content stringlengths 1 1.05M | input_ids listlengths 1 883k | ratio_char_token float64 1 22.9 | token_count int64 1 883k |
|---|---|---|---|
#!/usr/bin/env python
# encoding: utf-8
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"""In this module we provide a list paths, urls and other usefull settings."""
GITHUB_URL = "https://github.com/MarcSkovMadsen/awesome-panel/"
GITHUB_BLOB_MASTER_URL = "https://github.com/MarcSkovMadsen/awesome-panel/blob/master/"
GITHUB_RAW_URL = "https://raw.githubusercontent.com/MarcSkovMadsen/awesome-panel/master/"
GITHUB_THUMBNAILS_URL = (
"https://github.com/MarcSkovMadsen/awesome-panel/blob/master/assets/images/thumbnails/"
)
THUMBNAILS_ROOT = "assets/images/thumbnails/"
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... | 2.591623 | 191 |
import pickle
import pytest
import numpy as np
from astropy.coordinates import Longitude
from astropy import coordinates as coord
from astropy.tests.helper import pickle_protocol, check_pickling_recovery # noqa
# Can't test distances without scipy due to cosmology deps
from astropy.utils.compat.optional_deps import HAS_SCIPY # noqa
_names = [coord.Angle,
coord.Distance,
coord.DynamicMatrixTransform,
coord.ICRS,
coord.Latitude,
coord.Longitude,
coord.StaticMatrixTransform,
]
_xfail = [False,
not HAS_SCIPY,
True,
True,
False,
True,
False]
_args = [[0.0],
[],
[lambda *args: np.identity(3), coord.ICRS, coord.ICRS],
[0, 0],
[0],
[0],
[np.identity(3), coord.ICRS, coord.ICRS],
]
_kwargs = [{'unit': 'radian'},
{'z': 0.23},
{},
{'unit': ['radian', 'radian']},
{'unit': 'radian'},
{'unit': 'radian'},
{},
]
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... | 1.924028 | 566 |
#!/usr/bin/env python
#
# Copyright (C) 2017 ShadowMan
#
import operator
import numpy as np
group = np.array([
[1.0, 1.1],
[1.0, 1.0],
[0.0, 0.0],
[0.0, 0.1]
])
labels = ['A', 'A', 'B','B']
if __name__ == '__main__':
print(knn_classify((1.0, 0.5), group, labels, 2))
print(knn_classify((18, 90), np.array([
[3, 104], [2, 100], [1, 81], [101, 10], [99, 5], [98, 2]
]), [ 'M', 'M', 'M', 'A', 'A', 'A'], 5))
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... | 1.913793 | 232 |
import os
from typing import Tuple, List
from controller.invoker.invoker_task_exporting import TaskExportingInvoker
from controller import config
from controller.label_model.label_studio import LabelStudio
from controller.utils.app_logger import logger
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13,... | 3.909091 | 66 |
import os
from flask import Flask
from googleapiclient.discovery import build
import json
import sys
from google.oauth2 import service_account
import googleapiclient.discovery
app = Flask(__name__)
workflow_service = build('workflowexecutions', 'v1')
if __name__ == "__main__":
app.run(debug=True, host="0.0.0.0", port=int(os.environ.get("PORT", 8080)))
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11748,... | 2.837209 | 129 |
from math import pi, sin, cos
from panda3d.core import *
from direct.showbase.ShowBase import ShowBase
from direct.task import Task
from floorplan import Floorplan
import numpy as np
import random
import copy
app = Viewer()
app.run()
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... | 3.338028 | 71 |
#
# Autogenerated by Thrift Compiler (0.9.1)
#
# DO NOT EDIT UNLESS YOU ARE SURE THAT YOU KNOW WHAT YOU ARE DOING
#
# options string: py
#
from thrift.Thrift import TType, TMessageType, TException, TApplicationException
from ttypes import *
from thrift.Thrift import TProcessor
from thrift.transport import TTransport
from thrift.protocol import TBinaryProtocol, TProtocol
try:
from thrift.protocol import fastbinary
except:
fastbinary = None
# HELPER FUNCTIONS AND STRUCTURES
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2,... | 3.148387 | 155 |
import numpy as np
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45941,
628
] | 3.333333 | 6 |
import datetime
import logging
import os
import boto3
from qiskit import IBMQ
from qiskit import QuantumCircuit, execute
logger = logging.getLogger()
logger.setLevel(logging.INFO)
backend_candidates = [
"ibmq_athens",
"ibmq_santiago",
"ibmq_belem",
"ibmq_quito",
"ibmq_lima",
]
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1... | 2.318182 | 132 |
# -*- coding: utf-8 -*-
# SPDX-License-Identifier: GPL-2.0+
#
# Copyright 2017 Google, Inc
#
import contextlib
import os
import re
import shutil
import sys
import tempfile
import unittest
import gitutil
import patchstream
import settings
def testBasic(self):
"""Tests the basic flow of patman
This creates a series from some hard-coded patches build from a simple
tree with the following metadata in the top commit:
Series-to: u-boot
Series-prefix: RFC
Series-cc: Stefan Brns <stefan.bruens@rwth-aachen.de>
Cover-letter-cc: Lord Mlchett <clergy@palace.gov>
Series-version: 2
Series-changes: 4
- Some changes
Cover-letter:
test: A test patch series
This is a test of how the cover
leter
works
END
and this in the first commit:
Series-notes:
some notes
about some things
from the first commit
END
Commit-notes:
Some notes about
the first commit
END
with the following commands:
git log -n2 --reverse >/path/to/tools/patman/test/test01.txt
git format-patch --subject-prefix RFC --cover-letter HEAD~2
mv 00* /path/to/tools/patman/test
It checks these aspects:
- git log can be processed by patchstream
- emailing patches uses the correct command
- CC file has information on each commit
- cover letter has the expected text and subject
- each patch has the correct subject
- dry-run information prints out correctly
- unicode is handled correctly
- Series-to, Series-cc, Series-prefix, Cover-letter
- Cover-letter-cc, Series-version, Series-changes, Series-notes
- Commit-notes
"""
process_tags = True
ignore_bad_tags = True
stefan = u'Stefan Brns <stefan.bruens@rwth-aachen.de>'
rick = 'Richard III <richard@palace.gov>'
mel = u'Lord Mlchett <clergy@palace.gov>'
ed = u'Lond Edmund Blackaddr <weasel@blackadder.org'
fred = 'Fred Bloggs <f.bloggs@napier.net>'
add_maintainers = [stefan, rick]
dry_run = True
in_reply_to = mel
count = 2
settings.alias = {
'fdt': ['simon'],
'u-boot': ['u-boot@lists.denx.de'],
'simon': [ed],
'fred': [fred],
}
text = self.GetText('test01.txt')
series = patchstream.GetMetaDataForTest(text)
cover_fname, args = self.CreatePatchesForTest(series)
with capture() as out:
patchstream.FixPatches(series, args)
if cover_fname and series.get('cover'):
patchstream.InsertCoverLetter(cover_fname, series, count)
series.DoChecks()
cc_file = series.MakeCcFile(process_tags, cover_fname,
not ignore_bad_tags, add_maintainers,
None)
cmd = gitutil.EmailPatches(series, cover_fname, args,
dry_run, not ignore_bad_tags, cc_file,
in_reply_to=in_reply_to, thread=None)
series.ShowActions(args, cmd, process_tags)
cc_lines = open(cc_file).read().splitlines()
os.remove(cc_file)
lines = out[0].splitlines()
#print '\n'.join(lines)
self.assertEqual('Cleaned %s patches' % len(series.commits), lines[0])
self.assertEqual('Change log missing for v2', lines[1])
self.assertEqual('Change log missing for v3', lines[2])
self.assertEqual('Change log for unknown version v4', lines[3])
self.assertEqual("Alias 'pci' not found", lines[4])
self.assertIn('Dry run', lines[5])
self.assertIn('Send a total of %d patches' % count, lines[7])
line = 8
for i, commit in enumerate(series.commits):
self.assertEqual(' %s' % args[i], lines[line + 0])
line += 1
while 'Cc:' in lines[line]:
line += 1
self.assertEqual('To: u-boot@lists.denx.de', lines[line])
self.assertEqual('Cc: %s' % stefan.encode('utf-8'), lines[line + 1])
self.assertEqual('Version: 3', lines[line + 2])
self.assertEqual('Prefix:\t RFC', lines[line + 3])
self.assertEqual('Cover: 4 lines', lines[line + 4])
line += 5
self.assertEqual(' Cc: %s' % mel.encode('utf-8'), lines[line + 0])
self.assertEqual(' Cc: %s' % rick, lines[line + 1])
self.assertEqual(' Cc: %s' % fred, lines[line + 2])
self.assertEqual(' Cc: %s' % ed.encode('utf-8'), lines[line + 3])
expected = ('Git command: git send-email --annotate '
'--in-reply-to="%s" --to "u-boot@lists.denx.de" '
'--cc "%s" --cc-cmd "%s --cc-cmd %s" %s %s'
% (in_reply_to, stefan, sys.argv[0], cc_file, cover_fname,
' '.join(args))).encode('utf-8')
line += 4
self.assertEqual(expected, lines[line])
self.assertEqual(('%s %s, %s' % (args[0], rick, stefan))
.encode('utf-8'), cc_lines[0])
self.assertEqual(('%s %s, %s, %s, %s' % (args[1], fred, rick, stefan,
ed)).encode('utf-8'), cc_lines[1])
expected = '''
This is a test of how the cover
leter
works
some notes
about some things
from the first commit
Changes in v4:
- Some changes
Simon Glass (2):
pci: Correct cast for sandbox
fdt: Correct cast for sandbox in fdtdec_setup_memory_size()
cmd/pci.c | 3 ++-
fs/fat/fat.c | 1 +
lib/efi_loader/efi_memory.c | 1 +
lib/fdtdec.c | 3 ++-
4 files changed, 6 insertions(+), 2 deletions(-)
--\x20
2.7.4
'''
lines = open(cover_fname).read().splitlines()
#print '\n'.join(lines)
self.assertEqual(
'Subject: [RFC PATCH v3 0/2] test: A test patch series',
lines[3])
self.assertEqual(expected.splitlines(), lines[7:])
for i, fname in enumerate(args):
lines = open(fname).read().splitlines()
#print '\n'.join(lines)
subject = [line for line in lines if line.startswith('Subject')]
self.assertEqual('Subject: [RFC %d/%d]' % (i + 1, count),
subject[0][:18])
if i == 0:
# Check that we got our commit notes
self.assertEqual('---', lines[17])
self.assertEqual('Some notes about', lines[18])
self.assertEqual('the first commit', lines[19])
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from animeblkom import Animeblkom, animeblkom_episode
from downloader import blkom
from requests import get
download_list = []
anime_url = input('Anime Url: ') # ex. https://animeblkom.net/watch/one-piece
episodes = Animeblkom.get_anime_episodes(anime_url)
print(f'{len(episodes)} episodes found.')
episodes = episodes[ int(input('from: '))-1 : int(input('to: ')) ]
for episode in episodes:
print(episode.num)
episode_servers = episode.get_servers()
for i in range(len(episode_servers)):
curr_server = episode_servers[i]
print(f'{i}. {curr_server}')
choice = int(input('Choice: '))
download_list.append( episode_servers[choice].split(', ')[1].strip() )
print('\n')
for link in download_list:
file = blkom.download(link)
print(file.info)
file.save()
print('\n')
input('done')
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... | 2.439883 | 341 |
import logging
from fastapi import APIRouter
from spaceone.core.locator import Locator
_LOGGER = logging.getLogger(__name__)
router = APIRouter()
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"""
@author: mkowalska
"""
import os
import numpy as np
from numpy.linalg import LinAlgError
import matplotlib.pyplot as plt
from figure_properties import *
import matplotlib.gridspec as gridspec
from kcsd import KCSD1D
import targeted_basis as tb
__abs_file__ = os.path.abspath(__file__)
def stability_M(n_src, total_ele, ele_pos, pots, R_init=0.23):
"""
Investigates stability of reconstruction for different number of basis
sources
Parameters
----------
n_src: int
Number of basis sources.
total_ele: int
Number of electrodes.
ele_pos: numpy array
Electrodes positions.
pots: numpy array
Values of potentials at ele_pos.
R_init: float
Initial value of R parameter - width of basis source
Default: 0.23.
Returns
-------
obj_all: class object
eigenvalues: numpy array
Eigenvalues of k_pot matrix.
eigenvectors: numpy array
Eigen vectors of k_pot matrix.
"""
obj_all = []
eigenvectors = np.zeros((len(n_src), total_ele, total_ele))
eigenvalues = np.zeros((len(n_src), total_ele))
for i, value in enumerate(n_src):
pots = pots.reshape((len(ele_pos), 1))
obj = KCSD1D(ele_pos, pots, src_type='gauss', sigma=0.3, h=0.25,
gdx=0.01, n_src_init=n_src[i], ext_x=0, xmin=0, xmax=1,
R_init=R_init)
try:
eigenvalue, eigenvector = np.linalg.eigh(obj.k_pot +
obj.lambd *
np.identity
(obj.k_pot.shape[0]))
except LinAlgError:
raise LinAlgError('EVD is failing - try moving the electrodes'
'slightly')
idx = eigenvalue.argsort()[::-1]
eigenvalues[i] = eigenvalue[idx]
eigenvectors[i] = eigenvector[:, idx]
obj_all.append(obj)
return obj_all, eigenvalues, eigenvectors
def set_axis(ax, x, y, letter=None):
"""
Formats the plot's caption.
Parameters
----------
ax: Axes object.
x: float
X-position of caption.
y: float
Y-position of caption.
letter: string
Caption of the plot.
Default: None.
Returns
-------
ax: modyfied Axes object.
"""
ax.text(
x,
y,
letter,
fontsize=15,
weight='bold',
transform=ax.transAxes)
return ax
def generate_figure(csd_profile, R, MU, true_csd_xlims, total_ele, ele_lims,
noise=0, R_init=0.23):
"""
Generates figure for spectral structure decomposition.
Parameters
----------
csd_profile: function
Function to produce csd profile.
R: float
Thickness of the groundtruth source.
Default: 0.2.
MU: float
Central position of Gaussian source
Default: 0.25.
true_csd_xlims: list
Boundaries for ground truth space.
total_ele: int
Number of electrodes.
ele_lims: list
Electrodes limits.
noise: float
Determines the level of noise in the data.
Default: 0.
R_init: float
Initial value of R parameter - width of basis source
Default: 0.23.
Returns
-------
None
"""
csd_at, true_csd, ele_pos, pots, val = tb.simulate_data(csd_profile,
true_csd_xlims,
R, MU, total_ele,
ele_lims,
noise=noise)
n_src_M = [2, 4, 8, 16, 32, 64, 128, 256, 512]
OBJ_M, eigenval_M, eigenvec_M = stability_M(n_src_M,
total_ele, ele_pos, pots,
R_init=R_init)
plt_cord = [(2, 0), (2, 2), (2, 4),
(3, 0), (3, 2), (3, 4),
(4, 0), (4, 2), (4, 4),
(5, 0), (5, 2), (5, 4)]
letters = ['C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N']
BLACK = _html(0, 0, 0)
ORANGE = _html(230, 159, 0)
SKY_BLUE = _html(86, 180, 233)
GREEN = _html(0, 158, 115)
YELLOW = _html(240, 228, 66)
BLUE = _html(0, 114, 178)
VERMILION = _html(213, 94, 0)
PURPLE = _html(204, 121, 167)
colors = [BLUE, ORANGE, GREEN, PURPLE, VERMILION, SKY_BLUE, YELLOW, BLACK]
fig = plt.figure(figsize=(18, 16))
# heights = [1, 1, 1, 0.2, 1, 1, 1, 1]
heights = [4, 0.3, 1, 1, 1, 1]
markers = ['^', '.', '*', 'x', ',']
# linestyles = [':', '--', '-.', '-']
linestyles = ['-', '-', '-', '-']
src_idx = [0, 2, 3, 8]
gs = gridspec.GridSpec(6, 6, height_ratios=heights, hspace=0.3, wspace=0.6)
ax = fig.add_subplot(gs[0, :3])
for indx, i in enumerate(src_idx):
ax.plot(np.arange(1, total_ele + 1), eigenval_M[i],
linestyle=linestyles[indx], color=colors[indx],
marker=markers[indx], label='M='+str(n_src_M[i]),
markersize=10)
ht, lh = ax.get_legend_handles_labels()
set_axis(ax, -0.05, 1.05, letter='A')
ax.set_xlabel('Number of components')
ax.set_ylabel('Eigenvalues')
ax.set_yscale('log')
ax.set_ylim([1e-6, 1])
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax = fig.add_subplot(gs[0, 3:])
ax.plot(n_src_M, eigenval_M[:, 0], marker='s', color='k', markersize=5,
linestyle=' ')
set_axis(ax, -0.05, 1.05, letter='B')
ax.set_xlabel('Number of basis sources')
ax.set_xscale('log')
ax.set_ylabel('Eigenvalues')
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
for i in range(OBJ_M[0].k_interp_cross.shape[1]):
ax = fig.add_subplot(gs[plt_cord[i][0],
plt_cord[i][1]:plt_cord[i][1]+2])
for idx, j in enumerate(src_idx):
ax.plot(np.linspace(0, 1, 100), np.dot(OBJ_M[j].k_interp_cross,
eigenvec_M[j, :, i]),
linestyle=linestyles[idx], color=colors[idx],
label='M='+str(n_src_M[j]), lw=2)
ax.text(0.5, 1., r"$\tilde{K}\cdot{v_{{%(i)d}}}$" % {'i': i+1},
horizontalalignment='center', transform=ax.transAxes,
fontsize=20)
set_axis(ax, -0.10, 1.1, letter=letters[i])
if i < 9:
ax.get_xaxis().set_visible(False)
ax.spines['bottom'].set_visible(False)
else:
ax.set_xlabel('Depth ($mm$)')
if i % 3 == 0:
ax.set_ylabel('CSD ($mA/mm$)')
ax.yaxis.set_label_coords(-0.18, 0.5)
ax.ticklabel_format(style='sci', axis='y', scilimits=((0.0, 0.0)))
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
fig.legend(ht, lh, loc='lower center', ncol=5, frameon=False)
fig.savefig(os.path.join('vectors_' + '_noise_' +
str(noise) + 'R0_2' + '.png'), dpi=300)
plt.show()
if __name__ == '__main__':
ELE_LIMS = [0, 1.]
TRUE_CSD_XLIMS = [0., 1.]
TOTAL_ELE = 12
CSD_PROFILE = tb.csd_profile
R = 0.2
MU = 0.25
R_init = 0.2
generate_figure(CSD_PROFILE, R, MU, TRUE_CSD_XLIMS, TOTAL_ELE, ELE_LIMS,
noise=None, R_init=R_init)
| [
37811,
198,
31,
9800,
25,
33480,
322,
874,
4914,
198,
37811,
198,
11748,
28686,
198,
11748,
299,
32152,
355,
45941,
198,
6738,
299,
32152,
13,
75,
1292,
70,
1330,
5164,
2348,
70,
12331,
198,
11748,
2603,
29487,
8019,
13,
9078,
29487,
... | 1.838042 | 4,106 |
#!/usr/bin/env python
# coding=utf-8
from celery import Celery
from pprint import pprint
import time
app = Celery('tasks', backend='redis://localhost:6379/0', broker = 'redis://localhost:6379/0')
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
198,
2,
19617,
28,
40477,
12,
23,
198,
6738,
18725,
1924,
1330,
15248,
1924,
198,
198,
6738,
279,
4798,
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279,
4798,
198,
11748,
640,
198,
1324,
796,
15248,
1924,
10786,
83,
6791,
3256,... | 2.884058 | 69 |
import re
COMMENT_REGEX = re.compile(r'(checkov:skip=|bridgecrew:skip=) *([A-Z_\d]+)(:[^\n]+)?')
| [
11748,
302,
198,
198,
9858,
10979,
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6369,
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302,
13,
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32,
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57,
62,
59,
67,
48688,
5769,
33250,
61,
59,
77,
60,
28... | 2 | 49 |
# Generated by Django 2.1.4 on 2020-04-20 23:20
from django.db import migrations, models
| [
2,
2980,
515,
416,
37770,
362,
13,
16,
13,
19,
319,
12131,
12,
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6738,
42625,
14208,
13,
9945,
1330,
15720,
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11,
4981,
628
] | 2.84375 | 32 |
'''
Created on Dec 21, 2014
@author: Milos
'''
'''
Forma za eventualna prosirenja djangovog user-a
'''
from django import forms
from django.contrib.auth.forms import UserCreationForm
from django.contrib.auth.models import User
from django.core.urlresolvers import reverse
from django.shortcuts import get_object_or_404
from django.views.generic.detail import DetailView
from django.views.generic.edit import UpdateView, CreateView
from tasks.models import UserExtend
| [
7061,
6,
198,
41972,
319,
4280,
2310,
11,
1946,
198,
198,
31,
9800,
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4460,
418,
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6,
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6,
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42625,
648,
709,
519,
2836,
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198,
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198,
... | 2.993865 | 163 |
"""
Django settings for mysite project.
Generated by 'django-admin startproject' using Django 2.2.4.
For more information on this file, see
https://docs.djangoproject.com/en/2.2/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/2.2/ref/settings/
"""
import os
import re
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
# __file__ represents this file itself.
BASE_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/2.2/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
# this is the hardwired key for non-production
SECRET_KEY = '=yztu+7vgkkr%f1uy(fnk%zl=!jx*tg$v6ekzsp40a%2m7_d3!'
if "HCAT_SECRET_KEY" in os.environ:
SECRET_KEY = os.environ["HCAT_SECRET_KEY"] # this env var only used by production
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
ALLOWED_HOSTS = ["hgwdev.gi.ucsc.edu"]
# Application definition
INSTALLED_APPS = [
'hcat.apps.HcatConfig',
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'django.contrib.sites',
'allauth',
'allauth.account',
'allauth.socialaccount',
'allauth.socialaccount.providers.google',
]
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
ROOT_URLCONF = 'mysite.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [os.path.join(BASE_DIR, 'templates')],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = 'mysite.wsgi.application'
# Database
# https://docs.djangoproject.com/en/2.2/ref/settings/#databases
#DATABASES = {
# 'default': {
# 'ENGINE': 'django.db.backends.sqlite3',
# 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),
# }
#}
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.mysql',
'NAME': os.environ["HCAT_DB"],
'USER': os.environ["HCAT_USER"],
'PASSWORD': os.environ["HCAT_PASSWORD"],
'OPTIONS': {
'init_command': "SET sql_mode='STRICT_TRANS_TABLES'",
},
}
}
# Password validation
# https://docs.djangoproject.com/en/2.2/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
},
]
# Internationalization
# https://docs.djangoproject.com/en/2.2/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'America/Los_Angeles'
USE_I18N = True
USE_L10N = True
USE_TZ = True
SITE_ID = 1
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/2.2/howto/static-files/
STATIC_URL = '/static/'
# help it find our static files
STATICFILES_DIRS = [
os.path.join(BASE_DIR, 'static'), # this could have called i.e. assets instead
]
# Where wranglers can upload things, whoohoo!
MEDIA_ROOT = os.environ["HCAT_MEDIA"]
MEDIA_URL = '/media/'
LOGIN_REDIRECT_URL = '/'
# Hide Sensitive Secret Environment Variables.
# We do not want them to leak out if debug=true.
# Django should be doing this for us automatically, but it does not.
# runserver runs and forks and runs again. We only want to change env var for the main run process.
if os.environ.get('RUN_MAIN') == 'true':
HIDDEN_SETTINGS = re.compile('API|TOKEN|KEY|SECRET|PASS|SIGNATURE', flags=re.IGNORECASE)
CLEANSED_SUBSTITUTE = '********************'
for key in os.environ:
if key.isupper():
if HIDDEN_SETTINGS.search(key):
os.environ[key] = CLEANSED_SUBSTITUTE
| [
37811,
198,
35,
73,
14208,
6460,
329,
616,
15654,
1628,
13,
198,
198,
8645,
515,
416,
705,
28241,
14208,
12,
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923,
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6,
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198,
198,
1890,
517,
1321,
319,
428,
2393,
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766,
198,
5450... | 2.370904 | 2,014 |
"""Create the distribution file (pypi)."""
import importlib
import setuptools
from pathlib import Path
this_package_name = 'remote_import'
version_file = Path(__file__).absolute().parent / this_package_name / "__version__.py"
setuptools.setup(
name=this_package_name,
url="https://github.com/kiranmantri/python-remote_import",
author="Kiran Mantripragada (and Lydia.ai team)",
author_email="kiran.mantri@gmail.com",
description="Enable the Python import subsystem to load libraries from remote (e.g. HTTP, S3, SSH).",
version=importlib.import_module("remote_import.version", "__version__").__version__,
long_description=open("README.rst").read(),
packages=[this_package_name],
python_requires=">=3.10",
install_requires=[
line for line in open("requirements.txt").read().split("\n") if not line.startswith("#")
],
include_package_data=True,
setup_requires=["wheel"],
)
| [
37811,
16447,
262,
6082,
2393,
357,
79,
4464,
72,
21387,
15931,
198,
11748,
1330,
8019,
198,
11748,
900,
37623,
10141,
198,
6738,
3108,
8019,
1330,
10644,
198,
198,
5661,
62,
26495,
62,
3672,
796,
705,
47960,
62,
11748,
6,
198,
9641,
... | 2.832827 | 329 |
from flask import request
import pytest
import json
from app import create_app, create_rest_api
from db import get_db
| [
198,
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1330,
2581,
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9288,
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198,
6738,
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62,
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198,
6738,
20613,
1330,
651,
62,
9945,
628,
628,
198
] | 3.514286 | 35 |
import json
import os
import cv2
import imutils
import numpy as np
import torch
import torchvision.transforms.functional as tvf
from PIL import Image
from models.rapid import RAPiD
from tracker.deep_sort import DeepSort
from utils import utils
weights_path = "weights/pL1_HBCP608_Apr14_6000.ckpt"
model = RAPiD(backbone='dark53')
model.load_state_dict(torch.load(weights_path)['model_state_dict'])
print(f'Successfully loaded weights: {weights_path}')
model.eval()
if torch.cuda.is_available():
model = model.cuda()
device = "cuda"
image_dir = "datasets"
file_name = "75-279_LinhDam_HN-1"
ds = DeepSort("weights/osnet_x1_0.onnx")
vs = cv2.VideoCapture(f"/mnt/sdb1/Data/record/{file_name}.mkv")
input_size = 608
conf_thres = 0.3
nms_thres = 0.45
top_k = 100
DEBUG = False
frame_num = 0
interval = 10
ann_json = {
"annotations": [],
"images": [],
"categories": [
{
"id": 1,
"name": "person",
"supercategory": "person"
}]
}
while True:
image = vs.read()[1]
if image is None:
continue
image = image.copy()
pil_img = Image.fromarray(image)
# pad to square
input_img, _, pad_info = utils.rect_to_square(pil_img, None, input_size, 0)
input_ori = tvf.to_tensor(input_img)
input_ = input_ori.unsqueeze(0)
input_ = input_.to(device=device)
with torch.no_grad():
dts = model(input_).cpu()
dts = dts.squeeze()
# post-processing
dts = dts[dts[:,5] >= conf_thres]
if len(dts) > top_k:
_, idx = torch.topk(dts[:,5], k=top_k)
dts = dts[idx, :]
dts = utils.nms(dts, is_degree=True, nms_thres=nms_thres, img_size=input_size)
dts = utils.detection2original(dts, pad_info.squeeze())
boxes = []
infos= []
for bb in dts:
x,y,w,h,a,conf = bb
radian = a*np.pi/180
C, S = np.cos(radian), np.sin(radian)
R = np.asarray([[-C, -S], [S, -C]])
pts = np.asarray([[-w / 2, -h / 2], [w / 2, -h / 2],
[w / 2, h / 2], [-w / 2, h / 2]])
points = np.asarray([((x, y) + pt @ R).astype(int) for pt in pts])
# cv2.circle(image, (int(x), int(y)), 2, (0, 255, 0), 10)
# cv2.polylines(image, [points], True, (0, 0, 255), 5)
boxes.append(points)
infos.append([x,y,w,h,a])
tracked_boxes = ds.update(boxes, infos, image)
if frame_num % interval == 0:
with open(f'{file_name}.json', 'w') as f:
img_id = f"{file_name}_{frame_num}"
cv2.imwrite(os.path.join(image_dir, f"{img_id}.jpg"), image)
for trk in tracked_boxes:
x,y,w,h,a,trk_id = trk
obj_ann = {
"area": w*h,
"bbox": [x,y,w,h,a],
"category_id": 1,
"image_id": img_id,
"iscrowd": 0,
"segmentation": [],
"person_id": trk_id,
}
img_info = {
"file_name": f"{img_id}.jpg",
"id": img_id,
"width": image.shape[0],
"height": image.shape[1]
}
ann_json["images"].append(img_info)
ann_json["annotations"].append(obj_ann)
if DEBUG:
a = a*np.pi/180
C, S = np.cos(a), np.sin(a)
R = np.asarray([[-C, -S], [S, -C]])
pts = np.asarray([[-w / 2, -h / 2], [w / 2, -h / 2],
[w / 2, h / 2], [-w / 2, h / 2]])
points = np.asarray([((x, y) + pt @ R).astype(int) for pt in pts])
cv2.putText(image, str(trk_id), (x, y), cv2.FONT_HERSHEY_SIMPLEX, 2,
(0,255,0), 4, cv2.LINE_AA)
cv2.polylines(image, [points], True, (0, 0, 255), 5)
json.dump(ann_json, f, indent=4)
frame_num+=1
if DEBUG:
cv2.imshow("image", imutils.resize(image, width=720))
key = cv2.waitKey(1) & 0xff
if key == ord('q'):
break
cv2.destroyAllWindows()
| [
11748,
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198,
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28686,
198,
198,
11748,
269,
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198,
11748,
299,
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355,
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23914,
13,
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355,
31557,
69,
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6738,
350,
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13... | 1.794409 | 2,325 |
import os
import sys
from loguru import logger
from rich.console import Console
console_args = {}
if "pytest" in sys.modules:
console_args["width"] = 120
console = Console(**console_args)
cpu_count = None
def CPUs():
"""
Detects the number of CPUs on a system. Cribbed from pp.
"""
global cpu_count
if cpu_count is None:
cpu_count = 1 # default
# Linux, Unix and MacOS:
if hasattr(os, "sysconf"):
if "SC_NPROCESSORS_ONLN" in os.sysconf_names:
# Linux & Unix:
ncpus = os.sysconf("SC_NPROCESSORS_ONLN")
if isinstance(ncpus, int) and ncpus > 0:
cpu_count = ncpus
else: # OSX: pragma: no cover
cpu_count = int(
os.popen2("sysctl -n hw.ncpu")[1].read()
) # pragma: no cover
# Windows:
if "NUMBER_OF_PROCESSORS" in os.environ: # pragma: no cover
ncpus = int(os.environ["NUMBER_OF_PROCESSORS"])
if ncpus > 0:
cpu_count = ncpus
return cpu_count
def job_or_filename(job_or_filename, invariant_class=None):
"""Take a filename, or a job. Return Path(filename), dependency-for-that-file
ie. either the job, or a invariant_class (default: FileInvariant)"""
from .jobs import Job, FileInvariant
from pathlib import Path
if invariant_class is None: # pragma: no cover
invariant_class = FileInvariant
if isinstance(job_or_filename, Job):
filename = job_or_filename.files[0]
deps = [job_or_filename]
elif job_or_filename is not None:
filename = Path(job_or_filename)
deps = [invariant_class(filename)]
else:
filename = None
deps = []
return filename, deps
def assert_uniqueness_of_object(
object_with_name_attribute, pipegraph=None, also_check=None
):
"""Makes certain there is only one object with this class & .name.
This is necessary so the pipegraph jobs assign their data only to the
objects you're actually working with."""
if pipegraph is None: # pragma: no branch
from pypipegraph2 import global_pipegraph
pipegraph = global_pipegraph
if object_with_name_attribute.name.find("/") != -1:
raise ValueError(
"Names must not contain /, it confuses the directory calculations"
)
if pipegraph is None: # pragma: no cover
return
if not hasattr(pipegraph, "object_uniquifier"):
pipegraph.object_uniquifier = {}
typ = object_with_name_attribute.__class__
if typ not in pipegraph.object_uniquifier:
pipegraph.object_uniquifier[typ] = {}
if object_with_name_attribute.name in pipegraph.object_uniquifier[typ]:
raise ValueError(
"Doublicate object: %s, %s" % (typ, object_with_name_attribute.name)
)
if also_check:
if not isinstance(also_check, list):
also_check = [also_check]
for other_typ in also_check:
if (
other_typ in pipegraph.object_uniquifier
and object_with_name_attribute.name
in pipegraph.object_uniquifier[other_typ]
):
raise ValueError(
"Doublicate object: %s, %s"
% (other_typ, object_with_name_attribute.name)
)
object_with_name_attribute.unique_id = len(pipegraph.object_uniquifier[typ])
pipegraph.object_uniquifier[typ][object_with_name_attribute.name] = True
do_jobtrace_log = False
def pretty_log_errors(func):
"""capture exceptions (on a function outside of ppg)
and format it with our fancy local logging exception logger
"""
return inner
| [
11748,
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220,
220,
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... | 2.264458 | 1,660 |
# -*- coding: utf-8 -*-
# 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/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
import mock
from hwk import udev
from hwk.tests.unit import base
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
198,
2,
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739,
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362,
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15341,
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779,
428,
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2845,
287,
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351,
262,
13789,
... | 3.569832 | 179 |
from http import HTTPStatus
from flask_restful import Resource
from ..extensions import cache
from ..models.country import Country
from ..schemas.continent import ContinentSchema
continent_list_schema = ContinentSchema(many=True)
| [
6738,
2638,
1330,
14626,
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198,
198,
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913,
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1330,
12946,
198,
6738,
11485,
1416,
4411,
292,
13,
3642,
7233,
1330,
... | 3.774194 | 62 |
import sublime
import sublime_plugin
import threading
import os.path
from pickle import dump, load, UnpicklingError, PicklingError
from copy import deepcopy
from .common import *
from .bookmark import *
from .visibilityHandler import *
from .ui import *
BOOKMARKS = []
UID = None
#list of bookmarks that have ben deleted.
#This is used to remove bookmarks' buffer highlights. Without this, if a bookmark is removed,
#when a file is revisited, the buffer will still be marked. This will keep track of bookmarks
#that have been removed.
ERASED_BOOKMARKS = []
#whether all bookmarks (even unrelated) should be shown
BOOKMARKS_MODE = SHOW_ALL_BOOKMARKS()
| [
11748,
41674,
198,
11748,
41674,
62,
33803,
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278,
220,
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293,
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11,
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1359,
12331,
11,
12346,
1359,
12331,
198,
6738,
4866,
1330,
2769,
30073,
6... | 3.502646 | 189 |
import logging
import random
from typing import Dict
from xrpc.dsl import rpc, RPCType, regular, signal
from xrpc.error import TerminationException
from xrpc.runtime import sender, service
# todo: the issue is actually that not only the request-reply pattern wouldn't work
# todo: but also the fact that an RPC might have circular dependencies
from xrpc.transport import Origin
# todo: please note that Request-Reply pattern does would not work with a service that tries
# todo: to access itself.
| [
11748,
18931,
198,
11748,
4738,
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11,
6737,
198,
6738,
2124,
81,
14751,
13,
18224,
1330,
15527,
341... | 3.789474 | 133 |
import matplotlib.pyplot as plt
import pandas as pd
import pytest
from ml_matrics import ROOT
y_binary, y_proba, y_clf = pd.read_csv(f"{ROOT}/data/rand_clf.csv").to_numpy().T
xs, y_pred, y_true = pd.read_csv(f"{ROOT}/data/rand_regr.csv").to_numpy().T
| [
11748,
2603,
29487,
8019,
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... | 2.161017 | 118 |
# -*- coding: utf-8 -*-
#!/usr/bin/env python
from __future__ import print_function
import community_ext
import networkx as nx
fn1 = "datasets/polblogs/polblogs.edges"
fn2 = fn1.replace(".edges",".clusters")
print("DATASET:",fn1)
# load graph
G = nx.Graph()
for line in open(fn1):
from_node, to_node = map(int, line.rstrip().split("\t"))
if from_node not in G or to_node not in G[from_node]:
G.add_edge(from_node,to_node)
# load the ground-truth partition
groundtruth_partition = dict()
for line in open(fn2):
node, cluster = map(int, line.rstrip().split("\t"))
if node not in G.nodes(): continue
groundtruth_partition[node] = cluster
# print some general info
gt_mu = community_ext.estimate_mu(G,groundtruth_partition)
print("ground truth mu\t",gt_mu)
print("ground truth clusters\t",len(set(groundtruth_partition.values())))
print("ground truth modularity\t", community_ext.modularity(groundtruth_partition,G))
# now cycle thru the methods and optimize with each one
methods = ('ppm','dcppm','ilfrs')
for method in methods:
print('\nMethod', method)
# a starting parameter value depends on the method
if method in ('ilfrs',):
work_par = 0.5
else:
work_par = 1.
# now start the iterative process
prev_par, it = -1, 0
prev_pars = set()
while abs(work_par-prev_par)>1e-5: # stop if the size of improvement too small
it += 1
if it>100: break # stop after 100th iteration
# update the parameter value
prev_par = work_par
if prev_par in prev_pars: break # stop if we are in the cycle
prev_pars.add(prev_par)
# find the optimal partition with the current parameter value
if method in ('ilfrs',):
partition = community_ext.best_partition(G,model=method,pars={'mu':work_par})
else:
partition = community_ext.best_partition(G,model=method,pars={'gamma':work_par})
# calculate optimal parameter value for the current partition
if method in ('ilfrs',):
work_par = community_ext.estimate_mu(G,partition)
else:
work_par = community_ext.estimate_gamma(G,partition,model=method)
loglike = community_ext.model_log_likelihood(G,partition,model=method,pars={'gamma':work_par,'mu':work_par})
print('current par',work_par,'loglike',loglike)
# calculate and print the scores of resulting partition
part_scores = community_ext.compare_partitions(groundtruth_partition,partition)
loglike = community_ext.model_log_likelihood(G,partition,model=method,pars={'gamma':work_par,'mu':work_par})
print('best par',work_par)
print("rand\t% 0f\tjaccard\t% 0f\tnmi\t% 0f\tnmi_arithm\t% 0f\tsize\t%d\tloglike\t% 0f" %\
(part_scores['rand'], part_scores['jaccard'], part_scores['nmi'], part_scores['nmi_arithm'], len(set(partition.values())), loglike))
| [
2,
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299,
87,
1... | 2.484615 | 1,170 |
import os,sys,math,numpy as np, matplotlib.pyplot as plt
if __name__=='__main__':
runstuff()
| [
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2... | 2.295455 | 44 |
from keras.models import Model, Sequential
from keras.layers import Input, Convolution2D, ZeroPadding2D, MaxPooling2D, Flatten, Dense, Dropout, Activation
import numpy as np
from os import listdir,path
from os.path import isfile, join
from PIL import Image
from keras.preprocessing.image import load_img, save_img, img_to_array
from keras.applications.imagenet_utils import preprocess_input
from keras.preprocessing import image
# import matplotlib.pyplot as plt
import cv2 as cv
import boto3
# import sounddevice as sd
model = Sequential()
model.add(ZeroPadding2D((1,1),input_shape=(224,224, 3)))
model.add(Convolution2D(64, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(128, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(128, (3, 3), activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(256, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(256, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(256, (3, 3), activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, (3, 3), activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, (3, 3), activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))
model.add(Convolution2D(4096, (7, 7), activation='relu'))
model.add(Dropout(0.5))
model.add(Convolution2D(4096, (1, 1), activation='relu'))
model.add(Dropout(0.5))
model.add(Convolution2D(2622, (1, 1)))
model.add(Flatten())
model.add(Activation('softmax'))
model.load_weights('vgg_face_weights.h5')
vgg_face_descriptor = Model(inputs=model.layers[0].input, outputs=model.layers[-2].output)
face_cascade = cv.CascadeClassifier(join('haarcascades','haarcascade_frontalface_default.xml'))
faces_dir='faces'
faces={}
face_imgs = [f for f in listdir(faces_dir) if isfile(join(faces_dir, f))]
for face_file in face_imgs:
face_label=path.splitext(face_file)[0]
print(face_label)
face_representation= vgg_face_descriptor.predict(preprocess_image(join(faces_dir,face_file)))[0,:]
faces[face_label]=face_representation
vc = cv.VideoCapture(0)
if vc.isOpened():
is_capturing, frame = vc.read()
frame = cv.cvtColor(frame, cv.COLOR_BGR2RGB)
vc.release()
face=detect_face(frame)
# plt.imshow(face)
face=cv.resize(face,(224,224))
face = face[...,::-1]
face_representation= vgg_face_descriptor.predict(preprocess_loaded_image(face))[0,:]
min_sim=2
candidate=''
for key in faces.keys():
candidate_representation=faces[key]
cosine_similarity = findCosineSimilarity(face_representation, candidate_representation) # Should be less then 0.40
euclidean_distance = findEuclideanDistance(face_representation, candidate_representation) #Less then 120
print("Candidate {} CosineSimularity: {}, EuclideanDistance: {}" .format(key, cosine_similarity, euclidean_distance))
if cosine_similarity<min_sim:
min_sim=cosine_similarity
candidate=key
print(candidate)
# speak('Hello '+candidate+'. May I help you?')
# def speak(text):
# response = polly_client.synthesize_speech(VoiceId='Brian',OutputFormat='pcm',SampleRate="8000",Text = text)
# stream=response['AudioStream'].read()
# sound=np.frombuffer(stream,dtype=np.int16)
# sd.play(sound, 8000) | [
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11,
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11,
1... | 2.429429 | 1,665 |
"""Module with view functions that serve each uri."""
from datetime import datetime
from learning_journal.models.mymodel import Journal
from learning_journal.security import is_authenticated
from pyramid.httpexceptions import HTTPFound, HTTPNotFound
from pyramid.security import NO_PERMISSION_REQUIRED, forget, remember
from pyramid.view import view_config
| [
37811,
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62,
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1330,
4913,
198,
198,
6738,
4673,
62,
24891,
13,
12961,
1330,
... | 3.925532 | 94 |
"""
Created by Joseph Edradan
Github: https://github.com/josephedradan
Date created: 3/23/2020
Purpose:
The most generic DFS algorithm capable of being modified to Fit your needs.
It is also optimized.
Details:
Description:
Notes:
IMPORTANT NOTES:
Explanation:
Time Complexity:
Reference:
"""
from typing import Set, FrozenSet, List
# from joseph_resources.decorators._old.callable_called_count import print_callable_called_count, callable_called_count
# from joseph_resources.decorators.timer import timer
# @timer
def get_power_set(list_given: list) -> List[set]:
"""
Given a list, find the power set of it which is basically all the combinations for all
sizes from 0 to length of list_given
:param list_given: list given
:return: list of a list of solutions
"""
# Set containing frozensets which are solutions
set_frozenset_shared_solutions = set() # type: Set[FrozenSet]
# A Temp list that can potentially be a solution
list_temp_shared_generic_solution = [] # type: list
# Recursive DFS call
_get_power_set_helper(list_temp_shared_generic_solution, list_given, set_frozenset_shared_solutions)
# Convert frozen set with a tuple
list_sets = [set(i) for i in set_frozenset_shared_solutions]
# Add the empty set
list_sets.append(set())
# Sort the list
list_sets.sort()
return list_sets
# @callable_called_count
def _get_power_set_helper(list_temp_shared_generic_solution: list,
list_remaining_items: list,
set_frozenset_shared_solutions: set) -> None:
"""
Recursive DFS to get all permutations of a List, but making them unique via frozenset which is a combination
Notes: The amount of iterations should be - 1 because empty set is not involved
Total iterations (Permutation formula):
Less than (Due to not Recursive calling for an empty list_remaining_items)
Summation from r = 0 to n of (n!)/((n-r)!)
where r = sample size == len(list_remaining_items)
n = number of objects == len(list_remaining_items)
(n!)/((n-r)!) = permutation formula
Power Set iterations:
Greater than
Summation from r = 0 to n of (n!)/(r!(n-r)!) ==
2^(len(list_remaining_items))
where r = sample size == len(list_remaining_items)
n = number of objects == len(list_remaining_items)
(n!)/(r!(n-r)!) = combination formula
:param list_temp_shared_generic_solution: Temporary List of the current permutation (temp List is shared)
:param list_remaining_items: List of remaining items that need to be added to list_temp_shared_generic_solution
:param set_frozenset_shared_solutions: Set of a frozensets that are to solutions
:return: None
"""
# Loop through the length of list_remaining_items
for i in range(len(list_remaining_items)):
# Add the indexed item into the temp List
list_temp_shared_generic_solution.append(list_remaining_items[i])
# Create a copy of list_remaining_items
list_remaining_items_new = list_remaining_items.copy()
# Pop off the item with the index number
list_remaining_items_new.pop(i)
# Add a frozenset (immutable) which is hashable in a set (mutable)
set_frozenset_shared_solutions.add(frozenset(list_temp_shared_generic_solution))
# Don't recursive call if list_remaining_items_new is empty because you loop for no reason with a range(0)
if list_remaining_items_new:
# Recursive call into this function
_get_power_set_helper(list_temp_shared_generic_solution,
list_remaining_items_new,
set_frozenset_shared_solutions)
# Pop from list_temp_permutation for a new permutation
list_temp_shared_generic_solution.pop()
if __name__ == '__main__':
test_example()
| [
37811,
198,
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416,
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272,
198,
38,
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25,
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1378,
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14,
73,
577,
79,
704,
6335,
272,
198,
198,
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513,
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1954,
14,
42334,
198,
198,
30026,
3455,
25,
198,
220,
220,
220,
38... | 2.474955 | 1,657 |
from django.db import models
import json | [
6738,
42625,
14208,
13,
9945,
1330,
4981,
198,
198,
11748,
33918
] | 3.727273 | 11 |
import json
import logging
from PyQt5.QtCore import QObject, pyqtSignal
from ..core.constants import (
API_TEST_CASE_RECORD_TYPE,
HTTP_EXCHANGE_RECORD_TYPE,
ENVIRONMENT_RECORD_TYPE,
PROJECT_INFO_RECORD_TYPE,
APP_STATE_RECORD_TYPE,
API_CALL_RECORD_TYPE,
)
from ..model.app_data import (
ApiCall,
AppData,
ApiTestCase,
HttpExchange,
Environment,
ProjectInfo,
AppState,
)
| [
11748,
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357,
198,
220,
220,
220,
7824,
62,
51,
6465,
6... | 2.225131 | 191 |
import unittest
from dfttopif.parsers.pwscf import PwscfParser
from ..test_pif import unpack_example, delete_example
from pypif.obj.common.value import Value
import os
import shutil
if __name__ == '__main__':
unittest.main()
| [
11748,
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62,
20688,
198,
6738,
279,
... | 2.586957 | 92 |
# Generated by Django 3.1.3 on 2021-02-10 01:10
from django.db import migrations, models
| [
2,
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416,
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513,
13,
16,
13,
18,
319,
33448,
12,
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940,
198,
198,
6738,
42625,
14208,
13,
9945,
1330,
15720,
602,
11,
4981,
628
] | 2.84375 | 32 |
import numpy as np
| [
11748,
299,
32152,
355,
45941,
198
] | 3.166667 | 6 |
import model
sticker_storage = model.get_storage()
def my(_, update):
"""Prints stickers added by user"""
message = update.message
user_id = update.message.from_user.id
stickers = sticker_storage.get_for_owner(user_id, max_count=20, tagged=True)
text = '\n\n'.join(
'Tags: {tags}\n'
'Times used: {sticker.times_used}\n'
'/{sticker.id}'
.format(
sticker=sticker,
tags=', '.join(sticker.tags)
)
for sticker in stickers
)
message.reply_text(text, parse_mode='HTML')
| [
11748,
2746,
198,
198,
13915,
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628,
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4296,
... | 2.231373 | 255 |
#!/usr/bin/env python3
import argparse
import getpass
import os
import sys
import argcomplete
from datetime import datetime
from forest import cmake_tools
from forest.common.eval_handler import EvalHandler
from forest.common.install import install_package, write_setup_file, write_ws_file, check_ws_file, uninstall_package, \
clean
from forest.common.recipe import RecipeSource, Cookbook
from forest.common import sudo_refresh
from pprint import pprint
# define directories for source, build, install, and recipes
from forest.common.forest_dirs import *
# just a try-except wrapper to catch ctrl+c
# actual main
if __name__ == '__main__':
main() | [
2,
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14,
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21015,
18,
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198,
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651,
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198,
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8079,
1330,
4818,
8079,
198,
6738,
8222,
1330,
12067,
... | 3.460733 | 191 |
"""
Given an array of numbers, find the subarray that maximizes the sum of all
elements in the array. Note that these numbers can be negative
"""
import random
# Returns max subarray within a[i:j] (inclusive of i, exclusive of j)
if __name__ == "__main__":
a = createArray(100)
print(a)
bruteForce = bruteForceBest(a)
print(f'Brute force found {bruteForce} {total(bruteForce)}')
best = maxSubArray(a)
print(f'Best: {best} {total(best)}')
| [
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19... | 2.825301 | 166 |
#!/usr/bin/env python
# coding: utf-8
from setuptools import setup, find_packages
import os
migration_files = package_files(os.path.join('blag', 'migrations'), os.path.abspath('blag'))
package_data = [
os.path.join('templates', '*.html'),
os.path.join('templates', '*', '*.html'),
os.path.join('server-assets', '*'),
]
package_data.extend(migration_files)
setup(
name='blag',
version='0.1.0',
author='Tarjei Husy',
author_email='git@thusoy.com',
url='https://github.com/thusoy/blag',
description='Somewhere to ramble',
packages=find_packages(),
package_data={
'': package_data
},
zip_safe=False,
entry_points={
'console_scripts': [
'manage.py = blag.scripts:main',
]
}
)
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
198,
2,
19617,
25,
3384,
69,
12,
23,
198,
198,
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900,
37623,
10141,
1330,
9058,
11,
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62,
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198,
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28686,
628,
198,
76,
4254,
62,
16624,
796,
5301,
62,
16624,
7,
418,
... | 2.290801 | 337 |
#coding:utf-8
N = int(raw_input())
S = ''
stack = [S]
for _ in xrange(N):
s = raw_input()
if s.startswith('1'):
S = ''.join((S, s.split()[-1]))
stack.append(S)
elif s.startswith('2'):
k = int(s.split()[-1])
S = S[:-k]
stack.append(S)
elif s.startswith('3'):
k = int(s.split()[-1])
index = k-1
print S[index]
elif s.startswith('4'):
stack.pop()
S = stack[-1]
| [
2,
66,
7656,
25,
40477,
12,
23,
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45,
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7,
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7,
45,
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220,
220,
220,
264,
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8246,
62,
... | 1.693141 | 277 |
from datetime import datetime
from typing import Optional, List, Dict
import sys
sys.path.append("../../")
from backend.server.pd_model import *
from backend.queue.services import add_task, stop_all_ws_task
from backend.database.main import gis_stac
__all__ = ["preview_processing", "vector_processing", "refuse_processing"]
| [
6738,
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1330,
1635,
198,... | 3.316832 | 101 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from django.views.generic.base import TemplateView
from diario.models import Entry
from observations.models import Measurement
| [
2,
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13,
27530,
1330... | 3.259259 | 54 |
"""
Adds a ubt command which adds basic block counts to frames within a backtrace.
Usage: ubt
Contributors: Isa Smith, Toby Lloyd Davies
Copyright (C) 2019 Undo Ltd
"""
import gdb
from undodb.debugger_extensions import (
debugger_utils,
udb,
)
BacktraceWithTime()
| [
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8... | 3.021505 | 93 |
#
# @author Kevin Jesse
# @email kevin.r.jesse@gmail.com
#
"""
Database Connect serves to connect to the database postgres, as the master user. Autocommit has been turned on so
all inserts, updates, and deletes will be enforced atomically
"""
import psycopg2 | [
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... | 3.421053 | 76 |
"""
Integration test for remove_participants_under_18years module
Original Issues: DC-1724
The intent is to remove data for participants under 18 years old from all the domain tables."""
# Python Imports
import os
import datetime
# Project Imports
from common import VISIT_OCCURRENCE, OBSERVATION
from common import JINJA_ENV
from app_identity import PROJECT_ID
from cdr_cleaner.cleaning_rules.remove_participants_under_18years import (
RemoveParticipantsUnder18Years, AFFECTED_TABLES)
from tests.integration_tests.data_steward.cdr_cleaner.cleaning_rules.bigquery_tests_base import \
BaseTest
PERSON_DATA_TEMPLATE = JINJA_ENV.from_string("""
INSERT INTO `{{project_id}}.{{dataset_id}}.person`
(person_id, birth_datetime, gender_concept_id, year_of_birth, race_concept_id, ethnicity_concept_id)
VALUES
/* Adding participans with different ranges of birthdays.*/
/* Participant 4's birth_datetime was set to 2021.*/
/* The data belonging to this participant from all the domain tables should be dropped.*/
(1, '1970-01-01 00:00:00 UTC', 0, 1970, 0, 0),
(2, '2002-01-01 00:00:00 UTC', 0, 2002, 0, 0),
(3, '2003-01-01 00:00:00 UTC', 0, 2003, 0, 0),
(4, '2021-01-01 00:00:00 UTC', 0, 2015, 0, 0)
""")
VISIT_OCCURRENCE_DATA_TEMPLATE = JINJA_ENV.from_string("""
INSERT INTO `{{project_id}}.{{dataset_id}}.visit_occurrence`
(visit_occurrence_id, person_id, visit_start_date, visit_end_date, visit_concept_id, visit_type_concept_id)
VALUES
(1, 1, '2020-01-01', '2020-01-02', 0, 0),
(2, 3, '2020-01-02', '2020-01-03', 0, 0),
(3, 2, '2020-01-01', '2020-03-01', 0, 0),
(4, 4, '2020-01-02', '2022-01-03', 0, 0)
""")
OBSERVATION_DATA_TEMPLATE = JINJA_ENV.from_string("""
INSERT INTO `{{project_id}}.{{dataset_id}}.observation`
(observation_id, person_id, observation_date, observation_concept_id, observation_type_concept_id)
VALUES
(1, 1, '2020-01-01', 0, 0),
(2, 2, '2020-01-02', 0, 0),
(3, 3, '2020-03-01', 0, 0),
(4, 4, '2020-01-05', 0, 0),
(5, 3, '2020-05-05', 0, 0)
""")
| [
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... | 2.377854 | 876 |
import pandas as pd
canucks = pd.read_csv('data/canucks.csv')
# Identify any columns with null values with .info()
# Save this dataframe as canucks_info
canucks_info = canucks.info()
canucks_info
# Create a new column in the dataframe named Wealth
# where all the values equal "comfortable"
# Name the new dataframe canucks_comf
canucks_comf = canucks.assign(Wealth = "comfortable")
canucks_comf
# Do conditional replacement, where if the value in the salary column is null,
# we replace "comfortable" with "unknown"
canucks_comf.loc[canucks_comf['Salary'].isnull(), "Wealth"] = "unknown"
canucks_comf | [
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764,
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198,
2,
12793,
428... | 3.075758 | 198 |
#
# @lc app=leetcode.cn id=461 lang=python3
#
# [461]
#
# https://leetcode-cn.com/problems/hamming-distance/description/
#
# algorithms
# Easy (79.21%)
# Likes: 459
# Dislikes: 0
# Total Accepted: 137K
# Total Submissions: 170K
# Testcase Example: '1\n4'
#
#
#
# x y
#
#
# 0 x, y < 2^31.
#
# :
#
#
# : x = 1, y = 4
#
# : 2
#
# :
# 1 (0 0 0 1)
# 4 (0 1 0 0)
#
#
#
#
#
#
# @lc code=start
# @lc code=end
# def hammingDistance(self, x: int, y: int) -> int:
# count = 0
# for i in range(32):
# if x & 1 != y & 1:
# count += 1
# x >>= 1
# y >>= 1
# return count
# def hammingDistance(self, x: int, y: int) -> int:
# count = 0
# z = x ^ y
# for i in range(32):
# if z & 1:
# count += 1
# z >>= 1
# return count
# def hammingDistance(self, x: int, y: int) -> int:
# count = 0
# z = x ^ y
# while z:
# count += z & 1
# z >>= 1
# return count | [
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14,
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... | 1.715373 | 657 |
"""Database Layer for the Emmission API.
"""
from functools import wraps
from sqlalchemy import create_engine, Column, DateTime, Integer, Float, String
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker
import geoalchemy2
from emissionsapi.config import config
import emissionsapi.logger
# Logger
logger = emissionsapi.logger.getLogger('emission-api.db')
# Database uri as described in
# https://docs.sqlalchemy.org/en/13/core/engines.html#database-urls
# Retrieved as environment variable.
database = config('database') or 'postgresql://user:user@localhost/db'
# Global session variable. Set on initialization.
__session__ = None
# Base Class of all ORM objects.
Base = declarative_base()
def with_session(f):
"""Wrapper for f to make a SQLAlchemy session present within the function
:param f: function to call
:type f: function
:raises e: Possible Exception of f
:return: result of f
"""
return decorated
def get_session():
"""Get a new session.
Lazy load the database connection and create the tables.
Returns:
sqlalchemy.orm.session.Session -- SQLAlchemy Session Object
"""
global __session__
# Create Database Connection, Tables and Sessionmaker if neccessary.
if not __session__:
Engine = create_engine(database)
__session__ = sessionmaker(bind=Engine)
Base.metadata.create_all(Engine)
# Return new session object
return __session__()
def get_points_in_polygon(session, polygon):
"""Get all points from within the specified polygon.
:param session: SQL Alchemy Session
:type session: sqlalchemy.orm.session.Session
:param polygon: Polygon where to search for points
:type polygon: geoalchemy2.WKTElement
:return: SQLAlchemy Query Object with the points from within the polygon.
:rtype: sqlalchemy.orm.query.Query
"""
return session.query(Carbonmonoxide).filter(
geoalchemy2.func.ST_WITHIN(Carbonmonoxide.geom, polygon))
def get_points_in_rectangle(session, upper_left, lower_right):
"""Get all points from within a rectangle.
:param session: SQL Alchemy Session
:type session: sqlalchemy.orm.session.Session
:param polygon: Polygon where to search for points
:type polygon: geoalchemy2.WKTElement
:param upper_left: Upper left point of the rectangle
:type upper_left: tuple
:param lower_right: Lower right point of the rectangle
:type lower_right: tuple
:return: SQLAlchemy Query Object with the points from within the polygon.
:rtype: sqlalchemy.orm.query.Query
"""
# Defining the rectangle
rectangle = geoalchemy2.elements.WKTElement(
f'POLYGON(({upper_left[0]} {upper_left[1]},'
f' {lower_right[0]} {upper_left[1]},'
f' {lower_right[0]} {lower_right[1]},'
f' {upper_left[0]} {lower_right[1]},'
f' {upper_left[0]} {upper_left[1]}))')
return get_points_in_polygon(session, rectangle)
| [
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329,
262,
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3411,
7824,
13,
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198,
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6738,
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310,
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1330,
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198,
198,
6738,
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1330,
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11,
29201,
11,
7536,
7575,
11,
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11,
48436,
11,
10903,
198,
... | 2.909796 | 1,031 |
# This file is part of the Edison Project.
# Please refer to the LICENSE document that was supplied with this software for information on how it can be used.
# Create your views here.
from django.http import Http404, HttpResponse
from django.shortcuts import render_to_response, get_object_or_404
from django.template import RequestContext
from django.contrib.auth.decorators import login_required
from django.forms import ModelForm
from models import *
# Project specific imports
from models import *
def custom_proc(request):
"A context processor that provides 'app', 'user' and 'ip_address'."
return {
'app': 'edison',
'user': request.user,
'ip_address': request.META['REMOTE_ADDR']
}
# Setup the 'edit' form
class EditForm(ModelForm):
| [
2,
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318,
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13,
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2,
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534,
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13,
198,
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42625... | 3.232365 | 241 |
from setuptools import setup
setup(
name = 'pypdb',
packages = ['pypdb'], # same as 'name'
version = '1.310',
install_requires=[
'xmltodict',
'beautifulsoup4',
'requests'
],
description = 'A Python wrapper for the RCSB Protein Data Bank (PDB) API',
author = 'William Gilpin',
author_email = 'firstname_lastname@gmail.com',
url = 'https://github.com/williamgilpin/pypdb',
download_url = 'https://github.com/williamgilpin/pypdb/tarball/0.6',
keywords = ['protein','data','RESTful','api'],
classifiers = [],
)
| [
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6,
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... | 2.572093 | 215 |
wt1_10_10 = {'192.168.122.110': [5.5754, 5.5952, 9.2422, 8.5338, 9.0058, 8.4025, 8.7557, 8.4405, 8.1397, 8.1318, 8.0657, 7.8501, 8.1701, 8.0757, 7.8899, 7.7329, 7.6153, 7.9967, 7.9363, 7.838, 8.7936, 8.6351, 8.5151, 8.6381, 8.7262, 8.816, 8.8984, 8.7799, 8.8584, 8.9543, 8.8833, 8.794, 8.6935, 8.735, 8.635, 8.5429, 8.6163, 8.5476, 8.6108, 8.5577, 8.4882, 8.4211, 8.3623, 8.3054, 8.2413, 8.4337, 8.4888, 8.5373, 8.4713, 8.5225, 8.4593, 8.4011, 8.3452, 8.3259, 8.2905, 8.2449, 8.1937, 8.1445, 8.116, 8.1567, 8.1104, 8.0715, 8.115, 8.0737, 8.0345, 8.0136, 8.0019, 8.0049, 8.1192, 8.0796, 8.0452, 8.006, 7.981, 7.9482, 7.9175, 7.8876, 7.8733, 7.9073, 7.8801, 7.9114, 7.8796, 7.8656, 7.8337, 7.8213, 7.792, 7.8309, 7.8041, 7.8059, 7.7952, 7.7718, 7.8096, 7.7388, 7.7266, 7.7471, 7.7399, 7.7186, 7.7135, 7.6918, 7.7212, 7.7565, 7.7335, 7.711, 7.6962, 7.6741, 7.6596, 7.6417, 7.6303, 7.6106, 7.5902, 7.6216, 7.6199, 7.6491, 7.6785, 7.6596, 7.6464, 7.6277, 7.6561, 7.6506, 7.6396, 7.6222, 7.7245, 7.7135, 7.6971, 7.6861, 7.7189, 7.7939, 7.7734, 7.7552, 7.7516, 7.7743, 7.7593, 7.7427, 7.7249, 7.708, 7.6916, 7.6809, 7.703, 7.6865, 7.6734, 7.6572, 7.6484, 7.6386, 7.6612, 7.6835, 7.6686, 7.6592, 7.681, 7.6753, 7.7021, 7.6891, 7.6742, 7.665, 7.6538, 7.6739, 7.7405, 7.7304, 7.7693, 7.759, 7.7492, 7.7403, 7.7253, 7.7102, 7.6962, 7.7249, 7.7124, 7.7071, 7.7291, 7.7147, 7.7307, 7.717, 7.7031, 7.7357, 7.7238, 7.7431, 7.7364, 7.7563, 7.7441, 7.7358, 7.7272, 7.7473, 7.7372, 7.7443, 7.8068, 7.7982, 7.7906, 7.805, 7.7939, 7.7818, 7.8014, 7.7899, 7.7779, 7.7671, 7.7545, 7.9091, 7.8963, 7.8913, 7.8823, 7.8953, 7.9115, 7.9017, 7.9166, 7.9316, 7.9229, 7.9104, 7.9033, 7.8949, 7.8879, 7.9028, 7.8904, 7.8854, 7.8754, 7.8648, 7.8551, 7.8433, 7.8322, 7.8214, 7.8104, 7.8007, 7.7927, 7.7815, 7.7701, 7.783, 7.7734, 7.8037, 7.795, 7.7885, 7.7826, 7.7774, 7.7719, 7.7622, 7.7544, 7.7451, 7.7379, 7.7314, 7.7236, 7.7175, 7.7073, 7.7213, 7.7111, 7.7026, 7.6996, 7.6898, 7.7013, 7.6954, 7.7105, 7.7015, 7.692, 7.708, 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10.8814], '192.168.122.114': [6.6602, 21.8852, 10.5515, 10.7384, 5.3139, 5.2032, 10.8378, 5.3453, 5.4469, 11.3225, 5.7368, 0.5617, 6.1603, 6.4385, 5.2454, 21.4033, 9.8453, 0.5288, 6.2032, 10.8514, 10.9289, 6.386, 5.4474, 5.8727, 10.9434, 10.7694, 5.816, 5.5585, 11.3146, 10.4733, 5.4173, 11.1101, 5.7256, 5.4514, 5.5656, 5.9841, 5.5256, 12.8391, 5.3346, 5.3873, 5.2829, 10.6404, 5.2984, 10.735, 11.3688, 5.2152, 11.6599, 5.4834, 5.2633, 0.7706, 5.9247, 11.297, 0.6936, 10.814, 0.6723, 5.8286, 21.5101, 5.6531, 16.9098, 5.9524, 5.6846, 1.9381, 5.7724, 5.2226, 16.7112, 10.9046, 28.079, 6.5997, 5.4221, 5.3272, 5.2323, 0.6273, 5.3895, 0.8469, 10.8831, 5.3515, 10.9792, 5.6858, 10.6702, 5.4193, 10.6556, 10.8099, 10.7358, 16.3391, 10.8671, 5.5833, 5.3592, 5.6574, 5.4653, 0.8097, 10.8008, 10.9386, 5.4307, 5.481, 5.2984, 5.8599, 10.946, 11.0021, 10.8867, 5.3613, 5.3978, 5.3871, 10.9761, 5.5594, 5.224, 5.3334, 10.7236, 5.9152, 5.5037, 10.8681, 5.8761, 11.0388, 10.8972, 10.9205, 5.6214, 5.2702, 5.4736, 5.9876, 5.487, 5.8632, 5.671, 5.5416, 5.7781, 10.6444, 11.0345, 5.3198, 5.2545, 6.8128, 5.3844, 0.7143, 5.2736, 10.8373, 10.6473, 6.4766, 5.4076, 5.429, 5.6283, 11.054, 5.2774, 16.6681, 6.4287, 10.8495, 5.5344, 11.0664, 5.8348, 11.538, 5.5003, 5.7039, 5.3973, 5.8177, 10.7541, 11.601, 5.4624, 5.4364, 5.8844, 5.4607, 10.9727, 5.7366, 5.6095, 5.4739, 5.4507, 6.1328, 22.465, 5.7323, 5.5311, 6.263, 5.4953, 5.4088, 10.946, 5.6095, 10.9441, 0.9241, 13.8288, 10.8461, 6.3396, 5.9497, 5.8663, 5.8923, 5.4107, 10.5145, 27.3979, 5.8808, 6.2306, 11.0424, 5.4908, 5.5108, 6.0928, 7.2417, 5.2519, 11.4858, 28.2805, 5.6825, 5.61, 5.6183, 11.3459, 10.9723, 5.2614, 5.3639, 5.4107, 10.9742, 5.7142, 5.4798, 5.8181, 5.6889, 5.3797, 11.7629, 5.2879, 6.227, 5.3415, 5.2862, 11.1279, 10.7405, 5.4114, 5.3906, 10.9589, 5.3573, 10.8845, 21.9774, 10.8154, 10.8302, 5.5079, 5.5127, 5.3568, 5.7037, 26.2206, 5.8692, 5.446, 10.8614, 5.9435, 10.8948, 5.6939, 11.6425, 5.7316, 10.802, 30.3447, 10.4902, 11.0106, 11.0421, 6.2957, 5.7051, 5.4638, 11.0457, 10.6692, 6.2907, 5.9161, 6.4955, 5.4996, 0.7539, 11.1368, 5.8808, 11.5318, 10.7872, 5.5764, 5.4448, 5.6229, 5.7142, 5.4033, 6.5389, 10.3779, 10.5212], '192.168.122.113': [6.1097, 10.6566, 10.5853, 5.9462, 5.2862, 5.3642, 5.4536, 6.1572, 5.5923, 5.3971, 5.5206, 0.6919, 5.2652, 6.1638, 27.5834, 5.9998, 15.9521, 5.9268, 11.0595, 11.0445, 5.4104, 5.3883, 10.8278, 5.5466, 22.8617, 5.3542, 5.4598, 10.5743, 14.2767, 10.9401, 10.9687, 5.4798, 11.121, 5.4076, 5.4812, 6.4089, 0.6022, 20.9394, 5.336, 5.2462, 10.6268, 5.3275, 10.9079, 5.3456, 5.2767, 11.3921, 5.5587, 5.4827, 10.9241, 0.5558, 5.5449, 5.4462, 0.3741, 5.3287, 0.7129, 7.2215, 5.6663, 6.3317, 10.8392, 10.8936, 5.8303, 0.7582, 5.4543, 5.4324, 10.8788, 5.2931, 11.5926, 10.9506, 10.8185, 5.4824, 10.9649, 0.6731, 5.4259, 0.6688, 5.2674, 10.7324, 6.2621, 6.0894, 5.347, 5.3053, 10.6015, 5.4255, 9.4392, 5.4367, 5.4882, 5.6818, 10.9305, 11.426, 5.4784, 5.7657, 5.2328, 5.3585, 10.8802, 5.5373, 10.7126, 5.8601, 5.4152, 10.855, 11.8136, 10.7396, 5.8227, 5.4305, 10.9522, 5.8999, 6.0475, 6.012, 6.1071, 10.7541, 5.3461, 10.9253, 5.6303, 5.7094, 10.9172, 11.3692, 10.7694, 5.3012, 5.415, 5.3394, 5.3108, 11.1151, 5.4915, 20.6103, 5.8262, 11.1272, 5.4579, 23.2859, 10.6652, 11.3075, 5.4064, 6.2075, 10.4077, 10.2885, 5.672, 10.7863, 17.2386, 6.2072, 16.341, 10.9558, 5.5611, 6.0778, 10.9425, 10.8809, 5.5501, 5.5799, 11.1926, 6.1927, 6.0468, 5.3177, 10.803, 11.6098, 10.653, 6.3229, 6.8505, 10.89, 10.844, 10.6709, 5.465, 11.1008, 5.9712, 5.82, 10.7651, 6.3798, 5.4872, 16.1545, 5.6274, 6.2208, 5.3468, 5.3067, 5.6455, 32.4721, 5.3725, 0.6196, 5.3515, 6.1443, 5.3289, 5.8408, 10.5515, 5.8451, 5.2075, 5.2493, 6.038, 0.8898, 0.479, 5.4631, 10.9499, 5.5001, 5.6102, 5.4748, 5.7282, 6.1636, 6.8934, 10.7191, 11.6618, 5.8141, 11.0447, 5.6381, 5.3296, 10.7074, 11.4055, 10.963, 5.3792, 5.4297, 16.5396, 11.4932, 5.9912, 16.398, 10.8953, 21.0927, 10.9653, 5.4317, 5.4765, 10.6459, 10.3998, 5.4512, 11.0452, 16.9373, 5.3306, 5.4457, 10.7601, 10.7872, 5.486, 5.5089, 5.8868, 5.6283, 5.7862, 5.3961, 5.4438, 10.8128, 10.6032, 5.3902, 6.258, 5.4159, 11.1475, 5.2845, 5.4643, 10.6311, 5.3408, 5.8224, 5.7006, 5.595, 10.8099, 11.0126, 10.896, 12.9991, 10.89, 9.4335, 5.6505, 0.8337, 6.0415, 18.8353, 5.62, 5.5707, 17.1678, 5.7042, 11.3492, 7.1552, 8.1553, 5.4774, 5.6338, 10.798], '192.168.122.112': [6.6125, 5.5645, 10.5946, 11.8713, 15.9001, 11.2586, 10.8254, 16.2265, 10.9048, 6.3736, 5.605, 0.6585, 5.3959, 5.825, 11.2498, 5.2698, 5.7843, 5.2419, 5.8157, 10.6268, 5.3747, 6.341, 10.8926, 5.4038, 11.3842, 5.3983, 5.5132, 5.2459, 10.7982, 5.7573, 5.2838, 5.5094, 5.5149, 5.2421, 5.3141, 6.1073, 0.7484, 5.4691, 5.6562, 5.4846, 5.4369, 11.2696, 16.4752, 5.4097, 5.4548, 10.4196, 5.4026, 5.2888, 10.7374, 0.4601, 5.4896, 22.3451, 0.4125, 5.4975, 0.5937, 5.7178, 10.2999, 5.7499, 10.6742, 5.3937, 5.645, 0.5991, 5.4739, 10.69, 5.4615, 16.9995, 5.4548, 5.8846, 10.7071, 5.9607, 10.8147, 0.6783, 5.4412, 0.7348, 5.43, 5.3463, 5.6548, 5.3339, 10.7553, 5.4016, 5.5573, 5.3976, 21.6773, 5.5156, 11.0281, 10.3321, 5.2509, 5.8064, 5.7478, 11.3142, 10.581, 5.4367, 10.8495, 5.7001, 5.5792, 6.1207, 10.6704, 5.6777, 6.0284, 10.8283, 10.9849, 5.4097, 10.5689, 10.7875, 10.6878, 5.6207, 5.3773, 5.6381, 6.0358, 10.823, 10.9692, 5.2927, 10.9136, 7.3903, 5.2962, 5.4252, 5.8789, 5.4719, 16.3779, 22.0606, 16.4299, 10.8323, 21.1756, 5.7018, 11.0683, 5.7487, 5.3816, 5.645, 10.9463, 5.2621, 10.3791, 5.7228, 16.4721, 5.8875, 5.8603, 5.2803, 6.0284, 5.4231, 5.2381, 10.5226, 5.3709, 5.3816, 5.4293, 11.0567, 6.2199, 11.3499, 16.7074, 5.5611, 11.121, 11.5423, 10.4482, 5.5156, 21.7669, 10.92, 10.8056, 6.2876, 10.9017, 5.6219, 10.8156, 5.3685, 5.8391, 6.1989, 5.5017, 5.5075, 10.6542, 5.8732, 11.1272, 5.4305, 10.7915, 10.9906, 5.512, 3.5157, 5.3892, 10.5278, 5.4975, 5.4121, 5.3742, 5.8405, 5.9125, 5.2276, 5.6586, 6.9728, 0.7064, 5.3751, 6.356, 6.4003, 5.6314, 5.7282, 21.2114, 5.6839, 5.4758, 5.7142, 5.8277, 5.496, 11.5471, 5.5695, 10.926, 6.2068, 6.1648, 10.993, 5.5561, 5.6152, 10.8273, 5.867, 21.6129, 6.0785, 11.2967, 5.2211, 10.4733, 5.3942, 11.0958, 5.9552, 6.0847, 5.4493, 11.1403, 5.3926, 10.9742, 5.4233, 5.5892, 5.4414, 5.6138, 6.4259, 5.348, 0.622, 6.2153, 5.47, 10.9148, 5.4493, 5.8701, 10.7257, 6.2494, 5.9736, 0.9689, 11.4861, 5.4505, 5.1975, 5.1951, 11.0641, 11.4124, 5.9161, 5.4262, 11.0803, 28.1923, 5.5428, 11.0164, 6.4774, 10.9408, 0.5114, 16.1619, 26.4544, 5.758, 5.9094, 5.533, 5.3537, 11.272, 10.9181, 10.9501, 29.7203, 10.3443, 10.9313]}
cpu1_10_10 = [10.4, 57.2, 2.3, 3.6, 1.4, 0.3, 0.4, 0.5, 0.3, 0.3, 0.0, 0.6, 0.3, 1.3, 0.8, 0.1, 0.5, 0.2, 0.8, 1.2, 0.0, 0.8, 0.5, 0.1, 0.6, 0.0, 0.2, 0.1, 0.0, 0.0, 0.0, 0.1, 0.3, 0.2, 0.0, 0.1, 1.7, 2.2, 0.4, 0.5, 0.5, 0.1, 0.2, 0.2, 0.1, 0.3, 0.7, 0.9, 0.8, 0.2, 0.0, 0.4, 0.6, 1.0, 0.7, 2.4, 2.1, 0.1, 0.0, 0.0, 0.2, 0.2, 0.5, 0.9, 0.4, 0.2, 0.2, 0.5, 0.5, 0.2, 0.3, 0.7, 0.6, 0.1, 0.3, 1.2, 1.5, 1.8, 0.8, 0.8, 0.2, 0.2, 0.0, 0.4, 1.2, 0.7, 0.5, 1.6, 1.3, 0.1, 0.0, 0.4, 0.5, 0.1, 0.4, 0.0, 0.3, 0.5, 0.9, 1.1, 0.5, 2.1, 1.7, 0.7, 0.4, 0.3, 1.4, 1.2, 0.5, 0.2, 1.2, 0.4, 0.6, 0.9, 0.0, 0.7, 0.4, 0.0, 0.9, 1.0, 0.7, 0.1, 1.2, 0.9, 0.4, 0.7, 0.3, 1.1, 0.9, 0.1, 0.3, 0.6, 0.2, 0.4, 0.2, 0.5, 0.9, 0.3, 0.3, 0.5, 0.1, 0.7, 0.0, 0.7, 0.1, 0.8, 1.1, 0.8, 2.0, 3.2, 2.6, 0.2, 0.2, 0.4, 0.1, 0.3, 0.5, 1.3, 1.2, 0.3, 0.1, 0.5, 0.3, 1.1, 5.5, 4.8, 0.2, 0.4, 0.4, 1.4, 0.7, 0.2, 0.0, 0.4, 1.1, 0.6, 0.3, 0.1, 0.2, 0.4, 0.7, 0.4, 1.7, 1.8, 0.2, 0.5, 0.8, 0.5, 0.2, 0.2, 0.4, 0.1, 0.2, 0.5, 1.2, 0.4, 0.0, 0.5, 0.3, 0.1, 0.2, 0.1, 0.3, 0.4, 0.1, 0.3, 0.1, 0.1, 0.2, 0.2, 0.2, 1.3, 1.2, 5.0, 5.5, 0.4, 1.0, 0.9, 0.1, 0.3, 0.4, 0.2, 0.5, 0.5, 0.0, 0.8, 0.9, 0.1, 0.5, 3.2, 2.6, 3.5, 3.5, 2.2, 2.3, 0.6, 0.2, 0.6, 0.1, 3.2, 3.4, 0.2, 0.4, 0.8, 0.0, 0.7, 0.4, 0.0, 0.3, 0.1, 0.2, 0.8, 0.2, 1.0, 0.7, 2.1, 1.1, 0.7, 0.7, 0.4]
off_mec1_10_10 = 54
off_cloud1_10_10 = 114
inward_mec1_10_10 = 31
loc1_10_10 = 706
deadlock1_10_10 = [4]
memory1_10_10 = [0.2187, 0.2188, 0.2189, 0.2191, 0.2192, 0.2195, 0.2195, 0.2195, 0.2196, 0.2196, 0.2197, 0.2198, 0.2198, 0.2198, 0.2199, 0.2199, 0.22, 0.2202, 0.2204, 0.2204, 0.2205, 0.2205, 0.2206, 0.2206, 0.2206, 0.2207, 0.2207, 0.2208, 0.2209, 0.2209, 0.2209, 0.2211, 0.2211, 0.2212, 0.2212, 0.2212, 0.2212, 0.2212, 0.2213, 0.2213, 0.2213, 0.2214, 0.2214, 0.2214, 0.2215, 0.2216, 0.2216, 0.2217, 0.2219, 0.222, 0.2221, 0.2221, 0.2221, 0.2221, 0.2222, 0.2222, 0.2222, 0.2222, 0.2223, 0.2224, 0.2225, 0.2226, 0.2226, 0.2227, 0.2227, 0.2227, 0.2227, 0.2227, 0.2227, 0.2229, 0.223, 0.223, 0.2232, 0.2233, 0.2234, 0.2234, 0.2235, 0.2235, 0.2235, 0.2235, 0.2235, 0.2235, 0.2236, 0.2236, 0.2237, 0.2238, 0.2239, 0.2239, 0.2239, 0.2239, 0.2239, 0.2239, 0.2239, 0.2239, 0.2241, 0.2241, 0.2242, 0.2244, 0.2244, 0.2245, 0.2245, 0.2245, 0.2245, 0.2246, 0.2247, 0.2248, 0.2248, 0.225, 0.225, 0.2251, 0.2252, 0.2252, 0.2252, 0.2253, 0.2253, 0.2256, 0.2256, 0.2257, 0.2258, 0.2259, 0.2259, 0.2259, 0.2259, 0.226, 0.226, 0.226, 0.226, 0.226, 0.226, 0.2261, 0.2261, 0.2262, 0.2262, 0.2262, 0.2264, 0.2264, 0.2265, 0.2265, 0.2265, 0.2265, 0.2265, 0.2265, 0.2265, 0.2265, 0.2266, 0.2266, 0.2266, 0.2266, 0.2266, 0.2266, 0.2267, 0.2267, 0.2267, 0.2267, 0.2267, 0.2268, 0.2269, 0.2269, 0.227, 0.227, 0.227, 0.227, 0.227, 0.2271, 0.2271, 0.2271, 0.2273, 0.2273, 0.2273, 0.2273, 0.2274, 0.2274, 0.2274, 0.2274, 0.2275, 0.2275, 0.2275, 0.2276, 0.2276, 0.2276, 0.2276, 0.2282, 0.2282, 0.2282, 0.2283, 0.2283, 0.2283, 0.2283, 0.2283, 0.2284, 0.2284, 0.2284, 0.2284, 0.2284, 0.2284, 0.2284, 0.2285, 0.2285, 0.2285, 0.2286, 0.2286, 0.2286, 0.2287, 0.2287, 0.2287, 0.2287, 0.2288, 0.2288, 0.2288, 0.2288, 0.2291, 0.2291, 0.2296, 0.2296, 0.2297, 0.2297, 0.2297, 0.2298, 0.2298, 0.2298, 0.2299, 0.2299, 0.2299, 0.2299, 0.2299, 0.23, 0.23, 0.23, 0.23, 0.23, 0.2302, 0.2302, 0.2303, 0.2303, 0.2303, 0.2303, 0.2304, 0.2304, 0.2304, 0.2304, 0.2305, 0.231, 0.231, 0.2311, 0.2311, 0.2311, 0.2311, 0.2311, 0.2311, 0.2311, 0.2311, 0.2311, 0.2311, 0.2312, 0.2312, 0.2312, 0.2312, 0.2312, 0.2312, 0.2312]
task_received1_10_10 = 874
sent_t1_10_10 = {'124': 267, '125': 236, '126': 371}
cooperate1_10_10 = {'mec': 54, 'cloud': 114}
task_record1_10_10 = {}
outward_mec1_10_10 = 32
offload_check1_10_10 = [30, 2] | [
198,
46569,
16,
62,
940,
62,
940,
796,
1391,
6,
17477,
13,
14656,
13,
18376,
13,
11442,
10354,
685,
20,
13,
36189,
19,
11,
642,
13,
3270,
4309,
11,
860,
13,
1731,
1828,
11,
807,
13,
20,
28460,
11,
860,
13,
405,
3365,
11,
807,
... | 1.617242 | 253,319 |
from django.conf import settings
from django.conf.urls.static import static
from django.conf.urls import include,url
from . import views
urlpatterns=[
url(r'api/user/user-id/(?P<pk>[0-9]+)/$',
views.UserDescription.as_view()),
url(r'api/project/project-id/(?P<pk>[0-9]+)/$',
views.ProjectDescription.as_view()),
url(r'^profile/',views.profile,name='profile'),
url('^$',views.index,name ='index'),
url(r'^search/', views.search_results, name='search_results'),
url(r'^user/',views.user,name ='user'),
url(r'^tinymce/', include('tinymce.urls')),
url(r'^api/profile/', views.UserList.as_view()),
url(r'^api/project/', views.ProjectList.as_view()),
url(r'^project/',views.new_project,name ='newproject'),
url(r'^ajax/newsletter/$', views.newsletter, name='newsletter')
]
if settings.DEBUG:
urlpatterns+= static(settings.MEDIA_URL, document_root = settings.MEDIA_ROOT)
| [
220,
220,
198,
6738,
42625,
14208,
13,
10414,
1330,
6460,
198,
6738,
42625,
14208,
13,
10414,
13,
6371,
82,
13,
12708,
1330,
9037,
198,
6738,
42625,
14208,
13,
10414,
13,
6371,
82,
1330,
2291,
11,
6371,
198,
6738,
764,
1330,
5009,
198... | 2.410256 | 390 |
import yaml
import argparse
from ansible_vault import Vault
if __name__ == '__main__':
main()
| [
11748,
331,
43695,
198,
11748,
1822,
29572,
198,
6738,
9093,
856,
62,
85,
1721,
1330,
23450,
628,
628,
198,
361,
11593,
3672,
834,
6624,
705,
834,
12417,
834,
10354,
198,
220,
220,
220,
1388,
3419,
198
] | 2.833333 | 36 |
# -*- coding: utf-8 -*-
"""
The data is (should be) based on the lecture Energie und Klimasysteme II,
Erneuerbare Energieerzeugung am Gebude, FS 2019, Folie 30,
"""
from __future__ import print_function
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
37811,
198,
464,
1366,
318,
357,
21754,
307,
8,
1912,
319,
262,
19143,
412,
1008,
22699,
3318,
509,
2475,
4107,
927,
68,
220,
2873,
11,
198,
9139,
710,
15573,
49382,
4... | 2.72 | 75 |
import netCDF4
from os import path
from test_utils.utils import count_strings, read_log_file
import pytest
SCRIPT_PATH = path.dirname(path.realpath(__file__))
| [
11748,
2010,
34,
8068,
19,
198,
6738,
28686,
1330,
3108,
198,
6738,
1332,
62,
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62,
37336,
11,
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62,
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11748,
12972,
9288,
198,
198,
6173,
46023,
62,
34219,
796,
3108,
13,
15908,
3672,... | 3.037736 | 53 |
from django.contrib.auth.models import AbstractUser
from django.db import models
from .validators import validate_bet, validate_course
| [
6738,
42625,
14208,
13,
3642,
822,
13,
18439,
13,
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2024,
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26571,
62,
11181,
11,
26571,
62,
17319,
628,
198
] | 3.805556 | 36 |
# Copyright (c) 2021 Graphcore Ltd. All rights reserved.
import logging
import popdist
import popdist.poptorch
import horovod.torch as hvd
| [
2,
15069,
357,
66,
8,
33448,
29681,
7295,
12052,
13,
1439,
2489,
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13,
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18931,
198,
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1461,
17080,
198,
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1461,
17080,
13,
79,
8738,
273,
354,
198,
11748,
3076,
709,
375,
13,
13165,
354,
355,
289,
20306,
628,... | 3.27907 | 43 |
from .docs import docs
from .request import (
request_schema,
use_kwargs, # for backward compatibility
path_schema, # request_schema with locations=["path"]
querystring_schema, # request_schema with locations=["querystring"]
form_schema, # request_schema with locations=["form"]
json_schema, # request_schema with locations=["json"]
headers_schema, # request_schema with locations=["headers"]
cookies_schema, # request_schema with locations=["cookies"]
)
from .response import (
response_schema,
marshal_with, # for backward compatibility
)
| [
6738,
764,
31628,
1330,
34165,
198,
6738,
764,
25927,
1330,
357,
198,
220,
220,
220,
2581,
62,
15952,
2611,
11,
198,
220,
220,
220,
779,
62,
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22046,
11,
220,
1303,
329,
19528,
17764,
198,
220,
220,
220,
3108,
62,
15952,
2611,
... | 2.989848 | 197 |
# -*- coding: utf-8 -*-
#
# Copyright (C) 2018 Red Hat, Inc
#
# 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/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
from __future__ import unicode_literals
import pytest
from dci.common.exceptions import DCIException
from dci.common.schemas import check_json_is_valid, tag_schema
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
2,
198,
2,
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357,
34,
8,
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11,
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2,
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2,
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262,
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13789,
11,
10628,
362,
13,
15,
357,
1169,
366,
34156,
15341,
345,
... | 3.520548 | 219 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Logging Handler integrating RabbitMQ and
Graylog Extended Log Format (GELF)"""
import json
from logging import Filter
from logging.handlers import SocketHandler
from amqplib import client_0_8 as amqp # pylint: disable=import-error
from graypy.handler import BaseGELFHandler
try:
from urllib.parse import urlparse, unquote
except ImportError:
from urlparse import urlparse
from urllib import unquote
_ifnone = lambda v, x: x if v is None else v
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
198,
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
198,
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11187,
2667,
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32029,
25498,
49215,
290,
198,
46130,
6404,
24204,
5972,
18980,
357,
38,
37738,
8,
37811,... | 3.08982 | 167 |
import boto3
comprehend = boto3.client(service_name='comprehend')
translate = boto3.client(service_name='translate')
def detect_language(text):
"""
Detects the dominant language in a text
Parameters
----------
text: string, required
Input text
Returns
-------
string
Representing language code of the dominant language
"""
# Sending call to get language
result = comprehend.detect_dominant_language(Text = text)['Languages']
# Since the result can contain more than one language find the one with the highest score.
high_score = 0
best_guess = ''
for lang in range(len(result)):
if result[lang]['Score'] > high_score:
high_score = result[lang]['Score']
best_guess = result[lang]['LanguageCode']
return best_guess
def translate_text(text, source_lang, destination_lang):
"""
Translates given text from source language into destination language
Parameters
----------
text: string, required
Input text in source language
Returns
-------
string
Translated text in destination language
"""
result = translate.translate_text(Text=text,
SourceLanguageCode=source_lang, TargetLanguageCode=destination_lang)
return result.get('TranslatedText')
| [
11748,
275,
2069,
18,
198,
198,
785,
3866,
15631,
796,
275,
2069,
18,
13,
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7,
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62,
3672,
11639,
785,
3866,
15631,
11537,
198,
7645,
17660,
796,
275,
2069,
18,
13,
16366,
7,
15271,
62,
3672,
11639,
7645,
17660,
11537,
198,
... | 2.851293 | 464 |
# 2020.05.10
# update topNscore
# learner on subspace
# particular designed for encounter missing class in this subspace
# if one class do not exists in training data, probability for this class would be zeros under anytime
#
# learner: a regressor or classifier, must have methods named 'predict'
# num_class: total number of class in dataset
import numpy as np
from sklearn.metrics import accuracy_score
if __name__ == "__main__":
from sklearn.svm import SVC
from sklearn import datasets
from sklearn.model_selection import train_test_split
print(" > This is a test example: ")
digits = datasets.load_digits()
X = digits.images.reshape((len(digits.images), -1))
print(" input feature shape: %s"%str(X.shape))
X_train, X_test, y_train, y_test = train_test_split(X, digits.target, test_size=0.2, stratify=digits.target)
clf = myLearner(SVC(gamma='scale', probability=True), 10)
clf.fit(X_train, y_train)
print(" --> train acc: %s"%str(clf.score(X_train, y_train)))
print(" --> test acc.: %s"%str(clf.score(X_test, y_test)))
print(" --> test top3 acc.: %s"%str(clf.topNscore(X_test, y_test, 3)))
print("------- DONE -------\n")
| [
2,
12131,
13,
2713,
13,
940,
198,
2,
220,
4296,
1353,
45,
26675,
198,
2,
22454,
1008,
319,
850,
13200,
198,
2,
1948,
3562,
329,
8791,
4814,
1398,
287,
428,
850,
13200,
198,
2,
611,
530,
1398,
466,
407,
7160,
287,
3047,
1366,
11,
... | 2.729545 | 440 |
import time
import pritunl_wireguard_client.utils.random as utils
| [
11748,
640,
198,
11748,
778,
270,
403,
75,
62,
21809,
14864,
62,
16366,
13,
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13,
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355,
3384,
4487,
628
] | 3.190476 | 21 |
#!/usr/bin/env python3
from __future__ import print_function
import getpass
import os
import shutil
import time
from datetime import datetime
from github import Github, GithubException, GithubObject
from git import git_init
| [
2,
48443,
14629,
14,
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21015,
18,
198,
198,
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834,
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62,
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198,
198,
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651,
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198,
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198,
11748,
4423,
346,
198,
11748,
640,
198,
198,
6738,
4818,
8079,
1330,
4818,
8... | 3.615385 | 65 |
import logging
import os
import platform
from datetime import datetime
from moabb.analysis import plotting as plt
from moabb.analysis.meta_analysis import ( # noqa: E501
compute_dataset_statistics,
find_significant_differences,
)
from moabb.analysis.results import Results # noqa: F401
log = logging.getLogger(__name__)
def analyze(results, out_path, name="analysis", plot=False):
"""Analyze results.
Given a results dataframe, generates a folder with
results and a dataframe of the exact data used to generate those results,
aswell as introspection to return information on the computer
parameters
----------
out_path: location to store analysis folder
results: Dataframe generated from Results object
path: string/None
plot: whether to plot results
Either path or results is necessary
"""
# input checks #
if not isinstance(out_path, str):
raise ValueError("Given out_path argument is not string")
elif not os.path.isdir(out_path):
raise IOError("Given directory does not exist")
else:
analysis_path = os.path.join(out_path, name)
unique_ids = [plt._simplify_names(x) for x in results.pipeline.unique()]
simplify = True
print(unique_ids)
print(set(unique_ids))
if len(unique_ids) != len(set(unique_ids)):
log.warning("Pipeline names are too similar, turning off name shortening")
simplify = False
os.makedirs(analysis_path, exist_ok=True)
# TODO: no good cross-platform way of recording CPU info?
with open(os.path.join(analysis_path, "info.txt"), "a") as f:
dt = datetime.now()
f.write("Date: {:%Y-%m-%d}\n Time: {:%H:%M}\n".format(dt, dt))
f.write("System: {}\n".format(platform.system()))
f.write("CPU: {}\n".format(platform.processor()))
results.to_csv(os.path.join(analysis_path, "data.csv"))
stats = compute_dataset_statistics(results)
stats.to_csv(os.path.join(analysis_path, "stats.csv"))
P, T = find_significant_differences(stats)
if plot:
fig, color_dict = plt.score_plot(results)
fig.savefig(os.path.join(analysis_path, "scores.pdf"))
fig = plt.summary_plot(P, T, simplify=simplify)
fig.savefig(os.path.join(analysis_path, "ordering.pdf"))
| [
11748,
18931,
198,
11748,
28686,
198,
11748,
3859,
198,
6738,
4818,
8079,
1330,
4818,
8079,
198,
198,
6738,
6941,
6485,
13,
20930,
1330,
29353,
355,
458,
83,
198,
6738,
6941,
6485,
13,
20930,
13,
28961,
62,
20930,
1330,
357,
220,
1303,
... | 2.695193 | 853 |
from numba import guvectorize, float64, int64, njit
import numpy as np
from smm import numba_target as target_preset
| [
6738,
997,
7012,
1330,
915,
31364,
1096,
11,
12178,
2414,
11,
493,
2414,
11,
299,
45051,
198,
11748,
299,
32152,
355,
45941,
198,
6738,
895,
76,
1330,
997,
7012,
62,
16793,
355,
2496,
62,
18302,
316,
628,
628,
628,
198
] | 3.075 | 40 |
"""
How to find only the duplicates
"""
some_list = ['a', 'b', 'c', 'b', 'd', 'm', 'n', 'n']
duplicates = [] # o noua lista unde adaugam duplicatele
for value in some_list:
if some_list.count(value) > 1: # cand in lista numaram fiecare valoarea si se gaseste mai mult de o data
if value not in duplicates: # apoi cand acea valoare nu se gaseste deja in duplicates, o adaugam
duplicates.append(value)
print(duplicates)
some_list = ['a', 'b', 'c', 'b', 'd', 'm', 'n', 'n']
duplicates = list(set([x for x in some_list if some_list.count(x) > 1]))
print(duplicates)
# list = transformam in lista cum cere
# set = sa apara fara duplicate
# for x in some_list sa mearga prin lista, if = numai cand count(x) din lista some_list se gaseste mai mult de o data
| [
37811,
198,
2437,
284,
1064,
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262,
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16856,
198,
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198,
198,
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62,
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796,
37250,
64,
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198,
198,
... | 2.558824 | 306 |
#!/usr/bin/env python3
"""
ETH Gas Station
Primary backend.
"""
import argparse
from egs.main import master_control
from egs.output import Output
def main():
"""Parse command line options."""
parser = argparse.ArgumentParser(description="An adaptive gas price oracle for Ethereum.")
parser.add_argument('--generate-report', action='store_true', help="Generate reports for ethgasstation-frontend.")
args = parser.parse_args()
# kick off the egs main event loop
master_control(args)
if __name__ == '__main__':
o = Output()
o.banner()
main()
| [
2,
48443,
14629,
14,
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14,
24330,
21015,
18,
198,
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198,
220,
220,
220,
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220,
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1822,
29572,
198,
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304,
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13,
12417,
1330,
4958,
62,
137... | 2.984772 | 197 |
"""Contains tests for the functions found in ``controls/templates_helpers.py``
To run this particular test file use the following command line:
nose2 -v app.tests.controls.tests_template_helpers
"""
from app import db, create_app
import unittest
from unittest import TestCase
from config import Config
from app.tests.utils import (dummy_post, control_categories,
control_search_bar,
add_three_dummy_widget_positions)
from app.models import SearchBarControls, CategoriesControls
from app.controls.template_helpers import (ordered_widgets,
categories_presence,
sidebar_widget_count,
search_bar_placement)
if __name__ == '__main__':
unittest.main(verbosity=2) | [
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1708,
3141,
1627,
25,
198,
198,
77,
577,
17,
532,
85,
598... | 2.209845 | 386 |
import torch
import torch.nn as nn
from ..networks.basic.util import check
from ..networks.basic.predict import PredictNet, PredictLayer, OutLayer
from ..utils.util import get_shape_from_obs_space | [
11748,
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2198,
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13,
79,
17407,
1330,
49461,
7934,
11,
49461,
49925,
11,
3806,
49925,
198,... | 3.5 | 56 |
from google.appengine.ext import ndb
from repositories.gae.dataset import Dataset
from repositories.gae.dataset import calculate_max_hashes, get_random_bits | [
6738,
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13,
1324,
18392,
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316,
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62,
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62,
71,
7465,
11,
651,
62,
25... | 3.466667 | 45 |
"""Compare evolution matrices to v0.1 numerics"""
import wetrunner
import unittest
from pkg_resources import resource_filename
import numpy as np
import numpy.testing as npt
| [
37811,
41488,
6954,
2603,
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284,
410,
15,
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873,
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198,
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555,
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395,
198,
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279,
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62,
37540,
1330,
8271,
62,
34345,
198,
11748,
299,
32152,
355,
45941,
198,
11748,
... | 3.56 | 50 |
import configparser
import random
import string
from clockwork import utils
| [
11748,
4566,
48610,
198,
11748,
4738,
198,
11748,
4731,
198,
198,
6738,
8801,
1818,
1330,
3384,
4487,
628,
628,
628,
198
] | 3.952381 | 21 |
"""
Copyright (c) 2020 Intel 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.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import sys
from argparse import ArgumentParser
from typing import NamedTuple, Any
import torch
from os import listdir, makedirs
from os.path import isfile, join, exists
from shutil import copyfile
from nncf.torch.quantization.layers import SymmetricQuantizer, AsymmetricQuantizer
if __name__ == '__main__':
main(sys.argv[1:])
| [
37811,
198,
15069,
357,
66,
8,
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8180,
10501,
198,
49962,
739,
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11,
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362,
13,
15,
357,
1169,
366,
34156,
15341,
198,
345,
743,
407,
779,
428,
2393,
2845,
287,
11846,
351,
262,
13789,
13,
198,
921,
743,
7... | 3.704453 | 247 |
# Generated by Django 3.0.6 on 2020-05-19 14:32
from django.db import migrations, models
| [
2,
2980,
515,
416,
37770,
513,
13,
15,
13,
21,
319,
12131,
12,
2713,
12,
1129,
1478,
25,
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198,
198,
6738,
42625,
14208,
13,
9945,
1330,
15720,
602,
11,
4981,
628
] | 2.84375 | 32 |
# Copyright 2021 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from os.path import join, dirname
import numpy as np
import torch
from ..models.netinterface import NetInterface
from os import makedirs
import matplotlib.pyplot as plt
import matplotlib.cm
from matplotlib.colors import ListedColormap
from ..third_party.util_colormap import turbo_colormap_data
# matplotlib.cm.register_cmap('turbo', cmap=ListedColormap(turbo_colormap_data))
import matplotlib
import shutil
# %%
| [
2,
15069,
33448,
3012,
11419,
198,
2,
198,
2,
49962,
739,
262,
24843,
13789,
11,
10628,
362,
13,
15,
357,
1169,
366,
34156,
15341,
198,
2,
345,
743,
407,
779,
428,
2393,
2845,
287,
11846,
351,
262,
13789,
13,
198,
2,
921,
743,
733... | 3.477193 | 285 |
#!/usr/bin/python3
import subprocess
import yaml
try:
from yaml import CLoader as Loader, CDumper as Dumper
except ImportError:
from yaml import Loader, Dumper
#MAC address of the smart plug
MAC_ADDRESS = '84:f3:eb:32:e3:b4'
# netmask of your network
NET_MASK = '192.168.1.1/24'
#octopi config
OCTOPI_CONFIG = '/home/pi/.octoprint/config.yaml'
if __name__ == "__main__":
subprocess.check_call(['/usr/bin/nmap','-sP','-T4',NET_MASK])
p = subprocess.Popen(['/usr/sbin/arp','-n'], stdout=subprocess.PIPE)
for line in iter(p.stdout.readline, b''):
str = line.decode('utf-8')
if(MAC_ADDRESS in str):
ipaddress = str.split(' ')[0]
print('Found Plug with address %s' % ipaddress)
config = open(OCTOPI_CONFIG)
configy = yaml.load(config, Loader=Loader)
config.close()
# print(configy)
print(configy['plugins']['tuyasmartplug']['arrSmartplugs'][0]['ip'])
if configy['plugins']['tuyasmartplug']['arrSmartplugs'][0]['ip'] != ipaddress:
configy['plugins']['tuyasmartplug']['arrSmartplugs'][0]['ip'] = ipaddress
config = open(OCTOPI_CONFIG, mode='w')
config.write(yaml.safe_dump(configy, allow_unicode=False))
config.close()
break
| [
2,
48443,
14629,
14,
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14,
29412,
18,
198,
198,
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850,
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198,
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198,
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25,
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220,
220,
220,
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331,
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263,
355,
8778,
263,
11,
6458,
15829,
355,
360,
15829,
198,
16341,
... | 2.26055 | 545 |
from tkinter_gui_builder.panel_templates.widget_panel.widget_panel import AbstractWidgetPanel
from tkinter_gui_builder.widgets import basic_widgets
if __name__ == '__main__':
print(dir(AbstractWidgetPanel))
| [
6738,
256,
74,
3849,
62,
48317,
62,
38272,
13,
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62,
11498,
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13,
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256,
74,
3849,
62,
48317,
62,
38272,
13,
28029,
11407,
1330,
4096,
62,
28029,
11... | 3.147059 | 68 |
import torch
import numpy as np
import scipy.sparse as sp
def sparse_to_tuple(sparse_mx):
"""Convert sparse matrix to tuple representation."""
if isinstance(sparse_mx, list):
for i in range(len(sparse_mx)):
sparse_mx[i] = to_tuple(sparse_mx[i])
else:
sparse_mx = to_tuple(sparse_mx)
return sparse_mx
a = np.array([1,2,3])
b = np.array([4,5,6])
c = a + b.reshape((3,1))
print(c)
#a = torch.randint(1,100,(2,2,5))
#print(a)
#print(a.repeat(1,1,2))
#b = a.repeat(1,1,2).view(2,4,5)
#print(b)
#c = a.repeat(1,2,1)
#print(torch.cat([b,c],dim=2).view(2,))
#print(range(10))
#a = torch.randint(1,100,(3,3)).numpy()
#print(a)
#a = a + np.multiply(a.T,a.T > a) - np.multiply(a,a.T > a)
#print(a)
'''
a = torch.randint(0,2,(3,3))
b = sp.coo_matrix(a)
c = np.array(b.sum(1))
print(a)
print(b)
print(c)
'''
'''
a = torch.randn((3,3))
rowsum = np.array(a.sum(1))
r_inv = np.power(rowsum,-1).flatten()
r_inv[np.isinf(r_inv)] = 0.
r_mat_inv = sp.diags(r_inv)
print(r_inv)
print(r_mat_inv)
print(a)
features = r_mat_inv.dot(a)
print(features)
print(sparse_to_tuple(features))
'''
| [
11748,
28034,
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62,
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220,
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220,
37227,
3103,
1851,
29877,
17593,
284,
... | 1.918367 | 588 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
import mechkit
import mechmean
def evenly_distributed_vectors_on_sphere(nbr_vectors=1000):
"""
Define nbr_vectors evenly distributed vectors on a sphere
Using the golden spiral method kindly provided by
stackoverflow-user "CR Drost"
https://stackoverflow.com/a/44164075/8935243
"""
from numpy import pi, cos, sin, arccos, arange
indices = arange(0, nbr_vectors, dtype=float) + 0.5
phi = arccos(1 - 2 * indices / nbr_vectors)
theta = pi * (1 + 5 ** 0.5) * indices
x, y, z = cos(theta) * sin(phi), sin(theta) * sin(phi), cos(phi)
orientations = np.column_stack((x, y, z))
return orientations
def first_kind_discrete(orientations, order=4):
"""
Calc orientation tensors of ... kind
"""
# Normalize orientations
orientations = [np.array(v) / np.linalg.norm(v) for v in orientations]
# Symmetrize orientations
# orientations_reversed = [-v for v in orientations]
# orientations = orientations + orientations_reversed
einsumStrings = {
1: "ij -> j",
2: "ij, ik -> jk",
3: "ij, ik, il -> jkl",
4: "ij, ik, il, im -> jklm",
5: "ij, ik, il, im, in -> jklmn",
6: "ij, ik, il, im, in, ip -> jklmnp",
}
if order > 6:
einsumStrings[order] = einsum_str_fabric_tensor_first_kind_discrete(order=order)
einsumArgs = [orientations for i in range(order)]
N = 1.0 / len(orientations) * np.einsum(einsumStrings[order], *einsumArgs)
return N
def einsum_str_fabric_tensor_first_kind_discrete(order):
"""
Generalize to higher orders:
N = sum_i 'order'-times_dyad_product(vector)
=
1: 'ij -> j',
2: 'ij, ik -> jk',
3: 'ij, ik, il -> jkl',
4: 'ij, ik, il, im -> jklm',
5: 'ij, ik, il, im, in -> jklmn',
6: 'ij, ik, il, im, in, ip -> jklmnp',
...
"""
# Get list of all available characters
import string
letters = list(string.ascii_letters)
letters.remove("i")
# Create einsum string and arguments
einsumInput = ",".join(["i" + letters[index] for index in range(order)])
einsumOut = "".join(letters[0:order])
einsumString = einsumInput + "->" + einsumOut
return einsumString
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
18,
198,
2,
532,
9,
12,
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25,
3384,
69,
12,
23,
532,
9,
12,
198,
198,
11748,
299,
32152,
355,
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198,
11748,
36571,
15813,
198,
11748,
36571,
32604,
628,
198,
198,
4299,
21894,
... | 2.210526 | 1,064 |
from setuptools import setup, find_packages
setup(
name="fancylog",
version="0.2.6",
description="Fancier logging in Python",
install_requires=["packaging", "rich"],
extras_require={
"dev": [
"black",
"pytest-cov",
"pytest",
"coverage",
"bump2version",
"pre-commit",
"flake8",
]
},
python_requires=">=3.6",
packages=find_packages(),
include_package_data=True,
url="https://github.com/adamltyson/fancylog",
author="Adam Tyson",
author_email="code@adamltyson.com",
classifiers=[
"Development Status :: 3 - Alpha",
"Operating System :: POSIX :: Linux",
"Programming Language :: Python",
"Programming Language :: Python :: 3.7",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Intended Audience :: Developers",
],
)
| [
6738,
900,
37623,
10141,
1330,
9058,
11,
1064,
62,
43789,
628,
198,
40406,
7,
198,
220,
220,
220,
1438,
2625,
69,
3883,
6404,
1600,
198,
220,
220,
220,
2196,
2625,
15,
13,
17,
13,
21,
1600,
198,
220,
220,
220,
6764,
2625,
37,
1192... | 2.209195 | 435 |
from ymmsl import Identifier, Reference
import pytest
import yatiml
| [
6738,
331,
76,
907,
75,
1330,
11440,
7483,
11,
20984,
198,
198,
11748,
12972,
9288,
198,
11748,
331,
265,
320,
75,
628,
628,
628,
628,
628
] | 3 | 26 |
import os
import pytest
import testinfra.utils.ansible_runner
testinfra_hosts = testinfra.utils.ansible_runner.AnsibleRunner(
os.environ['MOLECULE_INVENTORY_FILE']).get_hosts('all')
def test_sysctl_non_local_bind(host):
non_local_bind = host.sysctl("net.ipv4.ip_nonlocal_bind")
assert non_local_bind == 1
| [
11748,
28686,
198,
11748,
12972,
9288,
198,
198,
11748,
1332,
10745,
430,
13,
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13,
504,
856,
62,
16737,
198,
198,
9288,
10745,
430,
62,
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82,
796,
1332,
10745,
430,
13,
26791,
13,
504,
856,
62,
16737,
13,
2025,
82,
856,
49... | 2.454545 | 132 |
"""Things that help you keep track of task results and how to decode them."""
import abc
import dataclasses
import threading
import uuid
from concurrent import futures
from typing import Callable, Generic, Optional, TypeVar, Union
import urllib3
from tiledb.cloud import rest_api
from tiledb.cloud import tiledb_cloud_error as tce
from tiledb.cloud._results import decoders
from tiledb.cloud._results import stored_params
TASK_ID_HEADER = "X-TILEDB-CLOUD-TASK-ID"
_T = TypeVar("_T")
# Not frozen to avoid generating unsafe methods like `__hash__`,
# but you should still *treat* these subclasses like they're frozen.
class AsyncResult(Generic[_T]):
"""Asynchronous wrapper for compatibility with the old array.TaskResult."""
def __init__(self, future: "futures.Future[Result[_T]]"):
"""Creates a new AsyncResponse wrapping the given Future."""
self._future = future
self._id_lock = threading.Lock()
self._task_id: Optional[uuid.UUID] = None
self._future.add_done_callback(self._set_task_id)
def get(self, timeout: Optional[float] = None) -> _T:
"""Gets the result from this response, with Future's timeout rules."""
return self._future.result(timeout).get()
def _set_task_id(self, _):
"""Sets the task ID once the Future has completed."""
try:
res = self._future.result()
except rest_api.ApiException as exc:
with self._id_lock:
self._task_id = extract_task_id(exc)
except: # noqa: E722 We don't care about other exceptions, period.
pass
else:
with self._id_lock:
self._task_id = res.task_id
def extract_task_id(
thing: Union[rest_api.ApiException, urllib3.HTTPResponse],
) -> Optional[uuid.UUID]:
"""Pulls the task ID out of a response or an exception."""
id_hdr = thing.headers and thing.headers.get(TASK_ID_HEADER)
return _maybe_uuid(id_hdr)
def _maybe_uuid(id_str: Optional[str]) -> Optional[uuid.UUID]:
"""Parses a hex string into a UUID if present and valid."""
if not id_str:
return None
try:
return uuid.UUID(hex=id_str)
except ValueError:
return None
| [
37811,
22248,
326,
1037,
345,
1394,
2610,
286,
4876,
2482,
290,
703,
284,
36899,
606,
526,
15931,
198,
198,
11748,
450,
66,
198,
11748,
4818,
330,
28958,
198,
11748,
4704,
278,
198,
11748,
334,
27112,
198,
6738,
24580,
1330,
25650,
198,... | 2.548055 | 874 |
import sys
sys.path.append('../../')
# Global variables
from test2.test_main import TREASURE_MAP
from test2.test_main import MINE_MAP
# From OceanOfCode
# Class
from OceanOfCode import MineAndTrigger
from OceanOfCode import Submarine
from OceanOfCode import Board
# Global
from OceanOfCode import EMPTY_SYMBOLS
from OceanOfCode import DIRS
# Method
from OceanOfCode import square
import unittest
if __name__ == '__main__':
unittest.main()
| [
11748,
25064,
198,
17597,
13,
6978,
13,
33295,
10786,
40720,
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198,
198,
2,
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17,
13,
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62,
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62,
33767,
198,
6738,
1332,
17,
13,
9288,
62,
12417,
1330,
337,
88... | 3.118056 | 144 |
# -*- coding: utf-8 -*-
"""Digital Forensics Date and Time (dfDateTime).
dfDateTime, or Digital Forensics date and time, provides date and time
objects to preserve accuracy and precision.
"""
# Imports for date time values factory.
from dfdatetime import apfs_time
from dfdatetime import cocoa_time
from dfdatetime import delphi_date_time
from dfdatetime import fat_date_time
from dfdatetime import filetime
from dfdatetime import hfs_time
from dfdatetime import golang_time
from dfdatetime import java_time
from dfdatetime import ole_automation_date
from dfdatetime import posix_time
from dfdatetime import rfc2579_date_time
from dfdatetime import semantic_time
from dfdatetime import systemtime
from dfdatetime import time_elements
from dfdatetime import uuid_time
from dfdatetime import webkit_time
__version__ = '20211229'
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
37811,
27640,
4558,
49242,
7536,
290,
3862,
357,
7568,
10430,
7575,
737,
198,
198,
7568,
10430,
7575,
11,
393,
10231,
4558,
49242,
3128,
290,
640,
11,
3769,
3128,
290,
6... | 3.22093 | 258 |
import logging
import json
import zlib
import os
import pymia.data.extraction as extr
| [
11748,
18931,
198,
11748,
33918,
198,
11748,
1976,
8019,
198,
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198,
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4948,
544,
13,
7890,
13,
2302,
7861,
355,
22820,
628,
628,
628,
198
] | 3.206897 | 29 |
import sys
from util.Timer import Timer
from util.FileOpener import FileOpener
from util.Logger import Logger
from util.PathExtractor import PathExtractor
from util.PathValidator import PathValidator
from service import SpacyModel
if __name__ == '__main__':
main()
| [
11748,
25064,
198,
6738,
7736,
13,
48801,
1330,
5045,
263,
198,
6738,
7736,
13,
8979,
18257,
877,
1330,
9220,
18257,
877,
198,
6738,
7736,
13,
11187,
1362,
1330,
5972,
1362,
198,
6738,
7736,
13,
15235,
11627,
40450,
1330,
10644,
11627,
... | 3.455696 | 79 |
# type: ignore
__all__ = [
"plotyy",
"figtoolset",
"getscribecontextmenu",
"getobj",
"scribeeventhandler",
"setscribecontextmenu",
"putdowntext",
"gtext",
"scribetextdlg",
"doresize",
"prepdrag",
"scriberestoresavefcns",
"zoom",
"scribeclearmode",
"domymenu",
"ylabel",
"getscribeobjectdata",
"enddrag",
"texlabel",
"setscribeobjectdata",
"polar",
"getcolumn",
"plotedit",
"semilogx",
"jpropeditutils",
"basicfitdatastat",
"plot",
"box",
"axis",
"title",
"loglog",
"dokeypress",
"semilogy",
"doclick",
"middrag",
"text",
"pan",
"subplot",
"xlabel",
"rbbox",
"grid",
"moveaxis",
"getorcreateobj",
]
from mat2py.common.backends import numpy as np
from ._internal import M
| [
2,
2099,
25,
8856,
198,
198,
834,
439,
834,
796,
685,
198,
220,
220,
220,
366,
29487,
22556,
1600,
198,
220,
220,
220,
366,
5647,
25981,
2617,
1600,
198,
220,
220,
220,
366,
11407,
66,
822,
721,
261,
5239,
26272,
1600,
198,
220,
2... | 1.997748 | 444 |
from app import app
if __name__ == '__main__':
app.run_server(port=5000, debug=True, host='localhost') | [
6738,
598,
1330,
598,
198,
198,
361,
11593,
3672,
834,
6624,
705,
834,
12417,
834,
10354,
198,
220,
220,
220,
598,
13,
5143,
62,
15388,
7,
634,
28,
27641,
11,
14257,
28,
17821,
11,
2583,
11639,
36750,
11537
] | 2.815789 | 38 |
from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
import logging
from rasa_core.agent import Agent
from rasa_core.policies.keras_policy import KerasPolicy
from rasa_core.policies.memoization import MemoizationPolicy
if __name__ == '__main__':
logging.basicConfig(level="INFO")
run_faq()
| [
6738,
11593,
37443,
834,
1330,
4112,
62,
11748,
198,
6738,
11593,
37443,
834,
1330,
7297,
198,
6738,
11593,
37443,
834,
1330,
28000,
1098,
62,
17201,
874,
198,
198,
11748,
18931,
198,
198,
6738,
374,
15462,
62,
7295,
13,
25781,
1330,
15... | 3.13913 | 115 |
# fmt: off
import logging
from pathlib import Path
from farm.data_handler.data_silo import DataSilo
from farm.data_handler.processor import RegressionProcessor, TextPairClassificationProcessor
from farm.experiment import initialize_optimizer
from farm.infer import Inferencer
from farm.modeling.adaptive_model import AdaptiveModel
from farm.modeling.language_model import LanguageModel
from farm.modeling.prediction_head import RegressionHead, TextClassificationHead
from farm.modeling.tokenization import Tokenizer
from farm.train import Trainer
from farm.utils import set_all_seeds, MLFlowLogger, initialize_device_settings, reformat_msmarco_train, reformat_msmarco_dev, write_msmarco_results
from farm.evaluation.msmarco_passage_farm import msmarco_evaluation
if __name__ == "__main__":
text_pair_classification()
# fmt: on
| [
2,
46996,
25,
572,
198,
11748,
18931,
198,
6738,
3108,
8019,
1330,
10644,
198,
198,
6738,
5318,
13,
7890,
62,
30281,
13,
7890,
62,
18217,
78,
1330,
6060,
15086,
78,
198,
6738,
5318,
13,
7890,
62,
30281,
13,
41341,
1330,
3310,
2234,
... | 3.454545 | 242 |
#!/usr/bin/env python
import numpy as np
import pandas as pd
import json
import pytz
def load():
""" Returns `X` and `y` arrays, the former being the training data and the former the targets. """
# Get data:
data = _get_data('coinbaseUSD_1-min_data_2014-12-01_to_2018-03-27.csv')
prices = _get_prices(data)
prices['day_of_week'] = prices['time'].dt.dayofweek
prices['day_of_month'] = prices['time'].dt.day
prices['day_of_month_scaled'] = prices['time'].dt.day / prices['time'].dt.days_in_month
prices['month'] = prices['time'].dt.month
prices['time_of_day'] = prices['time'].dt.time.apply(lambda time: str(time).split(':')[0]).astype(int)
return prices
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
198,
198,
11748,
299,
32152,
355,
45941,
198,
11748,
19798,
292,
355,
279,
67,
198,
11748,
33918,
198,
11748,
12972,
22877,
628,
628,
628,
198,
4299,
3440,
33529,
198,
220,
220,
220,
37227,
... | 2.584559 | 272 |
import sys
from django.core.management import CommandError, call_command
from django.test import TestCase
from .side_effects import bad_database_check
try:
from unittest.mock import patch
except ImportError:
from mock import patch
# Python 2.7 support
if sys.version_info > (3, 0):
from io import StringIO
else:
from io import BytesIO as StringIO
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... | 3.145299 | 117 |
from watchdog.observers import Observer
from watchdog.events import FileSystemEventHandler
# importing the modules
import os.path
import shutil
import datetime
import time
import re
# getting the current working directory
src_dir = os.getcwd()
# printing current directory
print("########## File-backup started ###########")
folder_to_track = f"{os.getcwd()}{os.path.sep}testsubject{os.path.sep}source"
destination = f"{os.getcwd()}{os.path.sep}testsubject{os.path.sep}destination"
print(f"{folder_to_track} --> {destination}")
event_handler = FileHandler()
observer = Observer()
observer.schedule(event_handler, folder_to_track, recursive=True)
observer.start()
try:
while True:
time.sleep(10)
except KeyboardInterrupt:
observer.stop()
observer.join()
print("########## File-backup ended ###########") | [
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198,... | 2.99278 | 277 |
import os
import pytest
import torch
import torch.distributed as dist
from ignite.distributed.comp_models import has_native_dist_support
if not has_native_dist_support:
pytest.skip("Skip if no native dist support", allow_module_level=True)
else:
from ignite.distributed.comp_models.native import _expand_hostlist, _NativeDistModel, _setup_ddp_vars_from_slurm_env
# tests from https://github.com/LLNL/py-hostlist/blob/master/hostlist/unittest_hostlist.py
def _assert_model(model, true_conf):
assert model.device() == torch.device(true_conf["device"])
assert model.get_local_rank() == true_conf["local_rank"]
assert model.get_rank() == true_conf["rank"]
assert model.get_world_size() == true_conf["world_size"]
assert model.get_node_rank() == true_conf["node_index"]
assert model.get_nnodes() == true_conf["nnodes"]
assert model.get_nproc_per_node() == true_conf["nproc_per_node"]
def _test__native_dist_model_create_from_backend_no_dist(backend, true_device):
from datetime import timedelta
model = _NativeDistModel.create_from_backend(backend=backend, timeout=timedelta(seconds=20))
assert dist.is_available() and dist.is_initialized()
assert dist.get_backend() == backend
_assert_model(
model,
{
"device": true_device,
"local_rank": 0,
"rank": 0,
"world_size": 1,
"node_index": 0,
"nnodes": 1,
"nproc_per_node": 1,
},
)
model.finalize()
def _test__native_dist_model_create_from_backend_dist(init_method, local_rank, rank, world_size, backend, true_device):
import os
from datetime import timedelta
timeout = timedelta(seconds=20)
os.environ["RANK"] = f"{rank}"
assert "MASTER_ADDR" not in os.environ
assert "MASTER_PORT" not in os.environ
model = _NativeDistModel.create_from_backend(backend=backend, timeout=timeout, init_method=init_method)
assert dist.is_available() and dist.is_initialized()
assert dist.get_backend() == backend
with pytest.raises(RuntimeError, match=r"Can not create new distributed process group if default one is"):
_NativeDistModel.create_from_backend(backend=backend, timeout=timeout)
_assert_model(
model,
{
"device": true_device,
"local_rank": local_rank,
"rank": rank,
"world_size": world_size,
"node_index": 0,
"nnodes": 1,
"nproc_per_node": world_size,
},
)
if init_method is None:
assert model._init_method == "env://"
else:
assert model._init_method == init_method
model.finalize()
del os.environ["RANK"]
assert "MASTER_ADDR" not in os.environ
assert "MASTER_PORT" not in os.environ
assert "RANK" not in os.environ
def _test__native_dist_model_create_from_backend_slurm(local_rank, rank, world_size, backend, true_device):
import os
from datetime import timedelta
timeout = timedelta(seconds=20)
assert "MASTER_ADDR" not in os.environ
assert "MASTER_PORT" not in os.environ
del os.environ["WORLD_SIZE"]
del os.environ["LOCAL_RANK"]
os.environ["SLURM_JOB_ID"] = "15000"
os.environ["SLURM_PROCID"] = str(rank)
os.environ["SLURM_LOCALID"] = str(local_rank)
os.environ["SLURM_NTASKS"] = str(world_size)
os.environ["SLURM_JOB_NODELIST"] = "localhost"
os.environ["SLURM_JOB_NUM_NODES"] = "1"
model = _NativeDistModel.create_from_backend(backend=backend, timeout=timeout)
assert dist.is_available() and dist.is_initialized()
assert dist.get_backend() == backend
with pytest.raises(RuntimeError, match=r"Can not create new distributed process group if default one is"):
_NativeDistModel.create_from_backend(backend=backend, timeout=timeout)
_assert_model(
model,
{
"device": true_device,
"local_rank": local_rank,
"rank": rank,
"world_size": world_size,
"node_index": 0,
"nnodes": 1,
"nproc_per_node": world_size,
},
)
model.finalize()
del os.environ["SLURM_JOB_ID"]
del os.environ["SLURM_PROCID"]
del os.environ["SLURM_LOCALID"]
del os.environ["SLURM_NTASKS"]
del os.environ["SLURM_JOB_NODELIST"]
del os.environ["SLURM_JOB_NUM_NODES"]
assert "MASTER_ADDR" not in os.environ
assert "MASTER_PORT" not in os.environ
assert "RANK" not in os.environ
os.environ["WORLD_SIZE"] = str(world_size)
os.environ["LOCAL_RANK"] = str(local_rank)
def _test__native_dist_model_create_from_context_no_local_rank():
if "LOCAL_RANK" in os.environ:
del os.environ["LOCAL_RANK"]
from ignite.distributed.comp_models.base import ComputationModel
if ComputationModel._ext_local_rank is not None:
ComputationModel._ext_local_rank = None
with pytest.warns(UserWarning, match=r"Local rank information for native distributed setting will be initialized"):
_NativeDistModel.create_from_context()
def _test__native_dist_model_create_from_context_env_local_rank(true_conf):
import os
remove_lrank = False
if "LOCAL_RANK" not in os.environ:
os.environ["LOCAL_RANK"] = str(true_conf["local_rank"])
remove_lrank = True
model = _NativeDistModel.create_from_context()
_assert_model(model, true_conf)
if remove_lrank:
del os.environ["LOCAL_RANK"]
def _test_dist_spawn_fn(local_rank, backend, world_size, device):
from ignite.distributed.utils import _model
assert dist.is_available() and dist.is_initialized()
assert dist.get_backend() == backend
assert isinstance(_model, _NativeDistModel), f"{type(_model)} vs _NativeDistModel"
assert _model.get_local_rank() == local_rank
assert _model.get_world_size() == world_size
assert _model.device().type == torch.device(device).type
def _test__native_dist_model_spawn(backend, num_workers_per_machine, device, init_method=None, **spawn_kwargs):
_NativeDistModel.spawn(
_test_dist_spawn_fn,
args=(backend, num_workers_per_machine, device),
kwargs_dict={},
backend=backend,
nproc_per_node=num_workers_per_machine,
init_method=init_method,
**spawn_kwargs,
)
def test__setup_ddp_vars_from_slurm_env_bad_configs():
with pytest.raises(
RuntimeError, match=r"Environment variable defined for PyTorch Distributed context is inconsistent"
):
environ = {
"SLURM_PROCID": "3",
"SLURM_LOCALID": "1",
"SLURM_NTASKS": "4",
"SLURM_JOB_NUM_NODES": "2",
"SLURM_JOB_NODELIST": "c1, c2",
"SLURM_JOB_ID": "12345",
"MASTER_ADDR": "another-addr",
"MASTER_PORT": "12233",
"RANK": "1",
"LOCAL_RANK": "1",
"WORLD_SIZE": "2",
}
_setup_ddp_vars_from_slurm_env(environ)
with pytest.raises(
RuntimeError, match=r"Environment variable defined for PyTorch Distributed context is inconsistent"
):
environ = {
"SLURM_PROCID": "1",
"SLURM_LOCALID": "1",
"SLURM_NTASKS": "4",
"SLURM_JOB_NUM_NODES": "1",
"SLURM_JOB_NODELIST": "c1",
"SLURM_JOB_ID": "12345",
"MASTER_ADDR": "another-addr",
"MASTER_PORT": "12233",
"RANK": "1",
"LOCAL_RANK": "1",
"WORLD_SIZE": "2",
}
_setup_ddp_vars_from_slurm_env(environ)
with pytest.warns(UserWarning, match=r"We detected the following env variables"):
environ = {
"SLURM_PROCID": "3",
"SLURM_LOCALID": "1",
"SLURM_NTASKS": "4",
"SLURM_JOB_NUM_NODES": "2",
"SLURM_JOB_NODELIST": "c1, c2",
"SLURM_JOB_ID": "12345",
"RANK": "1",
"LOCAL_RANK": "1",
"WORLD_SIZE": "2",
}
_setup_ddp_vars_from_slurm_env(environ)
with pytest.raises(RuntimeError, match=r"No hostname detected in SLURM_JOB_NODELIST by ignite"):
environ = {
"SLURM_PROCID": "1",
"SLURM_LOCALID": "1",
"SLURM_NTASKS": "4",
"SLURM_JOB_NUM_NODES": "1",
"SLURM_JOB_NODELIST": "[]",
"SLURM_JOB_ID": "12345",
}
_setup_ddp_vars_from_slurm_env(environ)
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... | 2.163104 | 3,930 |
import pandas as pd
from .apriori_opt import apriori as apriori_opt
from .apriori_basic import apriori as apriori_basic
# from memory_profiler import profile
from .utils import log
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19... | 3.066667 | 60 |