text stringlengths 232 16.3k | domain stringclasses 1
value | difficulty stringclasses 3
values | meta dict |
|---|---|---|---|
<|fim_prefix|># repo: Semprini/cbe-sport path: /sport/compete/motorsport/migrations/0001_initial.py
# -*- coding: utf-8 -*-
# Generated by Django 1.10.3 on 2017-02-20 05:41
from __future__ import unicode_literals
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Mi... | code_fim | hard | {
"lang": "python",
"repo": "Semprini/cbe-sport",
"path": "/sport/compete/motorsport/migrations/0001_initial.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>, models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='codriver', to='party.Individual')),
('current_lap', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='race_entry_current_lap', to='motorsp... | code_fim | hard | {
"lang": "python",
"repo": "Semprini/cbe-sport",
"path": "/sport/compete/motorsport/migrations/0001_initial.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: GeorgiStavrev/tensorflow-examples path: /m4_image_transpose.py
import tensorflow as tf
import matplotlib.image as mp_img
import matplotlib.pyplot as plot
import os
filename = './DandelionFlower.jpg'
<|fim_suffix|> transpose = tf.transpose(x, perm=[1,0,2])
result = sess.run(transpose)
... | code_fim | hard | {
"lang": "python",
"repo": "GeorgiStavrev/tensorflow-examples",
"path": "/m4_image_transpose.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>with tf.Session() as sess:
sess.run(init)
transpose = tf.transpose(x, perm=[1,0,2])
result = sess.run(transpose)
plot.imshow(result)
plot.show()<|fim_prefix|># repo: GeorgiStavrev/tensorflow-examples path: /m4_image_transpose.py
import tensorflow as tf
import matplotlib.image as mp_... | code_fim | easy | {
"lang": "python",
"repo": "GeorgiStavrev/tensorflow-examples",
"path": "/m4_image_transpose.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> for x in range(m):
for y in range(n):
if obstacleGrid[x][y] == 1:
dp[x][y] = 0
for p in range(1, m):
if obstacleGrid[p][0] != 1:
dp[p][0] = dp[p-1][0]
for q in range(1, n):
if obstacleGrid[0][... | code_fim | hard | {
"lang": "python",
"repo": "Coalin/Daily-LeetCode-Exercise",
"path": "/63_Unique-Paths-II.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> for p in range(1, m):
if obstacleGrid[p][0] != 1:
dp[p][0] = dp[p-1][0]
for q in range(1, n):
if obstacleGrid[0][q] != 1:
dp[0][q] = dp[0][q-1]
for i in range(1, m):
for j in range(1, n):
... | code_fim | hard | {
"lang": "python",
"repo": "Coalin/Daily-LeetCode-Exercise",
"path": "/63_Unique-Paths-II.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Coalin/Daily-LeetCode-Exercise path: /63_Unique-Paths-II.py
class Solution(object):
def uniquePathsWithObstacles(self, obstacleGrid):
"""
:type obstacleGrid: List[List[int]]
:rtype: int
"""
m = len(obstacleGrid)
n = len(obstacleGrid[0])
... | code_fim | hard | {
"lang": "python",
"repo": "Coalin/Daily-LeetCode-Exercise",
"path": "/63_Unique-Paths-II.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: nickmvincent/SerpScrap path: /scrapcore/parser/parser.py
# -*- coding: utf-8 -*-
import logging
import pprint
import re
from cssselect import HTMLTranslator
import lxml.html
from lxml.html.clean import Cleaner
logger = logging.getLogger(__name__)
class Parser():
"""Default Parse"""
... | code_fim | hard | {
"lang": "python",
"repo": "nickmvincent/SerpScrap",
"path": "/scrapcore/parser/parser.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
# only add items that have not None links.
# Avoid duplicates. Detect them by the link.
# If statement below: Lazy evaluation.
# The more probable case first.
found_container = False
se... | code_fim | hard | {
"lang": "python",
"repo": "nickmvincent/SerpScrap",
"path": "/scrapcore/parser/parser.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>te
from .is_not_in import IsNotInAttribute
from .not_equals import NotEqualsAttribute<|fim_prefix|># repo: phenobarbital/py-abac path: /py_abac/policy/conditions/attribute/__init__.py
"""
Attribute conditions
"""
from .all_in import AllInAttribute
from .all_not_i<|fim_middle|>n import AllNotInAttrib... | code_fim | medium | {
"lang": "python",
"repo": "phenobarbital/py-abac",
"path": "/py_abac/policy/conditions/attribute/__init__.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: phenobarbital/py-abac path: /py_abac/policy/conditions/attribute/__init__.py
"""
Attribute conditions
"""
from .all_in import AllInAttribute
from .all_not_in import AllNotInAttribute
from .any_in import AnyInAttribute
from .any_not_in import<|fim_suffix|>te
from .is_not_in import IsNotInAttr... | code_fim | medium | {
"lang": "python",
"repo": "phenobarbital/py-abac",
"path": "/py_abac/policy/conditions/attribute/__init__.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: avi1mizrahi/AdaptiveBatchingBlockchain path: /requestGenerator.py
import http.client
import json
from time import sleep
# payload = "{\n \"amount\": 50\n}"
#
#
#
# res = self.conn.getresponse()
# data = res.read()
# print(data.decode("utf-8"))
#
# j = json.loads(data)
class Client:
def... | code_fim | hard | {
"lang": "python",
"repo": "avi1mizrahi/AdaptiveBatchingBlockchain",
"path": "/requestGenerator.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>c1.transfer(acc11, acc12, 70)
c2.transfer(acc11, acc12, 70)
c2.transfer(acc21, acc12, 70)
print("c11 amount = ", c1.getAmount(acc11))
print("c11 amount = ", c2.getAmount(acc11))
print("c12 amount = ", c1.getAmount(acc12))
print("c12 amount = ", c2.getAmount(acc12))
print("c21 amount = ", c1.getAmount(a... | code_fim | hard | {
"lang": "python",
"repo": "avi1mizrahi/AdaptiveBatchingBlockchain",
"path": "/requestGenerator.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: danielmuthama/mindmeld path: /mindmeld/components/entity_resolver.py
logging.getLogger(__name__)
class EntityResolver:
"""An entity resolver is used to resolve entities in a given query to their canonical values
(usually linked to specific entries in a knowledge base).
"""
# pr... | code_fim | hard | {
"lang": "python",
"repo": "danielmuthama/mindmeld",
"path": "/mindmeld/components/entity_resolver.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> def _construct_phonetic_match_query(entity, weight=1):
return [
{
"match": {
"cname.double_metaphone": {
"query": entity.text,
"boost": 2 * weight,
... | code_fim | hard | {
"lang": "python",
"repo": "danielmuthama/mindmeld",
"path": "/mindmeld/components/entity_resolver.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: danielmuthama/mindmeld path: /mindmeld/components/entity_resolver.py
docs):
for doc in docs:
action = {}
# id
if doc.get("id"):
action["_id"] = doc["id"]
else:
# generate hash fro... | code_fim | hard | {
"lang": "python",
"repo": "danielmuthama/mindmeld",
"path": "/mindmeld/components/entity_resolver.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>ErrCode = MibTableColumn((1, 3, 6, 1, 4, 1, 1774, 4, 7, 1, 18), DisplayString().subtype(subtypeSpec=ValueSizeConstraint(1, 65))).setMaxAccess("readonly")
if mibBuilder.loadTexts: otxPrev3ErrCode.setStatus('mandatory')
otxPrev4Time = MibTableColumn((1, 3, 6, 1, 4, 1, 1774, 4, 7, 1, 19), TimeTicks()).setMax... | code_fim | hard | {
"lang": "python",
"repo": "agustinhenze/mibs.snmplabs.com",
"path": "/pysnmp/AUDITEC2-MIB.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: agustinhenze/mibs.snmplabs.com path: /pysnmp/AUDITEC2-MIB.py
ss("readonly")
if mibBuilder.loadTexts: ordTime.setStatus('mandatory')
ordValue = MibTableColumn((1, 3, 6, 1, 4, 1, 1774, 4, 6, 1, 27), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 2147483647))).setMaxAccess("readonly")
if mi... | code_fim | hard | {
"lang": "python",
"repo": "agustinhenze/mibs.snmplabs.com",
"path": "/pysnmp/AUDITEC2-MIB.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: agustinhenze/mibs.snmplabs.com path: /pysnmp/AUDITEC2-MIB.py
, 1, 4, 1, 1774, 4, 5, 1, 9), TimeTicks()).setMaxAccess("readonly")
if mibBuilder.loadTexts: sceStartTime.setStatus('mandatory')
sceAccumulationMeasureDuration = MibTableColumn((1, 3, 6, 1, 4, 1, 1774, 4, 5, 1, 10), Counter32()).setMaxA... | code_fim | hard | {
"lang": "python",
"repo": "agustinhenze/mibs.snmplabs.com",
"path": "/pysnmp/AUDITEC2-MIB.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: linyuxuanlin/Resources_for_Robotics path: /大一下/嵌入式系统设计（二）/资料/python 视觉资源/opencv001.py
# -*- coding: utf-8 -*-
import cv2
# from matplotlib import pyplot as plt
from pylab import *
<|fim_suffix|># 载入图像
im = cv2.imread('cat.jpg')
# 颜色空间转换
gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
# 显示原始图像... | code_fim | medium | {
"lang": "python",
"repo": "linyuxuanlin/Resources_for_Robotics",
"path": "/大一下/嵌入式系统设计（二）/资料/python 视觉资源/opencv001.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|># 显示原始图像
fig = plt.figure()
subplot(121)
plt.gray()
imshow(im)
title(u'彩色图')
axis('off')
# 显示灰度化图像
plt.subplot(122)
plt.gray()
imshow(gray)
title(u'灰度图')
axis('off')
show()<|fim_prefix|># repo: linyuxuanlin/Resources_for_Robotics path: /大一下/嵌入式系统设计（二）/资料/python 视觉资源/opencv001.py
# -*- coding: utf-8 -*-... | code_fim | hard | {
"lang": "python",
"repo": "linyuxuanlin/Resources_for_Robotics",
"path": "/大一下/嵌入式系统设计（二）/资料/python 视觉资源/opencv001.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Mewzyk/stephen_AI path: /graph_code/stephen_graph_test.py
from graph_code.stephen_graph import Graph
from graph_code.stephen_dfs import dfs
if __name__ == "__main__":
main_graph = Graph()
vertices = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i']
edges = [['a', 'b'], ['a', 'c'], ['b',... | code_fim | medium | {
"lang": "python",
"repo": "Mewzyk/stephen_AI",
"path": "/graph_code/stephen_graph_test.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> start = main_graph.graph_dict['i']
end = main_graph.graph_dict['f']
print('\nPrinting Path: ')
print(dfs(start, end))<|fim_prefix|># repo: Mewzyk/stephen_AI path: /graph_code/stephen_graph_test.py
from graph_code.stephen_graph import Graph
from graph_code.stephen_dfs import dfs
<|fim_mi... | code_fim | hard | {
"lang": "python",
"repo": "Mewzyk/stephen_AI",
"path": "/graph_code/stephen_graph_test.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>from yoyo import step
__depends__ = {"20210621_01_IRyiT-rename-qa-f1"}
steps = [
step(
"UPDATE rounds SET url = REPLACE(url, 'fhcxpbltv0', 'obws766r82')",
"UPDATE rounds SET url = REPLACE(url, 'obws766r82', 'fhcxpbltv0')",
)
]<|fim_prefix|># repo: vontell/dynabench path: /api/m... | code_fim | easy | {
"lang": "python",
"repo": "vontell/dynabench",
"path": "/api/migrations/20210630_01_s8Xod-update-model-url-to-authorized-endpoint.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: vontell/dynabench path: /api/migrations/20210630_01_s8Xod-update-model-url-to-authorized-endpoint.py
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
<|fim_suffix|>steps ... | code_fim | medium | {
"lang": "python",
"repo": "vontell/dynabench",
"path": "/api/migrations/20210630_01_s8Xod-update-model-url-to-authorized-endpoint.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>__depends__ = {"20210621_01_IRyiT-rename-qa-f1"}
steps = [
step(
"UPDATE rounds SET url = REPLACE(url, 'fhcxpbltv0', 'obws766r82')",
"UPDATE rounds SET url = REPLACE(url, 'obws766r82', 'fhcxpbltv0')",
)
]<|fim_prefix|># repo: vontell/dynabench path: /api/migrations/20210630_01_s8... | code_fim | easy | {
"lang": "python",
"repo": "vontell/dynabench",
"path": "/api/migrations/20210630_01_s8Xod-update-model-url-to-authorized-endpoint.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: winsphinx/Kindle path: /kindle.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
This is a ...
"""
from __future__ import unicode_literals
import codecs
import os
import re
import shutil
import sys
import tkinter as T
def get_path():
""" get Kindle path """
if sys.platform == "win3... | code_fim | hard | {
"lang": "python",
"repo": "winsphinx/Kindle",
"path": "/kindle.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>def show_clip(path):
""" Show clipper """
try:
f = codecs.open((os.path.join(path, "My Clippings.txt")), "r", "utf-8")
t = f.readlines()
f.close()
except IOError:
return "No Clipper File Found!"
else:
return format_text(t)
def format_text(text):
... | code_fim | hard | {
"lang": "python",
"repo": "winsphinx/Kindle",
"path": "/kindle.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> self.btn1 = T.Button(self.frm2, width=15, text="Clean")
self.btn1.grid(row=0, column=0, padx=20, pady=10)
self.btn1.config(command=self.cleanup)
self.btn2 = T.Button(self.frm2, width=15, text="Clipper")
self.btn2.grid(row=0, column=1, padx=20, pady=10)
self... | code_fim | hard | {
"lang": "python",
"repo": "winsphinx/Kindle",
"path": "/kindle.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>nge(1, n + 1):
for j in range(1, k + 1):
dp[j] += dp[j - 1]
for j in range(k, -1, -1):
if j - (i - 1) > 0:
dp[j] -= dp[j - (i - 1) - 1]
mod = 1000000007
return dp[-1] % mod<|fim_prefix|># repo: wyaadarsh/LeetCode-S... | code_fim | hard | {
"lang": "python",
"repo": "wyaadarsh/LeetCode-Solutions",
"path": "/Python3/0629-K-Inverse-Pairs-Array/soln.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: wyaadarsh/LeetCode-Solutions path: /Python3/0629-K-Inverse-Pairs-Array/soln.py
class Solution:
def kInversePairs(self, n: int, k: int) -> int:
# 1 to n
# exact k inverse
# f(n, k) = f(n - 1, j) i in [max(k - (n - 1), 0), k]
# f(0, k) = 0
# f(n, 0) = 1
... | code_fim | hard | {
"lang": "python",
"repo": "wyaadarsh/LeetCode-Solutions",
"path": "/Python3/0629-K-Inverse-Pairs-Array/soln.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> if j - (i - 1) > 0:
dp[j] -= dp[j - (i - 1) - 1]
mod = 1000000007
return dp[-1] % mod<|fim_prefix|># repo: wyaadarsh/LeetCode-Solutions path: /Python3/0629-K-Inverse-Pairs-Array/soln.py
class Solution:
def kInversePairs(self, n: int, k: int) -> int:
... | code_fim | hard | {
"lang": "python",
"repo": "wyaadarsh/LeetCode-Solutions",
"path": "/Python3/0629-K-Inverse-Pairs-Array/soln.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: suomichain/suomi-core path: /trx_libs/settings/suomi_settings/processor/handler.py
# Copyright 2017 Suomi Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
... | code_fim | hard | {
"lang": "python",
"repo": "suomichain/suomi-core",
"path": "/trx_libs/settings/suomi_settings/processor/handler.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> try:
entries_list = context.get_state([address], timeout=STATE_TIMEOUT_SEC)
except FutureTimeoutError:
LOGGER.warning('Timeout occured on context.get_state([%s])', address)
raise InternalError('Unable to get {}'.format(address))
if entries_list:
setting.ParseFr... | code_fim | hard | {
"lang": "python",
"repo": "suomichain/suomi-core",
"path": "/trx_libs/settings/suomi_settings/processor/handler.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: choonho/python-core path: /src/spaceone/core/logger/filters/exclude.py
# -*- coding: utf-8 -*-
import logging
class ExcludeFilter(logging.Filter):
def __init__(self, rules):
<|fim_suffix|> def filter(self, record):
for _rule in self.rules:
if getattr(record, _rule, No... | code_fim | easy | {
"lang": "python",
"repo": "choonho/python-core",
"path": "/src/spaceone/core/logger/filters/exclude.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> self.rules = rules
def filter(self, record):
for _rule in self.rules:
if getattr(record, _rule, None) in self.rules[_rule]:
return False
return True<|fim_prefix|># repo: choonho/python-core path: /src/spaceone/core/logger/filters/exclude.py
# -*- ... | code_fim | easy | {
"lang": "python",
"repo": "choonho/python-core",
"path": "/src/spaceone/core/logger/filters/exclude.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> def __init__(self, rules):
self.rules = rules
def filter(self, record):
for _rule in self.rules:
if getattr(record, _rule, None) in self.rules[_rule]:
return False
return True<|fim_prefix|># repo: choonho/python-core path: /src/spaceone/core/l... | code_fim | easy | {
"lang": "python",
"repo": "choonho/python-core",
"path": "/src/spaceone/core/logger/filters/exclude.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> # Update the new site
updated_site_config = copy.deepcopy(new_site_config)
updated_site_config["store_backend"]["base_directory"] = "/my_updated_site/"
ephemeral_context_with_defaults.update_data_docs_site(
new_site_name, updated_site_config
)
... | code_fim | hard | {
"lang": "python",
"repo": "great-expectations/great_expectations",
"path": "/tests/data_context/abstract_data_context/test_data_docs_config_crud.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: great-expectations/great_expectations path: /tests/data_context/abstract_data_context/test_data_docs_config_crud.py
import copy
from unittest import mock
import pytest
import great_expectations.exceptions as gx_exceptions
from great_expectations.data_context import EphemeralDataContext
@pytes... | code_fim | hard | {
"lang": "python",
"repo": "great-expectations/great_expectations",
"path": "/tests/data_context/abstract_data_context/test_data_docs_config_crud.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> @pytest.mark.unit
def test_delete_data_docs_site_persists(
self, ephemeral_context_with_defaults: EphemeralDataContext
):
# Check fixture configuration
existing_site_name = "local_site"
assert existing_site_name in ephemeral_context_with_defaults.get_site_names(... | code_fim | hard | {
"lang": "python",
"repo": "great-expectations/great_expectations",
"path": "/tests/data_context/abstract_data_context/test_data_docs_config_crud.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: tfqKR/prevision-quantum path: /examples/iris/load_iris.py
import numpy as np
import pandas as pd
from sklearn import datasets
<|fim_suffix|>if __name__ == "__main__":
application_params = "iris_params.json"
model_weights = "iris_weights_10.npz"
preprocessor_file = "iris_preprocessor... | code_fim | medium | {
"lang": "python",
"repo": "tfqKR/prevision-quantum",
"path": "/examples/iris/load_iris.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>if __name__ == "__main__":
application_params = "iris_params.json"
model_weights = "iris_weights_10.npz"
preprocessor_file = "iris_preprocessor.obj"
application = qnn.load_application(application_params,
model_weights=model_weights,
... | code_fim | medium | {
"lang": "python",
"repo": "tfqKR/prevision-quantum",
"path": "/examples/iris/load_iris.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: nadirhamid/django-oscar-wagtail path: /tests/project/apps/catalogue/migrations/0010_oscar_wagtail.py
# -*- coding: utf-8 -*-
# Generated by Django 1.9.8 on 2016-08-03 07:38
from __future__ import unicode_literals
from django.db import migrations, models
import django.db.models.deletion
import wa... | code_fim | hard | {
"lang": "python",
"repo": "nadirhamid/django-oscar-wagtail",
"path": "/tests/project/apps/catalogue/migrations/0010_oscar_wagtail.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> dependencies = [
('wagtailcore', '0028_merge'),
('catalogue', '0009_slugfield_noop'),
]
operations = [
migrations.AlterModelOptions(
name='category',
options={},
),
migrations.RemoveField(
model_name='category',
... | code_fim | hard | {
"lang": "python",
"repo": "nadirhamid/django-oscar-wagtail",
"path": "/tests/project/apps/catalogue/migrations/0010_oscar_wagtail.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> Message = Message
WantList = Message.Wantlist
WantType = Message.Wantlist.WantType
BlockPresenceType = Message.BlockPresenceType<|fim_prefix|># repo: VladislavSufyanov/py-bitswap path: /bitswap/message/proto_buff.py
from .pb.message_pb2 import Message
<|fim_middle|>class ProtoBuff:
| code_fim | easy | {
"lang": "python",
"repo": "VladislavSufyanov/py-bitswap",
"path": "/bitswap/message/proto_buff.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: VladislavSufyanov/py-bitswap path: /bitswap/message/proto_buff.py
from .pb.message_pb2 import Message
<|fim_suffix|> Message = Message
WantList = Message.Wantlist
WantType = Message.Wantlist.WantType
BlockPresenceType = Message.BlockPresenceType<|fim_middle|>class ProtoBuff:
| code_fim | easy | {
"lang": "python",
"repo": "VladislavSufyanov/py-bitswap",
"path": "/bitswap/message/proto_buff.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")
def max_pool_3x1_2_v(x):
return tf.nn.max_pool(x, ksize=[1, 3, 1, 1], strides=[1, 2, 1, 1], padding="VALID")
def avg_pool_2x2(x):
return tf.nn.avg_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAM... | code_fim | medium | {
"lang": "python",
"repo": "fakeface-mmc/fakeface-mmc",
"path": "/shared/train/SYN/model/Networks_Functions.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>def max_pool_3x1_2_v(x):
return tf.nn.max_pool(x, ksize=[1, 3, 1, 1], strides=[1, 2, 1, 1], padding="VALID")
def avg_pool_2x2(x):
return tf.nn.avg_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")
def FC(x,W):
return tf.matmul(x,W)
def ReLU(x):
return tf.nn.relu(x)<|fim_... | code_fim | hard | {
"lang": "python",
"repo": "fakeface-mmc/fakeface-mmc",
"path": "/shared/train/SYN/model/Networks_Functions.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: fakeface-mmc/fakeface-mmc path: /shared/train/SYN/model/Networks_Functions.py
import tensorflow as tf
import numpy as np
import math
#caclulate DCT basis
def cal_scale(p,q):
if p==0:
ap = 1/(math.sqrt(8))
else:
ap = math.sqrt(0.25)
if q==0:
aq = 1/(math.sqrt(8... | code_fim | hard | {
"lang": "python",
"repo": "fakeface-mmc/fakeface-mmc",
"path": "/shared/train/SYN/model/Networks_Functions.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: mmr12/DeepBlueSea path: /models/utils_model.py
import tensorflow as tf
import numpy as np
def create_weights(shape):
return tf.Variable(tf.truncated_normal(shape, stddev=0.05))
def create_biases(size):
return tf.Variable(tf.constant(0.05, shape=[size]))
def create_convolutional_layer... | code_fim | hard | {
"lang": "python",
"repo": "mmr12/DeepBlueSea",
"path": "/models/utils_model.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def create_convolution(input,
num_input_channels,
conv_filter_size,
num_filters,
stride=1,
data_format="NHWC"):
'''
Simplified version of create_convolutional_layer that doesn't inc... | code_fim | hard | {
"lang": "python",
"repo": "mmr12/DeepBlueSea",
"path": "/models/utils_model.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> layer = tf.matmul(input, weights) + biases
if use_relu:
layer = tf.nn.relu(layer)
return layer
def create_convolution(input,
num_input_channels,
conv_filter_size,
num_filters,
stride=1,
... | code_fim | hard | {
"lang": "python",
"repo": "mmr12/DeepBlueSea",
"path": "/models/utils_model.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ArminD93/Django_test path: /podstrony/views.py
from django.shortcuts import render
from django.views.generic import ListView, DetailView #Zawiera widoki generyczne, które zostały przygotowane przez twórców Django do obsługi najpopularniejszych obiektów
from .models import Budowa, Teoria, Przepis... | code_fim | hard | {
"lang": "python",
"repo": "ArminD93/Django_test",
"path": "/podstrony/views.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>################################################
class PrzepisyDetailView(DetailView):
model = Przepisy
class PrzepisyListView(ListView):
model = Przepisy
################################################
class ImageDetailView(DetailView):
model = Image
class ImageListView(ListView):
... | code_fim | hard | {
"lang": "python",
"repo": "ArminD93/Django_test",
"path": "/podstrony/views.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> return 'Name : {}\n'\
'Controller : {}\n'\
'Swapped : {}\n'\
'Left Diff : {}\n'\
'Right Diff : {}\n'\
'Type : {}\n'\
'Display : {}\n'\
'ROM Size : {}\n'\
'RA... | code_fim | hard | {
"lang": "python",
"repo": "NVlabs/cule",
"path": "/torchcule/atari/rom.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> def __repr__(self):
return 'Name : {}\n'\
'Controller : {}\n'\
'Swapped : {}\n'\
'Left Diff : {}\n'\
'Right Diff : {}\n'\
'Type : {}\n'\
'Display : {}\n'\
'ROM Size : {... | code_fim | hard | {
"lang": "python",
"repo": "NVlabs/cule",
"path": "/torchcule/atari/rom.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: NVlabs/cule path: /torchcule/atari/rom.py
"""CuLE (CUda Learning Environment module)
This module provides access to several RL environments that generate data
on the CPU or GPU.
"""
import atari_py
import gym
import os
<|fim_suffix|> def __repr__(self):
return 'Name : {}\n'\
... | code_fim | hard | {
"lang": "python",
"repo": "NVlabs/cule",
"path": "/torchcule/atari/rom.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> def print_detailed_score_log(self):
logger.info("--------------------")
logger.info("Detailed Tag-Based Score")
for tag in self.macro_f1:
logger.info("Tag: {} - Precision: {:.4f} - Recall: {:.4f} - F1: {:.4f}".format(self.ner_vocab.itos[tag],
... | code_fim | hard | {
"lang": "python",
"repo": "SunYanCN/nlp-experiments-in-pytorch",
"path": "/scorer/ner_scorer.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: SunYanCN/nlp-experiments-in-pytorch path: /scorer/ner_scorer.py
import logging.config
logging.config.fileConfig(fname='./config/config.logger', disable_existing_loggers=False)
logger = logging.getLogger("NerScorer")
class NerScorer(object):
def __init__(self, ner_vocab):
super(NerSc... | code_fim | hard | {
"lang": "python",
"repo": "SunYanCN/nlp-experiments-in-pytorch",
"path": "/scorer/ner_scorer.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> precision = {}
recall = {}
for tag in tp:
precision[tag] = tp[tag] / (tp[tag] + fp[tag] + 1e-16)
recall[tag] = tp[tag] / (tp[tag] + fn[tag] + 1e-16)
f1[tag] = (2 * precision[tag] * recall[tag] / (precision[tag] + recall[tag] + 1e-16)) * 100
... | code_fim | hard | {
"lang": "python",
"repo": "SunYanCN/nlp-experiments-in-pytorch",
"path": "/scorer/ner_scorer.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: stasvorosh/pythonintask path: /PINp/2014/Valkovskey_M_A/task_9_49.py
# Задача 9. Вариант 49.
#Создайте игру, в которой компьютер выбирает какое-либо слово, а игрок должен его отгадать. Компьютер сообщает игроку, сколько букв в слове, и дает пять попыток узнать, есть ли какая-либо буква в слове, п... | code_fim | hard | {
"lang": "python",
"repo": "stasvorosh/pythonintask",
"path": "/PINp/2014/Valkovskey_M_A/task_9_49.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> else:
print("/nПопытка №",i)
print("К сожалению, это не так.")
helpk = input("\nВам нужна подсказка?")
if helpk =="да":
vopr = input("\nНаличие какой буквы вы хотите узнать?")
if vopr in word:
print("В слове есть эта буква")
... | code_fim | hard | {
"lang": "python",
"repo": "stasvorosh/pythonintask",
"path": "/PINp/2014/Valkovskey_M_A/task_9_49.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> if len(fileFilters) == 0:
_usage()
sys.exit(2)
p4 = p4lib.P4()
changes = p4.changes(files=fileFilters)
changeNums = [c['change'] for c in changes]
for change in changeNums:
details = p4.describe(change=change, shortForm=True)
print changeHeader(details... | code_fim | hard | {
"lang": "python",
"repo": "edgauthier/p4changelog",
"path": "/p4cl.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: edgauthier/p4changelog path: /p4cl.py
#!/usr/bin/env python
import sys
import p4lib
import getopt
def changeHeader(details):
summary = changeSummary(details['description'])
return "[%s|CL:%s (%s)] - %s" % (details['date'], details['change'], details['user'], summary)
def changeSummary... | code_fim | hard | {
"lang": "python",
"repo": "edgauthier/p4changelog",
"path": "/p4cl.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
###########
# FUNCTIONS
###########
def concatenate_one(strip,
logfile = os.path.join(constants.LOGDIR, 'concatenation.log')):
with log.log_to_file(logfile):
# Strips are defined by the start longitude
log.info('Concatenating L={0}'.format(strip))
for mode... | code_fim | hard | {
"lang": "python",
"repo": "barentsen/iphas-dr2",
"path": "/dr2/concatenating.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> return status
###########
# FUNCTIONS
###########
def concatenate_one(strip,
logfile = os.path.join(constants.LOGDIR, 'concatenation.log')):
with log.log_to_file(logfile):
# Strips are defined by the start longitude
log.info('Concatenating L={0}'.format(s... | code_fim | hard | {
"lang": "python",
"repo": "barentsen/iphas-dr2",
"path": "/dr2/concatenating.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: barentsen/iphas-dr2 path: /dr2/concatenating.py
import Pool
from astropy import log
import constants
from constants import IPHASQC
import util
__author__ = 'Geert Barentsen'
__copyright__ = 'Copyright, The Authors'
__credits__ = ['Geert Barentsen', 'Hywel Farnhill', 'Janet Drew']
###########... | code_fim | hard | {
"lang": "python",
"repo": "barentsen/iphas-dr2",
"path": "/dr2/concatenating.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> TWITTER_AUTH.set_access_token(config('TWITTER_ACCESS_TOKEN'), config('TWITTER_ACCESS_TOKEN_SECRET'))
TWITTER = tweepy.API(TWITTER_AUTH)
BASILICA = basilica.Connection(config('BASILICA_KEY'))<|fim_prefix|># repo: nwilliams030/twitoff path: /TWITOFF/templates/twitter.py
""" Retrieve tweets, embedd... | code_fim | medium | {
"lang": "python",
"repo": "nwilliams030/twitoff",
"path": "/TWITOFF/templates/twitter.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: nwilliams030/twitoff path: /TWITOFF/templates/twitter.py
""" Retrieve tweets, embedding, save into database """
<|fim_suffix|> TWITTER = tweepy.API(TWITTER_AUTH)
BASILICA = basilica.Connection(config('BASILICA_KEY'))<|fim_middle|> import basilica
import tweepy
from decouple import conf... | code_fim | hard | {
"lang": "python",
"repo": "nwilliams030/twitoff",
"path": "/TWITOFF/templates/twitter.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: macecurb/IdeaBot path: /reactions/retry.py
from reactions import reactioncommand
class RetryCommand(reactioncommand.AdminReactionAddCommand):
def matches(self, reaction, user):
<|fim_suffix|> yield from client.on_message(reaction.message)<|fim_middle|> return reaction.emoji ==... | code_fim | hard | {
"lang": "python",
"repo": "macecurb/IdeaBot",
"path": "/reactions/retry.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> return reaction.emoji == (self.matchemoji(self.emoji) or False) and user == reaction.message.author
# (None or False) = False ; this prevents returning a NoneType when expecting a bool
def action(self, reaction, user, client):
yield from client.on_message(reaction.message)<|fi... | code_fim | easy | {
"lang": "python",
"repo": "macecurb/IdeaBot",
"path": "/reactions/retry.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def action(self, reaction, user, client):
yield from client.on_message(reaction.message)<|fim_prefix|># repo: macecurb/IdeaBot path: /reactions/retry.py
from reactions import reactioncommand
class RetryCommand(reactioncommand.AdminReactionAddCommand):
<|fim_middle|>
def matches(self, rea... | code_fim | hard | {
"lang": "python",
"repo": "macecurb/IdeaBot",
"path": "/reactions/retry.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: jcazallasc/lana-python-challenge path: /app/tests/checkout_backend/test_commands.py
import csv
from django.core.management import call_command
from django.test import TestCase
from checkout_backend.adapters.django.offer_repository import DjangoOfferRepository
from checkout_backend.adapters.djan... | code_fim | hard | {
"lang": "python",
"repo": "jcazallasc/lana-python-challenge",
"path": "/app/tests/checkout_backend/test_commands.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> offers_rates = len(DjangoOfferRepository().all())
self.assertEqual(
offers_rates + 1,
self._get_num_lines_from_csv('offers.csv'),
)
def test_load_offers_from_csv_twice(self):
"""Test load offers from CSV file twice to check no errors raise"""
... | code_fim | hard | {
"lang": "python",
"repo": "jcazallasc/lana-python-challenge",
"path": "/app/tests/checkout_backend/test_commands.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> self, _text: str, _subtype: str = ..., _charset: Optional[str] = ..., *, policy: Optional[Policy] = ...
) -> None: ...
else:
def __init__(self, _text: str, _subtype: str = ..., _charset: Optional[str] = ...) -> None: ...<|fim_prefix|># repo: aghasyedbilal/intellij-community... | code_fim | medium | {
"lang": "python",
"repo": "aghasyedbilal/intellij-community",
"path": "/python/helpers/typeshed/stdlib/3/email/mime/text.pyi",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: aghasyedbilal/intellij-community path: /python/helpers/typeshed/stdlib/3/email/mime/text.pyi
# Stubs for email.mime.text (Python 3.4)
import sys
from email.mime.nonmultipart import MIMENonMultipart
from email.policy import Policy
from typing import Optional
class MIMEText(MIMENonMultipart):
<|f... | code_fim | medium | {
"lang": "python",
"repo": "aghasyedbilal/intellij-community",
"path": "/python/helpers/typeshed/stdlib/3/email/mime/text.pyi",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: kokellab/klgists path: /klgists/misc/colored_notifications.py
from typing import Iterable, Mapping, Callable, Optional
from enum import Enum
from colorama import Fore, Style
class NotificationLevel(Enum):
INFO = 1
SUCCESS = 2
NOTICE = 3
WARNING = 4
FAILURE = 5
class ColorMessages:
DEFA... | code_fim | hard | {
"lang": "python",
"repo": "kokellab/klgists",
"path": "/klgists/misc/colored_notifications.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> def cl(text: str): print(str(color) + sides + text.center(line_length - 2 * len(sides)) + sides)
print(str(color) + top * line_length)
self._log(top * line_length)
for line in lines:
self._log(line)
cl(line)
print(str(color) + bottom * line_length)
self._log(bottom * line_length)
def _... | code_fim | hard | {
"lang": "python",
"repo": "kokellab/klgists",
"path": "/klgists/misc/colored_notifications.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> def _print(self, lines: Iterable[str], color: int, top: str = '_', bottom: str = '_', sides: str = '', line_length: int = 100):
def cl(text: str): print(str(color) + sides + text.center(line_length - 2 * len(sides)) + sides)
print(str(color) + top * line_length)
self._log(top * line_length)
for l... | code_fim | hard | {
"lang": "python",
"repo": "kokellab/klgists",
"path": "/klgists/misc/colored_notifications.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>Torch.
Note well that we provide no BC guarantees for torchgen. If you're interested
in using torchgen and want the PyTorch team to be aware, please reach out
on GitHub.
"""<|fim_prefix|># repo: pytorch/pytorch path: /torchgen/__init__.py
"""torchgen
This module contains codegeneration utilities for Py... | code_fim | medium | {
"lang": "python",
"repo": "pytorch/pytorch",
"path": "/torchgen/__init__.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: pytorch/pytorch path: /torchgen/__init__.py
"""torchgen
This module contains codegeneration utilities for PyTorch. It is used to
<|fim_suffix|>Torch.
Note well that we provide no BC guarantees for torchgen. If you're interested
in using torchgen and want the PyTorch team to be aware, please rea... | code_fim | medium | {
"lang": "python",
"repo": "pytorch/pytorch",
"path": "/torchgen/__init__.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> self.value = 0.0
def f(self) -> float:
self.value += 1.0
return torch.tensor(self.value)
def test_ensemble_mean():
f = F()
result = ensemble_mean(f.f, n_times=10)
expect = torch.tensor(5.5)
assert result == expect<|fim_prefix|># repo: rileymattr/pfhedge path... | code_fim | easy | {
"lang": "python",
"repo": "rileymattr/pfhedge",
"path": "/tests/_utils/test_operations.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> f = F()
result = ensemble_mean(f.f, n_times=10)
expect = torch.tensor(5.5)
assert result == expect<|fim_prefix|># repo: rileymattr/pfhedge path: /tests/_utils/test_operations.py
import torch
from pfhedge._utils.operations import ensemble_mean
<|fim_middle|>
class F:
def __init__(sel... | code_fim | medium | {
"lang": "python",
"repo": "rileymattr/pfhedge",
"path": "/tests/_utils/test_operations.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: rileymattr/pfhedge path: /tests/_utils/test_operations.py
import torch
from pfhedge._utils.operations import ensemble_mean
class F:
def __init__(self):
self.value = 0.0
def f(self) -> float:
self.value += 1.0
return torch.tensor(self.value)
<|fim_suffix|> ... | code_fim | easy | {
"lang": "python",
"repo": "rileymattr/pfhedge",
"path": "/tests/_utils/test_operations.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: LanguageAndIntelligence/v20-python-samples path: /m_src/account/summary.py
import sys
sys.path.append("/Users/thieut/Exercises/v20-python-samples/src")
import argparse
import common.config
from account.account import Account
<|fim_suffix|> parser=argparse.ArgumentParser()
common.config.a... | code_fim | easy | {
"lang": "python",
"repo": "LanguageAndIntelligence/v20-python-samples",
"path": "/m_src/account/summary.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>def main():
parser=argparse.ArgumentParser()
common.config.add_argument(parser)
args=parser.parse_args()
account_id=args.config.active_account
api=args.config.create_context()
response=api.account.summary(account_id)
account=Account(response.get("account","200"))
account.du... | code_fim | medium | {
"lang": "python",
"repo": "LanguageAndIntelligence/v20-python-samples",
"path": "/m_src/account/summary.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: jfleonUOC/Alpyne path: /alpyne/data/model_data.py
import math
from typing import Dict, Any
from alpyne.data.constants import InputTypes
class ModelData:
"""
Represents a single data element with a name, type, value, and (optional) units.
This class is what each of the collection t... | code_fim | hard | {
"lang": "python",
"repo": "jfleonUOC/Alpyne",
"path": "/alpyne/data/model_data.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # def to_jsonable(self) -> Dict[str, Any]:
# return {"name": self.name, "type": self.type_, "value": self.value, "units": self.units}
@staticmethod
def from_json(data: Dict[str, Any]) -> 'ModelData':
"""
Expands the values in a parsed JSON entry (a dictionary).
... | code_fim | hard | {
"lang": "python",
"repo": "jfleonUOC/Alpyne",
"path": "/alpyne/data/model_data.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ahelm/cython_oracle path: /tests/test_oracle.py
def test_direct_import():
"""Check calling function after direct import"""
from cython_oracle.oracle import answer_to_all_questions
assert answer_to_all_questions() == 42
def test_parent_module_import():
"""Check calling function ... | code_fim | medium | {
"lang": "python",
"repo": "ahelm/cython_oracle",
"path": "/tests/test_oracle.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> assert oracle.answer_to_all_questions() == 42
def test_root_module_import():
"""Check calling function after import of root module"""
import cython_oracle
assert cython_oracle.oracle.answer_to_all_questions() == 42<|fim_prefix|># repo: ahelm/cython_oracle path: /tests/test_oracle.py
de... | code_fim | medium | {
"lang": "python",
"repo": "ahelm/cython_oracle",
"path": "/tests/test_oracle.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def test_root_module_import():
"""Check calling function after import of root module"""
import cython_oracle
assert cython_oracle.oracle.answer_to_all_questions() == 42<|fim_prefix|># repo: ahelm/cython_oracle path: /tests/test_oracle.py
def test_direct_import():
"""Check calling functi... | code_fim | medium | {
"lang": "python",
"repo": "ahelm/cython_oracle",
"path": "/tests/test_oracle.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>for idx in res["group"]:
select_dict[int(systems[idx])] = []
score = db[idx]
for i in range(len(act_list)):
if score[i] == optim_score[i]:
select_dict[int(systems[idx])].append(act_list[i])
print(systems[idx])
json_str = json.dumps(select_dict,indent=4)
with open("./se... | code_fim | hard | {
"lang": "python",
"repo": "KevinQian97/diva_toolbox",
"path": "/scorer/select_combine.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: KevinQian97/diva_toolbox path: /scorer/select_combine.py
import csv
import os
from itertools import combinations
import numpy as np
import pandas as pd
import json
sys_num = 20
select_num = 3
class_num = 37
target = "metric_value"
csv_path = "/home/kevinq/repos/diva_toolbox/scorer/scores_by_acti... | code_fim | hard | {
"lang": "python",
"repo": "KevinQian97/diva_toolbox",
"path": "/scorer/select_combine.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> @type_check
def defuzz(self, x, mfx, method:str='centroid', **kwargs) -> float:
"""
Defuzzification of the aggregated membership functions.
Parameters
----------
x: numpy.ndarray
universe of discourse
... | code_fim | hard | {
"lang": "python",
"repo": "ErikSargsyann/FcmBci",
"path": "/fcmpy/expert_fcm/expert_based_fcm.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ErikSargsyann/FcmBci path: /fcmpy/expert_fcm/expert_based_fcm.py
import numpy as np
import pandas as pd
import functools
import collections
from abc import ABC, abstractmethod
from fcmpy.expert_fcm.input_validator import type_check
from fcmpy.store.methodsStore import EntropyStore
from fcmpy.sto... | code_fim | hard | {
"lang": "python",
"repo": "ErikSargsyann/FcmBci",
"path": "/fcmpy/expert_fcm/expert_based_fcm.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> @type_check
def build(self, data: collections.OrderedDict, implication_method:str='Mamdani',
aggregation_method:str='fMax', defuzz_method:str='centroid') -> pd.DataFrame:
"""
Build an FCM based on qualitative input data.
Parameters
... | code_fim | hard | {
"lang": "python",
"repo": "ErikSargsyann/FcmBci",
"path": "/fcmpy/expert_fcm/expert_based_fcm.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>def test_run_sambamba_missing(tmpdir, reset_path, bed_path, bam_path):
out_path = tmpdir.join('ccds.coverage.bed')
with pytest.raises(OSError):
run_sambamba(bam_path, bed_path, outfile=str(out_path),
cov_thresholds=THRESHOLDS)<|fim_prefix|># repo: Clinical-Genomics/cha... | code_fim | hard | {
"lang": "python",
"repo": "Clinical-Genomics/chanjo",
"path": "/tests/test_sambamba.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Clinical-Genomics/chanjo path: /tests/test_sambamba.py
# -*- coding: utf-8 -*-
import pytest
from chanjo.sambamba import run_sambamba
THRESHOLDS = (10, 20)
<|fim_suffix|> out_path = tmpdir.join('ccds.coverage.bed')
with pytest.raises(OSError):
run_sambamba(bam_path, bed_path, o... | code_fim | hard | {
"lang": "python",
"repo": "Clinical-Genomics/chanjo",
"path": "/tests/test_sambamba.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> for i in range(count + 1):
tag = TagFactory.build()
db.session.add(tag)
try:
db.session.commit()
except IntegrityError:
db.session.rollback()
def posts(count=100):
user_count = User.query.count()
category_count = Category.query.count()
tag_count = ... | code_fim | medium | {
"lang": "python",
"repo": "techouse/nordend",
"path": "/app/fake.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: techouse/nordend path: /app/fake.py
from random import randint
from sqlalchemy.exc import IntegrityError
from . import db
from .factories import UserFactory, PostFactory, CategoryFactory, TagFactory
from .models import User, Category, Role, Tag, PostCategory, PostAuthor, PostTag
<|fim_suffix|>... | code_fim | medium | {
"lang": "python",
"repo": "techouse/nordend",
"path": "/app/fake.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
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