text stringlengths 232 16.3k | domain stringclasses 1
value | difficulty stringclasses 3
values | meta dict |
|---|---|---|---|
<|fim_suffix|> def collect_statistics(self, stats_lambda, summary_lambda):
return summary_lambda([stats_lambda(item) for item in self])
train_augmentation = torchvision.transforms.Compose(
[
torchvision.transforms.AutoAugment(),
torchvision.transforms.RandomApply(
[
... | code_fim | hard | {
"lang": "python",
"repo": "ChenchaoZhao/GroceryStoreDataset",
"path": "/model_scripts/dataloader.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ChenchaoZhao/GroceryStoreDataset path: /model_scripts/dataloader.py
import os
import groot
import numpy as np
import PIL
import torch
import torchvision
class GroceryDataset(torch.utils.data.Dataset):
def __init__(
self,
root,
split="train",
string_labels=Tr... | code_fim | hard | {
"lang": "python",
"repo": "ChenchaoZhao/GroceryStoreDataset",
"path": "/model_scripts/dataloader.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def __ne__(self, other): ...
def __lt__(self, other): ...
def __le__(self, other): ...
def __gt__(self, other): ...
def __ge__(self, other): ...
@overload
def __init__(
self,*,
skill_id: Optional[str] = ...,
from_field: Optional[RuleConditionKey] =... | code_fim | hard | {
"lang": "python",
"repo": "IvanStelmakh/toloka-kit",
"path": "/src/client/actions.pyi",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: IvanStelmakh/toloka-kit path: /src/client/actions.pyi
from enum import Enum
from typing import Any, Dict, Optional, overload
from .conditions import RuleConditionKey
from .user_restriction import DurationUnit, UserRestriction
from .util._codegen import BaseParameters
class RuleType(Enum):
... | code_fim | hard | {
"lang": "python",
"repo": "IvanStelmakh/toloka-kit",
"path": "/src/client/actions.pyi",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> def __ge__(self, other): ...
def __init__(
self,*,
delta: Optional[int] = ...,
open_pool: Optional[bool] = ...
) -> None: ...
_unexpected: Optional[Dict[str, Any]]
delta: Optional[int]
open_pool: Optional[bool]
def ... | code_fim | hard | {
"lang": "python",
"repo": "IvanStelmakh/toloka-kit",
"path": "/src/client/actions.pyi",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: gkarthik/crawl-covid19-cases path: /covid19/items.py
# -*- coding: utf-8 -*-
# Define here the models for your scraped items
#
# See documentation in:
# https://doc.scrapy.org/en/latest/topics/items.html
import scrapy
from functools import reduce
from datetime import datetime as dt
class Cases... | code_fim | medium | {
"lang": "python",
"repo": "gkarthik/crawl-covid19-cases",
"path": "/covid19/items.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> # Get case categories for which data exists
categories = sorted([i for i in self.keys() if i!= "date"])
# Get all non null keys from lib import funs CasesCategory
case_categories = [list(self[i].keys()) for i in categories]
case_categories = reduce(lambda x,y: x+y,c... | code_fim | hard | {
"lang": "python",
"repo": "gkarthik/crawl-covid19-cases",
"path": "/covid19/items.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> def getCategoryTotal(self, key):
case_categories = [i for i in self[key].keys() if i not in ["name", "Hospitalized", "Deaths", "Intensive Care"]]
return sum([int(self[key][i]) for i in case_categories if self[key][i] != "NA"])
def toAsciiTable(self):
# Get case categories ... | code_fim | medium | {
"lang": "python",
"repo": "gkarthik/crawl-covid19-cases",
"path": "/covid19/items.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> print("It's higher!")
elif guess == random_number:
print("Congrats!! You guessed the right number")
print("You took {} tries to guess the right number.".format(attempts))
ask = input("Would you like to play again? (Yes/No) ")
... | code_fim | hard | {
"lang": "python",
"repo": "coderchris591/Number-guessing-game",
"path": "/guess.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: coderchris591/Number-guessing-game path: /guess.py
from random import randint
def start_game():
print('Welcome to the number guessing game!')
attempts = 0
random_number = randint(1,10)
while True:
attempts += 1
try:
guess = int(input("Guess a number ... | code_fim | hard | {
"lang": "python",
"repo": "coderchris591/Number-guessing-game",
"path": "/guess.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> raw_cards = c.fetchall()
cards = []
for card in raw_cards:
cards.append(
Flashcard(
id=card[0],
title=card[1],
description=card[2],
source=card[3],
image_... | code_fim | hard | {
"lang": "python",
"repo": "djbeadle/flashcard-backend",
"path": "/app/db_operations.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> r = c.fetchone()
if not r:
return None
return Flashcard(
id=r[0],
title=r[1],
description=r[2],
source=r[3],
image_url=r[4],
tags=json.loads(r[5])
)<|fim_prefix|># repo: djbeadle/fl... | code_fim | hard | {
"lang": "python",
"repo": "djbeadle/flashcard-backend",
"path": "/app/db_operations.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: djbeadle/flashcard-backend path: /app/db_operations.py
import sqlite3
from app.api.classes import Flashcard
from flask import current_app
import json
def create_flashcard(title: str, description='', source='', image_url='', tags=[]):
with sqlite3.connect(current_app.config['DB']) as db:
... | code_fim | hard | {
"lang": "python",
"repo": "djbeadle/flashcard-backend",
"path": "/app/db_operations.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> return self.len
def state_dict(self) -> Dict[str, Any]:
return {"pos": self.pos}
def load_state_dict(self, state: Dict[str, Any]):
self.pos = state["pos"]
class BucketedSampler(torch.utils.data.Sampler):
def __init__(self, data_source: torch.utils.data.Dataset, batc... | code_fim | hard | {
"lang": "python",
"repo": "RobertCsordas/ndr",
"path": "/framework/loader/sampler.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def state_dict(self) -> Dict[str, Any]:
return {"pos": self.pos}
def load_state_dict(self, state: Dict[str, Any]):
self.pos = state["pos"]
class BucketedSampler(torch.utils.data.Sampler):
def __init__(self, data_source: torch.utils.data.Dataset, batch_size: int, length_key_n... | code_fim | hard | {
"lang": "python",
"repo": "RobertCsordas/ndr",
"path": "/framework/loader/sampler.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: eddowh/Project-Euler path: /011_to_020/020_Factorial_Digit_Sum.py
# -*- coding: utf-8 -*-
# Conventions are according to NumPy Docstring.
"""
n! means n * (n − 1) * ... * 3 * 2 * 1
<|fim_suffix|>import time
import math
if __name__ == '__main__':
# input factorial number
inputNum = 100
... | code_fim | medium | {
"lang": "python",
"repo": "eddowh/Project-Euler",
"path": "/011_to_020/020_Factorial_Digit_Sum.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>import time
import math
if __name__ == '__main__':
# input factorial number
inputNum = 100
# initialize running time
start = time.time()
numFactorial = math.factorial(inputNum)
numFactorialString = str(numFactorial)
# sum of the digits initialized
sumDigits = 0
for cha... | code_fim | medium | {
"lang": "python",
"repo": "eddowh/Project-Euler",
"path": "/011_to_020/020_Factorial_Digit_Sum.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: bkimmig/silver-pancake path: /python/src/twins/v1.py
import pandas as pd
import numpy as np
import tensorflow as tf
from typing import List
import twins
# TODO - remove dep on the service in this repo; create a package that gets
# imported into both things (parent). But for sake of time just u... | code_fim | hard | {
"lang": "python",
"repo": "bkimmig/silver-pancake",
"path": "/python/src/twins/v1.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> df["sentence"] = df.apply(_join, axis=1)
return df
# --------------------------
def train():
steps = [_transform, _combine]
train_data = twins.pipeline.build(_load(), steps)
print("training model v1")
# TODO - persist this model then create a "predict" step that loads it in
... | code_fim | medium | {
"lang": "python",
"repo": "bkimmig/silver-pancake",
"path": "/python/src/twins/v1.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def _transform(data: List[dict]) -> List[dict]:
fcn = lambda x: " ".join(np.unique(x))
for i in range(len(data)):
data[i]["transformed"] = twins.utils.create_sentence(
data[i]["df"], group_col="user_handle", apply_col=data[i]["apply_col"], transform=fcn
)
return da... | code_fim | hard | {
"lang": "python",
"repo": "bkimmig/silver-pancake",
"path": "/python/src/twins/v1.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> def draw_kcf_trackers(self, tracks):
self.viewer.thickness = 2
for track in tracks:
if not track.is_confirmed() or track.time_since_update > 1:
continue
# if track.label == 'truck':
# self.viewer.thickness = 6
self.vie... | code_fim | hard | {
"lang": "python",
"repo": "818ajian/DTTM-Vehicle-Counting",
"path": "/deep_sort/application_util/frame_visualization.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: 818ajian/DTTM-Vehicle-Counting path: /deep_sort/application_util/frame_visualization.py
# vim: expandtab:ts=4:sw=4
import numpy as np
import colorsys
from .image_viewer import ImageViewer
import cv2
import time
def create_unique_color_float(tag, hue_step=0.41):
"""Create a unique RGB color c... | code_fim | hard | {
"lang": "python",
"repo": "818ajian/DTTM-Vehicle-Counting",
"path": "/deep_sort/application_util/frame_visualization.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> wallpaper = path + "/" + file
logger.info("Updating wallpaper.")
command = 'feh --bg-max "' + wallpaper + '"'
os.system(command)
def ResetI3():
logger.info("Resetting i3")
os.system('xrdb -merge ~/.Xresources-regolith && i3 reload')<|fim_prefix|># repo: Fave42/Wallpaper-Sorter p... | code_fim | medium | {
"lang": "python",
"repo": "Fave42/Wallpaper-Sorter",
"path": "/Changer.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Fave42/Wallpaper-Sorter path: /Changer.py
#!/usr/bin/python3
# -*- coding: utf-8 -*-
"""
@Author: Fabian Fey
"""
import os
from logzero import logger
COLORPATH = '/etc/regolith/styles/costum-theme/color'
def ChangeColors(colorsList):
colors = [
"#define color_base03 " + colorsL... | code_fim | medium | {
"lang": "python",
"repo": "Fave42/Wallpaper-Sorter",
"path": "/Changer.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: SaiTeja69/Hackathon_2020_MSME path: /Server/Budget_based_crop_suggestion.py
import csv
predict={}
with open('data/cost-of-cultivation.csv') as csvfile:
reader = csv.reader(csvfile)
for x in reader:
predict[x[0]]=int(x[1])
def bestcrop(budget):
rem=[]
cos=[int(i<|fim_suffix|>(i)
op... | code_fim | medium | {
"lang": "python",
"repo": "SaiTeja69/Hackathon_2020_MSME",
"path": "/Server/Budget_based_crop_suggestion.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>(i)
op={x:y for y,x in predict.items()}
for i in rem:
if(i!=-1):
print(op[i])
bestcrop(10000)<|fim_prefix|># repo: SaiTeja69/Hackathon_2020_MSME path: /Server/Budget_based_crop_suggestion.py
import csv
predict={}
with open('data/cost-of-cultivation.csv') as csvfile:
reader = csv.reader(csvfil... | code_fim | medium | {
"lang": "python",
"repo": "SaiTeja69/Hackathon_2020_MSME",
"path": "/Server/Budget_based_crop_suggestion.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> @classmethod
def get_argument(cls, input_console, argument_number):
return input_console.split(" ")[argument_number]
@classmethod
def print_incorrect_input(cls, input_console):
print("Error in input: " + input())
def join(self):
self._thread.join()<|fim_prefix... | code_fim | hard | {
"lang": "python",
"repo": "evowilliamson/py-pnd-crypto-tradebot",
"path": "/command_center.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: evowilliamson/py-pnd-crypto-tradebot path: /command_center.py
import threading
from trade_engine import TradeEngine
class CommandCenter:
def __init__(self, trade_engine):
self._trade_engine = trade_engine
self._thread = threading.Thread(target=self.run)
self._thread... | code_fim | hard | {
"lang": "python",
"repo": "evowilliamson/py-pnd-crypto-tradebot",
"path": "/command_center.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> @classmethod
def get_command(cls, input_console):
return input_console.split(" ")[0]
@classmethod
def get_argument(cls, input_console, argument_number):
return input_console.split(" ")[argument_number]
@classmethod
def print_incorrect_input(cls, input_console):
... | code_fim | hard | {
"lang": "python",
"repo": "evowilliamson/py-pnd-crypto-tradebot",
"path": "/command_center.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> # main
laFitness = LAFitness(monthlyFee, initFee)
LDai= Member("Dai Lian", datetime(2016,12,15))
LDai.pay(150)
LDai.pay(150)
KHuang = Member("Huang Kun", datetime(2017,5,13))
KHuang.pay(75)
JWang = Member("Wang Jinghao", datetime(2017,7,13))
JWang.pay(150)
laFi... | code_fim | medium | {
"lang": "python",
"repo": "oldteb/LAFtinessCalculator",
"path": "/LAFitnessMain.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> # load from config file...
# parameters
monthlyFee = 25
initFee = 25
# main
laFitness = LAFitness(monthlyFee, initFee)
LDai= Member("Dai Lian", datetime(2016,12,15))
LDai.pay(150)
LDai.pay(150)
KHuang = Member("Huang Kun", datetime(2017,5,13))
KHuang.pay(75... | code_fim | medium | {
"lang": "python",
"repo": "oldteb/LAFtinessCalculator",
"path": "/LAFitnessMain.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: oldteb/LAFtinessCalculator path: /LAFitnessMain.py
from LAFitness import LAFitness
from Member import Member
from datetime import datetime
if __name__ == "__main__":
# load from config file...
# parameters
monthlyFee = 25
initFee = 25
# main
laFitness = LAFitness(mont... | code_fim | medium | {
"lang": "python",
"repo": "oldteb/LAFtinessCalculator",
"path": "/LAFitnessMain.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: adityatanwar800/FSDP2019 path: /Day23/tshirts.py
# -*- coding: utf-8 -*-
"""
Created on Thu Jul 4 11:30:49 2019
@author: Aditya Tanwar
"""
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
#import dataset
dataset = pd.read_csv('tshirts.csv')
fea... | code_fim | medium | {
"lang": "python",
"repo": "adityatanwar800/FSDP2019",
"path": "/Day23/tshirts.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>from sklearn.cluster import KMeans
kmeans =KMeans(n_clusters = 3, init = 'k-means++', random_state = 0)
pred_cluster1 = kmeans.fit_predict(features)
plt.scatter(features[pred_cluster1 == 0, 0], features[pred_cluster1 == 0, 1], c = 'blue', label = 'small')
plt.scatter(features[pred_cluster1 == 1, 0], fea... | code_fim | medium | {
"lang": "python",
"repo": "adityatanwar800/FSDP2019",
"path": "/Day23/tshirts.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> def test_delete_image_by_wrong_tag(self, test_image):
"""
Only images with the matching tag are deleted if one is specified
"""
tag = f"{TEST_IMAGE_NAME}:wrong_tag"
assert image_exists(TEST_IMAGE_NAME)
assert not delete_image(tag, force=True)
ass... | code_fim | hard | {
"lang": "python",
"repo": "kreneskyp/ixian-docker",
"path": "/ixian_docker/tests/modules/docker/utils/test_images.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>class TestPull:
"""
Tests for pulling image from registry
"""
def test_pull(self, mock_docker_environment, snapshot, capsys):
"""
Test a successful push
"""
mock_client = mock_docker_environment
pull_image(TEST_IMAGE_NAME)
mock_client.api.pu... | code_fim | hard | {
"lang": "python",
"repo": "kreneskyp/ixian-docker",
"path": "/ixian_docker/tests/modules/docker/utils/test_images.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: kreneskyp/ixian-docker path: /ixian_docker/tests/modules/docker/utils/test_images.py
# Copyright [2018-2020] Peter Krenesky
#
# 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... | code_fim | hard | {
"lang": "python",
"repo": "kreneskyp/ixian-docker",
"path": "/ixian_docker/tests/modules/docker/utils/test_images.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: MarsStirner/sobiralka path: /int_service/lib/clients/abstract.py
# -*- coding: utf-8 -*-
from abc import ABCMeta, abstractmethod, abstractproperty
class AbstractClient(object):
<|fim_suffix|> pass
@abstractmethod
def enqueue(self):
pass<|fim_middle|> __metaclass__ = A... | code_fim | hard | {
"lang": "python",
"repo": "MarsStirner/sobiralka",
"path": "/int_service/lib/clients/abstract.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> @abstractmethod
def getPatientQueue(self):
pass
@abstractmethod
def getPatientInfo(self):
pass
@abstractmethod
def getWorkTimeAndStatus(self):
pass
@abstractmethod
def getWorkTimeAndStatus(self):
pass
@abstractmethod
def enqueue(s... | code_fim | medium | {
"lang": "python",
"repo": "MarsStirner/sobiralka",
"path": "/int_service/lib/clients/abstract.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> pass
@abstractmethod
def enqueue(self):
pass<|fim_prefix|># repo: MarsStirner/sobiralka path: /int_service/lib/clients/abstract.py
# -*- coding: utf-8 -*-
from abc import ABCMeta, abstractmethod, abstractproperty
class AbstractClient(object):
__metaclass__ = ABCMeta
@a... | code_fim | medium | {
"lang": "python",
"repo": "MarsStirner/sobiralka",
"path": "/int_service/lib/clients/abstract.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: tmu-nlp/100knock2017 path: /kurosawa/chapter02/knock11.py
f = open('hightemp.txt','r')
f1 = open('<|fim_suffix|>close()
# cat hightemp.txt | tr '\t' ' '<|fim_middle|>hightemp1.txt','w')
for line in f:
f1.write(line.expandtabs(1))
f.close()
f1. | code_fim | medium | {
"lang": "python",
"repo": "tmu-nlp/100knock2017",
"path": "/kurosawa/chapter02/knock11.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>.write(line.expandtabs(1))
f.close()
f1.close()
# cat hightemp.txt | tr '\t' ' '<|fim_prefix|># repo: tmu-nlp/100knock2017 path: /kurosawa/chapter02/knock11.py
f = open('hightemp.txt','r')
f1 = open('<|fim_middle|>hightemp1.txt','w')
for line in f:
f1 | code_fim | easy | {
"lang": "python",
"repo": "tmu-nlp/100knock2017",
"path": "/kurosawa/chapter02/knock11.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: sailor008/util_tool_excel path: /common_module/common_main.py
# -*- coding: utf-8 -*-
# from utils import FileUtil
<|fim_suffix|>
import sys
import os
current_folder_path = os.path.dirname(os.path.abspath(__file__))
sys.path.append(current_folder_path+'/utils')<|fim_middle|>
# fileNameLis... | code_fim | hard | {
"lang": "python",
"repo": "sailor008/util_tool_excel",
"path": "/common_module/common_main.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>
import sys
import os
current_folder_path = os.path.dirname(os.path.abspath(__file__))
sys.path.append(current_folder_path+'/utils')<|fim_prefix|># repo: sailor008/util_tool_excel path: /common_module/common_main.py
# -*- coding: utf-8 -*-
# from utils import FileUtil
<|fim_middle|># fileNameLi... | code_fim | hard | {
"lang": "python",
"repo": "sailor008/util_tool_excel",
"path": "/common_module/common_main.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: batxes/exocyst_scripts path: /output_exocyst/optimized_23424_notags.py
import _surface
import chimera
try:
import chimera.runCommand
except:
pass
from VolumePath import markerset as ms
try:
from VolumePath import Marker_Set, Link
new_marker_set=Marker_Set
except:
from VolumePath import ... | code_fim | hard | {
"lang": "python",
"repo": "batxes/exocyst_scripts",
"path": "/output_exocyst/optimized_23424_notags.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> marker_sets:
s=new_marker_set('Sec15_3')
marker_sets["Sec15_3"]=s
s= marker_sets["Sec15_3"]
mark=s.place_marker((355.443, 475.787, 389.417), (0.97, 0.51, 0.75), 2)
if "Sec15_4" not in marker_sets:
s=new_marker_set('Sec15_4')
marker_sets["Sec15_4"]=s
s= marker_sets["Sec15_4"]
mark=s.place_marker((... | code_fim | hard | {
"lang": "python",
"repo": "batxes/exocyst_scripts",
"path": "/output_exocyst/optimized_23424_notags.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: amiravni/Bowling-Project path: /hardware/cameras/usb_camera.py
import time
import io
import cv2
from threading import Thread, Lock
from thread import start_new_thread
camera = None
current_photo = None
current_photo_lock = Lock()
def init(res,shutter_speed):
global camera
exposure_tim... | code_fim | hard | {
"lang": "python",
"repo": "amiravni/Bowling-Project",
"path": "/hardware/cameras/usb_camera.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>def capture_image_sequence_time(time_sec):
global camera
if camera is None:
return
frames = int(20*time_sec)
for i in range(0,frames):
capture_image( 'images%03d.jpg' % i)<|fim_prefix|># repo: amiravni/Bowling-Project path: /hardware/cameras/usb_camera.py
import tim... | code_fim | hard | {
"lang": "python",
"repo": "amiravni/Bowling-Project",
"path": "/hardware/cameras/usb_camera.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: AhmedNusayer/Problem-Solving-Python- path: /Candies and Two Sisters.py
# -*- coding: utf-8 -*-
"""
Created on Wed Apr 15 02:49:31 2020
@author: User
"""
#Candies and two sisters
'''
import math
t = int(input())
ans = []
for i in range(t):<|fim_suffix|> mid = math.ceil(n/2)
... | code_fim | medium | {
"lang": "python",
"repo": "AhmedNusayer/Problem-Solving-Python-",
"path": "/Candies and Two Sisters.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> mid = math.ceil(n/2)
ans.append(n-mid)
for j in range(t):
print(ans[j])<|fim_prefix|># repo: AhmedNusayer/Problem-Solving-Python- path: /Candies and Two Sisters.py
# -*- coding: utf-8 -*-
"""
Created on Wed Apr 15 02:49:31 2020
@author: User
"""
#Candies and two sisters
'''
im... | code_fim | medium | {
"lang": "python",
"repo": "AhmedNusayer/Problem-Solving-Python-",
"path": "/Candies and Two Sisters.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> def get_address(self, instance):
if instance.address:
return AddressReadSerializer(instance.address).data
return None
def get_logo(self, instance):
if instance.logo:
return {
"id62": base62_encode(instance.logo_id),
... | code_fim | hard | {
"lang": "python",
"repo": "edgrmaulana/FinX",
"path": "/api/company/serializers/company.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: edgrmaulana/FinX path: /api/company/serializers/company.py
from rest_framework import serializers
from core.structures.company.models import Company
from enterprise.libs.base62 import base62_decode, base62_encode
from enterprise.libs.rest_module.exception import ErrorValidationException
from ent... | code_fim | medium | {
"lang": "python",
"repo": "edgrmaulana/FinX",
"path": "/api/company/serializers/company.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> class Meta:
model = Company
fields = (
"display_name",
"business_type",
"address",
"description",
"website",
"logo",
)<|fim_prefix|># repo: edgrmaulana/FinX path: /api/company/serializers/company.py
from r... | code_fim | hard | {
"lang": "python",
"repo": "edgrmaulana/FinX",
"path": "/api/company/serializers/company.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> snapshot = load_snapshot_to_dict(sc._archive_dest.with_suffix('.zip'))
e1_key = ('events', 'executions', 'e1')
e2_key = ('events', 'executions', 'e2')
e1_events = snapshot['tenants']['tenant1'][e1_key]
e2_events = snapshot['tenants']['tenant1'][e2_key]
asse... | code_fim | hard | {
"lang": "python",
"repo": "cloudify-cosmo/cloudify-manager",
"path": "/mgmtworker/cloudify_system_workflows/tests/snapshots/test_create.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: itsolutionscorp/AutoStyle-Clustering path: /all_data/exercism_data/python/difference-of-squares/b7a332ccdd9a40878b32dc868f48a396.py
"""from difference_of_squares import difference, square_of_sum, sum_of_squares
<|fim_suffix|>def square_of_sum(i):
""" square the sum """
return sum([j for ... | code_fim | hard | {
"lang": "python",
"repo": "itsolutionscorp/AutoStyle-Clustering",
"path": "/all_data/exercism_data/python/difference-of-squares/b7a332ccdd9a40878b32dc868f48a396.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> """ calculate the difference """
return square_of_sum(i) - sum_of_squares(i)
def sum_of_squares(i):
""" sum of squares """
return sum([j**2 for j in range(1, i+1)])
def square_of_sum(i):
""" square the sum """
return sum([j for j in range(1, i+1)])**2<|fim_prefix|># repo: itsol... | code_fim | easy | {
"lang": "python",
"repo": "itsolutionscorp/AutoStyle-Clustering",
"path": "/all_data/exercism_data/python/difference-of-squares/b7a332ccdd9a40878b32dc868f48a396.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>dom.choice(PATTERNS)
row = np.random.choice(np.arange(size))
col = np.random.choice(np.arange(size))
pattern.add_to(board, row, col)
gol = GOL(board)
gol.add_random_cells(coverage=0.3)
run(gol)<|fim_prefix|># repo: amansinclair/gol path: /example.py
if __name__ == "__m... | code_fim | medium | {
"lang": "python",
"repo": "amansinclair/gol",
"path": "/example.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: amansinclair/gol path: /example.py
if __name__ == "__main__":
from gol import run, GOL, PATTERNS
import numpy as np
size = 100
n_patterns = 20
board = np.zeros((size, size), dtype="int")
for i in range(n_patterns):
pattern = np.ran<|fim_suffix|>))
pattern.... | code_fim | medium | {
"lang": "python",
"repo": "amansinclair/gol",
"path": "/example.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> result_output = 'Case #%d: %s\n' % (case, result)
print(result_output)
f_out.write(result_output)<|fim_prefix|># repo: andy1li/codejam path: /2017/Qualification/A. Oversized Pancake Flipper/2017-q-a.py
# 2017 Qualification Round - A. Oversized Pancake Flipper
# https://code.google... | code_fim | hard | {
"lang": "python",
"repo": "andy1li/codejam",
"path": "/2017/Qualification/A. Oversized Pancake Flipper/2017-q-a.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: andy1li/codejam path: /2017/Qualification/A. Oversized Pancake Flipper/2017-q-a.py
# 2017 Qualification Round - A. Oversized Pancake Flipper
# https://code.google.com/codejam/contest/3264486/dashboard#s=p0
<|fim_suffix|>file = 'sample'
with open(file+'.in') as f_in, open(file+'.out', 'w') as f_o... | code_fim | hard | {
"lang": "python",
"repo": "andy1li/codejam",
"path": "/2017/Qualification/A. Oversized Pancake Flipper/2017-q-a.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> else:
dif = val-mtValue
newVal = mtValue+(dif*mult)
self.microTransformValues["%s_%s"%(nodeName, attrName)] = newVal
#xyz inverse
val = cmds.getAttr("%s.%s"%(nodeN... | code_fim | hard | {
"lang": "python",
"repo": "italic-r/maya-prefs",
"path": "/scripts/aTools/animTools/animBar/subUIs/specialTools_subUIs/microTransform.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>
#print "microTransform is ON."
else:
cmds.manipRotateContext('Rotate', edit=True, mode=self.rotationOrientMode)
self.removeMicroTransform()
#print "microTransform is OFF."
def changedMicroTransform(self... | code_fim | hard | {
"lang": "python",
"repo": "italic-r/maya-prefs",
"path": "/scripts/aTools/animTools/animBar/subUIs/specialTools_subUIs/microTransform.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: italic-r/maya-prefs path: /scripts/aTools/animTools/animBar/subUIs/specialTools_subUIs/microTransform.py
'''
========================================================================================================================
Author: Alan Camilo
www.alancamilo.com
Requirements: aTools Packag... | code_fim | hard | {
"lang": "python",
"repo": "italic-r/maya-prefs",
"path": "/scripts/aTools/animTools/animBar/subUIs/specialTools_subUIs/microTransform.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: maehler/Synergy path: /src/python/export_network.py
import sys
import argparse
import networkx as nx
import json
import urllib2
from matplotlib import pyplot as plt
from matplotlib.colors import ColorConverter
from collections import defaultdict
def as_network(d):
G = nx.Graph()
for n in d['no... | code_fim | hard | {
"lang": "python",
"repo": "maehler/Synergy",
"path": "/src/python/export_network.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>def main():
args = parse_args()
network = json.loads(urllib2.unquote(args.json.encode('utf8')))
G = as_network(network)
if args.type == 'gml':
nx.write_gml(G, sys.stdout)
elif args.type == 'png' or args.type == 'pdf':
ax = plt.axes(frameon=False)
ax.get_yaxis().set_visible(False)
ax.get_xa... | code_fim | hard | {
"lang": "python",
"repo": "maehler/Synergy",
"path": "/src/python/export_network.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>def get_edge_widths(G):
w = []
for e in G.edges(data=True):
w.append(e[2]['graphics']['width'])
wmax = max(w)
wmin = min(w)
return [x * (2 - 0.1) / (wmax - wmin) for x in w]
def get_node_types(G):
color = '#AAAAAA'
basket_color = '#219D1A'
types = defaultdict(list)
for n in G.nodes(data=True):... | code_fim | hard | {
"lang": "python",
"repo": "maehler/Synergy",
"path": "/src/python/export_network.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> result = 0
for element in valueList:
result = Addition.sum(result, element)
return result<|fim_prefix|># repo: HGNJIT/statsCalculator path: /MathOperations/addition.py
class Addition:
@staticmethod
def sum(augend,addend=None):
if isinstance(augend,list... | code_fim | easy | {
"lang": "python",
"repo": "HGNJIT/statsCalculator",
"path": "/MathOperations/addition.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: HGNJIT/statsCalculator path: /MathOperations/addition.py
class Addition:
@staticmethod
def sum(augend,addend=None):
<|fim_suffix|> @staticmethod
def sumList (valueList):
result = 0
for element in valueList:
result = Addition.sum(result, element)
... | code_fim | medium | {
"lang": "python",
"repo": "HGNJIT/statsCalculator",
"path": "/MathOperations/addition.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> S = (s-Fluid['swc'])/(1.0-Fluid['swc']-Fluid['sor'])
Mw = S/Fluid['vw']
Mo = 1.0-S/Fluid['vo']
dMw = 1.0/(Fluid['vw']*(1.0-Fluid['swc']-Fluid['sor']))
dMo = -1.0/(Fluid['vo']*(1.0-Fluid['swc']-Fluid['sor']))
return Mw, Mo, dMw, dMo<|fim_prefix|># repo: chanshing/python_msfv path:... | code_fim | hard | {
"lang": "python",
"repo": "chanshing/python_msfv",
"path": "/relperm_tracer.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: chanshing/python_msfv path: /relperm_tracer.py
from __future__ import division
import numpy as np
def relperm(s, Fluid): # Return Mw, Mo, dMw, dMo
<|fim_suffix|> S = (s-Fluid['swc'])/(1.0-Fluid['swc']-Fluid['sor'])
Mw = S/Fluid['vw']
Mo = 1.0-S/Fluid['vo']
dMw = 1.0/(Fluid['vw'... | code_fim | hard | {
"lang": "python",
"repo": "chanshing/python_msfv",
"path": "/relperm_tracer.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: MrHamdulay/csc3-capstone path: /examples/data/Assignment_6/glbnik001/question1.py
def namelist():
#This first set of instructions formulates the list to be aligned
y = []
x = input ("Enter strings (end with DONE):\n")
if x == str("DONE"):
s=2
else:
y.ap... | code_fim | hard | {
"lang": "python",
"repo": "MrHamdulay/csc3-capstone",
"path": "/examples/data/Assignment_6/glbnik001/question1.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> # This prints the list right-aligned
space = 0
print ("")
print ("Right-aligned list:")
for word in y:
space = start - len(word)
print (" "*space, word,sep="")
namelist()<|fim_prefix|># repo: MrHamdulay/csc3-capstone path: /examples/data/Assignment_6/g... | code_fim | hard | {
"lang": "python",
"repo": "MrHamdulay/csc3-capstone",
"path": "/examples/data/Assignment_6/glbnik001/question1.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> def __sub__(self, rhs):
assert isinstance(rhs, PoissonHist)
assert self.nbins == rhs.nbins
result = PoissonHist()
result.data = self.data - rhs.data
result.bins = self.bins
result.errors = np.sqrt(self.errors**2 + rhs.errors**2)
return result
... | code_fim | hard | {
"lang": "python",
"repo": "VitalyVorobyev/jpsipipi-lineshape",
"path": "/py/phist.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> def __add__(self, rhs):
assert isinstance(rhs, PoissonHist)
assert self.nbins == rhs.nbins
result = PoissonHist()
result.data = self.data + rhs.data
result.bins = self.bins
result.errors = np.sqrt(self.errors**2 + rhs.errors**2)
return result
... | code_fim | hard | {
"lang": "python",
"repo": "VitalyVorobyev/jpsipipi-lineshape",
"path": "/py/phist.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: VitalyVorobyev/jpsipipi-lineshape path: /py/phist.py
import numpy as np
import typing
class PoissonHist:
""" Binned data with symmetric Poisson error bars """
def __init__(self, data:typing.Iterable=None, lo=None, hi=None, nbins=100, dens=False, wght=None):
if data is not None:
... | code_fim | hard | {
"lang": "python",
"repo": "VitalyVorobyev/jpsipipi-lineshape",
"path": "/py/phist.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> Peso da caixa: ' ))
soma += p
print ' O peso total da carga �:',soma<|fim_prefix|># repo: Buorn/FDC path: /Lista 3/L3_EX_5.py
# -*- coding: cp1252 -*-
#UNIVERSIDADE DO ESTADO DO RIO DE JANEIRO - UERJ
<|fim_middle|>
#BRUNO BANDEIRA BRAND�O
#LISTA 3: FUNDAMENTOS DA COMPURA��O 2018/2
#EXERC�CIO 5
... | code_fim | medium | {
"lang": "python",
"repo": "Buorn/FDC",
"path": "/Lista 3/L3_EX_5.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Buorn/FDC path: /Lista 3/L3_EX_5.py
# -*- coding: cp1252 -*-
#UNIVERSIDADE DO ESTADO DO RIO DE JANEIRO - UERJ
<|fim_suffix|>C�CIO 5
soma = 0
for i in range (1, 26):
p = float (input ('Digite o Peso da caixa: ' ))
soma += p
print ' O peso total da carga �:',soma<|fim_middle|>
#BRUNO ... | code_fim | medium | {
"lang": "python",
"repo": "Buorn/FDC",
"path": "/Lista 3/L3_EX_5.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Znerual/FastLogin path: /configuration.py
import xml.etree.ElementTree as ET
class Configuration:
def __init__(self):
self.tree = ET.parse('config.xml')
self.root = self.tree.getroot()
<|fim_suffix|> courseList = self.root.findall('course')
for entry in course... | code_fim | hard | {
"lang": "python",
"repo": "Znerual/FastLogin",
"path": "/configuration.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> courseList = self.root.findall('course')
for entry in courseList:
if (entry.find('startDate') == date):
return int(entry.find('id').text), (entry.find('startDate').text), entry.find('gotPlace').text == 1<|fim_prefix|># repo: Znerual/FastLogin path: /configurati... | code_fim | hard | {
"lang": "python",
"repo": "Znerual/FastLogin",
"path": "/configuration.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> visited[i+1] = 1
elif x[i] == "S":
s += 1
elif x[i] == "T" and s:
ans += 2
s -= 1
print(len(x)-ans)<|fim_prefix|># repo: Aasthaengg/IBMdataset path: /Python_codes/p03986/s166996388.py
x = input()
visited = [0]*(len(x))
ans = 0
s = 0
t = 0
for i i... | code_fim | medium | {
"lang": "python",
"repo": "Aasthaengg/IBMdataset",
"path": "/Python_codes/p03986/s166996388.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Aasthaengg/IBMdataset path: /Python_codes/p03986/s166996388.py
x = input()
visited = [0]*(len(x))
ans = 0
s = 0
t = 0
for i in range(len(x)):<|fim_suffix|> visited[i+1] = 1
elif x[i] == "S":
s += 1
elif x[i] == "T" and s:
ans += 2
s -= 1
p... | code_fim | medium | {
"lang": "python",
"repo": "Aasthaengg/IBMdataset",
"path": "/Python_codes/p03986/s166996388.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>me'])
print(familiar_person['age'])
print(familiar_person['city'])<|fim_prefix|># repo: wy471x/learningNote path: /python/excise/basic/chapter6/6-1.py
#!/usr/bin/env python
# coding=utf-8
familiar_person = {'first_name':'Kobe','last_name':'Bryant','age':31,'city':'Los Angeles'}
pr<|fim_middle|>int(famili... | code_fim | medium | {
"lang": "python",
"repo": "wy471x/learningNote",
"path": "/python/excise/basic/chapter6/6-1.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: wy471x/learningNote path: /python/excise/basic/chapter6/6-1.py
#!/usr/bin/env python
# coding=utf-8
familiar_person = {'first_na<|fim_suffix|>me'])
print(familiar_person['age'])
print(familiar_person['city'])<|fim_middle|>me':'Kobe','last_name':'Bryant','age':31,'city':'Los Angeles'}
print(famili... | code_fim | medium | {
"lang": "python",
"repo": "wy471x/learningNote",
"path": "/python/excise/basic/chapter6/6-1.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>int(familiar_person['first_name'])
print(familiar_person['last_name'])
print(familiar_person['age'])
print(familiar_person['city'])<|fim_prefix|># repo: wy471x/learningNote path: /python/excise/basic/chapter6/6-1.py
#!/usr/bin/env python
# coding=utf-8
familiar_person = {'first_na<|fim_middle|>me':'Kobe'... | code_fim | medium | {
"lang": "python",
"repo": "wy471x/learningNote",
"path": "/python/excise/basic/chapter6/6-1.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Lin0l53/py4e path: /EX9/ex9.5.py
#partner was Logan Zipp
fname = "mbox-short.txt"
handle = open(fname)
counter = dict()
for line in handle :
if line.startswith("From:") :
line = line.rstrip()
print (line)
words = line.split()
sender = words[1]
pos = sender.find("@")
emai... | code_fim | easy | {
"lang": "python",
"repo": "Lin0l53/py4e",
"path": "/EX9/ex9.5.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>#print(word,max)
#print (words)
#print (sender)
#print (counter)<|fim_prefix|># repo: Lin0l53/py4e path: /EX9/ex9.5.py
#partner was Logan Zipp
fname = "mbox-short.txt"
handle = open(fname)
counter = dict()
for line in handle :
if line.startswith("From:") :
line = line.rstrip()
print (lin... | code_fim | easy | {
"lang": "python",
"repo": "Lin0l53/py4e",
"path": "/EX9/ex9.5.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: eric220/Wave_Height path: /image_work.py
import numpy as np
import glob
from keras.preprocessing import image
#input string img_path, returns tensors list of 6 slices of image
def get_tensors(file_name):
#tensor_stack = []
img = image.load_img(file_name<|fim_suffix|>list_of_tensors.appen... | code_fim | hard | {
"lang": "python",
"repo": "eric220/Wave_Height",
"path": "/image_work.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>list_of_tensors.append(np.expand_dims(img_slice, axis=0).astype('float32')/255)
#tensor_stack.append(np.vstack(list_of_tensors))
#tensor_stack = np.array(tensor_stack).reshape(1,6,224,224,3)
return tensor_stack<|fim_prefix|># repo: eric220/Wave_Height path: /image_work.py
import numpy as np
i... | code_fim | hard | {
"lang": "python",
"repo": "eric220/Wave_Height",
"path": "/image_work.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: lichguard/APR path: /documents/topdoc.py
from collections import defaultdict
import operator
from documents.scoretype import ScoreType
<|fim_suffix|> self.scores[score_type] = score
def calculate_score(self):
self.score = 0
self.score += self.scores[ScoreType.tf_idf] ... | code_fim | hard | {
"lang": "python",
"repo": "lichguard/APR",
"path": "/documents/topdoc.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> def __repr__(self):
return "\n" + str(self)
def display(self):
return sorted(self.scores.items(), key=operator.itemgetter(1), reverse=True)<|fim_prefix|># repo: lichguard/APR path: /documents/topdoc.py
from collections import defaultdict
import operator
from documents.scoretype i... | code_fim | hard | {
"lang": "python",
"repo": "lichguard/APR",
"path": "/documents/topdoc.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: gabriellaec/desoft-analise-exercicios path: /backup/user_088/ch20_2020_09_11_21_07_12_160901.py
distancia=float(input('digite a distancia'))
if (d<|fim_suffix|>ia)*0,45
print('o preco é R${0:.2f}'.format(preco))<|fim_middle|>istancia<200):
preco=0,5*(distancia)
print ('o preco é R${0:.2f}'.format... | code_fim | medium | {
"lang": "python",
"repo": "gabriellaec/desoft-analise-exercicios",
"path": "/backup/user_088/ch20_2020_09_11_21_07_12_160901.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>ia)*0,45
print('o preco é R${0:.2f}'.format(preco))<|fim_prefix|># repo: gabriellaec/desoft-analise-exercicios path: /backup/user_088/ch20_2020_09_11_21_07_12_160901.py
distancia=float(input('digite a distancia'))
if (d<|fim_middle|>istancia<200):
preco=0,5*(distancia)
print ('o preco é R${0:.2f}'.format... | code_fim | medium | {
"lang": "python",
"repo": "gabriellaec/desoft-analise-exercicios",
"path": "/backup/user_088/ch20_2020_09_11_21_07_12_160901.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>#-------------------------------------------------------------
# cache initialisation
#-------------------------------------------------------------
domoWebDataCache.domoWebDataCacheInit(config, logger, debugFlags)
#-------------------------------------------------------------
# Task management init
#---... | code_fim | hard | {
"lang": "python",
"repo": "Manu-31/domoweb",
"path": "/domoweb.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Manu-31/domoweb path: /domoweb.py
#!/usr/bin/python
# -*- coding: utf-8 -*-
#=============================================================
#=============================================================
# Some imports
#=============================================================
import os
import... | code_fim | hard | {
"lang": "python",
"repo": "Manu-31/domoweb",
"path": "/domoweb.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> slow -= 0.4*slow
fast -= 0.3*fast
print year, slow, fast
if(fast>=slow):
print year<|fim_prefix|># repo: sharanyaa/coursera_python path: /6/slow_fast_population.py
slow = 1000
fast = 1
year = 1
while fast < slow:
<|fim_middle|> slow += slow
fast += fast
year += 1
| code_fim | easy | {
"lang": "python",
"repo": "sharanyaa/coursera_python",
"path": "/6/slow_fast_population.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: sharanyaa/coursera_python path: /6/slow_fast_population.py
slow = 1000
fast = 1
year = 1
while fast < slow:
<|fim_suffix|>t year, slow, fast
if(fast>=slow):
print year<|fim_middle|> slow += slow
fast += fast
year += 1
slow -= 0.4*slow
fast -= 0.3*fast
prin | code_fim | medium | {
"lang": "python",
"repo": "sharanyaa/coursera_python",
"path": "/6/slow_fast_population.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> """rank 1"""
_, pred = torch.max(output, 1)
total += label.size(0)
acc_top1 += (pred == label).sum().item()
"""rank 5"""
_, rank5 = output.topk(5, 1, True, True)
rank5 = rank5.t()
... | code_fim | hard | {
"lang": "python",
"repo": "nicotina04/kmu-autonomous2021-20171717",
"path": "/hw2/compare_pretrained.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> model_label = [
'alexNet Top-1',
'alexNet Top-5',
'VGG16 Top-1',
'VGG16 Top-5',
'googLeNet Top-1',
'googLeNet Top-5',
'resnet18 Top-1',
'resnet18 Top-5'
]
fig, ax = plt.subplots()
ax.barh(model_label, eval_output, height=0.6,... | code_fim | hard | {
"lang": "python",
"repo": "nicotina04/kmu-autonomous2021-20171717",
"path": "/hw2/compare_pretrained.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: nicotina04/kmu-autonomous2021-20171717 path: /hw2/compare_pretrained.py
"""
Homework target model
AlexNet
GoogLeNet
vgg16
resnet18
Must need ILSVRC2012 Validation set for evaluation
"""
import torch
import torchvision
from torchvision import transforms
from torch.utils.data import DataLoader
imp... | code_fim | hard | {
"lang": "python",
"repo": "nicotina04/kmu-autonomous2021-20171717",
"path": "/hw2/compare_pretrained.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> R2 = cross_val_score(model, X_train, y_train).mean()
MSE = abs(cross_val_score(model, X_train, y_train, scoring = 'neg_mean_squared_error').mean())
return R2, MSE<|fim_prefix|># repo: tibrado/case-study-driver-churn-rate path: /src/models.py
import pandas as pd
import numpy as np
from sklear... | code_fim | hard | {
"lang": "python",
"repo": "tibrado/case-study-driver-churn-rate",
"path": "/src/models.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
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