text stringlengths 232 16.3k | domain stringclasses 1
value | difficulty stringclasses 3
values | meta dict |
|---|---|---|---|
<|fim_suffix|> name = 'Pineapple'
aliases = ['pine', 'pineapple']
filenames = ['*.pine', '*.pineapple']
flags = re.DOTALL | re.UNICODE | re.MULTILINE
tokens = {
'commentsandwhitespace': [
(r'\s+', Text),
(r'<!--', Comment),
(r'//.*?\n', Comment.Single),
... | code_fim | hard | {
"lang": "python",
"repo": "wongjiahau/Pineapple",
"path": "/pineapple.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: wongjiahau/Pineapple path: /pineapple.py
# -*- coding: utf-8 -*-
"""
pygments.lexers.pineapple
~~~~~~~~~~~~~~~~~~~~~~~~~~
Lexers for Pineapple language.
:copyright: Copyright 2018 by Wong Jia Hau.
:license: Apache 2.0
"""
"""
This file is modified from pygments.lexers.javasc... | code_fim | hard | {
"lang": "python",
"repo": "wongjiahau/Pineapple",
"path": "/pineapple.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>all_data = sorted(all_data, key = lambda i: i['count'], reverse=True)
ujson.dump(all_data,open(f"{viz_data_dir}wiki_identifier_links.json",'w'),indent=2)<|fim_prefix|># repo: thisismattmiller/swib-2020-resources path: /build_data_scripts/wiki_ident_links.py
import requests
import ujson
from pathlib impor... | code_fim | hard | {
"lang": "python",
"repo": "thisismattmiller/swib-2020-resources",
"path": "/build_data_scripts/wiki_ident_links.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: thisismattmiller/swib-2020-resources path: /build_data_scripts/wiki_ident_links.py
import requests
import ujson
from pathlib import Path
wikidata_data_file = f"{str(Path.home())}/data/swib_data/wikidata_entities.ndjson"
viz_data_dir = f"{str(Path.home())}/data/swib_data/viz_data_source/"
wikid... | code_fim | hard | {
"lang": "python",
"repo": "thisismattmiller/swib-2020-resources",
"path": "/build_data_scripts/wiki_ident_links.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> return self._approximator.predict(state, **self._predict_params)
def set_weights(self, weights):
self._approximator.set_weights(weights)
def get_weights(self):
return self._approximator.get_weights()
@property
def weights_size(self):
return self._approxim... | code_fim | hard | {
"lang": "python",
"repo": "MushroomRL/mushroom-rl",
"path": "/mushroom_rl/policy/deterministic_policy.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: MushroomRL/mushroom-rl path: /mushroom_rl/policy/deterministic_policy.py
import numpy as np
from .policy import ParametricPolicy
class DeterministicPolicy(ParametricPolicy):
"""
Simple parametric policy representing a deterministic policy. As
deterministic policies are degenerate pr... | code_fim | medium | {
"lang": "python",
"repo": "MushroomRL/mushroom-rl",
"path": "/mushroom_rl/policy/deterministic_policy.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def get_regressor(self):
"""
Getter.
Returns:
The regressor that is used to map state to actions.
"""
return self._approximator
def __call__(self, state, action):
policy_action = self._approximator.predict(state, **self._predict_params... | code_fim | hard | {
"lang": "python",
"repo": "MushroomRL/mushroom-rl",
"path": "/mushroom_rl/policy/deterministic_policy.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> if replacements != None:
replace_sets = replace_sets + replacements
for replace_set in replace_sets:
text = replace_set[0]
replacement = replace_set[1]
html = html.replace(text,replacement)
return html
def update_lookup(lookup,key,entry):
'''update_lo... | code_fim | hard | {
"lang": "python",
"repo": "rwblair/cogat-docker",
"path": "/cognitive/apps/atlas/utils.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: rwblair/cogat-docker path: /cognitive/apps/atlas/utils.py
from django.utils.crypto import get_random_string
from py2neo import Path, Node, Relationship
from cognitive.settings import graph
import pandas
def generate_uid(node_type):
'''generte_uid will generate a unique identifier for a new ... | code_fim | hard | {
"lang": "python",
"repo": "rwblair/cogat-docker",
"path": "/cognitive/apps/atlas/utils.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> pix_hw = [h//2,w//2]
# pix_hw = [h//2,w//2]
vBurst = vals.Burst[:,pix_hw[0],pix_hw[1],:].cpu().numpy()[0]
vSubBurst = vals.SubBurst[:,pix_hw[0],pix_hw[1],:].cpu().numpy()[0]
iBurstValid = np.where(vBurst < 1e10)
iSubBurstValid = np.where(vSubBurst < 1e10)
assert np.all(iBurstVa... | code_fim | hard | {
"lang": "python",
"repo": "gauenk/faiss_fork",
"path": "/tests/test_burst_patch_distance.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: gauenk/faiss_fork path: /tests/test_burst_patch_distance.py
hw[0]+flow_t[0]# - psHalf +0# dx
startW = pix_hw[1] + flow_t[1] + padOffset # dx
endW = startW + ps
sliceW = slice(startW,endW)
# print(pix_hw,startH,startW,burst.shape)
patch_t = burst[t,0,:,slic... | code_fim | hard | {
"lang": "python",
"repo": "gauenk/faiss_fork",
"path": "/tests/test_burst_patch_distance.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: gauenk/faiss_fork path: /tests/test_burst_patch_distance.py
ls,_locs = bnnf_utils.runBurstNnf(burst, patchsize,
nblocks, k = k,
valMean = valMean,
blockLabels=None,
... | code_fim | hard | {
"lang": "python",
"repo": "gauenk/faiss_fork",
"path": "/tests/test_burst_patch_distance.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> regex = DifferentiableRegex(Regex('(a * b | c) * d'))
correct = ['cd', 'aaabd', 'aabcd', 'abbd']
incorrect = ['dd', 'aaaad', 'ababc', 'q']
for word in correct:
assert(regex.accepts(word))
for word in incorrect:
assert(not regex.accepts(word))
def test_comp_regex_redu... | code_fim | hard | {
"lang": "python",
"repo": "AlekseiPrivalihin/FormalLanguageTheory",
"path": "/tests/test_ExtraTask01.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: AlekseiPrivalihin/FormalLanguageTheory path: /tests/test_ExtraTask01.py
from pyformlang.regular_expression import Regex
from DifferentiableRegex import DifferentiableRegex
def test_simple_regex():
regexes = ["a", "a b", "a | b", "a*"]
answers = ["a", "ab", "b", "aaa"]
for i in range(... | code_fim | hard | {
"lang": "python",
"repo": "AlekseiPrivalihin/FormalLanguageTheory",
"path": "/tests/test_ExtraTask01.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: 4DNucleome/PartSeg path: /package/PartSeg/common_gui/algorithms_description.py
s)
for name, value in values.items():
if name in self.widgets_dict:
self.widgets_dict[name].set_value(value)
def image_changed(self, image: Image):
if not image:
... | code_fim | hard | {
"lang": "python",
"repo": "4DNucleome/PartSeg",
"path": "/package/PartSeg/common_gui/algorithms_description.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: 4DNucleome/PartSeg path: /package/PartSeg/common_gui/algorithms_description.py
if ap.range is not None:
res.setRange(*ap.range)
return res
@classmethod
def _get_field_from_value_type(cls, ap: AlgorithmProperty):
if issubclass(ap.value_type, Channel):
... | code_fim | hard | {
"lang": "python",
"repo": "4DNucleome/PartSeg",
"path": "/package/PartSeg/common_gui/algorithms_description.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> def set_values(self, values: typing.Union[dict, BaseModel]):
if isinstance(values, BaseModel):
values = dict(values)
for name, value in values.items():
if name in self.widgets_dict:
self.widgets_dict[name].set_value(value)
def image_changed(... | code_fim | hard | {
"lang": "python",
"repo": "4DNucleome/PartSeg",
"path": "/package/PartSeg/common_gui/algorithms_description.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: yangsuhui/Boosted-OICR path: /code/tasks/test.py
lipped image.
Function signature is the same as for im_detect_bbox.
"""
# Compute predictions on the flipped image
im_hf = im[:, ::-1, :]
im_width = im.shape[1]
box_proposals_hf = box_utils.flip_boxes(box_proposals, im_widt... | code_fim | hard | {
"lang": "python",
"repo": "yangsuhui/Boosted-OICR",
"path": "/code/tasks/test.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> """Return empty results lists for boxes, masks, and keypoints.
Box detections are collected into:
all_boxes[cls][image] = N x 5 array with columns (x1, y1, x2, y2, score)
Instance mask predictions are collected into:
all_segms[cls][image] = [...] list of COCO RLE encoded masks that... | code_fim | hard | {
"lang": "python",
"repo": "yangsuhui/Boosted-OICR",
"path": "/code/tasks/test.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
# print("test_net_on_dataset")
"""Run inference on a dataset."""
dataset = JsonDataset(dataset_name)
test_timer = Timer()
test_timer.tic()
all_boxes = test_net(args, dataset_name, proposal_file, output_dir, gpu_id=gpu_id, early_stop=early_stop)
test_timer.to... | code_fim | hard | {
"lang": "python",
"repo": "yangsuhui/Boosted-OICR",
"path": "/code/tasks/test.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: myteksi/Flask-AppBuilder path: /flask_appbuilder/validators.py
from wtforms import ValidationError
class Unique(object):
"""
Checks field value unicity against specified table field.
<|fim_suffix|> def __init__(self, datamodel, col_name, message=None):
self.datamodel = d... | code_fim | hard | {
"lang": "python",
"repo": "myteksi/Flask-AppBuilder",
"path": "/flask_appbuilder/validators.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> def __call__(self, form, field):
filters = self.datamodel.get_filters().add_filter(
self.col_name, self.datamodel.FilterEqual, field.data
)
count, obj = self.datamodel.query(filters)
if count > 0:
# only test if Unique, if pk value is different o... | code_fim | hard | {
"lang": "python",
"repo": "myteksi/Flask-AppBuilder",
"path": "/flask_appbuilder/validators.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> def to_ListOffsetArray64(self, start_at_zero: bool = False) -> ListOffsetArray:
offsets = self._compact_offsets64(start_at_zero)
return self._broadcast_tooffsets64(offsets)
def to_RegularArray(self):
return self
def maybe_to_NumpyArray(self) -> ak.contents.NumpyArray ... | code_fim | hard | {
"lang": "python",
"repo": "scikit-hep/awkward",
"path": "/src/awkward/contents/regulararray.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> out = nextcontent._getitem_next(nexthead, nexttail, nextadvanced)
if advanced is None:
return ak._slicing.getitem_next_array_wrap(
out, head.metadata.get("shape", (head.length,)), self._length
)
... | code_fim | hard | {
"lang": "python",
"repo": "scikit-hep/awkward",
"path": "/src/awkward/contents/regulararray.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: scikit-hep/awkward path: /src/awkward/contents/regulararray.py
tent.backend,
)
else:
return None
def _getitem_nothing(self):
return self._content._getitem_range(0, 0)
def _getitem_at(self, where: IndexType):
index_nplike = self._backend.in... | code_fim | hard | {
"lang": "python",
"repo": "scikit-hep/awkward",
"path": "/src/awkward/contents/regulararray.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> def update_one_gpu(self, param, grad, h):
cuda.elementwise(
'T grad, T lr, T eps',
'T param, T h',
'''h += grad * grad;
param -= lr * grad / (sqrt(h) + eps);''',
'adagrad')(grad, self.lr, self.eps,
param, h)<... | code_fim | hard | {
"lang": "python",
"repo": "sw005320/chainer",
"path": "/chainer/optimizers/ada_grad.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def update_one_cpu(self, param, grad, h):
h += grad * grad
param -= self.lr * grad / (numpy.sqrt(h) + self.eps)
def update_one_gpu(self, param, grad, h):
cuda.elementwise(
'T grad, T lr, T eps',
'T param, T h',
'''h += grad * grad;
... | code_fim | medium | {
"lang": "python",
"repo": "sw005320/chainer",
"path": "/chainer/optimizers/ada_grad.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: sw005320/chainer path: /chainer/optimizers/ada_grad.py
import numpy
from chainer import cuda
from chainer import optimizer
class AdaGrad(optimizer.Optimizer):
"""AdaGrad implementation.
See: http://jmlr.org/papers/v12/duchi11a.html
"""
def __init__(self, lr=0.001, eps=1e-8)... | code_fim | medium | {
"lang": "python",
"repo": "sw005320/chainer",
"path": "/chainer/optimizers/ada_grad.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: the-mandarine/esiea-school-projects path: /pymas/pymas/modules/udp_sender.py
"""Description of the module goes here"""
from socket import socket, AF_INET, SOCK_DGRAM
# Is the module blocking or not ?
BLOCKING = False
# Beliefs that are impacted by this module
USEFUL_FOR = ['nothing']
# Comman... | code_fim | medium | {
"lang": "python",
"repo": "the-mandarine/esiea-school-projects",
"path": "/pymas/pymas/modules/udp_sender.py",
"mode": "psm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> """Execute the command string : udp_send::ip,port,message::opts"""
cmd_list = cmd_string.split('::')
cmd_args = '::'.join(cmd_list[1:-1]).split(',')
dest_host = cmd_args[0]
dest_port = int(cmd_args[1])
message = ','.join(cmd_args[2:]).encode()
sock = socket(AF_INET, SOCK_DGRAM... | code_fim | medium | {
"lang": "python",
"repo": "the-mandarine/esiea-school-projects",
"path": "/pymas/pymas/modules/udp_sender.py",
"mode": "spm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: lsst-camera-dh/EO-utilities path: /python/lsst/eo_utils/bias/correl_wrt_oscan.py
"""Tasks to analyze the correlation between the overscan and the imaging region"""
import numpy as np
from lsst.eo_utils.base.defaults import ALL_SLOTS
from lsst.eo_utils.base.config_utils import EOUtilOptions
fr... | code_fim | hard | {
"lang": "python",
"repo": "lsst-camera-dh/EO-utilities",
"path": "/python/lsst/eo_utils/bias/correl_wrt_oscan.py",
"mode": "psm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> outtables = TableDict()
outtables.make_datatable("biasoscorr_stats", data_dict)
return outtables
def plot(self, dtables, figs, **kwargs):
"""Plot the summary data from the statistics study
Parameters
----------
dtables : `TableDict`
... | code_fim | hard | {
"lang": "python",
"repo": "lsst-camera-dh/EO-utilities",
"path": "/python/lsst/eo_utils/bias/correl_wrt_oscan.py",
"mode": "spm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: MTES-MCT/envergo path: /envergo/pages/urls.py
from django.urls import include, path
from django.utils.translation import gettext_lazy as _
from django.views.generic import RedirectView, TemplateView
from envergo.geodata.views import ParcelsExport
from envergo.pages.views import (
Availabilit... | code_fim | hard | {
"lang": "python",
"repo": "MTES-MCT/envergo",
"path": "/envergo/pages/urls.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> _("envergo-news/feed/"),
NewsFeed(),
name="news_feed",
),
path(
_("available-departments/"),
AvailabilityInfo.as_view(),
name="faq_availability_info",
... | code_fim | hard | {
"lang": "python",
"repo": "MTES-MCT/envergo",
"path": "/envergo/pages/urls.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: pythonspeed/filprofiler path: /tests/test-scripts/oom.py
import os, signal
import numpy
# This is peak:
x = numpy.ones((200 * 1024 * 1024), dtype=nump<|fim_suffix|> Trigger a MemoryError:
toobig = numpy.ones((1024, 1024 * 1024, 1024 * 1024), dtype=numpy.int8)<|fim_middle|>y.int8)
del x
# Below p... | code_fim | medium | {
"lang": "python",
"repo": "pythonspeed/filprofiler",
"path": "/tests/test-scripts/oom.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>be dumped, not the deleted allocation:
x = numpy.ones((100 * 1024 * 1024), dtype=numpy.int8)
# Trigger a MemoryError:
toobig = numpy.ones((1024, 1024 * 1024, 1024 * 1024), dtype=numpy.int8)<|fim_prefix|># repo: pythonspeed/filprofiler path: /tests/test-scripts/oom.py
import os, signal
import numpy
# Th... | code_fim | medium | {
"lang": "python",
"repo": "pythonspeed/filprofiler",
"path": "/tests/test-scripts/oom.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> return self.model.predict(X)
def score(self, X, Y, metric='f1', verbose=True):
Y = convert_labels(Y, 'categorical', 'onezero')
Y_p = self.predict(X)
metric_list = metric if isinstance(metric, list) else [metric]
scores = []
for metric in metric_list:
... | code_fim | hard | {
"lang": "python",
"repo": "rit-git/snorkel-notebooks",
"path": "/babble/disc_model.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> def train(self, X, Y, X_dev=None, Y_dev=None, **kwargs):
Y_bin = convert_labels(Y, 'categorical', 'onezero')
self.model.fit(X, Y_bin)
def predict(self, X):
return self.model.predict(X)
def score(self, X, Y, metric='f1', verbose=True):
Y = convert_labels(Y, 'ca... | code_fim | hard | {
"lang": "python",
"repo": "rit-git/snorkel-notebooks",
"path": "/babble/disc_model.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: rit-git/snorkel-notebooks path: /babble/disc_model.py
import random
from sklearn.linear_model import LogisticRegression
from metal.utils import convert_labels
from metal.metrics import metric_score
<|fim_suffix|> return self.model.predict(X)
def score(self, X, Y, metric='f1', verbo... | code_fim | hard | {
"lang": "python",
"repo": "rit-git/snorkel-notebooks",
"path": "/babble/disc_model.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: DarklightGames/io_scene_psk_psa path: /io_scene_psk_psa/psk/data.py
from typing import List
from ..data import *
class Psk(object):
class Wedge(object):
def __init__(self):
self.point_index: int = 0
self.u: float = 0.0
self.v: float = 0.0
... | code_fim | hard | {
"lang": "python",
"repo": "DarklightGames/io_scene_psk_psa",
"path": "/io_scene_psk_psa/psk/data.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> _fields_ = [
('position_delta', Vector3),
('tangent_z_delta', Vector3),
('point_index', c_int32)
]
@property
def has_extra_uvs(self):
return len(self.extra_uvs) > 0
@property
def has_vertex_colors(self):
return len(self.... | code_fim | hard | {
"lang": "python",
"repo": "DarklightGames/io_scene_psk_psa",
"path": "/io_scene_psk_psa/psk/data.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def test_node_exporter_running_and_enabled(host):
nginx = host.service("node_exporter")
assert nginx.is_running
assert nginx.is_enabled<|fim_prefix|># repo: integritee-network/collator-setup path: /ansible/roles/node-exporter/molecule/default/tests/test_node_exporter.py
def test_node_exporte... | code_fim | hard | {
"lang": "python",
"repo": "integritee-network/collator-setup",
"path": "/ansible/roles/node-exporter/molecule/default/tests/test_node_exporter.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|>def test_node_exporter_running_and_enabled(host):
nginx = host.service("node_exporter")
assert nginx.is_running
assert nginx.is_enabled<|fim_prefix|># repo: integritee-network/collator-setup path: /ansible/roles/node-exporter/molecule/default/tests/test_node_exporter.py
def test_node_exporter... | code_fim | hard | {
"lang": "python",
"repo": "integritee-network/collator-setup",
"path": "/ansible/roles/node-exporter/molecule/default/tests/test_node_exporter.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: integritee-network/collator-setup path: /ansible/roles/node-exporter/molecule/default/tests/test_node_exporter.py
def test_node_exporter(host):
binary = host.file("/usr/local/bin/node_exporter")
assert binary.exists
assert binary.user == 'root'
assert binary.group == 'root'
as... | code_fim | medium | {
"lang": "python",
"repo": "integritee-network/collator-setup",
"path": "/ansible/roles/node-exporter/molecule/default/tests/test_node_exporter.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: architprasar/concric path: /concricv1/urls.py
from django.contrib import admin
from django.conf import settings
from django.conf.urls.static import static
from django.urls import path, include
<|fim_suffix|>urlpatterns = urlpatterns + \
static(settings.MEDIA_URL, document_root=settings.MEDI... | code_fim | hard | {
"lang": "python",
"repo": "architprasar/concric",
"path": "/concricv1/urls.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
urlpatterns = urlpatterns + \
static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)<|fim_prefix|># repo: architprasar/concric path: /concricv1/urls.py
from django.contrib import admin
from django.conf import settings
from django.conf.urls.static import static
from django.urls import path, i... | code_fim | hard | {
"lang": "python",
"repo": "architprasar/concric",
"path": "/concricv1/urls.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Rowing0914/TF2_RL path: /tf_rl/common/to_markdown.py
# reference: https://github.com/thombashi/pytablewriter
from pytablewriter import MarkdownTableWriter
def params_to_markdown(params_str):
<|fim_suffix|> writer = MarkdownTableWriter()
writer.table_name = "Hyper-parameters"
writer.h... | code_fim | medium | {
"lang": "python",
"repo": "Rowing0914/TF2_RL",
"path": "/tf_rl/common/to_markdown.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> for _str in list_str:
_str.replace(" ", "")
param = _str.split("=")
params[param[0]] = param[1]
return params<|fim_prefix|># repo: Rowing0914/TF2_RL path: /tf_rl/common/to_markdown.py
# reference: https://github.com/thombashi/pytablewriter
from pytablewriter import Markdo... | code_fim | hard | {
"lang": "python",
"repo": "Rowing0914/TF2_RL",
"path": "/tf_rl/common/to_markdown.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: nazrulworld/fhir.resources path: /fhir/resources/biologicallyderivedproduct.py
of this resource.
element_property=True,
)
division__ext: fhirtypes.FHIRPrimitiveExtensionType = Field(
None, alias="_division", title="Extension field for ``division``."
)
expirationDa... | code_fim | hard | {
"lang": "python",
"repo": "nazrulworld/fhir.resources",
"path": "/fhir/resources/biologicallyderivedproduct.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> valueCodeableConcept: fhirtypes.CodeableConceptType = Field(
None,
alias="valueCodeableConcept",
title="Property values",
description=None,
# if property is element of this resource.
element_property=True,
# Choice of Data Types. i.e value[x]
... | code_fim | hard | {
"lang": "python",
"repo": "nazrulworld/fhir.resources",
"path": "/fhir/resources/biologicallyderivedproduct.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|>class BiologicallyDerivedProductProperty(backboneelement.BackboneElement):
"""Disclaimer: Any field name ends with ``__ext`` doesn't part of
Resource StructureDefinition, instead used to enable Extensibility feature
for FHIR Primitive Data Types.
A property that is specific to this Biolog... | code_fim | hard | {
"lang": "python",
"repo": "nazrulworld/fhir.resources",
"path": "/fhir/resources/biologicallyderivedproduct.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> numbers = []
got = sum_numbers(numbers)
want = 0
assert got == want
def test_sum_with_floats():
numbers = [1.5, 2.0, 3.5]
got = sum_numbers(numbers)
want = 7.0
assert got == want<|fim_prefix|># repo: py-bootcamp/learn-python-with-tdd path: /lists/v4/test_sum.py
from ... | code_fim | medium | {
"lang": "python",
"repo": "py-bootcamp/learn-python-with-tdd",
"path": "/lists/v4/test_sum.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: py-bootcamp/learn-python-with-tdd path: /lists/v4/test_sum.py
from sum_numbers import sum_numbers
def test_sum():
numbers = [1, 2, 3, 4, 5]
got = sum_numbers(numbers)
want = 15
<|fim_suffix|> got = sum_numbers(numbers)
want = 7.0
assert got == want<|fim_middle|> ass... | code_fim | medium | {
"lang": "python",
"repo": "py-bootcamp/learn-python-with-tdd",
"path": "/lists/v4/test_sum.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>def validateModel(args):
with tf.Session() as sess:
data = DataMNIST()
model = NNModel()
model.load(sess, 'models/nn/')
model.predict(sess, data)
if __name__ == '__main__':
# Arguments to be parsed via command line
parser=argpar... | code_fim | hard | {
"lang": "python",
"repo": "chenkenie/tft",
"path": "/nn_model.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> with tf.Session() as sess:
data = DataMNIST()
model = NNModel()
model.load(sess, 'models/nn/')
model.predict(sess, data)
if __name__ == '__main__':
# Arguments to be parsed via command line
parser=argparse.ArgumentParser(descrip... | code_fim | medium | {
"lang": "python",
"repo": "chenkenie/tft",
"path": "/nn_model.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: chenkenie/tft path: /nn_model.py
#!/usr/bin/env python
import argparse
import tensorflow as tf
import numpy as np
from trainer_template import SimpleTrainer
from data_loader_template import DataMNIST
from model_template import TensorFlowModelTemplate
from model_template import TensorFlowClassif... | code_fim | medium | {
"lang": "python",
"repo": "chenkenie/tft",
"path": "/nn_model.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|># savefig(fname, dpi=None, facecolor='w', edgecolor='w',
# orientation='portrait', papertype=None, format=None,
# transparent=False, bbox_inches=None, pad_inches=0.1,
# frameon=None)
# x = np.arange(-9, 10)
# y = np.arange(-9, 10).reshape(-1, 1)
# base = np.hypot(x, y)
# ims = []
# f... | code_fim | hard | {
"lang": "python",
"repo": "v1thesource/ParaSweep",
"path": "/sensitivity_analysis.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>def superplot(batch):
plt.figure(figsize=(9,9)) # plot size
plt.figure().add_axes([0.1, 0.1, 0.6, 0.8])
lineNames = {
0: 'pO2',
1: 'mew_e',
2: 'electrons',
3: 'holes',
4: 'VO{2}',
5: 'VO{1}',
6: 'VO{0}',
7: 'VM{-4}',
8: 'VM{-3}',
9: 'VM{-2}',
10:'VM{-1}',
11:'VM{0}',
12:'Oi{-2... | code_fim | hard | {
"lang": "python",
"repo": "v1thesource/ParaSweep",
"path": "/sensitivity_analysis.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: v1thesource/ParaSweep path: /sensitivity_analysis.py
# Sensitivity analysis tool for Brouwer diagrams! By Alexandros Kenich
# Example usage (to be improved with argument passing):
# python matplotlib_test.py
import matplotlib
matplotlib.use('Agg')
import numpy as np
import matplotlib.pyplot as ... | code_fim | hard | {
"lang": "python",
"repo": "v1thesource/ParaSweep",
"path": "/sensitivity_analysis.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: s22l1g11/PyChat path: /src/client2.py
import pyNotificationCenter
import socket
host = input("Enter hostname: ")
if host == '': host="127.0.0.1"
port = 4446
server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
server.connect((host, port))
<|fim_suffix|>while running:
# handling differen... | code_fim | medium | {
"lang": "python",
"repo": "s22l1g11/PyChat",
"path": "/src/client2.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> if "#rename" in message:
nick = message.replace("#rename ","",1)
print("Your new username is: "+nick)
elif "#exit" in message:
server.close()
print("System stops...")
exit(0)
elif "#shutdown" in message:
server.send("!!shutdown!!")
server.close()
print("System stops...")
... | code_fim | hard | {
"lang": "python",
"repo": "s22l1g11/PyChat",
"path": "/src/client2.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> placed.append(block.arguments[0])
t += 1
return State(set(part_states))
def get_blocks(self) -> list:
"""Returns a list of blocks in the blocks world.
:return: a list of blocks
"""
self.clingo = ClingoBridge() # reset clingo
... | code_fim | hard | {
"lang": "python",
"repo": "fxgst/RLASP",
"path": "/BlocksWorld.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: fxgst/RLASP path: /BlocksWorld.py
from ClingoBridge import *
import random
from entities import *
import numpy as np
state_enumeration_limit = 9 # blocks worlds bigger than this don't try to enumerate all possible states
class BlocksWorld:
def __init__(self):
self.clingo = ClingoB... | code_fim | hard | {
"lang": "python",
"repo": "fxgst/RLASP",
"path": "/BlocksWorld.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def parse_part_state(self, atom: clingo.Symbol) -> PartState:
"""Parse a part-state.
:param atom: a clingo atom
:return: a part-state object representing one on/2 atom
"""
on_predicate = atom.arguments[0]
top_block = on_predicate.arguments[0]
bo... | code_fim | hard | {
"lang": "python",
"repo": "fxgst/RLASP",
"path": "/BlocksWorld.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: y95847frank/Semantic-Relations-Classifier path: /angelo_src/rnn.py
import numpy as np
import data
import os
import pickle
import keras
from keras.layers import Embedding , Dense ,Input , GlobalMaxPooling1D , Bidirectional , LSTM, GRU, Concatenate, Flatten ,Dropout
from keras.models import Model
f... | code_fim | hard | {
"lang": "python",
"repo": "y95847frank/Semantic-Relations-Classifier",
"path": "/angelo_src/rnn.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def main():
train_data_file = os.path.join(os.path.join(BASE_DIR , "dataset") , "TRAIN_FILE.txt")
train_data = data.Data(train_data_file , "train")
quit()
#rnn model
rnn_model = create_rnn_model(train_data.embedding_layer)
print(rnn_model.summary())
#train
model_file = 'model/lstm%ssum_drop2_h... | code_fim | hard | {
"lang": "python",
"repo": "y95847frank/Semantic-Relations-Classifier",
"path": "/angelo_src/rnn.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: microfossil/particle-classification path: /miso/data/image_dataset.py
import numpy as np
import skimage.io as skio
from miso.data.dataset import DatasetBase
from miso.data.image_loader import ParallelImageLoader
from miso.data.image_utils import resize_transform, resize_with_pad_transform, null_... | code_fim | hard | {
"lang": "python",
"repo": "microfossil/particle-classification",
"path": "/miso/data/image_dataset.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> if self.transform_fn is None:
self.transform_fn = null_transform
self.transform_args = [0]
# Get dataset unique identification hash
super().__init__(memmap_directory=memmap_directory, overwrite_memmap=overwrite_memmap, dtype=dtype)
self.hash_data = ... | code_fim | hard | {
"lang": "python",
"repo": "microfossil/particle-classification",
"path": "/miso/data/image_dataset.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|># for filename in filenames:
# startCalculation(filename)
# startCalculation(filenames[0])
# pool.map(startCalculation, [1, 2, 3])
if len(sys.argv) < 2:
print ("python op_fairness.py <directory>, e.g. 'python op_fairness pldi13/' for logfiles in /mnt/local_homes/ahaas/pldi13/")
else:
calcOpFair... | code_fim | hard | {
"lang": "python",
"repo": "cksystemsgroup/zeta",
"path": "/scripts/lin_point_linearization.py",
"mode": "spm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: cksystemsgroup/zeta path: /scripts/lin_point_linearization.py
import os;
import multiprocessing;
import subprocess;
import ah_config;
import sys;
def getLineCount(filename):
if not os.path.exists(filename):
return -1
logfile = open(filename, 'r')
lines = 0
for line in logfile:
... | code_fim | hard | {
"lang": "python",
"repo": "cksystemsgroup/zeta",
"path": "/scripts/lin_point_linearization.py",
"mode": "psm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> outputDir = "../results/{directory}".format(directory = directory)
if not os.path.exists(outputDir) :
os.makedirs(outputDir)
logDir = "/mnt/local_homes/ahaas/{directory}".format(directory = directory)
filenames = [{"filename":os.path.join(logDir, f), "directory":directory} for f in os.listdi... | code_fim | hard | {
"lang": "python",
"repo": "cksystemsgroup/zeta",
"path": "/scripts/lin_point_linearization.py",
"mode": "spm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: catapult-project/catapult path: /third_party/webapp2/tests/extras_json_test.py
# -*- coding: utf-8 -*-
from webapp2_extras import json
import test_base
class TestJson(test_base.BaseTestCase):
def test_encode(self):
self.assertEqual(json.encode(
'<script>alert("hello")</... | code_fim | medium | {
"lang": "python",
"repo": "catapult-project/catapult",
"path": "/third_party/webapp2/tests/extras_json_test.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> self.assertEqual(json.b64encode(
'<script>alert("hello")</script>'),
'IjxzY3JpcHQ+YWxlcnQoXCJoZWxsb1wiKTxcL3NjcmlwdD4i')
def test_b64decode(self):
self.assertEqual(json.b64decode(
'IjxzY3JpcHQ+YWxlcnQoXCJoZWxsb1wiKTxcL3NjcmlwdD4i'),
'<sc... | code_fim | medium | {
"lang": "python",
"repo": "catapult-project/catapult",
"path": "/third_party/webapp2/tests/extras_json_test.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: TR19006/robot_controller path: /run.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
########################################
# Robot Controller Script #
# Copyright (c) Takuya Tsukahara, 2019 #
########################################
import argparse
import cv2
import logging
from ... | code_fim | hard | {
"lang": "python",
"repo": "TR19006/robot_controller",
"path": "/run.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> '''
GPIO.output(m11, 1)
GPIO.output(m12, 0)
GPIO.output(m21, 1)
GPIO.output(m22, 0)
'''
print('FORWARD')
return 'forward'
@app.route('/left')
def left():
'''
GPIO.output(m11, 0)
GPIO.output(m12, 0)
GPIO.output(m21, 1)
GPIO.output(m22, 1)
'''
pri... | code_fim | hard | {
"lang": "python",
"repo": "TR19006/robot_controller",
"path": "/run.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ShengrongYang/Colors-of-Zhang-Yimou path: /scripts/HSV_K-means/GS_test.py
from datetime import datetime
import scipy
from scipy.spatial.distance import euclidean
from sklearn.cluster import KMeans
from sklearn.datasets.samples_generator import make_blobs
dst = euclidean
X, labels_true = make_bl... | code_fim | hard | {
"lang": "python",
"repo": "ShengrongYang/Colors-of-Zhang-Yimou",
"path": "/scripts/HSV_K-means/GS_test.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>KMeans_args_dict = {
'n_clusters': 0,
# drastically saves convergence time
'init': 'k-means++',
'random_state': 0,
'max_iter': 300,
'n_init': 10,
'verbose': 0,
# 'n_jobs':8
}
def gap(data, refs=None, nrefs=20, ks=range(3, 10)):
"""
I: NumPy array, reference matrix... | code_fim | hard | {
"lang": "python",
"repo": "ShengrongYang/Colors-of-Zhang-Yimou",
"path": "/scripts/HSV_K-means/GS_test.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> @token_auth.login_required(optional=True)
def get(self, organisation_id):
"""
Retrieves an organisation
---
tags:
- organisations
produces:
- application/json
parameters:
- in: header
name: Authorizat... | code_fim | hard | {
"lang": "python",
"repo": "hotosm/tasking-manager",
"path": "/backend/api/organisations/resources.py",
"mode": "spm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: hotosm/tasking-manager path: /backend/api/organisations/resources.py
from distutils.util import strtobool
from flask_restful import Resource, request, current_app
from schematics.exceptions import DataError
from backend.models.dtos.organisation_dto import (
NewOrganisationDTO,
UpdateOrga... | code_fim | hard | {
"lang": "python",
"repo": "hotosm/tasking-manager",
"path": "/backend/api/organisations/resources.py",
"mode": "psm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: omunroe-com/ietfdb2 path: /ietf/nomcom/models.py
# -*- coding: utf-8 -*-
import os
from django.db import models
from django.db.models.signals import post_delete
from django.conf import settings
from django.contrib.auth.models import User
from django.template.loader import render_to_string
from d... | code_fim | hard | {
"lang": "python",
"repo": "omunroe-com/ietfdb2",
"path": "/ietf/nomcom/models.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> def __unicode__(self):
if self.email.person and self.email.person.name:
return u'%s <%s> %s' % (self.email.person.plain_name(), self.email.address, self.nomcom.year())
else:
return u'%s %s' % (self.email.address, self.nomcom.year())
def name(self):
... | code_fim | hard | {
"lang": "python",
"repo": "omunroe-com/ietfdb2",
"path": "/ietf/nomcom/models.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> def get_questionnaire(self):
return render_to_string(self.questionnaire.path, {'position': self})
def get_requirement(self):
rendered = render_to_string(self.requirement.path, {'position': self})
if self.requirement.type_id=='plain':
rendered = linebreaks(rende... | code_fim | hard | {
"lang": "python",
"repo": "omunroe-com/ietfdb2",
"path": "/ietf/nomcom/models.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def to_timeseries(benchmark_data, x_label='Episode', y_label='Average Episode Reward',
target=rewards_by_episode, cut_x=1e12, smooth=0):
"""
Convert benchmark data to timeseries data, plottable my mathplotlib.
Args:
benchmark_data: BenchmarkData object
x_lab... | code_fim | hard | {
"lang": "python",
"repo": "afcarl/rl-benchmark",
"path": "/rl_benchmark/analyze/transform.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> if cut_x > len(rewards):
seconds = np.linspace(0, cut_x, 200)
rewards = n_step_average(rewards, 200)
else:
seconds = np.linspace(0, cut_x, cut_x)
rewards = n_step_average(rewards, cut_x)
return seconds, rewards
def to_timeseries(benchmark_data, x_label='Episo... | code_fim | hard | {
"lang": "python",
"repo": "afcarl/rl-benchmark",
"path": "/rl_benchmark/analyze/transform.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: afcarl/rl-benchmark path: /rl_benchmark/analyze/transform.py
# Copyright 2018 The RLgraph project. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
... | code_fim | hard | {
"lang": "python",
"repo": "afcarl/rl-benchmark",
"path": "/rl_benchmark/analyze/transform.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: suheb/cltk_api path: /metadata/translation/map_translation.py
"""
Map a translation to the original
Must already have definitions ingested for this to work
"""
import optparse
import pymongo
import re
import copy
import string
import numpy as np
from cltk_api.util.db import mongo
from nltk.c... | code_fim | hard | {
"lang": "python",
"repo": "suheb/cltk_api",
"path": "/metadata/translation/map_translation.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # split at words
words = text_unit.split(" ")
# lemmas
lemmas = []
for word in words:
if len(word):
word = self.lmtzr.lemmatize(word)
if word not in self.stops:
lemmas.append(word)
# syns
syns = []
for lemma in lemmas:
synsets = wn.synsets(lemma)
wor... | code_fim | hard | {
"lang": "python",
"repo": "suheb/cltk_api",
"path": "/metadata/translation/map_translation.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: m3h0w/TRAiVEL path: /back-end/skyscan/mainapp/__init__.py
# Import flask and template operators
from flask import Flask
from flask import jsonify, request
from prices import get_prices_from_cities, get_next_friday_sunday
from flask_cors import CORS
app = Flask(__name__, static_url_path='/stati... | code_fim | medium | {
"lang": "python",
"repo": "m3h0w/TRAiVEL",
"path": "/back-end/skyscan/mainapp/__init__.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>@app.route("/get_prices", methods=['POST'])
def get_prices():
if 'cities_list' in request.json and isinstance(request.json['cities_list'], list):
strings_list = request.json['cities_list']
data = get_prices_from_cities(strings_list)
# month = "2018-04"
# if 'month' in request.json and isin... | code_fim | medium | {
"lang": "python",
"repo": "m3h0w/TRAiVEL",
"path": "/back-end/skyscan/mainapp/__init__.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> data = get_prices_from_cities(strings_list)
# month = "2018-04"
# if 'month' in request.json and isinstance(request.json['month'], str):
# month = request.json['month']
return jsonify(data)<|fim_prefix|># repo: m3h0w/TRAiVEL path: /back-end/skyscan/mainapp/__init__.py
# Import flask and t... | code_fim | medium | {
"lang": "python",
"repo": "m3h0w/TRAiVEL",
"path": "/back-end/skyscan/mainapp/__init__.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>def setup(bot):
"""Sets up the extension."""
@bot.listen("on_command")
async def log_command(ctx):
message = (f"{ctx.message.content} | "
f"{ctx.author.id} in {ctx.guild.name}:{ctx.guild.id}")
logger.info(message)
message = f"{ctx.message.created_at.... | code_fim | hard | {
"lang": "python",
"repo": "DasWolke/kitsuchan-2",
"path": "/cogs/owner/command_log.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: DasWolke/kitsuchan-2 path: /cogs/owner/command_log.py
#!/usr/bin/env python3
# pylint: disable=C0103
"""Command logging functionality."""
import logging
FORMAT = "%(asctime)-15s: %(message)s"
formatter = logging.Formatter(FORMAT)
<|fim_suffix|>
def setup(bot):
"""Sets up the extension."""... | code_fim | hard | {
"lang": "python",
"repo": "DasWolke/kitsuchan-2",
"path": "/cogs/owner/command_log.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> fs = args["fs"]
fs_path = args["fs_path"]
self.trash(path=fs.getsyspath(fs_path), simulate=simulate)<|fim_prefix|># repo: iburunat/organize path: /organize/actions/trash.py
import logging
from .action import Action
logger = logging.getLogger(__name__)
class Trash(Action):
... | code_fim | hard | {
"lang": "python",
"repo": "iburunat/organize",
"path": "/organize/actions/trash.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: iburunat/organize path: /organize/actions/trash.py
import logging
from .action import Action
logger = logging.getLogger(__name__)
class Trash(Action):
"""Move a file or dir into the trash."""
<|fim_suffix|> from send2trash import send2trash
self.print(f'Trash "{path}"')
... | code_fim | medium | {
"lang": "python",
"repo": "iburunat/organize",
"path": "/organize/actions/trash.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: chenpaopao/- path: /classification/convNext/models/networks.py
"""
original code from facebook research:
https://github.com/facebookresearch/ConvNeXt
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
def drop_path(x, drop_prob: float = 0., training: bool = False):
"""D... | code_fim | hard | {
"lang": "python",
"repo": "chenpaopao/-",
"path": "/classification/convNext/models/networks.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> for i in range(4):
x = self.downsample_layers[i](x)
x = self.stages[i](x)
return self.norm(x.mean([-2, -1])) # global average pooling, (N, C, H, W) -> (N, C)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.forward_features(x)
x = ... | code_fim | hard | {
"lang": "python",
"repo": "chenpaopao/-",
"path": "/classification/convNext/models/networks.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.forward_features(x)
x = self.head(x)
return x
def convnext_tiny(num_classes: int):
# https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth
model = ConvNeXt(depths=[3, 3, 9, 3],
... | code_fim | hard | {
"lang": "python",
"repo": "chenpaopao/-",
"path": "/classification/convNext/models/networks.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: erjihaoshi/audio path: /torchaudio/compliance/kaldi.py
import math
import random
import torch
__all__ = [
'spectrogram'
]
# numeric_limits<float>::epsilon()
EPSILON = torch.tensor(1.19209290e-07, dtype=torch.get_default_dtype())
# 1 milliseconds = 0.001 seconds
MILLISECONDS_TO_SECONDS = 0.... | code_fim | hard | {
"lang": "python",
"repo": "erjihaoshi/audio",
"path": "/torchaudio/compliance/kaldi.py",
"mode": "psm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|>def spectrogram(
sig, blackman_coeff=0.42, channel=-1, dither=1.0, energy_floor=0.0,
frame_length=25.0, frame_shift=10.0, min_duration=0.0,
preemphasis_coefficient=0.97, raw_energy=True, remove_dc_offset=True,
round_to_power_of_two=True, sample_frequency=16000.0, snip_edges... | code_fim | hard | {
"lang": "python",
"repo": "erjihaoshi/audio",
"path": "/torchaudio/compliance/kaldi.py",
"mode": "spm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> inventory = configure_inventory(args)
try:
configdrive = json.loads(args.configdrive)
except (ValueError, TypeError):
configdrive = args.configdrive
extra_vars = args.extra_vars or []
if configdrive:
# Need to preserve JSON
extra_vars.append(json.dumps(... | code_fim | hard | {
"lang": "python",
"repo": "openstack/bifrost",
"path": "/bifrost/cli.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
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