text stringlengths 232 16.3k | domain stringclasses 1
value | difficulty stringclasses 3
values | meta dict |
|---|---|---|---|
<|fim_suffix|> @staticmethod
def save_es_query_history():
"""
记录es查询历史装饰器
"""
def _wrap(func):
@wraps(func)
def _deco(self, request, *args, **kwargs):
param = request.data
record = DatalabEsQueryHistory()
rec... | code_fim | hard | {
"lang": "python",
"repo": "Tencent/bk-base",
"path": "/src/api/datalab/es_query/result_table.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: lixiccccc/Population-Health-Trends path: /Optimization2.py
import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import scipy as sci
import sklearn
from sklearn.linear_model import LinearRegression
'#create Obesity array by interpolation. ... | code_fim | hard | {
"lang": "python",
"repo": "lixiccccc/Population-Health-Trends",
"path": "/Optimization2.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>trainerr_oneterm,testerr_oneterm,index_oneterm,coef_oneterm,intercept_oneterm,prediction_oneterm,index_mintest,index_mintrain\
= singleoptimal(HFCS,percentobese,lag)
'#Visualizing Single Term Regression'
plt.figure()
plt.xlabel('Index of Lagged Array')
plt.ylabel('Mean Square Error')
plt.title('Test/... | code_fim | hard | {
"lang": "python",
"repo": "lixiccccc/Population-Health-Trends",
"path": "/Optimization2.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> trainerr_threeterm = []
testerr_threeterm = []
index1_threeterm = []
index2_threeterm = []
index3_threeterm = []
coef_threeterm = []
intercept_threeterm = []
prediction_threeterm = []
for index1 in lag:
for index2 in lag:
for index3 in lag:
... | code_fim | hard | {
"lang": "python",
"repo": "lixiccccc/Population-Health-Trends",
"path": "/Optimization2.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: soleHats/Supreme-1 path: /supreme.py
#!/usr/bin/python
import os, sys, json, time, requests, urllib, random, threading, ConfigParser
from datetime import datetime
from functionCreate import copy_func
from colorCodes import *
from tokenContainer import *
global mobileStockJson
rootDirectory = os.... | code_fim | hard | {
"lang": "python",
"repo": "soleHats/Supreme-1",
"path": "/supreme.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>if __name__ == '__main__':
stopPoll = 0
checkedOut = 0
mobileStockJson = None
user_config = Config()
assert len(c.options('productName')) == len(c.options('productSize')) == len(c.options('productColor')) == len(c.options('productQty')),'Assertion Error: Product section lengths unmatch... | code_fim | hard | {
"lang": "python",
"repo": "soleHats/Supreme-1",
"path": "/supreme.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: BlakeMcc/healthFairDocRecs path: /health_fair/urls.py
from django.conf.urls import url, include
from django.contrib import admin
from django.contrib.auth import views as auth_views
from django.contrib.auth.decorators import login_required
from django.views.generic import TemplateView
from screene... | code_fim | hard | {
"lang": "python",
"repo": "BlakeMcc/healthFairDocRecs",
"path": "/health_fair/urls.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>key = Fernet.generate_key()
key = key.decode()
with open(KEY_PATH, 'w') as f:
f.write(f"export FERNET_KEY={key}")
print(f"Fernet key created and stored in {KEY_PATH}")<|fim_prefix|># repo: CSCfi/docker-airflow path: /fernet-key-generator/create_fernet_key.py
from cryptography.fernet import Fernet... | code_fim | hard | {
"lang": "python",
"repo": "CSCfi/docker-airflow",
"path": "/fernet-key-generator/create_fernet_key.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: CSCfi/docker-airflow path: /fernet-key-generator/create_fernet_key.py
from cryptography.fernet import Fernet
import os
<|fim_suffix|>KEY_PATH = '/tmp/fernet_key/fernet_key.env'
if os.path.exists(KEY_PATH):
print(f"File {KEY_PATH} exists already. Exiting without creating a new key")
exit... | code_fim | easy | {
"lang": "python",
"repo": "CSCfi/docker-airflow",
"path": "/fernet-key-generator/create_fernet_key.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|># loading actives
actives_file = Path(args.actives_file)
actives_props = []
if len(actives_file.parts) > 2:
# path/to/target/actives_final.smi
target = actives_file.parts[-2]
else:
# target.smi
target = actives_file.stem
if actives_file.suffix == '.gz':
f = gzip.open(actives_file, 'r'... | code_fim | hard | {
"lang": "python",
"repo": "hnlab/can-ai-do",
"path": "/dude/generate_decoys/genDecoys.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: hnlab/can-ai-do path: /dude/generate_decoys/genDecoys.py
"""Generating decoys from database based on active smiles.
"""
import json
import gzip
import random
import argparse
import numpy as np
from pathlib import Path
from datetime import datetime as dt
from rdkit import Chem
from rdkit import D... | code_fim | hard | {
"lang": "python",
"repo": "hnlab/can-ai-do",
"path": "/dude/generate_decoys/genDecoys.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: socialsoftware/mono2micro path: /backend/src/main/resources/evaluation/9_static_vs_dynamic_bestDecompositions.py
import json
import numpy as np
import pandas as pd
from py4j.java_gateway import JavaGateway
DISTR_SRC_FILE_PATH = '../../java/pt/ist/socialsoftware/mono2micro/utils/mojoCalculator/sr... | code_fim | hard | {
"lang": "python",
"repo": "socialsoftware/mono2micro",
"path": "/backend/src/main/resources/evaluation/9_static_vs_dynamic_bestDecompositions.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>for file in files:
print(file)
data = pd.read_csv("./data/" + file)
minComplexityClusters = []
for n in range(3, 11):
minWeights = []
minComplexity = float("inf")
minComplexityWeights = [] # a, w, r, s
for entry in data.values:
if entry[0] !=... | code_fim | hard | {
"lang": "python",
"repo": "socialsoftware/mono2micro",
"path": "/backend/src/main/resources/evaluation/9_static_vs_dynamic_bestDecompositions.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: WebPowerLabs/django-trainings path: /dtf/tags/models.py
from django.db import models
from django_extensions.db.fields import AutoSlugField
<|fim_suffix|> def __init__(self, *args, **kwargs):
super(Tag, self).__init__(*args, **kwargs)
def __unicode__(self):
return self.name<|fim_middle|>
cl... | code_fim | hard | {
"lang": "python",
"repo": "WebPowerLabs/django-trainings",
"path": "/dtf/tags/models.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> def __init__(self, *args, **kwargs):
super(Tag, self).__init__(*args, **kwargs)
def __unicode__(self):
return self.name<|fim_prefix|># repo: WebPowerLabs/django-trainings path: /dtf/tags/models.py
from django.db import models
from django_extensions.db.fields import AutoSlugField
<|fim_middle|>
cl... | code_fim | hard | {
"lang": "python",
"repo": "WebPowerLabs/django-trainings",
"path": "/dtf/tags/models.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> for item in items(kwargs):
print(f'Starting {item["id"]}')
old_item = item.copy()
new_item = transform_item(item)
if old_item == new_item:
print(f'Skipping {item["id"]}')
continue
print(f'Processing {item["id"]}')
table.put_item(I... | code_fim | hard | {
"lang": "python",
"repo": "support-kaaylabs/platform",
"path": "/migrate_dynamodb_items.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: support-kaaylabs/platform path: /migrate_dynamodb_items.py
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
import json
import re
import sys
import boto3
dynamodb = boto3.resource('dynamodb')
table = dynamodb.Table('SierraData_items')
def items(kwargs=None):
"""Generate all items from a ... | code_fim | hard | {
"lang": "python",
"repo": "support-kaaylabs/platform",
"path": "/migrate_dynamodb_items.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> item['data'] = json.dumps(data, separators=(',', ':'))
return item
def main():
try:
kwargs = {'ExclusiveStartKey': {'id': sys.argv[1]}}
except IndexError:
kwargs = {}
for item in items(kwargs):
print(f'Starting {item["id"]}')
old_item = item.copy()
... | code_fim | hard | {
"lang": "python",
"repo": "support-kaaylabs/platform",
"path": "/migrate_dynamodb_items.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|># don't assume the user has install dragonfly
sys.path.append(os.path.join(os.path.dirname(__file__), os.pardir, 'dragonfly'))
import translations
MIN_PYTHON = (3, 0)
if sys.version_info < MIN_PYTHON:
sys.exit("Python {}.{} or later is required.\n".format(*MIN_PYTHON))
parser = argparse.ArgumentPars... | code_fim | hard | {
"lang": "python",
"repo": "theAfricanQuant/dragonfly",
"path": "/scripts/export.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: theAfricanQuant/dragonfly path: /scripts/export.py
#!/usr/bin/env python3
# Copyright 2017-2019, The Johns Hopkins University Applied Physics Laboratory LLC
# All rights reserved.
# Distributed under the terms of the Apache 2.0 License.
#
# Export a translation dictionary
#
# usage: export.py l... | code_fim | hard | {
"lang": "python",
"repo": "theAfricanQuant/dragonfly",
"path": "/scripts/export.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>USE_TZ = True
# django-storages
DEFAULT_FILE_STORAGE = "storages.backends.s3boto3.S3Boto3Storage"
STATICFILES_STORAGE = "storages.backends.s3boto3.S3Boto3Storage"
AWS_ACCESS_KEY_ID = env("AWS_ACCESS_KEY_ID")
AWS_SECRET_ACCESS_KEY = env("AWS_SECRET_ACCESS_KEY")
AWS_STORAGE_BUCKET_NAME = env("AWS_STORAGE... | code_fim | hard | {
"lang": "python",
"repo": "gurleen/mercantile-api",
"path": "/mercantile/settings.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: gurleen/mercantile-api path: /mercantile/settings.py
"""
Django settings for mercantile project.
Generated by 'django-admin startproject' using Django 3.1.1.
For more information on this file, see
https://docs.djangoproject.com/en/3.1/topics/settings/
For the full list of settings and their va... | code_fim | hard | {
"lang": "python",
"repo": "gurleen/mercantile-api",
"path": "/mercantile/settings.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>LANGUAGE_CODE = "en-us"
TIME_ZONE = "US/Eastern"
USE_I18N = True
USE_L10N = True
USE_TZ = True
# django-storages
DEFAULT_FILE_STORAGE = "storages.backends.s3boto3.S3Boto3Storage"
STATICFILES_STORAGE = "storages.backends.s3boto3.S3Boto3Storage"
AWS_ACCESS_KEY_ID = env("AWS_ACCESS_KEY_ID")
AWS_SECRET... | code_fim | hard | {
"lang": "python",
"repo": "gurleen/mercantile-api",
"path": "/mercantile/settings.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: thesofakillers/SlowFast path: /tools/sandbox_net.py
from slowfast.datasets import loader
import slowfast.utils.logging as logging
import slowfast.utils.distributed as du
import slowfast.utils.misc as misc
import slowfast.utils.checkpoint as cu
<|fim_suffix|> # Setup logging format.
loggi... | code_fim | medium | {
"lang": "python",
"repo": "thesofakillers/SlowFast",
"path": "/tools/sandbox_net.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> # load weights
if cfg.INFERENCE.WEIGHTS_FILE_PATH != "":
cu.load_checkpoint(cfg.INFERENCE.WEIGHTS_FILE_PATH, model, cfg.NUM_GPUS > 1, None,
inflation=False, convert_from_caffe2=cfg.INFERENCE.WEIGHTS_TYPE == "caffe2")
else:
raise FileNotFoundError("Mod... | code_fim | hard | {
"lang": "python",
"repo": "thesofakillers/SlowFast",
"path": "/tools/sandbox_net.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> # Setup logging format.
logging.setup_logging()
# Print config.
logger.info("Infer with config:")
logger.info(cfg)
# Build the SlowFast model and print its statistics
model = build_model(cfg)
if du.is_master_proc():
misc.log_model_info(model, cfg, is_train=False)... | code_fim | medium | {
"lang": "python",
"repo": "thesofakillers/SlowFast",
"path": "/tools/sandbox_net.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>subprocess.run(["cargo", "build", "--release"], check=True, cwd=str(ROOT))
if platform.system() == "Windows":
SRC_NAME = "rocksdb3.dll"
DEST_NAME = "rocksdb3.pyd"
elif platform.system() == "Darwin":
SRC_NAME = "librocksdb3.dylib"
DEST_NAME = "rocksdb3.so"
else:
# Assume everything els... | code_fim | medium | {
"lang": "python",
"repo": "bobosui/rocksdb3",
"path": "/tests/build.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>if platform.system() == "Windows":
SRC_NAME = "rocksdb3.dll"
DEST_NAME = "rocksdb3.pyd"
elif platform.system() == "Darwin":
SRC_NAME = "librocksdb3.dylib"
DEST_NAME = "rocksdb3.so"
else:
# Assume everything else behaves like Linux.
SRC_NAME = "librocksdb3.so"
DEST_NAME = "rocks... | code_fim | medium | {
"lang": "python",
"repo": "bobosui/rocksdb3",
"path": "/tests/build.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: bobosui/rocksdb3 path: /tests/build.py
#! /usr/bin/env python3
# Build the shared library, and then copy it into this directory, so that
# test_rocksdb.py can see it. We use the filename that Python requires on the
# curent platform: rocksdb3.so on Linux and macOS, and rocksdb3.pyd on Windows.
... | code_fim | medium | {
"lang": "python",
"repo": "bobosui/rocksdb3",
"path": "/tests/build.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: DoctorChe/Python_DataBase_PyQT path: /server/server_app.py
import select
import threading
from typing import Tuple
from socket import socket, AF_INET, SOCK_STREAM
# from utils.config_jim import TO
from server.utils.config_server import WORKERS, MSG_SIZE
from server.utils.metaclasses import Serve... | code_fim | hard | {
"lang": "python",
"repo": "DoctorChe/Python_DataBase_PyQT",
"path": "/server/server_app.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> """
Метод отправки сообщений
:param sock: сокет
:param message: словарь сообщения
:return: None
"""
try:
# self.process_message(message, send_data_lst)
# TODO: сделать проверку: зарегистрирован ли клиент на сервере
... | code_fim | hard | {
"lang": "python",
"repo": "DoctorChe/Python_DataBase_PyQT",
"path": "/server/server_app.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> db = DataBook()
db.compile()
db.link()
if __name__ == "__main__":
log.info("Databook generator")
raise SystemExit(main())<|fim_prefix|># repo: normanlorrain/DataBookBinder path: /src/dbb/__main__.py
""" Main application entry point.
python -m DataBookBinder ...
<|fim_middle|>... | code_fim | medium | {
"lang": "python",
"repo": "normanlorrain/DataBookBinder",
"path": "/src/dbb/__main__.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: normanlorrain/DataBookBinder path: /src/dbb/__main__.py
""" Main application entry point.
python -m DataBookBinder ...
"""
<|fim_suffix|>if __name__ == "__main__":
log.info("Databook generator")
raise SystemExit(main())<|fim_middle|>from .util import config
from .util import log
f... | code_fim | medium | {
"lang": "python",
"repo": "normanlorrain/DataBookBinder",
"path": "/src/dbb/__main__.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>def write_predictions_conv_ema(name, mode, input_fn, fwords, ftags, generator_fn, estimator):
Path('results/score').mkdir(parents=True, exist_ok=True)
with Path('results/score/{}.{}.preds.txt'.format(name, mode)).open('wb') as f:
test_inpf = functools.partial(input_fn, fwords(n... | code_fim | hard | {
"lang": "python",
"repo": "MANASLU8/tf_ner",
"path": "/predictions_writer.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>def write_predictions_ema(name, mode, input_fn, fwords, ftags, generator_fn, estimator):
Path('results/score').mkdir(parents=True, exist_ok=True)
with Path('results/score/{}.{}.preds.txt'.format(name, mode)).open('wb') as f:
test_inpf = functools.partial(input_fn, fwords(name),... | code_fim | hard | {
"lang": "python",
"repo": "MANASLU8/tf_ner",
"path": "/predictions_writer.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: MANASLU8/tf_ner path: /predictions_writer.py
from pathlib import Path
import functools
def write_predictions(name, input_fn, fwords, ftags, generator_fn, estimator):
Path('results/score').mkdir(parents=True, exist_ok=True)
with Path('results/score/{}.preds.txt'.format(name)).open('wb') a... | code_fim | hard | {
"lang": "python",
"repo": "MANASLU8/tf_ner",
"path": "/predictions_writer.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: saberkia/MLand path: /dc_supervised/sk_knn.py
from sklearn.neighbors import KNeighborsClassifier
import pandas as pd
from sklearn import datasets
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
digits = datasets.load_digits()
print(digits.keys())
<|fim_suff... | code_fim | hard | {
"lang": "python",
"repo": "saberkia/MLand",
"path": "/dc_supervised/sk_knn.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|># Create a k-NN classifier with 7 neighbors: knn
knn = KNeighborsClassifier(n_neighbors=7)
# Fit the classifier to the training data
knn.fit(X_train, y_train)
# Print the accuracy
print(knn.score(X_test, y_test))<|fim_prefix|># repo: saberkia/MLand path: /dc_supervised/sk_knn.py
from sklearn.neighbors ... | code_fim | medium | {
"lang": "python",
"repo": "saberkia/MLand",
"path": "/dc_supervised/sk_knn.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|># Split into training and test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=42, stratify=y)
# Create a k-NN classifier with 7 neighbors: knn
knn = KNeighborsClassifier(n_neighbors=7)
# Fit the classifier to the training data
knn.fit(X_train, y_train)
# Pri... | code_fim | hard | {
"lang": "python",
"repo": "saberkia/MLand",
"path": "/dc_supervised/sk_knn.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> ft = (2 * frequency - 1) * epoch
lr = cosine_decay(ft, lr_min, lr_max, n_epoch)
lr *= gamma ** epoch
return lr<|fim_prefix|># repo: aiorhiroki/farmer path: /farmer/ncc/schedulers/functional.py
import numpy as np
def step_lr(epoch, base_lr, step_size, gamma):
reduce_num = epoch // st... | code_fim | hard | {
"lang": "python",
"repo": "aiorhiroki/farmer",
"path": "/farmer/ncc/schedulers/functional.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: aiorhiroki/farmer path: /farmer/ncc/schedulers/functional.py
import numpy as np
def step_lr(epoch, base_lr, step_size, gamma):
reduce_num = epoch // step_size
lr = base_lr * (gamma ** reduce_num)
return lr
def multi_step_lr(epoch, base_lr, milestones, milestone_num, gamma):
fo... | code_fim | hard | {
"lang": "python",
"repo": "aiorhiroki/farmer",
"path": "/farmer/ncc/schedulers/functional.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def t0_switch_config_helper(test_obj: 'T0TestBase'):
"""
Make t0 switch configurations base on the configuration in the test plan.
Set the configuration in test directly.
Set the following test_obj attributes:
int: switch_id
"""
configer = SwitchConfiger(test_obj)
te... | code_fim | medium | {
"lang": "python",
"repo": "lihuay/SAI",
"path": "/test/sai_test/config/switch_configer.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> Args:
switch_init_wait: switch init wait time (sec)
route_mac: route mac (switch mac)
Returns:
Vlan: vlan object
"""
switch_id = sai_thrift_create_switch(
self.test_obj.client, init_switch=True, src_mac_address=route_mac)
... | code_fim | hard | {
"lang": "python",
"repo": "lihuay/SAI",
"path": "/test/sai_test/config/switch_configer.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: lihuay/SAI path: /test/sai_test/config/switch_configer.py
# Copyright (c) 2021 Microsoft Open Technologies, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License a... | code_fim | hard | {
"lang": "python",
"repo": "lihuay/SAI",
"path": "/test/sai_test/config/switch_configer.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: danvalencia/deeppointcloud-benchmarks path: /models/pointnet2/nn.py
import torch
from torch import nn
import torch.nn.functional as F
from torch.nn import functional as FPModule
from models.base_model import MLP, FPModule, UnetBasedModel
from .modules import SAModule
<|fim_suffix|> def forwar... | code_fim | hard | {
"lang": "python",
"repo": "danvalencia/deeppointcloud-benchmarks",
"path": "/models/pointnet2/nn.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def __init__(self, opt, num_classes):
self.down_conv_cls = SAModule
self.up_conv_cls = FPModule
self._name = 'POINTNET++_MODEL'
super(SegmentationModel, self).__init__(opt, num_classes)
#self.mlp_cls = MLP(opt.mlp_cls + [num_classes], p_dropout=0.1)
self... | code_fim | medium | {
"lang": "python",
"repo": "danvalencia/deeppointcloud-benchmarks",
"path": "/models/pointnet2/nn.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> x = F.relu(self.lin1(x))
x = F.dropout(x, p=0.5, training=self.training)
x = self.lin2(x)
x = F.dropout(x, p=0.5, training=self.training)
x = self.lin3(x)
return F.log_softmax(x, dim=-1)
#return F.log_softmax(self.mlp_cls(x), dim=-1)<|fim_pre... | code_fim | hard | {
"lang": "python",
"repo": "danvalencia/deeppointcloud-benchmarks",
"path": "/models/pointnet2/nn.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: dimagi/commcare-hq path: /corehq/apps/hqwebapp/utils/translation.py
from django.utils.functional import lazy
from django<|fim_suffix|>_safe, str)
format_html_lazy = lazy(format_html, str)<|fim_middle|>.utils.safestring import mark_safe
from django.utils.html import format_html
mark_safe_lazy = l... | code_fim | medium | {
"lang": "python",
"repo": "dimagi/commcare-hq",
"path": "/corehq/apps/hqwebapp/utils/translation.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|>_safe, str)
format_html_lazy = lazy(format_html, str)<|fim_prefix|># repo: dimagi/commcare-hq path: /corehq/apps/hqwebapp/utils/translation.py
from django.utils.functional import lazy
from django.utils.safestring import mark_safe
from django.utils<|fim_middle|>.html import format_html
mark_safe_lazy = l... | code_fim | easy | {
"lang": "python",
"repo": "dimagi/commcare-hq",
"path": "/corehq/apps/hqwebapp/utils/translation.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|>iseDataRegistration, ntSecureRouter1002E=ntSecureRouter1002E, ntSecureRouterNE05=ntSecureRouterNE05, ntEthernetRoutingSwitch=ntEthernetRoutingSwitch, ntSecureRouterNE08=ntSecureRouterNE08, ntSecureRouter1001S=ntSecureRouter1001S, ntSecureRouter4000Series=ntSecureRouter4000Series, ntEnterpriseData=ntEnterp... | code_fim | hard | {
"lang": "python",
"repo": "agustinhenze/mibs.snmplabs.com",
"path": "/pysnmp/NT-ENTERPRISE-DATA-MIB.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: agustinhenze/mibs.snmplabs.com path: /pysnmp/NT-ENTERPRISE-DATA-MIB.py
#
# PySNMP MIB module NT-ENTERPRISE-DATA-MIB (http://snmplabs.com/pysmi)
# ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/NT-ENTERPRISE-DATA-MIB
# Produced by pysmi-0.3.4 at Mon Apr 29 17:24:49 2019
# On host D... | code_fim | hard | {
"lang": "python",
"repo": "agustinhenze/mibs.snmplabs.com",
"path": "/pysnmp/NT-ENTERPRISE-DATA-MIB.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: awabcodes/rms path: /rms/project/utils.py
from __future__ import unicode_literals
import frappe
@frappe.whitelist()
def query_task(doctype, txt, searchfield, start, page_len, filters):
from frappe.desk.reportview import build_match_conditions
<|fim_suffix|> return frappe.db.sql("""select name... | code_fim | medium | {
"lang": "python",
"repo": "awabcodes/rms",
"path": "/rms/project/utils.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> search_string = "%%%s%%" % txt
order_by_string = "%s%%" % txt
match_conditions = build_match_conditions("Task")
match_conditions = ("and" + match_conditions) if match_conditions else ""
return frappe.db.sql("""select name, subject from `tabTask`
where (`%s` like %s or `subject` like %s) %s
order... | code_fim | medium | {
"lang": "python",
"repo": "awabcodes/rms",
"path": "/rms/project/utils.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>polynominal1, m1 = interpolation(1, data)
polynominal2, m2 = interpolation(2, data)
polynominal3, m3 = interpolation(3, data)
polynominal4, m4 = interpolation(4, data)
polynominal5, m5 = interpolation(5, data)
polynominal6, m6 = interpolation(6, data)
z=detectPolynominal(m1, m2, m3, m4, m5, m6 )
wrtieE... | code_fim | hard | {
"lang": "python",
"repo": "nyucel/blm2010",
"path": "/final/170401035.py",
"mode": "spm",
"license": "Unlicense",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: nyucel/blm2010 path: /final/170401035.py
#Hasan Avcı 170401035
from sympy import Symbol,pprint
file = open("veriler.txt", "r")
data = file.readlines()
for i in range(len(data)):
data[i] = int(data[i])
def detectPolynominal(m1, m2, m3, m4, m5, m6):
if m1 > m6 and m1 > m5 and m1 > m4 and... | code_fim | hard | {
"lang": "python",
"repo": "nyucel/blm2010",
"path": "/final/170401035.py",
"mode": "psm",
"license": "Unlicense",
"source": "the-stack-v2"
} |
<|fim_suffix|>
z=detectPolynominal(m1, m2, m3, m4, m5, m6 )
wrtieEquationAndIntegral(polynominal1, polynominal2, polynominal3, polynominal4, polynominal5, polynominal6,z)
polinomsuzIntegral()
dosya = open('170401035_yorum.txt', 'w')
dosya.write("""Sonuçların farklı çıkmasının sebebi deltax'e verilen değerlerden kay... | code_fim | hard | {
"lang": "python",
"repo": "nyucel/blm2010",
"path": "/final/170401035.py",
"mode": "spm",
"license": "Unlicense",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Wiselab2/findPhasiRNAs path: /findPhasiRNAs.py
owtie_index==None:
cmd="lib/bowtie/bowtie-build"
cmd+=" --threads "+options.CPU+" "
cmd+=options.genome+" "
cmd+=options.output_directory+"/bowtie1_index"
os.system(cmd)
bowtie1_index=options.output_dir... | code_fim | hard | {
"lang": "python",
"repo": "Wiselab2/findPhasiRNAs",
"path": "/findPhasiRNAs.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Wiselab2/findPhasiRNAs path: /findPhasiRNAs.py
argparse.ArgumentParser(prog="findPhasiRNAs.py",description="findPhasiRNAs can be used to find genomic locations where phasing occurs. ")
optional_arg = parser.add_argument_group("Optional Arguments")
required_arg = parser.add_argument_group... | code_fim | hard | {
"lang": "python",
"repo": "Wiselab2/findPhasiRNAs",
"path": "/findPhasiRNAs.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> num_all_reads=0
for i in range(begin,finish+1):
try:
num_all_reads+=whole_mapped_data[chromosome][i]
except KeyError:
pass
if num_all_reads<min_reads_in_a_window:
... | code_fim | hard | {
"lang": "python",
"repo": "Wiselab2/findPhasiRNAs",
"path": "/findPhasiRNAs.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def plot_hyperplane(clf, min_x, max_x, linestyle, label):
# get the separating hyperplane
w = clf.coef_[0]
a = -w[0] / w[1]
xx = np.linspace(min_x, max_x)
yy = a * xx - (clf.intercept_[0]) / w[1]
pl.plot(xx, yy, linestyle, label=label)
X, Y = make_multilabel_classification(n_cla... | code_fim | hard | {
"lang": "python",
"repo": "forkloop/scikit-learn",
"path": "/examples/plot_multilabel.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: forkloop/scikit-learn path: /examples/plot_multilabel.py
"""
=========================
Multilabel classification
=========================
This example simulates a multi-label document classification problem. The
dataset is generated randomly based on the following process:
- pick the numbe... | code_fim | medium | {
"lang": "python",
"repo": "forkloop/scikit-learn",
"path": "/examples/plot_multilabel.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|>zero_class = np.where([0 in y for y in Y])
one_class = np.where([1 in y for y in Y])
pl.scatter(X[:, 0], X[:, 1], s=40, c='gray')
pl.scatter(X[zero_class, 0], X[zero_class, 1], s=160, edgecolors='b',
facecolors='none', linewidths=2, label='Class 1')
pl.scatter(X[one_class, 0], X[one_class, 1], ... | code_fim | hard | {
"lang": "python",
"repo": "forkloop/scikit-learn",
"path": "/examples/plot_multilabel.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: thoughteer/edera path: /tests/integration/monitoring/test_monitoring_agent.py
import logging
import threading
from edera.monitoring.agent import LogCapturingTaskWrapper
from edera.task import Task
def test_log_capturing_task_wrapper_ignores_messages_from_other_threads(mocker):
class T(Tas... | code_fim | medium | {
"lang": "python",
"repo": "thoughteer/edera",
"path": "/tests/integration/monitoring/test_monitoring_agent.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def interfere():
interferer = threading.Thread(target=log)
interferer.daemon = True
interferer.start()
interferer.join()
sink = logging.getLogger("edera.monitoring.sink")
sink.setLevel(logging.DEBUG)
agent = mocker.Mock()
wrapper = LogCapturingTaskWrapp... | code_fim | medium | {
"lang": "python",
"repo": "thoughteer/edera",
"path": "/tests/integration/monitoring/test_monitoring_agent.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: banare/simplegauges path: /datastores/datastore.py
# coding: utf-8
class GaugeDatastore(object):
def save_data(self, gauge_name, date_key, data):
"""Saves specified data to date_key in specified gauge.
date_key: str
"""
pass
def get_gauge_data(self, gaug... | code_fim | medium | {
"lang": "python",
"repo": "banare/simplegauges",
"path": "/datastores/datastore.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> Return format: [ {"key": date_key, "data": data}, ... ]
For missing data, data field will be returned as None.
"""
pass
def get_data(self, gauge_name, date_key):
"""Retrieves gauge data for a specific date key (e.g. day)
date_key: str
Return f... | code_fim | medium | {
"lang": "python",
"repo": "banare/simplegauges",
"path": "/datastores/datastore.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> If min_date_key (str) is specified, returns records after specified
date key (incl. min_date_key).
If max_date_key (str) is specified, returns records before specified
date key (excl. max_date_key).
Return format: [ {"key": date_key, "data": data}, ... ]
F... | code_fim | medium | {
"lang": "python",
"repo": "banare/simplegauges",
"path": "/datastores/datastore.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: yilin-wu98/gym path: /gym/envs/mujoco/assets/test.py
# import random
# from mujoco_py import load_model_from_path, MjSim, MjViewer
# import os
#
# model = load_model_from_path("cloth_v0.xml")
# sim = MjSim(model)
# viewer = MjViewer(sim)
# sim_state = sim.get_state()
#
# while True:
# sim.set... | code_fim | hard | {
"lang": "python",
"repo": "yilin-wu98/gym",
"path": "/gym/envs/mujoco/assets/test.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>.6 1" texcoord="true" inflate="0.005" subgrid="2"/>
<geom type="capsule" size="0.015 0.01" rgba=".8 .2 .1 1"/>
</composite>
</body>
</worldbody>
</mujoco>
"""
physics = mujoco.Physics.from_xml_string(xml_string)
# Render the default camera view as a numpy array of pixels.
pixels = physics.r... | code_fim | hard | {
"lang": "python",
"repo": "yilin-wu98/gym",
"path": "/gym/envs/mujoco/assets/test.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: xbnr/future_makers path: /python_samples/translate_server.py
# Translation API - https://azure.microsoft.com/en-gb/services/cognitive-services/translator-text-api/
# Example Request - http://localhost:3001/?lang=fr&text=hello
import http.server
import socketserver
import urllib.parse
import urll... | code_fim | hard | {
"lang": "python",
"repo": "xbnr/future_makers",
"path": "/python_samples/translate_server.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> queryStarts = self.path.find("?") + 1
if self.__data == "":
self.__data = self.rfile.read(int(self.headers['Content-Length'])).decode("utf-8")
from urllib.parse import parse_qs
parsed = parse_qs(self.path[queryStarts:])
parsed = parse_qs(self.__data)
... | code_fim | hard | {
"lang": "python",
"repo": "xbnr/future_makers",
"path": "/python_samples/translate_server.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> # This helper function formats parts of our response properly
def write(self,text):
self.wfile.write(str.encode(text))
def set_headers(self):
self.send_response(200) # 200 means everything is OK
self.send_header('Content-type', 'text/html') #Our response contains text
... | code_fim | hard | {
"lang": "python",
"repo": "xbnr/future_makers",
"path": "/python_samples/translate_server.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: calvinti12/impulse path: /impulse/alert/migrations/0001_initial.py
# -*- coding: utf-8 -*-
# Generated by Django 1.10 on 2016-11-08 03:07
from __future__ import unicode_literals
from django.db import migrations, models
<|fim_suffix|> initial = True
dependencies = [
]
operations... | code_fim | hard | {
"lang": "python",
"repo": "calvinti12/impulse",
"path": "/impulse/alert/migrations/0001_initial.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Qiskit/qiskit-ibmq-provider path: /test/utils.py
# This code is part of Qiskit.
#
# (C) Copyright IBM 2020.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www... | code_fim | hard | {
"lang": "python",
"repo": "Qiskit/qiskit-ibmq-provider",
"path": "/test/utils.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> """Cancel a job.
Args:
job: Job to cancel.
verify: Verify job status.
Returns:
Whether job has been cancelled.
"""
cancelled = False
for _ in range(2):
# Try twice in case job is not in a cancellable state
try:
cancelled = job.c... | code_fim | hard | {
"lang": "python",
"repo": "Qiskit/qiskit-ibmq-provider",
"path": "/test/utils.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Robpol86/FlashAirMusic path: /flash_air_music/upload/run.py
"""Main functions/coroutines that fire directory walking, song uploads, file/dir deletion, and retry logic."""
import asyncio
import logging
import time
from flash_air_music.configuration import GLOBAL_MUTABLE_CONFIG
from flash_air_mus... | code_fim | hard | {
"lang": "python",
"repo": "Robpol86/FlashAirMusic",
"path": "/flash_air_music/upload/run.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> :param str ip_addr: IP address of FlashAir to connect to.
:return: If sync was successful.
:rtype: bool
"""
log = logging.getLogger(__name__)
log.debug('Waiting for semaphore...')
sleep_for = 2
success = False
changed = False
with (yield from SEMAPHORE):
lo... | code_fim | hard | {
"lang": "python",
"repo": "Robpol86/FlashAirMusic",
"path": "/flash_air_music/upload/run.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> myHelper.__init__(self)
self.nameRe = re.compile(ur"\A( *)" + self.getReX11colors() + "( *)\Z", re.UNICODE|re.IGNORECASE)
def name(self, value):
value = value.strip()
for k, v in self.x11colorValues.iteritems():
if k.lower() == value.lower():
... | code_fim | medium | {
"lang": "python",
"repo": "s0ap/tex",
"path": "/page_designer/template_designer/code/colors/myname.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: s0ap/tex path: /page_designer/template_designer/code/colors/myname.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import re
from myhelper import myHelper
from x11colorvalues import x11colorValues
class myName(myHelper):
"""
This class defines contains all methods,
that convert from x11 ... | code_fim | medium | {
"lang": "python",
"repo": "s0ap/tex",
"path": "/page_designer/template_designer/code/colors/myname.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: PavelBlend/blender-xray path: /io_scene_xray/formats/bones/exp.py
# addon modules
from .. import obj
from ... import utils
from ... import log
from ... import rw
@log.with_context(name='bones-partitions')
def _export_partitions(context, bpy_obj):
log.update(object=bpy_obj.name)
packed_w... | code_fim | hard | {
"lang": "python",
"repo": "PavelBlend/blender-xray",
"path": "/io_scene_xray/formats/bones/exp.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> packed_writer = rw.write.PackedWriter()
packed_writer.putf('<f', xray.mass.value)
packed_writer.putv3f(cmass)
chunked_writer.put(chunks.MASS_PARAMS, packed_writer)
return chunked_writer
@log.with_context(name='export-bones')
@utils.stats.timer
def export_file(context):
utils.sta... | code_fim | hard | {
"lang": "python",
"repo": "PavelBlend/blender-xray",
"path": "/io_scene_xray/formats/bones/exp.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|>@log.with_context(name='export-bones')
@utils.stats.timer
def export_file(context):
utils.stats.status('Export File', context.filepath)
arm_obj = context.bpy_arm_obj
log.update(object=arm_obj.name)
chunked_writer = rw.write.ChunkedWriter()
# get armature scale
root_obj = utils.ob... | code_fim | hard | {
"lang": "python",
"repo": "PavelBlend/blender-xray",
"path": "/io_scene_xray/formats/bones/exp.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> """ Creates and returns the L9C script """
return RpycHost(c_instance)<|fim_prefix|># repo: enteleform-forks/AbletonAPI path: /python-api-materials/code/RpycHost/__init__.py
#****************************************************************************************
# File: __init__.py
#
# Copy... | code_fim | medium | {
"lang": "python",
"repo": "enteleform-forks/AbletonAPI",
"path": "/python-api-materials/code/RpycHost/__init__.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: enteleform-forks/AbletonAPI path: /python-api-materials/code/RpycHost/__init__.py
#****************************************************************************************
# File: __init__.py
#
# Copyright: 2011 Ableton AG, Berlin. All Rights reserved
#***************************************... | code_fim | easy | {
"lang": "python",
"repo": "enteleform-forks/AbletonAPI",
"path": "/python-api-materials/code/RpycHost/__init__.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: kirenguyen/unity-game-controllers path: /TapGameController/tests/test_fsm.py
# -*- coding: utf-8 -*-
from ..TapGameFSM import TapGameFSM
import unittest
class FSMTestSuite(unittest.TestCase):
<|fim_suffix|> def test_FSM(self):
return True
# def send_command(cmd, *args):
... | code_fim | medium | {
"lang": "python",
"repo": "kirenguyen/unity-game-controllers",
"path": "/TapGameController/tests/test_fsm.py",
"mode": "psm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> return True
# def send_command(cmd, *args):
# print(cmd)
#
# my_FSM = TapGameFSM()
# my_FSM.max_rounds = 2
# my_FSM.send_game_cmd = send_command
#
# self.assertEqual(my_FSM.state, 'GAME_START')
#
# my_FSM.init_firs... | code_fim | medium | {
"lang": "python",
"repo": "kirenguyen/unity-game-controllers",
"path": "/TapGameController/tests/test_fsm.py",
"mode": "spm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|>def clearRelevancy():
cypher.execute(GRAPHDB, "MATCH ()-[r:RELEVANCY]->() DELETE r")
def calculateRelevancy():
# clear old transformer
clearRelevancy()
# calculate transformer
if WHICH_RELEVANCY_ALGORITHM == "random":
randomRelevancyEdges()
elif WHICH_RELEVANCY_ALGORITH... | code_fim | medium | {
"lang": "python",
"repo": "R4chel/RecommendationGraph",
"path": "/recgraph/transformer/calculateRelevancy.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # calculate transformer
if WHICH_RELEVANCY_ALGORITHM == "random":
randomRelevancyEdges()
elif WHICH_RELEVANCY_ALGORITHM == "simple":
simpleRelevancyEdges()
else:
print "+EE+: Unknown Relevancy Algorithm"
# output graph to json file in static directory
if WH... | code_fim | medium | {
"lang": "python",
"repo": "R4chel/RecommendationGraph",
"path": "/recgraph/transformer/calculateRelevancy.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: R4chel/RecommendationGraph path: /recgraph/transformer/calculateRelevancy.py
import os
from py2neo import cypher
from recgraph.transformer.randomRelevancy import randomRelevancyEdges
from recgraph.transformer.simpleRelevancy import simpleRelevancyEdges
from recgraph.transformer.convertToJson im... | code_fim | medium | {
"lang": "python",
"repo": "R4chel/RecommendationGraph",
"path": "/recgraph/transformer/calculateRelevancy.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def test_recurrence(self):
recurrence = RecurringTransaction.objects.create(
title='some recurrence',
amount=25,
date=date.today(),
src=self.personal,
dst=self.foreign,
interval=RecurringTransaction.MONTHLY,
tr... | code_fim | hard | {
"lang": "python",
"repo": "agstrike/silverstrike",
"path": "/silverstrike/tests/models/test_splits.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: agstrike/silverstrike path: /silverstrike/tests/models/test_splits.py
from datetime import date
from django.test import TestCase
from django.urls import reverse
from silverstrike.models import (Account, AccountType, Category,
RecurringTransaction, Split, Transac... | code_fim | hard | {
"lang": "python",
"repo": "agstrike/silverstrike",
"path": "/silverstrike/tests/models/test_splits.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # all center frequencies of the filters
f = (N / fs) * invmelscale(
melscale(fl * fs)
+ (np.arange(M + 2) * (melscale(fh * fs) - melscale(fl * fs)) / (M + 1))
)
# Construct the triangular filter bank
H = np.zeros((M, N // 2 + 1))
k = np.arange(N // 2 + 1)
for m... | code_fim | hard | {
"lang": "python",
"repo": "LCAV/pyroomacoustics",
"path": "/pyroomacoustics/acoustics.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: LCAV/pyroomacoustics path: /pyroomacoustics/acoustics.py
port constants
from .transform import stft
def binning(S, bands):
"""
This function computes the sum of all columns of S in the subbands
enumerated in bands
"""
B = np.zeros((S.shape[0], len(bands)), dtype=S.dtype)
... | code_fim | hard | {
"lang": "python",
"repo": "LCAV/pyroomacoustics",
"path": "/pyroomacoustics/acoustics.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # perform STFT, X contains frames in rows
X = stft.analysis(x, L, hop, transform=np.fft.rfft)
# get and apply the mel filter bank
# and compute log energy
H = melfilterbank(M, L, fs=fs, fl=fl, fh=fh)
S = np.log(np.dot(H, np.abs(X.T) ** 2))
# Now take DCT of the result
C =... | code_fim | hard | {
"lang": "python",
"repo": "LCAV/pyroomacoustics",
"path": "/pyroomacoustics/acoustics.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> assert_equal(self.nodes[0].createrawscriptaddress("6ac1"), "3HnzbJ4TR9")
assert_equal(self.nodes[0].validateaddress("3HnzbJ4TR9")["isvalid"], True)
assert_equal(self.nodes[0].validateaddress("3HnzbJ4TR9")["scriptPubKey"], "6ac1")
self.nodes[0].staking(False)
self.n... | code_fim | medium | {
"lang": "python",
"repo": "David4860/navcoin-core",
"path": "/qa/rpc-tests/createrawscriptaddress.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: David4860/navcoin-core path: /qa/rpc-tests/createrawscriptaddress.py
#!/usr/bin/env python3
# Copyright (c) 2019 The Navcoin Core developers
# Distributed under the MIT software license, see the accompanying
# file COPYING or http://www.opensource.org/licenses/mit-license.php.
from test_framewor... | code_fim | hard | {
"lang": "python",
"repo": "David4860/navcoin-core",
"path": "/qa/rpc-tests/createrawscriptaddress.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: jakobkogler/CodeforcesAssistant path: /parse_testcases.py
#!/usr/bin/env python3
from bs4 import BeautifulSoup, NavigableString
from urllib.request import urlopen
import argparse
import os
import re
def format_testcase(test):
return '\n'.join(e for e in test.pre.contents if isinstance(e, Na... | code_fim | hard | {
"lang": "python",
"repo": "jakobkogler/CodeforcesAssistant",
"path": "/parse_testcases.py",
"mode": "psm",
"license": "WTFPL",
"source": "the-stack-v2"
} |
<|fim_suffix|> for idx, test_case in enumerate(parse_testcases(contest, problem), start=1):
for text_type, text in zip(("input", "output"), test_case):
file_path = os.path.join(dir_path, '{}{}'.format(text_type, idx))
with open(file_path, 'w') as f:
... | code_fim | hard | {
"lang": "python",
"repo": "jakobkogler/CodeforcesAssistant",
"path": "/parse_testcases.py",
"mode": "spm",
"license": "WTFPL",
"source": "the-stack-v2"
} |
<|fim_suffix|>image
# Written by Tianrui Hui
# --------------------------------------------------------<|fim_prefix|># repo: VitoChien/py-mask-rcnn path: /lib/crop_seg/__init__.py
# ------------------------------------------<|fim_middle|>--------------
# Crop the segmentation label | code_fim | easy | {
"lang": "python",
"repo": "VitoChien/py-mask-rcnn",
"path": "/lib/crop_seg/__init__.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>---------------------------------------------<|fim_prefix|># repo: VitoChien/py-mask-rcnn path: /lib/crop_seg/__init__.py
# --------------------------------------------------------
# Crop the segmentation label <|fim_middle|>image
# Written by Tianrui Hui
# ----------- | code_fim | easy | {
"lang": "python",
"repo": "VitoChien/py-mask-rcnn",
"path": "/lib/crop_seg/__init__.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.