text stringlengths 232 16.3k | domain stringclasses 1
value | difficulty stringclasses 3
values | meta dict |
|---|---|---|---|
<|fim_suffix|> default_label = "SpikeInterface - Quality Metrics"
pass
QualityMetricsPlotter.register(QualityMetricsWidget)<|fim_prefix|># repo: alejoe91/spikeinterface path: /src/spikeinterface/widgets/sortingview/quality_metrics.py
from .metrics import MetricsPlotter
from ..quality_metrics import QualityMe... | code_fim | easy | {
"lang": "python",
"repo": "alejoe91/spikeinterface",
"path": "/src/spikeinterface/widgets/sortingview/quality_metrics.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: alejoe91/spikeinterface path: /src/spikeinterface/widgets/sortingview/quality_metrics.py
from .metrics import MetricsPlotter
from ..quality_metrics import QualityMetricsWidget
<|fim_suffix|>QualityMetricsPlotter.register(QualityMetricsWidget)<|fim_middle|>class QualityMetricsPlotter(MetricsPlot... | code_fim | medium | {
"lang": "python",
"repo": "alejoe91/spikeinterface",
"path": "/src/spikeinterface/widgets/sortingview/quality_metrics.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: fabioderendinger/movie-website path: /generate_movie_page.py
import media
import fresh_tomatoes
# create movie objects of favorite movies
in_bruges = media.Movie("In Bruges",
"Guilt-stricken after a job gone wrong, hitman Ray and his partner await orders from their ruthl... | code_fim | hard | {
"lang": "python",
"repo": "fabioderendinger/movie-website",
"path": "/generate_movie_page.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>lord_of_the_rings = media.Movie("The Lord of the Rings: The Return of the King",
"Gandalf and Aragorn lead the World of Men against Sauron's army to draw his gaze from Frodo and Sam as they approach Mount Doom with the One Ring.",
"https://upload.wikimedia.... | code_fim | hard | {
"lang": "python",
"repo": "fabioderendinger/movie-website",
"path": "/generate_movie_page.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>setup(name='junkoda_cellularlib',
version='0.0.%d' % ver,
author='Jun Koda',
py_modules=['junkoda_cellularlib.cellularroot',
'junkoda_cellularlib.clusters',
'junkoda_cellularlib.data',
'junkoda_cellularlib.delaunay',
... | code_fim | hard | {
"lang": "python",
"repo": "junkoda/junkoda_cellularlib",
"path": "/py/setup.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: junkoda/junkoda_cellularlib path: /py/setup.py
#
# $ make
#
#from distutils.core import setup, Extension
from setuptools import setup, Extension
import numpy as np
import os
# Set default cellularroot dirctory
path, _ = os.path.split(os.path.abspath(__file__))
libroot = os.path.abspath(os.path.... | code_fim | hard | {
"lang": "python",
"repo": "junkoda/junkoda_cellularlib",
"path": "/py/setup.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def parse(self, response):
pass<|fim_prefix|># repo: redhead520/django-blog path: /src/novel/novel_spider/novel_spider/spiders/get_new_novel.py
import scrapy
class GetNewNovelSpider(scrapy.Spider):
<|fim_middle|> name = 'get_new_novel'
allowed_domains = ['https://www.qidian.com']
... | code_fim | medium | {
"lang": "python",
"repo": "redhead520/django-blog",
"path": "/src/novel/novel_spider/novel_spider/spiders/get_new_novel.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: redhead520/django-blog path: /src/novel/novel_spider/novel_spider/spiders/get_new_novel.py
import scrapy
class GetNewNovelSpider(scrapy.Spider):
<|fim_suffix|> def parse(self, response):
pass<|fim_middle|> name = 'get_new_novel'
allowed_domains = ['https://www.qidian.com']
... | code_fim | medium | {
"lang": "python",
"repo": "redhead520/django-blog",
"path": "/src/novel/novel_spider/novel_spider/spiders/get_new_novel.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> async def bind_axg_with_options_async(
self,
request: dyplsapi_20170525_models.BindAxgRequest,
runtime: util_models.RuntimeOptions,
) -> dyplsapi_20170525_models.BindAxgResponse:
UtilClient.validate_model(request)
query = {}
if not UtilClient.is_unse... | code_fim | hard | {
"lang": "python",
"repo": "aliyun/alibabacloud-python-sdk",
"path": "/dyplsapi-20170525/alibabacloud_dyplsapi20170525/client.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> def delete_secret_blacklist_with_options(
self,
request: dyplsapi_20170525_models.DeleteSecretBlacklistRequest,
runtime: util_models.RuntimeOptions,
) -> dyplsapi_20170525_models.DeleteSecretBlacklistResponse:
UtilClient.validate_model(request)
query = {}
... | code_fim | hard | {
"lang": "python",
"repo": "aliyun/alibabacloud-python-sdk",
"path": "/dyplsapi-20170525/alibabacloud_dyplsapi20170525/client.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: aliyun/alibabacloud-python-sdk path: /dyplsapi-20170525/alibabacloud_dyplsapi20170525/client.py
otocol='HTTPS',
pathname='/',
method='POST',
auth_type='AK',
style='RPC',
req_body_type='formData',
body_type='json'
)
... | code_fim | hard | {
"lang": "python",
"repo": "aliyun/alibabacloud-python-sdk",
"path": "/dyplsapi-20170525/alibabacloud_dyplsapi20170525/client.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> def checkpoint(self, epoch: int):
"""Method to save the state dictionaries of model, optimizer,etc.
Args:
epoch : The epoch at which model is saved.
"""
if self.exp.scheduler_stepper is not None:
torch.save(
{
... | code_fim | hard | {
"lang": "python",
"repo": "earlbabson/torchflare",
"path": "/torchflare/callbacks/model_checkpoint.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: earlbabson/torchflare path: /torchflare/callbacks/model_checkpoint.py
"""Implements Model Checkpoint Callback."""
from abc import ABC
from typing import Dict
import numpy as np
import torch
from torchflare.callbacks.callback import Callbacks
from torchflare.callbacks.states import CallbackOrder... | code_fim | hard | {
"lang": "python",
"repo": "earlbabson/torchflare",
"path": "/torchflare/callbacks/model_checkpoint.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> #fce.plot()
#with h5py.File("test_file_1",driver="core",
# backing_store=False) as f:
with tempfile.TemporaryFile() as f:
fce.save(f, test=True)
fce2 = DFunction()
fce2 = fce2.load(f, test=True)
... | code_fim | medium | {
"lang": "python",
"repo": "tmancal74/quantarhei",
"path": "/tests/unit/core/test_dfunction.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: tmancal74/quantarhei path: /tests/unit/core/test_dfunction.py
# -*- coding: utf-8 -*-
import unittest
import numpy
#import h5py
import tempfile
from quantarhei import DFunction, FrequencyAxis
class TestDFunction(unittest.TestCase):
"""Tests for the units package
"""
... | code_fim | hard | {
"lang": "python",
"repo": "tmancal74/quantarhei",
"path": "/tests/unit/core/test_dfunction.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> fce = DFunction(wa,fw)
val_mez_linear = fce.at(20.5)
val_mez_spline = fce.at(20.5, approx="spline")
# print("init", fce._splines_initialized)
# print(val_mez_linear)
# print(val_mez_spline)
#with h5py.File("test_file_1",driver="core",... | code_fim | hard | {
"lang": "python",
"repo": "tmancal74/quantarhei",
"path": "/tests/unit/core/test_dfunction.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: digital-science/dimcli path: /dimcli/core/dsl_grammar_categories.py
379",
"name": "Regenerative Medicine"
},
{
"count": 260234,
"id": "543",
"name": "Orphan Drug"
},
{
"count": 243219,
"id": "4... | code_fim | hard | {
"lang": "python",
"repo": "digital-science/dimcli",
"path": "/dimcli/core/dsl_grammar_categories.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> },
{
"count": 461587,
"id": "2539",
"name": "0406 Physical Geography and Environmental Geoscience"
},
{
"count": 448145,
"id": "2878",
"name": "0907 Environmental Engineering"
},
{
... | code_fim | hard | {
"lang": "python",
"repo": "digital-science/dimcli",
"path": "/dimcli/core/dsl_grammar_categories.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> "id": "529",
"name": "Health Effects of Indoor Air Pollution"
},
{
"count": 5530,
"id": "402",
"name": "Teenage Pregnancy"
},
{
"count": 5465,
"id": "590",
"name": "Youth Violence Preven... | code_fim | hard | {
"lang": "python",
"repo": "digital-science/dimcli",
"path": "/dimcli/core/dsl_grammar_categories.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: srz-zumix/wandbox-api path: /wandbox/__php__.py
import re
import os
from .cli import CLI
from .runner import Runner
from .utils import split_statements
class PhpRunner(Runner):
REQUIRE_INCLUDE_REGEX = re.compile(r'.*(require|require_once|include|include_once)\s*[\'"\(\)](.*?)[\'"\(\)]\s*;... | code_fim | hard | {
"lang": "python",
"repo": "srz-zumix/wandbox-api",
"path": "/wandbox/__php__.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> strings = tokens.split('.')
path = ""
for s in strings:
s = s.strip().strip('\'"()')
if s == 'PATH_SEPARATOR':
self.add_include_path(dir, path)
path = ""
else:
path += s
if len(path) > 0:
... | code_fim | hard | {
"lang": "python",
"repo": "srz-zumix/wandbox-api",
"path": "/wandbox/__php__.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Jiezhi/myleetcode path: /src/1207-UniqueNumberOfOccurrences.py
#!/usr/bin/env python3
"""
CREATED AT: 2022-11-30
URL: https://leetcode.com/problems/unique-number-of-occurrences/
GITHUB: https://github.com/Jiezhi/myleetcode
FileName: 1207-UniqueNumberOfOccurrences
<|fim_suffix|>Tag:
See:
"... | code_fim | medium | {
"lang": "python",
"repo": "Jiezhi/myleetcode",
"path": "/src/1207-UniqueNumberOfOccurrences.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> assert Solution().uniqueOccurrences(arr=[1, 2, 2, 1, 1, 3])
assert not Solution().uniqueOccurrences(arr=[1, 2])
assert Solution().uniqueOccurrences(arr=[-3, 0, 1, -3, 1, 1, 1, -3, 10, 0])
if __name__ == '__main__':
test()<|fim_prefix|># repo: Jiezhi/myleetcode path: /src/1207-UniqueNumb... | code_fim | hard | {
"lang": "python",
"repo": "Jiezhi/myleetcode",
"path": "/src/1207-UniqueNumberOfOccurrences.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: beer-garden/brewtils path: /brewtils/test/comparable.py
# -*- coding: utf-8 -*-
"""Module to simplify model comparisons.
.. warning::
This module was created to simplify testing. As such, it's not recommended for
production use.
.. warning::
This module subject to change outside of ... | code_fim | hard | {
"lang": "python",
"repo": "beer-garden/brewtils",
"path": "/brewtils/test/comparable.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def assert_role_equal(obj1, obj2, do_raise=False):
return _assert_wrapper(
obj1,
obj2,
expected_type=LegacyRole,
deep_fields={"roles": partial(assert_role_equal, do_raise=True)},
do_raise=do_raise,
)
def assert_system_equal(obj1, obj2, do_raise=False):
... | code_fim | hard | {
"lang": "python",
"repo": "beer-garden/brewtils",
"path": "/brewtils/test/comparable.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> try:
assert (
isinstance(dict1, dict) and "ids" in dict1
), "Not a job ID dict: %s" % str(dict1)
assert (
isinstance(dict2, dict) and "ids" in dict2
), "Not a job ID dict: %s" % str(dict2)
lst1, lst2 = dict1.get("ids", None), dict2.get("... | code_fim | hard | {
"lang": "python",
"repo": "beer-garden/brewtils",
"path": "/brewtils/test/comparable.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # plotting
fig, axs = plt.subplots(2, 2, figsize=(2 * 3.5, 2 * 2.45), constrained_layout=True)
ax0, ax1, ax2, ax3 = axs.flat
for ax in axs[0].flat:
ax.plot(xs, exact, label='exact', lw=1.5)
ax.plot(xs, x, label='xs irfft', ls=':', lw=1.5)
ax.legend(fontsize='x-smal... | code_fim | hard | {
"lang": "python",
"repo": "mynl/aggregate",
"path": "/aggregate/extensions/ft.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> #. Compute aggregate with enough space for the supported part of the
distribution (say, where density > 1e-15 or so)
#. Subtract the mean by rolling left (negative shift) by mean / bs buckets (mod n)
#. fft shift = roll (in either direction) by n / 2 buckets, because
the distributi... | code_fim | hard | {
"lang": "python",
"repo": "mynl/aggregate",
"path": "/aggregate/extensions/ft.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: mynl/aggregate path: /aggregate/extensions/ft.py
oll(wrap, B >> 1)
df = pd.DataFrame({'x': xs, 'FFT pmf': pmf})
po = poisson(en)
df['Exact pmf'] = po.pmf(df.x)
df = df.set_index('x', drop=True)
fig, [ax0, ax1] = plt.subplots(1, 2, figsize=(FIG_W * 2, FIG_H + 0.3), constrained_... | code_fim | hard | {
"lang": "python",
"repo": "mynl/aggregate",
"path": "/aggregate/extensions/ft.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Tetrite/cBinder path: /cBinder/HeaderFile.py
import os
import pathlib
from cBinder.Scrapers import ScrapedData
class HeaderFile:
"""
Class representing one C header file.
Attributes
----------
filepath : Path
Path object pointing to header file
includes : list
... | code_fim | medium | {
"lang": "python",
"repo": "Tetrite/cBinder",
"path": "/cBinder/HeaderFile.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> d = ScrapedData(h_path, export_symbols)
self.filepath = h_path
self.enums = d.enums
self.functions = d.functions
self.structs = d.structs
self.includes = d.includes
self.defines = d.defines
def __str__(self):
return 'Header file path: ' ... | code_fim | medium | {
"lang": "python",
"repo": "Tetrite/cBinder",
"path": "/cBinder/HeaderFile.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> """
Gets paths to all header files in directory (recursively)
Parameters
---------
dirpath : str
Library directory path string
export_symbols: dict
Dict containing lists of export symbols
Returns
-------
paths : list
List of HeaderFile objects
... | code_fim | medium | {
"lang": "python",
"repo": "Tetrite/cBinder",
"path": "/cBinder/HeaderFile.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: X-DataInitiative/tick path: /tick/base_model/model_lipschitz.py
# License: BSD 3 clause
from abc import abstractmethod
from . import Model
__author__ = 'Stephane Gaiffas'
class ModelLipschitz(Model):
"""An abstract base class for a model that implements lipschitz
constants
Parame... | code_fim | hard | {
"lang": "python",
"repo": "X-DataInitiative/tick",
"path": "/tick/base_model/model_lipschitz.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> """Returns the best Lipschitz constant, using all samples
Warning: this might take some time, since it requires a SVD computation.
Returns
-------
output : `float`
The best Lipschitz constant
"""
if self._fitted:
if self._rea... | code_fim | hard | {
"lang": "python",
"repo": "X-DataInitiative/tick",
"path": "/tick/base_model/model_lipschitz.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|>
# Evento pygame.KEYDOWN es generado al presionar una tecla
# Evento pygame.KEYUP es generado al soltar una tecla
# ----------------------- Bucle principal del programa-------
while not hecho:
# -------- Bucle principal de eventos ---------------
# Cerrar la pantalla de pygame al presionar en la ... | code_fim | hard | {
"lang": "python",
"repo": "osmandi/programarcadegames",
"path": "/Capítulo 10: Mando de juegos y gráficos/control_con_teclado.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
# Evento pygame.KEYDOWN es generado al presionar una tecla
# Evento pygame.KEYUP es generado al soltar una tecla
# ----------------------- Bucle principal del programa-------
while not hecho:
# -------- Bucle principal de eventos ---------------
# Cerrar la pantalla de pygame al presionar en la... | code_fim | hard | {
"lang": "python",
"repo": "osmandi/programarcadegames",
"path": "/Capítulo 10: Mando de juegos y gráficos/control_con_teclado.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: osmandi/programarcadegames path: /Capítulo 10: Mando de juegos y gráficos/control_con_teclado.py
# Importamos la librería de pygame
import pygame
# Iniciamos pygame
pygame.init()
# Definimos algunos colores
NEGRO = [0, 0, 0]
BLANCO = (255,255,255)
VERDE = (0,255,0)
ROJO = (255,0,0)
AZUL = (0,0,... | code_fim | hard | {
"lang": "python",
"repo": "osmandi/programarcadegames",
"path": "/Capítulo 10: Mando de juegos y gráficos/control_con_teclado.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>plugins.bsto.standalone(
# initial window-size. Tuple of (X,Y) or "fullscreen"
size = (1024,768),
#"Size" : "fullscreen",
# which mediaplayer do you use? I really like mplayer
playerCmd = "mplayer -fs '%s'")<|fim_prefix|># repo: inkeso/pselect path: /bsto_standalone.py
#!/usr/bin/env... | code_fim | medium | {
"lang": "python",
"repo": "inkeso/pselect",
"path": "/bsto_standalone.py",
"mode": "spm",
"license": "LicenseRef-scancode-warranty-disclaimer",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: inkeso/pselect path: /bsto_standalone.py
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
import plugins.bsto
<|fim_suffix|>plugins.bsto.standalone(
# initial window-size. Tuple of (X,Y) or "fullscreen"
size = (1024,768),
#"Size" : "fullscreen",
# which mediaplayer do you use? I... | code_fim | medium | {
"lang": "python",
"repo": "inkeso/pselect",
"path": "/bsto_standalone.py",
"mode": "psm",
"license": "LicenseRef-scancode-warranty-disclaimer",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Ankuraxz/Major_Project path: /inference_example.py
import cv2
import engine as eng
import inference as inf
import numpy as np
import tensorrt as trt
TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
trt_runtime = trt.Runtime(TRT_LOGGER)
class_list=['n000037', 'n000021', 'n000005', 'n000104', 'n00008... | code_fim | medium | {
"lang": "python",
"repo": "Ankuraxz/Major_Project",
"path": "/inference_example.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|># PREDICTION ON DEFAULT
h_input, d_input, h_output, d_output, stream = inf.allocate_buffers(engine, 1, trt.float32)
out1 = inf.do_inference(engine, img1, h_input, d_input, h_output, d_output, stream, 1, HEIGHT, WIDTH)
out2 = inf.do_inference(engine,img2,h_input,d_input,h_output,d_output,stream,1,HEIGHT,WI... | code_fim | hard | {
"lang": "python",
"repo": "Ankuraxz/Major_Project",
"path": "/inference_example.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> txid, rnd = self.proxy._call("name_new", 'd/' + name)
# XXX: Store txid and/or rnd for name_firstupdate, poll at/around expected time
def test_connection(self):
try:
self.proxy._call('help')
return True
except:
return False
def... | code_fim | hard | {
"lang": "python",
"repo": "probar/syncnet",
"path": "/namecoin.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: probar/syncnet path: /namecoin.py
#!/usr/bin/env python2
import json
from bitcoin.rpc import Proxy
import httplib
import os.path
import platform
def get_proxy(user, password, port=8336):
return Proxy(service_url="http://{0}:{1}@localhost:{2}".format(user, password, port))
class NameTake... | code_fim | medium | {
"lang": "python",
"repo": "probar/syncnet",
"path": "/namecoin.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def _build_json_value(self, secret, existing=None):
existing = dict(existing) or {}
existing['syncnet'] = existing.get('syncnet', {})
existing['syncnet']['secret'] = secret
return existing<|fim_prefix|># repo: probar/syncnet path: /namecoin.py
#!/usr/bin/env python2
i... | code_fim | hard | {
"lang": "python",
"repo": "probar/syncnet",
"path": "/namecoin.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def test_percent_encoding():
"""
Test whether percent encoding is working correctly based on an
artificial test file.
"""
file = gff.GFFFile.read(join(data_dir("sequence"), "percent_test.gff3"))
seqid, source, type, start, end, score, strand, phase, attrib \
= file[0]
... | code_fim | hard | {
"lang": "python",
"repo": "biotite-dev/biotite",
"path": "/tests/sequence/test_gff.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|>@pytest.mark.parametrize(
"path", ["bt_lysozyme.gp", "gg_avidin.gb", "ec_bl21.gb", "sc_chrom1.gb"]
)
def test_genbank_consistency(path):
"""
Test whether the same annotation (if reasonable) can be read from a
GFF3 file and a GenBank file.
"""
gb_file = gb.GenBankFile.read(join(data... | code_fim | hard | {
"lang": "python",
"repo": "biotite-dev/biotite",
"path": "/tests/sequence/test_gff.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: biotite-dev/biotite path: /tests/sequence/test_gff.py
# This source code is part of the Biotite package and is distributed
# under the 3-Clause BSD License. Please see 'LICENSE.rst' for further
# information.
from tempfile import TemporaryFile
from os.path import join
import biotite.sequence as ... | code_fim | hard | {
"lang": "python",
"repo": "biotite-dev/biotite",
"path": "/tests/sequence/test_gff.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> seconds = int((self - dt.datetime.min).total_seconds())
remainder = dt.timedelta(
seconds=seconds % delta.total_seconds(),
microseconds=self.microsecond,
)
quotient = self - remainder
return quotient, remainder
def __floordiv__(self, del... | code_fim | medium | {
"lang": "python",
"repo": "Lakerfield/timelapse",
"path": "/datetime_modulo.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Lakerfield/timelapse path: /datetime_modulo.py
import datetime as dt
#treyhunner: https://gist.github.com/treyhunner/6218526
class datetime(dt.datetime):
def __divmod__(self, delta):
seconds = int((self - dt.datetime.min).total_seconds())
remainder = dt.timedelta(
... | code_fim | medium | {
"lang": "python",
"repo": "Lakerfield/timelapse",
"path": "/datetime_modulo.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ryanfmurphy/c-sounds-fft path: /fastfour.py
import numpy as np
import sys
import matplotlib.pyplot as plt
'''
def arr_from_stdin(chunk_size)
data = sys.stdin.read(chunk_size)
arr = np.frombuffer(data,dtype=np.uint8)
if arr.shape != (chunk_size,):
raise IOError("not enough bytes ... | code_fim | medium | {
"lang": "python",
"repo": "ryanfmurphy/c-sounds-fft",
"path": "/fastfour.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>chunk_size = 128*1024
nbins = 128
#with open('funsounds.wav','rb') as fh:
if True:
data = sys.stdin.read(chunk_size)
arr = np.frombuffer(data,dtype=np.uint8)
bins = np.fft.fft(arr,n=nbins)
plt.plot(bins)
sys.stdout.write(data)
plt.show()<|fim_prefix|># repo: ryanfmurphy/c-sounds-f... | code_fim | medium | {
"lang": "python",
"repo": "ryanfmurphy/c-sounds-fft",
"path": "/fastfour.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: SocialFinanceDigitalLabs/AdventOfCode path: /solutions/2022/pughmds/day01/__main__.py
EXPECTED_TEST_ANSWER_PART1 = [24000]
EXPECTED_TEST_ANSWER_PART2 = [45000]
def set_max(calories, max_calories):
"""
Returns the larger value
"""
return calories if calories > max_calories else m... | code_fim | medium | {
"lang": "python",
"repo": "SocialFinanceDigitalLabs/AdventOfCode",
"path": "/solutions/2022/pughmds/day01/__main__.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> """
Takes a list of values in "data" and returns sum of the largest
group. Groups are separated by blank entries in the list.
"""
calorie_list = []
calories = 0
for item in data:
item = item.strip()
if item != "":
calories += int(item)
else:
... | code_fim | hard | {
"lang": "python",
"repo": "SocialFinanceDigitalLabs/AdventOfCode",
"path": "/solutions/2022/pughmds/day01/__main__.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> return max_calories
def run_p2(data):
"""
Takes a list of values in "data" and returns sum of the largest
group. Groups are separated by blank entries in the list.
"""
calorie_list = []
calories = 0
for item in data:
item = item.strip()
if item != "":
... | code_fim | hard | {
"lang": "python",
"repo": "SocialFinanceDigitalLabs/AdventOfCode",
"path": "/solutions/2022/pughmds/day01/__main__.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> self.fc2 = ln.Linear(inputs, 1, gain=1)
def encode(self, x, lod):
x = self.from_rgb[self.layer_count - lod - 1](x)
x = F.leaky_relu(x, 0.2)
for i in range(self.layer_count - lod - 1, self.layer_count):
x = self.encode_block[i](x)
return self.fc2(x... | code_fim | hard | {
"lang": "python",
"repo": "LendelTheGreat/SCALAE",
"path": "/net.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: LendelTheGreat/SCALAE path: /net.py
self.instance_norm_1 = nn.InstanceNorm2d(outputs, affine=False, eps=1e-8)
self.style_1 = ln.Linear(latent_size, 2 * outputs, gain=1)
self.popmap_1 = ln.Conv2d(1, 2 * outputs, 3, 1, 1, bias=False)
self.conv_2 = ln.Conv2d(outputs, o... | code_fim | hard | {
"lang": "python",
"repo": "LendelTheGreat/SCALAE",
"path": "/net.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: LendelTheGreat/SCALAE path: /net.py
nn.ModuleList()
resolution = 2 ** (self.layer_count + 1)
for i in range(self.layer_count):
outputs = min(self.maxf, startf * mul)
self.from_rgb.append(FromRGB(channels, inputs))
fused_scale = resolution >... | code_fim | hard | {
"lang": "python",
"repo": "LendelTheGreat/SCALAE",
"path": "/net.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Ryanb58/fastapi-saas-base path: /app/dependencies/auth.py
from fastapi import Depends, FastAPI, HTTPException
from sqlalchemy.orm import Session
from starlette.status import HTTP_401_UNAUTHORIZED
import jwt
from jwt import PyJWTError
from app.dependencies import get_db
from app.controllers.auth... | code_fim | hard | {
"lang": "python",
"repo": "Ryanb58/fastapi-saas-base",
"path": "/app/dependencies/auth.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
# async def get_current_active_account(current_user: Account = Depends(get_current_account)):
# if current_user.disabled:
# raise HTTPException(status_code=400, detail="Inactive user")
# return current_user<|fim_prefix|># repo: Ryanb58/fastapi-saas-base path: /app/dependencies/auth.py
fr... | code_fim | hard | {
"lang": "python",
"repo": "Ryanb58/fastapi-saas-base",
"path": "/app/dependencies/auth.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> dataframe['Hour'] = dataframe[column].dt.hour
dataframe['Minute'] = dataframe[column].dt.minute
dataframe['Second'] = dataframe[column].dt.second
return dataframe # Return new dataframe<|fim_prefix|># repo: KennethTBarrett/lambdata path: /dt.py
import pandas as pd
def date_splitter(data... | code_fim | hard | {
"lang": "python",
"repo": "KennethTBarrett/lambdata",
"path": "/dt.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>def time_splitter(dataframe, column): # Splits times into hour / minute / second
dataframe['Hour'] = dataframe[column].dt.hour
dataframe['Minute'] = dataframe[column].dt.minute
dataframe['Second'] = dataframe[column].dt.second
return dataframe # Return new dataframe<|fim_prefix|># repo: K... | code_fim | medium | {
"lang": "python",
"repo": "KennethTBarrett/lambdata",
"path": "/dt.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: KennethTBarrett/lambdata path: /dt.py
import pandas as pd
def date_splitter(dataframe, column): # Splits dates into month / day / year
dataframe['Month'] = dataframe[column].dt.month
dataframe['Day'] = dataframe[column].dt.day
dataframe['Year'] = dataframe[column].dt.year
return... | code_fim | medium | {
"lang": "python",
"repo": "KennethTBarrett/lambdata",
"path": "/dt.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Ascend/ModelZoo-PyTorch path: /PyTorch/built-in/cv/semantic_segmentation/Attention_R2U_Net_for_PyTorch/dataset.py
# BSD 3-Clause License
#
# Copyright (c) 2017 xxxx
# All rights reserved.
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Redistribution and use in source and binary forms, wi... | code_fim | hard | {
"lang": "python",
"repo": "Ascend/ModelZoo-PyTorch",
"path": "/PyTorch/built-in/cv/semantic_segmentation/Attention_R2U_Net_for_PyTorch/dataset.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> # model hyper-parameters
parser.add_argument('--train_ratio', type=float, default=0.7)
parser.add_argument('--valid_ratio', type=float, default=0.1)
parser.add_argument('--test_ratio', type=float, default=0.2)
# data path
parser.add_argument('--origin_data_path', type=str, d... | code_fim | hard | {
"lang": "python",
"repo": "Ascend/ModelZoo-PyTorch",
"path": "/PyTorch/built-in/cv/semantic_segmentation/Attention_R2U_Net_for_PyTorch/dataset.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> src = os.path.join(config.origin_data_path, data_list[idx])
dst = os.path.join(config.test_path,data_list[idx])
copyfile(src, dst)
src = os.path.join(config.origin_GT_path, GT_list[idx])
dst = os.path.join(config.test_GT_path, GT_list[idx])
co... | code_fim | hard | {
"lang": "python",
"repo": "Ascend/ModelZoo-PyTorch",
"path": "/PyTorch/built-in/cv/semantic_segmentation/Attention_R2U_Net_for_PyTorch/dataset.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: as23187/WeFe path: /kernel/components/feature/onehot/horzonehot/param.py
# Copyright 2021 Tianmian Tech. All Rights Reserved.
#
# 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 Lice... | code_fim | hard | {
"lang": "python",
"repo": "as23187/WeFe",
"path": "/kernel/components/feature/onehot/horzonehot/param.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> def __init__(self, transform_col_indexes=-1, transform_col_names=None, need_run=True, need_alignment=True):
super(HorzOneHotParam, self).__init__()
if transform_col_names is None:
transform_col_names = []
self.transform_col_indexes = transform_col_indexes
se... | code_fim | medium | {
"lang": "python",
"repo": "as23187/WeFe",
"path": "/kernel/components/feature/onehot/horzonehot/param.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: nasingfaund/iterm2-images path: /iterm2_images/transfer_cli.py
#!/usr/bin/env python3
"""Transfers files from the machine you are logged into to your desktop."""
<|fim_suffix|>from .payloads import FileEsc
@click.command()
@click.argument('file', type=click_pathlib.Path(readable=True))
def ma... | code_fim | easy | {
"lang": "python",
"repo": "nasingfaund/iterm2-images",
"path": "/iterm2_images/transfer_cli.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
@click.command()
@click.argument('file', type=click_pathlib.Path(readable=True))
def main(file):
"""The main function."""
FileEsc.open(file).write()<|fim_prefix|># repo: nasingfaund/iterm2-images path: /iterm2_images/transfer_cli.py
#!/usr/bin/env python3
"""Transfers files from the machine you... | code_fim | medium | {
"lang": "python",
"repo": "nasingfaund/iterm2-images",
"path": "/iterm2_images/transfer_cli.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>color(color3)
begin_fill()
forward(lado)
left(45)
forward(pequenoAngulo)
left(135)
forward(lado)
left(45)
forward(pequenoAngulo)
end_fill()
left(135)
forward(lado)
color(color2)
begin_fill()
left(45)
forward(pequenoAngulo)
right(135)
forward(lado)
right(45)
forward(pequenoAngulo)
right(135)
forward(lado... | code_fim | hard | {
"lang": "python",
"repo": "programingfrik/randomPython",
"path": "/fichaTetris.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: programingfrik/randomPython path: /fichaTetris.py
#!/usr/bin/python3
from turtle import *
from math import *
lado = 100
doble = lado * 2
mitad = lado * 0.5
pequeno = lado * 0.2
pequenoAngulo = (pequeno / sin(radians(45)))
interior = lado - (pequeno * 2)
dobleInterior = doble - (pequeno * 2)
co... | code_fim | hard | {
"lang": "python",
"repo": "programingfrik/randomPython",
"path": "/fichaTetris.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: antocuni/python-zeep path: /src/zeep/wsse/utils.py
import datetime
import pytz
from lxml import etree
from lxml.builder import ElementMaker
NSMAP = {
'wsse': 'http://docs.oasis-open.org/wss/2004/01/oasis-200401-wss-wssecurity-secext-1.0.xsd',
}
WSSE = ElementMaker(namespace=NSMAP['wsse'])
... | code_fim | hard | {
"lang": "python",
"repo": "antocuni/python-zeep",
"path": "/src/zeep/wsse/utils.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> """
header_qname = '{http://schemas.xmlsoap.org/soap/envelope/}Header'
header = doc.find(header_qname)
if header is None:
header = etree.Element(header_qname)
doc.insert(0, header)
security = header.find('wsse:Security', namespaces=NSMAP)
if security is None:
... | code_fim | medium | {
"lang": "python",
"repo": "antocuni/python-zeep",
"path": "/src/zeep/wsse/utils.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: fujimakishouten/unzip-cp932 path: /unzip.py
#!/usr/bin/env python3
# vim: set expandtab tabstop=4 shiftwidth=4 softtabstop=4:
import argparse
import chardet
import os
import zipfile
parser = argparse.ArgumentParser(description = "Extract zip file includes cp932 encoding file name")
parser.a... | code_fim | hard | {
"lang": "python",
"repo": "fujimakishouten/unzip-cp932",
"path": "/unzip.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>with zipfile.ZipFile(args.file, 'r') as archive:
for item in archive.namelist():
encoding = chardet.detect(item.encode("cp437"))
filename = os.path.join(args.directory, item.encode("cp437").decode(encoding['encoding']))
directory = os.path.dirname(filename)
if not os.p... | code_fim | hard | {
"lang": "python",
"repo": "fujimakishouten/unzip-cp932",
"path": "/unzip.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: pytorch/pytorch path: /torch/nn/quantized/modules/__init__.py
r"""Quantized Modules
Note::
The `torch.nn.quantized` namespace is in the process of being deprecated.
Please, use `torch.ao.nn.quantized` instead.
"""
from torch.ao.nn.quantized.modules.activation import ReLU6, Hardswish, EL... | code_fim | hard | {
"lang": "python",
"repo": "pytorch/pytorch",
"path": "/torch/nn/quantized/modules/__init__.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|>__all__ = [
'BatchNorm2d',
'BatchNorm3d',
'Conv1d',
'Conv2d',
'Conv3d',
'ConvTranspose1d',
'ConvTranspose2d',
'ConvTranspose3d',
'DeQuantize',
'ELU',
'Embedding',
'EmbeddingBag',
'GroupNorm',
'Hardswish',
'InstanceNorm1d',
'InstanceNorm2d',
... | code_fim | hard | {
"lang": "python",
"repo": "pytorch/pytorch",
"path": "/torch/nn/quantized/modules/__init__.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> return "prelink-" + self.version
def dependencies(self) -> Iterator[Package]:
yield self.libelf
def fetch(self, ctx: Context) -> None:
run(
ctx,
[
"svn",
"co",
"-r" + self.version,
"sv... | code_fim | hard | {
"lang": "python",
"repo": "vusec/instrumentation-infra",
"path": "/infra/packages/prelink/__init__.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> def __init__(self, version: str):
self.version = version
# assert version == '209'
self.libelf = LibElf("0.7.0")
def ident(self) -> str:
return "prelink-" + self.version
def dependencies(self) -> Iterator[Package]:
yield self.libelf
def fetch(self... | code_fim | hard | {
"lang": "python",
"repo": "vusec/instrumentation-infra",
"path": "/infra/packages/prelink/__init__.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: vusec/instrumentation-infra path: /infra/packages/prelink/__init__.py
import os
import shutil
from typing import Iterator
from ...context import Context
from ...package import Package
from ...util import apply_patch, download, run
class LibElf(Package):
"""
:identifier: libelf-<version... | code_fim | hard | {
"lang": "python",
"repo": "vusec/instrumentation-infra",
"path": "/infra/packages/prelink/__init__.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>url_domain_dict = {}
reader = csv.DictReader( csv_file, fieldnames)
for row in reader:
url_domain_dict[row["BASE_DOMAIN_PK"]] = row
url_domain_dict[row["BASE_DOMAIN_PK"]].pop("BASE_DOMAIN_PK", None)
helper.dump_json_to_file('resources/data/annotations/json/ALL_URL_DOMAIN_METADATA.json', url_domai... | code_fim | hard | {
"lang": "python",
"repo": "stevenzim/chiir-2019",
"path": "/resources/scripts/setup/all_annotations_to_json.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: stevenzim/chiir-2019 path: /resources/scripts/setup/all_annotations_to_json.py
# Scripts to convert 1) cochrane annotation csv files to json
import csv
from src import helper
# COCHRANE QUESTIONS CONVESRION
csv_file = open('resources/data/annotations/csv/COCHRANE_QUESTION_DEFINITIONS_FINAL.c... | code_fim | medium | {
"lang": "python",
"repo": "stevenzim/chiir-2019",
"path": "/resources/scripts/setup/all_annotations_to_json.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
# PAIN IN ASS.... needed to add title... there is some strange character in first postion of CSV...hence the correction
# of field name 0
# The file name is now XLS_JUDGMENTS_HELPFUL
csv_file = open('resources/data/annotations/csv/ALL_JUDGMENTS_v_1.csv', 'r')
reader = csv.reader(csv_file)
fieldnames = ne... | code_fim | medium | {
"lang": "python",
"repo": "stevenzim/chiir-2019",
"path": "/resources/scripts/setup/all_annotations_to_json.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> for data_file_name in data_filenames:
metadata_file_name = data_file_name.split('.')[0] + '_metadata.json'
shutil.copyfile(args.data_dir + data_file_name, output_dir + data_file_name)
shutil.copyfile(args.data_dir + metadata_file_name, output_dir + metadata_file_name)
copy_d... | code_fim | hard | {
"lang": "python",
"repo": "BlissChapman/SyntheticStatistics",
"path": "/brainpedia/tag_splitter.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> shutil.copyfile(args.data_dir + data_file_name, output_dir + data_file_name)
shutil.copyfile(args.data_dir + metadata_file_name, output_dir + metadata_file_name)
copy_data(data_filenames_with_tag, args.output_dir_with_tag)
copy_data(data_filenames_without_tag, args.output_dir_without_tag... | code_fim | hard | {
"lang": "python",
"repo": "BlissChapman/SyntheticStatistics",
"path": "/brainpedia/tag_splitter.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: BlissChapman/SyntheticStatistics path: /brainpedia/tag_splitter.py
import argparse
import os
import random
import shutil
import sys
from preprocessor import Preprocessor
parser = argparse.ArgumentParser(description="Utility script that given a folder of Brainpedia data can generate two new fold... | code_fim | hard | {
"lang": "python",
"repo": "BlissChapman/SyntheticStatistics",
"path": "/brainpedia/tag_splitter.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> tf.Variable(777)
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
start = global_step.eval()
for i in range(start,128):
for start,end in zip(range(0,len(trX),128),range(128,len(trX)+1,128)):
sess.run(train_op,feed_dict={X:trX[start:end... | code_fim | hard | {
"lang": "python",
"repo": "fanshaohua001/tfnote",
"path": "/code/tf_load.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: fanshaohua001/tfnote path: /code/tf_load.py
import tensorflow as tf
import numpy as np
import os
from tensorflow.examples.tutorials.mnist import input_data
def init_weights(shape):
return tf.Variable(tf.random_normal(shape,mean=0,stddev=0.01))
def model(X,w_h,w_h2,w_o,p_keep_input,p_keep_hidd... | code_fim | hard | {
"lang": "python",
"repo": "fanshaohua001/tfnote",
"path": "/code/tf_load.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Azure/autorest.az path: /test/unittest/expected/tests/cmdlet/test_positive_empty.py
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# ... | code_fim | medium | {
"lang": "python",
"repo": "Azure/autorest.az",
"path": "/test/unittest/expected/tests/cmdlet/test_positive_empty.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|># Test class for Scenario
class PositiveTest(ScenarioTest):
def __init__(self, *args, **kwargs):
super(PositiveTest, self).__init__(*args, **kwargs)<|fim_prefix|># repo: Azure/autorest.az path: /test/unittest/expected/tests/cmdlet/test_positive_empty.py
# ------------------------------------... | code_fim | medium | {
"lang": "python",
"repo": "Azure/autorest.az",
"path": "/test/unittest/expected/tests/cmdlet/test_positive_empty.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def __init__(self, *args, **kwargs):
super(PositiveTest, self).__init__(*args, **kwargs)<|fim_prefix|># repo: Azure/autorest.az path: /test/unittest/expected/tests/cmdlet/test_positive_empty.py
# --------------------------------------------------------------------------
# Copyright (c) Micros... | code_fim | medium | {
"lang": "python",
"repo": "Azure/autorest.az",
"path": "/test/unittest/expected/tests/cmdlet/test_positive_empty.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def fit(self, X, Y=None):
"""
Nothing to be done, but useful when included in a scikit-learn Pipeline.
"""
return self
def __transform(self, cells):
cells = np.asarray(cells)
if len(cells.shape) == 1 and self.min_persistence >= 0:
res = ... | code_fim | hard | {
"lang": "python",
"repo": "GUDHI/gudhi-devel",
"path": "/src/python/gudhi/sklearn/cubical_persistence.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: GUDHI/gudhi-devel path: /src/python/gudhi/sklearn/cubical_persistence.py
# This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT.
# See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details.
# Author(s): Vincent Rouvreau
#
... | code_fim | hard | {
"lang": "python",
"repo": "GUDHI/gudhi-devel",
"path": "/src/python/gudhi/sklearn/cubical_persistence.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: aws/aws-cli path: /tests/functional/ecs/test_execute_command.py
# Copyright 2021 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"). You
# may not use this file except in compliance with the License. A copy of
# the License ... | code_fim | hard | {
"lang": "python",
"repo": "aws/aws-cli",
"path": "/tests/functional/ecs/test_execute_command.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> @mock.patch('awscli.customizations.ecs.executecommand.check_call')
def test_execute_command_fails(self, mock_check_call):
cmdline = 'ecs execute-command --cluster someCluster ' \
'--task someTaskId ' \
'--interactive --command ls ' \
'-... | code_fim | hard | {
"lang": "python",
"repo": "aws/aws-cli",
"path": "/tests/functional/ecs/test_execute_command.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> matrix = [
[3, 0, 1, 4, 2],
[5, 6, 3, 2, 1],
[1, 2, 0, 1, 5],
[4, 1, 0, 1, 7],
[1, 0, 3, 0, 5]
]
obj = NumMatrix(matrix)
assert obj.sumRegion(2, 1, 4, 3) == 8
assert obj.sumRegion(1, 1, 2, 2) == 11
assert obj.sumRegion(1, 2, 3, 4)... | code_fim | medium | {
"lang": "python",
"repo": "shubhamoli/solutions",
"path": "/leetcode/medium/304-Range_sum_2D.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # call to this will be frequent, so try to avoid calculation in this
def sumRegion(self, row1: int, col1: int, row2: int, col2: int) -> int:
return self._sum[row2+1][col2+1] - self._sum[row1][col2+1] - self._sum[row2+1][col1] + self._sum[row1][col1]
if __name__ == "__main__":
matri... | code_fim | hard | {
"lang": "python",
"repo": "shubhamoli/solutions",
"path": "/leetcode/medium/304-Range_sum_2D.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: shubhamoli/solutions path: /leetcode/medium/304-Range_sum_2D.py
"""
Leetcode #304
"""
from typing import List
# Extension of LC#307
class NumMatrix:
def __init__(self, matrix: List[List[int]]):
if not matrix or not matrix[0]:
return
M = len(matrix)
... | code_fim | hard | {
"lang": "python",
"repo": "shubhamoli/solutions",
"path": "/leetcode/medium/304-Range_sum_2D.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: InsaneMonster/USienaRL path: /usienarl/interfaces/pass_through.py
#
# Copyright (C) 2019 Luca Pasqualini
# University of Siena - Artificial Intelligence Laboratory - SAILab
#
#
# USienaRL is licensed under a BSD 3-Clause.
#
# You should have received a copy of the license along with this
# work. ... | code_fim | hard | {
"lang": "python",
"repo": "InsaneMonster/USienaRL",
"path": "/usienarl/interfaces/pass_through.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> @property
def observation_space_shape(self):
# Just return the environment state space shape
return self._environment.state_space_shape
@property
def agent_action_space_type(self) -> SpaceType:
# Just return the environment action space type
return self._en... | code_fim | hard | {
"lang": "python",
"repo": "InsaneMonster/USienaRL",
"path": "/usienarl/interfaces/pass_through.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
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