text stringlengths 232 16.3k | domain stringclasses 1
value | difficulty stringclasses 3
values | meta dict |
|---|---|---|---|
<|fim_suffix|>from .python.pyWeight_problem import WeightProblem
from .python.pyWeight_problem import FuelCase
from .python.FluidProperties import FluidProperties
from .python.ICAOAtmosphere import ICAOAtmosphere
from .python.pyEngine_problem import EngineProblem
from .python.pyFieldPerformance_problem import FieldPe... | code_fim | hard | {
"lang": "python",
"repo": "nbons/baseclasses",
"path": "/__init__.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>from .python.FluidProperties import FluidProperties
from .python.ICAOAtmosphere import ICAOAtmosphere
from .python.pyEngine_problem import EngineProblem
from .python.pyFieldPerformance_problem import FieldPerformanceProblem
from .python.pyLG_problem import LGProblem
from .python.py3Util import getPy3Sa... | code_fim | medium | {
"lang": "python",
"repo": "nbons/baseclasses",
"path": "/__init__.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: nbons/baseclasses path: /__init__.py
from .python.pyAero_problem import AeroProblem
from .python.pyTransi_problem import TransiProblem
from .python.pyStruct_problem import StructProblem
from .python.pyAeroStruct_problem import AeroStructProblem
from .python.pyAero_solver import AeroSolver
from .... | code_fim | hard | {
"lang": "python",
"repo": "nbons/baseclasses",
"path": "/__init__.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: jbussy/nbgrader path: /nbgrader/converters/__init__.py
from .base import BaseConverter, NbGraderException
from .assig<|fim_suffix|>derException",
"Assign",
"Autograde",
"Feedback"
]<|fim_middle|>n import Assign
from .autograde import Autograde
from .feedback import Feedback
__all__ =... | code_fim | medium | {
"lang": "python",
"repo": "jbussy/nbgrader",
"path": "/nbgrader/converters/__init__.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|>derException",
"Assign",
"Autograde",
"Feedback"
]<|fim_prefix|># repo: jbussy/nbgrader path: /nbgrader/converters/__init__.py
from .base import BaseConverter, NbGraderException
from .assig<|fim_middle|>n import Assign
from .autograde import Autograde
from .feedback import Feedback
__all__ =... | code_fim | medium | {
"lang": "python",
"repo": "jbussy/nbgrader",
"path": "/nbgrader/converters/__init__.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: AlberCarri/ejercicio_Api path: /ejercicio_Api.py
import requests
import json
import os
#Para realizar este ejercicio he optado por elegir la siguiente página: https://date.nager.at/
#Esta página proporciona información sobre las diferentes fiestas nacionales de diferentes paises y de diferentes ... | code_fim | hard | {
"lang": "python",
"repo": "AlberCarri/ejercicio_Api",
"path": "/ejercicio_Api.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> url="https://date.nager.at/api/v2/publicholidays/2019/%s"%pais
print(url)
response = requests.get(url)
if response.status_code == 200:
datos = json.loads(response.content)
contador=1
for json_data in datos:
print ("Nomb... | code_fim | hard | {
"lang": "python",
"repo": "AlberCarri/ejercicio_Api",
"path": "/ejercicio_Api.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> if opc == 1:
mostrar_fiestas()
elif opc == 2:
mostrar_anyo()
elif opc == 3:
mostrar_otro()
elif opc == 4:
print("Gracias por su visita")
break
else:
print("Opción no disponible")
in... | code_fim | hard | {
"lang": "python",
"repo": "AlberCarri/ejercicio_Api",
"path": "/ejercicio_Api.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> @staticmethod
def generate_reports(percent_threshold):
str_full_report = ""
str_warnings = ""
for cs in CryptoStatus.select().order_by(CryptoStatus.current_percentage.desc(), CryptoStatus.crypto):
status = "%5s: %6.2f%% | %s | $%s\n" % (
cs.crypt... | code_fim | hard | {
"lang": "python",
"repo": "kdmukai/stop_loss_bot",
"path": "/src/stop_loss_bot/__init__.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> return cs
@staticmethod
def generate_reports(percent_threshold):
str_full_report = ""
str_warnings = ""
for cs in CryptoStatus.select().order_by(CryptoStatus.current_percentage.desc(), CryptoStatus.crypto):
status = "%5s: %6.2f%% | %s | $%s\n" % (
... | code_fim | hard | {
"lang": "python",
"repo": "kdmukai/stop_loss_bot",
"path": "/src/stop_loss_bot/__init__.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: kdmukai/stop_loss_bot path: /src/stop_loss_bot/__init__.py
from decimal import Decimal
from .models import CryptoStatus
class StopLossBot():
@staticmethod
def process_candle(candle, cs, is_current_candle=True):
candle_timestamp = candle['time']
if is_current_candle:
... | code_fim | hard | {
"lang": "python",
"repo": "kdmukai/stop_loss_bot",
"path": "/src/stop_loss_bot/__init__.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> return image_colors_df
def store_rug_colors(rug_dir):
def listdir_nohidden(path):
for f in os.listdir(path):
if not f.startswith('.'):
yield f
rug_filenames = listdir_nohidden(rug_dir)
# Setup dataframe
rug_colors_df = pd.DataFrame(columns... | code_fim | hard | {
"lang": "python",
"repo": "kswiftspong/style_match",
"path": "/model/color_clustering.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: kswiftspong/style_match path: /model/color_clustering.py
from sklearn.cluster import KMeans
import numpy as np
import cv2
from collections import Counter
from skimage.color import rgb2lab, deltaE_cie76
import os
import pandas as pd
import operator
from itertools import islice
import json
def RGB... | code_fim | hard | {
"lang": "python",
"repo": "kswiftspong/style_match",
"path": "/model/color_clustering.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> return rug_colors_df
def save_rug_colors(rug_dir):
rug_colors_df = store_rug_colors(rug_dir)
rug_colors_df['lab_1'] = rug_colors_df.apply (lambda row: row['Lab'][0][0][0], axis=1)
rug_colors_df['lab_2'] = rug_colors_df.apply (lambda row: row['Lab'][0][0][1], axis=1)
rug_colors_df['la... | code_fim | hard | {
"lang": "python",
"repo": "kswiftspong/style_match",
"path": "/model/color_clustering.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> return content
@app.route('/getrestbl/<task_id>',methods=['GET'])
def get_res_tbl(task_id):
# Get all mandatory informations we need
# Info about params: https://datatables.net/manual/server-side#DataTables_Table_1
draw = int(request.args['draw']) # not secure # TODO: make it secure?
... | code_fim | hard | {
"lang": "python",
"repo": "vincentiusmartin/QBiC-Pred",
"path": "/website/app/views/result.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: vincentiusmartin/QBiC-Pred path: /website/app/views/result.py
# clear the session given from index
return render_template("result.html",stats_url=url_for('task_status',task_id=job_id),parents=parents)
# /<taskid>/<filters>
@app.route('/files/<filetype>/<task_id>/<filters>')
def get_file_fro... | code_fim | hard | {
"lang": "python",
"repo": "vincentiusmartin/QBiC-Pred",
"path": "/website/app/views/result.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>def query_filter(search_filter):
query_or = {}
query = {}
inseq_substr = ""
ex_query = {} #exact, exclude
gene_or = ""
for q in search_filter:
if q["searchOpt"] == "in sequence":
inseq_substr += "%s|" % q["searchKey"]
elif q["searchOpt"] == "or":
... | code_fim | hard | {
"lang": "python",
"repo": "vincentiusmartin/QBiC-Pred",
"path": "/website/app/views/result.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: freekang/HPHG path: /src/main.py
'''
Reference implementation of HPHG and HPSG.
Author: Jie Huang
For more details, refer to the paper:
Hyper-Path-Based Representation Learning for Hyper-Networks
Jie Huang, Xin Liu, Yangqiu Song
'''
import numpy as np
import argparse
import networkx as nx
from... | code_fim | hard | {
"lang": "python",
"repo": "freekang/HPHG",
"path": "/src/main.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> elif args.method=='hpsg':
walks = [list(map(str,walk)) for walk in walks]
word2vec = Word2Vec(walks,size=args.dimensions,window=args.window_size,min_count=0,
sg=1,workers=args.workers,iter=args.iter,negative=5,compute_loss=True)
vectors = {}
... | code_fim | hard | {
"lang": "python",
"repo": "freekang/HPHG",
"path": "/src/main.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ishota/CarND-Traffic-Sign-Classifier path: /src/preprocess_data.py
# -*- coding: utf-8 -*-
import numpy as np
import tensorflow as tf
from copy import deepcopy
def shift_brightness(X_train):
s_bright_X = deepcopy(X_train)
for i in range(X_train.shape[0]):
random_delta = max(0, ... | code_fim | medium | {
"lang": "python",
"repo": "ishota/CarND-Traffic-Sign-Classifier",
"path": "/src/preprocess_data.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> r_hue_X = tf.image.random_hue(X_train, max_delta=0.2)
processed_X_train = np.concatenate((processed_X_train, r_hue_X), axis=0)
processed_y_train = np.concatenate((processed_y_train, y_train), axis=0)
return processed_X_train, processed_y_train
def input_normalize(images):
tensor_ima... | code_fim | hard | {
"lang": "python",
"repo": "ishota/CarND-Traffic-Sign-Classifier",
"path": "/src/preprocess_data.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> """
Get import records for `service_resource.py[i]`.
"""
if self.service_resource is None:
return []
import_records: set[ImportRecord] = set()
class_import_records = self.service_resource.get_required_import_records()
for import_record i... | code_fim | hard | {
"lang": "python",
"repo": "vemel/mypy_boto3_builder",
"path": "/mypy_boto3_builder/structures/service_package.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def _get_typed_dicts(self) -> set[TypeTypedDict]:
result: set[TypeTypedDict] = set()
for type_annotation in self.iterate_types():
if not isinstance(type_annotation, TypeTypedDict):
continue
result.add(type_annotation)
methods: set[Method... | code_fim | hard | {
"lang": "python",
"repo": "vemel/mypy_boto3_builder",
"path": "/mypy_boto3_builder/structures/service_package.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: vemel/mypy_boto3_builder path: /mypy_boto3_builder/structures/service_package.py
"""
Parsed Service package.
"""
from collections.abc import Iterable, Iterator
from typing import Literal
from mypy_boto3_builder.enums.service_module_name import ServiceModuleName
from mypy_boto3_builder.import_hel... | code_fim | hard | {
"lang": "python",
"repo": "vemel/mypy_boto3_builder",
"path": "/mypy_boto3_builder/structures/service_package.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: sohn21c/yoloMask path: /src/createDataset.py
"""
Author: James Sohn
Last Modified: 11/06/19
This script is to create a dataset to train YOLO by adding custom object masks to random backgrounds while rotating, scaling and jittering color. For more details about the code, please visit the github r... | code_fim | hard | {
"lang": "python",
"repo": "sohn21c/yoloMask",
"path": "/src/createDataset.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> if items[0] in self.target:
_x, _y, _w, _h = float(items[1]), float(items[2]), float(items[3]), float(items[4])
tx = _x - _w/2
ty = _y - _h/2
bx = _x + _w/2
by = _y + _h/2
... | code_fim | hard | {
"lang": "python",
"repo": "sohn21c/yoloMask",
"path": "/src/createDataset.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: chrippa/python-flashmedia path: /tests/test_4cc.py
from flashmedia.types import FourCC
from nose.tools import *
def test_pack():
assert FourCC("A") == b"A "
assert FourCC("AB") == b"AB "
assert FourCC("ABC") == b"ABC "
assert FourCC("ABCD") == b"ABCD"
<|fim_suffix|... | code_fim | medium | {
"lang": "python",
"repo": "chrippa/python-flashmedia",
"path": "/tests/test_4cc.py",
"mode": "psm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|>def test_unpack():
assert FourCC.unpack(b"A ")[0] == "A"
assert FourCC.unpack(b"AB ")[0] == "AB"
assert FourCC.unpack(b"ABC ")[0] == "ABC"
assert FourCC.unpack(b"ABCD")[0] == "ABCD"
def test_unpack_from():
assert FourCC.unpack_from(b"A ", 0)[0] == "A"
assert FourCC.unpack_fro... | code_fim | medium | {
"lang": "python",
"repo": "chrippa/python-flashmedia",
"path": "/tests/test_4cc.py",
"mode": "spm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> assert FourCC.unpack(b"A ")[0] == "A"
assert FourCC.unpack(b"AB ")[0] == "AB"
assert FourCC.unpack(b"ABC ")[0] == "ABC"
assert FourCC.unpack(b"ABCD")[0] == "ABCD"
def test_unpack_from():
assert FourCC.unpack_from(b"A ", 0)[0] == "A"
assert FourCC.unpack_from(b"AB ", 0)[0] ==... | code_fim | medium | {
"lang": "python",
"repo": "chrippa/python-flashmedia",
"path": "/tests/test_4cc.py",
"mode": "spm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: edebie/pyalcs path: /lcs/agents/racs/ClassifierList.py
from __future__ import annotations
from typing import Optional
from lcs import TypedList, Perception
from lcs.agents.racs import Configuration
from . import Classifier
from .components.alp import expected_case, unexpected_case, cover
clas... | code_fim | hard | {
"lang": "python",
"repo": "edebie/pyalcs",
"path": "/lcs/agents/racs/ClassifierList.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def apply_reinforcement_learning(self, reward: int, p: float) -> None:
"""
Reinforcement Learning. Applies RL according to
current reinforcement `reward` and back-propagated reinforcement
`maximum_fitness`.
Parameters
----------
reward: int
... | code_fim | hard | {
"lang": "python",
"repo": "edebie/pyalcs",
"path": "/lcs/agents/racs/ClassifierList.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> if t.is_element_exist_by_class_name(browser, "di-lic"):
sys.stderr.write("登录失败。请将以下内容与浏览器界面一并反馈给开发者: ")
raise ce.CustomBaseException(prefix="INFO", arg="di-lic exists: True.", code=100,
addition=traceback.format_exc())
print("登录成功。")
err_li... | code_fim | hard | {
"lang": "python",
"repo": "qzwxsaedc/CoolQ-PluginAutoDownloader",
"path": "/downloader.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: qzwxsaedc/CoolQ-PluginAutoDownloader path: /downloader.py
# -*- coding: UTF-8 -*-
from selenium import webdriver
from selenium.webdriver.support.wait import WebDriverWait
from selenium.webdriver.support import expected_conditions as ec
import sys
import os
import json
import time
import tracebac... | code_fim | hard | {
"lang": "python",
"repo": "qzwxsaedc/CoolQ-PluginAutoDownloader",
"path": "/downloader.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: douglasqsantos/DQSHackingTools path: /passwordcracking/sha1hash.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# https://docs.python.org/3/library/hashlib.html
# https://raw.githubusercontent.com/danielmiessler/SecLists/master/Passwords/Common-Credentials/10-million-password-list-top-10000.tx... | code_fim | medium | {
"lang": "python",
"repo": "douglasqsantos/DQSHackingTools",
"path": "/passwordcracking/sha1hash.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>def found(password,count):
end = time.time() - start
print('\n{}[+]{} The Password is: {}{}{} found in {:.2f} segs. Processed {}{}{} hashes'.format(GREEN,RESET,GREEN,str(password), RESET, end,GREEN,count,RESET))
sha1hash = input("[+] Enter Sha1 Hash: ")
# Start the crack
start = time.time()
# coun... | code_fim | medium | {
"lang": "python",
"repo": "douglasqsantos/DQSHackingTools",
"path": "/passwordcracking/sha1hash.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> end = time.time() - start
print('\n{}[+]{} The Password is: {}{}{} found in {:.2f} segs. Processed {}{}{} hashes'.format(GREEN,RESET,GREEN,str(password), RESET, end,GREEN,count,RESET))
sha1hash = input("[+] Enter Sha1 Hash: ")
# Start the crack
start = time.time()
# count the amount of passwords p... | code_fim | medium | {
"lang": "python",
"repo": "douglasqsantos/DQSHackingTools",
"path": "/passwordcracking/sha1hash.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: wtl5736/Introduction-to-Cryptography-Project path: /Project Code/Test Files/Test_2--Python_File.py
#!/usr/bin/python3
"""
Name: HW5 - Hash Function.py
Author: Wesley Lee
Assignment: HW5 #1
Date: 04-22-2018
"""
<|fim_suffix|> binary_number = int(binary_number, 2)
hash_func = (((((8192 * binar... | code_fim | medium | {
"lang": "python",
"repo": "wtl5736/Introduction-to-Cryptography-Project",
"path": "/Project Code/Test Files/Test_2--Python_File.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> hash_list = []
collision_list = []
for x in range(0, 256):
bin_num = bin(x)[2:].zfill(16)
hash_func = hash_function(bin_num)
#print("#" + str(x) + "\t", hash_func, "\t->\t", bin(hash_func)[2:].zfill(16))
#print(hash_func)
if hash_func in hash_list:
collision_list.append(hash_func)
el... | code_fim | medium | {
"lang": "python",
"repo": "wtl5736/Introduction-to-Cryptography-Project",
"path": "/Project Code/Test Files/Test_2--Python_File.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> #print()
#print(collision_list)
print("\nNumber of Collisions:", len(collision_list), "out of 256 Tests")
print("Ran Program in:", timeTaken, "seconds...")
main()<|fim_prefix|># repo: wtl5736/Introduction-to-Cryptography-Project path: /Project Code/Test Files/Test_2--Python_File.py
#!/usr/bin/pytho... | code_fim | hard | {
"lang": "python",
"repo": "wtl5736/Introduction-to-Cryptography-Project",
"path": "/Project Code/Test Files/Test_2--Python_File.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>if __name__ == "__main__":
context = Context(directory=".")
loader = PythonLoader(modules=["helpers", "escape_helpers"])
renderer = MarkdownRenderer(render_module_header=False, insert_header_anchors=True, code_headers=True, render_typehint_in_data_header=True, docstrings_as_blockquote=True)
... | code_fim | hard | {
"lang": "python",
"repo": "mu-semtech/mu-python-template",
"path": "/README.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: mu-semtech/mu-python-template path: /README.py
from re import search, RegexFlag
from pydoc_markdown.interfaces import Context
from pydoc_markdown.contrib.loaders.python import PythonLoader
from pydoc_markdown.contrib.renderers.markdown import MarkdownRenderer, MarkdownReferenceResolver
from pydo... | code_fim | hard | {
"lang": "python",
"repo": "mu-semtech/mu-python-template",
"path": "/README.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: LTTTDH/dataScienceHelpers path: /get_cv_params.py
def get_cv_params(X, y, estimator, params, cv=5, scoring=<|fim_suffix|>eturn estimator_cv.best_score_, estimator_cv.best_params_<|fim_middle|>None):
estimator_cv = GridSearchCV(estimator, params, cv=cv, scoring=scoring)
estimator_cv.fit(X,... | code_fim | medium | {
"lang": "python",
"repo": "LTTTDH/dataScienceHelpers",
"path": "/get_cv_params.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>eturn estimator_cv.best_score_, estimator_cv.best_params_<|fim_prefix|># repo: LTTTDH/dataScienceHelpers path: /get_cv_params.py
def get_cv_params(X, y, estimator, params, cv=5, scoring=<|fim_middle|>None):
estimator_cv = GridSearchCV(estimator, params, cv=cv, scoring=scoring)
estimator_cv.fit(X,... | code_fim | medium | {
"lang": "python",
"repo": "LTTTDH/dataScienceHelpers",
"path": "/get_cv_params.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>r=True,
setup_requires=['pbr'],
)<|fim_prefix|># repo: chrisgorgo/bids2datapackage path: /setup.py
from setuptools import find_packages
<|fim_middle|>from setuptools import setup
setup(
packages=find_packages(),
pb | code_fim | medium | {
"lang": "python",
"repo": "chrisgorgo/bids2datapackage",
"path": "/setup.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: chrisgorgo/bids2datapackage path: /setup.py
from setuptools import find_packages
<|fim_suffix|>
packages=find_packages(),
pbr=True,
setup_requires=['pbr'],
)<|fim_middle|>from setuptools import setup
setup(
| code_fim | easy | {
"lang": "python",
"repo": "chrisgorgo/bids2datapackage",
"path": "/setup.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
packages=find_packages(),
pbr=True,
setup_requires=['pbr'],
)<|fim_prefix|># repo: chrisgorgo/bids2datapackage path: /setup.py
from setuptools import find_packages
<|fim_middle|>from setuptools import setup
setup(
| code_fim | easy | {
"lang": "python",
"repo": "chrisgorgo/bids2datapackage",
"path": "/setup.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def test_inner_product_infer_no_shape(self):
graph = build_graph(nodes_attributes,
[('node_1', 'inner'),
('node_2', 'inner'),
('inner', 'node_3'),
('node_3', 'op_output')
... | code_fim | hard | {
"lang": "python",
"repo": "ni/dldt",
"path": "/model-optimizer/mo/front/common/partial_infer/inner_product_test.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>from mo.front.common.partial_infer.inner_product import caffe_inner_product
from mo.graph.graph import Node
from mo.utils.unittest.extractors import FakeValue
from mo.utils.unittest.graph import build_graph
nodes_attributes = {'node_1': {'value': None, 'kind': 'data'},
'inner': {'type... | code_fim | hard | {
"lang": "python",
"repo": "ni/dldt",
"path": "/model-optimizer/mo/front/common/partial_infer/inner_product_test.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ni/dldt path: /model-optimizer/mo/front/common/partial_infer/inner_product_test.py
"""
Copyright (c) 2018-2019 Intel 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 Licen... | code_fim | hard | {
"lang": "python",
"repo": "ni/dldt",
"path": "/model-optimizer/mo/front/common/partial_infer/inner_product_test.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: marrink-lab/cartographer path: /train_num_beads.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2018 University of Groningen
# 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 ... | code_fim | hard | {
"lang": "python",
"repo": "marrink-lab/cartographer",
"path": "/train_num_beads.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>
NUM_FEATURES = 7
FINGERPRINT_SIZE = 3
FILENAME = 'numbead_predictor.gz'
BASE_PATH = '/home/.../Documents/database'
XLS_FILE = os.path.join(BASE_PATH, 'DRUGS-06.xlsx')
AA_DIR = os.path.join(BASE_PATH, 'atomistic')
CG_DIR = os.path.join(BASE_PATH, 'Martini')
MAP_DIR = os.path.join(BASE_PATH, 'mapping')
s... | code_fim | hard | {
"lang": "python",
"repo": "marrink-lab/cartographer",
"path": "/train_num_beads.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: bhourahine/hsdparser path: /python/src/hsd/formatter.py
"""Formatting utilities for HSD content.
"""
import sys
from hsd.common import HSDATTR_EQUAL
__all__ = [ "HSDFormatter", "HSDStreamFormatter" ]
class HSDFormatter:
"""Event controlled formatter producing HSD output."""
def __... | code_fim | hard | {
"lang": "python",
"repo": "bhourahine/hsdparser",
"path": "/python/src/hsd/formatter.py",
"mode": "psm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> Args:
parser: Event controled parser to be used.
formatter: Formatter to be used.
"""
self._parser = parser
self._formatter = formatter
self._parser.start_handler = self._formatter.start_tag
self._parser.close_handler = self._formatt... | code_fim | hard | {
"lang": "python",
"repo": "bhourahine/hsdparser",
"path": "/python/src/hsd/formatter.py",
"mode": "spm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: qinggniq/Algorithm-Practice path: /test_cast/2019-11-13/ClosedNumber/solution.py
class Solution:
def findTheClosedNumbers(self, num: int) -> list:
def getNext(n):
c = n
c0 = 0
c1 = 0
while (c & 1) == 0 and c != 0:
c0 += 1... | code_fim | hard | {
"lang": "python",
"repo": "qinggniq/Algorithm-Practice",
"path": "/test_cast/2019-11-13/ClosedNumber/solution.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> c = n
c0 = 0
c1 = 0
while (c & 1) == 1:
c1 += 1
c >>= 1
if c == 0:
return -1
while (c & 1) == 0 and c != 0:
c0 += 1
c >>= 1
p = c0 + c1
... | code_fim | hard | {
"lang": "python",
"repo": "qinggniq/Algorithm-Practice",
"path": "/test_cast/2019-11-13/ClosedNumber/solution.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Codechef-SRM-NCR-Chapter/30-DaysOfCode-March-2021 path: /answers/Aryan Goyal/Day 10/Que 1.py
def pangram(s):
a = "abcdefghijklmnopqrstuvwxyz"
f<|fim_suffix|>False
return True
# main
string1 = input()
if(pangram(string1) == True):
print("Yes")
else:
print("No")<|fim_middle|>or i in ... | code_fim | easy | {
"lang": "python",
"repo": "Codechef-SRM-NCR-Chapter/30-DaysOfCode-March-2021",
"path": "/answers/Aryan Goyal/Day 10/Que 1.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>(string1) == True):
print("Yes")
else:
print("No")<|fim_prefix|># repo: Codechef-SRM-NCR-Chapter/30-DaysOfCode-March-2021 path: /answers/Aryan Goyal/Day 10/Que 1.py
def pangram(s):
a = "abcdefghijklmnopqrstuvwxyz"
f<|fim_middle|>or i in a:
if i not in s.lower():
return False
... | code_fim | medium | {
"lang": "python",
"repo": "Codechef-SRM-NCR-Chapter/30-DaysOfCode-March-2021",
"path": "/answers/Aryan Goyal/Day 10/Que 1.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: sbidy/pywizlight path: /pywizlight/tests/test_bulb_rgbw_1_21_4.py
"""Tests for the Bulb API with a rgbtw bulb."""
from typing import AsyncGenerator
import pytest
from pywizlight import wizlight
from pywizlight.bulblibrary import BulbClass, BulbType, Features, KelvinRange
from pywizlight.tests.f... | code_fim | hard | {
"lang": "python",
"repo": "sbidy/pywizlight",
"path": "/pywizlight/tests/test_bulb_rgbw_1_21_4.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>@pytest.mark.asyncio
async def test_model_description_rgbw_bulb(rgbw_bulb: wizlight) -> None:
"""Test fetching the model description rgbw bulb."""
bulb_type = await rgbw_bulb.get_bulbtype()
assert bulb_type == BulbType(
features=Features(
color=True, color_tmp=True, effect=... | code_fim | hard | {
"lang": "python",
"repo": "sbidy/pywizlight",
"path": "/pywizlight/tests/test_bulb_rgbw_1_21_4.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
@pytest.mark.asyncio
async def test_supported_scenes(rgbw_bulb: wizlight) -> None:
"""Test supported scenes."""
assert await rgbw_bulb.getSupportedScenes() == [
"Ocean",
"Romance",
"Sunset",
"Party",
"Fireplace",
"Cozy",
"Forest",
"P... | code_fim | hard | {
"lang": "python",
"repo": "sbidy/pywizlight",
"path": "/pywizlight/tests/test_bulb_rgbw_1_21_4.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: youngspinachIII/doepy-1 path: /doepy/model_discrimination/chi_squared_test.py
"""
MIT License
Copyright (c) 2019 Simon Olofsson
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 Softw... | code_fim | hard | {
"lang": "python",
"repo": "youngspinachIII/doepy-1",
"path": "/doepy/model_discrimination/chi_squared_test.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> D = np.asarray(D)
assert_is_shape(D, (num_models,))
# Squared Mahalanobis distance
mahaf1 = lambda d, is2: np.sum( d * np.matmul(is2, d) )
# Mahalanobis distances for all test points
mahaf2 = lambda Y, Z, iS: [ mahaf1(y-z, is2) for y,z,is2 in zip(Y,Z,iS) ]
iS = np.linalg.inv(S)
maha = [ np.sum... | code_fim | medium | {
"lang": "python",
"repo": "youngspinachIII/doepy-1",
"path": "/doepy/model_discrimination/chi_squared_test.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> self.build_system.cppflags = self.cppflags[self.required_thread]
self.maintainers = ['JG', 'AJ']
self.tags = {'production', 'craype'}
@run_before('compile')
def skip_nvidia_cray_ex(self):
envname = self.current_environ.name
sysname = self.current_system.nam... | code_fim | hard | {
"lang": "python",
"repo": "jgphpc/reframe",
"path": "/cscs-checks/prgenv/mpi.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: jgphpc/reframe path: /cscs-checks/prgenv/mpi.py
# Copyright 2016-2022 Swiss National Supercomputing Centre (CSCS/ETH Zurich)
# ReFrame Project Developers. See the top-level LICENSE file for details.
#
# SPDX-License-Identifier: BSD-3-Clause
import os
import reframe as rfm
import reframe.utility... | code_fim | hard | {
"lang": "python",
"repo": "jgphpc/reframe",
"path": "/cscs-checks/prgenv/mpi.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: fabien-roy/cardbot path: /app/games/entities/fuck_you_game.py
from app.games.entities.ordered_games import OrderedGame
<|fim_suffix|> def draw(self):
player, card = super().draw()
result = '{} of {}'.format(card.house_value.name, card.house_suit.name)
return player.na... | code_fim | easy | {
"lang": "python",
"repo": "fabien-roy/cardbot",
"path": "/app/games/entities/fuck_you_game.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> player, card = super().draw()
result = '{} of {}'.format(card.house_value.name, card.house_suit.name)
return player.name, result<|fim_prefix|># repo: fabien-roy/cardbot path: /app/games/entities/fuck_you_game.py
from app.games.entities.ordered_games import OrderedGame
<|fim_midd... | code_fim | medium | {
"lang": "python",
"repo": "fabien-roy/cardbot",
"path": "/app/games/entities/fuck_you_game.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> type = 'Fuck you'
def draw(self):
player, card = super().draw()
result = '{} of {}'.format(card.house_value.name, card.house_suit.name)
return player.name, result<|fim_prefix|># repo: fabien-roy/cardbot path: /app/games/entities/fuck_you_game.py
from app.games.entities.or... | code_fim | easy | {
"lang": "python",
"repo": "fabien-roy/cardbot",
"path": "/app/games/entities/fuck_you_game.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> dependencies = [
("news", "0006_article_copy_field"),
]
operations = [
migrations.RunPython(
article_content_to_copy, reverse_code=migrations.RunPython.noop
)
]<|fim_prefix|># repo: goodtune/vitriolic path: /touchtechnology/news/migrations/0007_article... | code_fim | hard | {
"lang": "python",
"repo": "goodtune/vitriolic",
"path": "/touchtechnology/news/migrations/0007_article_copy_field_data.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: goodtune/vitriolic path: /touchtechnology/news/migrations/0007_article_copy_field_data.py
from django.db import migrations
def article_content_to_copy(apps, schema_editor):
<|fim_suffix|>
class Migration(migrations.Migration):
dependencies = [
("news", "0006_article_copy_field"),
... | code_fim | hard | {
"lang": "python",
"repo": "goodtune/vitriolic",
"path": "/touchtechnology/news/migrations/0007_article_copy_field_data.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> operations = [
migrations.RunPython(
article_content_to_copy, reverse_code=migrations.RunPython.noop
)
]<|fim_prefix|># repo: goodtune/vitriolic path: /touchtechnology/news/migrations/0007_article_copy_field_data.py
from django.db import migrations
def article_conten... | code_fim | hard | {
"lang": "python",
"repo": "goodtune/vitriolic",
"path": "/touchtechnology/news/migrations/0007_article_copy_field_data.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: idaholab/raven path: /ravenframework/Samplers/SparseGridCollocation.py
# Copyright 2017 Battelle Energy Alliance, LLC
#
# 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": "idaholab/raven",
"path": "/ravenframework/Samplers/SparseGridCollocation.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> poly = OrthoPolynomials.factory.returnInstance(polyType)
poly.initialize(quad)
self.polyDict[varName] = poly
self.importanceDict[varName] = float(dat['weight'])
def localGenerateInput(self, model, oldInput):
"""
Function to select the next most informative point for r... | code_fim | hard | {
"lang": "python",
"repo": "idaholab/raven",
"path": "/ravenframework/Samplers/SparseGridCollocation.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> py_version = config_file.python_version_to_autoset(
partition.interpreter_constraints, python_setup.interpreter_versions_universe
)
named_cache_dir = ".cache/mypy_cache"
mypy_cache_dir = f"{named_cache_dir}/{sha256(build_root.path.encode()).hexdigest()}"
run_cache_dir = ".tmp_c... | code_fim | hard | {
"lang": "python",
"repo": "pantsbuild/pants",
"path": "/src/python/pants/backend/python/typecheck/mypy/rules.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: pantsbuild/pants path: /src/python/pants/backend/python/typecheck/mypy/rules.py
PexRequest,
PexResolveInfo,
VenvPex,
VenvPexProcess,
)
from pants.backend.python.util_rules.pex_from_targets import RequirementsPexRequest
from pants.backend.python.util_rules.python_sources import (
... | code_fim | hard | {
"lang": "python",
"repo": "pantsbuild/pants",
"path": "/src/python/pants/backend/python/typecheck/mypy/rules.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: KawaSwitch/Poke-Controller path: /SerialController/Camera.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import sys, os
import cv2
import time, datetime
class Camera:
def __init__(self):
self.camera = None
self.capture_size = (1280, 720)
self.capture_dir = "Captures"
de... | code_fim | medium | {
"lang": "python",
"repo": "KawaSwitch/Poke-Controller",
"path": "/SerialController/Camera.py",
"mode": "psm",
"license": "LicenseRef-scancode-warranty-disclaimer",
"source": "the-stack-v2"
} |
<|fim_suffix|> # Use the current time as the file name if not specified
filename = dt_now.strftime('%Y-%m-%d_%H-%M-%S') if len(str(name)) == 0 else name
ext = '.png'
path = str(filename) + ext
if not os.path.exists(self.capture_dir):
os.makedirs(self.capture_dir)
save_path = os.path.join(self.ca... | code_fim | hard | {
"lang": "python",
"repo": "KawaSwitch/Poke-Controller",
"path": "/SerialController/Camera.py",
"mode": "spm",
"license": "LicenseRef-scancode-warranty-disclaimer",
"source": "the-stack-v2"
} |
<|fim_suffix|> # 实现了去重，降低了时间复杂度
# 目标和为0
results = []
nums.sort() # 排序既是为了去重，也是为了使用双指针
n = len(nums)
for first in range(n):
# if nums[first] > 0:
# # 当第一个数大于目标值时，直接退出循环，降低时间消耗
# break
if first > 0 and nums[first] == n... | code_fim | hard | {
"lang": "python",
"repo": "showerhhh/leetcode_python",
"path": "/t15.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: showerhhh/leetcode_python path: /t15.py
class Solution:
def threeSum(self, nums):
# 仅实现了去重，未降低时间复杂度
# 目标和为0
results = []
nums.sort() # 排序为了去重
n = len(nums)
for first in range(n):
if first > 0 and nums[first] == nums[first - 1]:
... | code_fim | hard | {
"lang": "python",
"repo": "showerhhh/leetcode_python",
"path": "/t15.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> if self.level >= LoggerLevel.TRACE:
print(colored('[trace]', color='magenta', attrs=['bold']),
colored(' '.join(map(str, message)), color='magenta'))
def trace_current(self):
if self.level >= LoggerLevel.TRACE:
func = inspect.stack()[1][3]
... | code_fim | hard | {
"lang": "python",
"repo": "rloic/Caramel",
"path": "/tools/logger.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def log(self, level, *args, **kwargs):
if self.level >= level:
print(*args, **kwargs)
def fatal(self, *args, **kwargs):
if self.level >= LoggerLevel.FATAL:
print(colored('[fatal]', color='red', attrs=['bold']), *args, **kwargs)
def critical(self, *args... | code_fim | hard | {
"lang": "python",
"repo": "rloic/Caramel",
"path": "/tools/logger.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: rloic/Caramel path: /tools/logger.py
# coding: utf-8
# MIT License
#
# Copyright (c) 2018 Kalate Hexanome, 4IF, INSA Lyon
#
# 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 Softw... | code_fim | hard | {
"lang": "python",
"repo": "rloic/Caramel",
"path": "/tools/logger.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: pfassina/BattleFortune path: /src/battlefortune.py
import logging
import os
import shutil
from src import calculate, read, run, visualize
from src.config import CONFIG
<|fim_suffix|> """
Runs BattleFortune, simulate battles, and return results.
"""
# PREPARE
clone_game_files... | code_fim | medium | {
"lang": "python",
"repo": "pfassina/BattleFortune",
"path": "/src/battlefortune.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def clone_game_files() -> None:
logging.info("cloning game files")
for turn in CONFIG.data.simulation_turns:
if os.path.exists(CONFIG.data.simulation_path(turn)):
logging.info("previous simulation files detected. removing old files.")
shutil.rmtree(CONFIG.data.simu... | code_fim | medium | {
"lang": "python",
"repo": "pfassina/BattleFortune",
"path": "/src/battlefortune.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> Args:
self: An instance of the class
position: The position integer
Returns:
list: The list all the digits in the position.]
'''
return [int(d) for d in str(n)]
def sum_positions(self,x_position, y_position):
'''
Ret... | code_fim | hard | {
"lang": "python",
"repo": "kilonzi/calm",
"path": "/src/robot.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: kilonzi/calm path: /src/robot.py
class Robot:
''' Implement the EMP Mines on Grid avoiding Robot
'''
def __init__(self,condition) ->int:
if isinstance(condition, int):
self.condition = condition
else:
raise ValueError('Condition should be an In... | code_fim | hard | {
"lang": "python",
"repo": "kilonzi/calm",
"path": "/src/robot.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def split_digits(self,n) -> list:
'''
Returns the list of individual digits of the positions
Args:
self: An instance of the class
position: The position integer
Returns:
list: The list all the digits in the position.]
'''
... | code_fim | hard | {
"lang": "python",
"repo": "kilonzi/calm",
"path": "/src/robot.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>)
latitude = models.DecimalField(
max_digits=10,
decimal_places=6,
default=0,
)<|fim_prefix|># repo: yuriymironov96/traffic-optimizer path: /server/traffic_optimizer/locations/models.py
from django.db import models
class Location(models.Model):
name = models.CharFiel... | code_fim | medium | {
"lang": "python",
"repo": "yuriymironov96/traffic-optimizer",
"path": "/server/traffic_optimizer/locations/models.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: yuriymironov96/traffic-optimizer path: /server/traffic_optimizer/locations/models.py
from django.db import models
class Location(models.Model):
name = models.CharField(
max_length=6<|fim_suffix|>)
latitude = models.DecimalField(
max_digits=10,
decimal_places=6,
... | code_fim | hard | {
"lang": "python",
"repo": "yuriymironov96/traffic-optimizer",
"path": "/server/traffic_optimizer/locations/models.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: mesoscale-activity-map/map-ephys path: /pipeline/fixes/fix_0015_undo_amp_scaling_fix.py
#! /usr/bin/env python
import logging
import datajoint as dj
import pathlib
from tqdm import tqdm
from datetime import datetime
from pipeline import ephys, report
from pipeline.fixes import schema, FixHisto... | code_fim | hard | {
"lang": "python",
"repo": "mesoscale-activity-map/map-ephys",
"path": "/pipeline/fixes/fix_0015_undo_amp_scaling_fix.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> amp_scale = 1 / 3.01
units2fix = ephys.Unit & FixedAmpUnit # only fix those units that underwent fix_0007
units2fix = units2fix - (UndoFixedAmpUnit & 'fixed=1') # exclude those that were already fixed
if not units2fix:
return
# safety check, no jrclust results and no npx 1... | code_fim | hard | {
"lang": "python",
"repo": "mesoscale-activity-map/map-ephys",
"path": "/pipeline/fixes/fix_0015_undo_amp_scaling_fix.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # safety check, no jrclust results and no npx 1.0
assert len(units2fix & 'clustering_method LIKE "jrclust%"') == 0
assert len(units2fix.proj() * ephys.ProbeInsertion & 'probe_type LIKE "neuropixels 1.0%"') == 0
fix_hist_key = {'fix_name': pathlib.Path(__file__).name,
'... | code_fim | hard | {
"lang": "python",
"repo": "mesoscale-activity-map/map-ephys",
"path": "/pipeline/fixes/fix_0015_undo_amp_scaling_fix.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: renehorstmann/Visu path: /python/example_visuwidget.py
import gi
gi.require_version('Gtk', '3.0')
from gi.repository import Gtk, Gdk
from typing import Optional
import visu as vu
import numpy as np
visu: Optional[vu.visubase.Visu] = None
points: Optional[vu.object.RenderObject] = None
visuwidg... | code_fim | hard | {
"lang": "python",
"repo": "renehorstmann/Visu",
"path": "/python/example_visuwidget.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def render(visu: vu.visubase.Visu, data):
visu.mode3D()
points.render()
if __name__ == '__main__':
print('Start')
ortho = True
visu = vu.visubase.Visu(ortho, render, None)
add_object()
# gtk_init is called during import
window = create_window()
window.connect("des... | code_fim | hard | {
"lang": "python",
"repo": "renehorstmann/Visu",
"path": "/python/example_visuwidget.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Sejsel/GW2Bot path: /migrate.py
# Run this from the folder of old bot
import time
import json
from pymongo import MongoClient
start = time.time()
client = MongoClient()
old = client.gw2
new = client.toothy
def update_keys():
for key in old.keys.find():
updated = {
"_id"... | code_fim | hard | {
"lang": "python",
"repo": "Sejsel/GW2Bot",
"path": "/migrate.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def update_guilds():
with open("data/red/settings.json", encoding="utf-8", mode="r") as f:
data = json.load(f)
for guild in old.settings.find():
updates_channel = guild.get("channel")
updates_channel = int(updates_channel) if updates_channel else None
daily_updat... | code_fim | hard | {
"lang": "python",
"repo": "Sejsel/GW2Bot",
"path": "/migrate.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: jwesleye/system_checks path: /test_tensor.py
import tensorflow as tf
def test_torch_version():
<|fim_suffix|> assert len(tf.config.list_physical_devices('GPU')) != 0<|fim_middle|> assert tf.__version__ >= '2.9pip.0'
def test_gpu():
| code_fim | easy | {
"lang": "python",
"repo": "jwesleye/system_checks",
"path": "/test_tensor.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> assert tf.__version__ >= '2.9pip.0'
def test_gpu():
assert len(tf.config.list_physical_devices('GPU')) != 0<|fim_prefix|># repo: jwesleye/system_checks path: /test_tensor.py
import tensorflow as tf
<|fim_middle|>def test_torch_version():
| code_fim | easy | {
"lang": "python",
"repo": "jwesleye/system_checks",
"path": "/test_tensor.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> assert len(tf.config.list_physical_devices('GPU')) != 0<|fim_prefix|># repo: jwesleye/system_checks path: /test_tensor.py
import tensorflow as tf
def test_torch_version():
<|fim_middle|> assert tf.__version__ >= '2.9pip.0'
def test_gpu():
| code_fim | easy | {
"lang": "python",
"repo": "jwesleye/system_checks",
"path": "/test_tensor.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> assert len(inputs) == len(self.in_channels)
C4_lat = F.relu(self.conv4(inputs[-2]))
C5_lat = F.relu(self.conv5(inputs[-1]))
C5_lat = F.interpolate(C5_lat, scale_factor=2, mode="nearest")
avg_pool = self.avg_pool(inputs[-1])
Cglb_lat = F.relu(self.convlast(a... | code_fim | hard | {
"lang": "python",
"repo": "TonojiKiobya/thundernet_mmdetection",
"path": "/mmdet/models/necks/cem.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: TonojiKiobya/thundernet_mmdetection path: /mmdet/models/necks/cem.py
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import xavier_init
from mmdet.core import auto_fp16
from ..builder import NECKS
@NECKS.register_module
class CEM(nn.Module):
def __init__(self,
... | code_fim | medium | {
"lang": "python",
"repo": "TonojiKiobya/thundernet_mmdetection",
"path": "/mmdet/models/necks/cem.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
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