4637,
318,
4838,
4943,
198,
220,
220,
220,
3601,
7203,
39,
2357,
37477,
8922,
2163,
5201,
4943,
628,
220,
220,
220,
1303,
36472,
13686,
1366,
532,
49763,
11,
284,
5485,
7753,
198,
220,
220,
220,
850,
14681,
13,
13345,
7,
14692,
519,
81,
17,
519,
81,
1600,
27444,
69,
1600,
366,
1546,
7112,
25959,
7753,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
49763,
62,
22915,
11,
20613,
62,
38659,
11,
17465,
9127,
62,
11487,
12962,
198,
220,
220,
220,
3601,
7203,
39,
2357,
37477,
3781,
5072,
15680,
4943,
628,
220,
220,
220,
1303,
12280,
14520,
1096,
262,
606,
1512,
374,
1603,
13,
383,
1429,
2753,
656,
1848,
691,
198,
220,
220,
220,
1303,
220,
220,
530,
4097,
357,
259,
428,
1339,
532,
717,
4097,
737,
1680,
307,
973,
351,
807,
5884,
198,
220,
220,
220,
1303,
220,
220,
17848,
393,
351,
604,
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17848,
13,
198,
220,
220,
220,
5884,
1108,
796,
705,
12,
23,
6,
198,
220,
220,
220,
2723,
49,
1603,
796,
308,
31748,
13,
11505,
7,
81,
1603,
62,
4871,
361,
62,
7753,
8,
198,
220,
220,
220,
4097,
796,
2723,
49,
1603,
13,
3855,
49,
1603,
31407,
7,
16,
8,
198,
220,
220,
220,
264,
3808,
796,
267,
27891,
13,
4561,
34961,
26687,
7,
86,
21841,
28,
10459,
49,
1603,
13,
3855,
16775,
295,
28955,
198,
220,
220,
220,
29636,
62,
10724,
13292,
796,
7514,
14520,
1096,
62,
22915,
198,
220,
220,
220,
1553,
85,
796,
267,
2164,
13,
3855,
32103,
3886,
5376,
7203,
1546,
7112,
25959,
7753,
4943,
198,
220,
220,
220,
29636,
62,
9310,
796,
1553,
85,
13,
16447,
6601,
7416,
7,
67,
301,
62,
10724,
13292,
8,
198,
220,
220,
220,
29636,
62,
29289,
796,
29636,
62,
9310,
13,
16447,
49925,
7,
67,
301,
62,
10724,
13292,
11,
264,
3808,
28,
82,
3808,
8,
198,
220,
220,
220,
277,
67,
796,
267,
2164,
13,
15878,
7469,
77,
7203,
35504,
1600,
267,
2164,
13,
46,
9792,
46541,
8,
198,
220,
220,
220,
29636,
62,
29289,
13,
16447,
15878,
7,
16344,
8,
198,
220,
220,
220,
29636,
62,
3245,
796,
29636,
62,
29289,
13,
3855,
49925,
7469,
77,
22446,
3855,
15878,
15732,
7203,
35504,
4943,
198,
220,
220,
220,
308,
31748,
13,
34220,
14520,
1096,
7,
3903,
11,
6045,
11,
29636,
62,
29289,
11,
29636,
62,
3245,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
685,
15236,
1108,
4357,
23838,
28,
14202,
8,
198,
220,
220,
220,
29636,
62,
9310,
13,
49174,
3419,
628,
220,
220,
220,
1303,
17267,
7514,
14520,
1096,
1255,
284,
6831,
198,
220,
220,
220,
850,
14681,
13,
13345,
7,
14692,
519,
81,
17,
519,
81,
1600,
49312,
62,
18076,
11,
27444,
77,
2528,
1600,
4903,
296,
62,
4906,
11,
27444,
75,
1073,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4903,
296,
62,
3245,
62,
3672,
11,
27444,
77,
18755,
1600,
220,
7514,
14520,
1096,
62,
11487,
11,
27444,
69,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5072,
62,
18982,
11,
20613,
62,
38659,
11,
220,
7514,
14520,
1096,
62,
22915,
12962,
628,
220,
220,
220,
1303,
28531,
286,
262,
8893,
284,
307,
2727,
287,
262,
6831,
1141,
262,
7587,
198,
220,
220,
220,
13686,
62,
565,
13654,
796,
7514,
14520,
1096,
62,
11487,
1343,
45434,
565,
13654,
1,
198,
220,
220,
220,
13686,
62,
9517,
1292,
414,
796,
7514,
14520,
1096,
62,
11487,
1343,
45434,
9517,
259,
1,
198,
220,
220,
220,
1303,
1338,
34961,
1366,
284,
307,
6789,
532,
49796,
326,
481,
307,
10667,
329,
38691,
414,
198,
220,
220,
220,
1303,
220,
220,
357,
40,
892,
356,
815,
779,
262,
976,
1366,
355,
329,
49763,
8,
198,
220,
220,
220,
1303,
4941,
62,
11487,
796,
705,
35790,
62,
7890,
6,
198,
220,
220,
220,
1303,
26265,
1989,
329,
23163,
6356,
532,
691,
23163,
5749,
326,
262,
198,
220,
220,
220,
1303,
220,
220,
11387,
481,
307,
14789,
198,
220,
220,
220,
1303,
1989,
62,
400,
10126,
796,
4751,
628,
220,
220,
220,
1303,
32677,
262,
2947,
47701,
2163,
266,
488,
8794,
262,
38691,
414,
13,
383,
2163,
198,
220,
220,
220,
1303,
220,
220,
13040,
262,
22939,
329,
262,
21739,
1366,
8724,
422,
7514,
4593,
1096,
11,
19166,
198,
220,
220,
220,
1303,
220,
220,
262,
7514,
14520,
1096,
1255,
351,
262,
49796,
326,
2476,
284,
307,
10667,
11,
198,
220,
220,
220,
1303,
220,
220,
43707,
262,
1989,
286,
262,
23163,
2641,
1123,
25232,
11,
40573,
262,
198,
220,
220,
220,
1303,
220,
220,
23163,
326,
389,
517,
621,
530,
2099,
11,
1123,
286,
606,
5749,
326,
262,
198,
220,
220,
220,
1303,
220,
220,
11387,
11,
287,
1123,
25232,
13,
198,
220,
220,
220,
1303,
383,
2163,
8075,
734,
649,
8893,
25,
530,
351,
262,
23163,
326,
7466,
198,
220,
220,
220,
1303,
220,
220,
262,
3403,
11,
262,
584,
351,
1366,
284,
307,
6789,
290,
257,
649,
5721,
198,
220,
220,
220,
1303,
220,
220,
366,
6513,
72,
62,
66,
1,
266,
488,
318,
352,
611,
262,
25232,
468,
517,
326,
734,
3858,
286,
23163,
198,
220,
220,
220,
1303,
220,
220,
351,
262,
1989,
5749,
621,
262,
10811,
1477,
727,
628,
220,
220,
220,
1303,
5390,
8410,
25,
1234,
262,
3748,
27421,
355,
2163,
5772,
628,
220,
220,
220,
1949,
25,
198,
220,
220,
220,
220,
220,
220,
220,
26692,
62,
38659,
796,
20613,
62,
37043,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
26692,
62,
38659,
13,
2306,
420,
2002,
270,
796,
6407,
628,
220,
220,
220,
220,
220,
220,
220,
23493,
796,
26692,
62,
38659,
13,
66,
21471,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
869,
8574,
8771,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
23493,
13,
13345,
36942,
10786,
11377,
13,
9122,
62,
9517,
1292,
414,
3256,
357,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
220,
220,
220,
220,
7514,
14520,
1096,
62,
11487,
11,
4941,
62,
7890,
62,
11487,
11,
1989,
62,
400,
10126,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
23493,
13,
41049,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
34,
7036,
1171,
13,
9122,
62,
9517,
1292,
414,
62,
1676,
771,
495,
7,
4064,
82,
11,
4064,
82,
11,
4064,
82,
11,
4064,
82,
1776,
33172,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
35428,
14520,
1096,
62,
11487,
11,
4941,
62,
7890,
62,
11487,
11,
1989,
62,
400,
10126,
11,
33028,
4008,
628,
220,
220,
220,
220,
220,
220,
220,
3601,
7203,
28768,
2163,
284,
2198,
38691,
414,
4943,
628,
220,
220,
220,
2845,
357,
16922,
11,
17331,
22163,
70,
17,
13,
38105,
12331,
8,
355,
4049,
25,
198,
220,
220,
220,
220,
220,
220,
220,
3601,
7203,
12331,
981,
14320,
284,
2947,
47701,
1600,
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198,
220,
220,
220,
1388,
7,
17597,
13,
853,
85,
8,
198
] | 2.654606 | 5,113 |
import torch
import torch.nn as nn
import torch.optim as optim
from transformers import (
AdamW,
get_linear_schedule_with_warmup,
)
from .metrics import FullMetrics
import pytorch_lightning as pl
from pytorch_lightning import loggers as pl_loggers
| [
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] | 2.747475 | 99 |
input_file = open("input.txt", "r")
lines = input_file.read().splitlines() #Read whole file, split by newlines
lines = [int(i) for i in lines]
lines.sort()
lines.append(lines[-1]+3)
oneDifference = 0
threeDifference = 0
previousJoltage = 0
for outlet in lines:
if outlet - 3 == previousJoltage:
threeDifference += 1
else:
oneDifference += 1
previousJoltage = outlet
print(lines)
print("oneDifference:", oneDifference)
print("threeDifference:", threeDifference)
answer = oneDifference * threeDifference
print("Answer:", answer) | [
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1945,
198,
4798,
7203,
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25,
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8
] | 2.841837 | 196 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import logging
import argparse
from core.web import webapp
logging.basicConfig(format='%(levelname)s:%(module)s:%(message)s', level=logging.ERROR)
COMMANDS = {
'webserver': webserver,
'syncdb': syncdb,
}
if __name__ == '__main__':
main()
| [
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12417,
834,
10354,
198,
220,
220,
220,
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3419,
198
] | 2.393701 | 127 |
# Spaces
minsp = 0
maxsp = 500
# Admin's configuration
print("Hey Admin!")
sw = str(input("Make up a word : "))
guesses = float(input("How many guesses should the user have : "))
h = str(input("Do you want to give the user a hint? y/n : "))
guess = ''
gc = 0
# Admin's hint
if h == 'y':
hint = str(input("What hint do you wanna give to the user, when the user type down \'Hint\' : "))
elif h == 'n':
hint = str("I didn\'t give you a hint.")
# Spaces - So user can't see what the admin did.
while minsp != maxsp:
print("")
minsp = minsp + 1
print("Hey User!")
# The game starts here.
while guess != sw or gc != guesses:
guess = str(input("Enter a guess : "))
gc = gc + 1
# Events during/end the game.
if guess == sw:
print("You win!")
exit()
elif guess == 'Np =>':
print("You know something that nobody else knows!")
gc = gc - 1
elif guess == 'Amazing Grace':
print("Alan Jackson!")
gc = gc - 1
elif guess == 'Hint' or guess == 'hint':
print("Admin > " + str(hint))
elif guess == 'GCount' or guess == 'gcount':
print(str(gc) + "/" + str(guesses))
gc = gc - 1
elif gc == guesses:
print("You lose!")
exit()
| [
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8,
198,
220,
220,
220,
220,
220,
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220,
8420,
3419,
628
] | 2.383743 | 529 |
import enum
import requests
import sseclient
from json import loads
from typing import Callable
from typing import Generator
from typing import Tuple
from pycspr.api import NodeConnectionInfo
class NodeSseChannelType(enum.Enum):
""" Enumeration over set of exposed node SEE event types. """
deploys = enum.auto()
main = enum.auto()
sigs = enum.auto()
class NodeSseEventType(enum.Enum):
""" Enumeration over set of exposed node SEE event types. """
ApiVersion = enum.auto()
BlockAdded = enum.auto()
DeployAccepted = enum.auto()
DeployProcessed = enum.auto()
Fault = enum.auto()
FinalitySignature = enum.auto()
Step = enum.auto()
# Map: channel type <-> event type.
_CHANNEL_TO_TYPE = {
NodeSseChannelType.deploys: {
NodeSseEventType.ApiVersion,
NodeSseEventType.DeployAccepted
},
NodeSseChannelType.main: {
NodeSseEventType.ApiVersion,
NodeSseEventType.BlockAdded,
NodeSseEventType.DeployProcessed,
NodeSseEventType.Fault,
NodeSseEventType.Step
},
NodeSseChannelType.sigs: {
NodeSseEventType.ApiVersion,
NodeSseEventType.FinalitySignature
}
}
class EventsClient:
""" Bind to a node's event stream. """
def __init__(self, node: NodeConnectionInfo):
"""
Constructor EventChannel.
:param node: Information required to connect to a node.
"""
self._node = node
def get_events(self, callback: Callable, channel_type: NodeSseChannelType,
event_type: NodeSseEventType, event_id: int = 0) -> None:
"""
Binds to a node's event stream - events are passed to callback for
processing.
:param callback: Callback to invoke whenever an event of relevant type
is received.
:param channel_type: Type of event channel to which to bind.
:param event_type: Type of event type to listen for
(all if unspecified).
:param event_id: Identifer of event from which to start stream
listening.
"""
# validate that the channel supports the event type.
if channel_type not in _CHANNEL_TO_TYPE:
raise ValueError(f"Unsupported SSE channel: {channel_type.name}.")
if event_type not in _CHANNEL_TO_TYPE[channel_type]:
raise ValueError(f"Unsupported SSE channel/event permutation: "
f"{channel_type.name}:{event_type.name}.")
# get and set sse clieant
params = f"?start_from={event_id}" if event_id else ""
url = f"{self._node.address_sse}/{channel_type.name.lower()}{params}"
stream = requests.get(url, stream=True)
self._sse_client = sseclient.SSEClient(stream)
for event_type, event_id, payload in self._yield_events(self):
callback(self._channel_type, event_type, event_id, payload)
def _yield_events(self) -> Generator:
""" Yields events streaming from node. """
try:
for event in self._sse_client.events():
parsed = self._parse_event(event.id, loads(event.data))
if parsed:
yield parsed
except Exception as err:
try:
self._sse_client.close()
finally:
raise err
def _parse_event(self, event_id: int, payload: dict
) -> Tuple[NodeSseEventType, int, dict]:
""" Parses raw event data for upstream processing. """
events = {
"ApiVersion": NodeSseEventType.ApiVersion,
"BlockAdded": NodeSseEventType.BlockAdded,
"DeployProcessed": NodeSseEventType.DeployProcessed,
"Fault": NodeSseEventType.Fault,
"Step": NodeSseEventType.Step,
"DeployAccepted": NodeSseEventType.DeployAccepted,
"FinalitySignature": NodeSseEventType.FinalitySignature
}
for event_name in events:
if event_name in payload:
return events.get(event_name), event_id, payload
# @TODO: process unkown event_type
print(f"Unknown event occured. even_id: {event_id}\n"
f"payload: {payload}")
return None
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] | 2.304885 | 1,863 |
# library imports
from django.db import models
from django.core.validators import MaxValueValidator, MinValueValidator
# property imports
from .properties import *
# import socket # Koennte die richtige Loesung sein...
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#!/usr/bin/env python3
import sys
main()
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import unittest, types
from pyvisdk import Vim
from tests.common import get_options
from pyvisdk.facade.task import TaskManager
TASKS = ['hello world',
'I hate VMware',
'me too',
'This is a very long task name, a very very long name',
'school sucks',
'one more',
'last one']
if __name__ == "__main__":
#import sys;sys.argv = ['', 'Test.testHosts']
unittest.main()
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#! /usr/bin/python3
#############################################################
### Helper File for TFRecords and Image manipulation ########
#############################################################
import tensorflow as tf
import numpy as np
## Label mapping for Cityscapes (34 classes)
Cityscapes34_ID_2_RGB = [(0,0,0), (0,0,0), (0,0,0), (0,0,0), (0,0,0)
# 0=unlabeled, ego vehicle,rectification border, oo roi, static
,(111,74,0),(81,0,81),(128,64,128),(244,35,232),(250,170,160)
# 5=dynamic, 6=ground, 7=road, 8=sidewalk, 9=parking
,(230,150,140), (70,70,70), (102,102,156),(190,153,153),(180,165,180)
# 10=rail track, 11=building, 12=wall, 13=fence, 14=guard rail
,(150,100,100),(150,120, 90),(153,153,153),(153,153,153),(250,170, 30)
# 15= bridge, 16=tunnel, 17=pole, 18=polegroup, 19=traffic light
,(220,220,0),(107,142,35),(152,251,152),(70,130,180),(220,20,60)
# 20=traffic sign 21=vegetation, 22=terrain, 23=sky, 24=person
,(255,0,0),(0,0,142),(0,0,70),(0,60,100),(0,0,90), (0,0,110), (0,80,100), (0,0,230), (119, 11, 32)]
# 25=rider, 26=car, 22=terrain, 27=truck, 28=bus, 29=caravan, 30= trailer, 31=train, 32=motorcyle ,33=bicycle
## Label mapping for Cityscapes (19 classes + '255'=wildcard)
Cityscapes20_ID_2_RGB = [(128,64,128),(244,35,232), (70,70,70), (102,102,156),(190,153,153)
#0=road, 1=sidewalk, 2=building, 3=wall, 4=fence
,(153,153,153), (250,170, 30), (220,220,0),(107,142,35),(152,251,152),(70,130,180),(220,20,60)
# 5= pole, 6=traffic light, 7= traffic sign, 8= vegetation,9= terrain, 10=sky, 11=person
,(255,0,0),(0,0,142),(0,0,70),(0,60,100), (0,80,100), (0,0,230), (119, 11, 32), (255,255,255)]
# 12=rider, 13=car, 14=truck, 15=bus, 16=train, 17=motorcycle, 18=bicycle, #255 --cast via tf.minimum
Pred_2_ID = [7, 8, 11, 12, 13
#0=road, 1=sidewalk, 2=building, 3=wall, 4=fence
,17 , 19, 20, 21, 22, 23, 24
# 5= pole, 6=traffic light, 7= traffic sign, 8= vegetation,9= terrain, 10=sky, 11=person
,25 , 26, 27, 28, 31, 32, 33, -1]
# 12=rider, 13=car, 14=truck, 15=bus, 16=train, 17=motorcycle, 18=bicycle, #255 --cast via tf.minimum
##################################################################################
################## Functions for Image Preprocessing #############################
##################################################################################
def read_and_decode(filename_queue, hasDisparity=False, constHeight=1024, constWidth=1024):
"""Decode images from TF-Records Bytestream. TF-Record must be compiled with the "make_tf_record.py"-script!
Args:
filename_queue: String representation of TF-Records (returned from tf.train.string_input_producer([TFRECORD_FILENAME])
filename_queue: Boolean, needed for procession disparity maps
constHeight, constWidth: Expected shapes of Images to decode
Returns:
Decoded image and mask
"""
with tf.name_scope("Input_Decoder"):
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
if not hasDisparity:
features = tf.parse_single_example(
serialized_example,
features={
'image_raw': tf.FixedLenFeature([], tf.string),
'mask_raw': tf.FixedLenFeature([], tf.string)
})
image = tf.decode_raw(features['image_raw'], tf.uint8)
annotation = tf.decode_raw(features['mask_raw'], tf.uint8)
image_shape = tf.stack([constHeight, constWidth, 3])
annotation_shape = tf.stack([constHeight, constWidth, 1])
image = tf.reshape(image, image_shape)
annotation = tf.reshape(annotation, annotation_shape)
return image, annotation
else:
features = tf.parse_single_example(
serialized_example,
features={
'image_raw': tf.FixedLenFeature([], tf.string),
'mask_raw': tf.FixedLenFeature([], tf.string),
'disp_raw': tf.FixedLenFeature([], tf.string)
})
image = tf.decode_raw(features['image_raw'], tf.uint8)
annotation = tf.decode_raw(features['mask_raw'], tf.uint8)
disparity = tf.decode_raw(features['disp_raw'], tf.int16) #uint6
image_shape = tf.stack([constHeight, constWidth, 3])
masks_shape = tf.stack([constHeight, constWidth, 1])
image = tf.reshape(image, image_shape)
annotation = tf.reshape(annotation, masks_shape)
disparity = tf.reshape(disparity, masks_shape)
return image, annotation, disparity
def decode_labels(mask, num_images=1, num_classes=20, label=Cityscapes20_ID_2_RGB):
"""Decode batch of segmentation masks.
Args:
mask: result of inference after taking argmax.
num_images: number of images to decode from the batch.
num_classes: number of classes to predict (including background).
label: List, which value to assign for different classes
Returns:
A batch with num_images RGB images of the same size as the input.
"""
from PIL import Image
n, h, w, c = mask.shape
assert(n >= num_images), 'Batch size %d should be greater or equal than number of images to save %d.' % (n, num_images)
outputs = np.zeros((num_images, h, w, 3), dtype=np.uint8)
for i in range(num_images):
img = Image.new('RGB', (len(mask[i, 0]), len(mask[i])))
pixels = img.load()
for j_, j in enumerate(mask[i, :, :, 0]):
for k_, k in enumerate(j):
if k < num_classes:
pixels[k_,j_] = label[k]
outputs[i] = np.array(img)
return outputs
from tensorflow.python.ops import control_flow_ops
def flip_randomly_left_right_image_with_annotation(image_tensor, annotation_tensor):
"""Flips an image randomly and applies the same to an annotation tensor.
Args:
image_tensor, annotation_tensor: 3-D-Tensors
Returns:
Flipped image and gt.
"""
random_var = tf.random_uniform(maxval=2, dtype=tf.int32, shape=[])
randomly_flipped_img = control_flow_ops.cond(pred=tf.equal(random_var, 0),
fn1=lambda: tf.image.flip_left_right(image_tensor),
fn2=lambda: image_tensor)
randomly_flipped_annotation = control_flow_ops.cond(pred=tf.equal(random_var, 0),
fn1=lambda: tf.image.flip_left_right(annotation_tensor),
fn2=lambda: annotation_tensor)
return randomly_flipped_img, randomly_flipped_annotation
def random_crop_and_pad_image_and_labels(image, sem_labels, dep_labels, size):
"""Randomly crops `image` together with `labels`.
Args:
image: A Tensor with shape [D_1, ..., D_K, N]
labels: A Tensor with shape [D_1, ..., D_K, M]
size: A Tensor with shape [K] indicating the crop size.
Returns:
A tuple of (cropped_image, cropped_label).
"""
combined = tf.concat([image, sem_labels, dep_labels], axis=2)
print("combined : ", str(combined.get_shape()[:]))
combined_crop = tf.random_crop(combined, [size[0], size[1],5])
print("combined_crop : ", str(combined_crop.get_shape()[:]))
channels = tf.unstack(combined_crop, axis=-1)
image = tf.stack([channels[0],channels[1],channels[2]], axis=-1)
sem_label = tf.expand_dims(channels[3], axis=2)
dep_label = tf.expand_dims(channels[4], axis=2)
return image, sem_label, dep_label
##################################################################################
################## Functions for Image Postprocessing #############################
##################################################################################
def generate_prediction_Img(mask, num_images=1, num_classes= 20, label=Pred_2_ID):
"""Decode batch of segmentation masks.
Args:
mask: result of inference after taking argmax.
num_images: number of images to decode from the batch.
num_classes: number of classes to predict (including background).
label: List, which value to assign for different classes
Returns:
A batch with num_images RGB images of the same size as the input.
"""
from PIL import Image
n, h, w, c = mask.shape
assert(n >= num_images), 'Batch size %d should be greater or equal than number of images to save %d.' % (n, num_images)
outputs = np.zeros((num_images, h, w), dtype=np.uint8)
for i in range(num_images):
img = Image.new('L', (len(mask[i, 0]), len(mask[i])))
pixels = img.load()
for j_, j in enumerate(mask[i, :, :, 0]):
for k_, k in enumerate(j):
if k < num_classes:
pixels[k_,j_] = label[k]
outputs[i] = np.array(img)
return outputs
def plot_depthmap(mask):
"""Network output as [w, h, 1]-Tensor is transformed to a heatmap for easier visual interpretation
Args:
mask: result of inference (depth = 1)
Returns:
A RGB-Image (representation of the depth prediction as heatmap
"""
import matplotlib.pyplot as plt
cmap = plt.get_cmap('hot')
gray = mask[0,:,:,0].astype(np.uint16)
divisor = np.max(gray) - np.min(gray)
if divisor != 0:
normed = (gray - np.min(gray)) / divisor
else:
normed = (gray - np.min(gray))
rgba_img = cmap(normed)
rgb_img = np.delete(rgba_img, 3,2)
return (65535 * rgb_img).astype(np.float32) | [
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] | 2.256084 | 4,397 |
from widgetastic.utils import ParametrizedLocator
from widgetastic.widget import ClickableMixin
from widgetastic.widget import Widget
from widgetastic.xpath import quote
class Button(BaseButton, Widget, ClickableMixin):
"""A Patternfly button
You can match by text, partial text or by attributes, you can also add the patternfly classes
into the matching.
.. code-block:: python
Button("Text of button (unless it is an input ...)")
Button("contains", "Text of button (unless it is an input ...)")
Button(title="Show xyz") # And such
Button("Add", classes=[Button.PRIMARY])
Button(locator=".//xpath")
assert button.active
assert not button.disabled
"""
ROOT = ParametrizedLocator("{@locator}")
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] | 2.93609 | 266 |
import analyser.caption_factory
import data_connector.model_sentence
import data_connector.model_word
import data_connector.data_manager
from xml.etree.ElementTree import *
import re
import sys
sys.path.append("../")
from log_writer import LogWriter
| [
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from __future__ import absolute_import
# Copyright (c) 2010-2015 openpyxl
"""Reader for a single worksheet."""
from io import BytesIO
# compatibility imports
from openpyxl.xml.functions import iterparse
# package imports
from openpyxl.cell import Cell
from openpyxl.worksheet import Worksheet, ColumnDimension, RowDimension
from openpyxl.worksheet.iter_worksheet import IterableWorksheet
from openpyxl.worksheet.page import PageMargins, PrintOptions, PageSetup
from openpyxl.worksheet.protection import SheetProtection
from openpyxl.worksheet.views import SheetView
from openpyxl.xml.constants import SHEET_MAIN_NS, REL_NS
from openpyxl.xml.functions import safe_iterator
from openpyxl.styles import Color
from openpyxl.formatting import ConditionalFormatting
from openpyxl.worksheet.properties import parse_sheetPr
from openpyxl.utils import (
coordinate_from_string,
get_column_letter,
column_index_from_string
)
def _get_xml_iter(xml_source):
"""
Possible inputs: strings, bytes, members of zipfile, temporary file
Always return a file like object
"""
if not hasattr(xml_source, 'read'):
try:
xml_source = xml_source.encode("utf-8")
except (AttributeError, UnicodeDecodeError):
pass
return BytesIO(xml_source)
else:
try:
xml_source.seek(0)
except:
pass
return xml_source
def read_worksheet(xml_source, parent, preset_title, shared_strings,
style_table, color_index=None, worksheet_path=None):
"""Read an xml worksheet"""
if worksheet_path:
ws = IterableWorksheet(parent, preset_title,
worksheet_path, xml_source, shared_strings, style_table)
else:
ws = Worksheet(parent, preset_title)
fast_parse(ws, xml_source, shared_strings, style_table, color_index)
return ws
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] | 2.657746 | 710 |
"""
=================================
Creating a dataset class in MOABB
=================================
"""
# Authors: Pedro L. C. Rodrigues, Sylvain Chevallier
#
# https://github.com/plcrodrigues/Workshop-MOABB-BCI-Graz-2019
import numpy as np
from scipy.io import savemat, loadmat
import mne
from moabb.datasets.base import BaseDataset
from moabb.datasets import download as dl
from moabb.paradigms import LeftRightImagery
from moabb.evaluations import WithinSessionEvaluation
from pyriemann.classification import MDM
from pyriemann.estimation import Covariances
from sklearn.pipeline import make_pipeline
##############################################################################
# Creating some Data
# ------------------
#
# To illustrate the creation of a dataset class in MOABB, we first create an
# example dataset saved in .mat file. It contains a single fake recording on
# 8 channels lasting for 150 seconds (sampling frequency 256 Hz). We have
# included the script that creates this dataset and have uploaded it online.
# The fake dataset is available on the
# [Zenodo website](https://sandbox.zenodo.org/record/369543)
def create_example_dataset():
"""Create a fake example for a dataset"""
sfreq = 256
t_recording = 150
t_trial = 1 # duration of a trial
intertrial = 2 # time between end of a trial and the next one
n_chan = 8
x = np.zeros((n_chan + 1, t_recording * sfreq)) # electrodes + stimulus
stim = np.zeros(t_recording * sfreq)
t_offset = 1.0 # offset where the trials start
n_trials = 40
rep = np.linspace(0, 4 * t_trial, t_trial * sfreq)
signal = np.sin(2 * np.pi / t_trial * rep)
for n in range(n_trials):
label = n % 2 + 1 # alternate between class 0 and class 1
tn = int(t_offset * sfreq + n * (t_trial + intertrial) * sfreq)
stim[tn] = label
noise = 0.1 * np.random.randn(n_chan, len(signal))
x[:-1, tn:(tn + t_trial * sfreq)] = label * signal + noise
x[-1, :] = stim
return x, sfreq
# Create the fake data
for subject in [1, 2, 3]:
x, fs = create_example_dataset()
filename = 'subject_' + str(subject).zfill(2) + '.mat'
mdict = {}
mdict['x'] = x
mdict['fs'] = fs
savemat(filename, mdict)
##############################################################################
# Creating a Dataset Class
# ------------------------
#
# We will create now a dataset class using the fake data simulated with the
# code from above. For this, we first need to import the right classes from
# MOABB
# - `dl` is a very useful script that downloads automatically a dataset online
# if it is not yet available in the user's computer. The script knows where
# to download the files because we create a global variable telling the URL
# where to fetch the data.
# - `BaseDataset` is the basic class that we overload to create our dataset.
#
# The global variable with the dataset's URL should specify an online
# repository where all the files are stored.
ExampleDataset_URL = 'https://sandbox.zenodo.org/record/369543/files/'
# The `ExampleDataset` needs to implement only 3 functions:
# - `__init__` for indicating the parameter of the dataset
# - `_get_single_subject_data` to define how to process the data once they
# have been downloaded
# - `data_path` to define how the data are downloaded.
class ExampleDataset(BaseDataset):
'''
Dataset used to exemplify the creation of a dataset class in MOABB.
The data samples have been simulated and has no physiological meaning
whatsoever.
'''
def _get_single_subject_data(self, subject):
"""return data for a single subject"""
file_path_list = self.data_path(subject)
data = loadmat(file_path_list[0])
x = data['x']
fs = data['fs']
ch_names = ['ch' + str(i) for i in range(8)] + ['stim']
ch_types = ['eeg' for i in range(8)] + ['stim']
info = mne.create_info(ch_names, fs, ch_types)
raw = mne.io.RawArray(x, info)
sessions = {}
sessions['session_1'] = {}
sessions['session_1']['run_1'] = raw
return sessions
def data_path(self, subject, path=None, force_update=False,
update_path=None, verbose=None):
"""Download the data from one subject"""
if subject not in self.subject_list:
raise(ValueError("Invalid subject number"))
url = '{:s}subject_0{:d}.mat'.format(ExampleDataset_URL, subject)
path = dl.data_path(url, 'ExampleDataset')
return [path] # it has to return a list
##############################################################################
# Using the ExampleDataset
# ------------------------
#
# Now that the `ExampleDataset` is defined, it could be instanciated directly.
# The rest of the code follows the steps described in the previous tutorials.
dataset = ExampleDataset()
paradigm = LeftRightImagery()
X, labels, meta = paradigm.get_data(dataset=dataset, subjects=[1])
evaluation = WithinSessionEvaluation(paradigm=paradigm, datasets=dataset,
overwrite=True)
pipelines = {}
pipelines['MDM'] = make_pipeline(Covariances('oas'), MDM(metric='riemann'))
scores = evaluation.process(pipelines)
print(scores)
##############################################################################
# Pushing on MOABB Github
# -----------------------
#
# If you want to make your dataset available to everyone, you could upload
# your data on public server (like Zenodo or Figshare) and signal that you
# want to add your dataset to MOABB in the [dedicated issue](https://github.com/NeuroTechX/moabb/issues/1). # noqa: E501
# You could then follow the instructions on [how to contribute](https://github.com/NeuroTechX/moabb/blob/master/CONTRIBUTING.md) # noqa: E501
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] | 2.792734 | 2,147 |
# -*- encoding: utf-8 -*-
from unittest import TestCase
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
from sqlalchemy_filterparams_tests.models import (
Base,
)
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357,
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220,
220,
220,
7308,
11,
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198
] | 3.030303 | 66 |
from heisen.config import settings
from jsonrpclib.request import ConnectionPool
rpc_call = get_rpc_connection()
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] | 3.314286 | 35 |
import numpy as np
| [
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] | 3.166667 | 6 |
import subprocess
from functools import partial
from django.core.management.base import BaseCommand
from django.conf import settings
from pathlib import Path
import os
run = partial(subprocess.run, shell=True, check=True)
class Command(BaseCommand):
"""Update pytition install
"""
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] | 3.518072 | 83 |
# AUTOGENERATED! DO NOT EDIT! File to edit: ttbarzp.ipynb (unless otherwise specified).
__all__ = ['get_elijah_ttbarzp_cs', 'get_manuel_ttbarzp_cs', 'import47Ddata', 'get47Dfeatures']
# Cell
import numpy as np
import tensorflow as tf
# Cell
def get_elijah_ttbarzp_cs():
r"""
Contains cross section information produced by Elijah for $pp \to t\overline{t} \; Z'$ collider phenomenology.
Returns list containing signal masses, signal cross sections (for those masses, in pb), and background cross sections
(also in pb)
"""
# Z' masses (GeV) for which Elijah created signal samples
elijah_masses = [10, 50, 100, 200, 350, 500, 1000, 2000, 5000]
# signal cross sections (pb)
elijah_sig_css = [9.801, 0.5445, 0.1442, 0.03622, 0.009998, 0.003802, 0.0003936, 2.034e-05, 2.748e-08]
# background cross sections (pb)
elijah_bg_css = [0.106, 0.0117, 5.58]
return [elijah_masses, elijah_sig_css, elijah_bg_css]
# Cell
def get_manuel_ttbarzp_cs():
r"""
Contains cross section information produced through MadGraph by Manuel for collider phenomenology regarding
the semihadronic, semileptonic $pp \to t\overline{t} \; Z', Z' \to b\overline{b}$ channel
"""
# Z' masses (GeV) for which I (Elijah) created signal samples
manuel_masses = [350, 500, 750, 1000, 2000, 3000, 4000]
# signal cross sections (pb)
manuel_sig_css = [0.001395, 0.0007823, 0.0003429, 0.0001692, 1.808e-05, 1.325e-06, 4.456e-07]
# background cross sections (pb)
manuel_bg_css = [0.1339, 0.01187, 5.603]
return [manuel_masses, manuel_sig_css, manuel_bg_css]
# Cell
def import47Ddata(name):
r"""
Imports `name.npy` file containing 47-dimensional data for training
Available files:
- bgh.npy (Standard Model background 1, $pp \to t\overline{t}h$)
- bg4t.npy (Standard Model background 2, $pp \to t\overline{t}t\overline{t}$)
- bgnoh.npy (Standard Model background 3, $pp \to t\overline{t} \; \setminus \; h$)
- sig350G.npy ($Z'$ signal, $m_{Z'} = 350$ GeV)
- sig500G.npy ($Z'$ signal, $m_{Z'} = 500$ GeV)
- sig1T.npy ($Z'$ signal, $m_{Z'} = 1$ TeV)
- sig2T.npy ($Z'$ signal, $m_{Z'} = 2$ TeV)
- sig4T.npy ($Z'$ signal, $m_{Z'} = 4$ TeV)
"""
if name[-4:] == '.npy':
name = name[:-4]
url = 'https://storage.googleapis.com/ttbarzp/47dim/'
try:
path = tf.keras.utils.get_file(f'{name}.npy', url + name + '.npy')
data = np.load(path)
return data
except:
print(f"{name}.npy doesn't appear to exist")
# Cell
def get47Dfeatures():
"""
Returns list containing the names of the 47 features found in the data accessible through
`ttbarzp.import47Ddata()`
"""
return [
'pT b1', 'pT b2', 'pT b3', 'pT b4',
'sdEta b1 b2', 'sdEta b1 b3', 'sdEta b1 b4', 'sdEta b2 b3', 'sdEta b2 b4', 'sdEta b3 b4',
'sdPhi b1 b2', 'sdPhi b1 b3', 'sdPhi b1 b4', 'sdPhi b2 b3', 'sdPhi b2 b4', 'sdPhi b3 b4',
'dR b1 b2', 'dR b1 b3', 'dR b1 b4', 'dR b2 b3', 'dR b2 b4', 'dR b3 b4',
'MET', 'pT l', 'MT l MET',
'M b1 b2', 'M b1 b3', 'M b1 b4', 'M b2 b3', 'M b2 b4', 'M b3 b4',
'MT b1 l MET', 'MT b2 l MET', 'MT b3 l MET', 'MT b4 l MET',
'M j1 j2', 'pT j1', 'pT j2', 'dR j1 j2',
'dR b1 l', 'dR b2 l', 'dR b3 l', 'dR b4 l',
'sdPhi b1 l', 'sdPhi b2 l', 'sdPhi b3 l', 'sdPhi b4 l'] | [
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] | 2.133626 | 1,594 |
import os
import re
import sys
from .core import Migration
from .core.exceptions import MigrationException
from .helpers import Utils
from getpass import getpass
from .cli import CLI
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# Generated by Django 2.0.7 on 2018-08-06 02:02
from django.db import migrations, models
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from setuptools import setup, find_packages
setup(
name="PyRundeck",
version="0.3.7",
description="A thin, pure Python wrapper for the Rundeck API",
author="Panagiotis Koutsourakis",
author_email="kutsurak@ekt.gr",
license='BSD',
url='https://github.com/EKT/pyrundeck',
classifiers=[
'Development Status :: 3 - Alpha',
'Intended Audience :: Developers',
'Topic :: Internet :: REST API client',
'Topic :: Software Development :: Libraries :: Python Modules',
'License :: OSI Approved :: BSD License',
'Programming Language :: Python :: 2',
'Programming Language :: Python :: 2.7',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.2',
'Programming Language :: Python :: 3.3',
'Programming Language :: Python :: 3.4',
],
keywords='rest api client rundeck',
packages=find_packages(exclude=['tests', '*_virtualenv', 'doc']),
install_requires=[
'lxml>=3.4.4',
'requests>=2.7.0',
'pyopenssl>=0.15.1',
'ndg-httpsclient>=0.4.0',
'pyasn1>=0.1.8',
'pyyaml>=3.11'
]
)
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from abc import ABCMeta, abstractmethod
from multiprocessing import cpu_count
from numpy import exp, conj, zeros, complex64
from numba import jit
from pyfftw.builders import fft2, ifft2
class DiffractionExecutor(metaclass=ABCMeta):
"""
Abstract class for diffraction object.
The class takes on the input in the constructor a beam object, which contains all the necessary beam parameters
for further calculations.
Depending on the type of beam diffraction is implemented in different ways. To simulate diffraction in
a 2-dimensional beam and a 3-dimensional beam in the axisymmetric approximation, a sweep was used;
to simulate the diffraction of a non-axisymmetric 3-dimensional beam, we used fast Fourier transform with pyfftw
in several threads.
"""
@abstractmethod
def info(self):
"""DiffractionExecutor type"""
@abstractmethod
def process_diffraction(self, dz):
"""Process_diffraction"""
class SweepDiffractionExecutorX(DiffractionExecutor):
"""
Class for modeling the diffraction of a 2-dimensional beam using sweep.
"""
@property
@staticmethod
@jit(nopython=True)
def process_diffraction(self, dz):
"""
:param dz: current step along evolutionary coordinate z
:return: None
"""
self._beam._field = self.__fast_process(self._beam._field, self._beam.n_x, dz, self.__c1,
self.__c2, self.__alpha, self.__gamma, self.__delta, self.__xi,
self.__eta, self.__kappa_left, self.__mu_left, self.__kappa_right,
self.__mu_right)
class SweepDiffractionExecutorR(DiffractionExecutor):
"""
Class for modeling the diffraction of a 3-dimensional beam in axisymmetric approximation.
"""
@property
@staticmethod
@jit(nopython=True)
def process_diffraction(self, dz):
"""
:param dz: current step along evolutionary coordinate z
:return: None
"""
self._beam._field = self.__fast_process(self._beam._field, self._beam.n_r, dz, self.__c1,
self.__c3, self.__alpha, self.__beta, self.__gamma, self.__delta,
self.__xi, self.__eta, self.__vx, self.__kappa_left, self.__mu_left,
self.__kappa_right, self.__mu_right)
class FourierDiffractionExecutorXY(DiffractionExecutor):
"""
Class for modeling the diffraction of a 3-dimensional beam using fast Fourier transform in pyfftw.
"""
MAX_NUMBER_OF_CPUS = cpu_count() # number of threads for parallelization
@property
@staticmethod
@jit(nopython=True)
def __phase_increment(field_fft, n_x, n_y, k_xs, k_ys, current_lin_phase):
"""
:param field_fft: spatial spectrum of the field array
:param n_x: number of points in spatial grid along x
:param n_y: number of points in spatial grid along y
:param k_xs: wave vector grid nosed along x
:param k_ys: wave vector grid nosed along y
:param current_lin_phase: linear phase shift in spectral space
:return: spatial spectrum of the field array with linear phase shift along both axes
"""
for i in range(n_x):
field_fft[i, :] *= exp(current_lin_phase * k_xs[i] ** 2)
for j in range(n_y):
field_fft[:, j] *= exp(current_lin_phase * k_ys[j] ** 2)
return field_fft
def process_diffraction(self, dz, n_jobs=MAX_NUMBER_OF_CPUS):
"""
:param dz: current step along evolutionary coordinate z
:param n_jobs: number of threads for parallelization
:return: None
"""
# calculation of current linear phase shift
current_lin_phase = 0.5j * dz / self._beam.medium.k_0
# forward parallel fast Fourier transform
fft_obj = fft2(self._beam._field, threads=n_jobs)
# linear phase increment
field_fft = self.__phase_increment(fft_obj(), self._beam.n_x, self._beam.n_y, self._beam.k_xs,
self._beam.k_ys, current_lin_phase)
# backward parallel fast Fourier transform
ifft_obj = ifft2(field_fft, threads=n_jobs)
# field initialization with updated values
self._beam._field = ifft_obj()
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] | 2.363829 | 1,891 |
# Generated by Django 3.2.2 on 2021-05-16 03:23
from django.conf import settings
import django.core.validators
from django.db import migrations, models
import django.db.models.deletion
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] | 3.065574 | 61 |
#
# PySNMP MIB module APPN-DLUR-MIB (http://snmplabs.com/pysmi)
# ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/APPN-DLUR-MIB
# Produced by pysmi-0.3.4 at Mon Apr 29 17:08:22 2019
# On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4
# Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15)
#
SnaControlPointName, = mibBuilder.importSymbols("APPN-MIB", "SnaControlPointName")
OctetString, Integer, ObjectIdentifier = mibBuilder.importSymbols("ASN1", "OctetString", "Integer", "ObjectIdentifier")
NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues")
ValueRangeConstraint, SingleValueConstraint, ConstraintsIntersection, ConstraintsUnion, ValueSizeConstraint = mibBuilder.importSymbols("ASN1-REFINEMENT", "ValueRangeConstraint", "SingleValueConstraint", "ConstraintsIntersection", "ConstraintsUnion", "ValueSizeConstraint")
snanauMIB, = mibBuilder.importSymbols("SNA-NAU-MIB", "snanauMIB")
NotificationGroup, ModuleCompliance, ObjectGroup = mibBuilder.importSymbols("SNMPv2-CONF", "NotificationGroup", "ModuleCompliance", "ObjectGroup")
Unsigned32, IpAddress, Integer32, Counter32, Bits, Gauge32, ObjectIdentity, Counter64, MibScalar, MibTable, MibTableRow, MibTableColumn, NotificationType, MibIdentifier, ModuleIdentity, iso, TimeTicks = mibBuilder.importSymbols("SNMPv2-SMI", "Unsigned32", "IpAddress", "Integer32", "Counter32", "Bits", "Gauge32", "ObjectIdentity", "Counter64", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "NotificationType", "MibIdentifier", "ModuleIdentity", "iso", "TimeTicks")
DisplayString, TextualConvention, TruthValue = mibBuilder.importSymbols("SNMPv2-TC", "DisplayString", "TextualConvention", "TruthValue")
dlurMIB = ModuleIdentity((1, 3, 6, 1, 2, 1, 34, 5))
if mibBuilder.loadTexts: dlurMIB.setLastUpdated('9705101500Z')
if mibBuilder.loadTexts: dlurMIB.setOrganization('IETF SNA NAU MIB WG / AIW APPN/HPR MIBs SIG')
dlurObjects = MibIdentifier((1, 3, 6, 1, 2, 1, 34, 5, 1))
dlurNodeInfo = MibIdentifier((1, 3, 6, 1, 2, 1, 34, 5, 1, 1))
dlurNodeCapabilities = MibIdentifier((1, 3, 6, 1, 2, 1, 34, 5, 1, 1, 1))
dlurNodeCpName = MibScalar((1, 3, 6, 1, 2, 1, 34, 5, 1, 1, 1, 1), SnaControlPointName()).setMaxAccess("readonly")
if mibBuilder.loadTexts: dlurNodeCpName.setStatus('current')
dlurReleaseLevel = MibScalar((1, 3, 6, 1, 2, 1, 34, 5, 1, 1, 1, 2), DisplayString().subtype(subtypeSpec=ValueSizeConstraint(2, 2)).setFixedLength(2)).setMaxAccess("readonly")
if mibBuilder.loadTexts: dlurReleaseLevel.setStatus('current')
dlurAnsSupport = MibScalar((1, 3, 6, 1, 2, 1, 34, 5, 1, 1, 1, 3), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2))).clone(namedValues=NamedValues(("continueOrStop", 1), ("stopOnly", 2)))).setMaxAccess("readonly")
if mibBuilder.loadTexts: dlurAnsSupport.setStatus('current')
dlurMultiSubnetSupport = MibScalar((1, 3, 6, 1, 2, 1, 34, 5, 1, 1, 1, 4), TruthValue()).setMaxAccess("readonly")
if mibBuilder.loadTexts: dlurMultiSubnetSupport.setStatus('current')
dlurDefaultDefPrimDlusName = MibScalar((1, 3, 6, 1, 2, 1, 34, 5, 1, 1, 1, 5), SnaControlPointName()).setMaxAccess("readonly")
if mibBuilder.loadTexts: dlurDefaultDefPrimDlusName.setStatus('current')
dlurNetworkNameForwardingSupport = MibScalar((1, 3, 6, 1, 2, 1, 34, 5, 1, 1, 1, 6), TruthValue()).setMaxAccess("readonly")
if mibBuilder.loadTexts: dlurNetworkNameForwardingSupport.setStatus('current')
dlurNondisDlusDlurSessDeactSup = MibScalar((1, 3, 6, 1, 2, 1, 34, 5, 1, 1, 1, 7), TruthValue()).setMaxAccess("readonly")
if mibBuilder.loadTexts: dlurNondisDlusDlurSessDeactSup.setStatus('current')
dlurDefaultDefBackupDlusTable = MibTable((1, 3, 6, 1, 2, 1, 34, 5, 1, 1, 2), )
if mibBuilder.loadTexts: dlurDefaultDefBackupDlusTable.setStatus('current')
dlurDefaultDefBackupDlusEntry = MibTableRow((1, 3, 6, 1, 2, 1, 34, 5, 1, 1, 2, 1), ).setIndexNames((0, "APPN-DLUR-MIB", "dlurDefaultDefBackupDlusIndex"))
if mibBuilder.loadTexts: dlurDefaultDefBackupDlusEntry.setStatus('current')
dlurDefaultDefBackupDlusIndex = MibTableColumn((1, 3, 6, 1, 2, 1, 34, 5, 1, 1, 2, 1, 1), Unsigned32().subtype(subtypeSpec=ValueRangeConstraint(1, 4294967295)))
if mibBuilder.loadTexts: dlurDefaultDefBackupDlusIndex.setStatus('current')
dlurDefaultDefBackupDlusName = MibTableColumn((1, 3, 6, 1, 2, 1, 34, 5, 1, 1, 2, 1, 2), SnaControlPointName()).setMaxAccess("readonly")
if mibBuilder.loadTexts: dlurDefaultDefBackupDlusName.setStatus('current')
dlurPuInfo = MibIdentifier((1, 3, 6, 1, 2, 1, 34, 5, 1, 2))
dlurPuTable = MibTable((1, 3, 6, 1, 2, 1, 34, 5, 1, 2, 1), )
if mibBuilder.loadTexts: dlurPuTable.setStatus('current')
dlurPuEntry = MibTableRow((1, 3, 6, 1, 2, 1, 34, 5, 1, 2, 1, 1), ).setIndexNames((0, "APPN-DLUR-MIB", "dlurPuName"))
if mibBuilder.loadTexts: dlurPuEntry.setStatus('current')
dlurPuName = MibTableColumn((1, 3, 6, 1, 2, 1, 34, 5, 1, 2, 1, 1, 1), DisplayString().subtype(subtypeSpec=ValueSizeConstraint(1, 17)))
if mibBuilder.loadTexts: dlurPuName.setStatus('current')
dlurPuSscpSuppliedName = MibTableColumn((1, 3, 6, 1, 2, 1, 34, 5, 1, 2, 1, 1, 2), DisplayString().subtype(subtypeSpec=ValueSizeConstraint(0, 17))).setMaxAccess("readonly")
if mibBuilder.loadTexts: dlurPuSscpSuppliedName.setStatus('current')
dlurPuStatus = MibTableColumn((1, 3, 6, 1, 2, 1, 34, 5, 1, 2, 1, 1, 3), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3, 4, 5, 6, 7, 8, 9))).clone(namedValues=NamedValues(("reset", 1), ("pendReqActpuRsp", 2), ("pendActpu", 3), ("pendActpuRsp", 4), ("active", 5), ("pendLinkact", 6), ("pendDactpuRsp", 7), ("pendInop", 8), ("pendInopActpu", 9)))).setMaxAccess("readonly")
if mibBuilder.loadTexts: dlurPuStatus.setStatus('current')
dlurPuAnsSupport = MibTableColumn((1, 3, 6, 1, 2, 1, 34, 5, 1, 2, 1, 1, 4), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2))).clone(namedValues=NamedValues(("continue", 1), ("stop", 2)))).setMaxAccess("readonly")
if mibBuilder.loadTexts: dlurPuAnsSupport.setStatus('current')
dlurPuLocation = MibTableColumn((1, 3, 6, 1, 2, 1, 34, 5, 1, 2, 1, 1, 5), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2))).clone(namedValues=NamedValues(("internal", 1), ("downstream", 2)))).setMaxAccess("readonly")
if mibBuilder.loadTexts: dlurPuLocation.setStatus('current')
dlurPuLsName = MibTableColumn((1, 3, 6, 1, 2, 1, 34, 5, 1, 2, 1, 1, 6), DisplayString().subtype(subtypeSpec=ValueSizeConstraint(0, 10))).setMaxAccess("readonly")
if mibBuilder.loadTexts: dlurPuLsName.setStatus('current')
dlurPuDlusSessnStatus = MibTableColumn((1, 3, 6, 1, 2, 1, 34, 5, 1, 2, 1, 1, 7), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3, 4))).clone(namedValues=NamedValues(("reset", 1), ("pendingActive", 2), ("active", 3), ("pendingInactive", 4)))).setMaxAccess("readonly")
if mibBuilder.loadTexts: dlurPuDlusSessnStatus.setStatus('current')
dlurPuActiveDlusName = MibTableColumn((1, 3, 6, 1, 2, 1, 34, 5, 1, 2, 1, 1, 8), DisplayString().subtype(subtypeSpec=ValueSizeConstraint(0, 17))).setMaxAccess("readonly")
if mibBuilder.loadTexts: dlurPuActiveDlusName.setStatus('current')
dlurPuDefPrimDlusName = MibTableColumn((1, 3, 6, 1, 2, 1, 34, 5, 1, 2, 1, 1, 9), DisplayString().subtype(subtypeSpec=ValueSizeConstraint(0, 17))).setMaxAccess("readonly")
if mibBuilder.loadTexts: dlurPuDefPrimDlusName.setStatus('current')
dlurPuDefBackupDlusTable = MibTable((1, 3, 6, 1, 2, 1, 34, 5, 1, 2, 2), )
if mibBuilder.loadTexts: dlurPuDefBackupDlusTable.setStatus('current')
dlurPuDefBackupDlusEntry = MibTableRow((1, 3, 6, 1, 2, 1, 34, 5, 1, 2, 2, 1), ).setIndexNames((0, "APPN-DLUR-MIB", "dlurPuDefBackupDlusPuName"), (0, "APPN-DLUR-MIB", "dlurPuDefBackupDlusIndex"))
if mibBuilder.loadTexts: dlurPuDefBackupDlusEntry.setStatus('current')
dlurPuDefBackupDlusPuName = MibTableColumn((1, 3, 6, 1, 2, 1, 34, 5, 1, 2, 2, 1, 1), DisplayString().subtype(subtypeSpec=ValueSizeConstraint(1, 17)))
if mibBuilder.loadTexts: dlurPuDefBackupDlusPuName.setStatus('current')
dlurPuDefBackupDlusIndex = MibTableColumn((1, 3, 6, 1, 2, 1, 34, 5, 1, 2, 2, 1, 2), Unsigned32().subtype(subtypeSpec=ValueRangeConstraint(1, 4294967295)))
if mibBuilder.loadTexts: dlurPuDefBackupDlusIndex.setStatus('current')
dlurPuDefBackupDlusName = MibTableColumn((1, 3, 6, 1, 2, 1, 34, 5, 1, 2, 2, 1, 3), SnaControlPointName()).setMaxAccess("readonly")
if mibBuilder.loadTexts: dlurPuDefBackupDlusName.setStatus('current')
dlurDlusInfo = MibIdentifier((1, 3, 6, 1, 2, 1, 34, 5, 1, 3))
dlurDlusTable = MibTable((1, 3, 6, 1, 2, 1, 34, 5, 1, 3, 1), )
if mibBuilder.loadTexts: dlurDlusTable.setStatus('current')
dlurDlusEntry = MibTableRow((1, 3, 6, 1, 2, 1, 34, 5, 1, 3, 1, 1), ).setIndexNames((0, "APPN-DLUR-MIB", "dlurDlusName"))
if mibBuilder.loadTexts: dlurDlusEntry.setStatus('current')
dlurDlusName = MibTableColumn((1, 3, 6, 1, 2, 1, 34, 5, 1, 3, 1, 1, 1), SnaControlPointName())
if mibBuilder.loadTexts: dlurDlusName.setStatus('current')
dlurDlusSessnStatus = MibTableColumn((1, 3, 6, 1, 2, 1, 34, 5, 1, 3, 1, 1, 2), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3, 4))).clone(namedValues=NamedValues(("reset", 1), ("pendingActive", 2), ("active", 3), ("pendingInactive", 4)))).setMaxAccess("readonly")
if mibBuilder.loadTexts: dlurDlusSessnStatus.setStatus('current')
dlurConformance = MibIdentifier((1, 3, 6, 1, 2, 1, 34, 5, 2))
dlurCompliances = MibIdentifier((1, 3, 6, 1, 2, 1, 34, 5, 2, 1))
dlurGroups = MibIdentifier((1, 3, 6, 1, 2, 1, 34, 5, 2, 2))
dlurCompliance = ModuleCompliance((1, 3, 6, 1, 2, 1, 34, 5, 2, 1, 1)).setObjects(("APPN-DLUR-MIB", "dlurConfGroup"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
dlurCompliance = dlurCompliance.setStatus('current')
dlurConfGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 34, 5, 2, 2, 1)).setObjects(("APPN-DLUR-MIB", "dlurNodeCpName"), ("APPN-DLUR-MIB", "dlurReleaseLevel"), ("APPN-DLUR-MIB", "dlurAnsSupport"), ("APPN-DLUR-MIB", "dlurMultiSubnetSupport"), ("APPN-DLUR-MIB", "dlurNetworkNameForwardingSupport"), ("APPN-DLUR-MIB", "dlurNondisDlusDlurSessDeactSup"), ("APPN-DLUR-MIB", "dlurDefaultDefPrimDlusName"), ("APPN-DLUR-MIB", "dlurDefaultDefBackupDlusName"), ("APPN-DLUR-MIB", "dlurPuSscpSuppliedName"), ("APPN-DLUR-MIB", "dlurPuStatus"), ("APPN-DLUR-MIB", "dlurPuAnsSupport"), ("APPN-DLUR-MIB", "dlurPuLocation"), ("APPN-DLUR-MIB", "dlurPuLsName"), ("APPN-DLUR-MIB", "dlurPuDlusSessnStatus"), ("APPN-DLUR-MIB", "dlurPuActiveDlusName"), ("APPN-DLUR-MIB", "dlurPuDefPrimDlusName"), ("APPN-DLUR-MIB", "dlurPuDefBackupDlusName"), ("APPN-DLUR-MIB", "dlurDlusSessnStatus"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
dlurConfGroup = dlurConfGroup.setStatus('current')
mibBuilder.exportSymbols("APPN-DLUR-MIB", dlurPuTable=dlurPuTable, dlurPuDefBackupDlusName=dlurPuDefBackupDlusName, dlurNodeCpName=dlurNodeCpName, dlurPuStatus=dlurPuStatus, dlurNetworkNameForwardingSupport=dlurNetworkNameForwardingSupport, dlurPuDefBackupDlusTable=dlurPuDefBackupDlusTable, dlurDlusInfo=dlurDlusInfo, dlurPuAnsSupport=dlurPuAnsSupport, dlurDefaultDefBackupDlusIndex=dlurDefaultDefBackupDlusIndex, dlurGroups=dlurGroups, dlurPuName=dlurPuName, dlurCompliances=dlurCompliances, dlurPuDefPrimDlusName=dlurPuDefPrimDlusName, dlurPuDefBackupDlusEntry=dlurPuDefBackupDlusEntry, dlurMultiSubnetSupport=dlurMultiSubnetSupport, dlurNodeInfo=dlurNodeInfo, dlurPuLsName=dlurPuLsName, dlurDlusName=dlurDlusName, dlurConformance=dlurConformance, dlurNodeCapabilities=dlurNodeCapabilities, dlurDlusTable=dlurDlusTable, dlurCompliance=dlurCompliance, dlurDefaultDefPrimDlusName=dlurDefaultDefPrimDlusName, dlurDlusSessnStatus=dlurDlusSessnStatus, dlurPuDefBackupDlusIndex=dlurPuDefBackupDlusIndex, dlurPuLocation=dlurPuLocation, dlurDefaultDefBackupDlusTable=dlurDefaultDefBackupDlusTable, dlurPuInfo=dlurPuInfo, PYSNMP_MODULE_ID=dlurMIB, dlurObjects=dlurObjects, dlurConfGroup=dlurConfGroup, dlurPuDefBackupDlusPuName=dlurPuDefBackupDlusPuName, dlurNondisDlusDlurSessDeactSup=dlurNondisDlusDlurSessDeactSup, dlurPuActiveDlusName=dlurPuActiveDlusName, dlurReleaseLevel=dlurReleaseLevel, dlurAnsSupport=dlurAnsSupport, dlurDefaultDefBackupDlusEntry=dlurDefaultDefBackupDlusEntry, dlurPuSscpSuppliedName=dlurPuSscpSuppliedName, dlurDefaultDefBackupDlusName=dlurDefaultDefBackupDlusName, dlurPuEntry=dlurPuEntry, dlurPuDlusSessnStatus=dlurPuDlusSessnStatus, dlurMIB=dlurMIB, dlurDlusEntry=dlurDlusEntry)
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8,
198
] | 2.423964 | 5,116 |
from django.urls import path
from planning.views import main
app_name = 'planning'
urlpatterns = [
path('', main, name='main'),
]
| [
6738,
42625,
14208,
13,
6371,
82,
1330,
3108,
198,
198,
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13,
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198,
198,
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62,
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6,
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685,
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220,
220,
220,
3108,
10786,
3256,
1388,
11,
1438,
11639,
12417,
33809,
198,
60,
198
] | 2.833333 | 48 |
# Base conversion algorithm
# Fastest way to convert bases
# Note this does it in reverse so the strings and the strings elements
# must be reversed
usrNum = int(input("Enter a number to convert:\n"))
usrBase = int(input("Enter a base:\n"))
print(baseConvert(usrNum, usrBase))
| [
2,
7308,
11315,
11862,
198,
2,
12549,
395,
835,
284,
10385,
12536,
198,
2,
5740,
428,
857,
340,
287,
9575,
523,
262,
13042,
290,
262,
13042,
4847,
198,
2,
1276,
307,
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628,
198,
198,
14629,
33111,
796,
493,
7,
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7203,
17469,
257,
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3103,
1851,
7,
14629,
33111,
11,
514,
81,
14881,
4008,
198
] | 3.544304 | 79 |
from blinkstick import blinkstick
| [
6738,
21019,
13915,
1330,
21019,
13915,
198
] | 4.857143 | 7 |
"""Feature extraction tools based off a two channel EEG recording"""
import numpy as np
from classification.config.constants import (
EEG_CHANNELS,
AGE_FEATURE_BINS,
)
from classification.features.pipeline import get_feature_union
def get_eeg_features(epochs, in_bed_seconds, out_of_bed_seconds):
"""Returns the continuous feature matrix
Input
-------
epochs: mne.Epochs object with signals with or without annotations
in_bed_seconds: timespan, in seconds, from which the subject started
the recording and went to bed
out_of_bed_seconds: timespan, in seconds, from which the subject
started the recording and got out of bed
Returns
-------
Array of size (nb_epochs, nb_continuous_features)
"""
features = []
feature_union = get_feature_union()
for channel in EEG_CHANNELS:
channel_epochs = epochs.copy().pick_channels({channel})
channel_features = feature_union.transform(channel_epochs)
features.append(channel_features)
return np.hstack(tuple(features))
def get_non_eeg_features(age, sex, nb_epochs):
"""Returns the categorical feature matrix
Input
-------
age: Age of the subject
sex: Sex of the subject
nb_epochs: corresponds to the nb of epochs which will be analyzed.
Returns
-------
Array of size (nb_epochs,nb_categorical_features), which contains
(duplicated) value for all epochs because it concerns the same subject.
"""
age_category = next(
category_index
for category_index, age_range in enumerate(AGE_FEATURE_BINS)
if age >= age_range[0] and age <= age_range[1]
)
X_categorical = [sex.value, age_category]
return np.array(X_categorical * nb_epochs).reshape(nb_epochs, -1)
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7,
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62,
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62,
33,
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8,
198,
220,
220,
220,
220,
220,
220,
220,
611,
2479,
18189,
2479,
62,
9521,
58,
15,
60,
290,
2479,
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2479,
62,
9521,
58,
16,
60,
198,
220,
220,
220,
1267,
198,
220,
220,
220,
1395,
62,
66,
2397,
12409,
796,
685,
8044,
13,
8367,
11,
2479,
62,
22872,
60,
628,
220,
220,
220,
1441,
45941,
13,
18747,
7,
55,
62,
66,
2397,
12409,
1635,
299,
65,
62,
538,
5374,
82,
737,
3447,
1758,
7,
46803,
62,
538,
5374,
82,
11,
532,
16,
8,
198
] | 2.771739 | 644 |
preference_list_of_user=[] | [
3866,
4288,
62,
4868,
62,
1659,
62,
7220,
28,
21737
] | 2.6 | 10 |
import os
import re
from bs4 import BeautifulSoup
from string import ascii_lowercase
base_file = "OPTED/words_X.html"
fiveletter = []
for letter in ascii_lowercase:
file = base_file.replace("X",letter)
with open(file, "r") as f:
html = f.read()
words = BeautifulSoup(html, "html.parser").find_all("b")
for w in words:
wt = w.get_text().lower()
length = len(wt) == 5
duplicate = wt in fiveletter
ascii = not re.search("[\W]", wt)
if length and ascii and not duplicate:
fiveletter.append(wt)
with open("five_letter_words.txt", "w") as f:
for w in fiveletter:
f.write(w+"\n")
| [
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220,
220,
220,
220,
220,
220,
220,
1936,
9291,
13,
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7,
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8,
198,
198,
4480,
1280,
7203,
13261,
62,
9291,
62,
10879,
13,
14116,
1600,
366,
86,
4943,
355,
277,
25,
198,
220,
220,
220,
329,
266,
287,
1936,
9291,
25,
198,
220,
220,
220,
220,
220,
220,
220,
277,
13,
13564,
7,
86,
10,
1,
59,
77,
4943,
198
] | 2.260135 | 296 |
import unittest
from unittest.mock import patch
from models.coordinate import Coordinate
| [
11748,
555,
715,
395,
198,
6738,
555,
715,
395,
13,
76,
735,
1330,
8529,
198,
6738,
4981,
13,
37652,
4559,
1330,
22819,
4559,
628
] | 3.75 | 24 |
"""Realization of algorithms for char-by-char text encryption.
Functions:
rot_13(str) -> str
Character offset by 13 positions.
caesar_1(str) -> str
Character offset by 1 position.
"""
def rot_13(char_in: str) -> str:
"""Character offset by 13 positions.
Parameters:
char_in : function
Character of the source text.
Returns:
char_out : str
Character of the encrypted text.
"""
num = ord(char_in[0])
if (97 <= num <= 109) or (65 <= num <= 77):
char_out = chr(num + 13)
elif (110 <= num <= 122) or (78 <= num <= 90):
char_out = chr(num - 13)
else:
char_out = char_in
return char_out
def caesar_1(char_in: str) -> str:
"""Character offset by 1 position.
Parameters:
char_in : function
Character of the source text.
Returns:
char_out : str
Character of the encrypted text.
"""
num = ord(char_in[0])
if (97 <= num <= 121) or (65 <= num <= 89):
char_out = chr(num + 1)
elif num == 122 or num == 90:
char_out = chr(num - 25)
else:
char_out = char_in
return char_out
| [
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25,
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220,
220,
220,
220,
220,
220,
220,
1149,
62,
448,
796,
442,
81,
7,
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532,
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8,
198,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1149,
62,
448,
796,
1149,
62,
259,
628,
220,
220,
220,
1441,
1149,
62,
448,
198
] | 2.277567 | 526 |
import unittest
import asyncio
from lbry import utils
from torba.testcase import AsyncioTestCase
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