text stringlengths 232 16.3k | domain stringclasses 1
value | difficulty stringclasses 3
values | meta dict |
|---|---|---|---|
<|fim_suffix|> self.assertAllEqual(tf_out_values, out_values)
self.assertAllEqual(tf_out_indices, out_indices)
if __name__ == "__main__":
tf.test.main()<|fim_prefix|># repo: brodyh/tensorflow path: /tensorflow/python/kernel_tests/sparsemask_op_test.py
from __future__ import absolute_import
from __future_... | code_fim | hard | {
"lang": "python",
"repo": "brodyh/tensorflow",
"path": "/tensorflow/python/kernel_tests/sparsemask_op_test.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> t = tf.IndexedSlices(values_tensor, indices_tensor)
masked_t = tf.sparse_mask(t, mask_indices_tensor)
tf_out_values, tf_out_indices = sess.run([masked_t.values,
masked_t.indices])
self.assertAllEqual(tf_out_values, out_values)
... | code_fim | hard | {
"lang": "python",
"repo": "brodyh/tensorflow",
"path": "/tensorflow/python/kernel_tests/sparsemask_op_test.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: brodyh/tensorflow path: /tensorflow/python/kernel_tests/sparsemask_op_test.py
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.python.platform
import numpy as np
import tensorflow as tf
class SparseMaskTest(tf.test.... | code_fim | hard | {
"lang": "python",
"repo": "brodyh/tensorflow",
"path": "/tensorflow/python/kernel_tests/sparsemask_op_test.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> def compile(self, collection):
"""
Do our parameterized averaging procedure and whatever else doesn't happen every frame.
"""
return 0
def play(self, frame):
"""
Step between current frame and desired one somehow.
Intend... | code_fim | hard | {
"lang": "python",
"repo": "mhoaglund/TheShallows",
"path": "/TheShallows_DemoCV/outputstream.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: mhoaglund/TheShallows path: /TheShallows_DemoCV/outputstream.py
"""
Reads from a queue to retrieve collections of ROIs cut from a single frame by the corresponding input class.
Has two loops:
#1 carries out the aforementioned queue read
#2 runs in between, averaging the collections of ROIs into ... | code_fim | hard | {
"lang": "python",
"repo": "mhoaglund/TheShallows",
"path": "/TheShallows_DemoCV/outputstream.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>class DetPredict(xnn.Module):
def __init__(self, depth, plane, num_anchors, num_classes, num_loc_params):
super(DetPredict, self).__init__()
self.heads_cls = nn.ModuleList()
self.heads_loc = nn.ModuleList()
for i in range(depth):
self.heads_cls.append(Predic... | code_fim | hard | {
"lang": "python",
"repo": "ming71/SLA",
"path": "/model/net/utils/modules.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ming71/SLA path: /model/net/utils/modules.py
import torch
from torch import nn
from xtorch import xnn
import torch.nn.functional as F
class FeaturePyramidNet(xnn.Module):
def __init__(self, depth, plane):
super(FeaturePyramidNet, self).__init__()
self.link = nn.ModuleList(... | code_fim | hard | {
"lang": "python",
"repo": "ming71/SLA",
"path": "/model/net/utils/modules.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def forward(self, x):
x = self.body(x)
return x.permute(0, 2, 3, 1).reshape(x.size(0), -1, self.num_classes)
class DetPredict(xnn.Module):
def __init__(self, depth, plane, num_anchors, num_classes, num_loc_params):
super(DetPredict, self).__init__()
self.heads_cls... | code_fim | hard | {
"lang": "python",
"repo": "ming71/SLA",
"path": "/model/net/utils/modules.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def test_not_hidden(self, articles_kb_app, sphinx_app, valid_registration,
dummy_doctree, article_env, article_resources,
mocker,
):
# Turn on toctree support
sphinx_app.config.kaybee_settings.articles.use_toctree ... | code_fim | hard | {
"lang": "python",
"repo": "pauleveritt/kaybee",
"path": "/tests/unit/plugins/articles/test_articles_handlers.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: pauleveritt/kaybee path: /tests/unit/plugins/articles/test_articles_handlers.py
import dectate
import pytest
from kaybee.plugins.articles.handlers import (
articles_into_html_context,
register_template_directory,
render_toctrees,
resource_toctrees,
stamp_excerpt,
dump_set... | code_fim | hard | {
"lang": "python",
"repo": "pauleveritt/kaybee",
"path": "/tests/unit/plugins/articles/test_articles_handlers.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> valid_registration,
dummy_doctree, article_env, article_resources,
mocker,
):
# By default toctree support is turned off
sphinx_app.env = article_env
sphinx_app.resou... | code_fim | hard | {
"lang": "python",
"repo": "pauleveritt/kaybee",
"path": "/tests/unit/plugins/articles/test_articles_handlers.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: kubernetes/test-infra path: /releng/generate_tests_test.py
#!/usr/bin/env python3
# Copyright 2019 The Kubernetes Authors.
#
# 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 | medium | {
"lang": "python",
"repo": "kubernetes/test-infra",
"path": "/releng/generate_tests_test.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> shutil.rmtree(self.temp_directory)
def test_e2etests_testgrid_annotations_default(self):
generator = E2ETest(self.temp_directory, self.job_name, self.job, self.config)
_, prow_config, _ = generator.generate()
dashboards = prow_config["annotations"]["testgrid-dashboards... | code_fim | hard | {
"lang": "python",
"repo": "kubernetes/test-infra",
"path": "/releng/generate_tests_test.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: jphkun/aeroframe path: /src/lib/aeroframe/_wrappers/structure/framat.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# ----------------------------------------------------------------------
# Copyright 2019-2020 Airinnova AB and the AeroFrame authors
#
# Licensed under the Apache License, Vers... | code_fim | hard | {
"lang": "python",
"repo": "jphkun/aeroframe",
"path": "/src/lib/aeroframe/_wrappers/structure/framat.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> # Add loads to the FramAT model file
free_node_loads = []
for entry in load_field:
free_node_loads.append({'coord': list(entry[0:3]), 'load': list(entry[3:9])})
# Loads acting on a mirrored side
if component_uid.endswith('_m'):
... | code_fim | hard | {
"lang": "python",
"repo": "jphkun/aeroframe",
"path": "/src/lib/aeroframe/_wrappers/structure/framat.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>
#%%
class city(plan):
def __init__(self, H=2,W=2,grid=None, D = 5,B = 0, name = "cityX"):
checktype([name], [ str])
super().__init__(H,W, grid)
self.name = name
self.D = D
self.B = B
self.residences = []
self.utilities = []
self.manhatta... | code_fim | hard | {
"lang": "python",
"repo": "neroksi/hashcode18",
"path": "/src/urban.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: neroksi/hashcode18 path: /src/urban.py
#%%
import numpy as np
from enum import Enum
import itertools
from utils import project_from_file, checktype, btype
from hyperopt import fmin, tpe, hp, STATUS_OK, Trials
from matplotlib import pyplot as plt
#%%
class plan(object):
def __init__(sel... | code_fim | hard | {
"lang": "python",
"repo": "neroksi/hashcode18",
"path": "/src/urban.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>#%%
class building(plan):
def __init__(self,H=2, W=2, grid=None, kind = btype.RESIDENCE, capacity= None, utype = None, name = "BuildingX"):
checktype([kind, capacity, utype, name],
[ btype, (type(None),int), (type(None),int), str])
super().__init__(H,W, grid)
... | code_fim | hard | {
"lang": "python",
"repo": "neroksi/hashcode18",
"path": "/src/urban.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> def test_update_processed(self, api_key, card_payment):
card_payment.update(status="voided")
assert card_payment.status == "voided"
def test_transactions_not_found(self, api_key, card_payment):
with pytest.raises(NotFound):
pl.Transaction.get("invalid")<|fim_pr... | code_fim | hard | {
"lang": "python",
"repo": "payload-code/payload-python",
"path": "/tests/test_transaction.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> assert card_payment.risk_flag == "allowed"
def test_update_processed(self, api_key, card_payment):
card_payment.update(status="voided")
assert card_payment.status == "voided"
def test_transactions_not_found(self, api_key, card_payment):
with pytest.raises(NotFound... | code_fim | medium | {
"lang": "python",
"repo": "payload-code/payload-python",
"path": "/tests/test_transaction.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: payload-code/payload-python path: /tests/test_transaction.py
import pytest
import payload as pl
from payload.exceptions import NotFound
from .fixtures import Fixtures
class TestTransaction(Fixtures):
def test_transaction_ledger_empty(self, api_key, card_payment):
transaction = pl.... | code_fim | hard | {
"lang": "python",
"repo": "payload-code/payload-python",
"path": "/tests/test_transaction.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> if np.isscalar(a):
return a*b
return np.outer(a, b)
def newton(f, df, guess, max_n = 10, abstol = 0.000005):
n = 0
while np.linalg.norm(f(guess))>abstol and n<max_n:
guess -= solve_linear(df(guess), f(guess))
n+=1
return guess
def broyden(f, df... | code_fim | medium | {
"lang": "python",
"repo": "Markus28/Scientific-Computing",
"path": "/analysis/solve.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Markus28/Scientific-Computing path: /analysis/solve.py
import numpy as np
import matplotlib.pyplot as plt
from scipy.misc import derivative
import scipy
def solve_linear(A, w):
if np.isscalar(A):
return w/A
return scipy.linalg.solve(A, w)
def outer(a, b):
if np.isscalar... | code_fim | hard | {
"lang": "python",
"repo": "Markus28/Scientific-Computing",
"path": "/analysis/solve.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> guess = guess.copy()
n = 1
f_guess = f(guess)
J_inverse = np.linalg.inv(df)
s = np.matmul(J_inverse, f_guess)
guess -= s
f_guess = f(guess)
while np.linalg.norm(f_guess)>abstol and n<max_n:
w = np.matmul(J_inverse, f_guess)
s_norm = np.linalg.norm(s)**2... | code_fim | hard | {
"lang": "python",
"repo": "Markus28/Scientific-Computing",
"path": "/analysis/solve.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: openprocurement/openprocurement.tender.openuadefense path: /openprocurement/tender/openuadefense/tests/document.py
# -*- coding: utf-8 -*-
import unittest
from openprocurement.api.tests.base import snitch
from openprocurement.tender.belowthreshold.tests.document_blanks import (
# TenderDocu... | code_fim | hard | {
"lang": "python",
"repo": "openprocurement/openprocurement.tender.openuadefense",
"path": "/openprocurement/tender/openuadefense/tests/document.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> docservice = True
test_create_tender_document_json_invalid = snitch(create_tender_document_json_invalid)
test_create_tender_document_json = snitch(create_tender_document_json)
test_put_tender_document_json = snitch(put_tender_document_json)
def suite():
suite = unittest.TestSuite()
... | code_fim | medium | {
"lang": "python",
"repo": "openprocurement/openprocurement.tender.openuadefense",
"path": "/openprocurement/tender/openuadefense/tests/document.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: MinistereSupRecherche/bso path: /app/analyzers/html_parsers/iop.py
from bs4 import *
import re, bs4
from doi_utils import *
# doi 10.1088
def parse_iop(soup):
is_french = False
authors, affiliations = [], {}
for elt in soup.find_all(class_='mb-05'):
is_elt_ok = False
... | code_fim | hard | {
"lang": "python",
"repo": "MinistereSupRecherche/bso",
"path": "/app/analyzers/html_parsers/iop.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> if author_elt.find('sup') is None:
continue
nb_sups = author_elt.find('sup').get_text().split(',')
full_name = author_elt.find('span').get_text()
author['full_name'] = full_name
author['affiliations_info'] = []
for nb_sup in nb_sups:
... | code_fim | hard | {
"lang": "python",
"repo": "MinistereSupRecherche/bso",
"path": "/app/analyzers/html_parsers/iop.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> if isinstance(sub_elt, bs4.element.NavigableString):
full_name = sub_elt
if author_elt.find('sup') is None:
continue
nb_sups = author_elt.find('sup').get_text().split(',')
full_name = author_elt.find('span').get_text()
author['full_n... | code_fim | hard | {
"lang": "python",
"repo": "MinistereSupRecherche/bso",
"path": "/app/analyzers/html_parsers/iop.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: xuther/nyc-taxi-data path: /code/clustering/multi-dimensional-clustering/data-preparation.py
import numpy as np
import json
import csv
import os
import locale
from pyspark import SparkConf, SparkContext
conf = (SparkConf()
.setMaster("local[*]")
.setAppName("My app")
.set... | code_fim | hard | {
"lang": "python",
"repo": "xuther/nyc-taxi-data",
"path": "/code/clustering/multi-dimensional-clustering/data-preparation.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>splitArrivals = arrivals.map(lambda x: (x[0].split("/")[-1].split(".")[0], x[1].split()[1:]))
pointedArrivals = splitArrivals.map(lambda x: (x[0], labelPointsWithTractAndArriveDepart(x, 1)))
combined = sc.union([pointedDepartures, pointedArrivals])
combined = combined.reduceByKey(lambda x, y: x + y)
co... | code_fim | hard | {
"lang": "python",
"repo": "xuther/nyc-taxi-data",
"path": "/code/clustering/multi-dimensional-clustering/data-preparation.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>splitDepartures = departures.map(lambda x: (x[0].split("/")[-1].split(".")[0], x[1].split()[1:]))
pointedDepartures = splitDepartures.map(lambda x: (x[0], labelPointsWithTractAndArriveDepart(x, 0)))
splitArrivals = arrivals.map(lambda x: (x[0].split("/")[-1].split(".")[0], x[1].split()[1:]))
pointedArriv... | code_fim | hard | {
"lang": "python",
"repo": "xuther/nyc-taxi-data",
"path": "/code/clustering/multi-dimensional-clustering/data-preparation.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: VRI-UFPR/ocrd-gbn path: /gbn/sbb/predict.py
from gbn.lib.dl import Model, Prediction
from gbn.lib.struct import Contour, Polygon
from gbn.lib.util import pil_to_cv2_rgb, cv2_to_pil_gray
from gbn.tool import OCRD_TOOL
from ocrd import Processor
from ocrd_modelfactory import page_from_file
from oc... | code_fim | hard | {
"lang": "python",
"repo": "VRI-UFPR/ocrd-gbn",
"path": "/gbn/sbb/predict.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> lines = region.get_TextLine()
for line_idx, line in enumerate(lines):
line_id = "_region%04d" % line_idx
# Get image from TextLine:
line_image, line_xywh = self.workspace.image_from_segment(
... | code_fim | hard | {
"lang": "python",
"repo": "VRI-UFPR/ocrd-gbn",
"path": "/gbn/sbb/predict.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ivis-mizuguchi/ocs-templates path: /OpenHPC-v1/scripts/group.py
from __future__ import absolute_import
from __future__ import print_function
from __future__ import unicode_literals
import errno
import os
import os.path
import re
import yaml
import subprocess
from io import StringIO, BytesIO
impor... | code_fim | hard | {
"lang": "python",
"repo": "ivis-mizuguchi/ocs-templates",
"path": "/OpenHPC-v1/scripts/group.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> gvars = load_group_vars(target_group, dir)
return gvars[name]
def store_group_vars(target_group, gvars, work_dir=os.getcwd()):
path = os.path.join(work_dir, GROUP_VARS_DIR, target_group + '.yml')
mkdir_p(os.path.dirname(path))
with open(path, 'w') as f:
yaml.dump(gvars, f, de... | code_fim | hard | {
"lang": "python",
"repo": "ivis-mizuguchi/ocs-templates",
"path": "/OpenHPC-v1/scripts/group.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> """ Execute a command received from the server.
"""
if msg.startswith('PRINT '):
window.console.ori_log(msg[6:])
elif msg.startswith('EVAL '):
window._ = eval(msg[5:])
window.flexx.ws.send('RET ' + window._) # send back result
el... | code_fim | hard | {
"lang": "python",
"repo": "cy-fir/flexx",
"path": "/flexx/app/clientcore.py",
"mode": "spm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> def initSocket(self):
""" Make the connection to Python.
"""
# Check WebSocket support
if self.nodejs:
try:
WebSocket = require('ws')
except Exception:
# Better error message
raise "FAIL: y... | code_fim | hard | {
"lang": "python",
"repo": "cy-fir/flexx",
"path": "/flexx/app/clientcore.py",
"mode": "spm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: cy-fir/flexx path: /flexx/app/clientcore.py
"""
The client's core Flexx engine, implemented in PyScript.
"""
from ..pyscript import py2js, undefined, window
flexx_session_id = location = require = module = typeof = None # fool PyFlakes
@py2js(inline_stdlib=False)
class FlexxJS:
""" JavaS... | code_fim | hard | {
"lang": "python",
"repo": "cy-fir/flexx",
"path": "/flexx/app/clientcore.py",
"mode": "psm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: utsurius/django-actionable-messages path: /django_actionable_messages/elements.py
from typing import Union
from django_actionable_messages.mixins import CardElement
<|fim_suffix|> def __init__(self, name: str, value: Union[str, int], **kwargs):
self._data = {
"name": name... | code_fim | easy | {
"lang": "python",
"repo": "utsurius/django-actionable-messages",
"path": "/django_actionable_messages/elements.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> self._data = {
"name": name,
"value": value
}
super().__init__(**kwargs)<|fim_prefix|># repo: utsurius/django-actionable-messages path: /django_actionable_messages/elements.py
from typing import Union
from django_actionable_messages.mixins import CardEleme... | code_fim | medium | {
"lang": "python",
"repo": "utsurius/django-actionable-messages",
"path": "/django_actionable_messages/elements.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def compile_results(results, index):
"Compile results in a dictionary."
selection = dict()
for filename, haikus in results:
selection[filename] = index[filename]
selection[filename]['haikus'] = haikus
return selection
######################################################... | code_fim | hard | {
"lang": "python",
"repo": "Jaspervo159/dbnl-scripts",
"path": "/accidental_haiku.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Jaspervo159/dbnl-scripts path: /accidental_haiku.py
import glob
import json
import pyphen
import spacy
from utils import get_text, detokenize, load_dbnl_data, chunks, store_dbnl_data
from multiprocessing import Pool
from itertools import repeat
dic = pyphen.Pyphen(lang='nl_NL')
def count_syllab... | code_fim | hard | {
"lang": "python",
"repo": "Jaspervo159/dbnl-scripts",
"path": "/accidental_haiku.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> """
Checks whether is a sentence fits the (simplified) criteria for a haiku.
"""
first, second, third = 0, 0, 0
first_line = []
second_line = []
third_line = []
for token in sentence:
syllables = count_syllables(token)
if first < 5:
first += syll... | code_fim | hard | {
"lang": "python",
"repo": "Jaspervo159/dbnl-scripts",
"path": "/accidental_haiku.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: netvigator/auctions path: /auctionbot/taskapp/tasks.py
#from celery import shared_task
<|fim_suffix|>#@shared_task( name = 'auctionbot.taskapp.tasks.add' )
def add( i, j ): return i + j<|fim_middle|>from .celery import app
# 2021-05-24 celery not working, so giving up on it!
# instead, w... | code_fim | hard | {
"lang": "python",
"repo": "netvigator/auctions",
"path": "/auctionbot/taskapp/tasks.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|># 2021-05-24 celery not working, so giving up on it!
# instead, will set nice level on cron job processes
# will leave the celery structure in place, to allow retrying later if desired
#@shared_task( name = 'auctionbot.taskapp.tasks.add' )
def add( i, j ): return i + j<|fim_prefix|># repo: netvigator/auc... | code_fim | easy | {
"lang": "python",
"repo": "netvigator/auctions",
"path": "/auctionbot/taskapp/tasks.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: albertleers/GPflowBenchmarks path: /GPU_timed_mnist.py
import sys
import subprocess
import os
import consts
def runGPUExperiments(descriptor):
if descriptor=="short":
num_repeats = 2
elif descriptor=="full":
num_repeats = 5
else:
raise NotImplementedError
<|fim_suffix|>if __name__ == '... | code_fim | hard | {
"lang": "python",
"repo": "albertleers/GPflowBenchmarks",
"path": "/GPU_timed_mnist.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> software = "GPflow"
no_limit = -1
f= open(consts.GPU_results_file_name,'w')
for repeat_index in range(num_repeats):
text_out = subprocess.check_output(["python","timed_mnist.py",software,str(no_limit)])
comma = ","
line = software+comma+str(1)+comma+text_out.decode()
f.write(line)
f.close()
... | code_fim | medium | {
"lang": "python",
"repo": "albertleers/GPflowBenchmarks",
"path": "/GPU_timed_mnist.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: KathyLau/image2text path: /test.py
from pytesser import *
from PIL import Image
#import subprocess
#impor<|fim_suffix|>exe on image fnord
#print image_file_to_string('fnord.tif')# fnord ```<|fim_middle|>t util
#import errors
image = Image.open('fonts_test.png')
# Open image object using PIL
prin... | code_fim | medium | {
"lang": "python",
"repo": "KathyLau/image2text",
"path": "/test.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>ge object using PIL
print image_file_to_string(image)
# Run tesseract.exe on image fnord
#print image_file_to_string('fnord.tif')# fnord ```<|fim_prefix|># repo: KathyLau/image2text path: /test.py
from pytesser import *
from PIL import Image
#import subprocess
#impor<|fim_middle|>t util
#import errors
i... | code_fim | medium | {
"lang": "python",
"repo": "KathyLau/image2text",
"path": "/test.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: alercebroker/APF path: /tests/consumers/test_avro_file.py
from .test_core import GenericConsumerTest
from apf.consumers import AVROFileConsumer
import unittest
import os
FILE_PATH = os.path.dirname(os.path.abspath(__file__))
EXAMPLES_PATH = os.path.abspath(os.path.join(FILE_PATH, "../examples")... | code_fim | hard | {
"lang": "python",
"repo": "alercebroker/APF",
"path": "/tests/consumers/test_avro_file.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
class AVROFileConsumerTest(GenericConsumerTest, unittest.TestCase):
params = {
"DIRECTORY_PATH": os.path.join(EXAMPLES_PATH, "avro_test"),
"consume.messages": 1,
}
component = AVROFileConsumer(params)
__test__ = True
def test_consume_left_messages(self):
param... | code_fim | medium | {
"lang": "python",
"repo": "alercebroker/APF",
"path": "/tests/consumers/test_avro_file.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> params = {
"DIRECTORY_PATH": os.path.join(EXAMPLES_PATH, "avro_test"),
"consume.messages": 1,
}
component = AVROFileConsumer(params)
__test__ = True
def test_consume_left_messages(self):
params = self.params
params["consume.messages"] = 5
self.c... | code_fim | medium | {
"lang": "python",
"repo": "alercebroker/APF",
"path": "/tests/consumers/test_avro_file.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: uselessscat/python-practices path: /Galaxy simulation/spiral_galaxy_3.py
import numpy as np
import vpython as vp
from vpython import vector as v
from vpython import rotate as rot
# gravitacion
g_constant = 6.67408e-11
sagittarius_a_mass = 4e6 # 4e4 to 4e9
sun_mass = 1.989e30
distances_scale = ... | code_fim | hard | {
"lang": "python",
"repo": "uselessscat/python-practices",
"path": "/Galaxy simulation/spiral_galaxy_3.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> up = v(0.0, 0.0, 1.0)
initialSpeed = [np.sqrt(g_constant * sagittarius_a_mass * masses[i] / (positions[i].mag))
* positions[i].cross(up).norm() for i in range(len(positions))]
# add some randomness
for s in initialSpeed:
s.x = s.x + (np.random.rand() * 2.0 - 1.... | code_fim | hard | {
"lang": "python",
"repo": "uselessscat/python-practices",
"path": "/Galaxy simulation/spiral_galaxy_3.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>rate = 100
delta_tiempo = 1e-13
while True:
vp.rate(rate)
for i in range(count):
# centripetal acceleration
acceleration = (g_constant * sagittarius_a_mass *
masses[i] / (positions[i].mag2)) * -positions[i].norm()
# update speed an position
... | code_fim | hard | {
"lang": "python",
"repo": "uselessscat/python-practices",
"path": "/Galaxy simulation/spiral_galaxy_3.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>tape = Computer(int_code=data)
print(*tape.compute_n(niter=2, feed=(1, 5)))<|fim_prefix|># repo: krsnadatra/Contoh-Program path: /2019/day_05.py
from computer import Computer
<|fim_middle|>with open('input05', 'r') as data:
data = list(map(int, data.read().split(',')))
| code_fim | medium | {
"lang": "python",
"repo": "krsnadatra/Contoh-Program",
"path": "/2019/day_05.py",
"mode": "spm",
"license": "Unlicense",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: krsnadatra/Contoh-Program path: /2019/day_05.py
from computer import Computer
<|fim_suffix|>tape = Computer(int_code=data)
print(*tape.compute_n(niter=2, feed=(1, 5)))<|fim_middle|>with open('input05', 'r') as data:
data = list(map(int, data.read().split(',')))
| code_fim | medium | {
"lang": "python",
"repo": "krsnadatra/Contoh-Program",
"path": "/2019/day_05.py",
"mode": "psm",
"license": "Unlicense",
"source": "the-stack-v2"
} |
<|fim_suffix|>
@overload
def get_dummy(framework: str) -> Tensor:
...
def get_dummy(framework: str) -> Tensor:
x: Tensor
if framework == "pytorch":
x = modules.torch.zeros(0)
assert isinstance(x, PyTorchTensor)
elif framework == "pytorch-gpu":
x = modules.torch.zeros(0, device=... | code_fim | hard | {
"lang": "python",
"repo": "jonasrauber/eagerpy",
"path": "/eagerpy/utils.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> ...
@overload
def get_dummy(framework: Literal["jax"]) -> JAXTensor:
...
@overload
def get_dummy(framework: Literal["numpy"]) -> NumPyTensor:
...
@overload
def get_dummy(framework: str) -> Tensor:
...
def get_dummy(framework: str) -> Tensor:
x: Tensor
if framework == "pytor... | code_fim | medium | {
"lang": "python",
"repo": "jonasrauber/eagerpy",
"path": "/eagerpy/utils.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: jonasrauber/eagerpy path: /eagerpy/utils.py
from typing import overload
from typing_extensions import Literal
from .tensor import Tensor
from .tensor import PyTorchTensor
from .tensor import TensorFlowTensor
from .tensor import JAXTensor
from .tensor import NumPyTensor
from . import modules
<... | code_fim | hard | {
"lang": "python",
"repo": "jonasrauber/eagerpy",
"path": "/eagerpy/utils.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: amoudgl/habitat-sim path: /tests/test_examples.py
import multiprocessing
import os.path as osp
import shlex
import subprocess
import pytest
import examples.new_actions
import examples.stereo_agent
<|fim_suffix|> # This test needs to be done in its own process as there is a potentially for
... | code_fim | hard | {
"lang": "python",
"repo": "amoudgl/habitat-sim",
"path": "/tests/test_examples.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
@pytest.mark.gfxtest
@pytest.mark.skipif(
not osp.exists("data/scene_datasets/habitat-test-scenes/skokloster-castle.glb"),
reason="Requires the habitat-test-scenes",
)
@pytest.mark.parametrize(
"args",
[
"--compute_shortest_path",
"--compute_shortest_path --compute_action_... | code_fim | hard | {
"lang": "python",
"repo": "amoudgl/habitat-sim",
"path": "/tests/test_examples.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: kivancguckiran/torcs-rl-agent path: /algorithms/common/buffer/replay_buffer.py
# -*- coding: utf-8 -*-
"""Replay buffer for baselines."""
from collections import deque
from typing import Any, Deque, List, Tuple
import numpy as np
import torch
from algorithms.common.helper_functions import get_... | code_fim | hard | {
"lang": "python",
"repo": "kivancguckiran/torcs-rl-agent",
"path": "/algorithms/common/buffer/replay_buffer.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> if done:
if len(self.current_episode) > self.step_size:
self.episodes.append(self.current_episode)
if len(self.episodes) == self.episode_size:
self.episodes.pop(0)
self.current_episode = list()
def sample(self) -> T... | code_fim | hard | {
"lang": "python",
"repo": "kivancguckiran/torcs-rl-agent",
"path": "/algorithms/common/buffer/replay_buffer.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|># API配置
API_HOST = '127.0.0.1'
API_PORT = 5555
# 开关
#测试
TESTER_ENABLED = False
#获取
GETTER_ENABLED = False
#API
API_ENABLED = True
# 最大批测试量
BATCH_TEST_SIZE = 10<|fim_prefix|># repo: G-der/ProxyPool path: /proxypool/setting.py
# Redis数据库地址
REDIS_HOST = '127.0.0.1'
# Redis端口
REDIS_PORT = 6379
# Redis密码... | code_fim | medium | {
"lang": "python",
"repo": "G-der/ProxyPool",
"path": "/proxypool/setting.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: G-der/ProxyPool path: /proxypool/setting.py
# Redis数据库地址
REDIS_HOST = '127.0.0.1'
# Redis端口
REDIS_PORT = 6379
# Redis密码，如无填None
REDIS_PASSWORD = None
REDIS_KEY = 'proxies'
# 代理分数
MAX_SCORE = 5
MIN_SCORE = 1
INITIAL_SCORE = 3
VALID_STATUS_CODES = [200, 302]
# 代理池数量界限
POOL_UPPER_THRESHOLD = ... | code_fim | medium | {
"lang": "python",
"repo": "G-der/ProxyPool",
"path": "/proxypool/setting.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: jennyknuth/landlab path: /landlab/grid/tests/test_raster_funcs/test_flux_divergence_at_nodes.py
import numpy as np
from numpy.testing import assert_array_equal
try:
from nose.tools import assert_is
except ImportError:
from landlab.testing.tools import assert_is
from landlab import Raster... | code_fim | hard | {
"lang": "python",
"repo": "jennyknuth/landlab",
"path": "/landlab/grid/tests/test_raster_funcs/test_flux_divergence_at_nodes.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> """Test fluxes tha move from east to west."""
rmg = RasterModelGrid(4, 5)
active_link_flux = np.array([0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 1., 3., 6., 0., 1., 3., 6.])
divs = rmg.calculate_flux_divergence_at_nodes(active_link_flux)
assert_array_equ... | code_fim | hard | {
"lang": "python",
"repo": "jennyknuth/landlab",
"path": "/landlab/grid/tests/test_raster_funcs/test_flux_divergence_at_nodes.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: microsoft/onnxruntime path: /onnxruntime/test/providers/cpu/rnn/LSTM.py
= ["X", "W", "R"]
for i in required_inputs:
assert i in params, f"Missing Required Input: {i}"
X = params["X"] # noqa: N806
W = params["W"] # noqa: N806
R = params["R"] # noqa:... | code_fim | hard | {
"lang": "python",
"repo": "microsoft/onnxruntime",
"path": "/onnxruntime/test/providers/cpu/rnn/LSTM.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> input = (
np.array([-0.455351, -0.276391, -0.185934, -0.269585])
.reshape(seq_length, batch_size, input_size)
.astype(np.float32)
)
return input
class ONNXRuntimeUnitTests:
@staticmethod
def ONNXRuntime_TestLSTMBidirectionalBasic(): #... | code_fim | hard | {
"lang": "python",
"repo": "microsoft/onnxruntime",
"path": "/onnxruntime/test/providers/cpu/rnn/LSTM.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> input = ONNXRuntimeTestContext.DefaultInput()
W, R, B, P = ONNXRuntimeTestContext.OneDirectionWeights() # noqa: N806
lstm = LSTM_Helper(X=input, W=W, R=R, B=B, P=P, clip=0.1)
Y, Y_h, Y_c = lstm.run() # noqa: N806
print_results(Y, Y_h, Y_c)
@staticmethod
d... | code_fim | hard | {
"lang": "python",
"repo": "microsoft/onnxruntime",
"path": "/onnxruntime/test/providers/cpu/rnn/LSTM.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>from . import embeddings
from . import layers
from . import logger
from . import neighbor_funcs
from . import tf_funcs
from . import utility<|fim_prefix|># repo: SkafteNicki/Deep_LMNN path: /dlmnn/helper/__init__.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue May 29 08:53:33 2018
... | code_fim | easy | {
"lang": "python",
"repo": "SkafteNicki/Deep_LMNN",
"path": "/dlmnn/helper/__init__.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: SkafteNicki/Deep_LMNN path: /dlmnn/helper/__init__.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue May 29 08:53:33 2018
<|fim_suffix|>from . import embeddings
from . import layers
from . import logger
from . import neighbor_funcs
from . import tf_funcs
from . import utility... | code_fim | easy | {
"lang": "python",
"repo": "SkafteNicki/Deep_LMNN",
"path": "/dlmnn/helper/__init__.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: arseniybanayev/aiotime path: /tests/test_aiotime.py
import asyncio
import pytest
import datetime as dt
import aiotime
@pytest.mark.asyncio
async def test_asyncio_sleep():
loop = asyncio.get_event_loop()
# Try sleeping with normal loop behavior
start = dt.datetime.now()
sleep_tas... | code_fim | hard | {
"lang": "python",
"repo": "arseniybanayev/aiotime",
"path": "/tests/test_aiotime.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # call_later() with normal loop behavior now, after context manager exits
start = dt.datetime.now()
event = asyncio.Event()
def test():
event.set()
loop.call_later(0.25, test)
await asyncio.wait_for(event.wait(), 1) # timeout just in case
assert dt.datetime.now() - sta... | code_fim | hard | {
"lang": "python",
"repo": "arseniybanayev/aiotime",
"path": "/tests/test_aiotime.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> event.set()
loop.call_later(0.25, test)
await asyncio.wait_for(event.wait(), 1) # timeout just in case
assert dt.datetime.now() - start > dt.timedelta(seconds=0.25)<|fim_prefix|># repo: arseniybanayev/aiotime path: /tests/test_aiotime.py
import asyncio
import pytest
import datetime a... | code_fim | medium | {
"lang": "python",
"repo": "arseniybanayev/aiotime",
"path": "/tests/test_aiotime.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = decode(fm, i)
bbox_tensors.append(bbox_tensor)
self.model = tf.keras.Model(input_layer, bbox_tensors)
self.model.load_weights('./yolov3')
self.model.summary()
def get... | code_fim | hard | {
"lang": "python",
"repo": "polycart/polycart",
"path": "/PolyCart/Cart/cv_detection.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: polycart/polycart path: /PolyCart/Cart/cv_detection.py
import cv2
import numpy as np
import core.utils as utils
import tensorflow as tf
from core.yolov3 import YOLOv3, decode
from core.config import cfg
import time
from PIL import Image
class Detection:
''' 使用时, 通过 Detection.get_instance() ... | code_fim | hard | {
"lang": "python",
"repo": "polycart/polycart",
"path": "/PolyCart/Cart/cv_detection.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> for currSegment in segmentList:
rowData = [
currSegment['segid'], # ID
currSegment['permalink'], # PL
currRoadType, # Road type
"", # FWD Dir
"", ... | code_fim | hard | {
"lang": "python",
"repo": "TerryOtt/segmentcsv2kml",
"path": "/utils/jsonToCsv/jsonToCsv.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: TerryOtt/segmentcsv2kml path: /utils/jsonToCsv/jsonToCsv.py
#!/usr/bin/env python3
import argparse
import sys
import os
import json
# from pprint import pprint
import csv
def main():
args = parseArgs(sys.argv)
parsedData = readJson(args.jsonFile)
print( "Read all JSON data success... | code_fim | hard | {
"lang": "python",
"repo": "TerryOtt/segmentcsv2kml",
"path": "/utils/jsonToCsv/jsonToCsv.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> segmentCsvWriter.writerow(headerRow)
def addSegmentsToCsv( currRoadType, segmentList, csvWriter ):
for currSegment in segmentList:
rowData = [
currSegment['segid'], # ID
currSegment['permalink'], # PL
currRoadType, ... | code_fim | hard | {
"lang": "python",
"repo": "TerryOtt/segmentcsv2kml",
"path": "/utils/jsonToCsv/jsonToCsv.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Team-Alua/gcdv2gnf path: /gcdv2gnf.py
import os
header = 0x474E4620F8000000.to_bytes(8,'big')
folder = 'E:\GRR\gcd\\'
output_folder = 'E:\GRR\gnf\\'
files = []
# r=root, d=directories, f = files
for r, d, f in os.walk(folder):
for file in f:
if '.gcdm' in file:
... | code_fim | hard | {
"lang": "python",
"repo": "Team-Alua/gcdv2gnf",
"path": "/gcdv2gnf.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> while body is not None:
with open(r"%s%s_%d.gnf" % (output_folder, os.path.basename(f.name).split('.')[0], count), "wb") as output:
output.write(body)
print("Exported #%d" % count)
count += 1
hea... | code_fim | hard | {
"lang": "python",
"repo": "Team-Alua/gcdv2gnf",
"path": "/gcdv2gnf.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: anhnguyendepocen/unimib_masterbi_2021 path: /indiegogo_aws/aws_glue_pyspark.py
###### UNIMIB - 2021 Indiegogo
######
import sys
import json
import pyspark
from pyspark.sql.functions import col, collect_list, array_join
from awsglue.transforms import *
from awsglue.utils import getResolvedOptio... | code_fim | medium | {
"lang": "python",
"repo": "anhnguyendepocen/unimib_masterbi_2021",
"path": "/indiegogo_aws/aws_glue_pyspark.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
# CREATE THE AGGREGATE MODEL, ADD TAGS TO TEDX_DATASET
img_dataset_agg = img_dataset.groupBy(col("project_id").alias("project_id_ref")).agg(collect_list("name").alias("names"))
img_dataset_agg.printSchema()
projects_dataset_agg = projects_dataset.join(img_dataset_agg, projects_dataset.project_id == img_... | code_fim | hard | {
"lang": "python",
"repo": "anhnguyendepocen/unimib_masterbi_2021",
"path": "/indiegogo_aws/aws_glue_pyspark.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
if __name__ == "__main__":
logging.basicConfig(level=logging.INFO,
format='%(asctime)s - %(message)s',
datefmt='%Y-%m-%d %H:%M:%S')
# path = sys.argv[1] if len(sys.argv) > 1 else '.'
path = '/saber/'
# Find workflows
# Find jobs
c... | code_fim | hard | {
"lang": "python",
"repo": "aplbrain/saber",
"path": "/conduit/scripts/cwl_monitor",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>if __name__ == "__main__":
logging.basicConfig(level=logging.INFO,
format='%(asctime)s - %(message)s',
datefmt='%Y-%m-%d %H:%M:%S')
# path = sys.argv[1] if len(sys.argv) > 1 else '.'
path = '/saber/'
# Find workflows
# Find jobs
c ... | code_fim | hard | {
"lang": "python",
"repo": "aplbrain/saber",
"path": "/conduit/scripts/cwl_monitor",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: aplbrain/saber path: /conduit/scripts/cwl_monitor
#! /usr/bin/python
# Copyright 2019 The Johns Hopkins University Applied Physics Laboratory
#
# 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 c... | code_fim | hard | {
"lang": "python",
"repo": "aplbrain/saber",
"path": "/conduit/scripts/cwl_monitor",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: msduketown/PrettyQt path: /prettyqt/widgets/toolbutton.py
# -*- coding: utf-8 -*-
"""
@author: Philipp Temminghoff
"""
from qtpy import QtWidgets, QtCore
from prettyqt import widgets
from prettyqt.utils import bidict
POPUP_MODES = bidict(delayed=QtWidgets.QToolButton.DelayedPopup,
... | code_fim | hard | {
"lang": "python",
"repo": "msduketown/PrettyQt",
"path": "/prettyqt/widgets/toolbutton.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> possible values are "delayed", "menu_button", "instant"
Returns:
popup mode
"""
return POPUP_MODES.inv[self.popupMode()]
def set_arrow_type(self, mode: str):
"""sets the arrow type of the toolbutton
valid values are: "none", "up", "down", ... | code_fim | hard | {
"lang": "python",
"repo": "msduketown/PrettyQt",
"path": "/prettyqt/widgets/toolbutton.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: akxen/mpo-dev path: /project/api/serializers.py
from rest_framework import serializers
<|fim_suffix|> initial_weights = serializers.JSONField()
estimated_returns = serializers.JSONField()
parameters = serializers.JSONField()<|fim_middle|>
class ModelDataSerializer(serializers.Serializ... | code_fim | easy | {
"lang": "python",
"repo": "akxen/mpo-dev",
"path": "/project/api/serializers.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> initial_weights = serializers.JSONField()
estimated_returns = serializers.JSONField()
parameters = serializers.JSONField()<|fim_prefix|># repo: akxen/mpo-dev path: /project/api/serializers.py
from rest_framework import serializers
<|fim_middle|>class ModelDataSerializer(serializers.Serializ... | code_fim | easy | {
"lang": "python",
"repo": "akxen/mpo-dev",
"path": "/project/api/serializers.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ericgreveson/adventofcode path: /2018/day2/challenge.py
from collections import Counter
from aoc.challenge_base import ChallengeBase
class Challenge(ChallengeBase):
"""
Day 2 challenges
"""
def challenge1(self):
"""
Day 2 challenge 1
"""
ids_with... | code_fim | hard | {
"lang": "python",
"repo": "ericgreveson/adventofcode",
"path": "/2018/day2/challenge.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> """
Day 2 challenge 2
"""
# Remove one letter at each position from each ID and plonk them in a set
match_possibilities = set()
for id in self.lines:
sub_ids = set()
for letter_pos in range(len(id)):
sub_ids.add(id[:le... | code_fim | hard | {
"lang": "python",
"repo": "ericgreveson/adventofcode",
"path": "/2018/day2/challenge.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> # limit = 14 # days
# cond1 = Q(is_stood_down=True)
# cond2 = Q(end__lt=datetime.utcnow().replace(tzinfo=timezone.utc)-timedelta(days=limit))
return super().get_queryset().\
select_related('country')
# exclude(cond1 & cond2) # 'event' inclusion ^ to... | code_fim | hard | {
"lang": "python",
"repo": "IFRCGo/go-api",
"path": "/notifications/drf_views.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> serializer_class = SubscriptionSerializer
authentication_classes = (TokenAuthentication,)
permission_classes = (IsAuthenticated,)
search_fields = ('user__username', 'rtype') # for /docs
def get_queryset(self):
return Subscription.objects.filter(user=self.request.user)<|fim_pr... | code_fim | hard | {
"lang": "python",
"repo": "IFRCGo/go-api",
"path": "/notifications/drf_views.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: IFRCGo/go-api path: /notifications/drf_views.py
from datetime import datetime, timedelta, timezone
from django.db.models import Q
from django_filters import rest_framework as filters
from rest_framework.authentication import TokenAuthentication
from rest_framework.permissions import IsAuthenticat... | code_fim | hard | {
"lang": "python",
"repo": "IFRCGo/go-api",
"path": "/notifications/drf_views.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: jeraldlyh/HoloRPG path: /backend/api/room/models.py
from django.db import models
from django.db.models.signals import pre_save
from django.dispatch.dispatcher import receiver
from django.utils.translation import gettext as _
from ..user.models import UserProfile
from .utils import generate_unique... | code_fim | hard | {
"lang": "python",
"repo": "jeraldlyh/HoloRPG",
"path": "/backend/api/room/models.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> return self.id
@property
def get_profile_pictures(self) -> list:
profile_pictures = [self.host.image]
if self.player_two is not None:
profile_pictures.append(self.player_two.image)
if self.player_three is not None:
profile_pictures.appen... | code_fim | hard | {
"lang": "python",
"repo": "jeraldlyh/HoloRPG",
"path": "/backend/api/room/models.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
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