text stringlengths 232 16.3k | domain stringclasses 1
value | difficulty stringclasses 3
values | meta dict |
|---|---|---|---|
<|fim_suffix|>def _determines_copyright_dates() -> str:
"""Determines the years the copyright is in use for."""
this_year = datetime.now().year
copyright_start_date = configuration.get_value(ConfigurationVariable.COPYRIGHT_START_DATE)
return _to_copyright_date_string(copyright_start_date, this_year)
d... | code_fim | hard | {
"lang": "python",
"repo": "urutva/mbed-tools-ci-scripts",
"path": "/mbed_tools_ci_scripts/license_files.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> with codecs.open(args.input, "r", encoding='utf-8') as fin:
for line in fin:
if line == "" or line == "\n":
continue
else:
info = line.strip().split("\t")
gold_data = info[6]
pred_data = info[5]
... | code_fim | hard | {
"lang": "python",
"repo": "Aditi138/NeuralFactorGraph",
"path": "/evaluateNRF.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Aditi138/NeuralFactorGraph path: /evaluateNRF.py
import argparse, codecs
def manipulate_data(golds, hyps):
# log.info("Lemma acc, Lemma Levenshtein, morph acc, morph F1")
count = 0
morph_acc = 0
f1_precision_scores = 0
f1_precision_counts = 0
f1_recall_scores = 0
f1... | code_fim | hard | {
"lang": "python",
"repo": "Aditi138/NeuralFactorGraph",
"path": "/evaluateNRF.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: alipay/alipay-sdk-python-all path: /alipay/aop/api/domain/ShopDataDetail.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import json
from alipay.aop.api.constant.ParamConstants import *
from alipay.aop.api.domain.ShopScoreResultInfo import ShopScoreResultInfo
class ShopDataDetail(object):
... | code_fim | hard | {
"lang": "python",
"repo": "alipay/alipay-sdk-python-all",
"path": "/alipay/aop/api/domain/ShopDataDetail.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def to_alipay_dict(self):
params = dict()
if self.city_name:
if hasattr(self.city_name, 'to_alipay_dict'):
params['city_name'] = self.city_name.to_alipay_dict()
else:
params['city_name'] = self.city_name
if self.county_na... | code_fim | hard | {
"lang": "python",
"repo": "alipay/alipay-sdk-python-all",
"path": "/alipay/aop/api/domain/ShopDataDetail.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>
#3.2 Linear SVC
TARGET = "Linear_SVC"
FrovedisServer.initialize("mpirun -np 8 " + os.environ["FROVEDIS_SERVER"])
f_est = frovSVC(loss='hinge', max_iter=10000)
E_NM = TARGET + "_frovedis_" + frovedis.__version__
f_report = evaluate(f_est, E_NM, x_train, y_train, x_test, y_test)
f_est.release()
FrovedisSer... | code_fim | hard | {
"lang": "python",
"repo": "frovedis/frovedis",
"path": "/doc/notebook/python/01_1_ctr_binary_classification_sparse.py",
"mode": "spm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|>train_time = []
test_time = []
accuracy = []
precision = []
recall = []
f1 = []
estimator_name = []
def evaluate(estimator, estimator_nm,
x_train, y_train,
x_test, y_test):
'''
To generate performance report for both frovedis and sklearn estimators
'''
estimator_... | code_fim | hard | {
"lang": "python",
"repo": "frovedis/frovedis",
"path": "/doc/notebook/python/01_1_ctr_binary_classification_sparse.py",
"mode": "spm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: frovedis/frovedis path: /doc/notebook/python/01_1_ctr_binary_classification_sparse.py
#1. Description
'''
Click though rate prediction using logistic regression.
Please download the data from https://www.kaggle.com/c/avazu-ctr-prediction/
manually (registration required) and place `CTR_train` fi... | code_fim | hard | {
"lang": "python",
"repo": "frovedis/frovedis",
"path": "/doc/notebook/python/01_1_ctr_binary_classification_sparse.py",
"mode": "psm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|>
# Convert Jupyter notebooks to HTML
files = getfiles('.ipynb')
for f in files:
os.system(f"jupyter nbconvert --to html {f}")
# Move notebooks to docs/
os.system('mv *.html docs/.')
os.chdir('docs')
# Make index page.
header = """
---
title: Data Testing Tutorial
---
This contains static HTML vers... | code_fim | medium | {
"lang": "python",
"repo": "pydatawrangler/data-testing-tutorial",
"path": "/makedocs.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: pydatawrangler/data-testing-tutorial path: /makedocs.py
import os
def getfiles(extension):
return [f for f in os.listdir() if f.endswith(extension)]
def writefile(handle, string):
with open(handle, 'w+') as f:
f.write(string)
<|fim_suffix|># Make index page.
header = """
---
... | code_fim | hard | {
"lang": "python",
"repo": "pydatawrangler/data-testing-tutorial",
"path": "/makedocs.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> gt_match_indices = [torch.tensor([0, 1])]
proposal_list = [
torch.Tensor([[23.6667, 23.8757, 228.6326, 153.8874],
[23.6667, 23.8757, 228.6326, 153.8874]])
]
gt_bboxes = [
torch.Tensor([[23.6667, 23.8757, 228.6326, 153.8874],
[23.6... | code_fim | hard | {
"lang": "python",
"repo": "open-mmlab/mmtracking",
"path": "/tests/test_models/test_track_heads/test_quasi_dense_embed_head.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: open-mmlab/mmtracking path: /tests/test_models/test_track_heads/test_quasi_dense_embed_head.py
# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmdet.core import build_assigner, build_sampler
from mmtrack.models.track_heads import QuasiDenseEmbedHead
def test_quasi... | code_fim | hard | {
"lang": "python",
"repo": "open-mmlab/mmtracking",
"path": "/tests/test_models/test_track_heads/test_quasi_dense_embed_head.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> """Create sample results that can be passed to Head.get_targets."""
num_imgs = len(proposal_list)
assign_config = dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
ignore_iof_thr=-1)
sampler_config = dict(
type='... | code_fim | hard | {
"lang": "python",
"repo": "open-mmlab/mmtracking",
"path": "/tests/test_models/test_track_heads/test_quasi_dense_embed_head.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> return super(list_hardware, self).dispatch(request, *args, **kwargs)
###############################################
@login_required
def new_contact(request):
title = 'New Contact'
form = contactForm(request.POST or None)
if request.POST:
form = contactForm(request.POST)... | code_fim | hard | {
"lang": "python",
"repo": "danteio/Dante-Dev",
"path": "/atlas/views.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: danteio/Dante-Dev path: /atlas/views.py
from django.shortcuts import render, render_to_response, get_object_or_404, redirect
from django.core.paginator import Paginator, EmptyPage, PageNotAnInteger
from django.contrib.auth.decorators import login_required
from django.conf import settings
from dj... | code_fim | hard | {
"lang": "python",
"repo": "danteio/Dante-Dev",
"path": "/atlas/views.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> context['page_items'] = obj_z
return context
@method_decorator(login_required)
def dispatch(self, request, *args, **kwargs):
return super(list_airbill, self).dispatch(request, *args, **kwargs)
###############################################
@login_required
def new_pool(... | code_fim | hard | {
"lang": "python",
"repo": "danteio/Dante-Dev",
"path": "/atlas/views.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: haiiliin/pyabaqus path: /src/abaqus/Odb/OdbPretensionSection.py
from .OdbMeshNode import OdbMeshNode
from .OdbSet import OdbSet
class OdbPretensionSection:
"""The pretension section object is used to define an assembly load. It associates a
pretension node with a pretension section.
... | code_fim | medium | {
"lang": "python",
"repo": "haiiliin/pyabaqus",
"path": "/src/abaqus/Odb/OdbPretensionSection.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # An OdbSet object specifying the surface set that defines the pretension section.
surface: OdbSet = OdbSet("set", tuple[OdbMeshNode]())
# A tuple of Floats specifying the components of the normal to the pretension section.
normal: float = None<|fim_prefix|># repo: haiiliin/pyabaqus path... | code_fim | hard | {
"lang": "python",
"repo": "haiiliin/pyabaqus",
"path": "/src/abaqus/Odb/OdbPretensionSection.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # An OdbSet object specifying the element set that defines the pretension section.
element: OdbSet = OdbSet("set", tuple[OdbMeshNode]())
# An OdbSet object specifying the surface set that defines the pretension section.
surface: OdbSet = OdbSet("set", tuple[OdbMeshNode]())
# A tuple ... | code_fim | hard | {
"lang": "python",
"repo": "haiiliin/pyabaqus",
"path": "/src/abaqus/Odb/OdbPretensionSection.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: tractiming/trac-gae path: /apps/notifications/decorators.py
from functools import wraps
from django.conf import settings
try:
from google.appengine.api import taskqueue
except ImportError:
taskqueue = None
<|fim_suffix|>def do_maybe_notification(func):
"""Wrap a method that returns ... | code_fim | medium | {
"lang": "python",
"repo": "tractiming/trac-gae",
"path": "/apps/notifications/decorators.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> """Wrap a method that returns a serialized list of splits and send updates
to the notification task queue.
"""
@wraps(func)
def send_notification(*args, **kwargs):
resp = func(*args, **kwargs)
if settings.ENABLE_NOTIFICATIONS and taskqueue is not None:
data ... | code_fim | medium | {
"lang": "python",
"repo": "tractiming/trac-gae",
"path": "/apps/notifications/decorators.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: NukeA/deep-learning-from-scratch-3 path: /dezero/functions.py
=============================================================================
# Basic functions: sin / cos / tanh / exp / log
# =============================================================================
class Sin(Function):
def ... | code_fim | hard | {
"lang": "python",
"repo": "NukeA/deep-learning-from-scratch-3",
"path": "/dezero/functions.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: NukeA/deep-learning-from-scratch-3 path: /dezero/functions.py
ezero import cuda, utils
from dezero.core import Function, Variable, as_variable, as_array
# =============================================================================
# Basic functions: sin / cos / tanh / exp / log
# ============... | code_fim | hard | {
"lang": "python",
"repo": "NukeA/deep-learning-from-scratch-3",
"path": "/dezero/functions.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>class Sigmoid(Function):
def forward(self, x):
xp = cuda.get_array_module(x)
# y = 1 / (1 + xp.exp(-x))
y = xp.tanh(x * 0.5) * 0.5 + 0.5 # Better implementation
return y
def backward(self, gy):
y = self.outputs[0]()
gx = gy * y * (1 - y)
re... | code_fim | hard | {
"lang": "python",
"repo": "NukeA/deep-learning-from-scratch-3",
"path": "/dezero/functions.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> """Save the object to the database immediately."""
db.session.add(self)
db.session.commit()
@classmethod
def find_by_id(cls, request_tracker_id: int) -> RequestTracker:
"""Return the request tracker matching the id."""
request_tracker = None
if requ... | code_fim | hard | {
"lang": "python",
"repo": "bcgov/lear",
"path": "/legal-api/src/legal_api/models/request_tracker.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: bcgov/lear path: /legal-api/src/legal_api/models/request_tracker.py
# Copyright © 2022 Province of British Columbia
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# ... | code_fim | hard | {
"lang": "python",
"repo": "bcgov/lear",
"path": "/legal-api/src/legal_api/models/request_tracker.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> provider_class = config['provider']['class']
module_name, class_name = provider_class.rsplit('.', 1)
__import__(module_name)
module = sys.modules[module_name]
clazz = getattr(module, class_name)
if not service_type:
# if there is only one section (other then "provider")... | code_fim | hard | {
"lang": "python",
"repo": "benchmarking-suite/benchsuite-core",
"path": "/src/benchsuite/core/model/provider.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: benchmarking-suite/benchsuite-core path: /src/benchsuite/core/model/provider.py
# Benchmarking Suite
# Copyright 2014-2017 Engineering Ingegneria Informatica S.p.A.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the Licens... | code_fim | hard | {
"lang": "python",
"repo": "benchmarking-suite/benchsuite-core",
"path": "/src/benchsuite/core/model/provider.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: eferm/aoc-2020 path: /aoc_2020/day_08.py
from operator import add, sub
from _utils import *
inp = get_input(2020, 8)
tape = inp.strip().split("\n")
def step(i, acc):
op = {"+": add, "-": sub}
instr, arg = tape[i].split()
sign, num = arg[:1], arg[1:]
if instr == "nop":
... | code_fim | medium | {
"lang": "python",
"repo": "eferm/aoc-2020",
"path": "/aoc_2020/day_08.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def swaps(tape):
for i in range(len(tape)):
tape_ = list(tape)
t = tape_.pop(i)
instr, arg = t.split()
if instr == "jmp":
t = f"nop {arg}"
elif instr == "nop":
t = f"jmp {arg}"
tape_.insert(i, t)
yield tape_
for tape in... | code_fim | hard | {
"lang": "python",
"repo": "eferm/aoc-2020",
"path": "/aoc_2020/day_08.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>register = Library()
register.tag('food_network', do_get_food_network)<|fim_prefix|># repo: bhaugen/foodnetwork path: /distribution/templatetags/foodnetwork_tags.py
from django.template import Library, Node
from distribution.models import FoodNetwork
class FoodNet(Node):
def render(self, conte... | code_fim | medium | {
"lang": "python",
"repo": "bhaugen/foodnetwork",
"path": "/distribution/templatetags/foodnetwork_tags.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: bhaugen/foodnetwork path: /distribution/templatetags/foodnetwork_tags.py
from django.template import Library, Node
from distribution.models import FoodNetwork
<|fim_suffix|>register = Library()
register.tag('food_network', do_get_food_network)<|fim_middle|>class FoodNet(Node):
def rend... | code_fim | hard | {
"lang": "python",
"repo": "bhaugen/foodnetwork",
"path": "/distribution/templatetags/foodnetwork_tags.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def user_get(trans_id):
request_url = "https://aip.baidubce.com/rest/2.0/face/v3/faceset/face/getlist"
params = "{\"user_id\":\"" + str(trans_id) + "\",\"group_id\":\"students\"}"
access_token = get_at(ak, sk)
request_url = request_url + "?access_token=" + access_token
headers = {'con... | code_fim | hard | {
"lang": "python",
"repo": "Xchkoo/student_system_desktop",
"path": "/app_mask/face_detect.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> user_face_token = user_get(trans_id)
if user_face_token == -1:
return {"msg": "FAIL"}
access_token = get_at(ak, sk)
request_url = "https://aip.baidubce.com/rest/2.0/face/v3/faceset/face/delete"
params = "{\"user_id\":\""+str(trans_id)+"\",\"group_id\":\"students\",\"face_token\... | code_fim | hard | {
"lang": "python",
"repo": "Xchkoo/student_system_desktop",
"path": "/app_mask/face_detect.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Xchkoo/student_system_desktop path: /app_mask/face_detect.py
import requests
import base64
ak = 'QsPqs20yfvQ7QcdnYfdWC5Ei'
sk = 'EEMdjil0u1CW5uI3ts1mLD0VCQvTGYs6'
def get_at(api_key, secret_key):
host = "https://aip.baidubce.com/oauth/2.0/token?grant_type=client_credentials&client_id=" + a... | code_fim | hard | {
"lang": "python",
"repo": "Xchkoo/student_system_desktop",
"path": "/app_mask/face_detect.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>"PostForcastInteractor",
"GetPrizeInteractor",
"CreateUserInteractor",
"GetUserInteractor",
]<|fim_prefix|># repo: ojos/python-devenv path: /src/fastapi/src/usecase/interactor/__init__.py
from .forcast import PostForcastInteractor
from .prize import GetPrizeInteractor
from .rdb import Create<... | code_fim | hard | {
"lang": "python",
"repo": "ojos/python-devenv",
"path": "/src/fastapi/src/usecase/interactor/__init__.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ojos/python-devenv path: /src/fastapi/src/usecase/interactor/__init__.py
from .forcast import PostForcastInteractor
from .prize import GetPrizeInteractor
from .rdb import CreateTableInteractor, DropTableInteractor, ITableInteractor
from .user import CreateUserInteractor, GetUserInt<|fim_suffix|>"... | code_fim | medium | {
"lang": "python",
"repo": "ojos/python-devenv",
"path": "/src/fastapi/src/usecase/interactor/__init__.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: gitCommitWiL/ChatterBot path: /tests/training/test_training.py
from tests.base_case import ChatBotTestCase
from chatterbot.trainers import Trainer
from chatterbot.conversation import Statement
<|fim_suffix|> self.assertEqual(
[['Hello, how are you?', 'I am good.']], data
... | code_fim | hard | {
"lang": "python",
"repo": "gitCommitWiL/ChatterBot",
"path": "/tests/training/test_training.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> def test_generate_export_data(self):
self.chatbot.storage.create_many([
Statement(text='Hello, how are you?'),
Statement(text='I am good.', in_response_to='Hello, how are you?')
])
data = self.trainer._generate_export_data()
self.assertEqual(
... | code_fim | hard | {
"lang": "python",
"repo": "gitCommitWiL/ChatterBot",
"path": "/tests/training/test_training.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> def _read(self):
return TCPStreamReceiver.read(self)
def _release(self) -> None:
TCPStreamReceiver.release(self)<|fim_prefix|># repo: GreenBlitz/GBVision path: /gbvision/utils/net/async_tcp_stream_receiver.py
from .async_stream_receiver import AsyncStreamReceiver
from .tcp_stream... | code_fim | hard | {
"lang": "python",
"repo": "GreenBlitz/GBVision",
"path": "/gbvision/utils/net/async_tcp_stream_receiver.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> TCPStreamReceiver.__init__(self, ip, port, *args, **kwargs)
AsyncStreamReceiver.__init__(self, *args, **kwargs)
def _read(self):
return TCPStreamReceiver.read(self)
def _release(self) -> None:
TCPStreamReceiver.release(self)<|fim_prefix|># repo: GreenBlitz/GBVisio... | code_fim | medium | {
"lang": "python",
"repo": "GreenBlitz/GBVision",
"path": "/gbvision/utils/net/async_tcp_stream_receiver.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: GreenBlitz/GBVision path: /gbvision/utils/net/async_tcp_stream_receiver.py
from .async_stream_receiver import AsyncStreamReceiver
from .tcp_stream_receiver import TCPStreamReceiver
class AsyncTCPStreamReceiver(AsyncStreamReceiver, TCPStreamReceiver):
<|fim_suffix|> def _read(self):
... | code_fim | medium | {
"lang": "python",
"repo": "GreenBlitz/GBVision",
"path": "/gbvision/utils/net/async_tcp_stream_receiver.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: vivekmumbles/coding-challenges path: /didi/goldman/goldman.py
import sys, math
nums = map(int, sys.stdin.readlines()[1:])
gauss = lambda x: (x/2.0)*(1+x)
total = gauss(len(nums)-1)
a = max(nums)
nums.remove(a)
b = max(nums)
nums.remove(b)
<|fim_suffix|>shit_fmt = lambda x: math.floor(x*100.0)/... | code_fim | medium | {
"lang": "python",
"repo": "vivekmumbles/coding-challenges",
"path": "/didi/goldman/goldman.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>shit_fmt = lambda x: math.floor(x*100.0)/100.0 # b/c hackerrank is dumb.
print '{:.2f}'.format(shit_fmt(cnt/total))<|fim_prefix|># repo: vivekmumbles/coding-challenges path: /didi/goldman/goldman.py
import sys, math
nums = map(int, sys.stdin.readlines()[1:])
gauss = lambda x: (x/2.0)*(1+x)
total = gaus... | code_fim | medium | {
"lang": "python",
"repo": "vivekmumbles/coding-challenges",
"path": "/didi/goldman/goldman.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # merge t_scopes
scope = self.scope
card = self.card
for t_scope, t_card in zip(other.scope, other.card):
try:
scope.index(t_scope)
except:
scope.append(t_scope)
card.append(t_card)
# algo... | code_fim | hard | {
"lang": "python",
"repo": "Anaphory/libpgm",
"path": "/libpgm/tablecpdfactor.py",
"mode": "spm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Anaphory/libpgm path: /libpgm/tablecpdfactor.py
# Copyright (c) 2012, CyberPoint International, LLC
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistributions ... | code_fim | hard | {
"lang": "python",
"repo": "Anaphory/libpgm",
"path": "/libpgm/tablecpdfactor.py",
"mode": "psm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> if '{{' in line and '}}' in line and not ('#' in line and line.index('#') < line.index('{{')):
begin = line.index('{{')
end = line.index('}}', begin)
variable_name = line[begin:end].strip().replace('{{','').replace('}}','').strip()
try:
... | code_fim | hard | {
"lang": "python",
"repo": "brlrt/instagram-botnet",
"path": "/src/instabotnet/populate.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: brlrt/instagram-botnet path: /src/instabotnet/populate.py
from colorama import init, Fore
import os
from string import Formatter
import random
from .support import merge
def get_field_value(field_name, mapping):
try:
def recursive_get(field_name, mapping):
if '.' not... | code_fim | hard | {
"lang": "python",
"repo": "brlrt/instagram-botnet",
"path": "/src/instabotnet/populate.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: kfirstri/demisto-sdk path: /demisto_sdk/commands/common/tests/incident_field_test.py
import pytest
from demisto_sdk.commands.common.hook_validations.incident_field import (
GroupFieldTypes, IncidentFieldValidator)
from demisto_sdk.commands.common.hook_validations.structure import \
Struct... | code_fim | hard | {
"lang": "python",
"repo": "kfirstri/demisto-sdk",
"path": "/demisto_sdk/commands/common/tests/incident_field_test.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> current_file = {"cliName": cliname, "group": group}
with patch.object(StructureValidator, '__init__', lambda a, b: None):
structure = StructureValidator("")
structure.current_file = current_file
structure.old_file = None
structure.file_path =... | code_fim | hard | {
"lang": "python",
"repo": "kfirstri/demisto-sdk",
"path": "/demisto_sdk/commands/common/tests/incident_field_test.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: cash2one/xai path: /xai/brain/wordbase/nouns/_microphone.py
#calss header
class _MICROPHONE():
def __init__(self,):
<|fim_suffix|> self.parents = []
self.childen = []
self.properties = []
self.jsondata = {}
self.specie = 'nouns'
def run(self, obj1 = [], obj2 = []):
return self... | code_fim | medium | {
"lang": "python",
"repo": "cash2one/xai",
"path": "/xai/brain/wordbase/nouns/_microphone.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def pltDataFrame(df):
fig, axes = plt.subplots(nrows=2, ncols=4)
fig.set(alpha=0.2)
ans1onli = df.liNum[df["class"] == 1].value_counts()
ans0onli = df.liNum[df["class"] == 0].value_counts()
DataFrame({u'回答':ans1onli,
u'未回答':ans0onli}) \
.plot(kind='bar', stacked=Fa... | code_fim | hard | {
"lang": "python",
"repo": "bryandsy/ss_homework",
"path": "/MachineLearning/sshomework_stag1.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
# ans0onTags = df.popTagsNum[df["class"] == 0].value_counts()
# ans1onTags = df.popTagsNum[df["class"] == 1].value_counts()
# DataFrame({u'回答':ans1onTags,
# u'未回答':ans0onTags}) \
# .plot(kind='bar', stacked=False,
# ax=plt.subplot2grid((2,4),(1,0), colspan=2))
# ... | code_fim | hard | {
"lang": "python",
"repo": "bryandsy/ss_homework",
"path": "/MachineLearning/sshomework_stag1.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: bryandsy/ss_homework path: /MachineLearning/sshomework_stag1.py
# -*- coding: utf-8 -*-
"""
Created on Tue Jan 12 14:44:08 2016
@author: Jonater
"""
import math
import random
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib as mpl
im... | code_fim | hard | {
"lang": "python",
"repo": "bryandsy/ss_homework",
"path": "/MachineLearning/sshomework_stag1.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: heroku/fernet-py path: /fernet/fernet.py
__author__ = 'spersinger'
from .generator import Generator
from .verifier import Verifier
def generate(secret = None, message="", iv=None, now=None):
"""Public: generates a fernet token
Returns the fernet token as a string.
:param secret
... | code_fim | medium | {
"lang": "python",
"repo": "heroku/fernet-py",
"path": "/fernet/fernet.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> """Public: generates a fernet token
Returns the fernet token as a string.
:param secret
:param message
:param options
"""
return Generator(secret=secret, message=message, iv=iv, now=now).generate()
def verifier(secret, token, enforce_ttl=None, ttl=None, now=None):
return V... | code_fim | medium | {
"lang": "python",
"repo": "heroku/fernet-py",
"path": "/fernet/fernet.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> return Verifier(secret=secret, token=token, enforce_ttl = enforce_ttl, ttl=ttl, now=now)<|fim_prefix|># repo: heroku/fernet-py path: /fernet/fernet.py
__author__ = 'spersinger'
from .generator import Generator
from .verifier import Verifier
def generate(secret = None, message="", iv=None, now=None)... | code_fim | medium | {
"lang": "python",
"repo": "heroku/fernet-py",
"path": "/fernet/fernet.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>e = 'uploadPaperToChatGroup.html')),
path('getChatGroupName', views.getChatGroupName),
path('myChatGroupList.html', TemplateView.as_view(template_name = 'myChatGroupList.html')),
path('createChatGroup.html', TemplateView.as_view(template_name = 'createChatGroup.html')),
path('annotation-no... | code_fim | hard | {
"lang": "python",
"repo": "wxzsan/pQper",
"path": "/chatgroup/urls.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: wxzsan/pQper path: /chatgroup/urls.py
from django.urls import path
from . import views
from django.views.generic.base import TemplateView
urlpatterns = {
path('add_annotation', views.add_annotation),
path('getChatGroupPapers', views.getChatGroupPapers),
path('getChatGroupMembers', vi... | code_fim | hard | {
"lang": "python",
"repo": "wxzsan/pQper",
"path": "/chatgroup/urls.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>pdf.html', TemplateView.as_view(template_name = 'showpdf.html')),
path('memberInGroupPage.html', TemplateView.as_view(template_name = 'memberInGroupPage.html')),
path('singleGroupPage.html', TemplateView.as_view(template_name = 'singleGroupPage.html')),
path('uploadPaperToChatGroup.html', Temp... | code_fim | hard | {
"lang": "python",
"repo": "wxzsan/pQper",
"path": "/chatgroup/urls.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> rmin = r.min()
rmax = r.max()
ravg = r.mean()
rmed = np.median(r)
print ' '
print 'Minimum r-value = ', rmin
print 'Maximum r-value = ', rmax
print 'Mean r-value = ', ravg
print 'Median r-value = ', rmed
return r<|fim_prefix|># repo: gorsol/Pyroms-1 path: /p... | code_fim | hard | {
"lang": "python",
"repo": "gorsol/Pyroms-1",
"path": "/pyroms_toolbox/pyroms_toolbox/rfactor.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: gorsol/Pyroms-1 path: /pyroms_toolbox/pyroms_toolbox/rfactor.py
def rfactor(h,rmask):
"""
function r = rfactor(h,rmask)
This function computes the bathymetry slope from a SCRUM NetCDF file.
On Input:
h bathymetry at RHO-points.
rmask Land/Sea maski... | code_fim | medium | {
"lang": "python",
"repo": "gorsol/Pyroms-1",
"path": "/pyroms_toolbox/pyroms_toolbox/rfactor.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: vlandeiro/yamlett path: /yamlett/artifact.py
from typing import Any, Dict, Optional
import cloudpickle as pickle
from cloudpathlib import AnyPath
class Artifact:
MAGIC_KEY = "__yamlett_artifact__"
<|fim_suffix|> def load(self):
filepath = self.path.joinpath(f"{self.key}.pkl")
... | code_fim | hard | {
"lang": "python",
"repo": "vlandeiro/yamlett",
"path": "/yamlett/artifact.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> filepath = self.path.joinpath(f"{self.key}.pkl")
with filepath.open("rb") as fd:
return pickle.load(fd)
def save(self):
if self.value is not None:
filepath = self.path.joinpath(f"{self.key}.pkl")
with filepath.open("wb") as fd:
... | code_fim | hard | {
"lang": "python",
"repo": "vlandeiro/yamlett",
"path": "/yamlett/artifact.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> self.path = path
self.key = key
self.value = value
@staticmethod
def is_artifact(d: Dict):
if isinstance(d, dict):
return d.get(Artifact.MAGIC_KEY, False)
return False
def load(self):
filepath = self.path.joinpath(f"{self.key}.pkl")... | code_fim | hard | {
"lang": "python",
"repo": "vlandeiro/yamlett",
"path": "/yamlett/artifact.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|># outputs:
ana.registerFile( mergedBamKey, 'galaxyOutput',galaxyOutMergedBam)
resultsDir = ana.resultsDir(galaxyPath) # prefers nonGalaxyInput location over settings loc
ana.createOutFile(mergedBamKey,'nonGalaxyOutput','%s_%s_merged',ext='bam', \
input1=bamAkey, input2=bamBkey)
# Est... | code_fim | hard | {
"lang": "python",
"repo": "ENCODE-DCC/uniformAnalysis",
"path": "/src/galaxy/mergeBamsE3.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ENCODE-DCC/uniformAnalysis path: /src/galaxy/mergeBamsE3.py
#!/usr/bin/env python2.7
# mergeBamsE3.py ENCODE3 galaxy pipeline script for merging 2 bam replicates
# Must run from within galaxy sub-directory. Requires settingsE3.txt in same directory as script
#
# Usage: python(2.7) mergeBamsE3,p... | code_fim | medium | {
"lang": "python",
"repo": "ENCODE-DCC/uniformAnalysis",
"path": "/src/galaxy/mergeBamsE3.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: zielmicha/cloudrun-client path: /cloudrun/common.py
import requests, ssl, requests.adapters, socket, json
from requests.packages.urllib3.poolmanager import PoolManager
class HostNameIgnoringAdapter(requests.adapters.HTTPAdapter):
def init_poolmanager(self, connections, maxsize, block=False):... | code_fim | hard | {
"lang": "python",
"repo": "zielmicha/cloudrun-client",
"path": "/cloudrun/common.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> try:
while True:
data = sock1.recv(40960)
if not data: break
sock2.sendall(data)
except IOError as err:
print(err)<|fim_prefix|># repo: zielmicha/cloudrun-client path: /cloudrun/common.py
import requests, ssl, requests.adapters, socket, json
fro... | code_fim | hard | {
"lang": "python",
"repo": "zielmicha/cloudrun-client",
"path": "/cloudrun/common.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: taoky/gadgets path: /backTCP/testch.py
#!/usr/bin/python3
# Powered by FJW!
import sys
import os
import argparse
import random
import threading
import backTCP
from utils import *
# Actions: What to do for a stream of incoming packets
# 0: Do nothing and forward
# 1: Drop unless retransmit... | code_fim | hard | {
"lang": "python",
"repo": "taoky/gadgets",
"path": "/backTCP/testch.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> while True:
action = random.choice(ACTIONS)
log('debug', f"Action: {action}")
packet_needed = max(1, action)
packet_count = 0
while packet_count < packet_needed:
p = in_sock.recv()
if p is None:
# The last ones aren't man... | code_fim | hard | {
"lang": "python",
"repo": "taoky/gadgets",
"path": "/backTCP/testch.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> for p in packets:
out_sock.send(p)
packets = []
def parse_args():
parser = argparse.ArgumentParser(description="starts a backTCP test channel", epilog="This program is created by iBug")
parser.add_argument('-a', '--out-addr', '--address', metavar="addr", help="address... | code_fim | hard | {
"lang": "python",
"repo": "taoky/gadgets",
"path": "/backTCP/testch.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> for language_name in long_names:
truncated = language_name[:PREVIOUS_NAME_MAX_LENGTH]
try:
lang = Language.objects.get(name=truncated)
except Language.DoesNotExist:
pass
else:
lang.name = language_name
lang.save()
class ... | code_fim | hard | {
"lang": "python",
"repo": "pbanaszkiewicz/amy",
"path": "/amy/workshops/migrations/0139_fix_language_names.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # 1. (most inner) filter out non-language (sublanguages, dialects etc.)
# 2. (middle) apply ' '.join(language['Description']) and therefore make it
# a list of descriptions
# 3. (top) filter out shorter language names
long_names = filter(
lambda x: len(x) >= PREVIOUS_NAME_MA... | code_fim | medium | {
"lang": "python",
"repo": "pbanaszkiewicz/amy",
"path": "/amy/workshops/migrations/0139_fix_language_names.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: pbanaszkiewicz/amy path: /amy/workshops/migrations/0139_fix_language_names.py
# -*- coding: utf-8 -*-
# Generated by Django 1.9.12 on 2017-04-26 13:06
# Updated in Django 2.0.5 on 2018-06-02 11:15
from __future__ import unicode_literals
import json
from django.db import migrations, models
PREV... | code_fim | hard | {
"lang": "python",
"repo": "pbanaszkiewicz/amy",
"path": "/amy/workshops/migrations/0139_fix_language_names.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def get_queryset(self, request):
qs = super().get_queryset(request)
return qs.filter(parent__isnull=False)
class NavAdmin(nested_admin.NestedModelAdmin):
inlines = [
NavInline,
]
exclude = [
'parent',
]
list_display = (
'title',
'us... | code_fim | hard | {
"lang": "python",
"repo": "RoboLoCo-5338/website",
"path": "/blog/admin.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: RoboLoCo-5338/website path: /blog/admin.py
from django.contrib import admin
from .models.comment import Comment
from .models.post import Post
from .models.nav import Nav
from .models.files import Files
from .models.meeting import Meeting
from .models.member import Member
from .models.signin impo... | code_fim | hard | {
"lang": "python",
"repo": "RoboLoCo-5338/website",
"path": "/blog/admin.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>class MemberAdmin(admin.ModelAdmin):
inlines = [
SigninInline,
]
readonly_fields = ('hours', 'created', 'modified')
fields = ('user', 'team', 'name', 'slack', 'created', 'modified', 'hours')
list_display = (
'team',
'name',
'user',
'slack',
... | code_fim | hard | {
"lang": "python",
"repo": "RoboLoCo-5338/website",
"path": "/blog/admin.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: gridcoin-community/GRC-HUG-REST-API path: /WIP/extract.py
import gzip
import requests
import msgpack
from multiprocessing import Pool
import time
import xmltodict
WORKER_COUNT = 4 # Add CPUs & increase this value to supercharge processing downloaded
<|fim_suffix|>def download_extract_stats(proj... | code_fim | hard | {
"lang": "python",
"repo": "gridcoin-community/GRC-HUG-REST-API",
"path": "/WIP/extract.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # print("len: {}".format(len(file_content['users']['user'])))
pool = Pool(processes=WORKER_COUNT) # 4 workers
pool_xml_data = pool.map(extract_xml_step, file_content['users']['user']) # Deploy the pool workers
msg_packed_results = msgpack.packb(pool_xml_data, use_bin_type=... | code_fim | hard | {
"lang": "python",
"repo": "gridcoin-community/GRC-HUG-REST-API",
"path": "/WIP/extract.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ccampguilhem/Udacity-DataAnalyst path: /03-DataWranglingWithMongoDB/P02-WrangleOpenStreetMapData/completeness_audit.py
from utils import *
"""
Data completeness audit object in a form of a callback for SAX content handler.
This audit class checks compliance to gold standard. The nonconformities... | code_fim | hard | {
"lang": "python",
"repo": "ccampguilhem/Udacity-DataAnalyst",
"path": "/03-DataWranglingWithMongoDB/P02-WrangleOpenStreetMapData/completeness_audit.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> pass
"""
Method called back when an end event is encountered.
- name: element name
- children: element children
- locator: locator object from SAX parser
"""
def endEventCallback(self, name, children, locator):
#Find item with a tag child having amenit... | code_fim | hard | {
"lang": "python",
"repo": "ccampguilhem/Udacity-DataAnalyst",
"path": "/03-DataWranglingWithMongoDB/P02-WrangleOpenStreetMapData/completeness_audit.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def _config(self):
self.CLK_FREQ = Param(int(100e6))
def _declr(self):
addClkRstn(self)
super(SimpleIfStatementHls, self)._declr()
def _impl(self):
with Hls(self, freq=self.CLK_FREQ) as h:
io = h.io
a = io(self.a)
b = io(sel... | code_fim | medium | {
"lang": "python",
"repo": "daiwaka/hwtHls",
"path": "/hwtHls/tests/ifstm.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
if __name__ == "__main__": # alias python main function
from hwt.synthesizer.utils import toRtl
u = SimpleIfStatementHls()
p = VirtualHlsPlatform()
print(toRtl(u, targetPlatform=p))<|fim_prefix|># repo: daiwaka/hwtHls path: /hwtHls/tests/ifstm.py
#!/usr/bin/env python3
# -*- coding: ut... | code_fim | hard | {
"lang": "python",
"repo": "daiwaka/hwtHls",
"path": "/hwtHls/tests/ifstm.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: daiwaka/hwtHls path: /hwtHls/tests/ifstm.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from hwt.code import If
from hwt.interfaces.utils import addClkRstn
from hwt.synthesizer.param import Param
from hwtHls.hls import Hls
from hwtHls.platform.virtual import VirtualHlsPlatform
from hwtLib.sa... | code_fim | medium | {
"lang": "python",
"repo": "daiwaka/hwtHls",
"path": "/hwtHls/tests/ifstm.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
num_test_samples = 512
# evaluating on latest model
print("\nCalculating error over " + str(num_test_samples) + " test samples... (using latest model)")
predictions = model.predict_generator(generate_data(image_path, test, num_test_samples, patch_size), steps=1)
np.save("predictions_l... | code_fim | hard | {
"lang": "python",
"repo": "EdwardDixon/ml-and-security",
"path": "/transfer_student.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> model = Sequential()
model.add(conv_base)
model.add(Flatten())
model.add(Dense(32, activation='relu'))
model.add(BatchNormalization())
model.add(Dense(num_target_values, name="prediction"))
return (model)
def make_all_layers_trainable(mdl, is_trainable = True):
for layer... | code_fim | hard | {
"lang": "python",
"repo": "EdwardDixon/ml-and-security",
"path": "/transfer_student.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: EdwardDixon/ml-and-security path: /transfer_student.py
'''
Trains a simple convnet to recognise a smile
'''
from __future__ import print_function
from os.path import exists
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, BatchNormalization
fro... | code_fim | hard | {
"lang": "python",
"repo": "EdwardDixon/ml-and-security",
"path": "/transfer_student.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def send_post(submission, r2t):
total_size = clean_after_module()
r2t.send_text('Deleted: ' + str(round(total_size / (1024.0 ** 3), 3)) + 'GB.')
return SupplyResult.STOP_THIS_SUPPLY<|fim_prefix|># repo: Lambada10/reddit2telegram path: /reddit2telegram/channels/tech_cleaner/app.py
from utils ... | code_fim | easy | {
"lang": "python",
"repo": "Lambada10/reddit2telegram",
"path": "/reddit2telegram/channels/tech_cleaner/app.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> total_size = clean_after_module()
r2t.send_text('Deleted: ' + str(round(total_size / (1024.0 ** 3), 3)) + 'GB.')
return SupplyResult.STOP_THIS_SUPPLY<|fim_prefix|># repo: Lambada10/reddit2telegram path: /reddit2telegram/channels/tech_cleaner/app.py
from utils import SupplyResult, clean_after_... | code_fim | medium | {
"lang": "python",
"repo": "Lambada10/reddit2telegram",
"path": "/reddit2telegram/channels/tech_cleaner/app.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Lambada10/reddit2telegram path: /reddit2telegram/channels/tech_cleaner/app.py
from utils import SupplyResult, clean_after_module
from utils.tech import get_dev_channel
<|fim_suffix|>def send_post(submission, r2t):
total_size = clean_after_module()
r2t.send_text('Deleted: ' + str(round(t... | code_fim | easy | {
"lang": "python",
"repo": "Lambada10/reddit2telegram",
"path": "/reddit2telegram/channels/tech_cleaner/app.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def on_reduced_position(self, order):
self.take_profit = abs(self.position.qty), self.price
def go_short(self):
pass
def should_cancel_entry(self):
return False
def filters(self):
return []
def should_short(self):
return False<|fim_prefix|># ... | code_fim | hard | {
"lang": "python",
"repo": "jesse-ai/jesse",
"path": "/jesse/strategies/Test18/__init__.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def filters(self):
return []
def should_short(self):
return False<|fim_prefix|># repo: jesse-ai/jesse path: /jesse/strategies/Test18/__init__.py
from jesse.strategies import Strategy
# test_on_reduced_position
class Test18(Strategy):
def should_long(self):
<|fim_middle|> ... | code_fim | hard | {
"lang": "python",
"repo": "jesse-ai/jesse",
"path": "/jesse/strategies/Test18/__init__.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: jesse-ai/jesse path: /jesse/strategies/Test18/__init__.py
from jesse.strategies import Strategy
# test_on_reduced_position
class Test18(Strategy):
def should_long(self):
return self.price < 7
<|fim_suffix|> return []
def should_short(self):
return False<|fim_mid... | code_fim | hard | {
"lang": "python",
"repo": "jesse-ai/jesse",
"path": "/jesse/strategies/Test18/__init__.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: furas/python-examples path: /pyqt5/animation-after-animation/main.py
# date: 2019.08.01
# https://stackoverflow.com/questions/57308598/rectangle-moving/57309451#57309451
# https://www.qtcentre.org/threads/32958-multiple-QPropertyAnimations-after-each-other-how
# https://doc.qt.io/qt-5/qpropertya... | code_fim | hard | {
"lang": "python",
"repo": "furas/python-examples",
"path": "/pyqt5/animation-after-animation/main.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> self.anim = QPropertyAnimation(self.frame, b"geometry")
self.anim.setDuration(1000)
self.anim.setStartValue(QRect(0, 300, 100, 100))
self.anim.setEndValue(QRect(0, 0, 100, 100))
self.anim.finished.connect(self.doAnimation_1)
self.anim.start()
if __name__... | code_fim | hard | {
"lang": "python",
"repo": "furas/python-examples",
"path": "/pyqt5/animation-after-animation/main.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: tjd2002/spikeforest2 path: /repos/spiketoolkit/spiketoolkit/comparison/__init__.py
from .sortingcomparison import SortingComparison, MappedSortingExtractor, compute_per<|fim_suffix|>ingcomparison import MultiSortingComparison<|fim_middle|>formance, confusion_matrix
from .multisort | code_fim | easy | {
"lang": "python",
"repo": "tjd2002/spikeforest2",
"path": "/repos/spiketoolkit/spiketoolkit/comparison/__init__.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>ingcomparison import MultiSortingComparison<|fim_prefix|># repo: tjd2002/spikeforest2 path: /repos/spiketoolkit/spiketoolkit/comparison/__init__.py
from .sortingcomparison import SortingComp<|fim_middle|>arison, MappedSortingExtractor, compute_performance, confusion_matrix
from .multisort | code_fim | medium | {
"lang": "python",
"repo": "tjd2002/spikeforest2",
"path": "/repos/spiketoolkit/spiketoolkit/comparison/__init__.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> self._module = module
self.deploy_cfg = deploy_cfg
if not self._module.dec_gru:
rnn_decoder_layer1 = copy.deepcopy(self._module.rnn_decoder_layer1)
rnn_decoder_layer2 = copy.deepcopy(self._module.rnn_decoder_layer2)
self._module.rnn_decoder_layer... | code_fim | hard | {
"lang": "python",
"repo": "open-mmlab/mmdeploy",
"path": "/mmdeploy/codebase/mmocr/models/text_recognition/sar_decoder.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>
@MODULE_REWRITER.register_rewrite_module(
'mmocr.models.textrecog.decoders.SequentialSARDecoder', backend='default')
class SequentialSARDecoder(nn.Module):
"""Rewrite Sequential Decoder module in `SAR.
SequentialSARDecoder apply nn.LSTMCell inside, which brings obstacles to
deployment. L... | code_fim | hard | {
"lang": "python",
"repo": "open-mmlab/mmdeploy",
"path": "/mmdeploy/codebase/mmocr/models/text_recognition/sar_decoder.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
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