code stringlengths 22 1.05M | apis listlengths 1 3.31k | extract_api stringlengths 75 3.25M |
|---|---|---|
# _ __
# | |/ /___ ___ _ __ ___ _ _ ®
# | ' </ -_) -_) '_ \/ -_) '_|
# |_|\_\___\___| .__/\___|_|
# |_|
#
# <NAME>
# Copyright 2018 Keeper Security Inc.
# Contact: <EMAIL>
#
import argparse
import collections
import shlex
import logging
import json
import os
import re
import csv
import sys
import abc
... | [
"tabulate.tabulate",
"collections.OrderedDict",
"re.compile",
"shlex.split",
"csv.writer",
"os.path.splitext",
"logging.warning",
"logging.error",
"json.dump"
] | [((703, 716), 'collections.OrderedDict', 'OrderedDict', ([], {}), '()\n', (714, 716), False, 'from collections import OrderedDict\n'), ((6425, 6450), 're.compile', 're.compile', (['"""\\\\${(\\\\w+)}"""'], {}), "('\\\\${(\\\\w+)}')\n", (6435, 6450), False, 'import re\n'), ((3658, 3695), 'logging.error', 'logging.error'... |
import torch
import torch.nn as nn
import torch.utils.checkpoint as cp
class basic_conv(nn.Module):
def __init__(self, in_ch, out_ch, group=8, dilation_rate=1, memory_efficient=True):
super(basic_conv, self).__init__()
self.memory_efficient = memory_efficient
self.conv = nn.Conv2d(in_ch, ou... | [
"torch.nn.GroupNorm",
"torch.nn.ReLU",
"torch.nn.ModuleList",
"torch.utils.checkpoint.checkpoint",
"torch.jit.is_scripting",
"torch.nn.Conv2d",
"torch.cat"
] | [((301, 375), 'torch.nn.Conv2d', 'nn.Conv2d', (['in_ch', 'out_ch', '(3)'], {'dilation': 'dilation_rate', 'padding': 'dilation_rate'}), '(in_ch, out_ch, 3, dilation=dilation_rate, padding=dilation_rate)\n', (310, 375), True, 'import torch.nn as nn\n'), ((396, 417), 'torch.nn.ReLU', 'nn.ReLU', ([], {'inplace': '(True)'})... |
import pandas
import wti04_module
user_ratemovies = pandas.read_csv(filepath_or_buffer='./user_ratedmovies.dat', sep='\t')
movie_genres = pandas.read_csv(filepath_or_buffer='./movie_genres.dat', sep='\t')
join_table, genres = wti04_module.join_tables(user_ratemovies, movie_genres)
# print(wti04_module.build_datafra... | [
"wti04_module.avg_movies_by_genre",
"pandas.read_csv",
"wti04_module.avg_movie_by_user_and_genre",
"wti04_module.join_tables",
"wti04_module.profile_user"
] | [((54, 124), 'pandas.read_csv', 'pandas.read_csv', ([], {'filepath_or_buffer': '"""./user_ratedmovies.dat"""', 'sep': '"""\t"""'}), "(filepath_or_buffer='./user_ratedmovies.dat', sep='\\t')\n", (69, 124), False, 'import pandas\n'), ((140, 206), 'pandas.read_csv', 'pandas.read_csv', ([], {'filepath_or_buffer': '"""./mov... |
import pytest
from recipes.tests.share import create_recipes
from users.tests.share import create_user_api
pytestmark = [pytest.mark.django_db]
URL = '/api/users/subscriptions/'
RESPONSE_KEYS = (
'id',
'email',
'username',
'first_name',
'last_name',
'is_subscribed',
'recipes',
'recipe... | [
"recipes.tests.share.create_recipes",
"users.tests.share.create_user_api"
] | [((539, 563), 'users.tests.share.create_user_api', 'create_user_api', (['as_anon'], {}), '(as_anon)\n', (554, 563), False, 'from users.tests.share import create_user_api\n'), ((568, 611), 'recipes.tests.share.create_recipes', 'create_recipes', (['as_admin', 'ingredients', 'tags'], {}), '(as_admin, ingredients, tags)\n'... |
# Generated by Django 2.2.1 on 2019-05-16 11:41
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='Manufacturer',
fields=[
... | [
"django.db.models.DateField",
"django.db.models.TextField",
"django.db.models.IntegerField",
"django.db.models.ForeignKey",
"django.db.models.AutoField",
"django.db.models.CharField"
] | [((341, 434), 'django.db.models.AutoField', 'models.AutoField', ([], {'auto_created': '(True)', 'primary_key': '(True)', 'serialize': '(False)', 'verbose_name': '"""ID"""'}), "(auto_created=True, primary_key=True, serialize=False,\n verbose_name='ID')\n", (357, 434), False, 'from django.db import migrations, models\... |
"""Performs naive Bayes on the data from a given subreddit.
"""
import math
import datetime
import pandas as pd
from sklearn.naive_bayes import MultinomialNB
import numpy as np
import bag_of_words as bow
import sentimentAnalyzer as sa
MODEL = MultinomialNB()
def extract_features(data_frame: pd.DataFrame) -> pd.Data... | [
"math.floor",
"bag_of_words.bag_of_words",
"datetime.datetime.strptime",
"bag_of_words.csv_to_data_frame",
"numpy.array",
"sklearn.naive_bayes.MultinomialNB",
"sentimentAnalyzer.analyzeSentiments",
"bag_of_words.parse_arguments",
"pandas.DataFrame",
"numpy.percentile",
"pandas.concat"
] | [((246, 261), 'sklearn.naive_bayes.MultinomialNB', 'MultinomialNB', ([], {}), '()\n', (259, 261), False, 'from sklearn.naive_bayes import MultinomialNB\n'), ((608, 645), 'bag_of_words.bag_of_words', 'bow.bag_of_words', (['data_frame', '"""title"""'], {}), "(data_frame, 'title')\n", (624, 645), True, 'import bag_of_word... |
import shutil
import numpy as np
import tensorflow as tf
tf.logging.set_verbosity(tf.logging.INFO)
BUCKET = None # set from task.py
PATTERN = "of"
CSV_COLUMNS = [
"weight_pounds",
"is_male",
"mother_age",
"plurality",
"gestation_weeks",
]
LABEL_COLUMN = "weight_pounds"
DEFAULT... | [
"tensorflow.feature_column.crossed_column",
"tensorflow.estimator.RunConfig",
"tensorflow.estimator.train_and_evaluate",
"tensorflow.estimator.LatestExporter",
"tensorflow.placeholder",
"tensorflow.logging.set_verbosity",
"tensorflow.gfile.Glob",
"tensorflow.data.TextLineDataset",
"tensorflow.featur... | [((64, 105), 'tensorflow.logging.set_verbosity', 'tf.logging.set_verbosity', (['tf.logging.INFO'], {}), '(tf.logging.INFO)\n', (88, 105), True, 'import tensorflow as tf\n'), ((1548, 1670), 'tensorflow.feature_column.categorical_column_with_vocabulary_list', 'tf.feature_column.categorical_column_with_vocabulary_list', (... |
import re
class HostDetailsWrapper:
"""Klasa pomocnicza - wrapper parsujacy dane z serwisu https://www.shodan.io/."""
def __init__(self, ip, host_details):
self.__ip = ip
self.__data = host_details
@property
def ip(self):
"""Atrybut zwracajacy adres ip hosta."""
retur... | [
"re.findall"
] | [((1821, 1855), 're.findall', 're.findall', (['pattern', 'redirect_data'], {}), '(pattern, redirect_data)\n', (1831, 1855), False, 'import re\n')] |
from tests.base_test import BaseTest
from crc import session
from crc.api.common import ApiError
from crc.models.workflow import WorkflowSpecModel
from crc.services.file_service import FileService
class TestDuplicateWorkflowSpecFile(BaseTest):
def test_duplicate_workflow_spec_file(self):
# We want this t... | [
"crc.services.file_service.FileService.add_workflow_spec_file",
"crc.session.query"
] | [((534, 643), 'crc.services.file_service.FileService.add_workflow_spec_file', 'FileService.add_workflow_spec_file', (['spec'], {'name': '"""something.png"""', 'content_type': '"""text"""', 'binary_data': "b'1234'"}), "(spec, name='something.png', content_type\n ='text', binary_data=b'1234')\n", (568, 643), False, 'f... |
__author__ = 'guorongxu'
import re
#Parsing the impact factor file
def parse_impact_factor_file(impact_factor_file):
#impact_factor_file = "/Users/guorongxu/Desktop/SearchEngine/pubmed/2014_SCI_IF.txt"
journalList = {}
with open(impact_factor_file) as fp:
lines = fp.readlines()
for line... | [
"re.split"
] | [((352, 374), 're.split', 're.split', (['"""\\\\t+"""', 'line'], {}), "('\\\\t+', line)\n", (360, 374), False, 'import re\n')] |
'''
repo_downloader.py uses github api to get list of public repositories
that sizes are under max_repo_size size. When a repo's url is resolved and size is
lower that max_repo_size then it starts cloning it to the temp_repos_dir directory.
More info https://developer.github.com/v3/ .
If you have a github token (withou... | [
"os.path.exists",
"random.randrange",
"classifier.get_dict_of_top_extensions",
"git.Repo.clone_from",
"distribute_files.calculate_counters",
"os.path.join",
"requests.get",
"os.mkdir"
] | [((3173, 3201), 'classifier.get_dict_of_top_extensions', 'get_dict_of_top_extensions', ([], {}), '()\n', (3199, 3201), False, 'from classifier import get_dict_of_top_extensions\n'), ((3213, 3248), 'distribute_files.calculate_counters', 'calculate_counters', (['languages_types'], {}), '(languages_types)\n', (3231, 3248)... |
from datetime import datetime
import json
from evidently.analyzers.cat_target_drift_analyzer import CatTargetDriftAnalyzer
from evidently.profile_sections.base_profile_section import ProfileSection
class CatTargetDriftProfileSection(ProfileSection):
def part_id(self) -> str:
return 'cat_target_drift'
... | [
"datetime.datetime.now"
] | [((689, 703), 'datetime.datetime.now', 'datetime.now', ([], {}), '()\n', (701, 703), False, 'from datetime import datetime\n')] |
#!/usr/bin/env python
"""
Implements the Parity task from Graves 2016: determining the parity of a
statically-presented binary vector.
"""
import argparse
import random
from typing import Tuple, Optional
import pytorch_lightning as pl
import torch
from pytorch_adaptive_computation_time import models
class ParityDa... | [
"pytorch_lightning.Trainer.add_argparse_args",
"argparse.ArgumentParser",
"torch.randint",
"torch.zeros",
"torch.nn.Linear",
"torch.utils.data.DataLoader",
"pytorch_lightning.Trainer.from_argparse_args",
"random.randint",
"pytorch_adaptive_computation_time.models.AdaptiveRNNCell",
"torch.nn.functi... | [((4173, 4198), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {}), '()\n', (4196, 4198), False, 'import argparse\n'), ((4306, 4342), 'pytorch_lightning.Trainer.add_argparse_args', 'pl.Trainer.add_argparse_args', (['parser'], {}), '(parser)\n', (4334, 4342), True, 'import pytorch_lightning as pl\n'), ((4677... |
import logging
import json
import pprint
import os
import re
import glob
import pandas as pd
import rcs_csv_row_helper_functions as csv_helper
CACHED_SESSIONS_FILE_PATH = './database_jsons/cached_sessions.json'
DATABASE_BOOLEAN_PATH = './database_jsons/database_boolean.json'
PROJECT_SESSIONTYPES_PATH = './database_jso... | [
"logging.basicConfig",
"re.split",
"pandas.read_csv",
"pandas.DataFrame",
"os.path.join",
"os.symlink",
"logging.warning",
"os.path.isfile",
"os.path.isdir",
"glob.glob",
"os.mkdir",
"json.load",
"os.path.islink",
"logging.info",
"rcs_csv_row_helper_functions.collect_csv_info",
"json.d... | [((5496, 5583), 'rcs_csv_row_helper_functions.collect_csv_info', 'csv_helper.collect_csv_info', (['rcs', 'session', '{}', 'session_eventLog', 'session_jsons_path'], {}), '(rcs, session, {}, session_eventLog,\n session_jsons_path)\n', (5523, 5583), True, 'import rcs_csv_row_helper_functions as csv_helper\n'), ((6715,... |
import numpy as np
# reverse = True: descending order (TOPSIS, CODAS), False: ascending order (VIKOR, SPOTIS)
def rank_preferences(pref, reverse = True):
"""
Rank alternatives according to MCDM preference function values.
Parameters
----------
pref : ndarray
vector with MCDM prefer... | [
"numpy.where"
] | [((1001, 1037), 'numpy.where', 'np.where', (['(sorted_pref[i + 1] == pref)'], {}), '(sorted_pref[i + 1] == pref)\n', (1009, 1037), True, 'import numpy as np\n'), ((861, 893), 'numpy.where', 'np.where', (['(sorted_pref[i] == pref)'], {}), '(sorted_pref[i] == pref)\n', (869, 893), True, 'import numpy as np\n')] |
__author__ = "<NAME>"
__copyright__ = "Copyright 2018, <NAME>"
__license__ = "BSD 2-Clause"
__email__ = "<EMAIL>"
import idb
import gdb
def _image_base():
try:
mappings = gdb.execute("info proc mappings", to_string=True)
first_num_pos = mappings.find("0x")
return int(mappings[first_num_pos... | [
"gdb.lookup_type",
"gdb.execute",
"idb.from_file",
"idb.IDAPython"
] | [((185, 234), 'gdb.execute', 'gdb.execute', (['"""info proc mappings"""'], {'to_string': '(True)'}), "('info proc mappings', to_string=True)\n", (196, 234), False, 'import gdb\n'), ((2670, 2703), 'gdb.execute', 'gdb.execute', (["('break *0x%x' % addr)"], {}), "('break *0x%x' % addr)\n", (2681, 2703), False, 'import gdb... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright [2017] <NAME> [<EMAIL>]
#
# 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
#
# Unles... | [
"resource.getrusage",
"time.time"
] | [((990, 1001), 'time.time', 'time.time', ([], {}), '()\n', (999, 1001), False, 'import time\n'), ((1031, 1071), 'resource.getrusage', 'resource.getrusage', (['resource.RUSAGE_SELF'], {}), '(resource.RUSAGE_SELF)\n', (1049, 1071), False, 'import resource\n'), ((1217, 1257), 'resource.getrusage', 'resource.getrusage', ([... |
# Utilities for integration with Home Assistant (directly or via MQTT)
import logging
import re
_LOGGER = logging.getLogger(__name__)
class Instrument:
def __init__(self, component, attr, name, icon=None):
self._attr = attr
self._component = component
self._name = name
self._con... | [
"logging.getLogger",
"re.sub"
] | [((109, 136), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (126, 136), False, 'import logging\n'), ((639, 667), 're.sub', 're.sub', (['"""([A-Z])"""', '"""_\\\\1"""', 's'], {}), "('([A-Z])', '_\\\\1', s)\n", (645, 667), False, 'import re\n')] |
import argparse
import os
import sys
import logging
import numpy
import numpy as np
import torch
import torch.utils.data
import torchvision
from torch.utils.data import DataLoader
from tensorboardX import SummaryWriter
from tqdm import tqdm
from learning3d.ops import se3
# Only if the files are in example folder.
BASE... | [
"learning3d.data_utils.AnyData",
"learning3d.models.MaskNet",
"numpy.array",
"torch.cuda.is_available",
"numpy.arange",
"numpy.mean",
"os.path.exists",
"os.listdir",
"tensorboardX.SummaryWriter",
"argparse.ArgumentParser",
"os.path.isdir",
"numpy.random.seed",
"numpy.concatenate",
"pandas.... | [((343, 368), 'os.path.abspath', 'os.path.abspath', (['__file__'], {}), '(__file__)\n', (358, 368), False, 'import os\n'), ((941, 1029), 'os.system', 'os.system', (["('cp train.py checkpoints' + '/' + args.exp_name + '/' + 'train.py.backup')"], {}), "('cp train.py checkpoints' + '/' + args.exp_name + '/' +\n 'train.... |
import math
import time
import pickle
import argparse
from datetime import datetime
import tensorflow as tf
import numpy as np
import dataset_info
import model_info
# Check num of gpus
gpus = tf.config.experimental.list_physical_devices('GPU')
num_gpus = len(gpus)
for gpu in gpus:
print('Name:', gpu.name, ' Type... | [
"dataset_info.select_dataset",
"tensorflow.keras.utils.to_categorical",
"math.ceil",
"numpy.random.rand",
"argparse.ArgumentParser",
"math.floor",
"tensorflow.keras.callbacks.TensorBoard",
"model_info.select_model",
"pickle.dump",
"datetime.datetime.now",
"numpy.random.randint",
"tensorflow.di... | [((195, 246), 'tensorflow.config.experimental.list_physical_devices', 'tf.config.experimental.list_physical_devices', (['"""GPU"""'], {}), "('GPU')\n", (239, 246), True, 'import tensorflow as tf\n'), ((375, 400), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {}), '()\n', (398, 400), False, 'import argparse... |
from django.db import models
class FileType(models.Model):
name = models.CharField(max_length=200)
class File(models.Model):
url = models.URLField()
type = models.ForeignKey(FileType, on_delete=models.SET_NULL, null=True, blank=True)
| [
"django.db.models.URLField",
"django.db.models.CharField",
"django.db.models.ForeignKey"
] | [((72, 104), 'django.db.models.CharField', 'models.CharField', ([], {'max_length': '(200)'}), '(max_length=200)\n', (88, 104), False, 'from django.db import models\n'), ((143, 160), 'django.db.models.URLField', 'models.URLField', ([], {}), '()\n', (158, 160), False, 'from django.db import models\n'), ((173, 250), 'djan... |
import pygame
from pygame.locals import *
class Posicao:
def __init__(self, x=-1, y=-1, valor=0, estado=False, xmax=8, ymax=8, exposto=False, mostra=True):
# Atributos
self.X = x
self.Y = y
self.Valor = valor
self.Estado = estado
self.Xmax = xmax
self.Ymax = ... | [
"pygame.image.load",
"pygame.sprite.Group",
"pygame.sprite.Sprite.__init__"
] | [((3942, 3963), 'pygame.sprite.Group', 'pygame.sprite.Group', ([], {}), '()\n', (3961, 3963), False, 'import pygame\n'), ((3695, 3730), 'pygame.sprite.Sprite.__init__', 'pygame.sprite.Sprite.__init__', (['self'], {}), '(self)\n', (3724, 3730), False, 'import pygame\n'), ((3760, 3793), 'pygame.image.load', 'pygame.image... |
#!/usr/bin/env python
from clipper_admin import ClipperConnection, DockerContainerManager
clipper_conn = ClipperConnection(DockerContainerManager())
clipper_conn.stop_all() | [
"clipper_admin.DockerContainerManager"
] | [((123, 147), 'clipper_admin.DockerContainerManager', 'DockerContainerManager', ([], {}), '()\n', (145, 147), False, 'from clipper_admin import ClipperConnection, DockerContainerManager\n')] |
import mysql.connector
import sys
class MySqlWrapper:
def __init__(self, db_config):
self.db_config = db_config
def connect(self):
try:
self.cnx = mysql.connector.connect(**self.db_config)
self.cursor = self.cnx.cursor()
except mysql.connector.Error as err:
if err.errno == errorcode... | [
"sys.exc_info"
] | [((1030, 1044), 'sys.exc_info', 'sys.exc_info', ([], {}), '()\n', (1042, 1044), False, 'import sys\n')] |
import copy
from collections import OrderedDict
from typing import List
import numpy as np
from opticverge.core.chromosome.abstract_chromosome import AbstractChromosome
from opticverge.core.chromosome.function_chromosome import FunctionChromosome
from opticverge.core.generator.int_distribution_generator import rand_i... | [
"collections.OrderedDict",
"opticverge.core.generator.options_generator.rand_options",
"opticverge.core.generator.int_distribution_generator.rand_int",
"opticverge.core.generator.real_generator.rand_real",
"copy.copy",
"numpy.random.shuffle"
] | [((1003, 1016), 'collections.OrderedDict', 'OrderedDict', ([], {}), '()\n', (1014, 1016), False, 'from collections import OrderedDict\n'), ((1608, 1634), 'opticverge.core.generator.int_distribution_generator.rand_int', 'rand_int', (['(1)', 'self.__length'], {}), '(1, self.__length)\n', (1616, 1634), False, 'from opticv... |
#!/usr/bin/env python
#
# Baseline calculation script for NIfTI data sets. After specifying such
# a data set and an optional brain mask, converts each participant to an
# autocorrelation-based matrix representation.
#
# The goal is to summarise each participant as a voxel-by-voxel matrix.
#
# This script is specifical... | [
"os.path.exists",
"numpy.savez",
"argparse.ArgumentParser",
"os.path.join",
"os.path.splitext",
"warnings.warn",
"numpy.isnan",
"os.path.basename",
"numpy.ma.masked_invalid",
"math.log10",
"numpy.load",
"numpy.nan_to_num"
] | [((847, 873), 'os.path.basename', 'os.path.basename', (['filename'], {}), '(filename)\n', (863, 873), False, 'import os\n'), ((1150, 1175), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {}), '()\n', (1173, 1175), False, 'import argparse\n'), ((1649, 1668), 'numpy.load', 'np.load', (['args.input'], {}), '(a... |
from bs4 import BeautifulSoup
import re
import time
import requests
headers0 = {'User-Agent':"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/62.0.3202.62 Safari/537.36"}
def Musappend(Mdict,Items):
for it in Items:
title=it('a')[0].get_text(strip=True)
date=it(class_... | [
"bs4.BeautifulSoup",
"requests.Session",
"time.sleep",
"re.compile"
] | [((953, 971), 'requests.Session', 'requests.Session', ([], {}), '()\n', (969, 971), False, 'import requests\n'), ((1047, 1089), 'bs4.BeautifulSoup', 'BeautifulSoup', (['request.text', '"""html.parser"""'], {}), "(request.text, 'html.parser')\n", (1060, 1089), False, 'from bs4 import BeautifulSoup\n'), ((2792, 2810), 'r... |
import wx
app=wx.App()
frame=wx.Frame(parent=None, title='Hello')
frame.Show()
app.MainLoop()
| [
"wx.Frame",
"wx.App"
] | [((15, 23), 'wx.App', 'wx.App', ([], {}), '()\n', (21, 23), False, 'import wx\n'), ((31, 67), 'wx.Frame', 'wx.Frame', ([], {'parent': 'None', 'title': '"""Hello"""'}), "(parent=None, title='Hello')\n", (39, 67), False, 'import wx\n')] |
import platform
if platform.system() == 'Darwin':
from mac_graph_traversal import *
else:
from graph_traversal import * | [
"platform.system"
] | [((19, 36), 'platform.system', 'platform.system', ([], {}), '()\n', (34, 36), False, 'import platform\n')] |
import os
import time
from flask import Flask, json, request
app = Flask(__name__)
@app.route('/zanryu', methods=['POST'])
def api_message():
if request.headers['Content-Type'] == 'application/json':
# Read request
data = request.json
# Save different types of data to different dirs
... | [
"time.strftime",
"os.path.join",
"flask.json.dumps",
"flask.Flask"
] | [((69, 84), 'flask.Flask', 'Flask', (['__name__'], {}), '(__name__)\n', (74, 84), False, 'from flask import Flask, json, request\n'), ((336, 361), 'time.strftime', 'time.strftime', (['"""%m-%d-%Y"""'], {}), "('%m-%d-%Y')\n", (349, 361), False, 'import time\n'), ((803, 864), 'os.path.join', 'os.path.join', (['"""json"""... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Test the num_colors function with mocked environments."""
import collections
import platform
import sys
import pytest
import colorise
@pytest.fixture
def mock_base_itermapp(monkeypatch):
monkeypatch.setenv('COLORTERM', '')
monkeypatch.setenv('TERM_PROGRAM',... | [
"colorise.num_colors",
"collections.namedtuple"
] | [((1620, 1680), 'collections.namedtuple', 'collections.namedtuple', (['"""getwindowsversion_tuple"""', "['build']"], {}), "('getwindowsversion_tuple', ['build'])\n", (1642, 1680), False, 'import collections\n'), ((469, 490), 'colorise.num_colors', 'colorise.num_colors', ([], {}), '()\n', (488, 490), False, 'import colo... |
from csv import DictWriter
class Writer:
def __init__(self,filename):
headercsv = ['FRAME','INDEX','TYPE','CFLVL']
self.filename = filename #result.csv ## add csv extension using join
with open(filename, 'w', ) as csvfile:
dictwriter_obj = DictWriter(csvfile, fieldnames=heade... | [
"csv.DictWriter"
] | [((284, 325), 'csv.DictWriter', 'DictWriter', (['csvfile'], {'fieldnames': 'headercsv'}), '(csvfile, fieldnames=headercsv)\n', (294, 325), False, 'from csv import DictWriter\n')] |
"""Prediction of Users based on Tweet embeddings."""
import numpy as np
from sklearn.linear_model import LogisticRegression
from .models import User
from .twitter import BASILICA
def predict_user( user1_name, user2_name, tweet_text):
user1 = User.query.filter( User.name == user1_name).one()
user2 = User.qu... | [
"numpy.array",
"numpy.vstack",
"sklearn.linear_model.LogisticRegression"
] | [((386, 439), 'numpy.array', 'np.array', (['[tweet.embedding for tweet in user1.tweets]'], {}), '([tweet.embedding for tweet in user1.tweets])\n', (394, 439), True, 'import numpy as np\n'), ((464, 517), 'numpy.array', 'np.array', (['[tweet.embedding for tweet in user2.tweets]'], {}), '([tweet.embedding for tweet in use... |
# ##### BEGIN GPL LICENSE BLOCK #####
#
# This program is free software; you can redistribute it and / or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 2
# of the License, or (at your option) any later version.
#
# This program is distr... | [
"bpy.props.StringProperty",
"math.acos",
"math.sqrt",
"math.cos",
"bpy_extras.object_utils.object_data_add",
"math.hypot",
"bpy.types.VIEW3D_MT_curve_add.remove",
"bpy.utils.unregister_class",
"bpy.props.BoolProperty",
"mathutils.Vector",
"math.tan",
"bpy.ops.object.mode_set",
"bpy.ops.trans... | [((2419, 2433), 'math.radians', 'radians', (['angle'], {}), '(angle)\n', (2426, 2433), False, 'from math import sin, asin, sqrt, acos, cos, pi, radians, tan, hypot\n'), ((3892, 3911), 'math.radians', 'radians', (['startangle'], {}), '(startangle)\n', (3899, 3911), False, 'from math import sin, asin, sqrt, acos, cos, pi... |
# Copyright (C) 2018 Google Inc.
# Licensed under http://www.apache.org/licenses/LICENSE-2.0 <see LICENSE file>
"""REST service for Control app entities."""
from lib import decorator
from lib.entities import app_entity
from lib.rest import base_rest_service, rest_convert
class ControlRestService(base_rest_service.Obj... | [
"lib.rest.rest_convert.default_context",
"lib.rest.rest_convert.to_basic_rest_obj",
"lib.rest.rest_convert.build_access_control_list"
] | [((575, 618), 'lib.rest.rest_convert.build_access_control_list', 'rest_convert.build_access_control_list', (['obj'], {}), '(obj)\n', (613, 618), False, 'from lib.rest import base_rest_service, rest_convert\n'), ((752, 782), 'lib.rest.rest_convert.default_context', 'rest_convert.default_context', ([], {}), '()\n', (780,... |
import sys, os
sys.path.append(os.path.join(os.path.dirname(__file__), ".."))
import time
import math
import random
import numpy as np
import scipy as sp
from scipy.spatial import distance as scipydistance
# from numba import jit, njit, vectorize, float64, int64
from sc2.position import Point2
import pytest
from hy... | [
"random.uniform",
"math.dist",
"platform.python_version_tuple",
"numpy.asarray",
"numpy.sum",
"os.path.dirname",
"scipy.spatial.distance.euclidean",
"numpy.linalg.norm",
"math.hypot"
] | [((469, 500), 'platform.python_version_tuple', 'platform.python_version_tuple', ([], {}), '()\n', (498, 500), False, 'import platform\n'), ((3060, 3074), 'numpy.asarray', 'np.asarray', (['p1'], {}), '(p1)\n', (3070, 3074), True, 'import numpy as np\n'), ((3083, 3097), 'numpy.asarray', 'np.asarray', (['p2'], {}), '(p2)\... |
import time
import gex
# basic NDIR CO2 sensor readout
with gex.Client(gex.TrxRawUSB()) as client:
ser = gex.USART(client, 'ser')
while True:
ser.clear_buffer()
ser.write([0xFF, 0x01, 0x86, 0, 0, 0, 0, 0, 0x79])
data = ser.receive(9, decode=None)
pp = gex.PayloadParser(data,... | [
"gex.TrxRawUSB",
"gex.PayloadParser",
"time.sleep",
"gex.USART"
] | [((112, 136), 'gex.USART', 'gex.USART', (['client', '"""ser"""'], {}), "(client, 'ser')\n", (121, 136), False, 'import gex\n'), ((74, 89), 'gex.TrxRawUSB', 'gex.TrxRawUSB', ([], {}), '()\n', (87, 89), False, 'import gex\n'), ((391, 404), 'time.sleep', 'time.sleep', (['(1)'], {}), '(1)\n', (401, 404), False, 'import tim... |
from collections import deque
from aoc.utils import load_data, profiler
@profiler
def solution(data: deque[str]) -> tuple[int, int]:
pair = {"(": ")", "[": "]", "{": "}", "<": ">"}
points = {")": 3, "]": 57, "}": 1197, ">": 25137}
ipoints = {")": 1, "]": 2, "}": 3, ">": 4}
sum, isum = 0, []
queue... | [
"collections.deque",
"aoc.utils.load_data"
] | [((323, 330), 'collections.deque', 'deque', ([], {}), '()\n', (328, 330), False, 'from collections import deque\n'), ((973, 994), 'aoc.utils.load_data', 'load_data', ([], {'test': '(False)'}), '(test=False)\n', (982, 994), False, 'from aoc.utils import load_data, profiler\n')] |
"""
Baseline functions that can be used without fluffy.
"""
import numpy as np
import scipy.ndimage as ndi
import skimage.io
import tensorflow as tf
def predict_baseline(
image: np.ndarray, model: tf.keras.models.Model, bit_depth: int = 16
) -> np.ndarray:
"""
Returns a binary or categorical model based ... | [
"scipy.ndimage.binary_erosion",
"numpy.pad",
"numpy.argmax",
"numpy.unique"
] | [((1657, 1715), 'numpy.pad', 'np.pad', (['pred', '((0, pad_bottom), (0, pad_right))', '"""reflect"""'], {}), "(pred, ((0, pad_bottom), (0, pad_right)), 'reflect')\n", (1663, 1715), True, 'import numpy as np\n'), ((2475, 2532), 'scipy.ndimage.binary_erosion', 'ndi.binary_erosion', (['(pred_mask[..., 1] > 0.5)'], {'itera... |
import grokcore.component as grok
from zope import interface
class Cave(grok.Context):
pass
class Club(grok.Context):
pass
grok.context(Cave)
grok.context(Club)
| [
"grokcore.component.context"
] | [((134, 152), 'grokcore.component.context', 'grok.context', (['Cave'], {}), '(Cave)\n', (146, 152), True, 'import grokcore.component as grok\n'), ((153, 171), 'grokcore.component.context', 'grok.context', (['Club'], {}), '(Club)\n', (165, 171), True, 'import grokcore.component as grok\n')] |
from datetime import datetime, timedelta
from django.contrib.postgres.fields.jsonb import KeyTextTransform
from django.db.models import Q
from django.db.models.expressions import RawSQL
from django.utils.decorators import method_decorator
from django.views.decorators.cache import cache_page
from django_filters.rest_fr... | [
"sme.models.Sme.objects.order_by",
"restapi.serializers.sme.CustomerContractSerializer",
"restapi.serializers.sme.SmeSerializer",
"restapi.serializers.authentication.UserSerializer",
"restapi.serializers.sme.SmeTaskEmailSerializer",
"restapi.tasks.send_customer_welcome_email",
"sme.models.ContractRoute.... | [((2901, 2915), 'rest_framework.response.Response', 'Response', (['data'], {}), '(data)\n', (2909, 2915), False, 'from rest_framework.response import Response\n'), ((3120, 3155), 'rest_framework.response.Response', 'Response', ([], {'status': 'status.HTTP_200_OK'}), '(status=status.HTTP_200_OK)\n', (3128, 3155), False,... |
# -*- coding: utf-8 -*-
"""
Created on Sun Jun 16 07:34:10 2019
@author: Brendan
"""
import os
import numpy as np
class TestPreProcess():
def test_works(self):
raw_dir = './images/raw/demo'
processed_dir = './images/processed/demo'
Nfiles = 256
command = ('python preProcessDe... | [
"os.system",
"numpy.load",
"os.path.join",
"numpy.diff"
] | [((441, 459), 'os.system', 'os.system', (['command'], {}), '(command)\n', (450, 459), False, 'import os\n'), ((657, 700), 'os.path.join', 'os.path.join', (['processed_dir', '"""dataCube.npy"""'], {}), "(processed_dir, 'dataCube.npy')\n", (669, 700), False, 'import os\n'), ((1712, 1730), 'os.system', 'os.system', (['com... |
# -*- coding: utf-8 -*-
# Copyright 2021, CS GROUP - France, http://www.c-s.fr
#
# This file is part of EODAG project
# https://www.github.com/CS-SI/EODAG
#
# 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... | [
"logging.getLogger",
"os.makedirs",
"pathlib.Path",
"tests.context.EOProduct",
"os.path.join",
"yaml.load",
"tests.context.AwsDownload",
"os.path.dirname",
"os.path.isdir",
"shutil.rmtree"
] | [((1060, 1092), 'tests.context.AwsDownload', 'AwsDownload', (['"""some_provider"""', '{}'], {}), "('some_provider', {})\n", (1071, 1092), False, 'from tests.context import TEST_RESOURCES_PATH, TESTS_DOWNLOAD_PATH, AwsDownload, EOProduct\n'), ((1115, 1162), 'logging.getLogger', 'logging.getLogger', (['"""eodag.plugins.d... |
from django.core.management import BaseCommand
from corehq.apps.reports.models import ReportConfig
from dimagi.utils.couch.database import iter_docs
AFFECTED_REPORTS = ["daily_form_stats", "case_list", "case_activity", "completion_vs_submission", "completion_times",
"submissions_by_form", "submit_h... | [
"dimagi.utils.couch.database.iter_docs",
"corehq.apps.reports.models.ReportConfig.get_db"
] | [((1789, 1810), 'corehq.apps.reports.models.ReportConfig.get_db', 'ReportConfig.get_db', ([], {}), '()\n', (1808, 1810), False, 'from corehq.apps.reports.models import ReportConfig\n'), ((2049, 2090), 'dimagi.utils.couch.database.iter_docs', 'iter_docs', (['db', "[r['id'] for r in results]"], {}), "(db, [r['id'] for r ... |
from zeeguu.core.bookmark_quality import quality_bookmark
from zeeguu.core.definition_of_learned import is_learned_based_on_exercise_outcomes
from zeeguu.core.model.SortedExerciseLog import SortedExerciseLog
from zeeguu.core.util.timer_logging_decorator import time_this
def fit_for_study(bookmark):
exercise_log =... | [
"zeeguu.core.definition_of_learned.is_learned_based_on_exercise_outcomes",
"zeeguu.core.model.SortedExerciseLog.SortedExerciseLog",
"zeeguu.core.bookmark_quality.quality_bookmark"
] | [((321, 348), 'zeeguu.core.model.SortedExerciseLog.SortedExerciseLog', 'SortedExerciseLog', (['bookmark'], {}), '(bookmark)\n', (338, 348), False, 'from zeeguu.core.model.SortedExerciseLog import SortedExerciseLog\n'), ((377, 403), 'zeeguu.core.bookmark_quality.quality_bookmark', 'quality_bookmark', (['bookmark'], {}),... |
import zarr
from typing import Any, Tuple, List, Union
import numpy as np
from tqdm import tqdm
from .readers import CrReader, H5adReader, NaboH5Reader, LoomReader
import os
import pandas as pd
from .utils import controlled_compute
from .logging_utils import logger
from scipy.sparse import csr_matrix
__all__ = ['creat... | [
"os.path.exists",
"numpy.ones",
"numpy.hstack",
"tqdm.tqdm",
"numpy.array",
"zarr.open",
"numcodecs.Blosc",
"numpy.dtype",
"pandas.concat"
] | [((760, 814), 'numcodecs.Blosc', 'Blosc', ([], {'cname': '"""lz4"""', 'clevel': '(5)', 'shuffle': 'Blosc.BITSHUFFLE'}), "(cname='lz4', clevel=5, shuffle=Blosc.BITSHUFFLE)\n", (765, 814), False, 'from numcodecs import Blosc\n'), ((1137, 1151), 'numpy.array', 'np.array', (['data'], {}), '(data)\n', (1145, 1151), True, 'i... |
import os
import subprocess
cmd = "find ../electrum -type f -name '*.py' -o -name '*.kv'"
files = subprocess.check_output(cmd, shell=True)
with open("app.fil", "wb") as f:
f.write(files)
print("Found {} files to translate".format(len(files.splitlines())))
# Generate fresh translation template
cmd = 'xgettext -s... | [
"subprocess.check_output",
"os.path.exists",
"os.listdir",
"os.mkdir",
"os.system"
] | [((100, 140), 'subprocess.check_output', 'subprocess.check_output', (['cmd'], {'shell': '(True)'}), '(cmd, shell=True)\n', (123, 140), False, 'import subprocess\n'), ((447, 461), 'os.system', 'os.system', (['cmd'], {}), '(cmd)\n', (456, 461), False, 'import os\n'), ((495, 527), 'os.listdir', 'os.listdir', (['"""../elec... |
#!/usr/bin/env python3
from PyQt5.QtWidgets import QApplication
from main_window import main_window
def main():
connect_four_app = QApplication([])
main_window_cf = main_window()
main_window_cf.show()
exit(connect_four_app.exec_())
if __name__ == '__main__':
main()
| [
"main_window.main_window",
"PyQt5.QtWidgets.QApplication"
] | [((136, 152), 'PyQt5.QtWidgets.QApplication', 'QApplication', (['[]'], {}), '([])\n', (148, 152), False, 'from PyQt5.QtWidgets import QApplication\n'), ((174, 187), 'main_window.main_window', 'main_window', ([], {}), '()\n', (185, 187), False, 'from main_window import main_window\n')] |
# Copyright 2017 <NAME>
#
# 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... | [
"os.listdir",
"re.match",
"numpy.array",
"sys.exit",
"os.system",
"numpy.save",
"os.remove"
] | [((759, 796), 'os.system', 'os.system', (['"""R --no-save < catwrite.R"""'], {}), "('R --no-save < catwrite.R')\n", (768, 796), False, 'import os\n'), ((940, 952), 'os.listdir', 'os.listdir', ([], {}), '()\n', (950, 952), False, 'import os\n'), ((1639, 1657), 'numpy.array', 'np.array', (['catstack'], {}), '(catstack)\n... |
"""utils.py: utility functions for plotting figures."""
import os
OPT_COLORS = {
"eve": "#e7298a",
"adam": "#e6ab02",
"adamax": "#7570b3",
"rmsprop": "#66a61e",
"adagrad": "#d95f02",
"adadelta": "#a6761d",
"sgd": "#1b9e77",
}
"""Colors used to represent the different optimizers."""
OPT_... | [
"os.path.join",
"seaborn.set",
"matplotlib.pyplot.figure",
"matplotlib.rcParams.update"
] | [((1903, 1926), 'matplotlib.rcParams.update', 'mpl.rcParams.update', (['rc'], {}), '(rc)\n', (1922, 1926), True, 'import matplotlib as mpl\n'), ((2007, 2051), 'seaborn.set', 'sns.set', ([], {'context': 'context', 'style': 'style', 'rc': 'rc'}), '(context=context, style=style, rc=rc)\n', (2014, 2051), True, 'import seab... |
"""
Solves the incompressible Navier Stokes equations using "Stable Fluids" by <NAME>
in a closed box with a forcing that creates a bloom.
Momentum: ∂u/∂t + (u ⋅ ∇) u = − 1/ρ ∇p + ν ∇²u + f
Incompressibility: ∇ ⋅ u = 0
u: Velocity (2d vector)
p: Pressure
f: Forcing
ν: Kinematic Viscosity
ρ: Densit... | [
"numpy.clip",
"scipy.sparse.linalg.LinearOperator",
"numpy.array",
"matplotlib.pyplot.contourf",
"scipy.interpolate.interpn",
"matplotlib.pyplot.style.use",
"numpy.linspace",
"numpy.concatenate",
"numpy.meshgrid",
"numpy.maximum",
"matplotlib.pyplot.quiver",
"matplotlib.pyplot.pause",
"matpl... | [((5017, 5050), 'numpy.maximum', 'np.maximum', (['(2.0 - 0.5 * time)', '(0.0)'], {}), '(2.0 - 0.5 * time, 0.0)\n', (5027, 5050), True, 'import numpy as np\n'), ((5685, 5724), 'numpy.linspace', 'np.linspace', (['(0.0)', 'DOMAIN_SIZE', 'N_POINTS'], {}), '(0.0, DOMAIN_SIZE, N_POINTS)\n', (5696, 5724), True, 'import numpy ... |
"""
A collection of pre-built PIX object/models.
"""
import functools
from typing import TYPE_CHECKING, Any, MutableMapping
import six
from .exc import PIXError
from .factory import Factory
try:
from typing import GenericMeta
except ImportError:
import abc
class GenericMeta(abc.ABCMeta): # type: ignore... | [
"six.add_metaclass",
"functools.wraps"
] | [((4146, 4179), 'six.add_metaclass', 'six.add_metaclass', (['_ActiveProject'], {}), '(_ActiveProject)\n', (4163, 4179), False, 'import six\n'), ((3391, 3412), 'functools.wraps', 'functools.wraps', (['func'], {}), '(func)\n', (3406, 3412), False, 'import functools\n')] |
import math
t = int(input())
result = []
for _ in range(t):
T1,T2,R1,R2 = map(int, input().split())
if ((math.pow(T1,2)/math.pow(R1,3)) == (math.pow(T2,2)/math.pow(R2,3))):
result.append("Yes")
else:
result.append("No")
print(*result, sep = "\n") | [
"math.pow"
] | [((118, 133), 'math.pow', 'math.pow', (['T1', '(2)'], {}), '(T1, 2)\n', (126, 133), False, 'import math\n'), ((133, 148), 'math.pow', 'math.pow', (['R1', '(3)'], {}), '(R1, 3)\n', (141, 148), False, 'import math\n'), ((153, 168), 'math.pow', 'math.pow', (['T2', '(2)'], {}), '(T2, 2)\n', (161, 168), False, 'import math\... |
# -*- coding: utf-8 -*-
#------------------------------------------------------------------------------
# file: $Id$
# auth: <NAME> <<EMAIL>>
# date: 2013/12/18
# copy: (C) Copyright 2013-EOT Cadit Inc., All Rights Reserved.
#------------------------------------------------------------------------------
import os
impo... | [
"sqlalchemy.engine_from_config",
"sqlalchemy.create_engine",
"webtest.TestApp",
"uuid.uuid4",
"pyramid.config.Configurator",
"shutil.copyfile",
"os.unlink",
"tempfile.NamedTemporaryFile",
"pyramid.response.Response",
"threading.Condition",
"time.time",
"kombu.transport.sqlalchemy.models.metada... | [((766, 787), 'threading.Condition', 'threading.Condition', ([], {}), '()\n', (785, 787), False, 'import threading\n'), ((1019, 1065), 'sqlalchemy.engine_from_config', 'sa.engine_from_config', (['settings', '"""sqlalchemy."""'], {}), "(settings, 'sqlalchemy.')\n", (1040, 1065), True, 'import sqlalchemy as sa\n'), ((107... |
import numpy as np
import torch
import gym
import argparse
import os
from collections import deque
import utils
import TD3
import OurDDPG
import DDPG
import TD3_ad
import robosuite as suite
from torch.utils.tensorboard import SummaryWriter
import time
import multiprocessing as mp
from functools import partial
def c... | [
"numpy.array",
"numpy.save",
"robosuite.make",
"torch.utils.tensorboard.SummaryWriter",
"os.path.exists",
"numpy.mean",
"collections.deque",
"argparse.ArgumentParser",
"TD3_ad.TD3_ad",
"utils.ReplayBuffer",
"numpy.random.seed",
"numpy.concatenate",
"numpy.random.normal",
"OurDDPG.DDPG",
... | [((356, 368), 'numpy.array', 'np.array', (['[]'], {}), '([])\n', (364, 368), True, 'import numpy as np\n'), ((663, 801), 'robosuite.make', 'suite.make', (['args.env'], {'has_renderer': '(False)', 'has_offscreen_renderer': '(False)', 'use_object_obs': '(True)', 'use_camera_obs': '(False)', 'reward_shaping': '(True)'}), ... |
"""応答の格納・管理"""
import numpy as np
from asva.utils.wave import read_case_wave, divide_wave, add_wave_required_zero
class Response:
"""応答の格納・管理"""
def __init__(self, analysis):
self.analysis = analysis
self.n_dof_plus_1 = self.analysis.model.n_dof + 1
self.acc_00_origin = read_case_wav... | [
"numpy.abs",
"asva.utils.wave.divide_wave",
"asva.utils.wave.add_wave_required_zero",
"asva.utils.wave.read_case_wave",
"numpy.array",
"numpy.zeros",
"numpy.arange"
] | [((307, 366), 'asva.utils.wave.read_case_wave', 'read_case_wave', (['self.analysis.wave', 'self.analysis.case_conf'], {}), '(self.analysis.wave, self.analysis.case_conf)\n', (321, 366), False, 'from asva.utils.wave import read_case_wave, divide_wave, add_wave_required_zero\n'), ((395, 466), 'asva.utils.wave.add_wave_re... |
import socket
from capture import PCAPFile
def main():
server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
server.bind(('', 8080))
server.listen(1)
conn, addr = server.accept()
pcap = PCAPFile('remote.pcap')
with conn:
while True:
data = conn.recv(1024)
i... | [
"capture.PCAPFile",
"socket.socket"
] | [((70, 119), 'socket.socket', 'socket.socket', (['socket.AF_INET', 'socket.SOCK_STREAM'], {}), '(socket.AF_INET, socket.SOCK_STREAM)\n', (83, 119), False, 'import socket\n'), ((213, 236), 'capture.PCAPFile', 'PCAPFile', (['"""remote.pcap"""'], {}), "('remote.pcap')\n", (221, 236), False, 'from capture import PCAPFile\n... |
import argparse
import errno
import fnmatch
import os
import sys
import xml.sax
HELP_DESCRIPTION = '''Parse junit xml result from provided result dir.'''
EPILOG_TEXT = '''
-----------------------------------------------
PARSE JUNIT XML RESULT FROM PROVIDED RESULT DIR
-----------------------------------------------
... | [
"os.path.exists",
"argparse.ArgumentParser",
"os.path.join",
"fnmatch.filter",
"os.strerror",
"os.walk"
] | [((10717, 10841), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': 'HELP_DESCRIPTION', 'epilog': 'EPILOG_TEXT', 'formatter_class': 'argparse.RawTextHelpFormatter'}), '(description=HELP_DESCRIPTION, epilog=EPILOG_TEXT,\n formatter_class=argparse.RawTextHelpFormatter)\n', (10740, 10841), Fals... |
#!/usr/bin/env python
"""
Copyright (c) 2004-Present Pivotal Software, Inc.
This program and the accompanying materials are made available under
the terms of the 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 ... | [
"mpp.lib.gpdbSystem.GpdbSystem",
"tinctest.logger.info",
"mpp.lib.gpSystem.GpSystem",
"mpp.lib.PSQL.PSQL.run_sql_command",
"os.environ.get"
] | [((1439, 1449), 'mpp.lib.gpSystem.GpSystem', 'GpSystem', ([], {}), '()\n', (1447, 1449), False, 'from mpp.lib.gpSystem import GpSystem\n'), ((1465, 1477), 'mpp.lib.gpdbSystem.GpdbSystem', 'GpdbSystem', ([], {}), '()\n', (1475, 1477), False, 'from mpp.lib.gpdbSystem import GpdbSystem\n'), ((1770, 1808), 'os.environ.get'... |
#!/usr/bin/env python
#
# This code was copied from the data generation program of Tencent Alchemy
# project (https://github.com/tencent-alchemy).
#
#
# Copyright 2019 Tencent America LLC. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in com... | [
"pyscf.gto.Mole",
"pyscf.lib.logger.timer",
"pyscf.grad.rks.get_vxc",
"pyscf.grad.rks.get_vxc_full_response",
"pyscf.grad.rks.Gradients.extra_force",
"pyscf.lib.logger.process_clock",
"pyscf.lib.tag_array",
"pyscf.lib.current_memory",
"pyscf.lib.logger.perf_counter",
"pyscf.grad.rks.Gradients.__in... | [((2048, 2081), 'pyscf.lib.logger.timer', 'logger.timer', (['ks_grad', '"""vxc"""', '*t0'], {}), "(ks_grad, 'vxc', *t0)\n", (2060, 2081), False, 'from pyscf.lib import logger\n'), ((3926, 3936), 'pyscf.gto.Mole', 'gto.Mole', ([], {}), '()\n', (3934, 3936), False, 'from pyscf import gto\n'), ((1105, 1127), 'pyscf.lib.lo... |
# -*- coding: utf-8 -*-
# Copyright © tandemdude 2020-present
#
# This file is part of Lightbulb.
#
# Lightbulb is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at ... | [
"hikari.Permissions.all_permissions"
] | [((1484, 1520), 'hikari.Permissions.all_permissions', 'hikari.Permissions.all_permissions', ([], {}), '()\n', (1518, 1520), False, 'import hikari\n'), ((2448, 2484), 'hikari.Permissions.all_permissions', 'hikari.Permissions.all_permissions', ([], {}), '()\n', (2482, 2484), False, 'import hikari\n')] |
"""Tests for the fourohfour app."""
from unittest import TestCase, mock
import string
import random
from wsgi import create_web_app
from http import HTTPStatus as status
from werkzeug.exceptions import default_exceptions
class TestFourOhFour(TestCase):
"""Four oh four abounds."""
def setUp(self):
"... | [
"werkzeug.exceptions.default_exceptions.keys",
"wsgi.create_web_app"
] | [((373, 389), 'wsgi.create_web_app', 'create_web_app', ([], {}), '()\n', (387, 389), False, 'from wsgi import create_web_app\n'), ((1187, 1203), 'wsgi.create_web_app', 'create_web_app', ([], {}), '()\n', (1201, 1203), False, 'from wsgi import create_web_app\n'), ((1722, 1738), 'wsgi.create_web_app', 'create_web_app', (... |
# -*- coding: utf-8 -*-
# (c) Copyright 2020 Sensirion AG, Switzerland
##############################################################################
##############################################################################
# _____ _ _ _______ _____ ____ _ _
# / ____| ... | [
"logging.getLogger",
"struct.unpack",
"struct.pack"
] | [((1161, 1188), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (1178, 1188), False, 'import logging\n'), ((2593, 2616), 'struct.unpack', 'unpack', (['""">b"""', 'data[0:1]'], {}), "('>b', data[0:1])\n", (2599, 2616), False, 'from struct import pack, unpack\n'), ((2648, 2671), 'struct.unpa... |
"""Custom visual elements for schemdraw library"""
from schemdraw.segments import Segment, SegmentText, SegmentCircle
import schemdraw.elements as sd_elem
import math
class Constant(sd_elem.Element):
"""Element that holds one value all time"""
def __init__(self, *d, constant_value: bool = True, lbl_size: fl... | [
"schemdraw.segments.Segment",
"schemdraw.segments.SegmentCircle"
] | [((479, 678), 'schemdraw.segments.Segment', 'Segment', (['[(-self.clen / 2, -self.cheight / 2), (-self.clen / 2, self.cheight / 2), (\n self.clen / 2, self.cheight / 2), (self.clen / 2, -self.cheight / 2), (\n -self.clen / 2, -self.cheight / 2)]'], {}), '([(-self.clen / 2, -self.cheight / 2), (-self.clen / 2, sel... |
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: proto/clarifai/utils/jsonpb/test.proto
import sys
_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf im... | [
"google.protobuf.symbol_database.Default",
"google.protobuf.descriptor.FieldDescriptor"
] | [((505, 531), 'google.protobuf.symbol_database.Default', '_symbol_database.Default', ([], {}), '()\n', (529, 531), True, 'from google.protobuf import symbol_database as _symbol_database\n'), ((1255, 1586), 'google.protobuf.descriptor.FieldDescriptor', '_descriptor.FieldDescriptor', ([], {'name': '"""uint_32"""', 'full_... |
import numpy as np
from apps.keyboard.core.mfcc import mfcc
def mapminmax(x, ymin=-1, ymax=+1):
x = np.asanyarray(x)
xmax = x.max(axis=-1)
xmin = x.min(axis=-1)
if (xmax == xmin).any():
raise ValueError("some rows have no variation")
return (ymax - ymin) * (x - xmin) / (xmax - xmin) + ymi... | [
"apps.keyboard.core.mfcc.mfcc.mfcc",
"numpy.asanyarray"
] | [((107, 123), 'numpy.asanyarray', 'np.asanyarray', (['x'], {}), '(x)\n', (120, 123), True, 'import numpy as np\n'), ((1118, 1185), 'apps.keyboard.core.mfcc.mfcc.mfcc', 'mfcc.mfcc', (['speech', 'fs', 'Tw', 'Ts', 'alpha', 'np.hamming', '[LF, HF]', 'M', 'C', 'L'], {}), '(speech, fs, Tw, Ts, alpha, np.hamming, [LF, HF], M,... |
import datetime
import discord
from discord.ext import commands
import random
import time
import json
import sqlite3
#region globals (config, token and database)
# Config
config_file = open("config.json", "r").read()
config = json.loads(config_file)
token = config["token"]
bot = commands.Bot(command_prefix="/")
# ... | [
"json.loads",
"random.choice",
"sqlite3.connect",
"discord.utils.find",
"discord.ext.commands.Bot",
"discord.FFmpegPCMAudio",
"time.sleep",
"datetime.datetime.now",
"discord.Embed"
] | [((229, 252), 'json.loads', 'json.loads', (['config_file'], {}), '(config_file)\n', (239, 252), False, 'import json\n'), ((284, 316), 'discord.ext.commands.Bot', 'commands.Bot', ([], {'command_prefix': '"""/"""'}), "(command_prefix='/')\n", (296, 316), False, 'from discord.ext import commands\n'), ((902, 931), 'sqlite3... |
from django.db import models
# *****************************************************************************************
# Affairs
# *****************************************************************************************
class Affair(models.Model):
"""
model for affair
"""
# type choices
INQUIRY... | [
"django.db.models.DateField",
"django.db.models.TextField",
"django.db.models.ForeignKey",
"django.db.models.IntegerField",
"django.db.models.ManyToManyField",
"django.db.models.FileField",
"django.db.models.BooleanField",
"django.db.models.DateTimeField",
"django.db.models.CharField"
] | [((1314, 1346), 'django.db.models.CharField', 'models.CharField', ([], {'max_length': '(200)'}), '(max_length=200)\n', (1330, 1346), False, 'from django.db import models\n'), ((1365, 1417), 'django.db.models.CharField', 'models.CharField', ([], {'max_length': '(5)', 'choices': 'TYPE_CHOICES'}), '(max_length=5, choices=... |
import os
import pytest
basedir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
@pytest.fixture()
def setup_tests_directory():
return os.path.join(basedir, "tests")
| [
"pytest.fixture",
"os.path.join",
"os.path.abspath"
] | [((98, 114), 'pytest.fixture', 'pytest.fixture', ([], {}), '()\n', (112, 114), False, 'import pytest\n'), ((155, 185), 'os.path.join', 'os.path.join', (['basedir', '"""tests"""'], {}), "(basedir, 'tests')\n", (167, 185), False, 'import os\n'), ((67, 92), 'os.path.abspath', 'os.path.abspath', (['__file__'], {}), '(__fil... |
import datetime
from django.db import models, transaction
from djmoney.models.fields import MoneyField
from P2PLending.lending.signals import request_closed, proposal_closed
from P2PLending.users.models import User
class AdvertisementStatus(models.Model):
OPENED = "OPN"
CLOSED = "CLS"
STATUS_CHOICES = (
... | [
"django.db.models.FloatField",
"django.db.models.DateField",
"P2PLending.lending.signals.proposal_closed.send",
"django.db.models.ForeignKey",
"django.db.transaction.atomic",
"django.db.models.DurationField",
"P2PLending.lending.signals.request_closed.send",
"djmoney.models.fields.MoneyField",
"djan... | [((393, 463), 'django.db.models.CharField', 'models.CharField', ([], {'max_length': '(3)', 'choices': 'STATUS_CHOICES', 'default': 'OPENED'}), '(max_length=3, choices=STATUS_CHOICES, default=OPENED)\n', (409, 463), False, 'from django.db import models, transaction\n'), ((801, 840), 'django.db.models.DateTimeField', 'mo... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# MIT License
# Copyright (c) 2020 <NAME> // This file is part of AcuteBot
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONI... | [
"telegram.InlineKeyboardButton"
] | [((1374, 1452), 'telegram.InlineKeyboardButton', 'InlineKeyboardButton', ([], {'text': '"""🎞️ IMDb"""', 'url': 'f"""https://m.imdb.com/title/{imdbid}"""'}), "(text='🎞️ IMDb', url=f'https://m.imdb.com/title/{imdbid}')\n", (1394, 1452), False, 'from telegram import InlineKeyboardButton\n'), ((2266, 2363), 'telegram.Inl... |
import unittest
from unittest.mock import patch
import bucky3.carbon as carbon
def carbon_verify(carbon_module, expected_values):
for v in carbon_module.buffer:
if v in expected_values:
expected_values.remove(v)
else:
assert False, str(v) + " was not expected"
if expe... | [
"unittest.main",
"bucky3.carbon.CarbonClient",
"unittest.mock.patch"
] | [((2538, 2553), 'unittest.main', 'unittest.main', ([], {}), '()\n', (2551, 2553), False, 'import unittest\n'), ((477, 495), 'unittest.mock.patch', 'patch', (['"""time.time"""'], {}), "('time.time')\n", (482, 495), False, 'from unittest.mock import patch\n'), ((743, 788), 'bucky3.carbon.CarbonClient', 'carbon.CarbonClie... |
from urllib.parse import unquote
from django.forms import modelform_factory
from tastypie.validation import FormValidation
from tastypie.resources import ModelResource
from tastypie.cache import SimpleCache
from api.authentications import ApiKeyOrAnonymousAuthentication
from api.authorizations import AlwaysReadAuthori... | [
"django.forms.modelform_factory",
"api.authentications.ApiKeyOrAnonymousAuthentication",
"tastypie.cache.SimpleCache",
"metadata.models.Tag.objects.all",
"api.authorizations.AlwaysReadAuthorization",
"dataset.models.Dataset.objects.get",
"urllib.parse.unquote",
"api.serializers.Serializer"
] | [((891, 924), 'api.authentications.ApiKeyOrAnonymousAuthentication', 'ApiKeyOrAnonymousAuthentication', ([], {}), '()\n', (922, 924), False, 'from api.authentications import ApiKeyOrAnonymousAuthentication\n'), ((943, 968), 'api.authorizations.AlwaysReadAuthorization', 'AlwaysReadAuthorization', ([], {}), '()\n', (966,... |
import random
import torch as torch
import torch.nn as nn
from torch.autograd import Variable
import torchvision.models as models
from .baseRNN import BaseRNN
from dataset.collate_fn import calc_im_seq_len
class EncoderCRNN(BaseRNN):
r"""
Applies a multi-layer RNN to an input sequence.
Args:
voc... | [
"dataset.collate_fn.calc_im_seq_len",
"torch.nn.MaxPool2d",
"torch.nn.utils.rnn.pack_padded_sequence",
"torch.zeros",
"torch.nn.utils.rnn.pad_packed_sequence",
"random.randint",
"torch.rand"
] | [((2873, 2936), 'torch.nn.MaxPool2d', 'nn.MaxPool2d', ([], {'kernel_size': '(3, 1)', 'stride': '(2, 1)', 'padding': '(1, 0)'}), '(kernel_size=(3, 1), stride=(2, 1), padding=(1, 0))\n', (2885, 2936), True, 'import torch.nn as nn\n'), ((3036, 3099), 'torch.nn.MaxPool2d', 'nn.MaxPool2d', ([], {'kernel_size': '(3, 1)', 'st... |
#!/usr/bin/env python3
# note structure of code taken from poretools https://github.com/arq5x/poretools/blob/master/poretools/poretools_main.py
import os.path
import sys
import argparse
# AAFTF imports
from AAFTF.version import __version__
myversion = __version__
from AAFTF.utility import status
def run_subtool(par... | [
"AAFTF.pipeline.run",
"sys.stderr.write",
"argparse.ArgumentParser",
"sys.exit"
] | [((1248, 1275), 'AAFTF.pipeline.run', 'submodule.run', (['parser', 'args'], {}), '(parser, args)\n', (1261, 1275), True, 'import AAFTF.pipeline as submodule\n'), ((1905, 2003), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'prog': '"""AAFTF"""', 'formatter_class': 'argparse.ArgumentDefaultsHelpFormatter'}... |
import os
import zipfile
from collections import Counter
from django.core.management.base import BaseCommand, CommandError
from corehq.apps.dump_reload.couch.load import (
CouchDataLoader,
DomainLoader,
ToggleLoader,
)
from corehq.apps.dump_reload.exceptions import DataExistsException
from corehq.apps.dum... | [
"collections.Counter",
"os.path.exists",
"zipfile.ZipFile",
"os.path.isfile"
] | [((2319, 2328), 'collections.Counter', 'Counter', ([], {}), '()\n', (2326, 2328), False, 'from collections import Counter\n'), ((1979, 2009), 'os.path.isfile', 'os.path.isfile', (['dump_file_path'], {}), '(dump_file_path)\n', (1993, 2009), False, 'import os\n'), ((3608, 3634), 'os.path.exists', 'os.path.exists', (['tar... |
#
# MLDB-1267-bucketize-ts-test.py
# Mich, 2015-11-16
# This file is part of MLDB. Copyright 2015 mldb.ai inc. All rights reserved.
#
import unittest
from mldb import mldb
class BucketizeTest(unittest.TestCase):
def test_it(self):
url = '/v1/datasets/input'
mldb.put(url, {
'type' : 's... | [
"mldb.mldb.put",
"mldb.mldb.post",
"mldb.mldb.run_tests",
"mldb.mldb.query"
] | [((1228, 1244), 'mldb.mldb.run_tests', 'mldb.run_tests', ([], {}), '()\n', (1242, 1244), False, 'from mldb import mldb\n'), ((281, 322), 'mldb.mldb.put', 'mldb.put', (['url', "{'type': 'sparse.mutable'}"], {}), "(url, {'type': 'sparse.mutable'})\n", (289, 322), False, 'from mldb import mldb\n'), ((355, 430), 'mldb.mldb... |
import sys
from datetime import datetime
from io import BytesIO
import pytest
import requests_mock
from flask import current_app, request, url_for, json
from flask_login import current_user
from marshmallow import pprint
from benwaonline import mappers
from benwaonline import entities
from benwaonline.gallery import ... | [
"benwaonline.entities.Image",
"benwaonline.gallery.views.show_post",
"benwaonline.entities.Post",
"requests_mock.Mocker",
"io.BytesIO",
"flask.url_for",
"benwaonline.entities.Tag",
"utils.test_user",
"pytest.raises",
"utils.load_test_data",
"benwaonline.entities.Preview",
"flask.json.load",
... | [((424, 462), 'utils.load_test_data', 'utils.load_test_data', (['"""test_jwks.json"""'], {}), "('test_jwks.json')\n", (444, 462), False, 'import utils\n'), ((1087, 1104), 'utils.test_user', 'utils.test_user', ([], {}), '()\n', (1102, 1104), False, 'import utils\n'), ((1979, 2026), 'flask.url_for', 'url_for', (['"""gall... |
import os
from pathlib import Path
import subprocess
import paramiko
from paramiko.rsakey import RSAKey
from .box_config import BoxConfig
class SSH:
"""Manage SSH connections"""
ip: str
user: str
client: paramiko.SSHClient
def __init__(self, user: str, ip: str, cfg: BoxConfig) -> None:
se... | [
"paramiko.AutoAddPolicy",
"subprocess.run",
"paramiko.rsakey.RSAKey.generate",
"os.path.isfile",
"os.path.isdir",
"paramiko.SSHClient",
"os.remove"
] | [((378, 398), 'paramiko.SSHClient', 'paramiko.SSHClient', ([], {}), '()\n', (396, 398), False, 'import paramiko\n'), ((713, 791), 'subprocess.run', 'subprocess.run', (['f"""ssh {self.user}@{self.ip} -i {ssh_private_path}"""'], {'shell': '(True)'}), "(f'ssh {self.user}@{self.ip} -i {ssh_private_path}', shell=True)\n", (... |
import sys
import io
eng_letters = ["@suf", "yyCM", "yyCLN", "yyLRB", "yyQUOT", "yyDOT", "yyDASH", "yyRRB", "yyEXCL", "yyQM", "yySCLN", "yyELPS", "U", "O", "T", "F", "R", "Q", "C", "P", "E", "S", "N", "M", "L", "K", "I", "J", "X", "Z", "W", "H", "D", "G", "B", "A"]
heb_letters = ["~", ",", ":", "(", '"', ".", "-", ")"... | [
"io.TextIOWrapper"
] | [((885, 937), 'io.TextIOWrapper', 'io.TextIOWrapper', (['sys.stdin.buffer'], {'encoding': '"""utf-8"""'}), "(sys.stdin.buffer, encoding='utf-8')\n", (901, 937), False, 'import io\n')] |
from flask_restx import Namespace
admin = Namespace('Admin', 'Admin API and User API.', path='/')
| [
"flask_restx.Namespace"
] | [((43, 98), 'flask_restx.Namespace', 'Namespace', (['"""Admin"""', '"""Admin API and User API."""'], {'path': '"""/"""'}), "('Admin', 'Admin API and User API.', path='/')\n", (52, 98), False, 'from flask_restx import Namespace\n')] |
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
"""
Topic: 分数运算
Desc :
"""
from fractions import Fraction
def frac():
a = Fraction(5, 4)
b = Fraction(7, 16)
print(print(a + b))
print(a.numerator, a.denominator)
c = a + b
print(float(c))
print(type(c.limit_denominator(8)))
print(c.lim... | [
"fractions.Fraction"
] | [((129, 143), 'fractions.Fraction', 'Fraction', (['(5)', '(4)'], {}), '(5, 4)\n', (137, 143), False, 'from fractions import Fraction\n'), ((152, 167), 'fractions.Fraction', 'Fraction', (['(7)', '(16)'], {}), '(7, 16)\n', (160, 167), False, 'from fractions import Fraction\n')] |
# Copyright (c) 2021 <NAME>. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# 1. Redistributions of source code must retain the above copyright
# notice, this list of conditions and the followin... | [
"numpy.mean",
"cytoskeleton_analyser.position.empirical_data.mboc17.avg",
"cytoskeleton_analyser.position.empirical_data.mboc17.length",
"cytoskeleton_analyser.position.empirical_data.mboc17.curvature",
"cytoskeleton_analyser.fitting.Exponential.create",
"numpy.array",
"numpy.concatenate",
"numpy.std"... | [((8058, 8085), 'cytoskeleton_analyser.position.empirical_data.mboc17.length', 'mboc17.length', ([], {'density': '(True)'}), '(density=True)\n', (8071, 8085), True, 'import cytoskeleton_analyser.position.empirical_data.mboc17 as mboc17\n'), ((8218, 8235), 'cytoskeleton_analyser.position.empirical_data.mboc17.avg', 'mbo... |
# -*- coding: utf-8 -*-
"""
Created on Mon Aug 9 14:46:11 2021
To inject methods and properties into a asia model instance, so we dont have to recreate it
@author: bruger
"""
import pandas as pd
import ipywidgets as widgets
from ipysheet import sheet, cell, current
from ipysheet.pandas_loader import from_data... | [
"IPython.display.display",
"ipywidgets.VBox",
"ipysheet.pandas_loader.to_dataframe",
"ipysheet.pandas_loader.from_dataframe",
"ipywidgets.HBox",
"ipywidgets.Label",
"ipywidgets.Button",
"ipywidgets.Output",
"pandas.DataFrame",
"io.StringIO",
"matplotlib.pylab.close",
"dataclasses.field",
"ip... | [((698, 725), 'dataclasses.field', 'field', ([], {'default_factory': 'list'}), '(default_factory=list)\n', (703, 725), False, 'from dataclasses import dataclass, field\n'), ((2148, 2162), 'pandas.DataFrame', 'pd.DataFrame', ([], {}), '()\n', (2160, 2162), True, 'import pandas as pd\n'), ((12450, 12464), 'pandas.DataFra... |
import random
print("Random number: ",random.random()) | [
"random.random"
] | [((39, 54), 'random.random', 'random.random', ([], {}), '()\n', (52, 54), False, 'import random\n')] |
import src.Exceptions as Exception
from src.Parser import Parser
from src.Interpreteur import Interpreteur
import time
import sys
import json
settings = json.load(open("settings.json", "r"))
ERROR_MESSAGE = settings["main"]["ERROR_MESSAGE"]
args = sys.argv
if len(sys.argv) < 2:
raise Exception.NotEnoughArguments(... | [
"src.Interpreteur.Interpreteur",
"src.Parser.Parser",
"src.Exceptions.NotEnoughArguments",
"time.time"
] | [((291, 334), 'src.Exceptions.NotEnoughArguments', 'Exception.NotEnoughArguments', (['ERROR_MESSAGE'], {}), '(ERROR_MESSAGE)\n', (319, 334), True, 'import src.Exceptions as Exception\n'), ((692, 703), 'time.time', 'time.time', ([], {}), '()\n', (701, 703), False, 'import time\n'), ((754, 780), 'src.Parser.Parser', 'Par... |
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as sp
import math
import random as rm
import NumerosGenerados as ng
n = 100000
inicio = 0
ancho = 20
K = 3
numerosGamma = sp.gamma.rvs(size=n, a = K)
print("Media: ", round(np.mean(numerosGamma),3))
print("Desvio: ", round(np.sqrt(np.var(numerosGam... | [
"numpy.mean",
"random.choice",
"matplotlib.pyplot.hist",
"scipy.stats.gamma.rvs",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.xlabel",
"math.log",
"matplotlib.pyplot.title",
"NumerosGenerados.generarNumeros",
"numpy.var",
"matplotlib.pyplot.show"
] | [((193, 218), 'scipy.stats.gamma.rvs', 'sp.gamma.rvs', ([], {'size': 'n', 'a': 'K'}), '(size=n, a=K)\n', (205, 218), True, 'import scipy.stats as sp\n'), ((381, 469), 'matplotlib.pyplot.hist', 'plt.hist', (['numerosGamma'], {'bins': '(50)', 'color': '"""red"""', 'histtype': '"""bar"""', 'alpha': '(0.8)', 'ec': '"""blac... |
import subprocess
def run(s):
return subprocess.run(s, check=True, shell=True)
| [
"subprocess.run"
] | [((42, 83), 'subprocess.run', 'subprocess.run', (['s'], {'check': '(True)', 'shell': '(True)'}), '(s, check=True, shell=True)\n', (56, 83), False, 'import subprocess\n')] |
import torch
import torch.nn as nn
import torch.nn.functional as F
def manual_cross_entropy(
logits: torch.Tensor, labels: torch.Tensor, weight: torch.Tensor
) -> torch.Tensor:
ce = -weight * torch.sum(labels * F.log_softmax(logits, dim=-1), dim=-1)
return torch.mean(ce)
class DetConBLoss(nn.Module):
... | [
"torch.mean",
"torch.greater",
"torch.nn.functional.normalize",
"torch.tensor",
"torch.arange",
"torch.sum",
"torch.einsum",
"torch.nn.functional.log_softmax",
"torch.cat"
] | [((271, 285), 'torch.mean', 'torch.mean', (['ce'], {}), '(ce)\n', (281, 285), False, 'import torch\n'), ((517, 542), 'torch.tensor', 'torch.tensor', (['temperature'], {}), '(temperature)\n', (529, 542), False, 'import torch\n'), ((2640, 2666), 'torch.nn.functional.normalize', 'F.normalize', (['pred1'], {'dim': '(-1)'})... |
import argparse
import os
import sys
import torch
from torch import nn
import torchtext
from torchtext import data
from torchtext import datasets
from eval_args import get_arg_parser
from performance import size_metrics
import utils
from models.components.binarization import (
Binarize,
)
def main() -> None:
... | [
"models.components.binarization.Binarize",
"torchtext.datasets.TranslationDataset",
"torchtext.data.Field",
"torch.load",
"torchtext.datasets.Multi30k.splits",
"eval_args.get_arg_parser",
"torch.cuda.is_available",
"utils.load_torchtext_wmt_small_vocab",
"utils.build_model",
"performance.size_metr... | [((330, 346), 'eval_args.get_arg_parser', 'get_arg_parser', ([], {}), '()\n', (344, 346), False, 'from eval_args import get_arg_parser\n'), ((541, 650), 'torchtext.data.Field', 'data.Field', ([], {'include_lengths': '(True)', 'init_token': '"""<sos>"""', 'eos_token': '"""<eos>"""', 'batch_first': '(True)', 'fix_length'... |
#!/usr/bin/env python
import logging
logging.debug('this is deb')
logging.info('this is inf')
logging.warning('this is war')
logging.error('this is err')
logging.log(logging.INFO, 'this is inf too')
| [
"logging.debug",
"logging.warning",
"logging.log",
"logging.info",
"logging.error"
] | [((39, 67), 'logging.debug', 'logging.debug', (['"""this is deb"""'], {}), "('this is deb')\n", (52, 67), False, 'import logging\n'), ((68, 95), 'logging.info', 'logging.info', (['"""this is inf"""'], {}), "('this is inf')\n", (80, 95), False, 'import logging\n'), ((96, 126), 'logging.warning', 'logging.warning', (['""... |
import discord
import aiosqlite
import random
from discord.ext import commands
from discord_components import Button, ButtonStyle
from load import *
# set CHANNELID to channel u want to use guessgame in.
class Guess(commands.Cog):
def __init__(self, client):
self.client = client
descript... | [
"random.randint",
"aiosqlite.connect",
"discord.ext.commands.command",
"discord.Embed",
"discord_components.Button"
] | [((349, 379), 'discord.ext.commands.command', 'commands.command', ([], {'name': '"""guess"""'}), "(name='guess')\n", (365, 379), False, 'from discord.ext import commands\n'), ((10391, 10428), 'discord.ext.commands.command', 'commands.command', ([], {'name': '"""leaderboards"""'}), "(name='leaderboards')\n", (10407, 104... |
# -*- coding: utf-8 -*-
#
# Copyright (C) 2010-2016 PPMessage.
# <NAME>, <EMAIL>
#
from mdm.core.constant import MESSAGE_TYPE
from mdm.core.constant import MESSAGE_SUBTYPE
from mdm.core.constant import CONVERSATION_TYPE
from mdm.core.constant import PCSOCKET_SRV
from mdm.core.constant import TASK_STATUS
from mdm.core.... | [
"json.loads",
"json.dumps",
"mdm.db.models.ConversationInfo",
"uuid.uuid1",
"mdm.db.models.MessagePushTask",
"logging.error"
] | [((7073, 7097), 'mdm.db.models.MessagePushTask', 'MessagePushTask', ([], {}), '(**_task)\n', (7088, 7097), False, 'from mdm.db.models import MessagePushTask\n'), ((7194, 7259), 'mdm.db.models.ConversationInfo', 'ConversationInfo', ([], {'uuid': '_conversation_uuid', 'latest_task': '_task.uuid'}), '(uuid=_conversation_u... |
#!/usr/bin/env python
import simplejson as json
import time
import grovepi
import math
import datetime;
import urllib.request, urllib.parse
import datetime;
import requests
# Connect the Grove Light Sensor to analog port A0
# SIG,NC,VCC,GND
light_sensor = 0
sound_sensor = 1
dhtsensor = 4
header_content = {'Content-ty... | [
"requests.post",
"grovepi.dht",
"grovepi.digitalWrite",
"grovepi.analogRead",
"time.sleep",
"math.isnan",
"datetime.datetime.now",
"grovepi.pinMode"
] | [((481, 519), 'grovepi.pinMode', 'grovepi.pinMode', (['sound_sensor', '"""INPUT"""'], {}), "(sound_sensor, 'INPUT')\n", (496, 519), False, 'import grovepi\n'), ((519, 557), 'grovepi.pinMode', 'grovepi.pinMode', (['light_sensor', '"""INPUT"""'], {}), "(light_sensor, 'INPUT')\n", (534, 557), False, 'import grovepi\n'), (... |
import numpy as np
from pmesh.pm import ParticleMesh
from nbodykit.lab import BigFileCatalog, BigFileMesh, FFTPower
from nbodykit.source.mesh.field import FieldMesh
from nbodykit.lab import SimulationBox2PCF, FFTCorr
import os
import sys
sys.path.append('./utils')
import tools, dohod #
from time import ti... | [
"pmesh.pm.ParticleMesh",
"numpy.broadcast_to",
"numpy.ones",
"argparse.ArgumentParser",
"os.makedirs",
"nbodykit.lab.SimulationBox2PCF",
"tools.atoz",
"nbodykit.lab.BigFileMesh",
"dohod.make_galcat",
"dohod.assignH1mass",
"numpy.stack",
"os.path.dirname",
"nbodykit.lab.BigFileCatalog",
"ti... | [((239, 265), 'sys.path.append', 'sys.path.append', (['"""./utils"""'], {}), "('./utils')\n", (254, 265), False, 'import sys\n'), ((373, 398), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {}), '()\n', (396, 398), False, 'import argparse\n'), ((1724, 1768), 'pmesh.pm.ParticleMesh', 'ParticleMesh', ([], {'B... |
from __future__ import annotations
from typing import Dict, Set, Optional
from slovo import Gender, Term
from slovo import (
ImmutableClassError,
DiffLangsError,
SameLangsError,
)
class Word():
lang: Optional[str] = None
def __init__(self, word):
self._translations: Dict[str, Set[Word]]... | [
"slovo.ImmutableClassError",
"slovo.SameLangsError",
"slovo.DiffLangsError"
] | [((842, 904), 'slovo.SameLangsError', 'SameLangsError', (['"""Translate: accept word of the other language"""'], {}), "('Translate: accept word of the other language')\n", (856, 904), False, 'from slovo import ImmutableClassError, DiffLangsError, SameLangsError\n'), ((3596, 3633), 'slovo.DiffLangsError', 'DiffLangsErro... |
#!/usr/bin/env python
# coding: utf-8
# ### Modules ###
# In[1]:
import pandas as pd
import requests
from splinter import Browser
from bs4 import BeautifulSoup as bs
from webdriver_manager.chrome import ChromeDriverManager
# ### Setting Chrome Path ###
# In[2]:
def scrape():
# browser = init_browser()
e... | [
"bs4.BeautifulSoup",
"splinter.Browser",
"webdriver_manager.chrome.ChromeDriverManager",
"pandas.read_html"
] | [((404, 456), 'splinter.Browser', 'Browser', (['"""chrome"""'], {'headless': '(False)'}), "('chrome', **executable_path, headless=False)\n", (411, 456), False, 'from splinter import Browser\n'), ((875, 898), 'bs4.BeautifulSoup', 'bs', (['html', '"""html.parser"""'], {}), "(html, 'html.parser')\n", (877, 898), True, 'fr... |
import torch.utils.data as data
from torchvision import transforms
from PIL import Image
import os
import os.path
from .auto_augment import AutoAugment, ImageNetAutoAugment
IMG_EXTENSIONS = [
'.jpg', '.JPG', '.jpeg', '.JPEG',
'.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP',
]
def is_image_file(filename):
... | [
"PIL.Image.open",
"os.path.join",
"os.path.isdir",
"torchvision.transforms.Resize",
"torchvision.transforms.ToTensor",
"os.walk"
] | [((444, 462), 'os.path.isdir', 'os.path.isdir', (['dir'], {}), '(dir)\n', (457, 462), False, 'import os\n'), ((535, 547), 'os.walk', 'os.walk', (['dir'], {}), '(dir)\n', (542, 547), False, 'import os\n'), ((797, 813), 'PIL.Image.open', 'Image.open', (['path'], {}), '(path)\n', (807, 813), False, 'from PIL import Image\... |
# from url_encoding import make_url
# full url encoding
import urllib
try: # Python2 and python3 comatible code
from urllib.parse import urlparse
except ImportError:
from urlparse import urlparse
def make_url(string):
'''Encode every char in percentage format'''
return "".join("%{0:0>2}".format(form... | [
"urllib.quote",
"urllib.quote_plus"
] | [((597, 622), 'urllib.quote_plus', 'urllib.quote_plus', (['string'], {}), '(string)\n', (614, 622), False, 'import urllib\n'), ((662, 682), 'urllib.quote', 'urllib.quote', (['string'], {}), '(string)\n', (674, 682), False, 'import urllib\n')] |
import tkinter as tk
class ToolTip(object):
def __init__(self, widget, text):
self.widget = widget
self.text = text
def enter(event):
self.showTooltip()
def leave(event):
self.hideTooltip()
widget.bind('<Enter>', enter)
widget.bind('<Leave>',... | [
"tkinter.Toplevel",
"tkinter.Label"
] | [((390, 414), 'tkinter.Toplevel', 'tk.Toplevel', (['self.widget'], {}), '(self.widget)\n', (401, 414), True, 'import tkinter as tk\n'), ((626, 711), 'tkinter.Label', 'tk.Label', (['tw'], {'text': 'self.text', 'background': '"""#ffffe0"""', 'relief': '"""solid"""', 'borderwidth': '(1)'}), "(tw, text=self.text, backgroun... |