text stringlengths 232 16.3k | domain stringclasses 1
value | difficulty stringclasses 3
values | meta dict |
|---|---|---|---|
<|fim_prefix|># repo: shawn243343/comp9321Groj path: /rank.py
import pandas as pd
def ranked(country, variety, price, num):
data = pd.read_csv("wine_final.csv")
if num == 0:
return None
if country !='':
data = data.query("country==\"{}\"".format(country))
<|fim_suffix|> if price !=''... | code_fim | medium | {
"lang": "python",
"repo": "shawn243343/comp9321Groj",
"path": "/rank.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> if price !='':
# data['price']=data['price'].str.replace(',','').astype(int)
data = data.query("price=={}".format(price))
data = data.sort_values(by='points', ascending=False)
result=[]
count = 0
for index,row in data.iterrows():
if count >= num:
bre... | code_fim | medium | {
"lang": "python",
"repo": "shawn243343/comp9321Groj",
"path": "/rank.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> #从fund_info数据表中提取出fund_id，加入fund_nav_data数据表中的fund_id
for fund_name in sql_df['fund_name'].unique():
sql = "SELECT * FROM fund_info"
fund_info_sql_df = pd.read_sql(sql, con)
fund_id = fund_info_sql_df.loc[fund_info_sql_df.fund_name == fund_name, 'fund_id'].values[0]
... | code_fim | hard | {
"lang": "python",
"repo": "Geek-Lee/excel-upload-sqlite3",
"path": "/mins/website/views.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Geek-Lee/excel-upload-sqlite3 path: /mins/website/views.py
ame, 'group'].values[0])
fund_type_strategy = str(commit_data.loc[commit_data.fund_full_name == name, 'fund_type_strategy'].values[0])
reg_code = str(commit_data.loc[commit_data.fund_full_name == name, 'reg_code'].... | code_fim | hard | {
"lang": "python",
"repo": "Geek-Lee/excel-upload-sqlite3",
"path": "/mins/website/views.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> #把一行表格dataframe提取其中的值
user_name = str(name)
sex = str(commit_data.loc[commit_data.user_name == name, 'sex'].values[0])
org_name = str(commit_data.loc[commit_data.user_name == name, 'org_name'].values[0])
introduction = str(commit_data.loc[commit_... | code_fim | hard | {
"lang": "python",
"repo": "Geek-Lee/excel-upload-sqlite3",
"path": "/mins/website/views.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: allisson/loafer path: /loafer/ext/aws/routes.py
from ...routes import Route
from .providers import SQSProvider
from .message_translators import SQSMessageTranslator, SNSMessageTranslator
class SQSRoute(Route):
def __init__(self, provider_queue, provider_options=None, *args, **kwargs):
<|fim... | code_fim | hard | {
"lang": "python",
"repo": "allisson/loafer",
"path": "/loafer/ext/aws/routes.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def __init__(self, provider_queue, provider_options=None, *args, **kwargs):
provider_options = provider_options or {}
provider = SQSProvider(provider_queue, **provider_options)
kwargs['provider'] = provider
if 'message_translator' not in kwargs:
kwargs['mess... | code_fim | hard | {
"lang": "python",
"repo": "allisson/loafer",
"path": "/loafer/ext/aws/routes.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> for index in matrix:
jndex = 0
new_row = []
while jndex < len(index):
new_row.append(index[jndex] ** 2)
jndex += 1
new_matrix.append(new_row)
return new_matrix<|fim_prefix|># repo: MarcoANT9/holbertonschool-higher_level_programming path: /0x... | code_fim | easy | {
"lang": "python",
"repo": "MarcoANT9/holbertonschool-higher_level_programming",
"path": "/0x04-python-more_data_structures/0-square_matrix_simple.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: MarcoANT9/holbertonschool-higher_level_programming path: /0x04-python-more_data_structures/0-square_matrix_simple.py
#!/usr/bin/python3
def square_matrix_simple(matrix=[]):
'''This function will compute the square root of all integers in
a matrix. ... | code_fim | easy | {
"lang": "python",
"repo": "MarcoANT9/holbertonschool-higher_level_programming",
"path": "/0x04-python-more_data_structures/0-square_matrix_simple.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Anezeres/AprendizajeConPython path: /RStudio y Python/Python/01-Estructura de datos Python/00-Listas.py
L5 = [0]*10
print(L5)
L5[2] = 20
print(L5)
<|fim_suffix|>L6 = [1,2,3,4,5,6]
print(L6[1::2])
print(L6[::2])<|fim_middle|>print(L5[1:4])
L5.append(30)
print(L5)
L5.remove(30) #Elimina la pri... | code_fim | medium | {
"lang": "python",
"repo": "Anezeres/AprendizajeConPython",
"path": "/RStudio y Python/Python/01-Estructura de datos Python/00-Listas.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>print(L5[1:4])
L5.append(30)
print(L5)
L5.remove(30) #Elimina la primera ocurrencia del objeto
print(L5)
L6 = [1,2,3,4,5,6]
print(L6[1::2])
print(L6[::2])<|fim_prefix|># repo: Anezeres/AprendizajeConPython path: /RStudio y Python/Python/01-Estructura de datos Python/00-Listas.py
L5 = [0]*10
print(L5... | code_fim | easy | {
"lang": "python",
"repo": "Anezeres/AprendizajeConPython",
"path": "/RStudio y Python/Python/01-Estructura de datos Python/00-Listas.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>
L5.remove(30) #Elimina la primera ocurrencia del objeto
print(L5)
L6 = [1,2,3,4,5,6]
print(L6[1::2])
print(L6[::2])<|fim_prefix|># repo: Anezeres/AprendizajeConPython path: /RStudio y Python/Python/01-Estructura de datos Python/00-Listas.py
L5 = [0]*10
print(L5)
<|fim_middle|>L5[2] = 20
print(L5)
pr... | code_fim | medium | {
"lang": "python",
"repo": "Anezeres/AprendizajeConPython",
"path": "/RStudio y Python/Python/01-Estructura de datos Python/00-Listas.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: caopeirui/station-py path: /tests/test.py
#! /usr/bin/env python3
# -*- coding: utf-8 -*-
"""
DIM Station Test
~~~~~~~~~~~~~~~~
Unit test for DIM Station
"""
import unittest
from dimp import ID, NetworkID
class StationTestCase(unittest.TestCase):
<|fim_suffix|> total_mone... | code_fim | hard | {
"lang": "python",
"repo": "caopeirui/station-py",
"path": "/tests/test.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> total_money = 15 * 10000 * 10000
package = 2 ** 20
print('total money: %d, first package: %d' % (total_money, package))
spent = 0
day = 0
year = 0
while (spent + package) <= total_money and package >= 1:
spent += package
day +... | code_fim | hard | {
"lang": "python",
"repo": "caopeirui/station-py",
"path": "/tests/test.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: HpBoss/Cubor path: /TypeWriting/PyScripts/GetTones.py
import os
from typing import List
from pypinyin import pinyin, lazy_pinyin
# map vowel-number combination to unicode
toneMap = {
"d": ['ā', 'ē', 'ī', 'ō', 'ū', 'ǜ'],
"f": ['á', 'é', 'í', 'ó', 'ú', 'ǘ'],
"j": ['ǎ', 'ě', 'ǐ', 'ǒ', ... | code_fim | hard | {
"lang": "python",
"repo": "HpBoss/Cubor",
"path": "/TypeWriting/PyScripts/GetTones.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> tempToneKeys = []
for tone in tones:
toneKey = getToneKeys(tone)
if toneKey not in tempToneKeys: # 如果类似 啊 这样的字有多音 a e 都是一声就避免重复
tempToneKeys.append(toneKey)
# base-dict 来源于 pinyin_simp... | code_fim | hard | {
"lang": "python",
"repo": "HpBoss/Cubor",
"path": "/TypeWriting/PyScripts/GetTones.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ugiete/PPD-2021-1 path: /Trabalho-1.2/src/utils.py
from random import randint
import matplotlib.pyplot as plt
def generate_list(length: int) -> list:
"""Generate a list with given length with random integer values in the interval [0, length]
<|fim_suffix|> Args:
k (list): Threads... | code_fim | hard | {
"lang": "python",
"repo": "ugiete/PPD-2021-1",
"path": "/Trabalho-1.2/src/utils.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> Args:
k (list): Threads/Process used
deviation (list): Standard deviation of the timestamps
label (str): "Threads" or "Processos"
"""
plt.plot(threadList, timestamps.values(), 'o-')
plt.legend(mList, title = 'Total valores', loc='best', bbox_to_anchor=(0.5, 0., 0.5,... | code_fim | medium | {
"lang": "python",
"repo": "ugiete/PPD-2021-1",
"path": "/Trabalho-1.2/src/utils.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Faith-qa/alx-interview path: /0x02-minimum_operations/0-minoperations.py
#!/usr/bin/python3
"""minimum time time to write operations of copy and paste"""
<|fim_suffix|> """
a method that calculates the fewest number of operations needed
to result in exactly n H characters in the file
... | code_fim | easy | {
"lang": "python",
"repo": "Faith-qa/alx-interview",
"path": "/0x02-minimum_operations/0-minoperations.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> """loop for n number of times"""
for i in range(2, n + 1):
if n % i == 0:
return minOperations(int(n / i)) + i<|fim_prefix|># repo: Faith-qa/alx-interview path: /0x02-minimum_operations/0-minoperations.py
#!/usr/bin/python3
"""minimum time time to write operations of copy and ... | code_fim | medium | {
"lang": "python",
"repo": "Faith-qa/alx-interview",
"path": "/0x02-minimum_operations/0-minoperations.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> """
a method that calculates the fewest number of operations needed
to result in exactly n H characters in the file
"""
if n <= 1:
return 0
"""loop for n number of times"""
for i in range(2, n + 1):
if n % i == 0:
return minOperations(int(n / i)) + ... | code_fim | easy | {
"lang": "python",
"repo": "Faith-qa/alx-interview",
"path": "/0x02-minimum_operations/0-minoperations.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>print(c.display())<|fim_prefix|># repo: devansh27201/AkashTechnolabs_Internship path: /D-3-Tasks/cal4.py
class cal4:
def setdata(self,n1):
self.n1 = n1
def display(self):
<|fim_middle|> return n1*n1
n1 = int(input("Enter number: "))
c = cal4()
| code_fim | medium | {
"lang": "python",
"repo": "devansh27201/AkashTechnolabs_Internship",
"path": "/D-3-Tasks/cal4.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: devansh27201/AkashTechnolabs_Internship path: /D-3-Tasks/cal4.py
class cal4:
def setdata(self,n1):
<|fim_suffix|> return n1*n1
n1 = int(input("Enter number: "))
c = cal4()
print(c.display())<|fim_middle|> self.n1 = n1
def display(self):
| code_fim | easy | {
"lang": "python",
"repo": "devansh27201/AkashTechnolabs_Internship",
"path": "/D-3-Tasks/cal4.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: anbykova/web-poste path: /app/__init__.py
import os
from flask import Flask
from flask.ext.login import LoginManager
from config import basedir
from flask.ext.sqlalchemy import SQLAlchemy
from flask.ext.openid import OpenID
from momentjs import momentjs
<|fim_suffix|>lm = LoginManager()
lm.init_... | code_fim | medium | {
"lang": "python",
"repo": "anbykova/web-poste",
"path": "/app/__init__.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>lm = LoginManager()
lm.init_app(app)
app.jinja_env.globals['momentjs'] = momentjs
from app import views, models<|fim_prefix|># repo: anbykova/web-poste path: /app/__init__.py
import os
from flask import Flask
from flask.ext.login import LoginManager
from config import basedir
from flask.ext.sqlalchemy i... | code_fim | medium | {
"lang": "python",
"repo": "anbykova/web-poste",
"path": "/app/__init__.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: priyankakushi/machine-learning path: /018_011_019.py
'''import math
x = 5
print("sqrt of 5 is", math.sqrt(64))
str1 = "bollywood"
str2 = 'ody'
if str2 in str1:
print("String found")
else:
print("String not found")
print(10+20)'''
#try:
#block of code
#except Exception l:
#b... | code_fim | hard | {
"lang": "python",
"repo": "priyankakushi/machine-learning",
"path": "/018_011_019.py",
"mode": "psm",
"license": "CC-BY-3.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>try:
fileptr = open("file.txt", "w")
try:
fileptr.write("Hi I am good")
finally:
fileptr.close()
print("file.closed")
except:
print("Error")
else:
print("inside else block")
try:
age = int(input("Enter the age?"))
if age<18:
raise ValueError
... | code_fim | hard | {
"lang": "python",
"repo": "priyankakushi/machine-learning",
"path": "/018_011_019.py",
"mode": "spm",
"license": "CC-BY-3.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> node = self.Node(data)
if(self.tail is not None):
self.tail.next = node
self.tail = node
if (self.head is None):
self.head = node
def remove(self):
data = self.head.data
self.head = self.head.next
if (self.head is None):
... | code_fim | hard | {
"lang": "python",
"repo": "alexander-pang/interviewPrep",
"path": "/queue.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: alexander-pang/interviewPrep path: /queue.py
class Queue:
def __init__(self):
self.head = None
self.tail = None
class Node:
def __init__(self, data):
self.data = data
self.next = None
def isEmpty(self):
return self.head is N... | code_fim | hard | {
"lang": "python",
"repo": "alexander-pang/interviewPrep",
"path": "/queue.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>Thread import MainThread
from .nexonServer import NexonServer
from .tmLogging import TMLoggingThread
from .worldCheckboxStatus import WorldCheckBoxThread
from .setStartup import setStartupThread<|fim_prefix|># repo: parthspatel/TMRemote path: /python_app/backend/__init__.py
from .auth import Auth
from .b... | code_fim | medium | {
"lang": "python",
"repo": "parthspatel/TMRemote",
"path": "/python_app/backend/__init__.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>ead
from .worldCheckboxStatus import WorldCheckBoxThread
from .setStartup import setStartupThread<|fim_prefix|># repo: parthspatel/TMRemote path: /python_app/backend/__init__.py
from .auth import Auth
from .banDetection import BanDetectionThread
from .botLogging import BotLoggingThread
from .clientLaunch... | code_fim | medium | {
"lang": "python",
"repo": "parthspatel/TMRemote",
"path": "/python_app/backend/__init__.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: parthspatel/TMRemote path: /python_app/backend/__init__.py
from .auth import Auth
from .banDetection import BanDetectionThread
from .botLogging import BotLo<|fim_suffix|>Thread import MainThread
from .nexonServer import NexonServer
from .tmLogging import TMLoggingThread
from .worldCheckboxStatus ... | code_fim | medium | {
"lang": "python",
"repo": "parthspatel/TMRemote",
"path": "/python_app/backend/__init__.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>#%%
# Plot percent cytes with expression by bias*chrom*intronless
g = sns.FacetGrid(
df,
row="bias",
row_order=["cyte", "gonia", "NS"],
col="FB_chrom",
col_order=["X", "2L", "2R", "3L", "3R"],
sharex=True,
sharey=True,
margin_titles=True,
)
g.map(sns.boxplot, "intronless", ... | code_fim | hard | {
"lang": "python",
"repo": "jfear/larval_gonad",
"path": "/docs/x_escapers_and_intronless_genes.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>#%%
# Main effects model
model = smf.logit("intronless ~ cyte_bias + X", data=df.replace({True: 1, False: 0}))
results = model.fit()
plot_statsmodels_results(
"../output/docs/x_escapers_and_intronless_genes_main_effects.png", str(results.summary2())
)
display(results.summary2())
np.exp(results.params... | code_fim | hard | {
"lang": "python",
"repo": "jfear/larval_gonad",
"path": "/docs/x_escapers_and_intronless_genes.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Oran-G/crispr1011 path: /dataloaders.py
print(df.head())
average_value = list()
thisdata = list()
for line in df.to_dict("records"):
if line['cleavage_freq'] != '' and float(line['cleavage_freq']) >= 0:
thisdata.append([
... | code_fim | hard | {
"lang": "python",
"repo": "Oran-G/crispr1011",
"path": "/dataloaders.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> running_loss += loss.item()
if full_output == None:
full_output = outputs
else:
full_output = torch.cat((full_output, outputs), 0)
if full_labels == None:
full_labels = labels
... | code_fim | hard | {
"lang": "python",
"repo": "Oran-G/crispr1011",
"path": "/dataloaders.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> data.append(thisdata1)
print('time to load data: ', time.monotonic() - ftime, 'seconds')
return [data, dl]
def fullDataLoader(file="augmentcrisprsql.csv", batch=64, mode="target", target='rank'):
ftime = time.monotonic()
with open(file) as f:
d = list(csv.... | code_fim | hard | {
"lang": "python",
"repo": "Oran-G/crispr1011",
"path": "/dataloaders.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: sourjp/gof-design-patterns path: /adapter/adapter-jp.py
"""I referred below sample.
https://ja.wikipedia.org/wiki/Adapter_%E3%83%91%E3%82%BF%E3%83%BC%E3%83%B3#:~:text=Adapter%20%E3%83%91%E3%82%BF%E3%83%BC%E3%83%B3%EF%BC%88%E3%82%A2%E3%83%80%E3%83%97%E3%82%BF%E3%83%BC%E3%83%BB%E3%83%91%E3%82%BF%E... | code_fim | hard | {
"lang": "python",
"repo": "sourjp/gof-design-patterns",
"path": "/adapter/adapter-jp.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>
if __name__ == '__main__':
product = Product(cost=1000)
print(f'product cost {product.get_yen()} yen')
adapted_product = ProductAdapter(product)
print(f'product cost {adapted_product.get_doll():.1f} doll')<|fim_prefix|># repo: sourjp/gof-design-patterns path: /adapter/adapter-jp.py
"""I... | code_fim | hard | {
"lang": "python",
"repo": "sourjp/gof-design-patterns",
"path": "/adapter/adapter-jp.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> elif isinstance(li, list):
derivatives.update(resolve_data(li, derivatives_prefix))
else:
pass
else:
pass
else:
for li in raw_data:
if isinstance(li, ... | code_fim | hard | {
"lang": "python",
"repo": "ztttttttt/work_file_1",
"path": "/common.utility/mlx_utility/resolve_data.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ztttttttt/work_file_1 path: /common.utility/mlx_utility/resolve_data.py
def resolve_data(raw_data, derivatives_prefix):
derivatives = {}
if isinstance(raw_data, dict):
for k, v in raw_data.items():
if isinstance(v, dict):
derivatives.update(resolve_data... | code_fim | hard | {
"lang": "python",
"repo": "ztttttttt/work_file_1",
"path": "/common.utility/mlx_utility/resolve_data.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: yalaIrfan/python_setup path: /src/CommonUtil/SuperMethodsUtil.py
from google.cloud import vision
from google.cloud.vision import types
from google.oauth2 import service_account
import os
# import re
import io
import pdf2image
import tempfile
import datetime
<|fim_suffix|> return {'x1': bo... | code_fim | hard | {
"lang": "python",
"repo": "yalaIrfan/python_setup",
"path": "/src/CommonUtil/SuperMethodsUtil.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> "Creating temp directory.."
print("Creating temp directory.. with src and prefix .. ", prifx, src)
# temp_dir = tempfile.mkdtemp(("-"+str(datetime.datetime.now()).replace(":", "-")), "PMR_Claims", self.cwd+os.sep
# + "GENERATED"+os.sep+"CLAIMS")
temp_dir = ... | code_fim | medium | {
"lang": "python",
"repo": "yalaIrfan/python_setup",
"path": "/src/CommonUtil/SuperMethodsUtil.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>def createSubDir(self, src, subDirNameList):
print("Creating a subdirectory..")
for subfolder_name in subDirNameList:
os.makedirs(os.path.join(src, subfolder_name))
def getFilesindir(self, dire):
print('Fetching the file in the directory')
print(dire)
return os.listdir(dire)... | code_fim | hard | {
"lang": "python",
"repo": "yalaIrfan/python_setup",
"path": "/src/CommonUtil/SuperMethodsUtil.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>= img[100, 100, 1] # 63
r = img[100, 100, 2] # 68
r = img[100, 100, 2] = 99 # 设置red通道
# 获取和设置
piexl = img.item(100, 100, 2)
img.itemset((100, 100, 2), 99)<|fim_prefix|># repo: caok168/pythondemo path: /opencv_demos/demo2.py
import cv2
img = cv2.imread('imgs/1.png')
pixel = img[100, 100]
img[100, 100] ... | code_fim | medium | {
"lang": "python",
"repo": "caok168/pythondemo",
"path": "/opencv_demos/demo2.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: caok168/pythondemo path: /opencv_demos/demo2.py
import cv2
img = cv2.imread('imgs/1.png')
pixel = img[100, 100]
img[100, 100] <|fim_suffix|>= img[100, 100, 1] # 63
r = img[100, 100, 2] # 68
r = img[100, 100, 2] = 99 # 设置red通道
# 获取和设置
piexl = img.item(100, 100, 2)
img.itemset((100, 100, 2), 99)... | code_fim | medium | {
"lang": "python",
"repo": "caok168/pythondemo",
"path": "/opencv_demos/demo2.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Lord-Gusarov/holbertonschool-higher_level_programming path: /0x0F-python-object_relational_mapping/8-model_state_fetch_first.py
#!/usr/bin/python3
"""Prints the first State object from the database specified
"""
from sys import argv
import sqlalchemy
from sqlalchemy import create_engine, orm
from... | code_fim | hard | {
"lang": "python",
"repo": "Lord-Gusarov/holbertonschool-higher_level_programming",
"path": "/0x0F-python-object_relational_mapping/8-model_state_fetch_first.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> first = session.query(State).order_by(State.id).first()
out = 'Nothing' if first is None else '{}: {}'.format(first.id, first.name)
print(out)
session.close()<|fim_prefix|># repo: Lord-Gusarov/holbertonschool-higher_level_programming path: /0x0F-python-object_relational_mapping/8-model_s... | code_fim | hard | {
"lang": "python",
"repo": "Lord-Gusarov/holbertonschool-higher_level_programming",
"path": "/0x0F-python-object_relational_mapping/8-model_state_fetch_first.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: zihuaweng/leetcode-solutions path: /leetcode_python/math_leetcode.py
#!/usr/bin/env python3
# coding: utf-8
# Time complexity: O()
# Space complexity: O()
import math
# 最大公约数 Greatest common divisor
def get_gcd(a, b):
if b == 0:
return a
print(a, b)
return get_gcd(b, a % ... | code_fim | hard | {
"lang": "python",
"repo": "zihuaweng/leetcode-solutions",
"path": "/leetcode_python/math_leetcode.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> def myPow(self, x: float, n: int) -> float:
if n == 0:
return 1
if n < 0:
n = -n
x = 1 / x
if n & 1 == 0:
return self.myPow(x * x, n >> 1)
else:
return x * self.myPow(x * x, n >> 1)
# sqrt
class Solution:
... | code_fim | hard | {
"lang": "python",
"repo": "zihuaweng/leetcode-solutions",
"path": "/leetcode_python/math_leetcode.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|># power
class Solution:
def myPow(self, x: float, n: int) -> float:
if n == 0:
return 1
if n < 0:
n = -n
x = 1 / x
if n & 1 == 0:
return self.myPow(x * x, n >> 1)
else:
return x * self.myPow(x * x, n >> 1)
# s... | code_fim | hard | {
"lang": "python",
"repo": "zihuaweng/leetcode-solutions",
"path": "/leetcode_python/math_leetcode.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>data = LaserData()
#server = TCP(SERVER, PORT)
server = TCP()
server.start_server()
for i in range(100):
data = server.recv_server()
print data<|fim_prefix|># repo: maluethi/laser_tcp path: /test_server.py
__author__ = 'matthias'
from tcp import *
from data import *
<|fim_middle|>#SERVER = "13... | code_fim | easy | {
"lang": "python",
"repo": "maluethi/laser_tcp",
"path": "/test_server.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: maluethi/laser_tcp path: /test_server.py
__author__ = 'matthias'
from tcp import *
from data import *
<|fim_suffix|>data = LaserData()
#server = TCP(SERVER, PORT)
server = TCP()
server.start_server()
for i in range(100):
data = server.recv_server()
print data<|fim_middle|>#SERVER = "13... | code_fim | easy | {
"lang": "python",
"repo": "maluethi/laser_tcp",
"path": "/test_server.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: AhmedSakrr/Freqtrade_strategies-2 path: /Maro4h_bb_Adx.py
# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame
# --------------------------------
import datetime
import talib.... | code_fim | hard | {
"lang": "python",
"repo": "AhmedSakrr/Freqtrade_strategies-2",
"path": "/Maro4h_bb_Adx.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> """
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['ema'],dataframe['ema2']))
... | code_fim | hard | {
"lang": "python",
"repo": "AhmedSakrr/Freqtrade_strategies-2",
"path": "/Maro4h_bb_Adx.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|># data = minerl.data.make("MineRLNavigateDense-v0", data_dir="../dataset/navigate")
#
# # Iterate through a single epoch gathering sequences of at most 32 steps
# for current_state, action, reward, next_state, done in data.sarsd_iter(num_epochs=1, max_sequence_len=32):
# # Print the POV @ the first st... | code_fim | medium | {
"lang": "python",
"repo": "dsiegler2000/MineRL",
"path": "/src/ddqn/data_loader.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: dsiegler2000/MineRL path: /src/ddqn/data_loader.py
import numpy as np
import skimage
def preprocess_img(img, size):
img = np.rollaxis(img, 0, 3) # It becomes (640, 480, 3)
img = skimage.transform.resize(img, size)
img = skimage.color.rgb2gray(img)
<|fim_suffix|># data = minerl.dat... | code_fim | medium | {
"lang": "python",
"repo": "dsiegler2000/MineRL",
"path": "/src/ddqn/data_loader.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> for i in rows:
for j in cols:
rowVals = rows[i]
colVals = cols[j]
total = 0
p1, p2 = 0, 0
while p1 < len(rowVals) and p2 < len(colVals):
if rowVals[p1][1] ==... | code_fim | hard | {
"lang": "python",
"repo": "JeremyTsaii/LeetCode",
"path": "/sparse-matrix-multiplication/sparse-matrix-multiplication.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> for i in range(len(B)):
for j in range(len(B[0])):
if B[i][j]:
cols[j].append((B[i][j], i))
for i in rows:
for j in cols:
rowVals = rows[i]
colVals = cols[j]
total ... | code_fim | hard | {
"lang": "python",
"repo": "JeremyTsaii/LeetCode",
"path": "/sparse-matrix-multiplication/sparse-matrix-multiplication.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: JeremyTsaii/LeetCode path: /sparse-matrix-multiplication/sparse-matrix-multiplication.py
from collections import defaultdict
class Solution:
def multiply(self, A: List[List[int]], B: List[List[int]]) -> List[List[int]]:
<|fim_suffix|> for i in range(len(B)):
for j in range(... | code_fim | hard | {
"lang": "python",
"repo": "JeremyTsaii/LeetCode",
"path": "/sparse-matrix-multiplication/sparse-matrix-multiplication.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> def testPricingMultipleItemsWithMultipleDiscounts(self):
scanner = Scanner(self.multipleDiscountsItemList)
groceryList = ['Orange','Apple','Tomato','Orange','Tomato','Cucumber','Tomato','Tomato','Tomato',
'Apple','Cucumber','Apple','Tomato','Tomato','Apple','Toma... | code_fim | hard | {
"lang": "python",
"repo": "example-neitz/vogogo-shopping-cart",
"path": "/Checkout_unittests.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>
class CheckoutTests(unittest.TestCase):
def setUp(self):
pricingRulesWithSingleDiscount = { 'Apple': { 1 : '0.50' , 3 : '1.30' },
'Orange': {1 : '0.20'},
'Tomato': {1 : '1.25'},
'Cucumber': {1 : '0.10'}
... | code_fim | hard | {
"lang": "python",
"repo": "example-neitz/vogogo-shopping-cart",
"path": "/Checkout_unittests.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: example-neitz/vogogo-shopping-cart path: /Checkout_unittests.py
""" Unit test for the Supermarket checkout exercise """
import unittest
from decimal import *
from ShoppingCart import *
# Unit tests -----
class ScannerTests(unittest.TestCase):
def setUp(self):
pricingRulesWithSing... | code_fim | hard | {
"lang": "python",
"repo": "example-neitz/vogogo-shopping-cart",
"path": "/Checkout_unittests.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> des=request.args.get('description')
json_data=searchKnowledge.getTotalData_forKnowledgeSearch(des)
print(json_data)
return jsonify(json_data)
@app.route('/case_search_Test',methods=['get','post'])
def case_search_Test():
return render_template(
'case_search_Test.html',
... | code_fim | hard | {
"lang": "python",
"repo": "realcopycat/Athena_App",
"path": "/athena_App/athena_App/views.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> caseDes=request.args.get('caseDes')
#initialize graph search object
case_graph_result=caseQuery(caseDes)
pre_json_data=case_graph_result.getData()
print(pre_json_data)
return jsonify(pre_json_data)
@app.route('/knife',methods=['get','post'])
def knife():
return render_templa... | code_fim | hard | {
"lang": "python",
"repo": "realcopycat/Athena_App",
"path": "/athena_App/athena_App/views.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: realcopycat/Athena_App path: /athena_App/athena_App/views.py
"""
Routes and views for the flask application.
"""
from datetime import datetime
from flask import render_template, redirect, url_for, request, jsonify
from athena_App import app
from athena_App.formClass import QuestionForm
import t... | code_fim | hard | {
"lang": "python",
"repo": "realcopycat/Athena_App",
"path": "/athena_App/athena_App/views.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: tomhuntcouk/mayaSettings_2016.5 path: /python/th_utils/th_VertexColorToUVMoveNode.py
import pymel.all as pm
from collections import Counter
# example
# v.Create( sel[0], pm.datatypes.Color.red, sel[1], 'leftEye', 0.2 )
# select mesh 1st then the control
def Create( obj, targetColor, control, a... | code_fim | medium | {
"lang": "python",
"repo": "tomhuntcouk/mayaSettings_2016.5",
"path": "/python/th_utils/th_VertexColorToUVMoveNode.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> else :
pm.warning('The target must be a mesh')
# use this to connect the PolyMoveUV to the joint attribute you want FF (shader) to read
# example : ConnectToAttr( sel[0], sel[1], 'translateX' ) - select mesh 1st then joint
def ConnectToAttr( src, trgt, attr ) :
moveUVs = src.getShape().history(ty... | code_fim | hard | {
"lang": "python",
"repo": "tomhuntcouk/mayaSettings_2016.5",
"path": "/python/th_utils/th_VertexColorToUVMoveNode.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> if( len(moveUVs) > len(attr) ) :
pm.warning( 'There are more polyMoveUV nodes that attrs to connect to %s:%s' % ( len(moveUVs), len(attr) ) )
else :
for i, moveUV in enumerate(moveUVs) :
moveUV.translateV >> attr[i]<|fim_prefix|># repo: tomhuntcouk/mayaSettings_2016.5 path: /python/th_utils/th_... | code_fim | hard | {
"lang": "python",
"repo": "tomhuntcouk/mayaSettings_2016.5",
"path": "/python/th_utils/th_VertexColorToUVMoveNode.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>rt(kwic1.kwic(mystr) == [mystr])
#assert(len(kwic3.kwic(mystr))==2)
assert len(kwic.kwic(mystr)) == 3<|fim_prefix|># repo: DrakeSeifert/Software-Engineering-I path: /assignment1/finalProduct/testkwic3.py
import kwic
mystr = "hello world\nmy test\napple<|fim_middle|>s oranges"
#asseirt(kwic0.kwic(mystr)... | code_fim | easy | {
"lang": "python",
"repo": "DrakeSeifert/Software-Engineering-I",
"path": "/assignment1/finalProduct/testkwic3.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>.kwic(mystr))==2)
assert len(kwic.kwic(mystr)) == 3<|fim_prefix|># repo: DrakeSeifert/Software-Engineering-I path: /assignment1/finalProduct/testkwic3.py
import kwic
mystr = "hello world\nmy test\napple<|fim_middle|>s oranges"
#asseirt(kwic0.kwic(mystr) == [])
#assert(kwic1.kwic(mystr) == [mystr])
#ass... | code_fim | medium | {
"lang": "python",
"repo": "DrakeSeifert/Software-Engineering-I",
"path": "/assignment1/finalProduct/testkwic3.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: DrakeSeifert/Software-Engineering-I path: /assignment1/finalProduct/testkwic3.py
import kwic
mystr = "hello world\nmy test\napples oranges"
#asseirt(kwic0.kwic(mystr) == [])
#asse<|fim_suffix|>.kwic(mystr))==2)
assert len(kwic.kwic(mystr)) == 3<|fim_middle|>rt(kwic1.kwic(mystr) == [mystr])
#ass... | code_fim | easy | {
"lang": "python",
"repo": "DrakeSeifert/Software-Engineering-I",
"path": "/assignment1/finalProduct/testkwic3.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ajaniv/pytorch_explore path: /explore/neural_network_pytorch.py
"""
Simple neural network using pytorch
"""
import torch
import torch.nn as nn
# Prepare the data
# X represents the amount of hours studied and how much time students spent sleeping
X = torch.tensor(([2, 9], [1, 5], [3, 6]), dtype... | code_fim | hard | {
"lang": "python",
"repo": "ajaniv/pytorch_explore",
"path": "/explore/neural_network_pytorch.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> def save_weights(self, model):
# we will use the PyTorch internal storage functions
torch.save(model, "NN")
# you can reload model with all the weights and so forth with:
# torch.load("NN")
def predict(self):
"""predict"""
# @TODO: should be... | code_fim | hard | {
"lang": "python",
"repo": "ajaniv/pytorch_explore",
"path": "/explore/neural_network_pytorch.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>:
for j in range(i-1):
if not (sum_array[i]-sum_array[j])%k:
return True
return False<|fim_prefix|># repo: xwang322/Coding-Interview path: /python/P523.py
class Solution(object):
def checkSubarraySum(self, nums, k):
if not nums or ... | code_fim | hard | {
"lang": "python",
"repo": "xwang322/Coding-Interview",
"path": "/python/P523.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: xwang322/Coding-Interview path: /python/P523.py
class Solution(object):
def checkSubarraySum(self, nums, k):
if not nums or len(nums) == 1:
return False
sum_array = [0]*(len(nums)+1)
for i, num in enumerate(nums):
sum_array[i+1] = sum_arra... | code_fim | hard | {
"lang": "python",
"repo": "xwang322/Coding-Interview",
"path": "/python/P523.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> if sum_array[-1] == 0:
return True
else:
return False
for i in range(1, len(sum_array)):
for j in range(i-1):
if not (sum_array[i]-sum_array[j])%k:
return True
return False<|fim_prefix|># ... | code_fim | hard | {
"lang": "python",
"repo": "xwang322/Coding-Interview",
"path": "/python/P523.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: hai-zhu/bebop2_toolbox path: /bebop2_nonlinear_mpc/scripts/bebop_nmpc_node.py
#!/usr/bin/env python
import numpy as np
import rospy
import tf
from geometry_msgs.msg import PoseStamped, Twist, TwistStamped, Point
from nav_msgs.msg import Odometry
from visualization_msgs.msg import Marker
from beb... | code_fim | hard | {
"lang": "python",
"repo": "hai-zhu/bebop2_toolbox",
"path": "/bebop2_nonlinear_mpc/scripts/bebop_nmpc_node.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> # obtain solution
traj_opt = nlp_sol['x'].reshape((self.mpc_nu_ + self.mpc_nx_ + self.mpc_ns_, self.mpc_N_))
self.mpc_u_plan_ = np.array(traj_opt[:self.mpc_nu_, :])
self.mpc_x_plan_ = np.array(traj_opt[self.mpc_nu_:self.mpc_nu_+self.mpc_nx_, :])
self.mpc_s_plan_ = n... | code_fim | hard | {
"lang": "python",
"repo": "hai-zhu/bebop2_toolbox",
"path": "/bebop2_nonlinear_mpc/scripts/bebop_nmpc_node.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> for i, x in enumerate(p):
if type(x) is tuple:
new_tuple = tuple([_x[:, offset, :] for _x in x])
p[i] = new_tuple
else:
p[i] = x[offset, :]
# update action history
if self.relation_only... | code_fim | hard | {
"lang": "python",
"repo": "dertilo/MultiHopKG",
"path": "/src/rl/graph_search/graph_walk_agent.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: dertilo/MultiHopKG path: /src/rl/graph_search/graph_walk_agent.py
ace(
action_spaces: List[ActionSpace], inv_offset, kg: KnowledgeGraph
):
db_r_space, db_e_space, db_action_mask = [], [], []
forks = []
for acsp in action_spaces:
forks += acsp.forks
db_r_space.appen... | code_fim | hard | {
"lang": "python",
"repo": "dertilo/MultiHopKG",
"path": "/src/rl/graph_search/graph_walk_agent.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> A list of action space tensor representations grouped in n buckets, s.t.
r_space_b0.size(0) + r_space_b1.size(0) + ... + r_space_bn.size(0) = e.size(0)
:return db_references:
[l_batch_refs0, l_batch_refs1, ..., l_batch_refsn]
l_batch_refsi stores th... | code_fim | hard | {
"lang": "python",
"repo": "dertilo/MultiHopKG",
"path": "/src/rl/graph_search/graph_walk_agent.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: BerilBBJ/scraperwiki-scraper-vault path: /Users/R/russkel/wa_department_of_health_health_act_offenders.py
import scraperwiki, lxml.html, urllib2, re
from datetime import datetime
#html = scraperwiki.scrape("http://www.public.health.wa.gov.au/2/1035/2/publication_of_names_of_offenders_list.pm")
d... | code_fim | hard | {
"lang": "python",
"repo": "BerilBBJ/scraperwiki-scraper-vault",
"path": "/Users/R/russkel/wa_department_of_health_health_act_offenders.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>#select the table that contains the offenders, ignoring the first one that contains the header row
for tr in root.xpath("//div[@id='verdiSection10']/div/div/table/tbody/tr")[1:]:
data = {
'conviction_date': datetime.strptime(
re.match("(\d+/\d+/\d+)", tr[0].text_content().strip()).... | code_fim | hard | {
"lang": "python",
"repo": "BerilBBJ/scraperwiki-scraper-vault",
"path": "/Users/R/russkel/wa_department_of_health_health_act_offenders.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: enplus/parttime path: /singleton_abc/main.py
from common.utils import create_brokers
from Bot import DataGatherBot, ArbitrageBot
import api_config as config
<|fim_suffix|># brokers = create_brokers('BACKTEST', config.CURRENCIES, config.EXCHANGES)
# bot = ArbitrageBot(config, brokers) # this auto... | code_fim | medium | {
"lang": "python",
"repo": "enplus/parttime",
"path": "/singleton_abc/main.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>brokers = create_brokers('LIVE', config.CURRENCIES, config.EXCHANGES)
gp = brokers[2]
# gp.update_all_balances()
# gp.xchg.get_all_balances()
# gatherbot = DataGatherBot(config, brokers)
# maxdepth 체크할 호가 개수(-1)
# gatherbot.start(sleep=1, duration=60 * 60 * 4, maxdepth=4) # 5 hours of data, one minute in... | code_fim | hard | {
"lang": "python",
"repo": "enplus/parttime",
"path": "/singleton_abc/main.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> def reset(self):
for player, data in self.player_data.items():
data[1] = None
data[2] = None
def round_won(self):
sum = self.sum()
for player, data in self.player_data.items():
if data[2] == sum:
return player
re... | code_fim | hard | {
"lang": "python",
"repo": "thurbridi/INE5430",
"path": "/trabalhos/trabalho-01/palitos.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> for player, data in self.player_data.items():
data[1] = None
data[2] = None
def round_won(self):
sum = self.sum()
for player, data in self.player_data.items():
if data[2] == sum:
return player
return None
def wo... | code_fim | hard | {
"lang": "python",
"repo": "thurbridi/INE5430",
"path": "/trabalhos/trabalho-01/palitos.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: thurbridi/INE5430 path: /trabalhos/trabalho-01/palitos.py
from random import randint
class Game(object):
def __init__(self, players):
if len(players) < 2:
raise ValueError('Number of player must be at least 2')
self.play_order = players
self.player_data... | code_fim | hard | {
"lang": "python",
"repo": "thurbridi/INE5430",
"path": "/trabalhos/trabalho-01/palitos.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> return render_to_response(template_name, {
"form": form,
}, context_instance=RequestContext(request))<|fim_prefix|># repo: pydanny/whydjango path: /src/whydjango/casestudies/views.py
from django.core.urlresolvers import reverse
from django.http import HttpResponse, HttpR... | code_fim | hard | {
"lang": "python",
"repo": "pydanny/whydjango",
"path": "/src/whydjango/casestudies/views.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> if form.is_valid():
form.save()
return HttpResponseRedirect(reverse("submit_message"))
return render_to_response(template_name, {
"form": form,
}, context_instance=RequestContext(request))<|fim_prefix|># repo: pydanny/whydjango path: /src/... | code_fim | medium | {
"lang": "python",
"repo": "pydanny/whydjango",
"path": "/src/whydjango/casestudies/views.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: pydanny/whydjango path: /src/whydjango/casestudies/views.py
from django.core.urlresolvers import reverse
from django.http import HttpResponse, HttpResponseRedirect, HttpResponseNotFound
from django.shortcuts import render_to_response
from django.template import RequestContext
from whydjango.c... | code_fim | medium | {
"lang": "python",
"repo": "pydanny/whydjango",
"path": "/src/whydjango/casestudies/views.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> if k not in name:
print("ERROR:Key does not exists Enter a valid key!!")
else:
m = name[k]
if m[1] != 0:
if time.time() < m[1]:
print ( k + "-" + str(m[0]))
else:
print("ERROR: " + k + " Time expired")
... | code_fim | hard | {
"lang": "python",
"repo": "sivasa02/freshworks_assignment",
"path": "/testing/testing.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: sivasa02/freshworks_assignment path: /testing/testing.py
import time
import json
from threading import Thread
try:
with open('file.json') as f:
name = json.load(f)
except:
f = open("file.json", "w+")
name = {}
def create(k, v, t='0'):
if k in name:
... | code_fim | hard | {
"lang": "python",
"repo": "sivasa02/freshworks_assignment",
"path": "/testing/testing.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: xie-huan/Machine-Learning path: /Machine_Learning/LinearReg/LinearRegression.py
import numpy as np
from .metrics import r2_score
class LinearRegression:
def __init__(self):
self.coef_ = None # 系数
self.interception_ = None # 截距
self._theta = None
def fit_nor... | code_fim | hard | {
"lang": "python",
"repo": "xie-huan/Machine-Learning",
"path": "/Machine_Learning/LinearReg/LinearRegression.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>
assert X_train.shape[0] == y_train.shape[0], ""
def dJ_sgd(theta, X_b_i, y_i):
return X_b_i.T.dot(X_b_i.dot(theta) - y_i) * 2
# Stochastic gradient descent
def sgd(X_b, y, initial_theta, n_iter, t0=5, t1=50):
def learning_rate(t):
... | code_fim | hard | {
"lang": "python",
"repo": "xie-huan/Machine-Learning",
"path": "/Machine_Learning/LinearReg/LinearRegression.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: EnjambreBit/presentes-backend path: /presentes/migrations/0016_auto_20190523_1107.py
# Generated by Django 2.2.1 on 2019-05-23 14:07
from django.db import migrations, models
class Migration(migrations.Migration):
<|fim_suffix|> operations = [
migrations.AddField(
model_... | code_fim | medium | {
"lang": "python",
"repo": "EnjambreBit/presentes-backend",
"path": "/presentes/migrations/0016_auto_20190523_1107.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> dependencies = [
('presentes', '0015_caso_lugar_del_hecho'),
]
operations = [
migrations.AddField(
model_name='organizacion',
name='descripcion',
field=models.TextField(default=''),
),
migrations.AddField(
model_n... | code_fim | medium | {
"lang": "python",
"repo": "EnjambreBit/presentes-backend",
"path": "/presentes/migrations/0016_auto_20190523_1107.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: sliem/ScientificColorschemez path: /latest.py
from ScientificColorschemez import Colorschemez
import matplotlib.pyplot as plt
cs = Colorschemez.latest()
<|fim_suffix|>fig, ax = plt.subplots()
cs.example_plot(ax)
fig.savefig('latest.png', dpi=200, bbox_inches='tight')<|fim_middle|>for name, hexc... | code_fim | medium | {
"lang": "python",
"repo": "sliem/ScientificColorschemez",
"path": "/latest.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>fig, ax = plt.subplots()
cs.example_plot(ax)
fig.savefig('latest.png', dpi=200, bbox_inches='tight')<|fim_prefix|># repo: sliem/ScientificColorschemez path: /latest.py
from ScientificColorschemez import Colorschemez
import matplotlib.pyplot as plt
cs = Colorschemez.latest()
<|fim_middle|>for name, hexc... | code_fim | medium | {
"lang": "python",
"repo": "sliem/ScientificColorschemez",
"path": "/latest.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
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