text stringlengths 232 16.3k | domain stringclasses 1
value | difficulty stringclasses 3
values | meta dict |
|---|---|---|---|
<|fim_suffix|> # @param A : list of integers
# @return a list of integers
def subUnsort(self, A):
n = len(A)
left_index = -1
right_index = -1
B = sorted(A)
if A == B:
return [-1]
for i in range(n):
if A[i] != B[i]:
left_index = i
break
... | code_fim | medium | {
"lang": "python",
"repo": "arnabs542/Data-Structures-And-Algorithms",
"path": "/Sorting/Maximum Unsorted Subarray.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: arnabs542/Data-Structures-And-Algorithms path: /Sorting/Maximum Unsorted Subarray.py
"""
Maximum Unsorted Subarray
Problem Description
Given an array A of non-negative integers of size N. Find the minimum sub-array Al, Al+1 ,..., Ar such that if we sort(in ascending order) that sub-array, then t... | code_fim | hard | {
"lang": "python",
"repo": "arnabs542/Data-Structures-And-Algorithms",
"path": "/Sorting/Maximum Unsorted Subarray.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> def __init__(self, img):
self.img = img
def _notch_kernel(self, d0=1000):
"""
理想陷波带阻滤波器
"""
r, c = self.img.shape
u0, v0 = 0, c/8
h = np.empty((r, c, ))
for u in range(r):
for v in range(c):
... | code_fim | hard | {
"lang": "python",
"repo": "Stareven233/fzu_homework",
"path": "/cv_homework/3/3_1.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Stareven233/fzu_homework path: /cv_homework/3/3_1.py
import numpy as np
import matplotlib.pyplot as plt
from utils import draw_picture
"""
作业3：
在网上寻找一张福州大学校园图像，并将之转换为灰度图像，完成以下题目：
1编程实现陷波滤波器，对该图进行频率域滤波。
2编程实现巴特沃思低通滤波器，对该图进行图像滤波。
3编程实现理想低通滤波器，对该图进行图像滤波，并分析一下振铃现象。
... | code_fim | hard | {
"lang": "python",
"repo": "Stareven233/fzu_homework",
"path": "/cv_homework/3/3_1.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>def main():
img = plt.imread('boy_L.jpg')
# 对带噪声的图片滤波效果更加明显，有针对性
# img = plt.imread('Fzu_shutong_L.jpg')
filters = Filter(img)
# draw = draw_picture(2, 2)
# plt.figure(figsize=(10, 6))
# f_shift, f_shift_h, img_h = filters.run('notch')
# draw(1, img, '原图')
# ... | code_fim | hard | {
"lang": "python",
"repo": "Stareven233/fzu_homework",
"path": "/cv_homework/3/3_1.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Persifer/Machine_Learning_Training path: /classification_algorithm/classification_algorithm.py
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
def training_class... | code_fim | medium | {
"lang": "python",
"repo": "Persifer/Machine_Learning_Training",
"path": "/classification_algorithm/classification_algorithm.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> model = DecisionTreeClassifier()
model.fit(X_train, y_train)
prediction_train = model.predict(X_train)
prediction_test = model.predict(X_test)
# calculate the accuracy of the model for the training
accuracy_train = accuracy_score(y_train, prediction_train)
# calculate the acc... | code_fim | medium | {
"lang": "python",
"repo": "Persifer/Machine_Learning_Training",
"path": "/classification_algorithm/classification_algorithm.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> operations = [
migrations.AlterField(
model_name='attachments',
name='content',
field=models.FileField(default=None, help_text='Add important documents or pictures', upload_to=ToDo.models.get_attachment_dir),
preserve_default=False,
),
... | code_fim | medium | {
"lang": "python",
"repo": "arafat-ar13/Regular-ToDoList",
"path": "/ToDo/migrations/0022_auto_20200525_1444.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: arafat-ar13/Regular-ToDoList path: /ToDo/migrations/0022_auto_20200525_1444.py
# Generated by Django 3.0.3 on 2020-05-25 08:44
import ToDo.models
from django.db import migrations, models
<|fim_suffix|> operations = [
migrations.AlterField(
model_name='attachments',
... | code_fim | medium | {
"lang": "python",
"repo": "arafat-ar13/Regular-ToDoList",
"path": "/ToDo/migrations/0022_auto_20200525_1444.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: haamis/wisdom-tree path: /pull_tournament.py
import json, multiprocessing.pool, sys
import requests
ids = []
def make_request(id):
return requests.get("https://fftbg.com/api/tournament/" + str(id))
<|fim_suffix|>for t in tournaments:
print(json.dumps(t.json()))<|fim_middle|>with open(s... | code_fim | medium | {
"lang": "python",
"repo": "haamis/wisdom-tree",
"path": "/pull_tournament.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>with multiprocessing.pool.ThreadPool(12) as p:
tournaments = p.map(make_request, ids)
for t in tournaments:
print(json.dumps(t.json()))<|fim_prefix|># repo: haamis/wisdom-tree path: /pull_tournament.py
import json, multiprocessing.pool, sys
import requests
ids = []
def make_request(id):
re... | code_fim | medium | {
"lang": "python",
"repo": "haamis/wisdom-tree",
"path": "/pull_tournament.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> cls.optimizer_params = {'func': optimizer.ClipOptimizer,
'optimizer_class': tf.train.MomentumOptimizer,
'clip': True,
'optimizer_kwargs':{'momentum': 0.9}}
cls.learning_rate_params = {'learning... | code_fim | hard | {
"lang": "python",
"repo": "apvadaparty/tfutils",
"path": "/tfutils/tests/test_dbinterface.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def load_test_checkpoint(self, save_path):
reader = tf.train.NewCheckpointReader(save_path)
saved_shapes = reader.get_variable_to_shape_map()
self.log.info('Saved Vars:\n' + str(saved_shapes.keys()))
for name in saved_shapes.keys():
self.log.info(
... | code_fim | hard | {
"lang": "python",
"repo": "apvadaparty/tfutils",
"path": "/tfutils/tests/test_dbinterface.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: apvadaparty/tfutils path: /tfutils/tests/test_dbinterface.py
"""Test DBInterface."""
import os
import re
import sys
import time
import errno
import shutil
import logging
import pymongo
import unittest
import pdb
import tensorflow as tf
import mnist_data as data
sys.path.insert(0, "..")
import... | code_fim | hard | {
"lang": "python",
"repo": "apvadaparty/tfutils",
"path": "/tfutils/tests/test_dbinterface.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> elif choice == '9':
op = ROp.SEARCH_TAG.value
tag = get_tag()
request = (op, tag)
error, result = self.send_request(request)
if error:
response = result
else:
... | code_fim | hard | {
"lang": "python",
"repo": "d-nagy/distributed-systems",
"path": "/client.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> print()
print(' --- Movie Database ---')
print()
[print(option) for option in self.menu_options]
print()
print(f' {len(self.menu_options) + 1}. Exit')
print()
print('Enter option: ', end='')
def main(self):
'''
Main loop ... | code_fim | hard | {
"lang": "python",
"repo": "d-nagy/distributed-systems",
"path": "/client.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: d-nagy/distributed-systems path: /client.py
import Pyro4
from enums import ROp
def get_user_id():
uid = input('Enter a user ID (number): ')
while not uid.isdigit():
print('- ' * 32)
print(f'Invalid user ID [ {uid} ]. User ID must be a number.')
print('- ' * 32)
... | code_fim | hard | {
"lang": "python",
"repo": "d-nagy/distributed-systems",
"path": "/client.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> def get_feeds(self):
sections = json.load(urllib2.urlopen(self.server + '/today/sections.json'))
feeds = []
for section in sections:
feeds.append((section, self.server + '/today/feed-' + section.replace(" ", "_").replace("&", "und") + '.xml'))
return fe... | code_fim | hard | {
"lang": "python",
"repo": "jhbruhn/nwz-rss",
"path": "/nwz_calibre.recipe",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: jhbruhn/nwz-rss path: /nwz_calibre.recipe
#!/usr/bin/env python2
# vim:fileencoding=utf-8
from __future__ import unicode_literals, division, absolute_import, print_function
from calibre.web.feeds.news import BasicNewsRecipe
from datetime import date
import json
import urllib2
class Advan... | code_fim | hard | {
"lang": "python",
"repo": "jhbruhn/nwz-rss",
"path": "/nwz_calibre.recipe",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def __init_capsule_begin_end(self): pass # capsule BEGIN .. END
def __init_param_converters(self):
self.M3param_converters = {'p1.b' : (),'p1.a' : (),}
self.M3port_converters = {'a': 'p1.a', 'b': 'p1.b'}
def runcap():
import Simulator
Simulator.run(createCapsule(1,'top'))
def createCapsule(level,h... | code_fim | hard | {
"lang": "python",
"repo": "sofayam/m3",
"path": "/doc/m3lib/ConjC2CapMod.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> self.runtimeName = runtimeName
self.__level = level
RTSTypes.M3CapsuleRuntimeType.__init__(self,level)
self.__init_capsule_connect()
self.__init_capsule_begin_end()
self.__init_param_converters()
def __init_capsule_connect(self): pass
def __init_capsule_begin_end(self): pass # capsule BEGIN ... | code_fim | medium | {
"lang": "python",
"repo": "sofayam/m3",
"path": "/doc/m3lib/ConjC2CapMod.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: sofayam/m3 path: /doc/m3lib/ConjC2CapMod.py
import M3Objects
import M3Types
import RTSTypes
import M3Predefined
import ConjTypesInt as CT
import TimerInt as Timer
import M3TypeLib
import M3ProcLib
import CapsuleMap
from Statistics import M3incStat
M3TL=M3TypeLib.internaliseTypes(r'm3lib/ConjC2Cap... | code_fim | medium | {
"lang": "python",
"repo": "sofayam/m3",
"path": "/doc/m3lib/ConjC2CapMod.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> return ans
def main():
mat = [None] * 3
mat[0] = [1, 1, 2, 9]
mat[1] = [2, 4, -3, 1]
mat[2] = [3, 6, -5, 0]
X = GaussianElimination(3, mat)
print('X = %.1lf, Y = %.1lf, Z = %.1lf' % (X[0], X[1], X[2]))
main()<|fim_prefix|># repo: stevenhalim/cpbook-code path: /ch9/GaussianElimination.p... | code_fim | medium | {
"lang": "python",
"repo": "stevenhalim/cpbook-code",
"path": "/ch9/GaussianElimination.py",
"mode": "spm",
"license": "UPL-1.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>def main():
mat = [None] * 3
mat[0] = [1, 1, 2, 9]
mat[1] = [2, 4, -3, 1]
mat[2] = [3, 6, -5, 0]
X = GaussianElimination(3, mat)
print('X = %.1lf, Y = %.1lf, Z = %.1lf' % (X[0], X[1], X[2]))
main()<|fim_prefix|># repo: stevenhalim/cpbook-code path: /ch9/GaussianElimination.py
def GaussianE... | code_fim | medium | {
"lang": "python",
"repo": "stevenhalim/cpbook-code",
"path": "/ch9/GaussianElimination.py",
"mode": "spm",
"license": "UPL-1.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: stevenhalim/cpbook-code path: /ch9/GaussianElimination.py
def GaussianElimination(N, mat):
for i in range(N-1):
l = i
for j in range(i+1, N):
if abs(mat[j][i]) > abs(mat[l][i]):
l = j
for k in range(i, N+1):
mat[i][k], mat[l][k] = mat[l][k], mat[i][k]
for j i... | code_fim | medium | {
"lang": "python",
"repo": "stevenhalim/cpbook-code",
"path": "/ch9/GaussianElimination.py",
"mode": "psm",
"license": "UPL-1.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>if __name__ == "__main__":
filepath = "demo_airhistory"
lines = get_lines(filepath)
new_csv(lines, filepath)<|fim_prefix|># repo: tianchen2215/txt-to-csv path: /run.py
#-*- coding: utf-8 -*-
def get_lines(filepath):
with open(filepath + '.txt') as file_object:
lines = list(file_ob... | code_fim | hard | {
"lang": "python",
"repo": "tianchen2215/txt-to-csv",
"path": "/run.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: tianchen2215/txt-to-csv path: /run.py
#-*- coding: utf-8 -*-
def get_lines(filepath):
with open(filepath + '.txt') as file_object:
lines = list(file_object.readlines())
return lines
def new_csv(lines, filepath):
<|fim_suffix|>if __name__ == "__main__":
filepath = "demo_ai... | code_fim | hard | {
"lang": "python",
"repo": "tianchen2215/txt-to-csv",
"path": "/run.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> fileindex = 0
fp = open(filepath + '.csv', 'w')
count = len(lines)
print("总行数:" + str(count))
for index, line in enumerate(lines):
index += 1
# print(str(index)+' : '+line)
oneline = line.strip() # 逐行读取，剔除空白
fp.write(oneline) # 写文件
fp.writ... | code_fim | medium | {
"lang": "python",
"repo": "tianchen2215/txt-to-csv",
"path": "/run.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>name = 'testing'),
path('search/', views.searchfunc, name = 'searchfunc'),
path('contactus/', views.contactuss, name = 'contactus'),
path('testimonials/', views.testimonial, name = 'testimonial'),
path('sellcar/', views.sellcar, name = 'sellcar'),
path('buycar/', views.buycar, name = '... | code_fim | hard | {
"lang": "python",
"repo": "carinfinity/testing",
"path": "/myapp/urls.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: carinfinity/testing path: /myapp/urls.py
from django.contrib import admin
from django.urls import include, path
from django.conf import settings
from django.conf.urls.static import static
from . import views
from myapp.views import newcarpage
urlpatterns = [
path('admin/', admin.site.urls),
... | code_fim | hard | {
"lang": "python",
"repo": "carinfinity/testing",
"path": "/myapp/urls.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: jk983294/morph path: /book/tensorflow/core/variables.py
import tensorflow as tf
import os
import cProfile
def variable_turn_off_gradient():
step_counter = tf.Variable(1, trainable=False)
print(step_counter)
def variable_placing():
with tf.device('CPU:0'):
# Create some ten... | code_fim | medium | {
"lang": "python",
"repo": "jk983294/morph",
"path": "/book/tensorflow/core/variables.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> # This creates a new tensor; it does not reshape the variable.
print("\nCopying and reshaping: ", tf.reshape(my_variable, [1, 4]))
# Variables can be all kinds of types, just like tensors
bool_variable = tf.Variable([False, False, False, True])
complex_variable = tf.Variable([5 + 4j, ... | code_fim | hard | {
"lang": "python",
"repo": "jk983294/morph",
"path": "/book/tensorflow/core/variables.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> # copy, two variables will not share the same memory
a = tf.Variable([2.0, 3.0])
b = tf.Variable(a) # Create b based on the value of a
a.assign([5, 6])
print(a.numpy()) # [5. 6.]
print(b.numpy()) # [2. 3.]
print(a.assign_add([2, 3]).numpy()) # [7. 9.]
print(a.assign_sub... | code_fim | hard | {
"lang": "python",
"repo": "jk983294/morph",
"path": "/book/tensorflow/core/variables.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>treatments_names = ['text_blob', 'vader', 'naive_bayes', 'neural_network']
graphics = ["ROC", "visualize_data"]<|fim_prefix|># repo: Mattross45/RottenTomatoes2.0 path: /tweet_analyser/main.py
import pandas as pd
import numpy as np
from get_data import *
from clean_text import *
from tweet_analyser... | code_fim | medium | {
"lang": "python",
"repo": "Mattross45/RottenTomatoes2.0",
"path": "/tweet_analyser/main.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Mattross45/RottenTomatoes2.0 path: /tweet_analyser/main.py
import pandas as pd
import numpy as np
from get_data import *
from clean_text import *
from tweet_analyser.treatment_algorithms.text_blob_treatement import text_blob_treatement
from tweet_analyser.treatment_algorithms.vader_treatment... | code_fim | hard | {
"lang": "python",
"repo": "Mattross45/RottenTomatoes2.0",
"path": "/tweet_analyser/main.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42, stratify=y)
y_train = y_train.values.ravel()
y_test = y_test.values.ravel()
print('Data cleaning and train-test split is done')
print ('Train set shape:', X_train.shape, y_train.shape)
print ('Test set shape: ', ... | code_fim | hard | {
"lang": "python",
"repo": "AIMPED/ML_Classification_Kickstarter",
"path": "/train_save_model.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|># Start year and month of the projects
df['start_month']= df['launched_at'].dt.month
df['start_year']= df['launched_at'].dt.year
# Splitting the text in column category, keeping only the left part of the string --> main category
df.category = df.category.apply(lambda x: x.split('/')[0])
# change to lowe... | code_fim | hard | {
"lang": "python",
"repo": "AIMPED/ML_Classification_Kickstarter",
"path": "/train_save_model.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: AIMPED/ML_Classification_Kickstarter path: /train_save_model.py
import numpy as np
import pandas as pd
import pickle
# Scikit Learn
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from datetime import datetime
RSEED = 42
df = pd.read_cs... | code_fim | hard | {
"lang": "python",
"repo": "AIMPED/ML_Classification_Kickstarter",
"path": "/train_save_model.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Himmalay-Devulapalli/Speech-to-Text-Bot path: /speech_to_text_bot.py
from telegram.ext import Updater, CommandHandler, MessageHandler, Filters
import speech_recognition as sr
from pydub import AudioSegment
import pydub
from gtts import gTTS
#default commands handlers
def start(update,cont... | code_fim | hard | {
"lang": "python",
"repo": "Himmalay-Devulapalli/Speech-to-Text-Bot",
"path": "/speech_to_text_bot.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> """
telegram bots have a default command '/start',
when you try to make a conversation with the bot for the first time, you can use the /start command
You can add your custom commands using add_handler method.
CommandHandler is responsible for handling the comm... | code_fim | hard | {
"lang": "python",
"repo": "Himmalay-Devulapalli/Speech-to-Text-Bot",
"path": "/speech_to_text_bot.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> """
Transforms function signatures with line continuations to a function
on a single line with () appended. Required because pygments cannot
handle this situation correctly.
:param code:
:type code: str
:return: Code string with functions on single line
"""
pat = r"""^... | code_fim | hard | {
"lang": "python",
"repo": "sphinx-contrib/matlabdomain",
"path": "/sphinxcontrib/mat_parser.py",
"mode": "spm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|>def fix_function_signatures(code):
"""
Transforms function signatures with line continuations to a function
on a single line with () appended. Required because pygments cannot
handle this situation correctly.
:param code:
:type code: str
:return: Code string with functions on ... | code_fim | hard | {
"lang": "python",
"repo": "sphinx-contrib/matlabdomain",
"path": "/sphinxcontrib/mat_parser.py",
"mode": "spm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: sphinx-contrib/matlabdomain path: /sphinxcontrib/mat_parser.py
# -*- coding: utf-8 -*-
"""
Functions for parsing MatlabLexer output.
:copyright: Copyright 2023 Jørgen Cederberg
:license: BSD, see LICENSE for details.
"""
import re
import sphinx.util
logger = sphinx.util.logging.getLogger("matl... | code_fim | hard | {
"lang": "python",
"repo": "sphinx-contrib/matlabdomain",
"path": "/sphinxcontrib/mat_parser.py",
"mode": "psm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: guideontoshar/piaskownica path: /lecture2/instructions/assignements.py
name = 'Euzebiusz'
a, b = 1,2 # przypisanie rozpakowujące krotkę
c, d = [1,2] # przypisanie rozpakowujące listę
a,b,c,d = [1,2,3,4]
<|fim_suffix|>first,*other,last = [1,2,3,4]
print(first, other, last)
a +... | code_fim | medium | {
"lang": "python",
"repo": "guideontoshar/piaskownica",
"path": "/lecture2/instructions/assignements.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>
a += 42 # przypisanie rozszerzone<|fim_prefix|># repo: guideontoshar/piaskownica path: /lecture2/instructions/assignements.py
name = 'Euzebiusz'
a, b = 1,2 # przypisanie rozpakowujące krotkę
c, d = [1,2] # przypisanie rozpakowujące listę
a,b,c,d = [1,2,3,4]
first, *other = [1, 2, 3, ... | code_fim | easy | {
"lang": "python",
"repo": "guideontoshar/piaskownica",
"path": "/lecture2/instructions/assignements.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> get_flavors: Callable[[], List[int]], get_player_count: Callable[[], int],
get_served: Callable[[], List[Dict[int, int]]], get_turns_received: Callable[[], List[int]]
) -> Dict[str, Union[Tuple[int, int], int]]:
remain = 24 - self.state[-1]
choices... | code_fim | hard | {
"lang": "python",
"repo": "jueerEcho/icecream",
"path": "/players/g1_player.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: jueerEcho/icecream path: /players/g1_player.py
import logging
import math
from typing import Callable, Dict, List, Tuple, Union, NamedTuple
import numpy as np
from collections import defaultdict
class Choice(NamedTuple):
flavors: List[int]
max_depth: int
index: Tuple[int, int]
de... | code_fim | hard | {
"lang": "python",
"repo": "jueerEcho/icecream",
"path": "/players/g1_player.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> # events seem to be on their own.
for category in ['_trackEvent']:
for i in self.data_struct[category]:
if single_push:
single_pushes.append(i)
else:
script.append(u"""_gaq.push(%s);""" % i)
return ... | code_fim | hard | {
"lang": "python",
"repo": "rprots/gaq_hub",
"path": "/gaq_hub/__init__.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: rprots/gaq_hub path: /gaq_hub/__init__.py
import types
def escape_text(text=''):
text = str(text)
return text.replace("\'", "\\'")
class GaqHub(object):
data_struct = None
def __init__(self, account_id, single_push=False):
"""Sets up self.data_struct dict which we use... | code_fim | hard | {
"lang": "python",
"repo": "rprots/gaq_hub",
"path": "/gaq_hub/__init__.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: GISer18/UCMerced_LandUse path: /classification2.py
import keras
from keras.models import Model
from keras import backend as K
import matplotlib.pyplot as plt
import h5py
from keras.callbacks import ModelCheckpoint,ReduceLROnPlateau,TensorBoard
from sklearn.model_selection import train_test_split
... | code_fim | hard | {
"lang": "python",
"repo": "GISer18/UCMerced_LandUse",
"path": "/classification2.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>net = keras.layers.Dense(units=num_classes,activation="sigmoid")(net)
model = keras.Model(inputs=images,outputs=net)
model.summary()
#%%
optimizer = keras.optimizers.Adadelta()
class_weights= calculating_class_weights(y_train)
model.compile(optimizer= optimizer,
loss = get_weighted_loss... | code_fim | hard | {
"lang": "python",
"repo": "GISer18/UCMerced_LandUse",
"path": "/classification2.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>net = keras.layers.Conv2DTranspose(filters=256, kernel_size=(3, 3),strides=(2,2), padding="same")(net)
net = keras.layers.concatenate([net,shortcut1], axis=-1)
net = keras.layers.BatchNormalization()(net)
net = keras.layers.Activation("relu")(net)
net = keras.layers.Conv2D(filters=512, kernel_size=(3, 3)... | code_fim | hard | {
"lang": "python",
"repo": "GISer18/UCMerced_LandUse",
"path": "/classification2.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: pirateWorley/UnDomainer path: /src/undomainer.py
import requests
import sys
from optparse import OptionParser
def scanDomains(domain, port):
sub_list = open("Wordlists/subdomains-10000.txt").read()
subs = sub_list.splitlines()
<|fim_suffix|>if __name__ == '__main__':
# Set up option... | code_fim | hard | {
"lang": "python",
"repo": "pirateWorley/UnDomainer",
"path": "/src/undomainer.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>if __name__ == '__main__':
# Set up options from the commandline
usage = "usage: %prog [options] filename"
parser = OptionParser(usage=usage)
parser.add_option("-p", "--port", dest="port",help="set target port to PORT", metavar="PORT", default="80")
parser.add_option("-s", "--secure", ... | code_fim | hard | {
"lang": "python",
"repo": "pirateWorley/UnDomainer",
"path": "/src/undomainer.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>#capture all the option and print the items
all_option=drop.options
for option in all_option:
print(option.text)
time.sleep(5)
a.quit()<|fim_prefix|># repo: kandeepanveera/Selenium path: /DropDown.py
"""
Select Any drop down from option
Find out how many options exist in drop down
count how... | code_fim | medium | {
"lang": "python",
"repo": "kandeepanveera/Selenium",
"path": "/DropDown.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: kandeepanveera/Selenium path: /DropDown.py
"""
Select Any drop down from option
Find out how many options exist in drop down
count how many option present
capture option from drop down and print them
"""
from selenium import webdriver
import time
#select class need to import
from seleniu... | code_fim | hard | {
"lang": "python",
"repo": "kandeepanveera/Selenium",
"path": "/DropDown.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: cheatm/fxdayu_sinta path: /fxdayu_sinta/adjust/env.py
from fxdayu_sinta.IO.config import root
import json
import os
config_path = os.path.join(root, "adjust.json")
def read_config():
try:
return json.load(open(config_path))
except IOError:
from fxdayu_sinta.adjust.conf... | code_fim | medium | {
"lang": "python",
"repo": "cheatm/fxdayu_sinta",
"path": "/fxdayu_sinta/adjust/env.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> from fxdayu_sinta.adjust import CLIENT, DB
from fxdayu_sinta.utils.mongo import create_client
config = read_config()
return create_client(**config.get(CLIENT, {}))[config.get(DB, "adjust")]
def get_home():
from fxdayu_sinta.adjust import HOME
return read_config().get(HOME, "/rqa... | code_fim | medium | {
"lang": "python",
"repo": "cheatm/fxdayu_sinta",
"path": "/fxdayu_sinta/adjust/env.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>def generate():
import click
return {'create': click.Command("create", callback=create,
help="Create adjust config file with rqalpha bundle adjust data path.",
params=[click.Argument(["path"], nargs=1)])}<|fim_prefix|># repo:... | code_fim | hard | {
"lang": "python",
"repo": "cheatm/fxdayu_sinta",
"path": "/fxdayu_sinta/adjust/env.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> if grade_points > 92.4: # A
return 4
elif grade_points > 89.9: # A-
return 3.7
elif grade_points > 87.4: # B+
return 3.33
elif grade_points > 82.4: # B
return 3
elif grade_points > 79.9: # B-
return 2.... | code_fim | hard | {
"lang": "python",
"repo": "tik26/gpaAnalyzer",
"path": "/src/analyzeGPA_us.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: tik26/gpaAnalyzer path: /src/analyzeGPA_us.py
class AnalyzeGPA_US:
def __init__(self, cource_list):
self.cource_list = cource_list
def get_gpa(self):
sum_gp_by_credits = 0.0
sum_credits = 0.0
for cource_dict in self.cource_list:
gp_us = self.g... | code_fim | hard | {
"lang": "python",
"repo": "tik26/gpaAnalyzer",
"path": "/src/analyzeGPA_us.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> gpa_us = sum_gp_by_credits / sum_credits
return gpa_us
def grade_points_to_gp(self, grade_points):
if grade_points > 92.4: # A
return 4
elif grade_points > 89.9: # A-
return 3.7
elif grade_points > 87.4: # B+
return 3.33
... | code_fim | hard | {
"lang": "python",
"repo": "tik26/gpaAnalyzer",
"path": "/src/analyzeGPA_us.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ganzourii/SE2018G10 path: /CareHub/Staff/migrations/0008_auto_20190207_2254.py
# Generated by Django 2.1.5 on 2019-02-07 21:54
from django.db import migrations, models
<|fim_suffix|>
dependencies = [
('Staff', '0007_auto_20190206_1854'),
]
operations = [
migrations.... | code_fim | medium | {
"lang": "python",
"repo": "ganzourii/SE2018G10",
"path": "/CareHub/Staff/migrations/0008_auto_20190207_2254.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> operations = [
migrations.AlterField(
model_name='doctor',
name='email',
field=models.EmailField(max_length=70, unique=True),
),
migrations.AlterField(
model_name='doctor',
name='image',
field=models.ImageF... | code_fim | medium | {
"lang": "python",
"repo": "ganzourii/SE2018G10",
"path": "/CareHub/Staff/migrations/0008_auto_20190207_2254.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>print('O total de terminais comuns encontrados foi')
print(Total)
print('')
print('ANГЃLISE CONCLUГЌDA COM SUCESSO!')<|fim_prefix|># repo: gustavoalecrim/python path: /comparar DF.py
# coding: utf-8
from datetime import datetime
now = datetime.now()
print('INICIANDO A ANГЃLISE DE DADOS...')
print('Este ... | code_fim | hard | {
"lang": "python",
"repo": "gustavoalecrim/python",
"path": "/comparar DF.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: gustavoalecrim/python path: /comparar DF.py
# coding: utf-8
from datetime import datetime
now = datetime.now()
print('INICIANDO A ANГЃLISE DE DADOS...')
print('Este procedimento requer alguns minutos, por favor aguarde!')
print('')
print('')
<|fim_suffix|>data = open('testlist.txt','r')
for... | code_fim | medium | {
"lang": "python",
"repo": "gustavoalecrim/python",
"path": "/comparar DF.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> loss_meter.reset()
train_acc_meter.reset()
test_acc_meter.reset()
if (epoch % 5 == 0) or (epoch == args.final_eval_epoch):
torch.save({
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
... | code_fim | hard | {
"lang": "python",
"repo": "ekitanidis/SimSiam",
"path": "/classifier.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> data, labels = data.to(device), labels.to(device)
output = model(data)
loss = criterion(output, labels)
loss.backward()
optimizer.step()
loss_meter.update(loss.item())
model.zero_grad()
if batch_id % 10 == 0:... | code_fim | hard | {
"lang": "python",
"repo": "ekitanidis/SimSiam",
"path": "/classifier.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ekitanidis/SimSiam path: /classifier.py
from config import load_args
from transforms import baseline
from models import Encoder, Predictor, SimSiam, LinearClassifier
from schedulers import SimpleCosineDecayLR
from utils import accuracy, AverageMeter
import time
import os
import torch
from to... | code_fim | hard | {
"lang": "python",
"repo": "ekitanidis/SimSiam",
"path": "/classifier.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>= 11
# about the optimization
self.batch_size = 32
self.num_step = 300000
# about the saver
self.save_period = 2000
self.save_dir = './models/'
self.summary_dir = './logs/'<|fim_prefix|># repo: caoquanjie/SVHN-multi-digits-recogniton path: /config.p... | code_fim | medium | {
"lang": "python",
"repo": "caoquanjie/SVHN-multi-digits-recogniton",
"path": "/config.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: caoquanjie/SVHN-multi-digits-recogniton path: /config.py
class Config(object):
""" Wrapper class for various (hyper)parameters. """
def __init__(self):
# about the model architecture
self.image_size = 64
self.label_length = 6
self.num_classes <|fim_suffix|... | code_fim | medium | {
"lang": "python",
"repo": "caoquanjie/SVHN-multi-digits-recogniton",
"path": "/config.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>the saver
self.save_period = 2000
self.save_dir = './models/'
self.summary_dir = './logs/'<|fim_prefix|># repo: caoquanjie/SVHN-multi-digits-recogniton path: /config.py
class Config(object):
""" Wrapper class for various (hyper)parameters. """
def __init__(self):
... | code_fim | medium | {
"lang": "python",
"repo": "caoquanjie/SVHN-multi-digits-recogniton",
"path": "/config.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> dependencies = [
('simulations', '0005_useraction_simulation'),
]
operations = [
migrations.AddField(
model_name='useraction',
name='time',
field=models.DateTimeField(null=True),
preserve_default=False,
),
migrati... | code_fim | medium | {
"lang": "python",
"repo": "mdivband/arcade_swarm",
"path": "/web_interface/simulations/migrations/0006_auto_20210103_1812.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: RoardFruit/leetcode path: /oddEvenList.py
# Definition for singly-linked list.
# class ListNode(object):
# def __init__(self, x):
# self.val = x
# self.next = None
<|fim_suffix|> def oddEvenList(self, head):
"""
:type head: ListNode
:rtype:... | code_fim | medium | {
"lang": "python",
"repo": "RoardFruit/leetcode",
"path": "/oddEvenList.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> """
:type head: ListNode
:rtype: ListNode
"""
if head is None or head.next is None or head.next.next is None:
return head
pre=head
prenode=head.next
node=prenode.next
while node:
prenode.next=node.nex... | code_fim | medium | {
"lang": "python",
"repo": "RoardFruit/leetcode",
"path": "/oddEvenList.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: yuhonghai123/Flok_muitimodal_operators path: /Batch_AudioLogMel.py
import pandas as pd
from FlokAlgorithmLocal import FlokDataFrame, FlokAlgorithmLocal
import json
import sys, os
import numpy as np
import librosa
# import cv2
from pandas import Series, DataFrame
class Batch_AudioLogMe... | code_fim | hard | {
"lang": "python",
"repo": "yuhonghai123/Flok_muitimodal_operators",
"path": "/Batch_AudioLogMel.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> params = all_info["parameters"]
inputPaths = all_info["input"]
inputTypes = all_info["inputFormat"]
inputLocation = all_info["inputLocation"]
outputPaths = all_info["output"]
outputTypes = all_info["outputFormat"]
outputLocation = all_info["outputLocation"]
algorithm... | code_fim | hard | {
"lang": "python",
"repo": "yuhonghai123/Flok_muitimodal_operators",
"path": "/Batch_AudioLogMel.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>if __name__ == "__main__":
all_info = json.loads(sys.argv[1])
# f = open("test.json", encoding = 'utf-8')
# all_info = json.loads(f)
# all_info = {
# "input": ["in.mp3"],
# "inputFormat": ["mp3"],
# "inputLocation":["local_fs"],
# "ou... | code_fim | hard | {
"lang": "python",
"repo": "yuhonghai123/Flok_muitimodal_operators",
"path": "/Batch_AudioLogMel.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: SyedMaazHassan/math-practice path: /application/models.py
from hashlib import new
from django.db import models
from datetime import datetime
from django.contrib.auth.models import User, auth
from django.forms.models import ModelFormOptions, model_to_dict
import random
# Create your models here.
... | code_fim | hard | {
"lang": "python",
"repo": "SyedMaazHassan/math-practice",
"path": "/application/models.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> condition_list = self.conditions.all()
new_list = []
for i in condition_list:
single_string = f'NUMBER {i.key} {i.limit}'
new_list.append(single_string)
condition_string = " and ".join(new_list)
return condition_string
class question(model... | code_fim | hard | {
"lang": "python",
"repo": "SyedMaazHassan/math-practice",
"path": "/application/models.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>print(correct_signs("13 > 44 > 33 > 1"))
#➞ False
print(correct_signs("1 < 2 < 6 < 9 > 3"))
#➞ True<|fim_prefix|># repo: ravalrupalj/BrainTeasers path: /Edabit/Correct_Inequality_Signs.py
#Correct Inequality Signs
#Create a function that returns true if a given inequality expression is correct and false... | code_fim | easy | {
"lang": "python",
"repo": "ravalrupalj/BrainTeasers",
"path": "/Edabit/Correct_Inequality_Signs.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ravalrupalj/BrainTeasers path: /Edabit/Correct_Inequality_Signs.py
#Correct Inequality Signs
#Create a function that returns true if a given inequality expression is correct and false otherwise.
def correct_signs(string):
<|fim_suffix|>print(correct_signs("13 > 44 > 33 > 1"))
#➞ False
print(corr... | code_fim | medium | {
"lang": "python",
"repo": "ravalrupalj/BrainTeasers",
"path": "/Edabit/Correct_Inequality_Signs.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>
"""
r3 = RBM(num_visible = 220, num_hidden = 200)
train_data3 = output_train2
test_data3 = output_test2
r3.train(train_data3, max_epochs = 5000)
output_train3,prob_train3=r3.run_visible(train_data3)
output_test3,prob_test3=r3.run_visible(test_data3)
print output_test3
"""<|fim_prefix|># repo: vishals... | code_fim | hard | {
"lang": "python",
"repo": "vishalsubbiah/Kernel-Methods-for-Pattern-Analysis",
"path": "/Assignment 2/task5/test.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>r2.train(train_data2, max_epochs = 5000)
output_train2,prob_train2=r2.run_visible(train_data2)
output_test2,prob_test2=r2.run_visible(test_data2)
X_train = output_train2
X_test=output_test2
Y_train=train_label
Y_test=test_label
logistic = linear_model.LogisticRegression()
rbm = BernoulliRBM(random_stat... | code_fim | hard | {
"lang": "python",
"repo": "vishalsubbiah/Kernel-Methods-for-Pattern-Analysis",
"path": "/Assignment 2/task5/test.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: vishalsubbiah/Kernel-Methods-for-Pattern-Analysis path: /Assignment 2/task5/test.py
import numpy as np
import matplotlib.pyplot as plt
from scipy.ndimage import convolve
from sklearn import linear_model, datasets, metrics
from sklearn.cross_validation import train_test_split
from sklearn.neural_... | code_fim | hard | {
"lang": "python",
"repo": "vishalsubbiah/Kernel-Methods-for-Pattern-Analysis",
"path": "/Assignment 2/task5/test.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: gama79530/DesignPattern path: /AbstractFactoryPattern/python/PizzaStore/Pizza.py
from enum import Enum
import abc
from .Ingredient import *
class PizzaType(Enum):
CHEESE_PIZZA = 0
class Pizza(metaclass=abc.ABCMeta):
def __init__(self, ingredients:list[Ingredient]) -> None:
self... | code_fim | hard | {
"lang": "python",
"repo": "gama79530/DesignPattern",
"path": "/AbstractFactoryPattern/python/PizzaStore/Pizza.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def __init__(self, cheese:Cheese, ingredients:list[Ingredient]) -> None:
super().__init__(cheese, ingredients)
def getName(self) -> str:
return "store A cheese pizze"
def showIngredients(self) -> str:
ingredientsStr = self.cheese.getInfo()
for ingr... | code_fim | hard | {
"lang": "python",
"repo": "gama79530/DesignPattern",
"path": "/AbstractFactoryPattern/python/PizzaStore/Pizza.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def showIngredients(self) -> str:
ingredientsStr = self.cheese.getInfo()
for ingredient in self.ingredients:
ingredientsStr += (", " + ingredient.getInfo())
return ingredientsStr
class CheesePizzeOfStoreB(CheesePizza):
def __init__(self, cheese:Cheese, ingred... | code_fim | medium | {
"lang": "python",
"repo": "gama79530/DesignPattern",
"path": "/AbstractFactoryPattern/python/PizzaStore/Pizza.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: kevin510610/Book_AGuideToPython_Kaiching-Chang path: /unit05/exercise0502.py
a = int(input("Please input the first number: "))
b = int<|fim_suffix|>名: exercise0502.py
# 作者: Kaiching Chang
# 時間: July, 2014<|fim_middle|>(input("Please input the second number: "))
print(a + b)
print(a - b)
print(a... | code_fim | medium | {
"lang": "python",
"repo": "kevin510610/Book_AGuideToPython_Kaiching-Chang",
"path": "/unit05/exercise0502.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>名: exercise0502.py
# 作者: Kaiching Chang
# 時間: July, 2014<|fim_prefix|># repo: kevin510610/Book_AGuideToPython_Kaiching-Chang path: /unit05/exercise0502.py
a = int(input("Please input the first number: "))
b = int(input("Please input the second number: "))
print(a + b)
p<|fim_middle|>rint(a - b)
print(a... | code_fim | easy | {
"lang": "python",
"repo": "kevin510610/Book_AGuideToPython_Kaiching-Chang",
"path": "/unit05/exercise0502.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> elif field.one_to_one:
if model_cls:
related_obj_data = model_cls.construct(
**{_field: getattr(related_obj, _field) for _field in model_cls.get_fields()}
)
... | code_fim | hard | {
"lang": "python",
"repo": "julyzergcn/pydantic-django",
"path": "/pydantic_django/main.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> if model_cls:
related_fields = [field for field in model_cls.get_fields() if field != "content_object"]
related_obj_data = [
model_cls.construct(**obj_vals) for obj_vals in related_qs.values(*re... | code_fim | hard | {
"lang": "python",
"repo": "julyzergcn/pydantic-django",
"path": "/pydantic_django/main.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: julyzergcn/pydantic-django path: /pydantic_django/main.py
from inspect import isclass
from itertools import chain
from typing import Type, Optional, Union, Any
from pydantic import BaseModel, create_model, validate_model, Field, ConfigError
from pydantic.main import ModelMetaclass
import django... | code_fim | hard | {
"lang": "python",
"repo": "julyzergcn/pydantic-django",
"path": "/pydantic_django/main.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> pass
class ParenthesesAdmin(admin.ModelAdmin):
pass
class ScreenplayElementTypeAdmin(admin.ModelAdmin):
pass
admin.site.register(Slug, SlugAdmin)
admin.site.register(Action, ActionAdmin)
admin.site.register(Dialogue, DialogueAdmin)
admin.site.register(Character, CharacterAdmin)
admin.site.reg... | code_fim | hard | {
"lang": "python",
"repo": "chemcnabb/django_ultimate_screenwriter",
"path": "/ultimate_screenwriter/ultimate_screenwriter/common/admin.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: chemcnabb/django_ultimate_screenwriter path: /ultimate_screenwriter/ultimate_screenwriter/common/admin.py
__author__ = 'Che'
from django.contrib import admin
from screenwriter.models import Slug, Action, Dialogue, Character, Screenplay, ScreenplayElements, Parentheses, ScreenplayElementType
clas... | code_fim | medium | {
"lang": "python",
"repo": "chemcnabb/django_ultimate_screenwriter",
"path": "/ultimate_screenwriter/ultimate_screenwriter/common/admin.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> pass
admin.site.register(Slug, SlugAdmin)
admin.site.register(Action, ActionAdmin)
admin.site.register(Dialogue, DialogueAdmin)
admin.site.register(Character, CharacterAdmin)
admin.site.register(Screenplay, ScreenplayAdmin)
admin.site.register(ScreenplayElements, ScreenplayElementsAdmin)
admin.site.r... | code_fim | hard | {
"lang": "python",
"repo": "chemcnabb/django_ultimate_screenwriter",
"path": "/ultimate_screenwriter/ultimate_screenwriter/common/admin.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: jakestaab/online_calculators path: /calculators.py
from flask import request
from nec_lib import NECTables as NEC
from nec_lib import ConduitFill as CF
from nec_lib import WireDerate as WD
def get_voltage_drop(material, phase, size, length, current, voltage):
form = VD_Form()
if form.is_... | code_fim | hard | {
"lang": "python",
"repo": "jakestaab/online_calculators",
"path": "/calculators.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> centripetal_force = (mass_kg * (velocity_in_meters ** 2)) / radius_meters
centrifugal_force = centripetal_force * .224809
horsepower = centripetal_force * .00134102209
output_list = [velocity_in_inch, velocity_in_meters, centripetal_force,
centrifuga... | code_fim | hard | {
"lang": "python",
"repo": "jakestaab/online_calculators",
"path": "/calculators.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> for y in WD.temp_lst:
if tmp > y:
continue
elif tmp < y:
temp_factor = WD.temp_dict_90[y]
break
required_ampacity = (crnt / fill_factor / temp_factor) * cntns
for z in WD.cu_ampacity_90:
if requir... | code_fim | hard | {
"lang": "python",
"repo": "jakestaab/online_calculators",
"path": "/calculators.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> args = get_args()
loader = jinja2.FileSystemLoader([
os.path.join(SCRIPT_PATH, 'user', 'templates'),
os.path.join(SCRIPT_PATH, 'templates')
])
env = jinja2.Environment(loader=loader)
template = loader.load(env, 'index.md')
rendered = template.render({
'da... | code_fim | hard | {
"lang": "python",
"repo": "ihadgraft/lighthouse-reporter",
"path": "/lighthouse2md.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
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