code stringlengths 22 1.05M | apis listlengths 1 3.31k | extract_api stringlengths 75 3.25M |
|---|---|---|
import numpy as np
import pickle
from pathlib import Path
source_path = "./data/unirep/stability"
for path in Path(source_path).rglob('*.npz'):
pickle_file = str(path).replace('npz', 'p')
seq_dict = dict()
data = np.load(path, allow_pickle=True)
print(path)
dups = set()
dup_count = 0
for ... | [
"numpy.load",
"pickle.dump",
"pathlib.Path"
] | [((228, 260), 'numpy.load', 'np.load', (['path'], {'allow_pickle': '(True)'}), '(path, allow_pickle=True)\n', (235, 260), True, 'import numpy as np\n'), ((111, 128), 'pathlib.Path', 'Path', (['source_path'], {}), '(source_path)\n', (115, 128), False, 'from pathlib import Path\n'), ((764, 788), 'pickle.dump', 'pickle.du... |
import setuptools
import versioneer
from pathlib import Path
# Extract information from the README file and embed it in the package.
readme_path = Path(__file__).absolute().parent / "README.md"
with open(readme_path, "r") as fh:
long_description = fh.read()
setuptools.setup(
author="<NAME>",
author_emai... | [
"versioneer.get_cmdclass",
"setuptools.find_packages",
"versioneer.get_version",
"pathlib.Path"
] | [((706, 731), 'versioneer.get_cmdclass', 'versioneer.get_cmdclass', ([], {}), '()\n', (729, 731), False, 'import versioneer\n'), ((959, 985), 'setuptools.find_packages', 'setuptools.find_packages', ([], {}), '()\n', (983, 985), False, 'import setuptools\n'), ((1086, 1110), 'versioneer.get_version', 'versioneer.get_vers... |
import torch
from torch_geometric.nn import GCNConv
from torch_geometric.nn import GraphConv, TopKPooling
from torch_geometric.nn import global_mean_pool as gap, global_max_pool as gmp
import torch.nn.functional as F
from layers import SAGPool
class Net(torch.nn.Module):
def __init__(self, num_features, ... | [
"torch_geometric.nn.global_max_pool",
"torch_geometric.nn.global_mean_pool",
"layers.SAGPool",
"torch.nn.functional.dropout",
"torch_geometric.nn.GCNConv",
"torch.nn.Linear"
] | [((655, 692), 'torch_geometric.nn.GCNConv', 'GCNConv', (['self.num_features', 'self.nhid'], {}), '(self.num_features, self.nhid)\n', (662, 692), False, 'from torch_geometric.nn import GCNConv\n'), ((715, 759), 'layers.SAGPool', 'SAGPool', (['self.nhid'], {'ratio': 'self.pooling_ratio'}), '(self.nhid, ratio=self.pooling... |
"""Script designed to concatenate files from
publications database for required years"""
import pandas as pd
# Import years 2013-2020 (for 2021 years of interest)
fac_2013 = pd.read_excel(
"INSERT DATA FILE NAME.xlsx",
sheet_name="Publications",
header=0,
engine="openpyxl",
keep_default_na=False,
... | [
"pandas.concat",
"pandas.read_excel"
] | [((176, 302), 'pandas.read_excel', 'pd.read_excel', (['"""INSERT DATA FILE NAME.xlsx"""'], {'sheet_name': '"""Publications"""', 'header': '(0)', 'engine': '"""openpyxl"""', 'keep_default_na': '(False)'}), "('INSERT DATA FILE NAME.xlsx', sheet_name='Publications',\n header=0, engine='openpyxl', keep_default_na=False)... |
from pydantic import validator, ValidationError, Field
from .types import BaseModel, Union, Optional, Literal, List
from typing import Dict
import pathlib
class ParasiticValues(BaseModel):
mean: int = 0
min: int = 0
max: int = 0
class Layer(BaseModel):
name: str
gds_layer_number: int
gds_dat... | [
"pydantic.Field",
"pydantic.validator"
] | [((355, 398), 'pydantic.Field', 'Field', ([], {'default_factory': "(lambda : {'draw': 0})"}), "(default_factory=lambda : {'draw': 0})\n", (360, 398), False, 'from pydantic import validator, ValidationError, Field\n'), ((865, 882), 'pydantic.validator', 'validator', (['"""name"""'], {}), "('name')\n", (874, 882), False,... |
# Generated by Django 3.1.3 on 2020-11-18 19:38
from django.db import migrations, models
import django.utils.timezone
class Migration(migrations.Migration):
dependencies = [
('ghostpost_app', '0008_auto_20201118_1937'),
]
operations = [
migrations.AlterField(
model_name='gho... | [
"django.db.models.DateTimeField"
] | [((378, 433), 'django.db.models.DateTimeField', 'models.DateTimeField', ([], {'default': 'django.utils.timezone.now'}), '(default=django.utils.timezone.now)\n', (398, 433), False, 'from django.db import migrations, models\n')] |
# Project Repository : https://github.com/robertapplin/N-Body-Simulations
# Authored by <NAME>, 2020
from n_body_simulations.body_marker import BodyMarker
from n_body_simulations.error_catcher import catch_errors
from n_body_simulations.simulation_animator import SimulationAnimator
from NBodySimulations import Vector2D... | [
"n_body_simulations.error_catcher.catch_errors",
"PyQt5.QtCore.pyqtSignal",
"matplotlib.figure.Figure",
"matplotlib.backends.backend_qt5agg.FigureCanvasQTAgg",
"NBodySimulations.Vector2D",
"n_body_simulations.simulation_animator.SimulationAnimator"
] | [((670, 698), 'PyQt5.QtCore.pyqtSignal', 'pyqtSignal', (['QTableWidgetItem'], {}), '(QTableWidgetItem)\n', (680, 698), False, 'from PyQt5.QtCore import pyqtSignal\n'), ((1042, 1056), 'n_body_simulations.error_catcher.catch_errors', 'catch_errors', ([], {}), '()\n', (1054, 1056), False, 'from n_body_simulations.error_ca... |
import datetime
import time
from django.db import models
from django.core.serializers.json import DjangoJSONEncoder
from django.utils import simplejson as json
def get_timestamp(date_time):
"""
Create a `timestamp` from a `datetime` object. A `timestamp` is defined
as the number of milliseconds ... | [
"django.utils.simplejson.loads",
"django.utils.simplejson.dumps"
] | [((1090, 1130), 'django.utils.simplejson.dumps', 'json.dumps', (['value'], {'cls': 'DjangoJSONEncoder'}), '(value, cls=DjangoJSONEncoder)\n', (1100, 1130), True, 'from django.utils import simplejson as json\n'), ((1709, 1749), 'django.utils.simplejson.dumps', 'json.dumps', (['value'], {'cls': 'DjangoJSONEncoder'}), '(v... |
print('===== DESAFIO 92 =====')
from datetime import datetime
pessoa = {}
pessoa['nome'] = str(input('Nome: '))
pessoa['idade'] = datetime.now().year - int(input('Ano de Nascimento: '))
pessoa['ctps'] = int(input('Carteira de Trabalho (0 não tem): '))
if pessoa['ctps'] != 0:
pessoa['contr'] = int(input('Ano de Cont... | [
"datetime.datetime.now"
] | [((130, 144), 'datetime.datetime.now', 'datetime.now', ([], {}), '()\n', (142, 144), False, 'from datetime import datetime\n'), ((455, 469), 'datetime.datetime.now', 'datetime.now', ([], {}), '()\n', (467, 469), False, 'from datetime import datetime\n')] |
"""
A set of large-scale tests which test code updates against previously-run "golden"
results.
The idea here is that any new updates (except for major versions) should be non-breaking;
firstly, they should not break the API, so that the tests should run without crashing without
being changed.
Secondly, the actual res... | [
"logging.getLogger",
"numpy.allclose",
"py21cmfast.global_params.use",
"py21cmfast.config.use",
"numpy.isclose",
"numpy.testing.assert_allclose",
"pytest.mark.parametrize",
"numpy.sum"
] | [((1632, 1661), 'logging.getLogger', 'logging.getLogger', (['"""21cmFAST"""'], {}), "('21cmFAST')\n", (1649, 1661), False, 'import logging\n'), ((1818, 1858), 'pytest.mark.parametrize', 'pytest.mark.parametrize', (['"""name"""', 'options'], {}), "('name', options)\n", (1841, 1858), False, 'import pytest\n'), ((3046, 30... |
# -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'Akshita_02.ui'
#
# Created by: PyQt5 UI code generator 5.15.4
#
# WARNING: Any manual changes made to this file will be lost when pyuic5 is
# run again. Do not edit this file unless you know what you are doing.
from PyQt5 import QtCore, Q... | [
"PyQt5.QtWidgets.QWidget",
"PyQt5.QtWidgets.QTextEdit",
"PyQt5.QtGui.QFont",
"PyQt5.QtWidgets.QSpacerItem",
"PyQt5.QtWidgets.QSizePolicy",
"PyQt5.QtCore.QMetaObject.connectSlotsByName",
"PyQt5.QtWidgets.QFrame",
"PyQt5.QtWidgets.QHBoxLayout",
"PyQt5.QtWidgets.QGridLayout",
"PyQt5.QtWidgets.QApplic... | [((34514, 34546), 'PyQt5.QtWidgets.QApplication', 'QtWidgets.QApplication', (['sys.argv'], {}), '(sys.argv)\n', (34536, 34546), False, 'from PyQt5 import QtCore, QtGui, QtWidgets\n'), ((34558, 34577), 'PyQt5.QtWidgets.QWidget', 'QtWidgets.QWidget', ([], {}), '()\n', (34575, 34577), False, 'from PyQt5 import QtCore, QtG... |
#Wordle clone made by lightflix
from flask import Flask, request, render_template, session, redirect, url_for
import re
import random
import uuid
import datetime
app = Flask(__name__)
app.secret_key = "m1lktrUckjUsT4rr1v"
app.permanent_session_lifetime = datetime.timedelta(days=365)
@app.errorhandler(405)
def method... | [
"flask.render_template",
"random.choice",
"flask.Flask",
"re.match",
"uuid.uuid4",
"flask.request.form.get",
"flask.url_for",
"datetime.timedelta"
] | [((170, 185), 'flask.Flask', 'Flask', (['__name__'], {}), '(__name__)\n', (175, 185), False, 'from flask import Flask, request, render_template, session, redirect, url_for\n'), ((257, 285), 'datetime.timedelta', 'datetime.timedelta', ([], {'days': '(365)'}), '(days=365)\n', (275, 285), False, 'import datetime\n'), ((34... |
#!/usr/bin/env python
# This work was created by participants in the DataONE project, and is
# jointly copyrighted by participating institutions in DataONE. For
# more information on DataONE, see our web site at http://dataone.org.
#
# Copyright 2009-2019 DataONE
#
# Licensed under the Apache License, Version 2.0 (t... | [
"logging.getLogger",
"io.BytesIO",
"optparse.OptionParser",
"sys.exit"
] | [((1302, 1329), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (1319, 1329), False, 'import logging\n'), ((1399, 1422), 'optparse.OptionParser', 'optparse.OptionParser', ([], {}), '()\n', (1420, 1422), False, 'import optparse\n'), ((4075, 4099), 'io.BytesIO', 'io.BytesIO', (['sciobj_bytes... |
from django.test import TestCase
from monsters.models import Monster
class MonsterTestCase(TestCase):
fixtures = [
'monsters.json',
'series.json',
]
def test_monster(self):
monster = Monster.objects.get(pk=2)
self.assertIsInstance(monster, Monster)
self.assertEqua... | [
"monsters.models.Monster.objects.get"
] | [((223, 248), 'monsters.models.Monster.objects.get', 'Monster.objects.get', ([], {'pk': '(2)'}), '(pk=2)\n', (242, 248), False, 'from monsters.models import Monster\n'), ((402, 427), 'monsters.models.Monster.objects.get', 'Monster.objects.get', ([], {'pk': '(2)'}), '(pk=2)\n', (421, 427), False, 'from monsters.models i... |
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: messenger.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 import reflection as _ref... | [
"google.protobuf.symbol_database.Default",
"google.protobuf.descriptor.MethodDescriptor"
] | [((482, 508), 'google.protobuf.symbol_database.Default', '_symbol_database.Default', ([], {}), '()\n', (506, 508), True, 'from google.protobuf import symbol_database as _symbol_database\n'), ((8331, 8512), 'google.protobuf.descriptor.MethodDescriptor', '_descriptor.MethodDescriptor', ([], {'name': '"""Send"""', 'full_n... |
"""
REST API Resource Routing
http://flask-restplus.readthedocs.io
"""
import json
from datetime import datetime
from flask import request
from flask_restplus import fields
from werkzeug.datastructures import FileStorage
from .base import BaseResource, SecureResource
from .helper import mock_mod, mock_data, query_par... | [
"flask_restplus.fields.String",
"datetime.datetime.utcnow"
] | [((451, 528), 'flask_restplus.fields.String', 'fields.String', ([], {'description': '"""The resource name"""', 'required': '(True)', 'example': '"""Jack"""'}), "(description='The resource name', required=True, example='Jack')\n", (464, 528), False, 'from flask_restplus import fields\n'), ((2249, 2266), 'datetime.dateti... |
"""
Orbitscalc
Copyright 2021 <NAME>
Licensed under the Apache License, Version 2.0
"""
from skyfield.api import EarthSatellite, load
from datetime import timedelta
from copy import copy
from orbitscalc.general_utility import data_with_unit, CustomEnum, to_percent_max100
from orbitscalc.ground_station import GroundSta... | [
"orbitscalc.general_utility.data_with_unit",
"orbitscalc.ground_station.GroundStation",
"orbitscalc.contact_utility.best_contact_sequence_from_sorted_group",
"copy.copy",
"skyfield.api.load.timescale",
"orbitscalc.contact_utility.ContactSequence",
"datetime.timedelta",
"orbitscalc.general_utility.to_p... | [((5406, 5423), 'orbitscalc.contact_utility.ContactSequence', 'ContactSequence', ([], {}), '()\n', (5421, 5423), False, 'from orbitscalc.contact_utility import ContactSequence, determine_sorted_contact_groups, best_contact_sequence_from_sorted_group\n'), ((2645, 2669), 'orbitscalc.general_utility.to_percent_max100', 't... |
##########################################################
# Python Tkinter Save To Dat File Instead of Databases
# Guardar en archivo Dat en lugar de bases de datos
##########################################################
from tkinter import *
import pickle
root = Tk()
root.title('Python Tkinter Save To Dat File I... | [
"pickle.load",
"pickle.dump"
] | [((1007, 1038), 'pickle.dump', 'pickle.dump', (['stuff', 'output_file'], {}), '(stuff, output_file)\n', (1018, 1038), False, 'import pickle\n'), ((1258, 1281), 'pickle.load', 'pickle.load', (['input_file'], {}), '(input_file)\n', (1269, 1281), False, 'import pickle\n')] |
import threading, os, functools
from utils import fileUtil, folderUtil;
from components.SortOptions import DataType;
from collections import deque;
from datetime import datetime;
class SortObj:
param = [];
value = "";
def __init__(self, param, value):
self.param = param;
self.value = v... | [
"threading.Thread.__init__",
"functools.cmp_to_key",
"collections.deque",
"utils.fileUtil.getWriterWithHeader",
"datetime.datetime.strptime",
"utils.folderUtil.stagingResult",
"os.path.basename"
] | [((2061, 2088), 'os.path.basename', 'os.path.basename', (['file.name'], {}), '(file.name)\n', (2077, 2088), False, 'import threading, os, functools\n'), ((2537, 2556), 'collections.deque', 'deque', (['objContainer'], {}), '(objContainer)\n', (2542, 2556), False, 'from collections import deque\n'), ((2575, 2632), 'utils... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Advent of Code 2018 day 21 module."""
from __future__ import division, print_function
def valid_inputs():
'''Determine the possible values for r0 which will cause the program to exit.'''
# the input program is as follows:
# # 00
# r3 = 123
# # 01
... | [
"fileinput.input",
"argparse.ArgumentParser"
] | [((1826, 1851), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {}), '()\n', (1849, 1851), False, 'import argparse\n'), ((2162, 2190), 'fileinput.input', 'fileinput.input', (['args.infile'], {}), '(args.infile)\n', (2177, 2190), False, 'import fileinput\n')] |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from neodroid.models import Displayable
__author__ = "<NAME>"
def get_actor_configuration(environment, candidate):
state_ob, _ = environment.configure(state=candidate)
if environment:
goal_pos_x = environment.description.configurable(
"ActorT... | [
"neodroid.models.Displayable"
] | [((1024, 1103), 'neodroid.models.Displayable', 'Displayable', (['frontier_displayer_name', '(success_estimates, actor_configurations)'], {}), '(frontier_displayer_name, (success_estimates, actor_configurations))\n', (1035, 1103), False, 'from neodroid.models import Displayable\n')] |
# --------------
import pandas as pd
df = pd.read_csv(path)
df["state"] = df["state"].apply(lambda x : x.lower())
df["total"] = df["Jan"] + df["Feb"] + df["Mar"]
sum_row = df[['Jan','Feb','Mar','total']].sum()
df_final = pd.read_csv(path)
df_final = df_final.append(sum_row,ignore_index=True)
print(df_final)
# ... | [
"pandas.read_html",
"requests.get",
"pandas.read_csv"
] | [((43, 60), 'pandas.read_csv', 'pd.read_csv', (['path'], {}), '(path)\n', (54, 60), True, 'import pandas as pd\n'), ((226, 243), 'pandas.read_csv', 'pd.read_csv', (['path'], {}), '(path)\n', (237, 243), True, 'import pandas as pd\n'), ((453, 470), 'requests.get', 'requests.get', (['url'], {}), '(url)\n', (465, 470), Fa... |
"""
Database models
Based on: https://docs.djangoproject.com/en/3.1/topics/db/models/#many-to-many-relationships
"""
from django.db import models
class Motifs(models.Model):
""" Saved motif sequences """
sequence = models.CharField(
max_length=1000,
unique=True,
primary_key=True
)
... | [
"django.db.models.TextField",
"django.db.models.ManyToManyField",
"django.db.models.CharField",
"django.db.models.ForeignKey"
] | [((224, 288), 'django.db.models.CharField', 'models.CharField', ([], {'max_length': '(1000)', 'unique': '(True)', 'primary_key': '(True)'}), '(max_length=1000, unique=True, primary_key=True)\n', (240, 288), False, 'from django.db import models\n'), ((337, 393), 'django.db.models.TextField', 'models.TextField', ([], {'m... |
# Question 9
# Get the size of an object in bytes
import sys
var1 = input("Enter a value of the object: ")
print(sys.getsizeof(var1), "bytes")
| [
"sys.getsizeof"
] | [((115, 134), 'sys.getsizeof', 'sys.getsizeof', (['var1'], {}), '(var1)\n', (128, 134), False, 'import sys\n')] |
# vim: tabstop=4 shiftwidth=4 softtabstop=4
# Copyright 2012 OpenStack LLC
#
# 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 requ... | [
"keystone.common.serializer.to_xml",
"re.sub",
"keystone.common.serializer.from_xml"
] | [((1241, 1268), 'keystone.common.serializer.to_xml', 'serializer.to_xml', (['d', 'xmlns'], {}), '(d, xmlns)\n', (1258, 1268), False, 'from keystone.common import serializer\n'), ((1300, 1324), 'keystone.common.serializer.from_xml', 'serializer.from_xml', (['xml'], {}), '(xml)\n', (1319, 1324), False, 'from keystone.com... |
# Preppin' Data 2021 Week 01
import os
import pandas
import numpy
# Load csv
data = pandas.read_csv('unprepped_data\\PD 2021 Wk 1 Input - Bike Sales.csv')
# Split the 'Store-Bike' into 'Store' and 'Bike'
data[['Store','Bike']] = data['Store - Bike'].str.split(' - ', expand=True)
# Clean up the 'Bike' field to: Mou... | [
"numpy.where",
"pandas.to_datetime",
"pandas.read_csv"
] | [((87, 157), 'pandas.read_csv', 'pandas.read_csv', (['"""unprepped_data\\\\PD 2021 Wk 1 Input - Bike Sales.csv"""'], {}), "('unprepped_data\\\\PD 2021 Wk 1 Input - Bike Sales.csv')\n", (102, 157), False, 'import pandas\n'), ((582, 614), 'pandas.to_datetime', 'pandas.to_datetime', (["data['Date']"], {}), "(data['Date'])... |
from django.contrib import admin
from .models import *
# Register your models here.
admin.site.register(User)
admin.site.register(Appointment)
admin.site.register(Service)
admin.site.register(Review)
admin.site.register(Membership)
admin.site.register(Cart)
admin.site.register(Payment)
| [
"django.contrib.admin.site.register"
] | [((86, 111), 'django.contrib.admin.site.register', 'admin.site.register', (['User'], {}), '(User)\n', (105, 111), False, 'from django.contrib import admin\n'), ((112, 144), 'django.contrib.admin.site.register', 'admin.site.register', (['Appointment'], {}), '(Appointment)\n', (131, 144), False, 'from django.contrib impo... |
from StatisticLabSupport import statistic_lab_support
from StatisticLab import statistic_lab_toolkits
from StatisticLabVisualizer import statistic_lab_vizard
a = [7,6,1,7,9]
b = [3,2,6,9,5]
x=[-2,-1,0,1,2]
y=[-2,1,4,5,5]
z = [100, 120, 400, 100000, -1, -200000, 400, 4700]
Flight = [7.43,7.21, 8.69, 8.64, 9.76, 6.85,
... | [
"StatisticLab.statistic_lab_toolkits",
"StatisticLabVisualizer.statistic_lab_vizard"
] | [((484, 508), 'StatisticLab.statistic_lab_toolkits', 'statistic_lab_toolkits', ([], {}), '()\n', (506, 508), False, 'from StatisticLab import statistic_lab_toolkits\n'), ((520, 542), 'StatisticLabVisualizer.statistic_lab_vizard', 'statistic_lab_vizard', ([], {}), '()\n', (540, 542), False, 'from StatisticLabVisualizer ... |
#interface for loading and saving json lists of events
import simplejson as json
def SaveFile(events_list):
try:
events_json = json.dumps(events_list)
with open('data.json', 'w') as write_file:
json.dump(events_json, write_file)
print("list saved")
print(events_json)
... | [
"simplejson.dumps",
"simplejson.dump",
"simplejson.load"
] | [((141, 164), 'simplejson.dumps', 'json.dumps', (['events_list'], {}), '(events_list)\n', (151, 164), True, 'import simplejson as json\n'), ((480, 500), 'simplejson.load', 'json.load', (['read_file'], {}), '(read_file)\n', (489, 500), True, 'import simplejson as json\n'), ((228, 262), 'simplejson.dump', 'json.dump', ([... |
from django.db import models
import datetime
# Импортируем настройки приложения polls
from polls import settings
# Create your models here.
class Question(models.Model):
"""Вопрос"""
title = models.CharField(max_length=200, verbose_name="Вопрос")
date_published = models.DateTimeField(verbose_name="Дата пу... | [
"django.db.models.ForeignKey",
"django.db.models.IntegerField",
"django.db.models.BooleanField",
"datetime.datetime.now",
"django.db.models.CharField"
] | [((201, 256), 'django.db.models.CharField', 'models.CharField', ([], {'max_length': '(200)', 'verbose_name': '"""Вопрос"""'}), "(max_length=200, verbose_name='Вопрос')\n", (217, 256), False, 'from django.db import models\n'), ((422, 469), 'django.db.models.BooleanField', 'models.BooleanField', ([], {'verbose_name': '""... |
''' Send email module of the LS CI/CD Integration toolkit,
implements the necessary methods to notify of the results
of the CI/CD process'''
import os
import smtplib
import sys
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
from string import Template
import traceback
from ... | [
"modules.utils.get_salutation",
"traceback.format_exc",
"modules.utils.get_smtp_server",
"string.Template",
"email.mime.text.MIMEText",
"modules.utils.get_allure_url",
"os.environ.get",
"modules.custom_argparser.parse_args",
"email.mime.multipart.MIMEMultipart",
"modules.utils.print_key_value_list... | [((686, 728), 'os.environ.get', 'os.environ.get', (['"""gitMergeRequestId"""', '"""125"""'], {}), "('gitMergeRequestId', '125')\n", (700, 728), False, 'import os\n'), ((740, 802), 'os.environ.get', 'os.environ.get', (['"""gitMergeRequestTitle"""', '"""This is a Mock Title"""'], {}), "('gitMergeRequestTitle', 'This is a... |
from django.conf.urls import url, include
from . import views
from rest_framework.routers import DefaultRouter
# Create a router and register our viewsets with it.
router = DefaultRouter()
router.register(r'organization', views.OrganizationViewSet, 'organization')
router.register(r'user', views.UserViewSet, base_name=... | [
"django.conf.urls.include",
"django.conf.urls.url",
"rest_framework.routers.DefaultRouter"
] | [((174, 189), 'rest_framework.routers.DefaultRouter', 'DefaultRouter', ([], {}), '()\n', (187, 189), False, 'from rest_framework.routers import DefaultRouter\n'), ((465, 494), 'django.conf.urls.url', 'url', (['"""^callback/"""', 'views.line'], {}), "('^callback/', views.line)\n", (468, 494), False, 'from django.conf.ur... |
#!/Users/caihaocui/opt/miniconda3/bin/python
# %% load the package
from os import name
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.python.keras.engine.sequential import relax_input_shape
print(tf.__version__)
# %% Load the data
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_te... | [
"tensorflow.keras.losses.SparseCategoricalCrossentropy",
"tensorflow.keras.layers.Dropout",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.layers.Softmax",
"tensorflow.keras.layers.Flatten"
] | [((644, 707), 'tensorflow.keras.losses.SparseCategoricalCrossentropy', 'tf.keras.losses.SparseCategoricalCrossentropy', ([], {'from_logits': '(True)'}), '(from_logits=True)\n', (689, 707), True, 'import tensorflow as tf\n'), ((481, 517), 'tensorflow.keras.layers.Flatten', 'layers.Flatten', ([], {'input_shape': '(28, 28... |
from ray import tune
from tensorflow import keras
class TuneReporter(keras.callbacks.Callback):
"""Tune Callback for Keras."""
def __init__(self, metric="val_mcc"):
super().__init__()
self.metric = metric
def on_epoch_end(self, epoch, logs=None):
print(logs)
tune.report(
... | [
"ray.tune.report"
] | [((307, 399), 'ray.tune.report', 'tune.report', ([], {'keras_info': 'logs', 'val_loss': "logs['val_loss']", 'val_accuracy': 'logs[self.metric]'}), "(keras_info=logs, val_loss=logs['val_loss'], val_accuracy=logs[\n self.metric])\n", (318, 399), False, 'from ray import tune\n')] |
import asyncio
import pytest
from panini.async_test_client import AsyncTestClient
from panini import app as panini_app
def run_panini():
app = panini_app.App(
service_name="async_test_client_test_error_handling",
host="127.0.0.1",
port=4222,
)
@app.listen("async_test_client.test... | [
"panini.async_test_client.AsyncTestClient",
"panini.app.App",
"pytest.raises"
] | [((151, 253), 'panini.app.App', 'panini_app.App', ([], {'service_name': '"""async_test_client_test_error_handling"""', 'host': '"""127.0.0.1"""', 'port': '(4222)'}), "(service_name='async_test_client_test_error_handling', host=\n '127.0.0.1', port=4222)\n", (165, 253), True, 'from panini import app as panini_app\n')... |
import os
import torch
import torch.nn as nn
import engine
from dataset import BengaliDatasetTrain
import config
from model_dispatcher import MODEL_DISPATCHER
from torch.utils.data import DataLoader
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def run():
model = MODEL_DISPATCHER[config.B... | [
"torch.optim.lr_scheduler.ReduceLROnPlateau",
"torch.cuda.device_count",
"torch.nn.DataParallel",
"engine.train",
"torch.cuda.is_available",
"torch.utils.data.DataLoader",
"engine.evaluate",
"dataset.BengaliDatasetTrain"
] | [((390, 556), 'dataset.BengaliDatasetTrain', 'BengaliDatasetTrain', ([], {'folds': 'config.TRAINING_FOLDS', 'image_height': 'config.IMG_HEIGHT', 'image_width': 'config.IMG_WIDTH', 'mean': 'config.MODEL_MEAN', 'std': 'config.MODEL_STD'}), '(folds=config.TRAINING_FOLDS, image_height=config.\n IMG_HEIGHT, image_width=c... |
import copy
import numpy as np
import imageio
import torch
import torch.nn.functional as F
from models.rendering import get_rays_tourism, sample_points, volume_render
def test_time_optimize(args, model, meta_state_dict, tto_view):
"""
quicky optimize the meta trained model to a target appearance
and retur... | [
"torch.nn.functional.mse_loss",
"torch.as_tensor",
"numpy.ones",
"models.rendering.get_rays_tourism",
"imageio.mimwrite",
"models.rendering.sample_points",
"numpy.stack",
"numpy.linspace",
"torch.randint",
"torch.no_grad",
"models.rendering.volume_render",
"torch.cat"
] | [((543, 630), 'models.rendering.get_rays_tourism', 'get_rays_tourism', (["tto_view['H']", "tto_view['W']", "tto_view['kinv']", "tto_view['pose']"], {}), "(tto_view['H'], tto_view['W'], tto_view['kinv'], tto_view[\n 'pose'])\n", (559, 630), False, 'from models.rendering import get_rays_tourism, sample_points, volume_... |
# -*- coding: utf-8 -*-
import pandas as pd
import re
from urllib.parse import quote_plus
from vespid import setup_logger
import gzip
import os
import shutil
from vespid import setup_logger, set_global_log_level
logger = setup_logger(module_name=__name__)
def replace_special_solr_characters(data, columns=None):
... | [
"pandas.Series",
"shutil.copyfileobj",
"gzip.open",
"vespid.setup_logger",
"pandas.DataFrame",
"os.remove"
] | [((222, 256), 'vespid.setup_logger', 'setup_logger', ([], {'module_name': '__name__'}), '(module_name=__name__)\n', (234, 256), False, 'from vespid import setup_logger, set_global_log_level\n'), ((4376, 4392), 'pandas.Series', 'pd.Series', (['lists'], {}), '(lists)\n', (4385, 4392), True, 'import pandas as pd\n'), ((48... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import division, print_function
"""
Example of a 4 dimensional root finidng problem (chemical
acid/base equilbria)
"""
from sympy import pprint
from symneqsys import SimpleNEQSys, Problem
from symneqsys.minpack import MINPACK_Solver
class ChemSys(Simpl... | [
"logging.basicConfig",
"sympy.pprint",
"logging.getLogger",
"symneqsys.minpack.MINPACK_Solver"
] | [((1471, 1484), 'sympy.pprint', 'pprint', (['sys.v'], {}), '(sys.v)\n', (1477, 1484), False, 'from sympy import pprint\n'), ((1586, 1601), 'sympy.pprint', 'pprint', (['sys.jac'], {}), '(sys.jac)\n', (1592, 1601), False, 'from sympy import pprint\n'), ((1615, 1690), 'symneqsys.minpack.MINPACK_Solver', 'MINPACK_Solver', ... |
import numpy as np
from keras.models import Sequential
from keras.layers.core import Dense, Activation
from keras.optimizers import SGD
from grid.clients.keras import KerasClient
from grid.workers.compute import GridCompute
import time
from threading import Thread
import pytest
client = None
compute_id = None
@pytes... | [
"keras.layers.core.Activation",
"grid.clients.keras.KerasClient",
"time.sleep",
"keras.models.Sequential",
"numpy.array",
"keras.optimizers.SGD",
"keras.layers.core.Dense"
] | [((357, 370), 'grid.clients.keras.KerasClient', 'KerasClient', ([], {}), '()\n', (368, 370), False, 'from grid.clients.keras import KerasClient\n'), ((485, 499), 'time.sleep', 'time.sleep', (['(30)'], {}), '(30)\n', (495, 499), False, 'import time\n'), ((1034, 1076), 'numpy.array', 'np.array', (['[[0, 0], [0, 1], [1, 0... |
from combination_wall import ConbinationWall
from constants import WIDTH, HEIGHT, UP, DOWN, NORMAL, SPECIAL
from coin import Coin
from wall import Wall
import pyxel
from leotti import Leotti
from typing import List
class MainScreen:
def __init__(self):
self.reset()
def update(self):
if self.g... | [
"coin.Coin",
"pyxel.text",
"pyxel.btnp",
"pyxel.playm",
"pyxel.play",
"pyxel.stop",
"pyxel.btn",
"combination_wall.ConbinationWall",
"leotti.Leotti"
] | [((1693, 1715), 'leotti.Leotti', 'Leotti', (['(20)', '(HEIGHT / 2)'], {}), '(20, HEIGHT / 2)\n', (1699, 1715), False, 'from leotti import Leotti\n'), ((1829, 1856), 'pyxel.play', 'pyxel.play', (['(3)', '(2)'], {'loop': '(True)'}), '(3, 2, loop=True)\n', (1839, 1856), False, 'import pyxel\n'), ((345, 368), 'pyxel.btnp',... |
from pynemo.core.base.abstract.expression import Expression
from pynemo.core.base.operation import CreateOperation, MatchOperation, ReturnOperation
class Statement(Expression):
def __init__(self):
self.operations = []
def match(self, *exp: Expression):
op = MatchOperation(*exp)
self.o... | [
"pynemo.core.base.operation.MatchOperation",
"pynemo.core.base.operation.ReturnOperation",
"pynemo.core.base.operation.CreateOperation"
] | [((285, 305), 'pynemo.core.base.operation.MatchOperation', 'MatchOperation', (['*exp'], {}), '(*exp)\n', (299, 305), False, 'from pynemo.core.base.operation import CreateOperation, MatchOperation, ReturnOperation\n'), ((429, 450), 'pynemo.core.base.operation.CreateOperation', 'CreateOperation', (['*exp'], {}), '(*exp)\... |
""" CCOBRA evaluation handler.
"""
import copy
import pandas as pd
import numpy as np
from .. import tuple_to_string
class EvaluationHandler():
""" Evaluation handler class used to handle an evaluation setting.
"""
def __init__(self, data_column, comparator, predict_fn_name, adapt_fn_name, task_encode... | [
"pandas.DataFrame",
"copy.deepcopy"
] | [((2148, 2167), 'copy.deepcopy', 'copy.deepcopy', (['item'], {}), '(item)\n', (2161, 2167), False, 'import copy\n'), ((2182, 2200), 'copy.deepcopy', 'copy.deepcopy', (['aux'], {}), '(aux)\n', (2195, 2200), False, 'import copy\n'), ((6091, 6110), 'copy.deepcopy', 'copy.deepcopy', (['item'], {}), '(item)\n', (6104, 6110)... |
# -*- coding: utf-8 -*-
import pytest
import numpy as np
from ...hypothesiser.probability import PDAHypothesiser
from ...hypothesiser.distance import DistanceHypothesiser
from ...measures import Mahalanobis
from ...models.measurement.linear import LinearGaussian
from ...types.array import CovarianceMatrix
from ...mode... | [
"pytest.fixture",
"numpy.diag"
] | [((507, 523), 'pytest.fixture', 'pytest.fixture', ([], {}), '()\n', (521, 523), False, 'import pytest\n'), ((681, 697), 'pytest.fixture', 'pytest.fixture', ([], {}), '()\n', (695, 697), False, 'import pytest\n'), ((930, 946), 'pytest.fixture', 'pytest.fixture', ([], {}), '()\n', (944, 946), False, 'import pytest\n'), (... |
import torch
from collections import namedtuple
import copy
from rlpyt.utils.tensor import valid_mean
from rlpyt.ul.algos.ul_for_rl.base import BaseUlAlgorithm
from rlpyt.utils.quick_args import save__init__args
from rlpyt.utils.logging import logger
from rlpyt.ul.replays.offline_ul_replay import OfflineUlReplayBuffer
... | [
"collections.namedtuple",
"torch.nn.CrossEntropyLoss",
"torch.nn.ModuleList",
"torch.max",
"torch.argmax",
"rlpyt.utils.logging.logger.log",
"rlpyt.ul.algos.utils.data_augs.random_shift",
"torch.tensor",
"torch.arange",
"torch.matmul",
"torch.nn.Linear",
"copy.deepcopy",
"torch.no_grad",
"... | [((660, 844), 'collections.namedtuple', 'namedtuple', (['"""OptInfo"""', "['stcLoss', 'sprLoss', 'contrastLoss', 'cpcAccuracy1', 'cpcAccuracy2',\n 'cpcAccuracyTm1', 'cpcAccuracyTm2', 'contrast_accuracy', 'gradNorm',\n 'current_lr']"], {}), "('OptInfo', ['stcLoss', 'sprLoss', 'contrastLoss', 'cpcAccuracy1',\n '... |
import wandb
if __name__ == "__main__":
run = wandb.init()
run.finish()
| [
"wandb.init"
] | [((52, 64), 'wandb.init', 'wandb.init', ([], {}), '()\n', (62, 64), False, 'import wandb\n')] |
from fastapi import FastAPI, HTTPException, Query
from starlette.staticfiles import StaticFiles
from starlette.responses import FileResponse
from geojson import FeatureCollection, Feature, LineString
import redis
import json
redis_connection = redis.Redis(decode_responses=True)
app = FastAPI()
app.mount("/static", St... | [
"json.loads",
"fastapi.FastAPI",
"geojson.FeatureCollection",
"fastapi.HTTPException",
"geojson.LineString",
"starlette.responses.FileResponse",
"redis.Redis",
"starlette.staticfiles.StaticFiles",
"fastapi.Query"
] | [((245, 279), 'redis.Redis', 'redis.Redis', ([], {'decode_responses': '(True)'}), '(decode_responses=True)\n', (256, 279), False, 'import redis\n'), ((286, 295), 'fastapi.FastAPI', 'FastAPI', ([], {}), '()\n', (293, 295), False, 'from fastapi import FastAPI, HTTPException, Query\n'), ((318, 352), 'starlette.staticfiles... |
import datetime
from django.contrib.auth.models import User
from django.core.validators import MinValueValidator
from django.db import models
from rest_framework import serializers
def validate_duration(value):
if value % 15 != 0:
raise serializers.ValidationError('only even quarters allowed, for example... | [
"django.db.models.DateField",
"django.db.models.TextField",
"django.db.models.TimeField",
"django.db.models.ForeignKey",
"rest_framework.serializers.ValidationError",
"django.db.models.ManyToManyField",
"django.db.models.BooleanField",
"django.core.validators.MinValueValidator",
"django.db.models.Ch... | [((383, 415), 'django.db.models.CharField', 'models.CharField', ([], {'max_length': '(128)'}), '(max_length=128)\n', (399, 415), False, 'from django.db import models\n'), ((431, 464), 'django.db.models.BooleanField', 'models.BooleanField', ([], {'default': '(True)'}), '(default=True)\n', (450, 464), False, 'from django... |
"""
本体内容查询
对象、关系、备注
"""
from owlready2 import *
from app.operautils.OtherUtils import OtherUtils
class OntoContentSearch:
# 1 查询本体库内所有对象
@classmethod
def searchOwlClass(cls, fileName):
filepath = "../owl/%s.owl" % (fileName)
onto = get_ontology(filepath).load()
ow... | [
"app.operautils.OtherUtils.OtherUtils.changeToD3"
] | [((3115, 3152), 'app.operautils.OtherUtils.OtherUtils.changeToD3', 'OtherUtils.changeToD3', (['classLayerList'], {}), '(classLayerList)\n', (3136, 3152), False, 'from app.operautils.OtherUtils import OtherUtils\n')] |
from gym_risk.envs.game.ai import AI
import random
import collections
class BetterAI(AI):
"""
BetterAI: Thinks about what it is doing a little more - picks a priority
continent and priorities holding and reinforcing it.
"""
def start(self):
self.area_priority = list(self.world.areas)
... | [
"random.choice",
"collections.defaultdict",
"random.shuffle"
] | [((324, 358), 'random.shuffle', 'random.shuffle', (['self.area_priority'], {}), '(self.area_priority)\n', (338, 358), False, 'import random\n'), ((1008, 1036), 'collections.defaultdict', 'collections.defaultdict', (['int'], {}), '(int)\n', (1031, 1036), False, 'import collections\n'), ((1081, 1104), 'random.choice', 'r... |
"""adding_social_group
Revision ID: 13241dd1fd5b
Revises: <PASSWORD>f<PASSWORD>
Create Date: 2021-10-26 15:15:10.855822
"""
# revision identifiers, used by Alembic.
revision = '13241dd1fd5b'
down_revision = '922f78ec3698'
from alembic import op
import sqlalchemy as sa
import sqlalchemy_utils
import app
import app.... | [
"sqlalchemy.DateTime",
"alembic.op.drop_table",
"alembic.op.f",
"app.extensions.JSON",
"app.extensions.GUID",
"alembic.op.batch_alter_table",
"sqlalchemy.String"
] | [((3085, 3136), 'alembic.op.drop_table', 'op.drop_table', (['"""social_group_individual_membership"""'], {}), "('social_group_individual_membership')\n", (3098, 3136), False, 'from alembic import op\n'), ((3348, 3377), 'alembic.op.drop_table', 'op.drop_table', (['"""social_group"""'], {}), "('social_group')\n", (3361, ... |
from django.contrib import admin
from .models import Movie, UserRating, UserList
# Register your models here.
admin.site.register(Movie)
admin.site.register(UserRating)
admin.site.register(UserList) | [
"django.contrib.admin.site.register"
] | [((111, 137), 'django.contrib.admin.site.register', 'admin.site.register', (['Movie'], {}), '(Movie)\n', (130, 137), False, 'from django.contrib import admin\n'), ((138, 169), 'django.contrib.admin.site.register', 'admin.site.register', (['UserRating'], {}), '(UserRating)\n', (157, 169), False, 'from django.contrib imp... |
#!/usr/bin/env python
__author__ = '<NAME>'
#============================================================================
import os
import sys
import time
import uuid
import shutil
import importlib
import subprocess
import numpy as np
from Utils.utils import Printer, ParserJSON
#================================... | [
"main.get_suggestion",
"importlib.import_module",
"spearmint.resources.resource.parse_resources_from_config",
"time.time",
"numpy.linalg.norm",
"time.sleep",
"os.getcwd",
"os.chdir",
"Utils.utils.ParserJSON",
"numpy.array",
"shutil.rmtree",
"os.mkdir",
"subprocess.call",
"numpy.random.unif... | [((373, 384), 'os.getcwd', 'os.getcwd', ([], {}), '()\n', (382, 384), False, 'import os\n'), ((385, 439), 'sys.path.append', 'sys.path.append', (["('%s/ParamGenerator/Spearmint/' % home)"], {}), "('%s/ParamGenerator/Spearmint/' % home)\n", (400, 439), False, 'import sys\n'), ((440, 503), 'sys.path.append', 'sys.path.ap... |
#!/usr/bin/env python
# system imports
import argparse
# local/app imports
from audio2rgb import Audio2RGB
# setup the argument parser
parser = argparse.ArgumentParser(description='audio2rgb CLI')
parser.add_argument('filename', help='filename of audio input')
parser.add_argument("--verbose", help="enable verbose mo... | [
"audio2rgb.Audio2RGB",
"argparse.ArgumentParser"
] | [((147, 199), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '"""audio2rgb CLI"""'}), "(description='audio2rgb CLI')\n", (170, 199), False, 'import argparse\n'), ((626, 676), 'audio2rgb.Audio2RGB', 'Audio2RGB', (['args.filename', 'args.verbose', 'args.debug'], {}), '(args.filename, args.verb... |
from fractions import Fraction as frac
from solver import oper
res = []
def solve_all_rec(l, r):
if(len(l) == 1):
if(l[0][0] == r):
res.append(l[0][1])
for i in range(len(l)):
for j in range(i+1, len(l)):
for t in range(6):
try:
op = oper(t,l[i],l[j])
except:
continue
else:
nl =... | [
"fractions.Fraction",
"solver.oper"
] | [((503, 510), 'fractions.Fraction', 'frac', (['r'], {}), '(r)\n', (507, 510), True, 'from fractions import Fraction as frac\n'), ((461, 472), 'fractions.Fraction', 'frac', (['nl[i]'], {}), '(nl[i])\n', (465, 472), True, 'from fractions import Fraction as frac\n'), ((257, 276), 'solver.oper', 'oper', (['t', 'l[i]', 'l[j... |
#!/usr/bin/python3
import random
s=[]
with open('dirstart.txt','r') as fp:
for line in fp:
s.append(int(line))
g=[]
with open('dirdest.txt','r') as fp:
for line in fp:
g.append(int(line))
v={}
with open('dirvertices.txt','r') as vfp:
for line in vfp:
l=line.split(' ')
v[int(l[0])]=[int(f) for f... | [
"numpy.array",
"random.randint"
] | [((870, 890), 'random.randint', 'random.randint', (['(0)', '(1)'], {}), '(0, 1)\n', (884, 890), False, 'import random\n'), ((402, 413), 'numpy.array', 'np.array', (['a'], {}), '(a)\n', (410, 413), True, 'import numpy as np\n'), ((414, 425), 'numpy.array', 'np.array', (['b'], {}), '(b)\n', (422, 425), True, 'import nump... |
import os
from dotenv import load_dotenv
basedir = os.path.abspath(os.path.dirname(__file__))
load_dotenv(os.path.join(basedir, ".env"))
class Config(object):
SECRET_KEY = os.environ.get("SECRET_KEY") or "very-difficult-random-bstring"
SQLALCHEMY_DATABASE_URI = os.environ.get(
"DATABASE_U... | [
"os.path.dirname",
"os.path.join",
"os.environ.get"
] | [((73, 98), 'os.path.dirname', 'os.path.dirname', (['__file__'], {}), '(__file__)\n', (88, 98), False, 'import os\n'), ((113, 142), 'os.path.join', 'os.path.join', (['basedir', '""".env"""'], {}), "(basedir, '.env')\n", (125, 142), False, 'import os\n'), ((189, 217), 'os.environ.get', 'os.environ.get', (['"""SECRET_KEY... |
from src.datetime import DateTime
from src.filemanager import FileManager
class Logger:
__LOG_DIR = '/var/log/'
__INFO_LOG_FILE = 'info.log'
__ERROR_LOG_FILE = 'exception.log'
def __init__(self):
self.datetime = DateTime()
self.filemanager = FileManager()
def info(self, message):... | [
"src.datetime.DateTime",
"src.filemanager.FileManager"
] | [((239, 249), 'src.datetime.DateTime', 'DateTime', ([], {}), '()\n', (247, 249), False, 'from src.datetime import DateTime\n'), ((277, 290), 'src.filemanager.FileManager', 'FileManager', ([], {}), '()\n', (288, 290), False, 'from src.filemanager import FileManager\n')] |
# This is a sample Python script.
# Press ⌃R to execute it or replace it with your code.
# Press Double ⇧ to search everywhere for classes, files, tool windows, actions, and settings.
import nltk
from nltk import punkt
from nltk.corpus import stopwords
import matplotlib.pyplot as plt
LINES = ['-', ':', '--'] # Line s... | [
"nltk.pos_tag",
"nltk.corpus.stopwords.words",
"nltk.word_tokenize",
"nltk.FreqDist",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.ion",
"matplotlib.pyplot.legend"
] | [((1506, 1519), 'matplotlib.pyplot.figure', 'plt.figure', (['(1)'], {}), '(1)\n', (1516, 1519), True, 'import matplotlib.pyplot as plt\n'), ((1524, 1533), 'matplotlib.pyplot.ion', 'plt.ion', ([], {}), '()\n', (1531, 1533), True, 'import matplotlib.pyplot as plt\n'), ((1964, 1976), 'matplotlib.pyplot.legend', 'plt.legen... |
import tensorflow as tf
def deconvLayer(x,kernelSize,outMaps,stride): #default caffe style MRSA
with tf.variable_scope(None,default_name="deconv"):
inMaps = x.get_shape()[3]
kShape = [kernelSize,kernelSize,outMaps,inMaps]
w = tf.get_variable("weights",shape=kShape,initializer=tf.uniform_unit_scaling_initialize... | [
"tensorflow.variable_scope",
"tensorflow.uniform_unit_scaling_initializer",
"tensorflow.nn.conv2d_transpose",
"tensorflow.add_to_collection",
"tensorflow.stack"
] | [((103, 149), 'tensorflow.variable_scope', 'tf.variable_scope', (['None'], {'default_name': '"""deconv"""'}), "(None, default_name='deconv')\n", (120, 149), True, 'import tensorflow as tf\n'), ((327, 361), 'tensorflow.add_to_collection', 'tf.add_to_collection', (['"""weights"""', 'w'], {}), "('weights', w)\n", (347, 36... |
# AntiBiofilm Peptide Research
# Department of Computer Science and Engineering, Santa Clara University
# Author: <NAME>
# A python script that performs forward selection and hyperparameter optimization
# in order to find the best performing SVR model for the MBEC peptides
# Loss function used is RMSE
# The script dum... | [
"sklearn.model_selection.RepeatedKFold",
"numpy.mean",
"numpy.sqrt",
"pandas.read_csv",
"numpy.min",
"sklearn.metrics.mean_squared_error",
"numpy.around",
"sklearn.utils.validation.column_or_1d",
"copy.deepcopy",
"numpy.argmin",
"sys.stdout.flush",
"sklearn.svm.SVR",
"sklearn.preprocessing.M... | [((1012, 1045), 'warnings.filterwarnings', 'warnings.filterwarnings', (['"""ignore"""'], {}), "('ignore')\n", (1035, 1045), False, 'import warnings\n'), ((1785, 1833), 'pandas.read_csv', 'pd.read_csv', (['"""../../data/mbec_training_data.csv"""'], {}), "('../../data/mbec_training_data.csv')\n", (1796, 1833), True, 'imp... |
import asyncio
from timeit import default_timer
import settings
from backends import aiohttp, requests
def extract_trips(data):
return sum([item.get('trips') or 0 for item in data['data']])
if __name__ == "__main__":
url = input(f"Input URL [default: {settings.URL}]: ") or settings.URL
prompt = """
... | [
"timeit.default_timer",
"asyncio.get_event_loop",
"backends.aiohttp.fetch_many",
"backends.requests.fetch_many"
] | [((726, 741), 'timeit.default_timer', 'default_timer', ([], {}), '()\n', (739, 741), False, 'from timeit import default_timer\n'), ((753, 777), 'asyncio.get_event_loop', 'asyncio.get_event_loop', ([], {}), '()\n', (775, 777), False, 'import asyncio\n'), ((815, 830), 'timeit.default_timer', 'default_timer', ([], {}), '(... |
import tensorflow as tf
from tensorflow.keras.applications.vgg16 import preprocess_input
from featureExtractorBase import FeatureExtractorBase
class FeatureExtractorVGG16_Block5(FeatureExtractorBase):
"""Feature extractor based on VGG16 at block 5 without the last max pooling layer (trained on ImageNet)."""
I... | [
"tensorflow.keras.applications.VGG16",
"tensorflow.keras.applications.vgg16.preprocess_input",
"tensorflow.cast",
"tensorflow.image.resize"
] | [((624, 741), 'tensorflow.keras.applications.VGG16', 'tf.keras.applications.VGG16', ([], {'input_shape': '(self.IMG_SIZE, self.IMG_SIZE, 3)', 'include_top': '(False)', 'weights': '"""imagenet"""'}), "(input_shape=(self.IMG_SIZE, self.IMG_SIZE, 3),\n include_top=False, weights='imagenet')\n", (651, 741), True, 'impor... |
from django.test import testcases
from graphql_jwt.testcases import JSONWebTokenClient
from gw_viterbi.schema import schema
from graphql_jwt.shortcuts import get_token
from graphql_jwt.settings import jwt_settings
class ViterbiJSONWebTokenClient(JSONWebTokenClient):
"""Viterbi test client with a custom authentica... | [
"graphql_jwt.shortcuts.get_token"
] | [((608, 652), 'graphql_jwt.shortcuts.get_token', 'get_token', (['user'], {'userId': 'user.id', 'isLigo': '(True)'}), '(user, userId=user.id, isLigo=True)\n', (617, 652), False, 'from graphql_jwt.shortcuts import get_token\n')] |
import gc
import os
import pickle
from skopt import gp_minimize
from skopt.callbacks import CheckpointSaver
from skopt import load
import pandas as pd
from EEG_Model.EEG_gpt2 import EEG_GPT2
from EEG_Model.EventDataset import EventDataset
""""
Global variables are necessary due to the Bayesian Optimization used
"""
t... | [
"pandas.read_pickle",
"skopt.callbacks.CheckpointSaver",
"os.path.exists",
"pickle.dump",
"os.rename",
"skopt.gp_minimize",
"os.remove",
"gc.collect",
"EEG_Model.EventDataset.EventDataset",
"skopt.load",
"EEG_Model.EEG_gpt2.EEG_GPT2"
] | [((1599, 1609), 'EEG_Model.EEG_gpt2.EEG_GPT2', 'EEG_GPT2', ([], {}), '()\n', (1607, 1609), False, 'from EEG_Model.EEG_gpt2 import EEG_GPT2\n'), ((1914, 1926), 'gc.collect', 'gc.collect', ([], {}), '()\n', (1924, 1926), False, 'import gc\n'), ((2085, 2133), 'skopt.callbacks.CheckpointSaver', 'CheckpointSaver', (['opt_ch... |
"""
Scripts to parse the gender data for the PhD recipients.
genderize.io
gender-api.com
"""
import urllib
import json
import yaml
import glob
import numpy as np
import astropy
from astropy.io import ascii
import requests
import os
### gender-api.com
GENDER_API_KEY = os.getenv(GENDER_API_KEY)
### genederize.io
GEND... | [
"numpy.unique",
"os.getenv",
"requests.get",
"numpy.array",
"numpy.sum",
"os.system",
"astropy.io.ascii.read",
"numpy.arange"
] | [((271, 296), 'os.getenv', 'os.getenv', (['GENDER_API_KEY'], {}), '(GENDER_API_KEY)\n', (280, 296), False, 'import os\n'), ((336, 364), 'os.getenv', 'os.getenv', (['GENDERIZE_API_KEY'], {}), '(GENDERIZE_API_KEY)\n', (345, 364), False, 'import os\n'), ((2254, 2270), 'astropy.io.ascii.read', 'ascii.read', (['file'], {}),... |
import webbrowser
from threading import Thread
from time import sleep
def open_browser_tab(url):
def _open_tab():
sleep(1)
webbrowser.open_new_tab(url)
thread = Thread(target=_open_tab)
thread.daemon = True
thread.start()
| [
"webbrowser.open_new_tab",
"threading.Thread",
"time.sleep"
] | [((188, 212), 'threading.Thread', 'Thread', ([], {'target': '_open_tab'}), '(target=_open_tab)\n', (194, 212), False, 'from threading import Thread\n'), ((128, 136), 'time.sleep', 'sleep', (['(1)'], {}), '(1)\n', (133, 136), False, 'from time import sleep\n'), ((145, 173), 'webbrowser.open_new_tab', 'webbrowser.open_ne... |
import unittest
from mockito import mock
from eventcore.event import Event
from eventcore.dummy import DummyProducer, DummyQueue
class TestEvent(unittest.TestCase):
"""
"""
A_SUBJECT = 'a-subject'
A_EVENT = mock({
'name': 'a-name',
'topic': 'a-topic',
'subject': A_SUBJECT,
... | [
"eventcore.event.Event.dispatch",
"eventcore.event.Event.discover_topics",
"eventcore.dummy.DummyProducer",
"mockito.mock"
] | [((228, 328), 'mockito.mock', 'mock', (["{'name': 'a-name', 'topic': 'a-topic', 'subject': A_SUBJECT, 'data': 'a-data'}"], {'spec': 'Event'}), "({'name': 'a-name', 'topic': 'a-topic', 'subject': A_SUBJECT, 'data':\n 'a-data'}, spec=Event)\n", (232, 328), False, 'from mockito import mock\n'), ((433, 448), 'eventcore.... |
"""Application Models."""
from marshmallow import fields, Schema
from marshmallow.validate import OneOf
from ..enums import *
from ..models.BaseSchema import BaseSchema
class ValidateCustomerRequest(BaseSchema):
# Payment swagger.json
transaction_amount_in_paise = fields.Int(required=False)
... | [
"marshmallow.fields.Int",
"marshmallow.fields.Str",
"marshmallow.fields.Dict"
] | [((292, 318), 'marshmallow.fields.Int', 'fields.Int', ([], {'required': '(False)'}), '(required=False)\n', (302, 318), False, 'from marshmallow import fields, Schema\n'), ((343, 369), 'marshmallow.fields.Str', 'fields.Str', ([], {'required': '(False)'}), '(required=False)\n', (353, 369), False, 'from marshmallow import... |
from pathlib import Path
from typing import Tuple, Optional
import streamlit as st
import pandas as pd
from src.utils import io
@st.cache
def load_data(file_name: str, src_dir: str) -> pd.DataFrame:
if str(src_dir) == 'raw':
return io.load_csv_data(file_name, src_dir, io.filter_dt_session)
else:
... | [
"src.utils.io.load_csv_data",
"streamlit.spinner",
"streamlit.error",
"streamlit.sidebar.selectbox",
"pandas.DataFrame",
"src.utils.io.get_available_datasets"
] | [((766, 800), 'src.utils.io.get_available_datasets', 'io.get_available_datasets', (['src_dir'], {}), '(src_dir)\n', (791, 800), False, 'from src.utils import io\n'), ((914, 1000), 'streamlit.sidebar.selectbox', 'st.sidebar.selectbox', (['"""Source file: """'], {'options': 'available_files', 'index': 'default_idx'}), "(... |
#coding:utf-8
# 读取Mongo中短评数据，对其进行中文分词，并生成词云
# 读取Mongo中的短评数据
# https://pypi.org/project/pymongo/
# http://github.com/mongodb/mongo-python-driver
import pymongo
import jieba
from jieba import analyse
import collections
from matplotlib import pyplot
from wordcloud import WordCloud
from pyecharts import Bar
from pyecharts... | [
"matplotlib.pyplot.imshow",
"jieba.load_userdict",
"matplotlib.pyplot.axis",
"collections.Counter",
"wordcloud.WordCloud",
"matplotlib.pyplot.figure",
"pyecharts.Funnel",
"pymongo.MongoClient",
"jieba.analyse.set_stop_words",
"jieba.analyse.extract_tags"
] | [((754, 802), 'jieba.load_userdict', 'jieba.load_userdict', (['"""../analysis/user_dict.txt"""'], {}), "('../analysis/user_dict.txt')\n", (773, 802), False, 'import jieba\n'), ((821, 872), 'jieba.analyse.set_stop_words', 'analyse.set_stop_words', (['"""../analysis/stopwords.txt"""'], {}), "('../analysis/stopwords.txt')... |
from distutils.core import setup, Extension
m = Extension('tinyobjloader',
sources = ['main.cpp', '../tiny_obj_loader.cc'])
setup (name = 'tinyobjloader',
version = '0.1',
description = 'Python module for tinyobjloader',
ext_modules = [m])
| [
"distutils.core.Extension",
"distutils.core.setup"
] | [((50, 123), 'distutils.core.Extension', 'Extension', (['"""tinyobjloader"""'], {'sources': "['main.cpp', '../tiny_obj_loader.cc']"}), "('tinyobjloader', sources=['main.cpp', '../tiny_obj_loader.cc'])\n", (59, 123), False, 'from distutils.core import setup, Extension\n'), ((142, 253), 'distutils.core.setup', 'setup', (... |
import keras.backend as K
from keras.layers import Lambda, concatenate
def triplet_loss(y_true, y_pred):
"""
y_true : FAKE
y_pred : (3,embedding_units) vector
"""
alpha = 0.1
anchor = y_pred[0, :]
positive = y_pred[1,:]
negative = y_pred[2,:]
loss = K.sqrt(K.sum(K.square(anchor-posi... | [
"keras.backend.mean",
"keras.layers.Lambda",
"keras.backend.square",
"keras.layers.concatenate",
"keras.backend.maximum"
] | [((390, 410), 'keras.backend.maximum', 'K.maximum', (['(0.0)', 'loss'], {}), '(0.0, loss)\n', (399, 410), True, 'import keras.backend as K\n'), ((1204, 1235), 'keras.backend.maximum', 'K.maximum', (['(0.0)', 'loss_per_sample'], {}), '(0.0, loss_per_sample)\n', (1213, 1235), True, 'import keras.backend as K\n'), ((1257,... |
# -*- coding: utf-8 -*-
from enigma import eTimer, getDesktop, gFont, RT_HALIGN_CENTER, RT_VALIGN_CENTER
from Components.ActionMap import NumberActionMap
from Components.Label import Label
from Components.Sources.CanvasSource import CanvasSource
from Components.Sources.StaticText import StaticText
from Screens.Sc... | [
"Components.ActionMap.NumberActionMap",
"Tools.Directories.resolveFilename",
"__init__._",
"Components.Sources.CanvasSource.CanvasSource",
"enigma.eTimer",
"Screens.Screen.Screen.__init__",
"enigma.getDesktop",
"enigma.gFont",
"Components.Label.Label",
"random.randint"
] | [((5137, 5150), '__init__._', '_', (['"""Beginner"""'], {}), "('Beginner')\n", (5138, 5150), False, 'from __init__ import _\n'), ((5152, 5163), '__init__._', '_', (['"""Simple"""'], {}), "('Simple')\n", (5153, 5163), False, 'from __init__ import _\n'), ((5165, 5176), '__init__._', '_', (['"""Medium"""'], {}), "('Medium... |
import numpy as np
import pandas as pd
from tqdm import tqdm
from joblib import Parallel, delayed
import os
bitsize = 1024
total_sample = 110913349
data_save_folder = './data'
file = './data/%s_%s.npy' % (total_sample, bitsize)
f = np.memmap(file, dtype = np.bool, shape = (total_sample, bitsize))
def _sum(memmap... | [
"joblib.Parallel",
"joblib.delayed",
"numpy.memmap",
"pandas.Series"
] | [((236, 297), 'numpy.memmap', 'np.memmap', (['file'], {'dtype': 'np.bool', 'shape': '(total_sample, bitsize)'}), '(file, dtype=np.bool, shape=(total_sample, bitsize))\n', (245, 297), True, 'import numpy as np\n'), ((360, 379), 'joblib.Parallel', 'Parallel', ([], {'n_jobs': '(16)'}), '(n_jobs=16)\n', (368, 379), False, ... |
"""
---------------------------------------------------------------------
-- Author: <NAME>
---------------------------------------------------------------------
Util functions for partitioning input data
"""
import numpy as np
def partition_train_val(x_train, y_train, proportion, num_classes, shuffle=True):
""... | [
"numpy.prod",
"numpy.hstack",
"numpy.where",
"numpy.random.permutation",
"numpy.array",
"numpy.vstack",
"numpy.random.shuffle"
] | [((1105, 1133), 'numpy.array', 'np.array', (['[]'], {'dtype': 'np.int32'}), '([], dtype=np.int32)\n', (1113, 1133), True, 'import numpy as np\n'), ((1157, 1185), 'numpy.array', 'np.array', (['[]'], {'dtype': 'np.int32'}), '([], dtype=np.int32)\n', (1165, 1185), True, 'import numpy as np\n'), ((4392, 4412), 'numpy.prod'... |
annotations_dic = \
{"lipsUpperOuter": [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291,78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],
"lipsLowerOuter": [146, 91, 181, 84, 17, 314, 405, 321, 375, 291,78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],
"lipsUpperInner": [78, 191, 80, 81, 82, 13, 312, 311, 310, ... | [
"numpy.zeros",
"numpy.int32"
] | [((2534, 2579), 'numpy.zeros', 'np.zeros', (['(image.shape[0], image.shape[1], 3)'], {}), '((image.shape[0], image.shape[1], 3))\n', (2542, 2579), True, 'import numpy as np\n'), ((2975, 3020), 'numpy.zeros', 'np.zeros', (['(image.shape[0], image.shape[1], 3)'], {}), '((image.shape[0], image.shape[1], 3))\n', (2983, 302... |
__copyright__ = "Copyright (c) Microsoft Corporation and Mila - Quebec AI Institute"
__license__ = "MIT"
"""Metrics for MDPs
"""
import numpy as np
import ot
from segar.factors.number_factors import NumericFactor
from segar.factors.bools import BooleanFactor
from segar.factors.arrays import VectorFactor
from segar.m... | [
"ot.emd2",
"numpy.zeros",
"segar.metrics.wasserstein_distance",
"numpy.ones"
] | [((1981, 1999), 'numpy.zeros', 'np.zeros', (['(n1, n2)'], {}), '((n1, n2))\n', (1989, 1999), True, 'import numpy as np\n'), ((2821, 2849), 'segar.metrics.wasserstein_distance', 'wasserstein_distance', (['s1', 's2'], {}), '(s1, s2)\n', (2841, 2849), False, 'from segar.metrics import wasserstein_distance\n'), ((3377, 339... |
import sys, os
from read_struc import read_struc
from math import sin, cos
import numpy as np
def euler2rotmat(phi,ssi,rot):
cs=cos(ssi)
cp=cos(phi)
ss=sin(ssi)
sp=sin(phi)
cscp=cs*cp
cssp=cs*sp
sscp=ss*cp
sssp=ss*sp
crot=cos(rot)
srot=sin(rot)
r1 = crot * cscp + srot * sp
... | [
"numpy.eye",
"json.dumps",
"math.cos",
"numpy.array",
"math.sin"
] | [((133, 141), 'math.cos', 'cos', (['ssi'], {}), '(ssi)\n', (136, 141), False, 'from math import sin, cos\n'), ((149, 157), 'math.cos', 'cos', (['phi'], {}), '(phi)\n', (152, 157), False, 'from math import sin, cos\n'), ((165, 173), 'math.sin', 'sin', (['ssi'], {}), '(ssi)\n', (168, 173), False, 'from math import sin, c... |
import logging
import random
from typing import List, Any
from src.models import *
from src.helpers import viz_maze
logger = logging.getLogger(__name__)
class MazeGenerator:
def __init__(self, traps: dict, dimension: int):
self.dimension = dimension
self.traps = traps
self.maze = None
... | [
"logging.getLogger",
"src.helpers.viz_maze",
"random.choice"
] | [((128, 155), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (145, 155), False, 'import logging\n'), ((1231, 1250), 'src.helpers.viz_maze', 'viz_maze', (['self.maze'], {}), '(self.maze)\n', (1239, 1250), False, 'from src.helpers import viz_maze\n'), ((1259, 1285), 'src.helpers.viz_maze', ... |
#raise NotImplementedError
#import dbm
import time
class GDATA1:
def __init__(self):
pass
gdata1 = GDATA1()
SIM_MAX=1022
SIM_STEP=30
class ADC:
def __init__(self,pin):
self.pin=pin
self.v=0
self.dir=SIM_STEP
def read(self):
self.v+=self.dir
if self.... | [
"time.localtime"
] | [((2268, 2284), 'time.localtime', 'time.localtime', ([], {}), '()\n', (2282, 2284), False, 'import time\n')] |
# Generated by Django 3.0.4 on 2020-04-19 18:40
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
ope... | [
"django.db.models.TextField",
"django.db.models.IntegerField",
"django.db.models.ForeignKey",
"django.db.models.DurationField",
"django.db.models.AutoField",
"django.db.models.DateTimeField",
"django.db.migrations.swappable_dependency",
"django.db.models.CharField"
] | [((247, 304), 'django.db.migrations.swappable_dependency', 'migrations.swappable_dependency', (['settings.AUTH_USER_MODEL'], {}), '(settings.AUTH_USER_MODEL)\n', (278, 304), False, 'from django.db import migrations, models\n'), ((436, 529), 'django.db.models.AutoField', 'models.AutoField', ([], {'auto_created': '(True)... |
from rest_framework.documentation import include_docs_urls
from rest_framework.routers import DefaultRouter
from django.urls import path,include
from .views import *
router = DefaultRouter()
router.register('label',LableViewSet ,base_name='label')
router.register('song',SongViewSet ,base_name='song')
router.register... | [
"rest_framework.routers.DefaultRouter",
"django.urls.include"
] | [((177, 192), 'rest_framework.routers.DefaultRouter', 'DefaultRouter', ([], {}), '()\n', (190, 192), False, 'from rest_framework.routers import DefaultRouter\n'), ((462, 482), 'django.urls.include', 'include', (['router.urls'], {}), '(router.urls)\n', (469, 482), False, 'from django.urls import path, include\n')] |
#!/usr/bin/env python
import rospy
from waypoint_generator.msg import point_list
from geometry_msgs.msg import Point
from sensor_msgs.msg import NavSatFix
from geometry_msgs.msg import PoseStamped
from std_msgs.msg import Float64
# import tf
drone_gps = Point()
msg0 = point_list()
i = 0
def gps_callback(data):
g... | [
"rospy.Subscriber",
"rospy.is_shutdown",
"rospy.init_node",
"waypoint_generator.msg.point_list",
"geometry_msgs.msg.Point",
"rospy.Rate",
"rospy.Publisher"
] | [((255, 262), 'geometry_msgs.msg.Point', 'Point', ([], {}), '()\n', (260, 262), False, 'from geometry_msgs.msg import Point\n'), ((270, 282), 'waypoint_generator.msg.point_list', 'point_list', ([], {}), '()\n', (280, 282), False, 'from waypoint_generator.msg import point_list\n'), ((447, 454), 'geometry_msgs.msg.Point'... |
# 2017.01.30 15:56:30 IST
# Embedded file name: pyswitch/isis.py
"""
Copyright 2015 Brocade Communications Systems, Inc.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/li... | [
"pyswitch.utilities.Util"
] | [((8967, 8984), 'pyswitch.utilities.Util', 'Util', (['output.data'], {}), '(output.data)\n', (8971, 8984), False, 'from pyswitch.utilities import Util\n'), ((15024, 15041), 'pyswitch.utilities.Util', 'Util', (['output.data'], {}), '(output.data)\n', (15028, 15041), False, 'from pyswitch.utilities import Util\n')] |
import settings
from django.db.models import Q
from legacy.legacyprojects.models import Project as LegacyProject, Link as LegacyLink, Testimonial as LegacyTestimonial, Need
from apps.projects.models import Project, IdeaPhase, FundPhase, ActPhase, ResultsPhase, Link, Testimonial, BudgetLine, PartnerOrganization
from .b... | [
"logging.getLogger",
"django.db.models.Q",
"apps.projects.models.PartnerOrganization.objects.get"
] | [((834, 861), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (851, 861), False, 'import logging\n'), ((4399, 4441), 'apps.projects.models.PartnerOrganization.objects.get', 'PartnerOrganization.objects.get', ([], {'slug': 'slug'}), '(slug=slug)\n', (4430, 4441), False, 'from apps.projects.... |
import numpy as np
from loadsounds import parse_file, load_data_definition, reshape_dataset
data_def_file = 'sounddata-csv.yml'
datafile = 'cherry-sound-20200218113643319806.csv'
data_chunk = load_data_definition(data_def_file)
csv_dataset = parse_file(datafile,np.array([]),data_chunk, by_channel=True)
#sound_dataset ... | [
"loadsounds.reshape_dataset",
"loadsounds.load_data_definition",
"json.dumps",
"numpy.array",
"datetime.datetime.now",
"tensorflow.keras.models.load_model"
] | [((193, 228), 'loadsounds.load_data_definition', 'load_data_definition', (['data_def_file'], {}), '(data_def_file)\n', (213, 228), False, 'from loadsounds import parse_file, load_data_definition, reshape_dataset\n'), ((719, 753), 'tensorflow.keras.models.load_model', 'models.load_model', (['model_file_path'], {}), '(mo... |
import logging
import resource
logging.basicConfig(format='%(asctime)s - %(message)s', level=logging.INFO)
def print(info, include_mem=False):
logging.info(info)
if include_mem:
mem = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
logging.info(f'Memory consumption (Kb): {mem}')
def inter... | [
"logging.basicConfig",
"resource.getrusage",
"logging.info"
] | [((32, 107), 'logging.basicConfig', 'logging.basicConfig', ([], {'format': '"""%(asctime)s - %(message)s"""', 'level': 'logging.INFO'}), "(format='%(asctime)s - %(message)s', level=logging.INFO)\n", (51, 107), False, 'import logging\n'), ((149, 167), 'logging.info', 'logging.info', (['info'], {}), '(info)\n', (161, 167... |
"""Manages database connections and queries."""
import sqlite3
import os
import binascii
import hmac
from typing import cast, Optional
from typing_extensions import Final
import uita.auth
class Database():
"""Holds a single database connection and generates queries.
Args:
uri: URI pointing to datab... | [
"typing.cast",
"os.urandom",
"hmac.compare_digest",
"sqlite3.connect"
] | [((454, 474), 'sqlite3.connect', 'sqlite3.connect', (['uri'], {}), '(uri)\n', (469, 474), False, 'import sqlite3\n'), ((2511, 2561), 'hmac.compare_digest', 'hmac.compare_digest', (['db_session[0]', 'session.secret'], {}), '(db_session[0], session.secret)\n', (2530, 2561), False, 'import hmac\n'), ((3576, 3594), 'typing... |
"""
Copyright (c) 2020 COTOBA DESIGN, Inc.
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
documentation files (the "Software"), to deal in the Software without restriction, including without limitation
the rights to use, copy, modify, merge, publish, distri... | [
"xml.etree.ElementTree.fromstring"
] | [((1511, 1633), 'xml.etree.ElementTree.fromstring', 'ET.fromstring', (['"""\n <template>\n <resetlearn />\n </template>\n """'], {}), '(\n """\n <template>\n <resetlearn />\n </template>\n """\n )\n', (1524, 1633), Tru... |
import matplotlib.pyplot as plt
from cleanco import cleanco
from nltk.corpus import names, gazetteers
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from nltk.stem.lancaster import LancasterStemmer
from nltk.tokenize import TweetTokenizer
plt.style.use('ggplot')
import nltk
import scipy.sta... | [
"nltk.tokenize.TweetTokenizer",
"sklearn.grid_search.RandomizedSearchCV",
"nltk.corpus.stopwords.words",
"nltk.corpus.brown.words",
"matplotlib.pyplot.gca",
"nltk.stem.WordNetLemmatizer",
"nltk.stem.lancaster.LancasterStemmer",
"matplotlib.pyplot.style.use",
"sklearn.metrics.make_scorer",
"nltk.co... | [((267, 290), 'matplotlib.pyplot.style.use', 'plt.style.use', (['"""ggplot"""'], {}), "('ggplot')\n", (280, 290), True, 'import matplotlib.pyplot as plt\n'), ((531, 562), 'nltk.corpus.conll2002.fileids', 'nltk.corpus.conll2002.fileids', ([], {}), '()\n', (560, 562), False, 'import nltk\n'), ((1381, 1399), 'nltk.stem.la... |
import sys
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from transformers_interpret import SequenceClassificationExplainer
if __name__ == '__main__':
text = str(sys.argv[0])
model_name = 'garynguyen1174/disaster_tweet_bert'
model = AutoModelForSequenceClassification.from_pre... | [
"transformers.AutoModelForSequenceClassification.from_pretrained",
"transformers_interpret.SequenceClassificationExplainer",
"transformers.AutoTokenizer.from_pretrained"
] | [((277, 339), 'transformers.AutoModelForSequenceClassification.from_pretrained', 'AutoModelForSequenceClassification.from_pretrained', (['model_name'], {}), '(model_name)\n', (327, 339), False, 'from transformers import AutoModelForSequenceClassification, AutoTokenizer\n'), ((356, 397), 'transformers.AutoTokenizer.from... |
import logging
import sys
from time import time
import numpy as np
import itertools as it
import csv
import torch
from torch import nn
from torch import optim
from torch.utils.data import DataLoader
from torch.utils.data.sampler import SubsetRandomSampler
from zensols.actioncli import persisted
from zensols.dltools imp... | [
"logging.getLogger",
"torch.nn.CrossEntropyLoss",
"torch.max",
"torch.exp",
"zensols.actioncli.persisted",
"zensols.dlqaclass.Net",
"torch.utils.data.sampler.SubsetRandomSampler",
"torch.sort",
"csv.writer",
"numpy.floor",
"zensols.dlqaclass.QADataLoader",
"time.time",
"torch.cat",
"iterto... | [((406, 433), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (423, 433), False, 'import logging\n'), ((1892, 1917), 'zensols.actioncli.persisted', 'persisted', (['"""_data_loader"""'], {}), "('_data_loader')\n", (1901, 1917), False, 'from zensols.actioncli import persisted\n'), ((1233, 12... |
# coding=utf-8
# date: 2018/11/5, 18:40
# name: smz
import tensorflow as tf
from tensorflow.python.tools.inspect_checkpoint import print_tensors_in_checkpoint_file
def demo_one():
W = tf.Variable(4, name='W_op')
# saver = tf.train.Saver({v.op.name: v for v in [W]}) # tensor_name: W_op，这里可以看出我们命名的对象其实是操作
... | [
"tensorflow.Variable",
"tensorflow.Session",
"tensorflow.train.Saver",
"tensorflow.python.tools.inspect_checkpoint.print_tensors_in_checkpoint_file",
"tensorflow.global_variables_initializer"
] | [((191, 218), 'tensorflow.Variable', 'tf.Variable', (['(4)'], {'name': '"""W_op"""'}), "(4, name='W_op')\n", (202, 218), True, 'import tensorflow as tf\n'), ((396, 420), 'tensorflow.train.Saver', 'tf.train.Saver', (["{'W': W}"], {}), "({'W': W})\n", (410, 420), True, 'import tensorflow as tf\n'), ((616, 649), 'tensorfl... |
#!/usr/bin/python3
# -*- coding: utf-8 -*-
"""
.. module:: find_in_youtube
:platform: Unix
:synopsis: the top-level submodule of Dragonfire.commands that contains the classes related to Dragonfire's simple if-else struct of Searching in Youtube ability.
.. moduleauthors:: <NAME> <<EMAIL>>
<... | [
"time.sleep",
"pykeyboard.PyKeyboard",
"youtube_dl.YoutubeDL",
"ava.utilities.nostdout",
"ava.utilities.nostderr"
] | [((1690, 1700), 'ava.utilities.nostdout', 'nostdout', ([], {}), '()\n', (1698, 1700), False, 'from ava.utilities import nostdout, nostderr\n'), ((1723, 1733), 'ava.utilities.nostderr', 'nostderr', ([], {}), '()\n', (1731, 1733), False, 'from ava.utilities import nostdout, nostderr\n'), ((3141, 3153), 'pykeyboard.PyKeyb... |
import numpy as np
import torch
from relnet.state.graph_embedding import EmbedMeanField, EmbedLoopyBP
from relnet.utils.config_utils import get_device_placement
class GNNRegressor(object):
def __init__(self, hyperparams, s2v_module):
super(GNNRegressor, self).__init__()
self.hyperparams = hyperpa... | [
"relnet.utils.config_utils.get_device_placement"
] | [((712, 734), 'relnet.utils.config_utils.get_device_placement', 'get_device_placement', ([], {}), '()\n', (732, 734), False, 'from relnet.utils.config_utils import get_device_placement\n')] |
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: tensorflow_serving/core/test_util/fake_loader_source_adapter.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 _mes... | [
"google.protobuf.symbol_database.Default"
] | [((490, 516), 'google.protobuf.symbol_database.Default', '_symbol_database.Default', ([], {}), '()\n', (514, 516), True, 'from google.protobuf import symbol_database as _symbol_database\n')] |
import pandas as pd
import os
import numpy as np
import glob
inf = glob.glob('/home/wequ0318/sleep/data/eeg_fpz_cz/*.npz')
for _f in inf:
with np.load(_f) as f:
data = f["x"]
labels = f["y"]
sampling_rate = f["fs"]
df_data = pd.DataFrame(np.squeeze(data))
df_label = pd.DataFr... | [
"numpy.repeat",
"numpy.arange",
"numpy.squeeze",
"os.path.basename",
"pandas.DataFrame",
"numpy.load",
"pandas.concat",
"glob.glob"
] | [((67, 122), 'glob.glob', 'glob.glob', (['"""/home/wequ0318/sleep/data/eeg_fpz_cz/*.npz"""'], {}), "('/home/wequ0318/sleep/data/eeg_fpz_cz/*.npz')\n", (76, 122), False, 'import glob\n'), ((147, 158), 'numpy.load', 'np.load', (['_f'], {}), '(_f)\n', (154, 158), True, 'import numpy as np\n'), ((311, 331), 'pandas.DataFra... |
from os import path
import re
from typing import List, Dict, Pattern
def handle_toxins_3_5(file_name: str) -> None:
rna_translation: Dict[str, str] = {"A": "U", "T": "A", "C": "G", "G": "C"}
re_toxins: Pattern = re.compile(r">(.*)\n([ATCG\n]*)\n?")
rna_sequences: List[str] = []
with open(file_name)... | [
"os.path.join",
"re.compile"
] | [((223, 261), 're.compile', 're.compile', (['""">(.*)\\\\n([ATCG\\\\n]*)\\\\n?"""'], {}), "('>(.*)\\\\n([ATCG\\\\n]*)\\\\n?')\n", (233, 261), False, 'import re\n'), ((870, 910), 'os.path.join', 'path.join', (['path.curdir', '"""toxins_3-5.fna"""'], {}), "(path.curdir, 'toxins_3-5.fna')\n", (879, 910), False, 'from os i... |
import functools
import numpy as np
from estimagic.batch_evaluators import joblib_batch_evaluator
from src.manfred.minimize_manfred import minimize_manfred
def minimize_manfred_estimagic(
internal_criterion_and_derivative,
x,
lower_bounds,
upper_bounds,
convergence_relative_params_tolerance=0.00... | [
"functools.partial",
"numpy.random.seed"
] | [((6673, 6774), 'functools.partial', 'functools.partial', (['internal_criterion_and_derivative'], {'algorithm_info': 'algo_info', 'task': '"""criterion"""'}), "(internal_criterion_and_derivative, algorithm_info=\n algo_info, task='criterion')\n", (6690, 6774), False, 'import functools\n'), ((7916, 7993), 'functools.... |