text stringlengths 232 16.3k | domain stringclasses 1
value | difficulty stringclasses 3
values | meta dict |
|---|---|---|---|
<|fim_prefix|># repo: by46/muggle path: /gallery/admin.py
from django.contrib import admin
from .models import Photo, Item
<|fim_suffix|>
admin.site.register(Item, ItemAdmin)
admin.site.register(Photo)<|fim_middle|># Register your models here.
class PhotoInline(admin.StackedInline):
model = Photo
class ItemA... | code_fim | medium | {
"lang": "python",
"repo": "by46/muggle",
"path": "/gallery/admin.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: by46/muggle path: /gallery/admin.py
from django.contrib import admin
<|fim_suffix|># Register your models here.
class PhotoInline(admin.StackedInline):
model = Photo
class ItemAdmin(admin.ModelAdmin):
inlines = [PhotoInline]
admin.site.register(Item, ItemAdmin)
admin.site.register(P... | code_fim | easy | {
"lang": "python",
"repo": "by46/muggle",
"path": "/gallery/admin.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: enthought/pyql path: /test/test_variance_swap.py
import datetime
import unittest
import numpy as np
from quantlib.settings import Settings
from quantlib.instruments.option import EuropeanExercise, OptionType
from quantlib.instruments.variance_swap import VarianceSwap, SwapType
from quantlib.mat... | code_fim | hard | {
"lang": "python",
"repo": "enthought/pyql",
"path": "/test/test_variance_swap.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> engine = MCVarianceSwapEngine(stoch_process,
time_steps_per_year=250,
required_samples=1023,
seed=42,
)
variance_swap = VarianceSwap(sel... | code_fim | hard | {
"lang": "python",
"repo": "enthought/pyql",
"path": "/test/test_variance_swap.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|>
@event.listens_for(Engine, 'connect')
def set_sqlite_pragma(dbapi_connection, connection_record):
cursor = dbapi_connection.cursor()
cursor.execute('PRAGMA foreign_keys=ON')
cursor.close()
_engine = None
_Session = None
_session_store = threading.local()<|fim_prefix|># repo: AnnaKudriasheva... | code_fim | hard | {
"lang": "python",
"repo": "AnnaKudriasheva/vgs-satellite",
"path": "/satellite/db/__init__.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: AnnaKudriasheva/vgs-satellite path: /satellite/db/__init__.py
import threading
from sqlalchemy import create_engine
from sqlalchemy import event
from sqlalchemy.engine import Engine
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker
from sqlalchemy.or... | code_fim | medium | {
"lang": "python",
"repo": "AnnaKudriasheva/vgs-satellite",
"path": "/satellite/db/__init__.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: project-koku/koku path: /koku/masu/api/status.py
#
# Copyright 2021 Red Hat Inc.
# SPDX-License-Identifier: Apache-2.0
#
"""View for server status."""
import logging
import platform
import sys
from django.conf import settings
from django.db import connection
from django.db import InterfaceError
... | code_fim | hard | {
"lang": "python",
"repo": "project-koku/koku",
"path": "/koku/masu/api/status.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>@never_cache
@api_view(http_method_names=["GET"])
@permission_classes((AllowAny,))
@renderer_classes(tuple(api_settings.DEFAULT_RENDERER_CLASSES))
def get_status(request):
"""Packages response for class-based view."""
if "liveness" in request.query_params:
return Response({"alive": True})
... | code_fim | hard | {
"lang": "python",
"repo": "project-koku/koku",
"path": "/koku/masu/api/status.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> """Collect the installed modules.
:returns: A dictonary of module names and versions.
"""
return self._modules
@modules.setter
def modules(self, value):
module_data = {
str(name): str(module.__version__)
for name, module in sorted(s... | code_fim | hard | {
"lang": "python",
"repo": "project-koku/koku",
"path": "/koku/masu/api/status.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: MrDenexi/smb-flowers path: /app.py
import sys
from time import sleep
from datetime import datetime, timedelta
from random import randint, choice
import asyncio
import concurrent.futures
import RPi.GPIO as GPIO
import pyfirmata
from sqlalchemy import func, text
from sqlalchemy.orm import joine... | code_fim | hard | {
"lang": "python",
"repo": "MrDenexi/smb-flowers",
"path": "/app.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # make currentState the loginstate using this user
self.currentState = LoginState(self, user)
# user register flow (existing user)
def userRegister(self, card):
# find the least popular flower
# using least amount of accesses of the last 30 days
leastPopula... | code_fim | hard | {
"lang": "python",
"repo": "MrDenexi/smb-flowers",
"path": "/app.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Yvonmu/2O8JbAWhoGk6CZk0 path: /view_web_app.py
import pandas
from flask import Flask, render_template
<|fim_suffix|>if __name__ == "__main__":
app.run()<|fim_middle|>app = Flask(__name__)
@app.route("/", methods=['GET'])
def index():
filename = 'request for startup.csv'
data = pand... | code_fim | hard | {
"lang": "python",
"repo": "Yvonmu/2O8JbAWhoGk6CZk0",
"path": "/view_web_app.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
if __name__ == "__main__":
app.run()<|fim_prefix|># repo: Yvonmu/2O8JbAWhoGk6CZk0 path: /view_web_app.py
import pandas
from flask import Flask, render_template
<|fim_middle|>app = Flask(__name__)
@app.route("/", methods=['GET'])
def index():
filename = 'request for startup.csv'
data = pan... | code_fim | hard | {
"lang": "python",
"repo": "Yvonmu/2O8JbAWhoGk6CZk0",
"path": "/view_web_app.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: yenchenlin/self-attention-gan path: /non_local.py
# Copyright 2018 Google 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
#
# https://www.apache.org/licenses/LICE... | code_fim | hard | {
"lang": "python",
"repo": "yenchenlin/self-attention-gan",
"path": "/non_local.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> # g path
g = sn_conv1x1(x, num_channels // 2, update_collection, init, 'sn_conv_g')
g = tf.layers.max_pooling2d(inputs=g, pool_size=[2, 2], strides=2)
g = tf.reshape(
g, [batch_size, downsampled_num, num_channels // 2])
attn_g = tf.matmul(attn, g)
attn_g = tf.reshape(attn_g,... | code_fim | hard | {
"lang": "python",
"repo": "yenchenlin/self-attention-gan",
"path": "/non_local.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> series_data = pd.concat(series_list, axis = 1)
return series_data
def main(argv):
countries = util.get_countries(argv)
if len(countries) == 0:
return
db_connection = util.login()
queries = get_queries(countries)
series = get_series_from_queries(db_connection, queries, 1930, 2018)
util.plot_t... | code_fim | hard | {
"lang": "python",
"repo": "kzl/world-cup-analysis",
"path": "/scripts/country_record.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: kzl/world-cup-analysis path: /scripts/country_record.py
from __future__ import print_function
import datetime
import sys
import numpy as np
import pandas as pd
import mysql.connector
import matplotlib.pyplot as plt
import util
def get_queries(countries):
queries = []
for country in countries:... | code_fim | hard | {
"lang": "python",
"repo": "kzl/world-cup-analysis",
"path": "/scripts/country_record.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> cursor = db_connection.cursor()
series_list = []
for country, query in queries:
cursor.execute(query)
series = get_country_series(cursor, country, range_min, range_max)
series_list.append(series)
cursor.close()
series_data = pd.concat(series_list, axis = 1)
return series_data
def main(arg... | code_fim | hard | {
"lang": "python",
"repo": "kzl/world-cup-analysis",
"path": "/scripts/country_record.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|># Paths
# GT
process.pGTPass = cms.Path(
process.genericTriggerEventFlagGTPass
)
process.pGTFail = cms.Path(
process.genericTriggerEventFlagGTFail
)
process.pGTTest = cms.Path(
process.genericTriggerEventFlagGTTest
)
process.pGTTestFail = cms.Path(
process.genericTriggerEventFlagGTTestFail
)
# L1
... | code_fim | hard | {
"lang": "python",
"repo": "cms-sw/cmssw",
"path": "/CommonTools/TriggerUtils/test/genericTriggerEventFlagTest_cfg.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: cms-sw/cmssw path: /CommonTools/TriggerUtils/test/genericTriggerEventFlagTest_cfg.py
import FWCore.ParameterSet.Config as cms
process = cms.Process( "TEST" )
## Logging
process.load("FWCore.MessageLogger.MessageLogger_cfi")
process.MessageLogger.cerr.threshold = 'INFO'
process.MessageLogger.ce... | code_fim | hard | {
"lang": "python",
"repo": "cms-sw/cmssw",
"path": "/CommonTools/TriggerUtils/test/genericTriggerEventFlagTest_cfg.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: TheImaginaryOne/image_text_claim_detection path: /vilbert_code/extract_features.py
from pytorch_transformers.tokenization_bert import BertTokenizer
from vilbert.datasets._image_features_reader import ImageFeaturesH5Reader
from helper_functions import *
import argparse
parser = argparse.Argument... | code_fim | hard | {
"lang": "python",
"repo": "TheImaginaryOne/image_text_claim_detection",
"path": "/vilbert_code/extract_features.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>##ImgNet Model for images where no objects detected
img_model = models.resnet152(pretrained=True)
img_model = nn.Sequential(*list(img_model.children())[:-1])
img_model.eval()
img_model.to(device)
##--------------------------------------------------
## Training, Validation and Test Text
data_dict = json.l... | code_fim | hard | {
"lang": "python",
"repo": "TheImaginaryOne/image_text_claim_detection",
"path": "/vilbert_code/extract_features.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> if 'ar' in dset:
text = process_tweet(data_dict[txt_id]['text_en'])
else:
text = process_tweet(data_dict[txt_id]['full_text'])
## First get text tokens, ids and mask for bert
tokens, segment_ids, input_mask = tokenize(text)
## Get image features and boxes
try:... | code_fim | hard | {
"lang": "python",
"repo": "TheImaginaryOne/image_text_claim_detection",
"path": "/vilbert_code/extract_features.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ZhangYaxu/gerrit_backup_tool path: /gerrit_backup_tool/tar/Tar.py
"""SSH Module."""
import os
import shell
class Tar(object):
"""Tar Class."""
def __init__(self, dry_run=False, verbose=False):
"""Init."""
super(Tar, self).__init__()
self.dry_run = dry_run
... | code_fim | medium | {
"lang": "python",
"repo": "ZhangYaxu/gerrit_backup_tool",
"path": "/gerrit_backup_tool/tar/Tar.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> def extract(self, file_path):
"""Extract TAR file."""
path = os.path.dirname(file_path)
if path == '':
path = '.'
extra_tar_options = ""
if self.verbose:
extra_tar_options += "v"
cmd = "tar -%sxzf %s" % (extra_tar_options, file... | code_fim | hard | {
"lang": "python",
"repo": "ZhangYaxu/gerrit_backup_tool",
"path": "/gerrit_backup_tool/tar/Tar.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: blackberry/UBCIS path: /Vagrant/plugins/clair.py
################################################################################
# Name : Clair - Clair plugin
# Author : Alexander Parent
#
# Copyright 2020 BlackBerry Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
#... | code_fim | hard | {
"lang": "python",
"repo": "blackberry/UBCIS",
"path": "/Vagrant/plugins/clair.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> os.system("touch clair-whitelist.yml")
subprocess.run("docker run -d -p 5432:5432 --name db arminc/clair-db:latest", shell=True)
os.system("sleep 15")
subprocess.run("docker run -d -p 6060:6060 --link db:postgres --name clair arminc/clair-local-scan:v2.0.1", shell=True)
os.system("slee... | code_fim | hard | {
"lang": "python",
"repo": "blackberry/UBCIS",
"path": "/Vagrant/plugins/clair.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: mcpython4-coding/core path: /mcpython/common/entity/EntityManager.py
"""
mcpython - a minecraft clone written in python licenced under the MIT-licence
(https://github.com/mcpython4-coding/core)
Contributors: uuk, xkcdjerry (inactive)
Based on the game of fogleman (https://github.com/fogleman/M... | code_fim | hard | {
"lang": "python",
"repo": "mcpython4-coding/core",
"path": "/mcpython/common/entity/EntityManager.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> if uuid is not None:
entity.uuid = uuid
self.entity_map[entity.uuid] = entity
entity.teleport(entity.position, force_chunk_save_update=True)
return entity
async def tick(self, dt: float):
# todo: move to dimensions
# todo: move most of thi... | code_fim | hard | {
"lang": "python",
"repo": "mcpython4-coding/core",
"path": "/mcpython/common/entity/EntityManager.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def _plan_route(self, current_grid_pos):
basic_plan = self.path_planner.calculate_path(
current_grid_pos,
tuple(self.dest_coords)
)
rospy.logdebug(
'{} created new path plan from {} to {}: {}'.format(
self.swarmie_name,
current_grid_pos,
... | code_fim | hard | {
"lang": "python",
"repo": "rjnieves/SwarmiesRL",
"path": "/src/rl_behavior/src/action/moveto.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: rjnieves/SwarmiesRL path: /src/rl_behavior/src/action/moveto.py
"""Definition of the MoveToCellAction class.
"""
import math
import numpy as np
import rospy
from utility import PathPlanning
from action import TurnAction, DriveAction
from swarmie_msgs.msg import Skid
from utility import YawBearin... | code_fim | hard | {
"lang": "python",
"repo": "rjnieves/SwarmiesRL",
"path": "/src/rl_behavior/src/action/moveto.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|># Convert to json, removing zeros, and not
segregated_nonzero = df_to_json(segregated)
integrated_nonzero = df_to_json(integrated)
seg_output = os.path.abspath(os.path.join(pwd,"../data/segregated_state.json"))
write_json(segregated_nonzero,seg_output)
int_output = os.path.abspath(os.path.join(pwd,"../dat... | code_fim | medium | {
"lang": "python",
"repo": "vsoch/network-integration-vis",
"path": "/prep/prep_transitional.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: vsoch/network-integration-vis path: /prep/prep_transitional.py
# prep_transitional.py will read in Matlab exported integrated and segreated state data matrices and parse into JSON objects for visualizations
import igraph as ig
import os
import json
import pandas
import sys
from utils import pwd... | code_fim | hard | {
"lang": "python",
"repo": "vsoch/network-integration-vis",
"path": "/prep/prep_transitional.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: CICATA/TensorflowCertificationStudy path: /_21_CatsDogsAugmentation.py
import os
import zipfile
import tensorflow as tf
from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from matplotlib import pyplot as plt
import wget
os.environ[... | code_fim | hard | {
"lang": "python",
"repo": "CICATA/TensorflowCertificationStudy",
"path": "/_21_CatsDogsAugmentation.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|># Image augmentation and data generator
train_datagen = ImageDataGenerator(
rescale=1. / 255,
rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
fill_mode='nearest')
# Normalize test dataset
test_datagen... | code_fim | hard | {
"lang": "python",
"repo": "CICATA/TensorflowCertificationStudy",
"path": "/_21_CatsDogsAugmentation.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|># Normalize test dataset
test_datagen = ImageDataGenerator(rescale=1. / 255)
# Flow training images in batches of 20 using train_datagen generator
train_generator = train_datagen.flow_from_directory(
train_dir, # This is the source directory for training images
target_size=(150, 150), # All ima... | code_fim | hard | {
"lang": "python",
"repo": "CICATA/TensorflowCertificationStudy",
"path": "/_21_CatsDogsAugmentation.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>)
print(binascii.hexlify(lora.mac()).upper().decode('utf-8'))<|fim_prefix|># repo: bhargavbhat/lopy path: /src/ttn/lopy-dev-eui/main.py
# Required to add device to TTN
# Taken from: https://www.thethingsnetwork.org/forum/t/lopy-otaa-example/4471/6
from netw<|fim_middle|>ork import LoRa
import binascii
l... | code_fim | medium | {
"lang": "python",
"repo": "bhargavbhat/lopy",
"path": "/src/ttn/lopy-dev-eui/main.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: bhargavbhat/lopy path: /src/ttn/lopy-dev-eui/main.py
# Required to add device to TTN
# Taken from: https://www.the<|fim_suffix|>ork import LoRa
import binascii
lora = LoRa(mode=LoRa.LORAWAN)
print(binascii.hexlify(lora.mac()).upper().decode('utf-8'))<|fim_middle|>thingsnetwork.org/forum/t/lopy-ot... | code_fim | medium | {
"lang": "python",
"repo": "bhargavbhat/lopy",
"path": "/src/ttn/lopy-dev-eui/main.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: hykeegj/calculator path: /python/UI.py
# -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'untitled.ui'
#
# Created by: PyQt5 UI code generator 5.13.2
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore, QtGui, QtWidgets
class Ui_Dialog... | code_fim | hard | {
"lang": "python",
"repo": "hykeegj/calculator",
"path": "/python/UI.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ZichaoDi/Di_MATLABTool path: /numerical-tours/python/nt_toolbox/plot_vf.py
import numpy as np
import matplotlib.pyplot as plt
from numpy import random
<|fim_suffix|> """
velocities is supposed to be of shape nxnx2
"""
n = np.shape(velocities)[0]
u = velocities[:,:,0]
v... | code_fim | easy | {
"lang": "python",
"repo": "ZichaoDi/Di_MATLABTool",
"path": "/numerical-tours/python/nt_toolbox/plot_vf.py",
"mode": "psm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> """
velocities is supposed to be of shape nxnx2
"""
n = np.shape(velocities)[0]
u = velocities[:,:,0]
v = velocities[:,:,1]
x,y = np.meshgrid(np.arange(n), np.arange(n))
plt.quiver(x,y,u,v,color="b")
plt.xlim(0,n)
plt.ylim(0,n)
plt.axis("off")
plt.show()... | code_fim | easy | {
"lang": "python",
"repo": "ZichaoDi/Di_MATLABTool",
"path": "/numerical-tours/python/nt_toolbox/plot_vf.py",
"mode": "spm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> return
def build(self, input_shape):
self._head = dict()
for key in self._key_list:
scale = 2**int(key)
self._head[key] = nn_blocks.ConvBN(
bias_initializer=self.bias_init(scale, input_shape[key][-1]),
**self._conv_config)
def call(self, inputs):
outp... | code_fim | hard | {
"lang": "python",
"repo": "ananya-singhh/TensorFlowModels",
"path": "/yolo/modeling/heads/yolo_head.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ananya-singhh/TensorFlowModels path: /yolo/modeling/heads/yolo_head.py
import tensorflow as tf
import math
from yolo.modeling.layers import nn_blocks
class YoloHead(tf.keras.layers.Layer):
"""YOLO Prediction Head"""
def __init__(self,
min_level,
max_level,
... | code_fim | hard | {
"lang": "python",
"repo": "ananya-singhh/TensorFlowModels",
"path": "/yolo/modeling/heads/yolo_head.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: shen3443/paramak path: /examples/example_parametric_reactors/make_all_parametric_reactors_images_for_docs.py
"""
This python script demonstrates the creation of all parametric shapes available
in the paramak tool
"""
from make_all_parametric_reactors import main
from cadquery import exporters
... | code_fim | medium | {
"lang": "python",
"repo": "shen3443/paramak",
"path": "/examples/example_parametric_reactors/make_all_parametric_reactors_images_for_docs.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
all_reactors = main()
for reactor in all_reactors:
with open(reactor.name + ".svg", "w") as f:
exporters.exportShape(reactor.solid, "SVG", f)
reactor.export_stp(output_folder=reactor.name)
if __name__ == "__main__":
export_images()<|fim_prefix|># repo: shen3443... | code_fim | easy | {
"lang": "python",
"repo": "shen3443/paramak",
"path": "/examples/example_parametric_reactors/make_all_parametric_reactors_images_for_docs.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|># Read / Write SPI bit justification
SPI_IOC_RD_LSB_FIRST = _IOR(SPI_IOC_MAGIC, 2, c_uint8)
SPI_IOC_WR_LSB_FIRST = _IOW(SPI_IOC_MAGIC, 2, c_uint8)
# Read / Write SPI device word length (1..N)
SPI_IOC_RD_BITS_PER_WORD = _IOR(SPI_IOC_MAGIC, 3, c_uint8)
SPI_IOC_WR_BITS_PER_WORD = _IOW(SPI_IOC_MAGIC, 3, c_... | code_fim | hard | {
"lang": "python",
"repo": "PotatoSpudowski/malnou",
"path": "/L.I.S.A/quick2wire/spi_ctypes.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: PotatoSpudowski/malnou path: /L.I.S.A/quick2wire/spi_ctypes.py
# Warning: not part of the published Quick2Wire API.
#
# User space versions of kernel symbols for SPI clocking modes,
# matching <linux/spi/spi.h>
#
# Ported to Python ctypes from <linux/spi/spidev.h>
from ctypes import *
from quic... | code_fim | hard | {
"lang": "python",
"repo": "PotatoSpudowski/malnou",
"path": "/L.I.S.A/quick2wire/spi_ctypes.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>print("%d eh o maior" %MAIOR)<|fim_prefix|># repo: antuniooh/uri-resolutions path: /1. Beginner/URI1013.py
a,b,c = input().split(" ")
A = int(a)
B = int(b)
C = int(c)
<|fim_middle|>MAIORAB = (A + B + abs(A-B))/2
MAIOR = (MAIORAB + C + abs(MAIORAB - C))/2
| code_fim | medium | {
"lang": "python",
"repo": "antuniooh/uri-resolutions",
"path": "/1. Beginner/URI1013.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: antuniooh/uri-resolutions path: /1. Beginner/URI1013.py
a,b,c = input().split(" ")
<|fim_suffix|>print("%d eh o maior" %MAIOR)<|fim_middle|>A = int(a)
B = int(b)
C = int(c)
MAIORAB = (A + B + abs(A-B))/2
MAIOR = (MAIORAB + C + abs(MAIORAB - C))/2
| code_fim | medium | {
"lang": "python",
"repo": "antuniooh/uri-resolutions",
"path": "/1. Beginner/URI1013.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: nagyist/sentry path: /src/sentry/models/dashboard_widget.py
from django.contrib.postgres.fields import ArrayField as DjangoArrayField
from django.db import models
from django.utils import timezone
from sentry.db.models import (
ArrayField,
BoundedPositiveIntegerField,
FlexibleForeign... | code_fim | hard | {
"lang": "python",
"repo": "nagyist/sentry",
"path": "/src/sentry/models/dashboard_widget.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>@region_silo_only_model
class DashboardWidgetQuery(Model):
"""
A query in a dashboard widget.
"""
__include_in_export__ = True
widget = FlexibleForeignKey("sentry.DashboardWidget")
name = models.CharField(max_length=255)
fields = ArrayField()
conditions = models.TextField... | code_fim | hard | {
"lang": "python",
"repo": "nagyist/sentry",
"path": "/src/sentry/models/dashboard_widget.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>
class DashboardWidgetDisplayTypes(TypesClass):
LINE_CHART = 0
AREA_CHART = 1
STACKED_AREA_CHART = 2
BAR_CHART = 3
TABLE = 4
WORLD_MAP = 5
BIG_NUMBER = 6
TOP_N = 7
TYPES = [
(LINE_CHART, "line"),
(AREA_CHART, "area"),
(STACKED_AREA_CHART, "stacke... | code_fim | hard | {
"lang": "python",
"repo": "nagyist/sentry",
"path": "/src/sentry/models/dashboard_widget.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: elifesciences/update-iam-human path: /src/tests/test_utils.py
from src.utils import ensure
import pytest
<|fim_suffix|> ensure(1 == 1, "working")
with pytest.raises(AssertionError):
ensure(1 == 2, "not working")<|fim_middle|>def test_ensure():
| code_fim | easy | {
"lang": "python",
"repo": "elifesciences/update-iam-human",
"path": "/src/tests/test_utils.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> ensure(1 == 1, "working")
with pytest.raises(AssertionError):
ensure(1 == 2, "not working")<|fim_prefix|># repo: elifesciences/update-iam-human path: /src/tests/test_utils.py
from src.utils import ensure
import pytest
<|fim_middle|>def test_ensure():
| code_fim | easy | {
"lang": "python",
"repo": "elifesciences/update-iam-human",
"path": "/src/tests/test_utils.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def setUp(self):
self.server.reset()<|fim_prefix|># repo: timgates42/grab path: /tests/misc.py
from pprint import pprint # pylint: disable=unused-import
<|fim_middle|>from test_server import Response # pylint: disable=unused-import
from tests.util import build_grab # pylint: disable=unus... | code_fim | hard | {
"lang": "python",
"repo": "timgates42/grab",
"path": "/tests/misc.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: timgates42/grab path: /tests/misc.py
from pprint import pprint # pylint: disable=unused-import
from test_server import Response # pylint: disable=unused-import
<|fim_suffix|>class TestMisc(BaseGrabTestCase):
def setUp(self):
self.server.reset()<|fim_middle|>from tests.util import ... | code_fim | medium | {
"lang": "python",
"repo": "timgates42/grab",
"path": "/tests/misc.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> self_pointing = pd.DataFrame(columns=["NODEA", "NODEB", "EDGE_WEIGHT"])
self_pointing["NODEA"] = nodes_left
self_pointing["NODEB"] = nodes_left
self_pointing["EDGE_WEIGHT"] = 0
partial_df = pd.concat([dataframe, self_pointing])
self.le = LabelEncoder()
... | code_fim | hard | {
"lang": "python",
"repo": "trevorWieland/maxcutpy",
"path": "/maxcutpy/solvers/abstractmaxcut.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: trevorWieland/maxcutpy path: /maxcutpy/solvers/abstractmaxcut.py
import numpy as np
import pandas as pd
import networkx as nx
from typing import Optional, List
from abc import ABC, abstractmethod
from sklearn.preprocessing import LabelEncoder
class AbstractMaxCut(ABC):
def __init__(self... | code_fim | hard | {
"lang": "python",
"repo": "trevorWieland/maxcutpy",
"path": "/maxcutpy/solvers/abstractmaxcut.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> return cut_vectors
@abstractmethod
def batch_split(self) -> np.array:
"""A function to split the batches.
This function is an abstract method in the class AbstractMaxCut.
Should rely on internal attributes, and take no input parameters.
Should return a si... | code_fim | hard | {
"lang": "python",
"repo": "trevorWieland/maxcutpy",
"path": "/maxcutpy/solvers/abstractmaxcut.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: UcarLab/CoRE-ATAC path: /tensorloader/PEASUtil.py
import pandas as pd
import numpy as np
from sklearn import preprocessing
from sklearn.metrics import accuracy_score, roc_curve, auc, precision_recall_curve,average_precision_score, confusion_matrix
import matplotlib
matplotlib.use('agg')
import ma... | code_fim | hard | {
"lang": "python",
"repo": "UcarLab/CoRE-ATAC",
"path": "/tensorloader/PEASUtil.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> fig = plt.figure(figsize=(10, 10))
plt.imshow(ncm, interpolation='nearest', cmap=cmap, vmin=0, vmax=1)
plt.title(title+" Acc: "+str(accuracy)+")")
plt.colorbar()
for i in range(0,len(labels)):
for j in range(0,len(labels)):
plt.text(j,i,cm[i,j],va='center',ha='cente... | code_fim | hard | {
"lang": "python",
"repo": "UcarLab/CoRE-ATAC",
"path": "/tensorloader/PEASUtil.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Platingamer42/DigitRecognition path: /Windows/AI_KERAS.py
from keras import models
import numpy as np
class AI_KERAS:
cnn = True
def __init__(self):
<|fim_suffix|> if self.cnn:
x = x.reshape(1,28,28,1)
output = self.model.predict(x)
else:
... | code_fim | medium | {
"lang": "python",
"repo": "Platingamer42/DigitRecognition",
"path": "/Windows/AI_KERAS.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> self.model = models.load_model("data/saves/model_cnn (3).h5")
def sendThrough(self, x):
if self.cnn:
x = x.reshape(1,28,28,1)
output = self.model.predict(x)
else:
output = self.model.predict(x)
return output<|fim_prefix|># repo: Plat... | code_fim | easy | {
"lang": "python",
"repo": "Platingamer42/DigitRecognition",
"path": "/Windows/AI_KERAS.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> def sendThrough(self, x):
if self.cnn:
x = x.reshape(1,28,28,1)
output = self.model.predict(x)
else:
output = self.model.predict(x)
return output<|fim_prefix|># repo: Platingamer42/DigitRecognition path: /Windows/AI_KERAS.py
from keras impor... | code_fim | medium | {
"lang": "python",
"repo": "Platingamer42/DigitRecognition",
"path": "/Windows/AI_KERAS.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: xkortex/VIAME path: /plugins/flask/Python/gen_filelist.py
from __future__ import division
import itertools
import os
import random
import numpy as np
import sys
import json
_, label_file = sys.argv
reader = open(os.path.join(os.environ['FS_ROOT'], label_file), 'rt')
label = json.load(reader)
re... | code_fim | hard | {
"lang": "python",
"repo": "xkortex/VIAME",
"path": "/plugins/flask/Python/gen_filelist.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|>f = open(train_file, "w")
for i in range(int(max_num*3/4)) :
for element in pos_dics :
label = pos_dics[element]
imglist = pos_list[label]
k = (i%len(imglist))
f.write("%s %d\n" % (imglist[k], label))
for element in neg_dics :
label = neg_dics[eleme... | code_fim | medium | {
"lang": "python",
"repo": "xkortex/VIAME",
"path": "/plugins/flask/Python/gen_filelist.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: learnables/learn2learn path: /learn2learn/nn/metaoptnet.py
#!/usr/bin/env python3
import torch
try:
from qpth.qp import QPFunction
except ImportError:
from learn2learn.utils import _ImportRaiser
QPFunction = _ImportRaiser('qpth', 'pip install qpth')
EPS = 1e-8
def kronecker(A, B):... | code_fim | hard | {
"lang": "python",
"repo": "learnables/learn2learn",
"path": "/learn2learn/nn/metaoptnet.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
if __name__ == "__main__":
from learn2learn.utils import accuracy
IMAGE_SHAPES = (1, 16, 16)
NUM_CLASSES = 10
NUM_SHOTS = 5
NOISE = 0.0
for normalize in [True, False]:
X = []
y = []
for i in range(NUM_CLASSES):
images = torch.randn(1, *IMAGE_S... | code_fim | hard | {
"lang": "python",
"repo": "learnables/learn2learn",
"path": "/learn2learn/nn/metaoptnet.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> flickr_ur = "https://farm%s.staticflickr.com/%s/%s_%s.%s"
return flickr_ur % (row[5], row[6], row[0], row[7], row[8])
# COMMAND ----------
unclassified_images_df = spark.read.parquet('/mnt/group07/final_data_product/classification_result/unclassified_images.parquet').cache()
unclassified_images_df.p... | code_fim | hard | {
"lang": "python",
"repo": "LSDE-Flickr-ML-Classification/data-pipeline",
"path": "/notebooks/group07/download_failed_images.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: LSDE-Flickr-ML-Classification/data-pipeline path: /notebooks/group07/download_failed_images.py
# Databricks notebook source
import json
import os
import urllib.parse
from pyspark.sql.functions import desc, asc, monotonically_increasing_id, collect_list, col, dense_rank, row_number, lit, floor, c... | code_fim | medium | {
"lang": "python",
"repo": "LSDE-Flickr-ML-Classification/data-pipeline",
"path": "/notebooks/group07/download_failed_images.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Micro-sheep/PaddleOCR path: /ppstructure/predict_system.py
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License ... | code_fim | hard | {
"lang": "python",
"repo": "Micro-sheep/PaddleOCR",
"path": "/ppstructure/predict_system.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> if not flag:
img = cv2.imread(image_file)
if img is None:
logger.error("error in loading image:{}".format(image_file))
continue
starttime = time.time()
res = structure_sys(img)
save_structure_res(res, save_folder, img_name)
... | code_fim | hard | {
"lang": "python",
"repo": "Micro-sheep/PaddleOCR",
"path": "/ppstructure/predict_system.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: cms-sw/cmssw path: /Configuration/Generator/python/Pythia8CUEP8S1Herapdf15LOSettings_cfi.py
import FWCore.ParameterSet.Config as cms
pythia<|fim_suffix|>hia8CUEP8S1herapdfSettings = cms.vstring(
'Tune:pp 16',
'Tune:ee 3',
)
)<|fim_middle|>8CUEP8S1herapdfSettingsBlock = c... | code_fim | easy | {
"lang": "python",
"repo": "cms-sw/cmssw",
"path": "/Configuration/Generator/python/Pythia8CUEP8S1Herapdf15LOSettings_cfi.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> 'Tune:pp 16',
'Tune:ee 3',
)
)<|fim_prefix|># repo: cms-sw/cmssw path: /Configuration/Generator/python/Pythia8CUEP8S1Herapdf15LOSettings_cfi.py
import FWCore.ParameterSet.Config as cms
pythia<|fim_middle|>8CUEP8S1herapdfSettingsBlock = cms.PSet(
pythia8CUEP8S1herapdfSettings = cms... | code_fim | medium | {
"lang": "python",
"repo": "cms-sw/cmssw",
"path": "/Configuration/Generator/python/Pythia8CUEP8S1Herapdf15LOSettings_cfi.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: houseofmackee/BrightcovePY path: /brightcove/DynamicIngest.py
"""
Implements wrapper class and methods to work with Brightcove's Dynamic Ingest API.
See: https://apis.support.brightcove.com/dynamic-ingest/references/reference.html
"""
from typing import Callable, Optional
import functools
from ... | code_fim | hard | {
"lang": "python",
"repo": "houseofmackee/BrightcovePY",
"path": "/brightcove/DynamicIngest.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> """
Submits an ingest request to the Dynamic Ingest API.
Args:
video_id (str): Video ID to ingest video to.
source_url (str): URL of the source video asset to ingest.
capture_images (bool, optional): [description]. Defaults to True.
... | code_fim | hard | {
"lang": "python",
"repo": "houseofmackee/BrightcovePY",
"path": "/brightcove/DynamicIngest.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> """
Args:
oauth (OAuth): OAuth instance to use for the API calls.
ingest_profile (str, optional): Default ingest profile to use for ingests. Defaults to ''.
priority_queue (str, optional): Default priority queue to use for ingests. Defaults to 'normal'.
... | code_fim | hard | {
"lang": "python",
"repo": "houseofmackee/BrightcovePY",
"path": "/brightcove/DynamicIngest.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: lnadi17/lights-yes path: /projects/Game/Gameboard.py
# import all necessary libraries
import numpy as np
import queue
# final implementation of the Gameboard class
class Gameboard:
def __init__(self, shape):
self.nrows, self.ncols = shape, shape
self.col_labels, self.row_la... | code_fim | hard | {
"lang": "python",
"repo": "lnadi17/lights-yes",
"path": "/projects/Game/Gameboard.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> while True:
# make a recorded set of moves
moves = q.get()
if q.empty():
# print("answer not found")
return None
self.play_multiple(moves)
# check for win
if self.is_game_over():
... | code_fim | hard | {
"lang": "python",
"repo": "lnadi17/lights-yes",
"path": "/projects/Game/Gameboard.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> results = []
i, piv, col = 0, 0, 0
to_fix = []
while col < len(A):
if A[i, i + piv] == 1:
i += 1
else:
to_fix.append(col)
piv += 1
col += 1
no_fix = np.delete(np.arange(0, len(A), 1)... | code_fim | hard | {
"lang": "python",
"repo": "lnadi17/lights-yes",
"path": "/projects/Game/Gameboard.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: edelhirsch/brain-tumor-classification path: /main.py
#!/usr/bin/python3
import argparse
import keras
from keras import layers
import matplotlib.pyplot as plt
import numpy as np
import os
import tensorflow as tf
from tensorflow.keras.applications.xception import decode_predictions
base_dir = "/h... | code_fim | hard | {
"lang": "python",
"repo": "edelhirsch/brain-tumor-classification",
"path": "/main.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> train_ds, validation_ds, test_ds = create_datasets()
for index, value in zip(train_ds.class_names, predictions):
print(f'{index}: {value}')
plt.figure(figsize=(5, 5))
plt.imshow(image)
index = np.argmax(predictions)
title = os.path.basename(image_path) + "\nprediction: " ... | code_fim | hard | {
"lang": "python",
"repo": "edelhirsch/brain-tumor-classification",
"path": "/main.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> cordenates=jsan_data_features[events]['geometry']['coordinates']
print("title :"+str(title)+" cordenadas :" + str(cordenates))
print("="*50)<|fim_prefix|># repo: jjgilces/Where-There-s-a-Link-There-s-a-Way path: /events_eonet.py
import requests
import json
parameters={"limit":1000,... | code_fim | medium | {
"lang": "python",
"repo": "jjgilces/Where-There-s-a-Link-There-s-a-Way",
"path": "/events_eonet.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: jjgilces/Where-There-s-a-Link-There-s-a-Way path: /events_eonet.py
import requests
import json
parameters={"limit":1000,"days":5000}
response=requests.get('https://eonet.sci.gsfc.nasa.gov/api/v3/events/geojson')
if response.stat<|fim_suffix|>xt)
jsan_data_features=json_data['features']
date=[]
f... | code_fim | medium | {
"lang": "python",
"repo": "jjgilces/Where-There-s-a-Link-There-s-a-Way",
"path": "/events_eonet.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>+= str(a[max_index])
del a[max_index]
return res
if __name__ == '__main__':
input = int(input()) # number of numbers in list
a = [int(x) for x in input().split()] # input list of numbers
print(largest_number(a))<|fim_prefix|># repo: VAR-solutions/Algorithms path: /Greedy Algorith... | code_fim | medium | {
"lang": "python",
"repo": "VAR-solutions/Algorithms",
"path": "/Greedy Algorithms/Largest Number/largest_number.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: VAR-solutions/Algorithms path: /Greedy Algorithms/Largest Number/largest_number.py
def largest_number(a):
res = ""
while a:
max_index = 0
for i in range(1, len(a)):
<|fim_suffix|>+= str(a[max_index])
del a[max_index]
return res
if __name__ == '__ma... | code_fim | medium | {
"lang": "python",
"repo": "VAR-solutions/Algorithms",
"path": "/Greedy Algorithms/Largest Number/largest_number.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> assert(self.history.shape == (self.steps, self.dim))
return rtn
# need to override in derived classes
def _step(self, whence):
"""
Perform one step of the algorithm, starting from the point "whence".
This must be overridden by specific walk implementation... | code_fim | hard | {
"lang": "python",
"repo": "dawsboss/convexgeometry",
"path": "/convexgeometry/walks.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: dawsboss/convexgeometry path: /convexgeometry/walks.py
import numpy as np
import numpy.linalg as la
def angle2vec(theta):
return np.array([np.cos(theta), np.sin(theta)])
class RandomWalk:
def __init__(self, memberfunc, start, space=1, *args, **kwargs):
"""
Create the wal... | code_fim | hard | {
"lang": "python",
"repo": "dawsboss/convexgeometry",
"path": "/convexgeometry/walks.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: brainma/ASRNet path: /run_test.py
import argparse
import os
import sys
import re
import cv2
import pydicom as pyd
import torch
import torch.nn as nn
import numpy as np
from math import log10, sqrt
from skimage.measure import compare_ssim
parser = argparse.ArgumentParser()
parser.add_argument("-... | code_fim | hard | {
"lang": "python",
"repo": "brainma/ASRNet",
"path": "/run_test.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> files = os.listdir(outPath)
pre_num = 0
psnr_case = float(0)
ssim_case = float(0)
ie_case = float(0)
print(outPath)
for file in files:
m = predictedFilePattern.match(file)
if m:
predicted_img_path = os.path.join(outPath, file)
label_img_... | code_fim | hard | {
"lang": "python",
"repo": "brainma/ASRNet",
"path": "/run_test.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> else:
continue
print('Case Num: ' + folder)
psnr_case = psnr_case/float(pre_num)
ie_case = ie_case/float(pre_num)
ssim_case = ssim_case/float(pre_num)
print(str(folder) + '\t' + str(psnr_case) + '\t' + str(ie_case) + '\t' + str(ssim_case))
psnr_total += ps... | code_fim | hard | {
"lang": "python",
"repo": "brainma/ASRNet",
"path": "/run_test.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> parent_frame = rcall("sys.frame", -1)
things = [
"import",
"import_builtins",
"py_call",
"py_copy",
"py_eval",
"py_get_attr",
"py_get_item",
"py_object",
"py_set_attr",
"... | code_fim | hard | {
"lang": "python",
"repo": "randy3k/rchitect",
"path": "/rchitect/py_tools.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> return robject("function", x, **kwargs)
e = new_env(parent=lib.R_GlobalEnv)
kwarg = {"rchitect.py_tools": e}
rcall(("base", "options"), **kwarg)
assign("import", _rfunction(py_import, convert=False), e)
assign("import_builtins", _rfunction(py_import_builtins, convert=False), ... | code_fim | hard | {
"lang": "python",
"repo": "randy3k/rchitect",
"path": "/rchitect/py_tools.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: randy3k/rchitect path: /rchitect/py_tools.py
from __future__ import unicode_literals, absolute_import
from rchitect._cffi import lib
import operator
import sys
import importlib
from six import text_type
from types import ModuleType
from .interface import rcopy, robject, rcall_p, rcall, sexp, se... | code_fim | hard | {
"lang": "python",
"repo": "randy3k/rchitect",
"path": "/rchitect/py_tools.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> suite = unittest.TestSuite()
if sys.version_info >= (2,4):
suite.addTests([make_doctest('test_clean.txt')])
if LIBXML_VERSION >= (2,6,31):
suite.addTests([make_doctest('test_clean_embed.txt')])
return suite<|fim_prefix|># repo: sarvex/hue path: /desktop/core/ext-py... | code_fim | easy | {
"lang": "python",
"repo": "sarvex/hue",
"path": "/desktop/core/ext-py/lxml/src/lxml/html/tests/test_clean.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: sarvex/hue path: /desktop/core/ext-py/lxml/src/lxml/html/tests/test_clean.py
import unittest, sys
from lxml.tests.common_imports import make_doctest
from lxml.etree import LIBXML_VERSION
<|fim_suffix|> suite = unittest.TestSuite()
if sys.version_info >= (2,4):
suite.addTests([make... | code_fim | easy | {
"lang": "python",
"repo": "sarvex/hue",
"path": "/desktop/core/ext-py/lxml/src/lxml/html/tests/test_clean.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: gabbpuy/vindauga path: /vindauga/menus/menu_popup.py
# -*- coding: utf-8 -*-
import logging
from vindauga.constants.event_codes import evKeyDown, evCommand
from vindauga.misc.character_codes import getCtrlChar, getAltChar
from .menu_box import MenuBox
logger = logging.getLogger(__name__)
cla... | code_fim | hard | {
"lang": "python",
"repo": "gabbpuy/vindauga",
"path": "/vindauga/menus/menu_popup.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> if item and self.commandEnabled(item.command):
event.what = evCommand
event.message.command = item.command
event.message.infoPtr = None
self.putEvent(event)
self.clearEvent(event)
elif getAltChar(event.... | code_fim | hard | {
"lang": "python",
"repo": "gabbpuy/vindauga",
"path": "/vindauga/menus/menu_popup.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> validator = DateRequired()
class TestYearSpec:
year = None
mock_form = Mock(spec=TestYearSpec)
mock_form.year.data = ''
mock_field = Mock()
with self.assertRaises(StopValidation) as ite:
validator(mock_form, mock_field)
se... | code_fim | hard | {
"lang": "python",
"repo": "ONSdigital/eq-survey-runner",
"path": "/tests/app/validation/test_date_required.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ONSdigital/eq-survey-runner path: /tests/app/validation/test_date_required.py
import unittest
from unittest.mock import Mock
from wtforms.validators import StopValidation
from app.validation.error_messages import error_messages
from app.validation.validators import DateRequired
class TestDateR... | code_fim | hard | {
"lang": "python",
"repo": "ONSdigital/eq-survey-runner",
"path": "/tests/app/validation/test_date_required.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
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