text stringlengths 232 16.3k | domain stringclasses 1
value | difficulty stringclasses 3
values | meta dict |
|---|---|---|---|
<|fim_suffix|>
# =============================================================================
# >> COMMANDS
# =============================================================================
@TypedSayCommand('!test')
def typed_say_test(command_info):
global urls_sent
urls_sent += 1
url = URL_TEMPLATE.format(... | code_fim | hard | {
"lang": "python",
"repo": "KirillMysnik/sp-motd-issue-test",
"path": "/addons/source-python/plugins/motd_issue_test/motd_issue_test.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> n = n // div
break
s += n
print(s)<|fim_prefix|># repo: shaarangg/CP-codes path: /codeforces/Python/Rectangular Game(Easy).py
n = int(input())
s = n
div = 2
while n > 1:
if n % div == 0:
n = n // div
else:
for i in range(div + 1, <|fim_middle|>n + 1)... | code_fim | medium | {
"lang": "python",
"repo": "shaarangg/CP-codes",
"path": "/codeforces/Python/Rectangular Game(Easy).py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: shaarangg/CP-codes path: /codeforces/Python/Rectangular Game(Easy).py
n = int(input())
s = n
div = 2
while n > 1:
if n % div == 0:<|fim_suffix|>n + 1):
if n % i == 0:
div = i
n = n // div
break
s += n
print(s)<|fim_middle|>
... | code_fim | medium | {
"lang": "python",
"repo": "shaarangg/CP-codes",
"path": "/codeforces/Python/Rectangular Game(Easy).py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|># this Method helps give info about the passed in function or Argument!
#help(str)
# finding strings in strings
print("corn" in "Buttered Popcorn")<|fim_prefix|># repo: Coryf65/Python-Basics path: /PythonBasics.py
#Python uses Snake Case for naming conventions
first_name = "Ada"
last_name = "Lovelace"
... | code_fim | hard | {
"lang": "python",
"repo": "Coryf65/Python-Basics",
"path": "/PythonBasics.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Coryf65/Python-Basics path: /PythonBasics.py
#Python uses Snake Case for naming conventions
first_name = "Ada"
last_name = "Lovelace"
<|fim_suffix|># displaying the mood !
print("I am glad that you are ,", current_mood)
# PEMDAS...
print("PEMDAS: ", 0.1 + 0.1 + 0.1 - 0.3)
# we get 0.0000005
#... | code_fim | hard | {
"lang": "python",
"repo": "Coryf65/Python-Basics",
"path": "/PythonBasics.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|># we get 0.0000005
# now we can round this
print("rounding: ",round(0.1 + 0.1 + 0.1 - 0.3))
# this Method helps give info about the passed in function or Argument!
#help(str)
# finding strings in strings
print("corn" in "Buttered Popcorn")<|fim_prefix|># repo: Coryf65/Python-Basics path: /PythonBasics... | code_fim | hard | {
"lang": "python",
"repo": "Coryf65/Python-Basics",
"path": "/PythonBasics.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: msaad1311/Flight-Fare path: /Scripts/Utils.py
import pandas as pd
import numpy as np
# from IPython.display import display
import holidays
from math import sqrt
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.metrics import mean... | code_fim | hard | {
"lang": "python",
"repo": "msaad1311/Flight-Fare",
"path": "/Scripts/Utils.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>def timing(x):
if (x > 7) and (x <= 12):
return 'Morning'
elif (x > 12) and (x <= 17):
return 'Afternoon'
elif (x > 17) and (x <= 20):
return 'Evening'
elif (x > 20) or (x <= 7):
return 'Night'
def times(df):
df_temp = df.copy(deep=True)
df_temp['... | code_fim | hard | {
"lang": "python",
"repo": "msaad1311/Flight-Fare",
"path": "/Scripts/Utils.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> df_temp = dropper(df_temp, ['Arrival_Time1', 'Dep_Time', 'Arrival_Time'])
# display(df_temp.head())
return df_temp
def encoder(df, name):
source = df[[name]]
df_temp = pd.get_dummies(source, drop_first=True)
return df_temp
def metric(actual, predicted):
e_mse = mse(actual,... | code_fim | hard | {
"lang": "python",
"repo": "msaad1311/Flight-Fare",
"path": "/Scripts/Utils.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def test_datasource_tables(ndjson_test_data_dir, pathling_ctx):
pathling_ctx.read.ndjson(ndjson_test_data_dir).write.tables()
data_source = pathling_ctx.read.tables()
result = ndjson_query(data_source)
assert result.columns == list(ResultRow)
assert result.collect() == [
Resu... | code_fim | hard | {
"lang": "python",
"repo": "aehrc/pathling",
"path": "/lib/python/tests/test_datasource.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> pathling_ctx.read.delta(delta_test_data_dir).write.delta(temp_delta_dir)
data_source = pathling_ctx.read.delta(temp_delta_dir)
result = delta_query(data_source)
assert result.columns == list(ResultRow)
assert result.collect() == [
ResultRow(71),
]
def test_datasource_del... | code_fim | hard | {
"lang": "python",
"repo": "aehrc/pathling",
"path": "/lib/python/tests/test_datasource.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: aehrc/pathling path: /lib/python/tests/test_datasource.py
# Copyright 2023 Commonwealth Scientific and Industrial Research
# Organisation (CSIRO) ABN 41 687 119 230.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the L... | code_fim | hard | {
"lang": "python",
"repo": "aehrc/pathling",
"path": "/lib/python/tests/test_datasource.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: DataDog/datadog-api-client-python path: /examples/v1/azure-integration/DeleteAzureIntegration.py
"""
Delete an Azure integration returns "OK" response
"""
from datadog_api_client import ApiClient, Configuration
from datadog_api_client.v1.api.azure_integration_api import AzureIntegrationApi
from ... | code_fim | hard | {
"lang": "python",
"repo": "DataDog/datadog-api-client-python",
"path": "/examples/v1/azure-integration/DeleteAzureIntegration.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>configuration = Configuration()
with ApiClient(configuration) as api_client:
api_instance = AzureIntegrationApi(api_client)
response = api_instance.delete_azure_integration(body=body)
print(response)<|fim_prefix|># repo: DataDog/datadog-api-client-python path: /examples/v1/azure-integration/... | code_fim | hard | {
"lang": "python",
"repo": "DataDog/datadog-api-client-python",
"path": "/examples/v1/azure-integration/DeleteAzureIntegration.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: 1036225283/python path: /face/Config.py
import model as models
DATA_SIZE = 320
# DATA_SIZE = 304
IMAGE_SIZE = 224
B<|fim_suffix|> models.Point68_residual
MODEL_SAVE_PATH = model.path
print(MODEL_SAVE_PATH)<|fim_middle|>ATCH_SIZE = 32
EPOCH = 400
# model = models.Point68_y1
# model = models.Po... | code_fim | medium | {
"lang": "python",
"repo": "1036225283/python",
"path": "/face/Config.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> models.Point68_residual
MODEL_SAVE_PATH = model.path
print(MODEL_SAVE_PATH)<|fim_prefix|># repo: 1036225283/python path: /face/Config.py
import model as models
DATA_SIZE = 320
# DATA_SIZE = 304
IMAGE_SIZE = 224
BATCH_SIZE = 32
EPOCH = 400
# model = models.Point68_y1
# model = models.Poin<|fim_middle... | code_fim | medium | {
"lang": "python",
"repo": "1036225283/python",
"path": "/face/Config.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: adafruit/Adafruit_Learning_System_Guides path: /Motion_Buddy/code.py
# SPDX-FileCopyrightText: 2022 Charlyn G for Adafruit Industries
#
# SPDX-License-Identifier: MIT
#
# Code for the Adafruit Learning System tutorial
# Exercise Buddy: Motion aware BLE media controller
# https://learn.adafrui... | code_fim | hard | {
"lang": "python",
"repo": "adafruit/Adafruit_Learning_System_Guides",
"path": "/Motion_Buddy/code.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> if paused:
print("play!")
paused = False
ams.play()
except OSError:
supervisor.reload()
except UnsupportedCommand:
# This means that we tried to pause but th... | code_fim | hard | {
"lang": "python",
"repo": "adafruit/Adafruit_Learning_System_Guides",
"path": "/Motion_Buddy/code.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: eruffaldi/ukfmanifold path: /task_headpose/headPose.py
import csv
import livestat
import sys
a = csv.DictReader(open(sys.argv[1] if len(sys.argv) > 1 else "headPose.txt","rb"))
o = csv.DictWriter(open(sys.argv[2] if len(sys.argv) > 2 else "headPoseout.csv","wb"),fieldnames=[x.strip() for x in a.... | code_fim | medium | {
"lang": "python",
"repo": "eruffaldi/ukfmanifold",
"path": "/task_headpose/headPose.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> print d["pose_Tx"],d["pose_Ty"],d["pose_Tz"],d["pose_Rx"],d["pose_Ry"],d["pose_Rz"]
o.writerow(d)
for k in sorted(w.keys()):
print w[k]<|fim_prefix|># repo: eruffaldi/ukfmanifold path: /task_headpose/headPose.py
import csv
import livestat
import sys
a = csv.DictReader(open(sys.argv[1] if len(sys.ar... | code_fim | medium | {
"lang": "python",
"repo": "eruffaldi/ukfmanifold",
"path": "/task_headpose/headPose.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>if __name__ == "__main__":
bank = Cardbank()
res = set_next_round_ressources()
cards = set_drafted_card()
scenarios = list(itertools.product([0, 1], repeat=7))
for scenario in scenarios:
early_ressources = set_early_ressources(cards, scenario)
pass<|fim_prefix|># repo... | code_fim | hard | {
"lang": "python",
"repo": "parking52/ITWW",
"path": "/wonderfulworld.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: parking52/ITWW path: /wonderfulworld.py
from cardbank import Cardbank
import time
import itertools
def set_next_round_ressources():
grey = input("Enter your value for grey: ") or 0
black = input("Enter your value for black: ") or 0
green = input("Enter your value for green: ") or 0
... | code_fim | hard | {
"lang": "python",
"repo": "parking52/ITWW",
"path": "/wonderfulworld.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> grey, black, green, yellow, blue = 0, 0, 0, 0, 0
for i in range(len(cards)):
if scenario[i]:
grey = grey + cards[i].discard_reward.grey
black = black + cards[i].discard_reward.black
green = green + cards[i].discard_reward.green
yellow = yello... | code_fim | medium | {
"lang": "python",
"repo": "parking52/ITWW",
"path": "/wonderfulworld.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: salshaw749/Fyyur_FSND path: /app.py
g')
db = SQLAlchemy(app)
# TODO: connect to a local postgresql database
app.config['SQLALCHEMY_DATABASE_URI']
app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False
migrate = Migrate(app, db)
db.create_all()
#----------------------------------------------------... | code_fim | hard | {
"lang": "python",
"repo": "salshaw749/Fyyur_FSND",
"path": "/app.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> form = VenueForm()
#venue = Venue.query.filter(Venue.id == venue_id)
venue=Venue.query.filter(Venue.id == venue_id).one()
# TODO: populate form with values from venue with ID <venue_id>
return render_template('forms/edit_venue.html', form=form, venue=venue)
@app.route('/venues/<int:venue_id... | code_fim | hard | {
"lang": "python",
"repo": "salshaw749/Fyyur_FSND",
"path": "/app.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: salshaw749/Fyyur_FSND path: /app.py
information
data = []
# render the upcoming shows
def upcoming_shows():
# create empty arr
upcoming_shows = []
# loop through each show that we have recieved
for show in shows:
# now compare the time of a speci... | code_fim | hard | {
"lang": "python",
"repo": "salshaw749/Fyyur_FSND",
"path": "/app.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> return {
'auth': BitbucketOAuth(access_token)
}
elif self.username != '' and self.password != '':
return {
'auth': HTTPBasicAuth(self.username, self.password)
}
else:
if self.client_id == '':
... | code_fim | hard | {
"lang": "python",
"repo": "SHyx0rmZ/bitbucket-build-status-resource",
"path": "/scripts/bitbucket/bitbucket_cloud.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: SHyx0rmZ/bitbucket-build-status-resource path: /scripts/bitbucket/bitbucket_cloud.py
from bitbucket import BitbucketDriver, BitbucketOAuth, request_access_token
from concourse import ConcourseResource, MissingSourceException, print_error
from requests.auth import HTTPBasicAuth
class BitbucketCl... | code_fim | hard | {
"lang": "python",
"repo": "SHyx0rmZ/bitbucket-build-status-resource",
"path": "/scripts/bitbucket/bitbucket_cloud.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>env.Program("bin/MockServer",
["src/Mock/MockServer.cpp"] + loc["util_src"] + loc["tpl_src"],
CPPPATH=["include"] + loc["openni_inc"] + loc["tpl_inc"] + loc["util_inc"],
LIBPATH=loc["openni_lib"], LIBS="OpenNI")<|fim_prefix|># repo: kinect-fun/ocv_kinect path: /tools/GestureServer... | code_fim | hard | {
"lang": "python",
"repo": "kinect-fun/ocv_kinect",
"path": "/tools/GestureServer/SConscript",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: kinect-fun/ocv_kinect path: /tools/GestureServer/SConscript
Import("loc")
Import("env")
env.Program("bin/GestureServer",
["src/GestureServer.cpp"] + loc["tpl_src"] + loc["util_src"],
CPPPATH=["include"] + loc["openni_inc"] + loc["tpl_inc"] + loc["util_inc"],
LIBPATH=loc["openni_lib"]... | code_fim | hard | {
"lang": "python",
"repo": "kinect-fun/ocv_kinect",
"path": "/tools/GestureServer/SConscript",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> pub_date = models.DateTimeField(default=timezone.now)
def __str__(self):
#return self.cart_id
return str(self.title)<|fim_prefix|># repo: lurdray/study_aid path: /theory/models.py
from django.db import models
from django.utils import timezone
# Create your models here.
class Theory(models.Model):... | code_fim | medium | {
"lang": "python",
"repo": "lurdray/study_aid",
"path": "/theory/models.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: lurdray/study_aid path: /theory/models.py
from django.db import models
from django.utils import timezone
# Create your models here.
class Theory(models.Model):
<|fim_suffix|> def __str__(self):
#return self.cart_id
return str(self.title)<|fim_middle|> title = models.CharField(max_length=500,... | code_fim | hard | {
"lang": "python",
"repo": "lurdray/study_aid",
"path": "/theory/models.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> category = models.CharField(max_length=500, default="general")
level = models.CharField(max_length=500, default="simple")
pub_date = models.DateTimeField(default=timezone.now)
def __str__(self):
#return self.cart_id
return str(self.title)<|fim_prefix|># repo: lurdray/study_aid path: /theory/mo... | code_fim | medium | {
"lang": "python",
"repo": "lurdray/study_aid",
"path": "/theory/models.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> operations = [
migrations.RemoveField(
model_name='data_type_collection',
name='num_tables',
),
]<|fim_prefix|># repo: fzhao99/washu-act-db path: /hello/migrations/0007_remove_data_type_collection_num_tables.py
# Generated by Django 2.1 on 2018-08-06 16:52
... | code_fim | medium | {
"lang": "python",
"repo": "fzhao99/washu-act-db",
"path": "/hello/migrations/0007_remove_data_type_collection_num_tables.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>
dependencies = [
('hello', '0006_auto_20180801_1203'),
]
operations = [
migrations.RemoveField(
model_name='data_type_collection',
name='num_tables',
),
]<|fim_prefix|># repo: fzhao99/washu-act-db path: /hello/migrations/0007_remove_data_t... | code_fim | easy | {
"lang": "python",
"repo": "fzhao99/washu-act-db",
"path": "/hello/migrations/0007_remove_data_type_collection_num_tables.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: fzhao99/washu-act-db path: /hello/migrations/0007_remove_data_type_collection_num_tables.py
# Generated by Django 2.1 on 2018-08-06 16:52
<|fim_suffix|> operations = [
migrations.RemoveField(
model_name='data_type_collection',
name='num_tables',
),
... | code_fim | medium | {
"lang": "python",
"repo": "fzhao99/washu-act-db",
"path": "/hello/migrations/0007_remove_data_type_collection_num_tables.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: valechitarivera/Fase3_Rivera_Garrido_HerreraL path: /locallibreria/catalogo/migrations/0013_auto_20201112_2207.py
# Generated by Django 3.1.2 on 2020-11-13 01:07
from django.db import migrations, models
class Migration(migrations.Migration):
<|fim_suffix|> operations = [
migrations... | code_fim | hard | {
"lang": "python",
"repo": "valechitarivera/Fase3_Rivera_Garrido_HerreraL",
"path": "/locallibreria/catalogo/migrations/0013_auto_20201112_2207.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> operations = [
migrations.AddField(
model_name='producto',
name='img1',
field=models.ImageField(null=True, upload_to=''),
),
migrations.AddField(
model_name='producto',
name='img2',
field=models.ImageField(... | code_fim | hard | {
"lang": "python",
"repo": "valechitarivera/Fase3_Rivera_Garrido_HerreraL",
"path": "/locallibreria/catalogo/migrations/0013_auto_20201112_2207.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>
# In[14]:
# Using k-means clustering to group into 5 sets of players
# In[15]:
# Define number of clusters#Define n
clusters = 5
# In[16]:
# Train a model using KMeans() machine learning method
model = KMeans(init='k-means++',n_clusters=clusters,n_init=20).fit(groups)
# In[17]:
# Counting ... | code_fim | hard | {
"lang": "python",
"repo": "sundarram98/Footbal-Training-Drill-Suggestion-System",
"path": "/Project.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: sundarram98/Footbal-Training-Drill-Suggestion-System path: /Project.py
# coding: utf-8
# <h3> Football Drill Suggestion </h3>
#
#
# <p>
# This program is used to analyze the data of 18,000+ soccer players from European Soccer leagues. The dataset used here is an open European Soccer Database... | code_fim | hard | {
"lang": "python",
"repo": "sundarram98/Footbal-Training-Drill-Suggestion-System",
"path": "/Project.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|># In[16]:
# Train a model using KMeans() machine learning method
model = KMeans(init='k-means++',n_clusters=clusters,n_init=20).fit(groups)
# In[17]:
# Counting the number of players in each cluster
print(60*"_")
print("\nCount of players in each cluster : ")
print(60*"_")
pd.value_counts(model.labe... | code_fim | hard | {
"lang": "python",
"repo": "sundarram98/Footbal-Training-Drill-Suggestion-System",
"path": "/Project.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: njread/New-Python path: /functions.py
def print_two(name, age):
print("your name is %s" % name)
print("your age is %d" % age)
<|fim_suffix|> print("I got nothing")
print_two("Nicholas", 22)
print_one("Nichoals")
print_none()<|fim_middle|>def print_one(name):
print("your name is %s" % name)
... | code_fim | medium | {
"lang": "python",
"repo": "njread/New-Python",
"path": "/functions.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>def print_one(name):
print("your name is %s" % name)
def print_none():
print("I got nothing")
print_two("Nicholas", 22)
print_one("Nichoals")
print_none()<|fim_prefix|># repo: njread/New-Python path: /functions.py
def print_two(name, age):
<|fim_middle|> print("your name is %s" % name)
print("your a... | code_fim | medium | {
"lang": "python",
"repo": "njread/New-Python",
"path": "/functions.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: njread/New-Python path: /functions.py
def print_two(name, age):
<|fim_suffix|>def print_none():
print("I got nothing")
print_two("Nicholas", 22)
print_one("Nichoals")
print_none()<|fim_middle|> print("your name is %s" % name)
print("your age is %d" % age)
def print_one(name):
print("your nam... | code_fim | medium | {
"lang": "python",
"repo": "njread/New-Python",
"path": "/functions.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: jkaariain/python-exercises path: /Kierros6/tehtava6.10.py
def laske_abbat(string):
abba_lkm = 0
for i in range(0, len(string)):
<|fim_suffix|>"b" and string[i + 2] == "b" \
and string[i + 3] == "a":
abba_lkm += 1
else:
conti... | code_fim | hard | {
"lang": "python",
"repo": "jkaariain/python-exercises",
"path": "/Kierros6/tehtava6.10.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>"b" and string[i + 2] == "b" \
and string[i + 3] == "a":
abba_lkm += 1
else:
continue
return abba_lkm<|fim_prefix|># repo: jkaariain/python-exercises path: /Kierros6/tehtava6.10.py
def laske_abbat(string):
abba_lkm = 0
for i i... | code_fim | hard | {
"lang": "python",
"repo": "jkaariain/python-exercises",
"path": "/Kierros6/tehtava6.10.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> abba_lkm += 1
else:
continue
return abba_lkm<|fim_prefix|># repo: jkaariain/python-exercises path: /Kierros6/tehtava6.10.py
def laske_abbat(string):
abba_lkm = 0
for i in range(0, len(string)):
if string[i] == "a" and i+3 < len(string):
... | code_fim | hard | {
"lang": "python",
"repo": "jkaariain/python-exercises",
"path": "/Kierros6/tehtava6.10.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: abhishekambastha/PedestrianDetection path: /evaluation/main.py
#! /usr/bin/env python
import glob
import os
from detect import Detector
from model import *
# define model_path, image_path
image_path = '/home/ambastha/abhishek/matlab-eval/caltech_tool_kit/data-INRIA/images/set01/V000/'
model_path... | code_fim | medium | {
"lang": "python",
"repo": "abhishekambastha/PedestrianDetection",
"path": "/evaluation/main.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>
d = Detector(model_path, image_path)
frames = list()
for i, img in enumerate(img_list):
img_obj = Image(i+1) # 1 based index for matlab
img_obj.bboxes.extend(d.detect(img))
print '[INFO]: Detecting {}'.format(img)
frames.append(img_obj)
os.chdir(curr_dir)
with open('V000.txt', 'w') as f... | code_fim | medium | {
"lang": "python",
"repo": "abhishekambastha/PedestrianDetection",
"path": "/evaluation/main.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>frames = list()
for i, img in enumerate(img_list):
img_obj = Image(i+1) # 1 based index for matlab
img_obj.bboxes.extend(d.detect(img))
print '[INFO]: Detecting {}'.format(img)
frames.append(img_obj)
os.chdir(curr_dir)
with open('V000.txt', 'w') as f:
for fr in frames:
boxes =... | code_fim | medium | {
"lang": "python",
"repo": "abhishekambastha/PedestrianDetection",
"path": "/evaluation/main.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>def setup(app):
# type: (Sphinx) -> Dict[unicode, Any]
app.add_builder(IpynbBuilder)
app.add_config_value('ipynb_kernel', 'python', False)
app.add_config_value('ipynb_indent', '>>> ', False)
app.add_config_value('ipynb_continue', '... ', False)
return {'version': sphinx.__display_v... | code_fim | hard | {
"lang": "python",
"repo": "haraldschilly/sphinx-ipynbbuilder",
"path": "/ipynbbuilder.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> def write_doc(self, docname, doctree):
self.current_docname = docname
destination = StringOutput(encoding='utf-8')
self.writer.write(doctree, destination)
outfilename = path.join(self.outdir, os_path(docname) + self.out_suffix)
ensuredir(path.dirname(outfilename... | code_fim | hard | {
"lang": "python",
"repo": "haraldschilly/sphinx-ipynbbuilder",
"path": "/ipynbbuilder.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: lucaskmiyazaki/document_reader path: /houghOCR.py
import cv2
import numpy as np
from pdf2image import convert_from_path
import pytesseract as ocr
import sys
import time
import requests
# parametrizar variaveis
# enviar imagem com filtro para tesseract
def region_growing(pts, map_pts, img, n):
... | code_fim | hard | {
"lang": "python",
"repo": "lucaskmiyazaki/document_reader",
"path": "/houghOCR.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>def microsoftOCR(frame, debug):
subscription_key = "12w1asde663bsdasd9eeeqc0a7"
endpoint = "https://v360testingocr.cognitiveservices.azure.com/"
ocr_url = endpoint + "vision/v3.0/ocr"
image_path = "test.jpg"
image_data = open(image_path, "rb").read()
headers = {'Ocp-Apim-Subscript... | code_fim | hard | {
"lang": "python",
"repo": "lucaskmiyazaki/document_reader",
"path": "/houghOCR.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> if debug:
print(full_text)
return full_text
def ocr_manager(file_path, mode='tesseract', debug=False, th_filter=False):
# preprocess document
time0 = time.time()
res = 0.80
max_gap = 0
pages=convert_from_path(file_path)
pages[0].save("tax.jpeg","jpeg")
img... | code_fim | hard | {
"lang": "python",
"repo": "lucaskmiyazaki/document_reader",
"path": "/houghOCR.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: jeremybrachle/Merge-Sort path: /Assignment2_JeremyBrachle_Q1-2.py
#This program will implement merge sort
#Jeremy Brachle 46659942
#23 February 2017
#import necessary libraries
import sys
import time
import re
#create a list for containing the numbers
numbers = []
#get the command line argumen... | code_fim | hard | {
"lang": "python",
"repo": "jeremybrachle/Merge-Sort",
"path": "/Assignment2_JeremyBrachle_Q1-2.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> #build back up in ascending order
#loop through each half until equally sorted (or until one uneven half finishes)
while i < len(leftHalfSide) and j < len(rightHalfSide):
if leftHalfSide[i] < rightHalfSide[j]:
values[k]=leftHalfSide[i]
i=i+1
else:
values[k]=rightHalfSide[j]
j=j... | code_fim | hard | {
"lang": "python",
"repo": "jeremybrachle/Merge-Sort",
"path": "/Assignment2_JeremyBrachle_Q1-2.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>else:
if i%2 != 0: res[i], right = right - 1, right - 1
else: res[i], left = left + 1, left + 1
if i != 0: k -= 1<|fim_prefix|># repo: jxhangithub/leetcode path: /solutions/python3/667.py
class Solution:
def constructArray(self, n, k):
"""
... | code_fim | hard | {
"lang": "python",
"repo": "jxhangithub/leetcode",
"path": "/solutions/python3/667.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: jxhangithub/leetcode path: /solutions/python3/667.py
class Solution:
def constructArray(self, n, k):
"""
:type n: int
:type k: int
:rtype: List[int]
"""
left, ri<|fim_suffix|>else:
if i%2 != 0: res[i], right = right - 1, right - ... | code_fim | hard | {
"lang": "python",
"repo": "jxhangithub/leetcode",
"path": "/solutions/python3/667.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: dtran39/Programming_Preparation path: /01_Arrays/384_Shuffle_Array/ShuffleArray.py
'''
Problem:
- Shuffle a set of numbers without duplicates.
----------------------------------------------------------------------------------------------------
Examples:
// Init an array with set 1, 2, and 3.... | code_fim | hard | {
"lang": "python",
"repo": "dtran39/Programming_Preparation",
"path": "/01_Arrays/384_Shuffle_Array/ShuffleArray.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> def shuffle(self):
"""
Returns a random shuffling of the array.
:rtype: List[int]
"""
new_nums = self.nums[:]
n = len(new_nums)
import random
for i in range(n):
rand_num = random.randint(0, n - 1)
# Swap nums[i] wi... | code_fim | medium | {
"lang": "python",
"repo": "dtran39/Programming_Preparation",
"path": "/01_Arrays/384_Shuffle_Array/ShuffleArray.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: imalkov97/Python path: /Week 2/Lab 2 Q5 Mitroi.py
#Labsheet 2
#Question 5
hoursStr=input("How many hours?")
inpHours=int(hoursStr)
minutesStr=input("How many minutes?")
minutes=int(minutesStr)
currHours=inpHours
midi="in the morning."
periodHour=''
#-----------------------------------------------... | code_fim | hard | {
"lang": "python",
"repo": "imalkov97/Python",
"path": "/Week 2/Lab 2 Q5 Mitroi.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> elif (minutes>=8) and (minutes<=22):
periodHour='quarter past '
elif (minutes>=23) and (minutes<=37):
periodHour='half past '
#This bit transforms the integer currHours into the word representing the number
#It also prints the message
numToWords=['twelve ', 'one ', 'two ', 'thr... | code_fim | hard | {
"lang": "python",
"repo": "imalkov97/Python",
"path": "/Week 2/Lab 2 Q5 Mitroi.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>
class GPS:
gpgga_info = "$GPGGA,"
ser = serial.Serial("/dev/ttyS0")
GPGGA_buffer = 0
NMEA_buff = 0
EARTH_RADIUS = 6371
DIST_TRAVELLED = 0
DIST_INITIAL = 0
CURRENT_COORDINATES = []
PREV_COORDINATES = []
COUNTER = 0
# convert raw NMEA string into degree decimal ... | code_fim | hard | {
"lang": "python",
"repo": "MarcoGrazi/PR-05-Talos2020",
"path": "/Software/ANIMA/Anima1.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> CURRENT_COORDINATES[0] = self.convert_to_degrees(lat) # get latitude in degree decimal format
CURRENT_COORDINATES[1] = self.convert_to_degrees(longi) # get longitude in degree decimal format
PREV_COORDINATES.append(CURRENT_COORDINATES)
COUNTER += 1
def CalculateDist... | code_fim | hard | {
"lang": "python",
"repo": "MarcoGrazi/PR-05-Talos2020",
"path": "/Software/ANIMA/Anima1.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: MarcoGrazi/PR-05-Talos2020 path: /Software/ANIMA/Anima1.py
import tflite
import Adafruit_PCA9685
import adafruit_mpu6050
import threading
import cv2
import math
import sys
import serial
from time import sleep
from picamera import PiCamera
import BlynkLib as B
BLYNK_AUTH = ""
CAM = PiCamera()
BL... | code_fim | hard | {
"lang": "python",
"repo": "MarcoGrazi/PR-05-Talos2020",
"path": "/Software/ANIMA/Anima1.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> url = baseurl+'/api/range/?name=plot'
data = {}
response = self.client.get(url, data, format='json')
print json.loads(response.content)
self.assertEqual(json.loads(response.content),[{u'id': 1, u'value': 5000000, u'name': u'plot'}])
class HowSoonTest(APITestCase):
... | code_fim | hard | {
"lang": "python",
"repo": "harikakoye/code",
"path": "/core tests.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: harikakoye/code path: /core tests.py
from __future__ import unicode_literals
import json
from rest_framework import status
from django.test import TestCase, Client
from django.urls import reverse
from core.models import *
from rest_framework.test import APIClient
from rest_framework.test import A... | code_fim | hard | {
"lang": "python",
"repo": "harikakoye/code",
"path": "/core tests.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def get_basic_model_results(X_train, X_test, y_train, y_test):
scaler = preprocessing.StandardScaler().fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
print("\n TRAIN AND TEST SHAPES ARE AS FOLLOWS")
print("\n X TRAIN : ",X_train.shape)
pri... | code_fim | hard | {
"lang": "python",
"repo": "avik-samanta99/Fake-News-Detection",
"path": "/basic_model.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: avik-samanta99/Fake-News-Detection path: /basic_model.py
import time
import sys
sys.path.insert(0,"/content/FakeNewsPropagation")
import matplotlib
import numpy as np
from sklearn import preprocessing, svm
from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier
from sklearn.l... | code_fim | hard | {
"lang": "python",
"repo": "avik-samanta99/Fake-News-Detection",
"path": "/basic_model.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> matplotlib.rcParams['figure.figsize'] = 5, 2
# Plot the feature importances of the forest
plt.figure()
plt.bar(range(X_train.shape[1]), importances[indices],
color="b", yerr=std[indices], align="center")
plt.xticks(range(X_train.shape[1]), np.array(short_feature_names)[in... | code_fim | hard | {
"lang": "python",
"repo": "avik-samanta99/Fake-News-Detection",
"path": "/basic_model.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ModaoT/firstkill path: /utils.py
from collections import Counter
from sklearn.metrics import roc_curve, auc
import pandas as pd
import numpy as np
import sys
def create_lookup_tables(words):
word_counts = Counter(words)
sorted_vocab = sorted(word_counts, key=word_counts.get, reverse=Tr... | code_fim | hard | {
"lang": "python",
"repo": "ModaoT/firstkill",
"path": "/utils.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> merged = source.copy()
f1 = source[feature1].copy()
f2 = source[feature2].fillna('NaN')
length = len(f1)
step = length // 10
for i, e in enumerate(f1.fillna('NaN')):
if e == 'NaN':
f1[i] = f2[i]
else:
if f2[i] != 'NaN':
formed... | code_fim | hard | {
"lang": "python",
"repo": "ModaoT/firstkill",
"path": "/utils.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: joshianshul2/Python path: /floorceilandrint.py
import numpy
numpy.set_printoptions(sign=' ')# setprintoption provied space b/w float values
arr = input().split()
a = numpy.array(arr,dtype=float)
print(numpy.floor(a))
print(numpy.ceil(a))
print(numpy.rint(a))
'''0/p:
1 2.3 4.5 67.78
[ 1.... | code_fim | medium | {
"lang": "python",
"repo": "joshianshul2/Python",
"path": "/floorceilandrint.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> print(numpy.floor(a))
print(numpy.ceil(a))
print(numpy.rint(a))
'''<|fim_prefix|># repo: joshianshul2/Python path: /floorceilandrint.py
import numpy
numpy.set_printoptions(sign=' ')# setprintoption provied space b/w float values
arr = input().split()
a = numpy.array(arr,dtype=float)
print... | code_fim | hard | {
"lang": "python",
"repo": "joshianshul2/Python",
"path": "/floorceilandrint.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> operations = [
migrations.CreateModel(
name='Product',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(max_length=120)),
('description'... | code_fim | hard | {
"lang": "python",
"repo": "ed85w/stream_3_project",
"path": "/shop/migrations/0001_initial.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ed85w/stream_3_project path: /shop/migrations/0001_initial.py
# -*- coding: utf-8 -*-
# Generated by Django 1.11.5 on 2017-10-22 13:04
from __future__ import unicode_literals
from django.conf import settings
import django.core.validators
from django.db import migrations, models
import django.db.... | code_fim | hard | {
"lang": "python",
"repo": "ed85w/stream_3_project",
"path": "/shop/migrations/0001_initial.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: WarlockD/tapeimgr path: /tapeimgr/shared.py
#! /usr/bin/env python3
"""Shared functions module"""
import os
import logging
import glob
import hashlib
import datetime
import subprocess as sub
import pytz
def launchSubProcess(args, writeLog=True):
"""Launch subprocess and return exit code, st... | code_fim | hard | {
"lang": "python",
"repo": "WarlockD/tapeimgr",
"path": "/tapeimgr/shared.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> # All files in directory
allFiles = glob.glob(directory + "/*." + extension)
# Dictionary for storing results
checksums = {}
for thisFile in allFiles:
hashString = generate_file_sha512(thisFile)
fName = os.path.basename(thisFile)
checksums[fName] = hashString
... | code_fim | hard | {
"lang": "python",
"repo": "WarlockD/tapeimgr",
"path": "/tapeimgr/shared.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>input("\n\nPress ENTER to exit the program.")<|fim_prefix|># repo: Markay12/learningPython path: /Chapter2/repeat_string.py
# String Repetition
# You can repeat the same string multiple times by using a '*' operator
<|fim_middle|>print('pie' * 10)
| code_fim | easy | {
"lang": "python",
"repo": "Markay12/learningPython",
"path": "/Chapter2/repeat_string.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Markay12/learningPython path: /Chapter2/repeat_string.py
# String Repetition
# You can repeat the same string multiple times by using a '*' operator
<|fim_suffix|>
input("\n\nPress ENTER to exit the program.")<|fim_middle|>print('pie' * 10)
| code_fim | easy | {
"lang": "python",
"repo": "Markay12/learningPython",
"path": "/Chapter2/repeat_string.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> admin.site.urls),
path('',mediaApp_views.home,name='home'),
path('query/', mediaApp_views.youtube_query, name='youtube_query'),
path('query/<int:vid>', mediaApp_views.show_video, name='show_video'),
path('query/<vid>', mediaApp_views.show_local_video, name='show_local_video'),
path('d... | code_fim | hard | {
"lang": "python",
"repo": "Shiru99/Software-Engineering",
"path": "/miniproject-cs305autumn2020-develop/DjangoProject/mediaWebApp/urls.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Shiru99/Software-Engineering path: /miniproject-cs305autumn2020-develop/DjangoProject/mediaWebApp/urls.py
"""mediaWebApp URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/2.2/topics/http/urls/
Examples:
Function ... | code_fim | hard | {
"lang": "python",
"repo": "Shiru99/Software-Engineering",
"path": "/miniproject-cs305autumn2020-develop/DjangoProject/mediaWebApp/urls.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Pascal-tgn/gb-python-basics path: /lesson_3/task-02.py
def user_info(first_name, last_name, birth_year, city, email, phone):
print(", ".join([first_name, last_name, birth_year, city, email, phone]))
<|fim_suffix|>user_info(first_name=first_name, last_name=last_name, birth_year=birth_year, city... | code_fim | hard | {
"lang": "python",
"repo": "Pascal-tgn/gb-python-basics",
"path": "/lesson_3/task-02.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>first_name = input("Введите имя: ")
last_name = input("Введите фамилию: ")
birth_year = input("Введите год рождения: ")
city = input("Введите город: ")
email = input("Введите e-mail: ")
phone = input("Введите телефон: ")
user_info(first_name=first_name, last_name=last_name, birth_year=bi... | code_fim | medium | {
"lang": "python",
"repo": "Pascal-tgn/gb-python-basics",
"path": "/lesson_3/task-02.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>if is_integer(args.device):
args.device = int(args.device)
if args.device < 0:
use_oak = True
arg_vid_cap = args.device
else:
use_oak = False
arg_vid_cap = args.device
else:
use_oak = False
arg_vid_cap = args.device
pipeline = nobita_vision.VisionPipeli... | code_fim | medium | {
"lang": "python",
"repo": "wbawakate/nobita",
"path": "/demo/face_landmark.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: wbawakate/nobita path: /demo/face_landmark.py
import os
import sys
sys.path.append(os.path.join(os.path.dirname(__file__), ".."))
from nobita import nobita_vision
from nobita.modules import FaceDetection, FaceLandmark
import cv2
import numpy as np
import argparse
parser = argparse.ArgumentParse... | code_fim | hard | {
"lang": "python",
"repo": "wbawakate/nobita",
"path": "/demo/face_landmark.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> print ("after prunning #non zero parameters " + str(np.sum(weight_arr_pruned!=0)))
sess.run(dense_w[target_name].assign(weight_arr_pruned))
dict_nzidx[target_name] = mask
return dict_nzidx
def apply_prune_on_grads(grads_and_vars,dict_nzidx):
for key, nzidx in dict_nzidx.items():
c... | code_fim | hard | {
"lang": "python",
"repo": "Originofamonia/admm-pruning",
"path": "/tensorflow-mnist-code/prune_utility.py",
"mode": "spm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Originofamonia/admm-pruning path: /tensorflow-mnist-code/prune_utility.py
import numpy as np
import tensorflow as tf
class PruneConfiguration():
P1 = 80
P2 = 92
P3 = 99.1
P4 = 93
@staticmethod
def display():
print("P1 is %f"% PruneConfiguration.P1)
print("P2 is %f"% PruneC... | code_fim | hard | {
"lang": "python",
"repo": "Originofamonia/admm-pruning",
"path": "/tensorflow-mnist-code/prune_utility.py",
"mode": "psm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> res = requests.get(Nasa_api_url)
res.raise_for_status()
result = json.loads(res.content)
return result
except Exception as e:
msg = f'Exception occur while retrieve response from {url}. Exception :: {e}'
... | code_fim | hard | {
"lang": "python",
"repo": "svijay87/Python-Learning-Assignment",
"path": "/Assignment-Complete.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: svijay87/Python-Learning-Assignment path: /Assignment-Complete.py
import requests
from abc import ABC,abstractmethod
import logging
import json
import unittest
logging.basicConfig(filename="Nasa-shooting-star.log",filemode="a",format="%(asctime)s|%(process)s|%(level)s|%(message)s",level=logging... | code_fim | hard | {
"lang": "python",
"repo": "svijay87/Python-Learning-Assignment",
"path": "/Assignment-Complete.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> option.add_experimental_option("detach", True)
driver = webdriver.Chrome(options=option)
log.info("启动{}浏览器".format(browser))
driver.get(url)
log.info("打开链接：{}".format(url))
return driver
if __name__ == '__main__':
open_browser()<|fim_prefix|># repo: Yuen... | code_fim | hard | {
"lang": "python",
"repo": "Yuenleung/UIautomation",
"path": "/public/browser.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Yuenleung/UIautomation path: /public/browser.py
from selenium import webdriver
from public.log import Log
from public.readconfig import ReadConfig
log = Log()
def open_browser():
read = ReadConfig()
browser = read.getValue("browserType", "browserName")
url = read.getValue("testServer"... | code_fim | hard | {
"lang": "python",
"repo": "Yuenleung/UIautomation",
"path": "/public/browser.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>ome正受到自动测试软件的控制”
# V75以及以下版本
# option.add_argument('disable-infobars')
# V76以及以上版本
option.add_experimental_option('useAutomationExtension', False)
option.add_experimental_option('excludeSwitches', ['enable-automation'])
# 不自动关闭浏览器
option.add_experime... | code_fim | hard | {
"lang": "python",
"repo": "Yuenleung/UIautomation",
"path": "/public/browser.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: rabernat/oocgcm path: /examples/scripts/make_movie_cartopy_ffmpeg.py
#!/usr/bin/env python
# coding=utf-8
#
""" Create a movie of a 2D variable
requires: cartopy, ffmpeg
to do:
- automatically choose lon,lat bounds and ticks
- fix loading of the library
- improve t... | code_fim | hard | {
"lang": "python",
"repo": "rabernat/oocgcm",
"path": "/examples/scripts/make_movie_cartopy_ffmpeg.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|># create repertory if need be
if not os.path.exists('./movies'): os.makedirs('./movies')
os.system("rm -rf movies/*.jpg >& /dev/null")
vmin, vmax=(-1.0,1.3)
# setup the plot
fig = plt.figure(figsize=(8,3))
ax=plt.axes(projection=ccrs.PlateCarree())
ax.set_extent([-90, 15, 25, 70],ccrs.PlateCarree())
plt... | code_fim | hard | {
"lang": "python",
"repo": "rabernat/oocgcm",
"path": "/examples/scripts/make_movie_cartopy_ffmpeg.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>for i in xrange(v_xr.shape[0]):
plt_hdl.remove()
plt_hdl = v_xr[i, ...].plot.pcolormesh('nav_lon','nav_lat',ax=ax,
vmin=vmin, vmax=vmax,
transform=ccrs.PlateCarree(),
... | code_fim | hard | {
"lang": "python",
"repo": "rabernat/oocgcm",
"path": "/examples/scripts/make_movie_cartopy_ffmpeg.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ksingla025/multimodal path: /lib/model.py
#!/usr/bin/python
#standard python imports
import sys
#reload(sys)
#sys.setdefaultencoding('utf-8')
#standard python imports
from collections import Counter
import math
import os
import random
import zipfile
import glob
import ntpath
import re
import r... | code_fim | hard | {
"lang": "python",
"repo": "ksingla025/multimodal",
"path": "/lib/model.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> # Now, let's access and create placeholders variables and
# create feed-dict to feed new data
graph = tf.get_default_graph()
self.input = graph.get_tensor_by_name("input_placeholder:0")
enc_state = graph.get_tensor_by_name("enc_state:0")
w2 = graph.get_tensor_by_name("w2:0")
feed_dict ={w... | code_fim | hard | {
"lang": "python",
"repo": "ksingla025/multimodal",
"path": "/lib/model.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|># One hot encoding for resolution
train_output = pd.get_dummies(train_output,prefix=['Resolution'], dummy_na=True)
# Convert to MLB encoded format
from sklearn.preprocessing import MultiLabelBinarizer
mlb_afn = MultiLabelBinarizer() # Altered File Names
mlb_data_afn = pd.DataFrame(mlb_afn.fit_transform(t... | code_fim | hard | {
"lang": "python",
"repo": "Archmonger/Xanadu-Parse",
"path": "/Xanadu-Parse/XanaduParse.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
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