text stringlengths 232 16.3k | domain stringclasses 1
value | difficulty stringclasses 3
values | meta dict |
|---|---|---|---|
<|fim_prefix|># repo: sammypudjianto/PythonLib path: /Finance/PerformanceComparison/numba_cuda_cython.py
"""
Speed comparison between straigt python loop vs numba
"""
import time
import matplotlib.pyplot as plt
import numba
import numpy as np
<|fim_suffix|> res = []
for iteration in iterations:
start... | code_fim | hard | {
"lang": "python",
"repo": "sammypudjianto/PythonLib",
"path": "/Finance/PerformanceComparison/numba_cuda_cython.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> print(' all list', res)
x = [x for x, y, z in res]
y = [y for x, y, z in res]
z = [z for x, y, z in res]
plt.figure(figsize=(10, 5))
plt.plot(x, y, 'r')
plt.plot(x, z, 'b')
plt.grid(True)
plt.xlabel('iterations')
plt.ylabel('time (ms)')
plt.show()<|fim_prefix|>#... | code_fim | hard | {
"lang": "python",
"repo": "sammypudjianto/PythonLib",
"path": "/Finance/PerformanceComparison/numba_cuda_cython.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> flair = nib.load(os.path.join(path_to_file, base_name + str(i), base_name + str(i) + "_flair.nii.gz"))
t2 = nib.load(os.path.join(path_to_file, base_name + str(i), base_name + str(i) + "_t2.nii.gz"))
t1 = nib.load(os.path.join(path_to_file, base_name + str(i), base_name + str(i) + "_t1.nii.gz"... | code_fim | hard | {
"lang": "python",
"repo": "jordan-colman/DR-Unet104",
"path": "/convert_nii_to_png.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: jordan-colman/DR-Unet104 path: /convert_nii_to_png.py
from PIL import Image
import numpy as np
import os
import nibabel as nib
import cv2
path_to_file = 'Data\MICCAI_BraTS2020_ValidationData'
path_to_mask_output = 'Data\BRATS_20_Training_masks_png'
path_to_output = 'Data\BRATS_20_Validation_data... | code_fim | hard | {
"lang": "python",
"repo": "jordan-colman/DR-Unet104",
"path": "/convert_nii_to_png.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> ##include if converting segmentation mask
'''
slice_m[:,:,0] = cv2.resize(MASK[:,:,j].astype(np.uint8),(H,W))
slice_m[:, :, 1] = cv2.resize(MASK[:, :, j].astype(np.uint8), (H, W))
slice_m[:, :, 2] = cv2.resize(MASK[:, :, j].astype(np.uint8), (H, W))
sm_png =... | code_fim | hard | {
"lang": "python",
"repo": "jordan-colman/DR-Unet104",
"path": "/convert_nii_to_png.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>a2 = np.array([[1.0, -1.0, 2.0], [3.0, 2.0, 1.0], [2.0, -3.0, -2.0]])
b2 = np.array([5.0, 10.0, -10.0])
print(30*'-')
print("Przyklad B\n")
print("Sprawdzenie czy macierz a jest diagonalnie dominujaca. Jesli zwrocony zostal wektor jedynek to jest diagonalnie dominujaca.")
print(diag_dom_test(a2))
print("J... | code_fim | hard | {
"lang": "python",
"repo": "matbocz/kurs-mn-python-pwsz-elblag",
"path": "/MN_lab_12/MN_lab12_zad_1.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: matbocz/kurs-mn-python-pwsz-elblag path: /MN_lab_12/MN_lab12_zad_1.py
#ZADANIE 1
#Wykorzystaj metode iteracyjna Jacobiego do
#znalezienia rozwiazan ukladow rownan a) – d). Wykonaj
#obliczenia w przy uzyciu arkusza kalkulacyjnego oraz programu
#napisanego w Python.
import numpy as np;
def diag_d... | code_fim | hard | {
"lang": "python",
"repo": "matbocz/kurs-mn-python-pwsz-elblag",
"path": "/MN_lab_12/MN_lab12_zad_1.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: nearkyh/artik-project path: /artik_server/face_net.py
import tensorflow as tf
import re
flags = tf.app.flags
FLAGS = flags.FLAGS
FLAGS.image_size = 96
FLAGS.image_color = 3
FLAGS.maxpool_filter_size = 2
FLAGS.num_classes = 5
FLAGS.batch_size = 100
FLAGS.learning_rate = 0.0001
FLAGS.lo... | code_fim | hard | {
"lang": "python",
"repo": "nearkyh/artik-project",
"path": "/artik_server/face_net.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|># fully connected layer 2
def fc2(input_data):
FLAGS.fc2_layer_size = 256
with tf.name_scope('fc_2'):
W_fc2 = tf.Variable(tf.truncated_normal([FLAGS.fc1_layer_size, FLAGS.fc2_layer_size], stddev=0.1))
b_fc2 = tf.Variable(tf.truncated_normal(
[FLAGS.fc2_layer_siz... | code_fim | hard | {
"lang": "python",
"repo": "nearkyh/artik-project",
"path": "/artik_server/face_net.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> global lastToken
lastToken = token
def writeHeader():
state = State()
h = state.h
toc1 = state.toc1
toc2 = state.toc2
mt = state.mt
if not h:
h = state.title
if not toc1:
toc1 = state.title
if not toc2:
toc2 = state.title
if not mt:... | code_fim | hard | {
"lang": "python",
"repo": "mondele/tools",
"path": "/usfm/usfm2rc.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>def takeAsIs(key, value):
state = State()
if state.chapter < 1: # header has not been written, chapter 1 has not started
state.addPostHeader(key, value)
# sys.stdout.write(u"addPostHeader(" + key + u", " + str(len(value)) + u")\n")
else:
# sys.stdout.write(u"takeA... | code_fim | hard | {
"lang": "python",
"repo": "mondele/tools",
"path": "/usfm/usfm2rc.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: mondele/tools path: /usfm/usfm2rc.py
# -*- coding: utf-8 -*-
# This script produces one or more .usfm files in resource container format from valid USFM source text.
# Chunk division and paragraph locations are based on an English resource container of the same Bible book.
# Uses parseUsfm module... | code_fim | hard | {
"lang": "python",
"repo": "mondele/tools",
"path": "/usfm/usfm2rc.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: magic428/work_note path: /tools-dl-cv/useful_examples/code/train_eassy/calc_amount_and_extract_fixed_amount_data.py
#coding=gbk
import os
import random
import shutil
def mkdir_if_not_exists(path):
if not os.path.exists(path):
os.makedirs(path)
data_dir = "roi"
origin_dir = "error... | code_fim | hard | {
"lang": "python",
"repo": "magic428/work_note",
"path": "/tools-dl-cv/useful_examples/code/train_eassy/calc_amount_and_extract_fixed_amount_data.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> classes = os.listdir(origin_dir)
files_of_class = {}
for c in classes:
copy_files = []
c_full = os.path.join(origin_dir, c)
c_dst = os.path.join(dst_dir, c)
mkdir_if_not_exists(c_dst)
files = os.listdir(c_full)
for f in files:
copy_files.append(os.path.join(c_full, f))
... | code_fim | medium | {
"lang": "python",
"repo": "magic428/work_note",
"path": "/tools-dl-cv/useful_examples/code/train_eassy/calc_amount_and_extract_fixed_amount_data.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>os.chdir(FUZZDIR)
g = gdb.Gdb(FUZZBIN, timeout=10)
g.setup()
todo = []
for f in files:
h = check_output(['sha1sum', f]).split(b' ')[0].decode('utf-8')
crashname = ROOTDIR + '/crashes/' + h + '.bsp'
if not os.path.exists(crashname):
todo.append((f, h))
print("TODO %d files" % len(todo... | code_fim | hard | {
"lang": "python",
"repo": "fengjixuchui/bspfuzz",
"path": "/triage/triage.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: fengjixuchui/bspfuzz path: /triage/triage.py
#!/usr/bin/env python3
import argparse
import gdb
import glob
import hashlib
import os
import random
import shutil
import sys
import tempfile
from subprocess import Popen, check_output
ROOTDIR = os.path.dirname(os.path.abspath(__file__))
FUZZDIR = ROO... | code_fim | hard | {
"lang": "python",
"repo": "fengjixuchui/bspfuzz",
"path": "/triage/triage.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|>-Send your location to get Vaccine availability in your Pin-Code
-Enter your district to search for Vaccine availability in your district
Type *Help* anytime to to learn how to interact with me.
'''
help_message = f'''
-Say *Hi* to begin an interaction with me anytime.
-Enter your pincode to get vaccine ... | code_fim | medium | {
"lang": "python",
"repo": "adybose/cowin-whatsapp-tracker",
"path": "/constants.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: adybose/cowin-whatsapp-tracker path: /constants.py
# Constants and default messages for the WhatsApp bot
greeting_tokens = ['hi', 'hello', 'hey']
# Default messages
welcome_message = f'''
Hi there! I am a bot that gives you the latest information on Covid-19 Vaccine availability in India nearby... | code_fim | hard | {
"lang": "python",
"repo": "adybose/cowin-whatsapp-tracker",
"path": "/constants.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>def query(db,statement):
DBV = e32db.Db_view()
DBV.prepare(db, unicode(statement))
n = DBV.count_line()
DBV.first_line()
data=[]
for i in xrange(n):
DBV.get_line()
line=[]
for j in xrange(DBV.col_count()):
line.append(DBV.col(1+j))
t=DBV.col_type(1+j)
print "Colum... | code_fim | hard | {
"lang": "python",
"repo": "tuankien2601/python222",
"path": "/ext/e32db/test_e32db.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: tuankien2601/python222 path: /ext/e32db/test_e32db.py
# Copyright (c) 2005 Nokia Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.o... | code_fim | hard | {
"lang": "python",
"repo": "tuankien2601/python222",
"path": "/ext/e32db/test_e32db.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>db=e32db.Dbms()
FILE=u'c:\\bardb'
if os.path.isfile(FILE):
os.remove(FILE)
db.create(FILE)
db.open(FILE)
db.execute(u'create table data (a long varchar, b integer)')
db.execute(u"insert into data values('"+('x'*10)+"',42)")
print query(db,u'select * from data')
#db.execute(u"insert into data values('"+('... | code_fim | hard | {
"lang": "python",
"repo": "tuankien2601/python222",
"path": "/ext/e32db/test_e32db.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: eclee25/flu-SDI-exploratory-age path: /scripts/OR_allweeks_hhsreg.py
pstat_zip3_season_cl.csv, /home/elee/Dropbox/Elizabeth_Bansal_Lab/SDI_Data/explore/R_export/OR_zip3_week_cl.csv
###Command Line: python
##############################################
### notes ###
### packages/modules ###
... | code_fim | hard | {
"lang": "python",
"repo": "eclee25/flu-SDI-exploratory-age",
"path": "/scripts/OR_allweeks_hhsreg.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # d_ILI[(zip3, season, agegroup, wk)] = ILI
for week in sorted(weeklist):
incidwk_child = sum([d_ILI[(zip3, seasonnum, 'C', week)] for zip3 in zip3list if (zip3, seasonnum, 'C', week) in d_ILI])
incidwk_adult = sum([d_ILI[(zip3, seasonnum, 'A', week)] for zip3 in zip3list if (zip3, seasonnum, 'A', w... | code_fim | hard | {
"lang": "python",
"repo": "eclee25/flu-SDI-exploratory-age",
"path": "/scripts/OR_allweeks_hhsreg.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: eclee25/flu-SDI-exploratory-age path: /scripts/OR_allweeks_hhsreg.py
faultdict
from itertools import product
from time import clock
## local modules ##
### data structures ###
# d_pop[(zip3, season, agegroup)] = popstat
# d_z3hhs[zip3] = hhs region
d_pop, d_z3hhs = {}, {} # from pop data
# d_I... | code_fim | hard | {
"lang": "python",
"repo": "eclee25/flu-SDI-exploratory-age",
"path": "/scripts/OR_allweeks_hhsreg.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: stfxecutables/MLE-Image-Classification path: /src/CNN_Answer_Predictor.py
import numpy as np
from sklearn.model_selection import train_test_split
from tensorflow import keras
from tensorflow.keras.callbacks import EarlyStopping, TerminateOnNaN
from tensorflow.keras.layers import Dropout, Dense, F... | code_fim | hard | {
"lang": "python",
"repo": "stfxecutables/MLE-Image-Classification",
"path": "/src/CNN_Answer_Predictor.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> train_labels = keras.utils.to_categorical(
train_labels, num_classes=self.num_classes
)
val_labels = keras.utils.to_categorical(
val_labels, num_classes=self.num_classes
)
self.model.fit(
x=train_data,
y=train_labels,... | code_fim | hard | {
"lang": "python",
"repo": "stfxecutables/MLE-Image-Classification",
"path": "/src/CNN_Answer_Predictor.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> """
Trains CNN model to generate softmax scores from
:param input_scores: numpy array of prediction matrices
:param true_labels: numpy array of labels corresponding to each prediction matrix
:return: None
"""
self.model.compile(
loss="cat... | code_fim | hard | {
"lang": "python",
"repo": "stfxecutables/MLE-Image-Classification",
"path": "/src/CNN_Answer_Predictor.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: universalturtles/Lisibilite path: /code/common/Constants.py
HEADER_CORE_METRICS = "Core Metrics"
HEADER_TOTAL_COUNT = "Total Count"
<|fim_suffix|>FRES = "Flesch Reading Ease Score"
FKGL = "Flesch-Kincaid Grade Level"
GFI = "Gunning Fog Index"
ARI = "Automated Readability Index"
SMOG = "Simple M... | code_fim | hard | {
"lang": "python",
"repo": "universalturtles/Lisibilite",
"path": "/code/common/Constants.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>METRICS_HEADERS = [HEADER_ALGORITHM, HEADER_SCORE, HEADER_DESCRIPTION]
FRES = "Flesch Reading Ease Score"
FKGL = "Flesch-Kincaid Grade Level"
GFI = "Gunning Fog Index"
ARI = "Automated Readability Index"
SMOG = "Simple Measure of Gobbledygook"
CLI = "Coleman-Liau Index"
LWS = "Linsear Write Score"
FRY = ... | code_fim | hard | {
"lang": "python",
"repo": "universalturtles/Lisibilite",
"path": "/code/common/Constants.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>HEADER_ALGORITHM = "Algorithm"
HEADER_SCORE = "Score"
HEADER_DESCRIPTION = "Description"
METRICS_HEADERS = [HEADER_ALGORITHM, HEADER_SCORE, HEADER_DESCRIPTION]
FRES = "Flesch Reading Ease Score"
FKGL = "Flesch-Kincaid Grade Level"
GFI = "Gunning Fog Index"
ARI = "Automated Readability Index"
SMOG = "Sim... | code_fim | medium | {
"lang": "python",
"repo": "universalturtles/Lisibilite",
"path": "/code/common/Constants.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>ulate
__all__ = [
"Learner",
"emulate",
"BaseEmulator",
"CholeskyNnEmulator",
"InterpolationEmulator",
"TorchEmulator",
]<|fim_prefix|># repo: auckland-cosmo/LearnAsYouGoEmulator path: /layg/__init__.py
from .emulator import (
BaseEmulator,
CholeskyNnEmulator,
Int<|fi... | code_fim | medium | {
"lang": "python",
"repo": "auckland-cosmo/LearnAsYouGoEmulator",
"path": "/layg/__init__.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: auckland-cosmo/LearnAsYouGoEmulator path: /layg/__init__.py
from .emulator import (
BaseEmulator,
CholeskyNnEmulator,
Int<|fim_suffix|>ulate
__all__ = [
"Learner",
"emulate",
"BaseEmulator",
"CholeskyNnEmulator",
"InterpolationEmulator",
"TorchEmulator",
]<|fi... | code_fim | medium | {
"lang": "python",
"repo": "auckland-cosmo/LearnAsYouGoEmulator",
"path": "/layg/__init__.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>"CholeskyNnEmulator",
"InterpolationEmulator",
"TorchEmulator",
]<|fim_prefix|># repo: auckland-cosmo/LearnAsYouGoEmulator path: /layg/__init__.py
from .emulator import (
BaseEmulator,
CholeskyNnEmulator,
Int<|fim_middle|>erpolationEmulator,
TorchEmulator,
)
from .learner import L... | code_fim | medium | {
"lang": "python",
"repo": "auckland-cosmo/LearnAsYouGoEmulator",
"path": "/layg/__init__.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> def __init__(self, *args, **kwargs):
self.args = args
self.kwargs = kwargs
def pipe_reader(self, input_pipe):
return self.wrapper_cls(input_pipe, *self.args, **self.kwargs)
def pipe_writer(self, output_pipe):
return self.wrapper_cls(output_pipe, *self.args, **... | code_fim | hard | {
"lang": "python",
"repo": "targets-fs/targets-python",
"path": "/targets/format/wrapped.py",
"mode": "spm",
"license": "ISC",
"source": "the-stack-v2"
} |
<|fim_suffix|> def __del__(self, *args):
# io.TextIOWrapper close the file on __del__, let the underlying file decide
if not self.closed and self.writable():
super(TextWrapper, self).flush()
try:
self._stream.__del__(*args)
except AttributeError:
p... | code_fim | hard | {
"lang": "python",
"repo": "targets-fs/targets-python",
"path": "/targets/format/wrapped.py",
"mode": "spm",
"license": "ISC",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: targets-fs/targets-python path: /targets/format/wrapped.py
import io
import locale
import os
import re
import warnings
from targets import six
from targets.format.format import Format
class BaseWrapper(object):
def __init__(self, stream, *args, **kwargs):
self._stream = stream
... | code_fim | hard | {
"lang": "python",
"repo": "targets-fs/targets-python",
"path": "/targets/format/wrapped.py",
"mode": "psm",
"license": "ISC",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: skazi0/yaia path: /migrations/versions/d19921d7f126_added_sent_on_field.py
"""added sent_on field
Revision ID: d19921d7f126
Revises: 534381d04908
Create Date: 2016-08-11 12:11:11.940043
<|fim_suffix|>
def downgrade():
op.drop_column('invoices', 'sent_on')<|fim_middle|>"""
# revision identi... | code_fim | hard | {
"lang": "python",
"repo": "skazi0/yaia",
"path": "/migrations/versions/d19921d7f126_added_sent_on_field.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>def downgrade():
op.drop_column('invoices', 'sent_on')<|fim_prefix|># repo: skazi0/yaia path: /migrations/versions/d19921d7f126_added_sent_on_field.py
"""added sent_on field
Revision ID: d19921d7f126
Revises: 534381d04908
Create Date: 2016-08-11 12:11:11.940043
"""
# revision identifiers, used by ... | code_fim | medium | {
"lang": "python",
"repo": "skazi0/yaia",
"path": "/migrations/versions/d19921d7f126_added_sent_on_field.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> op.add_column('invoices', sa.Column('sent_on', sa.DateTime(), nullable=True))
def downgrade():
op.drop_column('invoices', 'sent_on')<|fim_prefix|># repo: skazi0/yaia path: /migrations/versions/d19921d7f126_added_sent_on_field.py
"""added sent_on field
Revision ID: d19921d7f126
Revises: 534381d... | code_fim | medium | {
"lang": "python",
"repo": "skazi0/yaia",
"path": "/migrations/versions/d19921d7f126_added_sent_on_field.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: PingLu8/django-twitter path: /inbox/api/views.py
from inbox.api.serializers import (
NotificationSerializer,
NotificationSerializerForUpdate,
)
from rest_framework import viewsets, status
from rest_framework.decorators import action
from rest_framework.permissions import IsAuthenticated
f... | code_fim | hard | {
"lang": "python",
"repo": "PingLu8/django-twitter",
"path": "/inbox/api/views.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> # GET /api/notifications/unread-count/
count = self.get_queryset().filter(unread=True).count()
return Response({'unread_count':count}, status=status.HTTP_200_OK)
@action(methods=['POST'], detail=False, url_path='mark-all-as-read')
@method_decorator(ratelimit(key='user', ra... | code_fim | hard | {
"lang": "python",
"repo": "PingLu8/django-twitter",
"path": "/inbox/api/views.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>def format_time(t):
"""Format seconds into a human readable form.
>>> format_time(10.4)
'10.4s'
>>> format_time(1000.4)
'16min 40.4s'
>>> format_time(100000.4)
'27hr 46min 40.4s'
"""
m, s = divmod(t, 60)
h, m = divmod(m, 60)
if h:
return f"{h:2.0f}hr {m... | code_fim | hard | {
"lang": "python",
"repo": "dask/distributed",
"path": "/distributed/diagnostics/progress.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: dask/distributed path: /distributed/diagnostics/progress.py
from __future__ import annotations
import asyncio
import logging
import warnings
from collections import defaultdict
from timeit import default_timer
from typing import ClassVar
from tlz import groupby, valmap
from dask.base import to... | code_fim | hard | {
"lang": "python",
"repo": "dask/distributed",
"path": "/distributed/diagnostics/progress.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|>dif -%]
[% if extract_product -%]
# extract_product=True in your pipeline.yaml file, declare a "product"
# variable inside this function
[% endif -%]
pass<|fim_prefix|># repo: ploomber/ploomber path: /src/ploomber/resources/ploomber_add/function.py
def [[function_name]](product):
... | code_fim | medium | {
"lang": "python",
"repo": "ploomber/ploomber",
"path": "/src/ploomber/resources/ploomber_add/function.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ploomber/ploomber path: /src/ploomber/resources/ploomber_add/function.py
def [[function_name]](product):
"""Add description here
"""
[% if extract_upstream -%]
# extract_upstream=True in your pipeline.yaml file, if this task has<|fim_suffix|> pipeline.yaml file, if this task has... | code_fim | medium | {
"lang": "python",
"repo": "ploomber/ploomber",
"path": "/src/ploomber/resources/ploomber_add/function.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> context = {'person': flavio}
result = schema.execute(query, context=context)
full_name = result.data['person']['fullName']
self.assertEqual(full_name, 'Flavio Fernandes')<|fim_prefix|># repo: flaviogf/examples path: /hello_graphql_python/tests/test_exemplo_02.py
import ... | code_fim | hard | {
"lang": "python",
"repo": "flaviogf/examples",
"path": "/hello_graphql_python/tests/test_exemplo_02.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: flaviogf/examples path: /hello_graphql_python/tests/test_exemplo_02.py
import unittest
from hello_graphql_python.exemplo_02 import Person, schema
class PersonTests(unittest.TestCase):
def test_should_returns_full_name(self):
query = '''
query {
person {
... | code_fim | hard | {
"lang": "python",
"repo": "flaviogf/examples",
"path": "/hello_graphql_python/tests/test_exemplo_02.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: atviriduomenys/spinta path: /spinta/types/namespace.py
import uuid
import collections
from typing import NamedTuple
from typing import Union
from typing import overload
import itertools
from typing import Any
from typing import Dict
from typing import Iterable
from typing import Iterator
from ty... | code_fim | hard | {
"lang": "python",
"repo": "atviriduomenys/spinta",
"path": "/spinta/types/namespace.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>@commands.getall.register(Context, Namespace)
def getall(
context: Context,
ns: Namespace,
*,
action: Optional[Action] = None,
dataset_: Optional[str] = None,
resource: Optional[str] = None,
**kwargs
):
return _query_data(context, ns, action, dataset_, resource, **kwargs)
... | code_fim | hard | {
"lang": "python",
"repo": "atviriduomenys/spinta",
"path": "/spinta/types/namespace.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> context: Context,
ns: Namespace,
action: Action,
dataset_: Optional[str] = None,
resource: Optional[str] = None,
) -> Iterable[_NodeAndData]:
items: Iterable[Union[Namespace, Model]] = itertools.chain(
ns.names.values(),
ns.models.values(),
)
for item in it... | code_fim | hard | {
"lang": "python",
"repo": "atviriduomenys/spinta",
"path": "/spinta/types/namespace.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: lanxinplus/lanxinplus-openapi-python-sdk path: /lanxinplus_openapi/api/addrbk_tags_api.py
1_tags_meta_fetch',
'http_method': 'POST',
'servers': None,
},
params_map={
'all': [
'app_token',
... | code_fim | hard | {
"lang": "python",
"repo": "lanxinplus/lanxinplus-openapi-python-sdk",
"path": "/lanxinplus_openapi/api/addrbk_tags_api.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> Args:
app_token (str): app_token
v1_tag_groups_create_request_body (V1TagGroupsCreateRequestBody): Request Body
Keyword Args:
user_token (str): user_token. [optional]
Returns:
V1TagGroupsCreateResponse
If the method ... | code_fim | hard | {
"lang": "python",
"repo": "lanxinplus/lanxinplus-openapi-python-sdk",
"path": "/lanxinplus_openapi/api/addrbk_tags_api.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> Returns:
V1TagGroupsFetchResponse
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
... | code_fim | hard | {
"lang": "python",
"repo": "lanxinplus/lanxinplus-openapi-python-sdk",
"path": "/lanxinplus_openapi/api/addrbk_tags_api.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: IFarhankhan/qb path: /cli.py
import click
from qanta import logging
from qanta.util.environment import ENVIRONMENT
from qanta.util.vw import format_audit
from qanta.wikipedia.cached_wikipedia import CachedWikipedia
from qanta.wikipedia import wikification
from qanta.streaming import start_qanta_... | code_fim | hard | {
"lang": "python",
"repo": "IFarhankhan/qb",
"path": "/cli.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
@main.command()
def spark_stream():
start_spark_streaming()
@main.command()
def qanta_stream():
start_qanta_streaming()
@main.command()
@click.argument('wiki_cache')
def init_wiki_cache(wiki_cache):
CachedWikipedia.initialize_cache(wiki_cache)
@main.command()
@click.argument('output')
d... | code_fim | medium | {
"lang": "python",
"repo": "IFarhankhan/qb",
"path": "/cli.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>@main.command()
def qanta_stream():
start_qanta_streaming()
@main.command()
@click.argument('wiki_cache')
def init_wiki_cache(wiki_cache):
CachedWikipedia.initialize_cache(wiki_cache)
@main.command()
@click.argument('output')
def wikify(output):
wikification.wikify(output)
@main.command(... | code_fim | hard | {
"lang": "python",
"repo": "IFarhankhan/qb",
"path": "/cli.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>for i in range(10):
t = threading.Thread(name="Thread no. " + str(i), target=func, args=(i,))
t.start()<|fim_prefix|># repo: tsarpaul/poolhub path: /test.py
import threading
from time import sleep
import poolhub
a = 5
def bunk():
sleep(3)
a = 5
a.b
<|fim_middle|>
def func(b):
... | code_fim | medium | {
"lang": "python",
"repo": "tsarpaul/poolhub",
"path": "/test.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: tsarpaul/poolhub path: /test.py
import threading
from time import sleep
import poolhub
a = 5
<|fim_suffix|>
for i in range(10):
t = threading.Thread(name="Thread no. " + str(i), target=func, args=(i,))
t.start()<|fim_middle|>
def bunk():
sleep(3)
a = 5
a.b
def func(b):
... | code_fim | medium | {
"lang": "python",
"repo": "tsarpaul/poolhub",
"path": "/test.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: mhogg/cgtools path: /cgtools/mesh/laplacian.py
import numpy as np
import scipy.sparse as sparse
from .. import vector as V
def compute_mesh_laplacian(verts, tris, weight_type='cotangent',
return_vertex_area=True, area_type='mixed',
add_di... | code_fim | hard | {
"lang": "python",
"repo": "mhogg/cgtools",
"path": "/cgtools/mesh/laplacian.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> for all weight types:
w_ii = sum(w_ij for j in [1..n])
if area_type == 'mixed':
compute the vertex area as the voronoi area for non-obtuse triangles,
use the barycentric area for obtuse triangles
(according to Mark Meyer's 2002 paper)
if area_type == 'lumped... | code_fim | hard | {
"lang": "python",
"repo": "mhogg/cgtools",
"path": "/cgtools/mesh/laplacian.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Leek727/InverseKinematics path: /table_generator_no_visual.py
import math
import time
import json
# arm length
arm_lengths = [100, 100, 100]
# sums two vectors
def sum_vector(A, B):
# A = (x, y)
return (A[0] + B[0], A[1] + B[1])
# converts polar to the other one
def convert_cartesian(A... | code_fim | hard | {
"lang": "python",
"repo": "Leek727/InverseKinematics",
"path": "/table_generator_no_visual.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
# ------------------ hash map making part ------------------
end_point = []
key = str(round(cum_vector[0])) + "," + str(round(cum_vector[1]))
try:
end_point = data[key]
except:
... | code_fim | hard | {
"lang": "python",
"repo": "Leek727/InverseKinematics",
"path": "/table_generator_no_visual.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: parametersky/ServerTest path: /data.py
from sqlalchemy import (create_engine,Table,Column,Integer,String,MetaData)
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker
engine = create_engine("mysql://root:YES@localhost/orders")
Base = declarative_base... | code_fim | medium | {
"lang": "python",
"repo": "parametersky/ServerTest",
"path": "/data.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> @property
def serial(self):
return {
"uid":self.uid,
"validTime":self.validTime,
"length":self.length
}
Base.metadata.bind=engine
Base.metadata.create_all()
Session = sessionmaker(bind=engine)
ses = Session()<|fim_prefix|># repo: parametersky/Ser... | code_fim | medium | {
"lang": "python",
"repo": "parametersky/ServerTest",
"path": "/data.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> with connection:
with connection.cursor() as cursor:
cursor.execute('DROP TABLE test_table')
if __name__ == '__main__':
create_test_table()
read_test_table()
cleanup()<|fim_prefix|># repo: regananalytics/kubism.dev path: /old/sql/test.py
import os
import psycopg2
fr... | code_fim | medium | {
"lang": "python",
"repo": "regananalytics/kubism.dev",
"path": "/old/sql/test.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: regananalytics/kubism.dev path: /old/sql/test.py
import os
import psycopg2
from dotenv import load_dotenv
load_dotenv()
connection = psycopg2.connect(os.environ['DATABASE_URL'])
CREATE_TEST_TABLE = """CREATE TABLE IF NOT EXISTS test_table (
id SERIAL PRIMARY KEY,
test_field TEXT,
... | code_fim | hard | {
"lang": "python",
"repo": "regananalytics/kubism.dev",
"path": "/old/sql/test.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: jsmartini/Cubesat-Telemetry path: /node_test.py
import util.radio as radio
<|fim_suffix|>node = radio.zumlink("COM11")
config = radio.generate_config(radioMode="Gateway", txPower=0)
node.setup(config)
node.Terminal()<|fim_middle|>#device = "COM8" #device serial port if windows i.e. COM8; if li... | code_fim | medium | {
"lang": "python",
"repo": "jsmartini/Cubesat-Telemetry",
"path": "/node_test.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: jsmartini/Cubesat-Telemetry path: /node_test.py
import util.radio as radio
<|fim_suffix|>node = radio.zumlink("COM11")
config = radio.generate_config(radioMode="Gateway", txPower=0)
node.setup(config)
node.Terminal()<|fim_middle|>
#device = "COM8" #device serial port if windows i.e. COM8; if li... | code_fim | medium | {
"lang": "python",
"repo": "jsmartini/Cubesat-Telemetry",
"path": "/node_test.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>config = radio.generate_config(radioMode="Gateway", txPower=0)
node.setup(config)
node.Terminal()<|fim_prefix|># repo: jsmartini/Cubesat-Telemetry path: /node_test.py
import util.radio as radio
<|fim_middle|>
#device = "COM8" #device serial port if windows i.e. COM8; if linux tr /dev/tty..... or similar... | code_fim | medium | {
"lang": "python",
"repo": "jsmartini/Cubesat-Telemetry",
"path": "/node_test.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: vasilydenisenko/modbus_rtu_slave path: /tests/mb_stop_bit_tests.py
# MIT License
# Copyright (c) 2021 Vasily Denisenko, Sergey Kuznetsov
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to... | code_fim | hard | {
"lang": "python",
"repo": "vasilydenisenko/modbus_rtu_slave",
"path": "/tests/mb_stop_bit_tests.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # Check if response received
response_received = mb_bsp.get_pdu_status('Master', 'PDU status')
if response_received:
print('Response received')
else:
print('Response isn\'t received')
mb_util.print_error_count()
def run_test_m_stop_bit(speed, conf_bit):
print('Run test for ma... | code_fim | hard | {
"lang": "python",
"repo": "vasilydenisenko/modbus_rtu_slave",
"path": "/tests/mb_stop_bit_tests.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> ActivationFunction.__init__(
self, lambda x: x, lambda x: 1.0)<|fim_prefix|># repo: DomenicD/dom_ml_playground path: /python/src/neurons/activation_functions/linear_activation.py
from python.src.neurons.activation_functions.activation_function import ActivationFunction
class Linea... | code_fim | easy | {
"lang": "python",
"repo": "DomenicD/dom_ml_playground",
"path": "/python/src/neurons/activation_functions/linear_activation.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def __init__(self):
ActivationFunction.__init__(
self, lambda x: x, lambda x: 1.0)<|fim_prefix|># repo: DomenicD/dom_ml_playground path: /python/src/neurons/activation_functions/linear_activation.py
from python.src.neurons.activation_functions.activation_function import Activatio... | code_fim | easy | {
"lang": "python",
"repo": "DomenicD/dom_ml_playground",
"path": "/python/src/neurons/activation_functions/linear_activation.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: DomenicD/dom_ml_playground path: /python/src/neurons/activation_functions/linear_activation.py
from python.src.neurons.activation_functions.activation_function import ActivationFunction
<|fim_suffix|> ActivationFunction.__init__(
self, lambda x: x, lambda x: 1.0)<|fim_middl... | code_fim | medium | {
"lang": "python",
"repo": "DomenicD/dom_ml_playground",
"path": "/python/src/neurons/activation_functions/linear_activation.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Shatha1978/Optimisation-algorithm-examples path: /src/CircularList.py
# Import the collections package for the circular list
import collections
# Import the numpy package for mean and std
import numpy as np
<|fim_suffix|> return list(self.data);
def mean(self):
return np.mea... | code_fim | hard | {
"lang": "python",
"repo": "Shatha1978/Optimisation-algorithm-examples",
"path": "/src/CircularList.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> self.data = collections.deque(maxlen=maxlen);
self.last_element = None;
if not isinstance(default_value, NoneType):
for i in range(maxlen):
self.data.append(default_value);
self.last_element = default_value;
def append(self, i):
... | code_fim | medium | {
"lang": "python",
"repo": "Shatha1978/Optimisation-algorithm-examples",
"path": "/src/CircularList.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|>=======================================================================<|fim_prefix|># repo: tejasukhavasi24/my-repo path: /teja.py
I love mmy India
#!/usr/bin/python
#title :teja.py
#description :good guy
#author :tsukha
#date :20180320
#version :123456
#usage :<|fim_middle|>python teja.py dum... | code_fim | medium | {
"lang": "python",
"repo": "tejasukhavasi24/my-repo",
"path": "/teja.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>python teja.py dummy
#notes :
#python_version :2.6.6.143.142
#=============================================================================<|fim_prefix|># repo: tejasukhavasi24/my-repo path: /teja.py
I love mmy India
#!/usr/bin/python
#title :teja.py
#description :go<|fim_middle|>od guy
#author :t... | code_fim | medium | {
"lang": "python",
"repo": "tejasukhavasi24/my-repo",
"path": "/teja.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: tejasukhavasi24/my-repo path: /teja.py
I love mmy India
#!/usr/bin/python
#title :teja.py
#description :good guy
#author :tsukha
#date :20180320
#version :123456
#usage :<|fim_suffix|>=======================================================================<|fim_middle|>python teja.py dum... | code_fim | medium | {
"lang": "python",
"repo": "tejasukhavasi24/my-repo",
"path": "/teja.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: chemlove/polyply_1.0 path: /polyply/src/gen_seq.py
# Copyright 2020 University of Groningen
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org... | code_fim | hard | {
"lang": "python",
"repo": "chemlove/polyply_1.0",
"path": "/polyply/src/gen_seq.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def _find_terminal_nodes(graph):
"""
Find all termini of a graph and return the node key.
A termini is defined as a node with degree of 1. Note
that graph is assumed to be undirected.
"""
termini = []
for node in graph.nodes:
if graph.degree(node) == 1:
ter... | code_fim | hard | {
"lang": "python",
"repo": "chemlove/polyply_1.0",
"path": "/polyply/src/gen_seq.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> """
Find all termini of a graph and return the node key.
A termini is defined as a node with degree of 1. Note
that graph is assumed to be undirected.
"""
termini = []
for node in graph.nodes:
if graph.degree(node) == 1:
termini.append(node)
return term... | code_fim | hard | {
"lang": "python",
"repo": "chemlove/polyply_1.0",
"path": "/polyply/src/gen_seq.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Ryan-Amaral/general-game-playing-tpg path: /random-scores.py
# imports and helper methods
import gym
import gym.spaces
import multiprocessing as mp
import psutil
import os
"""
Run each agent in this method for parallization.
Args:
args: (TpgAgent, envName, scoreList, numEpisodes, numFrames)... | code_fim | medium | {
"lang": "python",
"repo": "Ryan-Amaral/general-game-playing-tpg",
"path": "/random-scores.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|># all of the titles we will be general game playing on
# we chose games that we know TPG does OK in alone
envNames = ['Alien-v0','Asteroids-v0','Atlantis-v0','BankHeist-v0',
'BattleZone-v0','Bowling-v0','Boxing-v0','Centipede-v0',
'ChopperCommand-v0','DoubleDunk-v0','FishingD... | code_fim | medium | {
"lang": "python",
"repo": "Ryan-Amaral/general-game-playing-tpg",
"path": "/random-scores.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> print(str(env.env) + ': ' + str(scoreTotal/10))
env.close()
# https://stackoverflow.com/questions/42103367/limit-total-cpu-usage-in-python-multiprocessing/42130713
def limit_cpu():
p = psutil.Process(os.getpid())
p.nice(10)
# all of the titles we will be general game play... | code_fim | hard | {
"lang": "python",
"repo": "Ryan-Amaral/general-game-playing-tpg",
"path": "/random-scores.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # We have to write the rules for programming the switch to the file:
st = "table_set_default send_frame _drop"
print >> f_handle,st
st = "table_set_default forward _drop"
print >> f_handle,st
st = "table_set_default ipv4_lpm _drop"
... | code_fim | hard | {
"lang": "python",
"repo": "bramamurthy/P4SwitchesInMaxiNet",
"path": "/MaxiNet/WorkerServer/mygraph.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: bramamurthy/P4SwitchesInMaxiNet path: /MaxiNet/WorkerServer/mygraph.py
witch_list = []
switch_list = self.__graph_dict[switch_name]
return switch_list
def diameter(self):
""" Calculates the diameter of the graph """
v = self.vertices()
pairs = [ (v[i]... | code_fim | hard | {
"lang": "python",
"repo": "bramamurthy/P4SwitchesInMaxiNet",
"path": "/MaxiNet/WorkerServer/mygraph.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: bramamurthy/P4SwitchesInMaxiNet path: /MaxiNet/WorkerServer/mygraph.py
)
(vertex1, vertex2) = tuple(edge)
if vertex1 not in self.__graph_dict:
self.__graph_dict[vertex1] = []
dbg_str = "Vertex being initialized .." + str(vertex1)
# logging.debug... | code_fim | hard | {
"lang": "python",
"repo": "bramamurthy/P4SwitchesInMaxiNet",
"path": "/MaxiNet/WorkerServer/mygraph.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> try:
callback(*args)
except Exception as err:
msg = 'Library import callback threw an unexpected exception'
RideLogException(message=msg, exception=err, level='WARN').publish()
def fetch_keywords(self, library_name, library_args, callback):
... | code_fim | hard | {
"lang": "python",
"repo": "robotframework/RIDE",
"path": "/src/robotide/spec/librarymanager.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> def _handle_insert_keywords_message(self, message):
_, library_name, library_args, result_queue = message
keywords = self._fetch_keywords(library_name, library_args)
self._insert(library_name, library_args, keywords,
lambda res: result_queue.put(res, timeou... | code_fim | hard | {
"lang": "python",
"repo": "robotframework/RIDE",
"path": "/src/robotide/spec/librarymanager.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: robotframework/RIDE path: /src/robotide/spec/librarymanager.py
# Copyright 2008-2015 Nokia Networks
# Copyright 2016- Robot Framework Foundation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You ma... | code_fim | hard | {
"lang": "python",
"repo": "robotframework/RIDE",
"path": "/src/robotide/spec/librarymanager.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>
np.random.seed(0)
self.input_shape = [3, 4, 10, 10]
self.output_shape = [3, 4, 5, 5]
self.pool_size = (2, 2)
self.maxpool = compile_maxpool(self.output_shape, self.pool_size)
def test_maxpool_non_edge_case(self):
"""
Test MaxPooling on a non-... | code_fim | hard | {
"lang": "python",
"repo": "oval-group/pl-cnn",
"path": "/src/tests/maxpool.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> np.random.seed(0)
self.input_shape = [3, 4, 10, 10]
self.output_shape = [3, 4, 5, 5]
self.pool_size = (2, 2)
self.maxpool = compile_maxpool(self.output_shape, self.pool_size)
def test_maxpool_non_edge_case(self):
"""
Test MaxPooling on a non-e... | code_fim | hard | {
"lang": "python",
"repo": "oval-group/pl-cnn",
"path": "/src/tests/maxpool.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: oval-group/pl-cnn path: /src/tests/maxpool.py
import unittest
import numpy as np
import theano
import theano.tensor as T
from utils.patches import my_pool_2d
def compile_maxpool(output_shape, pool_size):
X = T.tensor4()
# compute output with both methods
out1 = T.signal.pool.pool... | code_fim | hard | {
"lang": "python",
"repo": "oval-group/pl-cnn",
"path": "/src/tests/maxpool.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> ok, frame = capture.read()
position = capture.get(0)
print('Current position:', position)
if ok == True:
current_state, frame_count = frame_handler(frame, frame_count, current_state)
else:
break
except Key... | code_fim | hard | {
"lang": "python",
"repo": "sahupr/highlight-bot",
"path": "/stream-capture-service/main.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: sahupr/highlight-bot path: /stream-capture-service/main.py
import os
import time
import streamlink
from PIL import Image
import cv2
import game_screen
import goal_event_producer
CHANNEL_NAME = os.getenv('CHANNEL_NAME')
KAFKA_TOPIC_GOAL_EVENTS = 'hbot.core.goal-events'
def frame_handler(frame,... | code_fim | hard | {
"lang": "python",
"repo": "sahupr/highlight-bot",
"path": "/stream-capture-service/main.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # Hacky
# TODO: Formalize this
current_state = None
# Debug:
# time.sleep(14)
# print('Away goal detected')
# goal_event_producer.send_goal_event(int(time.time()), KAFKA_TOPIC_GOAL_EVENTS)
while capture.isOpened():
try:
# Capture every
cap... | code_fim | hard | {
"lang": "python",
"repo": "sahupr/highlight-bot",
"path": "/stream-capture-service/main.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: somuncudayi/depthai-experiments path: /gen2-age-gender/main.py
import argparse
import queue
import time
from pathlib import Path
import blobconverter
import cv2
import depthai as dai
import numpy as np
from imutils.video import FPS
parser = argparse.ArgumentParser()
parser.add_argument('-nd', '-... | code_fim | hard | {
"lang": "python",
"repo": "somuncudayi/depthai-experiments",
"path": "/gen2-age-gender/main.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # If there is a face detected, there will also be an age/gender
# inference result available soon, so we can wait for it
det = age_gender_q.get()
age = int(float(np.squeeze(np.array(det.getLayerFp16('age_conv3')))) * 100)
... | code_fim | hard | {
"lang": "python",
"repo": "somuncudayi/depthai-experiments",
"path": "/gen2-age-gender/main.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>model = Model(input=[input_var], output=[softmax])
model.compile(loss='categorical_crossentropy',
optimizer='adadelta',
metrics=['accuracy'])
model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch,
verbose=1, validation_data=(X_test, Y_test))
model.save... | code_fim | hard | {
"lang": "python",
"repo": "mark14wu/Fashion-MNIST_Contest",
"path": "/fashion-mnist.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
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