text stringlengths 232 16.3k | domain stringclasses 1
value | difficulty stringclasses 3
values | meta dict |
|---|---|---|---|
<|fim_suffix|> self.fit_result = minimize(self.residuals_wrapper, self.parameters, args = (x, data, weights), kws = kwargs)
logging.info('Fit Result')
logging.info('==========')
return self.fit_result
def get_opt_parameters(self):
if self.fit_result is None:
rais... | code_fim | hard | {
"lang": "python",
"repo": "jamesbate/phd_code",
"path": "/code/lib/FitTemplate.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: lucascantos/weather-alerts-crud path: /src/schemas/schemas.py
user_schema = {
'id': {
'type': 'string',
'required': True,
'coerce': (str, lambda x: x.lower())
},
'latitude':{
'type': 'float',
'required': True,
'min': -60.0,
'm... | code_fim | hard | {
"lang": "python",
"repo": "lucascantos/weather-alerts-crud",
"path": "/src/schemas/schemas.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|># Schema of AWS event
event_schema = {
'pathParameters':{
'type': 'dict',
'default': {},
'schema':{
'uid':{
'type': 'string',
'required': True,
},
}
}
}<|fim_prefix|># repo: lucascantos/weather-alerts-crud pat... | code_fim | hard | {
"lang": "python",
"repo": "lucascantos/weather-alerts-crud",
"path": "/src/schemas/schemas.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> updater = training.StandardUpdater(self.train_iter, opt, device=gpu)
self.trainer = training.Trainer(updater, (n_epoch, 'epoch'), out=out_dir)
self.trainer.extend(extensions.Evaluator(self.test_iter, self.model, device=gpu))
self.trainer.extend(extensions.dump_graph('main/l... | code_fim | hard | {
"lang": "python",
"repo": "trtd56/NlpUtil",
"path": "/old/trainer.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: trtd56/NlpUtil path: /old/trainer.py
# -*- coding: utf-8 -*-
import chainer.links as L
import chainer.functions as F
from chainer import optimizer, optimizers, training, iterators
from chainer.training import extensions
from chainer.datasets import tuple_dataset
class SoftMaxTrainer():
def... | code_fim | hard | {
"lang": "python",
"repo": "trtd56/NlpUtil",
"path": "/old/trainer.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>err_u = df['cModelMagErr_u'].values
err_g = df['cModelMagErr_g'].values
err_r = df['cModelMagErr_r'].values
err_i = df['cModelMagErr_i'].values
err_z = df['cModelMagErr_z'].values
dered_u = mod_u - ext_u
dered_g = mod_g - ext_g
dered_r = mod_r - ext_r
dered_i = mod_i - ext_i
dered_z = mod_z - ext_z
b =... | code_fim | hard | {
"lang": "python",
"repo": "patogallardo/iskay",
"path": "/misc/kcorrection/lups2maggies.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: patogallardo/iskay path: /misc/kcorrection/lups2maggies.py
''' Converts luptitudes to maggies and stores in folder output
Written by P. Gallardo
'''
import numpy as np
import pandas as pd
import sys
assert len(sys.argv) == 2 # usage: lups2maggies.py /path/to/cat.csv
fname = sys.argv[1]
<|f... | code_fim | hard | {
"lang": "python",
"repo": "patogallardo/iskay",
"path": "/misc/kcorrection/lups2maggies.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: fleapx/python-learning path: /base/get-starting/call_func.py
# -*- coding: utf-8 -*-
# author : rovo98
# date: 2018.3.19
<|fim_suffix|>print(hex(n1))
print(hex(n2))
print(abs(-119999))<|fim_middle|>
# this is a demo for test calling functions.
n1 = 255
n2 = 1000
| code_fim | medium | {
"lang": "python",
"repo": "fleapx/python-learning",
"path": "/base/get-starting/call_func.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>print(hex(n1))
print(hex(n2))
print(abs(-119999))<|fim_prefix|># repo: fleapx/python-learning path: /base/get-starting/call_func.py
# -*- coding: utf-8 -*-
# author : rovo98
# date: 2018.3.19
<|fim_middle|>
# this is a demo for test calling functions.
n1 = 255
n2 = 1000
| code_fim | medium | {
"lang": "python",
"repo": "fleapx/python-learning",
"path": "/base/get-starting/call_func.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def compute_f2(output, target):
true_and_pred = target * output
ttp_sum = torch.sum(true_and_pred, 1)
tpred_sum = torch.sum(output, 1)
ttrue_sum = torch.sum(target, 1)
tprecision = ttp_sum / tpred_sum
trecall = ttp_sum / ttrue_sum
f2 = ((1 + 4) * tprecision * trecall) / (4 *... | code_fim | hard | {
"lang": "python",
"repo": "chinmay5/deepLearningProject",
"path": "/planet/boilerplate.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>def train(train_loader, model, criterion, optimizer, epoch, is_multi_fc=False):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
predictions = AverageMeter()
# switch to train mode
model.train()
end = time.time()
for i, (input, target) in enu... | code_fim | hard | {
"lang": "python",
"repo": "chinmay5/deepLearningProject",
"path": "/planet/boilerplate.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: chinmay5/deepLearningProject path: /planet/boilerplate.py
#-*- coding: utf8 -*-
#credits to https://github.com/pytorch/examples/blob/master/imagenet/main.py
import shutil, time, logging
import torch
import torch.optim
import numpy as np
import visdom, copy
from datetime import datetime
from colle... | code_fim | hard | {
"lang": "python",
"repo": "chinmay5/deepLearningProject",
"path": "/planet/boilerplate.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ita93/qca-hex-analyzer path: /qca_hex_analyzer/__main__.py
age parsing. " \
"This subcommand is used to extract WMI control messages from the input. "
wmi_ctrl_description = \
"Extracts WMI control message hexdata from an input (--input-file). " \
"The extracted messages will be prin... | code_fim | hard | {
"lang": "python",
"repo": "ita93/qca-hex-analyzer",
"path": "/qca_hex_analyzer/__main__.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> global parsed_args
load_options()
try:
if parsed_args.input_file:
infp = open(parsed_args.input_file, "r")
else:
infp = sys.stdin
if parsed_args.output_file:
outfp = open(parsed_args.output_file, "w")
else:
outfp ... | code_fim | hard | {
"lang": "python",
"repo": "ita93/qca-hex-analyzer",
"path": "/qca_hex_analyzer/__main__.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ita93/qca-hex-analyzer path: /qca_hex_analyzer/__main__.py
r HTC control message parsing. " \
"This subcommand is used to extract HTC control messages from the input. "
htc_ctrl_description = \
"Extracts HTC control message hexdata from an input (--input-file). " \
"The extracted mes... | code_fim | hard | {
"lang": "python",
"repo": "ita93/qca-hex-analyzer",
"path": "/qca_hex_analyzer/__main__.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: B-Step62/pytorch-motiongan-open path: /core/datasets/matlab_to_bvh.py
### Script to convert matlab structure file (/motiongan/data/style-dataset/style_motion_database.mat')
import os
import sys
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
import argparse
import math
import numpy... | code_fim | hard | {
"lang": "python",
"repo": "B-Step62/pytorch-motiongan-open",
"path": "/core/datasets/matlab_to_bvh.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> # Convert data to list of string
frames = []
for i in range(joint_quarternions.shape[0]):
# Root pos
root_pos_i = root_pos[i]
frame = '{0:.05f} {1:.05f} {2:.05f} '.format(*root_pos_i.tolist())
for j in range(joint_quarternions.shape... | code_fim | hard | {
"lang": "python",
"repo": "B-Step62/pytorch-motiongan-open",
"path": "/core/datasets/matlab_to_bvh.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>def cleaning(sentences):
words = []
for s in sentences:
clean = re.sub(r'[^ a-z A-Z 0-9]', " ", s)
w = nltk.word_tokenize(clean)
# lemmatizing
words.append([lemmatizer.lemmatize(i.lower()) for i in w])
return words
def create_tokenizer(words,
... | code_fim | medium | {
"lang": "python",
"repo": "utk61198/Amar-Ujala-Analytics-",
"path": "/svoExtraction.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> tuple_to_lists=list(tuple_data)
return tuple_to_lists
def displaySubjectVerbObject(tuples_to_lists):
for item in tuples_to_lists:
print(item)
tuple_data=findTriplets(str)
list=creatingLists(tuple_data)
displaySubjectVerbObject(list)<|fim_prefix|># repo: utk61198/Amar-Ujala-Analytic... | code_fim | hard | {
"lang": "python",
"repo": "utk61198/Amar-Ujala-Analytics-",
"path": "/svoExtraction.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: utk61198/Amar-Ujala-Analytics- path: /svoExtraction.py
import nltk
import spacy
import textacy
from keras.layers import Embedding, Bidirectional, Dense, Dropout, BatchNormalization
from keras_preprocessing.sequence import pad_sequences
from keras_preprocessing.text import Tokenizer
from nltk impo... | code_fim | medium | {
"lang": "python",
"repo": "utk61198/Amar-Ujala-Analytics-",
"path": "/svoExtraction.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: zhushaoquan/recommend-sys path: /deep_learning/utils/__init__.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2019/8/15 <|fim_suffix|>*
from .logger import *
from .metric import *
from .input_fn import *<|fim_middle|>下午5:04
# @Author : Zessay
from .ffm import *
from .fm import *
... | code_fim | medium | {
"lang": "python",
"repo": "zhushaoquan/recommend-sys",
"path": "/deep_learning/utils/__init__.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> .utils import *
from .base_model import *
from .base_trainer import *
from .logger import *
from .metric import *
from .input_fn import *<|fim_prefix|># repo: zhushaoquan/recommend-sys path: /deep_learning/utils/__init__.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2019/8/15 <|fim_midd... | code_fim | medium | {
"lang": "python",
"repo": "zhushaoquan/recommend-sys",
"path": "/deep_learning/utils/__init__.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>*
from .logger import *
from .metric import *
from .input_fn import *<|fim_prefix|># repo: zhushaoquan/recommend-sys path: /deep_learning/utils/__init__.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2019/8/15 下午5:04
# @Author : Zessay
from .ffm import *
from .fm import *
from<|fim_midd... | code_fim | medium | {
"lang": "python",
"repo": "zhushaoquan/recommend-sys",
"path": "/deep_learning/utils/__init__.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> COLLECT = "x-stats-collect"
# Param Dict Prefix
class ParamDictPrefix:
PostKey = "x-" # Used in http POST params from HTML forms<|fim_prefix|># repo: RxJellyBot/Jelly-Bot path: /JellyBot/keys.py
# Cookies Keys
class Cookies:
USER_TOKEN = "utoken"
<|fim_middle|>
# Session Keys
class Se... | code_fim | hard | {
"lang": "python",
"repo": "RxJellyBot/Jelly-Bot",
"path": "/JellyBot/keys.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: RxJellyBot/Jelly-Bot path: /JellyBot/keys.py
# Cookies Keys
class Cookies:
USER_TOKEN = "utoken"
<|fim_suffix|> USER_ROOT_ID = "x-root-id"
class APIStatisticsCollection:
API_ACTION = "x-stats-api-action"
DICT_PARAMS = "x-stats-param-dict"
DICT_RESPONSE = "x-s... | code_fim | easy | {
"lang": "python",
"repo": "RxJellyBot/Jelly-Bot",
"path": "/JellyBot/keys.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: loganriggs/zero_shot_learning path: /plot3d.py
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
<|fim_suffix|> fig = plt.figure(figure)
ax = plt.axes(projection='3d')
colors = ["r", "b", "y", "c", "m"]
for i in range(numberOfClasses+1):
... | code_fim | medium | {
"lang": "python",
"repo": "loganriggs/zero_shot_learning",
"path": "/plot3d.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> fig = plt.figure(figure)
ax = plt.axes(projection='3d')
colors = ["r", "b", "y", "c", "m"]
for i in range(numberOfClasses+1):
classLocation = np.argwhere(labels == i+minClass)
ax.scatter3D(xValues[classLocation, 0], xValues[classLocation, 1], xValues[classLocation, 2]) #3D<... | code_fim | medium | {
"lang": "python",
"repo": "loganriggs/zero_shot_learning",
"path": "/plot3d.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> print('Number of pairs: {}'.format(len(self._pairs)))
if __name__ == '__main__':
pairs = lfwdata()<|fim_prefix|># repo: Tushn/triplet path: /triplet/lfwdata.py
import os
import config as cfg
import numpy as np
class lfwdata():
def __init__(self):
self._pairs = []
<|fim_middl... | code_fim | hard | {
"lang": "python",
"repo": "Tushn/triplet",
"path": "/triplet/lfwdata.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Tushn/triplet path: /triplet/lfwdata.py
import os
import config as cfg
import numpy as np
class lfwdata():
def __init__(self):
self._pairs = []
<|fim_suffix|> print('Number of pairs: {}'.format(len(self._pairs)))
if __name__ == '__main__':
pairs = lfwdata()<|fim_middl... | code_fim | hard | {
"lang": "python",
"repo": "Tushn/triplet",
"path": "/triplet/lfwdata.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: nicomateucci/tpSimulacion path: /colaSimpleMM1LIFO.py
# Simulador de sistema M/M/1.
#
# Variables de respuesta:
# - Demora promedio por cliente
# - Número promedio de clientes en cola
# - Utilización promedio de cliente
#
# Funciones:
# arribo()
# partida()
# nuevoEvento()
... | code_fim | hard | {
"lang": "python",
"repo": "nicomateucci/tpSimulacion",
"path": "/colaSimpleMM1LIFO.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> global reloj
global proximoEvento
global listaEventos
if listaEventos[0] <= listaEventos[1]:
reloj = listaEventos[0]
proximoEvento = "ARRIBO"
else:
reloj = listaEventos[1]
proximoEvento = "PARTIDA"
#Inicio del programa principal
#Tiempo de ... | code_fim | hard | {
"lang": "python",
"repo": "nicomateucci/tpSimulacion",
"path": "/colaSimpleMM1LIFO.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>model = Sequential()
model.add(Dense(5, input_dim=(len(X[0]))))
model.add(Dense(32, activation="relu"))
model.add(Dense(len(onehot_Y[0]), activation="softmax"))
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
model.fit(X, onehot_Y, validation_split=0.33, epochs=1000)... | code_fim | hard | {
"lang": "python",
"repo": "bjotho/Gynecological-cancer-multi-class-classification",
"path": "/task_3/task3_Keras.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|># encode class values as integers
encoder = LabelEncoder()
encoder.fit(Y)
encoded_Y = encoder.transform(Y)
# convert integers to dummy variables (i.e. one-hot encoded)
onehot_Y = np_utils.to_categorical(encoded_Y)
model = Sequential()
model.add(Dense(5, input_dim=(len(X[0]))))
model.add(Dense(32, activat... | code_fim | medium | {
"lang": "python",
"repo": "bjotho/Gynecological-cancer-multi-class-classification",
"path": "/task_3/task3_Keras.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: bjotho/Gynecological-cancer-multi-class-classification path: /task_3/task3_Keras.py
import pyreadstat
import matplotlib.pyplot as plt
import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from keras.utils import np_utils
from sklearn.preprocessing import LabelEncod... | code_fim | medium | {
"lang": "python",
"repo": "bjotho/Gynecological-cancer-multi-class-classification",
"path": "/task_3/task3_Keras.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: k1nk33/NukeBox2000 path: /tests/test_nukeboxDB.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
test_nukeboxQueue
----------------------------------
Tests for `nukebox2000` module.
"""
import sys
import unittest
from nukebox2000.MongoBox import NukeBoxDB
class TestNukeBoxDB(unittest.Tes... | code_fim | hard | {
"lang": "python",
"repo": "k1nk33/NukeBox2000",
"path": "/tests/test_nukeboxDB.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> '''
B{Test 02}
Tests User entry creation in the DB
- createUser first checks if an matching entry already exists,
updating the existing entry if it does
- either way it returns the entries object id
'''
nbdb = NukeBoxDB()
us... | code_fim | hard | {
"lang": "python",
"repo": "k1nk33/NukeBox2000",
"path": "/tests/test_nukeboxDB.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: neoformit/primerdesign path: /design/forms.py
"""Primer3 input form.
For details on input params see:
https://primer3.org/manual.html#globalTags
"""
from django import forms
from django.core.exceptions import ValidationError
from .fasta import Fasta
<|fim_suffix|>def validate_fasta(data):
... | code_fim | hard | {
"lang": "python",
"repo": "neoformit/primerdesign",
"path": "/design/forms.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> """Validate and return user input."""
data = self.cleaned_data
data['fasta'] = Fasta.from_string(data['fasta'])
validate_fasta(data)
return data
def validate_fasta(data):
"""Validate input sequence lengths."""
for sequence in data['fasta'].values():
... | code_fim | medium | {
"lang": "python",
"repo": "neoformit/primerdesign",
"path": "/design/forms.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>def validate_fasta(data):
"""Validate input sequence lengths."""
for sequence in data['fasta'].values():
print(f'Sequence length {len(sequence)} nt')
if len(sequence) < data['amplicon_min']:
raise ValidationError({'fasta':
f'Input sequence must be longer... | code_fim | medium | {
"lang": "python",
"repo": "neoformit/primerdesign",
"path": "/design/forms.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: mtatum7/Robomath_Project path: /pca/create_final.py
#! /usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import division
import os
from solid import *
from solid.utils import *
from shapes import *
import sys
# Assumes SolidPython is in site-packages or elsewhwere in sys.path
from sol... | code_fim | hard | {
"lang": "python",
"repo": "mtatum7/Robomath_Project",
"path": "/pca/create_final.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> f.closed
print("Success")
if __name__ == '__main__':
out_dir = sys.argv[1] if len(sys.argv) > 1 else os.curdir
file_out = os.path.join(out_dir, 'basic_geometry.scad')
shape_list = basic_geometry()
for i, shape in enumerate(shape_list):
export(shape, "output" + str(i))
... | code_fim | hard | {
"lang": "python",
"repo": "mtatum7/Robomath_Project",
"path": "/pca/create_final.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>@coroutine
def grep_python_coroutine():
g = grep('python')
yield from g
g = grep('python')
#next(g) #g.send(None)
g.send("php is better")
g.send("python is simplier")
g.close()<|fim_prefix|># repo: YevhenKhomenko/dive_into_python_coursera path: /week5/practice/coroutine.py
def coroutine(func):
def s... | code_fim | medium | {
"lang": "python",
"repo": "YevhenKhomenko/dive_into_python_coursera",
"path": "/week5/practice/coroutine.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: YevhenKhomenko/dive_into_python_coursera path: /week5/practice/coroutine.py
def coroutine(func):
def start_coroutine(*args, **kwargs):
<|fim_suffix|>@coroutine
def grep_python_coroutine():
g = grep('python')
yield from g
g = grep('python')
#next(g) #g.send(None)
g.send("php is better")
g.sen... | code_fim | hard | {
"lang": "python",
"repo": "YevhenKhomenko/dive_into_python_coursera",
"path": "/week5/practice/coroutine.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> g = grep('python')
yield from g
g = grep('python')
#next(g) #g.send(None)
g.send("php is better")
g.send("python is simplier")
g.close()<|fim_prefix|># repo: YevhenKhomenko/dive_into_python_coursera path: /week5/practice/coroutine.py
def coroutine(func):
def start_coroutine(*args, **kwargs):
<|fim_m... | code_fim | hard | {
"lang": "python",
"repo": "YevhenKhomenko/dive_into_python_coursera",
"path": "/week5/practice/coroutine.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: katreashish120/ml-pipeline path: /temperature/databricks/train_temperature.py
#!/usr/bin/env python
# coding: utf-8
# In[ ]:
import numpy as np
import pickle
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_absolute_error
from pyspark.sql.functions import s... | code_fim | hard | {
"lang": "python",
"repo": "katreashish120/ml-pipeline",
"path": "/temperature/databricks/train_temperature.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>
# In[ ]:
input_df = input_df.withColumn('Year_Month', concat(col('Year'), col('Month')))
cols = ['Year_Month','Day','Mean_Temperature']
input_df = input_df[cols]
if test:
display(input_df)
# In[ ]:
input_pivot_df = input_df.groupBy("Year_Month").pivot("Day").sum("Mean_Temperature")
# In[ ]:
... | code_fim | hard | {
"lang": "python",
"repo": "katreashish120/ml-pipeline",
"path": "/temperature/databricks/train_temperature.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>if test:
print(dbutils.widgets.get("input_path"))
print(dbutils.widgets.get("model_path"))
if input_path == 'Not found':
input_path = '/mnt/<mount-name>/<path>/temperature/data/*.csv'
if model_path == 'Not found':
model_path = '/dbfs/mnt/<mount-name>/<path>/temperature/model/temperature... | code_fim | hard | {
"lang": "python",
"repo": "katreashish120/ml-pipeline",
"path": "/temperature/databricks/train_temperature.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>sha256", b"hallo", b"salt", 2)
print(b)
print(c)<|fim_prefix|># repo: rupali-adhikari/pythonprojecthashlib path: /saltingandit.py
import hashlib
a = hashlib.pbkdf2_hmac("sha256", b"hallo", b"salt", 1)
b = hashlib.pbkdf2_hmac("s<|fim_middle|>ha256", a, b"salt", 1)
c = hashlib.pbkdf2_hmac(" | code_fim | easy | {
"lang": "python",
"repo": "rupali-adhikari/pythonprojecthashlib",
"path": "/saltingandit.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: rupali-adhikari/pythonprojecthashlib path: /saltingandit.py
import hashlib
a = hashlib.pbkdf2_hmac("sha256",<|fim_suffix|>ha256", a, b"salt", 1)
c = hashlib.pbkdf2_hmac("sha256", b"hallo", b"salt", 2)
print(b)
print(c)<|fim_middle|> b"hallo", b"salt", 1)
b = hashlib.pbkdf2_hmac("s | code_fim | easy | {
"lang": "python",
"repo": "rupali-adhikari/pythonprojecthashlib",
"path": "/saltingandit.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> if reinterpreted_batch_ndims is None:
reinterpreted_batch_ndims = len(self.batch_shape)
base_dist = self.base_dist
sample_shape = self.sample_shape
reinterpreted_batch_ndims = self.reinterpreted_batch_ndims + reinterpreted_batch_ndims
return ReshapedDist... | code_fim | hard | {
"lang": "python",
"repo": "neerajprad/pyro",
"path": "/pyro/distributions/torch_distribution.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: neerajprad/pyro path: /pyro/distributions/torch_distribution.py
from __future__ import absolute_import, division, print_function
import numbers
import torch
from torch.distributions import constraints
from pyro.distributions.distribution import Distribution
from pyro.distributions.score_parts ... | code_fim | hard | {
"lang": "python",
"repo": "neerajprad/pyro",
"path": "/pyro/distributions/torch_distribution.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> TorchDistributions provide a method ``.shape()`` for the tensor shape of samples::
x = d.sample(sample_shape)
assert x.shape == d.shape(sample_shape)
Pyro follows the same distribution shape semantics as PyTorch. It distinguishes
between three different roles for tensor shapes of... | code_fim | hard | {
"lang": "python",
"repo": "neerajprad/pyro",
"path": "/pyro/distributions/torch_distribution.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Wong-James/Python path: /week 1/day 3/Ninja.py
from pet import Pet
class Ninja:
def __init__(self, first_name, last_name, treats, pet_food, pet):
self.first_name = first_name
self.last_name = last_name
self.treats = treats
self.pet_food = pet_food
sel... | code_fim | medium | {
"lang": "python",
"repo": "Wong-James/Python",
"path": "/week 1/day 3/Ninja.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>Fox = Pet("Ninetailed Fox", "Fox", "Fire-Breathing")
Naruto = Ninja("Naruto", "Izumaki", "Rice Balls", "Ground Beef", Fox)
Naruto.feed()
print(Naruto.pet.energy)
print(Naruto.pet.health)
Naruto.bathe()
Naruto.walk()
print(Naruto.pet.energy)
print(Naruto.pet.health)<|fim_prefix|># repo: Wong-James/Python... | code_fim | medium | {
"lang": "python",
"repo": "Wong-James/Python",
"path": "/week 1/day 3/Ninja.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> def bathe(self):
self.pet.noise()
Fox = Pet("Ninetailed Fox", "Fox", "Fire-Breathing")
Naruto = Ninja("Naruto", "Izumaki", "Rice Balls", "Ground Beef", Fox)
Naruto.feed()
print(Naruto.pet.energy)
print(Naruto.pet.health)
Naruto.bathe()
Naruto.walk()
print(Naruto.pet.energy)
print(Naruto.p... | code_fim | medium | {
"lang": "python",
"repo": "Wong-James/Python",
"path": "/week 1/day 3/Ninja.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: rkBiswal/Python_Basics path: /More Review + More Linked Lists.py
## More Review + More Linked Lists ##
##Given a pointer to the head node of a linked list whose data elements are in non-decreasing order, you must delete any duplicate nodes and print the updated list.
##Code handling I/O is pro... | code_fim | hard | {
"lang": "python",
"repo": "rkBiswal/Python_Basics",
"path": "/More Review + More Linked Lists.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> if head==None or head.next ==None: return head
tmp = head;
while tmp.next!=None:
if tmp.data==tmp.next.data: tmp.next=tmp.next.next;
else: tmp=tmp.next;
return head
mylist= Solution()
T=int(input())
head=None
for i in range(T):
data=int(input())... | code_fim | hard | {
"lang": "python",
"repo": "rkBiswal/Python_Basics",
"path": "/More Review + More Linked Lists.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> print ('d : ', d)
r = 256
mod = 2 ** r
d0 = d % mod
d0e = d0 * e
print (bin(d0e)[-10:])
if d0e & (1 << 2):
x = RSA.construct((p*q, e, d, p, q))
output = x.exportKey("PEM")
with open('pri.pem', 'w') as f:
... | code_fim | hard | {
"lang": "python",
"repo": "b04902036/balsnctf-2019",
"path": "/shellcode_writer/share/solution/gen_key_and_solve_parital_key.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: b04902036/balsnctf-2019 path: /shellcode_writer/share/solution/gen_key_and_solve_parital_key.py
#!/usr/bin/env python2
from Crypto.PublicKey import RSA
from Crypto.Util.number import *
from timeit import default_timer as timer
import os
import gmpy2
import itertools as it
def extract2(inp):
... | code_fim | hard | {
"lang": "python",
"repo": "b04902036/balsnctf-2019",
"path": "/shellcode_writer/share/solution/gen_key_and_solve_parital_key.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>def printTOADChildren():
wordFinder = WordFinder()
print wordFinder.spellingDictionary.dictionary.children('t')
print wordFinder.spellingDictionary.dictionary.children('to')
print wordFinder.spellingDictionary.dictionary.children('toa')
print wordFinder.spellingDictionary.dict... | code_fim | hard | {
"lang": "python",
"repo": "BuffaloBuffalo/Wordly",
"path": "/base/WordFinder.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: BuffaloBuffalo/Wordly path: /base/WordFinder.py
from base.SpellingDictionary import SpellingDictionary
from datastructure.trie import NeedMore
class WordFinder:
def __init__(self):
self.spellingDictionary = SpellingDictionary()
#self.dictionary.add(["toad", "to", "do", "d... | code_fim | hard | {
"lang": "python",
"repo": "BuffaloBuffalo/Wordly",
"path": "/base/WordFinder.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ratchet3789/Handy-Blender-Plugins path: /object_center_zero.py
import bpy
bl_info = {
"name": "Ratchets Center All Objects",
"author": "Ratchet3789",
"version": (0, 1, 0),
"description": "Centers all selected objects. Built for Game Development.",
"category": "Object",
}
cla... | code_fim | hard | {
"lang": "python",
"repo": "ratchet3789/Handy-Blender-Plugins",
"path": "/object_center_zero.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> bpy.utils.unregister_class(CenterOriginToZero)
bpy.utils.unregister_class(SnapMeshToOrigin)
bpy.utils.unregister_class(AbsoluteCenterObjects)
# This allows you to run the script directly from blenders text editor
# to test the addon without having to install it.
if __name__ == "__main__":
... | code_fim | hard | {
"lang": "python",
"repo": "ratchet3789/Handy-Blender-Plugins",
"path": "/object_center_zero.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> self.campoDeTreinamento = CampoDeTreinamentoPage(self.driver)
self.campoDeTreinamento.fill_name("Everton")
self.campoDeTreinamento.fill_sobrenome("Araujo")
self.campoDeTreinamento.select_sexo_masculino()
self.campoDeTreinamento.cadastra()
time.sleep(3)<|fim_... | code_fim | medium | {
"lang": "python",
"repo": "jairoalm/PythonProject001",
"path": "/tests/test_campo_de_treinamento.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: jairoalm/PythonProject001 path: /tests/test_campo_de_treinamento.py
import time
from tests.test_base import BaseTest
from pages.campo_de_treinamento_page import CampoDeTreinamentoPage
<|fim_suffix|> self.campoDeTreinamento = CampoDeTreinamentoPage(self.driver)
self.campoDeTreiname... | code_fim | medium | {
"lang": "python",
"repo": "jairoalm/PythonProject001",
"path": "/tests/test_campo_de_treinamento.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>
x = sym.Symbol('x')
t = sym.Symbol('t')
dlfl_integral = sym.integrate(del_flu_sym(x, t), (x))
print(dlfl_integral(2))
sym.pprint(dlfl_integral)<|fim_prefix|># repo: CucumentoJolaz/scattering_model path: /optical_integr.py
#Интегрирование точного решения кинетик затухания люминесценции символьным методом... | code_fim | medium | {
"lang": "python",
"repo": "CucumentoJolaz/scattering_model",
"path": "/optical_integr.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>x = sym.Symbol('x')
t = sym.Symbol('t')
dlfl_integral = sym.integrate(del_flu_sym(x, t), (x))
print(dlfl_integral(2))
sym.pprint(dlfl_integral)<|fim_prefix|># repo: CucumentoJolaz/scattering_model path: /optical_integr.py
#Интегрирование точного решения кинетик затухания люминесценции символьным методом
... | code_fim | medium | {
"lang": "python",
"repo": "CucumentoJolaz/scattering_model",
"path": "/optical_integr.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: CucumentoJolaz/scattering_model path: /optical_integr.py
#Интегрирование точного решения кинетик затухания люминесценции символьным методом
#Из за сложности получаемых уравнений. Последующий подбор коэффициентов методом МНК
# и печать результата
#
<|fim_suffix|>x = sym.Symbol('x')
t = sym.Symbol... | code_fim | medium | {
"lang": "python",
"repo": "CucumentoJolaz/scattering_model",
"path": "/optical_integr.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> def update_overall_average_value(self):
value_sum = 0
for event in self.events:
value_sum += event.value
value_count = len(self.events)
if value_count > 0:
self.overall_average_value = value_sum / value_count<|fim_prefix|># repo: HalAltran/EventP... | code_fim | hard | {
"lang": "python",
"repo": "HalAltran/EventProcessing",
"path": "/event_processing/location.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: HalAltran/EventProcessing path: /event_processing/location.py
from datetime import datetime
class Location:
def __init__(self, location_dict):
self.x = location_dict['x']
self.y = location_dict['y']
self.id = location_dict['id']
self.events = []
<|fim_suff... | code_fim | hard | {
"lang": "python",
"repo": "HalAltran/EventProcessing",
"path": "/event_processing/location.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>ik Informatika 2018 A")
print ("Kampus : Universitas Nahdlatul Ulama Sidoarjo")
print ("===================================================")<|fim_prefix|># repo: AGUNGDHARMA1/Hello path: /Hello.py
print ("hello")
print ("=================================================<|fim_middle|>==")
print ("N... | code_fim | medium | {
"lang": "python",
"repo": "AGUNGDHARMA1/Hello",
"path": "/Hello.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: AGUNGDHARMA1/Hello path: /Hello.py
print ("hello")
print ("===================================================")
print ("Nama Lengkap : Agung Dharmawan")
print ("Kelas : Tekn<|fim_suffix|>a Sidoarjo")
print ("===================================================")<|fim_middle|>ik Informatika... | code_fim | medium | {
"lang": "python",
"repo": "AGUNGDHARMA1/Hello",
"path": "/Hello.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>a Sidoarjo")
print ("===================================================")<|fim_prefix|># repo: AGUNGDHARMA1/Hello path: /Hello.py
print ("hello")
print ("=================================================<|fim_middle|>==")
print ("Nama Lengkap : Agung Dharmawan")
print ("Kelas : Teknik Informatika... | code_fim | medium | {
"lang": "python",
"repo": "AGUNGDHARMA1/Hello",
"path": "/Hello.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: YuboLuo/budgetrnn_backup path: /data_preparation/emg/tokenize_dataset.py
import os
import numpy as np
from argparse import ArgumentParser
from collections import Counter
from typing import Iterable, Dict, Any, Tuple
from utils.constants import TRAIN, VALID, TEST, SAMPLE_ID, INPUTS, OUTPUT
from u... | code_fim | hard | {
"lang": "python",
"repo": "YuboLuo/budgetrnn_backup",
"path": "/data_preparation/emg/tokenize_dataset.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> partition_counters = {
TRAIN: Counter(),
VALID: Counter(),
TEST: Counter()
}
for i, (sample, partition) in enumerate(data_generator(input_folder)):
data_writers[partition].add(sample)
partition_counters[partition][sample[OUTPUT]] += 1
if (i + 1... | code_fim | hard | {
"lang": "python",
"repo": "YuboLuo/budgetrnn_backup",
"path": "/data_preparation/emg/tokenize_dataset.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>def tokenize_dataset(input_folder: str, output_folder: str, chunk_size: int):
make_dir(output_folder)
data_writers = {
TRAIN: DataWriter(os.path.join(output_folder, TRAIN), file_prefix='data', file_suffix='jsonl.gz', chunk_size=chunk_size),
VALID: DataWriter(os.path.join(output_fol... | code_fim | hard | {
"lang": "python",
"repo": "YuboLuo/budgetrnn_backup",
"path": "/data_preparation/emg/tokenize_dataset.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> if leftPointer > rightPointer:
return -1
else:
midPointer = (leftPointer + rightPointer) // 2
if target == array[midPointer]:
return midPointer
elif target < array[midPointer]:
return binarySearchR(array, target, leftPointer, midPointer - 1)
... | code_fim | medium | {
"lang": "python",
"repo": "Chunkygoo/Algorithms",
"path": "/Searching/binarySearch.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Chunkygoo/Algorithms path: /Searching/binarySearch.py
# O(logn) T O(1) S
def binarySearch(array, target):
if len(array) == 0:
return -1
else:
return binarySearchR(array, target, 0, len(array) - 1)
<|fim_suffix|> if leftPointer > rightPointer:
return -1
else... | code_fim | medium | {
"lang": "python",
"repo": "Chunkygoo/Algorithms",
"path": "/Searching/binarySearch.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> # 添加下面一句，在记录日志之后移除句柄
# self.logger.info('记录数据')
# self.logger.removeHandler(fh)
# 关闭打开的文件
fh.close()
return self.logger
def log(name):
def wraaper(func):
def inner(*args, **kwargs): # 如果想返回result必须再包裹一层
log = IC... | code_fim | hard | {
"lang": "python",
"repo": "coffeeTeaOne/spiderpy",
"path": "/1.1.0/SpidersLog/icrwler_log.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|># Start
cache_path = "%s/fvwm/menu" % xdg_cache_home
icon_theme = gtk.icon_theme_get_default()
if not os.path.exists(cache_path):
os.makedirs(cache_path)
# Parse commandline
parser = OptionParser()
parser.add_option("-d", "--dynamic", dest="fvwm_menu", default=None, help="Use in DynamicPopupAction... | code_fim | hard | {
"lang": "python",
"repo": "jcmenguito/config",
"path": "/.fvwm/EN/scripts/xdgmenu-updated.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: jcmenguito/config path: /.fvwm/EN/scripts/xdgmenu-updated.py
#!/usr/bin/python2
#
# Author: Victor Ananjevsky, 2007 - 2010
# based on xdg-menu.py, written by Piotr Zielinski (http://www.cl.cam.ac.uk/~pz215/)
# License: GPL
#
# This script takes names of menu files conforming to the XDG Desktop
# ... | code_fim | hard | {
"lang": "python",
"repo": "jcmenguito/config",
"path": "/.fvwm/EN/scripts/xdgmenu-updated.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>for arg in args:
filename = ""
if os.path.exists(arg) or arg == "recent":
filename = arg
else:
tmpfile = "%s/menus/%s.menu" % (xdg_config_home, arg)
if os.path.exists(tmpfile):
filename = tmpfile
else:
for dir in xdg_config_dirs:
... | code_fim | hard | {
"lang": "python",
"repo": "jcmenguito/config",
"path": "/.fvwm/EN/scripts/xdgmenu-updated.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Replicant74/Pythonsec path: /portscanner_v1.5.py
# Original code from http://www.pythonforbeginners.com/code-snippets-source-code/port-scanner-in-python
#!/usr/bin/env python
# modules
import threading
import socket
import subprocess
import sys
import time
import scapy
from threading import Thr... | code_fim | hard | {
"lang": "python",
"repo": "Replicant74/Pythonsec",
"path": "/portscanner_v1.5.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|># Setting some values
ports = range(int(startPort), int(endPort)+1)
t1 = datetime.now()
SYNACK = 0x12
RSTACK = 0x14
# Banner displaying which host is being scanned
print ("-" * 60)
print ("Please wait, scanning remote host...", targetIP)
localtime = time.asctime(time.localtime())
print ("Scan started at:... | code_fim | hard | {
"lang": "python",
"repo": "Replicant74/Pythonsec",
"path": "/portscanner_v1.5.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: 1tianjing/scrapy path: /爬虫/周末作业/zhilian.py
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
import requests
import time
driver = webdriver.Chrome(executable_path='/home/bc/桌面/chromedriver')
<|fim_suffix|>kw2').send_keys('技术')
driver.find_element_by_class_name('doSea... | code_fim | medium | {
"lang": "python",
"repo": "1tianjing/scrapy",
"path": "/爬虫/周末作业/zhilian.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>
driver.get('https://www.zhaopin.com/')
time.sleep(5)
driver.find_element_by_id('KeyWord_kw2').send_keys('技术')
driver.find_element_by_class_name('doSearch').click()
time.sleep(5)<|fim_prefix|># repo: 1tianjing/scrapy path: /爬虫/周末作业/zhilian.py
from selenium import webdriver
from selenium.webdriver.common.... | code_fim | medium | {
"lang": "python",
"repo": "1tianjing/scrapy",
"path": "/爬虫/周末作业/zhilian.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: globocom/redis-pyinterval path: /tests/test_iadd.py
from redis_interval.client import RedisInterval
class TestRedisIntervalIADD(object):
<|fim_suffix|> """ Add simple text inside an interval """
value = self.redis.iadd("test", 0, 10, "simple text")
assert value == 'OK'<|f... | code_fim | medium | {
"lang": "python",
"repo": "globocom/redis-pyinterval",
"path": "/tests/test_iadd.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> @classmethod
def setup_class(cls):
cls.redis = RedisInterval(host="localhost")
def test_add_simple_text(self):
""" Add simple text inside an interval """
value = self.redis.iadd("test", 0, 10, "simple text")
assert value == 'OK'<|fim_prefix|># repo: globocom/re... | code_fim | medium | {
"lang": "python",
"repo": "globocom/redis-pyinterval",
"path": "/tests/test_iadd.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def test_add_simple_text(self):
""" Add simple text inside an interval """
value = self.redis.iadd("test", 0, 10, "simple text")
assert value == 'OK'<|fim_prefix|># repo: globocom/redis-pyinterval path: /tests/test_iadd.py
from redis_interval.client import RedisInterval
clas... | code_fim | medium | {
"lang": "python",
"repo": "globocom/redis-pyinterval",
"path": "/tests/test_iadd.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: acattle/HumourTools path: /HumourDetection/src/fwaDifference.py
'''
Created on Sep 23, 2016
@author: Andrew
'''
from pymongo import MongoClient
import re
client = MongoClient()
atMentions = re.compile(ur"@\w+", flags=re.I|re.U)
atMidnight = re.compile(u"@midnight", flags=re.I|re.U)
... | code_fim | hard | {
"lang": "python",
"repo": "acattle/HumourTools",
"path": "/HumourDetection/src/fwaDifference.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>len(hashtag.findall(tweet["text"])) > 1: #if there's more than 1 hashtag
continue
if (tweet[featureF] > 0) and (tweet[featureB] > 0):
tweet[featureD] = tweet[featureF] - tweet[featureB]
sign = 0 #assume forward and back are equa... | code_fim | hard | {
"lang": "python",
"repo": "acattle/HumourTools",
"path": "/HumourDetection/src/fwaDifference.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|># Get Secret
puzzle_input = sys.argv[1]
input_num = 0
# Calcuate
for i in range(sys.maxsize):
digest = hashlib.md5(puzzle_input.encode('utf-8')+str(i).encode('utf-8')).hexdigest()
if (digest.startswith('000000')): # must start with 6 zeros
input_num = i
break;
# Print Results
print(f'puzzle_inp... | code_fim | medium | {
"lang": "python",
"repo": "babint/AoC-2015",
"path": "/04/part2.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: babint/AoC-2015 path: /04/part2.py
#!/usr/bin/env python3
import sys
import hashlib
<|fim_suffix|># Print Results
print(f'puzzle_input: {puzzle_input} solved with {input_num}')
print("\ndone.");<|fim_middle|># Usage
if len(sys.argv) != 2:
print("usage: part2.py puzzle_input")
exit(1)
# Ge... | code_fim | hard | {
"lang": "python",
"repo": "babint/AoC-2015",
"path": "/04/part2.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: babint/AoC-2015 path: /04/part2.py
#!/usr/bin/env python3
import sys
import hashlib
# Usage
if len(sys.argv) != 2:
print("usage: part2.py puzzle_input")
exit(1)
<|fim_suffix|># Calcuate
for i in range(sys.maxsize):
digest = hashlib.md5(puzzle_input.encode('utf-8')+str(i).encode('utf-8')).h... | code_fim | medium | {
"lang": "python",
"repo": "babint/AoC-2015",
"path": "/04/part2.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: John-Titor/py68k path: /targets/cb030.py
from emulator import Emulator
from device import Device
from devices.compactflash import CompactFlash
from devices.mc68681 import MC68681
from musashi import m68k
def add_arguments(parser):
parser.add_argument('--rom',
type=st... | code_fim | hard | {
"lang": "python",
"repo": "John-Titor/py68k",
"path": "/targets/cb030.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> if self._tick_fired:
self._tick_fired = False
return M68K_IRQ_AUTOVECTOR
return M68K_IRQ_SPURIOUS
def configure(args):
"""create and configure an emulator"""
emu = Emulator(args,
cpu='68030',
frequency=24 * 1000 * 100... | code_fim | hard | {
"lang": "python",
"repo": "John-Titor/py68k",
"path": "/targets/cb030.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def get_datetime_from_utc_timestamp(utc_timestamp):
return datetime.datetime.utcfromtimestamp(utc_timestamp).replace(tzinfo=datetime.timezone.utc)
def get_local_datetime(date_time):
return date_time.astimezone(datetime.datetime.utcnow().astimezone().tzinfo)
def get_string_from_datetime(date_t... | code_fim | medium | {
"lang": "python",
"repo": "corneliusroemer/diagnosis-keys",
"path": "/lib/conversions.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def get_string_from_datetime(date_time):
return date_time.strftime('%Y-%m-%d %H:%M:%S %Z')<|fim_prefix|># repo: corneliusroemer/diagnosis-keys path: /lib/conversions.py
import datetime
interval_length_minutes = 10 # 10 minutes per interval
tek_rolling_period = 144 # 24*60//10 - 24 hours per day, ... | code_fim | medium | {
"lang": "python",
"repo": "corneliusroemer/diagnosis-keys",
"path": "/lib/conversions.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: corneliusroemer/diagnosis-keys path: /lib/conversions.py
import datetime
interval_length_minutes = 10 # 10 minutes per interval
tek_rolling_period = 144 # 24*60//10 - 24 hours per day, 60 minutes per hour, 10 minutes per interval
<|fim_suffix|> return datetime.datetime.utcfromtimestamp(ut... | code_fim | medium | {
"lang": "python",
"repo": "corneliusroemer/diagnosis-keys",
"path": "/lib/conversions.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>boy = None
Start_menu = None
menu_time =None
def enter():
global Start_menu
Start_menu = Menu()
menu_world.add_object(Start_menu, 0)
def exit():
menu_world.clear()
def pause():
pass
def resume():
pass
def handle_events():
global Start_menu,menu_time
events = get_event... | code_fim | medium | {
"lang": "python",
"repo": "gic91/2DGP_project",
"path": "/2D_GAME/game_source/menu_state.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.