text stringlengths 232 16.3k | domain stringclasses 1
value | difficulty stringclasses 3
values | meta dict |
|---|---|---|---|
<|fim_suffix|>The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPO... | code_fim | hard | {
"lang": "python",
"repo": "DexterInd/GrovePi",
"path": "/Software/Python/grove_barometer_sensors/high_accuracy_hp206c_barometer/high_accuracy_barometer_example.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>ret=h.isAvailable()
if h.OK_HP20X_DEV == ret:
print("HP20x_dev is available.")
else:
print("HP20x_dev isn't available.")
temp=h.ReadTemperature()
pressure=h.ReadPressure()
altitude=h.ReadAltitude()
print("Temperature\t: %.2f C\nPressure\t: %.2f hPa\nAltitude\t: %.2f m" %(temp,pressure,altitud... | code_fim | hard | {
"lang": "python",
"repo": "DexterInd/GrovePi",
"path": "/Software/Python/grove_barometer_sensors/high_accuracy_hp206c_barometer/high_accuracy_barometer_example.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> Example::
>>> from keystoneclient import discover
>>> disc = discover.Discovery(auth_url='http://localhost:5000')
>>> disc.raw_version_data()
[{'id': 'v3.0',
'links': [{'href': 'http://127.0.0.1:5000/v3/',
... | code_fim | hard | {
"lang": "python",
"repo": "openstack/python-keystoneclient",
"path": "/keystoneclient/discover.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: openstack/python-keystoneclient path: /keystoneclient/discover.py
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# dis... | code_fim | hard | {
"lang": "python",
"repo": "openstack/python-keystoneclient",
"path": "/keystoneclient/discover.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: openstack/python-keystoneclient path: /keystoneclient/discover.py
License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | code_fim | hard | {
"lang": "python",
"repo": "openstack/python-keystoneclient",
"path": "/keystoneclient/discover.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: liusulin/Ax path: /ax/benchmark/benchmark_result.py
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
from dataclasses import datac... | code_fim | hard | {
"lang": "python",
"repo": "liusulin/Ax",
"path": "/ax/benchmark/benchmark_result.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> Returns:
An array representing the optimization trace as a function of time.
"""
if any(isinstance(trial, BatchTrial) for trial in experiment.trials.values()):
raise NotImplementedError("Batched trials are not yet supported.")
def get_completed_time(row):
time = ex... | code_fim | hard | {
"lang": "python",
"repo": "liusulin/Ax",
"path": "/ax/benchmark/benchmark_result.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def get_completed_time(row):
time = experiment.trials[row.trial_index].run_metadata[completed_time_key]
return pd.Series({"completed_time": time})
if include_only_completed_trials:
completed_trials = experiment.trial_indices_by_status[TrialStatus.COMPLETED]
data_df... | code_fim | hard | {
"lang": "python",
"repo": "liusulin/Ax",
"path": "/ax/benchmark/benchmark_result.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> try:
msg = self.format(record)
self.logs.append(msg)
self.logs = self.logs[-1000:]
self.flush()
except Exception:
self.handleError(record)<|fim_prefix|># repo: salesforce/Merlion path: /merlion/dashboard/utils/log.py
#
# Copyrigh... | code_fim | medium | {
"lang": "python",
"repo": "salesforce/Merlion",
"path": "/merlion/dashboard/utils/log.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: salesforce/Merlion path: /merlion/dashboard/utils/log.py
#
# Copyright (c) 2023 salesforce.com, inc.
# All rights reserved.
# SPDX-License-Identifier: BSD-3-Clause
# For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
#
import logging
cla... | code_fim | medium | {
"lang": "python",
"repo": "salesforce/Merlion",
"path": "/merlion/dashboard/utils/log.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: capusta/handyAutomation path: /tensorflow/t.py
#! /usr/bin/env python
import tensorflow as tf
import os, sys, argparse
from tensorflow.contrib import lookup
from tensorflow.python.platform import gfile
os.environ['WORKSPACE'] = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
#sys.pa... | code_fim | medium | {
"lang": "python",
"repo": "capusta/handyAutomation",
"path": "/tensorflow/t.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # the text to be classified
vocab_processor = tf.contrib.learn.preprocessing.VocabularyProcessor(MAX_DOC_LENGTH)
vocab_processor.fit(LINES)
with gfile.Open(outfilename, 'wb') as f:
f.write("{}\n".format(PADWORD))
for w, i in vocab_processor.vocabulary_._mapping.iteritems()... | code_fim | hard | {
"lang": "python",
"repo": "capusta/handyAutomation",
"path": "/tensorflow/t.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: simonsdave/ecs path: /tests/load/locustfile.py
#
# this module is a locustfile drives load into a ECS deployment
#
# this locustfile is expected to called from a BASH script
#
import httplib
import os
import random
import re
import uuid
from locust import HttpLocust
import requests
from locust ... | code_fim | hard | {
"lang": "python",
"repo": "simonsdave/ecs",
"path": "/tests/load/locustfile.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
class QuickHealthBehavior(ECSTaskSet):
min_wait = 500
max_wait = 1000
@task
def quick_health_check(self):
response = self.client.get('/v1.1/_health?quick=true', auth=_get_random_credentials())
self.log_on_response('Health-Check-Quick', response, httplib.OK)
class Quick... | code_fim | hard | {
"lang": "python",
"repo": "simonsdave/ecs",
"path": "/tests/load/locustfile.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
min_wait = 500
max_wait = 1000
@task
def quick_health_check(self):
response = self.client.get('/v1.1/_health?quick=true', auth=_get_random_credentials())
self.log_on_response('Health-Check-Quick', response, httplib.OK)
class QuickHealthLocust(ECSHttpLocust):
task_s... | code_fim | hard | {
"lang": "python",
"repo": "simonsdave/ecs",
"path": "/tests/load/locustfile.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> Args:
xml (str or bytes)
root (str): (optional) name of root element
Returns:
lxml document object.
'''
if isinstance(xml, str) or isinstance(xml, bytes):
dom = etree.XML(xml)
else:
dom = etree.XML(xml)
... | code_fim | hard | {
"lang": "python",
"repo": "pmartin23/metapub",
"path": "/metapub/base.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: pmartin23/metapub path: /metapub/base.py
from __future__ import absolute_import, unicode_literals
import six
from lxml import etree
from .exceptions import MetaPubError, BaseXMLError
def parse_elink_response(xmlstr):
""" return all Ids from an elink XML response
:param xmlstr:
:... | code_fim | hard | {
"lang": "python",
"repo": "pmartin23/metapub",
"path": "/metapub/base.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def ratio(img, height=800):
"""Getting scale ratio."""
return img.shape[0] / height<|fim_prefix|># repo: gaurav879/PlagiarismChecker path: /backend/OCR/utils.py
import cv2
def resize(img, height=800):
<|fim_middle|> """Resize image to given height"""
ratio = height / img.shape[0]
re... | code_fim | medium | {
"lang": "python",
"repo": "gaurav879/PlagiarismChecker",
"path": "/backend/OCR/utils.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> """Getting scale ratio."""
return img.shape[0] / height<|fim_prefix|># repo: gaurav879/PlagiarismChecker path: /backend/OCR/utils.py
import cv2
def resize(img, height=800):
"""Resize image to given height"""
ratio = height / img.shape[0]
return cv2.resize(img, (int(ratio * img.shape... | code_fim | easy | {
"lang": "python",
"repo": "gaurav879/PlagiarismChecker",
"path": "/backend/OCR/utils.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: gaurav879/PlagiarismChecker path: /backend/OCR/utils.py
import cv2
def resize(img, height=800):
<|fim_suffix|> """Getting scale ratio."""
return img.shape[0] / height<|fim_middle|> """Resize image to given height"""
ratio = height / img.shape[0]
return cv2.resize(img, (int(rat... | code_fim | medium | {
"lang": "python",
"repo": "gaurav879/PlagiarismChecker",
"path": "/backend/OCR/utils.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>def getMean(numbers):
return sum(numbers) / len(numbers) if numbers else None
out = getMean(numbers)
print(out)<|fim_prefix|># repo: PedroBernini/ipl-2021 path: /set_1/p1_2_1.py
# Programa para calcular a média aritmética de uma lista de números (que podem ser ints ou floats).
<|fim_middle|>number... | code_fim | easy | {
"lang": "python",
"repo": "PedroBernini/ipl-2021",
"path": "/set_1/p1_2_1.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>out = getMean(numbers)
print(out)<|fim_prefix|># repo: PedroBernini/ipl-2021 path: /set_1/p1_2_1.py
# Programa para calcular a média aritmética de uma lista de números (que podem ser ints ou floats).
numbers = [2, 7, 3, 9, 13]
def getMean(numbers):
<|fim_middle|> return sum(numbers) / len(numbers) ... | code_fim | medium | {
"lang": "python",
"repo": "PedroBernini/ipl-2021",
"path": "/set_1/p1_2_1.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: PedroBernini/ipl-2021 path: /set_1/p1_2_1.py
# Programa para calcular a média aritmética de uma lista de números (que podem ser ints ou floats).
numbers = [2, 7, 3, 9, 13]
<|fim_suffix|>out = getMean(numbers)
print(out)<|fim_middle|>def getMean(numbers):
return sum(numbers) / len(numbers) ... | code_fim | medium | {
"lang": "python",
"repo": "PedroBernini/ipl-2021",
"path": "/set_1/p1_2_1.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> data_root=data_root,
ann_file=data_root + f's3dis_infos_Area_{i}.pkl',
pipeline=train_pipeline,
filter_empty_gt=False,
classes=class_names,
box_type_3d='Depth') for i in train_area
... | code_fim | hard | {
"lang": "python",
"repo": "OpenGVLab/InternImage",
"path": "/autonomous_driving/occupancy_prediction/projects/configs/_base_/datasets/s3dis-3d-5class.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: OpenGVLab/InternImage path: /autonomous_driving/occupancy_prediction/projects/configs/_base_/datasets/s3dis-3d-5class.py
# dataset settings
dataset_type = 'S3DISDataset'
data_root = './data/s3dis/'
class_names = ('table', 'chair', 'sofa', 'bookcase', 'board')
train_area = [1, 2, 3, 4, 6]
test_are... | code_fim | hard | {
"lang": "python",
"repo": "OpenGVLab/InternImage",
"path": "/autonomous_driving/occupancy_prediction/projects/configs/_base_/datasets/s3dis-3d-5class.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>dle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
])
]
# construct a pipeline for data and gt loading in show function
# please keep its loading function consistent with test_pipeline (e.g. client)
eval_pipeline ... | code_fim | hard | {
"lang": "python",
"repo": "OpenGVLab/InternImage",
"path": "/autonomous_driving/occupancy_prediction/projects/configs/_base_/datasets/s3dis-3d-5class.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> This function is called automatically when a Python process exits. Normally,
the training script does not need to invoke this function at the end.
In the case that the training script needs to initialize the distributed module
multiple times (so far, this is needed in the unit tests), the... | code_fim | hard | {
"lang": "python",
"repo": "hengruizhang98/dgl",
"path": "/python/dgl/distributed/dist_context.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: hengruizhang98/dgl path: /python/dgl/distributed/dist_context.py
"""Initialize the distributed services"""
import multiprocessing as mp
import traceback
import atexit
import time
import os
import sys
from . import rpc
from .constants import MAX_QUEUE_SIZE
from .kvstore import init_kvstore, clos... | code_fim | hard | {
"lang": "python",
"repo": "hengruizhang98/dgl",
"path": "/python/dgl/distributed/dist_context.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> """join the worker close process"""
global SAMPLER_POOL
if SAMPLER_POOL is not None:
SAMPLER_POOL.join()
SAMPLER_POOL = None
def is_initialized():
"""Is RPC initialized?
"""
return INITIALIZED
def exit_client():
"""Trainer exits
This function is called automa... | code_fim | hard | {
"lang": "python",
"repo": "hengruizhang98/dgl",
"path": "/python/dgl/distributed/dist_context.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: pyzh/ix path: /ix/cmd.py
import os
import os.path
from argparse import ArgumentParser
from functools import wraps
import logging
logging.captureWarnings(True)
logger = logging.getLogger('')
logger.setLevel(logging.DEBUG)
handler = logging.StreamHandler()
formatter = logging.Formatter(fmt='%(mess... | code_fim | hard | {
"lang": "python",
"repo": "pyzh/ix",
"path": "/ix/cmd.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>@command(
argument("-r", "--recompile", action="store_true", help="recompile if already compiled before test"),
argument("filename", nargs='?', help="path to solution"))
def test(cfg, filename=None, recompile=False):
"""check solution against sample testcases"""
for filename, (oj, problem)... | code_fim | hard | {
"lang": "python",
"repo": "pyzh/ix",
"path": "/ix/cmd.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: geertj/draco2 path: /draco2/command/config.py
# vi: ts=8 sts=4 sw=4 et
#
# config.py: config commands
#
# This file is part of Draco2. Draco2 is free software and is made available
# under the MIT license. Consult the file "LICENSE" that is distributed
# together with this file for the exact lice... | code_fim | hard | {
"lang": "python",
"repo": "geertj/draco2",
"path": "/draco2/command/config.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> name = 'config'
description = 'manage configuration'
def __init__(self):
super(ConfigCommand, self).__init__()
self.add_subcommand(SetConfig())
self.add_subcommand(DeleteConfig())
self.add_subcommand(ListConfig())<|fim_prefix|># repo: geertj/draco2 path: /drac... | code_fim | hard | {
"lang": "python",
"repo": "geertj/draco2",
"path": "/draco2/command/config.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: scivision/mbta_salary path: /mbtasalary/plots.py
from matplotlib.pyplot import figure
import typing
import pandas
import numpy as np
def doplot(
data: pandas.DataFrame, ind: np.ndarray, saltype: str, thres: float, ax
) -> typing.Tuple[float, float, float, float, float]:
# %% for analysi... | code_fim | hard | {
"lang": "python",
"repo": "scivision/mbta_salary",
"path": "/mbtasalary/plots.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> fg.suptitle(f'{year} MBTA salary histograms', fontsize='xx-large')
fg.tight_layout()
fg.subplots_adjust(top=0.93)
maxearner = data.loc[data['Salary'].idxmax(), :]
maxsalary = maxearner['Salary']
try:
maxearnerOT = maxsalary - maxearner['ProjSal']
print(
... | code_fim | hard | {
"lang": "python",
"repo": "scivision/mbta_salary",
"path": "/mbtasalary/plots.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: VTranCS/NewsTrust path: /sentimentTest.py
# Imports the Google Cloud client library
from google.cloud import language_v1
import math
<|fim_suffix|> client = language_v1.LanguageServiceClient()
document = language_v1.Document(content=tweet, type_=language_v1.Document.Type.PLAIN_TEXT)
s... | code_fim | medium | {
"lang": "python",
"repo": "VTranCS/NewsTrust",
"path": "/sentimentTest.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> client = language_v1.LanguageServiceClient()
document = language_v1.Document(content=tweet, type_=language_v1.Document.Type.PLAIN_TEXT)
sentiment = client.analyze_sentiment(request={'document': document}).document_sentiment
normDirection = (3.7 / (.3 * math.sqrt(2 * math.pi))) * math.e ** ... | code_fim | medium | {
"lang": "python",
"repo": "VTranCS/NewsTrust",
"path": "/sentimentTest.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> res = self.client.get(urlunquote(url))
self.assertEqual(res.status_code, 200)
self.assertTemplateUsed(res, 'project/volumes/index.html')
self.mox.UnsetStubs()
return res
def ensure_attachments_exist(self, volumes):
volumes = copy.copy(volumes)
... | code_fim | hard | {
"lang": "python",
"repo": "etforshell/horizon",
"path": "/openstack_dashboard/dashboards/project/volumes/test.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>from openstack_dashboard import api
from openstack_dashboard.dashboards.project.volumes.volumes \
import tables as volume_tables
from openstack_dashboard.test import helpers as test
INDEX_URL = reverse('horizon:project:volumes:index')
class VolumeAndSnapshotsAndBackupsTests(test.TestCase):
@te... | code_fim | hard | {
"lang": "python",
"repo": "etforshell/horizon",
"path": "/openstack_dashboard/dashboards/project/volumes/test.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: etforshell/horizon path: /openstack_dashboard/dashboards/project/volumes/test.py
# Copyright 2012 Nebula, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
... | code_fim | hard | {
"lang": "python",
"repo": "etforshell/horizon",
"path": "/openstack_dashboard/dashboards/project/volumes/test.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ctn-waterloo/nengo_pushbot path: /nengo_pushbot/pushbot2.py
import socket
import time
import numpy as np
import struct
import atexit
class PushBot2(object):
def __init__(self, address, port=56000, message_delay=0.01):
self.socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
... | code_fim | hard | {
"lang": "python",
"repo": "ctn-waterloo/nengo_pushbot",
"path": "/nengo_pushbot/pushbot2.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def stop(self):
if self.socket is not None:
self.beep(0, force=True)
#self.laser(0, force=True)
#self.led(0, force=True)
self.socket.send('!M-\n')
self.socket.send('E-\n')
#self.send_motor(0, 0, force=True)<|fim_prefix|># ... | code_fim | hard | {
"lang": "python",
"repo": "ctn-waterloo/nengo_pushbot",
"path": "/nengo_pushbot/pushbot2.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> data_out = pd.DataFrame(res, index=['absolute']).drop('chunk_height', axis=1).transpose()
total_utxos = data_out.sum(axis=0)[0]
print(str(total_utxos))
data_out['relative'] = (100. * data_out['absolute']) / (1. * total_utxos)
print(str(data_out))<|fim_prefix|># repo: COMSYS/coinprune-s... | code_fim | hard | {
"lang": "python",
"repo": "COMSYS/coinprune-scripts",
"path": "/read_utxo_histogram.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: COMSYS/coinprune-scripts path: /read_utxo_histogram.py
#!/usr/bin/env python3
""" This file parses a histogram CSV file generated via get_utxo_histogram.py. """
import sys
import argparse
import pandas as pd
<|fim_suffix|> with open(f'{args.folder}/{args.prefix}{args.snapshot_height:010d}_h... | code_fim | hard | {
"lang": "python",
"repo": "COMSYS/coinprune-scripts",
"path": "/read_utxo_histogram.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> res = dict()
res['chunk_height'] = args.snapshot_height
for column in data:
if column in ['chunk_height', 'chunk_offset']:
continue
res[column] = data[column].sum()
data_out = pd.DataFrame(res, index=['absolute']).drop('chunk_height', axis=1).transpose()
to... | code_fim | medium | {
"lang": "python",
"repo": "COMSYS/coinprune-scripts",
"path": "/read_utxo_histogram.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def f():
raise IgnoredException('any exception')
try:
utils.ignore_exception(f)
except IgnoredException:
self.fail('should not raise any exception.')
def test_assure_cleanup(self):
data = [0]
def _enter():
data[... | code_fim | hard | {
"lang": "python",
"repo": "emc-openstack/unity-cinder-driver",
"path": "/cinder/tests/unit/volume/drivers/dell_emc/unity/test_utils.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: emc-openstack/unity-cinder-driver path: /cinder/tests/unit/volume/drivers/dell_emc/unity/test_utils.py
# Copyright (c) 2017 Dell Inc. or its subsidiaries.
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance wi... | code_fim | hard | {
"lang": "python",
"repo": "emc-openstack/unity-cinder-driver",
"path": "/cinder/tests/unit/volume/drivers/dell_emc/unity/test_utils.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> start_time = time.time()
orders_analysis = model_inventory.analyse_orders_abcxyz_from_file(file_path="data.csv", z_value=Decimal(1.28),
reorder_cost=Decimal(5000), file_type="csv")
sim = simulate.run_monte_carlo(orders_ana... | code_fim | hard | {
"lang": "python",
"repo": "Jcaffert/supplychainpy",
"path": "/supplychainpy/supplychain.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Jcaffert/supplychainpy path: /supplychainpy/supplychain.py
#!/usr/bin/env python3
import os
from _decimal import Decimal
import time
from supplychainpy import simulate
from supplychainpy import model_inventory
__author__ = 'kevin'
def main():
start_time = time.time()
orders_analysis... | code_fim | hard | {
"lang": "python",
"repo": "Jcaffert/supplychainpy",
"path": "/supplychainpy/supplychain.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> train_op = self.get_train_op(loss)
return train_op, loss
def predict_no_crf(self, xs):
feed_dict = self._fill_feed_dict(xs)
pred_idxs, mask = self.sess.run([self._y_pred, self.mask_ph], feed_dict)
# Filter by sequece length
sequence_lengths = np.sum(ma... | code_fim | hard | {
"lang": "python",
"repo": "vintagexav/DeepPavlov",
"path": "/deeppavlov/models/ner/network.py",
"mode": "spm",
"license": "Python-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: vintagexav/DeepPavlov path: /deeppavlov/models/ner/network.py
# Copyright 2017 Neural Networks and Deep Learning lab, MIPT
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License ... | code_fim | hard | {
"lang": "python",
"repo": "vintagexav/DeepPavlov",
"path": "/deeppavlov/models/ner/network.py",
"mode": "psm",
"license": "Python-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> try:
osutils.remove(os.path.join('c:\\Users\\Hasee\\abaqus_plugins\\Fibre_insert', '_rsgTmp322_DB.py'), force=True )
osutils.remove(os.path.join('c:\\Users\\Hasee\\abaqus_plugins\\Fibre_insert', '_rsgTmp322_DB.pyc'), force=True )
except:
pass
... | code_fim | hard | {
"lang": "python",
"repo": "ericheshenghao/researh",
"path": "/Fibre_insert/_rsgTmp322_Form.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ericheshenghao/researh path: /Fibre_insert/_rsgTmp322_Form.py
from abaqusGui import *
from abaqusConstants import ALL
import osutils, os
###########################################################################
# Class definition
########################################################... | code_fim | medium | {
"lang": "python",
"repo": "ericheshenghao/researh",
"path": "/Fibre_insert/_rsgTmp322_Form.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: xairy/Facedancer path: /facedancer/backends/MAXUSBApp.py
# MAXUSBApp.py
#
# Contains class definition for MAXUSBApp.
import time
from ..core import FacedancerApp
from ..USB import *
from ..USBDevice import USBDeviceRequest
class MAXUSBApp(FacedancerApp):
reg_ep0_fifo = 0... | code_fim | hard | {
"lang": "python",
"repo": "xairy/Facedancer",
"path": "/facedancer/backends/MAXUSBApp.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> def disconnect(self):
self.write_register(self.reg_usb_control, self.usb_control_vbgate)
if self.verbose > 0:
print(self.app_name, "disconnected device", self.connected_device.name)
self.connected_device = None
def clear_irq_bit(self, reg, bit):
self.... | code_fim | hard | {
"lang": "python",
"repo": "xairy/Facedancer",
"path": "/facedancer/backends/MAXUSBApp.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> signed_angle_degs = Util.Utils().get_signed_angle_between_degs(
relative_hinge_reference_axis,
this_bone_inner_to_outer_uv,
relative_hinge_rotation_axis)
... | code_fim | hard | {
"lang": "python",
"repo": "Atiehmerikh/FABRIK_chain_3D",
"path": "/fabrik_chain_3d/FABRIK.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Atiehmerikh/FABRIK_chain_3D path: /fabrik_chain_3d/FABRIK.py
e == "GLOBAL_HINGE":
# Project this bone outer-to-inner direction onto the hinge rotation axis
this_bone_outer_to_inner_uv = Util.Utils().project_on_to_plane(this_bone_outer_to_inner_uv,
... | code_fim | hard | {
"lang": "python",
"repo": "Atiehmerikh/FABRIK_chain_3D",
"path": "/fabrik_chain_3d/FABRIK.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> scale = [i * this_bone_length for i in this_bone_inner_to_outer_uv]
start_location = this_bone.get_start_point_position()
new_end_location = [x + y for x, y in zip(start_location, scale)]
this_bone.set_end_point_position(new_end_location)
... | code_fim | hard | {
"lang": "python",
"repo": "Atiehmerikh/FABRIK_chain_3D",
"path": "/fabrik_chain_3d/FABRIK.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> X = X.reshape(64,64,3)
result = loaded_model.predict(X)
confusion_matrix[np.argmax(y), np.argmax(result[0])] += 1
print('Confusion Matrix'+confusion_matrix)
# Results Visualization
for i, (X, y)in enumerate(zip(X_test, y_test)):
plt.imshow(X)
plt.show()
print('prediction: ', ... | code_fim | hard | {
"lang": "python",
"repo": "LilyHeAsamiko/TUT-Advanced-Signal-Processing-Lab",
"path": "/4/image_recognition.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>model.add(Conv2D(64, (w, h),
activation = 'relu',
padding = 'same'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.5))
model.add(Conv2D(128, (w, h),
activation = 'relu',
padding = 'same'))
model.add(BatchNormaliza... | code_fim | hard | {
"lang": "python",
"repo": "LilyHeAsamiko/TUT-Advanced-Signal-Processing-Lab",
"path": "/4/image_recognition.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: LilyHeAsamiko/TUT-Advanced-Signal-Processing-Lab path: /4/image_recognition.py
# -*- coding: utf-8 -*-
"""
Spyder Editor
This is a temporary script file.
"""
from __future__ import print_function
import keras
from keras.application.vgg16 import VGG16
from keras.models import Sequential , Model... | code_fim | hard | {
"lang": "python",
"repo": "LilyHeAsamiko/TUT-Advanced-Signal-Processing-Lab",
"path": "/4/image_recognition.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def test_detect_short_sliders_time(self):
short_sliders = StdMapPatterns.detect_short_sliders_time(self.map_data, min_time=100)
def test_reinterpret_short_sliders(self):
map_data = StdMapPatterns.reinterpret_short_sliders(self.map_data, min_time=100, cs_px=4)<|fim_prefix|># repo:... | code_fim | hard | {
"lang": "python",
"repo": "abraker95/ultimate_osu_analyzer",
"path": "/unit_tests/test_std_map_patterns.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def test_reinterpret_short_sliders(self):
map_data = StdMapPatterns.reinterpret_short_sliders(self.map_data, min_time=100, cs_px=4)<|fim_prefix|># repo: abraker95/ultimate_osu_analyzer path: /unit_tests/test_std_map_patterns.py
import unittest
from osu.local.beatmap.beatmapIO import BeatmapI... | code_fim | hard | {
"lang": "python",
"repo": "abraker95/ultimate_osu_analyzer",
"path": "/unit_tests/test_std_map_patterns.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: abraker95/ultimate_osu_analyzer path: /unit_tests/test_std_map_patterns.py
import unittest
from osu.local.beatmap.beatmapIO import BeatmapIO
from analysis.osu.std.map_data import StdMapData
from analysis.osu.std.map_patterns import StdMapPatterns
class TestStdMapPatterns(unittest.TestCase):
... | code_fim | hard | {
"lang": "python",
"repo": "abraker95/ultimate_osu_analyzer",
"path": "/unit_tests/test_std_map_patterns.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> tx = np.genfromtxt(file_x, delimiter = ' ', skip_header = 1)
test = np.genfromtxt(file_t, delimiter = ' ', skip_header = 1)
ty = tx[:, 0]
tx = tx[:, 2:]
test = test[:, 2:]
#randomly shuffle tx:
np.random.shuffle(tx)
#Split train and test
ind = int(tx.shape[0... | code_fim | hard | {
"lang": "python",
"repo": "amirbawab/image_recognition",
"path": "/tools/python/cnn_keras2.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: amirbawab/image_recognition path: /tools/python/cnn_keras2.py
import keras
import numpy as np
from sklearn.preprocessing import OneHotEncoder
from keras.models import Sequential
from keras.layers import Dense, Activation, Conv2D, MaxPooling2D, Flatten, Dropout, BatchNormalization
from keras.optim... | code_fim | hard | {
"lang": "python",
"repo": "amirbawab/image_recognition",
"path": "/tools/python/cnn_keras2.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> #Create a mapping between indice in one hot encoded labels to actual label
labels = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 24, 25, 27, 28, 30, 32, 35, 36, 40, 42, 45, 48, 49, 54, 56, 63, 64, 72, 81]
ind = [i for i in range(40)]
mapping = dict()
fo... | code_fim | hard | {
"lang": "python",
"repo": "amirbawab/image_recognition",
"path": "/tools/python/cnn_keras2.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>def feature_normalize(dataset):
'''
TODO: Documentation
'''
mu = np.mean(dataset, axis=0)
sigma = np.std(dataset, axis=0)
return (dataset - mu)/sigma
def plot_axis(ax, x, y, title):
'''
TODO: Documentation
'''
ax.plot(x, y)
ax.set_title(title)
ax.xaxis.set... | code_fim | hard | {
"lang": "python",
"repo": "vixadd/cnn_lab",
"path": "/utils.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: vixadd/cnn_lab path: /utils.py
# Utility functions meant to aid our convolutional neural network.
from scipy import stats
import pandas as pb
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
<|fim_suffix|> '''
TODO: Documentation
... | code_fim | hard | {
"lang": "python",
"repo": "vixadd/cnn_lab",
"path": "/utils.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Fruitkwan/fastapi-template path: /application/main/infrastructure/database/mongodb/operations.py
from abc import ABC
from typing import Dict
import motor.motor_asyncio
from application.main.config import settings
from application.main.infrastructure.database.db_interface import DataBaseOperatio... | code_fim | hard | {
"lang": "python",
"repo": "Fruitkwan/fastapi-template",
"path": "/application/main/infrastructure/database/mongodb/operations.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> async def update_multiple_db_record(self, record: Dict):
connection_uri = 'mongodb://' + \
str(self.db_config.test.host) + str(self.db_config.test.port)
client = motor.motor_asyncio.AsyncIOMotorClient(connection_uri)
async def fetch_multiple_db_record(self, unique_id: ... | code_fim | hard | {
"lang": "python",
"repo": "Fruitkwan/fastapi-template",
"path": "/application/main/infrastructure/database/mongodb/operations.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: kymatio/kymatio path: /kymatio/scattering2d/backend/torch_skcuda_backend.py
from collections import namedtuple
import torch
import cupy
from string import Template
from ...backend.torch_skcuda_backend import TorchSkcudaBackend
from .torch_backend import TorchBackend2D
# As of v8, cupy.util ha... | code_fim | hard | {
"lang": "python",
"repo": "kymatio/kymatio",
"path": "/kymatio/scattering2d/backend/torch_skcuda_backend.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> out = out.reshape(batch_shape + out.shape[-3:])
return out
class Modulus(object):
"""This class implements a modulus transform for complex numbers.
Usage
-----
modulus = Modulus()
x_mod = modulus(x)
Parameters
---------
x : ten... | code_fim | hard | {
"lang": "python",
"repo": "kymatio/kymatio",
"path": "/kymatio/scattering2d/backend/torch_skcuda_backend.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: izzatum/BoMb-OT path: /DeepGM/experiments.py
import torch
from torchvision.utils import save_image
def reconstruct(filename,input,encoder,decoder,image_size,num_chanel,device):
with torch.no_grad():
x_sample = input.to(device)
x_reconstruct_mean = decoder(encoder(x_sample))
... | code_fim | medium | {
"lang": "python",
"repo": "izzatum/BoMb-OT",
"path": "/DeepGM/experiments.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> save_image(sample.view(num_sample, num_chanel, image_size, image_size), filename,scale_each=True,normalize=True)
def sampling_eps(filename,fixednoise,decoder,num_sample,image_size,num_chanel):
with torch.no_grad():
sample = decoder(fixednoise)
save_image(sample.view(num_samp... | code_fim | medium | {
"lang": "python",
"repo": "izzatum/BoMb-OT",
"path": "/DeepGM/experiments.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # Get an S3 resource reference
s3_resource = boto3.resource('s3')
# Download/Restore from tsbak
if len(tsbaks)>0:
# Get the local and remote paths
local_backup_path = os.path.join(backup_full_path(tsm=tsm), "backup.tsbak")
s3_backu... | code_fim | hard | {
"lang": "python",
"repo": "aws-quickstart/quickstart-tableau-server",
"path": "/scripts/backup-restore-s3.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: aws-quickstart/quickstart-tableau-server path: /scripts/backup-restore-s3.py
from ast import arg
import boto3
from botocore.exceptions import ClientError
import argparse, os, glob, logging, json, sys
from subprocess import check_output
from datetime import datetime
#########################
# ... | code_fim | hard | {
"lang": "python",
"repo": "aws-quickstart/quickstart-tableau-server",
"path": "/scripts/backup-restore-s3.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: theonaunheim/tutorials path: /python_sessions/202_intro_for_devs_part_2/_07_file_io.py
r'''
__ _ _ _
/ _(_) | ___ (_) ___
| |_| | |/ _ \ | |/ _ \
| _| | | __/ | | (_) |
|_| |_|_|\___| |_|\___/
'''
##================================================... | code_fim | hard | {
"lang": "python",
"repo": "theonaunheim/tutorials",
"path": "/python_sessions/202_intro_for_devs_part_2/_07_file_io.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|># Side note, you can nest if absolutely necessary.
# Open StringIO
with file_like_object_txt as f1:
# Open BytesIO
with file_like_object_bin as f2:
# Read StringIO
print(f1.read())
# Write binary data, seek to zero, read data, and print.
f2.write(bytearray([1, 1, 2,... | code_fim | hard | {
"lang": "python",
"repo": "theonaunheim/tutorials",
"path": "/python_sessions/202_intro_for_devs_part_2/_07_file_io.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: graphql-python/graphql-ws path: /examples/django_channels2/django_channels2/settings.py
"""
Django settings for django_channels2 project.
"""
SECRET_KEY = "0%1c709jhmggqhk&=tci06iy+%jedfxpcoai69jd8wjzm+k2f0"
DEBUG = True
INSTALLED_APPS = ["channels", "graphql_ws.django", "graphene_django"]
TEM... | code_fim | medium | {
"lang": "python",
"repo": "graphql-python/graphql-ws",
"path": "/examples/django_channels2/django_channels2/settings.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
CHANNEL_LAYERS = {"default": {"BACKEND": "channels.layers.InMemoryChannelLayer"}}
GRAPHENE = {"MIDDLEWARE": [], "SCHEMA": "django_channels2.schema.schema"}<|fim_prefix|># repo: graphql-python/graphql-ws path: /examples/django_channels2/django_channels2/settings.py
"""
Django settings for django_channels... | code_fim | hard | {
"lang": "python",
"repo": "graphql-python/graphql-ws",
"path": "/examples/django_channels2/django_channels2/settings.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>MIDDLEWARE = [
'django.middleware.common.CommonMiddleware',
]
ROOT_URLCONF = "django_channels2.urls"
ASGI_APPLICATION = "graphql_ws.django.routing.application"
CHANNEL_LAYERS = {"default": {"BACKEND": "channels.layers.InMemoryChannelLayer"}}
GRAPHENE = {"MIDDLEWARE": [], "SCHEMA": "django_channels2... | code_fim | hard | {
"lang": "python",
"repo": "graphql-python/graphql-ws",
"path": "/examples/django_channels2/django_channels2/settings.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>a=json.dumps(read_csv(path))
# print a
f= open("tripti.json","w")
f.write(a)
f.close()<|fim_prefix|># repo: mallatripti/Visualization path: /data_filter/data.py
#!/usr/bin/env python
import json
import os
import csv
import random
import string
currentdirpath = os.getcwd()
filename = 'choices.csv'
fil... | code_fim | hard | {
"lang": "python",
"repo": "mallatripti/Visualization",
"path": "/data_filter/data.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: mallatripti/Visualization path: /data_filter/data.py
#!/usr/bin/env python
import json
import os
import csv
import random
import string
currentdirpath = os.getcwd()
filename = 'choices.csv'
file_path = os.path.join(os.getcwd(), filename)
def get_file_path(filename):
currentdirpath = os.getcwd... | code_fim | hard | {
"lang": "python",
"repo": "mallatripti/Visualization",
"path": "/data_filter/data.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: egdeliya/hangman path: /tests/test_hangman.py
import sys
from hangman import hangman
class MyInput:
def __init__(self, input_values):
self.__input_values = input_values
def readline(self):
return self.__input_values.pop(0)
def test_hangman(capsys):
sys.stdin = MyI... | code_fim | hard | {
"lang": "python",
"repo": "egdeliya/hangman",
"path": "/tests/test_hangman.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> expected = "".join(["Guess a letter:\n",
"Missed, mistake 1 out of 5\n",
"The word: *****\n",
"Guess a letter:\n",
"Missed, mistake 2 out of 5\n",
"The word: ... | code_fim | hard | {
"lang": "python",
"repo": "egdeliya/hangman",
"path": "/tests/test_hangman.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> assert "You won!" in out
expected = "".join(["Guess a letter:\n",
"Missed, mistake 1 out of 5\n",
"The word: *****\n",
"Guess a letter:\n",
"Missed, mistake 2 out of 5\n",
... | code_fim | hard | {
"lang": "python",
"repo": "egdeliya/hangman",
"path": "/tests/test_hangman.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> Examples::
>>> rnn = nn.SubLSTMCell(10, 20)
>>> input = Variable(torch.randn(6, 3, 10))
>>> hx = Variable(torch.randn(3, 20))
>>> cx = Variable(torch.randn(3, 20))
>>> output = []
>>> for i in range(6):
... hx, cx = rnn(input[i], (hx, cx))
... output.append(hx)
"""... | code_fim | hard | {
"lang": "python",
"repo": "Junaid199f/Testing",
"path": "/subLSTM-master/pytorch-sublstm-master/pytorch-sublstm-master/subLSTM/nn/cell.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Junaid199f/Testing path: /subLSTM-master/pytorch-sublstm-master/pytorch-sublstm-master/subLSTM/nn/cell.py
#!/usr/bin/env python3
import torch.nn as nn
import torch as T
import torch.nn.functional as F
from torch.nn.modules.rnn import RNNCellBase
from subLSTM.functional import SubLSTMCell as Sub... | code_fim | hard | {
"lang": "python",
"repo": "Junaid199f/Testing",
"path": "/subLSTM-master/pytorch-sublstm-master/pytorch-sublstm-master/subLSTM/nn/cell.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> super(SubLSTMCell, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.bias = bias
self.weight_ih = nn.Parameter(T.Tensor(4 * hidden_size, input_size))
self.weight_hh = nn.Parameter(T.Tensor(4 * hidden_size, hidden_size))
if bias:
self.bias_i... | code_fim | hard | {
"lang": "python",
"repo": "Junaid199f/Testing",
"path": "/subLSTM-master/pytorch-sublstm-master/pytorch-sublstm-master/subLSTM/nn/cell.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>@responses.activate
def test_get_all_emoticons():
response = {"emoticons": [example_emote]}
responses.add(
responses.GET,
"{}chat/emoticons".format(BASE_URL),
body=json.dumps(response),
status=200,
content_type="application/json",
)
client = TwitchC... | code_fim | hard | {
"lang": "python",
"repo": "tsifrer/python-twitch-client",
"path": "/tests/api/test_chat.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: tsifrer/python-twitch-client path: /tests/api/test_chat.py
import json
import responses
from twitch.client import TwitchClient
from twitch.constants import BASE_URL
example_emote = {"code": "TwitchLit", "id": 115390}
@responses.activate
def test_get_badges_by_channel():
channel_id = 7236... | code_fim | hard | {
"lang": "python",
"repo": "tsifrer/python-twitch-client",
"path": "/tests/api/test_chat.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: romanalexander/opendota path: /dotastats/exceptions.py
class SteamAPIError(Exception):
<|fim_suffix|> self.errormessage = value<|fim_middle|> """ Error raised when the Steam API has issues. """
def __init__(self, value):
| code_fim | medium | {
"lang": "python",
"repo": "romanalexander/opendota",
"path": "/dotastats/exceptions.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> self.errormessage = value<|fim_prefix|># repo: romanalexander/opendota path: /dotastats/exceptions.py
class SteamAPIError(Exception):
<|fim_middle|> """ Error raised when the Steam API has issues. """
def __init__(self, value):
| code_fim | medium | {
"lang": "python",
"repo": "romanalexander/opendota",
"path": "/dotastats/exceptions.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> stack_4_widgets = []
stack_4_widgets.append(get_tooltip('cluster-type'))
stack_4_widgets.append(choose_option("process.executor", paths['nf']))
stack_4_widgets.append(filler)
stack_4_widgets.append(get_tooltip('cluster-arguments'))
stack_4_widgets.append(cluster_arguments("process.... | code_fim | hard | {
"lang": "python",
"repo": "statisticalbiotechnology/quandenser-pipeline",
"path": "/dependencies/ui/tab2/init_tab2.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> stack_3_widgets = []
stack_3_widgets.append(get_tooltip('cluster-type'))
stack_3_widgets.append(choose_option("process.executor", paths['nf']))
stack_3_widgets.append(filler)
stack_3_widgets.append(get_tooltip('cluster-arguments'))
stack_3_widgets.append(cluster_arguments("process.... | code_fim | hard | {
"lang": "python",
"repo": "statisticalbiotechnology/quandenser-pipeline",
"path": "/dependencies/ui/tab2/init_tab2.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: statisticalbiotechnology/quandenser-pipeline path: /dependencies/ui/tab2/init_tab2.py
from PySide2 import QtCore
from PySide2.QtWidgets import QWidget, QHBoxLayout, QVBoxLayout, QFormLayout, QLabel, QGridLayout, QSizePolicy
from PySide2.QtWidgets import QStackedLayout
from tab2.workflow import w... | code_fim | hard | {
"lang": "python",
"repo": "statisticalbiotechnology/quandenser-pipeline",
"path": "/dependencies/ui/tab2/init_tab2.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> # setup
origin_text = 'Hola mundo'
expected_text = 'Hola ...'
# exercise
target = get_short_text(origin_text, 8)
# verify
self.assertEquals(expected_text, target)
def test_long_text_retuns_the_shorter_text_if_has_to_cut_words(self):
# ... | code_fim | hard | {
"lang": "python",
"repo": "gsorianob/excelutils",
"path": "/tests_string_utils.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: gsorianob/excelutils path: /tests_string_utils.py
# -*- coding: utf-8 -*-
from django.test import TestCase
from excelutils.string_utils import agregar_espacios_luego_de_cada_coma_y_cada_punto, get_short_text
class StringUtilsTest(TestCase):
def test_agregar_espacios_en_blanco_a_string_con_c... | code_fim | hard | {
"lang": "python",
"repo": "gsorianob/excelutils",
"path": "/tests_string_utils.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>if __name__ == "__main__":
rect = RectangularArea(10, 12)
print(rect.square())
print(rect.perimeter())
dot1 = Dot(20,20)
print(dot1.dist_from_zero_version1())
print(dot1.dist_from_zero_version2())
print(dot1.between_two_dots(34.4, 45))
print(dot1.three_dimensional(12))<|fim... | code_fim | hard | {
"lang": "python",
"repo": "JuveVR/Homework_5",
"path": "/Exercise_2.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
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