text stringlengths 232 16.3k | domain stringclasses 1
value | difficulty stringclasses 3
values | meta dict |
|---|---|---|---|
<|fim_suffix|>
loop = asyncio.get_event_loop()
loop.create_task(
main()
)
for signame in ('SIGINT', 'SIGTERM'):
loop.add_signal_handler(
getattr(signal, signame),
lambda: asyncio.ensure_future(ask_exit(signame))
)
loop.run_forever()<|fim_prefix|># repo: lianraru/aiokraken path: /aiokraken/... | code_fim | hard | {
"lang": "python",
"repo": "lianraru/aiokraken",
"path": "/aiokraken/examples/wss_example.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: lianraru/aiokraken path: /aiokraken/examples/wss_example.py
import asyncio
import signal
from aiokraken import WssClient
def process_message(message):
print(f'processed message {message}')
<|fim_suffix|>loop.create_task(
main()
)
for signame in ('SIGINT', 'SIGTERM'):
loop.add_sign... | code_fim | hard | {
"lang": "python",
"repo": "lianraru/aiokraken",
"path": "/aiokraken/examples/wss_example.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: gczarnocki/grovepi-logger path: /Logger/logger.py
# Logowanie temperatury, poziomu swiatla, dzwieku, odleglosci za pomoca RPi
# GrovePi + Sound Sensor + Light Sensor +
# Temperature Sensor + Ultrasonic Ranger Sensor + LED
# http://www.seeedstudio.com/wiki/Grove_-_Sound_Sensor
# http://www.seeed... | code_fim | hard | {
"lang": "python",
"repo": "gczarnocki/grovepi-logger",
"path": "/Logger/logger.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>timestamp = time.strftime('%Y-%m-%d:%H:%M:%S')
log_file = 'logs/' + timestamp + '.log'
log = open(log_file, 'a')
# Polaczenia
light_sensor = 0 # port A0
sound_sensor = 1 # port A1
temperature_sensor = 2 # port D2
led = 3 # port D3
ranger = 4 # port D4
led2 = 5 # port D5
button ... | code_fim | hard | {
"lang": "python",
"repo": "gczarnocki/grovepi-logger",
"path": "/Logger/logger.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> dependencies = [
('dashboard', '0004_profile_student_id'),
('student', '0002_auto_20181113_1920'),
]
operations = [
migrations.AddField(
model_name='student',
name='toprofile',
field=models.ForeignKey(default='', on_delete=django.db.... | code_fim | medium | {
"lang": "python",
"repo": "vasundhara7/College-EWallet",
"path": "/student/migrations/0003_student_toprofile.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: vasundhara7/College-EWallet path: /student/migrations/0003_student_toprofile.py
# Generated by Django 2.0.9 on 2018-12-10 17:59
from django.db import migrations, models
import django.db.models.deletion
<|fim_suffix|> dependencies = [
('dashboard', '0004_profile_student_id'),
... | code_fim | medium | {
"lang": "python",
"repo": "vasundhara7/College-EWallet",
"path": "/student/migrations/0003_student_toprofile.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> def __del__(self):
#print(self.__class__.__name__, self.market, self.ktype, "__del__")
pass
@hku_catch(trace=True)
def __call__(self):
self.status = "no run"
capture_multiprocess_all_logger(self.log_queue)
use_hdf = False
if self.config.getboole... | code_fim | hard | {
"lang": "python",
"repo": "fasiondog/hikyuu",
"path": "/hikyuu/gui/data/ImportTdxToH5Task.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> count = 0
try:
progress = ProgressBar(self)
if use_hdf:
count = import_data(
connect, self.market, self.ktype, self.quotations, self.src_dir, self.dest_dir, progress
)
else:
count = impo... | code_fim | hard | {
"lang": "python",
"repo": "fasiondog/hikyuu",
"path": "/hikyuu/gui/data/ImportTdxToH5Task.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: fasiondog/hikyuu path: /hikyuu/gui/data/ImportTdxToH5Task.py
# coding:utf-8
#
# The MIT License (MIT)
#
# Copyright (c) 2010-2017 fasiondog/hikyuu
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"... | code_fim | hard | {
"lang": "python",
"repo": "fasiondog/hikyuu",
"path": "/hikyuu/gui/data/ImportTdxToH5Task.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|># with open('test.txt', 'r') as f:
# input = [int(i) for i in f.read().strip()]
# solve(input)
with open('input.txt', 'r') as f:
input = [int(i) for i in f.read().strip()]
solve(input)<|fim_prefix|># repo: amochtar/adventofcode path: /2019/day-16/part1.py
#!/usr/bin/env pypy3
def gen_p... | code_fim | hard | {
"lang": "python",
"repo": "amochtar/adventofcode",
"path": "/2019/day-16/part1.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: amochtar/adventofcode path: /2019/day-16/part1.py
#!/usr/bin/env pypy3
def gen_pattern(i):
p = [0] * (i+1)
p.extend([1]*(i+1))
p.extend([0]*(i+1))
p.extend([-1]*(i+1))
return p[1:]+p[:1]
def phase(inp):
output = [0] * len(inp)
for i, x in enumerate(inp):
pa... | code_fim | hard | {
"lang": "python",
"repo": "amochtar/adventofcode",
"path": "/2019/day-16/part1.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: JSJeong-me/2021-K-Digital-Training path: /Web_Crawling/python-crawler/chapter_6/run_crawl.py
"""scrapy의 quotes 크롤러 호출하기"""
from scrapy.crawler import CrawlerProcess
from scrapy.utils.project import get_project_settings
<|fim_suffix|> """크롤링 실행"""
process = CrawlerProcess(get_project_... | code_fim | easy | {
"lang": "python",
"repo": "JSJeong-me/2021-K-Digital-Training",
"path": "/Web_Crawling/python-crawler/chapter_6/run_crawl.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>if __name__ == '__main__':
run_crawl()<|fim_prefix|># repo: JSJeong-me/2021-K-Digital-Training path: /Web_Crawling/python-crawler/chapter_6/run_crawl.py
"""scrapy의 quotes 크롤러 호출하기"""
from scrapy.crawler import CrawlerProcess
from scrapy.utils.project import get_project_settings
def run_crawl():... | code_fim | medium | {
"lang": "python",
"repo": "JSJeong-me/2021-K-Digital-Training",
"path": "/Web_Crawling/python-crawler/chapter_6/run_crawl.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: datarail/msda path: /msda/kmeans.py
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_samples, silhouette_score
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
import numpy as np
import matplotlib.cm as cm
def cluster(dfi, samples, num_clusters=8... | code_fim | hard | {
"lang": "python",
"repo": "datarail/msda",
"path": "/msda/kmeans.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # Initialize the clusterer with n_clusters value and a random generator
# seed of 10 for reproducibility.
clusterer = KMeans(n_clusters=n_clusters, random_state=10)
cluster_labels = clusterer.fit_predict(X)
# The silhouette_score gives the average value for all the... | code_fim | hard | {
"lang": "python",
"repo": "datarail/msda",
"path": "/msda/kmeans.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: nikwl/mime-release path: /mime/scene/chain.py
import numpy as np
import pybullet as pb
from .body import Body
from .joint import JointArray
class Chain(JointArray):
def __init__(self, body_id, tip_link_name, client_id):
<|fim_suffix|> joints = [i.info for i in body if not i.info.is_... | code_fim | medium | {
"lang": "python",
"repo": "nikwl/mime-release",
"path": "/mime/scene/chain.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> super(Chain, self).__init__(body_id, chain_indices, client_id)
self._tip = tip
self._lowers = lowers
self._uppers = uppers
self._ranges = np.subtract(uppers, lowers)
self._chain_mask = chain_mask
@property
def tip(self):
return self._tip
... | code_fim | medium | {
"lang": "python",
"repo": "nikwl/mime-release",
"path": "/mime/scene/chain.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: mpab/SemanticSearch path: /app/api/context_execute.py
# pylint: disable=missing-docstring
from typing import Tuple
from context_args_parse import Args, ContextArgs
from context_types import (
ExecState,
SearchContext,
SearchContextExt,
SearchRequest,
)
from document_utilities im... | code_fim | hard | {
"lang": "python",
"repo": "mpab/SemanticSearch",
"path": "/app/api/context_execute.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> while status > 0:
status, info = context_execute_by_identifier_hash_step(identifier_hash)
print(status, info)
return status, info
def context_execute_by_context_parameters(
context_parameters: ContextArgs,
) -> Tuple[int, str]:
request = SearchRequest(context_parameters.data... | code_fim | hard | {
"lang": "python",
"repo": "mpab/SemanticSearch",
"path": "/app/api/context_execute.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: JoelLigma/Image-Classification-with-PyTorch path: /train.py
# Module Imports
import numpy as np
import pandas as pd
import json
import torchvision
from torchvision import datasets, transforms, models
import torch
from torch import nn, optim
import torch.nn.functional as F
import time
import PIL
... | code_fim | medium | {
"lang": "python",
"repo": "JoelLigma/Image-Classification-with-PyTorch",
"path": "/train.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # user inputs from command line
in_arg = get_input_args()
# load and process data into training, validation and test data sets
trainloader, validationloader, testloader, train_data = load_and_transform(in_arg.data_dir)
# load pre-trained nn and build classifier with user inputs (loss c... | code_fim | medium | {
"lang": "python",
"repo": "JoelLigma/Image-Classification-with-PyTorch",
"path": "/train.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>def parse_args():
parser = ArgumentParser()
parser.add_argument("--text", help="input string or path to a .txt file", default=None, type=str)
parser.add_argument(
"--input_case", help="input capitalization", choices=["lower_cased", "cased"], default="cased", type=str
)
parser.a... | code_fim | hard | {
"lang": "python",
"repo": "blisc/NeMo",
"path": "/nemo_text_processing/text_normalization/normalize_with_audio.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> line = json.loads(line)
audio = line['audio_filepath']
if 'transcript' in line:
transcript = line['transcript']
else:
transcript = asr_model.transcribe([audio])[0]
normalized_texts = normalizer.normalize(
text=line['text'],
verbose=args.verbose,
... | code_fim | hard | {
"lang": "python",
"repo": "blisc/NeMo",
"path": "/nemo_text_processing/text_normalization/normalize_with_audio.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: blisc/NeMo path: /nemo_text_processing/text_normalization/normalize_with_audio.py
# Copyright (c) 2021, NVIDIA CORPORATION. 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... | code_fim | hard | {
"lang": "python",
"repo": "blisc/NeMo",
"path": "/nemo_text_processing/text_normalization/normalize_with_audio.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>"-c", type=int, help="How many times to print the greeting")
parser.add_argument('-f', '--foo', default=42)
# we will parse the arguments now
args = parser.parse_args()
print(args.foo, type(args.foo))
for _ in range(args.c):
print(f"Hello {args.name}")<|fim_prefix|># repo: ValR... | code_fim | medium | {
"lang": "python",
"repo": "ValRCS/LU_PySem_2020_1",
"path": "/src/argreet.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>ments now
args = parser.parse_args()
print(args.foo, type(args.foo))
for _ in range(args.c):
print(f"Hello {args.name}")<|fim_prefix|># repo: ValRCS/LU_PySem_2020_1 path: /src/argreet.py
import argparse
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Prin... | code_fim | hard | {
"lang": "python",
"repo": "ValRCS/LU_PySem_2020_1",
"path": "/src/argreet.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ValRCS/LU_PySem_2020_1 path: /src/argreet.py
import argparse
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Print a greeting')
# we will define some a<|fim_suffix|>"-c", type=int, help="How many times to print the greeting")
parser.add_argument('-f', '--foo'... | code_fim | medium | {
"lang": "python",
"repo": "ValRCS/LU_PySem_2020_1",
"path": "/src/argreet.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # table_pat to erase all html tag and get only those data between those tags
table_pat = re.compile('>([^<^&]+?)<')
stock_list = [table_head]
for i in tr_list:
s = re.findall(table_pat, i)
stock_list.append(s)
return stock_list
dji_list = retrieve_dji_list()
for i in d... | code_fim | hard | {
"lang": "python",
"repo": "HawkingLaugh/Data-Processing-Using-Python",
"path": "/Week2/11. CNN NASDAQ.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: HawkingLaugh/Data-Processing-Using-Python path: /Week2/11. CNN NASDAQ.py
import re
import requests
def retrieve_dji_list():
r = requests.get('https://money.cnn.com/data/markets/nasdaq/')
# the first row, with each col's title
# re.findall(regular expression pattern, string(reque... | code_fim | hard | {
"lang": "python",
"repo": "HawkingLaugh/Data-Processing-Using-Python",
"path": "/Week2/11. CNN NASDAQ.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> async def check_local_raylet_liveness(self) -> bool:
if self._local_node_address is None:
return False
liveness = await self._gcs_aio_client.check_alive(
[self._local_node_address.encode()], 0.1
)
return liveness[0]
async def check_gcs_live... | code_fim | hard | {
"lang": "python",
"repo": "ray-project/ray",
"path": "/dashboard/modules/healthz/utils.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ray-project/ray path: /dashboard/modules/healthz/utils.py
from typing import Optional
from ray._private.gcs_utils import GcsAioClient
class HealthChecker:
def __init__(
self, gcs_aio_client: GcsAioClient, local_node_address: Optional[str] = None
):
self._gcs_aio_client =... | code_fim | medium | {
"lang": "python",
"repo": "ray-project/ray",
"path": "/dashboard/modules/healthz/utils.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: devinmatte/packet path: /packet/ldap.py
"""
Helper functions for working with the csh_ldap library
"""
from functools import lru_cache
from datetime import date
from packet import _ldap
def _ldap_get_group_members(group):
"""
:return: A list of CSHMember instances
"""
return _... | code_fim | hard | {
"lang": "python",
"repo": "devinmatte/packet",
"path": "/packet/ldap.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def ldap_get_drink_admins():
"""
All drink admins
:return: A list of CSHMember instances
"""
return [member.uid for member in _ldap_get_group_members('drink')]
def ldap_get_eboard_role(member):
"""
:param member: A CSHMember instance
:return: A String or None
"""
... | code_fim | hard | {
"lang": "python",
"repo": "devinmatte/packet",
"path": "/packet/ldap.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: elibs/epython path: /src/epython/filters.py
# -*- coding: utf-8 -*-
"""
Description:
This module contains all types of filters useful for QA
Author:
Ray Gomez
Date:
3/22/21
"""
import os
import re
from epython import errors
from epython.environment import _LOG
def generic_log_fi... | code_fim | hard | {
"lang": "python",
"repo": "elibs/epython",
"path": "/src/epython/filters.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # Capture the log line if we are within the capturing state
if capturing:
processed_file += line
# Find the end position
if capturing and end in line:
break
else:
_LOG.info(f"End of {logfile} not found, ca... | code_fim | hard | {
"lang": "python",
"repo": "elibs/epython",
"path": "/src/epython/filters.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # Find the start position
if start in line:
capturing = True
# Capture the log line if we are within the capturing state
if capturing:
processed_file += line
# Find the end position
if capturing and e... | code_fim | hard | {
"lang": "python",
"repo": "elibs/epython",
"path": "/src/epython/filters.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: vishalbelsare/HyperStream path: /hyperstream/channels/channel_manager.py
# The MIT License (MIT) # Copyright (c) 2014-2017 University of Bristol
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"... | code_fim | hard | {
"lang": "python",
"repo": "vishalbelsare/HyperStream",
"path": "/hyperstream/channels/channel_manager.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> self.update_channels()
@property
def tool_channels(self):
"""
The tool channels as a list
"""
return [c for c in self.values() if isinstance(c, ToolChannel)]
@property
def memory_channels(self):
"""
The memory channels as a list
... | code_fim | hard | {
"lang": "python",
"repo": "vishalbelsare/HyperStream",
"path": "/hyperstream/channels/channel_manager.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> tool_stream_view = None
# Look in the main tool channel first
if tool_id in self.tools:
tool_stream_view = self.tools[tool_id].window((MIN_DATE, self.tools.up_to_timestamp))
else:
# Otherwise look through all the channels in the order they were defi... | code_fim | hard | {
"lang": "python",
"repo": "vishalbelsare/HyperStream",
"path": "/hyperstream/channels/channel_manager.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # Create and start the RPC server
server = ThreadedServer(SlaveService,port=server_port)
t = threading.Thread(target=server.start)
t.setDaemon(True)
t.start()
# Send heartbeats
while True:
heartbeat_sender.send(config.state)
time.sleep(1)<|fim_prefix|># repo:... | code_fim | hard | {
"lang": "python",
"repo": "cnwangfeng/integration-prototype",
"path": "/slave/sip_slave/main.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: cnwangfeng/integration-prototype path: /slave/sip_slave/main.py
""" Skeleton slave controller
A handler for SIGTERM is set up that just exits because that is what
'Docker stop' sends.
"""
import os
from rpyc.utils.server import ThreadedServer
import threading
import time
from sip_common impor... | code_fim | hard | {
"lang": "python",
"repo": "cnwangfeng/integration-prototype",
"path": "/slave/sip_slave/main.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> args = [
'predictions',
'create',
service.create_document_id(),
service.create_model_id(),
*preprocess_config,
*postprocess_config,
]
util.main_parser(parser, client, args)<|fim_prefix|># repo: LucidtechAI/las-cli path: /tests/test_predictions.p... | code_fim | hard | {
"lang": "python",
"repo": "LucidtechAI/las-cli",
"path": "/tests/test_predictions.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: LucidtechAI/las-cli path: /tests/test_predictions.py
import json
import pytest
from tests import service, util
@pytest.mark.parametrize('sort_by', [('--sort-by', 'createdTime')])
@pytest.mark.parametrize('order', [('--order', 'ascending'), ('--order', 'descending')])
def test_predictions_list(p... | code_fim | hard | {
"lang": "python",
"repo": "LucidtechAI/las-cli",
"path": "/tests/test_predictions.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> path_to_directory = os.path.join(str(tmpdir), 'src')
result = run_cli_rename(path_to_directory, file_with_the_imports_to_move)
assert result.exit_code == 0
with open(file_path, mode='r') as file:
assert file.read() == "from x.x import c\nfrom d.e import f\n"
def test_run_rename... | code_fim | hard | {
"lang": "python",
"repo": "ESSS/module-renamer",
"path": "/tests/test_rename_imports.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ESSS/module-renamer path: /tests/test_rename_imports.py
import os
import pytest
from module_renamer.cli import rename
@pytest.fixture
def run_cli_rename():
def _run_cli_rename(project_path, file_path):
from click.testing import CliRunner
runner = CliRunner()
return ... | code_fim | hard | {
"lang": "python",
"repo": "ESSS/module-renamer",
"path": "/tests/test_rename_imports.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> with open(file_path, 'w+') as file:
file.writelines(['from a.b impot c\n'])
# Create the file with the list of imports to move
file_with_the_imports_to_move = os.path.join(str(tmpdir), "list_output.py")
with open(file_with_the_imports_to_move, 'w+') as file:
file.writelin... | code_fim | hard | {
"lang": "python",
"repo": "ESSS/module-renamer",
"path": "/tests/test_rename_imports.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>def load_jupyter_server_extension(nb_server_app):
"""
Called when the extension is loaded.
Args:
nb_server_app (NotebookWebApplication): handle to the Notebook webserver instance.
"""
web_app = nb_server_app.web_app
host_pattern = '.*$'
route_pattern = url_path_join(we... | code_fim | medium | {
"lang": "python",
"repo": "Carreau/remote_ikernel",
"path": "/remote_ikernel/webui.py",
"mode": "spm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> Args:
nb_server_app (NotebookWebApplication): handle to the Notebook webserver instance.
"""
web_app = nb_server_app.web_app
host_pattern = '.*$'
route_pattern = url_path_join(web_app.settings['base_url'], '/hello')
web_app.add_handlers(host_pattern, [(route_pattern, HelloW... | code_fim | medium | {
"lang": "python",
"repo": "Carreau/remote_ikernel",
"path": "/remote_ikernel/webui.py",
"mode": "spm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Carreau/remote_ikernel path: /remote_ikernel/webui.py
from notebook.utils import url_path_join
from notebook.base.handlers import IPythonHandler
<|fim_suffix|> Args:
nb_server_app (NotebookWebApplication): handle to the Notebook webserver instance.
"""
web_app = nb_server_app.... | code_fim | medium | {
"lang": "python",
"repo": "Carreau/remote_ikernel",
"path": "/remote_ikernel/webui.py",
"mode": "psm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> with open(os.path.join(model_path, "svm-model.pkl"), "w") as out:
pickle.dump(svm, out)
print("Training has been completed.")
except Exception as e:
trc = traceback.format_exc()
with open(os.path.join(output_path, "failure"), "w") as s:
s.wr... | code_fim | hard | {
"lang": "python",
"repo": "mauriciomani/Ecce-Homo",
"path": "/sagemaker_examples/pure_genius/folder_all_data/train",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> train_data = pd.read_csv(input_files[0])
X = train_data.drop("DEATH_EVENT", axis = 1)
y = train_data["DEATH_EVENT"]
C = trainingParams.get("C", 1.0)
if C is not 1.0:
C = float(C)
#train support vector machine
svm = SVC(C = C)
... | code_fim | hard | {
"lang": "python",
"repo": "mauriciomani/Ecce-Homo",
"path": "/sagemaker_examples/pure_genius/folder_all_data/train",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: mauriciomani/Ecce-Homo path: /sagemaker_examples/pure_genius/folder_all_data/train
#!/usr/bin/env python
import os
import json
import pickle
import sys
import traceback
import pandas as pd
from sklearn.svm import SVC
prefix = "/opt/ml/"
input_path = prefix + "input/data"
output_path = os.pat... | code_fim | hard | {
"lang": "python",
"repo": "mauriciomani/Ecce-Homo",
"path": "/sagemaker_examples/pure_genius/folder_all_data/train",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: fnyaoke/School-MS path: /migrations/versions/6661c5d6588c_add_student_id_column.py
"""Add student id column
Revision ID: 6661c5d6588c
Revises: 07b82f889002
Create Date: 2020-11-04 16:12:28.307073
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revis... | code_fim | medium | {
"lang": "python",
"repo": "fnyaoke/School-MS",
"path": "/migrations/versions/6661c5d6588c_add_student_id_column.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: D33pBlue/Apprendimento-Automatico path: /sms_spam/sms_evolutionaryNN_classification.py
# -*- coding: utf-8 -*-
from __future__ import division
import csv,random,pickle
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
from skl... | code_fim | hard | {
"lang": "python",
"repo": "D33pBlue/Apprendimento-Automatico",
"path": "/sms_spam/sms_evolutionaryNN_classification.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>X,y = [],[]
# with open('sms_spam.csv', 'rb') as csvfile:
# recordfile = csv.reader(csvfile, delimiter=',', quotechar='"')
# for row in recordfile:
# X.append(row[1].decode('latin-1'))
# if row[0]=='spam':
# y.append(1)
# else:
# y.append(0)
#
# save... | code_fim | hard | {
"lang": "python",
"repo": "D33pBlue/Apprendimento-Automatico",
"path": "/sms_spam/sms_evolutionaryNN_classification.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>keras.utils.plot_model(model, "keras_LSTM_autoencoder.png", show_shapes=True)
#%%
model.summary()
model.compile(optimizer=keras.optimizers.Adam(1e-3), loss='mse') #tf.keras.metrics.mean_squared_error
history = model.fit(x_train, y_train,
validation_data = (x_val, y_val),
... | code_fim | hard | {
"lang": "python",
"repo": "MachineLearningJournalClub/SSVEP_IEEE_SMC_2021",
"path": "/Code/ModelSelection/LSTM/LSTM_autoencoder.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: MachineLearningJournalClub/SSVEP_IEEE_SMC_2021 path: /Code/ModelSelection/LSTM/LSTM_autoencoder.py
#%%
import numpy as np
import pandas as pd
import tensorflow as tf
import scipy.io
import random
import xgboost as xgb
import seaborn as sns
from tensorflow import keras
from tensorflow.keras import... | code_fim | hard | {
"lang": "python",
"repo": "MachineLearningJournalClub/SSVEP_IEEE_SMC_2021",
"path": "/Code/ModelSelection/LSTM/LSTM_autoencoder.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> BaseCommand[WaitForTemperatureParams, WaitForTemperatureResult]
):
"""A command to wait for a Temperature Module's target temperature."""
commandType: WaitForTemperatureCommandType = "temperatureModule/waitForTemperature"
params: WaitForTemperatureParams
result: Optional[WaitForTemper... | code_fim | hard | {
"lang": "python",
"repo": "Opentrons/opentrons",
"path": "/api/src/opentrons/protocol_engine/commands/temperature_module/wait_for_temperature.py",
"mode": "spm",
"license": "LicenseRef-scancode-warranty-disclaimer",
"source": "the-stack-v2"
} |
<|fim_suffix|> _ImplementationCls: Type[WaitForTemperatureImpl] = WaitForTemperatureImpl
class WaitForTemperatureCreate(BaseCommandCreate[WaitForTemperatureParams]):
"""A request to create a Temperature Module's wait for temperature command."""
commandType: WaitForTemperatureCommandType = "temperatureModu... | code_fim | hard | {
"lang": "python",
"repo": "Opentrons/opentrons",
"path": "/api/src/opentrons/protocol_engine/commands/temperature_module/wait_for_temperature.py",
"mode": "spm",
"license": "LicenseRef-scancode-warranty-disclaimer",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Opentrons/opentrons path: /api/src/opentrons/protocol_engine/commands/temperature_module/wait_for_temperature.py
"""Command models to wait for target temperature of a Temperature Module."""
from __future__ import annotations
from typing import Optional, TYPE_CHECKING
from typing_extensions import... | code_fim | hard | {
"lang": "python",
"repo": "Opentrons/opentrons",
"path": "/api/src/opentrons/protocol_engine/commands/temperature_module/wait_for_temperature.py",
"mode": "psm",
"license": "LicenseRef-scancode-warranty-disclaimer",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Ruturaj123/SARKAR.AI path: /jack/io/embeddings/memory_map.py
# -*- coding: utf-8 -*-
import json
import os
import numpy as np
from jack.io.embeddings import Embeddings
def load_memory_map_dir(directory: str) -> Embeddings:
"""
Loads embeddings from a memory map directory to allow laz... | code_fim | hard | {
"lang": "python",
"repo": "Ruturaj123/SARKAR.AI",
"path": "/jack/io/embeddings/memory_map.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> """
Saves the given embeddings as memory map file and corresponding meta data in a directory.
Args:
directory: the directory to store the memory map file in (called `memory_map`) and the meta file (called
`meta.json` that stores the shape of the memory map and the actual vocabu... | code_fim | hard | {
"lang": "python",
"repo": "Ruturaj123/SARKAR.AI",
"path": "/jack/io/embeddings/memory_map.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>def save_as_memory_map_dir(directory: str, emb: Embeddings):
"""
Saves the given embeddings as memory map file and corresponding meta data in a directory.
Args:
directory: the directory to store the memory map file in (called `memory_map`) and the meta file (called
`meta.json` ... | code_fim | hard | {
"lang": "python",
"repo": "Ruturaj123/SARKAR.AI",
"path": "/jack/io/embeddings/memory_map.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: kanaadp/actionflow path: /actionflow/scripts/config_generator.py
import numpy as np
import cv2
import matplotlib.pyplot as plt
import numpy as np
from sklearn.cluster import KMeans
import json
import argparse
import sys
import tty, termios
import rospy
from std_msgs.msg import String
av_car = ... | code_fim | hard | {
"lang": "python",
"repo": "kanaadp/actionflow",
"path": "/actionflow/scripts/config_generator.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> dst = np.float32([(ts(0, scale), ts(0, scale)),
(ts(800, scale), ts(0, scale)),
(ts(800, scale), ts(800, scale)),
(ts(0, scale), ts(800, scale))])
calibrate_im, _ = unwarp(im, src, dst)
calibrate_im = cv2.GaussianBlur(calibrate_im, (... | code_fim | hard | {
"lang": "python",
"repo": "kanaadp/actionflow",
"path": "/actionflow/scripts/config_generator.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>def get_closest(point, list):
find_min = lambda point_1: lambda point_2: similarity(point_1, point_2)
closest_point = min(list, key=find_min(point))
return closest_point
def unwarp(img, src, dst):
h, w = img.shape[:2]
# use cv2.getPerspectiveTransform() to get M, the transform matrix,... | code_fim | hard | {
"lang": "python",
"repo": "kanaadp/actionflow",
"path": "/actionflow/scripts/config_generator.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> try:
self.pnl.unbind()
except AttributeError:
pass
if appType == "viewer":
self.pnl = TraceViewerSubApp(toolbarName="Experiment", parent=self)
if self.controller.expt is not None:
self.controller.experimentLoaded.emit... | code_fim | hard | {
"lang": "python",
"repo": "rpauszek/Scripps-smTIRF-GUI",
"path": "/smtirf_viewer.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: rpauszek/Scripps-smTIRF-GUI path: /smtirf_viewer.py
# -*- coding: utf-8 -*-
"""
@author: Raymond F. Pauszek III, Ph.D. (2020)
Single-Molecule TIRF Viewer App
"""
from PyQt5.QtWidgets import QApplication, QSizePolicy
from PyQt5 import QtWidgets, QtCore, QtGui
import sys
from collections import Ord... | code_fim | hard | {
"lang": "python",
"repo": "rpauszek/Scripps-smTIRF-GUI",
"path": "/smtirf_viewer.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> _BUST.__init__(self)
self.name = "BUSTING"
self.specie = 'nouns'
self.basic = "bust"
self.jsondata = {}<|fim_prefix|># repo: cash2one/xai path: /xai/brain/wordbase/nouns/_busting.py
from xai.brain.wordbase.nouns._bust import _BUST
#calss header
class _BUSTING(_BUST, ):
<|fim_middle|> def __i... | code_fim | easy | {
"lang": "python",
"repo": "cash2one/xai",
"path": "/xai/brain/wordbase/nouns/_busting.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: cash2one/xai path: /xai/brain/wordbase/nouns/_busting.py
from xai.brain.wordbase.nouns._bust import _BUST
<|fim_suffix|> _BUST.__init__(self)
self.name = "BUSTING"
self.specie = 'nouns'
self.basic = "bust"
self.jsondata = {}<|fim_middle|>#calss header
class _BUSTING(_BUST, ):
def __i... | code_fim | medium | {
"lang": "python",
"repo": "cash2one/xai",
"path": "/xai/brain/wordbase/nouns/_busting.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: pcmagic/stokes_flow path: /head_Force/diskVane_strain_rate.py
import sys
import petsc4py
petsc4py.init(sys.argv)
import numpy as np
import pickle
# from time import time
# from scipy.io import loadmat
# from src.stokes_flow import problem_dic, obj_dic
from src.geo import *
from petsc4py import... | code_fim | hard | {
"lang": "python",
"repo": "pcmagic/stokes_flow",
"path": "/head_Force/diskVane_strain_rate.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>if __name__ == '__main__':
# pythonmpi ../diskVane_strain_rate.py -sm lg_rs -legendre_m 3 -legendre_k 2 -epsilon 3 -ffweight 2 -main_fun_E 1 -diskVane_r1 1 -diskVane_rz 1 -diskVane_r2 0.3 -diskVane_ds 0.05 -diskVane_ph_loc 0 -diskVane_nr 2 -diskVane_nz 2 -diskVane_th_loc 0.7853981633974483
# pytho... | code_fim | hard | {
"lang": "python",
"repo": "pcmagic/stokes_flow",
"path": "/head_Force/diskVane_strain_rate.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: lightfold/Nbdler path: /nbdler/url/response.py
from nbdler.url.basic import BasicUrl
from nbdler.struct.dump import UrlResponseDumpedData
class UrlResponse(BasicUrl):
def __init__(self, url, headers, code, length):
<|fim_suffix|> return UrlResponseDumpedData(url=self.url, headers=di... | code_fim | medium | {
"lang": "python",
"repo": "lightfold/Nbdler",
"path": "/nbdler/url/response.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> return UrlResponseDumpedData(url=self.url, headers=dict(self.headers),
code=self.code, length=self.length)<|fim_prefix|># repo: lightfold/Nbdler path: /nbdler/url/response.py
from nbdler.url.basic import BasicUrl
from nbdler.struct.dump import UrlResponseDump... | code_fim | medium | {
"lang": "python",
"repo": "lightfold/Nbdler",
"path": "/nbdler/url/response.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> return y
#### Not finished
class AdapterPooler(nn.Module):
def __init__(self, input_dim, adapter_dim, init_scale = 1e-3, shared_weights = True):
super().__init__()
self.adapter_dim = adapter_dim
if shared_weights:
self.pooler_layer = TimeDistributed(
... | code_fim | hard | {
"lang": "python",
"repo": "afogarty85/BERTVision",
"path": "/code/tensorflow/utils/model_zoo_torch.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: afogarty85/BERTVision path: /code/tensorflow/utils/model_zoo_torch.py
import torch
from torch import nn
from scipy.stats import truncnorm
class BertConcat(nn.Module):
def __init__(self, seq_length, embeddings, ha, bias=True):
super().__init__()
#Will only work curren... | code_fim | hard | {
"lang": "python",
"repo": "afogarty85/BERTVision",
"path": "/code/tensorflow/utils/model_zoo_torch.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def _get_file_path_by_file_name(self, dependency_file_name: str):
# Get dependency file path by file name
result = ""
for _, files in self.m_pe_dependency_files.items():
for file in files:
file_dir, file_name = file
if dependency_file_name.lower() == file_name.lower():
... | code_fim | hard | {
"lang": "python",
"repo": "vn-os/Dependency-Walker",
"path": "/DependencyWalker.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: vn-os/Dependency-Walker path: /DependencyWalker.py
import sys, os, pefile, ctypes, pprint, shutil, json, time
from PyVutils import File
class DependencyWalker:
# Dependency Walker
g_list_checked_files = set()
def __init__(self, target: str, dirs: list, exts: list, verbose: bool):
# C... | code_fim | hard | {
"lang": "python",
"repo": "vn-os/Dependency-Walker",
"path": "/DependencyWalker.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # Normalize all directories
result = []
for dir in dirs: result.append(File.NormalizePath(dir, True))
return result
def _get_relative_current_dir(self):
# Get relative current directory
result = ""
if getattr(sys, "frozen", False):
result = os.path.dirname(sys.executab... | code_fim | hard | {
"lang": "python",
"repo": "vn-os/Dependency-Walker",
"path": "/DependencyWalker.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> facts = {}
for item in object.keys():
facts['FACTER_{}'.format(item)] = object[item]
return facts<|fim_prefix|># repo: digitalascension/fact-inject path: /fact_inject/parse/__init__.py
import json
# Read the JSON file file and convert to a dictionary.
def parse_input(json_file):
... | code_fim | medium | {
"lang": "python",
"repo": "digitalascension/fact-inject",
"path": "/fact_inject/parse/__init__.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: digitalascension/fact-inject path: /fact_inject/parse/__init__.py
import json
# Read the JSON file file and convert to a dictionary.
def parse_input(json_file):
input_json = None
input_obj = None
# Read the json file into memory.
try:
input_json = open(json_file, 'r').rea... | code_fim | hard | {
"lang": "python",
"repo": "digitalascension/fact-inject",
"path": "/fact_inject/parse/__init__.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: TTOFFLINE-LEAK/ttoffline path: /v2.5.7/toontown/safezone/DistributedFishingSpotAI.py
from direct.directnotify import DirectNotifyGlobal
from direct.distributed.DistributedObjectAI import DistributedObjectAI
from toontown.fishing import FishGlobals
from toontown.fishing.FishBase import FishBase
fr... | code_fim | hard | {
"lang": "python",
"repo": "TTOFFLINE-LEAK/ttoffline",
"path": "/v2.5.7/toontown/safezone/DistributedFishingSpotAI.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def d_setOccupied(self, avId):
self.sendUpdate('setOccupied', [avId])
def b_setOccupied(self, avId):
self.setOccupied(avId)
self.d_setOccupied(avId)
def doCast(self, p, h):
avId = self.air.getAvatarIdFromSender()
if self.avId != avId:
self.... | code_fim | hard | {
"lang": "python",
"repo": "TTOFFLINE-LEAK/ttoffline",
"path": "/v2.5.7/toontown/safezone/DistributedFishingSpotAI.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> pass
def d_setMovie(self, mode, code, genus, species, weight, p, h):
self.sendUpdate('setMovie', [mode, code, genus, species, weight, p, h])
def removeFromPier(self):
taskMgr.remove('timeOut%d' % self.doId)
self.cancelAnimation()
self.d_setOccupied(0)
... | code_fim | hard | {
"lang": "python",
"repo": "TTOFFLINE-LEAK/ttoffline",
"path": "/v2.5.7/toontown/safezone/DistributedFishingSpotAI.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> print("[{}-client_thread]({}): {}".format(self.name, self.port, msg))
pass
def send(self, data):
return self.sock.sendto(data, (self.host, self.port))
def onReceive(self, ip, message) -> None:
file = open(ip, "a")
file.write(message)
file.close... | code_fim | hard | {
"lang": "python",
"repo": "witjon/BACnet",
"path": "/redez-sem-hs20/groups/07-decentTCP/src/TCPClient.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: witjon/BACnet path: /redez-sem-hs20/groups/07-decentTCP/src/TCPClient.py
import socket
import sys
import struct
import os
from importlib import reload
from threading import Thread
import Parser as parser
class ClientTCP(Thread):
def __init__(self, host, port, name):
super(ClientTCP... | code_fim | hard | {
"lang": "python",
"repo": "witjon/BACnet",
"path": "/redez-sem-hs20/groups/07-decentTCP/src/TCPClient.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def debug(self, msg):
print("[{}-client_thread]({}): {}".format(self.name, self.port, msg))
pass
def send(self, data):
return self.sock.sendto(data, (self.host, self.port))
def onReceive(self, ip, message) -> None:
file = open(ip, "a")
file.write(m... | code_fim | hard | {
"lang": "python",
"repo": "witjon/BACnet",
"path": "/redez-sem-hs20/groups/07-decentTCP/src/TCPClient.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: DeepLearnPhysics/larcv2 path: /larcv/app/arxiv/mac/dump_img.py
from larcv import larcv
larcv.IOManager
import matplotlib.pyplot as plt
from ROOT import TChain
import sys
<|fim_suffix|>img_tree_name='image2d_%s_tree' % IMAGE_PRODUCER
img_br_name='image2d_%s_branch' % IMAGE_PRODUCER
img_ch = TChai... | code_fim | medium | {
"lang": "python",
"repo": "DeepLearnPhysics/larcv2",
"path": "/larcv/app/arxiv/mac/dump_img.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>start=0
cutoff=0
if len(sys.argv) > 3:
cutoff = int(sys.argv[3])
if len(sys.argv) > 4:
start = int(sys.argv[3])
cutoff = int(sys.argv[4])
for entry in xrange(img_ch.GetEntries()):
if entry<start: continue
img_ch.GetEntry(entry)
img_br=None
exec('img_br=img_ch.%s' % img_br_name... | code_fim | medium | {
"lang": "python",
"repo": "DeepLearnPhysics/larcv2",
"path": "/larcv/app/arxiv/mac/dump_img.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: YauHsien/pyetl path: /scripts/sqns_access/.unittest.py
import unittest
import helpers
class TestHelperMethods(unittest.TestCase):
def test_header_empty(self):
r = helpers.header('', lambda d: {'size': 0})
self.assertEqual(('record', {'header': {'size': 0},
... | code_fim | hard | {
"lang": "python",
"repo": "YauHsien/pyetl",
"path": "/scripts/sqns_access/.unittest.py",
"mode": "psm",
"license": "CC0-1.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> def test_header(self):
r = helpers.header('hello,world', lambda d: {0: 'hello',
1: 'world',
'size': 2})
self.assertEqual(('header', {0: 'hello',
... | code_fim | hard | {
"lang": "python",
"repo": "YauHsien/pyetl",
"path": "/scripts/sqns_access/.unittest.py",
"mode": "spm",
"license": "CC0-1.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> imgrefs = []
cursor = labelfiles.find({})
for rec in cursor:
print(rec)
imgrefs.append({"img": f"/thumbnail/{rec['unique_filename']}", "name": rec['userfilename']})
return jsonify({'imgrefs': imgrefs})
@app.route("/thumbnail/<uniquefilename>")
def render_thumbnail(uniquef... | code_fim | hard | {
"lang": "python",
"repo": "apurvasharan/labeltool",
"path": "/labelserver/app.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: apurvasharan/labeltool path: /labelserver/app.py
from functools import reduce
from flask import Flask, request, jsonify, send_from_directory, send_file
from flask_cors import CORS
import os, sys, random, string, traceback
from pymongo import MongoClient
import cv2
from werkzeug.utils import secu... | code_fim | hard | {
"lang": "python",
"repo": "apurvasharan/labeltool",
"path": "/labelserver/app.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> Args:
df_in: transactions dataframe
categories: categories to use
years: years to include in pie
"""
df = u.dfs.filter_data(u.uos.b64_to_df(df_in), categories)
... | code_fim | hard | {
"lang": "python",
"repo": "villoro/expensor",
"path": "/src/pages/page_pies.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> """
Updates the incomes pies plots
Args:
df_in: transactions dataframe
categories: categories to use
years: years to include in pie
"""
... | code_fim | hard | {
"lang": "python",
"repo": "villoro/expensor",
"path": "/src/pages/page_pies.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: villoro/expensor path: /src/pages/page_pies.py
"""
Dash app
"""
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
import utilities as u
import constants as c
import layout as lay
from plots import plots_pies as plots
class P... | code_fim | hard | {
"lang": "python",
"repo": "villoro/expensor",
"path": "/src/pages/page_pies.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ehmatthes/legs_of_steel path: /xml_grabber.py
# Grab all the trackpoint data.
# A trackpoint is a point on the track, not a waypoint?
import xml.etree.ElementTree as ET
import sys
from datetime import datetime
tree = ET.parse('tracks.gpx')
root = tree.getroot()
lats, lons, timestamps, elevati... | code_fim | hard | {
"lang": "python",
"repo": "ehmatthes/legs_of_steel",
"path": "/xml_grabber.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|># Find unique days that have tracks.
mdy_prev = (0,0,0)
for ts in timestamps:
month = datetime.strftime(ts, '%B')
day = datetime.strftime(ts, '%d')
year = datetime.strftime(ts, '%Y')
if (month, day, year) != mdy_prev:
print month, day, year
mdy_prev = (month, day, year)<|fim_pr... | code_fim | medium | {
"lang": "python",
"repo": "ehmatthes/legs_of_steel",
"path": "/xml_grabber.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|># Assert all lists same length.
print(len(lats), len(lons), len(timestamps), len(elevations))
# Find unique days that have tracks.
mdy_prev = (0,0,0)
for ts in timestamps:
month = datetime.strftime(ts, '%B')
day = datetime.strftime(ts, '%d')
year = datetime.strftime(ts, '%Y')
if (month, d... | code_fim | hard | {
"lang": "python",
"repo": "ehmatthes/legs_of_steel",
"path": "/xml_grabber.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> #
elif horloge_monde.contient_l_element_passe_en_parametre_dans_le_tableau_passe_en_parametre("CONFIGURER",tableau_de_la_commande_vocale_de_l_uttilisateur) and horloge_monde.contient_l_element_passe_en_parametre_dans_le_tableau_passe_en_parametre("FAHRENHEIT",tableau_de_la_commande_vocale_de_l_uttil... | code_fim | hard | {
"lang": "python",
"repo": "Vicken-Ghoubiguian/smart_connected_alarm_clock",
"path": "/modules_python_du_projet/interface_graphique.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
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