seq_id string | text string | repo_name string | sub_path string | file_name string | file_ext string | file_size_in_byte int64 | program_lang string | lang string | doc_type string | stars int64 | dataset string | pt string | api list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
41325207 | """
PERFORMANCE ANALYSIS: Evaluate the system performance, according to settings in 'config.yaml'
Results are stored in 'result.csv' and can be visualized running the Matlab script 'result.m'
"""
from core.simulation.simulation import Simulation as Simulation
from core.simulation.model.config import load_configuration... | null | exp/simulation/performance_analysis.py | performance_analysis.py | py | 2,246 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "logging.basicConfig",
"line_number": 14,
"usage_type": "call"
},
{
"api_name": "logging.INFO",
"line_number": 14,
"usage_type": "attribute"
},
{
"api_name": "core.utils.logutils.ConsoleHandler",
"line_number": 14,
"usage_type": "call"
},
{
"api_name... |
366747823 | from django.conf.urls import url
from sample_app import views
#for template tagging
app_name = 'sample_app'
urlpatterns = [
url(r'^$', views.index, name='index'),
url(r'formpage/', views.form_name_view, name='form_name'),
]
| null | sample_project/sample_app/urls.py | urls.py | py | 235 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "django.conf.urls.url",
"line_number": 8,
"usage_type": "call"
},
{
"api_name": "sample_app.views.index",
"line_number": 8,
"usage_type": "attribute"
},
{
"api_name": "sample_app.views",
"line_number": 8,
"usage_type": "name"
},
{
"api_name": "django... |
19742931 | from lxml import etree
# 写入csv文件
import csv
# 电影名称、电影类型、电影评分、上映场次
# 获取网页内容
def get_content():
path = 'www1/北京影讯,北京电影院-在线选座购票-购电影票.html'
htmlfile = open(path, 'r', encoding='utf-8')
htmlhandle = htmlfile.read()
return htmlhandle
# 解析数据并写入文件
def get_information(cont):
item = {}
cont = etree.HTML... | null | 测试/题目三/ans0101.py | ans0101.py | py | 1,233 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "lxml.etree.HTML",
"line_number": 16,
"usage_type": "call"
},
{
"api_name": "lxml.etree",
"line_number": 16,
"usage_type": "name"
},
{
"api_name": "csv.writer",
"line_number": 20,
"usage_type": "call"
}
] |
613551772 | import json
import numpy as np
import os
from PIL import Image
import tarfile
import re
from keras.layers import Embedding
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.utils.data_utils import get_file
EMBEDDING_DIM = 50
tokenizer = Tokenizer()
def ... | null | prepare.py | prepare.py | py | 10,964 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "keras.preprocessing.text.Tokenizer",
"line_number": 14,
"usage_type": "call"
},
{
"api_name": "json.loads",
"line_number": 21,
"usage_type": "call"
},
{
"api_name": "keras.preprocessing.sequence.pad_sequences",
"line_number": 33,
"usage_type": "call"
},
... |
165456308 | from django import forms
from .models import Persona, ObraSocial
class PersonaForm(forms.ModelForm):
class Meta:
model = Persona
fields = ['dni', 'nombre', 'apellido', 'fecha_nac', 'telefono']
labels = {
'dni': 'Ingrese el DNI',
'nombre': 'Ingrese el/los nombres ',
... | null | apps/modelsGeneral/forms.py | forms.py | py | 2,613 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "django.forms.ModelForm",
"line_number": 4,
"usage_type": "attribute"
},
{
"api_name": "django.forms",
"line_number": 4,
"usage_type": "name"
},
{
"api_name": "models.Persona",
"line_number": 6,
"usage_type": "name"
},
{
"api_name": "django.forms.Tex... |
256958275 | from PyQt5 import QtCore, QtGui, QtWidgets, uic
from Model import *
from typing import List, Callable
from Database import *
class ListWidget(QtWidgets.QWidget):
class Mediator:
def load(self) -> List[Model]:
return []
def edit_available(self) -> bool:
return True
... | null | bullshit/UI/ListWidget.py | ListWidget.py | py | 3,680 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "PyQt5.QtWidgets.QWidget",
"line_number": 7,
"usage_type": "attribute"
},
{
"api_name": "PyQt5.QtWidgets",
"line_number": 7,
"usage_type": "name"
},
{
"api_name": "typing.List",
"line_number": 9,
"usage_type": "name"
},
{
"api_name": "PyQt5.QtWidgets... |
428285147 | """
SimpleTwitter
Feature Extractors:
1. Word Counts
* Split on whitespace
* No stop words
* unigrams only
Final Classifier:
Naive Bayes
"""
from sklearn.pipeline import Pipeline
# Feature Extractors
from sklearn.feature_extraction.text import CountVectorizer
f1 = CountVectorizer(
input=u'content',
... | null | foodbornenyc/pipelines/simple_naivebayes.py | simple_naivebayes.py | py | 1,055 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "sklearn.feature_extraction.text.CountVectorizer",
"line_number": 19,
"usage_type": "call"
},
{
"api_name": "sklearn.naive_bayes.MultinomialNB",
"line_number": 41,
"usage_type": "call"
},
{
"api_name": "sklearn.pipeline.Pipeline",
"line_number": 48,
"usage_t... |
61640179 | from setuptools import setup
from Cython.Build import cythonize
from distutils.extension import Extension
import numpy as np
#compilation mode for debuging
#extensions = [
#Extension("differentiable_renderer_cython", ["DEODR/differentiable_renderer_cython.pyx"]
#,extra_compile_args=["-Zi", "/Od"]
#,extra_l... | null | setup.py | setup.py | py | 760 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "Cython.Build.cythonize",
"line_number": 17,
"usage_type": "call"
},
{
"api_name": "setuptools.setup",
"line_number": 20,
"usage_type": "call"
},
{
"api_name": "numpy.get_include",
"line_number": 28,
"usage_type": "call"
}
] |
457994625 | #!/usr/bin/env python
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import numpy as np
import os.path as osp
import os
from resnet import build_model
from skimage.io import imread, imsave
DATASET_PATH = '/home/jorge/Documents/cos4... | null | resnet_predict.py | resnet_predict.py | py | 2,857 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "numpy.arange",
"line_number": 26,
"usage_type": "call"
},
{
"api_name": "numpy.zeros",
"line_number": 33,
"usage_type": "call"
},
{
"api_name": "numpy.float32",
"line_number": 33,
"usage_type": "attribute"
},
{
"api_name": "numpy.zeros",
"line_n... |
107663802 | # Data Loading & Setting Library
from glob import glob
import warnings
import pickle
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.font_manager as fm
import pandas as pd
import numpy as np
import re
import os
from konlpy.tag import Okt
from sklearn.feature_selection import SelectKBest, f_classif
... | null | dacon/2_climate/dc_climate_4.py | dc_climate_4.py | py | 14,160 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "os.environ",
"line_number": 27,
"usage_type": "attribute"
},
{
"api_name": "warnings.simplefilter",
"line_number": 30,
"usage_type": "call"
},
{
"api_name": "warnings.filterwarnings",
"line_number": 31,
"usage_type": "call"
},
{
"api_name": "matplot... |
223301261 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
cal_WAVE_NEW_E2DS_spirou.py [night_directory] [HCfitsfilename] [FPfitsfilename]
Wavelength calibration incorporating the FP lines
Following C. Lovis's method for Espresso
Created on 2018-06-08 at 16:00
@author: mhobson
"""
from __future__ import division
import numpy... | null | old_code/INTROOT/misc/cal_WAVE_NEW_E2DS_spirou.py | cal_WAVE_NEW_E2DS_spirou.py | py | 58,870 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "astropy.constants.c.to",
"line_number": 32,
"usage_type": "call"
},
{
"api_name": "astropy.constants.c",
"line_number": 32,
"usage_type": "attribute"
},
{
"api_name": "astropy.constants",
"line_number": 32,
"usage_type": "name"
},
{
"api_name": "ast... |
353892067 | import sys
from PyQt5.QtWidgets import QMainWindow,QApplication,QDialog,QFileDialog,QMessageBox
from monitor import Ui_MainWindow
import subprocess
import re
import time
class parent_MainWindow(QMainWindow,Ui_MainWindow):
def __init__(self):
QMainWindow.__init__(self)
self.main_ui=Ui_MainWindow()
self.setupUi(... | null | Android_monitor_log/monitor_dut_log.py | monitor_dut_log.py | py | 3,591 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "PyQt5.QtWidgets.QMainWindow",
"line_number": 9,
"usage_type": "name"
},
{
"api_name": "monitor.Ui_MainWindow",
"line_number": 9,
"usage_type": "name"
},
{
"api_name": "PyQt5.QtWidgets.QMainWindow.__init__",
"line_number": 11,
"usage_type": "call"
},
{
... |
277523217 | from __future__ import division
## to get the data
import cPickle
import gzip
## to visualize data
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import theano
import theano.tensor as T
import numpy as np
filename = "/Users/ducprogram/Dropbox/Programming/DigitRecognizer"
filename += "/neural-networks-a... | null | get_data.py | get_data.py | py | 1,916 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "gzip.open",
"line_number": 38,
"usage_type": "call"
},
{
"api_name": "cPickle.load",
"line_number": 40,
"usage_type": "call"
},
{
"api_name": "theano.shared",
"line_number": 53,
"usage_type": "call"
},
{
"api_name": "numpy.asarray",
"line_number... |
314778512 | import tensorflow as tf
import numpy as np
from tools.preprocessing import preprocess_images
from tools.kfold import KFold_cross_validation_split
from tools.extraction_and_metrics import extract_features, compute_confusion_matrix
from .network import Net
import torchvision.models as models
import torch
import os
im... | null | src/frameworks/detrac_torch/feature_extractor.py | feature_extractor.py | py | 2,501 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "tools.preprocessing.preprocess_images",
"line_number": 44,
"usage_type": "call"
},
{
"api_name": "tools.kfold.KFold_cross_validation_split",
"line_number": 53,
"usage_type": "call"
},
{
"api_name": "network.Net",
"line_number": 64,
"usage_type": "call"
},... |
314254496 | #!/home/cimatori/installed/anaconda/bin/python
"""
Compute and compare pdfs in different time intervals and at different scales
"""
import numpy as np
import numexpr as ne
import matplotlib.pyplot as ppl
from MyLib import Statistics,hist2plot
from NIOZhst import load_chain
# Load paramters
import ConfigPdfs
reload(C... | null | LIS132/Layering/dens_incr_pdf.py | dens_incr_pdf.py | py | 2,479 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "matplotlib.pyplot.close",
"line_number": 18,
"usage_type": "call"
},
{
"api_name": "matplotlib.pyplot",
"line_number": 18,
"usage_type": "name"
},
{
"api_name": "NIOZhst.load_chain",
"line_number": 25,
"usage_type": "call"
},
{
"api_name": "socket.g... |
428199193 | # !/usr/bin/env python
# -*- coding: utf-8 -*-
# @Date : 2020/05/22 14:21:36
# @File : asyncio使用requests.py
# @Link : https://www.cnblogs.com/BenLam/
# @Version : 1.0
import os
import asyncio
import multiprocessing
import random
import time
import requests
from concurrent.futures import ProcessPoolExecuto... | null | Python/协程/asyncio使用requests.py | asyncio使用requests.py | py | 2,415 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "time.time",
"line_number": 19,
"usage_type": "call"
},
{
"api_name": "requests.get",
"line_number": 22,
"usage_type": "call"
},
{
"api_name": "time.sleep",
"line_number": 24,
"usage_type": "call"
},
{
"api_name": "asyncio.Queue",
"line_number": ... |
520571497 | import numpy as np
import pandas as pd
import random
import math
from sklearn.feature_extraction import DictVectorizer as DVec
import matplotlib.pyplot as ploty
import time
import sys
file_name = sys.argv[1]
start_time = time.clock()
dataset = pd.read_csv(file_name)
dataset.columns =["Patient_ID","Local_tumor_recurren... | null | ml/es14btech11017/3B.py | 3B.py | py | 2,605 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "sys.argv",
"line_number": 10,
"usage_type": "attribute"
},
{
"api_name": "time.clock",
"line_number": 11,
"usage_type": "call"
},
{
"api_name": "pandas.read_csv",
"line_number": 12,
"usage_type": "call"
},
{
"api_name": "random.sample",
"line_nu... |
202897127 | from gensim.models.word2vec import Word2Vec
from gensim.models.keyedvectors import KeyedVectors
from gensim.parsing.porter import PorterStemmer
from nltk.corpus import stopwords
import numpy as np
import pickle
from sklearn.cluster import MiniBatchKMeans
import operator
from os import system
from vaderSentiment.vaderSe... | null | Creighton/MASA.py | MASA.py | py | 25,814 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "os.path.isfile",
"line_number": 122,
"usage_type": "call"
},
{
"api_name": "os.path",
"line_number": 122,
"usage_type": "attribute"
},
{
"api_name": "pickle.load",
"line_number": 128,
"usage_type": "call"
},
{
"api_name": "os.path.isfile",
"line... |
650023850 | from collections import OrderedDict
from typing import Sequence, Any
import numpy as np
import tensorflow as tf
import sys, traceback
import pdb
import optparse
from dyPred import Model
from utils import glorot_init
def graph_to_adj_mat(graph, max_n_vertices, num_edge_types, tie_fwd_bkwd=True):
bwd_edge_offset =... | null | DyPred/main.py | main.py | py | 12,130 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "numpy.zeros",
"line_number": 15,
"usage_type": "call"
},
{
"api_name": "numpy.asarray",
"line_number": 28,
"usage_type": "call"
},
{
"api_name": "numpy.float32",
"line_number": 28,
"usage_type": "attribute"
},
{
"api_name": "dyPred.Model",
"line... |
262423198 | import itertools
import os
import json
import argparse
import re
platform_map = {
"linux-aarch64": ["aarch64_linux"],
"linux-ppc64le": ["ppc64le_linux"],
"linux-arm": ["arm_linux"],
"linux-s390x": ["s390x_linux"],
"linux-x64": ["x86-64_linux"],
"macosx-x64": ["x86-64_mac", "aarch64_mac"],
"... | null | disabledTestParser/generateDisabledTestListJson.py | generateDisabledTestListJson.py | py | 5,349 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "re.search",
"line_number": 35,
"usage_type": "call"
},
{
"api_name": "itertools.chain",
"line_number": 70,
"usage_type": "call"
},
{
"api_name": "argparse.ArgumentParser",
"line_number": 85,
"usage_type": "call"
},
{
"api_name": "os.path.dirname",
... |
279129513 | """
SALTS XBMC Addon
Copyright (C) 2014 tknorris
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
T... | null | scrapers/hdmovie14_scraper.py | hdmovie14_scraper.py | py | 7,887 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "scraper.Scraper",
"line_number": 34,
"usage_type": "attribute"
},
{
"api_name": "scraper.DEFAULT_TIMEOUT",
"line_number": 37,
"usage_type": "attribute"
},
{
"api_name": "salts_lib.kodi.get_setting",
"line_number": 39,
"usage_type": "call"
},
{
"api_... |
175759900 |
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import logging
from sklearn.decomposition import PCA
def log():
logging.basicConfig(level=logging.INFO,
format='%(asctime)s %(levelname)s %(message)s')
logger = logging.getLogger('KDD2009')
return log... | null | kdd2009_modified.py | kdd2009_modified.py | py | 16,505 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "logging.basicConfig",
"line_number": 9,
"usage_type": "call"
},
{
"api_name": "logging.INFO",
"line_number": 9,
"usage_type": "attribute"
},
{
"api_name": "logging.getLogger",
"line_number": 11,
"usage_type": "call"
},
{
"api_name": "pandas.read_tab... |
237387082 | import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
NameFiles = [ "SingleJulySingleNode.dat", "SingleJulyTwoNodes.dat", "SingleJulyFourNodes.dat"]
NFiles = len(NameFiles)
NRep = np.zeros((NFiles), dtype = int)
NConf = np.zeros((NFiles), dtype = int)
NTmp = -1 * np.ones((NFile... | null | MultipleNodesPlot/ShowData.py | ShowData.py | py | 4,615 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "numpy.zeros",
"line_number": 8,
"usage_type": "call"
},
{
"api_name": "numpy.zeros",
"line_number": 9,
"usage_type": "call"
},
{
"api_name": "numpy.ones",
"line_number": 10,
"usage_type": "call"
},
{
"api_name": "numpy.array",
"line_number": 39,... |
141378986 | bl_info = {
"name": "PLANEX",
"description": "Display the stroke points intersect with the plane across the 3d cursor",
"author": "V Ven",
"version": (0, 0, 3),
"blender": (2, 80, 0),
"category": "Learnbgame",
}
import bpy
import blf
from mathutils import (
Quaternion,
... | null | All_In_One/addons/PLANEX/__init__.py | __init__.py | py | 3,744 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "bpy.context",
"line_number": 39,
"usage_type": "attribute"
},
{
"api_name": "mathutils.Vector",
"line_number": 42,
"usage_type": "call"
},
{
"api_name": "bpy_extras.view3d_utils.location_3d_to_region_2d",
"line_number": 63,
"usage_type": "call"
},
{
... |
529413236 | import json
import os
from algoliasearch import algoliasearch
client = algoliasearch.Client("7EK9KHJW8M", os.environ["ALGOLIA_API_KEY"])
index = client.init_index("schema")
p = json.load(open("_data/plotschema.json"))
schema = []
skippable_keys = [
"src",
"_deprecated",
"impliedEdits",
"uid",
"ed... | null | update_ref_search.py | update_ref_search.py | py | 3,452 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "algoliasearch.algoliasearch.Client",
"line_number": 5,
"usage_type": "call"
},
{
"api_name": "algoliasearch.algoliasearch",
"line_number": 5,
"usage_type": "name"
},
{
"api_name": "os.environ",
"line_number": 5,
"usage_type": "attribute"
},
{
"api_n... |
268108056 |
import requests
from bs4 import BeautifulSoup
from collections import OrderedDict
fname_out = 'output.txt'
def getinfo(url):
sep = '\t'
html = requests.get(url).text
soup = BeautifulSoup(html, 'lxml')
title = soup.select('div.title > h1')[0].text.strip()
loc = soup.find('span', {'class': 'location'}).text.st... | null | olx/olx.py | olx.py | py | 2,299 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "requests.get",
"line_number": 12,
"usage_type": "call"
},
{
"api_name": "bs4.BeautifulSoup",
"line_number": 13,
"usage_type": "call"
},
{
"api_name": "collections.OrderedDict",
"line_number": 26,
"usage_type": "call"
},
{
"api_name": "requests.get",... |
371740446 | from config import env
from collections import OrderedDict
EXCHANGE_INFO = OrderedDict()
EXCHANGE_INFO['binance'] = 'Binance'
EXCHANGE_INFO['poloniex'] = 'Poloniex'
EXCHANGE_INFO['okex'] = 'OKEx'
EXCHANGE_INFO['huobipro'] = 'Huobi'
EXCHANGE_INFO['bitfinex'] = 'Bitfinex'
EXCHANGE_INFO['bitstamp'] = 'Bitstamp'
EXCHANGE_... | null | config/common.py | common.py | py | 4,555 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "collections.OrderedDict",
"line_number": 4,
"usage_type": "call"
},
{
"api_name": "collections.OrderedDict",
"line_number": 29,
"usage_type": "call"
},
{
"api_name": "collections.OrderedDict",
"line_number": 48,
"usage_type": "call"
},
{
"api_name":... |
103328237 | # -*- coding: utf-8 -*-
"""
Created on Sun Feb 17 20:29:46 2019
@author: Formateur IT
"""
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
X = pd.read_csv('credit_bank.csv').values
from scipy.cluster.hierarchy import linkage, dendrogram
fusions = linkage(X, method='comple... | null | hiérarchie.py | hiérarchie.py | py | 1,296 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "pandas.read_csv",
"line_number": 13,
"usage_type": "call"
},
{
"api_name": "scipy.cluster.hierarchy.linkage",
"line_number": 19,
"usage_type": "call"
},
{
"api_name": "scipy.cluster.hierarchy.dendrogram",
"line_number": 21,
"usage_type": "call"
},
{
... |
248788133 | #!/usr/bin/python -u
# +
from SeqDataset import *
#import pandas as pd
import numpy as np
import argparse
import random
import math
import time
#import matplotlib.pyplot as plt
from torch.utils.data import DataLoader
from torch.utils.data.sampler import SubsetRandomSampler
from torch.nn.functional import softmax, relu... | null | scripts/torchtext_cnn_supervised_learning.py | torchtext_cnn_supervised_learning.py | py | 10,655 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "argparse.ArgumentParser",
"line_number": 32,
"usage_type": "call"
},
{
"api_name": "random.seed",
"line_number": 40,
"usage_type": "call"
},
{
"api_name": "random.getstate",
"line_number": 41,
"usage_type": "call"
},
{
"api_name": "numpy.random.seed... |
349262818 | from tqdm import tqdm
class evaluater(nn.Module):
def __init__(self):
pass
def evaluate(self, dev, matric_best, slot_temp, early_stop=None):
# Set to not-training mode to disable dropout
self.encoder.train(False)
self.decoder.train(False)
print("STARTING EVALUATION")
... | null | test/test_util.py | test_util.py | py | 6,388 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "tqdm.tqdm",
"line_number": 14,
"usage_type": "call"
}
] |
201987002 | # -*- coding: utf-8 -*-
"""
Created on Wed Mar 13 10:55:21 2019
@author: bsoni
"""
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output, State
import datetime
import plotly.graph_objs as go
import pandas as pd
import numpy as np
from datetime i... | null | DashBoards/AllDashBoardsMerged/CancellationApps/CA2.py | CA2.py | py | 21,752 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "pandas.read_csv",
"line_number": 27,
"usage_type": "call"
},
{
"api_name": "pandas.read_csv",
"line_number": 28,
"usage_type": "call"
},
{
"api_name": "pandas.read_csv",
"line_number": 29,
"usage_type": "call"
},
{
"api_name": "pandas.concat",
"... |
193852476 | import cv2
import numpy as np
#import utilitis as utlis
curveList = []
avgVal = 10
def getHist(img,minPer=0.1,disp=False,region=1):
if region == 1:
histVal = np.sum(img, axis=0)
else:
histVal = np.sum(img[img.shape[0] // region:, :], axis=0)
# print(histValues)
maxVal = np.max(histVal)
minVal... | null | Python/WIP/main.py | main.py | py | 6,795 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "numpy.sum",
"line_number": 10,
"usage_type": "call"
},
{
"api_name": "numpy.sum",
"line_number": 12,
"usage_type": "call"
},
{
"api_name": "numpy.max",
"line_number": 15,
"usage_type": "call"
},
{
"api_name": "numpy.where",
"line_number": 18,
... |
580472352 | from django import forms
from .models import Notes
class NoteForm(forms.ModelForm):
description = forms.CharField(widget=forms.Textarea(attrs={'rows': 5, 'placeholder': 'What is on your mind?'}),
max_length=4000,
help_text='The max length of the text is ... | null | notes/forms.py | forms.py | py | 449 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "django.forms.ModelForm",
"line_number": 5,
"usage_type": "attribute"
},
{
"api_name": "django.forms",
"line_number": 5,
"usage_type": "name"
},
{
"api_name": "django.forms.CharField",
"line_number": 6,
"usage_type": "call"
},
{
"api_name": "django.f... |
646009412 | import contextlib
import datetime
import io
import json
import math
import os
import re
import requests
import time
from dcicutils.env_utils import EnvUtils, full_env_name, is_stg_or_prd_env
from dcicutils.ff_utils import authorized_request
from dcicutils.lang_utils import disjoined_list, conjoined_list
from dcicutils... | null | dcicutils/ff_mocks.py | ff_mocks.py | py | 36,918 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "dcicutils.misc_utils.environ_bool",
"line_number": 35,
"usage_type": "call"
},
{
"api_name": "dcicutils.qa_utils.MockBotoElasticBeanstalkClient",
"line_number": 39,
"usage_type": "name"
},
{
"api_name": "dcicutils.qa_utils.make_mock_beanstalk",
"line_number": 4... |
123739395 | #!/usr/bin/env python3
"""
My first Flask app
"""
# Importera relevanta moduler
from flask import Flask, render_template, request, session
from person import Person
from data import Data
import functions as func
app = Flask(__name__)
person = Person("John", "Cena", 24, "Sweden")
data = Data('static/img/earthporn.j... | null | flask/app.py | app.py | py | 1,500 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "flask.Flask",
"line_number": 12,
"usage_type": "call"
},
{
"api_name": "person.Person",
"line_number": 15,
"usage_type": "call"
},
{
"api_name": "data.Data",
"line_number": 16,
"usage_type": "call"
},
{
"api_name": "flask.render_template",
"line... |
575033465 | from Bio import SeqIO, Seq
import os
import io
import sys
import time
import argparse
if __name__ == '__main__':
parser = argparse.ArgumentParser(description = "Split reference assembly.")
split_params = parser.add_argument_group('Split parameters.')
split_params.add_argument('-a', '--assembly', requ... | null | ref_splitter.py | ref_splitter.py | py | 1,974 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "argparse.ArgumentParser",
"line_number": 13,
"usage_type": "call"
},
{
"api_name": "Bio.SeqIO.parse",
"line_number": 29,
"usage_type": "call"
},
{
"api_name": "Bio.SeqIO",
"line_number": 29,
"usage_type": "name"
}
] |
608350754 | # -*- coding: utf-8 -*-
"""
spider.tender_hangzhou.py
~~~~~~~~~~~~~~
:copyright:...
"""
import datetime
import logging
import re
from copy import deepcopy
from bs4 import BeautifulSoup
from .spider import Spider
class TenderHangzhou(object):
def __init__(self):
self.logger = logging.getLogge... | null | spider/tender_hangzhou.py | tender_hangzhou.py | py | 1,602 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "logging.getLogger",
"line_number": 19,
"usage_type": "call"
},
{
"api_name": "spider.Spider",
"line_number": 20,
"usage_type": "call"
},
{
"api_name": "datetime.datetime.now",
"line_number": 21,
"usage_type": "call"
},
{
"api_name": "datetime.dateti... |
93733600 | #! /usr/bin/env python
import numpy as np
from setuptools import setup, Extension, find_packages
descr = """Convolutional dictionary learning for noisy signals"""
DISTNAME = 'alphacsc'
DESCRIPTION = descr
MAINTAINER = 'Mainak Jas'
MAINTAINER_EMAIL = 'mainakjas@gmail.com'
LICENSE = 'BSD (3-clause)'
DOWNLOAD_URL = 'ht... | null | setup.py | setup.py | py | 2,603 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "setuptools.Extension",
"line_number": 26,
"usage_type": "call"
},
{
"api_name": "setuptools.Extension",
"line_number": 29,
"usage_type": "call"
},
{
"api_name": "setuptools.Extension",
"line_number": 36,
"usage_type": "call"
},
{
"api_name": "Cython... |
258597930 | import requests
from bs4 import BeautifulSoup
import json
def getGeoCode(address):
if ("/" in address[0:int(len(address)/2)]):
address = deleteslashes(address)
address += " Singapore"
requesteddata = requests.get('https://maps.googleapis.com/maps/api/geocode/json?address={}&key=AIzaSyCZFz2PlrkMfum... | null | results/geocoding.py | geocoding.py | py | 1,130 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "requests.get",
"line_number": 10,
"usage_type": "call"
},
{
"api_name": "bs4.BeautifulSoup",
"line_number": 11,
"usage_type": "call"
},
{
"api_name": "json.loads",
"line_number": 12,
"usage_type": "call"
}
] |
276503982 | #!/usr/bin/env/python3
import re
from collections import defaultdict
m = open("locust.fsa_nt.out", "r")
f = open("./database/nucl_all.accession2taxid", "r")
w = open("./database/taxid_50557.txt", "r")
h = open("locust.fsa_nt", "r")
n = open("locust_insect.fasta", "w")
p = open("locust10_insect.fasta", "w")
query_dict =... | null | filterAc.py | filterAc.py | py | 1,628 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "collections.defaultdict",
"line_number": 10,
"usage_type": "call"
}
] |
104232830 | import os
from datetime import date
import dash
import dash_core_components as dcc
import dash_html_components as html
import plotly.express as px
import pandas as pd
import json
import numpy as np
import datetime
# Make daily map
# --------- GEOJSON ---------- #
uk_counties = json.load(open('../data/Geojson/Ceremonial... | null | utils/daily_map.py | daily_map.py | py | 10,314 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "json.load",
"line_number": 13,
"usage_type": "call"
},
{
"api_name": "json.load",
"line_number": 14,
"usage_type": "call"
},
{
"api_name": "json.load",
"line_number": 15,
"usage_type": "call"
},
{
"api_name": "pandas.read_csv",
"line_number": 10... |
239043285 | import torch
import torch.nn as nn
import torch.nn.functional as F
class QNetwork(nn.Module):
def __init__(self, state_size, action_size, hidden_size1, hidden_size2, seed=1):
"""Initialize parameters and build model.
Params
======
state_size (int): Dimension of each state
... | null | 20Agents/model.py | model.py | py | 943 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "torch.nn.Module",
"line_number": 6,
"usage_type": "attribute"
},
{
"api_name": "torch.nn",
"line_number": 6,
"usage_type": "name"
},
{
"api_name": "torch.manual_seed",
"line_number": 18,
"usage_type": "call"
},
{
"api_name": "torch.nn.Sequential",
... |
527419680 | import ast
import _ast
from dataclasses import dataclass
import re
import yaml
from jinja2 import Template, Environment, DictLoader
from hy.compiler import hy_compile, hy_parse
from coconut.convenience import parse, setup
from . import blocks, expressions, utils
class transpiler:
tmpls = {
'types': {},... | null | transPYler/core.py | core.py | py | 4,520 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "_ast.Assign",
"line_number": 33,
"usage_type": "attribute"
},
{
"api_name": "_ast.AnnAssign",
"line_number": 34,
"usage_type": "attribute"
},
{
"api_name": "_ast.Expr",
"line_number": 35,
"usage_type": "attribute"
},
{
"api_name": "_ast.AugAssign",
... |
314306441 | import numpy as np
import functions as rr
from constants import const
import h5py
import time
def linkage(par):
st = time.time()
C = const()
f_red = h5py.File("spatial_reduced.hdf5", 'r')
f_link = h5py.File("responses.hdf5", 'r')
"""gather the calibration data"""
n_cal_tot = len(C['set_id... | null | fip_collab/2016_06_10_polycrystal_FIP/get_linkage.py | get_linkage.py | py | 4,337 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "time.time",
"line_number": 10,
"usage_type": "call"
},
{
"api_name": "constants.const",
"line_number": 12,
"usage_type": "call"
},
{
"api_name": "h5py.File",
"line_number": 14,
"usage_type": "call"
},
{
"api_name": "h5py.File",
"line_number": 15... |
471711357 | import os
import sys
import pandas as pd
from sklearn.externals import joblib
import umap
import logging
from datetime import datetime
from doppler import cols_conv_feats, skip_hash, filename_without_extension
import doppler.mosaic_utils_stories as mosaic_utils
logger = logging.getLogger(__file__)
DATA_DIR = os.path... | null | doppler/mosaics_stories.py | mosaics_stories.py | py | 7,585 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "logging.getLogger",
"line_number": 12,
"usage_type": "call"
},
{
"api_name": "os.path.join",
"line_number": 14,
"usage_type": "call"
},
{
"api_name": "os.path",
"line_number": 14,
"usage_type": "attribute"
},
{
"api_name": "os.path.dirname",
"li... |
440453199 | # # WRITE TO DISK
# tmp = ar5.ds.to_dataset('pre')
# tmp.to_netcdf( out_fn.replace('.tif', '.nc') )
import xarray as xr
import numpy as np
import rasterio, os
class WriteDataset( object ):
def __init__( self, fn, variable, lat='lat', lon='lon', northup=False ):
self.fn = fn
self.variable = variable
self.lat = ... | null | snap_scripts/epscor_sc/write_lowres_dataset_layer_todisk.py | write_lowres_dataset_layer_todisk.py | py | 2,208 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "xarray.open_dataset",
"line_number": 22,
"usage_type": "call"
},
{
"api_name": "numpy.flipud",
"line_number": 27,
"usage_type": "call"
},
{
"api_name": "numpy.array",
"line_number": 29,
"usage_type": "call"
},
{
"api_name": "numpy.flipud",
"line... |
479131719 | # -*- encoding: utf-8 -*-
##############################################################################
#
# Part of Odoo.
# Copyright (C) 2021 Allegro IT (<http://www.allegro.lv/>)
# E-mail: <info@allegro.lv>
# Address: <Vienibas gatve 109 LV-1058 Riga Latvia>
# ... | null | l10n_lv_partner_title/models/res_partner.py | res_partner.py | py | 3,462 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "odoo.models.Model",
"line_number": 28,
"usage_type": "attribute"
},
{
"api_name": "odoo.models",
"line_number": 28,
"usage_type": "name"
},
{
"api_name": "xml.dom.minidom.parseString",
"line_number": 34,
"usage_type": "call"
},
{
"api_name": "odoo.a... |
640645242 | # Source code modified from scipy.stats._distn_infrastructure.py
#
# Copyright (c) 2001, 2002 Enthought, Inc.
# All rights reserved.
#
# Copyright (c) 2003-2019 SciPy Developers.
# All rights reserved.
import numpy as onp
import scipy.stats as osp_stats
from scipy._lib._util import getargspec_no_self
from scipy.stats.... | null | numpyro/distributions/distribution.py | distribution.py | py | 5,638 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "scipy.stats.rv_continuous",
"line_number": 21,
"usage_type": "attribute"
},
{
"api_name": "scipy.stats",
"line_number": 21,
"usage_type": "name"
},
{
"api_name": "jax.random._is_prng_key",
"line_number": 27,
"usage_type": "call"
},
{
"api_name": "ja... |
509336066 | # -*- coding: utf-8 -*-
from django.utils import timezone
from django.contrib import admin
from django.core.urlresolvers import reverse
from django import forms
from django.conf.urls import url
from django.core.urlresolvers import reverse
from django.contrib.admin.utils import quote
from django.core.exceptions import ... | null | apps/events/admin.py | admin.py | py | 7,014 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "core.abs.admin.BaseCategoryInline",
"line_number": 26,
"usage_type": "name"
},
{
"api_name": "apps.events.models.EventType",
"line_number": 28,
"usage_type": "name"
},
{
"api_name": "core.abs.admin.BaseCategoryAdmin",
"line_number": 30,
"usage_type": "name"... |
613667853 | import os
import pytest
from selenium import webdriver
from heracles.pages.general_page import GeneralPage
# recomment to use chrome driver
# chromedriver should be in root directory of repository
#browser = webdriver.Chrome(executable_path=os.getcwd() + '/chromedriver')
browser = webdriver.PhantomJS(executable_path... | null | test_money_formatter/test_money_formatter.py | test_money_formatter.py | py | 1,821 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "selenium.webdriver.PhantomJS",
"line_number": 11,
"usage_type": "call"
},
{
"api_name": "selenium.webdriver",
"line_number": 11,
"usage_type": "name"
},
{
"api_name": "os.getcwd",
"line_number": 11,
"usage_type": "call"
},
{
"api_name": "pytest.fixt... |
509252692 | import folium
import pandas
data = pandas.read_csv("Volcanoes.txt")
lat = list(data["LAT"])
lon = list(data["LON"])
name = list(data["NAME"])
elev = list(data["ELEV"])
type_of = list(data["TYPE"])
circle_icon = False
words = """<h4>Volcano information:</h4>
<a href="https://www.google.com/search?q=%%22%... | null | Map/map1.py | map1.py | py | 2,732 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "pandas.read_csv",
"line_number": 4,
"usage_type": "call"
},
{
"api_name": "folium.Map",
"line_number": 52,
"usage_type": "call"
},
{
"api_name": "folium.FeatureGroup",
"line_number": 54,
"usage_type": "call"
},
{
"api_name": "folium.IFrame",
"li... |
508891011 | '''
Analysis of maximum profit as a function of multiplierIR or gamma
'''
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import time
import copy
import pickle
from multiprocessing import Pool
from config import *
from profit_optimization_core import *
def dist_from_delta... | null | profit_vs_multiplierIR.py | profit_vs_multiplierIR.py | py | 6,804 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "numpy.unravel_index",
"line_number": 27,
"usage_type": "call"
},
{
"api_name": "numpy.array",
"line_number": 29,
"usage_type": "call"
},
{
"api_name": "numpy.zeros",
"line_number": 45,
"usage_type": "call"
},
{
"api_name": "time.time",
"line_num... |
109535096 | from flask import Blueprint, render_template
from flask import request, flash, redirect, url_for
import boto3
import os
import collections
from datetime import datetime, timedelta
from operator import itemgetter
from app import db
from ..models.configuration import HttpRequestCount
from ..settings import BUCKET_NAME
... | null | app/controllers/manager.py | manager.py | py | 5,598 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "flask.Blueprint",
"line_number": 14,
"usage_type": "call"
},
{
"api_name": "os.getenv",
"line_number": 17,
"usage_type": "call"
},
{
"api_name": "os.getenv",
"line_number": 18,
"usage_type": "call"
},
{
"api_name": "os.getenv",
"line_number": 19... |
445865458 | import requests
import json
# url = "http://fanyi.baidu.com/extendtrans"
url = "http://fanyi.baidu.com/basetrans"
query_str = input("请输入要翻译的中文：")
data = {"query":query_str,
"from":"zh",
"to": "en"}
headers = {"User-Agent":"Mozilla/5.0 (Linux; Android 5.0; SM-G900P Build/LRX21T) Appl... | null | 06_try_json.py | 06_try_json.py | py | 724 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "requests.post",
"line_number": 15,
"usage_type": "call"
},
{
"api_name": "json.loads",
"line_number": 21,
"usage_type": "call"
}
] |
95673082 | import numpy as np
import matplotlib as mpl
mpl.use('tkagg')
import matplotlib.pyplot as plt
from func import *
#Point of intersection
n1=np.array([4,3])
n2=np.array([3,4])
c=np.array([12,12])
n=np.vstack((n1,n2))
point_inter=np.matmul(np.linalg.inv(n),c)
plt.plot(point_inter[0],point_inter[1],'o')
#plotting Li... | null | Computing/StraightLines/Ex3.py | Ex3.py | py | 522 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "matplotlib.use",
"line_number": 3,
"usage_type": "call"
},
{
"api_name": "numpy.array",
"line_number": 8,
"usage_type": "call"
},
{
"api_name": "numpy.array",
"line_number": 9,
"usage_type": "call"
},
{
"api_name": "numpy.array",
"line_number": ... |
346103445 | import errno
from flask import abort
from flask import current_app
from flask import g, jsonify
from flask import redirect
from flask import render_template
from flask import request
from info import constants
from info import db
from info.models import News, Category, User
from info.modules.user import user_blu
from... | null | info/modules/user/views.py | views.py | py | 11,478 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "flask.g.user",
"line_number": 27,
"usage_type": "attribute"
},
{
"api_name": "flask.g",
"line_number": 27,
"usage_type": "name"
},
{
"api_name": "flask.redirect",
"line_number": 30,
"usage_type": "call"
},
{
"api_name": "flask.render_template",
... |
589023668 | import os
from celery import Celery
from dagster_graphql.client.mutations import execute_execute_plan_mutation
from dagster import ExecutionTargetHandle
from dagster.core.instance import InstanceRef
from dagster.core.serdes import serialize_dagster_namedtuple
from .config import CeleryConfig
DEFAULT_BROKER = 'pyamq... | null | python_modules/dagster-celery/dagster_celery/tasks.py | tasks.py | py | 1,327 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "os.getenv",
"line_number": 13,
"usage_type": "call"
},
{
"api_name": "dagster.core.instance.InstanceRef.from_dict",
"line_number": 20,
"usage_type": "call"
},
{
"api_name": "dagster.core.instance.InstanceRef",
"line_number": 20,
"usage_type": "name"
},
... |
28418569 | import sys, pygame, random, time
from threading import Timer
from ww import *
from pygame.locals import *
pygame.init()
ww=Stage(20, 20, 24)
def setup():
'''
Initializes the setup of the game, adding your player, monsters, walls, sticky boxes and normal boxes across the stage.
'''
ww.set_player(Ke... | null | wwgame.py | wwgame.py | py | 3,403 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "pygame.init",
"line_number": 8,
"usage_type": "call"
},
{
"api_name": "ww.set_player",
"line_number": 16,
"usage_type": "call"
},
{
"api_name": "ww.add_actor",
"line_number": 17,
"usage_type": "call"
},
{
"api_name": "ww.add_actor",
"line_number... |
411262790 | # -*- coding: utf-8 -*-
"""A demonstration of Comet streaming functionality using Sijax."""
import os
import sys
import flask
from shelljob import proc
path = os.path.join('.', os.path.dirname(__file__), '../')
sys.path.append(path)
from flask import Flask, g, render_template
import flask_sijax
app = Flask(__name... | null | sijaxExample/comet.py | comet.py | py | 2,744 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "os.path.join",
"line_number": 11,
"usage_type": "call"
},
{
"api_name": "os.path",
"line_number": 11,
"usage_type": "attribute"
},
{
"api_name": "os.path.dirname",
"line_number": 11,
"usage_type": "call"
},
{
"api_name": "sys.path.append",
"line... |
332927134 | # uncompyle6 version 3.7.4
# Python bytecode 3.5 (3351)
# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04)
# [GCC 8.4.0]
# Embedded file name: /home/ale/progr/github/wasp-general/wasp_general/network/clients/file.py
# Compiled at: 2018-03-02 16:08:04
# Size of source mod 2**32: 3929 bytes
from wasp_gener... | null | pycfiles/wasp-general-0.0.3.3.tar/file.cpython-35.py | file.cpython-35.py | py | 3,516 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "wasp_general.network.clients.base.WBasicNetworkClientProto",
"line_number": 20,
"usage_type": "name"
},
{
"api_name": "wasp_general.network.clients.base.WBasicNetworkClientProto.__init__",
"line_number": 23,
"usage_type": "call"
},
{
"api_name": "wasp_general.netwo... |
301728411 | from logzero import logger
from managers.DatabaseManager import DatabaseManager
from managers.ExchangeManager import ExchangeManager
class BalanceManager:
"""Class to handle dealing with index balances."""
em = ExchangeManager()
def rebalance_coins(self, coins_above_threshold, coins_below_thre... | null | managers/BalanceManager.py | BalanceManager.py | py | 5,997 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "managers.ExchangeManager.ExchangeManager",
"line_number": 9,
"usage_type": "call"
},
{
"api_name": "logzero.logger.debug",
"line_number": 26,
"usage_type": "call"
},
{
"api_name": "logzero.logger",
"line_number": 26,
"usage_type": "name"
},
{
"api_n... |
56718470 | # -*- coding: utf-8 -*-
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
def sklearn_tfidf():
tag_list = ['iphone guuci huawei watch huawei',
'huawei watch iphone watch iphone guuci',
's... | null | TFIDF.py | TFIDF.py | py | 4,017 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "sklearn.feature_extraction.text.CountVectorizer",
"line_number": 14,
"usage_type": "call"
},
{
"api_name": "sklearn.feature_extraction.text.TfidfTransformer",
"line_number": 17,
"usage_type": "call"
},
{
"api_name": "numpy.array",
"line_number": 22,
"usage_... |
503982801 | #!/usr/bin/python
from cortex import app
import cortex.lib.user
from flask import Flask, request, session, redirect, url_for, flash, g, abort, make_response, render_template, jsonify
import os
import MySQLdb as mysql
################################################################################
@app.route('/about... | null | views.py | views.py | py | 3,493 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "flask.render_template",
"line_number": 13,
"usage_type": "call"
},
{
"api_name": "cortex.app.route",
"line_number": 11,
"usage_type": "call"
},
{
"api_name": "cortex.app",
"line_number": 11,
"usage_type": "name"
},
{
"api_name": "flask.render_templa... |
477850560 | #!/usr/bin/env python3
import yaml
from jinja2 import Template
var_file = open('variables.yml')
var_data = var_file.read()
my_vars = yaml.full_load(var_data)
template_file = open('template.j2')
template_data = template_file.read()
template = Template(template_data)
for device in my_vars:
print("Creating config for... | null | lessons/template-generation-device/stage2/build-configs.py | build-configs.py | py | 474 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "yaml.full_load",
"line_number": 8,
"usage_type": "call"
},
{
"api_name": "jinja2.Template",
"line_number": 12,
"usage_type": "call"
}
] |
447824132 | import base_task
import jax
import jax.numpy as jnp
class SinusoidTask(base_task.BaseTask):
def __init__(self, x_range, amplitude, phase):
self._x_range = x_range
self._amplitude = amplitude
self._phase = phase
super().__init__()
def sample_data(self, key, num_datapoints):
... | null | sinusoidal_task.py | sinusoidal_task.py | py | 654 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "base_task.BaseTask",
"line_number": 6,
"usage_type": "attribute"
},
{
"api_name": "jax.random.uniform",
"line_number": 14,
"usage_type": "call"
},
{
"api_name": "jax.random",
"line_number": 14,
"usage_type": "attribute"
},
{
"api_name": "jax.numpy.s... |
462960420 | # Written by Seonwoo Min, Seoul National University (mswzeus@gmail.com)
# Some parts of the code were referenced from or inspired by below
# - Tristan Bepler's code (https://github.com/tbepler/protein-sequence-embedding-iclr2019)
""" Biological sequence encoding functions """
import numpy as np
from utils import get_... | null | alphabets.py | alphabets.py | py | 2,371 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "numpy.frombuffer",
"line_number": 14,
"usage_type": "call"
},
{
"api_name": "numpy.uint8",
"line_number": 14,
"usage_type": "attribute"
},
{
"api_name": "numpy.zeros",
"line_number": 15,
"usage_type": "call"
},
{
"api_name": "numpy.uint8",
"line... |
280566295 | from __future__ import print_function
import glob
import shutil
from itertools import chain
import os
import random
import zipfile
import click
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from lin... | null | main.py | main.py | py | 11,640 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "torch.__version__",
"line_number": 42,
"usage_type": "attribute"
},
{
"api_name": "random.seed",
"line_number": 46,
"usage_type": "call"
},
{
"api_name": "os.environ",
"line_number": 47,
"usage_type": "attribute"
},
{
"api_name": "numpy.random.seed"... |
85615637 | import pandas as pd
import streamlit as st
import numpy as np
import matplotlib.pyplot as plt
import plotly.offline as pyo
import plotly.graph_objects as go
import plotly.express as px
# Set notebook mode to work in offline
pyo.init_notebook_mode()
#data_url = "https://drive.google.com/file/d/1jJOFfcLoRmY8Vs... | null | Top_rest.py | Top_rest.py | py | 3,423 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "plotly.offline.init_notebook_mode",
"line_number": 9,
"usage_type": "call"
},
{
"api_name": "plotly.offline",
"line_number": 9,
"usage_type": "name"
},
{
"api_name": "pandas.read_csv",
"line_number": 13,
"usage_type": "call"
},
{
"api_name": "pandas... |
167739843 | # coding:utf8
import scrapy
import re
from bs4 import BeautifulSoup
import sys
reload(sys)
sys.setdefaultencoding('utf8')
class ss_spider(scrapy.Spider):
name = "sh"
allowed_domins = ["sh17.cn"]
start_urls = ['http://www.sh17.cn/product/category-1/',
'http://www.sh17.cn/product/categ... | null | sh17/sh17/spiders/sh1.py | sh1.py | py | 3,595 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "sys.setdefaultencoding",
"line_number": 11,
"usage_type": "call"
},
{
"api_name": "scrapy.Spider",
"line_number": 14,
"usage_type": "attribute"
},
{
"api_name": "scrapy.Request",
"line_number": 48,
"usage_type": "call"
},
{
"api_name": "scrapy.Reque... |
145093872 | import gc
import os
import random
import numpy as np
import pandas as pd
import wandb
from more_itertools import chunked
from scipy.spatial.distance import cdist
from sklearn.neighbors import NearestNeighbors
from code_search import prepare_data
from code_search import shared
from code_search import train_model
from ... | null | code_search/evaluate_model.py | evaluate_model.py | py | 4,969 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "numpy.random.seed",
"line_number": 18,
"usage_type": "call"
},
{
"api_name": "numpy.random",
"line_number": 18,
"usage_type": "attribute"
},
{
"api_name": "random.seed",
"line_number": 19,
"usage_type": "call"
},
{
"api_name": "code_search.build_cod... |
647448108 | import os
import torch
import torch.utils.data as data
import pandas as pd
import cv2
import numpy as np
from ast import literal_eval
from sklearn.model_selection import train_test_split
from functools import partial
MIX_CLASSES = ('__background__', 'person')
# for making bounding boxes pretty
COLORS = ((255, 0, 0, 1... | null | detector_back/worker/detector/detector/train/dataset/mix_dataset.py | mix_dataset.py | py | 3,829 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "pandas.read_csv",
"line_number": 32,
"usage_type": "call"
},
{
"api_name": "os.path.join",
"line_number": 33,
"usage_type": "call"
},
{
"api_name": "os.path",
"line_number": 33,
"usage_type": "attribute"
},
{
"api_name": "ast.literal_eval",
"lin... |
118505889 | # 导入numpy工具包
import numpy as np
# 使用listdir模块，用于访问本地文件
from os import listdir
from sklearn.neural_network import MLPClassifier
def img2vector(fileName):
"""
将加载的32*32的图片矩阵展开成一列向量
"""
retMat = np.zeros([1024], int) # 定义返回的矩阵，大小为1*1024
fr = open(fileName) # 打开包含32*32大小的数字文件
lines = fr.readlines() # 读取文件的所有行
for i... | null | sklearn-examples/mlp_DBRHD.py | mlp_DBRHD.py | py | 2,820 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "numpy.zeros",
"line_number": 11,
"usage_type": "call"
},
{
"api_name": "os.listdir",
"line_number": 23,
"usage_type": "call"
},
{
"api_name": "numpy.zeros",
"line_number": 25,
"usage_type": "call"
},
{
"api_name": "numpy.zeros",
"line_number": 2... |
525065867 | from django.core.management.base import BaseCommand, CommandError
from django.core.management import call_command
from django.db import connection
from datetime import datetime, timedelta
from django.db import IntegrityError, transaction
from plans.backup.remote import Table
from plans.backup.models import ImportModel... | null | plans/backup/management/commands/populate.py | populate.py | py | 6,173 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "datetime.datetime.today",
"line_number": 16,
"usage_type": "call"
},
{
"api_name": "datetime.datetime",
"line_number": 16,
"usage_type": "name"
},
{
"api_name": "datetime.timedelta",
"line_number": 16,
"usage_type": "call"
},
{
"api_name": "django.c... |
251242868 | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
class Migration(migrations.Migration):
dependencies = [
('dictionary', '0009_word_lang'),
]
operations = [
migrations.RemoveField(
model_name='word',
name='tr... | null | mysite/dictionary/migrations/0010_auto_20150516_1020.py | 0010_auto_20150516_1020.py | py | 758 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "django.db.migrations.Migration",
"line_number": 7,
"usage_type": "attribute"
},
{
"api_name": "django.db.migrations",
"line_number": 7,
"usage_type": "name"
},
{
"api_name": "django.db.migrations.RemoveField",
"line_number": 14,
"usage_type": "call"
},
... |
648039094 | from django.shortcuts import render
from django.http import HttpResponse
from dashboard.models import Feedback
# Create your views here.
def index(request):
return render(request, 'dashboard/index.html')
def contact(request):
if request.POST:
name = request.POST['name']
email = request.POST['em... | null | dashboard/views.py | views.py | py | 601 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "django.shortcuts.render",
"line_number": 6,
"usage_type": "call"
},
{
"api_name": "dashboard.models.Feedback",
"line_number": 14,
"usage_type": "call"
},
{
"api_name": "django.http.HttpResponse",
"line_number": 17,
"usage_type": "call"
},
{
"api_nam... |
45291718 | import json
import time
import grpc
from kafka import KafkaProducer, KafkaConsumer, TopicPartition
from concurrent import futures
from proto import WorkProtocol
from proto import WorkProtocolService_pb2
from proto import WorkProtocolService_pb2_grpc
def start_rpc_server(addr, port):
print("Started gRPC Server... ... | null | collector/network.py | network.py | py | 1,410 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "proto.WorkProtocol",
"line_number": 13,
"usage_type": "call"
},
{
"api_name": "grpc.server",
"line_number": 14,
"usage_type": "call"
},
{
"api_name": "concurrent.futures.ThreadPoolExecutor",
"line_number": 14,
"usage_type": "call"
},
{
"api_name": "... |
29854758 | try:
from packaging import version
except ImportError:
from pip._vendor.packaging import version
import chainer
import chainer.functions as F
chainer_version = version.parse(chainer.__version__)
if chainer_version < version.parse('3.0.0a1'):
"""Chainer's PR #2426 changed the behavior of matmul
Simu... | null | chainerrl/misc/chainer_compat.py | chainer_compat.py | py | 733 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "pip._vendor.packaging.version.parse",
"line_number": 10,
"usage_type": "call"
},
{
"api_name": "pip._vendor.packaging.version",
"line_number": 10,
"usage_type": "name"
},
{
"api_name": "chainer.__version__",
"line_number": 10,
"usage_type": "attribute"
},... |
189998321 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jun 22 13:53:13 2017
@author: danifili
"""
import matplotlib.pyplot as plt
# import cv2
import numpy as np
#import scipy.cluster.vq as kmeans
# from sklearn.cluster import KMeans
class Plot(object):
@staticmethod
def plot(image, displacem... | null | backend/src/motion_analysis/Plot.py | Plot.py | py | 9,374 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "matplotlib.pyplot.imshow",
"line_number": 56,
"usage_type": "call"
},
{
"api_name": "matplotlib.pyplot",
"line_number": 56,
"usage_type": "name"
},
{
"api_name": "matplotlib.pyplot.quiver",
"line_number": 65,
"usage_type": "call"
},
{
"api_name": "m... |
551209684 | import json
import sys
import os
from urllib.parse import urlparse
from shutil import copyfile
def main(bep_path):
raw_data = ""
with open(bep_path) as f:
raw_data = f.read()
decoder = json.JSONDecoder()
pos = 0
while pos < len(raw_data):
json_dict, size = decoder.raw_decode(raw_data[pos:])
if "... | null | .buildkite/failed_testlogs.py | failed_testlogs.py | py | 1,004 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "json.JSONDecoder",
"line_number": 11,
"usage_type": "call"
},
{
"api_name": "os.makedirs",
"line_number": 23,
"usage_type": "call"
},
{
"api_name": "os.path.dirname",
"line_number": 23,
"usage_type": "call"
},
{
"api_name": "os.path",
"line_numb... |
5294827 | from google.cloud import bigquery
def query_stackoverflow():
client = bigquery.Client()
query_job = client.query("""
SELECT *
FROM (
SELECT
outputs[OFFSET(0)].output_satoshis AS sat, outputs[OFFSET(0)].output_pubkey_base58 as key
FROM `bigquery-public-data.bitcoin_blockchain.tr... | null | modules/testbitcoinbigquery.py | testbitcoinbigquery.py | py | 596 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "google.cloud.bigquery.Client",
"line_number": 5,
"usage_type": "call"
},
{
"api_name": "google.cloud.bigquery",
"line_number": 5,
"usage_type": "name"
}
] |
401265604 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import ast
import pandas
import numpy
from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
from mpl_toolkits.axes_grid1.inset_locator import mark_inset
from matplotlib.ticker import (MultipleLocator, FormatStrFormatter,
... | null | scripts/plotting/iter_vqe_plots/lih_iters.py | lih_iters.py | py | 5,212 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "pandas.read_csv",
"line_number": 15,
"usage_type": "call"
},
{
"api_name": "pandas.read_csv",
"line_number": 17,
"usage_type": "call"
},
{
"api_name": "pandas.read_csv",
"line_number": 19,
"usage_type": "call"
},
{
"api_name": "pandas.read_csv",
... |
70047417 | import requests
import re
import os
from bs4 import BeautifulSoup
def get_links(pages):
base_url = 'http://desk.zol.com.cn'
links = list()
for page in pages:
html = requests.get(page)
soup = BeautifulSoup(html.text,'lxml')
for li in soup('li',class_='photo-list-padding'):
... | null | zol.py | zol.py | py | 1,758 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "requests.get",
"line_number": 10,
"usage_type": "call"
},
{
"api_name": "bs4.BeautifulSoup",
"line_number": 11,
"usage_type": "call"
},
{
"api_name": "requests.get",
"line_number": 20,
"usage_type": "call"
},
{
"api_name": "re.findall",
"line_nu... |
464437500 | import psutil
from flask import Blueprint
from flask import render_template
from app.tasks.tasks import say_hi
from app.tasks.tasks import long_task
from app import socketio
import time
from threading import Lock
from flask import jsonify
import eventlet
eventlet.monkey_patch()
blue_hello = Blueprint('hello', __name__... | null | app/api/views.py | views.py | py | 3,472 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "eventlet.monkey_patch",
"line_number": 11,
"usage_type": "call"
},
{
"api_name": "flask.Blueprint",
"line_number": 13,
"usage_type": "call"
},
{
"api_name": "threading.Lock",
"line_number": 15,
"usage_type": "call"
},
{
"api_name": "app.tasks.tasks.... |
209471337 | # -*- coding: utf-8 -*-
from __future__ import division, print_function
__all__ = ["CeleriteFlowMatrix"]
import tensorflow as tf
from .ops import to_dense, get_matrices, factor, solve, matmul
class CeleriteFlowMatrix(object):
def __init__(self, a_real, c_real, a_comp, b_comp, c_comp, d_comp, x, diag,
... | null | celeriteflow/celeriteflow.py | celeriteflow.py | py | 1,163 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "tensorflow.name_scope",
"line_number": 15,
"usage_type": "call"
},
{
"api_name": "ops.get_matrices",
"line_number": 16,
"usage_type": "call"
},
{
"api_name": "ops.factor",
"line_number": 19,
"usage_type": "call"
},
{
"api_name": "tensorflow.reduce_s... |
481057032 | # -*- coding: utf-8 -*-
# gthnk (c) 2014-2017 Ian Dennis Miller
import re
import io
import datetime
import sys
from collections import defaultdict
from gthnk import models
def split_filename_list(filename_str):
"""
"""
return([x.strip() for x in filename_str.split(',')])
class JournalBuffer(object):
... | null | gthnk/adaptors/journal_buffer.py | journal_buffer.py | py | 3,876 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "sys.version_info",
"line_number": 26,
"usage_type": "attribute"
},
{
"api_name": "collections.defaultdict",
"line_number": 28,
"usage_type": "call"
},
{
"api_name": "collections.defaultdict",
"line_number": 30,
"usage_type": "call"
},
{
"api_name": ... |
168835525 | """Helper functions for the NAPALM base."""
# std libs
import os
import sys
# third party libs
import jinja2
import textfsm
# local modules
import napalm_base.exceptions
def load_template(cls, template_name, template_source=None, template_path=None, **template_vars):
try:
if isinstance(template_source... | null | napalm_base/helpers.py | helpers.py | py | 5,540 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "jinja2.Template",
"line_number": 19,
"usage_type": "call"
},
{
"api_name": "os.path.dirname",
"line_number": 21,
"usage_type": "call"
},
{
"api_name": "os.path",
"line_number": 21,
"usage_type": "attribute"
},
{
"api_name": "os.path.abspath",
"l... |
590300978 | import numpy as np
from sklearn import datasets
from sklearn.preprocessing import scale
from sklearn import svm
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
digits = datasets.load_digits()
# 数据归一化
data = scale(digits.data)
X_train, X_test, y_train, y_test, images_train, images_t... | null | Pattern-Recognition-Lab/lab1-svm.py | lab1-svm.py | py | 1,910 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "sklearn.datasets.load_digits",
"line_number": 8,
"usage_type": "call"
},
{
"api_name": "sklearn.datasets",
"line_number": 8,
"usage_type": "name"
},
{
"api_name": "sklearn.preprocessing.scale",
"line_number": 10,
"usage_type": "call"
},
{
"api_name"... |
429143219 | #!/usr/bin/python
import argparse
import sys
import yaml
from PIL import Image
class TileBuilder(object):
def __init__(self):
self.tiles = {}
def ConvertImage(self, data):
origin = data.get('origin', (0, 0))
width = int(data['width']) * 8
height = int(data['height']) * 8
... | null | resources/mktiles.py | mktiles.py | py | 4,384 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "PIL.Image.open",
"line_number": 18,
"usage_type": "call"
},
{
"api_name": "PIL.Image",
"line_number": 18,
"usage_type": "name"
},
{
"api_name": "yaml.load",
"line_number": 113,
"usage_type": "call"
},
{
"api_name": "argparse.ArgumentParser",
"li... |
144155445 | import os
from collections import defaultdict
from fabric.api import * # noqa
from fabric.colors import (blue, cyan, green, magenta, red, white, # noqa
yellow)
from fabric.contrib.files import exists # noqa
def update_content_versions(pth):
pth = os.path.join(pth, 'content')
to_... | null | bin/fabric/utils/versioning.py | versioning.py | py | 3,828 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "os.path.join",
"line_number": 11,
"usage_type": "call"
},
{
"api_name": "os.path",
"line_number": 11,
"usage_type": "attribute"
},
{
"api_name": "collections.defaultdict",
"line_number": 19,
"usage_type": "call"
},
{
"api_name": "os.path.isdir",
... |
369337449 | #!/usr/bin/env python
# coding: utf-8
import io
from django.http import HttpResponse
from django.db.models import Count
from django.shortcuts import render
from django.utils.translation import gettext as _
from api.models import *
from urllib.request import Request
from PIL import Image
#-----------------------... | null | front/.~c9_invoke_8aysu5.py | .~c9_invoke_8aysu5.py | py | 25,253 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "django.http.HttpResponse",
"line_number": 113,
"usage_type": "call"
},
{
"api_name": "urllib.request.Request",
"line_number": 133,
"usage_type": "call"
},
{
"api_name": "io.BytesIO",
"line_number": 134,
"usage_type": "call"
},
{
"api_name": "urllib.... |
72962958 | # -*- coding: utf-8 -*-
#
# Copyright (C) 2019 CERN.
#
# inspirehep is free software; you can redistribute it and/or modify it under
# the terms of the MIT License; see LICENSE file for more details.
import structlog
from invenio_db import db
from sqlalchemy.exc import OperationalError
from inspirehep.records.api im... | null | backend/inspirehep/records/tasks.py | tasks.py | py | 1,509 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "structlog.getLogger",
"line_number": 15,
"usage_type": "call"
},
{
"api_name": "invenio_db.db.session.begin_nested",
"line_number": 29,
"usage_type": "call"
},
{
"api_name": "invenio_db.db.session",
"line_number": 29,
"usage_type": "attribute"
},
{
... |
78986933 | import sqlite3
from flask import Flask, render_template, request, session, redirect, url_for, flash
import os
app = Flask(__name__)
app.secret_key = """b'(\x9a^\xde\xf8tZm_/&?X\xeb\x00\xda'"""
UPLOAD_FOLDER = "./static/images/"
ALLOWED_EXTENSIONS = {"png", "jpg", "jpeg", "jfif"}
app.config["UPLOAD_FOLDER"] = UPLOAD_FO... | null | main.py | main.py | py | 17,773 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "flask.Flask",
"line_number": 5,
"usage_type": "call"
},
{
"api_name": "sqlite3.connect",
"line_number": 32,
"usage_type": "call"
},
{
"api_name": "flask.session.get",
"line_number": 51,
"usage_type": "call"
},
{
"api_name": "flask.session",
"lin... |
583994720 | from django.shortcuts import render, HttpResponse
from mimetypes import MimeTypes
import os
import json
import requests
def index(request):
cl_platform = str(request.user_agent.os.family)
cl_ver = str(request.user_agent.os.version_string)
query_s = cl_platform + ' ' + cl_ver
query = {'query': query_s... | null | main/views.py | views.py | py | 1,426 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "requests.post",
"line_number": 19,
"usage_type": "call"
},
{
"api_name": "json.dumps",
"line_number": 19,
"usage_type": "call"
},
{
"api_name": "json.dump",
"line_number": 23,
"usage_type": "call"
},
{
"api_name": "django.shortcuts.render",
"lin... |
372710267 | #!/usr/bin/env python
# coding: utf-8
# In[1]:
import nltk
from nltk import sent_tokenize, word_tokenize, pos_tag, ne_chunk
from nltk import ngrams
# In[2]:
datafile = "Development_data/set1/a1.txt"
datafile2 = "Development_data/set1/a2.txt"
questionfile = "Development_data/set1/a1q.txt"
data = open(datafile, "... | null | Documents/CMU/NLP (Spring 2020)/project/answering-NER.py | answering-NER.py | py | 2,598 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "nltk.sent_tokenize",
"line_number": 28,
"usage_type": "call"
},
{
"api_name": "nltk.sent_tokenize",
"line_number": 31,
"usage_type": "call"
},
{
"api_name": "nltk.word_tokenize",
"line_number": 39,
"usage_type": "call"
},
{
"api_name": "nltk.ngrams"... |
576337512 | #!/usr/bin/python3.2
'''
Python-Digital-Rain v1.0
========================
Simple project that tries to simulate the digital rain effect on screen (terminal).
Environment:
------------
OS: Linux (required)
Python: v3.2 (required)
Modules: curses, random, time (all standard modules)
Usage:
------
Launches... | null | python练习/第九章/python-digital-rain-v1.py | python-digital-rain-v1.py | py | 7,398 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "curses.initscr",
"line_number": 38,
"usage_type": "call"
},
{
"api_name": "curses.curs_set",
"line_number": 39,
"usage_type": "call"
},
{
"api_name": "curses.start_color",
"line_number": 40,
"usage_type": "call"
},
{
"api_name": "curses.init_pair",
... |
453027339 | #!/usr/bin/env python
# -- coding: utf-8 --
"""
Copyright (c) 2019. All rights reserved.
Created by C. L. Wang on 2019/5/17
"""
import os
import sys
import json
import re
import fnmatch
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from PIL import Image
from datetime import datetime
from skim... | null | preprocess/csv2coco_processor.py | csv2coco_processor.py | py | 13,065 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "os.path.dirname",
"line_number": 21,
"usage_type": "call"
},
{
"api_name": "os.path",
"line_number": 21,
"usage_type": "attribute"
},
{
"api_name": "os.path.abspath",
"line_number": 21,
"usage_type": "call"
},
{
"api_name": "sys.path",
"line_num... |
333929334 | import paramiko
import time
import re
import getpass
#Define global regexes
ip_regex=re.compile(r'[0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3}')
subnet_regex=re.compile(r'[0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3}\.')
mac_regex=re.compile(r'[0-9a-f]{4}\.[0-9a-f]{4}\.[0-9a-f]{4}')
int_regex=re.compile(r'Fa{1}\d/\S*\... | null | cisco_ip_trace.py | cisco_ip_trace.py | py | 9,277 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "re.compile",
"line_number": 7,
"usage_type": "call"
},
{
"api_name": "re.compile",
"line_number": 8,
"usage_type": "call"
},
{
"api_name": "re.compile",
"line_number": 9,
"usage_type": "call"
},
{
"api_name": "re.compile",
"line_number": 10,
... |
24645426 | #!/usr/bin/env python
# encoding: utf-8
'''
@author: DeltaF
@software: pycharm
@file: 9.6饼图：电影语种统计.py
@desc:
'''
import C8处理与分析.pandas_def as pdef
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = ['Arial Unicode MS']
# 获取统计数据
data = pdef.language_tj()
# print(data)
# 对数据进行排序
data = data.sort_values('... | null | Coder_Old/data-analysis/09-Python-Matplotlib/9.6饼图：电影语种统计.py | 9.6饼图：电影语种统计.py | py | 710 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "matplotlib.pyplot.rcParams",
"line_number": 12,
"usage_type": "attribute"
},
{
"api_name": "matplotlib.pyplot",
"line_number": 12,
"usage_type": "name"
},
{
"api_name": "C8处理与分析.pandas_def.language_tj",
"line_number": 14,
"usage_type": "call"
},
{
"... |
103083276 | import xlrd
#import jieba_fast as jieba
#import jieba
import re
import pandas as pd
import jieba_fast as jieba
jieba.initialize()
#0正常 1贷款 2信用卡 3广告 4其他
def preProcess(flag = 'train'):
with open(flag + '.csv', 'w', encoding='utf-8') as w:
if flag == 'test':
fix = ''
... | null | process.py | process.py | py | 3,129 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "jieba_fast.initialize",
"line_number": 8,
"usage_type": "call"
},
{
"api_name": "xlrd.open_workbook",
"line_number": 28,
"usage_type": "call"
},
{
"api_name": "jieba_fast.cut",
"line_number": 40,
"usage_type": "call"
},
{
"api_name": "re.findall",
... |
81721036 | from io import open
class Histograma():
def lectura_de_archivo(self):
archivo = open("archivo_ejercicio4.txt")
lista = archivo.read()
lista=list(lista.replace(" ", ""))
return lista
def crear_histograma(self):
lista = self.lectura_de_archivo()
print ("Sus datos... | null | alumnos/58018-Valentin-Faraz/clase01/ejercicio4.py | ejercicio4.py | py | 485 | python | en | code | null | code-starcoder2 | 76 | [
{
"api_name": "io.open",
"line_number": 6,
"usage_type": "call"
}
] |
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