text stringlengths 232 16.3k | domain stringclasses 1
value | difficulty stringclasses 3
values | meta dict |
|---|---|---|---|
<|fim_prefix|># repo: maxkrivich/SlowLoris path: /pyslowloris/attack.py
"""
MIT License
Copyright (c) 2020 Maxim Krivich
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, incl... | code_fim | hard | {
"lang": "python",
"repo": "maxkrivich/SlowLoris",
"path": "/pyslowloris/attack.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> internal_dict = {key: getattr(self, key) for key in self.__slots__}
args = ",".join([f"{k}={repr(v)}" for (k, v) in internal_dict.items()])
return f"{self.__class__.__name__}({args.rstrip(',')})"
async def _atack_coroutine(self) -> None:
while True:
try:
... | code_fim | hard | {
"lang": "python",
"repo": "maxkrivich/SlowLoris",
"path": "/pyslowloris/attack.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>class TestMultiLabelClassificationTask:
num_classes = 10
@pytest.fixture(scope="class")
def datamodule(self, request: SubRequest) -> DummyDataModule:
dm = DummyDataModule(
num_channels=3,
num_classes=self.num_classes,
multilabel=True,
b... | code_fim | hard | {
"lang": "python",
"repo": "sxjscience/torchgeo",
"path": "/tests/trainers/test_classification.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: sxjscience/torchgeo path: /tests/trainers/test_classification.py
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
import os
from typing import Any, Dict, Generator, Optional, cast
import pytest
import pytorch_lightning as pl
import torch
import torch... | code_fim | hard | {
"lang": "python",
"repo": "sxjscience/torchgeo",
"path": "/tests/trainers/test_classification.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> self, datamodule: DummyDataModule, task: ClassificationTask
) -> None:
batch = next(iter(datamodule.val_dataloader()))
task.validation_step(batch, 0)
task.validation_epoch_end(0)
def test_test(self, datamodule: DummyDataModule, task: ClassificationTask) -> None:
... | code_fim | hard | {
"lang": "python",
"repo": "sxjscience/torchgeo",
"path": "/tests/trainers/test_classification.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> reorganized = {}
for p in places:
photos = p.get("photos", [])
if "types" not in p or len(p["types"]) == 0:
p["types"] = ["others"]
for key in p["types"]:
obj = {
"name": p["name"],
"photos": photos,
"... | code_fim | hard | {
"lang": "python",
"repo": "Hasan-Jawaheri/traveller",
"path": "/server/webapi/api.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Hasan-Jawaheri/traveller path: /server/webapi/api.py
from django.shortcuts import HttpResponse, HttpResponseRedirect
from django.conf import settings
import requests, json, time
def get_nearby(r):
try:
airport = r.GET["airport"]
duration = r.GET["duration"]
except: pass
#... | code_fim | hard | {
"lang": "python",
"repo": "Hasan-Jawaheri/traveller",
"path": "/server/webapi/api.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> response = s.get(url).json()
if response["status"] == "ZERO_RESULTS":
break
elif response["status"] == "OVER_QUERY_LIMIT":
return HttpResponse(json.dumps({"result": "Query limit"}), content_type="application/json")
elif response["status"] == "INVALID... | code_fim | hard | {
"lang": "python",
"repo": "Hasan-Jawaheri/traveller",
"path": "/server/webapi/api.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>params = {'name': 'public'}
group.create(params)
params = {'id': '1234', 'name': 'new'}
group.create(params)
params = {'id': 'abcd', 'name': 'old'}
group.create(params)
params = {'id': '1234'}
group.find(params)
params = {'ids': ['1234', 'tt']}
group.find(params)
params = {}
group.find(params)<|fim_pre... | code_fim | medium | {
"lang": "python",
"repo": "SungardAS/porper-core",
"path": "/tests/models/test_group.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>params = {'id': '1234'}
group.find(params)
params = {'ids': ['1234', 'tt']}
group.find(params)
params = {}
group.find(params)<|fim_prefix|># repo: SungardAS/porper-core path: /tests/models/test_group.py
import sys
sys.path.append('../../porper')
import os
region = os.environ.get('AWS_DEFAULT_REGION')... | code_fim | hard | {
"lang": "python",
"repo": "SungardAS/porper-core",
"path": "/tests/models/test_group.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: SungardAS/porper-core path: /tests/models/test_group.py
import sys
sys.path.append('../../porper')
import os
region = os.environ.get('AWS_DEFAULT_REGION')
import boto3
dynamodb = boto3.resource('dynamodb',region_name=region)
<|fim_suffix|>params = {'id': '1234'}
group.find(params)
params = {... | code_fim | hard | {
"lang": "python",
"repo": "SungardAS/porper-core",
"path": "/tests/models/test_group.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ashudeep/ranking-fairness-uncertainty path: /evaluation.py
import numpy as np
from sample_rankings_util import (get_posteriors, get_mean_merits, sample_rankings,
optimal_ranking, get_mean_merits,
compute_marginal_rank_probabiliti... | code_fim | hard | {
"lang": "python",
"repo": "ashudeep/ranking-fairness-uncertainty",
"path": "/evaluation.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def get_mean_dcg(sampled_rankings, means, v_vec):
num_docs = len(means)
dcgs = []
for ranking in sampled_rankings:
dcgs.append(get_dcg(ranking, means, v_vec))
return np.mean(dcgs)
def compute_unfairness(movieids, matrix, v, num_samples=10000, constraint_probabilities=None):
... | code_fim | hard | {
"lang": "python",
"repo": "ashudeep/ranking-fairness-uncertainty",
"path": "/evaluation.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: kesia-barros/exercicios-python path: /ex001 a ex114/ex034.py
sal = float(input("Qaul é o seu salário?"))
if sa<|fim_suffix|> aumento = (sal * 0.10) + sal
print("Seu salario aumentou para R$ {:.3f} reais!".format(aumento))<|fim_middle|>l <= 1250:
aumento = (sal * 0.15) + sal
else:
| code_fim | easy | {
"lang": "python",
"repo": "kesia-barros/exercicios-python",
"path": "/ex001 a ex114/ex034.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> aumento = (sal * 0.10) + sal
print("Seu salario aumentou para R$ {:.3f} reais!".format(aumento))<|fim_prefix|># repo: kesia-barros/exercicios-python path: /ex001 a ex114/ex034.py
sal = float(input("Qaul é o seu salário?"))
if sa<|fim_middle|>l <= 1250:
aumento = (sal * 0.15) + sal
else:
| code_fim | easy | {
"lang": "python",
"repo": "kesia-barros/exercicios-python",
"path": "/ex001 a ex114/ex034.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: danasaur/wf-process-pose-data path: /process_pose_data/honeycomb_io.py
device_id') for datum in result]
logger.info('Found {} camera IDs that match specified properties'.format(len(camera_ids)))
return camera_ids
return None
def fetch_pose_model_id(
pose_model_id=None,
... | code_fim | hard | {
"lang": "python",
"repo": "danasaur/wf-process-pose-data",
"path": "/process_pose_data/honeycomb_io.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> camera_ids,
start=None,
end=None,
chunk_size=100,
uri=None,
token_uri=None,
audience=None,
client_id=None,
client_secret=None
):
client = minimal_honeycomb.MinimalHoneycombClient(
uri=uri,
token_uri=token_uri,
audience=audience,
clien... | code_fim | hard | {
"lang": "python",
"repo": "danasaur/wf-process-pose-data",
"path": "/process_pose_data/honeycomb_io.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: danasaur/wf-process-pose-data path: /process_pose_data/honeycomb_io.py
ch_3d_pose_tracks(
query_list,
return_data,
chunk_size=100,
uri=None,
token_uri=None,
audience=None,
client_id=None,
client_secret=None
):
logger.info('Searching for 3D pose tracks that matc... | code_fim | hard | {
"lang": "python",
"repo": "danasaur/wf-process-pose-data",
"path": "/process_pose_data/honeycomb_io.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> self._image_id = value
def parse_response_content(self, response_content):
response = super(AlipayOpenIotmbsImageUploadResponse, self).parse_response_content(response_content)
if 'audit_status' in response:
self.audit_status = response['audit_status']
if 'i... | code_fim | hard | {
"lang": "python",
"repo": "alipay/alipay-sdk-python-all",
"path": "/alipay/aop/api/response/AlipayOpenIotmbsImageUploadResponse.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> @image_id.setter
def image_id(self, value):
self._image_id = value
def parse_response_content(self, response_content):
response = super(AlipayOpenIotmbsImageUploadResponse, self).parse_response_content(response_content)
if 'audit_status' in response:
self.a... | code_fim | medium | {
"lang": "python",
"repo": "alipay/alipay-sdk-python-all",
"path": "/alipay/aop/api/response/AlipayOpenIotmbsImageUploadResponse.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: alipay/alipay-sdk-python-all path: /alipay/aop/api/response/AlipayOpenIotmbsImageUploadResponse.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import json
from alipay.aop.api.response.AlipayResponse import AlipayResponse
class AlipayOpenIotmbsImageUploadResponse(AlipayResponse):
def __i... | code_fim | medium | {
"lang": "python",
"repo": "alipay/alipay-sdk-python-all",
"path": "/alipay/aop/api/response/AlipayOpenIotmbsImageUploadResponse.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> # Select all data from database
c.execute("SELECT * FROM data")
cdata = c.fetchall()
conn.close()
return cdata
def get_salary_month():
# Create set to store month name, when a salary was written to
set_salary = set()
conn = sqlite3.connect(r"database\database.db")
... | code_fim | hard | {
"lang": "python",
"repo": "optionalg/Xlsx-Account-Book",
"path": "/functions.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> conn.commit()
conn.close()
def get_data_sql():
conn = sqlite3.connect(r"database\database.db")
c = conn.cursor()
# Select all data from database
c.execute("SELECT * FROM data")
cdata = c.fetchall()
conn.close()
return cdata
def get_salary_month():
# Create s... | code_fim | hard | {
"lang": "python",
"repo": "optionalg/Xlsx-Account-Book",
"path": "/functions.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: optionalg/Xlsx-Account-Book path: /functions.py
import sqlite3
def show_classes(chosen_month_number):
# At first let the user chose a month to make entries
if chosen_month_number == "":
print("At first a month has to be chosen:")
print("")
select_class = "3"
... | code_fim | hard | {
"lang": "python",
"repo": "optionalg/Xlsx-Account-Book",
"path": "/functions.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> return str(key).lower()
def __and__(self, other):
"""
join version specifiers, consuming a mapping object.
"""
for k, v in other.items():
if k in self._values:
self._values[k] = str(SpecifierSet(self._values[k]) & v)
else... | code_fim | hard | {
"lang": "python",
"repo": "toumorokoshi/uranium",
"path": "/uranium/packages/versions.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: toumorokoshi/uranium path: /uranium/packages/versions.py
from collections import MutableMapping
from packaging.specifiers import SpecifierSet
class Versions(MutableMapping):
""" a dictionary containing version specs. """
def __init__(self):
self._values = {}
def __setitem_... | code_fim | hard | {
"lang": "python",
"repo": "toumorokoshi/uranium",
"path": "/uranium/packages/versions.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>#Define variables for later
g_input_shape = 100
d_input_shape = (28,28)
hidden_1_num_units = 500
hidden_2_num_units = 500
g_output_num_units = 784
d_output_num_units = 1
epochs = 25
batch_size = 128
#Generator network
model_1 = Sequential()
model_1.add(Dense(hidden_1_num_units, input_dim=g_in... | code_fim | hard | {
"lang": "python",
"repo": "GreensboroAI/GANBasicMnist",
"path": "/GANtest1.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>history = model.fit(x=train_x, y=gan_targets(train_x.shape[0]), epochs=epochs, batch_size=batch_size)
plt.plot(history.history['player_0_loss'])
plt.plot(history.history['player_1_loss'])
plt.plot(history.history['loss'])
plt.show()
zsamples = np.random.normal(size=(10,100))
pred = model_1.predi... | code_fim | hard | {
"lang": "python",
"repo": "GreensboroAI/GANBasicMnist",
"path": "/GANtest1.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: GreensboroAI/GANBasicMnist path: /GANtest1.py
import os
import numpy as np
import pandas as pd
from scipy.misc import imread
import pylab as pyl
import matplotlib.pyplot as plt
import keras
from keras.models import Sequential
from keras.layers import Dense, Flatten, Reshape, InputLayer
... | code_fim | hard | {
"lang": "python",
"repo": "GreensboroAI/GANBasicMnist",
"path": "/GANtest1.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: miriad/nanoleaf-aurora-python path: /aurora.py
# aurora.py - Nanoleaf Aurora python library
#
# Copyright 2017 Zachary Cornelius
#
# 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 o... | code_fim | hard | {
"lang": "python",
"repo": "miriad/nanoleaf-aurora-python",
"path": "/aurora.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> def get_hue_min(self):
return self._get_json("/state/hue")["min"]
def set_hue(self, new_hue):
self._put_json("/state/hue", {"hue": {"value": int(new_hue)}})
return self.get_hue()
def increment_hue(self, hue_increment):
self._put_json("/state/hue", {"hue": {"in... | code_fim | hard | {
"lang": "python",
"repo": "miriad/nanoleaf-aurora-python",
"path": "/aurora.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ahmadchatha/Scalable-PaQL-Queries path: /src/paql_eval/ilp_direct/ilp_interface/ilp_solver.py
#######################
# MIN linear formulation
######################################################
# MIN(Y) >= (ti.y)xi - Y_diff*(1-mj) ==>
# (ti.y)xi - r*my + (Y_diff)mj <= Y_diff+r-1
... | code_fim | hard | {
"lang": "python",
"repo": "ahmadchatha/Scalable-PaQL-Queries",
"path": "/src/paql_eval/ilp_direct/ilp_interface/ilp_solver.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # Add base constraints
self.add_base_constraints()
# Add global constraints
self.add_global_constraints()
print "TODO: YOU SHOULD PROBABLY CLEAN DATA HERE, BUT FOR NOW I'M DISABLING IT"
self.clear_data()
# Problem must be a (M)ILP ((Mixed) Integer Linear Program)
if self.problem... | code_fim | hard | {
"lang": "python",
"repo": "ahmadchatha/Scalable-PaQL-Queries",
"path": "/src/paql_eval/ilp_direct/ilp_interface/ilp_solver.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> try:
with _active_limbo_lock:
del _active[self._Thread__ident]
except KeyError:
if 'dummy_threading' not in _sys.modules:
raise
threading.Thread._Thread__delete = _delete
else:
def _delete(... | code_fim | hard | {
"lang": "python",
"repo": "circus-tent/circus",
"path": "/circus/_patch.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> try:
with _active_limbo_lock:
del _active[self._ident]
except KeyError:
if 'dummy_threading' not in _sys.modules:
raise
threading.Thread._delete = _delete<|fim_prefix|># repo: circus-tent/circus path:... | code_fim | hard | {
"lang": "python",
"repo": "circus-tent/circus",
"path": "/circus/_patch.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: circus-tent/circus path: /circus/_patch.py
import threading
from threading import _active_limbo_lock, _active, _sys
debugger = False
try:
# noinspection PyUnresolvedReferences
import pydevd
debugger = pydevd.GetGlobalDebugger()
except ImportError:
pass
<|fim_suffix|> thr... | code_fim | hard | {
"lang": "python",
"repo": "circus-tent/circus",
"path": "/circus/_patch.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: stjordanis/flor path: /examples/deprecated/fib/clean_fib.py
import flor
log = flor.log
@flor.track
def fib(idx):
<|fim_suffix|>
with flor.Context('fib'):
fib(5)<|fim_middle|> fib = {}
fib[log.param(0)] = log.metric(0)
fib[log.param(1)] = log.metric(1)
fib[log.param(2)] = log.m... | code_fim | hard | {
"lang": "python",
"repo": "stjordanis/flor",
"path": "/examples/deprecated/fib/clean_fib.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>with flor.Context('fib'):
fib(5)<|fim_prefix|># repo: stjordanis/flor path: /examples/deprecated/fib/clean_fib.py
import flor
log = flor.log
@flor.track
def fib(idx):
fib = {}
fib[log.param(0)] = log.metric(0)
fib[log.param(1)] = log.metric(1)
fib[log.param(2)] = log.metric(2)
<|fim... | code_fim | medium | {
"lang": "python",
"repo": "stjordanis/flor",
"path": "/examples/deprecated/fib/clean_fib.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> operations = [
migrations.AlterModelOptions(
name='order',
options={'verbose_name_plural': '订单管理'},
),
migrations.AlterField(
model_name='order',
name='state',
field=models.CharField(choices=[(0, '未完成'), (1, '已完成')], d... | code_fim | medium | {
"lang": "python",
"repo": "fangduozhi/LearnGit",
"path": "/bishe/order/migrations/0008_auto_20190521_1221.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: fangduozhi/LearnGit path: /bishe/order/migrations/0008_auto_20190521_1221.py
# Generated by Django 2.1.7 on 2019-05-21 04:21
from django.db import migrations, models
class Migration(migrations.Migration):
<|fim_suffix|> operations = [
migrations.AlterModelOptions(
name='... | code_fim | medium | {
"lang": "python",
"repo": "fangduozhi/LearnGit",
"path": "/bishe/order/migrations/0008_auto_20190521_1221.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> code_end = start + dt.timedelta(seconds=code)
pause_start = code_end
pause_end = pause_start + dt.timedelta(seconds=pause)
return (code_start, code_end), (pause_start, pause_end)
pass
def run_as_events_n(self, start, n):
array = []
for _ in rang... | code_fim | hard | {
"lang": "python",
"repo": "timolesterhuis/diagnostics",
"path": "/src/diagnostics/demo.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: timolesterhuis/diagnostics path: /src/diagnostics/demo.py
import datetime as dt
import random
from .classes import Report
class TimeGenerator(object):
min = 0
max = None
mean = 0
stdev = 1
def __init__(self):
pass
def run(self):
value = random.gauss(se... | code_fim | hard | {
"lang": "python",
"repo": "timolesterhuis/diagnostics",
"path": "/src/diagnostics/demo.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> array = self.run_as_events_n(*args, **kwargs)
reports = [Report(t0=s, te=e, name=self.name) for s, e in array]
return reports
def run_as_events_for_t(self, start, t, start_mu=0):
array = []
if start_mu:
offset = random.gauss(0, start_mu)
... | code_fim | hard | {
"lang": "python",
"repo": "timolesterhuis/diagnostics",
"path": "/src/diagnostics/demo.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: delcypher/klee-runner path: /kleeanalysis/rank.py
f one tools has no false positives and
# the other has one of more false positives are the tools ranked differently.
#
# The motivation behind doing this is that ranking based on the number of false
# positives implicitly assumes t... | code_fim | hard | {
"lang": "python",
"repo": "delcypher/klee-runner",
"path": "/kleeanalysis/rank.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: delcypher/klee-runner path: /kleeanalysis/rank.py
available_indices = []
else:
# Retrieve coverage information
index_to_coverage_info = _get_index_to_coverage_infos(
native_program_name,
index_to_number_of_repeat_runs_map,
... | code_fim | hard | {
"lang": "python",
"repo": "delcypher/klee-runner",
"path": "/kleeanalysis/rank.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> assert isinstance(values, list)
lower_bound = min(values)
upper_bound = max(values)
median = statistics.median(values)
return (lower_bound, median, upper_bound)
def get_arithmetic_mean_and_confidence_intervals(values, confidence_interval_factor):
assert isinstance(values, list)
... | code_fim | hard | {
"lang": "python",
"repo": "delcypher/klee-runner",
"path": "/kleeanalysis/rank.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> if cv2.waitKey(2) & 0xFF == ord('d'):
print(dir)
try:
shutil.rmtree(dir)
except OSError as e:
print("Error: %s : %s" % (dir, e.strerror))
break
# Press Q on keyboard to exit
... | code_fim | hard | {
"lang": "python",
"repo": "brycekroencke/workout_tracker",
"path": "/AutoWorkoutTracker/generator/show_full_dataset.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: brycekroencke/workout_tracker path: /AutoWorkoutTracker/generator/show_full_dataset.py
import cv2
import os
import shutil
import glob
import re
def split_num(s):
return list(filter(None, re.split(r'(\d+)', s)))
def show_dataset(img_dir):
<|fim_suffix|> if cv2.waitKey(2) & 0xFF =... | code_fim | hard | {
"lang": "python",
"repo": "brycekroencke/workout_tracker",
"path": "/AutoWorkoutTracker/generator/show_full_dataset.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> profile_dir = temp_dir_path/"profile"
profile_dir.mkdir()
profile_result_dir = temp_dir_path/"result_profile"
profile_result_dir.mkdir()
# Convert sequence-to-sequence results to profile
convert_mmseqs_result_to_profile(
query_dir, serch_dir, re... | code_fim | hard | {
"lang": "python",
"repo": "sacdallago/bio_embeddings",
"path": "/tests/test_mmseqs2.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: sacdallago/bio_embeddings path: /tests/test_mmseqs2.py
import os
from pathlib import Path
from tempfile import TemporaryDirectory
import pytest
from bio_embeddings.align import (
check_mmseqs, convert_mmseqs_result_to_profile,
create_mmseqs_database, mmseqs_search, MMseqsSearchOptions,
... | code_fim | hard | {
"lang": "python",
"repo": "sacdallago/bio_embeddings",
"path": "/tests/test_mmseqs2.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: huizhi-li/swarmlib path: /swarmlib/util/problem_base.py
# ------------------------------------------------------------------------------------------------------
# Copyright (c) Leo Hanisch. All rights reserved.
# Licensed under the BSD 3-Clause License. See LICENSE.txt in the project root for l... | code_fim | medium | {
"lang": "python",
"repo": "huizhi-li/swarmlib",
"path": "/swarmlib/util/problem_base.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> def replay(self) -> None:
"""
Start the problems visualization.
"""
self._visualizer.replay()<|fim_prefix|># repo: huizhi-li/swarmlib path: /swarmlib/util/problem_base.py
# ------------------------------------------------------------------------------------------------... | code_fim | medium | {
"lang": "python",
"repo": "huizhi-li/swarmlib",
"path": "/swarmlib/util/problem_base.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: softsys4ai/unicorn path: /causallearn/utils/ChoiceGenerator.py
class ChoiceGenerator:
'''
Generates (nonrecursively) all of the combinations of a choose b, where a, b
are nonnegative integers and a >= b. The values of a and b are given in the
constructor, and the sequence of choi... | code_fim | hard | {
"lang": "python",
"repo": "softsys4ai/unicorn",
"path": "/causallearn/utils/ChoiceGenerator.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def next(self):
i = self.b
while i > 0:
i -= 1
if self.choiceLocal[i] < (i + self.diff):
self.fill(i)
self.begun = True
for j in range(self.b):
self.choiceReturned[j] = self.choiceLocal[j]
... | code_fim | hard | {
"lang": "python",
"repo": "softsys4ai/unicorn",
"path": "/causallearn/utils/ChoiceGenerator.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: agenteAND/jeito path: /members/serializers.py
from rest_framework import serializers
from members.models import Adhesion, Nomination, Structure
class NominationSerializer(serializers.ModelSerializer):
structure = serializers.CharField(source='structure.name')
structure_type = serializer... | code_fim | medium | {
"lang": "python",
"repo": "agenteAND/jeito",
"path": "/members/serializers.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> class Meta:
model = Adhesion
fields = (
'number', 'first_name', 'last_name', 'gender', 'email',
'structure', 'structure_type', 'region', 'rate', 'nominations',
'adhesions_resp_email', 'structure_resp_email',
)
class StructureSerializer(seri... | code_fim | hard | {
"lang": "python",
"repo": "agenteAND/jeito",
"path": "/members/serializers.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>class AdhesionSerializer(serializers.ModelSerializer):
number = serializers.CharField(source='person.number')
first_name = serializers.CharField(source='person.first_name')
last_name = serializers.CharField(source='person.last_name')
gender = serializers.IntegerField(source='person.gender'... | code_fim | medium | {
"lang": "python",
"repo": "agenteAND/jeito",
"path": "/members/serializers.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>les.fix_imports()
except ImportError:
pass<|fim_prefix|># repo: statusz/pygrow path: /grow/__init__.py
import os
import sys
sys.path.extend([os.pa<|fim_middle|>th.join(os.path.dirname(__file__), '..')])
try:
from grow import submodules
submodu | code_fim | medium | {
"lang": "python",
"repo": "statusz/pygrow",
"path": "/grow/__init__.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: statusz/pygrow path: /grow/__init__.py
import os
import sys
sys.path.extend([os.pa<|fim_suffix|>try:
from grow import submodules
submodules.fix_imports()
except ImportError:
pass<|fim_middle|>th.join(os.path.dirname(__file__), '..')])
| code_fim | easy | {
"lang": "python",
"repo": "statusz/pygrow",
"path": "/grow/__init__.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>try:
from grow import submodules
submodules.fix_imports()
except ImportError:
pass<|fim_prefix|># repo: statusz/pygrow path: /grow/__init__.py
import os
import sys
sys.path.extend([os.pa<|fim_middle|>th.join(os.path.dirname(__file__), '..')])
| code_fim | easy | {
"lang": "python",
"repo": "statusz/pygrow",
"path": "/grow/__init__.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: cs-fullstack-2019-spring/django-formclassv2-cw-DB225-1 path: /frmProject/frmApp/forms.py
from django import forms
<|fim_suffix|> name = forms.CharField()
birthday = forms.DateField()
applyingTo = forms.CharField()
salary = forms.IntegerField()<|fim_middle|>class EmpApplication(for... | code_fim | easy | {
"lang": "python",
"repo": "cs-fullstack-2019-spring/django-formclassv2-cw-DB225-1",
"path": "/frmProject/frmApp/forms.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> name = forms.CharField()
birthday = forms.DateField()
applyingTo = forms.CharField()
salary = forms.IntegerField()<|fim_prefix|># repo: cs-fullstack-2019-spring/django-formclassv2-cw-DB225-1 path: /frmProject/frmApp/forms.py
from django import forms
<|fim_middle|>class EmpApplication(for... | code_fim | easy | {
"lang": "python",
"repo": "cs-fullstack-2019-spring/django-formclassv2-cw-DB225-1",
"path": "/frmProject/frmApp/forms.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> self.input = self.input.lower().split()
self.run = True
def run_cmd(self):
if self.input[0] in self.commands.list:
return getattr(self.commands, self.input[0])()
else:
print 'Command not found. Use "help"\n'<|fim_prefix|># repo: michaelti... | code_fim | hard | {
"lang": "python",
"repo": "michaeltintiuc/pygame-demo",
"path": "/terminal.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: michaeltintiuc/pygame-demo path: /terminal.py
from commands import *
class Terminal:
def __init__(self, engine=None):
<|fim_suffix|> if self.input[0] in self.commands.list:
return getattr(self.commands, self.input[0])()
else:
print 'Command not... | code_fim | hard | {
"lang": "python",
"repo": "michaeltintiuc/pygame-demo",
"path": "/terminal.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> while not self.input:
self.input = raw_input('>> ')
self.input = self.input.lower().split()
self.run = True
def run_cmd(self):
if self.input[0] in self.commands.list:
return getattr(self.commands, self.input[0])()
else:
... | code_fim | medium | {
"lang": "python",
"repo": "michaeltintiuc/pygame-demo",
"path": "/terminal.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: r4k0nb4k0n/CTF-Writeups path: /2020/utctf_2020/Cryptography/Random_ECB/get_flag.py
from pwn import *
r = remote('ecb.utctf.live', 9003)
#r = process(['python', 'server.py'])
def get_hash_block(plaintext, block_idx):
r.sendlineafter("Input a string to encrypt (input 'q' to quit):",plaintext)
... | code_fim | hard | {
"lang": "python",
"repo": "r4k0nb4k0n/CTF-Writeups",
"path": "/2020/utctf_2020/Cryptography/Random_ECB/get_flag.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>def guess():
plaintext = 'A'*16
block_idx = 0
FLAG = ''
dummy_block = get_hash_block(plaintext, block_idx)
while True:
part = ''
for i in range(1,16):
target_block = find_block(plaintext[i:16+block_idx], dummy_block, block_idx)
ch = brute_force(plaintext[i:] + part, target_block, dummy_bloc... | code_fim | medium | {
"lang": "python",
"repo": "r4k0nb4k0n/CTF-Writeups",
"path": "/2020/utctf_2020/Cryptography/Random_ECB/get_flag.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def guess():
plaintext = 'A'*16
block_idx = 0
FLAG = ''
dummy_block = get_hash_block(plaintext, block_idx)
while True:
part = ''
for i in range(1,16):
target_block = find_block(plaintext[i:16+block_idx], dummy_block, block_idx)
ch = brute_force(plaintext[i:] + part, target_block, dummy_blo... | code_fim | medium | {
"lang": "python",
"repo": "r4k0nb4k0n/CTF-Writeups",
"path": "/2020/utctf_2020/Cryptography/Random_ECB/get_flag.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> if question_num == scrap_questions_num:
to_stop = True
break
question_num += 1
page_num += 1
if to_stop ==True:
break<|fim_prefix|># repo: xiaodongzi/pytohon_teach_material path: /11/homework11/zhihu_top100.py
# coding: utf-8
import requests
from p... | code_fim | medium | {
"lang": "python",
"repo": "xiaodongzi/pytohon_teach_material",
"path": "/11/homework11/zhihu_top100.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: xiaodongzi/pytohon_teach_material path: /11/homework11/zhihu_top100.py
# coding: utf-8
import requests
from pyquery import PyQuery as pq
question_num = 1
page_num = 1
to_stop = False
scrap_questions_num = 100
while True:
url = "http://www.zhihu.com/topic/19776749/top-answers?page=%d" % (pag... | code_fim | medium | {
"lang": "python",
"repo": "xiaodongzi/pytohon_teach_material",
"path": "/11/homework11/zhihu_top100.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> @staticmethod
def load_image_into_numpy_array(image):
(im_width, im_height) = image.size
return np.array(image.getdata()).reshape(
(im_height, im_width, 3)).astype(np.uint8)
def api(self, image):
if self.sess is None:
self.sess = tf.Session(grap... | code_fim | hard | {
"lang": "python",
"repo": "fengrk/docker-practice",
"path": "/tensorflow/tensorflow-object-detection-server/server.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: fengrk/docker-practice path: /tensorflow/tensorflow-object-detection-server/server.py
# coding:utf-8
from io import BytesIO
import numpy as np
import requests
import tensorflow as tf
from PIL import Image
from flask import Flask, request, make_response
from ml_tools.object_detection.utils impor... | code_fim | hard | {
"lang": "python",
"repo": "fengrk/docker-practice",
"path": "/tensorflow/tensorflow-object-detection-server/server.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: amadev/open4k path: /open4k/controllers/instance.py
import kopf
import pykube
from open4k import utils
from open4k import kube
from open4k import client
from open4k import settings
from open4k import hooks
LOG = utils.get_logger(__name__)
kopf_on_args = ["open4k.amadev.ru", "v1alpha1", "instanc... | code_fim | hard | {
"lang": "python",
"repo": "amadev/open4k",
"path": "/open4k/controllers/instance.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> if not body.get("status", {}).get("applied"):
LOG.info(f"{name} was not applied successfully")
return
klass = Instance
os_obj_id = body["status"].get("object", {}).get("id")
if not os_obj_id:
LOG.info(f"Cannot get id for {name}")
return
c = client.get... | code_fim | hard | {
"lang": "python",
"repo": "amadev/open4k",
"path": "/open4k/controllers/instance.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> """ Wrapper class for all mpi4py communication variables. """
comm = MPI.COMM_WORLD
size = MPI.COMM_WORLD.Get_size()
uid = MPI.COMM_WORLD.Get_rank()
name = MPI.Get_processor_name()
# Attach buffer
BUFF_SISE = 32064000 * (1 + MPI.BSEND_OVERHEAD)
buff = empty(BUFF_SISE, dty... | code_fim | medium | {
"lang": "python",
"repo": "jiaqi61/AsySPA",
"path": "/asyspa/gossip_comm.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: jiaqi61/AsySPA path: /asyspa/gossip_comm.py
""" Wrapper for MPI communication variables to improve process timing. """
from numpy import empty
<|fim_suffix|>class GossipComm(object):
""" Wrapper class for all mpi4py communication variables. """
comm = MPI.COMM_WORLD
size = MPI.COMM... | code_fim | easy | {
"lang": "python",
"repo": "jiaqi61/AsySPA",
"path": "/asyspa/gossip_comm.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> def create_row(self, well=None, artifact=None):
header = collections.OrderedDict()
header["Well"] = well.alpha_num_key if well else None
header["Sample Name"] = artifact.name if artifact else None
header["Target Name"] = artifact.name if artifact else None
heade... | code_fim | hard | {
"lang": "python",
"repo": "ctmrbio/claritylims",
"path": "/clarity-ext-scripts/clarity_ext_scripts/covid/pcr/example_result_file_rt_pcr.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ctmrbio/claritylims path: /clarity-ext-scripts/clarity_ext_scripts/covid/pcr/example_result_file_rt_pcr.py
import xlwt
import collections
import datetime
import random
from clarity_ext.extensions import GeneralExtension
from clarity_ext_scripts.covid.parse_pcr import CT_HEADER
class Extension(G... | code_fim | hard | {
"lang": "python",
"repo": "ctmrbio/claritylims",
"path": "/clarity-ext-scripts/clarity_ext_scripts/covid/pcr/example_result_file_rt_pcr.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> inputs = inspect.getcallargs(function, *args, **kwargs) # pylint: disable=deprecated-method
self = inputs.pop('self', function) # We test whether function is a method by looking for a `self` argument. If not we store the cache in the function itself.
if not hasattr(self, '_cache'):
... | code_fim | medium | {
"lang": "python",
"repo": "MarkCBell/flipper",
"path": "/flipper/kernel/decorators.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: MarkCBell/flipper path: /flipper/kernel/decorators.py
''' A module for decorators. '''
import inspect
from decorator import decorator
@decorator
def memoize(function, *args, **kwargs):
''' A decorator that memoizes a function. '''
<|fim_suffix|> result = self._cache[key]
if isi... | code_fim | hard | {
"lang": "python",
"repo": "MarkCBell/flipper",
"path": "/flipper/kernel/decorators.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> result = self._cache[key]
if isinstance(result, Exception):
raise result
else:
return result<|fim_prefix|># repo: MarkCBell/flipper path: /flipper/kernel/decorators.py
''' A module for decorators. '''
import inspect
from decorator import decorator
@decorator
def memoize(fun... | code_fim | hard | {
"lang": "python",
"repo": "MarkCBell/flipper",
"path": "/flipper/kernel/decorators.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> overlay = Curve(range(10), label='A') * Curve(range(10), label='B')
plot = mpl_renderer.get_plot(overlay)
legend = plot.handles['legend']
legend_labels = [l.get_text() for l in legend.texts]
self.assertEqual(legend_labels, ['A', 'B'])
def test_overlay_legend_wi... | code_fim | hard | {
"lang": "python",
"repo": "holoviz/holoviews",
"path": "/holoviews/tests/plotting/matplotlib/test_overlayplot.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: holoviz/holoviews path: /holoviews/tests/plotting/matplotlib/test_overlayplot.py
import numpy as np
from holoviews.core import Overlay, NdOverlay, DynamicMap, HoloMap
from holoviews.element import Curve, Scatter
from ...utils import LoggingComparisonTestCase
from .test_plot import TestMPLPlot, ... | code_fim | hard | {
"lang": "python",
"repo": "holoviz/holoviews",
"path": "/holoviews/tests/plotting/matplotlib/test_overlayplot.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> def cb(X):
return NdOverlay({i: Curve(np.arange(10)+i) for i in range(X)})
dmap = DynamicMap(cb, kdims=['X']).redim.range(X=(1, 10))
plot = mpl_renderer.get_plot(dmap)
self.assertEqual(len(plot.subplots), 1)
plot.update((3,))
self.assertEqual(len... | code_fim | hard | {
"lang": "python",
"repo": "holoviz/holoviews",
"path": "/holoviews/tests/plotting/matplotlib/test_overlayplot.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> uri=input("uri of the remote i/o server, or enter for local i/o:").strip()
print(repr(uri))
if uri:
try:
remoteIO = Pyro4.Proxy(uri)
remote_stdout, remote_stdin = remoteIO.getInputOutput()
print("Replacing sys.stdin and sys.stdout. Read and typ... | code_fim | medium | {
"lang": "python",
"repo": "delmic/Pyro4",
"path": "/examples/stdinstdout/program.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: delmic/Pyro4 path: /examples/stdinstdout/program.py
# this is the program whose input/output you can redirect
from __future__ import print_function
import sys
import Pyro4
if sys.version_info<(3,0):
input=raw_input
sys.excepthook=Pyro4.util.excepthook
def interaction():
<|fim_... | code_fim | hard | {
"lang": "python",
"repo": "delmic/Pyro4",
"path": "/examples/stdinstdout/program.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>class CommandsTest(TestCase):
def test_relay_events(self):
cmd = relay_events.Command()
cmd.stdout = StringIO()
cmd.handle()
self.assertEqual(
cmd.stdout.getvalue(),
'Relaying 0 events in batches of %s.\nDone.\n' % (
settings.GAR... | code_fim | medium | {
"lang": "python",
"repo": "smn/garelay",
"path": "/garelay/tests/test_commands.py",
"mode": "spm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: smn/garelay path: /garelay/tests/test_commands.py
from StringIO import StringIO
from django.test import TestCase
from django.conf import settings
from garelay.management.commands import relay_events, register_events
<|fim_suffix|> cmd = register_events.Command()
cmd.stdout = Str... | code_fim | hard | {
"lang": "python",
"repo": "smn/garelay",
"path": "/garelay/tests/test_commands.py",
"mode": "psm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> cmd = register_events.Command()
cmd.stdout = StringIO()
cmd.handle()
self.assertEqual(
cmd.stdout.getvalue(),
'Registering 0 events in batches of %s.\nDone.\n' % (
settings.GARELAY_REGISTER_BATCH_SIZE,))<|fim_prefix|># repo: smn/garel... | code_fim | hard | {
"lang": "python",
"repo": "smn/garelay",
"path": "/garelay/tests/test_commands.py",
"mode": "spm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> """Return authorization view object using corp type and business identifier.
Mainly used for service accounts.Sorted using the membership since service accounts gets all access
"""
return cls.query.filter_by(product_code=product_code, business_identifier=business_identifi... | code_fim | hard | {
"lang": "python",
"repo": "bcgov/sbc-auth",
"path": "/auth-api/src/auth_api/models/views/authorization.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> @classmethod
def find_authorization_for_admin_by_org_id(cls, org_id: int):
"""Return authorization view object for staff."""
# staff gets ADMIN level access
return cls.query.filter_by(org_id=org_id, org_membership=ADMIN).first()
@classmethod
def find_account_author... | code_fim | hard | {
"lang": "python",
"repo": "bcgov/sbc-auth",
"path": "/auth-api/src/auth_api/models/views/authorization.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: bcgov/sbc-auth path: /auth-api/src/auth_api/models/views/authorization.py
# Copyright © 2019 Province of British Columbia
#
# 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": "bcgov/sbc-auth",
"path": "/auth-api/src/auth_api/models/views/authorization.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: willettk/SpaceWarps path: /analysis/SWAPSHOP.py
import os
import subprocess
import pdb
import swap
from optparse import OptionParser
'''
Need to run SWAP.py multiple times -- once for every "day" in GZ2
Take that output and feed it into my machine classifiers
Take that output and determine reti... | code_fim | hard | {
"lang": "python",
"repo": "willettk/SpaceWarps",
"path": "/analysis/SWAPSHOP.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # Check if SWAP.py set the "keep going" cookie to False
#more = os.system("grep 'running' .swap.cookie | wc -l")
more = subprocess.check_output("grep 'running' .swap.cookie | wc -l",
shell=True)
# Read the results of that call -- they're goofy
... | code_fim | hard | {
"lang": "python",
"repo": "willettk/SpaceWarps",
"path": "/analysis/SWAPSHOP.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # Define "today's" logfile name -- the count specifies which day
logfile = "%s/GZ2_%i.log"%(log_dir,count)
# run SWAP.py with the chosen configfile (and specific logfile?)
#os.system("python SWAP.py %s > %s"%(config,logfile))
os.system("python SWAP.py %s"%(config))
# ALWAYS run M... | code_fim | hard | {
"lang": "python",
"repo": "willettk/SpaceWarps",
"path": "/analysis/SWAPSHOP.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> velocities_list = []
distances_list = []
for param in params_to_change:
velocities_dict = {}
distances_dict = {}
dists = []
velocity_limits = []
for motor_name, motor_obj in motors.items():
velocity_limits.append(tuple(motor_obj.velocity.limi... | code_fim | hard | {
"lang": "python",
"repo": "NSLS-II-TES/profile_collection",
"path": "/startup/.test_calc_velocity.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: NSLS-II-TES/profile_collection path: /startup/.test_calc_velocity.py
import pytest
def calc_velocity(motors, dists, velocity_limits):
ret_vels = []
# find max distance to move
max_dist = np.max(dists)
max_dist_index = dists.index(max_dist)
max_dist_vel = velocity_limits[max_... | code_fim | hard | {
"lang": "python",
"repo": "NSLS-II-TES/profile_collection",
"path": "/startup/.test_calc_velocity.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> for pair in gradients:
gradient, variable = pair
summary_name = ('%s_gradient' % variable.name).replace(':', '_')
tf.summary.histogram(summary_name, gradient)
return tf.estimator.EstimatorSpec(
mode,
loss=loss,
train_... | code_fim | hard | {
"lang": "python",
"repo": "williamwhe/char-cnn",
"path": "/charcnn/cnn.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: williamwhe/char-cnn path: /charcnn/cnn.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
An implementation of
Character-level Convolutional Networks for Text Classification
Zhang and LeCun, 2015 (See https://arxiv.org/abs/1509.01626)
"""
import numpy as np
import json
import tenso... | code_fim | hard | {
"lang": "python",
"repo": "williamwhe/char-cnn",
"path": "/charcnn/cnn.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # add ground truth to the output if it's there
if 'ground_truth' in features:
predictions['ground_truth'] = features['ground_truth']
return tf.estimator.EstimatorSpec(
mode,
predictions=predictions,
export_outputs={
'... | code_fim | hard | {
"lang": "python",
"repo": "williamwhe/char-cnn",
"path": "/charcnn/cnn.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
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