text stringlengths 232 16.3k | domain stringclasses 1
value | difficulty stringclasses 3
values | meta dict |
|---|---|---|---|
<|fim_prefix|># repo: eladroz/funnel-rocket path: /tests/test_apiserver.py
# Copyright 2021 The Funnel Rocket Maintainers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://... | code_fim | hard | {
"lang": "python",
"repo": "eladroz/funnel-rocket",
"path": "/tests/test_apiserver.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: baidu/baiduads-sdk path: /python/baiduads-sdk-auto/baiduads/wtpfeed/api/__init__.py
from __future__ import absolute_import
<|fim_suffix|># import apis into api package
from baiduads.wtpfeed.api.wtp_feed_service import WtpFeedService<|fim_middle|># flake8: noqa
| code_fim | easy | {
"lang": "python",
"repo": "baidu/baiduads-sdk",
"path": "/python/baiduads-sdk-auto/baiduads/wtpfeed/api/__init__.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|># import apis into api package
from baiduads.wtpfeed.api.wtp_feed_service import WtpFeedService<|fim_prefix|># repo: baidu/baiduads-sdk path: /python/baiduads-sdk-auto/baiduads/wtpfeed/api/__init__.py
from __future__ import absolute_import
<|fim_middle|># flake8: noqa
| code_fim | easy | {
"lang": "python",
"repo": "baidu/baiduads-sdk",
"path": "/python/baiduads-sdk-auto/baiduads/wtpfeed/api/__init__.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>
input = [0,0,0,0,0,0]
print(input, end= " : ")
print(remove_deuplicate(input))
input = [i for i in range(21)]
print(input, end= " : ")
print(remove_deuplicate(input))<|fim_prefix|># repo: ohmema/interview path: /python/interviews/Array/remove_duplicates.py
#memory bound implimataion
def remove_deuplic... | code_fim | medium | {
"lang": "python",
"repo": "ohmema/interview",
"path": "/python/interviews/Array/remove_duplicates.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ohmema/interview path: /python/interviews/Array/remove_duplicates.py
#memory bound implimataion
def remove_deuplicate(nums):
single, i = 0, 1
while i < len(nums):
if not isDuplicate(nums, single, nums[i]):
single += 1
nums[single], nums[i] = nums[i], nums[... | code_fim | medium | {
"lang": "python",
"repo": "ohmema/interview",
"path": "/python/interviews/Array/remove_duplicates.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: boneil3/backtest path: /macro.py
__author__ = 'brendan'
import main
import pandas as pd
import numpy as np
from datetime import datetime as dt
from matplotlib import pyplot as plt
import random
import itertools
import time
import dateutil
from datetime import timedelta
data = pd.read_csv('raw_d... | code_fim | hard | {
"lang": "python",
"repo": "boneil3/backtest",
"path": "/macro.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>fig4, ax4 = plt.subplots()
data['US_M2'].dropna().pct_change(periods=1).plot(ax=ax4, label='US')
data['UK_M2'].dropna().pct_change(periods=1).plot(ax=ax4, label='UK')
data['GER_M2'].dropna().pct_change(periods=1).plot(ax=ax4, label='GER')
data['ITA_M2'].dropna().pct_change(periods=1).plot(ax=ax4, label='I... | code_fim | hard | {
"lang": "python",
"repo": "boneil3/backtest",
"path": "/macro.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Mhmdaris15/free-python-project path: /Code Politan/while_loop.py
#Belajar while-loop
data = ""
# pada while loop perintah diulang berdasarkan data boolean
# jika data true maka akan terus di eksekusi sampai data tsb false
while data != "x":
print("masukkan perulangan") #kata ini a... | code_fim | hard | {
"lang": "python",
"repo": "Mhmdaris15/free-python-project",
"path": "/Code Politan/while_loop.py",
"mode": "psm",
"license": "Unlicense",
"source": "the-stack-v2"
} |
<|fim_suffix|>for ii in range(0, banyak):
print(f"data ke {ii}")
data_nama = input("nama :")
data_tinggi_badan = int(input("tinggi :"))
data_berat_badan = int(input("berat :"))
nama.append(data_nama)
tinggi_badan.append(data_tinggi_badan)
berat_badan.append(data_berat_badan)
prin... | code_fim | medium | {
"lang": "python",
"repo": "Mhmdaris15/free-python-project",
"path": "/Code Politan/while_loop.py",
"mode": "spm",
"license": "Unlicense",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: demirkirans/PlaDat-BLG411E path: /AWS_Services/matchPlacements.py
import json
import urllib3
import boto3
import time
from boto3.dynamodb.conditions import Key,Attr
from botocore.exceptions import ClientError
def matching(student_skills, job_skills):
counter = 0
for s_skill in student_skills:
... | code_fim | hard | {
"lang": "python",
"repo": "demirkirans/PlaDat-BLG411E",
"path": "/AWS_Services/matchPlacements.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> table = dynamodb.Table('Skills_jobs')
db_response = table.scan(FilterExpression=Attr('Jobs_Company_ID').eq(Company_ID)&Attr('Jobs_ID').eq(Placement_ID))
for attribute in db_response['Items']:
job_skills.append(attribute['name'])... | code_fim | hard | {
"lang": "python",
"repo": "demirkirans/PlaDat-BLG411E",
"path": "/AWS_Services/matchPlacements.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> ############Scan all jobs ################
table = dynamodb.Table('Jobs')
response = table.scan()
if response['Count'] == 0:
return {
"statusCode": 500,
'body': json.dumps('Internal server error- There is no placement to show *-*')
}
... | code_fim | hard | {
"lang": "python",
"repo": "demirkirans/PlaDat-BLG411E",
"path": "/AWS_Services/matchPlacements.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: tim6her/listb path: /tests/test_doctests.py
import doctest
import unittest
import os.path
import listb.mrtools
import listb.normalizetex
import listb.pybibtools
<|fim_suffix|>runner = unittest.TextTestRunner(verbosity=2)
runner.run(suite)<|fim_middle|>suite = unittest.TestSuite()
flags = docte... | code_fim | hard | {
"lang": "python",
"repo": "tim6her/listb",
"path": "/tests/test_doctests.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>runner = unittest.TextTestRunner(verbosity=2)
runner.run(suite)<|fim_prefix|># repo: tim6her/listb path: /tests/test_doctests.py
import doctest
import unittest
import os.path
import listb.mrtools
import listb.normalizetex
import listb.pybibtools
<|fim_middle|>suite = unittest.TestSuite()
flags = docte... | code_fim | hard | {
"lang": "python",
"repo": "tim6her/listb",
"path": "/tests/test_doctests.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>flags = doctest.NORMALIZE_WHITESPACE
suite.addTest(doctest.DocTestSuite(listb.mrtools,
optionflags=flags))
suite.addTest(doctest.DocTestSuite(listb.normalizetex,
optionflags=flags))
suite.addTest(doctest.DocTestSuite(listb.pybibtools,
... | code_fim | medium | {
"lang": "python",
"repo": "tim6her/listb",
"path": "/tests/test_doctests.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> payload = response.json()
self.assertIsInstance(payload, dict)
self.assertDictEqual(
payload,
{
"id": ph.pk,
"shop": ph.shop,
"name_of_discount": ph.name_of_discount,
"text": ph.text,
... | code_fim | hard | {
"lang": "python",
"repo": "rko619619/Skidon",
"path": "/src/apps/api/tests/test_discount.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> user_headers = {"HTTP_AUTHORIZATION": self.user_token}
admin_headers = {"HTTP_AUTHORIZATION": self.admin_token}
data = {
"shop": "shop",
"name_of_discount": "sad",
"text": "sad",
"price": "sdsa",
"additional_media": "http:... | code_fim | hard | {
"lang": "python",
"repo": "rko619619/Skidon",
"path": "/src/apps/api/tests/test_discount.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: rko619619/Skidon path: /src/apps/api/tests/test_discount.py
from typing import Optional
from rest_framework import status
from apps.api.tests.base import ApiTest
class DiscountApiTest(ApiTest):
def test_read(self):
discount1 = self.create_discount("discount1")
discount2 = ... | code_fim | hard | {
"lang": "python",
"repo": "rko619619/Skidon",
"path": "/src/apps/api/tests/test_discount.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: sdss/sdss_install path: /python/sdss_install/application/Argument.py
from __future__ import absolute_import, division, print_function, unicode_literals
# The line above will help with 2to3 support.
from sys import argv
from os import environ, getenv
from os.path import basename
from argparse impo... | code_fim | hard | {
"lang": "python",
"repo": "sdss/sdss_install",
"path": "/python/sdss_install/application/Argument.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> '''Add command line arguments for bin file sdss_install'''
xct = basename(argv[0])
parser = ArgumentParser(description=__doc__,prog=xct)
parser.add_argument('-e', '--level', help='set logging level', metavar='LEVEL', choices=['debug','info','warning','error'], default='debug')
parser.a... | code_fim | hard | {
"lang": "python",
"repo": "sdss/sdss_install",
"path": "/python/sdss_install/application/Argument.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def sdss_install():
'''Add command line arguments for bin file sdss_install'''
xct = basename(argv[0])
parser = ArgumentParser(description=__doc__,prog=xct)
parser.add_argument('-e', '--level', help='set logging level', metavar='LEVEL', choices=['debug','info','warning','error'], defa... | code_fim | hard | {
"lang": "python",
"repo": "sdss/sdss_install",
"path": "/python/sdss_install/application/Argument.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: mpasternak/django-multiseek path: /multiseek/urls.py
# -*- encoding: utf-8 -*-
try:
from django.conf.urls import url
except ImportError:
from django.urls import re_path as url
from django.conf import settings
from django.views.decorators.csrf import csrf_exempt
from django.views.i18n i... | code_fim | hard | {
"lang": "python",
"repo": "mpasternak/django-multiseek",
"path": "/multiseek/urls.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> url(r'^remove-from-results/(?P<pk>\d+)$',
views.remove_by_hand,
name="remove_from_results"),
url(r'^remove-from-removed-results/(?P<pk>\d+)$',
views.remove_from_removed_by_hand,
name="remove_from_removed_results"),
url(r'^reenable-removed-ids/$',
views... | code_fim | hard | {
"lang": "python",
"repo": "mpasternak/django-multiseek",
"path": "/multiseek/urls.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> url(r'^results/$',
csrf_exempt(views.MultiseekResults.as_view(
registry=settings.MULTISEEK_REGISTRY,
template_name="multiseek/results.html"
)), name="results"),
url(r'^save_form/$',
csrf_exempt(views.MultiseekSaveForm.as_view(
registry=s... | code_fim | hard | {
"lang": "python",
"repo": "mpasternak/django-multiseek",
"path": "/multiseek/urls.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> """Test failure"""
self.helper_file_negative(
"test/fixtures/templates/bad/properties_sg_ingress.yaml", 1
)<|fim_prefix|># repo: trav-c/cfn-python-lint path: /test/unit/rules/resources/ec2/test_sg_ingress.py
"""
Copyright Amazon.com, Inc. or its affiliates. All Rights ... | code_fim | hard | {
"lang": "python",
"repo": "trav-c/cfn-python-lint",
"path": "/test/unit/rules/resources/ec2/test_sg_ingress.py",
"mode": "spm",
"license": "MIT-0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: trav-c/cfn-python-lint path: /test/unit/rules/resources/ec2/test_sg_ingress.py
"""
Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
SPDX-License-Identifier: MIT-0
"""
from test.unit.rules import BaseRuleTestCase
from cfnlint.rules.resources.ectwo.SecurityGroupIngress import (
... | code_fim | hard | {
"lang": "python",
"repo": "trav-c/cfn-python-lint",
"path": "/test/unit/rules/resources/ec2/test_sg_ingress.py",
"mode": "psm",
"license": "MIT-0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: tsouvarev/django-firebird path: /tests/test_main/test_base/models.py
#-*- utf-8 -*-
from django.db import models
<|fim_suffix|>class BigS(models.Model):
s = models.SlugField(max_length=255)<|fim_middle|>
class FieldsTest(models.Model):
date_field = models.DateTimeField()
| code_fim | medium | {
"lang": "python",
"repo": "tsouvarev/django-firebird",
"path": "/tests/test_main/test_base/models.py",
"mode": "psm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|>class BigS(models.Model):
s = models.SlugField(max_length=255)<|fim_prefix|># repo: tsouvarev/django-firebird path: /tests/test_main/test_base/models.py
#-*- utf-8 -*-
from django.db import models
<|fim_middle|>
class FieldsTest(models.Model):
date_field = models.DateTimeField()
| code_fim | medium | {
"lang": "python",
"repo": "tsouvarev/django-firebird",
"path": "/tests/test_main/test_base/models.py",
"mode": "spm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|>def get_points_from_city(cities):
points_list = []
for value in keys.values():
for city in cities:
if city in value:
val = value.split('-')
points_list.append(int(val[0]))
return sum(points_list)
'''def get_point_city_from_learned... | code_fim | medium | {
"lang": "python",
"repo": "saikiran278/website-for-event-and-team-management",
"path": "/index.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: saikiran278/website-for-event-and-team-management path: /index.py
keys = {
'if-statement': '5-Paris',
'comparison': '10-New York',
'match': '8-Sydney',
'boolean': '7-Barcelona',
'list': '2-London',
'slice': '10-Rome',
'iterable': '3-San Francisco',
'languag... | code_fim | hard | {
"lang": "python",
"repo": "saikiran278/website-for-event-and-team-management",
"path": "/index.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: aschnapp/TheHaloMod path: /halomod_app/views.py
import datetime
# import logging
import io
import logging
import zipfile
from collections import OrderedDict
import numpy as np
from django.conf import settings
from django.core.mail import send_mail
from django.http import HttpResponse, HttpRespo... | code_fim | hard | {
"lang": "python",
"repo": "aschnapp/TheHaloMod",
"path": "/halomod_app/views.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
class ContactFormView(FormView):
form_class = forms.ContactForm
template_name = "email_form.html"
success_url = "/email-sent/"
def form_valid(self, form):
message = "{name} / {email} said: ".format(
name=form.cleaned_data.get("name"), email=form.cleaned_data.get("emai... | code_fim | hard | {
"lang": "python",
"repo": "aschnapp/TheHaloMod",
"path": "/halomod_app/views.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ripssr/Code-Combat path: /1_Kithgard_Dungeon/042-Tactical_Strike/tactical_strike.py
# You need the Elemental codex 1+ to cast "Haste<|fim_suffix|>.moveRight(2)
hero.moveDown(0.1)
hero.manaBlast()<|fim_middle|>"
# You need an unique hero to perform "Mana Blast"
hero.cast("haste", hero)
hero.moveD... | code_fim | medium | {
"lang": "python",
"repo": "ripssr/Code-Combat",
"path": "/1_Kithgard_Dungeon/042-Tactical_Strike/tactical_strike.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>.moveRight(2)
hero.moveDown(0.1)
hero.manaBlast()<|fim_prefix|># repo: ripssr/Code-Combat path: /1_Kithgard_Dungeon/042-Tactical_Strike/tactical_strike.py
# You need the Elemental codex 1+ to cast "Haste<|fim_middle|>"
# You need an unique hero to perform "Mana Blast"
hero.cast("haste", hero)
hero.moveD... | code_fim | medium | {
"lang": "python",
"repo": "ripssr/Code-Combat",
"path": "/1_Kithgard_Dungeon/042-Tactical_Strike/tactical_strike.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def _learn(self, state):
"""update Q-value for the last taken action"""
p_state, p_action = self.prev
if p_state is None:
return
self.Q[p_state][p_action] = self.learning_rate * (self.R(state) + self.discount * max(self.Q[state].values())) - self.Q[p_state][... | code_fim | medium | {
"lang": "python",
"repo": "frnsys/cess",
"path": "/cess/agent/learn.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: frnsys/cess path: /cess/agent/learn.py
import random
class QLearner():
def __init__(self, states_actions, rewards, discount=0.5, explore=0.0, learning_rate=0.5):
"""basic Q-learning. given an environment where actions result in uncertain states,
Q-learning allows the agent t... | code_fim | hard | {
"lang": "python",
"repo": "frnsys/cess",
"path": "/cess/agent/learn.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> return 1 - classifierlabelandprobability[classifierlabelandprobability[0]+1]
def calculateUncertaintyMarginSampling(classifierlabelandprobability):
prob = classifierlabelandprobability[1:]
best = prob[classifierlabelandprobability[0]]
second = 0
for p in prob:
if p < best and ... | code_fim | hard | {
"lang": "python",
"repo": "Ichaelus/Github-Classifier",
"path": "/Application/Models/ClassificationModules/ActiveLearningSpecific.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Ichaelus/Github-Classifier path: /Application/Models/ClassificationModules/ActiveLearningSpecific.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from math import log
def calculateUncertaintyEntropyBased(classifierlabelandprobability):
prob = classifierlabelandprobability[1:]
sum = 0
... | code_fim | medium | {
"lang": "python",
"repo": "Ichaelus/Github-Classifier",
"path": "/Application/Models/ClassificationModules/ActiveLearningSpecific.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|># The number of processes to spawn of in multiprocessing.
PROCESSES = cpu_count() - 1
# We need to pass pieces of the array to each process so it can do some work;
# however, pieces that are too large cannot be passed. SPLITS determines how
# arrays as subspliced to reduce their size.
SPLITS = int(100 * ... | code_fim | hard | {
"lang": "python",
"repo": "crcresearch/GOS",
"path": "/examples/migration/constants.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: crcresearch/GOS path: /examples/migration/constants.py
from multiprocessing import cpu_count
# The minimum size of a country (in population) to be added to the model.
MIN_POPULATION = 1900000
<|fim_suffix|># Any income above this level multiplied by the country's GDP is brought
# down to this l... | code_fim | hard | {
"lang": "python",
"repo": "crcresearch/GOS",
"path": "/examples/migration/constants.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|># We need to pass pieces of the array to each process so it can do some work;
# however, pieces that are too large cannot be passed. SPLITS determines how
# arrays as subspliced to reduce their size.
SPLITS = int(100 * POPULATION_SCALE) if POPULATION_SCALE > 1 / 100 else 1<|fim_prefix|># repo: crcresearch... | code_fim | medium | {
"lang": "python",
"repo": "crcresearch/GOS",
"path": "/examples/migration/constants.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>def remove_double_curly(text):
while True:
before = len(text)
text = re.sub('{{[^{]*?}}', '', text)
after = len(text)
if before == after:
return text
body = remove_double_curly(body)
def remove_double_brackets(text):
while True:
before = ... | code_fim | hard | {
"lang": "python",
"repo": "deadbranch-forkarchive/EncyclopedicRecall",
"path": "/dewiki.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def remove_double_curly(text):
while True:
before = len(text)
text = re.sub('{{[^{]*?}}', '', text)
after = len(text)
if before == after:
return text
body = remove_double_curly(body)
def remove_double_brackets(text):
while True:
before =... | code_fim | hard | {
"lang": "python",
"repo": "deadbranch-forkarchive/EncyclopedicRecall",
"path": "/dewiki.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: deadbranch-forkarchive/EncyclopedicRecall path: /dewiki.py
import re
from ast import literal_eval
with open('wiki.txt', 'r', encoding='utf-8') as infile:
#body = infile.read()
body = literal_eval(f'"""{infile.read()}"""')
<|fim_suffix|>body = remove_double_curly(body)
def remove_dou... | code_fim | hard | {
"lang": "python",
"repo": "deadbranch-forkarchive/EncyclopedicRecall",
"path": "/dewiki.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> while True:
print( "\n"
"This script provides the ability to work with test sets\n"
"You can enter:\n"
"0 - exit from the script\n"
"1 - create a test set\n"
"2 - add some tests to an existing test set\n"
... | code_fim | hard | {
"lang": "python",
"repo": "dmitriyklebanov/tsp",
"path": "/scripts/test_gen.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: dmitriyklebanov/tsp path: /scripts/test_gen.py
import sys
import re
import os
from subprocess import call
import random
test_sets_folder = os.path.join("..", "test_sets")
inputs_folder_name = "inputs"
test_set_log_file = "test_set.log"
def readInt(var_name, check_func):
while True:
... | code_fim | hard | {
"lang": "python",
"repo": "dmitriyklebanov/tsp",
"path": "/scripts/test_gen.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> number_of_tests = int(log[i]); i += 1
number_of_points = [int(t) for t in log[i].split(" ")]; i += 1
x = [int(t) for t in log[i].split(" ")]; i += 1
y = [int(t) for t in log[i].split(" ")]; i += 1
generateTests(folder_name, number_of_tests, number_of_points, x, y)
... | code_fim | hard | {
"lang": "python",
"repo": "dmitriyklebanov/tsp",
"path": "/scripts/test_gen.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: yangkang779/WSNet path: /WS-Faster-RCNN/ResNet.py
import torch.nn as nn
import torch
import torchvision
import torch.backends.cudnn as cudnn
import torch.utils.data as data
import math
class Bottleneck(nn.Module):
<|fim_suffix|> """
:param inplanes: 输入通道维数
:param planes: 输... | code_fim | medium | {
"lang": "python",
"repo": "yangkang779/WSNet",
"path": "/WS-Faster-RCNN/ResNet.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> """
:param inplanes: 输入通道维数
:param planes: 输出通道纬数
:param baseWidth: 基本宽度
:param cardinality: 卷积组数量
:param stride: 步副
:param dawnsample:是否下采样
"""
super(Bottleneck,self).__init__()
D=int(math.floor(planes*(basewidth/64)))
... | code_fim | medium | {
"lang": "python",
"repo": "yangkang779/WSNet",
"path": "/WS-Faster-RCNN/ResNet.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def FtVspcGetFraming(port_handle):
result = c_uint(0)
ret = api.FtVspcGetFraming(port_handle, byref(result))
if ret == 0:
print_api_error()
return None
return result.value
def FtVspcGetInQueueBytes(port_handle):
result = c_ulong(0)
ret = api.FtVspcGetInQueueBytes... | code_fim | hard | {
"lang": "python",
"repo": "thingsroot/power_vsp",
"path": "/vspc/__init__.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: thingsroot/power_vsp path: /vspc/__init__.py
spcPortEventInX = 28 # fInX SERIAL_AUTO_RECEIVE
ftvspcPortEventNull = 29 # fNull SERIAL_NULL_STRIPPING
ftvspcPortEventRtsControl = 30 # fRtsControl SERIAL_RTS_MASK = SERIAL_RTS_CONTROL | SERIAL_RTS_H... | code_fim | hard | {
"lang": "python",
"repo": "thingsroot/power_vsp",
"path": "/vspc/__init__.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: thingsroot/power_vsp path: /vspc/__init__.py
3)
# FtVspc_DataBits
(ftvspcDataBits5, ftvspcDataBits6, ftvspcDataBits7, ftvspcDataBits8) = (0, 1, 2, 3)
# FtVspc_Parity
(ftvspcParityNone, ftvspcParityOdd, ftvspcParityEven, ftvspcParityMark, ftvspcParitySpace) = (0, 1, 2, 3, 4)
# FtVspc_StopBits
(... | code_fim | hard | {
"lang": "python",
"repo": "thingsroot/power_vsp",
"path": "/vspc/__init__.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: gracetian-11/ketchup path: /backend/sentiment.py
import argparse
from google.cloud import language_v1
# export GOOGLE_APPLICATION_CREDENTIALS="/Users/gracetian/Desktop/hackduke2020/backend/google-app-cred.json"
def get_sentiment(text_content):
"""
Analyzing Sentiment in a String
A... | code_fim | hard | {
"lang": "python",
"repo": "gracetian-11/ketchup",
"path": "/backend/sentiment.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|># print(
# "Overall Sentiment: score of {} with magnitude of {}".format(score, magnitude)
# )
# return 0
# if __name__ == "__main__":
# text_content = 'I am so happy and joyful.'
# annotations = sample_analyze_sentiment(text_content)
# print_result(annotations)<|fim_prefix... | code_fim | hard | {
"lang": "python",
"repo": "gracetian-11/ketchup",
"path": "/backend/sentiment.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: maurosilber/donkeykong path: /donkeykong/target/tifffile.py
from tifffile import TiffFile
from .local_target import LocalTarget
class LocalTiff(LocalTarget):
tif = None
<|fim_suffix|> self.tif = TiffFile(self.path)
return self
def close(self):
if self.tif is no... | code_fim | hard | {
"lang": "python",
"repo": "maurosilber/donkeykong",
"path": "/donkeykong/target/tifffile.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> return (len(self), *self.tif.pages[0].shape)
def __getitem__(self, item):
if isinstance(item, tuple):
if isinstance(item[0], int):
return self[item[0]][item[1:]]
else:
return self[item[0]][item]
else:
out = se... | code_fim | hard | {
"lang": "python",
"repo": "maurosilber/donkeykong",
"path": "/donkeykong/target/tifffile.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: corbinsmart/metan path: /python/metan/exception.py
# -*- coding:utf-8 -*-
__all__ = [u"MayaCommandError", u"MetanRuntimeError", u"MetanObjectNotFoundError", u"MetanAttributeError",
u"MetanTypeError", u"MetanArgumentError"]
class MayaCommandError(RuntimeError):pass
<|fim_suffix|>
clas... | code_fim | medium | {
"lang": "python",
"repo": "corbinsmart/metan",
"path": "/python/metan/exception.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>class MetanAttributeError(AttributeError):pass
class MetanTypeError(TypeError):pass
class MetanArgumentError(TypeError):pass<|fim_prefix|># repo: corbinsmart/metan path: /python/metan/exception.py
# -*- coding:utf-8 -*-
__all__ = [u"MayaCommandError", u"MetanRuntimeError", u"MetanObjectNotFoundError", u... | code_fim | hard | {
"lang": "python",
"repo": "corbinsmart/metan",
"path": "/python/metan/exception.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Wenhui1008/ML_KEDF path: /pbcpy_new/src/pbcpy/semilocal_xc.py
# Drivers for LibXC
import numpy as np
from .field import DirectField
from .functionals import *
import pylibxc
from pylibxc.functional import LibXCFunctional
def Get_LibXC_Input(density,do_sigma=True):
if not isinstance(density... | code_fim | hard | {
"lang": "python",
"repo": "Wenhui1008/ML_KEDF",
"path": "/pbcpy_new/src/pbcpy/semilocal_xc.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> return XC(density=density,x_str='gga_x_pbe',c_str='gga_c_pbe',polarization='unpolarized')
def LDA(density,polarization):
return XC(density=density,x_str='lda_x',c_str='lda_c_pz',polarization='unpolarized')
def KEDF(density,k_str,polarization):
'''
Output:
- Functional_KEDF: a... | code_fim | hard | {
"lang": "python",
"repo": "Wenhui1008/ML_KEDF",
"path": "/pbcpy_new/src/pbcpy/semilocal_xc.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> ok = True
for i in range(count):
if not generator.available(True):
print("No pointcloud available?")
break
pc = generator.get()
assert pc
cwipc.cwipc_write_debugdump(f"{outdir}/pointcloud-{i}.cwipcdump", pc)
pc.free()
generator.f... | code_fim | medium | {
"lang": "python",
"repo": "cwi-dis/cwipc_util",
"path": "/python/examples/record.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: cwi-dis/cwipc_util path: /python/examples/record.py
import sys
import cwipc
def main():
if len(sys.argv) != 3:
print(f"Usage: {sys.argv[0]} count dir", file=sys.stderr)
print("Capture pointclouds store them in a directory", file=sys.stderr)
sys.exit(2)
<|fim_suff... | code_fim | medium | {
"lang": "python",
"repo": "cwi-dis/cwipc_util",
"path": "/python/examples/record.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: webclinic017/TradingEvolved path: /examples/old/zipline_model5.py
from zipline.api import order_target_percent, order_target, symbol, record
from pandas_datareader import data as pdr
import matplotlib.pyplot as plt
from datetime import datetime
import numpy as np
import pandas as pd
aapl = pdr.g... | code_fim | hard | {
"lang": "python",
"repo": "webclinic017/TradingEvolved",
"path": "/examples/old/zipline_model5.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> short_mavg = data.history(context.sym, 'price', 100, '1d').mean()
long_mavg = data.history(context.sym, 'price', 300, '1d').mean()
if short_mavg > long_mavg:
order_target(context.sym, 100)
elif short_mavg < long_mavg:
order_target(context.sym, 0)
record(AAPL=data.curre... | code_fim | hard | {
"lang": "python",
"repo": "webclinic017/TradingEvolved",
"path": "/examples/old/zipline_model5.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|># Plot the buy signals
ax1.plot(signals.loc[signals.positions == 1.0].index,
signals.short_mavg[signals.positions == 1.0],
'^', markersize=10, color='m')
# Plot the sell signals
ax1.plot(signals.loc[signals.positions == -1.0].index,
signals.short_mavg[signals.positions == -1.0]... | code_fim | hard | {
"lang": "python",
"repo": "webclinic017/TradingEvolved",
"path": "/examples/old/zipline_model5.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>if __name__ == '__main__':
os.chdir(build_dir)
run_setup('setup.py', script_args=['sdist', 'bdist'])<|fim_prefix|># repo: abeusher/fastapi-crudrouter path: /scripts/build.py
import os
import pathlib
<|fim_middle|>from distutils.core import run_setup
build_dir = pathlib.Path(__file__).parent.par... | code_fim | medium | {
"lang": "python",
"repo": "abeusher/fastapi-crudrouter",
"path": "/scripts/build.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: abeusher/fastapi-crudrouter path: /scripts/build.py
import os
import pathlib
<|fim_suffix|>build_dir = pathlib.Path(__file__).parent.parent
if __name__ == '__main__':
os.chdir(build_dir)
run_setup('setup.py', script_args=['sdist', 'bdist'])<|fim_middle|>from distutils.core import run_se... | code_fim | easy | {
"lang": "python",
"repo": "abeusher/fastapi-crudrouter",
"path": "/scripts/build.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> "3x3 convolution with padding"
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self... | code_fim | hard | {
"lang": "python",
"repo": "alisure-fork/BASNet",
"path": "/src/MyTrain2.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> @staticmethod
def cam(feature_for_clustering, feature_for_cam, k=5):
top_k_value, top_k_index = torch.topk(feature_for_clustering, k, 1)
cam = torch.cat([feature_for_cam[i:i+1, top_k_index[i], :, :].mean(
1, keepdim=True) for i in range(feature_for_cam.size()[0])])
... | code_fim | hard | {
"lang": "python",
"repo": "alisure-fork/BASNet",
"path": "/src/MyTrain2.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: alisure-fork/BASNet path: /src/MyTrain2.py
import os
import glob
import torch
import numpy as np
import torch.nn as nn
from PIL import Image
import torch.optim as optim
from torchvision import models
import torch.nn.functional as F
from skimage import io, transform
from torchvision import transfo... | code_fim | hard | {
"lang": "python",
"repo": "alisure-fork/BASNet",
"path": "/src/MyTrain2.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> collectiveArgs.MMout = torch.mm(collectiveArgs.MMin1, collectiveArgs.MMin2)
# Memory related
def get_mem_size(self, collectiveArgs):
return (
collectiveArgs.ipTensor.nelement() * collectiveArgs.ipTensor.element_size()
)
def alloc_random(self, sizeArr, curR... | code_fim | hard | {
"lang": "python",
"repo": "andrei-pokrovsky/param",
"path": "/train/comms/pt/pytorch_tpu_backend.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: andrei-pokrovsky/param path: /train/comms/pt/pytorch_tpu_backend.py
#!/usr/bin/env python3
import torch
import os
import torch_xla.core.xla_model as xm
import torch_xla.distributed.xla_multiprocessing as xmp
import torch.nn as nn
import numpy as np
from comms_utils import backendFunctions
class... | code_fim | hard | {
"lang": "python",
"repo": "andrei-pokrovsky/param",
"path": "/train/comms/pt/pytorch_tpu_backend.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> pass
def get_groups(self):
pass
# Init functions
def __init__(self, comms_world_info, commsParams):
self.comms_world_info = comms_world_info
self.commsParams = commsParams
def initialize_backend(self, master_ip, master_port, backend="gloo"):
pass
... | code_fim | hard | {
"lang": "python",
"repo": "andrei-pokrovsky/param",
"path": "/train/comms/pt/pytorch_tpu_backend.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>or j in range(1, i+1):
print("#", end="")
print("")<|fim_prefix|># repo: thembones79/cs50x path: /pset6/mario/less/mario.py
height = 0
while height < 1 or height > 8:
height = int(in<|fim_middle|>put("Height: "))
for i in range(1, height+1):
k = height
while k > i:
print(... | code_fim | medium | {
"lang": "python",
"repo": "thembones79/cs50x",
"path": "/pset6/mario/less/mario.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: thembones79/cs50x path: /pset6/mario/less/mario.py
height = 0
while height < 1 or height > 8:
height = int(in<|fim_suffix|> while k > i:
print(" ", end="")
k -= 1
for j in range(1, i+1):
print("#", end="")
print("")<|fim_middle|>put("Height: "))
for i in ran... | code_fim | medium | {
"lang": "python",
"repo": "thembones79/cs50x",
"path": "/pset6/mario/less/mario.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Kingpin007/SC-Lab path: /plot-functions.py
# -*- coding: utf-8 -*-
"""
Created on Thu Jul 26 09:34:42 2018
@author: Kingpin007
Equation: 1/(1+x^2)
"""
import matplotlib.pyplot as plt
import numpy as np
from scipy.special import gamma as Gamma
from scipy import signal
from scipy.integrate import ... | code_fim | hard | {
"lang": "python",
"repo": "Kingpin007/SC-Lab",
"path": "/plot-functions.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> return signal.triang(x)
def trapezoidal(f, a, b, n):
h = float(b - a) / n
s = 0.0
s += f(a)/2.0
for i in range(1, n):
s += f(a + i*h)
s += f(b)/2.0
return s * h
def sigmoid(x):
a = []
for item in x:
#(the sigmoid function)
a.appen... | code_fim | hard | {
"lang": "python",
"repo": "Kingpin007/SC-Lab",
"path": "/plot-functions.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: heyestom/CD4ML-Scenarios path: /cd4ml/pipeline_helpers.py
from cd4ml.run_ml import run_all
from cd4ml.download_data import run_download_data
<|fim_suffix|>
def train_and_validate_model(pipeline_params):
print('Training and validating model')
run_all(pipeline_params)
print('Done train... | code_fim | medium | {
"lang": "python",
"repo": "heyestom/CD4ML-Scenarios",
"path": "/cd4ml/pipeline_helpers.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def train_and_validate_model(pipeline_params):
print('Training and validating model')
run_all(pipeline_params)
print('Done training and validating model')<|fim_prefix|># repo: heyestom/CD4ML-Scenarios path: /cd4ml/pipeline_helpers.py
from cd4ml.run_ml import run_all
from cd4ml.download_data ... | code_fim | medium | {
"lang": "python",
"repo": "heyestom/CD4ML-Scenarios",
"path": "/cd4ml/pipeline_helpers.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: YangZhou1997/openwhisk-lambda-mpi path: /CS260Tests/func-data-transferal.py
import socket
import os
import time
import sys
from builtins import bytes
import random
import string
import struct
PORT = 65432
def get_peerips():
f = open("/addrMap/addrMap.txt", 'r')
idipStr = f.readline().s... | code_fim | hard | {
"lang": "python",
"repo": "YangZhou1997/openwhisk-lambda-mpi",
"path": "/CS260Tests/func-data-transferal.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> else: # server
print("server: ")
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.bind((selfip, PORT))
s.listen()
conn, addr = s.accept()
print('Connected by', addr)
while True:
data = recv_msg(conn)
# print(data)
... | code_fim | hard | {
"lang": "python",
"repo": "YangZhou1997/openwhisk-lambda-mpi",
"path": "/CS260Tests/func-data-transferal.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> selfid = args.get("instanceID") # myname0 or myname1: String
if selfid == "myname0":
peerid = "myname1"
else:
peerid = "myname0"
idipMap = get_peerips()
peerip = idipMap[peerid]
selfip = idipMap[selfid]
socket_type = selfid
if socket_type == "myname1": # ... | code_fim | hard | {
"lang": "python",
"repo": "YangZhou1997/openwhisk-lambda-mpi",
"path": "/CS260Tests/func-data-transferal.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def _do_additional_simulations(
target,
radiuses,
surface_index,
cluster_index,
cluster_centers,
cluster_labels,
propagated_points,
statistics_from_propagation,
inner,
outer,
num_trials,
time_step,
use_parallel,
n_split,
):
surfaces = _get_surfa... | code_fim | hard | {
"lang": "python",
"repo": "StannisZhou/entropic_barrier",
"path": "/golf_course/estimate/capacity.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: StannisZhou/entropic_barrier path: /golf_course/estimate/capacity.py
ner,
outer,
num_points,
num_clusters,
num_trials,
time_step,
use_parallel,
n_split,
)
middle_index = outer + 1
cluster_labels = cluster_labels[middle_index]
... | code_fim | hard | {
"lang": "python",
"repo": "StannisZhou/entropic_barrier",
"path": "/golf_course/estimate/capacity.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> print('Doing additional simulations.')
# Do more simulations and update statistics_from_propagation
for ii in range(1, num_surfaces - 1):
for jj in range(num_clusters):
print('Doing simulations for surface {}, cluster {}.'.format(ii, jj))
_do_additional_simulati... | code_fim | hard | {
"lang": "python",
"repo": "StannisZhou/entropic_barrier",
"path": "/golf_course/estimate/capacity.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> outputs = generator(input_data)
output_values = self.evaluate(outputs)
self.assertLen(outputs, 3)
for index, output_value in enumerate(output_values):
self.assertSequenceEqual(output_value.shape,
(batch_size, 2**(index + 2), 2**(index + 2), 3))
def ... | code_fim | hard | {
"lang": "python",
"repo": "tensorflow/graphics",
"path": "/tensorflow_graphics/projects/gan/architectures_progressive_gan_test.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: tensorflow/graphics path: /tensorflow_graphics/projects/gan/architectures_progressive_gan_test.py
# Copyright 2020 The TensorFlow Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy ... | code_fim | hard | {
"lang": "python",
"repo": "tensorflow/graphics",
"path": "/tensorflow_graphics/projects/gan/architectures_progressive_gan_test.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: marcowurth/w2w_ensembleplots path: /historical/contourplot_example_tot_prec.py
import numpy as np
import eccodes
import netCDF4 as nc
import Ngl
def main():
########################################################################
### settings ... | code_fim | hard | {
"lang": "python",
"repo": "marcowurth/w2w_ensembleplots",
"path": "/historical/contourplot_example_tot_prec.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> resources.cnFillOn = True
resources.cnFillMode = 'CellFill'
#resources.cnCellFillEdgeColor = 'black' # uncomment this for plotting the cell edges
resources.cnMissingValFillColor = 'black'
resources.cnFillPalette = 'WhiteBlueGr... | code_fim | hard | {
"lang": "python",
"repo": "marcowurth/w2w_ensembleplots",
"path": "/historical/contourplot_example_tot_prec.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> def update(self, obj, data):
obj.title = data.get('title', data.title)
obj.code = data.get('code', data.code)
obj.lineos = data.get('lineos', data.lineos)
obj.language = data.get('language', data.language)
obj.style = data.get('style', data.style)
obj.sa... | code_fim | hard | {
"lang": "python",
"repo": "flaviogf/examples",
"path": "/tutorial_django_rest/api/serializers.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: flaviogf/examples path: /tutorial_django_rest/api/serializers.py
from rest_framework import serializers
from api.models import LANGUAGE_CHOICES, STYLE_CHOICES, Snippet
"""
class SnippetSerializer(serializers.Serializer):
<|fim_suffix|>
class SnippetSerializer(serializers.ModelSerializer):
... | code_fim | hard | {
"lang": "python",
"repo": "flaviogf/examples",
"path": "/tutorial_django_rest/api/serializers.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def generate_content(self):
self.add_title("EVENTS")
events={}
cur=""
lines = f_readlines('events')
for line in lines:
line = line.decode("utf-8")
line = line.strip("\n")
if line != "":
if line[0] == "#":
... | code_fim | hard | {
"lang": "python",
"repo": "mscroggs/KLBFAX",
"path": "/pages/103.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: mscroggs/KLBFAX path: /pages/103.py
import os
from os.path import join,expanduser
from page import Page
from file_handler import f_readlines
class EventPage(Page):
def __init__(self):
<|fim_suffix|> for date in sorted(events):
event = events[date]
self.add_text... | code_fim | hard | {
"lang": "python",
"repo": "mscroggs/KLBFAX",
"path": "/pages/103.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> self.add_title("EVENTS")
events={}
cur=""
lines = f_readlines('events')
for line in lines:
line = line.decode("utf-8")
line = line.strip("\n")
if line != "":
if line[0] == "#":
line = line.strip... | code_fim | hard | {
"lang": "python",
"repo": "mscroggs/KLBFAX",
"path": "/pages/103.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def list_gliders(bounding_box):
"""
"""
advanced_search = {
'institution': 'ooi_coastal_endurance',
'min_lat': bounding_box[0],
'max_lat': bounding_box[1],
'min_lon': bounding_box[2],
'max_lon': bounding_box[3],
}
search_url = GLIDER_DAC.get_se... | code_fim | hard | {
"lang": "python",
"repo": "cwingard/ooi-data-explorations",
"path": "/python/ooi_data_explorations/qartod/endurance/qartod_ce_gliders.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> :param latitude:
:param longitude:
:return:
"""
# based on the bounding box, create a list of glider datasets to download
bounding_box = create_box(latitude, longitude)
gliders = list_gliders(bounding_box)
# download the data for each of the datasets
partial_glider = p... | code_fim | hard | {
"lang": "python",
"repo": "cwingard/ooi-data-explorations",
"path": "/python/ooi_data_explorations/qartod/endurance/qartod_ce_gliders.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: cwingard/ooi-data-explorations path: /python/ooi_data_explorations/qartod/endurance/qartod_ce_gliders.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
@author Christopher Wingard
@brief Load Coastal Endurance glider data from the IOOS GilderDAC for use in
creating QARTOD test values for d... | code_fim | hard | {
"lang": "python",
"repo": "cwingard/ooi-data-explorations",
"path": "/python/ooi_data_explorations/qartod/endurance/qartod_ce_gliders.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
sentenceMatrix = np.array(sentenceMatrix, dtype='int32')
positionMatrix_e = np.array(positionMatrix_e, dtype='int32')
positionMatrix_t = np.array(positionMatrix_t, dtype='int32')
return {'labels': labels,
'event':eventMatrix,
'time':timeMatrix,
... | code_fim | hard | {
"lang": "python",
"repo": "xinyi1214/tacl2017-event-time-extraction",
"path": "/1_CreatePKLFiles.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> f = open('pkl/embeddings.pkl', 'rb')
embeddings = pkl.load(f)
word2Idx = pkl.load(f)
f.close()
aspectMapping = {}
typeMapping = {}
tenseMapping = {}
eventClassMapping = {}
distanceMapping = {'PADDING': 0, 'LowerMin': 1, 'GreaterMax': 2}
minDistan... | code_fim | hard | {
"lang": "python",
"repo": "xinyi1214/tacl2017-event-time-extraction",
"path": "/1_CreatePKLFiles.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: xinyi1214/tacl2017-event-time-extraction path: /1_CreatePKLFiles.py
import numpy as np
import cPickle as pkl
import gzip
import os
def createEmbeddingsFile():
folder = 'input/'
embeddingsPath = '0_Preprocessing/embeddings/levy_dependency_based.words.vocab.gz'
words = {}
... | code_fim | hard | {
"lang": "python",
"repo": "xinyi1214/tacl2017-event-time-extraction",
"path": "/1_CreatePKLFiles.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
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