code stringlengths 22 1.05M | apis listlengths 1 3.31k | extract_api stringlengths 75 3.25M |
|---|---|---|
from moz_books.log import get_logger
from moz_books.opendb.opendb_book_factory import OpenDBBookFactory
from moz_books.opendb.opendb_request_params_factory import OpenDBRequestParamsFactory
from moz_books.service import Service
LOGGER = get_logger(__name__)
class OpenDBService(Service):
# https://openbd.jp/
... | [
"moz_books.opendb.opendb_request_params_factory.OpenDBRequestParamsFactory",
"moz_books.log.get_logger",
"moz_books.opendb.opendb_book_factory.OpenDBBookFactory"
] | [((238, 258), 'moz_books.log.get_logger', 'get_logger', (['__name__'], {}), '(__name__)\n', (248, 258), False, 'from moz_books.log import get_logger\n'), ((566, 585), 'moz_books.opendb.opendb_book_factory.OpenDBBookFactory', 'OpenDBBookFactory', ([], {}), '()\n', (583, 585), False, 'from moz_books.opendb.opendb_book_fa... |
# -*- coding: utf-8 -*-
# Generated by Django 1.11 on 2017-07-30 16:11
from __future__ import unicode_literals
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('webmarks_storage', '0001_initial'),
]
operations = [
migrations.RemoveField(
... | [
"django.db.migrations.DeleteModel",
"django.db.migrations.RemoveField"
] | [((288, 349), 'django.db.migrations.RemoveField', 'migrations.RemoveField', ([], {'model_name': '"""archive"""', 'name': '"""bookmark"""'}), "(model_name='archive', name='bookmark')\n", (310, 349), False, 'from django.db import migrations\n'), ((394, 432), 'django.db.migrations.DeleteModel', 'migrations.DeleteModel', (... |
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import json
import warnings
import pulumi
import pulumi.runtime
from typing import Union
from .. import utilities, tables
class GetCer... | [
"pulumi.InvokeOptions",
"pulumi.runtime.invoke"
] | [((3647, 3669), 'pulumi.InvokeOptions', 'pulumi.InvokeOptions', ([], {}), '()\n', (3667, 3669), False, 'import pulumi\n'), ((3760, 3851), 'pulumi.runtime.invoke', 'pulumi.runtime.invoke', (['"""gcp:compute/getCertificate:getCertificate"""', '__args__'], {'opts': 'opts'}), "('gcp:compute/getCertificate:getCertificate', ... |
# ----------------------------------------------------------------------------
# Copyright (c) 2020, <NAME>.
#
# Your use of this software as distributed in this GitHub repository, is
# governed by the Apache License 2.0
#
# Your use of the Shotgun Pipeline Toolkit is governed by the applicable
# license agreement betw... | [
"os.path.basename"
] | [((2532, 2563), 'os.path.basename', 'os.path.basename', (['node.filepath'], {}), '(node.filepath)\n', (2548, 2563), False, 'import os\n')] |
import os
from flask import Flask
from flask import render_template
from flask_restful import Api
from shield_app.utils.route_utils import error_handler
from shield_app.api.route import Certificate
current_path = os.path.dirname(os.path.abspath(__file__))
ui_path = os.path.join(current_path, os.pardir, "shield_ui")
... | [
"flask.render_template",
"flask_restful.Api",
"flask.Flask",
"os.path.join",
"os.path.abspath"
] | [((269, 319), 'os.path.join', 'os.path.join', (['current_path', 'os.pardir', '"""shield_ui"""'], {}), "(current_path, os.pardir, 'shield_ui')\n", (281, 319), False, 'import os\n'), ((326, 413), 'flask.Flask', 'Flask', (['"""shield"""'], {'template_folder': 'ui_path', 'static_folder': "(ui_path + os.sep + 'static')"}), ... |
"""
Module that contains the command line app.
Why does this file exist, and why not put this in __main__?
You might be tempted to import things from __main__ later, but that will cause
problems: the code will get executed twice:
- When you run `python -m sa_pathfinding` python will execute
``__main__.py``... | [
"sa_pathfinding.heuristics.grid_heuristic.OctileGridHeuristic",
"sa_pathfinding.environments.grids.octile_grid.OctileGrid",
"argparse.ArgumentParser",
"sa_pathfinding.algorithms.generics.search_node.SearchNode",
"os.path.abspath",
"time.time"
] | [((2312, 2337), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {}), '()\n', (2335, 2337), False, 'import argparse\n'), ((4546, 4557), 'time.time', 'time.time', ([], {}), '()\n', (4555, 4557), False, 'import time\n'), ((4600, 4611), 'time.time', 'time.time', ([], {}), '()\n', (4609, 4611), False, 'import tim... |
import traffic_lights
import travis_ci_client
import time
print("Running TravisCI XFD...")
lights = traffic_lights.TrafficLights("clewarecontrol", "902971")
greenCount = 0
while True:
buildSuccess = travis_ci_client.travisCiBuildWasSuccessfull("TerrySoba", "retro-game", "master")
if buildSuccess:
gr... | [
"traffic_lights.TrafficLights",
"travis_ci_client.travisCiBuildWasSuccessfull",
"time.sleep"
] | [((102, 158), 'traffic_lights.TrafficLights', 'traffic_lights.TrafficLights', (['"""clewarecontrol"""', '"""902971"""'], {}), "('clewarecontrol', '902971')\n", (130, 158), False, 'import traffic_lights\n'), ((207, 292), 'travis_ci_client.travisCiBuildWasSuccessfull', 'travis_ci_client.travisCiBuildWasSuccessfull', (['"... |
#!/usr/bin/python
import unittest
import omniture
import os
from datetime import date
import pandas
import datetime
import requests_mock
creds = {}
creds['username'] = os.environ['OMNITURE_USERNAME']
creds['secret'] = os.environ['OMNITURE_SECRET']
test_report_suite = 'omniture.api-gateway'
class ReportTest(unittes... | [
"requests_mock.mock",
"datetime.date.today",
"os.path.dirname",
"omniture.authenticate",
"unittest.main",
"unittest.skip",
"omniture.sync"
] | [((4446, 4508), 'unittest.skip', 'unittest.skip', (['"""skip inline segments because checked in Query"""'], {}), "('skip inline segments because checked in Query')\n", (4459, 4508), False, 'import unittest\n'), ((4914, 4934), 'requests_mock.mock', 'requests_mock.mock', ([], {}), '()\n', (4932, 4934), False, 'import req... |
from plugins.debate.layout import ServerSetup
plugin_data = {"name": "Debate Plugins", "database": True}
def setup(bot):
bot.add_cog(ServerSetup(bot))
| [
"plugins.debate.layout.ServerSetup"
] | [((140, 156), 'plugins.debate.layout.ServerSetup', 'ServerSetup', (['bot'], {}), '(bot)\n', (151, 156), False, 'from plugins.debate.layout import ServerSetup\n')] |
import yaml
import os
dir = os.path.dirname(__file__)
config_path = os.path.join(dir, "config.yaml")
# 读取配置文件
def read_config(config_path):
with open(config_path, "r", encoding="utf-8") as f:
data = yaml.load(f, Loader=yaml.FullLoader)
return data
# 配置获取函数，支持二级配置，供其他模块调用
def get_config(key=None):
... | [
"os.path.dirname",
"os.path.join",
"yaml.load"
] | [((29, 54), 'os.path.dirname', 'os.path.dirname', (['__file__'], {}), '(__file__)\n', (44, 54), False, 'import os\n'), ((69, 101), 'os.path.join', 'os.path.join', (['dir', '"""config.yaml"""'], {}), "(dir, 'config.yaml')\n", (81, 101), False, 'import os\n'), ((213, 249), 'yaml.load', 'yaml.load', (['f'], {'Loader': 'ya... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Sep 8 13:43:52 2021
@author: renaud
https://stackoverflow.com/questions/29211794/how-to-bind-a-click-event-to-a-canvas-in-tkinter
"""
import tkinter as tk
import random
import time
haut = 10 # table heigth
larg = 10 # table width
cote = 40 # cell... | [
"tkinter.Menu",
"tkinter.Toplevel",
"tkinter.Canvas",
"tkinter.Tk",
"tkinter.Label",
"time.time",
"tkinter.Frame",
"random.randint"
] | [((4819, 4826), 'tkinter.Tk', 'tk.Tk', ([], {}), '()\n', (4824, 4826), True, 'import tkinter as tk\n'), ((4864, 4877), 'tkinter.Menu', 'tk.Menu', (['root'], {}), '(root)\n', (4871, 4877), True, 'import tkinter as tk\n'), ((4911, 4938), 'tkinter.Menu', 'tk.Menu', (['top'], {'tearoff': '(False)'}), '(top, tearoff=False)\... |
"""
IDE: PyCharm
Project: simulating-doctor-patient-interviews-using-neural-networks
Author: Robin
Filename: testing.py
Date: 16.07.2019
"""
import csv
import scipy
import torch
from sklearn.metrics import classification_report
from torchtext.data import TabularDataset, BucketIterator, Field
from tqdm import tqdm
f... | [
"utility.evaluation._calculate_map",
"utility.evaluation.build_relevance_list",
"sklearn.metrics.classification_report",
"scipy.stats.binom_test",
"torch.unsqueeze",
"tqdm.tqdm",
"csv.writer",
"torchtext.data.BucketIterator",
"torch.no_grad",
"torchtext.data.TabularDataset",
"torch.cuda.current_... | [((575, 602), 'torch.cuda.current_device', 'torch.cuda.current_device', ([], {}), '()\n', (600, 602), False, 'import torch\n'), ((1195, 1237), 'scipy.stats.binom_test', 'scipy.stats.binom_test', (['differ', 'a_better_b'], {}), '(differ, a_better_b)\n', (1217, 1237), False, 'import scipy\n'), ((6575, 6666), 'torchtext.d... |
from django.db import models
from django.contrib.auth import get_user_model
from django.template.defaultfilters import slugify
from django.db.models.signals import pre_save, post_save
from django.dispatch import receiver
from core.utils.unique_slug import unique_slug_generator
import uuid
import os
class Subject(mode... | [
"django.contrib.auth.get_user_model",
"django.db.models.TextField",
"django.db.models.IntegerField",
"django.db.models.ForeignKey",
"core.utils.unique_slug.unique_slug_generator",
"django.db.models.FileField",
"django.db.models.DateTimeField",
"django.db.models.BooleanField",
"django.db.models.SlugF... | [((1373, 1404), 'django.dispatch.receiver', 'receiver', (['pre_save'], {'sender': 'Quiz'}), '(pre_save, sender=Quiz)\n', (1381, 1404), False, 'from django.dispatch import receiver\n'), ((4226, 4258), 'django.dispatch.receiver', 'receiver', (['post_save'], {'sender': 'Quiz'}), '(post_save, sender=Quiz)\n', (4234, 4258),... |
from typing import Optional, List
from sonosrestapi.favourite import Favourite
from sonosrestapi.group import Group
from sonosrestapi.player import Player
from sonosrestapi.playlist import Playlist
class Household:
def __init__ (self, id, mySonos):
self.id = id
self.groups: List[Group] = []
... | [
"sonosrestapi.player.Player",
"sonosrestapi.playlist.Playlist",
"sonosrestapi.group.Group",
"sonosrestapi.favourite.Favourite"
] | [((1230, 1332), 'sonosrestapi.favourite.Favourite', 'Favourite', (["favourites['id']", "favourites['name']", "favourites['description']", "favourites['imageUrl']"], {}), "(favourites['id'], favourites['name'], favourites['description'],\n favourites['imageUrl'])\n", (1239, 1332), False, 'from sonosrestapi.favourite ... |
import pibayer
Nimg = 2
exposure_sec = 0.1
def test_acq_seq():
img = pibayer.bayerseq(Nimg, exposure_sec)
assert img.shape[0] == Nimg
| [
"pibayer.bayerseq"
] | [((76, 112), 'pibayer.bayerseq', 'pibayer.bayerseq', (['Nimg', 'exposure_sec'], {}), '(Nimg, exposure_sec)\n', (92, 112), False, 'import pibayer\n')] |
#!/usr/bin/env python
from flask import Flask, render_template, Response, Blueprint, request
import cv2, sys, threading
sys.path.append(sys.path[0] + '/Helpers')
from Robot_Helper import robot
sys.path.append(sys.path[0] + '/Programs')
from Calibrate_Sticker_Location_Program import Calibrate_Sticker_Location_Program
... | [
"flask.Blueprint",
"sys.path.append",
"Robot_Helper.robot.cameras.Get_Frames",
"Calibrate_Sticker_Location_Program.Calibrate_Sticker_Location_Program"
] | [((121, 162), 'sys.path.append', 'sys.path.append', (["(sys.path[0] + '/Helpers')"], {}), "(sys.path[0] + '/Helpers')\n", (136, 162), False, 'import cv2, sys, threading\n'), ((195, 237), 'sys.path.append', 'sys.path.append', (["(sys.path[0] + '/Programs')"], {}), "(sys.path[0] + '/Programs')\n", (210, 237), False, 'imp... |
import json
from unittest import mock
import pytest
from src.findings.import_security_hub_finding import lambda_handler
from .test_fixtures import os_environment_setup
class TestImportSecurityHubFinding:
@mock.patch("boto3.client")
def test_iam_user_creation_event(self, mock_client, os_environment_setup):
... | [
"src.findings.import_security_hub_finding.lambda_handler",
"unittest.mock.patch",
"pytest.raises"
] | [((213, 239), 'unittest.mock.patch', 'mock.patch', (['"""boto3.client"""'], {}), "('boto3.client')\n", (223, 239), False, 'from unittest import mock\n'), ((485, 511), 'unittest.mock.patch', 'mock.patch', (['"""boto3.client"""'], {}), "('boto3.client')\n", (495, 511), False, 'from unittest import mock\n'), ((408, 433), ... |
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.lines as lines
import matplotlib.text as text
import matplotlib.patches as patches
import argparse
import sys
from matplotlib.backends.backend_pdf import PdfPages
from emma.processing.dsp import butter_filter
from common import *
d... | [
"matplotlib.patches.Rectangle",
"argparse.ArgumentParser",
"matplotlib.font_manager.FontProperties",
"numpy.fft.fftfreq",
"matplotlib.pyplot.rcParams.update",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.tight_layout",
"matplotlib.backends.backend_pdf.PdfPages",
"numpy.fft.fftshift",
"numpy.lo... | [((4107, 4170), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '"""Draw DES problem figure."""'}), "(description='Draw DES problem figure.')\n", (4130, 4170), False, 'import argparse\n'), ((698, 771), 'matplotlib.font_manager.FontProperties', 'matplotlib.font_manager.FontProperties', ([], {'... |
import re
import ijson
from typing import Iterable
import langumo_ko.utils as utils
from langumo.building import Parser
from langumo.utils import AuxiliaryFile
from langumo_ko.namuwiki.rendering import NamuWikiRenderer
class NamuWikiParser(Parser):
single_quotes_pattern = re.compile('[\x60\xb4\u2018\u2019]')
... | [
"langumo_ko.utils.remove_duplicated_spaces",
"ijson.parse",
"re.compile",
"langumo_ko.utils.korean_character_ratio",
"langumo_ko.utils.normalize_quotes",
"langumo_ko.namuwiki.rendering.NamuWikiRenderer.render"
] | [((279, 299), 're.compile', 're.compile', (['"""[`´‘’]"""'], {}), "('[`´‘’]')\n", (289, 299), False, 'import re\n'), ((344, 362), 're.compile', 're.compile', (['"""[“”]"""'], {}), "('[“”]')\n", (354, 362), False, 'import re\n'), ((829, 858), 'langumo_ko.namuwiki.rendering.NamuWikiRenderer.render', 'NamuWikiRenderer.ren... |
# This file was automatically generated by SWIG (http://www.swig.org).
# Version 4.0.2
#
# Do not make changes to this file unless you know what you are doing--modify
# the SWIG interface file instead.
from sys import version_info as _swig_python_version_info
if _swig_python_version_info < (2, 7, 0):
raise Runtime... | [
"_ledcontrol_rpi_ws281x_driver.lerp",
"_ledcontrol_rpi_ws281x_driver.wave_triangle",
"_ledcontrol_rpi_ws281x_driver.render_rgb_float",
"_ledcontrol_rpi_ws281x_driver.ws2811_t_swigregister",
"_ledcontrol_rpi_ws281x_driver.perlin_noise_3d",
"_ledcontrol_rpi_ws281x_driver.ws2811_led_get",
"_ledcontrol_rpi_... | [((5240, 5317), '_ledcontrol_rpi_ws281x_driver.ws2811_channel_t_swigregister', '_ledcontrol_rpi_ws281x_driver.ws2811_channel_t_swigregister', (['ws2811_channel_t'], {}), '(ws2811_channel_t)\n', (5299, 5317), False, 'import _ledcontrol_rpi_ws281x_driver\n'), ((6499, 6560), '_ledcontrol_rpi_ws281x_driver.ws2811_t_swigreg... |
import tensorflow as tf
from bot_code.conversions.input.normalization_input_formatter import NormalizationInputFormatter
class DataNormalizer:
normalization_array = None
boolean = [0.0, 1.0]
def __init__(self, batch_size, feature_creator=None):
self.batch_size = batch_size
self.... | [
"bot_code.conversions.input.normalization_input_formatter.NormalizationInputFormatter",
"tensorflow.name_scope",
"tensorflow.constant",
"tensorflow.check_numerics",
"tensorflow.cast"
] | [((332, 399), 'bot_code.conversions.input.normalization_input_formatter.NormalizationInputFormatter', 'NormalizationInputFormatter', (['(0)', '(0)', 'self.batch_size', 'feature_creator'], {}), '(0, 0, self.batch_size, feature_creator)\n', (359, 399), False, 'from bot_code.conversions.input.normalization_input_formatter... |
import pytest
from dbt.tests.util import run_dbt, check_relations_equal
snapshot_sql = """
{% snapshot snapshot_check_cols_new_column %}
{{
config(
target_database=database,
target_schema=schema,
strategy='check',
unique_key='id',
check_cols=var("... | [
"pytest.fixture",
"dbt.tests.util.check_relations_equal",
"dbt.tests.util.run_dbt"
] | [((923, 952), 'pytest.fixture', 'pytest.fixture', ([], {'scope': '"""class"""'}), "(scope='class')\n", (937, 952), False, 'import pytest\n'), ((1037, 1066), 'pytest.fixture', 'pytest.fixture', ([], {'scope': '"""class"""'}), "(scope='class')\n", (1051, 1066), False, 'import pytest\n'), ((1156, 1185), 'pytest.fixture', ... |
#!/usr/bin/env python
import os
import subprocess
from unittest import TestCase
from boutiques import __file__ as bfile
import boutiques as bosh
class TestExample2(TestCase):
def get_examples_dir(self):
return os.path.join(os.path.dirname(bfile),
"schema", "examples")
de... | [
"os.path.dirname",
"os.path.join"
] | [((239, 261), 'os.path.dirname', 'os.path.dirname', (['bfile'], {}), '(bfile)\n', (254, 261), False, 'import os\n'), ((490, 533), 'os.path.join', 'os.path.join', (['example2_dir', '"""example2.json"""'], {}), "(example2_dir, 'example2.json')\n", (502, 533), False, 'import os\n'), ((635, 680), 'os.path.join', 'os.path.j... |
# -*- coding: utf-8 -*-
"""
# @file name : cross_entropy.py
# @author : JLChen
# @date : 2020-03-12
# @brief : cross_entropy
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
class CrossEntropyLossFloat(nn.Module):
"""
浮点类型的CE实现，适用于标签是连续变量
（补充说明：PyTorch提供的CE Loss，只适用于标... | [
"torch.randint",
"torch.nn.functional.log_softmax",
"torch.nn.CrossEntropyLoss",
"torch.tensor"
] | [((925, 955), 'torch.randint', 'torch.randint', (['(1)', 'c', '(b, h, w)'], {}), '(1, c, (b, h, w))\n', (938, 955), False, 'import torch\n'), ((1489, 1524), 'torch.tensor', 'torch.tensor', (['[0]'], {'dtype': 'torch.long'}), '([0], dtype=torch.long)\n', (1501, 1524), False, 'import torch\n'), ((1622, 1643), 'torch.nn.C... |
# Copyright 2021 <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, softw... | [
"yaml.full_load",
"pathlib.Path",
"yaml.dump"
] | [((801, 818), 'yaml.full_load', 'yaml.full_load', (['f'], {}), '(f)\n', (815, 818), False, 'import yaml\n'), ((1067, 1087), 'yaml.dump', 'yaml.dump', (['config', 'f'], {}), '(config, f)\n', (1076, 1087), False, 'import yaml\n'), ((684, 698), 'pathlib.Path', 'Path', (['__file__'], {}), '(__file__)\n', (688, 698), False,... |
# Generated by Django 3.1.7 on 2021-02-27 12:37
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('blog', '0006_auto_20210227_1348'),
]
operations = [
migrations.AlterField(
model_name='post',
name='thumbnail',
... | [
"django.db.models.ImageField"
] | [((334, 398), 'django.db.models.ImageField', 'models.ImageField', ([], {'default': '"""default.jpg"""', 'upload_to': '"""simpleblog"""'}), "(default='default.jpg', upload_to='simpleblog')\n", (351, 398), False, 'from django.db import migrations, models\n')] |
from PIL import ImageDraw, Image
import numpy as np
import hashlib
import random
# array_list = [1]
background_color = '#F2F1F2'
colors = ['#CD00CD', 'Red', 'Orange', "#66FF00", "#2A52BE"]
def generate_array(bytes):
## Generate array
for i in range(100):
# Array 6 * 12
need_array = np.arr... | [
"PIL.Image.new",
"PIL.ImageDraw.Draw",
"random.choice",
"numpy.concatenate"
] | [((974, 1018), 'PIL.Image.new', 'Image.new', (['"""RGB"""', 'img_size', 'background_color'], {}), "('RGB', img_size, background_color)\n", (983, 1018), False, 'from PIL import ImageDraw, Image\n'), ((1030, 1049), 'PIL.ImageDraw.Draw', 'ImageDraw.Draw', (['img'], {}), '(img)\n', (1044, 1049), False, 'from PIL import Ima... |
import numpy as np
import scipy.signal
__all__ = ['instant_parameters']
#-----------------------------------
def instant_parameters(signal, fs = None):
'''
Instant parameters estimation:
..math::
analitc_signal = hilbert(signal)
envelope = |analitc_signal|
phase = unwrap(angle(a... | [
"numpy.abs",
"numpy.unwrap",
"numpy.asarray",
"numpy.diff",
"numpy.angle"
] | [((867, 885), 'numpy.asarray', 'np.asarray', (['signal'], {}), '(signal)\n', (877, 885), True, 'import numpy as np\n'), ((1023, 1039), 'numpy.abs', 'np.abs', (['analytic'], {}), '(analytic)\n', (1029, 1039), True, 'import numpy as np\n'), ((1055, 1073), 'numpy.angle', 'np.angle', (['analytic'], {}), '(analytic)\n', (10... |
r"""TELNET negotiation filter
This code was adapted from the telnetlib library included with Python 2.7,
and is being used under the PSF License agreement included below.
All changes to the original telnetlib code are copyright (c) 2019 <NAME>,
and licensed under the same terms.
=====================================... | [
"filters.telnetiac.mssp.handle_mssp",
"collections.deque",
"filters.telnetiac.naws.handle_naws",
"filters.telnetiac.mtts.handle_mtts"
] | [((6002, 6013), 'collections.deque', 'deque', (['line'], {}), '(line)\n', (6007, 6013), False, 'from collections import deque\n'), ((10942, 10956), 'collections.deque', 'deque', (['sbdataq'], {}), '(sbdataq)\n', (10947, 10956), False, 'from collections import deque\n'), ((11026, 11052), 'filters.telnetiac.mssp.handle_m... |
from mgetool.tool import tt
from mgetool.tool import tt
from sklearn.datasets import load_boston
from fastgplearn.skflow import SymbolicRegressor as FSR
from gplearn.genetic import SymbolicRegressor as SR
from bgp.skflow import SymbolLearning
x, y = load_boston(return_X_y=True)
sr1 = FSR(population_size=10000, gener... | [
"bgp.skflow.SymbolLearning",
"gplearn.genetic.SymbolicRegressor",
"sklearn.datasets.load_boston",
"fastgplearn.skflow.SymbolicRegressor"
] | [((252, 280), 'sklearn.datasets.load_boston', 'load_boston', ([], {'return_X_y': '(True)'}), '(return_X_y=True)\n', (263, 280), False, 'from sklearn.datasets import load_boston\n'), ((288, 694), 'fastgplearn.skflow.SymbolicRegressor', 'FSR', ([], {'population_size': '(10000)', 'generations': '(10)', 'stopping_criteria'... |
# -*- coding: utf-8 -*-
"""
@author: derobest
brief : used to compare concurrent and non concurrent modes
args :
Return :
Raises :
"""
import subprocess
import threading
import os
def publishing():
iterations=500#00
#concurrent mode
subprocess.call( ['python','queue_publish_read.py','-... | [
"threading.Thread"
] | [((976, 1025), 'threading.Thread', 'threading.Thread', ([], {'target': 'runReader', 'args': '[1, 0.1]'}), '(target=runReader, args=[1, 0.1])\n', (992, 1025), False, 'import threading\n'), ((1045, 1094), 'threading.Thread', 'threading.Thread', ([], {'target': 'runReader', 'args': '[2, 0.1]'}), '(target=runReader, args=[... |
#!/usr/bin/env python
from __future__ import absolute_import, division, print_function
import io
import random
import flask as fl
import potsim
app = fl.Flask(__name__)
app.config['MAX_CONTENT_LENGTH'] = 32 * 1024 * 1024
app.debug = True
@app.route('/json', methods=['GET', 'POST'])
def pots_processor_json():
i... | [
"flask.render_template",
"flask.Flask",
"io.BytesIO",
"flask.redirect",
"flask.request.get_json",
"potsim.POTSFilter",
"flask.send_file",
"random.randint"
] | [((154, 172), 'flask.Flask', 'fl.Flask', (['__name__'], {}), '(__name__)\n', (162, 172), True, 'import flask as fl\n'), ((371, 403), 'flask.request.get_json', 'fl.request.get_json', ([], {'cache': '(False)'}), '(cache=False)\n', (390, 403), True, 'import flask as fl\n'), ((960, 976), 'flask.redirect', 'fl.redirect', ([... |
"""
Helper functions for comparison
"""
import math
def fuzzyEqual(a,b,thre):
res = False
if math.fabs(a-b) < thre:
res = True
return res
| [
"math.fabs"
] | [((102, 118), 'math.fabs', 'math.fabs', (['(a - b)'], {}), '(a - b)\n', (111, 118), False, 'import math\n')] |
import numpy as np
import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.utils.data import Dataset
import pickle
class CharDataset(Dataset):
def __init__(self, data, block_size):
chars = sorted(list(set(data)))
data_size, vocab_size = len(data), len(chars)
prin... | [
"mingpt.trainer.TrainerConfig",
"mingpt.model.GPTConfig",
"mingpt.model.GPT",
"torch.tensor",
"mingpt.utils.sample",
"mingpt.trainer.Trainer"
] | [((1087, 1185), 'mingpt.model.GPTConfig', 'GPTConfig', (['train_dataset.vocab_size', 'train_dataset.block_size'], {'n_layer': '(8)', 'n_head': '(8)', 'n_embd': '(512)'}), '(train_dataset.vocab_size, train_dataset.block_size, n_layer=8,\n n_head=8, n_embd=512)\n', (1096, 1185), False, 'from mingpt.model import GPT, G... |
#Start up torch dist package
import torch
import torch.distributed as dist
dist.init_process_group(backend='mpi')
#Load classes for simulations and controls
from brownian_fts import BrownianParticle
import numpy as np
#Starting and ending configuration.
start = torch.tensor([[-1.0]])
end = torch.tensor([[1.0]])
def i... | [
"numpy.mean",
"numpy.random.choice",
"numpy.std",
"torch.tensor",
"numpy.array",
"torch.distributed.get_rank",
"torch.distributed.init_process_group",
"torch.distributed.get_world_size"
] | [((75, 113), 'torch.distributed.init_process_group', 'dist.init_process_group', ([], {'backend': '"""mpi"""'}), "(backend='mpi')\n", (98, 113), True, 'import torch.distributed as dist\n'), ((264, 286), 'torch.tensor', 'torch.tensor', (['[[-1.0]]'], {}), '([[-1.0]])\n', (276, 286), False, 'import torch\n'), ((293, 314),... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Imports
import os
import pickle
import pandas as pd
from warnings import simplefilter
from model_funs import fasta_frame, ohe_fun, flatten_sequence
import numpy as np
from numpy import array
from sklearn import preprocessing
from sklearn.model_selection import train_tes... | [
"pandas.read_csv",
"keras.utils.to_categorical",
"tensorflow.keras.utils.plot_model",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.layers.MaxPooling1D",
"sklearn.preprocessing.LabelBinarizer",
"tensorflow.compat.v1.random.set_random_seed",
"numpy.random.seed",
"warnings.simplefilter",
"model... | [((679, 732), 'warnings.simplefilter', 'simplefilter', ([], {'action': '"""ignore"""', 'category': 'FutureWarning'}), "(action='ignore', category=FutureWarning)\n", (691, 732), False, 'from warnings import simplefilter\n'), ((1030, 1071), 'tensorflow.compat.v1.random.set_random_seed', 'tf.compat.v1.random.set_random_se... |
# -*- coding: utf-8 -*-
""" REST API - Endpoint routing
Author(s): <NAME>, <EMAIL>
"""
from flask import current_app, request
from flask_rebar import HeaderApiKeyAuthenticator, Rebar, response
from rest_api.schemas import *
authenticator = HeaderApiKeyAuthenticator(header='X-MyApp-ApiKey')
authenticator.registe... | [
"flask_rebar.HeaderApiKeyAuthenticator",
"flask_rebar.Rebar"
] | [((248, 298), 'flask_rebar.HeaderApiKeyAuthenticator', 'HeaderApiKeyAuthenticator', ([], {'header': '"""X-MyApp-ApiKey"""'}), "(header='X-MyApp-ApiKey')\n", (273, 298), False, 'from flask_rebar import HeaderApiKeyAuthenticator, Rebar, response\n'), ((362, 369), 'flask_rebar.Rebar', 'Rebar', ([], {}), '()\n', (367, 369)... |
# Generated by Django 3.1.14 on 2022-05-13 10:25
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('idp_user', '0002_auto_20220120_1617'),
]
operations = [
migrations.RenameField(
model_name='userrole',
old_name='app_config... | [
"django.db.migrations.RenameField"
] | [((229, 339), 'django.db.migrations.RenameField', 'migrations.RenameField', ([], {'model_name': '"""userrole"""', 'old_name': '"""app_config"""', 'new_name': '"""app_entities_restrictions"""'}), "(model_name='userrole', old_name='app_config',\n new_name='app_entities_restrictions')\n", (251, 339), False, 'from djang... |
#!/usr/bin/env python
import cdt
from sam.sam import SAM
d, g = cdt.data.load_dataset('sachs')
m = SAM()
m.predict(d, nruns=1)
| [
"sam.sam.SAM",
"cdt.data.load_dataset"
] | [((66, 96), 'cdt.data.load_dataset', 'cdt.data.load_dataset', (['"""sachs"""'], {}), "('sachs')\n", (87, 96), False, 'import cdt\n'), ((101, 106), 'sam.sam.SAM', 'SAM', ([], {}), '()\n', (104, 106), False, 'from sam.sam import SAM\n')] |
import imageio
import sys
if __name__ == '__main__':
if len(sys.argv) == 2:
_, filename = sys.argv
img = imageio.imread(filename).astype(dtype='float32')
print('DTYPE:', img.dtype)
print('SHAPE:', img.shape)
elif len(sys.argv) == 3:
_, filename, type = sys.argv
i... | [
"imageio.imread"
] | [((126, 150), 'imageio.imread', 'imageio.imread', (['filename'], {}), '(filename)\n', (140, 150), False, 'import imageio\n'), ((325, 349), 'imageio.imread', 'imageio.imread', (['filename'], {}), '(filename)\n', (339, 349), False, 'import imageio\n')] |
import frappe
from frappe.utils import flt, cint
from erpnext.accounts.doctype.sales_invoice.sales_invoice import SalesInvoice
class OverrideSalesInvoice(SalesInvoice):
@frappe.whitelist()
def set_advances(self):
"""Returns list of advances against Account, Party, Reference"""
res = self.get_... | [
"frappe.whitelist",
"frappe.utils.flt"
] | [((176, 194), 'frappe.whitelist', 'frappe.whitelist', ([], {}), '()\n', (192, 194), False, 'import frappe\n'), ((902, 923), 'frappe.utils.flt', 'flt', (['allocated_amount'], {}), '(allocated_amount)\n', (905, 923), False, 'from frappe.utils import flt, cint\n'), ((1233, 1246), 'frappe.utils.flt', 'flt', (['d.amount'], ... |
# Generated by Django 3.2.6 on 2021-08-30 18:45
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('users', '0004_auto_20210830_1844'),
]
operations = [
migrations.RenameField(
model_name='colaborador',
old_name='emailInstit... | [
"django.db.migrations.RenameField"
] | [((225, 335), 'django.db.migrations.RenameField', 'migrations.RenameField', ([], {'model_name': '"""colaborador"""', 'old_name': '"""emailInstitucional"""', 'new_name': '"""address_email"""'}), "(model_name='colaborador', old_name=\n 'emailInstitucional', new_name='address_email')\n", (247, 335), False, 'from django... |
import torch
from torch.backends import cudnn
cudnn.enabled = True
from torch.utils.data import DataLoader
import torch.nn.functional as F
import importlib
import voc12.dataloader
from misc import pyutils, torchutils
def validate(model, data_loader):
print('validating ... ', flush=True, end='')
val_loss_m... | [
"torch.nn.functional.multilabel_soft_margin_loss",
"importlib.import_module",
"torch.nn.DataParallel",
"torch.pow",
"misc.pyutils.Timer",
"torch.nn.functional.relu",
"torch.utils.data.DataLoader",
"misc.pyutils.AverageMeter",
"torch.no_grad",
"torch.cuda.empty_cache",
"misc.torchutils.PolyOptimi... | [((327, 365), 'misc.pyutils.AverageMeter', 'pyutils.AverageMeter', (['"""loss1"""', '"""loss2"""'], {}), "('loss1', 'loss2')\n", (347, 365), False, 'from misc import pyutils, torchutils\n'), ((1198, 1336), 'torch.utils.data.DataLoader', 'DataLoader', (['train_dataset'], {'batch_size': 'args.cam_batch_size', 'shuffle': ... |
"""Example demonstrating a basic usage of choke package."""
from time import sleep
from redis import StrictRedis
from choke import RedisChokeManager, CallLimitExceededError
REDIS = StrictRedis() # Tweak this to reflect your setup
CHOKE_MANAGER = RedisChokeManager(redis=REDIS)
# Example configuration: enforce limit o... | [
"redis.StrictRedis",
"time.sleep",
"choke.RedisChokeManager"
] | [((183, 196), 'redis.StrictRedis', 'StrictRedis', ([], {}), '()\n', (194, 196), False, 'from redis import StrictRedis\n'), ((248, 278), 'choke.RedisChokeManager', 'RedisChokeManager', ([], {'redis': 'REDIS'}), '(redis=REDIS)\n', (265, 278), False, 'from choke import RedisChokeManager, CallLimitExceededError\n'), ((913,... |
from datetime import datetime
from user import unfollow_accounts
import schedule
import time
# # # TIME INTERVAL # # #
MINUTES_INTERVAL = 20 # How often should the script be executed (in minutes)
def unfollow():
result = unfollow_accounts()
now = datetime.now()
dt = now.strftime("%d/%m/%Y %H:%M:%S")
... | [
"schedule.run_pending",
"schedule.cancel_job",
"time.sleep",
"datetime.datetime.now",
"schedule.every",
"user.unfollow_accounts"
] | [((230, 249), 'user.unfollow_accounts', 'unfollow_accounts', ([], {}), '()\n', (247, 249), False, 'from user import unfollow_accounts\n'), ((260, 274), 'datetime.datetime.now', 'datetime.now', ([], {}), '()\n', (272, 274), False, 'from datetime import datetime\n'), ((708, 730), 'schedule.run_pending', 'schedule.run_pen... |
import rospy
from std_msgs.msg import Int8
# servo should publish a nonzero warning code here
STATUS_TOPIC = 'servo_server/status'
def wait_for_servo_initialization(timeout=15):
try:
rospy.wait_for_message(STATUS_TOPIC, Int8, timeout=timeout)
except rospy.ROSException as exc:
rospy.logerr("The ser... | [
"rospy.wait_for_message"
] | [((195, 254), 'rospy.wait_for_message', 'rospy.wait_for_message', (['STATUS_TOPIC', 'Int8'], {'timeout': 'timeout'}), '(STATUS_TOPIC, Int8, timeout=timeout)\n', (217, 254), False, 'import rospy\n')] |
from django import forms
class ClientErrorForm(forms.Form):
msg = forms.CharField(max_length=1024, required=False)
url = forms.CharField(max_length=256, required=False)
line = forms.CharField(max_length=4, required=False)
| [
"django.forms.CharField"
] | [((72, 120), 'django.forms.CharField', 'forms.CharField', ([], {'max_length': '(1024)', 'required': '(False)'}), '(max_length=1024, required=False)\n', (87, 120), False, 'from django import forms\n'), ((131, 178), 'django.forms.CharField', 'forms.CharField', ([], {'max_length': '(256)', 'required': '(False)'}), '(max_l... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Program: Fit peaks with Lorentzian distribution
Version: 20201123
@author: <NAME> (GitHub: @pranabdas)
data = suv.fit_lorentz(x, y, a='', x0='', gamma='', xmin='', xmax='')
"""
def fit_lorentz(x, y, a='', x0='', gamma='', xmin='', xmax='', num=1000):
import numpy ... | [
"scipy.optimize.curve_fit",
"numpy.linspace"
] | [((1003, 1055), 'scipy.optimize.curve_fit', 'optimize.curve_fit', (['lorentz', 'x', 'y'], {'p0': '[a, x0, gamma]'}), '(lorentz, x, y, p0=[a, x0, gamma])\n', (1021, 1055), False, 'from scipy import optimize\n'), ((1179, 1207), 'numpy.linspace', 'np.linspace', (['xmin', 'xmax', 'num'], {}), '(xmin, xmax, num)\n', (1190, ... |
# -*- coding: utf-8 -*-
# @Time : 2021/08/14 16:30
# @Author : srcrs
# @Email : <EMAIL>
import requests,json,time,re,login,logging,traceback,os,random,notify,datetime
from lxml.html import fromstring
#游戏任务中心每日打卡领积分，游戏任务自然数递增至7，游戏频道每日1积分
#位置: 首页 --> 游戏 --> 每日打卡
class game_signin:
def run(self, client, user):
... | [
"traceback.format_exc",
"logging.info",
"time.sleep"
] | [((621, 634), 'time.sleep', 'time.sleep', (['(2)'], {}), '(2)\n', (631, 634), False, 'import requests, json, time, re, login, logging, traceback, os, random, notify, datetime\n'), ((1408, 1421), 'time.sleep', 'time.sleep', (['(1)'], {}), '(1)\n', (1418, 1421), False, 'import requests, json, time, re, login, logging, tr... |
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: MIT-0
""" Originally from CRESI project. We modified the code to accomodate
different number of input channels, e.g. 5-channel RGB+LIDAR input images.
"""
import math
import torch
from torch import nn
###################... | [
"torch.nn.BatchNorm2d",
"torch.nn.ReLU",
"torch.nn.Sequential",
"torch.sigmoid",
"math.sqrt",
"torch.nn.Conv2d",
"torch.nn.MaxPool2d",
"torch.nn.Upsample",
"torch.cat"
] | [((2157, 2248), 'torch.nn.Conv2d', 'nn.Conv2d', (['in_filters', 'out_filters'], {'kernel_size': '(3)', 'stride': 'stride', 'padding': '(1)', 'bias': '(False)'}), '(in_filters, out_filters, kernel_size=3, stride=stride, padding=1,\n bias=False)\n', (2166, 2248), False, 'from torch import nn\n'), ((2489, 2511), 'torch... |
from rdkit import Chem
from smdt.descriptors import AtomProperty
import numpy
import pandas as pd
def _CalculateGearyAutocorrelation(mol, lag=1, propertylabel='m'):
"""
**Internal used only**
Calculation of Geary autocorrelation descriptors based on
different property weights.
"""
... | [
"rdkit.Chem.GetDistanceMatrix",
"rdkit.Chem.MolFromSmiles",
"pandas.DataFrame",
"numpy.square"
] | [((655, 682), 'rdkit.Chem.GetDistanceMatrix', 'Chem.GetDistanceMatrix', (['mol'], {}), '(mol)\n', (677, 682), False, 'from rdkit import Chem\n'), ((3986, 4017), 'pandas.DataFrame', 'pd.DataFrame', (['geary_descriptors'], {}), '(geary_descriptors)\n', (3998, 4017), True, 'import pandas as pd\n'), ((582, 609), 'numpy.squ... |
from django.conf import settings
from solana.rpc.api import Client
solana_client = Client(settings.SOLANA_NETWORK_URL)
| [
"solana.rpc.api.Client"
] | [((84, 119), 'solana.rpc.api.Client', 'Client', (['settings.SOLANA_NETWORK_URL'], {}), '(settings.SOLANA_NETWORK_URL)\n', (90, 119), False, 'from solana.rpc.api import Client\n')] |
from main import Solver, is_prime
solver = Solver()
sum = 0
for i in range(1, 2000000):
if is_prime(i):
print(i)
sum += i
solver.solve(10, sum)
| [
"main.Solver",
"main.is_prime"
] | [((43, 51), 'main.Solver', 'Solver', ([], {}), '()\n', (49, 51), False, 'from main import Solver, is_prime\n'), ((97, 108), 'main.is_prime', 'is_prime', (['i'], {}), '(i)\n', (105, 108), False, 'from main import Solver, is_prime\n')] |
import sys
import os
sys.path.append(os.path.abspath('./photo'))
from photo_face_recognition import PhotoFaceRecognition
pr = PhotoFaceRecognition()
pr.train_model()
pr.eval_model()
pr.label_data()
| [
"photo_face_recognition.PhotoFaceRecognition",
"os.path.abspath"
] | [((127, 149), 'photo_face_recognition.PhotoFaceRecognition', 'PhotoFaceRecognition', ([], {}), '()\n', (147, 149), False, 'from photo_face_recognition import PhotoFaceRecognition\n'), ((37, 63), 'os.path.abspath', 'os.path.abspath', (['"""./photo"""'], {}), "('./photo')\n", (52, 63), False, 'import os\n')] |
import os
import random
import numpy as np
import argparse
import logging
import pickle
from pprint import pformat
from exps.data import get_modelnet40_data_fps
from settree.set_data import SetDataset, OPERATIONS, flatten_datasets
import exps.eval_utils as eval
if __name__ == '__main__':
parser = argparse.Argu... | [
"pickle.dump",
"settree.set_data.SetDataset",
"argparse.ArgumentParser",
"exps.data.get_modelnet40_data_fps",
"random.seed",
"exps.eval_utils.create_logger",
"numpy.random.seed",
"os.path.abspath",
"exps.eval_utils.train_and_predict_xgboost",
"logging.info",
"exps.eval_utils.train_and_predict_se... | [((307, 332), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {}), '()\n', (330, 332), False, 'import argparse\n'), ((681, 755), 'exps.eval_utils.create_logger', 'eval.create_logger', ([], {'log_dir': 'log_dir', 'log_name': 'args.exp_name', 'dump': 'args.log'}), '(log_dir=log_dir, log_name=args.exp_name, dum... |
#Pluginname="Protobuf Decoder"
#Type=Generic
import struct
import os
from Library import protobuf
import binascii
def main():
ctx.gui_setMainLabel("Protobuf: Parsing Strings");
ctx.gui_setMainProgressBar(0)
cell=ctx.gui_get_currentcell()
row=int(cell[0])
col=int(cell[1])
try:
... | [
"Library.protobuf.pseudoxml"
] | [((538, 561), 'Library.protobuf.pseudoxml', 'protobuf.pseudoxml', (['dat'], {}), '(dat)\n', (556, 561), False, 'from Library import protobuf\n')] |
from flask import Flask
from flask_script import Manager
from flask_migrate import MigrateCommand
from App.ext_init import init_ext, migrate
from App.settings import Development
from App.views import blue, init_api
# 初始化 APP模块
def create_app():
# 创建 flask实例
app = Flask(__name__)
# 初始化接口
... | [
"App.ext_init.init_ext",
"flask_script.Manager",
"App.views.init_api",
"flask.Flask"
] | [((287, 302), 'flask.Flask', 'Flask', (['__name__'], {}), '(__name__)\n', (292, 302), False, 'from flask import Flask\n'), ((323, 336), 'App.views.init_api', 'init_api', (['app'], {}), '(app)\n', (331, 336), False, 'from App.views import blue, init_api\n'), ((442, 455), 'App.ext_init.init_ext', 'init_ext', (['app'], {}... |
import numpy as np
import cupy as cp
from typing import Optional, Union, List
from .projections import RandomProjection, TensorizedRandomProjection
from .utils import ArrayOnCPU, ArrayOnGPU, ArrayOnCPUOrGPU, RandomStateOrSeed
# ----------------------------------------------------------------------------------------... | [
"cupy.isscalar",
"cupy.ones",
"cupy.stack",
"cupy.reshape",
"cupy.pad",
"cupy.cumsum",
"cupy.empty",
"cupy.concatenate",
"cupy.sum",
"cupy.diff",
"cupy.copy"
] | [((3345, 3355), 'cupy.copy', 'cp.copy', (['M'], {}), '(M)\n', (3352, 3355), True, 'import cupy as cp\n'), ((7065, 7097), 'cupy.ones', 'cp.ones', (['(n_X, 1)'], {'dtype': 'U.dtype'}), '((n_X, 1), dtype=U.dtype)\n', (7072, 7097), True, 'import cupy as cp\n'), ((8876, 8908), 'cupy.ones', 'cp.ones', (['(n_X, 1)'], {'dtype'... |
# Generated by Django 3.2.9 on 2021-11-18 07:36
import django.core.validators
from django.db import migrations, models
import django.db.models.deletion
import src.base.services
class Migration(migrations.Migration):
initial = True
dependencies = [
('oauth', '0001_initial'),
]
operations = ... | [
"django.db.models.TextField",
"django.db.models.ForeignKey",
"django.db.models.ManyToManyField",
"django.db.models.BooleanField",
"django.db.models.PositiveIntegerField",
"django.db.models.BigAutoField",
"django.db.models.DateTimeField",
"django.db.models.CharField"
] | [((424, 520), 'django.db.models.BigAutoField', 'models.BigAutoField', ([], {'auto_created': '(True)', 'primary_key': '(True)', 'serialize': '(False)', 'verbose_name': '"""ID"""'}), "(auto_created=True, primary_key=True, serialize=False,\n verbose_name='ID')\n", (443, 520), False, 'from django.db import migrations, m... |
# Generated by Django 2.0.7 on 2019-04-13 07:39
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='Category',
fields=[
... | [
"django.db.models.ForeignKey",
"django.db.models.IntegerField",
"django.db.models.AutoField",
"django.db.models.DateTimeField",
"django.db.models.CharField"
] | [((337, 430), '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", (353, 430), False, 'from django.db import migrations, models\... |
from .. import map_generators
import collections
def test_raw_generation():
_map = map_generators.raw(8, 5)
assert isinstance(_map, map_generators.Raw)
assert _map.vertexes.issuperset({map_generators.Vertex(i, 0) for i in range(8)})
# contains at_least 8 areas numbered 0 to 7
reached = {_map.fina... | [
"collections.deque"
] | [((335, 366), 'collections.deque', 'collections.deque', (['[_map.final]'], {}), '([_map.final])\n', (352, 366), False, 'import collections\n')] |
from django.db import models
from django.contrib.auth.models import AbstractBaseUser, BaseUserManager
from rest_framework.authtoken.models import Token
# class for creating the user
class UsersManager(BaseUserManager):
def create_user(self, first_name, last_name, email, password):
if not email:
... | [
"django.db.models.EmailField",
"django.db.models.CharField",
"django.db.models.BooleanField"
] | [((1565, 1623), 'django.db.models.CharField', 'models.CharField', ([], {'verbose_name': '"""first_name"""', 'max_length': '(50)'}), "(verbose_name='first_name', max_length=50)\n", (1581, 1623), False, 'from django.db import models\n'), ((1640, 1697), 'django.db.models.CharField', 'models.CharField', ([], {'verbose_name... |
import numpy
import h5py
import scipy.sparse
from pyscf import gto, scf, mcscf, fci, ao2mo, lib
from pauxy.systems.generic import Generic
from pauxy.utils.from_pyscf import generate_integrals
from pauxy.utils.io import (
write_qmcpack_wfn,
write_qmcpack_dense,
write_input
)
mol = gto.M(... | [
"numpy.abs",
"pyscf.gto.M",
"pyscf.fci.addons.large_ci",
"numpy.array",
"pyscf.mcscf.CASSCF",
"pauxy.utils.io.write_qmcpack_wfn",
"numpy.linalg.eigh",
"pyscf.scf.RHF",
"pauxy.utils.io.write_input"
] | [((314, 406), 'pyscf.gto.M', 'gto.M', ([], {'atom': "[('N', 0, 0, 0), ('N', (0, 0, 3.0))]", 'basis': '"""sto-3g"""', 'verbose': '(3)', 'unit': '"""Bohr"""'}), "(atom=[('N', 0, 0, 0), ('N', (0, 0, 3.0))], basis='sto-3g', verbose=3,\n unit='Bohr')\n", (319, 406), False, 'from pyscf import gto, scf, mcscf, fci, ao2mo, ... |
import json
import numpy as np
from collections import OrderedDict
from src.evaluation.summary_loader import load_processed_dataset
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
sns.set()
sns.set_style("darkgrid")
n_videos = 50
videos = {}
n_splits = 5
x_axis = []
y_axis = []
df = pd.Da... | [
"pandas.Series",
"seaborn.set",
"numpy.arange",
"seaborn.set_style",
"json.load",
"pandas.DataFrame",
"seaborn.relplot",
"matplotlib.pyplot.show"
] | [((208, 217), 'seaborn.set', 'sns.set', ([], {}), '()\n', (215, 217), True, 'import seaborn as sns\n'), ((218, 243), 'seaborn.set_style', 'sns.set_style', (['"""darkgrid"""'], {}), "('darkgrid')\n", (231, 243), True, 'import seaborn as sns\n'), ((315, 374), 'pandas.DataFrame', 'pd.DataFrame', ([], {'columns': "['Videos... |
from flask import url_for
from flask_sqlalchemy import SQLAlchemy
from sqlalchemy import func
from sqlalchemy.orm import backref
from idManager.settings import TOKEN_HOST, REDIS_URL
import redis
db = SQLAlchemy()
if TOKEN_HOST == 'redis':
db_redis = redis.from_url(REDIS_URL)
else:
db_redis = ''
class Group(... | [
"sqlalchemy.func.now",
"redis.from_url",
"sqlalchemy.orm.backref",
"flask.url_for",
"flask_sqlalchemy.SQLAlchemy"
] | [((201, 213), 'flask_sqlalchemy.SQLAlchemy', 'SQLAlchemy', ([], {}), '()\n', (211, 213), False, 'from flask_sqlalchemy import SQLAlchemy\n'), ((256, 281), 'redis.from_url', 'redis.from_url', (['REDIS_URL'], {}), '(REDIS_URL)\n', (270, 281), False, 'import redis\n'), ((710, 768), 'flask.url_for', 'url_for', (['"""api.ge... |
import os
import json
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
from segmenter.collectors.BaseCollector import BaseCollector
import glob
import numpy as np
from typing import Dict
from segmenter.helpers.p_tqdm import p_uimap as mapper
class VarianceCollector(BaseCollector):
resu... | [
"os.path.exists",
"pandas.read_csv",
"segmenter.helpers.p_tqdm.p_uimap",
"os.path.join",
"pandas.DataFrame",
"os.path.abspath",
"os.remove"
] | [((326, 340), 'pandas.DataFrame', 'pd.DataFrame', ([], {}), '()\n', (338, 340), True, 'import pandas as pd\n'), ((453, 472), 'pandas.read_csv', 'pd.read_csv', (['result'], {}), '(result)\n', (464, 472), True, 'import pandas as pd\n'), ((786, 809), 'os.path.exists', 'os.path.exists', (['outfile'], {}), '(outfile)\n', (8... |
import sqlite3
import pandas as pd
"""
Load the data (use `pandas`) from the provided file `buddymove_holidayiq.csv`
"""
df = pd.read_csv('buddymove_holidayiq.csv')
"""
- Open a connection to a new (blank) database file `buddymove_holidayiq.sqlite3`
"""
conn = sqlite3.connect('buddymove_holidayiq.sqlite3')
c = conn.cu... | [
"sqlite3.connect",
"pandas.read_csv"
] | [((126, 164), 'pandas.read_csv', 'pd.read_csv', (['"""buddymove_holidayiq.csv"""'], {}), "('buddymove_holidayiq.csv')\n", (137, 164), True, 'import pandas as pd\n'), ((262, 308), 'sqlite3.connect', 'sqlite3.connect', (['"""buddymove_holidayiq.sqlite3"""'], {}), "('buddymove_holidayiq.sqlite3')\n", (277, 308), False, 'i... |
#!/usr/bin/env python3
import argparse
import random
import json
import logging
import pandas as pd
from scipy import stats
import spacy
import time
from tqdm import tqdm, trange
import torch
import torch.nn.functional as F
import numpy as np
import os
from pytorch_pretrained_bert import GPT2LMHeadModel, GPT2Tokeniz... | [
"logging.getLogger",
"pytorch_pretrained_bert.GPT2LMHeadModel.from_pretrained",
"torch.cuda.is_available",
"pytorch_pretrained_bert.GPT2Tokenizer.from_pretrained",
"torch.nn.functional.softmax",
"torch.random.manual_seed",
"os.path.exists",
"argparse.ArgumentParser",
"random.Random",
"spacy.load",... | [((416, 559), 'logging.basicConfig', 'logging.basicConfig', ([], {'format': '"""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""', 'datefmt': '"""%m/%d/%Y %H:%M:%S"""', 'level': 'logging.INFO'}), "(format=\n '%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt=\n '%m/%d/%Y %H:%M:%S', level=l... |
from transformers import T5Tokenizer, MT5ForConditionalGeneration
import torch
import numpy as np
from scipy.special import softmax
class BertRanker:
def __init__(self):
self.model_name = 'unicamp-dl/mt5-base-mmarco-v2'
self.tokenizer = T5Tokenizer.from_pretrained(self.model_name)
self.mode... | [
"transformers.MT5ForConditionalGeneration.from_pretrained",
"transformers.T5Tokenizer.from_pretrained",
"torch.log10",
"torch.softmax",
"torch.sum",
"torch.gather"
] | [((258, 302), 'transformers.T5Tokenizer.from_pretrained', 'T5Tokenizer.from_pretrained', (['self.model_name'], {}), '(self.model_name)\n', (285, 302), False, 'from transformers import T5Tokenizer, MT5ForConditionalGeneration\n'), ((324, 384), 'transformers.MT5ForConditionalGeneration.from_pretrained', 'MT5ForConditiona... |
import numpy as np
from scipy.stats import entropy
from scipy.spatial.distance import cosine
def jsd(p1, p2) -> float:
'''Returns the Jensen Shannon Divergence'''
p1 = np.asarray(p1)
p2 = np.asarray(p2)
p1 /= p1.sum()
p2 /= p2.sum()
m = (p1 + p2) / 2
return (entropy(p1, m) + en... | [
"scipy.spatial.distance.cosine",
"numpy.asarray",
"numpy.unique",
"scipy.stats.entropy"
] | [((185, 199), 'numpy.asarray', 'np.asarray', (['p1'], {}), '(p1)\n', (195, 199), True, 'import numpy as np\n'), ((210, 224), 'numpy.asarray', 'np.asarray', (['p2'], {}), '(p2)\n', (220, 224), True, 'import numpy as np\n'), ((431, 444), 'numpy.asarray', 'np.asarray', (['a'], {}), '(a)\n', (441, 444), True, 'import numpy... |
"""
Mesh class containing geometry information
"""
from pyrr import matrix44
import numpy
class Mesh:
"""Mesh info and geometry"""
def __init__(self, name, vao=None, material=None, attributes=None, bbox_min=None, bbox_max=None):
"""
:param name: Name of the mesh
:param vao: VAO
... | [
"numpy.append",
"numpy.asarray",
"pyrr.matrix44.apply_to_vector"
] | [((2345, 2380), 'numpy.append', 'numpy.append', (['self.bbox_min[:]', '(1.0)'], {}), '(self.bbox_min[:], 1.0)\n', (2357, 2380), False, 'import numpy\n'), ((2395, 2430), 'numpy.append', 'numpy.append', (['self.bbox_max[:]', '(1.0)'], {}), '(self.bbox_max[:], 1.0)\n', (2407, 2430), False, 'import numpy\n'), ((2483, 2525)... |
# -*- coding: utf-8 -*-
from kivy.app import App
from kivy.uix.floatlayout import FloatLayout
from kivy.uix.relativelayout import RelativeLayout
from kivy.uix.gridlayout import GridLayout
from kivy.uix.scatterlayout import ScatterLayout
from kivy.uix.boxlayout import BoxLayout
from kivy.uix.label import Label
from k... | [
"kivy.uix.button.Button",
"kivy.properties.NumericProperty",
"kivy.lang.Builder.load_string",
"kivy.uix.boxlayout.BoxLayout",
"kivy.uix.label.Label",
"kivy.clock.Clock.schedule_once",
"kivy.properties.ListProperty",
"time.localtime"
] | [((591, 879), 'kivy.lang.Builder.load_string', 'Builder.load_string', (['"""\n<ColoredGridLayout@GridLayout>:\n size_hint: None, 1\n size: self.height, self.height\n bcolor: 1, 1, 1, 1\n #pos_hint: {\'center\': (.5, .5)}\n canvas.before:\n Color:\n rgba: self.bcolor\n Rectangle:\n pos: self.pos\n ... |
import os
import pathlib
import zipfile
import pytest
from cihai.data.unihan.constants import UNIHAN_FILES
@pytest.fixture
def fixture_path():
return os.path.abspath(os.path.join(os.path.dirname(__file__), "fixtures"))
@pytest.fixture
def test_config_file(fixture_path):
return os.path.join(fixture_path, "... | [
"pytest.fixture",
"os.path.dirname",
"os.path.join",
"os.path.basename"
] | [((933, 965), 'pytest.fixture', 'pytest.fixture', ([], {'scope': '"""function"""'}), "(scope='function')\n", (947, 965), False, 'import pytest\n'), ((292, 337), 'os.path.join', 'os.path.join', (['fixture_path', '"""test_config.yml"""'], {}), "(fixture_path, 'test_config.yml')\n", (304, 337), False, 'import os\n'), ((18... |
# Import the openAI gym module
import gym
# Create an environment for the specific game - "breakout"
env = gym.make('BreakoutDeterministic-v4')
# Reset the frame to start from the first frame
frame = env.reset()
# Render
env.render()
is_done = False
while not is_done:
# Perform an action that is random by sampling... | [
"gym.make"
] | [((108, 144), 'gym.make', 'gym.make', (['"""BreakoutDeterministic-v4"""'], {}), "('BreakoutDeterministic-v4')\n", (116, 144), False, 'import gym\n')] |
from sklearn import datasets, linear_model, preprocessing, decomposition, manifold, svm
from sklearn.metrics import make_scorer, accuracy_score
import numpy as np
from sklearn.model_selection import cross_validate, cross_val_score, train_test_split
import matplotlib.pyplot as plt
import time
##########################... | [
"sklearn.preprocessing.LabelEncoder",
"numpy.sqrt",
"numpy.unique",
"numpy.where",
"sklearn.linear_model.LogisticRegression",
"numpy.dot",
"numpy.savetxt",
"numpy.linalg.lstsq",
"numpy.loadtxt",
"time.time"
] | [((492, 620), 'numpy.loadtxt', 'np.loadtxt', (['"""/home/cristianopatricio/Documents/Datasets/Animals_with_Attributes2/Features/ResNet101/AwA2-features.txt"""'], {}), "(\n '/home/cristianopatricio/Documents/Datasets/Animals_with_Attributes2/Features/ResNet101/AwA2-features.txt'\n )\n", (502, 620), True, 'import n... |
from PyQt5.QtCore import QObject, pyqtSignal, QTimer
class BackInicio(QObject):
senal_nombre_verificado = pyqtSignal(bool) # Envía al front-end si el nombre es valido
def __init__(self):
super().__init__()
def verificacion(self, nombre):
if nombre.isalnum() and len(nombre)... | [
"PyQt5.QtCore.pyqtSignal"
] | [((118, 134), 'PyQt5.QtCore.pyqtSignal', 'pyqtSignal', (['bool'], {}), '(bool)\n', (128, 134), False, 'from PyQt5.QtCore import QObject, pyqtSignal, QTimer\n')] |
from django.contrib.auth.mixins import LoginRequiredMixin, PermissionRequiredMixin
from django.shortcuts import get_object_or_404
from django.views.generic import TemplateView
from cajas.inventory.models.category import Category
from cajas.office.models.officeCountry import OfficeCountry
class OfficeBox(LoginRequir... | [
"cajas.office.models.officeCountry.OfficeCountry.objects.select_related",
"django.shortcuts.get_object_or_404",
"cajas.inventory.models.category.Category.objects.all",
"cajas.office.models.officeCountry.OfficeCountry.objects.get"
] | [((638, 674), 'cajas.office.models.officeCountry.OfficeCountry.objects.get', 'OfficeCountry.objects.get', ([], {'slug': 'slug'}), '(slug=slug)\n', (663, 674), False, 'from cajas.office.models.officeCountry import OfficeCountry\n'), ((935, 978), 'django.shortcuts.get_object_or_404', 'get_object_or_404', (['OfficeCountry... |
####################################################
# The Constexpr file is used by the compiler to evaluate
# specifically constant expressions ( expressions containing
# only constant / macro values. )
#
# Constexpr evaluation is done through a slight modification
# to the ExpressionEvaluators,... | [
"globals.DOUBLE.copy",
"Postfixer.Postfixer",
"globals.LITERAL.copy",
"Classes.ExpressionComponent.ExpressionComponent",
"globals.operatorISO"
] | [((7373, 7425), 'Classes.ExpressionComponent.ExpressionComponent', 'EC.ExpressionComponent', (['values', 'set'], {'token': 'tokens[0]'}), '(values, set, token=tokens[0])\n', (7395, 7425), True, 'import Classes.ExpressionComponent as EC\n'), ((7580, 7614), 'Postfixer.Postfixer', 'Postfixer', (['tokens', 'fn', 'globalSco... |
from functools import lru_cache
from buildbot.process.build import Build
from buildbot.process.properties import renderer
from buildbot.locks import WorkerLock
_current_builds = {}
@lru_cache(None)
def get_lock(name, count):
return WorkerLock(name, maxCount=count)
@renderer
def builder_locks(props):
build... | [
"functools.lru_cache",
"buildbot.locks.WorkerLock"
] | [((186, 201), 'functools.lru_cache', 'lru_cache', (['None'], {}), '(None)\n', (195, 201), False, 'from functools import lru_cache\n'), ((240, 272), 'buildbot.locks.WorkerLock', 'WorkerLock', (['name'], {'maxCount': 'count'}), '(name, maxCount=count)\n', (250, 272), False, 'from buildbot.locks import WorkerLock\n')] |
# /!usr/bin/env python3
"""
Define decision makers (either human participants or CNN models).
"""
from modelvshuman import constants as c
from modelvshuman.plotting.colors import *
from modelvshuman.plotting.decision_makers import DecisionMaker
def plotting_definition_template(df):
"""Decision makers to compare... | [
"modelvshuman.plotting.decision_makers.DecisionMaker"
] | [((4945, 5067), 'modelvshuman.plotting.decision_makers.DecisionMaker', 'DecisionMaker', ([], {'name_pattern': '"""simclr_resnet50x1"""', 'color': 'orange2', 'marker': '"""o"""', 'df': 'df', 'plotting_name': '"""SimCLR: ResNet-50x1"""'}), "(name_pattern='simclr_resnet50x1', color=orange2, marker='o',\n df=df, plottin... |
################################################################################
# Project: AuShadha
# Description: Pane of the UI
# Author ; Dr.<NAME>
# Date: 04-11-2013
# License: GNU-GPL Version3, see LICENSE.txt for details
################################################################################
from cStri... | [
"AuShadha.apps.ui.ui.UI.get_module"
] | [((748, 784), 'AuShadha.apps.ui.ui.UI.get_module', 'UI.get_module', (['"""PatientRegistration"""'], {}), "('PatientRegistration')\n", (761, 784), False, 'from AuShadha.apps.ui.ui import UI\n'), ((799, 825), 'AuShadha.apps.ui.ui.UI.get_module', 'UI.get_module', (['"""OPD_Visit"""'], {}), "('OPD_Visit')\n", (812, 825), F... |
import os
import json
from pyramid.response import Response
from pyramid.response import FileResponse
from pyramid.view import view_config
import pyramid.httpexceptions as httpexceptions
# database stuff
from sqlalchemy.exc import DBAPIError
from sqlalchemy import or_
from climasng.models import *
# json data stuff
... | [
"climasng.docassembly.sectiondata.SectionData",
"json.dumps",
"os.path.join",
"os.path.isfile",
"os.path.dirname",
"pyramid.response.Response",
"climasng.data.datafinder.createBiodiversityJson",
"pyramid.view.view_config",
"climasng.data.datafinder.createSpeciesJson"
] | [((581, 611), 'pyramid.view.view_config', 'view_config', ([], {'route_name': '"""data"""'}), "(route_name='data')\n", (592, 611), False, 'from pyramid.view import view_config\n'), ((727, 752), 'os.path.dirname', 'os.path.dirname', (['__file__'], {}), '(__file__)\n', (742, 752), False, 'import os\n'), ((2029, 2070), 'js... |
# Generated by Django 2.0.3 on 2018-06-01 20:45
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('cdr_rates', '0012_cachedrate'),
]
operations = [
migrations.RemoveField(
model_name='cachedrate... | [
"django.db.models.OneToOneField",
"django.db.migrations.RemoveField"
] | [((262, 320), 'django.db.migrations.RemoveField', 'migrations.RemoveField', ([], {'model_name': '"""cachedrate"""', 'name': '"""id"""'}), "(model_name='cachedrate', name='id')\n", (284, 320), False, 'from django.db import migrations, models\n'), ((475, 600), 'django.db.models.OneToOneField', 'models.OneToOneField', ([]... |
#!/usr/bin/python
#
# Utility to extract OpenCV cmake options
#
import argparse
import collections
import fnmatch
import os
import re
def findfiles(path, pat):
res = []
if isinstance(pat, list):
pat_list = pat
else:
pat_list = [pat]
for pat in pat_list:
pat_dir = os.path.dirn... | [
"collections.OrderedDict",
"argparse.ArgumentParser",
"re.compile",
"os.path.join",
"os.path.dirname",
"fnmatch.fnmatch",
"os.path.basename"
] | [((641, 658), 're.compile', 're.compile', (['regex'], {}), '(regex)\n', (651, 658), False, 'import re\n'), ((1018, 1043), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {}), '()\n', (1041, 1043), False, 'import argparse\n'), ((1396, 1442), 'os.path.join', 'os.path.join', (['args.build_dir', '"""CMakeCache.t... |
#-*- coding:utf-8 -*-
from app.models import *
from app import app,db
'''
a_gv=Gv_Files()
a_gv.gv_title="first_one"
a_gv.gv_content="try it"
db.session.add(a_gv)
db.session.commit()
a_conception=Conceptions()
a_conception.conception_id="test1"
a_conception.conception_style="conception"
a_conception.conception_title... | [
"app.db.session.commit",
"app.db.session.add"
] | [((1416, 1440), 'app.db.session.add', 'db.session.add', (['a_writer'], {}), '(a_writer)\n', (1430, 1440), False, 'from app import app, db\n'), ((1441, 1460), 'app.db.session.commit', 'db.session.commit', ([], {}), '()\n', (1458, 1460), False, 'from app import app, db\n')] |
import sys
import os
import pprint
def main():
print("python version:", sys.version)
pprint.pprint(os.environ._data)
if __name__ == "__main__":
main()
| [
"pprint.pprint"
] | [((95, 126), 'pprint.pprint', 'pprint.pprint', (['os.environ._data'], {}), '(os.environ._data)\n', (108, 126), False, 'import pprint\n')] |
"""
All functions that take input (other than the game mainloop).
interface.poll() returns (libtcod.Key, libtcod.Mouse) with key *presses*
and mouse events, but not key *releases*.
interface.parse_move(key) translates a libtcod.Key into directional movement.
interface.log_display(width=60)
interface.target_tile(acto... | [
"libtcodpy.console_set_char_background",
"libtcodpy.line_init",
"libtcodpy.line_step",
"renderer.ScreenCoords.fromWorldCoords",
"libtcodpy.console_set_default_background",
"libtcodpy.map_is_in_fov",
"renderer.write_log",
"libtcodpy.Mouse",
"libtcodpy.console_new",
"libtcodpy.sys_check_for_event",
... | [((527, 540), 'libtcodpy.Key', 'libtcod.Key', ([], {}), '()\n', (538, 540), True, 'import libtcodpy as libtcod\n'), ((553, 568), 'libtcodpy.Mouse', 'libtcod.Mouse', ([], {}), '()\n', (566, 568), True, 'import libtcodpy as libtcod\n'), ((573, 663), 'libtcodpy.sys_check_for_event', 'libtcod.sys_check_for_event', (['(libt... |
#Functions used for gibbs sampling
# <NAME>
#02 April 2019
import pandas as pd
import numpy as np
class gibbs:
def gibbs_difference(y, ind, mu0 = 50, tau0 = 1/625, del0 = 0, gamma0 = 1/625, a0 = 0.5, b0 = 50, maxiter = 5000):
y1 = y[ind == 1]
y2 = y[ind == 2]
n1 = len(y1)
n2 = len... | [
"numpy.mean",
"numpy.sqrt",
"numpy.random.gamma",
"pandas.DataFrame",
"numpy.var"
] | [((457, 508), 'pandas.DataFrame', 'pd.DataFrame', ([], {'columns': "['mu', 'del', 'tau', 'theta']"}), "(columns=['mu', 'del', 'tau', 'theta'])\n", (469, 508), True, 'import pandas as pd\n'), ((1639, 1653), 'numpy.mean', 'np.mean', (['theta'], {}), '(theta)\n', (1646, 1653), True, 'import numpy as np\n'), ((1904, 1972),... |
from __future__ import division
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
import cv2
# Note origin is top-left corner of the image
# Formula for bx,by,bh,w
# bx=sigmoid(tx)+cx , by=sigmoid(ty)+cy
# where tx,ty is prediction ... | [
"torch.sort",
"torch.unique",
"torch.max",
"torch.sigmoid",
"torch.clamp",
"torch.min",
"torch.exp",
"torch.cat",
"torch.nonzero",
"numpy.meshgrid",
"torch.from_numpy",
"numpy.full",
"cv2.resize",
"torch.FloatTensor",
"numpy.arange",
"torch.true_divide"
] | [((887, 949), 'cv2.resize', 'cv2.resize', (['img', '(new_w, new_h)'], {'interpolation': 'cv2.INTER_CUBIC'}), '(img, (new_w, new_h), interpolation=cv2.INTER_CUBIC)\n', (897, 949), False, 'import cv2\n'), ((966, 1007), 'numpy.full', 'np.full', (['(inp_dim[1], inp_dim[0], 3)', '(128)'], {}), '((inp_dim[1], inp_dim[0], 3),... |
from django.test import TestCase
from django.utils.timezone import now, timedelta
from emoticonvis.apps.corpus import models as corpus_models
from emoticonvis.apps.corpus import utils as corpus_utils
from emoticonvis.apps.coding import models as coding_models
from emoticonvis.apps.api import serializers
from django.con... | [
"emoticonvis.apps.corpus.models.Message.objects.filter",
"emoticonvis.apps.corpus.models.Dataset.objects.create",
"emoticonvis.apps.api.serializers.CodeDefinitionSerializer",
"emoticonvis.apps.corpus.models.Code.objects.filter",
"emoticonvis.apps.coding.models.CodeDefinition.objects.create",
"emoticonvis.... | [((528, 571), 'django.contrib.auth.models.User.objects.create_user', 'User.objects.create_user', ([], {'username': '"""master"""'}), "(username='master')\n", (552, 571), False, 'from django.contrib.auth.models import User\n'), ((588, 630), 'django.contrib.auth.models.User.objects.create_user', 'User.objects.create_user... |
import numpy as np
from envs.EnvWrapper import EnvWrapper
class LunarLanderWithNoise(EnvWrapper):
def __init__(self, random_state):
super(LunarLanderWithNoise, self).__init__("LunarLander-v2", random_state)
self.state_sz = 256
def transform_obs(self, obs):
return np.concatenate((obs, ... | [
"numpy.random.uniform"
] | [((320, 347), 'numpy.random.uniform', 'np.random.uniform', ([], {'size': '(248)'}), '(size=248)\n', (337, 347), True, 'import numpy as np\n')] |
import cv2
from queue import Queue
import threading
from fellbeast.drone import Drone
from fellbeast.routines import follow_person
q = Queue()
def display():
print("Start Displaying")
while True:
if q.empty() != True:
frame = q.get()
cv2.imshow("frame1", frame)
if cv2... | [
"fellbeast.drone.Drone",
"fellbeast.routines.follow_person",
"cv2.imshow",
"cv2.destroyAllWindows",
"threading.Thread",
"queue.Queue",
"cv2.waitKey",
"cv2.namedWindow"
] | [((136, 143), 'queue.Queue', 'Queue', ([], {}), '()\n', (141, 143), False, 'from queue import Queue\n'), ((396, 432), 'fellbeast.drone.Drone', 'Drone', ([], {'known_face_path': '"""../face_db/"""'}), "(known_face_path='../face_db/')\n", (401, 432), False, 'from fellbeast.drone import Drone\n'), ((492, 523), 'fellbeast.... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 5/18/2019 1:38 PM
# @Author : chinshin
# @FileName: ggnn_preprocessor.py
from __future__ import unicode_literals
from collections import defaultdict
import numpy as np
from rdkit import Chem
from chainer_chemistry.dataset.preprocessors.common \
i... | [
"chainer_chemistry.dataset.preprocessors.common.construct_discrete_edge_matrix",
"chainer_chemistry.dataset.preprocessors.common.type_check_num_atoms",
"numpy.array",
"collections.defaultdict",
"rdkit.Chem.GetAdjacencyMatrix"
] | [((2821, 2862), 'chainer_chemistry.dataset.preprocessors.common.type_check_num_atoms', 'type_check_num_atoms', (['mol', 'self.max_atoms'], {}), '(mol, self.max_atoms)\n', (2841, 2862), False, 'from chainer_chemistry.dataset.preprocessors.common import type_check_num_atoms\n'), ((3062, 3097), 'chainer_chemistry.dataset.... |
#!/usr/bin/env python
import os
import sys
def main():
s = 'This Python script prepares all files needed to run a replica exchange, including' \
' .mdp files and .tpr files, given the .gro file(s) and a template .mdp file.' \
' The .gro file could be only one common .gro file for all the replicas, ... | [
"os.chdir",
"os.system",
"sys.exit"
] | [((2466, 2525), 'os.system', 'os.system', (["('cp *template.mdp state_%s/%s.mdp' % (i, prefix))"], {}), "('cp *template.mdp state_%s/%s.mdp' % (i, prefix))\n", (2475, 2525), False, 'import os\n'), ((2534, 2664), 'os.system', 'os.system', (['(\'sed -i -e "s/init-lambda-state = X/init-lambda-state = %s/g" s... |
# Generated by Django 3.2 on 2021-05-17 13:38
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('api', '0003_auto_20210517_1034'),
]
operations = [
migrations.AlterModelOptions(
name='pavilhao',
options={'ordering':... | [
"django.db.migrations.AlterModelOptions",
"django.db.models.IntegerField"
] | [((229, 372), 'django.db.migrations.AlterModelOptions', 'migrations.AlterModelOptions', ([], {'name': '"""pavilhao"""', 'options': "{'ordering': ['id'], 'verbose_name': 'Pavilhão', 'verbose_name_plural':\n 'Pavilhões'}"}), "(name='pavilhao', options={'ordering': ['id'],\n 'verbose_name': 'Pavilhão', 'verbose_name... |
"""Integration tests for DIDComm resolver."""
# pylint: disable=redefined-outer-name
from . import DID_MOCK, DID_SOV, DID_MOCK_FAIL
from acapy_backchannel import Client
from acapy_backchannel.api.resolver import resolve
def test_no_resolver_connection_returns_error(requester: Client):
"""Test resolution over D... | [
"acapy_backchannel.api.resolver.resolve.sync",
"acapy_backchannel.api.resolver.resolve.sync_detailed"
] | [((353, 406), 'acapy_backchannel.api.resolver.resolve.sync_detailed', 'resolve.sync_detailed', ([], {'client': 'requester', 'did': 'DID_MOCK'}), '(client=requester, did=DID_MOCK)\n', (374, 406), False, 'from acapy_backchannel.api.resolver import resolve\n'), ((584, 636), 'acapy_backchannel.api.resolver.resolve.sync_det... |
#!/usr/bin/env python
"""
Convert text data to embeddings
__author__ = "<NAME>"
__copyright__ = "Copyright 2018, <NAME>"
__license__ = "The MIT License"
__email__ = "<EMAIL>"
"""
import os
import logging
import re
import numpy as np
import keras
from gensim.models.word2vec import Word2Vec
from project.text_to_id im... | [
"logging.getLogger",
"gensim.models.word2vec.Word2Vec.load",
"project.text_to_id.map_text_to_word_list",
"os.environ.get",
"keras.utils.to_categorical",
"numpy.array",
"numpy.sum",
"numpy.concatenate"
] | [((354, 381), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (371, 381), False, 'import logging\n'), ((1259, 1284), 'gensim.models.word2vec.Word2Vec.load', 'Word2Vec.load', (['MODEL_PATH'], {}), '(MODEL_PATH)\n', (1272, 1284), False, 'from gensim.models.word2vec import Word2Vec\n'), ((271... |
import contextlib
import redis
@contextlib.contextmanager
def get_db(db_url):
yield redis.StrictRedis.from_url(db_url)
| [
"redis.StrictRedis.from_url"
] | [((89, 123), 'redis.StrictRedis.from_url', 'redis.StrictRedis.from_url', (['db_url'], {}), '(db_url)\n', (115, 123), False, 'import redis\n')] |
import numpy as np
import six
import collections
import requests ... | [
"MDAnalysis.Universe.from_smiles"
] | [((8108, 8146), 'MDAnalysis.Universe.from_smiles', 'mda.Universe.from_smiles', (['smilesString'], {}), '(smilesString)\n', (8132, 8146), True, 'import MDAnalysis as mda\n')] |
"""
frosch - Better runtime errors
<NAME>
betterthannothing.blog
<EMAIL>
License MIT
"""
import sys
from typing import Any, List
import colorama
from pygments import highlight
from pygments.formatters.terminal256 import Terminal256Formatter
from pygments.lexers.python import Python3Lexer, Pyth... | [
"pygments.highlight",
"pygments.lexers.python.Python3TracebackLexer",
"pygments.formatters.terminal256.Terminal256Formatter",
"pygments.lexers.python.Python3Lexer"
] | [((1380, 1420), 'pygments.formatters.terminal256.Terminal256Formatter', 'Terminal256Formatter', ([], {'style': 'MonokaiStyle'}), '(style=MonokaiStyle)\n', (1400, 1420), False, 'from pygments.formatters.terminal256 import Terminal256Formatter\n'), ((1449, 1463), 'pygments.lexers.python.Python3Lexer', 'Python3Lexer', ([]... |