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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1cf352409236639cbafbd2ba47ff641f1c9803a8 | 44 | py | Python | src/experiment.py | windar427/find_alpha | dbca4e677c6cdc144f20f6259c07291b5d3e6eed | [
"MIT"
] | null | null | null | src/experiment.py | windar427/find_alpha | dbca4e677c6cdc144f20f6259c07291b5d3e6eed | [
"MIT"
] | null | null | null | src/experiment.py | windar427/find_alpha | dbca4e677c6cdc144f20f6259c07291b5d3e6eed | [
"MIT"
] | null | null | null | from .lib.DownloadData import DownloadData
| 14.666667 | 42 | 0.840909 |
1cf437df5479619203c624b6e001a3eebc26d12e | 127 | py | Python | src/__init__.py | songchenwen/icloud-drive-docker | 7188dfbcc34e29ddbeeb1324c62ea77bed8f0576 | [
"BSD-3-Clause"
] | null | null | null | src/__init__.py | songchenwen/icloud-drive-docker | 7188dfbcc34e29ddbeeb1324c62ea77bed8f0576 | [
"BSD-3-Clause"
] | null | null | null | src/__init__.py | songchenwen/icloud-drive-docker | 7188dfbcc34e29ddbeeb1324c62ea77bed8f0576 | [
"BSD-3-Clause"
] | null | null | null | __author__ = 'Mandar Patil (mandarons@pm.me)'
import warnings
warnings.filterwarnings('ignore', category=DeprecationWarning)
| 21.166667 | 62 | 0.80315 |
1cf4fd8e02d4b81f15724ee999de5c59e9d28d5e | 3,210 | py | Python | setup.py | rohernandezz/coldtype | 724234fce454699a469d17b6c78ae50fa8138169 | [
"Apache-2.0"
] | null | null | null | setup.py | rohernandezz/coldtype | 724234fce454699a469d17b6c78ae50fa8138169 | [
"Apache-2.0"
] | null | null | null | setup.py | rohernandezz/coldtype | 724234fce454699a469d17b6c78ae50fa8138169 | [
"Apache-2.0"
] | null | null | null | import setuptools
long_description = """
# Coldtype
### Programmatic display typography
More info available at: [coldtype.goodhertz.com](https://coldtype.goodhertz.com)
"""
setuptools.setup(
name="coldtype",
version="0.6.6",
author="Rob Stenson / Goodhertz",
author_email="rob@goodhertz.com",
description="Functions for manual vectorized typesetting",
long_description=long_description,
long_description_content_type="text/markdown",
url="https://github.com/goodhertz/coldtype",
#package_dir={"": "coldtype"},
packages=[
"coldtype",
"coldtype.sh",
"coldtype.fx",
"coldtype.img",
"coldtype.time",
"coldtype.midi",
"coldtype.pens",
"coldtype.text",
"coldtype.grid",
"coldtype.color",
"coldtype.capture",
"coldtype.blender",
"coldtype.geometry",
"coldtype.time.nle",
"coldtype.renderer",
"coldtype.webserver",
"coldtype.renderable",
"coldtype.fontgoggles",
"coldtype.interpolation",
"coldtype.renderer.winman",
"coldtype.fontgoggles.font",
"coldtype.fontgoggles.misc",
"coldtype.fontgoggles.compile",
],
include_package_data=True,
package_data={
"": [
"webserver/webviewer.html",
"demo/RecMono-CasualItalic.ttf",
"demo/ColdtypeObviously-VF.ttf",
"demo/MutatorSans.ttf",
"demo/demo.py",
"demo/midi.py",
"demo/blank.py",
"demo/boiler.py",
"renderer/picklejar.py",
"renderer/.coldtype.py"
],
},
entry_points={
'console_scripts': [
'coldtype = coldtype.renderer:main'
],
},
extras_require={
"skia": [
"skia-python>=86.0",
],
"viewer": [
"glfw",
"PyOpenGL",
"PyOpenGL-accelerate",
"skia-python>=86.0",
"skia-pathops", # can this be taken from skia-python?
"SimpleWebSocketServer",
"watchdog<2.0.0", # https://github.com/gorakhargosh/watchdog/issues/702
"noise",
"ufo2ft",
"numpy",
],
"webviewer": [
"SimpleWebSocketServer",
"watchdog<2.0.0", # https://github.com/gorakhargosh/watchdog/issues/702
],
"experimental": [
"pynput",
"rtmidi",
"noise",
],
"c": [
"srt",
"noise",
],
"unicode": [
"unicodedata2"
],
"blender": [
"skia-pathops"
],
"notebook": [
"skia-pathops",
"skia-python",
]
},
install_requires=[
"lxml",
"fonttools[ufo]",
"fontPens",
"fontParts",
"more-itertools",
"easing-functions",
"timecode",
"mido",
"defcon",
"freetype-py",
"uharfbuzz>=0.14.0",
"python-bidi"
],
classifiers=[
"Programming Language :: Python :: 3",
"Operating System :: OS Independent",
],
)
| 25.68 | 83 | 0.50405 |
1cf68294fceda7fcab026e615d3065d3c8dac6d6 | 5,064 | py | Python | GFOLD_problem.py | xdedss/SuccessiveConvexification | 8b330b64a31f546ce92c1e34036c212484cbae5e | [
"MIT"
] | null | null | null | GFOLD_problem.py | xdedss/SuccessiveConvexification | 8b330b64a31f546ce92c1e34036c212484cbae5e | [
"MIT"
] | null | null | null | GFOLD_problem.py | xdedss/SuccessiveConvexification | 8b330b64a31f546ce92c1e34036c212484cbae5e | [
"MIT"
] | 1 | 2021-01-18T11:47:19.000Z | 2021-01-18T11:47:19.000Z | # -*- coding: utf-8 -*-
# GFOLD_static_p3p4
min_=min
from cvxpy import *
import cvxpy_codegen as cpg
from time import time
import numpy as np
import sys
import GFOLD_params
''' As defined in the paper...
PROBLEM 3: Minimum Landing Error (tf roughly solved)
MINIMIZE : norm of landing error vector
SUBJ TO :
0) initial conditions satisfied (position, velocity)
1) final conditions satisfied (altitude, velocity)
2) dynamics always satisfied
3) x stays in cone at all times
4) relaxed convexified mass and thrust constraints
5) thrust pointing constraint
6) sub-surface flight constraint
PROBLEM 4: Minimum Fuel Use
MAXIMIZE : landing mass, opt variables are dynamical and
SUBJ TO :
0) same constraints as p1, plus:
1) landing point must be equal or better than that found by p1
'''
if __name__ == '__main__':
if (len(sys.argv) > 2 and sys.argv[1] == 'codegen'):
codegen_path = sys.argv[2]
solve(None, None, True)
else:
print("invalid input")
print(sys.argv)
| 33.536424 | 102 | 0.540482 |
1cf6d2a88472187827c03695f3dd1ceab02478dc | 6,154 | py | Python | Hints.py | SarienFates/MMRandomizer | 7c677140d83e94167fecee35e8c25216a51bdd56 | [
"MIT"
] | 36 | 2018-08-23T05:01:33.000Z | 2021-03-17T03:25:20.000Z | Hints.py | SarienFates/MMRandomizer | 7c677140d83e94167fecee35e8c25216a51bdd56 | [
"MIT"
] | 11 | 2018-09-07T21:43:13.000Z | 2019-02-10T22:40:11.000Z | Hints.py | SarienFates/MMRandomizer | 7c677140d83e94167fecee35e8c25216a51bdd56 | [
"MIT"
] | 7 | 2018-08-22T09:49:12.000Z | 2020-01-12T20:23:29.000Z | import io
import hashlib
import logging
import os
import struct
import random
from HintList import getHint, getHintGroup, Hint
from Utils import local_path
#builds out general hints based on location and whether an item is required or not
# builds boss reward text that is displayed at the temple of time altar for child and adult, pull based off of item in a fixed order.
# pulls text string from hintlist for reward after sending the location to hintlist.
# alternates through color set commands in child and adult boss reward hint strings setting the colors at the start of the string to correspond with the reward found at the location.
# skips over color commands at the end of stings to set color back to white.
#sets the end of text byte in the text box.
# reads array of characters and converts them to an array of bytes.
| 41.581081 | 183 | 0.666071 |
1cf7cd31d835d561c98ce25133f916ba86e3848f | 304 | py | Python | examen_2/p2/p2.py | Jhoselyn-Carballo/computacion_para_ingenieria | 4b5ed7d4aa0017fb4993ccfdcc9fcef0fb5b3898 | [
"Apache-2.0"
] | null | null | null | examen_2/p2/p2.py | Jhoselyn-Carballo/computacion_para_ingenieria | 4b5ed7d4aa0017fb4993ccfdcc9fcef0fb5b3898 | [
"Apache-2.0"
] | null | null | null | examen_2/p2/p2.py | Jhoselyn-Carballo/computacion_para_ingenieria | 4b5ed7d4aa0017fb4993ccfdcc9fcef0fb5b3898 | [
"Apache-2.0"
] | null | null | null | # -*- coding: utf-8 -*-
"""
Created on Thu Feb 17 09:10:05 2022
@author: JHOSS
"""
from tkinter import *
| 17.882353 | 35 | 0.644737 |
1cf8bcf99614d3811354f1616a7df6d5dc7e2771 | 786 | py | Python | bokeh/models/tests/test_callbacks.py | ndepal/bokeh | 1b514f28fe40eeb71954eac0c113b2debdb2eda9 | [
"BSD-3-Clause"
] | 1 | 2018-11-14T19:08:18.000Z | 2018-11-14T19:08:18.000Z | bokeh/models/tests/test_callbacks.py | ndepal/bokeh | 1b514f28fe40eeb71954eac0c113b2debdb2eda9 | [
"BSD-3-Clause"
] | 1 | 2021-05-09T02:45:17.000Z | 2021-05-09T02:45:17.000Z | bokeh/models/tests/test_callbacks.py | ndepal/bokeh | 1b514f28fe40eeb71954eac0c113b2debdb2eda9 | [
"BSD-3-Clause"
] | 1 | 2020-06-17T05:47:16.000Z | 2020-06-17T05:47:16.000Z | from pytest import raises
from bokeh.models import CustomJS, Slider
| 22.457143 | 56 | 0.605598 |
1cf8fa369efacd3241e998562ea192fd61a8484a | 639 | py | Python | tests/test_0150-attributeerrors.py | martindurant/awkward-1.0 | a3221ee1bab6551dd01d5dd07a1d2dc24fd02c38 | [
"BSD-3-Clause"
] | null | null | null | tests/test_0150-attributeerrors.py | martindurant/awkward-1.0 | a3221ee1bab6551dd01d5dd07a1d2dc24fd02c38 | [
"BSD-3-Clause"
] | null | null | null | tests/test_0150-attributeerrors.py | martindurant/awkward-1.0 | a3221ee1bab6551dd01d5dd07a1d2dc24fd02c38 | [
"BSD-3-Clause"
] | null | null | null | # BSD 3-Clause License; see https://github.com/jpivarski/awkward-1.0/blob/master/LICENSE
from __future__ import absolute_import
import sys
import pytest
import numpy
import awkward1
| 23.666667 | 88 | 0.666667 |
1cf95ca934131253ff13ec5e02a3e110c4125705 | 1,838 | py | Python | scripts/preprocess.py | umd-lib/solr-irroc | 860be84ea1847cbb96c1a7a70b03f59dc6e0366b | [
"Apache-2.0"
] | null | null | null | scripts/preprocess.py | umd-lib/solr-irroc | 860be84ea1847cbb96c1a7a70b03f59dc6e0366b | [
"Apache-2.0"
] | null | null | null | scripts/preprocess.py | umd-lib/solr-irroc | 860be84ea1847cbb96c1a7a70b03f59dc6e0366b | [
"Apache-2.0"
] | 1 | 2019-11-04T13:19:34.000Z | 2019-11-04T13:19:34.000Z | #!/user/bin/env python3
# -*- coding: utf8 -*-
#===================================================#
# cleanup.py #
# Joshua Westgard #
# 2015-08-13 #
# #
# Data preprocessing script for IRRoC DB #
# Usage: python3 cleanup.py [in.csv] [out.csv] #
#===================================================#
import sys, csv, re
infields = ['id', 'str_resource', 'str_description', 'website', 'meta_title',
'meta_description', 'stage_list', 'task_list']
outfields = infields + ['stage_list_facet', 'task_list_facet']
with open(sys.argv[1], 'r') as infile, open(sys.argv[2], 'w') as outfile:
# skip header row in order to use own fieldnames
next(infile)
# instantiate the reader and writer objects
dr = csv.DictReader(infile, fieldnames=infields)
dw = csv.DictWriter(outfile, fieldnames=outfields)
dw.writeheader()
exp = re.compile(r'\d+::([^\b])')
# loop over the input file, writing results to output file
for row in dr:
# remove hash marks from URL
m = re.search('#(.+)#', row['website'])
if m:
row['website'] = m.group(1)
# remove spaces from all multivalued fields
row['stage_list_facet'] = row['stage_list'].replace('; ', ';')
row['task_list_facet'] = row['task_list'].replace('; ', ';')
row['meta_description'] = row['meta_description'].replace(', ', ',')
# create stage_list_facet and task_list_facet cols and strip numbers
row['stage_list'] = re.sub(exp, r'\1', row['stage_list_facet'])
row['task_list'] = re.sub(exp, r'\1', row['task_list_facet'])
# write row
dw.writerow(row)
| 36.039216 | 77 | 0.516322 |
1cf9ec2d86459244af14ac26fb2ee812ead9cfcb | 1,262 | py | Python | ievv_opensource/demo/batchframeworkdemo/apps.py | appressoas/ievv_opensource | 63e87827952ddc8f6f86145b79478ef21d6a0990 | [
"BSD-3-Clause"
] | null | null | null | ievv_opensource/demo/batchframeworkdemo/apps.py | appressoas/ievv_opensource | 63e87827952ddc8f6f86145b79478ef21d6a0990 | [
"BSD-3-Clause"
] | 37 | 2015-10-26T09:14:12.000Z | 2022-02-10T10:35:33.000Z | ievv_opensource/demo/batchframeworkdemo/apps.py | appressoas/ievv_opensource | 63e87827952ddc8f6f86145b79478ef21d6a0990 | [
"BSD-3-Clause"
] | 1 | 2015-11-06T07:56:34.000Z | 2015-11-06T07:56:34.000Z | from django.apps import AppConfig
from ievv_opensource import ievv_batchframework
from ievv_opensource.ievv_batchframework import batchregistry
| 33.210526 | 89 | 0.655309 |
1cf9fe0aeeda656a56200c7ffd6e72f3aa0fd6c3 | 783 | py | Python | fmoe/gates/utils.py | GODVIX/fastmoe | 7f6463f0367205a1e95139c6d7e930be6e7fa746 | [
"Apache-2.0"
] | null | null | null | fmoe/gates/utils.py | GODVIX/fastmoe | 7f6463f0367205a1e95139c6d7e930be6e7fa746 | [
"Apache-2.0"
] | 1 | 2021-05-24T03:13:50.000Z | 2021-05-24T03:13:50.000Z | fmoe/gates/utils.py | Co1lin/fastmoe | ff7333c7a164a8e1f54954b1b56095dc4cde7bfc | [
"Apache-2.0"
] | null | null | null | r"""
Utilities that may be used in the gates
"""
import torch
from fmoe.functions import count_by_gate
import fmoe_cuda as fmoe_native
| 30.115385 | 79 | 0.711367 |
1cfa6189a373dd681dccafae6be7e17e2a430784 | 4,438 | py | Python | evaluate.py | DeppMeng/DANNet | 831eb70d44a4a0b6f6f57ca2014521fc64d1906c | [
"Apache-2.0"
] | null | null | null | evaluate.py | DeppMeng/DANNet | 831eb70d44a4a0b6f6f57ca2014521fc64d1906c | [
"Apache-2.0"
] | null | null | null | evaluate.py | DeppMeng/DANNet | 831eb70d44a4a0b6f6f57ca2014521fc64d1906c | [
"Apache-2.0"
] | null | null | null | import os
import torch
import numpy as np
from PIL import Image
import torch.nn as nn
from torch.utils import data
from network import *
from dataset.zurich_night_dataset import zurich_night_DataSet
from configs.test_config import get_arguments
palette = [128, 64, 128, 244, 35, 232, 70, 70, 70, 102, 102, 156, 190, 153, 153, 153, 153, 153, 250, 170, 30,
220, 220, 0, 107, 142, 35, 152, 251, 152, 70, 130, 180, 220, 20, 60, 255, 0, 0, 0, 0, 142, 0, 0, 70,
0, 60, 100, 0, 80, 100, 0, 0, 230, 119, 11, 32]
zero_pad = 256 * 3 - len(palette)
for i in range(zero_pad):
palette.append(0)
if __name__ == '__main__':
main()
| 37.294118 | 116 | 0.630689 |
1cff9155874f3a3bbd07b635d9ad399b58f00a6d | 1,581 | py | Python | decorator.py | zengboming/python | 13018f476554adc3bff831af27c08f7c216d4b09 | [
"Apache-2.0"
] | null | null | null | decorator.py | zengboming/python | 13018f476554adc3bff831af27c08f7c216d4b09 | [
"Apache-2.0"
] | null | null | null | decorator.py | zengboming/python | 13018f476554adc3bff831af27c08f7c216d4b09 | [
"Apache-2.0"
] | null | null | null | #decorator
f=now
f()
print now.__name__
print f.__name__
now1()
now1=log(now1)
now1()
now2()
import functools
now3()
now4()
now5()
now6() | 17.566667 | 53 | 0.655914 |
e8000d1d11cc25ead163054e538c9037a92ad52f | 2,090 | py | Python | test/pyfrechet_visualize.py | compgeomTU/frechetForCurves | 625bfe32a45d23b194226b4ac7713ded09bd2825 | [
"MIT"
] | null | null | null | test/pyfrechet_visualize.py | compgeomTU/frechetForCurves | 625bfe32a45d23b194226b4ac7713ded09bd2825 | [
"MIT"
] | null | null | null | test/pyfrechet_visualize.py | compgeomTU/frechetForCurves | 625bfe32a45d23b194226b4ac7713ded09bd2825 | [
"MIT"
] | null | null | null | # Author: Will Rodman
# wrodman@tulane.edu
#
# Command line to run program:
# python3 pyfrechet_visualize.py
import sys, os, unittest
sys.path.insert(0, "../")
from pyfrechet.distance import StrongDistance
from pyfrechet.visualize import FreeSpaceDiagram, Trajectories
TEST_DATA = "sp500"
if TEST_DATA == "sp500":
REACHABLE_EPSILON = 5
UNREACHABLE_EPSILON = 1
REVERSE_CURVE = False
elif TEST_DATA == "trajectory":
REACHABLE_EPSILON = 70
UNREACHABLE_EPSILON = 60
REVERSE_CURVE = True
CURVE_1 = f"{TEST_DATA}_data/sample_1.txt"
CURVE_2 = f"{TEST_DATA}_data/sample_2.txt"
if __name__ == '__main__':
unittest.main()
| 27.866667 | 71 | 0.704306 |
e800c566ae2b10b4aec8821bd3d2096d44b1f7e9 | 6,403 | py | Python | py_ser_freeastro/core.py | nww2007/py_ser_freeastro | 5806cf83316f48a6db0abe4a88e4485fc04a1b4d | [
"MIT"
] | null | null | null | py_ser_freeastro/core.py | nww2007/py_ser_freeastro | 5806cf83316f48a6db0abe4a88e4485fc04a1b4d | [
"MIT"
] | null | null | null | py_ser_freeastro/core.py | nww2007/py_ser_freeastro | 5806cf83316f48a6db0abe4a88e4485fc04a1b4d | [
"MIT"
] | null | null | null | #!/usr/bin/env python3
# vim:fileencoding=UTF-8
# -*- coding: UTF-8 -*-
"""
Created on 15 juny 2019 y.
@author: Vlsdimir Nekrasov nww2007@mail.ru
"""
import sys
import struct
import numpy as np
from progress.bar import Bar
import logging
logging.basicConfig(format = u'%(filename)s:%(lineno)d: %(levelname)-8s [%(asctime)s] %(message)s', level = logging.DEBUG, stream=sys.stdout)
# class ser(np.array):
if __name__ == '__main__':
sys.exit(main(sys.argv))
| 38.341317 | 165 | 0.580353 |
e801eb5a0287658d171711d53067b93a3c272ccf | 10,915 | py | Python | sgdml_dataset_generation/readers/fchk.py | humeniuka/sGDML_dataset_generation | a99f792b6aac7ff869ebcd1bd7a7226ca81f43ee | [
"MIT"
] | null | null | null | sgdml_dataset_generation/readers/fchk.py | humeniuka/sGDML_dataset_generation | a99f792b6aac7ff869ebcd1bd7a7226ca81f43ee | [
"MIT"
] | null | null | null | sgdml_dataset_generation/readers/fchk.py | humeniuka/sGDML_dataset_generation | a99f792b6aac7ff869ebcd1bd7a7226ca81f43ee | [
"MIT"
] | null | null | null | #!/usr/bin/env python
# -*- coding: utf-8 -*-
__all__ = ["FormattedCheckpointFile"]
# # Imports
import numpy as np
import scipy.linalg as sla
from collections import OrderedDict
import re
import logging
# # Local Imports
from sgdml_dataset_generation import units
from sgdml_dataset_generation.units import hbar
# # Logging
logger = logging.getLogger(__name__)
logging.basicConfig(format="[%(module)-12s] %(message)s", level=logging.INFO)
| 38.298246 | 132 | 0.536143 |
e80208494724abb41e0606d412c1f9f663383e0b | 227 | py | Python | 2020_01_01/max_values/max_values.py | 94JuHo/Algorithm_study | e2c10ec680d966e5bcc4e7cb88d9514f9ccbbf15 | [
"MIT"
] | null | null | null | 2020_01_01/max_values/max_values.py | 94JuHo/Algorithm_study | e2c10ec680d966e5bcc4e7cb88d9514f9ccbbf15 | [
"MIT"
] | null | null | null | 2020_01_01/max_values/max_values.py | 94JuHo/Algorithm_study | e2c10ec680d966e5bcc4e7cb88d9514f9ccbbf15 | [
"MIT"
] | null | null | null | values = []
for i in range(9):
values.append(int(input('')))
max_value = 0
location = 0
for i in range(9):
if values[i] > max_value:
max_value = values[i]
location = i+1
print(max_value)
print(location) | 18.916667 | 33 | 0.61674 |
e803805e9e6f0689766fc36aba000586323cb80c | 3,097 | py | Python | fuzzywuzzy/process.py | rhasspy/fuzzywuzzy | e5b486c756b392481ec8e1382eedce280e56fd69 | [
"MIT"
] | 3 | 2015-10-18T18:00:08.000Z | 2021-03-23T06:42:02.000Z | fuzzywuzzy/process.py | rhasspy/fuzzywuzzy | e5b486c756b392481ec8e1382eedce280e56fd69 | [
"MIT"
] | 20 | 2015-01-16T18:46:53.000Z | 2016-02-18T22:01:00.000Z | process.py | agile-geoscience/fuzzylas | 0298292878d7fb6c0a788a1a2a21b543f49432bd | [
"Apache-2.0"
] | 10 | 2015-08-11T10:12:56.000Z | 2022-02-20T14:45:50.000Z | #!/usr/bin/env python
# encoding: utf-8
"""
process.py
Copyright (c) 2011 Adam Cohen
Permission is hereby granted, free of charge, to any person obtaining
a copy of this software and associated documentation files (the
"Software"), to deal in the Software without restriction, including
without limitation the rights to use, copy, modify, merge, publish,
distribute, sublicense, and/or sell copies of the Software, and to
permit persons to whom the Software is furnished to do so, subject to
the following conditions:
The above copyright notice and this permission notice shall be
included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
"""
from fuzz import *
import sys, os
import utils
#######################################
# Find Best Matchs In List Of Choices #
#######################################
##########################
# Find Single Best Match #
##########################
| 35.193182 | 119 | 0.668389 |
e803dd122dcc777403d95c61024e9847e1e285a3 | 2,063 | py | Python | day03/day03.py | robfalck/AoC2017 | fa19f3fb42d979b60888a1954bea571c9d4ee735 | [
"Apache-2.0"
] | null | null | null | day03/day03.py | robfalck/AoC2017 | fa19f3fb42d979b60888a1954bea571c9d4ee735 | [
"Apache-2.0"
] | null | null | null | day03/day03.py | robfalck/AoC2017 | fa19f3fb42d979b60888a1954bea571c9d4ee735 | [
"Apache-2.0"
] | null | null | null | from __future__ import print_function, division, absolute_import
import numpy as np
INPUT = 265149
def part2(number):
"""
A brute-force approach to part 2.
"""
map = np.zeros((11, 11), dtype=int)
row = 5
col = 5
map[row, col] = 1
heading = 'RIGHT'
dcol = 1
drow = 0
nsteps = 70
for i in range(nsteps):
row += drow
col += dcol
sum_at_next = map[row-1:row+2, col-1:col+2].sum()
map[row, col] = sum_at_next
if sum_at_next > number:
return sum_at_next
# Determine if we need to change heading
if heading == 'RIGHT' and map[row-1, col] == 0:
heading = 'UP'
drow = -1
dcol = 0
elif heading == 'UP' and map[row, col-1] == 0:
heading = 'LEFT'
drow = 0
dcol = -1
elif heading == 'LEFT' and map[row+1, col] == 0:
heading = 'DOWN'
drow = 1
dcol = 0
elif heading == 'DOWN' and map[row, col+1] == 0:
heading = 'RIGHT'
drow = 0
dcol = 1
if __name__ == '__main__':
print(part1(number=INPUT))
print(part2(number=INPUT))
| 21.946809 | 64 | 0.448376 |
e80430c85971413d707279b7c247a402de4c954e | 916 | py | Python | core/migrations/0004_auto_20210929_2354.py | codefair114/Inventory-App-Django | f09f43ca282f82be981cac26a92d614fdf2ff5ef | [
"MIT"
] | null | null | null | core/migrations/0004_auto_20210929_2354.py | codefair114/Inventory-App-Django | f09f43ca282f82be981cac26a92d614fdf2ff5ef | [
"MIT"
] | null | null | null | core/migrations/0004_auto_20210929_2354.py | codefair114/Inventory-App-Django | f09f43ca282f82be981cac26a92d614fdf2ff5ef | [
"MIT"
] | null | null | null | # Generated by Django 3.2.7 on 2021-09-29 23:54
from django.db import migrations, models
import django.db.models.deletion
| 30.533333 | 117 | 0.618996 |
e804b98c7267ddc9714ddf384d8bd8fa54ddef35 | 5,249 | py | Python | nova/api/openstack/compute/legacy_v2/contrib/console_auth_tokens.py | bopopescu/nova-token | ec98f69dea7b3e2b9013b27fd55a2c1a1ac6bfb2 | [
"Apache-2.0"
] | null | null | null | nova/api/openstack/compute/legacy_v2/contrib/console_auth_tokens.py | bopopescu/nova-token | ec98f69dea7b3e2b9013b27fd55a2c1a1ac6bfb2 | [
"Apache-2.0"
] | null | null | null | nova/api/openstack/compute/legacy_v2/contrib/console_auth_tokens.py | bopopescu/nova-token | ec98f69dea7b3e2b9013b27fd55a2c1a1ac6bfb2 | [
"Apache-2.0"
] | 2 | 2017-07-20T17:31:34.000Z | 2020-07-24T02:42:19.000Z | begin_unit
comment|'# Copyright 2013 Cloudbase Solutions Srl'
nl|'\n'
comment|'# All Rights Reserved.'
nl|'\n'
comment|'#'
nl|'\n'
comment|'# Licensed under the Apache License, Version 2.0 (the "License"); you may'
nl|'\n'
comment|'# not use this file except in compliance with the License. You may obtain'
nl|'\n'
comment|'# a copy of the License at'
nl|'\n'
comment|'#'
nl|'\n'
comment|'# http://www.apache.org/licenses/LICENSE-2.0'
nl|'\n'
comment|'#'
nl|'\n'
comment|'# Unless required by applicable law or agreed to in writing, software'
nl|'\n'
comment|'# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT'
nl|'\n'
comment|'# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the'
nl|'\n'
comment|'# License for the specific language governing permissions and limitations'
nl|'\n'
comment|'# under the License.'
nl|'\n'
nl|'\n'
name|'import'
name|'webob'
newline|'\n'
nl|'\n'
name|'from'
name|'nova'
op|'.'
name|'api'
op|'.'
name|'openstack'
name|'import'
name|'extensions'
newline|'\n'
name|'from'
name|'nova'
op|'.'
name|'api'
op|'.'
name|'openstack'
name|'import'
name|'wsgi'
newline|'\n'
name|'from'
name|'nova'
op|'.'
name|'consoleauth'
name|'import'
name|'rpcapi'
name|'as'
name|'consoleauth_rpcapi'
newline|'\n'
name|'from'
name|'nova'
op|'.'
name|'i18n'
name|'import'
name|'_'
newline|'\n'
nl|'\n'
nl|'\n'
DECL|variable|authorize
name|'authorize'
op|'='
name|'extensions'
op|'.'
name|'extension_authorizer'
op|'('
string|"'compute'"
op|','
string|"'console_auth_tokens'"
op|')'
newline|'\n'
nl|'\n'
nl|'\n'
DECL|class|ConsoleAuthTokensController
name|'class'
name|'ConsoleAuthTokensController'
op|'('
name|'wsgi'
op|'.'
name|'Controller'
op|')'
op|':'
newline|'\n'
DECL|member|__init__
indent|' '
name|'def'
name|'__init__'
op|'('
name|'self'
op|','
op|'*'
name|'args'
op|','
op|'**'
name|'kwargs'
op|')'
op|':'
newline|'\n'
indent|' '
name|'self'
op|'.'
name|'_consoleauth_rpcapi'
op|'='
name|'consoleauth_rpcapi'
op|'.'
name|'ConsoleAuthAPI'
op|'('
op|')'
newline|'\n'
name|'super'
op|'('
name|'ConsoleAuthTokensController'
op|','
name|'self'
op|')'
op|'.'
name|'__init__'
op|'('
op|'*'
name|'args'
op|','
op|'**'
name|'kwargs'
op|')'
newline|'\n'
nl|'\n'
DECL|member|show
dedent|''
name|'def'
name|'show'
op|'('
name|'self'
op|','
name|'req'
op|','
name|'id'
op|')'
op|':'
newline|'\n'
indent|' '
string|'"""Checks a console auth token and returns the related connect info."""'
newline|'\n'
name|'context'
op|'='
name|'req'
op|'.'
name|'environ'
op|'['
string|"'nova.context'"
op|']'
newline|'\n'
name|'authorize'
op|'('
name|'context'
op|')'
newline|'\n'
nl|'\n'
name|'token'
op|'='
name|'id'
newline|'\n'
name|'connect_info'
op|'='
name|'self'
op|'.'
name|'_consoleauth_rpcapi'
op|'.'
name|'check_token'
op|'('
name|'context'
op|','
name|'token'
op|')'
newline|'\n'
name|'if'
name|'not'
name|'connect_info'
op|':'
newline|'\n'
indent|' '
name|'raise'
name|'webob'
op|'.'
name|'exc'
op|'.'
name|'HTTPNotFound'
op|'('
name|'explanation'
op|'='
name|'_'
op|'('
string|'"Token not found"'
op|')'
op|')'
newline|'\n'
nl|'\n'
dedent|''
name|'console_type'
op|'='
name|'connect_info'
op|'.'
name|'get'
op|'('
string|"'console_type'"
op|')'
newline|'\n'
comment|'# This is currently required only for RDP consoles'
nl|'\n'
name|'if'
name|'console_type'
op|'!='
string|'"rdp-html5"'
op|':'
newline|'\n'
indent|' '
name|'raise'
name|'webob'
op|'.'
name|'exc'
op|'.'
name|'HTTPUnauthorized'
op|'('
nl|'\n'
name|'explanation'
op|'='
name|'_'
op|'('
string|'"The requested console type details are not "'
nl|'\n'
string|'"accessible"'
op|')'
op|')'
newline|'\n'
nl|'\n'
dedent|''
name|'return'
op|'{'
string|"'console'"
op|':'
nl|'\n'
op|'{'
name|'i'
op|':'
name|'connect_info'
op|'['
name|'i'
op|']'
nl|'\n'
name|'for'
name|'i'
name|'in'
op|'['
string|"'instance_uuid'"
op|','
string|"'host'"
op|','
string|"'port'"
op|','
nl|'\n'
string|"'internal_access_path'"
op|']'
nl|'\n'
name|'if'
name|'i'
name|'in'
name|'connect_info'
op|'}'
op|'}'
newline|'\n'
nl|'\n'
nl|'\n'
DECL|class|Console_auth_tokens
dedent|''
dedent|''
name|'class'
name|'Console_auth_tokens'
op|'('
name|'extensions'
op|'.'
name|'ExtensionDescriptor'
op|')'
op|':'
newline|'\n'
indent|' '
string|'"""Console token authentication support."""'
newline|'\n'
DECL|variable|name
name|'name'
op|'='
string|'"ConsoleAuthTokens"'
newline|'\n'
DECL|variable|alias
name|'alias'
op|'='
string|'"os-console-auth-tokens"'
newline|'\n'
DECL|variable|namespace
name|'namespace'
op|'='
op|'('
string|'"http://docs.openstack.org/compute/ext/"'
nl|'\n'
string|'"consoles-auth-tokens/api/v2"'
op|')'
newline|'\n'
DECL|variable|updated
name|'updated'
op|'='
string|'"2013-08-13T00:00:00Z"'
newline|'\n'
nl|'\n'
DECL|member|get_resources
name|'def'
name|'get_resources'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'controller'
op|'='
name|'ConsoleAuthTokensController'
op|'('
op|')'
newline|'\n'
name|'ext'
op|'='
name|'extensions'
op|'.'
name|'ResourceExtension'
op|'('
string|"'os-console-auth-tokens'"
op|','
nl|'\n'
name|'controller'
op|')'
newline|'\n'
name|'return'
op|'['
name|'ext'
op|']'
newline|'\n'
dedent|''
dedent|''
endmarker|''
end_unit
| 14.62117 | 88 | 0.634597 |
e805511a04665665499fc07f9e4ac3855c825235 | 1,801 | py | Python | ahrs/common/geometry.py | jaluebbe/ahrs | 4b4a33b1006e0d455a71ac8379a2697202361758 | [
"MIT"
] | 184 | 2019-09-06T07:58:52.000Z | 2022-03-31T04:27:09.000Z | ahrs/common/geometry.py | geoKinga/ahrs | 87f9210cfcf6c545d86ae8588a93f012020164ee | [
"MIT"
] | 48 | 2019-11-13T15:42:46.000Z | 2022-03-31T23:53:53.000Z | ahrs/common/geometry.py | geoKinga/ahrs | 87f9210cfcf6c545d86ae8588a93f012020164ee | [
"MIT"
] | 34 | 2019-12-19T16:22:00.000Z | 2022-03-14T09:51:50.000Z | # -*- coding: utf-8 -*-
"""
Geometrical functions
---------------------
References
----------
.. [W1] Wikipedia: https://de.wikipedia.org/wiki/Ellipse#Ellipsengleichung_(Parameterform)
.. [WAE] Wolfram Alpha: Ellipse. (http://mathworld.wolfram.com/Ellipse.html)
"""
import numpy as np
from typing import Union
def circle(center: Union[list, np.ndarray], radius: float = 1.0, num_points: int = 20) -> np.ndarray:
"""
Build a circle with the given characteristics.
Parameters
----------
c : array-like
2D Coordinates of center.
r : float
Radius of the circle.
num_points : int
Number of points to build.
Returns
-------
points : numpy.ndarray
N-by-2 array with the coordinates of the circle.
"""
R = np.linspace(0.0, 2.0*np.pi, num_points+1)
x = center[0] + radius*np.cos(R)
y = center[1] + radius*np.sin(R)
return np.array([x, y]).transpose()
def ellipse(center: Union[list, np.ndarray], phi: float, axes: Union[list, np.ndarray], num_points: int = 20) -> np.ndarray:
"""
Build an ellipse with the given characteristics.
Parameters
----------
center : array-like
2D Coordinates of center.
phi : float
Angle, in radians, of the major axis w.r.t. the X-axis
axes : array-like
Lengths of major and minor axes, respectively.
num_points : int
Number of points. Defaults to 20.
Returns
-------
points : numpy.ndarray
N-by-2 array with the coordinates of the ellipse.
"""
R = np.linspace(0.0, 2.0*np.pi, num_points+1)
a, b = axes
x = center[0] + a*np.cos(R)*np.cos(phi) - b*np.sin(R)*np.sin(phi)
y = center[1] + a*np.cos(R)*np.sin(phi) + b*np.sin(R)*np.cos(phi)
return np.array([x, y]).transpose()
| 27.287879 | 124 | 0.599667 |
e8072be9245c30cff262ca9e32de353f2b9c7c66 | 4,435 | py | Python | htdocs/plotting/auto/scripts100/p116.py | jamayfieldjr/iem | 275b77a65f3b12e26e6cbdb230786b9c7d2b9c9a | [
"MIT"
] | 1 | 2019-10-07T17:01:24.000Z | 2019-10-07T17:01:24.000Z | htdocs/plotting/auto/scripts100/p116.py | jamayfieldjr/iem | 275b77a65f3b12e26e6cbdb230786b9c7d2b9c9a | [
"MIT"
] | null | null | null | htdocs/plotting/auto/scripts100/p116.py | jamayfieldjr/iem | 275b77a65f3b12e26e6cbdb230786b9c7d2b9c9a | [
"MIT"
] | null | null | null | """Monthly HDD/CDD Totals."""
import datetime
from pandas.io.sql import read_sql
from pyiem.plot.use_agg import plt
from pyiem.util import get_dbconn, get_autoplot_context
from pyiem.exceptions import NoDataFound
PDICT = {'cdd': 'Cooling Degree Days',
'hdd': 'Heating Degree Days'}
def get_description():
""" Return a dict describing how to call this plotter """
desc = dict()
desc['data'] = True
desc['report'] = True
desc['description'] = """This chart presents monthly cooling degree days
or heating degree days for a 20 year period of your choice. The 20 year
limit is for plot usability only, the data download has all available
years contained."""
y20 = datetime.date.today().year - 19
desc['arguments'] = [
dict(type='station', name='station', default='IATDSM',
label='Select Station', network='IACLIMATE'),
dict(type='select', options=PDICT, default='cdd', name='var',
label='Select Variable'),
dict(type='year', name='syear', default=y20,
label='For plotting, year to start 20 years of plot'),
]
return desc
def plotter(fdict):
""" Go """
import seaborn as sns
ctx = get_autoplot_context(fdict, get_description())
pgconn = get_dbconn('coop')
station = ctx['station']
varname = ctx['var']
table = "alldata_%s" % (station[:2], )
df = read_sql("""
SELECT year, month, sum(precip) as sum_precip,
avg(high) as avg_high,
avg(low) as avg_low,
sum(cdd(high,low,60)) as cdd60,
sum(cdd(high,low,65)) as cdd65,
sum(hdd(high,low,60)) as hdd60,
sum(hdd(high,low,65)) as hdd65,
sum(case when precip >= 0.01 then 1 else 0 end) as rain_days,
sum(case when snow >= 0.1 then 1 else 0 end) as snow_days
from """+table+""" WHERE station = %s GROUP by year, month
""", pgconn, params=(station,), index_col=None)
if df.empty:
raise NoDataFound("No Data Found.")
df['monthdate'] = df[['year', 'month']].apply(
lambda x: datetime.date(x[0], x[1], 1), axis=1)
df.set_index('monthdate', inplace=True)
res = """\
# IEM Climodat https://mesonet.agron.iastate.edu/climodat/
# Report Generated: %s
# Climate Record: %s -> %s
# Site Information: [%s] %s
# Contact Information: Daryl Herzmann akrherz@iastate.edu 515.294.5978
""" % (datetime.date.today().strftime("%d %b %Y"),
ctx['_nt'].sts[station]['archive_begin'].date(),
datetime.date.today(), station, ctx['_nt'].sts[station]['name'])
res += """# THESE ARE THE MONTHLY %s (base=65) FOR STATION %s
YEAR JAN FEB MAR APR MAY JUN JUL AUG SEP \
OCT NOV DEC
""" % (PDICT[varname].upper(), station)
second = """# THESE ARE THE MONTHLY %s (base=60) FOR STATION %s
YEAR JAN FEB MAR APR MAY JUN JUL AUG SEP \
OCT NOV DEC
""" % (
PDICT[varname].upper(), station)
minyear = df['year'].min()
maxyear = df['year'].max()
for yr in range(minyear, maxyear + 1):
res += ("%4i" % (yr,))
second += "%4i" % (yr,)
for mo in range(1, 13):
ts = datetime.date(yr, mo, 1)
if ts not in df.index:
res += ("%7s" % ("M",))
second += "%7s" % ("M",)
continue
row = df.loc[ts]
res += ("%7.0f" % (row[varname+"65"],))
second += "%7.0f" % (row[varname+"60"],)
res += ("\n")
second += "\n"
res += ("MEAN")
second += "MEAN"
for mo in range(1, 13):
df2 = df[df['month'] == mo]
res += ("%7.0f" % (df2[varname+"65"].mean(), ))
second += "%7.0f" % (df2[varname+"60"].mean(), )
res += ("\n")
second += "\n"
res += second
y1 = int(fdict.get('syear', 1990))
fig, ax = plt.subplots(1, 1, figsize=(8., 6.))
fig.text(0.5, 0.95, "[%s] %s (%s-%s)" % (
station, ctx['_nt'].sts[station]['name'], y1, y1 + 20), ha='center',
fontsize=16)
ax.set_title(r"%s base=60$^\circ$F" % (PDICT[varname], ))
filtered = df[(df['year'] >= y1) & (df['year'] <= (y1 + 20))]
df2 = filtered[
['month', 'year', varname + '60']
].pivot('year', 'month', varname + '60')
sns.heatmap(df2, annot=True, fmt=".0f", linewidths=.5, ax=ax)
return fig, df, res
if __name__ == '__main__':
plotter(dict(syear=1990))
| 34.92126 | 76 | 0.555355 |
e807b65f64a7deff0b619d067868e319ecf01061 | 12,502 | py | Python | examples/horovod/ray_torch_shuffle.py | krfricke/ray_shuffling_data_loader | b238871d45218c655cd0fcd78b8bf2a3940087f9 | [
"Apache-2.0"
] | 16 | 2021-05-13T08:03:03.000Z | 2021-09-30T00:20:01.000Z | examples/horovod/ray_torch_shuffle.py | krfricke/ray_shuffling_data_loader | b238871d45218c655cd0fcd78b8bf2a3940087f9 | [
"Apache-2.0"
] | 12 | 2021-05-04T22:18:01.000Z | 2021-07-14T12:10:40.000Z | examples/horovod/ray_torch_shuffle.py | krfricke/ray_shuffling_data_loader | b238871d45218c655cd0fcd78b8bf2a3940087f9 | [
"Apache-2.0"
] | 5 | 2021-05-18T02:57:50.000Z | 2021-07-01T11:23:05.000Z | import os
import pickle
import time
import timeit
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
import torch
import tempfile
import horovod.torch as hvd
from horovod.ray import RayExecutor
from ray_shuffling_data_loader.torch_dataset import (TorchShufflingDataset)
from ray_shuffling_data_loader.data_generation import (generate_data,
DATA_SPEC)
import argparse
DEFAULT_DATA_DIR = "s3://shuffling-data-loader-benchmarks/data/"
numpy_to_torch_dtype = {
np.bool: torch.bool,
np.uint8: torch.uint8,
np.int8: torch.int8,
np.int16: torch.int16,
np.int32: torch.int32,
np.int64: torch.int64,
np.float16: torch.float16,
np.float32: torch.float32,
np.float64: torch.float64,
np.complex64: torch.complex64,
np.complex128: torch.complex128
}
# Training settings
parser = argparse.ArgumentParser(description="PyTorch MNIST Example")
parser.add_argument(
"--batch-size",
type=int,
default=250000,
metavar="N",
help="input batch size for training (default: 64)")
parser.add_argument(
"--test-batch-size",
type=int,
default=250000,
metavar="N",
help="input batch size for testing (default: 1000)")
parser.add_argument(
"--epochs",
type=int,
default=10,
metavar="N",
help="number of epochs to train (default: 10)")
parser.add_argument(
"--lr",
type=float,
default=0.01,
metavar="LR",
help="learning rate (default: 0.01)")
parser.add_argument(
"--momentum",
type=float,
default=0.5,
metavar="M",
help="SGD momentum (default: 0.5)")
parser.add_argument(
"--no-cuda",
action="store_true",
default=False,
help="disables CUDA training")
parser.add_argument(
"--seed",
type=int,
default=42,
metavar="S",
help="random seed (default: 42)")
parser.add_argument(
"--log-interval",
type=int,
default=10,
metavar="N",
help=("how many batches to wait before logging training "
"status"))
parser.add_argument(
"--fp16-allreduce",
action="store_true",
default=False,
help="use fp16 compression during allreduce")
parser.add_argument(
"--use-adasum",
action="store_true",
default=False,
help="use adasum algorithm to do reduction")
parser.add_argument(
"--gradient-predivide-factor",
type=float,
default=1.0,
help=("apply gradient predivide factor in optimizer "
"(default: 1.0)"))
parser.add_argument("--num-workers", type=int, default=None)
parser.add_argument("--num-hosts", type=int, default=None)
parser.add_argument("--num-workers-per-host", type=int, default=None)
parser.add_argument("--cpus-per-worker", type=int, default=1)
parser.add_argument("--mock-train-step-time", type=float, default=1.0)
# Synthetic training data generation settings.
parser.add_argument("--cache-files", action="store_true", default=False)
parser.add_argument("--num-rows", type=int, default=2 * (10**7))
parser.add_argument("--num-files", type=int, default=25)
parser.add_argument("--max-row-group-skew", type=float, default=0.0)
parser.add_argument("--num-row-groups-per-file", type=int, default=5)
parser.add_argument("--data-dir", type=str, default=DEFAULT_DATA_DIR)
# Shuffling data loader settings.
parser.add_argument("--num-reducers", type=int, default=32)
parser.add_argument("--max-concurrent-epochs", type=int, default=2)
parser.add_argument("--address", default="auto")
if __name__ == "__main__":
args = parser.parse_args()
from ray_shuffling_data_loader.stats import human_readable_size
import ray
print("Connecting to Ray cluster...")
ray.init(address=args.address)
num_rows = args.num_rows
num_files = args.num_files
num_row_groups_per_file = args.num_row_groups_per_file
max_row_group_skew = args.max_row_group_skew
data_dir = args.data_dir
cache_path = os.path.join(tempfile.gettempdir(), "data_cache")
filenames = None
if args.cache_files and os.path.exists(cache_path):
try:
with open(cache_path, "rb") as f:
filenames, num_bytes = pickle.load(f)
except Exception as exc:
print(f"Cache load failed - {exc}")
if not filenames:
print(f"Generating {num_rows} rows over {num_files} files, with "
f"{num_row_groups_per_file} row groups per file and at most "
f"{100 * max_row_group_skew:.1f}% row group skew.")
filenames, num_bytes = generate_data(num_rows, num_files,
num_row_groups_per_file,
max_row_group_skew, data_dir)
if args.cache_files:
with open(os.path.join(tempfile.gettempdir(), "data_cache"),
"wb") as f:
pickle.dump((filenames, num_bytes), f)
print(f"Generated {len(filenames)} files containing {num_rows} rows "
f"with {num_row_groups_per_file} row groups per file, totalling "
f"{human_readable_size(num_bytes)}.")
print("Create Ray executor")
worker_kwargs = {}
num_workers = args.num_workers
num_hosts = args.num_hosts
num_workers_per_host = args.num_workers_per_host
if num_workers is not None:
if num_hosts is not None:
raise ValueError(
"Only one of --num-workers and --num-hosts should be used.")
worker_kwargs["num_workers"] = num_workers
elif num_hosts is not None:
worker_kwargs["num_hosts"] = num_hosts
if num_workers_per_host is None:
raise ValueError("When giving --num-hosts, --num-workers-per-host "
"must also be given.")
worker_kwargs["num_workers_per_host"] = num_workers_per_host
cpus_per_worker = args.cpus_per_worker
settings = RayExecutor.create_settings(timeout_s=30)
executor = RayExecutor(
settings,
use_gpu=True,
gpus_per_worker=1,
cpus_per_worker=cpus_per_worker,
**worker_kwargs)
executor.start()
executor.run(train_main, args=[args, filenames])
executor.shutdown()
print("Done consuming batches.")
| 35.925287 | 79 | 0.654375 |
e80814f0c20b1ee061421e9446a7dafff2db52a5 | 13,051 | py | Python | tests/test_main/test_base/tests.py | PitonX60/django-firebird | 407bd5916a8ae37184d06adb3b943d6bb4f7076f | [
"BSD-3-Clause"
] | 51 | 2015-01-13T00:16:36.000Z | 2022-01-28T12:18:22.000Z | tests/test_main/test_base/tests.py | PitonX60/django-firebird | 407bd5916a8ae37184d06adb3b943d6bb4f7076f | [
"BSD-3-Clause"
] | 84 | 2015-01-28T19:08:22.000Z | 2022-03-26T02:04:03.000Z | tests/test_main/test_base/tests.py | PitonX60/django-firebird | 407bd5916a8ae37184d06adb3b943d6bb4f7076f | [
"BSD-3-Clause"
] | 47 | 2015-01-13T00:53:27.000Z | 2021-08-03T04:20:31.000Z | # -*- coding: utf-8 -*-
from datetime import datetime, timedelta
from django.conf import settings
from django.db import connection, DatabaseError
from django.db.models import F, DateField, DateTimeField, IntegerField, TimeField, CASCADE
from django.db.models.fields.related import ForeignKey
from django.db.models.functions import (
Extract, ExtractDay, ExtractHour, ExtractMinute, ExtractMonth,
ExtractSecond, ExtractWeek, ExtractWeekDay, ExtractYear, Trunc, TruncDate,
TruncDay, TruncHour, TruncMinute, TruncMonth, TruncSecond, TruncTime,
TruncYear,
)
from django.test import TestCase, TransactionTestCase, override_settings
from django.utils import timezone
from .models import BigS, FieldsTest, Foo, Bar, DTModel
| 42.930921 | 257 | 0.64815 |
e808484d2e1b639627bded840c79fb6fdbdc05fc | 7,200 | py | Python | tests/test_past_failures.py | justinbois/eqtk | 7363b8c09e35088d2cb2cb5a62d315b52cce0d9b | [
"MIT"
] | 2 | 2020-05-17T05:34:40.000Z | 2020-05-17T12:40:14.000Z | tests/test_past_failures.py | justinbois/eqtk | 7363b8c09e35088d2cb2cb5a62d315b52cce0d9b | [
"MIT"
] | 1 | 2021-12-07T01:20:48.000Z | 2021-12-07T01:20:48.000Z | tests/test_past_failures.py | justinbois/eqtk | 7363b8c09e35088d2cb2cb5a62d315b52cce0d9b | [
"MIT"
] | null | null | null | import pytest
import numpy as np
import eqtk
| 24 | 87 | 0.321806 |
e80867dca8c2989991af1d5f2e487d3e8fa8b0f5 | 13,314 | py | Python | sdk/python/pulumi_azure/lb/outbound_rule.py | suresh198526/pulumi-azure | bf27206a38d7a5c58b3c2c57ec8769fe3d0fc5d7 | [
"ECL-2.0",
"Apache-2.0"
] | null | null | null | sdk/python/pulumi_azure/lb/outbound_rule.py | suresh198526/pulumi-azure | bf27206a38d7a5c58b3c2c57ec8769fe3d0fc5d7 | [
"ECL-2.0",
"Apache-2.0"
] | null | null | null | sdk/python/pulumi_azure/lb/outbound_rule.py | suresh198526/pulumi-azure | bf27206a38d7a5c58b3c2c57ec8769fe3d0fc5d7 | [
"ECL-2.0",
"Apache-2.0"
] | null | null | null | # coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union
from .. import _utilities, _tables
from . import outputs
from ._inputs import *
__all__ = ['OutboundRule']
| 53.043825 | 207 | 0.686646 |
e8094e0dbe640e5e2094dda3ca82426a4117f2ae | 21,377 | py | Python | orbit_predictor/predictors/base.py | Juanlu001/orbit-predictor | ca67e2e859932938627ed24e5cbf58c887cd99c0 | [
"MIT"
] | null | null | null | orbit_predictor/predictors/base.py | Juanlu001/orbit-predictor | ca67e2e859932938627ed24e5cbf58c887cd99c0 | [
"MIT"
] | null | null | null | orbit_predictor/predictors/base.py | Juanlu001/orbit-predictor | ca67e2e859932938627ed24e5cbf58c887cd99c0 | [
"MIT"
] | null | null | null | # MIT License
#
# Copyright (c) 2017 Satellogic SA
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import datetime as dt
import logging
import warnings
from collections import namedtuple
from math import pi, acos, degrees, radians
import numpy as np
try:
from scipy.optimize import brentq, minimize_scalar
except ImportError:
warnings.warn('scipy module was not found, some features may not work properly.',
ImportWarning)
from orbit_predictor.constants import MU_E
from orbit_predictor.exceptions import NotReachable, PropagationError
from orbit_predictor import coordinate_systems
from orbit_predictor.keplerian import rv2coe
from orbit_predictor.utils import (
angle_between,
cross_product,
dot_product,
reify,
vector_diff,
vector_norm,
gstime_from_datetime,
get_shadow,
get_sun,
eclipse_duration,
get_satellite_minus_penumbra_verticals,
)
logger = logging.getLogger(__name__)
ONE_SECOND = dt.timedelta(seconds=1)
def get_only_position(self, when_utc=None):
"""Return a tuple in ECEF coordinate system"""
return self.get_position(when_utc).position_ecef
def get_eclipse_duration(self, when_utc=None, tolerance=1e-1):
"""Gets eclipse duration at given time, in minutes"""
ecc = self.get_position(when_utc).osculating_elements[1]
if ecc > tolerance:
raise NotImplementedError("Non circular orbits are not supported")
beta = self.get_beta(when_utc)
return eclipse_duration(beta, self.period)
def passes_over(self, location, when_utc, limit_date=None, max_elevation_gt=0, aos_at_dg=0):
return LocationPredictor(location, self, when_utc, limit_date,
max_elevation_gt, aos_at_dg)
def get_next_pass(self, location, when_utc=None, max_elevation_gt=5,
aos_at_dg=0, limit_date=None):
"""Return a PredictedPass instance with the data of the next pass over the given location
location_llh: point on Earth we want to see from the satellite.
when_utc: datetime UTC after which the pass is calculated, default to now.
max_elevation_gt: filter passes with max_elevation under it.
aos_at_dg: This is if we want to start the pass at a specific elevation.
The next pass with a LOS strictly after when_utc will be returned,
possibly the current pass.
"""
if when_utc is None:
when_utc = dt.datetime.utcnow()
for pass_ in self.passes_over(location, when_utc, limit_date,
max_elevation_gt=max_elevation_gt,
aos_at_dg=aos_at_dg):
return pass_
else:
raise NotReachable('Propagation limit date exceeded')
def eclipses_since(self, when_utc=None, limit_date=None):
"""
An iterator that yields all eclipses start and end times between
when_utc and limit_date.
The next eclipse with a end strictly after when_utc will be returned,
possibly the current eclipse.
The last eclipse returned starts before limit_date, but it can end
strictly after limit_date.
No circular orbits are not supported, and will raise NotImplementedError.
"""
if when_utc is None:
when_utc = dt.datetime.utcnow()
orbital_period_s = self.period * 60
# A third of the orbit period is used as the base window of the search.
# This window ensures the function get_satellite_minus_penumbra_verticals
# will not have more than one local minimum (one in the illuminated phase and
# the other in penumbra).
base_search_window_s = orbital_period_s / 3
start = when_utc
while limit_date is None or start < limit_date:
# a minimum negative value is aproximatelly the middle point of the eclipse
minimum_illumination = minimize_scalar(
_get_illumination,
bounds=(0, base_search_window_s),
method="bounded",
options={"xatol": 1e-2},
)
eclipse_center_candidate_delta_s = minimum_illumination.x
# If found a minimum that is not illuminated, there is an eclipse here
if _get_illumination(eclipse_center_candidate_delta_s) < 0:
# The small time interval to search zeros around the center
# is estimated with the expected eclipse duration (which generally
# is smaller than expected, and that is the reason of the 1.5 coeficient).
# Also a minimum of 180 seconds was added because
# in some cases the estimation is 0 even though there is an eclipse.
eclipse_duration_estimation_s = self.get_eclipse_duration(start) * 60
zero_search_window_s = max(180, 1.5 * eclipse_duration_estimation_s)
# Search now both zeros to get the start and end of the eclipse
eclipse_start_delta_s = brentq(
_get_illumination,
eclipse_center_candidate_delta_s - zero_search_window_s,
eclipse_center_candidate_delta_s,
xtol=1e-2,
full_output=False,
)
eclipse_end_delta_s = brentq(
_get_illumination,
eclipse_center_candidate_delta_s,
eclipse_center_candidate_delta_s + zero_search_window_s,
xtol=1e-2,
full_output=False,
)
eclipse_start = start + dt.timedelta(seconds=eclipse_start_delta_s)
eclipse_end = start + dt.timedelta(seconds=eclipse_end_delta_s)
yield eclipse_start, eclipse_end
start = eclipse_end + dt.timedelta(seconds=base_search_window_s)
else:
start += dt.timedelta(seconds=base_search_window_s)
class GPSPredictor(Predictor):
pass
class LocationPredictor:
"""Predicts passes over a given location
Exposes an iterable interface
"""
def _build_predicted_pass(self, accuratepass):
"""Returns a classic predicted pass"""
tca_position = self.predictor.get_position(accuratepass.tca)
return PredictedPass(self.location, self.predictor.sate_id,
max_elevation_deg=accuratepass.max_elevation_deg,
aos=accuratepass.aos,
los=accuratepass.los,
duration_s=accuratepass.duration.total_seconds(),
max_elevation_position=tca_position,
max_elevation_date=accuratepass.tca,
)
def _find_nearest_descending(self, ascending_date):
for candidate in self._sample_points(ascending_date):
if not self.is_ascending(candidate):
return candidate
else:
logger.error('Could not find a descending pass over %s start date: %s - TLE: %s',
self.location, ascending_date, self.predictor.tle)
raise PropagationError("Can not find an descending phase")
def _find_nearest_ascending(self, descending_date):
for candidate in self._sample_points(descending_date):
if self.is_ascending(candidate):
return candidate
else:
logger.error('Could not find an ascending pass over %s start date: %s - TLE: %s',
self.location, descending_date, self.predictor.tle)
raise PropagationError('Can not find an ascending phase')
def _sample_points(self, date):
"""Helper method to found ascending or descending phases of elevation"""
start = date
end = date + self._orbit_step(0.99)
mid = self.midpoint(start, end)
mid_right = self.midpoint(mid, end)
mid_left = self.midpoint(start, mid)
return [end, mid, mid_right, mid_left]
def _refine_pass(self, ascending_date, descending_date):
tca = self._find_tca(ascending_date, descending_date)
elevation = self._elevation_at(tca)
if elevation > self.max_elevation_gt:
aos = self._find_aos(tca)
los = self._find_los(tca)
else:
aos = los = None
return AccuratePredictedPass(aos, tca, los, elevation)
def _find_tca(self, ascending_date, descending_date):
while not self._precision_reached(ascending_date, descending_date):
midpoint = self.midpoint(ascending_date, descending_date)
if self.is_ascending(midpoint):
ascending_date = midpoint
else:
descending_date = midpoint
return ascending_date
def _precision_reached(self, start, end):
# TODO: Allow the precision to change from the outside
return end - start <= ONE_SECOND
def is_passing(self, when_utc):
"""Returns a boolean indicating if satellite is actually visible"""
return bool(self._elevation_at(when_utc))
def is_ascending(self, when_utc):
"""Check is elevation is ascending or descending on a given point"""
elevation = self._elevation_at(when_utc)
next_elevation = self._elevation_at(when_utc + ONE_SECOND)
return elevation <= next_elevation
def _orbit_step(self, size):
"""Returns a time step, that will make the satellite advance a given number of orbits"""
step_in_radians = size * 2 * pi
seconds = (step_in_radians / self.predictor.mean_motion) * 60
return dt.timedelta(seconds=seconds)
class AccuratePredictedPass:
def __init__(self, aos, tca, los, max_elevation):
self.aos = round_datetime(aos) if aos is not None else None
self.tca = round_datetime(tca)
self.los = round_datetime(los) if los is not None else None
self.max_elevation = max_elevation
| 38.105169 | 98 | 0.635449 |
e80a42577d50ff4b5707bc38cca297d3bcb73ab4 | 170 | py | Python | vilmedic/scorers/NLG/__init__.py | jbdel/vilmedic | 17d462a540a2632811cc2a78edd2861800a33b07 | [
"MIT"
] | 15 | 2021-07-24T10:41:07.000Z | 2022-03-27T14:40:47.000Z | vilmedic/scorers/NLG/__init__.py | jbdel/vilmedic | 17d462a540a2632811cc2a78edd2861800a33b07 | [
"MIT"
] | null | null | null | vilmedic/scorers/NLG/__init__.py | jbdel/vilmedic | 17d462a540a2632811cc2a78edd2861800a33b07 | [
"MIT"
] | 2 | 2022-02-22T17:37:22.000Z | 2022-03-20T12:55:40.000Z | from .rouge import ROUGEScorer
from .bleu.bleu import BLEUScorer
from .meteor.meteor import METEORScorer
from .cider.cider import Cider
from .ciderd.ciderd import CiderD
| 28.333333 | 39 | 0.829412 |
e80f9dcf1fbaf95d64db548a68a4a0e2a7c25dc9 | 3,993 | py | Python | tests/test_liif.py | Yshuo-Li/mmediting-test | ff8349a183b3d266495a53be0c8ad8e342e8b461 | [
"Apache-2.0"
] | 2 | 2021-05-16T14:49:23.000Z | 2022-03-28T01:16:44.000Z | tests/test_liif.py | Yshuo-Li/mmediting-test | ff8349a183b3d266495a53be0c8ad8e342e8b461 | [
"Apache-2.0"
] | null | null | null | tests/test_liif.py | Yshuo-Li/mmediting-test | ff8349a183b3d266495a53be0c8ad8e342e8b461 | [
"Apache-2.0"
] | 2 | 2021-04-22T12:10:14.000Z | 2021-05-19T02:09:48.000Z | import numpy as np
import torch
import torch.nn as nn
from mmcv.runner import obj_from_dict
from mmcv.utils.config import Config
from mmedit.models import build_model
from mmedit.models.losses import L1Loss
from mmedit.models.registry import COMPONENTS
| 34.721739 | 77 | 0.615577 |
e80fc2194a7c4cdddb23cee4ca11cad0caafde7b | 3,739 | py | Python | database/signals.py | ccraddock/beiwe-backend-cc | b37c2604800aafcf81c93bc14673ada6aed17a39 | [
"BSD-3-Clause"
] | null | null | null | database/signals.py | ccraddock/beiwe-backend-cc | b37c2604800aafcf81c93bc14673ada6aed17a39 | [
"BSD-3-Clause"
] | null | null | null | database/signals.py | ccraddock/beiwe-backend-cc | b37c2604800aafcf81c93bc14673ada6aed17a39 | [
"BSD-3-Clause"
] | null | null | null |
from django.utils import timezone
from django.core.exceptions import ObjectDoesNotExist
from django.db.models.signals import post_save, pre_save
from django.dispatch import receiver
from database.study_models import DeviceSettings, Study, Survey, SurveyArchive
| 46.7375 | 127 | 0.70099 |
e810f0787ee0e3fe022cdf8b169ed014bf9c5752 | 673 | py | Python | docs/examples/notify/notify_skeleton.py | Blakstar26/npyscreen | d47f9c78dc9fea6f66aaef60403e748bb89e52f7 | [
"BSD-2-Clause"
] | null | null | null | docs/examples/notify/notify_skeleton.py | Blakstar26/npyscreen | d47f9c78dc9fea6f66aaef60403e748bb89e52f7 | [
"BSD-2-Clause"
] | null | null | null | docs/examples/notify/notify_skeleton.py | Blakstar26/npyscreen | d47f9c78dc9fea6f66aaef60403e748bb89e52f7 | [
"BSD-2-Clause"
] | null | null | null | import npyscreen
if __name__ == '__main__':
TestApp = MyApplication().run() | 29.26087 | 96 | 0.707281 |
e8112e27cfea3db2efc0a5a799487bb00e1650b2 | 5,656 | py | Python | practicioner_bundle/ch15-neural_style/pyimagesearch/nn/conv/minigooglenet.py | romanroson/pis_code | 1221c39c23bec62ba419f9a324f88b0d8e5e4b5b | [
"MIT"
] | 1 | 2020-06-07T04:02:16.000Z | 2020-06-07T04:02:16.000Z | practicioner_bundle/ch15-neural_style/pyimagesearch/nn/conv/minigooglenet.py | romanroson/pis_code | 1221c39c23bec62ba419f9a324f88b0d8e5e4b5b | [
"MIT"
] | null | null | null | practicioner_bundle/ch15-neural_style/pyimagesearch/nn/conv/minigooglenet.py | romanroson/pis_code | 1221c39c23bec62ba419f9a324f88b0d8e5e4b5b | [
"MIT"
] | 2 | 2020-03-25T10:51:54.000Z | 2020-09-18T09:36:44.000Z | # -*- coding: utf-8 -*-
"""Implementation of MiniGoogLeNet architecture.
This implementation is based on the original implemetation of GoogLeNet.
The authors of the net used BN before Activation layer.
This should be switched.
"""
from keras.layers.normalization import BatchNormalization
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import AveragePooling2D
from keras.layers.convolutional import MaxPooling2D
from keras.layers.core import Activation
from keras.layers.core import Dropout
from keras.layers.core import Dense
from keras.layers import Flatten
from keras.layers import Input
from keras.models import Model
from keras.layers import concatenate
from keras import backend as K
| 39.277778 | 102 | 0.647454 |
e81163dfe2d224db06c949798465e50a5cb2ce2e | 7,702 | py | Python | python/GafferUI/ScriptEditor.py | PaulDoessel/gaffer-play | 8b72dabb388e12424c230acfb0bd209049b01bd6 | [
"BSD-3-Clause"
] | null | null | null | python/GafferUI/ScriptEditor.py | PaulDoessel/gaffer-play | 8b72dabb388e12424c230acfb0bd209049b01bd6 | [
"BSD-3-Clause"
] | null | null | null | python/GafferUI/ScriptEditor.py | PaulDoessel/gaffer-play | 8b72dabb388e12424c230acfb0bd209049b01bd6 | [
"BSD-3-Clause"
] | 1 | 2020-02-15T16:15:54.000Z | 2020-02-15T16:15:54.000Z | ##########################################################################
#
# Copyright (c) 2011-2012, John Haddon. All rights reserved.
# Copyright (c) 2011-2013, Image Engine Design Inc. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above
# copyright notice, this list of conditions and the following
# disclaimer.
#
# * Redistributions in binary form must reproduce the above
# copyright notice, this list of conditions and the following
# disclaimer in the documentation and/or other materials provided with
# the distribution.
#
# * Neither the name of John Haddon nor the names of
# any other contributors to this software may be used to endorse or
# promote products derived from this software without specific prior
# written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS
# IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
# THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
# LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
##########################################################################
import ast
import sys
import traceback
import IECore
import Gaffer
import GafferUI
QtGui = GafferUI._qtImport( "QtGui" )
QtCore = GafferUI._qtImport( "QtCore" )
## \todo Custom right click menu with script load, save, execute file, undo, redo etc.
## \todo Standard way for users to customise all menus
## \todo Tab completion and popup help. rlcompleter module should be useful for tab completion. Completer( dict ) constructs a completer
# that works in a specific namespace.
GafferUI.EditorWidget.registerType( "ScriptEditor", ScriptEditor )
| 36.67619 | 141 | 0.692288 |
e811afcf8e2f5fbd26eb4879dec2b68ca870f0fa | 1,475 | py | Python | 03/triangle.py | machinelearningdeveloper/aoc_2016 | e2c2f7909b09c2ad27f87e05a80f2b2feee6a3a2 | [
"MIT"
] | null | null | null | 03/triangle.py | machinelearningdeveloper/aoc_2016 | e2c2f7909b09c2ad27f87e05a80f2b2feee6a3a2 | [
"MIT"
] | null | null | null | 03/triangle.py | machinelearningdeveloper/aoc_2016 | e2c2f7909b09c2ad27f87e05a80f2b2feee6a3a2 | [
"MIT"
] | null | null | null | """Test whether putative triangles, specified as triples of side lengths,
in fact are possible."""
def load_triangles(filename):
"""Load triangles from filename."""
triangles = []
with open(filename) as f:
for line in f:
if line.strip():
triangles.append(tuple([int(side) for side in line.split()]))
return triangles
def load_triangles_from_cols(filename):
"""Instead of loading one triangle per line,
load one-third each of three triangles per line."""
xs = []
ys = []
zs = []
with open(filename) as f:
for line in f:
if line.strip():
x, y, z = [int(side) for side in line.split()]
xs.append(x)
ys.append(y)
zs.append(z)
return ([(xs[i], xs[i+1], xs[i+2]) for i in range(0, len(xs), 3)]
+ [(ys[i], ys[i+1], ys[i+2]) for i in range(0, len(ys), 3)]
+ [(zs[i], zs[i+1], zs[i+2]) for i in range(0, len(zs), 3)])
def is_possible(*sides):
"""The sum of the lengths of every pair of sides in a, b, c
must be larger than the length of the remaining side,
or the putative triangle is impossible."""
for a in [0, 1]:
for b in range(a + 1, 3):
if a == 0:
c = 1 if b == 2 else 2
elif a == 1:
c = 0
if sum([sides[a], sides[b]]) <= sides[c]:
return False
return True
| 31.382979 | 76 | 0.51661 |
e811c36f36c477d41124bae1d31894772c187da0 | 135 | py | Python | ExerciciosdePython/ex049.py | aleksandromelo/Exercicios | 782ff539efa1286180eaf8df8c25c4eca7a5e669 | [
"MIT"
] | null | null | null | ExerciciosdePython/ex049.py | aleksandromelo/Exercicios | 782ff539efa1286180eaf8df8c25c4eca7a5e669 | [
"MIT"
] | null | null | null | ExerciciosdePython/ex049.py | aleksandromelo/Exercicios | 782ff539efa1286180eaf8df8c25c4eca7a5e669 | [
"MIT"
] | null | null | null | num = int(input('Digite um nmero para ver sua tabuada: '))
for i in range(1, 11):
print('{} x {:2} = {}'.format(num, i, num * i)) | 33.75 | 59 | 0.57037 |
e811dc5802ea397bf5ec6753cbdbdf5b70c81146 | 151 | py | Python | ebmeta/actions/version.py | bkidwell/ebmeta-old | 2279ddd14235ea31b27f0eaa7e9bb26cb43d4133 | [
"0BSD"
] | 1 | 2021-01-05T10:24:13.000Z | 2021-01-05T10:24:13.000Z | ebmeta/actions/version.py | bkidwell/ebmeta-old | 2279ddd14235ea31b27f0eaa7e9bb26cb43d4133 | [
"0BSD"
] | null | null | null | ebmeta/actions/version.py | bkidwell/ebmeta-old | 2279ddd14235ea31b27f0eaa7e9bb26cb43d4133 | [
"0BSD"
] | null | null | null | """Print ebmeta version number."""
import sys
import ebmeta
| 16.777778 | 61 | 0.668874 |
e812c612130c7c8d3bd444f6ef7c0936cdb5b8cc | 9,594 | py | Python | backend/api/tests/mixins/credit_trade_relationship.py | amichard/tfrs | ed3973016cc5c2ae48999d550a23b41a5ddad807 | [
"Apache-2.0"
] | 18 | 2017-05-10T21:55:11.000Z | 2021-03-01T16:41:32.000Z | backend/api/tests/mixins/credit_trade_relationship.py | amichard/tfrs | ed3973016cc5c2ae48999d550a23b41a5ddad807 | [
"Apache-2.0"
] | 1,167 | 2017-03-04T00:18:43.000Z | 2022-03-03T22:31:51.000Z | backend/api/tests/mixins/credit_trade_relationship.py | amichard/tfrs | ed3973016cc5c2ae48999d550a23b41a5ddad807 | [
"Apache-2.0"
] | 48 | 2017-03-09T17:19:39.000Z | 2022-02-24T16:38:17.000Z | # -*- coding: utf-8 -*-
# pylint: disable=no-member,invalid-name,duplicate-code
"""
REST API Documentation for the NRS TFRS Credit Trading Application
The Transportation Fuels Reporting System is being designed to streamline
compliance reporting for transportation fuel suppliers in accordance with
the Renewable & Low Carbon Fuel Requirements Regulation.
OpenAPI spec version: v1
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 datetime
import json
import logging
from typing import Callable
from collections import namedtuple, defaultdict
from enum import Enum
from api.models.CreditTrade import CreditTrade
from api.models.CreditTradeStatus import CreditTradeStatus
| 36.340909 | 96 | 0.575985 |
e81446098f632747c9bb69739c5cdbc90d7e2461 | 946 | py | Python | superset/superset_config.py | panchohumeres/dynamo-covid | cf473be3eeca436efccd8891a61b721192cf6d34 | [
"MIT"
] | 4 | 2020-08-10T07:35:10.000Z | 2022-03-31T23:03:32.000Z | superset/superset_config.py | panchohumeres/superJupyter | cc0ce98da2c58a7ab0ae502b5b07250db0c78b89 | [
"MIT"
] | 1 | 2021-06-02T02:51:12.000Z | 2021-06-02T02:51:12.000Z | superset/superset_config.py | panchohumeres/dynamo-covid | cf473be3eeca436efccd8891a61b721192cf6d34 | [
"MIT"
] | 1 | 2022-02-08T01:46:19.000Z | 2022-02-08T01:46:19.000Z | import os
SERVER_NAME = os.getenv('DOMAIN_SUPERSET')
PUBLIC_ROLE_LIKE_GAMMA = True
SESSION_COOKIE_SAMESITE = None # One of [None, 'Lax', 'Strict']
SESSION_COOKIE_HTTPONLY = False
MAPBOX_API_KEY = os.getenv('MAPBOX_API_KEY', '')
POSTGRES_DB=os.getenv('POSTGRES_DB')
POSTGRES_PASSWORD=os.getenv('POSTGRES_PASSWORD')
POSTGRES_USER=os.getenv('POSTGRES_USER')
POSTGRES_PORT=str(os.getenv('POSTGRES_PORT'))
HTTP_HEADERS = {'X-Frame-Options': 'ALLOWALL'}
sql_alchemy_string='postgresql+psycopg2://'+POSTGRES_USER+':'+POSTGRES_PASSWORD+'@postgres:'+POSTGRES_PORT+'/'+POSTGRES_DB
CACHE_CONFIG = {
'CACHE_TYPE': 'redis',
'CACHE_DEFAULT_TIMEOUT': 300,
'CACHE_KEY_PREFIX': 'superset_',
'CACHE_REDIS_HOST': 'redis',
'CACHE_REDIS_PORT': 6379,
'CACHE_REDIS_DB': 1,
'CACHE_REDIS_URL': 'redis://redis:6379/1'}
SQLALCHEMY_DATABASE_URI = \
sql_alchemy_string
SQLALCHEMY_TRACK_MODIFICATIONS = True
SECRET_KEY = 'thisISaSECRET_1234' | 35.037037 | 122 | 0.756871 |
e814b12d587f4fdbbd8d27aa48d2d332f3fb169b | 14,345 | py | Python | mybot.py | johnnyboiii3020/matchmaking-bot | c36df430fd8b3292f34fb2e156e65d9914e0e497 | [
"MIT"
] | null | null | null | mybot.py | johnnyboiii3020/matchmaking-bot | c36df430fd8b3292f34fb2e156e65d9914e0e497 | [
"MIT"
] | null | null | null | mybot.py | johnnyboiii3020/matchmaking-bot | c36df430fd8b3292f34fb2e156e65d9914e0e497 | [
"MIT"
] | null | null | null | import discord
import json
import random
import os
from discord.ext import commands
TOKEN = ""
client = commands.Bot(command_prefix = '--')
os.chdir(r'D:\Programming\Projects\Discord bot\jsonFiles')
SoloCounter = 30
SolominCounter = 10
Queueiter = 1
T_Queueiter = 1
TeamCounter = 50
TeamminCounter = 20
extensions = [
"cogs.Matchmaking",
"cogs.Moderator"
]
######################### Register Team #################################
############################ Register Solo ###################################
############################### Win Team ################################
###############CReate Team Queue Channel###########################
########################## Join Team Queue ###################
################Show Queue#################
###############Show Team Points##########
####################Show Points ###############
######################### Win Solo ##############################
####Leave Queue #####
###Create Queue Channel ####
#############Join Queue#########
if __name__ == '__main__':
for extension in extensions:
try:
client.load_extension(extension)
except Exception as e:
print('Failed to load extension {}\n{}: {}'.format(extension, type(e).__name__, e))
client.run(TOKEN)
| 40.408451 | 171 | 0.594772 |
e814cda6088e4f32617041b4185b65bf218042f4 | 18,547 | py | Python | conversation.py | markemus/economy | d7b3be9b2095393d7ee5c8967b9fcee8998776bb | [
"MIT"
] | 2 | 2017-05-09T22:46:18.000Z | 2021-09-07T06:04:57.000Z | conversation.py | markemus/economy | d7b3be9b2095393d7ee5c8967b9fcee8998776bb | [
"MIT"
] | null | null | null | conversation.py | markemus/economy | d7b3be9b2095393d7ee5c8967b9fcee8998776bb | [
"MIT"
] | 3 | 2017-07-20T21:22:30.000Z | 2020-10-17T13:04:28.000Z | import database as d
import numpy as np
import random
from transitions import Machine
#Conversations are markov chains. Works as follows: a column vector for each CURRENT state j, a row vector for each TARGET state i.
#Each entry i,j = the probability of moving to state i from state j.
#target state D = end of conversation. We start in state D when initializing conversation.
#row vectors sum to 1, internal lists are columns.
#Conversation is a singleton. DO NOT CREATE NEW CONVERSATION OBJECTS.
Convo = Conversation() | 40.942605 | 153 | 0.586025 |
e815853c059c203edb8fd8cdf216a9a5dcd145cf | 218 | py | Python | src/createData.py | saijananiganesan/SimPathFinder | 1634f2cb82c8056256d191be72589c4c531a3f67 | [
"MIT"
] | null | null | null | src/createData.py | saijananiganesan/SimPathFinder | 1634f2cb82c8056256d191be72589c4c531a3f67 | [
"MIT"
] | 1 | 2021-09-27T18:46:29.000Z | 2021-10-01T19:19:48.000Z | src/createData.py | saijananiganesan/SimPathFinder | 1634f2cb82c8056256d191be72589c4c531a3f67 | [
"MIT"
] | null | null | null | from __init__ import ExtractUnlabeledData, SampleUnlabeledData, ExtractLabeledData
E = ExtractLabeledData(data_dir='../labeldata/')
E.get_pathways()
E.get_pathway_names()
E.get_classes_dict()
E.create_df_all_labels()
| 27.25 | 82 | 0.825688 |
e815ba1c0be05931c138a14af77c58193d1bc5db | 3,812 | py | Python | blog/views.py | farman99ahmed/diyblog | 2e4548037c95b5563d2fdba3d05b488330a5e2b4 | [
"MIT"
] | null | null | null | blog/views.py | farman99ahmed/diyblog | 2e4548037c95b5563d2fdba3d05b488330a5e2b4 | [
"MIT"
] | null | null | null | blog/views.py | farman99ahmed/diyblog | 2e4548037c95b5563d2fdba3d05b488330a5e2b4 | [
"MIT"
] | null | null | null | from django.shortcuts import render, redirect
from .forms import AuthorForm, BlogForm, NewUserForm
from .models import Author, Blog
from django.contrib.auth import login, authenticate, logout
from django.contrib import messages
from django.contrib.auth.forms import AuthenticationForm
from django.contrib.auth.decorators import login_required
# Create your views here.
def get_blogs(request):
context = {'blogs': Blog.objects.all()}
return render(request, "blog/get_blogs.html", context)
def register_request(request):
if request.method == "POST":
form = NewUserForm(request.POST)
if form.is_valid():
user = form.save()
login(request, user)
messages.success(request, "Registration successful." )
return redirect("get_blogs")
messages.error(request, "Unsuccessful registration. Invalid information.")
form = NewUserForm()
return render (request=request, template_name="blog/register.html", context={"register_form":form})
def login_request(request):
if request.method == "POST":
form = AuthenticationForm(request, data=request.POST)
if form.is_valid():
username = form.cleaned_data.get('username')
password = form.cleaned_data.get('password')
user = authenticate(username=username, password=password)
if user is not None:
login(request, user)
messages.info(request, f"You are now logged in as {username}.")
return redirect("get_blogs")
else:
messages.error(request,"Invalid username or password.")
else:
messages.error(request,"Invalid username or password.")
form = AuthenticationForm()
return render(request=request, template_name="blog/login.html", context={"login_form":form})
def logout_request(request):
logout(request)
messages.info(request, "You have successfully logged out.")
return redirect("get_blogs")
| 33.147826 | 100 | 0.670776 |
e8163cfdf2d7fa0a32158347c9c5f81ab0a3df6a | 1,759 | py | Python | tests/conftest.py | SolomidHero/speech-regeneration-enhancer | eb43907ff085d68a707ff7bc3af14e93ff66fd65 | [
"MIT"
] | 8 | 2021-03-27T00:17:17.000Z | 2022-01-26T05:26:19.000Z | tests/conftest.py | SolomidHero/speech-regeneration-enhancer | eb43907ff085d68a707ff7bc3af14e93ff66fd65 | [
"MIT"
] | 1 | 2021-09-26T02:29:21.000Z | 2021-09-27T08:56:13.000Z | tests/conftest.py | SolomidHero/speech-regeneration-enhancer | eb43907ff085d68a707ff7bc3af14e93ff66fd65 | [
"MIT"
] | 1 | 2021-03-26T12:30:14.000Z | 2021-03-26T12:30:14.000Z | # here we make fixtures of toy data
# real parameters are stored and accessed from config
import pytest
import librosa
import os
import numpy as np
from hydra.experimental import compose, initialize
# It is not clear if we should cleanup the test directories
# or leave them for debugging
# https://github.com/pytest-dev/pytest/issues/3051 | 25.867647 | 77 | 0.74133 |
e8175b6a8041e64371cfdfe4643c61104dee988c | 2,189 | py | Python | dataclassses_howto.py | CvanderStoep/VideosSampleCode | 38a8d2538a041d5664d0040807ffac463d0fb79c | [
"MIT"
] | 285 | 2021-02-16T21:08:57.000Z | 2022-03-31T10:58:41.000Z | dataclassses_howto.py | CvanderStoep/VideosSampleCode | 38a8d2538a041d5664d0040807ffac463d0fb79c | [
"MIT"
] | 10 | 2021-05-01T17:20:50.000Z | 2022-03-09T21:46:46.000Z | dataclassses_howto.py | CvanderStoep/VideosSampleCode | 38a8d2538a041d5664d0040807ffac463d0fb79c | [
"MIT"
] | 96 | 2021-02-15T14:23:15.000Z | 2022-03-31T10:58:50.000Z | import dataclasses
import inspect
from dataclasses import dataclass, field
from pprint import pprint
import attr
def main():
comment = Comment(1, "I just subscribed!")
# comment.id = 3 # can't immutable
print(comment)
print(dataclasses.astuple(comment))
print(dataclasses.asdict(comment))
copy = dataclasses.replace(comment, id=3)
print(copy)
pprint(inspect.getmembers(Comment, inspect.isfunction))
if __name__ == '__main__':
main()
| 25.752941 | 87 | 0.616263 |
e81917ac02c5b07e90d8b7911baf13d1cd91a319 | 574 | py | Python | downloadMusic/main.py | yaosir0317/my_first | 387fe21aa529bca1d08ed45e13269aca23dce251 | [
"MIT"
] | null | null | null | downloadMusic/main.py | yaosir0317/my_first | 387fe21aa529bca1d08ed45e13269aca23dce251 | [
"MIT"
] | null | null | null | downloadMusic/main.py | yaosir0317/my_first | 387fe21aa529bca1d08ed45e13269aca23dce251 | [
"MIT"
] | null | null | null | from enum import Enum
import requests
PRE_URL = "http://www.musictool.top/"
headers = {"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/85.0.4183.102 Safari/537.36"}
if __name__ == '__main__':
get_music_list("", MusicAPP.qq)
| 24.956522 | 149 | 0.663763 |
e81b190b66eb2d495bc411eca3c4ded7a86955c8 | 892 | py | Python | app/api/serializers.py | michelmarcondes/django-study-with-docker | 248e41db3f16a5d26662c5e93ebf32716a20195e | [
"MIT"
] | null | null | null | app/api/serializers.py | michelmarcondes/django-study-with-docker | 248e41db3f16a5d26662c5e93ebf32716a20195e | [
"MIT"
] | null | null | null | app/api/serializers.py | michelmarcondes/django-study-with-docker | 248e41db3f16a5d26662c5e93ebf32716a20195e | [
"MIT"
] | null | null | null | from rest_framework import serializers
from projects.models import Project, Tag, Review
from users.models import Profile
| 24.777778 | 57 | 0.696188 |
e81b3e433344ddf1234f763d89e92536bfcf4280 | 108,927 | py | Python | corehq/apps/domain/views.py | johan--/commcare-hq | 86ee99c54f55ee94e4c8f2f6f30fc44e10e69ebd | [
"BSD-3-Clause"
] | null | null | null | corehq/apps/domain/views.py | johan--/commcare-hq | 86ee99c54f55ee94e4c8f2f6f30fc44e10e69ebd | [
"BSD-3-Clause"
] | 1 | 2022-03-12T01:03:25.000Z | 2022-03-12T01:03:25.000Z | corehq/apps/domain/views.py | johan--/commcare-hq | 86ee99c54f55ee94e4c8f2f6f30fc44e10e69ebd | [
"BSD-3-Clause"
] | null | null | null | import copy
import datetime
from decimal import Decimal
import logging
import uuid
import json
import cStringIO
from couchdbkit import ResourceNotFound
import dateutil
from django.core.paginator import Paginator
from django.views.generic import View
from django.db.models import Sum
from django.conf import settings
from django.template.loader import render_to_string
from django.utils.decorators import method_decorator
from django.utils.safestring import mark_safe
from django.core.urlresolvers import reverse
from django.http import HttpResponseRedirect, HttpResponse, Http404
from django.shortcuts import redirect, render
from django.contrib import messages
from django.views.decorators.http import require_POST
from PIL import Image
from django.utils.translation import ugettext as _, ugettext_noop, ugettext_lazy
from corehq.const import USER_DATE_FORMAT
from custom.dhis2.forms import Dhis2SettingsForm
from custom.dhis2.models import Dhis2Settings
from casexml.apps.case.mock import CaseBlock
from casexml.apps.case.xml import V2
from corehq.apps.accounting.async_handlers import Select2BillingInfoHandler
from corehq.apps.accounting.invoicing import DomainWireInvoiceFactory
from corehq.apps.accounting.decorators import (
requires_privilege_with_fallback,
)
from corehq.apps.hqwebapp.tasks import send_mail_async
from corehq.apps.accounting.exceptions import (
NewSubscriptionError,
PaymentRequestError,
)
from corehq.apps.accounting.payment_handlers import (
BulkStripePaymentHandler,
CreditStripePaymentHandler,
InvoiceStripePaymentHandler,
)
from corehq.apps.accounting.subscription_changes import DomainDowngradeStatusHandler
from corehq.apps.accounting.forms import EnterprisePlanContactForm
from corehq.apps.accounting.utils import (
get_change_status, get_privileges, fmt_dollar_amount,
quantize_accounting_decimal, get_customer_cards,
)
from corehq.apps.hqwebapp.async_handler import AsyncHandlerMixin
from corehq.apps.smsbillables.async_handlers import SMSRatesAsyncHandler, SMSRatesSelect2AsyncHandler
from corehq.apps.smsbillables.forms import SMSRateCalculatorForm
from corehq.apps.users.models import DomainInvitation
from corehq.apps.fixtures.models import FixtureDataType
from corehq.toggles import NAMESPACE_DOMAIN, all_toggles, CAN_EDIT_EULA, TRANSFER_DOMAIN
from corehq.util.context_processors import get_domain_type
from dimagi.utils.couch.resource_conflict import retry_resource
from corehq import privileges, feature_previews
from django_prbac.utils import has_privilege
from corehq.apps.accounting.models import (
Subscription, CreditLine, SoftwareProductType, SubscriptionType,
DefaultProductPlan, SoftwarePlanEdition, BillingAccount,
BillingAccountType,
Invoice, BillingRecord, InvoicePdf, PaymentMethodType,
PaymentMethod, EntryPoint, WireInvoice, SoftwarePlanVisibility, FeatureType,
StripePaymentMethod,
)
from corehq.apps.accounting.usage import FeatureUsageCalculator
from corehq.apps.accounting.user_text import (
get_feature_name,
PricingTable,
DESC_BY_EDITION,
get_feature_recurring_interval,
)
from corehq.apps.hqwebapp.models import ProjectSettingsTab
from corehq.apps import receiverwrapper
from corehq.apps.domain.calculations import CALCS, CALC_FNS, CALC_ORDER, dom_calc
from corehq.apps.domain.decorators import (
domain_admin_required, login_required, require_superuser, login_and_domain_required
)
from corehq.apps.domain.forms import (
DomainGlobalSettingsForm, DomainMetadataForm, SnapshotSettingsForm,
SnapshotApplicationForm, DomainInternalForm, PrivacySecurityForm,
ConfirmNewSubscriptionForm, ProBonoForm, EditBillingAccountInfoForm,
ConfirmSubscriptionRenewalForm, SnapshotFixtureForm, TransferDomainForm,
SelectSubscriptionTypeForm, INTERNAL_SUBSCRIPTION_MANAGEMENT_FORMS)
from corehq.apps.domain.models import Domain, LICENSES, TransferDomainRequest
from corehq.apps.domain.utils import normalize_domain_name
from corehq.apps.hqwebapp.views import BaseSectionPageView, BasePageView, CRUDPaginatedViewMixin
from corehq.apps.orgs.models import Organization, OrgRequest, Team
from corehq.apps.domain.forms import ProjectSettingsForm
from dimagi.utils.decorators.memoized import memoized
from dimagi.utils.web import get_ip, json_response, get_site_domain
from corehq.apps.users.decorators import require_can_edit_web_users
from corehq.apps.receiverwrapper.forms import GenericRepeaterForm, FormRepeaterForm
from corehq.apps.receiverwrapper.models import FormRepeater, CaseRepeater, ShortFormRepeater, AppStructureRepeater, \
RepeatRecord
from dimagi.utils.post import simple_post
from toggle.models import Toggle
from corehq.apps.hqwebapp.tasks import send_html_email_async
accounting_logger = logging.getLogger('accounting')
PAYMENT_ERROR_MESSAGES = {
400: ugettext_lazy('Your request was not formatted properly.'),
403: ugettext_lazy('Forbidden.'),
404: ugettext_lazy('Page not found.'),
500: ugettext_lazy("There was an error processing your request."
" We're working quickly to fix the issue. Please try again shortly."),
}
# Domain not required here - we could be selecting it for the first time. See notes domain.decorators
# about why we need this custom login_required decorator
class DomainViewMixin(object):
"""
Paving the way for a world of entirely class-based views.
Let's do this, guys. :-)
Set strict_domain_fetching to True in subclasses to bypass the cache.
"""
strict_domain_fetching = False
class BaseDomainView(LoginAndDomainMixin, BaseSectionPageView, DomainViewMixin):
class EditMyProjectSettingsView(BaseProjectSettingsView):
template_name = 'domain/admin/my_project_settings.html'
urlname = 'my_project_settings'
page_title = ugettext_lazy("My Timezone")
def post(self, request, *args, **kwargs):
if self.my_project_settings_form.is_valid():
self.my_project_settings_form.save(self.request.couch_user, self.domain)
messages.success(request, _("Your project settings have been saved!"))
return self.get(request, *args, **kwargs)
class EditDhis2SettingsView(BaseProjectSettingsView):
template_name = 'domain/admin/dhis2_settings.html'
urlname = 'dhis2_settings'
page_title = ugettext_lazy("DHIS2 API settings")
def post(self, request, *args, **kwargs):
if self.dhis2_settings_form.is_valid():
if self.dhis2_settings_form.save(self.domain_object):
messages.success(request, _('DHIS2 API settings successfully updated'))
else:
messages.error(request, _('There seems to have been an error. Please try again.'))
return self.get(request, *args, **kwargs)
def autocomplete_fields(request, field):
prefix = request.GET.get('prefix', '')
results = Domain.field_by_prefix(field, prefix)
return HttpResponse(json.dumps(results))
def logo(request, domain):
logo = Domain.get_by_name(domain).get_custom_logo()
if logo is None:
raise Http404()
return HttpResponse(logo[0], content_type=logo[1])
class DomainAccountingSettings(BaseAdminProjectSettingsView):
def _credit_grand_total(self, credit_lines):
return sum([c.balance for c in credit_lines]) if credit_lines else Decimal('0.00')
def get_product_summary(self, plan_version, account, subscription):
product_rates = plan_version.product_rates.all()
if len(product_rates) > 1:
# Models and UI are both written to support multiple products,
# but for now, each subscription can only have one product.
accounting_logger.error(
"[BILLING] "
"There seem to be multiple ACTIVE NEXT subscriptions for the subscriber %s. "
"Odd, right? The latest one by date_created was used, but consider this an issue."
% self.account
)
product_rate = product_rates[0]
product_info = {
'name': product_rate.product.product_type,
'monthly_fee': _("USD %s /month") % product_rate.monthly_fee,
'credit': None,
'type': product_rate.product.product_type,
}
credit_lines = None
if subscription is not None:
credit_lines = CreditLine.get_credits_by_subscription_and_features(
subscription, product_type=product_rate.product.product_type
)
elif account is not None:
credit_lines = CreditLine.get_credits_for_account(
account, product_type=product_rate.product.product_type
)
if credit_lines:
product_info['credit'] = self._fmt_credit(self._credit_grand_total(credit_lines))
return product_info
def get_feature_summary(self, plan_version, account, subscription):
feature_summary = []
for feature_rate in plan_version.feature_rates.all():
usage = FeatureUsageCalculator(feature_rate, self.domain).get_usage()
feature_info = {
'name': get_feature_name(feature_rate.feature.feature_type, self.product),
'usage': usage,
'remaining': (
feature_rate.monthly_limit - usage
if feature_rate.monthly_limit != -1
else _('Unlimited')
),
'credit': self._fmt_credit(),
'type': feature_rate.feature.feature_type,
'recurring_interval': get_feature_recurring_interval(feature_rate.feature.feature_type),
}
credit_lines = None
if subscription is not None:
credit_lines = CreditLine.get_credits_by_subscription_and_features(
subscription, feature_type=feature_rate.feature.feature_type
)
elif account is not None:
credit_lines = CreditLine.get_credits_for_account(
account, feature_type=feature_rate.feature.feature_type)
if credit_lines:
feature_info['credit'] = self._fmt_credit(self._credit_grand_total(credit_lines))
feature_summary.append(feature_info)
return feature_summary
def post(self, *args, **kwargs):
return self.paginate_crud_response
def dispatch(self, request, *args, **kwargs):
if self.account is None:
raise Http404()
return super(DomainBillingStatementsView, self).dispatch(request, *args, **kwargs)
class BaseStripePaymentView(DomainAccountingSettings):
http_method_names = ['post']
def get_or_create_payment_method(self):
return StripePaymentMethod.objects.get_or_create(
web_user=self.domain_admin,
method_type=PaymentMethodType.STRIPE,
)[0]
def get_payment_handler(self):
"""Returns a StripePaymentHandler object
"""
raise NotImplementedError("You must impmenent get_payment_handler()")
def post(self, request, *args, **kwargs):
try:
payment_handler = self.get_payment_handler()
response = payment_handler.process_request(request)
except PaymentRequestError as e:
accounting_logger.error(
"[BILLING] Failed to process Stripe Payment due to bad "
"request for domain %(domain)s user %(web_user)s: "
"%(error)s" % {
'domain': self.domain,
'web_user': self.request.user.username,
'error': e,
}
)
response = {
'error': {
'message': _(
"There was an issue processing your payment. No "
"charges were made. We're looking into the issue "
"as quickly as possible. Sorry for the inconvenience."
)
}
}
return json_response(response)
class CreditsStripePaymentView(BaseStripePaymentView):
urlname = 'domain_credits_payment'
class CreditsWireInvoiceView(DomainAccountingSettings):
http_method_names = ['post']
urlname = 'domain_wire_payment'
class InvoiceStripePaymentView(BaseStripePaymentView):
urlname = 'domain_invoice_payment'
def get_payment_handler(self):
return InvoiceStripePaymentHandler(
self.get_or_create_payment_method(), self.domain, self.invoice
)
class BulkStripePaymentView(BaseStripePaymentView):
urlname = 'domain_bulk_payment'
class WireInvoiceView(View):
http_method_names = ['post']
urlname = 'domain_wire_invoice'
class BillingStatementPdfView(View):
urlname = 'domain_billing_statement_download'
class InternalSubscriptionManagementView(BaseAdminProjectSettingsView):
template_name = 'domain/internal_subscription_management.html'
urlname = 'internal_subscription_mgmt'
page_title = ugettext_lazy("Dimagi Internal Subscription Management")
form_classes = INTERNAL_SUBSCRIPTION_MANAGEMENT_FORMS
class SelectedEnterprisePlanView(SelectPlanView):
template_name = 'domain/selected_enterprise_plan.html'
urlname = 'enterprise_request_quote'
step_title = ugettext_lazy("Contact Dimagi")
edition = SoftwarePlanEdition.ENTERPRISE
def post(self, request, *args, **kwargs):
if self.is_not_redirect and self.enterprise_contact_form.is_valid():
self.enterprise_contact_form.send_message()
messages.success(request, _("Your request was sent to Dimagi. "
"We will try our best to follow up in a timely manner."))
return HttpResponseRedirect(reverse(DomainSubscriptionView.urlname, args=[self.domain]))
return self.get(request, *args, **kwargs)
class ConfirmSelectedPlanView(SelectPlanView):
template_name = 'domain/confirm_plan.html'
urlname = 'confirm_selected_plan'
step_title = ugettext_lazy("Confirm Plan")
def get(self, request, *args, **kwargs):
return HttpResponseRedirect(reverse(SelectPlanView.urlname, args=[self.domain]))
def post(self, request, *args, **kwargs):
if self.edition == SoftwarePlanEdition.ENTERPRISE and not self.request.couch_user.is_superuser:
return HttpResponseRedirect(reverse(SelectedEnterprisePlanView.urlname, args=[self.domain]))
return super(ConfirmSelectedPlanView, self).get(request, *args, **kwargs)
class ConfirmBillingAccountInfoView(ConfirmSelectedPlanView, AsyncHandlerMixin):
template_name = 'domain/confirm_billing_info.html'
urlname = 'confirm_billing_account_info'
step_title = ugettext_lazy("Confirm Billing Information")
is_new = False
async_handlers = [
Select2BillingInfoHandler,
]
def post(self, request, *args, **kwargs):
if self.async_response is not None:
return self.async_response
if self.edition == SoftwarePlanEdition.ENTERPRISE and not self.request.couch_user.is_superuser:
return HttpResponseRedirect(reverse(SelectedEnterprisePlanView.urlname, args=[self.domain]))
if self.is_form_post and self.billing_account_info_form.is_valid():
is_saved = self.billing_account_info_form.save()
software_plan_name = DESC_BY_EDITION[self.selected_plan_version.plan.edition]['name'].encode('utf-8')
if not is_saved:
messages.error(
request, _("It appears there was an issue subscribing your project to the %s Software Plan. You "
"may try resubmitting, but if that doesn't work, rest assured someone will be "
"contacting you shortly.") % software_plan_name)
else:
messages.success(
request, _("Your project has been successfully subscribed to the %s Software Plan."
% software_plan_name)
)
return HttpResponseRedirect(reverse(DomainSubscriptionView.urlname, args=[self.domain]))
return super(ConfirmBillingAccountInfoView, self).post(request, *args, **kwargs)
class SubscriptionMixin(object):
class ExchangeSnapshotsView(BaseAdminProjectSettingsView):
template_name = 'domain/snapshot_settings.html'
urlname = 'domain_snapshot_settings'
page_title = ugettext_lazy("CommCare Exchange")
class ManageProjectMediaView(BaseAdminProjectSettingsView):
urlname = 'domain_manage_multimedia'
page_title = ugettext_lazy("Multimedia Sharing")
template_name = 'domain/admin/media_manager.html'
def post(self, request, *args, **kwargs):
if self.add_repeater_form.is_valid():
repeater = self.make_repeater()
repeater.save()
messages.success(request, _("Forwarding set up to %s" % repeater.url))
return HttpResponseRedirect(reverse(DomainForwardingOptionsView.urlname, args=[self.domain]))
return self.get(request, *args, **kwargs)
class AddFormRepeaterView(AddRepeaterView):
urlname = 'add_form_repeater'
repeater_form_class = FormRepeaterForm
class OrgSettingsView(BaseAdminProjectSettingsView):
template_name = 'domain/orgs_settings.html'
urlname = 'domain_org_settings'
page_title = ugettext_lazy("Organization")
class EditInternalCalculationsView(BaseInternalDomainSettingsView):
urlname = 'domain_internal_calculations'
page_title = ugettext_lazy("Calculated Properties")
template_name = 'domain/internal_calculations.html'
def _publish_snapshot(request, domain, published_snapshot=None):
snapshots = domain.snapshots()
for snapshot in snapshots:
if snapshot.published:
snapshot.published = False
if not published_snapshot or snapshot.name != published_snapshot.name:
snapshot.save()
if published_snapshot:
if published_snapshot.copied_from.name != domain.name:
messages.error(request, "Invalid snapshot")
return False
# cda stuff. In order to publish a snapshot, a user must have agreed to this
published_snapshot.cda.signed = True
published_snapshot.cda.date = datetime.datetime.utcnow()
published_snapshot.cda.type = 'Content Distribution Agreement'
if request.couch_user:
published_snapshot.cda.user_id = request.couch_user.get_id
published_snapshot.cda.user_ip = get_ip(request)
published_snapshot.published = True
published_snapshot.save()
_notification_email_on_publish(domain, published_snapshot, request.couch_user)
return True
def _notification_email_on_publish(domain, snapshot, published_by):
params = {"domain": domain, "snapshot": snapshot,
"published_by": published_by, "url_base": get_site_domain()}
text_content = render_to_string(
"domain/email/published_app_notification.txt", params)
html_content = render_to_string(
"domain/email/published_app_notification.html", params)
recipients = settings.EXCHANGE_NOTIFICATION_RECIPIENTS
subject = "New App on Exchange: %s" % snapshot.title
try:
for recipient in recipients:
send_html_email_async.delay(subject, recipient, html_content,
text_content=text_content,
email_from=settings.DEFAULT_FROM_EMAIL)
except Exception:
logging.warning("Can't send notification email, "
"but the message was:\n%s" % text_content)
def post(self, request, *args, **kwargs):
form = self.transfer_domain_form
if form.is_valid():
# Initiate domain transfer
transfer = form.save()
transfer.send_transfer_request()
return HttpResponseRedirect(self.page_url)
context = self.get_context_data(**kwargs)
return self.render_to_response(context)
class ActivateTransferDomainView(BasePageView):
urlname = 'activate_transfer_domain'
page_title = 'Activate Domain Transfer'
template_name = 'domain/activate_transfer_domain.html'
def get(self, request, guid, *args, **kwargs):
self.guid = guid
if (self.active_transfer and
self.active_transfer.to_username != request.user.username and
not request.user.is_superuser):
return HttpResponseRedirect(reverse("no_permissions"))
return super(ActivateTransferDomainView, self).get(request, *args, **kwargs)
from corehq.apps.smsbillables.forms import PublicSMSRateCalculatorForm
from corehq.apps.smsbillables.async_handlers import PublicSMSRatesAsyncHandler
def _send_request_notification_email(request, org, dom):
params = {"org": org, "dom": dom, "requestee": request.couch_user,
"url_base": get_site_domain()}
text_content = render_to_string(
"domain/email/org_request_notification.txt", params)
html_content = render_to_string(
"domain/email/org_request_notification.html", params)
recipients = [member.email for member in org.get_members()
if member.is_org_admin(org.name)]
subject = "New request to add a project to your organization! -- CommcareHQ"
try:
for recipient in recipients:
send_html_email_async.delay(subject, recipient, html_content,
text_content=text_content,
email_from=settings.DEFAULT_FROM_EMAIL)
except Exception:
logging.warning("Can't send notification email, "
"but the message was:\n%s" % text_content)
| 38.750267 | 130 | 0.636215 |
e81ba3ad3b0b7d369560fab0313b86525e0c16f1 | 5,668 | py | Python | src/anmi/T2/funcs_met_iters.py | alexmascension/ANMI | 9c51a497a5fa2650f1429f847c7f9df69271168b | [
"BSD-3-Clause"
] | 1 | 2021-11-30T23:30:45.000Z | 2021-11-30T23:30:45.000Z | src/anmi/T2/funcs_met_iters.py | alexmascension/ANMI | 9c51a497a5fa2650f1429f847c7f9df69271168b | [
"BSD-3-Clause"
] | 2 | 2021-04-11T20:39:15.000Z | 2021-04-13T17:45:43.000Z | src/anmi/T2/funcs_met_iters.py | alexmascension/ANMI | 9c51a497a5fa2650f1429f847c7f9df69271168b | [
"BSD-3-Clause"
] | 1 | 2021-11-30T23:31:11.000Z | 2021-11-30T23:31:11.000Z | from sympy import simplify, zeros
from sympy import Matrix as mat
import numpy as np
from ..genericas import print_verbose, matriz_inversa
def metodo_iterativo(
A, b=None, x0=None, metodo="jacobi", w=1.5, n_iter=10, verbose=True,
):
"""Aplica el mtodo iterativo designado
Args:
A (matriz): Matriz de valores
b (vector, optional): Vector de rhs. Por defecto es 1, 1, ..., 1.
x0 (vector, optional): Vector con elementos de la primera iteracin. Por defecto es 1, 1, ..., 1.
metodo (str, optional): mtodo de resolucin, puede ser "jacobi", "gs" o "sor".
w (float, optional): Peso para mtodo sor. Defaults to 1.5.
n_iter (int, optional): Nmero de iteraciones del mtodo. Defaults to 10.
verbose (bool, optional): Imprime resultados intermedios. Defaults to True.
Returns:
dict: 'x': vector de resultados para Ax=b, 'diff': diferencia entre Ax y b para cada iteracin.
"""
if b is None:
b = mat([[1] * A.shape[0]]).T
if x0 is None:
x0 = mat([[1] * A.shape[1]]).T
D, L, U = (
zeros(A.shape[0], A.shape[1]),
zeros(A.shape[0], A.shape[1]),
zeros(A.shape[0], A.shape[1]),
)
for r in range(A.shape[0]):
for c in range(A.shape[1]):
if r == c:
D[r, c] = A[r, c]
elif r < c:
U[r, c] = -A[r, c]
else:
L[r, c] = -A[r, c]
if metodo == "jacobi":
M = D
elif metodo == "gs":
M = D - L
elif metodo == "sor":
M = D / w - L
N = simplify(M - A)
# Aplicamos criterios!
criterio_radio_espectral(matriz_inversa(M) * N, verbose)
criterio_diagonal_dominante(A, verbose)
criterio_simetrica_definida_positiva(A, verbose)
criterio_SOR(verbose)
criterio_m_matriz(A, verbose)
diff = []
for iter in range(n_iter): # Aplica el mtodo
x0 = (matriz_inversa(M)) * (N * x0 + b)
diff.append(np.sum(np.abs(A * x0 - b)))
return {"x": x0, "diff": diff}
| 32.574713 | 165 | 0.572689 |
e81c315cccb3d9ee19bee096605d603f1e698137 | 282 | py | Python | {{cookiecutter.project_hyphen}}/{{cookiecutter.project_slug}}/__init__.py | zhangxianbing/cookiecutter-pypackage | 28f7f305d3baf96771881c3359227bed1bc7d182 | [
"BSD-3-Clause"
] | 1 | 2021-05-20T06:20:13.000Z | 2021-05-20T06:20:13.000Z | {{cookiecutter.project_hyphen}}/{{cookiecutter.project_slug}}/__init__.py | zhangxianbing/cookiecutter-pypackage | 28f7f305d3baf96771881c3359227bed1bc7d182 | [
"BSD-3-Clause"
] | null | null | null | {{cookiecutter.project_hyphen}}/{{cookiecutter.project_slug}}/__init__.py | zhangxianbing/cookiecutter-pypackage | 28f7f305d3baf96771881c3359227bed1bc7d182 | [
"BSD-3-Clause"
] | null | null | null | """{{ cookiecutter.project_name }} - {{ cookiecutter.project_short_description }}"""
__version__ = "{{ cookiecutter.project_version }}"
__author__ = """{{ cookiecutter.author_name }}"""
__email__ = "{{ cookiecutter.author_email }}"
prog_name = "{{ cookiecutter.project_hyphen }}"
| 35.25 | 84 | 0.70922 |
e81d0332787bf5b5558e04aef3e4c8b78112e963 | 200 | py | Python | services/osparc-gateway-server/tests/integration/_dask_helpers.py | mguidon/osparc-dask-gateway | accd850c15cb3a36cf4421a1d070a4db29843013 | [
"MIT"
] | 1 | 2022-01-25T09:18:59.000Z | 2022-01-25T09:18:59.000Z | services/osparc-gateway-server/tests/integration/_dask_helpers.py | ITISFoundation/osparc-dask-gateway | 13d533de582382fcca3c379df30534ca0e6e145f | [
"MIT"
] | 16 | 2021-12-06T16:16:17.000Z | 2022-03-21T13:26:48.000Z | services/osparc-gateway-server/tests/integration/_dask_helpers.py | sanderegg/osparc-dask-gateway | 13d533de582382fcca3c379df30534ca0e6e145f | [
"MIT"
] | 2 | 2021-11-26T15:56:00.000Z | 2021-12-07T21:27:41.000Z | from typing import NamedTuple
from dask_gateway_server.app import DaskGateway
| 18.181818 | 47 | 0.775 |
e81d5f97eebe858da4677a4562a912397f01a885 | 5,487 | py | Python | rdkit/ML/InfoTheory/BitRank.py | kazuyaujihara/rdkit | 06027dcd05674787b61f27ba46ec0d42a6037540 | [
"BSD-3-Clause"
] | 1,609 | 2015-01-05T02:41:13.000Z | 2022-03-30T21:57:24.000Z | rdkit/ML/InfoTheory/BitRank.py | kazuyaujihara/rdkit | 06027dcd05674787b61f27ba46ec0d42a6037540 | [
"BSD-3-Clause"
] | 3,412 | 2015-01-06T12:13:33.000Z | 2022-03-31T17:25:41.000Z | rdkit/ML/InfoTheory/BitRank.py | kazuyaujihara/rdkit | 06027dcd05674787b61f27ba46ec0d42a6037540 | [
"BSD-3-Clause"
] | 811 | 2015-01-11T03:33:48.000Z | 2022-03-28T11:57:49.000Z | #
# Copyright (C) 2001,2002,2003 greg Landrum and Rational Discovery LLC
#
""" Functionality for ranking bits using info gains
**Definitions used in this module**
- *sequence*: an object capable of containing other objects which supports
__getitem__() and __len__(). Examples of these include lists, tuples, and
Numeric arrays.
- *IntVector*: an object containing integers which supports __getitem__() and
__len__(). Examples include lists, tuples, Numeric Arrays, and BitVects.
**NOTE**: Neither *sequences* nor *IntVectors* need to support item assignment.
It is perfectly acceptable for them to be read-only, so long as they are
random-access.
"""
import numpy
from rdkit.ML.InfoTheory import entropy
def FormCounts(bitVects, actVals, whichBit, nPossibleActs, nPossibleBitVals=2):
""" generates the counts matrix for a particular bit
**Arguments**
- bitVects: a *sequence* containing *IntVectors*
- actVals: a *sequence*
- whichBit: an integer, the bit number to use.
- nPossibleActs: the (integer) number of possible activity values.
- nPossibleBitVals: (optional) if specified, this integer provides the maximum
value attainable by the (increasingly inaccurately named) bits in _bitVects_.
**Returns**
a Numeric array with the counts
**Notes**
This is really intended for internal use.
"""
if len(bitVects) != len(actVals):
raise ValueError('var and activity lists should be the same length')
res = numpy.zeros((nPossibleBitVals, nPossibleActs), numpy.integer)
for i in range(len(bitVects)):
res[bitVects[i][whichBit], actVals[i]] += 1
return res
def CalcInfoGains(bitVects, actVals, nPossibleActs, nPossibleBitVals=2):
""" Calculates the information gain for a set of points and activity values
**Arguments**
- bitVects: a *sequence* containing *IntVectors*
- actVals: a *sequence*
- nPossibleActs: the (integer) number of possible activity values.
- nPossibleBitVals: (optional) if specified, this integer provides the maximum
value attainable by the (increasingly inaccurately named) bits in _bitVects_.
**Returns**
a list of floats
"""
if len(bitVects) != len(actVals):
raise ValueError('var and activity lists should be the same length')
nBits = len(bitVects[0])
res = numpy.zeros(nBits, numpy.float)
for bit in range(nBits):
counts = FormCounts(bitVects, actVals, bit, nPossibleActs, nPossibleBitVals=nPossibleBitVals)
res[bit] = entropy.InfoGain(counts)
return res
def RankBits(bitVects, actVals, nPossibleBitVals=2, metricFunc=CalcInfoGains):
""" Rank a set of bits according to a metric function
**Arguments**
- bitVects: a *sequence* containing *IntVectors*
- actVals: a *sequence*
- nPossibleBitVals: (optional) if specified, this integer provides the maximum
value attainable by the (increasingly inaccurately named) bits in _bitVects_.
- metricFunc: (optional) the metric function to be used. See _CalcInfoGains()_
for a description of the signature of this function.
**Returns**
A 2-tuple containing:
- the relative order of the bits (a list of ints)
- the metric calculated for each bit (a list of floats)
"""
nPossibleActs = max(actVals) + 1
metrics = metricFunc(bitVects, actVals, nPossibleActs, nPossibleBitVals=nPossibleBitVals)
bitOrder = list(numpy.argsort(metrics))
bitOrder.reverse()
return bitOrder, metrics
def AnalyzeSparseVects(bitVects, actVals):
""" #DOC
**Arguments**
- bitVects: a *sequence* containing SBVs
- actVals: a *sequence*
**Returns**
a list of floats
**Notes**
- these need to be bit vects and binary activities
"""
nPts = len(bitVects)
if nPts != len(actVals):
raise ValueError('var and activity lists should be the same length')
nBits = bitVects[0].GetSize()
actives = numpy.zeros(nBits, numpy.integer)
inactives = numpy.zeros(nBits, numpy.integer)
nActives, nInactives = 0, 0
for i in range(nPts):
sig, act = bitVects[i], actVals[i]
onBitList = sig.GetOnBits()
if act:
for bit in onBitList:
actives[bit] += 1
nActives += 1
else:
for bit in onBitList:
inactives[bit] += 1
nInactives += 1
resTbl = numpy.zeros((2, 2), numpy.integer)
res = []
gains = []
for bit in range(nBits):
nAct, nInact = actives[bit], inactives[bit]
if nAct or nInact:
resTbl[0, 0] = nAct
resTbl[1, 0] = nPts - nAct
resTbl[0, 1] = nInact
resTbl[1, 1] = nPts - nInact
gain = entropy.InfoGain(resTbl)
gains.append(gain)
res.append((bit, gain, nAct, nInact))
return res, gains
def SparseRankBits(bitVects, actVals, metricFunc=AnalyzeSparseVects):
""" Rank a set of bits according to a metric function
**Arguments**
- bitVects: a *sequence* containing SBVs
- actVals: a *sequence*
- metricFunc: (optional) the metric function to be used. See _SparseCalcInfoGains()_
for a description of the signature of this function.
**Returns**
A 2-tuple containing:
- the relative order of the bits (a list of ints)
- the metric calculated for each bit (a list of floats)
**Notes**
- these need to be bit vects and binary activities
"""
info, metrics = metricFunc(bitVects, actVals)
bitOrder = list(numpy.argsort(metrics))
bitOrder.reverse()
return bitOrder, info
| 27.163366 | 97 | 0.68635 |
e81d7c04c616831d43d69c14fef3ddcc06add40b | 5,766 | py | Python | trainer/dataset.py | vinay-swamy/gMVP | 62202baa0769dfe0e47c230e78dffa42fb1280f1 | [
"MIT"
] | 2 | 2021-04-24T03:23:40.000Z | 2021-06-28T11:51:10.000Z | trainer/dataset.py | vinay-swamy/gMVP | 62202baa0769dfe0e47c230e78dffa42fb1280f1 | [
"MIT"
] | null | null | null | trainer/dataset.py | vinay-swamy/gMVP | 62202baa0769dfe0e47c230e78dffa42fb1280f1 | [
"MIT"
] | 2 | 2021-09-14T13:03:38.000Z | 2022-03-23T02:49:19.000Z | import tensorflow as tf
import os
import pickle
import numpy as np
from constant_params import input_feature_dim, window_size
| 33.137931 | 125 | 0.601283 |
e81d9b0b490485d24940928f241dddb40fd49833 | 209,857 | py | Python | layers/eight_mile/pytorch/layers.py | dpressel/baseline | 2f46f3b043f2d20bc348495cc54c834f31f71098 | [
"Apache-2.0"
] | 241 | 2016-04-25T20:02:31.000Z | 2019-09-03T05:44:09.000Z | layers/eight_mile/pytorch/layers.py | dpressel/baseline | 2f46f3b043f2d20bc348495cc54c834f31f71098 | [
"Apache-2.0"
] | 42 | 2017-08-21T16:04:36.000Z | 2019-09-30T20:45:17.000Z | layers/eight_mile/pytorch/layers.py | dpressel/baseline | 2f46f3b043f2d20bc348495cc54c834f31f71098 | [
"Apache-2.0"
] | 75 | 2016-06-28T01:18:58.000Z | 2019-08-29T06:47:22.000Z | import copy
import math
import logging
from typing import Dict, List, Optional, Tuple, Union
import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.jit as jit
import torch.autograd
import contextlib
import glob
from eight_mile.utils import listify, Offsets, is_sequence, str2bool, get_alibi_slopes
from eight_mile.utils import transition_mask as transition_mask_np
MASK_FALSE = False
logger = logging.getLogger("mead.layers")
def sequence_mask(lengths: torch.Tensor, max_len: int = -1) -> torch.Tensor:
"""Generate a sequence mask of shape `BxT` based on the given lengths
:param lengths: A `B` tensor containing the lengths of each example
:param max_len: The maximum width (length) allowed in this mask (default to None)
:return: A mask
"""
lens = lengths.cpu()
if max_len < 0:
max_len_v = torch.max(lens)
else:
max_len_v = max_len
# 1 x T
row = torch.arange(0, max_len_v).type_as(lens).view(1, -1)
# B x 1
col = lens.view(-1, 1)
# Broadcast to B x T, compares increasing number to max
mask = row < col
return mask
def sequence_mask_mxlen(lengths: torch.Tensor, max_len: int) -> torch.Tensor:
"""Generate a sequence mask of shape `BxT` based on the given lengths, with a maximum value
This function primarily exists to make ONNX tracing work better
:param lengths: A `B` tensor containing the lengths of each example
:param max_len: The maximum width (length) allowed in this mask (default to None)
:return: A mask
"""
lens = lengths.cpu()
max_len_v = max_len
# 1 x T
row = torch.arange(0, max_len_v).type_as(lens).view(1, -1)
# B x 1
col = lens.view(-1, 1)
# Broadcast to B x T, compares increasing number to max
mask = row < col
return mask
def vec_log_sum_exp(vec: torch.Tensor, dim: int) -> torch.Tensor:
"""Vectorized version of log-sum-exp
:param vec: Vector
:param dim: What dimension to operate on
:return:
"""
max_scores, idx = torch.max(vec, dim, keepdim=True)
max_scores_broadcast = max_scores.expand_as(vec)
return max_scores + torch.log(torch.sum(torch.exp(vec - max_scores_broadcast), dim, keepdim=True))
def unsort_batch(batch: torch.Tensor, perm_idx: torch.Tensor) -> torch.Tensor:
"""Undo the sort on a batch of tensors done for packing the data in the RNN.
:param batch: The batch of data batch first `[B, ...]`
:param perm_idx: The permutation index returned from the torch.sort.
:returns: The batch in the original order.
"""
# Add ones to the shape of the perm_idx until it can broadcast to the batch
perm_idx = perm_idx.to(batch.device)
diff = len(batch.shape) - len(perm_idx.shape)
extra_dims = [1] * diff
perm_idx = perm_idx.view([-1] + extra_dims)
return torch.scatter(torch.zeros_like(batch), 0, perm_idx.expand_as(batch), batch)
def infer_lengths(tensor, dim=1):
"""Infer the lengths of an input based on the idea the Offsets.PAD was used as the padding token.
:param tensor: The data to infer the length of, should be either [B, T] or [T, B]
:param dim: The dimension which contains the sequential signal
:returns: A Tensor of shape `[B]` that has the lengths for example item in the batch
"""
if len(tensor.shape) != 2:
raise ValueError(f"infer_lengths only works with tensors wit two dims right now, got {len(tensor.shape)}")
offsets = torch.arange(1, tensor.shape[dim] + 1, device=tensor.device, dtype=tensor.dtype).unsqueeze(1 - dim)
non_pad_loc = (tensor != Offsets.PAD).to(tensor.dtype)
return torch.argmax(non_pad_loc * offsets, dim=dim) + 1
def tensor_and_lengths(inputs) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
"""Return either the unpacked inputs (2), or a `Tuple` of the input with None
TODO: this function should probably be changed to always return the lengths second.
To do this, we just need a sentinel value, e.g. <PAD> (0). The problem with doing this is
that it might be possible to generate <PAD> in the middle of the tensor which would make that
length invalid.
:param inputs: Either a sequence of the `(tensor, length)` or just the `tensor`
:return: A `Tuple` of `(tensor, length)` or `(tensor, None)`
"""
if isinstance(inputs, (list, tuple)):
in_tensor, lengths = inputs
else:
in_tensor = inputs
lengths = None
return in_tensor, lengths
# Torch only added this module in 1.4.0, shim
#Code taken from: https://github.com/huggingface/transformers/blob/766d4bf7920213bdd8a8afb42a72719190124568/src/transformers/activations.py#L27
def get_activation(name: str = "relu") -> nn.Module:
"""Get back an `nn.Module` by string name of the activation operator
:param name: A string name of the operation
:return: A module associated with that string
"""
if name is None or name == "ident":
return nn.Identity()
if name == "tanh":
return nn.Tanh()
if name == "gelu":
return GeLU()
if name == "hardtanh":
return nn.Hardtanh()
if name == "leaky_relu":
return nn.LeakyReLU()
if name == "prelu":
return nn.PReLU()
if name == "sigmoid":
return nn.Sigmoid()
if name == "log_sigmoid":
return nn.LogSigmoid()
if name == "log_softmax":
return nn.LogSoftmax(dim=-1)
if name == "softmax":
return nn.Softmax(dim=-1)
if name == "gpt2_gelu":
return Gpt2GELU()
return nn.ReLU()
def _cat_dir(h: torch.Tensor) -> torch.Tensor:
"""Concat forward and backword state vectors.
The shape of the hidden is `[#layers * #dirs, B, H]`. The docs say you can
separate directions with `h.view(#l, #dirs, B, H)` with the forward dir being
index 0 and backwards dir being 1.
This means that before separating with the view the forward dir are the even
indices in the first dim while the backwards dirs are the odd ones. Here we select
the even and odd values and concatenate them
:param h: The hidden shape as it comes back from PyTorch modules
"""
return torch.cat([h[0 : h.size(0) : 2], h[1 : h.size(0) : 2]], dim=-1)
def concat_state_dirs(state):
"""Convert the bidirectional out of an RNN so the forward and backward values are a single vector."""
if isinstance(state, tuple):
return tuple(_cat_dir(h) for h in state)
return _cat_dir(state)
def bth2bht(t: torch.Tensor) -> torch.Tensor:
"""Transpose the 2nd and 3rd dim of a tensor"""
return t.transpose(1, 2).contiguous()
def tbh2bht(t: torch.Tensor) -> torch.Tensor:
"""Permute the dimensions, first goes to third, second goes to first, last moves to second"""
return t.permute(1, 2, 0).contiguous()
def tbh2bth(t: torch.Tensor) -> torch.Tensor:
"""Transpose the first 2 dims"""
return t.transpose(0, 1).contiguous()
def bth2tbh(t: torch.Tensor) -> torch.Tensor:
"""Transpose the first 2 dims"""
return t.transpose(0, 1).contiguous()
def pytorch_linear(in_sz: int, out_sz: int, unif: float = 0, initializer: str = None, bias: bool = True):
"""Utility function that wraps a linear (AKA dense) layer creation, with options for weight init and bias"""
l = nn.Linear(in_sz, out_sz, bias=bias)
if unif > 0:
l.weight.data.uniform_(-unif, unif)
elif initializer == "ortho":
nn.init.orthogonal(l.weight)
elif initializer == "he" or initializer == "kaiming":
nn.init.kaiming_uniform(l.weight)
else:
nn.init.xavier_uniform_(l.weight)
if bias:
l.bias.data.zero_()
return l
def rnn_cell(insz: int, hsz: int, rnntype: str, nlayers: int, dropout: float):
"""This is a wrapper function around a stacked RNN cell
:param insz: The input dimensions
:param hsz: The hidden dimensions
:param rnntype: An RNN type `gru` or `lstm`
:param nlayers: The number of layers to stack
:param dropout: The amount of dropout
:return:
"""
if rnntype == "gru":
rnn = StackedGRUCell(nlayers, insz, hsz, dropout)
else:
rnn = StackedLSTMCell(nlayers, insz, hsz, dropout)
return rnn
def pytorch_lstm(
insz: int,
hsz: int,
rnntype: str,
nlayers: int,
dropout: float,
unif: float = 0,
batch_first: bool = False,
initializer: str = None,
) -> torch.nn.LSTM:
"""Wrapper around `torch.nn.LSTM`, mainly for weight initialization options
:param insz: The input dimension
:param hsz: The number of hidden units
:param rnntype: A string description of the type of LSTM: `bi?lstm` or `lstm`
:param nlayers: The number of layers
:param dropout: How much dropout to apply
:param unif: if uniform initialization, what range?
:param batch_first: Should we do the RNN batch first or time first
:param initializer: An optional string representing a style of initialization `ortho`, `he`/`kaiming`, `xavier`/`glorot`
:return: An LSTM
"""
if nlayers == 1:
dropout = 0.0
ndir = 2 if rnntype.startswith("b") else 1
layer_hsz = hsz // ndir
rnn = torch.nn.LSTM(
insz, layer_hsz, nlayers, dropout=dropout, bidirectional=True if ndir > 1 else False, batch_first=batch_first
) # , bias=False)
if initializer == "ortho":
nn.init.orthogonal(rnn.weight_hh_l0)
nn.init.orthogonal(rnn.weight_ih_l0)
elif initializer == "he" or initializer == "kaiming":
nn.init.kaiming_uniform(rnn.weight_hh_l0)
nn.init.kaiming_uniform(rnn.weight_ih_l0)
elif unif > 0:
for weight in rnn.parameters():
weight.data.uniform_(-unif, unif)
else:
nn.init.xavier_uniform_(rnn.weight_hh_l0)
nn.init.xavier_uniform_(rnn.weight_ih_l0)
return rnn
# TODO: this module only exists in pytorch. Do we eliminate it or put it in both?
# TODO: this module only exists in pytorch. Do we eliminate it or put it in both?
# TODO: Add this to TF or remove
# TODO: normalize across backends or remove
def dot_product_attention_weights(query_t: torch.Tensor,
keys_bth: torch.Tensor,
keys_mask: torch.Tensor) -> torch.Tensor:
a = keys_bth @ query_t.unsqueeze(2)
a = a.squeeze(2).masked_fill(keys_mask == MASK_FALSE, -1e9)
a = F.softmax(a, dim=-1)
return a
def dot_product_attention_weights_lengths(query_t: torch.Tensor,
keys_bth: torch.Tensor,
keys_lengths: torch.Tensor) -> torch.Tensor:
mask = sequence_mask(keys_lengths, keys_bth.shape[1]).to(keys_bth.device)
return dot_product_attention_weights(query_t, keys_bth, mask)
def transition_mask(vocab, span_type, s_idx, e_idx, pad_idx=None):
"""Create a mask to enforce span sequence transition constraints.
Returns a Tensor with valid transitions as a 0 and invalid as a 1 for easy use with `masked_fill`
"""
np_mask = transition_mask_np(vocab, span_type, s_idx, e_idx, pad_idx=pad_idx)
return torch.from_numpy(np_mask) == 0
class ViterbiBatchSize1(nn.Module):
class Viterbi(nn.Module):
def forward(
self, unary: torch.Tensor, trans: torch.Tensor, lengths: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Do Viterbi decode on a batch.
:param unary: torch.FloatTensor: [T, B, N]
:param trans: torch.FloatTensor: [1, N, N]
:param norm: Callable: This function should take the initial and a dim to
normalize along.
:return: torch.LongTensor: [T, B] the padded paths
:return: torch.FloatTensor: [B] the path scores
"""
seq_len, batch_size, tag_size = unary.size()
min_length = torch.min(lengths)
backpointers = []
# Alphas: [B, 1, N]
alphas = torch.full((batch_size, 1, tag_size), -1e4, device=unary.device)
alphas[:, 0, self.start_idx] = 0
# alphas = self.norm(alphas)
for i, unary_t in enumerate(unary):
next_tag_var = alphas + trans
viterbi, best_tag_ids = torch.max(next_tag_var, 2)
backpointers.append(best_tag_ids)
new_alphas = viterbi + unary_t
new_alphas.unsqueeze_(1)
# This part generates a warning
if i >= min_length:
mask = (i < lengths).view(-1, 1, 1)
alphas = alphas.masked_fill(mask, 0) + new_alphas.masked_fill(mask == MASK_FALSE, 0)
else:
alphas = new_alphas
# Add end tag
terminal_var = alphas.squeeze(1) + trans[:, self.end_idx, :]
path_score, best_tag_id = torch.max(terminal_var, 1)
# Flip lengths
rev_len = seq_len - lengths - 1
best_path = [best_tag_id]
for i in range(len(backpointers)):
t = len(backpointers) - i - 1
backpointer_t = backpointers[t]
# Get new best tag candidate
new_best_tag_id = backpointer_t.gather(1, best_tag_id.unsqueeze(1)).squeeze(1)
# We are going backwards now, if flipped length was passed
# these you aren't in your real results yet
mask = i > rev_len
best_tag_id = best_tag_id.masked_fill(mask, 0) + new_best_tag_id.masked_fill(mask == MASK_FALSE, 0)
best_path.append(best_tag_id)
_ = best_path.pop()
best_path.reverse()
best_path = torch.stack(best_path)
# Mask out the extra tags (This might be pointless given thathatt anything that
# will use this as a dense tensor downstream will mask it itself?)
seq_mask = sequence_mask(lengths, seq_len).to(best_path.device).transpose(0, 1)
best_path = best_path.masked_fill(seq_mask == MASK_FALSE, 0)
return best_path, path_score
def ident(x):
return x
def pytorch_embedding(weights: torch.Tensor, finetune: bool = True) -> nn.Embedding:
"""Creation function for making an nn.Embedding with the given weights
:param weights: The weights to use
:param finetune: Should we fine-tune the embeddings or freeze them
"""
lut = nn.Embedding(weights.shape[0], weights.shape[1], padding_idx=Offsets.PAD)
del lut.weight
lut.weight = nn.Parameter(torch.FloatTensor(weights), requires_grad=finetune)
return lut
def subsequent_mask(size: int):
"""
Creates a lower triangular mask to mask future
:param size: Temporal length
:return: A tensor of type `uint8` that is 1s along diagonals and below, zero o.w
"""
attn_shape = (1, 1, size, size)
sub_mask = np.tril(np.ones(attn_shape)).astype("uint8")
return torch.from_numpy(sub_mask)
def unfold_tensor(tensor, dim, window_sz):
"""Unfold a tensor by applying a sliding window on a certain dimension with step 1 and padding of 0's. The window
dimension is added as the last dimension
:param tensor: the tensor to be unfolded, with shape [d_1, d_2, ..., T, ..., d_n]
:param dim: the dimension along which unfolding is applied
:param window_sz: sliding window size, need to be an odd number
:return: the unfolded tensor with shape [d_1, d_2, ..., T, ..., d_n, window_sz]
"""
half_window = (window_sz - 1) // 2
if dim < 0:
dim = len(tensor.shape) + dim
# torch.nn.functional.pad apply backwardly from the last dimension
padding = [0, 0] * (len(tensor.shape) - dim - 1) + [half_window, half_window]
return F.pad(tensor, padding).unfold(dim, window_sz, 1)
def update_lengths(lengths, eoses, idx):
"""Update the length of a generated tensor based on the first EOS found.
This is useful for a decoding situation where tokens after an EOS
can be something other than EOS. This also makes sure that a second
generated EOS doesn't affect the lengths.
:param lengths: `torch.LongTensor`: The lengths where zero means an
unfinished sequence.
:param eoses: `torch.ByteTensor`: A mask that has 1 for sequences that
generated an EOS.
:param idx: `int`: What value to fill the finished lengths with (normally
the current decoding timestep).
:returns: `torch.Tensor`: The updated lengths tensor (same shape and type).
"""
# If a length is 0 it has never had a length set so it is eligible to have
# this EOS be the length.
updatable_lengths = lengths == 0
# If this length can be updated AND this token is an eos
lengths_mask = updatable_lengths & eoses
return lengths.masked_fill(lengths_mask, idx)
def gnmt_length_penalty(lengths, alpha=0.8):
"""Calculate a length penalty from https://arxiv.org/pdf/1609.08144.pdf
The paper states the penalty as (5 + |Y|)^a / (5 + 1)^a. This is implemented
as ((5 + |Y|) / 6)^a for a (very) tiny performance boost
:param lengths: `torch.LongTensor`: [B, K] The lengths of the beams.
:param alpha: `float`: A hyperparameter. See Table 2 for a search on this
parameter.
:returns:
`torch.FloatTensor`: [B, K, 1] The penalties.
"""
lengths = lengths.to(torch.float)
penalty = torch.pow(((5 + lengths) / 6), alpha)
return penalty.unsqueeze(-1)
def no_length_penalty(lengths):
"""A dummy function that returns a no penalty (1)."""
return torch.ones_like(lengths).to(torch.float).unsqueeze(-1)
def repeat_batch(t, K, dim=0):
"""Repeat a tensor while keeping the concept of a batch.
:param t: `torch.Tensor`: The tensor to repeat.
:param K: `int`: The number of times to repeat the tensor.
:param dim: `int`: The dimension to repeat in. This should be the
batch dimension.
:returns: `torch.Tensor`: The repeated tensor. The new shape will be
batch size * K at dim, the rest of the shapes will be the same.
Example::
>>> a = torch.arange(10).view(2, -1)
>>> a
tensor([[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9]])
>>> a.repeat(2, 1)
tensor([[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9],
[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9]])
>>> repeat_batch(a, 2)
tensor([[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9],
[5, 6, 7, 8, 9]])
"""
shape = t.shape
tiling = [1] * (len(shape) + 1)
tiling[dim + 1] = K
tiled = t.unsqueeze(dim + 1).repeat(tiling)
old_bsz = shape[dim]
new_bsz = old_bsz * K
new_shape = list(shape[:dim]) + [new_bsz] + list(shape[dim + 1 :])
return tiled.view(new_shape)
def pytorch_conv1d(in_channels, out_channels, fsz, unif=0, padding=0, initializer=None, stride=1, bias=True, groups=1):
c = nn.Conv1d(in_channels, out_channels, fsz, padding=padding, stride=stride, bias=bias, groups=groups)
if unif > 0:
c.weight.data.uniform_(-unif, unif)
elif initializer == "ortho":
nn.init.orthogonal_(c.weight)
if bias:
nn.init.constant_(c.bias, 0)
elif initializer == "he" or initializer == "kaiming":
nn.init.kaiming_uniform_(c.weight)
if bias:
nn.init.constant_(c.bias, 0)
elif initializer == "normal":
nn.init.normal(mean=0, std=unif)
if bias:
nn.init.constant_(c.bias, 0)
else:
nn.init.xavier_uniform_(c.weight)
if bias:
nn.init.constant_(c.bias, 0)
return c
def tie_weight(to_layer, from_layer):
"""Assigns a weight object to the layer weights.
This method exists to duplicate baseline functionality across packages.
:param to_layer: the pytorch layer to assign weights to
:param from_layer: pytorch layer to retrieve weights from
"""
to_layer.weight = from_layer.weight
| 41.613524 | 202 | 0.62778 |
e81e5aa3bb914c6f0bd68fa448159f5b4cf4de2f | 348 | py | Python | setup.py | flother/pdf-search | fa4c519a673bf5a5d25e1ab44e971690ab3cf781 | [
"MIT"
] | 5 | 2018-06-18T10:31:20.000Z | 2020-06-10T01:05:02.000Z | setup.py | flother/pdf-search | fa4c519a673bf5a5d25e1ab44e971690ab3cf781 | [
"MIT"
] | null | null | null | setup.py | flother/pdf-search | fa4c519a673bf5a5d25e1ab44e971690ab3cf781 | [
"MIT"
] | null | null | null | from setuptools import setup
setup(
name='espdf',
version='0.1.0-dev',
url='https://github.com/flother/pdf-search',
py_modules=(
'espdf',
),
install_requires=(
'certifi',
'elasticsearch-dsl',
),
entry_points={
'console_scripts': (
'espdf=espdf:cli',
),
},
)
| 16.571429 | 48 | 0.514368 |
e82007b9763c2cfe490672fce1bfbb724173644a | 2,776 | py | Python | conanfile.py | sintef-ocean/conan-clapack | 9c472130eaadee71253ced9b5fe25ee1b868bcb3 | [
"MIT"
] | null | null | null | conanfile.py | sintef-ocean/conan-clapack | 9c472130eaadee71253ced9b5fe25ee1b868bcb3 | [
"MIT"
] | null | null | null | conanfile.py | sintef-ocean/conan-clapack | 9c472130eaadee71253ced9b5fe25ee1b868bcb3 | [
"MIT"
] | null | null | null | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from conans import ConanFile, CMake, tools
import shutil
| 35.139241 | 84 | 0.610231 |
e820480eb7c2b0f9e9f4f3e9695e4d3110de174e | 20,078 | py | Python | yacos/algorithm/metaheuristics.py | ComputerSystemsLaboratory/YaCoS | abd5d3c6e227e5c7a563493f7855ebf58ba3de05 | [
"Apache-2.0"
] | 8 | 2022-02-03T16:41:01.000Z | 2022-02-09T11:29:20.000Z | yacos/algorithm/metaheuristics.py | ComputerSystemsLaboratory/YaCoS | abd5d3c6e227e5c7a563493f7855ebf58ba3de05 | [
"Apache-2.0"
] | null | null | null | yacos/algorithm/metaheuristics.py | ComputerSystemsLaboratory/YaCoS | abd5d3c6e227e5c7a563493f7855ebf58ba3de05 | [
"Apache-2.0"
] | null | null | null | """
Copyright 2021 Anderson Faustino da Silva.
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 os
from dataclasses import dataclass
import pygmo as pg
from yacos.essential import Sequence
from yacos.essential import IO
from yacos.essential import Engine
class SGA(Pygmo):
"""Simple Genetic Algorithm."""
__version__ = '1.0.0'
__flags = None
def __init__(self,
generations,
population,
cr,
m,
param_m,
param_s,
crossover,
mutation,
selection,
seed,
dimension,
passes_filename,
goals,
compiler,
benchmarks_directory,
working_set,
times,
tool,
verify_output):
"""Initialize a SGA object.
Parameters
----------
generations : int
population : int
cr : float
Crossover probability
m : float
Mutation probability
param_m : float
Distribution index (polynomial mutation),
gaussian width (gaussian mutation) or
inactive (uniform mutation)
param_s : float
The number of best individuals to use in truncated
selection or the size of the tournament in
tournament selection.
crossover : str
exponential, binomial or single
mutation : str
gaussian, polynomial or uniform
selection : str
tournament or truncated
seed : int
dimension : int
The length of a sequence.
passes_filename : str
The file that describes the passes to use.
goals : dict
compiler : str
benchmarks_directory : str
working_set : int
The dataset to execute the benchmark.
times : int
Execution times
tool : str
Execution tool
verify_output: bool
The goal is valid only if the execution status is OK.
"""
self.__flags = self.Flags(generations,
cr,
m,
param_m,
param_s,
crossover,
mutation,
selection,
seed)
super().__init__(dimension,
population,
passes_filename,
goals,
compiler,
benchmarks_directory,
working_set,
times,
tool,
verify_output)
def run(self, benchmark):
"""Execute the algorithm.
Parameter
--------
benchmark: str
"""
if self.__flags.seed is None:
algorithm = pg.sga(gen=self.__flags.generations,
cr=self.__flags.cr,
m=self.__flags.m,
param_m=self.__flags.param_m,
param_s=self.__flags.param_s,
crossover=self.__flags.crossover,
mutation=self.__flags.mutation,
selection=self.__flags.selection)
else:
algorithm = pg.sga(gen=self.__flags.generations,
cr=self.__flags.cr,
m=self.__flags.m,
param_m=self.__flags.param_m,
param_s=self.__flags.param_s,
crossover=self.__flags.crossover,
mutation=self.__flags.mutation,
selection=self.__flags.selection,
seed=self.__flags.seed)
# Execute
super().exec(algorithm, benchmark)
class PSO(Pygmo):
"""Particle Swarm Optimization."""
__version__ = '1.0.0'
__flags = None
def __init__(self,
generations,
population,
omega,
eta1,
eta2,
max_vel,
variant,
neighb_type,
neighb_param,
memory,
seed,
dimension,
passes_filename,
goals,
compiler,
benchmarks_directory,
working_set,
times,
tool,
verify_output):
"""Initialize a PSO object.
Parameters
----------
generations : int
population : int
omega : float
Inertia weight (or constriction factor)
eta1 : float
Social component
eta2 : float
Cognitive component
max_vel : float
Maximum allowed particle velocities
(normalized with respect to the bounds width)
variant : int
Algorithmic variant
neighb_type : int
Swarm topology (defining each particles neighbours)
neighb_param : int
Topology parameter (defines how many neighbours to consider)
memory : bool
When true the velocities are not reset between successive
calls to the evolve method
seed : int
Seed used by the internal random number generator.
"""
self.__flags = self.Flags(generations,
omega,
eta1,
eta2,
max_vel,
variant,
neighb_type,
neighb_param,
memory,
seed)
super().__init__(dimension,
population,
passes_filename,
goals,
compiler,
benchmarks_directory,
working_set,
times,
tool,
verify_output)
def run(self, benchmark):
"""Execute the algorithm.
Parameter
--------
benchmark : str
"""
if self.__flags.seed:
algorithm = pg.pso(self.__flags.generations,
self.__flags.omega,
self.__flags.eta1,
self.__flags.eta2,
self.__flags.max_vel,
self.__flags.variant,
self.__flags.neighb_type,
self.__flags.neighb_param,
self.__flags.memory,
self.__flags.seed)
else:
algorithm = pg.pso(self.__flags.generations,
self.__flags.omega,
self.__flags.eta1,
self.__flags.eta2,
self.__flags.max_vel,
self.__flags.variant,
self.__flags.neighb_type,
self.__flags.neighb_param,
self.__flags.memory)
# Execute
super().exec(algorithm, benchmark)
| 28.60114 | 76 | 0.456121 |
e82053dc2380e925870bdf15395c02b067f76451 | 8,924 | py | Python | lab_03/main.py | solnishko-pvs/Modeling_BMSTU | 0ecb82aea23b6726912f72d3230097d7b679eaf9 | [
"MIT"
] | null | null | null | lab_03/main.py | solnishko-pvs/Modeling_BMSTU | 0ecb82aea23b6726912f72d3230097d7b679eaf9 | [
"MIT"
] | null | null | null | lab_03/main.py | solnishko-pvs/Modeling_BMSTU | 0ecb82aea23b6726912f72d3230097d7b679eaf9 | [
"MIT"
] | null | null | null | import tkinter as tk
from scipy.stats import chi2, chisquare
COLOR = '#dddddd'
COLUMNS_COLOR = '#ffffff'
MAX_SIZE = 10
WIDGET_WIDTH = 25
generator = LinearCongruent()
root = tk.Tk()
root['bg'] = COLOR
root.geometry('540x390')
first_block = Block(root)
first_block.make_view()
root.mainloop()
| 40.748858 | 140 | 0.632788 |
e820d40fdfc773bb5cbef40f764f0c231d8e4804 | 107 | py | Python | VacationPy/api_keys.py | tylermneher/python-api-challenge | 28c88b4fff13c8b752096b0776a3d4645ad5fddb | [
"ADSL"
] | null | null | null | VacationPy/api_keys.py | tylermneher/python-api-challenge | 28c88b4fff13c8b752096b0776a3d4645ad5fddb | [
"ADSL"
] | null | null | null | VacationPy/api_keys.py | tylermneher/python-api-challenge | 28c88b4fff13c8b752096b0776a3d4645ad5fddb | [
"ADSL"
] | null | null | null | # OpenWeatherMap API Key
weather_api_key = "MyOpenWeatherMapAPIKey"
# Google API Key
g_key = "MyGoogleKey" | 21.4 | 42 | 0.794393 |
e8213e750200485b12b34726d6bd13c4476d653d | 843 | py | Python | aries_cloudagent/protocols/actionmenu/v1_0/messages/menu_request.py | panickervinod/aries-cloudagent-python | bb4627fe62ee42ffeeb435cf3d8bfbd66c10d02f | [
"Apache-2.0"
] | null | null | null | aries_cloudagent/protocols/actionmenu/v1_0/messages/menu_request.py | panickervinod/aries-cloudagent-python | bb4627fe62ee42ffeeb435cf3d8bfbd66c10d02f | [
"Apache-2.0"
] | 1 | 2020-06-16T20:20:55.000Z | 2020-06-16T20:20:55.000Z | aries_cloudagent/protocols/actionmenu/v1_0/messages/menu_request.py | panickervinod/aries-cloudagent-python | bb4627fe62ee42ffeeb435cf3d8bfbd66c10d02f | [
"Apache-2.0"
] | null | null | null | """Represents a request for an action menu."""
from .....messaging.agent_message import AgentMessage, AgentMessageSchema
from ..message_types import MENU_REQUEST, PROTOCOL_PACKAGE
HANDLER_CLASS = f"{PROTOCOL_PACKAGE}.handlers.menu_request_handler.MenuRequestHandler"
| 26.34375 | 86 | 0.702254 |
e8218935d90ebb72cee991e2f05f077a46339325 | 846 | py | Python | porthole/management/commands/brocade.py | jsayles/Porthole | a4176aad632e319eba88dfbe40cb96a4c437725d | [
"Apache-2.0"
] | null | null | null | porthole/management/commands/brocade.py | jsayles/Porthole | a4176aad632e319eba88dfbe40cb96a4c437725d | [
"Apache-2.0"
] | null | null | null | porthole/management/commands/brocade.py | jsayles/Porthole | a4176aad632e319eba88dfbe40cb96a4c437725d | [
"Apache-2.0"
] | null | null | null | from django.core.management.base import BaseCommand, CommandError
from django.conf import settings
from porthole import models, brocade
| 29.172414 | 106 | 0.634752 |
e8221f351de78d1c4212826efb9039a3b827c105 | 410 | py | Python | joulia/unit_conversions_test.py | willjschmitt/joulia-webserver | 712decb749c2d1bda71af49ecab245378bf30078 | [
"FTL"
] | null | null | null | joulia/unit_conversions_test.py | willjschmitt/joulia-webserver | 712decb749c2d1bda71af49ecab245378bf30078 | [
"FTL"
] | 95 | 2016-08-04T01:59:37.000Z | 2021-06-10T18:41:46.000Z | joulia/unit_conversions_test.py | willjschmitt/joulia-webserver | 712decb749c2d1bda71af49ecab245378bf30078 | [
"FTL"
] | null | null | null | """Tests joulia.unit_conversions.
"""
from django.test import TestCase
from joulia import unit_conversions
| 24.117647 | 77 | 0.77561 |
e82434209f26613cf2f4d4081789832c2affad67 | 1,127 | py | Python | Django/blog/tests.py | zarif007/Blog-site | e20e3f73fedbd7acb2f3d22398c36f3dcd4f88b4 | [
"MIT"
] | 1 | 2021-03-15T22:28:26.000Z | 2021-03-15T22:28:26.000Z | Django/blog/tests.py | zarif007/Blog-site | e20e3f73fedbd7acb2f3d22398c36f3dcd4f88b4 | [
"MIT"
] | null | null | null | Django/blog/tests.py | zarif007/Blog-site | e20e3f73fedbd7acb2f3d22398c36f3dcd4f88b4 | [
"MIT"
] | null | null | null | from django.contrib.auth.models import User
from django.test import TestCase
from blog.models import Category, Post
| 36.354839 | 104 | 0.616681 |
e824b59433bad7c9a547eb3af6d1a2e1fb6af9c5 | 2,504 | py | Python | scripts/train_presets/beads.py | kreshuklab/hylfm-net | 9f1013640e40e998674b65176023367b1e978782 | [
"MIT"
] | 8 | 2020-11-13T05:46:59.000Z | 2022-01-30T06:12:04.000Z | scripts/train_presets/beads.py | kreshuklab/hylfm-net | 9f1013640e40e998674b65176023367b1e978782 | [
"MIT"
] | 1 | 2020-11-13T08:29:23.000Z | 2022-02-10T16:45:19.000Z | scripts/train_presets/beads.py | kreshuklab/hylfm-net | 9f1013640e40e998674b65176023367b1e978782 | [
"MIT"
] | 2 | 2020-10-30T11:02:42.000Z | 2021-01-12T06:51:33.000Z | from pathlib import Path
from hylfm.hylfm_types import (
CriterionChoice,
DatasetChoice,
LRSchedThresMode,
LRSchedulerChoice,
MetricChoice,
OptimizerChoice,
PeriodUnit,
)
from hylfm.model import HyLFM_Net
from hylfm.train import train
if __name__ == "__main__":
train(
dataset=DatasetChoice.beads_highc_b,
batch_multiplier=2,
batch_size=1,
crit_apply_weight_above_threshold=False,
crit_beta=1.0,
crit_decay_weight_by=0.8,
crit_decay_weight_every_unit=PeriodUnit.epoch,
crit_decay_weight_every_value=1,
crit_decay_weight_limit=1.0,
crit_ms_ssim_weight=0.01,
crit_threshold=0.5,
crit_weight=0.001,
criterion=CriterionChoice.WeightedSmoothL1,
data_range=1.0,
eval_batch_size=1,
interpolation_order=2,
lr_sched_factor=0.5,
lr_sched_patience=10,
lr_sched_thres=0.0001,
lr_sched_thres_mode=LRSchedThresMode.abs,
lr_scheduler=LRSchedulerChoice.ReduceLROnPlateau,
max_epochs=10,
model_weights=None, # Path()
opt_lr=3e-4,
opt_momentum=0.0,
opt_weight_decay=0.0,
optimizer=OptimizerChoice.Adam,
patience=5,
score_metric=MetricChoice.MS_SSIM,
seed=None,
validate_every_unit=PeriodUnit.epoch,
validate_every_value=1,
win_sigma=1.5,
win_size=11,
# model
nnum=19,
z_out=51,
kernel2d=3,
c00_2d=976,
c01_2d=976,
c02_2d=0,
c03_2d=0,
c04_2d=0,
up0_2d=488,
c10_2d=488,
c11_2d=0,
c12_2d=0,
c13_2d=0,
c14_2d=0,
up1_2d=244,
c20_2d=244,
c21_2d=0,
c22_2d=0,
c23_2d=0,
c24_2d=0,
up2_2d=0,
c30_2d=0,
c31_2d=0,
c32_2d=0,
c33_2d=0,
c34_2d=0,
last_kernel2d=1,
cin_3d=7,
kernel3d=3,
c00_3d=7,
c01_3d=0,
c02_3d=0,
c03_3d=0,
c04_3d=0,
up0_3d=7,
c10_3d=7,
c11_3d=7,
c12_3d=0,
c13_3d=0,
c14_3d=0,
up1_3d=0,
c20_3d=0,
c21_3d=0,
c22_3d=0,
c23_3d=0,
c24_3d=0,
up2_3d=0,
c30_3d=0,
c31_3d=0,
c32_3d=0,
c33_3d=0,
c34_3d=0,
init_fn=HyLFM_Net.InitName.xavier_uniform_,
final_activation=None,
)
| 23.185185 | 57 | 0.5623 |
e82619fe802bc3c2cff480de32dc1445ec9e1c68 | 17,055 | py | Python | TrainingPreprocess/filtered_to_dataset.py | CsekM8/LVH-THESIS | b0dc60daaf0825ad43951e6895289da4e3ed911b | [
"MIT"
] | null | null | null | TrainingPreprocess/filtered_to_dataset.py | CsekM8/LVH-THESIS | b0dc60daaf0825ad43951e6895289da4e3ed911b | [
"MIT"
] | null | null | null | TrainingPreprocess/filtered_to_dataset.py | CsekM8/LVH-THESIS | b0dc60daaf0825ad43951e6895289da4e3ed911b | [
"MIT"
] | null | null | null | import os
import pickle
from PIL import Image
if __name__ == "__main__":
sourceFolder = 'D:/BME/7felev/Szakdolgozat/whole_dataset/filtered_data'
imageFolderArranger = PatientToImageFolder(sourceFolder)
imageFolderArranger.createImageFolderDatasets()
| 55.373377 | 121 | 0.595075 |
e82731c89300ff664f879da568d6bdf2c014d59b | 2,140 | py | Python | scripts/pipeline/a06a_submission.py | Iolaum/Phi1337 | c73b01cb85c0187ed5c23c672d4f3d05a6934a9f | [
"Apache-2.0"
] | null | null | null | scripts/pipeline/a06a_submission.py | Iolaum/Phi1337 | c73b01cb85c0187ed5c23c672d4f3d05a6934a9f | [
"Apache-2.0"
] | null | null | null | scripts/pipeline/a06a_submission.py | Iolaum/Phi1337 | c73b01cb85c0187ed5c23c672d4f3d05a6934a9f | [
"Apache-2.0"
] | null | null | null | import pandas as pd
import numpy as np
import pickle
from sklearn.cross_validation import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from math import sqrt
from sklearn.svm import SVR
from sklearn.svm import LinearSVR
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestRegressor
if __name__ == "__main__":
# Change between:
# svr
# linear
# rfr
regression_type = 'svr'
standardize_df = True
regression(regression_type, standardize_df, debug=False) | 21.616162 | 95 | 0.706075 |
e82976f9a06173b4cb3e6c53bd388cb5ba53c170 | 602 | py | Python | Data Gathering/PythonPlottingScript.py | Carter-eng/SeniorDesign | 5305531d251ea749f909cc09eb1ccfe21714cebd | [
"MIT"
] | null | null | null | Data Gathering/PythonPlottingScript.py | Carter-eng/SeniorDesign | 5305531d251ea749f909cc09eb1ccfe21714cebd | [
"MIT"
] | null | null | null | Data Gathering/PythonPlottingScript.py | Carter-eng/SeniorDesign | 5305531d251ea749f909cc09eb1ccfe21714cebd | [
"MIT"
] | null | null | null | import numpy as np
import serial
import time
import matplotlib.pyplot as plt
if __name__ == '__main__':
sensorReadings = getData()
plotter(sensorReadings)
| 24.08 | 46 | 0.642857 |
e82a07131fefc0b08096ef65e989b7c1655ac2a3 | 732 | py | Python | Examples/Rich_Message_Example.py | robinvoogt/text-sdk-python | 1acd624991f396cc673dad22cfa3272b4b3fb53b | [
"MIT"
] | 2 | 2021-05-29T07:22:55.000Z | 2022-01-15T18:23:39.000Z | Examples/Rich_Message_Example.py | robinvoogt/text-sdk-python | 1acd624991f396cc673dad22cfa3272b4b3fb53b | [
"MIT"
] | 2 | 2021-08-04T13:13:50.000Z | 2021-12-31T12:54:00.000Z | Examples/Rich_Message_Example.py | robinvoogt/text-sdk-python | 1acd624991f396cc673dad22cfa3272b4b3fb53b | [
"MIT"
] | 3 | 2020-12-23T15:24:40.000Z | 2021-04-09T11:43:03.000Z | from CMText.TextClient import TextClient
# Message to be send
message = 'Examples message to be send'
# Media to be send
media = {
"mediaName": "conversational-commerce",
"mediaUri": "https://www.cm.com/cdn/cm/cm.png",
"mimeType": "image/png"
}
# AllowedChannels in this case Whatsapp
allowedChannels = ['Whatsapp']
# Recipients
to = ['003156789000', '002134567890']
# Instantiate client with your own api-key
client = TextClient(apikey=UNIQUE_API_KEY)
# Add a Rich message to the queue
client.AddRichMessage(message=message, from_='pythonSDK', to=to, allowedChannels=allowedChannels, media=media)
# Send the messages
response = client.send()
# Print response
print(response.text) | 25.241379 | 110 | 0.709016 |
e82b3f9736e429583940ccca0c1055b7a0f8dda1 | 2,630 | py | Python | client/audio.py | Dmitry450/asynciogame | 47a2a118155805db37f2c3bd916bd6c68c504cff | [
"MIT"
] | null | null | null | client/audio.py | Dmitry450/asynciogame | 47a2a118155805db37f2c3bd916bd6c68c504cff | [
"MIT"
] | null | null | null | client/audio.py | Dmitry450/asynciogame | 47a2a118155805db37f2c3bd916bd6c68c504cff | [
"MIT"
] | 1 | 2022-03-10T16:05:09.000Z | 2022-03-10T16:05:09.000Z | import pygame
from pygame.math import Vector2
| 30.229885 | 91 | 0.53308 |
e82d0945219e52cb12d826deeb2607fdf4a302a4 | 1,353 | py | Python | bot/ganjoor/category_choose.py | MmeK/ganjoor-telegram-bot | 3992bdd860ea3626dccd79b0c1993a3662e92aa5 | [
"MIT"
] | null | null | null | bot/ganjoor/category_choose.py | MmeK/ganjoor-telegram-bot | 3992bdd860ea3626dccd79b0c1993a3662e92aa5 | [
"MIT"
] | 3 | 2021-11-17T08:03:59.000Z | 2021-11-17T14:00:23.000Z | bot/ganjoor/category_choose.py | MmeK/ganjoor-telegram-bot | 3992bdd860ea3626dccd79b0c1993a3662e92aa5 | [
"MIT"
] | null | null | null |
# Copyright 2021 Mohammad Kazemi <kazemi.me.222@gmail.com>.
# SPDX-License-Identifier: MIT
# Telegram API framework core imports
from collections import namedtuple
from functools import partial
from ganjoor.ganjoor import Ganjoor
from telegram.ext import Dispatcher, CallbackContext
from telegram import Update
# Helper methods import
from utils.logger import get_logger
from utils.telegram.keyboards import category_keyboard
# Telegram API framework handlers imports
from telegram.ext import CallbackQueryHandler
# Init logger
logger = get_logger(__name__)
CallbackData = namedtuple('CallbackData', "menu_name doto")
def init(dispatcher: Dispatcher, ganjoor: Ganjoor):
"""Provide handlers initialization."""
dispatcher.add_handler(CallbackQueryHandler(
partial(category_id, ganjoor=ganjoor), pattern=r'^category_*'))
def category_id(update: Update, context: CallbackContext, ganjoor: Ganjoor) -> int:
"""Process a /start command."""
query = update.callback_query
message_id = '_'.join(query.data.split('_')[2:])
cat_id = query.data.split('_')[1]
cat = ganjoor.find_category_by_id(cat_id, with_poems=True)
# query.answer()
query.answer()
context.bot.edit_message_reply_markup(
inline_message_id=message_id, reply_markup=category_keyboard(cat, message_id))
# query.edit_reply_markup()
| 33 | 86 | 0.764967 |
e82e3c8a5f7b2a855ae8103d8f7b3d7858fce12c | 457 | py | Python | python/util/md_utils.py | walterfan/snippets | 62f87720c411093fcff888f25b338afd1d99a6f9 | [
"Apache-2.0"
] | 1 | 2021-06-18T09:31:59.000Z | 2021-06-18T09:31:59.000Z | python/util/md_utils.py | walterfan/snippets | 62f87720c411093fcff888f25b338afd1d99a6f9 | [
"Apache-2.0"
] | 10 | 2020-12-12T08:12:06.000Z | 2022-03-02T06:54:10.000Z | python/util/md_utils.py | walterfan/snippets | 62f87720c411093fcff888f25b338afd1d99a6f9 | [
"Apache-2.0"
] | null | null | null | import os
import sys
import struct
import re
import logging
logging.basicConfig(stream=sys.stderr, level=logging.DEBUG)
logger = logging.getLogger(__name__)
| 19.869565 | 59 | 0.66302 |
e82febc9f75bb1499d62773b17eb275855bf705c | 374 | py | Python | board/main.py | Josverl/micropython-stubber | 3dc57ea9e957bd9f36fce6abfe051a2fa7ace522 | [
"MIT"
] | 96 | 2019-04-10T15:03:27.000Z | 2022-03-29T09:07:14.000Z | board/main.py | Josverl/micropython-stubber | 3dc57ea9e957bd9f36fce6abfe051a2fa7ace522 | [
"MIT"
] | 154 | 2019-06-02T21:30:56.000Z | 2022-03-30T12:19:00.000Z | board/main.py | Josverl/micropython-stubber | 3dc57ea9e957bd9f36fce6abfe051a2fa7ace522 | [
"MIT"
] | 7 | 2019-06-14T19:54:22.000Z | 2022-03-29T05:02:32.000Z | import uos as os
import time
try:
# only run import if no stubs yet
os.listdir("stubs")
print("stub folder was found, stubbing is not automatically started")
except OSError:
countdown()
| 17.809524 | 73 | 0.636364 |
e83023e586a214491a4865df8d7fd4b1c0c4034a | 5,110 | py | Python | third_party/blink/tools/blinkpy/web_tests/breakpad/dump_reader_multipart_unittest.py | zipated/src | 2b8388091c71e442910a21ada3d97ae8bc1845d3 | [
"BSD-3-Clause"
] | 2,151 | 2020-04-18T07:31:17.000Z | 2022-03-31T08:39:18.000Z | third_party/blink/tools/blinkpy/web_tests/breakpad/dump_reader_multipart_unittest.py | cangulcan/src | 2b8388091c71e442910a21ada3d97ae8bc1845d3 | [
"BSD-3-Clause"
] | 395 | 2020-04-18T08:22:18.000Z | 2021-12-08T13:04:49.000Z | third_party/blink/tools/blinkpy/web_tests/breakpad/dump_reader_multipart_unittest.py | cangulcan/src | 2b8388091c71e442910a21ada3d97ae8bc1845d3 | [
"BSD-3-Clause"
] | 338 | 2020-04-18T08:03:10.000Z | 2022-03-29T12:33:22.000Z | # Copyright (C) 2013 Google Inc. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above
# copyright notice, this list of conditions and the following disclaimer
# in the documentation and/or other materials provided with the
# distribution.
# * Neither the name of Google Inc. nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import unittest
from blinkpy.common.host import Host
from blinkpy.common.host_mock import MockHost
from blinkpy.web_tests.breakpad.dump_reader_multipart import DumpReaderMultipart
| 45.221239 | 104 | 0.724462 |
e8308aa03da4eb51baf9d04fa863c427d1808871 | 257,168 | py | Python | scripts/emoji-to-scl.py | SilverWingedSeraph/sws-dotfiles | 6bee2b2ece03439101848673d6bcd9196359f7c4 | [
"Apache-2.0"
] | 3 | 2018-03-07T15:15:50.000Z | 2018-05-22T17:50:34.000Z | scripts/emoji-to-scl.py | SilverWingedSeraph/sws-dotfiles | 6bee2b2ece03439101848673d6bcd9196359f7c4 | [
"Apache-2.0"
] | null | null | null | scripts/emoji-to-scl.py | SilverWingedSeraph/sws-dotfiles | 6bee2b2ece03439101848673d6bcd9196359f7c4 | [
"Apache-2.0"
] | 1 | 2020-01-30T15:07:29.000Z | 2020-01-30T15:07:29.000Z | #!/usr/bin/python3
# -*- coding: utf-8 -*-
from subprocess import Popen, PIPE
emojis=""" Helmet With White Cross, Type-1-2
Helmet With White Cross, Type-3
Helmet With White Cross, Type-4
Helmet With White Cross, Type-5
Helmet With White Cross, Type-6
Kiss, Type-1-2
Kiss, Type-3
Kiss, Type-4
Kiss, Type-5
Kiss, Type-6
Couple With Heart, Type-1-2
Couple With Heart, Type-3
Couple With Heart, Type-4
Couple With Heart, Type-5
Couple With Heart, Type-6
Skier, Type-1-2
Skier, Type-3
Skier, Type-4
Skier, Type-5
Skier, Type-6
Grinning Face
Grinning Face With Smiling Eyes
Face With Tears of Joy
Rolling on the Floor Laughing
Smiling Face With Open Mouth
Smiling Face With Open Mouth & Smiling Eyes
Smiling Face With Open Mouth & Cold Sweat
Smiling Face With Open Mouth & Closed Eyes
Winking Face
Smiling Face With Smiling Eyes
Face Savouring Delicious Food
Smiling Face With Sunglasses
Smiling Face With Heart-Eyes
Face Blowing a Kiss
Kissing Face
Kissing Face With Smiling Eyes
Kissing Face With Closed Eyes
Smiling Face
Slightly Smiling Face
Hugging Face
Star-Struck
Thinking Face
Face With Raised Eyebrow
Neutral Face
Expressionless Face
Face Without Mouth
Face With Rolling Eyes
Smirking Face
Persevering Face
Disappointed but Relieved Face
Face With Open Mouth
Zipper-Mouth Face
Hushed Face
Sleepy Face
Tired Face
Sleeping Face
Relieved Face
Face With Stuck-Out Tongue
Face With Stuck-Out Tongue & Winking Eye
Face With Stuck-Out Tongue & Closed Eyes
Drooling Face
Unamused Face
Face With Cold Sweat
Pensive Face
Confused Face
Upside-Down Face
Money-Mouth Face
Astonished Face
Frowning Face
Slightly Frowning Face
Confounded Face
Disappointed Face
Worried Face
Face With Steam From Nose
Crying Face
Loudly Crying Face
Frowning Face With Open Mouth
Anguished Face
Fearful Face
Weary Face
Exploding Head
Grimacing Face
Face With Open Mouth & Cold Sweat
Face Screaming in Fear
Flushed Face
Crazy Face
Dizzy Face
Pouting Face
Angry Face
Face With Symbols Over Mouth
Face With Medical Mask
Face With Thermometer
Face With Head-Bandage
Nauseated Face
Face Vomiting
Sneezing Face
Smiling Face With Halo
Cowboy Hat Face
Clown Face
Lying Face
Shushing Face
Face With Hand Over Mouth
Face With Monocle
Nerd Face
Smiling Face With Horns
Angry Face With Horns
Ogre
Goblin
Skull
Skull and Crossbones
Ghost
Alien
Alien Monster
Robot Face
Pile of Poo
Smiling Cat Face With Open Mouth
Grinning Cat Face With Smiling Eyes
Cat Face With Tears of Joy
Smiling Cat Face With Heart-Eyes
Cat Face With Wry Smile
Kissing Cat Face With Closed Eyes
Weary Cat Face
Crying Cat Face
Pouting Cat Face
See-No-Evil Monkey
Hear-No-Evil Monkey
Speak-No-Evil Monkey
Baby
Baby: Light Skin Tone
Baby: Medium-Light Skin Tone
Baby: Medium Skin Tone
Baby: Medium-Dark Skin Tone
Baby: Dark Skin Tone
Child
Child: Light Skin Tone
Child: Medium-Light Skin Tone
Child: Medium Skin Tone
Child: Medium-Dark Skin Tone
Child: Dark Skin Tone
Boy
Boy: Light Skin Tone
Boy: Medium-Light Skin Tone
Boy: Medium Skin Tone
Boy: Medium-Dark Skin Tone
Boy: Dark Skin Tone
Girl
Girl: Light Skin Tone
Girl: Medium-Light Skin Tone
Girl: Medium Skin Tone
Girl: Medium-Dark Skin Tone
Girl: Dark Skin Tone
Adult
Adult: Light Skin Tone
Adult: Medium-Light Skin Tone
Adult: Medium Skin Tone
Adult: Medium-Dark Skin Tone
Adult: Dark Skin Tone
Man
Man: Light Skin Tone
Man: Medium-Light Skin Tone
Man: Medium Skin Tone
Man: Medium-Dark Skin Tone
Man: Dark Skin Tone
Woman
Woman: Light Skin Tone
Woman: Medium-Light Skin Tone
Woman: Medium Skin Tone
Woman: Medium-Dark Skin Tone
Woman: Dark Skin Tone
Older Adult
Older Adult: Light Skin Tone
Older Adult: Medium-Light Skin Tone
Older Adult: Medium Skin Tone
Older Adult: Medium-Dark Skin Tone
Older Adult: Dark Skin Tone
Old Man
Old Man: Light Skin Tone
Old Man: Medium-Light Skin Tone
Old Man: Medium Skin Tone
Old Man: Medium-Dark Skin Tone
Old Man: Dark Skin Tone
Old Woman
Old Woman: Light Skin Tone
Old Woman: Medium-Light Skin Tone
Old Woman: Medium Skin Tone
Old Woman: Medium-Dark Skin Tone
Old Woman: Dark Skin Tone
Man Health Worker
Man Health Worker: Light Skin Tone
Man Health Worker: Medium-Light Skin Tone
Man Health Worker: Medium Skin Tone
Man Health Worker: Medium-Dark Skin Tone
Man Health Worker: Dark Skin Tone
Woman Health Worker
Woman Health Worker: Light Skin Tone
Woman Health Worker: Medium-Light Skin Tone
Woman Health Worker: Medium Skin Tone
Woman Health Worker: Medium-Dark Skin Tone
Woman Health Worker: Dark Skin Tone
Man Student
Man Student: Light Skin Tone
Man Student: Medium-Light Skin Tone
Man Student: Medium Skin Tone
Man Student: Medium-Dark Skin Tone
Man Student: Dark Skin Tone
Woman Student
Woman Student: Light Skin Tone
Woman Student: Medium-Light Skin Tone
Woman Student: Medium Skin Tone
Woman Student: Medium-Dark Skin Tone
Woman Student: Dark Skin Tone
Man Teacher
Man Teacher: Light Skin Tone
Man Teacher: Medium-Light Skin Tone
Man Teacher: Medium Skin Tone
Man Teacher: Medium-Dark Skin Tone
Man Teacher: Dark Skin Tone
Woman Teacher
Woman Teacher: Light Skin Tone
Woman Teacher: Medium-Light Skin Tone
Woman Teacher: Medium Skin Tone
Woman Teacher: Medium-Dark Skin Tone
Woman Teacher: Dark Skin Tone
Man Judge
Man Judge: Light Skin Tone
Man Judge: Medium-Light Skin Tone
Man Judge: Medium Skin Tone
Man Judge: Medium-Dark Skin Tone
Man Judge: Dark Skin Tone
Woman Judge
Woman Judge: Light Skin Tone
Woman Judge: Medium-Light Skin Tone
Woman Judge: Medium Skin Tone
Woman Judge: Medium-Dark Skin Tone
Woman Judge: Dark Skin Tone
Man Farmer
Man Farmer: Light Skin Tone
Man Farmer: Medium-Light Skin Tone
Man Farmer: Medium Skin Tone
Man Farmer: Medium-Dark Skin Tone
Man Farmer: Dark Skin Tone
Woman Farmer
Woman Farmer: Light Skin Tone
Woman Farmer: Medium-Light Skin Tone
Woman Farmer: Medium Skin Tone
Woman Farmer: Medium-Dark Skin Tone
Woman Farmer: Dark Skin Tone
Man Cook
Man Cook: Light Skin Tone
Man Cook: Medium-Light Skin Tone
Man Cook: Medium Skin Tone
Man Cook: Medium-Dark Skin Tone
Man Cook: Dark Skin Tone
Woman Cook
Woman Cook: Light Skin Tone
Woman Cook: Medium-Light Skin Tone
Woman Cook: Medium Skin Tone
Woman Cook: Medium-Dark Skin Tone
Woman Cook: Dark Skin Tone
Man Mechanic
Man Mechanic: Light Skin Tone
Man Mechanic: Medium-Light Skin Tone
Man Mechanic: Medium Skin Tone
Man Mechanic: Medium-Dark Skin Tone
Man Mechanic: Dark Skin Tone
Woman Mechanic
Woman Mechanic: Light Skin Tone
Woman Mechanic: Medium-Light Skin Tone
Woman Mechanic: Medium Skin Tone
Woman Mechanic: Medium-Dark Skin Tone
Woman Mechanic: Dark Skin Tone
Man Factory Worker
Man Factory Worker: Light Skin Tone
Man Factory Worker: Medium-Light Skin Tone
Man Factory Worker: Medium Skin Tone
Man Factory Worker: Medium-Dark Skin Tone
Man Factory Worker: Dark Skin Tone
Woman Factory Worker
Woman Factory Worker: Light Skin Tone
Woman Factory Worker: Medium-Light Skin Tone
Woman Factory Worker: Medium Skin Tone
Woman Factory Worker: Medium-Dark Skin Tone
Woman Factory Worker: Dark Skin Tone
Man Office Worker
Man Office Worker: Light Skin Tone
Man Office Worker: Medium-Light Skin Tone
Man Office Worker: Medium Skin Tone
Man Office Worker: Medium-Dark Skin Tone
Man Office Worker: Dark Skin Tone
Woman Office Worker
Woman Office Worker: Light Skin Tone
Woman Office Worker: Medium-Light Skin Tone
Woman Office Worker: Medium Skin Tone
Woman Office Worker: Medium-Dark Skin Tone
Woman Office Worker: Dark Skin Tone
Man Scientist
Man Scientist: Light Skin Tone
Man Scientist: Medium-Light Skin Tone
Man Scientist: Medium Skin Tone
Man Scientist: Medium-Dark Skin Tone
Man Scientist: Dark Skin Tone
Woman Scientist
Woman Scientist: Light Skin Tone
Woman Scientist: Medium-Light Skin Tone
Woman Scientist: Medium Skin Tone
Woman Scientist: Medium-Dark Skin Tone
Woman Scientist: Dark Skin Tone
Man Technologist
Man Technologist: Light Skin Tone
Man Technologist: Medium-Light Skin Tone
Man Technologist: Medium Skin Tone
Man Technologist: Medium-Dark Skin Tone
Man Technologist: Dark Skin Tone
Woman Technologist
Woman Technologist: Light Skin Tone
Woman Technologist: Medium-Light Skin Tone
Woman Technologist: Medium Skin Tone
Woman Technologist: Medium-Dark Skin Tone
Woman Technologist: Dark Skin Tone
Man Singer
Man Singer: Light Skin Tone
Man Singer: Medium-Light Skin Tone
Man Singer: Medium Skin Tone
Man Singer: Medium-Dark Skin Tone
Man Singer: Dark Skin Tone
Woman Singer
Woman Singer: Light Skin Tone
Woman Singer: Medium-Light Skin Tone
Woman Singer: Medium Skin Tone
Woman Singer: Medium-Dark Skin Tone
Woman Singer: Dark Skin Tone
Man Artist
Man Artist: Light Skin Tone
Man Artist: Medium-Light Skin Tone
Man Artist: Medium Skin Tone
Man Artist: Medium-Dark Skin Tone
Man Artist: Dark Skin Tone
Woman Artist
Woman Artist: Light Skin Tone
Woman Artist: Medium-Light Skin Tone
Woman Artist: Medium Skin Tone
Woman Artist: Medium-Dark Skin Tone
Woman Artist: Dark Skin Tone
Man Pilot
Man Pilot: Light Skin Tone
Man Pilot: Medium-Light Skin Tone
Man Pilot: Medium Skin Tone
Man Pilot: Medium-Dark Skin Tone
Man Pilot: Dark Skin Tone
Woman Pilot
Woman Pilot: Light Skin Tone
Woman Pilot: Medium-Light Skin Tone
Woman Pilot: Medium Skin Tone
Woman Pilot: Medium-Dark Skin Tone
Woman Pilot: Dark Skin Tone
Man Astronaut
Man Astronaut: Light Skin Tone
Man Astronaut: Medium-Light Skin Tone
Man Astronaut: Medium Skin Tone
Man Astronaut: Medium-Dark Skin Tone
Man Astronaut: Dark Skin Tone
Woman Astronaut
Woman Astronaut: Light Skin Tone
Woman Astronaut: Medium-Light Skin Tone
Woman Astronaut: Medium Skin Tone
Woman Astronaut: Medium-Dark Skin Tone
Woman Astronaut: Dark Skin Tone
Man Firefighter
Man Firefighter: Light Skin Tone
Man Firefighter: Medium-Light Skin Tone
Man Firefighter: Medium Skin Tone
Man Firefighter: Medium-Dark Skin Tone
Man Firefighter: Dark Skin Tone
Woman Firefighter
Woman Firefighter: Light Skin Tone
Woman Firefighter: Medium-Light Skin Tone
Woman Firefighter: Medium Skin Tone
Woman Firefighter: Medium-Dark Skin Tone
Woman Firefighter: Dark Skin Tone
Police Officer
Police Officer: Light Skin Tone
Police Officer: Medium-Light Skin Tone
Police Officer: Medium Skin Tone
Police Officer: Medium-Dark Skin Tone
Police Officer: Dark Skin Tone
Man Police Officer
Man Police Officer: Light Skin Tone
Man Police Officer: Medium-Light Skin Tone
Man Police Officer: Medium Skin Tone
Man Police Officer: Medium-Dark Skin Tone
Man Police Officer: Dark Skin Tone
Woman Police Officer
Woman Police Officer: Light Skin Tone
Woman Police Officer: Medium-Light Skin Tone
Woman Police Officer: Medium Skin Tone
Woman Police Officer: Medium-Dark Skin Tone
Woman Police Officer: Dark Skin Tone
Detective
Detective: Light Skin Tone
Detective: Medium-Light Skin Tone
Detective: Medium Skin Tone
Detective: Medium-Dark Skin Tone
Detective: Dark Skin Tone
Man Detective
Man Detective: Light Skin Tone
Man Detective: Medium-Light Skin Tone
Man Detective: Medium Skin Tone
Man Detective: Medium-Dark Skin Tone
Man Detective: Dark Skin Tone
Woman Detective
Woman Detective: Light Skin Tone
Woman Detective: Medium-Light Skin Tone
Woman Detective: Medium Skin Tone
Woman Detective: Medium-Dark Skin Tone
Woman Detective: Dark Skin Tone
Guard
Guard: Light Skin Tone
Guard: Medium-Light Skin Tone
Guard: Medium Skin Tone
Guard: Medium-Dark Skin Tone
Guard: Dark Skin Tone
Man Guard
Man Guard: Light Skin Tone
Man Guard: Medium-Light Skin Tone
Man Guard: Medium Skin Tone
Man Guard: Medium-Dark Skin Tone
Man Guard: Dark Skin Tone
Woman Guard
Woman Guard: Light Skin Tone
Woman Guard: Medium-Light Skin Tone
Woman Guard: Medium Skin Tone
Woman Guard: Medium-Dark Skin Tone
Woman Guard: Dark Skin Tone
Construction Worker
Construction Worker: Light Skin Tone
Construction Worker: Medium-Light Skin Tone
Construction Worker: Medium Skin Tone
Construction Worker: Medium-Dark Skin Tone
Construction Worker: Dark Skin Tone
Man Construction Worker
Man Construction Worker: Light Skin Tone
Man Construction Worker: Medium-Light Skin Tone
Man Construction Worker: Medium Skin Tone
Man Construction Worker: Medium-Dark Skin Tone
Man Construction Worker: Dark Skin Tone
Woman Construction Worker
Woman Construction Worker: Light Skin Tone
Woman Construction Worker: Medium-Light Skin Tone
Woman Construction Worker: Medium Skin Tone
Woman Construction Worker: Medium-Dark Skin Tone
Woman Construction Worker: Dark Skin Tone
Prince
Prince: Light Skin Tone
Prince: Medium-Light Skin Tone
Prince: Medium Skin Tone
Prince: Medium-Dark Skin Tone
Prince: Dark Skin Tone
Princess
Princess: Light Skin Tone
Princess: Medium-Light Skin Tone
Princess: Medium Skin Tone
Princess: Medium-Dark Skin Tone
Princess: Dark Skin Tone
Person Wearing Turban
Person Wearing Turban: Light Skin Tone
Person Wearing Turban: Medium-Light Skin Tone
Person Wearing Turban: Medium Skin Tone
Person Wearing Turban: Medium-Dark Skin Tone
Person Wearing Turban: Dark Skin Tone
Man Wearing Turban
Man Wearing Turban: Light Skin Tone
Man Wearing Turban: Medium-Light Skin Tone
Man Wearing Turban: Medium Skin Tone
Man Wearing Turban: Medium-Dark Skin Tone
Man Wearing Turban: Dark Skin Tone
Woman Wearing Turban
Woman Wearing Turban: Light Skin Tone
Woman Wearing Turban: Medium-Light Skin Tone
Woman Wearing Turban: Medium Skin Tone
Woman Wearing Turban: Medium-Dark Skin Tone
Woman Wearing Turban: Dark Skin Tone
Man With Chinese Cap
Man With Chinese Cap: Light Skin Tone
Man With Chinese Cap: Medium-Light Skin Tone
Man With Chinese Cap: Medium Skin Tone
Man With Chinese Cap: Medium-Dark Skin Tone
Man With Chinese Cap: Dark Skin Tone
Woman With Headscarf
Person With Headscarf: Light Skin Tone
Person With Headscarf: Medium-Light Skin Tone
Person With Headscarf: Medium Skin Tone
Person With Headscarf: Medium-Dark Skin Tone
Person With Headscarf: Dark Skin Tone
Bearded Person
Bearded Person: Light Skin Tone
Bearded Person: Medium-Light Skin Tone
Bearded Person: Medium Skin Tone
Bearded Person: Medium-Dark Skin Tone
Bearded Person: Dark Skin Tone
Blond-Haired Person
Blond-Haired Person: Light Skin Tone
Blond-Haired Person: Medium-Light Skin Tone
Blond-Haired Person: Medium Skin Tone
Blond-Haired Person: Medium-Dark Skin Tone
Blond-Haired Person: Dark Skin Tone
Blond-Haired Man
Blond-Haired Man: Light Skin Tone
Blond-Haired Man: Medium-Light Skin Tone
Blond-Haired Man: Medium Skin Tone
Blond-Haired Man: Medium-Dark Skin Tone
Blond-Haired Man: Dark Skin Tone
Blond-Haired Woman
Blond-Haired Woman: Light Skin Tone
Blond-Haired Woman: Medium-Light Skin Tone
Blond-Haired Woman: Medium Skin Tone
Blond-Haired Woman: Medium-Dark Skin Tone
Blond-Haired Woman: Dark Skin Tone
Man in Tuxedo
Man in Tuxedo: Light Skin Tone
Man in Tuxedo: Medium-Light Skin Tone
Man in Tuxedo: Medium Skin Tone
Man in Tuxedo: Medium-Dark Skin Tone
Man in Tuxedo: Dark Skin Tone
Bride With Veil
Bride With Veil: Light Skin Tone
Bride With Veil: Medium-Light Skin Tone
Bride With Veil: Medium Skin Tone
Bride With Veil: Medium-Dark Skin Tone
Bride With Veil: Dark Skin Tone
Pregnant Woman
Pregnant Woman: Light Skin Tone
Pregnant Woman: Medium-Light Skin Tone
Pregnant Woman: Medium Skin Tone
Pregnant Woman: Medium-Dark Skin Tone
Pregnant Woman: Dark Skin Tone
Breast-Feeding
Breast-Feeding: Light Skin Tone
Breast-Feeding: Medium-Light Skin Tone
Breast-Feeding: Medium Skin Tone
Breast-Feeding: Medium-Dark Skin Tone
Breast-Feeding: Dark Skin Tone
Baby Angel
Baby Angel: Light Skin Tone
Baby Angel: Medium-Light Skin Tone
Baby Angel: Medium Skin Tone
Baby Angel: Medium-Dark Skin Tone
Baby Angel: Dark Skin Tone
Santa Claus
Santa Claus: Light Skin Tone
Santa Claus: Medium-Light Skin Tone
Santa Claus: Medium Skin Tone
Santa Claus: Medium-Dark Skin Tone
Santa Claus: Dark Skin Tone
Mrs. Claus
Mrs. Claus: Light Skin Tone
Mrs. Claus: Medium-Light Skin Tone
Mrs. Claus: Medium Skin Tone
Mrs. Claus: Medium-Dark Skin Tone
Mrs. Claus: Dark Skin Tone
Mage
Mage: Light Skin Tone
Mage: Medium-Light Skin Tone
Mage: Medium Skin Tone
Mage: Medium-Dark Skin Tone
Mage: Dark Skin Tone
Woman Mage
Woman Mage: Light Skin Tone
Woman Mage: Medium-Light Skin Tone
Woman Mage: Medium Skin Tone
Woman Mage: Medium-Dark Skin Tone
Woman Mage: Dark Skin Tone
Man Mage
Man Mage: Light Skin Tone
Man Mage: Medium-Light Skin Tone
Man Mage: Medium Skin Tone
Man Mage: Medium-Dark Skin Tone
Man Mage: Dark Skin Tone
Fairy
Fairy: Light Skin Tone
Fairy: Medium-Light Skin Tone
Fairy: Medium Skin Tone
Fairy: Medium-Dark Skin Tone
Fairy: Dark Skin Tone
Woman Fairy
Woman Fairy: Light Skin Tone
Woman Fairy: Medium-Light Skin Tone
Woman Fairy: Medium Skin Tone
Woman Fairy: Medium-Dark Skin Tone
Woman Fairy: Dark Skin Tone
Man Fairy
Man Fairy: Light Skin Tone
Man Fairy: Medium-Light Skin Tone
Man Fairy: Medium Skin Tone
Man Fairy: Medium-Dark Skin Tone
Man Fairy: Dark Skin Tone
Vampire
Vampire: Light Skin Tone
Vampire: Medium-Light Skin Tone
Vampire: Medium Skin Tone
Vampire: Medium-Dark Skin Tone
Vampire: Dark Skin Tone
Woman Vampire
Woman Vampire: Light Skin Tone
Woman Vampire: Medium-Light Skin Tone
Woman Vampire: Medium Skin Tone
Woman Vampire: Medium-Dark Skin Tone
Woman Vampire: Dark Skin Tone
Man Vampire
Man Vampire: Light Skin Tone
Man Vampire: Medium-Light Skin Tone
Man Vampire: Medium Skin Tone
Man Vampire: Medium-Dark Skin Tone
Woman With Bunny Ears, Type-1-2
Woman With Bunny Ears, Type-3
Man Vampire: Dark Skin Tone
Woman With Bunny Ears, Type-4
Woman With Bunny Ears, Type-5
Merperson
Woman With Bunny Ears, Type-6
Merperson: Light Skin Tone
Men With Bunny Ears Partying, Type-1-2
Merperson: Medium-Light Skin Tone
Men With Bunny Ears Partying, Type-3
Merperson: Medium Skin Tone
Men With Bunny Ears Partying, Type-4
Merperson: Medium-Dark Skin Tone
Men With Bunny Ears Partying, Type-5
Merperson: Dark Skin Tone
Men With Bunny Ears Partying, Type-6
Mermaid
Women With Bunny Ears Partying, Type-1-2
Mermaid: Light Skin Tone
Women With Bunny Ears Partying, Type-3
Mermaid: Medium-Light Skin Tone
Women With Bunny Ears Partying, Type-4
Women With Bunny Ears Partying, Type-5
Mermaid: Medium Skin Tone
Women With Bunny Ears Partying, Type-6
Mermaid: Medium-Dark Skin Tone
Mermaid: Dark Skin Tone
Merman
Merman: Light Skin Tone
Merman: Medium-Light Skin Tone
Man and Woman Holding Hands, Type-1-2
Merman: Medium Skin Tone
Man and Woman Holding Hands, Type-3
Man and Woman Holding Hands, Type-4
Merman: Medium-Dark Skin Tone
Man and Woman Holding Hands, Type-5
Man and Woman Holding Hands, Type-6
Merman: Dark Skin Tone
Two Men Holding Hands, Type-1-2
Elf
Two Men Holding Hands, Type-3
Two Men Holding Hands, Type-4
Elf: Light Skin Tone
Two Men Holding Hands, Type-5
Elf: Medium-Light Skin Tone
Two Men Holding Hands, Type-6
Elf: Medium Skin Tone
Elf: Medium-Dark Skin Tone
Two Women Holding Hands, Type-1-2
Elf: Dark Skin Tone
Woman Elf
Two Women Holding Hands, Type-3
Two Women Holding Hands, Type-4
Woman Elf: Light Skin Tone
Two Women Holding Hands, Type-5
Two Women Holding Hands, Type-6
Woman Elf: Medium-Light Skin Tone
Woman Elf: Medium Skin Tone
Woman Elf: Medium-Dark Skin Tone
Woman Elf: Dark Skin Tone
Man Elf
Family, Type-1-2
Man Elf: Light Skin Tone
Family, Type-3
Family, Type-4
Man Elf: Medium-Light Skin Tone
Family, Type-5
Family, Type-6
Man Elf: Medium Skin Tone
Man Elf: Medium-Dark Skin Tone
Man Elf: Dark Skin Tone
Genie
Woman Genie
Man Genie
Zombie
Woman Zombie
Man Zombie
Person Frowning
Person Frowning: Light Skin Tone
Person Frowning: Medium-Light Skin Tone
Person Frowning: Medium Skin Tone
Person Frowning: Medium-Dark Skin Tone
Person Frowning: Dark Skin Tone
Man Frowning
Man Frowning: Light Skin Tone
Light Skin Tone
Medium-Light Skin Tone
Man Frowning: Medium-Light Skin Tone
Medium Skin Tone
Man Frowning: Medium Skin Tone
Medium-Dark Skin Tone
Dark Skin Tone
Man Frowning: Medium-Dark Skin Tone
Man Frowning: Dark Skin Tone
Woman Frowning
Woman Frowning: Light Skin Tone
Woman Frowning: Medium-Light Skin Tone
Woman Frowning: Medium Skin Tone
Woman Frowning: Medium-Dark Skin Tone
Woman Frowning: Dark Skin Tone
Person Pouting
Person Pouting: Light Skin Tone
Person Pouting: Medium-Light Skin Tone
Person Pouting: Medium Skin Tone
Person Pouting: Medium-Dark Skin Tone
Person Pouting: Dark Skin Tone
Man Pouting
Man Pouting: Light Skin Tone
Man Pouting: Medium-Light Skin Tone
Man Pouting: Medium Skin Tone
Man Pouting: Medium-Dark Skin Tone
Man Pouting: Dark Skin Tone
Woman Pouting
Woman Pouting: Light Skin Tone
Woman Pouting: Medium-Light Skin Tone
Woman Pouting: Medium Skin Tone
Woman Pouting: Medium-Dark Skin Tone
Woman Pouting: Dark Skin Tone
Person Gesturing No
Person Gesturing No: Light Skin Tone
Person Gesturing No: Medium-Light Skin Tone
Person Gesturing No: Medium Skin Tone
Person Gesturing No: Medium-Dark Skin Tone
Person Gesturing No: Dark Skin Tone
Man Gesturing No
Man Gesturing No: Light Skin Tone
Man Gesturing No: Medium-Light Skin Tone
Man Gesturing No: Medium Skin Tone
Man Gesturing No: Medium-Dark Skin Tone
Man Gesturing No: Dark Skin Tone
Woman Gesturing No
Woman Gesturing No: Light Skin Tone
Woman Gesturing No: Medium-Light Skin Tone
Woman Gesturing No: Medium Skin Tone
Woman Gesturing No: Medium-Dark Skin Tone
Woman Gesturing No: Dark Skin Tone
Person Gesturing OK
Person Gesturing OK: Light Skin Tone
Person Gesturing OK: Medium-Light Skin Tone
Person Gesturing OK: Medium Skin Tone
Person Gesturing OK: Medium-Dark Skin Tone
Person Gesturing OK: Dark Skin Tone
Man Gesturing OK
Man Gesturing OK: Light Skin Tone
Man Gesturing OK: Medium-Light Skin Tone
Man Gesturing OK: Medium Skin Tone
Man Gesturing OK: Medium-Dark Skin Tone
Man Gesturing OK: Dark Skin Tone
Woman Gesturing OK
Woman Gesturing OK: Light Skin Tone
Woman Gesturing OK: Medium-Light Skin Tone
Woman Gesturing OK: Medium Skin Tone
Woman Gesturing OK: Medium-Dark Skin Tone
Woman Gesturing OK: Dark Skin Tone
Person Tipping Hand
Person Tipping Hand: Light Skin Tone
Person Tipping Hand: Medium-Light Skin Tone
Person Tipping Hand: Medium Skin Tone
Person Tipping Hand: Medium-Dark Skin Tone
Person Tipping Hand: Dark Skin Tone
Man Tipping Hand
Man Tipping Hand: Light Skin Tone
Man Tipping Hand: Medium-Light Skin Tone
Man Tipping Hand: Medium Skin Tone
Man Tipping Hand: Medium-Dark Skin Tone
Man Tipping Hand: Dark Skin Tone
Woman Tipping Hand
Woman Tipping Hand: Light Skin Tone
Woman Tipping Hand: Medium-Light Skin Tone
Woman Tipping Hand: Medium Skin Tone
Woman Tipping Hand: Medium-Dark Skin Tone
Woman Tipping Hand: Dark Skin Tone
Person Raising Hand
Person Raising Hand: Light Skin Tone
Person Raising Hand: Medium-Light Skin Tone
Person Raising Hand: Medium Skin Tone
Person Raising Hand: Medium-Dark Skin Tone
Person Raising Hand: Dark Skin Tone
Man Raising Hand
Man Raising Hand: Light Skin Tone
Man Raising Hand: Medium-Light Skin Tone
Man Raising Hand: Medium Skin Tone
Man Raising Hand: Medium-Dark Skin Tone
Man Raising Hand: Dark Skin Tone
Woman Raising Hand
Woman Raising Hand: Light Skin Tone
Woman Raising Hand: Medium-Light Skin Tone
Woman Raising Hand: Medium Skin Tone
Woman Raising Hand: Medium-Dark Skin Tone
Woman Raising Hand: Dark Skin Tone
Person Bowing
Person Bowing: Light Skin Tone
Person Bowing: Medium-Light Skin Tone
Person Bowing: Medium Skin Tone
Person Bowing: Medium-Dark Skin Tone
Person Bowing: Dark Skin Tone
Man Bowing
Man Bowing: Light Skin Tone
Handshake, Type-1-2
Man Bowing: Medium-Light Skin Tone
Handshake, Type-3
Handshake, Type-4
Man Bowing: Medium Skin Tone
Handshake, Type-5
Handshake, Type-6
Man Bowing: Medium-Dark Skin Tone
Man Bowing: Dark Skin Tone
Woman Bowing
Woman Bowing: Light Skin Tone
Woman Bowing: Medium-Light Skin Tone
Woman Bowing: Medium Skin Tone
Woman Bowing: Medium-Dark Skin Tone
Woman Bowing: Dark Skin Tone
Person Facepalming
Person Facepalming: Light Skin Tone
Person Facepalming: Medium-Light Skin Tone
Person Facepalming: Medium Skin Tone
Person Facepalming: Medium-Dark Skin Tone
Person Facepalming: Dark Skin Tone
Man Facepalming
Man Facepalming: Light Skin Tone
Man Facepalming: Medium-Light Skin Tone
Man Facepalming: Medium Skin Tone
Man Facepalming: Medium-Dark Skin Tone
Man Facepalming: Dark Skin Tone
Woman Facepalming
Woman Facepalming: Light Skin Tone
Woman Facepalming: Medium-Light Skin Tone
Woman Facepalming: Medium Skin Tone
Woman Facepalming: Medium-Dark Skin Tone
Woman Facepalming: Dark Skin Tone
Person Shrugging
Person Shrugging: Light Skin Tone
Person Shrugging: Medium-Light Skin Tone
Person Shrugging: Medium Skin Tone
Person Shrugging: Medium-Dark Skin Tone
Person Shrugging: Dark Skin Tone
Man Shrugging
Man Shrugging: Light Skin Tone
Man Shrugging: Medium-Light Skin Tone
Man Shrugging: Medium Skin Tone
Man Shrugging: Medium-Dark Skin Tone
Man Shrugging: Dark Skin Tone
Woman Shrugging
Woman Shrugging: Light Skin Tone
Woman Shrugging: Medium-Light Skin Tone
Woman Shrugging: Medium Skin Tone
Woman Shrugging: Medium-Dark Skin Tone
Woman Shrugging: Dark Skin Tone
Person Getting Massage
Person Getting Massage: Light Skin Tone
Person Getting Massage: Medium-Light Skin Tone
Person Getting Massage: Medium Skin Tone
Person Getting Massage: Medium-Dark Skin Tone
Person Getting Massage: Dark Skin Tone
Man Getting Massage
Man Getting Massage: Light Skin Tone
Man Getting Massage: Medium-Light Skin Tone
Man Getting Massage: Medium Skin Tone
Man Getting Massage: Medium-Dark Skin Tone
Man Getting Massage: Dark Skin Tone
Woman Getting Massage
Woman Getting Massage: Light Skin Tone
Woman Getting Massage: Medium-Light Skin Tone
Woman Getting Massage: Medium Skin Tone
Woman Getting Massage: Medium-Dark Skin Tone
Woman Getting Massage: Dark Skin Tone
Person Getting Haircut
Person Getting Haircut: Light Skin Tone
Person Getting Haircut: Medium-Light Skin Tone
Person Getting Haircut: Medium Skin Tone
Person Getting Haircut: Medium-Dark Skin Tone
Person Getting Haircut: Dark Skin Tone
Man Getting Haircut
Man Getting Haircut: Light Skin Tone
Man Getting Haircut: Medium-Light Skin Tone
Man Getting Haircut: Medium Skin Tone
Man Getting Haircut: Medium-Dark Skin Tone
Man Getting Haircut: Dark Skin Tone
Woman Getting Haircut
Woman Getting Haircut: Light Skin Tone
Woman Getting Haircut: Medium-Light Skin Tone
Woman Getting Haircut: Medium Skin Tone
Woman Getting Haircut: Medium-Dark Skin Tone
Woman Getting Haircut: Dark Skin Tone
Person Walking
Person Walking: Light Skin Tone
Person Walking: Medium-Light Skin Tone
Person Walking: Medium Skin Tone
Person Walking: Medium-Dark Skin Tone
Person Walking: Dark Skin Tone
Man Walking
Man Walking: Light Skin Tone
Man Walking: Medium-Light Skin Tone
Man Walking: Medium Skin Tone
Man Walking: Medium-Dark Skin Tone
Man Walking: Dark Skin Tone
Woman Walking
Woman Walking: Light Skin Tone
Woman Walking: Medium-Light Skin Tone
Woman Walking: Medium Skin Tone
Woman Walking: Medium-Dark Skin Tone
Woman Walking: Dark Skin Tone
Person Running
Person Running: Light Skin Tone
Person Running: Medium-Light Skin Tone
Person Running: Medium Skin Tone
Person Running: Medium-Dark Skin Tone
Person Running: Dark Skin Tone
Man Running
Man Running: Light Skin Tone
Man Running: Medium-Light Skin Tone
Man Running: Medium Skin Tone
Man Running: Medium-Dark Skin Tone
Man Running: Dark Skin Tone
Woman Running
Woman Running: Light Skin Tone
Woman Running: Medium-Light Skin Tone
Woman Running: Medium Skin Tone
Woman Running: Medium-Dark Skin Tone
Woman Running: Dark Skin Tone
Woman Dancing
Woman Dancing: Light Skin Tone
Woman Dancing: Medium-Light Skin Tone
Woman Dancing: Medium Skin Tone
Woman Dancing: Medium-Dark Skin Tone
Woman Dancing: Dark Skin Tone
Man Dancing
Man Dancing: Light Skin Tone
Man Dancing: Medium-Light Skin Tone
Man Dancing: Medium Skin Tone
Man Dancing: Medium-Dark Skin Tone
Man Dancing: Dark Skin Tone
People With Bunny Ears Partying
Men With Bunny Ears Partying
Women With Bunny Ears Partying
Person in Steamy Room
Person in Steamy Room: Light Skin Tone
Person in Steamy Room: Medium-Light Skin Tone
Person in Steamy Room: Medium Skin Tone
Person in Steamy Room: Medium-Dark Skin Tone
Person in Steamy Room: Dark Skin Tone
Woman in Steamy Room
Woman in Steamy Room: Light Skin Tone
Woman in Steamy Room: Medium-Light Skin Tone
Woman in Steamy Room: Medium Skin Tone
Woman in Steamy Room: Medium-Dark Skin Tone
Woman in Steamy Room: Dark Skin Tone
Man in Steamy Room
Man in Steamy Room: Light Skin Tone
Man in Steamy Room: Medium-Light Skin Tone
Man in Steamy Room: Medium Skin Tone
Man in Steamy Room: Medium-Dark Skin Tone
Man in Steamy Room: Dark Skin Tone
Person Climbing
Person Climbing: Light Skin Tone
Person Climbing: Medium-Light Skin Tone
Person Climbing: Medium Skin Tone
Person Climbing: Medium-Dark Skin Tone
Person Climbing: Dark Skin Tone
Woman Climbing
Woman Climbing: Light Skin Tone
Woman Climbing: Medium-Light Skin Tone
Woman Climbing: Medium Skin Tone
Woman Climbing: Medium-Dark Skin Tone
Woman Climbing: Dark Skin Tone
Man Climbing
Man Climbing: Light Skin Tone
Man Climbing: Medium-Light Skin Tone
Man Climbing: Medium Skin Tone
Man Climbing: Medium-Dark Skin Tone
Man Climbing: Dark Skin Tone
Person in Lotus Position
Person in Lotus Position: Light Skin Tone
Person in Lotus Position: Medium-Light Skin Tone
Person in Lotus Position: Medium Skin Tone
Person in Lotus Position: Medium-Dark Skin Tone
Person in Lotus Position: Dark Skin Tone
Woman in Lotus Position
Woman in Lotus Position: Light Skin Tone
Woman in Lotus Position: Medium-Light Skin Tone
Woman in Lotus Position: Medium Skin Tone
Woman in Lotus Position: Medium-Dark Skin Tone
Woman in Lotus Position: Dark Skin Tone
Man in Lotus Position
Man in Lotus Position: Light Skin Tone
Man in Lotus Position: Medium-Light Skin Tone
Man in Lotus Position: Medium Skin Tone
Man in Lotus Position: Medium-Dark Skin Tone
Man in Lotus Position: Dark Skin Tone
Person Taking Bath
Person Taking Bath: Light Skin Tone
Person Taking Bath: Medium-Light Skin Tone
Person Taking Bath: Medium Skin Tone
Person Taking Bath: Medium-Dark Skin Tone
Person Taking Bath: Dark Skin Tone
Person in Bed
Person in Bed: Light Skin Tone
Person in Bed: Medium-Light Skin Tone
Person in Bed: Medium Skin Tone
Person in Bed: Medium-Dark Skin Tone
Person in Bed: Dark Skin Tone
Man in Business Suit Levitating
Man in Business Suit Levitating: Light Skin Tone
Man in Business Suit Levitating: Medium-Light Skin Tone
Man in Business Suit Levitating: Medium Skin Tone
Man in Business Suit Levitating: Medium-Dark Skin Tone
Man in Business Suit Levitating: Dark Skin Tone
Speaking Head
Bust in Silhouette
Busts in Silhouette
Person Fencing
Horse Racing
Horse Racing: Light Skin Tone
Horse Racing: Medium-Light Skin Tone
Horse Racing: Medium Skin Tone
Horse Racing: Medium-Dark Skin Tone
Horse Racing: Dark Skin Tone
Skier
Snowboarder
Snowboarder: Light Skin Tone
Snowboarder: Medium-Light Skin Tone
Snowboarder: Medium Skin Tone
Snowboarder: Medium-Dark Skin Tone
Snowboarder: Dark Skin Tone
Person Golfing
Person Golfing: Light Skin Tone
Person Golfing: Medium-Light Skin Tone
Person Golfing: Medium Skin Tone
Person Golfing: Medium-Dark Skin Tone
Person Golfing: Dark Skin Tone
Man Golfing
Man Golfing: Light Skin Tone
Man Golfing: Medium-Light Skin Tone
Man Golfing: Medium Skin Tone
Man Golfing: Medium-Dark Skin Tone
Man Golfing: Dark Skin Tone
Woman Golfing
Woman Golfing: Light Skin Tone
Woman Golfing: Medium-Light Skin Tone
Woman Golfing: Medium Skin Tone
Woman Golfing: Medium-Dark Skin Tone
Woman Golfing: Dark Skin Tone
Person Surfing
Person Surfing: Light Skin Tone
Person Surfing: Medium-Light Skin Tone
Person Surfing: Medium Skin Tone
Person Surfing: Medium-Dark Skin Tone
Person Surfing: Dark Skin Tone
Man Surfing
Man Surfing: Light Skin Tone
Man Surfing: Medium-Light Skin Tone
Man Surfing: Medium Skin Tone
Man Surfing: Medium-Dark Skin Tone
Man Surfing: Dark Skin Tone
Woman Surfing
Woman Surfing: Light Skin Tone
Woman Surfing: Medium-Light Skin Tone
Woman Surfing: Medium Skin Tone
Woman Surfing: Medium-Dark Skin Tone
Woman Surfing: Dark Skin Tone
Person Rowing Boat
Person Rowing Boat: Light Skin Tone
Person Rowing Boat: Medium-Light Skin Tone
Person Rowing Boat: Medium Skin Tone
Person Rowing Boat: Medium-Dark Skin Tone
Person Rowing Boat: Dark Skin Tone
Man Rowing Boat
Man Rowing Boat: Light Skin Tone
Man Rowing Boat: Medium-Light Skin Tone
Man Rowing Boat: Medium Skin Tone
Man Rowing Boat: Medium-Dark Skin Tone
Man Rowing Boat: Dark Skin Tone
Woman Rowing Boat
Woman Rowing Boat: Light Skin Tone
Woman Rowing Boat: Medium-Light Skin Tone
Woman Rowing Boat: Medium Skin Tone
Woman Rowing Boat: Medium-Dark Skin Tone
Woman Rowing Boat: Dark Skin Tone
Person Swimming
Person Swimming: Light Skin Tone
Person Swimming: Medium-Light Skin Tone
Person Swimming: Medium Skin Tone
Person Swimming: Medium-Dark Skin Tone
Person Swimming: Dark Skin Tone
Man Swimming
Man Swimming: Light Skin Tone
Man Swimming: Medium-Light Skin Tone
Man Swimming: Medium Skin Tone
Man Swimming: Medium-Dark Skin Tone
Man Swimming: Dark Skin Tone
Woman Swimming
Woman Swimming: Light Skin Tone
Woman Swimming: Medium-Light Skin Tone
Woman Swimming: Medium Skin Tone
Woman Swimming: Medium-Dark Skin Tone
Woman Swimming: Dark Skin Tone
Person Bouncing Ball
Person Bouncing Ball: Light Skin Tone
Person Bouncing Ball: Medium-Light Skin Tone
Person Bouncing Ball: Medium Skin Tone
Person Bouncing Ball: Medium-Dark Skin Tone
Person Bouncing Ball: Dark Skin Tone
Man Bouncing Ball
Man Bouncing Ball: Light Skin Tone
Man Bouncing Ball: Medium-Light Skin Tone
Man Bouncing Ball: Medium Skin Tone
Man Bouncing Ball: Medium-Dark Skin Tone
Man Bouncing Ball: Dark Skin Tone
Woman Bouncing Ball
Woman Bouncing Ball: Light Skin Tone
Woman Bouncing Ball: Medium-Light Skin Tone
Woman Bouncing Ball: Medium Skin Tone
Woman Bouncing Ball: Medium-Dark Skin Tone
Woman Bouncing Ball: Dark Skin Tone
Person Lifting Weights
Person Lifting Weights: Light Skin Tone
Person Lifting Weights: Medium-Light Skin Tone
Person Lifting Weights: Medium Skin Tone
Person Lifting Weights: Medium-Dark Skin Tone
Person Lifting Weights: Dark Skin Tone
Man Lifting Weights
Man Lifting Weights: Light Skin Tone
Man Lifting Weights: Medium-Light Skin Tone
Man Lifting Weights: Medium Skin Tone
Man Lifting Weights: Medium-Dark Skin Tone
Man Lifting Weights: Dark Skin Tone
Woman Lifting Weights
Woman Lifting Weights: Light Skin Tone
Woman Lifting Weights: Medium-Light Skin Tone
Woman Lifting Weights: Medium Skin Tone
Woman Lifting Weights: Medium-Dark Skin Tone
Woman Lifting Weights: Dark Skin Tone
Person Biking
Person Biking: Light Skin Tone
Person Biking: Medium-Light Skin Tone
Person Biking: Medium Skin Tone
Person Biking: Medium-Dark Skin Tone
Person Biking: Dark Skin Tone
Man Biking
Man Biking: Light Skin Tone
Man Biking: Medium-Light Skin Tone
Man Biking: Medium Skin Tone
Man Biking: Medium-Dark Skin Tone
Man Biking: Dark Skin Tone
Woman Biking
Woman Biking: Light Skin Tone
Woman Biking: Medium-Light Skin Tone
Woman Biking: Medium Skin Tone
Woman Biking: Medium-Dark Skin Tone
Woman Biking: Dark Skin Tone
Person Mountain Biking
Person Mountain Biking: Light Skin Tone
Person Mountain Biking: Medium-Light Skin Tone
Person Mountain Biking: Medium Skin Tone
Person Mountain Biking: Medium-Dark Skin Tone
Person Mountain Biking: Dark Skin Tone
Man Mountain Biking
Man Mountain Biking: Light Skin Tone
Man Mountain Biking: Medium-Light Skin Tone
Man Mountain Biking: Medium Skin Tone
Man Mountain Biking: Medium-Dark Skin Tone
Man Mountain Biking: Dark Skin Tone
Woman Mountain Biking
Woman Mountain Biking: Light Skin Tone
Woman Mountain Biking: Medium-Light Skin Tone
Woman Mountain Biking: Medium Skin Tone
Woman Mountain Biking: Medium-Dark Skin Tone
Woman Mountain Biking: Dark Skin Tone
Racing Car
Motorcycle
Person Cartwheeling
Person Cartwheeling: Light Skin Tone
Person Cartwheeling: Medium-Light Skin Tone
Person Cartwheeling: Medium Skin Tone
Person Cartwheeling: Medium-Dark Skin Tone
Person Cartwheeling: Dark Skin Tone
Man Cartwheeling
Man Cartwheeling: Light Skin Tone
Man Cartwheeling: Medium-Light Skin Tone
Man Cartwheeling: Medium Skin Tone
Man Cartwheeling: Medium-Dark Skin Tone
Man Cartwheeling: Dark Skin Tone
Woman Cartwheeling
Woman Cartwheeling: Light Skin Tone
Woman Cartwheeling: Medium-Light Skin Tone
Woman Cartwheeling: Medium Skin Tone
Woman Cartwheeling: Medium-Dark Skin Tone
Woman Cartwheeling: Dark Skin Tone
People Wrestling
Men Wrestling
Women Wrestling
Person Playing Water Polo
Person Playing Water Polo: Light Skin Tone
Person Playing Water Polo: Medium-Light Skin Tone
Person Playing Water Polo: Medium Skin Tone
Person Playing Water Polo: Medium-Dark Skin Tone
Person Playing Water Polo: Dark Skin Tone
Man Playing Water Polo
Man Playing Water Polo: Light Skin Tone
Man Playing Water Polo: Medium-Light Skin Tone
Man Playing Water Polo: Medium Skin Tone
Man Playing Water Polo: Medium-Dark Skin Tone
Man Playing Water Polo: Dark Skin Tone
Woman Playing Water Polo
Woman Playing Water Polo: Light Skin Tone
Woman Playing Water Polo: Medium-Light Skin Tone
Woman Playing Water Polo: Medium Skin Tone
Woman Playing Water Polo: Medium-Dark Skin Tone
Woman Playing Water Polo: Dark Skin Tone
Person Playing Handball
Person Playing Handball: Light Skin Tone
Person Playing Handball: Medium-Light Skin Tone
Person Playing Handball: Medium Skin Tone
Person Playing Handball: Medium-Dark Skin Tone
Person Playing Handball: Dark Skin Tone
Man Playing Handball
Man Playing Handball: Light Skin Tone
Man Playing Handball: Medium-Light Skin Tone
Man Playing Handball: Medium Skin Tone
Man Playing Handball: Medium-Dark Skin Tone
Man Playing Handball: Dark Skin Tone
Woman Playing Handball
Woman Playing Handball: Light Skin Tone
Woman Playing Handball: Medium-Light Skin Tone
Woman Playing Handball: Medium Skin Tone
Woman Playing Handball: Medium-Dark Skin Tone
Woman Playing Handball: Dark Skin Tone
Person Juggling
Person Juggling: Light Skin Tone
Person Juggling: Medium-Light Skin Tone
Person Juggling: Medium Skin Tone
Person Juggling: Medium-Dark Skin Tone
Person Juggling: Dark Skin Tone
Man Juggling
Man Juggling: Light Skin Tone
Man Juggling: Medium-Light Skin Tone
Man Juggling: Medium Skin Tone
Man Juggling: Medium-Dark Skin Tone
Man Juggling: Dark Skin Tone
Woman Juggling
Woman Juggling: Light Skin Tone
Woman Juggling: Medium-Light Skin Tone
Woman Juggling: Medium Skin Tone
Woman Juggling: Medium-Dark Skin Tone
Woman Juggling: Dark Skin Tone
Wrestlers, Type-1-2
Wrestlers, Type-3
Man and Woman Holding Hands
Wrestlers, Type-4
Two Men Holding Hands
Wrestlers, Type-5
Two Women Holding Hands
Wrestlers, Type-6
Kiss
Kiss: Woman, Man
Men Wrestling, Type-1-2
Men Wrestling, Type-3
Men Wrestling, Type-4
Kiss: Man, Man
Men Wrestling, Type-5
Men Wrestling, Type-6
Kiss: Woman, Woman
Women Wrestling, Type-1-2
Couple With Heart
Women Wrestling, Type-3
Couple With Heart: Woman, Man
Women Wrestling, Type-4
Women Wrestling, Type-5
Couple With Heart: Man, Man
Women Wrestling, Type-6
Couple With Heart: Woman, Woman
Family
Family: Man, Woman, Boy
Family: Man, Woman, Girl
Family: Man, Woman, Girl, Boy
Family: Man, Woman, Boy, Boy
Family: Man, Woman, Girl, Girl
Family: Man, Man, Boy
Family: Man, Man, Girl
Family: Man, Man, Girl, Boy
Family: Man, Man, Boy, Boy
Family: Man, Man, Girl, Girl
Family: Woman, Woman, Boy
Family: Woman, Woman, Girl
Family: Woman, Woman, Girl, Boy
Family: Woman, Woman, Boy, Boy
Family: Woman, Woman, Girl, Girl
Family: Man, Boy
Family: Man, Boy, Boy
Family: Man, Girl
Family: Man, Girl, Boy
Family: Man, Girl, Girl
Family: Woman, Boy
Family: Woman, Boy, Boy
Family: Woman, Girl
Family: Woman, Girl, Boy
Family: Woman, Girl, Girl
Selfie
Selfie: Light Skin Tone
Selfie: Medium-Light Skin Tone
Selfie: Medium Skin Tone
Selfie: Medium-Dark Skin Tone
Selfie: Dark Skin Tone
Flexed Biceps
Flexed Biceps: Light Skin Tone
Flexed Biceps: Medium-Light Skin Tone
Flexed Biceps: Medium Skin Tone
Flexed Biceps: Medium-Dark Skin Tone
Flexed Biceps: Dark Skin Tone
Backhand Index Pointing Left
Backhand Index Pointing Left: Light Skin Tone
Backhand Index Pointing Left: Medium-Light Skin Tone
Backhand Index Pointing Left: Medium Skin Tone
Backhand Index Pointing Left: Medium-Dark Skin Tone
Backhand Index Pointing Left: Dark Skin Tone
Backhand Index Pointing Right
Backhand Index Pointing Right: Light Skin Tone
Backhand Index Pointing Right: Medium-Light Skin Tone
Backhand Index Pointing Right: Medium Skin Tone
Backhand Index Pointing Right: Medium-Dark Skin Tone
Backhand Index Pointing Right: Dark Skin Tone
Index Pointing Up
Index Pointing Up: Light Skin Tone
Index Pointing Up: Medium-Light Skin Tone
Index Pointing Up: Medium Skin Tone
Index Pointing Up: Medium-Dark Skin Tone
Index Pointing Up: Dark Skin Tone
Backhand Index Pointing Up
Backhand Index Pointing Up: Light Skin Tone
Backhand Index Pointing Up: Medium-Light Skin Tone
Backhand Index Pointing Up: Medium Skin Tone
Backhand Index Pointing Up: Medium-Dark Skin Tone
Backhand Index Pointing Up: Dark Skin Tone
Middle Finger
Middle Finger: Light Skin Tone
Middle Finger: Medium-Light Skin Tone
Middle Finger: Medium Skin Tone
Middle Finger: Medium-Dark Skin Tone
Middle Finger: Dark Skin Tone
Backhand Index Pointing Down
Backhand Index Pointing Down: Light Skin Tone
Backhand Index Pointing Down: Medium-Light Skin Tone
Backhand Index Pointing Down: Medium Skin Tone
Backhand Index Pointing Down: Medium-Dark Skin Tone
Backhand Index Pointing Down: Dark Skin Tone
Victory Hand
Victory Hand: Light Skin Tone
Victory Hand: Medium-Light Skin Tone
Victory Hand: Medium Skin Tone
Victory Hand: Medium-Dark Skin Tone
Victory Hand: Dark Skin Tone
Crossed Fingers
Crossed Fingers: Light Skin Tone
Crossed Fingers: Medium-Light Skin Tone
Crossed Fingers: Medium Skin Tone
Crossed Fingers: Medium-Dark Skin Tone
Crossed Fingers: Dark Skin Tone
Vulcan Salute
Vulcan Salute: Light Skin Tone
Vulcan Salute: Medium-Light Skin Tone
Vulcan Salute: Medium Skin Tone
Vulcan Salute: Medium-Dark Skin Tone
Vulcan Salute: Dark Skin Tone
Sign of the Horns
Sign of the Horns: Light Skin Tone
Sign of the Horns: Medium-Light Skin Tone
Sign of the Horns: Medium Skin Tone
Sign of the Horns: Medium-Dark Skin Tone
Sign of the Horns: Dark Skin Tone
Call Me Hand
Call Me Hand: Light Skin Tone
Call Me Hand: Medium-Light Skin Tone
Call Me Hand: Medium Skin Tone
Call Me Hand: Medium-Dark Skin Tone
Call Me Hand: Dark Skin Tone
Raised Hand With Fingers Splayed
Raised Hand With Fingers Splayed: Light Skin Tone
Raised Hand With Fingers Splayed: Medium-Light Skin Tone
Raised Hand With Fingers Splayed: Medium Skin Tone
Raised Hand With Fingers Splayed: Medium-Dark Skin Tone
Raised Hand With Fingers Splayed: Dark Skin Tone
Raised Hand
Raised Hand: Light Skin Tone
Raised Hand: Medium-Light Skin Tone
Raised Hand: Medium Skin Tone
Raised Hand: Medium-Dark Skin Tone
Raised Hand: Dark Skin Tone
OK Hand
OK Hand: Light Skin Tone
OK Hand: Medium-Light Skin Tone
OK Hand: Medium Skin Tone
OK Hand: Medium-Dark Skin Tone
OK Hand: Dark Skin Tone
Thumbs Up
Thumbs Up: Light Skin Tone
Thumbs Up: Medium-Light Skin Tone
Thumbs Up: Medium Skin Tone
Thumbs Up: Medium-Dark Skin Tone
Thumbs Up: Dark Skin Tone
Thumbs Down
Thumbs Down: Light Skin Tone
Thumbs Down: Medium-Light Skin Tone
Thumbs Down: Medium Skin Tone
Thumbs Down: Medium-Dark Skin Tone
Thumbs Down: Dark Skin Tone
Raised Fist
Raised Fist: Light Skin Tone
Raised Fist: Medium-Light Skin Tone
Raised Fist: Medium Skin Tone
Raised Fist: Medium-Dark Skin Tone
Raised Fist: Dark Skin Tone
Oncoming Fist
Oncoming Fist: Light Skin Tone
Oncoming Fist: Medium-Light Skin Tone
Oncoming Fist: Medium Skin Tone
Oncoming Fist: Medium-Dark Skin Tone
Oncoming Fist: Dark Skin Tone
Left-Facing Fist
Left-Facing Fist: Light Skin Tone
Left-Facing Fist: Medium-Light Skin Tone
Left-Facing Fist: Medium Skin Tone
Left-Facing Fist: Medium-Dark Skin Tone
Left-Facing Fist: Dark Skin Tone
Right-Facing Fist
Right-Facing Fist: Light Skin Tone
Right-Facing Fist: Medium-Light Skin Tone
Right-Facing Fist: Medium Skin Tone
Right-Facing Fist: Medium-Dark Skin Tone
Right-Facing Fist: Dark Skin Tone
Raised Back of Hand
Raised Back of Hand: Light Skin Tone
Raised Back of Hand: Medium-Light Skin Tone
Raised Back of Hand: Medium Skin Tone
Raised Back of Hand: Medium-Dark Skin Tone
Raised Back of Hand: Dark Skin Tone
Waving Hand
Waving Hand: Light Skin Tone
Waving Hand: Medium-Light Skin Tone
Waving Hand: Medium Skin Tone
Waving Hand: Medium-Dark Skin Tone
Waving Hand: Dark Skin Tone
Love-You Gesture
Love-You Gesture: Light Skin Tone
Love-You Gesture: Medium-Light Skin Tone
Love-You Gesture: Medium Skin Tone
Love-You Gesture: Medium-Dark Skin Tone
Love-You Gesture: Dark Skin Tone
Writing Hand
Writing Hand: Light Skin Tone
Writing Hand: Medium-Light Skin Tone
Writing Hand: Medium Skin Tone
Writing Hand: Medium-Dark Skin Tone
Writing Hand: Dark Skin Tone
Clapping Hands
Clapping Hands: Light Skin Tone
Clapping Hands: Medium-Light Skin Tone
Clapping Hands: Medium Skin Tone
Clapping Hands: Medium-Dark Skin Tone
Clapping Hands: Dark Skin Tone
Open Hands
Open Hands: Light Skin Tone
Open Hands: Medium-Light Skin Tone
Open Hands: Medium Skin Tone
Open Hands: Medium-Dark Skin Tone
Open Hands: Dark Skin Tone
Raising Hands
Raising Hands: Light Skin Tone
Raising Hands: Medium-Light Skin Tone
Raising Hands: Medium Skin Tone
Raising Hands: Medium-Dark Skin Tone
Raising Hands: Dark Skin Tone
Palms Up Together
Palms Up Together: Light Skin Tone
Palms Up Together: Medium-Light Skin Tone
Palms Up Together: Medium Skin Tone
Palms Up Together: Medium-Dark Skin Tone
Palms Up Together: Dark Skin Tone
Folded Hands
Folded Hands: Light Skin Tone
Folded Hands: Medium-Light Skin Tone
Folded Hands: Medium Skin Tone
Folded Hands: Medium-Dark Skin Tone
Folded Hands: Dark Skin Tone
Handshake
Nail Polish
Nail Polish: Light Skin Tone
Nail Polish: Medium-Light Skin Tone
Nail Polish: Medium Skin Tone
Nail Polish: Medium-Dark Skin Tone
Nail Polish: Dark Skin Tone
Ear
Ear: Light Skin Tone
Ear: Medium-Light Skin Tone
Ear: Medium Skin Tone
Ear: Medium-Dark Skin Tone
Ear: Dark Skin Tone
Nose
Nose: Light Skin Tone
Nose: Medium-Light Skin Tone
Nose: Medium Skin Tone
Nose: Medium-Dark Skin Tone
Nose: Dark Skin Tone
Footprints
Eyes
Eye
Eye in Speech Bubble
Brain
Tongue
Mouth
Kiss Mark
Heart With Arrow
Red Heart
Beating Heart
Broken Heart
Two Hearts
Sparkling Heart
Growing Heart
Blue Heart
Green Heart
Yellow Heart
Orange Heart
Purple Heart
Black Heart
Heart With Ribbon
Revolving Hearts
Heart Decoration
Heavy Heart Exclamation
Love Letter
Zzz
Anger Symbol
Bomb
Collision
Sweat Droplets
Dashing Away
Dizzy
Speech Balloon
Left Speech Bubble
Right Anger Bubble
Thought Balloon
Hole
Glasses
Sunglasses
Necktie
T-Shirt
Jeans
Scarf
Gloves
Coat
Socks
Dress
Kimono
Bikini
Womans Clothes
Purse
Handbag
Clutch Bag
Shopping Bags
School Backpack
Mans Shoe
Running Shoe
High-Heeled Shoe
Womans Sandal
Womans Boot
Crown
Womans Hat
Top Hat
Graduation Cap
Billed Cap
Rescue Workers Helmet
Prayer Beads
Lipstick
Ring
Gem Stone
Monkey Face
Monkey
Gorilla
Dog Face
Dog
Poodle
Wolf Face
Fox Face
Cat Face
Cat
Lion Face
Tiger Face
Tiger
Leopard
Horse Face
Horse
Unicorn Face
Zebra
Deer
Cow Face
Ox
Water Buffalo
Cow
Pig Face
Pig
Boar
Pig Nose
Ram
Ewe
Goat
Camel
Two-Hump Camel
Giraffe
Elephant
Rhinoceros
Mouse Face
Mouse
Rat
Hamster Face
Rabbit Face
Rabbit
Chipmunk
Hedgehog
Bat
Bear Face
Koala
Panda Face
Paw Prints
Turkey
Chicken
Rooster
Hatching Chick
Baby Chick
Front-Facing Baby Chick
Bird
Penguin
Dove
Eagle
Duck
Owl
Frog Face
Crocodile
Turtle
Lizard
Snake
Dragon Face
Dragon
Sauropod
T-Rex
Spouting Whale
Whale
Dolphin
Fish
Tropical Fish
Blowfish
Shark
Octopus
Spiral Shell
Crab
Shrimp
Squid
Snail
Butterfly
Bug
Ant
Honeybee
Lady Beetle
Cricket
Spider
Spider Web
Scorpion
Bouquet
Cherry Blossom
White Flower
Rosette
Rose
Wilted Flower
Hibiscus
Sunflower
Blossom
Tulip
Seedling
Evergreen Tree
Deciduous Tree
Palm Tree
Cactus
Sheaf of Rice
Herb
Shamrock
Four Leaf Clover
Maple Leaf
Fallen Leaf
Leaf Fluttering in Wind
Grapes
Melon
Watermelon
Tangerine
Lemon
Banana
Pineapple
Red Apple
Green Apple
Pear
Peach
Cherries
Strawberry
Kiwi Fruit
Tomato
Coconut
Avocado
Eggplant
Potato
Carrot
Ear of Corn
Hot Pepper
Cucumber
Broccoli
Mushroom
Peanuts
Chestnut
Bread
Croissant
Baguette Bread
Pretzel
Pancakes
Cheese Wedge
Meat on Bone
Poultry Leg
Cut of Meat
Bacon
Hamburger
French Fries
Pizza
Hot Dog
Sandwich
Taco
Burrito
Stuffed Flatbread
Egg
Cooking
Shallow Pan of Food
Pot of Food
Bowl With Spoon
Green Salad
Popcorn
Canned Food
Bento Box
Rice Cracker
Rice Ball
Cooked Rice
Curry Rice
Steaming Bowl
Spaghetti
Roasted Sweet Potato
Oden
Sushi
Fried Shrimp
Fish Cake With Swirl
Dango
Dumpling
Fortune Cookie
Takeout Box
Soft Ice Cream
Shaved Ice
Ice Cream
Doughnut
Cookie
Birthday Cake
Shortcake
Pie
Chocolate Bar
Candy
Lollipop
Custard
Honey Pot
Baby Bottle
Glass of Milk
Hot Beverage
Teacup Without Handle
Sake
Bottle With Popping Cork
Wine Glass
Cocktail Glass
Tropical Drink
Beer Mug
Clinking Beer Mugs
Clinking Glasses
Tumbler Glass
Cup With Straw
Chopsticks
Fork and Knife With Plate
Fork and Knife
Spoon
Kitchen Knife
Amphora
Globe Showing Europe-Africa
Globe Showing Americas
Globe Showing Asia-Australia
Globe With Meridians
World Map
Map of Japan
Snow-Capped Mountain
Mountain
Volcano
Mount Fuji
Camping
Beach With Umbrella
Desert
Desert Island
National Park
Stadium
Classical Building
Building Construction
House
Cityscape
Derelict House
House
House With Garden
Office Building
Japanese Post Office
Post Office
Hospital
Bank
Hotel
Love Hotel
Convenience Store
School
Department Store
Factory
Japanese Castle
Castle
Wedding
Tokyo Tower
Statue of Liberty
Church
Mosque
Synagogue
Shinto Shrine
Kaaba
Fountain
Tent
Foggy
Night With Stars
Sunrise Over Mountains
Sunrise
Cityscape at Dusk
Sunset
Bridge at Night
Hot Springs
Milky Way
Carousel Horse
Ferris Wheel
Roller Coaster
Barber Pole
Circus Tent
Performing Arts
Framed Picture
Artist Palette
Slot Machine
Locomotive
Railway Car
High-Speed Train
High-Speed Train With Bullet Nose
Train
Metro
Light Rail
Station
Tram
Monorail
Mountain Railway
Tram Car
Bus
Oncoming Bus
Trolleybus
Minibus
Ambulance
Fire Engine
Police Car
Oncoming Police Car
Taxi
Oncoming Taxi
Automobile
Oncoming Automobile
Sport Utility Vehicle
Delivery Truck
Articulated Lorry
Tractor
Bicycle
Kick Scooter
Motor Scooter
Bus Stop
Motorway
Railway Track
Fuel Pump
Police Car Light
Horizontal Traffic Light
Vertical Traffic Light
Construction
Stop Sign
Anchor
Sailboat
Canoe
Speedboat
Passenger Ship
Ferry
Motor Boat
Ship
Airplane
Small Airplane
Airplane Departure
Airplane Arrival
Seat
Helicopter
Suspension Railway
Mountain Cableway
Aerial Tramway
Satellite
Rocket
Flying Saucer
Bellhop Bell
Door
Bed
Couch and Lamp
Toilet
Shower
Bathtub
Hourglass
Hourglass With Flowing Sand
Watch
Alarm Clock
Stopwatch
Timer Clock
Mantelpiece Clock
Twelve Oclock
Twelve-Thirty
One Oclock
One-Thirty
Two Oclock
Two-Thirty
Three Oclock
Three-Thirty
Four Oclock
Four-Thirty
Five Oclock
Five-Thirty
Six Oclock
Six-Thirty
Seven Oclock
Seven-Thirty
Eight Oclock
Eight-Thirty
Nine Oclock
Nine-Thirty
Ten Oclock
Ten-Thirty
Eleven Oclock
Eleven-Thirty
New Moon
Waxing Crescent Moon
First Quarter Moon
Waxing Gibbous Moon
Full Moon
Waning Gibbous Moon
Last Quarter Moon
Waning Crescent Moon
Crescent Moon
New Moon Face
First Quarter Moon With Face
Last Quarter Moon With Face
Thermometer
Sun
Full Moon With Face
Sun With Face
White Medium Star
Glowing Star
Shooting Star
Cloud
Sun Behind Cloud
Cloud With Lightning and Rain
Sun Behind Small Cloud
Sun Behind Large Cloud
Sun Behind Rain Cloud
Cloud With Rain
Cloud With Snow
Cloud With Lightning
Tornado
Fog
Wind Face
Cyclone
Rainbow
Closed Umbrella
Umbrella
Umbrella With Rain Drops
Umbrella on Ground
High Voltage
Snowflake
Snowman
Snowman Without Snow
Comet
Fire
Droplet
Water Wave
Jack-O-Lantern
Christmas Tree
Fireworks
Sparkler
Sparkles
Balloon
Party Popper
Confetti Ball
Tanabata Tree
Pine Decoration
Japanese Dolls
Carp Streamer
Wind Chime
Moon Viewing Ceremony
Ribbon
Wrapped Gift
Reminder Ribbon
Admission Tickets
Ticket
Military Medal
Trophy
Sports Medal
1st Place Medal
2nd Place Medal
3rd Place Medal
Soccer Ball
Baseball
Basketball
Volleyball
American Football
Rugby Football
Tennis
Pool 8 Ball
Bowling
Cricket
Field Hockey
Ice Hockey
Ping Pong
Badminton
Boxing Glove
Martial Arts Uniform
Goal Net
Direct Hit
Flag in Hole
Ice Skate
Fishing Pole
Running Shirt
Skis
Sled
Curling Stone
Video Game
Joystick
Game Die
Spade Suit
Heart Suit
Diamond Suit
Club Suit
Joker
Mahjong Red Dragon
Flower Playing Cards
Muted Speaker
Speaker Low Volume
Speaker Medium Volume
Speaker High Volume
Loudspeaker
Megaphone
Postal Horn
Bell
Bell With Slash
Musical Score
Musical Note
Musical Notes
Studio Microphone
Level Slider
Control Knobs
Microphone
Headphone
Radio
Saxophone
Guitar
Musical Keyboard
Trumpet
Violin
Drum
Mobile Phone
Mobile Phone With Arrow
Telephone
Telephone Receiver
Pager
Fax Machine
Battery
Electric Plug
Laptop Computer
Desktop Computer
Printer
Keyboard
Computer Mouse
Trackball
Computer Disk
Floppy Disk
Optical Disk
DVD
Movie Camera
Film Frames
Film Projector
Clapper Board
Television
Camera
Camera With Flash
Video Camera
Videocassette
Left-Pointing Magnifying Glass
Right-Pointing Magnifying Glass
Microscope
Telescope
Satellite Antenna
Candle
Light Bulb
Flashlight
Red Paper Lantern
Notebook With Decorative Cover
Closed Book
Open Book
Green Book
Blue Book
Orange Book
Books
Notebook
Ledger
Page With Curl
Scroll
Page Facing Up
Newspaper
Rolled-Up Newspaper
Bookmark Tabs
Bookmark
Label
Money Bag
Yen Banknote
Dollar Banknote
Euro Banknote
Pound Banknote
Money With Wings
Credit Card
Chart Increasing With Yen
Currency Exchange
Heavy Dollar Sign
Envelope
E-Mail
Incoming Envelope
Envelope With Arrow
Outbox Tray
Inbox Tray
Package
Closed Mailbox With Raised Flag
Closed Mailbox With Lowered Flag
Open Mailbox With Raised Flag
Open Mailbox With Lowered Flag
Postbox
Ballot Box With Ballot
Pencil
Black Nib
Fountain Pen
Pen
Paintbrush
Crayon
Memo
Briefcase
File Folder
Open File Folder
Card Index Dividers
Calendar
Tear-Off Calendar
Spiral Notepad
Spiral Calendar
Card Index
Chart Increasing
Chart Decreasing
Bar Chart
Clipboard
Pushpin
Round Pushpin
Paperclip
Linked Paperclips
Straight Ruler
Triangular Ruler
Scissors
Card File Box
File Cabinet
Wastebasket
Locked
Unlocked
Locked With Pen
Locked With Key
Key
Old Key
Hammer
Pick
Hammer and Pick
Hammer and Wrench
Dagger
Crossed Swords
Pistol
Bow and Arrow
Shield
Wrench
Nut and Bolt
Gear
Clamp
Alembic
Balance Scale
Link
Chains
Syringe
Pill
Cigarette
Coffin
Funeral Urn
Moai
Oil Drum
Crystal Ball
Shopping Cart
Atm Sign
Litter in Bin Sign
Potable Water
Wheelchair Symbol
Mens Room
Womens Room
Restroom
Baby Symbol
Water Closet
Passport Control
Customs
Baggage Claim
Left Luggage
Warning
Children Crossing
No Entry
Prohibited
No Bicycles
No Smoking
No Littering
Non-Potable Water
No Pedestrians
No Mobile Phones
No One Under Eighteen
Radioactive
Biohazard
Up Arrow
Up-Right Arrow
Right Arrow
Down-Right Arrow
Down Arrow
Down-Left Arrow
Left Arrow
Up-Left Arrow
Up-Down Arrow
Left-Right Arrow
Right Arrow Curving Left
Left Arrow Curving Right
Right Arrow Curving Up
Right Arrow Curving Down
Clockwise Vertical Arrows
Anticlockwise Arrows Button
Back Arrow
End Arrow
On! Arrow
Soon Arrow
Top Arrow
Place of Worship
Atom Symbol
Om
Star of David
Wheel of Dharma
Yin Yang
Latin Cross
Orthodox Cross
Star and Crescent
Peace Symbol
Menorah
Dotted Six-Pointed Star
Aries
Taurus
Gemini
Cancer
Leo
Virgo
Libra
Scorpius
Sagittarius
Capricorn
Aquarius
Pisces
Ophiuchus
Shuffle Tracks Button
Repeat Button
Repeat Single Button
Play Button
Fast-Forward Button
Next Track Button
Play or Pause Button
Reverse Button
Fast Reverse Button
Last Track Button
Up Button
Fast Up Button
Down Button
Fast Down Button
Pause Button
Stop Button
Record Button
Eject Button
Cinema
Dim Button
Bright Button
Antenna Bars
Vibration Mode
Mobile Phone Off
Female Sign
Male Sign
Medical Symbol
Recycling Symbol
Fleur-De-Lis
Trident Emblem
Name Badge
Japanese Symbol for Beginner
Heavy Large Circle
White Heavy Check Mark
Ballot Box With Check
Heavy Check Mark
Heavy Multiplication X
Cross Mark
Cross Mark Button
Heavy Plus Sign
Heavy Minus Sign
Heavy Division Sign
Curly Loop
Double Curly Loop
Part Alternation Mark
Eight-Spoked Asterisk
Eight-Pointed Star
Sparkle
Double Exclamation Mark
Exclamation Question Mark
Question Mark
White Question Mark
White Exclamation Mark
Exclamation Mark
Wavy Dash
Copyright
Registered
Trade Mark
# Keycap Number Sign
* Keycap Asterisk
0 Keycap Digit Zero
1 Keycap Digit One
2 Keycap Digit Two
3 Keycap Digit Three
4 Keycap Digit Four
5 Keycap Digit Five
6 Keycap Digit Six
7 Keycap Digit Seven
8 Keycap Digit Eight
9 Keycap Digit Nine
Keycap 10
Hundred Points
Input Latin Uppercase
Input Latin Lowercase
Input Numbers
Input Symbols
Input Latin Letters
A Button (blood Type)
Ab Button (blood Type)
B Button (blood Type)
CL Button
Cool Button
Free Button
Information
ID Button
Circled M
New Button
NG Button
O Button (blood Type)
OK Button
P Button
SOS Button
Up! Button
Vs Button
Japanese here Button
Japanese service Charge Button
Japanese monthly Amount Button
Japanese not Free of Charge Button
Japanese reserved Button
Japanese bargain Button
Japanese discount Button
Japanese free of Charge Button
Japanese prohibited Button
Japanese acceptable Button
Japanese application Button
Japanese passing Grade Button
Japanese vacancy Button
Japanese congratulations Button
Japanese secret Button
Japanese open for Business Button
Japanese no Vacancy Button
Black Small Square
White Small Square
White Medium Square
Black Medium Square
White Medium-Small Square
Black Medium-Small Square
Black Large Square
White Large Square
Large Orange Diamond
Large Blue Diamond
Small Orange Diamond
Small Blue Diamond
Red Triangle Pointed Up
Red Triangle Pointed Down
Diamond With a Dot
Radio Button
Black Square Button
White Square Button
White Circle
Black Circle
Red Circle
Blue Circle
Chequered Flag
Triangular Flag
Crossed Flags
Black Flag
White Flag
Rainbow Flag
Ascension Island
Andorra
United Arab Emirates
Afghanistan
Antigua & Barbuda
Anguilla
Albania
Armenia
Angola
Antarctica
Argentina
American Samoa
Austria
Australia
Aruba
land Islands
Azerbaijan
Bosnia & Herzegovina
Barbados
Bangladesh
Belgium
Burkina Faso
Bulgaria
Bahrain
Burundi
Benin
St. Barthlemy
Bermuda
Brunei
Bolivia
Caribbean Netherlands
Brazil
Bahamas
Bhutan
Bouvet Island
Botswana
Belarus
Belize
Canada
Cocos (Keeling) Islands
Congo - Kinshasa
Central African Republic
Congo - Brazzaville
Switzerland
Cte DIvoire
Cook Islands
Chile
Cameroon
China
Colombia
Clipperton Island
Costa Rica
Cuba
Cape Verde
Curaao
Christmas Island
Cyprus
Czechia
Germany
Diego Garcia
Djibouti
Denmark
Dominica
Dominican Republic
Algeria
Ceuta & Melilla
Ecuador
Estonia
Egypt
Western Sahara
Eritrea
Spain
Ethiopia
European Union
Finland
Fiji
Falkland Islands
Micronesia
Faroe Islands
France
Gabon
United Kingdom
Grenada
Georgia
French Guiana
Guernsey
Ghana
Gibraltar
Greenland
Gambia
Guinea
Guadeloupe
Equatorial Guinea
Greece
South Georgia & South Sandwich Islands
Guatemala
Guam
Guinea-Bissau
Guyana
Hong Kong Sar China
Heard & Mcdonald Islands
Honduras
Croatia
Haiti
Hungary
Canary Islands
Indonesia
Ireland
Israel
Isle of Man
India
British Indian Ocean Territory
Iraq
Iran
Iceland
Italy
Jersey
Jamaica
Jordan
Japan
Kenya
Kyrgyzstan
Cambodia
Kiribati
Comoros
St. Kitts & Nevis
North Korea
South Korea
Kuwait
Cayman Islands
Kazakhstan
Laos
Lebanon
St. Lucia
Liechtenstein
Sri Lanka
Liberia
Lesotho
Lithuania
Luxembourg
Latvia
Libya
Morocco
Monaco
Moldova
Montenegro
St. Martin
Madagascar
Marshall Islands
Macedonia
Mali
Myanmar (Burma)
Mongolia
Macau Sar China
Northern Mariana Islands
Martinique
Mauritania
Montserrat
Malta
Mauritius
Maldives
Malawi
Mexico
Malaysia
Mozambique
Namibia
New Caledonia
Niger
Norfolk Island
Nigeria
Nicaragua
Netherlands
Norway
Nepal
Nauru
Niue
New Zealand
Oman
Panama
Peru
French Polynesia
Papua New Guinea
Philippines
Pakistan
Poland
St. Pierre & Miquelon
Pitcairn Islands
Puerto Rico
Palestinian Territories
Portugal
Palau
Paraguay
Qatar
Runion
Romania
Serbia
Russia
Rwanda
Saudi Arabia
Solomon Islands
Seychelles
Sudan
Sweden
Singapore
St. Helena
Slovenia
Svalbard & Jan Mayen
Slovakia
Sierra Leone
San Marino
Senegal
Somalia
Suriname
South Sudan
So Tom & Prncipe
El Salvador
Sint Maarten
Syria
Swaziland
Tristan Da Cunha
Turks & Caicos Islands
Chad
French Southern Territories
Togo
Thailand
Tajikistan
Tokelau
Timor-Leste
Turkmenistan
Tunisia
Tonga
Turkey
Trinidad & Tobago
Tuvalu
Taiwan
Tanzania
Ukraine
Uganda
U.S. Outlying Islands
United Nations
United States
Uruguay
Uzbekistan
Vatican City
St. Vincent & Grenadines
Venezuela
British Virgin Islands
U.S. Virgin Islands
Vietnam
Vanuatu
Wallis & Futuna
Samoa
Kosovo
Yemen
Mayotte
South Africa
Zambia
Zimbabwe
Flag for England (GB-ENG)
Flag for Scotland (GB-SCT)
Flag for Wales (GB-WLS)
Rifle
Modern Pentathlon
Pirate Flag
Regional Indicator Symbol Letter A
Regional Indicator Symbol Letter B
Regional Indicator Symbol Letter C
Regional Indicator Symbol Letter D
Regional Indicator Symbol Letter E
Regional Indicator Symbol Letter F
Regional Indicator Symbol Letter G
Regional Indicator Symbol Letter H
Regional Indicator Symbol Letter I
Regional Indicator Symbol Letter J
Regional Indicator Symbol Letter K
Regional Indicator Symbol Letter L
Regional Indicator Symbol Letter M
Regional Indicator Symbol Letter N
Regional Indicator Symbol Letter O
Regional Indicator Symbol Letter P
Regional Indicator Symbol Letter Q
Regional Indicator Symbol Letter R
Regional Indicator Symbol Letter S
Regional Indicator Symbol Letter T
Regional Indicator Symbol Letter U
Regional Indicator Symbol Letter V
Regional Indicator Symbol Letter W
Regional Indicator Symbol Letter X
Regional Indicator Symbol Letter Y
Regional Indicator Symbol Letter Z
Dino Cat
Astro Cat
Ninja Cat
Hacker Cat
Stunt Cat
Hipster Cat
Olympic Rings
Flag for Baiti (NR-05)
Flag for Nord-Trndelag (NO-17)
Flag for Hordaland (NO-12)
Flag for Akershus (NO-02)
Flag for Sr-Trndelag (NO-16)
Flag for Telemark (NO-08)
Flag for Utrecht (NL-UT)
Flag for Mre og Romsdal (NO-15)
Flag for Svalbard (NO-21)
Flag for Purwanchal (NP-4)
Flag for Central (NP-1)
Flag for Oslo (NO-03)
Flag for Boe (NR-06)
Family - Man: Medium-Dark Skin Tone, Man: Medium-Dark Skin Tone, Boy: Medium-Dark Skin Tone, Girl: Medium-Dark Skin Tone
Flag for North Brabant (NL-NB)
Flag for Aust-Agder (NO-09)
Flag for Anabar (NR-02)
Flag for Limburg (NL-LI)
Flag for Buskerud (NO-06)
Flag for Hedmark (NO-04)
Flag for Vestfold (NO-07)
Flag for Anibare (NR-04)
Flag for Finnmark (NO-20)
Flag for Overijssel (NL-OV)
Flag for Rogaland (NO-11)
Flag for stfold (NO-01)
Flag for Aiwo (NR-01)
Flag for Zeeland (NL-ZE)
Flag for Buada (NR-07)
Flag for Troms (NO-19)
Flag for Oppland (NO-05)
Flag for Madhya Pashchimanchal (NP-2)
Flag for Anetan (NR-03)
Flag for Western (NP-3)
Flag for Jan Mayen (NO-22)
Flag for Nordland (NO-18)
Flag for Bocas del Toro (PA-1)
Flag for Coln (PA-3)
Flag for Ad Dakhiliyah (OM-DA)
Flag for Muscat (OM-MA)
Flag for Ewa (NR-09)
Flag for Taranaki (NZ-TKI)
Flag for Ijuw (NR-10)
Flag for West Coast (NZ-WTC)
Flag for Southland (NZ-STL)
Flag for Tasman (NZ-TAS)
Flag for Manawatu-Wanganui (NZ-MWT)
Flag for Waikato (NZ-WKO)
Flag for Marl (NZ-MBH)
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Flag for Bong (LR-BG)
Flag for Deir al-Balah (PS-DEB)
Family - Man: Dark Skin Tone, Man: Dark Skin Tone, Girl: Dark Skin Tone
Flag for Saint Thomas (JM-03)
Flag for Kayangel (PW-100)
Flag for Pool (CG-12)
Couple With Heart - Man, Man: Medium-Dark Skin Tone
Flag for Balearic Islands (ES-IB)
Family: Woman, Man, Boy
Flag for Uusimaa (FI-18)
Family - Man: Light Skin Tone, Woman: Light Skin Tone, Boy: Light Skin Tone, Girl: Light Skin Tone
Flag for Cear (BR-CE)
Family: Man, Woman, Boy, Baby
Family - Man: Light Skin Tone, Man: Light Skin Tone, Girl: Light Skin Tone, Boy: Light Skin Tone
Flag for Demir Hisar (MK-25)
Flag for Antofagasta (CL-AN)
Flag for Christ Church (BB-01)
Flag for Harju (EE-37)
Kiss - Man: Dark Skin Tone, Woman: Medium Skin Tone
Flag for Yaren (NR-14)
Couple With Heart - Woman, Woman: Light Skin Tone
Flag for Selangor (MY-10)
Family - Man: Medium-Light Skin Tone, Man: Medium-Light Skin Tone, Girl: Medium-Light Skin Tone, Boy: Medium-Light Skin Tone
Flag for Apurmac (PE-APU)
Family: Woman, Man, Boy, Girl
Family - Man: Dark Skin Tone, Woman: Dark Skin Tone, Girl: Dark Skin Tone
Flag for Abkhazia (GE-AB)
Flag for Schellenberg (LI-08)
Flag for Dzce (TR-81)
Family - Woman: Medium-Dark Skin Tone, Girl: Medium-Dark Skin Tone, Girl: Medium-Dark Skin Tone
Family: Woman, Man, Baby, Boy
Flag for Sonora (MX-SON)
Flag for Sassandra-Marahou (CI-SM)
Flag for Arequipa (PE-ARE)
Couple With Heart - Woman: Medium Skin Tone, Woman: Medium-Light Skin Tone
Flag for Bouenza (CG-11)
Flag for Saint Catherine (JM-14)
Flag for kofja Loka (SI-122)
Kiss - Woman: Light Skin Tone, Man: Medium-Light Skin Tone
Flag for Hsinchu (TW-HSZ)
Family - Woman: Medium-Light Skin Tone, Girl: Medium-Light Skin Tone, Boy: Medium-Light Skin Tone
Flag for Southern (LK-3)
Kiss - Man, Man: Medium-Light Skin Tone
Family - Man: Medium Skin Tone, Man: Medium Skin Tone, Girl: Medium Skin Tone, Boy: Medium Skin Tone
Flag for Len (NI-LE)
Flag for Varadin (HR-05)
Flag for Antioquia (CO-ANT)
Flag for Sainte-Dvote Chapel (MC-SD)
Flag for Plasnica (MK-61)
Couple With Heart - Man: Medium-Dark Skin Tone, Man: Light Skin Tone
Flag for West Greece (GR-G)
Flag for North Province (MV-NO)
Couple With Heart - Man, Woman: Light Skin Tone
Flag for Apure (VE-C)
Mercury
Flag for Montana (US-MT)
Couple With Heart - Woman: Medium-Light Skin Tone, Man: Medium-Dark Skin Tone
Family - Man: Medium-Dark Skin Tone, Man: Medium-Dark Skin Tone, Girl: Medium-Dark Skin Tone, Boy: Medium-Dark Skin Tone
Flag for Esmeraldas (EC-E)
Flag for Bchar (DZ-08)
Flag for North Holland (NL-NH)
Flag for St. Barthlemy (FR-BL)
Flag for Ouaka (CF-UK)
Flag for Red Sea (SD-RS)
Flag for Tabasco (MX-TAB)
Flag for Macau SAR China (CN-92)
Flag for Eger (HU-EG)
Flag for North Ossetia-Alania (RU-SE)
Flag for quateur (CD-EQ)
Family - Man: Dark Skin Tone, Man: Dark Skin Tone, Girl: Dark Skin Tone, Boy: Dark Skin Tone
Flag for Basque Country (ES-PV)
Kiss - Man: Medium Skin Tone, Man: Light Skin Tone
Flag for Gafsa (TN-71)
Flag for Tavastia Proper (FI-06)
Flag for Razavi Khorasan (IR-30)
Flag for Dobje (SI-154)
Kiss - Man: Medium-Light Skin Tone, Man: Light Skin Tone
Flag for Retalhuleu (GT-RE)
Flag for Line Islands (KI-L)
Flag for West Azarbaijan (IR-02)
Flag for Nario (CO-NAR)
Flag for Mashonaland Central (ZW-MC)
Couple With Heart - Man: Light Skin Tone, Man: Light Skin Tone
Flag for Emilia-Romagna (IT-45)
Flag for Valencian Community (ES-VC)
Flag for Samut Songkhram (TH-75)
Flag for le-de-France (FR-IDF)
Flag for Maseru (LS-A)
Flag for Marsabit (KE-25)
Flag for Adrar (DZ-01)
Flag for Usulutn (SV-US)
Flag for Mazsalaca (LV-060)
Kiss - Woman: Light Skin Tone, Woman: Medium-Dark Skin Tone
Family - Man: Medium-Dark Skin Tone, Boy: Medium-Dark Skin Tone, Boy: Medium-Dark Skin Tone
Flag for Chaiyaphum (TH-36)
Flag for Central Visayas (PH-07)
Flag for Chumphon (TH-86)
Flag for Zanzan (CI-ZZ)
Flag for Castile and Len (ES-CL)
Family - Man: Light Skin Tone, Man: Light Skin Tone, Girl: Light Skin Tone, Girl: Light Skin Tone
Flag for Al Bahah (SA-11)
Flag for Sint Eustatius (BQ-SE)
Flag for land Islands (FI-01)
Flag for Heredia (CR-H)
Flag for Ktahya (TR-43)
Flag for Vaisigano (WS-VS)
Kiss - Man: Dark Skin Tone, Woman: Medium-Light Skin Tone
Flag for Kranj (SI-052)
Flag for Zulia (VE-V)
Kiss - Woman: Medium Skin Tone, Man: Medium-Light Skin Tone
Flag for Capellen (LU-CA)
Couple With Heart - Woman: Medium Skin Tone, Woman: Medium-Dark Skin Tone
Family - Man: Medium-Light Skin Tone, Man: Medium-Light Skin Tone, Girl: Medium-Light Skin Tone, Girl: Medium-Light Skin Tone
Flag for East Berbice-Corentyne (GY-EB)
Flag for Lopburi (TH-16)
Flag for Luqa (MT-25)
Couple With Heart - Man: Light Skin Tone, Man: Medium-Light Skin Tone
Family - Man: Medium Skin Tone, Man: Medium Skin Tone, Girl: Medium Skin Tone, Girl: Medium Skin Tone
Couple With Heart - Woman: Light Skin Tone, Woman: Medium Skin Tone
Flag for Baja California Sur (MX-BCS)
Flag for Beni Suef (EG-BNS)
Flag for Phatthalung (TH-93)
Flag for Tanga (TZ-25)
Flag for Oriental (MA-04)
Family - Man: Medium-Dark Skin Tone, Man: Medium-Dark Skin Tone, Girl: Medium-Dark Skin Tone, Girl: Medium-Dark Skin Tone
Family - Man: Dark Skin Tone, Woman: Dark Skin Tone, Baby: Dark Skin Tone
Flag for Gorenja VasPoljane (SI-027)
Flag for Sangre Grande (TT-SGE)
Flag for Koknese (LV-046)
Flag for Odranci (SI-086)
Flag for Nelson (NZ-NSN)
Flag for Szabolcs-Szatmr-Bereg (HU-SZ)
Kiss - Woman: Medium-Dark Skin Tone, Man: Medium Skin Tone
Flag for Sveti Jurij v Slovenskih Goricah (SI-210)
NKo Symbol Gbakurunen
Flag for Delta (NG-DE)
Flag for Cueni (MD-CS)
Family - Woman: Medium Skin Tone, Girl: Medium Skin Tone, Boy: Medium Skin Tone
Flag for Isla de la Juventud (CU-99)
Flag for Svay Rieng (KH-20)
Flag for Hadjer-Lamis (TD-HL)
Flag for Gifu (JP-21)
Flag for Jelgava Municipality (LV-041)
Flag for Federally Administered Tribal Areas (PK-TA)
Flag for Xewkija (MT-62)
Flag for Guidimaka (MR-10)
Flag for Arainovo (MK-02)
Flag for LogDragomer (SI-208)
Flag for martno ob Paki (SI-125)
Flag for Capital District (CO-DC)
Flag for Ventspils Municipality (LV-106)
Flag for South Central Province (MV-SC)
Flag for Assam (IN-AS)
Flag for Alytus Municipality (LT-02)
Flag for Hng Yn (VN-66)
Family - Man: Light Skin Tone, Man: Light Skin Tone, Girl: Light Skin Tone, Baby: Light Skin Tone
Family - Man: Medium-Light Skin Tone, Man: Medium-Light Skin Tone, Girl: Medium-Light Skin Tone, Baby: Medium-Light Skin Tone
Flag for San Marcos (GT-SM)
Family - Man: Medium-Light Skin Tone, Man: Medium-Light Skin Tone, Boy: Medium-Light Skin Tone, Boy: Medium-Light Skin Tone
Flag for Schleswig-Holstein (DE-SH)
Family: Man, Man, Baby, Girl
Variation Selector-16
Family - Man: Medium Skin Tone, Man: Medium Skin Tone, Girl: Medium Skin Tone, Baby: Medium Skin Tone
Family - Man: Medium-Dark Skin Tone, Man: Medium-Dark Skin Tone, Girl: Medium-Dark Skin Tone, Baby: Medium-Dark Skin Tone
Family - Man: Dark Skin Tone, Man: Dark Skin Tone, Girl: Dark Skin Tone, Baby: Dark Skin Tone
Family - Man: Light Skin Tone, Man: Light Skin Tone, Baby: Light Skin Tone
Family - Man: Medium-Light Skin Tone, Man: Medium-Light Skin Tone, Baby: Medium-Light Skin Tone
Family - Man: Medium Skin Tone, Man: Medium Skin Tone, Baby: Medium Skin Tone
Family - Man: Medium-Dark Skin Tone, Man: Medium-Dark Skin Tone, Baby: Medium-Dark Skin Tone
Family - Man: Dark Skin Tone, Man: Dark Skin Tone, Baby: Dark Skin Tone
Couple With Heart - Woman, Man: Dark Skin Tone
Flag for Cantabria (ES-CB)
Flag for Unity (SS-UY)
Family - Woman: Medium-Light Skin Tone, Baby: Medium-Light Skin Tone, Boy: Medium-Light Skin Tone
Family - Woman: Medium Skin Tone, Baby: Medium Skin Tone, Boy: Medium Skin Tone
Family - Woman: Medium-Dark Skin Tone, Baby: Medium-Dark Skin Tone, Boy: Medium-Dark Skin Tone
Family - Woman: Dark Skin Tone, Baby: Dark Skin Tone, Boy: Dark Skin Tone
Family - Woman: Light Skin Tone, Baby: Light Skin Tone, Girl: Light Skin Tone
Family - Woman: Medium-Light Skin Tone, Baby: Medium-Light Skin Tone, Girl: Medium-Light Skin Tone
Family - Woman: Medium Skin Tone, Baby: Medium Skin Tone, Girl: Medium Skin Tone
Family - Woman: Medium-Dark Skin Tone, Baby: Medium-Dark Skin Tone, Girl: Medium-Dark Skin Tone
Family - Woman: Medium Skin Tone, Baby: Medium Skin Tone, Baby: Medium Skin Tone
Family - Woman: Medium-Dark Skin Tone, Baby: Medium-Dark Skin Tone, Baby: Medium-Dark Skin Tone
Family - Woman: Dark Skin Tone, Baby: Dark Skin Tone, Baby: Dark Skin Tone
Family - Woman: Light Skin Tone, Man: Light Skin Tone, Boy: Light Skin Tone
Family - Woman: Medium-Light Skin Tone, Man: Medium-Light Skin Tone, Boy: Medium-Light Skin Tone
Family - Woman: Medium Skin Tone, Man: Medium Skin Tone, Boy: Medium Skin Tone
Family - Woman: Medium-Dark Skin Tone, Man: Medium-Dark Skin Tone, Boy: Medium-Dark Skin Tone
Family - Woman: Dark Skin Tone, Man: Dark Skin Tone, Boy: Dark Skin Tone
Family - Woman: Light Skin Tone, Man: Light Skin Tone, Boy: Light Skin Tone, Boy: Light Skin Tone
Family - Woman: Medium-Light Skin Tone, Baby: Medium-Light Skin Tone, Baby: Medium-Light Skin Tone
Family - Woman: Medium-Light Skin Tone, Man: Medium-Light Skin Tone, Boy: Medium-Light Skin Tone, Boy: Medium-Light Skin Tone
Family - Woman: Medium Skin Tone, Man: Medium Skin Tone, Boy: Medium Skin Tone, Boy: Medium Skin Tone
Family - Woman: Medium-Dark Skin Tone, Man: Medium-Dark Skin Tone, Boy: Medium-Dark Skin Tone, Boy: Medium-Dark Skin Tone
Family - Woman: Dark Skin Tone, Man: Dark Skin Tone, Boy: Dark Skin Tone, Boy: Dark Skin Tone
Family - Woman: Medium Skin Tone, Man: Medium Skin Tone, Boy: Medium Skin Tone, Girl: Medium Skin Tone
Family - Woman: Medium-Dark Skin Tone, Man: Medium-Dark Skin Tone, Boy: Medium-Dark Skin Tone, Girl: Medium-Dark Skin Tone
Family - Woman: Dark Skin Tone, Man: Dark Skin Tone, Boy: Dark Skin Tone, Girl: Dark Skin Tone
Family - Woman: Medium-Light Skin Tone, Man: Medium-Light Skin Tone, Boy: Medium-Light Skin Tone, Baby: Medium-Light Skin Tone
Family - Woman: Medium Skin Tone, Man: Medium Skin Tone, Boy: Medium Skin Tone, Baby: Medium Skin Tone
Family - Woman: Medium-Dark Skin Tone, Man: Medium-Dark Skin Tone, Boy: Medium-Dark Skin Tone, Baby: Medium-Dark Skin Tone
Family - Woman: Light Skin Tone, Man: Light Skin Tone, Girl: Light Skin Tone
Family - Woman: Medium Skin Tone, Man: Medium Skin Tone, Girl: Medium Skin Tone
Family - Woman: Light Skin Tone, Man: Light Skin Tone, Boy: Light Skin Tone, Baby: Light Skin Tone
Family - Woman: Medium-Light Skin Tone, Man: Medium-Light Skin Tone, Boy: Medium-Light Skin Tone, Girl: Medium-Light Skin Tone
Family - Woman: Medium-Light Skin Tone, Man: Medium-Light Skin Tone, Girl: Medium-Light Skin Tone
Family - Woman: Light Skin Tone, Man: Light Skin Tone, Girl: Light Skin Tone, Boy: Light Skin Tone
Family - Woman: Medium-Light Skin Tone, Man: Medium-Light Skin Tone, Girl: Medium-Light Skin Tone, Boy: Medium-Light Skin Tone
Family - Woman: Medium Skin Tone, Man: Medium Skin Tone, Girl: Medium Skin Tone, Boy: Medium Skin Tone
Family - Woman: Medium-Dark Skin Tone, Man: Medium-Dark Skin Tone, Girl: Medium-Dark Skin Tone, Boy: Medium-Dark Skin Tone
Family - Woman: Dark Skin Tone, Man: Dark Skin Tone, Girl: Dark Skin Tone, Boy: Dark Skin Tone
Family - Woman: Light Skin Tone, Man: Light Skin Tone, Girl: Light Skin Tone, Girl: Light Skin Tone
Family - Woman: Medium Skin Tone, Man: Medium Skin Tone, Girl: Medium Skin Tone, Girl: Medium Skin Tone
Family - Woman: Dark Skin Tone, Man: Dark Skin Tone, Girl: Dark Skin Tone, Girl: Dark Skin Tone
Family - Woman: Light Skin Tone, Man: Light Skin Tone, Girl: Light Skin Tone, Baby: Light Skin Tone
Family - Woman: Medium-Light Skin Tone, Man: Medium-Light Skin Tone, Girl: Medium-Light Skin Tone, Baby: Medium-Light Skin Tone
Family - Woman: Medium Skin Tone, Man: Medium Skin Tone, Girl: Medium Skin Tone, Baby: Medium Skin Tone
Family - Woman: Medium-Dark Skin Tone, Man: Medium-Dark Skin Tone, Girl: Medium-Dark Skin Tone, Baby: Medium-Dark Skin Tone
Family - Woman: Dark Skin Tone, Man: Dark Skin Tone, Girl: Dark Skin Tone, Baby: Dark Skin Tone
Family - Woman: Light Skin Tone, Man: Light Skin Tone, Baby: Light Skin Tone
Family - Woman: Medium-Light Skin Tone, Man: Medium-Light Skin Tone, Baby: Medium-Light Skin Tone
Family - Woman: Light Skin Tone, Man: Light Skin Tone, Baby: Light Skin Tone, Boy: Light Skin Tone
Family - Woman: Medium-Light Skin Tone, Man: Medium-Light Skin Tone, Baby: Medium-Light Skin Tone, Boy: Medium-Light Skin Tone
Family - Woman: Medium-Dark Skin Tone, Man: Medium-Dark Skin Tone, Baby: Medium-Dark Skin Tone, Boy: Medium-Dark Skin Tone
Family - Woman: Dark Skin Tone, Man: Dark Skin Tone, Baby: Dark Skin Tone, Boy: Dark Skin Tone
Family - Woman: Light Skin Tone, Man: Light Skin Tone, Baby: Light Skin Tone, Girl: Light Skin Tone
Family - Woman: Medium-Light Skin Tone, Man: Medium-Light Skin Tone, Baby: Medium-Light Skin Tone, Girl: Medium-Light Skin Tone
Family - Woman: Medium Skin Tone, Man: Medium Skin Tone, Baby: Medium Skin Tone, Girl: Medium Skin Tone
Family - Woman: Dark Skin Tone, Man: Dark Skin Tone, Baby: Dark Skin Tone, Girl: Dark Skin Tone
Family - Woman: Light Skin Tone, Man: Light Skin Tone, Baby: Light Skin Tone, Baby: Light Skin Tone
Family - Woman: Medium-Light Skin Tone, Man: Medium-Light Skin Tone, Baby: Medium-Light Skin Tone, Baby: Medium-Light Skin Tone
Family - Woman: Medium Skin Tone, Man: Medium Skin Tone, Baby: Medium Skin Tone, Baby: Medium Skin Tone
Family - Woman: Medium-Dark Skin Tone, Man: Medium-Dark Skin Tone, Baby: Medium-Dark Skin Tone, Baby: Medium-Dark Skin Tone
Family - Woman: Dark Skin Tone, Man: Dark Skin Tone, Baby: Dark Skin Tone, Baby: Dark Skin Tone
Family - Woman: Medium-Light Skin Tone, Woman: Medium-Light Skin Tone, Boy: Medium-Light Skin Tone
Family - Woman: Light Skin Tone, Woman: Light Skin Tone, Boy: Light Skin Tone, Girl: Light Skin Tone
Family - Woman: Medium-Light Skin Tone, Woman: Medium-Light Skin Tone, Boy: Medium-Light Skin Tone, Boy: Medium-Light Skin Tone
Family - Woman: Medium-Dark Skin Tone, Woman: Medium-Dark Skin Tone, Boy: Medium-Dark Skin Tone, Boy: Medium-Dark Skin Tone
Family - Woman: Dark Skin Tone, Woman: Dark Skin Tone, Boy: Dark Skin Tone, Boy: Dark Skin Tone
Family - Woman: Dark Skin Tone, Woman: Dark Skin Tone, Boy: Dark Skin Tone, Baby: Dark Skin Tone
Family - Woman: Medium Skin Tone, Woman: Medium Skin Tone, Boy: Medium Skin Tone, Girl: Medium Skin Tone
Family - Woman: Medium-Dark Skin Tone, Woman: Medium-Dark Skin Tone, Boy: Medium-Dark Skin Tone, Girl: Medium-Dark Skin Tone
Family - Woman: Dark Skin Tone, Woman: Dark Skin Tone, Boy: Dark Skin Tone, Girl: Dark Skin Tone
Family - Woman: Light Skin Tone, Woman: Light Skin Tone, Boy: Light Skin Tone, Baby: Light Skin Tone
Family - Woman: Medium-Light Skin Tone, Woman: Medium-Light Skin Tone, Boy: Medium-Light Skin Tone, Baby: Medium-Light Skin Tone
Family - Woman: Medium Skin Tone, Woman: Medium Skin Tone, Boy: Medium Skin Tone, Baby: Medium Skin Tone
Family - Woman: Medium-Dark Skin Tone, Woman: Medium-Dark Skin Tone, Boy: Medium-Dark Skin Tone, Baby: Medium-Dark Skin Tone
Family - Woman: Light Skin Tone, Woman: Light Skin Tone, Girl: Light Skin Tone
Family - Woman: Medium-Light Skin Tone, Woman: Medium-Light Skin Tone, Girl: Medium-Light Skin Tone
Family - Woman: Medium Skin Tone, Woman: Medium Skin Tone, Boy: Medium Skin Tone, Boy: Medium Skin Tone
Family - Woman: Medium Skin Tone, Woman: Medium Skin Tone, Girl: Medium Skin Tone, Boy: Medium Skin Tone
Family - Woman: Light Skin Tone, Woman: Light Skin Tone, Girl: Light Skin Tone, Girl: Light Skin Tone
Family - Woman: Medium Skin Tone, Woman: Medium Skin Tone, Girl: Medium Skin Tone, Girl: Medium Skin Tone
Family - Woman: Medium-Dark Skin Tone, Woman: Medium-Dark Skin Tone, Girl: Medium-Dark Skin Tone, Girl: Medium-Dark Skin Tone
Family - Woman: Dark Skin Tone, Woman: Dark Skin Tone, Girl: Dark Skin Tone, Girl: Dark Skin Tone
Family - Woman: Light Skin Tone, Woman: Light Skin Tone, Girl: Light Skin Tone, Baby: Light Skin Tone
Family - Woman: Medium-Light Skin Tone, Woman: Medium-Light Skin Tone, Girl: Medium-Light Skin Tone, Baby: Medium-Light Skin Tone
Family - Woman: Medium Skin Tone, Woman: Medium Skin Tone, Girl: Medium Skin Tone, Baby: Medium Skin Tone
Family - Woman: Medium-Dark Skin Tone, Woman: Medium-Dark Skin Tone, Girl: Medium-Dark Skin Tone, Baby: Medium-Dark Skin Tone
Family - Woman: Dark Skin Tone, Woman: Dark Skin Tone, Girl: Dark Skin Tone, Baby: Dark Skin Tone
Family - Woman: Light Skin Tone, Woman: Light Skin Tone, Baby: Light Skin Tone
Family - Man: Medium-Dark Skin Tone, Woman: Medium-Dark Skin Tone, Girl: Medium-Dark Skin Tone, Girl: Medium-Dark Skin Tone
Family - Woman: Medium-Light Skin Tone, Woman: Medium-Light Skin Tone, Baby: Medium-Light Skin Tone
Family - Woman: Medium Skin Tone, Woman: Medium Skin Tone, Baby: Medium Skin Tone
Family - Woman: Medium-Dark Skin Tone, Woman: Medium-Dark Skin Tone, Baby: Medium-Dark Skin Tone
Family - Woman: Dark Skin Tone, Woman: Dark Skin Tone, Baby: Dark Skin Tone
Family - Woman: Light Skin Tone, Woman: Light Skin Tone, Baby: Light Skin Tone, Boy: Light Skin Tone
Family - Woman: Medium-Light Skin Tone, Woman: Medium-Light Skin Tone, Baby: Medium-Light Skin Tone, Boy: Medium-Light Skin Tone
Family - Woman: Medium Skin Tone, Woman: Medium Skin Tone, Baby: Medium Skin Tone, Boy: Medium Skin Tone
Family - Woman: Medium-Dark Skin Tone, Woman: Medium-Dark Skin Tone, Baby: Medium-Dark Skin Tone, Boy: Medium-Dark Skin Tone
Family - Woman: Dark Skin Tone, Woman: Dark Skin Tone, Baby: Dark Skin Tone, Boy: Dark Skin Tone
Family - Woman: Light Skin Tone, Woman: Light Skin Tone, Baby: Light Skin Tone, Girl: Light Skin Tone
Family - Woman: Medium Skin Tone, Woman: Medium Skin Tone, Baby: Medium Skin Tone, Girl: Medium Skin Tone
Family - Woman: Medium-Dark Skin Tone, Woman: Medium-Dark Skin Tone, Baby: Medium-Dark Skin Tone, Girl: Medium-Dark Skin Tone
Family - Woman: Dark Skin Tone, Woman: Dark Skin Tone, Baby: Dark Skin Tone, Girl: Dark Skin Tone
Family - Woman: Light Skin Tone, Woman: Light Skin Tone, Baby: Light Skin Tone, Baby: Light Skin Tone
Family - Woman: Medium-Light Skin Tone, Woman: Medium-Light Skin Tone, Baby: Medium-Light Skin Tone, Baby: Medium-Light Skin Tone
Family - Woman: Medium Skin Tone, Woman: Medium Skin Tone, Baby: Medium Skin Tone, Baby: Medium Skin Tone
Family - Woman: Medium-Dark Skin Tone, Woman: Medium-Dark Skin Tone, Baby: Medium-Dark Skin Tone, Baby: Medium-Dark Skin Tone
Family - Woman: Dark Skin Tone, Woman: Dark Skin Tone, Baby: Dark Skin Tone, Baby: Dark Skin Tone
Family - Woman: Medium-Light Skin Tone, Girl: Medium-Light Skin Tone
Flag for Maluku Islands (ID-ML)
Family - Woman: Dark Skin Tone, Baby: Dark Skin Tone, Girl: Dark Skin Tone
Flag for Southern Denmark (DK-83)
Flag for Skopje (MK-85)
Kiss - Man: Medium-Light Skin Tone, Woman
Flag for Beja (PT-02)
Flag for Sardinia (IT-88)
Flag for Bavaria (DE-BY)
Flag for East New Britain (PG-EBR)
Flag for Trentino-South Tyrol (IT-32)
Flag for Tennessee (US-TN)
Flag for Saskatchewan (CA-SK)
Flag for Funafuti (TV-FUN)
Flag for Gorno-Badakhshan (TJ-GB)
Flag for Banaadir (SO-BN)
Flag for Radenci (SI-100)
Flag for Baden-Wrttemberg (DE-BW)
Family - Woman: Dark Skin Tone, Girl: Dark Skin Tone
Flag for Carabobo (VE-G)
Zero Width Joiner
Flag for Nakuru (KE-31)
Flag for Maritime (TG-M)
Flag for Borno (NG-BO)
Flag for Transnistria (MD-SN)
Flag for Tehran (IR-07)
Flag for Dagestan (RU-DA)
Flag for Al Wusta (OM-WU)
Flag for steck kraj (CZ-42)
Flag for Kuala Lumpur (MY-14)
Flag for Ayacucho (PE-AYA)
Flag for Kiev (UA-30)
Flag for Saint Philip (AG-08)
Flag for Mdina (MT-29)
Flag for Northern Ireland (GB-NIR)
Flag for Auvergne-Rhne-Alpes (FR-ARA)
Flag for Durango (MX-DUR)
Family - Man: Medium-Light Skin Tone, Woman: Medium-Light Skin Tone, Girl: Medium-Light Skin Tone
Flag for Eastern (LK-5)
Flag for Ogun (NG-OG)
Flag for Jafara (LY-JI)
Flag for Skne (SE-M)
Family - Man: Medium Skin Tone, Woman: Medium Skin Tone, Girl: Medium Skin Tone, Boy: Medium Skin Tone
Family - Woman: Medium-Dark Skin Tone, Woman: Medium-Dark Skin Tone, Girl: Medium-Dark Skin Tone, Boy: Medium-Dark Skin Tone
Flag for Mato Grosso do Sul (BR-MS)
Flag for Santa Rosa (GT-SR)
Family - Man: Medium-Light Skin Tone, Woman: Medium-Light Skin Tone, Girl: Medium-Light Skin Tone, Girl: Medium-Light Skin Tone
Flag for Braslove (SI-151)
Flag for Madeira (PT-30)
Flag for San Vicente (SV-SV)
Flag for Alborz (IR-32)
Flag for Faasaleleaga (WS-FA)
Family - Man: Medium-Light Skin Tone, Man: Medium-Light Skin Tone, Boy: Medium-Light Skin Tone
Flag for Newfoundland and Labrador (CA-NL)
Flag for Peloponnese (GR-J)
Flag for Sint Maarten (NL-SX)
Flag for St. Julians (MT-48)
Flag for Adamawa (NG-AD)
Family - Woman: Dark Skin Tone, Woman: Dark Skin Tone, Girl: Dark Skin Tone, Boy: Dark Skin Tone
Flag for So Tom (ST-S)
Family - Woman: Light Skin Tone, Woman: Light Skin Tone, Girl: Light Skin Tone, Boy: Light Skin Tone
Flag for Auce (LV-010)
Flag for Cordillera Administrative (PH-15)
Flag for Fukui (JP-18)
Family - Man: Dark Skin Tone, Woman: Dark Skin Tone, Boy: Dark Skin Tone
Flag for Kakheti (GE-KA)
Flag for Jeju (KR-49)
Flag for Souss-Massa-Dra (MA-13)
Flag for Inukalns (LV-037)
Flag for French Southern Territories (FR-TF)
Flag for Quintana Roo (MX-ROO)
Family - Woman: Light Skin Tone, Baby: Light Skin Tone, Baby: Light Skin Tone
Family - Man: Medium-Dark Skin Tone, Man: Medium-Dark Skin Tone, Boy: Medium-Dark Skin Tone, Boy: Medium-Dark Skin Tone
Flag for Gyr-Moson-Sopron (HU-GS)
Family - Woman: Dark Skin Tone, Woman: Dark Skin Tone, Girl: Dark Skin Tone
Family - Woman: Light Skin Tone, Woman: Light Skin Tone, Boy: Light Skin Tone, Boy: Light Skin Tone
Shibuya
Couple With Heart - Woman, Man: Medium Skin Tone
Flag for Gagaifomauga (WS-GI)
Flag for Nord-Est (HT-NE)
Flag for Central Singapore (SG-01)
Flag for Tungurahua (EC-T)
# Number Sign
Family - Man: Light Skin Tone, Man: Light Skin Tone, Baby: Light Skin Tone, Boy: Light Skin Tone
1 Digit One
Flag for Tarija (BO-T)
Family - Man: Medium-Dark Skin Tone, Woman: Medium-Dark Skin Tone, Girl: Medium-Dark Skin Tone
Flag for Cibitoke (BI-CI)
Flag for Upper South Province (MV-US)
Flag for Canillo (AD-02)
Flag for Bamyan (AF-BAM)
Flag for Encamp (AD-03)
Flag for Northern Mariana Islands (US-MP)
Flag for Babte (LV-012)
Flag for Cotopaxi (EC-X)
Flag for Ngouni (GA-4)
* Asterisk
Tag Latin Small Letter Z
Flag for La Massana (AD-04)
Tag Digit Three
Kiss - Woman: Medium-Light Skin Tone, Woman: Light Skin Tone
Flag for Berane (ME-03)
Kiss - Man: Dark Skin Tone, Man: Medium Skin Tone
Flag for El Valle (DO-37)
Couple With Heart - Woman: Medium-Dark Skin Tone, Woman: Light Skin Tone
Flag for Baringo (KE-01)
Flag for Amanat Al Asimah (YE-SA)
Family - Man: Medium-Light Skin Tone, Man: Medium-Light Skin Tone, Baby: Medium-Light Skin Tone, Boy: Medium-Light Skin Tone
Tag Digit Two
Flag for Senglea (MT-20)
Woman in Business Suit Levitating
Flag for Haut-Mbomou (CF-HM)
Tag Digit One
Tag Digit Four
Flag for Absheron (AZ-ABS)
6 Digit Six
Flag for Savannakhet (LA-SV)
Flag for Kayes (ML-1)
Flag for Abu Dhabi (AE-AZ)
Flag for Asturias (ES-AS)
Flag for Kirkuk (IQ-KI)
Couple With Heart - Woman, Woman: Medium Skin Tone
Flag for Berlin (DE-BE)
8 Digit Eight
Flag for Escaldes-Engordany (AD-08)
Flag for Ningxia (CN-64)
Flag for Caar (EC-F)
Flag for Ajman (AE-AJ)
Woman in Business Suit Levitating: Light Skin Tone
Kiss - Man: Light Skin Tone, Woman
Tag Digit Eight
Flag for Fars (IR-14)
Flag for Fujairah (AE-FU)
Family - Man: Medium-Light Skin Tone, Boy: Medium-Light Skin Tone, Boy: Medium-Light Skin Tone
Flag for Virovitica-Podravina (HR-10)
Tag Latin Small Letter I
7 Digit Seven
Tag Digit Seven
Tag Latin Small Letter E
Family - Woman: Medium-Light Skin Tone, Woman: Medium-Light Skin Tone, Girl: Medium-Light Skin Tone, Boy: Medium-Light Skin Tone
Flag for Ratak Chain (MH-T)
Flag for Sharjah (AE-SH)
Tag Latin Small Letter F
Flag for Vilniaus Municipality (LT-57)
Flag for Westfjords (IS-4)
Flag for British Columbia (CA-BC)
4 Digit Four
Flag for Balkh (AF-BAL)
Family: Man, Baby, Boy
Flag for Hsinchu County (TW-HSQ)
Family: Woman, Baby, Girl
Flag for Jalisco (MX-JAL)
Flag for Kitui (KE-18)
Flag for Azores (PT-20)
Flag for Manipur (IN-MN)
Flag for Badakhshan (AF-BDS)
Couple With Heart - Woman: Light Skin Tone, Woman: Medium-Light Skin Tone
Flag for Ordino (AD-05)
Kiss - Woman: Medium Skin Tone, Woman
Flag for Baghlan (AF-BGL)
Flag for Cross River (NG-CR)
Flag for Colorado (US-CO)
Tag Latin Small Letter T
Flag for Radovi (MK-64)
Flag for Wellington (NZ-WGN)
Family - Man: Medium Skin Tone, Man: Medium Skin Tone, Baby: Medium Skin Tone, Boy: Medium Skin Tone
Flag for Kurdistan (IR-16)
Kiss - Man: Medium Skin Tone, Man: Dark Skin Tone
Tag Latin Small Letter S
Family: Woman, Baby, Baby
Flag for Daykundi (AF-DAY)
Kiss - Man: Light Skin Tone, Man: Medium-Dark Skin Tone
Flag for Farah (AF-FRA)
Tag Latin Small Letter Q
Flag for Guatemala (GT-GU)
Flag for Thurgau (CH-TG)
Flag for Chechen (RU-CE)
Tag Digit Five
Flag for Ghr (AF-GHO)
Flag for Vienna (AT-9)
Flag for Ghazni (AF-GHA)
Tag Latin Small Letter U
Flag for Gaborone (BW-GA)
Tag Latin Small Letter Y
Cancel Tag
Tag Latin Small Letter W
Couple With Heart - Woman: Medium Skin Tone, Woman: Dark Skin Tone
Flag for Amazonas (CO-AMA)
Tag Latin Small Letter N
Kiss - Woman, Woman: Medium Skin Tone
Family: Man, Baby
Flag for Burgenland (AT-1)
Flag for Helmand (AF-HEL)
Tag Digit Six
Flag for Jowzjan (AF-JOW)
Woman With Headscarf
Tag Latin Small Letter B
Tag Digit Zero
Flag for Herat (AF-HER)
Flag for Saint Mark (GD-05)
3 Digit Three
Tag Latin Small Letter G
Woman in Business Suit Levitating: Medium-Dark Skin Tone
Kiss - Woman: Medium Skin Tone, Man: Medium Skin Tone
Flag for Alaska (US-AK)
Tag Latin Small Letter R
Flag for Lautm (TL-LA)
Flag for Kabul (AF-KAB)
Kiss - Man, Man: Dark Skin Tone
Man With Headscarf
Tag Latin Small Letter V
Tag Latin Small Letter D
Flag for Kandahar (AF-KAN)
Flag for Kapisa (AF-KAP)
Flag for Saint Roman (MC-SR)
Flag for Hiiu (EE-39)
Tag Latin Small Letter M
Flag for Khost (AF-KHO)
Man With Headscarf: Light Skin Tone
Flag for Kunduz (AF-KDZ)
Couple With Heart - Woman: Dark Skin Tone, Man
Flag for South Dakota (US-SD)
Flag for Badghis (AF-BDG)
Flag for Southern (IS-8)
Flag for Kunar (AF-KNR)
Family: Man, Man, Baby, Baby
Flag for Tokyo (JP-13)
Flag for Laghman (AF-LAG)
Man With Headscarf: Medium Skin Tone
Flag for Logar (AF-LOG)
5 Digit Five
Tag Latin Small Letter C
Flag for Faryab (AF-FYB)
Tag Latin Small Letter P
Flag for Nangarhar (AF-NAN)
Tag Digit Nine
Flag for Navarra Chartered Community (ES-NC)
Family - Woman: Medium-Light Skin Tone, Boy: Medium-Light Skin Tone, Girl: Medium-Light Skin Tone
Flag for Nayarit (MX-NAY)
Flag for Pernambuco (BR-PE)
Flag for Campania (IT-72)
Man With Headscarf: Medium-Dark Skin Tone
Kiss - Woman: Medium Skin Tone, Woman: Medium-Dark Skin Tone
Flag for Nuristan (AF-NUR)
Family: Man, Man, Girl, Baby
Flag for West New Britain (PG-WBK)
Family - Man: Medium-Light Skin Tone, Woman: Medium-Light Skin Tone, Girl: Medium-Light Skin Tone, Boy: Medium-Light Skin Tone
Flag for Upper Demerara-Berbice (GY-UD)
Kiss - Man, Woman
Flag for Afar (ET-AF)
Flag for Parwan (AF-PAR)
Flag for Nimruz (AF-NIM)
Flag for Karlovac (HR-04)
Flag for Paktia (AF-PIA)
Man With Headscarf: Dark Skin Tone
Man With Headscarf: Medium-Light Skin Tone
Flag for Baja California (MX-BCN)
Flag for Paktika (AF-PKA)
Flag for Phoenix Islands (KI-P)
Tag Latin Small Letter O
Flag for Panjshir (AF-PAN)
Flag for Ticino (CH-TI)
Flag for irovnica (SI-192)
Flag for Halland (SE-N)
Tag Latin Small Letter J
Kiss - Woman: Medium Skin Tone, Woman: Light Skin Tone
Family - Man: Medium-Dark Skin Tone, Man: Medium-Dark Skin Tone, Baby: Medium-Dark Skin Tone, Boy: Medium-Dark Skin Tone
Family - Man: Dark Skin Tone, Man: Dark Skin Tone, Baby: Dark Skin Tone, Boy: Dark Skin Tone
Flag for Northern Bahr el Ghazal (SS-BN)
Kiss - Man: Medium Skin Tone, Woman
Flag for Basse-Kotto (CF-BK)
Couple With Heart - Man, Man: Light Skin Tone
Couple With Heart - Man: Medium Skin Tone, Man
Flag for Butnan (LY-BU)
Family: Woman, Baby
Flag for Sabaragamuwa (LK-9)
Flag for Samangan (AF-SAM)
Flag for Nukulaelae (TV-NKL)
Flag for Ras al-Khaimah (AE-RK)
Flag for Ceuta (ES-CE)
Flag for Dubai (AE-DU)
Family - Man: Light Skin Tone, Man: Light Skin Tone, Baby: Light Skin Tone, Girl: Light Skin Tone
Flag for Okinawa (JP-47)
Flag for Sar-e Pol (AF-SAR)
Family - Woman: Medium-Light Skin Tone, Woman: Medium-Light Skin Tone, Boy: Medium-Light Skin Tone, Girl: Medium-Light Skin Tone
Tag Latin Small Letter L
Flag for Urozgan (AF-URU)
9 Digit Nine
Family - Woman: Medium-Dark Skin Tone, Boy: Medium-Dark Skin Tone
Kiss - Man, Man: Medium Skin Tone
Flag for Saint Joseph (DM-06)
Flag for Saint John (AG-04)
Flag for Vichada (CO-VID)
Flag for Ngarchelong (PW-218)
Flag for Arkhangelsk (RU-ARK)
Flag for Zabul (AF-ZAB)
Flag for Saint George (AG-03)
Flag for Lombardy (IT-25)
Kiss - Man: Light Skin Tone, Man: Light Skin Tone
Flag for Pardubick kraj (CZ-53)
Flag for Saint Paul (AG-06)
Flag for Tr Vinh (VN-51)
Family: Woman, Man, Baby, Girl
Flag for South Gyeongsang (KR-48)
Flag for Saint Mary (AG-05)
Flag for North Aegean (GR-K)
Family: Woman, Woman, Baby, Girl
Flag for Zamora-Chinchipe (EC-Z)
Flag for Masaya (NI-MS)
Flag for Gilbert Islands (KI-G)
Flag for Chihuahua (MX-CHH)
Family - Man: Medium-Light Skin Tone, Man: Medium-Light Skin Tone, Baby: Medium-Light Skin Tone, Girl: Medium-Light Skin Tone
Family - Man: Medium Skin Tone, Man: Medium Skin Tone, Baby: Medium Skin Tone, Girl: Medium Skin Tone
Family - Woman: Medium Skin Tone, Girl: Medium Skin Tone, Girl: Medium Skin Tone
Family: Woman, Man, Baby, Baby
Flag for Redonda (AG-11)
Family: Woman, Woman, Baby
Kiss - Man, Woman: Light Skin Tone
Kiss - Man, Man: Medium-Dark Skin Tone
Flag for Berat County (AL-01)
Tag Latin Small Letter A
Flag for Barbuda (AG-10)
Flag for San Andrs & Providencia (CO-SAP)
Flag for Elbasan County (AL-03)
Family - Man: Medium-Dark Skin Tone, Man: Medium-Dark Skin Tone, Baby: Medium-Dark Skin Tone, Girl: Medium-Dark Skin Tone
Family - Man: Dark Skin Tone, Man: Dark Skin Tone, Boy: Dark Skin Tone, Baby: Dark Skin Tone
Flag for Karnataka (IN-KA)
Flag for Gjirokastr County (AL-05)
Flag for Hokkaid (JP-01)
Family - Woman: Medium-Dark Skin Tone, Man: Medium-Dark Skin Tone, Baby: Medium-Dark Skin Tone, Girl: Medium-Dark Skin Tone
Flag for Central (UG-C)
Kiss - Man: Medium-Light Skin Tone, Man
Flag for Durrs County (AL-02)
Flag for Fier County (AL-04)
Flag for Kor County (AL-06)
Flag for Alto Paraguay (PY-16)
Flag for Kuks County (AL-07)
Kiss - Man: Dark Skin Tone, Man
Flag for Upper Takutu-Upper Essequibo (GY-UT)
Family - Man: Medium-Dark Skin Tone, Baby: Medium-Dark Skin Tone, Girl: Medium-Dark Skin Tone
Family - Man: Dark Skin Tone, Man: Dark Skin Tone, Baby: Dark Skin Tone, Girl: Dark Skin Tone
Family - Man: Light Skin Tone, Man: Light Skin Tone, Baby: Light Skin Tone, Baby: Light Skin Tone
Flag for Dibr County (AL-09)
Flag for Lezh County (AL-08)
Family - Man: Medium-Light Skin Tone, Man: Medium-Light Skin Tone, Baby: Medium-Light Skin Tone, Baby: Medium-Light Skin Tone
Flag for Tirana County (AL-11)
Flag for Sant Juli de Lria (AD-06)
Flag for Bahia (BR-BA)
Flag for Shkodr County (AL-10)
Kiss - Woman, Man: Dark Skin Tone
Family - Man: Medium Skin Tone, Man: Medium Skin Tone, Baby: Medium Skin Tone, Baby: Medium Skin Tone
Family - Man: Medium-Dark Skin Tone, Man: Medium-Dark Skin Tone, Baby: Medium-Dark Skin Tone, Baby: Medium-Dark Skin Tone
Kiss - Woman, Man: Medium Skin Tone
Flag for Vlor County (AL-12)
Flag for Trat (TH-23)
Flag for Gegharkunik (AM-GR)
Family - Man: Dark Skin Tone, Man: Dark Skin Tone, Baby: Dark Skin Tone, Baby: Dark Skin Tone
Flag for Aragatsotn (AM-AG)
Flag for Ararat (AM-AR)
Flag for Yerevan (AM-ER)
Flag for Kotayk (AM-KT)
Flag for Corse (FR-COR)
Flag for Armavir (AM-AV)
Kiss - Woman, Woman: Dark Skin Tone
Flag for Minas Gerais (BR-MG)
Flag for Pointe-Noire (CG-16)
Flag for Lori (AM-LO)
Flag for Skikda (DZ-21)
Flag for Shirak (AM-SH)
Kiss - Woman, Woman: Medium-Dark Skin Tone
Flag for Andorra la Vella (AD-07)
Flag for Altai Krai (RU-ALT)
Flag for Lovrenc na Pohorju (SI-167)
Kiss - Woman, Woman: Medium-Light Skin Tone
Kiss - Man: Dark Skin Tone, Woman: Light Skin Tone
Flag for Panevys County (LT-PN)
Flag for Cibao Norte (DO-35)
Flag for Vest-Agder (NO-10)
Kiss - Man, Woman: Dark Skin Tone
Flag for Vayots Dzor (AM-VD)
Kiss - Woman: Light Skin Tone, Woman: Light Skin Tone
Flag for Vermont (US-VT)
Kiss - Man: Medium Skin Tone, Man
Flag for Bengo (AO-BGO)
Kiss - Woman: Light Skin Tone, Woman
Flag for Meta (CO-MET)
Flag for Saba (NL-BQ2)
Kiss - Woman: Medium Skin Tone, Woman: Medium-Light Skin Tone
Family - Man: Medium Skin Tone, Woman: Medium Skin Tone, Boy: Medium Skin Tone
Flag for Benguela (AO-BGU)
Flag for Sucre (CO-SUC)
Flag for Cuando Cubango (AO-CCU)
Flag for Madre de Dios (PE-MDD)
Flag for Vaud (CH-VD)
Flag for Bi (AO-BIE)
Flag for Cabinda (AO-CAB)
Flag for Hula (AO-HUI)
Flag for Cuanza Sul (AO-CUS)
Kiss - Man, Woman: Medium Skin Tone
Family: Woman, Woman, Boy, Baby
Flag for Huambo (AO-HUA)
Couple With Heart - Man: Medium-Light Skin Tone, Woman: Medium-Dark Skin Tone
Flag for Kyrenia (CY-06)
Kiss - Woman: Medium-Light Skin Tone, Man: Light Skin Tone
Flag for Umm al-Quwain (AE-UQ)
Flag for Lunda Sul (AO-LSU)
Flag for Grand Cape Mount (LR-CM)
Flag for Lunda Norte (AO-LNO)
Couple With Heart - Woman: Medium Skin Tone, Man: Dark Skin Tone
Couple With Heart - Man: Medium-Dark Skin Tone, Woman: Medium-Dark Skin Tone
Flag for Cuanza Norte (AO-CNO)
Flag for Malanje (AO-MAL)
Kiss - Woman: Medium-Light Skin Tone, Woman
Family - Man: Medium-Light Skin Tone, Woman: Medium-Light Skin Tone, Boy: Medium-Light Skin Tone, Boy: Medium-Light Skin Tone
Flag for Moxico (AO-MOX)
Flag for Namibe (AO-NAM)
Family - Man: Medium-Dark Skin Tone, Woman: Medium-Dark Skin Tone, Boy: Medium-Dark Skin Tone, Boy: Medium-Dark Skin Tone
Tag Latin Small Letter K
Woman in Business Suit Levitating: Medium-Light Skin Tone
Flag for Salta (AR-A)
Family - Man: Medium-Dark Skin Tone, Woman: Medium-Dark Skin Tone, Boy: Medium-Dark Skin Tone
Flag for Lualaba (CD-LU)
Flag for Buenos Aires Province (AR-B)
Family - Man: Dark Skin Tone, Woman: Dark Skin Tone, Boy: Dark Skin Tone, Boy: Dark Skin Tone
Flag for San Luis (AR-D)
Flag for Zaire (AO-ZAI)
Flag for Afyonkarahisar (TR-03)
0 Digit Zero
Flag for Qung Tr (VN-25)
Woman in Business Suit Levitating: Dark Skin Tone
Flag for Uge (AO-UIG)
Family - Woman: Medium-Dark Skin Tone, Girl: Medium-Dark Skin Tone, Boy: Medium-Dark Skin Tone
Flag for Zhytomyrshchyna (UA-18)
Kiss - Man: Medium-Dark Skin Tone, Man: Medium Skin Tone
Flag for Cesar (CO-CES)
Flag for Syunik (AM-SU)
Flag for Entre Ros (AR-E)
Kiss - Man: Dark Skin Tone, Woman
Flag for La Rioja (AR-F)
Flag for East Kazakhstan (KZ-VOS)
Flag for Maidan Wardak (AF-WAR)
Flag for San Juan (AR-J)
Family - Woman: Medium-Dark Skin Tone, Woman: Medium-Dark Skin Tone, Girl: Medium-Dark Skin Tone
Flag for Luanda (AO-LUA)
Flag for La Pampa (AR-L)
Kiss - Woman: Medium-Light Skin Tone, Woman: Medium Skin Tone
Family - Man: Medium-Light Skin Tone, Woman: Medium-Light Skin Tone, Boy: Medium-Light Skin Tone, Girl: Medium-Light Skin Tone
Family - Man: Medium-Light Skin Tone, Woman: Medium-Light Skin Tone, Boy: Medium-Light Skin Tone
Flag for Catamarca (AR-K)
Flag for Ro Negro (AR-R)
Flag for Chaco (AR-H)
Flag for Formosa (AR-P)
Flag for Mendoza (AR-M)
Flag for Misiones (AR-N)
Flag for Neuqun (AR-Q)
Family - Man: Medium Skin Tone, Woman: Medium Skin Tone, Boy: Medium Skin Tone, Girl: Medium Skin Tone
Flag for Tucumn (AR-T)
Flag for Santa Fe (AR-S)
Flag for Corrientes (AR-W)
Flag for Jujuy (AR-Y)
Flag for Tierra del Fuego (AR-V)
Flag for Chubut (AR-U)
Flag for Crdoba (AR-X)
Flag for Santa Cruz (AR-Z)
Flag for Santiago del Estero (AR-G)
Flag for Carinthia (AT-2)
Flag for Basel-Landschaft (CH-BL)
Family - Woman: Dark Skin Tone, Girl: Dark Skin Tone, Girl: Dark Skin Tone
Family - Man: Light Skin Tone, Woman: Light Skin Tone, Boy: Light Skin Tone, Baby: Light Skin Tone
Family - Woman: Light Skin Tone, Girl: Light Skin Tone, Baby: Light Skin Tone
Family: Man, Man, Boy, Girl
Flag for Lower Austria (AT-3)
Family: Woman, Baby, Boy
Flag for Nouakchott Ouest (MR-13)
Family - Man: Medium-Light Skin Tone, Woman: Medium-Light Skin Tone, Boy: Medium-Light Skin Tone, Baby: Medium-Light Skin Tone
Flag for Mbomou (CF-MB)
Flag for Styria (AT-6)
Flag for Ilocos (PH-01)
Flag for Tyrol (AT-7)
Flag for Guizhou (CN-52)
Flag for Xaisomboun (LA-XS)
Flag for Vorarlberg (AT-8)
Family - Man: Medium-Light Skin Tone, Man: Medium-Light Skin Tone, Boy: Medium-Light Skin Tone, Girl: Medium-Light Skin Tone
Flag for Salzburg (AT-5)
Family - Man: Dark Skin Tone, Woman: Dark Skin Tone, Boy: Dark Skin Tone, Girl: Dark Skin Tone
Family: Woman, Woman, Baby, Baby
Family: Woman, Man, Girl, Boy
Family: Woman, Man, Girl
Family: Woman, Boy, Baby
Flag for New South Wales (AU-NSW)
Family: Woman, Man, Girl, Baby
Family - Woman: Medium Skin Tone, Girl: Medium Skin Tone, Baby: Medium Skin Tone
Flag for Northern Territory (AU-NT)
Family - Woman: Dark Skin Tone, Girl: Dark Skin Tone, Boy: Dark Skin Tone
Flag for Queensland (AU-QLD)
2 Digit Two
Family: Woman, Man, Girl, Girl
Family - Woman: Medium-Light Skin Tone, Girl: Medium-Light Skin Tone, Baby: Medium-Light Skin Tone
Flag for Upper Austria (AT-4)
Flag for East Macedonia and Thrace (GR-A)
Family - Man: Medium Skin Tone, Woman: Medium Skin Tone, Boy: Medium Skin Tone, Baby: Medium Skin Tone
Family - Man: Medium-Dark Skin Tone, Woman: Medium-Dark Skin Tone, Boy: Medium-Dark Skin Tone, Baby: Medium-Dark Skin Tone
Family: Man, Baby, Girl
Family - Man: Light Skin Tone, Woman: Light Skin Tone, Girl: Light Skin Tone
Family - Man: Dark Skin Tone, Woman: Dark Skin Tone, Boy: Dark Skin Tone, Baby: Dark Skin Tone
Family: Woman, Man, Baby
Flag for Nebraska (US-NE)
Flag for Agstafa (AZ-AGA)
Flag for Takhar (AF-TAK)
Flag for Western Australia (AU-WA)
Flag for Aghjabadi (AZ-AGC)
Flag for Astara (AZ-AST)
Flag for Balakan (AZ-BAL)
Kiss - Woman, Man: Medium-Light Skin Tone
Flag for California (US-CA)
Flag for Agdash (AZ-AGS)
Flag for Baku (AZ-BA)
Kiss - Man: Light Skin Tone, Woman: Dark Skin Tone
Flag for Victoria (AU-VIC)
Flag for Agdam (AZ-AGM)
Family - Man: Light Skin Tone, Girl: Light Skin Tone
Flag for Barda (AZ-BAR)
Family - Man: Medium Skin Tone, Woman: Medium Skin Tone, Girl: Medium Skin Tone
Family - Woman: Medium-Dark Skin Tone, Girl: Medium-Dark Skin Tone, Baby: Medium-Dark Skin Tone
Flag for Agsu (AZ-AGU)
Flag for Tanganyika (CD-TA)
Couple With Heart - Woman: Light Skin Tone, Man: Medium-Light Skin Tone
Flag for Bilasuvar (AZ-BIL)
Flag for Jalilabad (AZ-CAL)
Flag for Jabrayil (AZ-CAB)
Flag for Beylagan (AZ-BEY)
Flag for Novo Mesto (SI-085)
Flag for Niari (CG-9)
Flag for Dashkasan (AZ-DAS)
Flag for Fizuli (AZ-FUZ)
Kiss - Woman: Dark Skin Tone, Man: Medium Skin Tone
Couple With Heart - Man: Dark Skin Tone, Man: Medium-Dark Skin Tone
Flag for Goychay (AZ-GOY)
Flag for Goranboy (AZ-GOR)
Flag for Ganja (AZ-GA)
Flag for Umm Salal (QA-US)
Flag for Eastern (FJ-E)
Flag for Goygol (AZ-GYG)
Flag for Hajigabul (AZ-HAC)
Kiss - Woman: Dark Skin Tone, Woman
Flag for Rzekne Municipality (LV-077)
Flag for Australian Capital Territory (AU-ACT)
Kiss - Man: Medium Skin Tone, Woman: Medium-Dark Skin Tone
Flag for Federal Capital Territory (NG-FC)
Flag for Bryansk (RU-BRY)
Flag for Tavush (AM-TV)
Flag for Santo Domingo de los Tschilas (EC-SD)
Couple With Heart - Woman: Medium-Light Skin Tone, Woman
Flag for Imishli (AZ-IMI)
Flag for Agabat (TM-S)
Couple With Heart - Man, Woman: Medium-Dark Skin Tone
Flag for Sekong (LA-XE)
Flag for Gorj (RO-GJ)
Couple With Heart - Man: Light Skin Tone, Man
Flag for Kurdamir (AZ-KUR)
Family - Woman: Light Skin Tone, Man: Light Skin Tone, Boy: Light Skin Tone, Girl: Light Skin Tone
Flag for Kalbajar (AZ-KAL)
Flag for Gadabay (AZ-GAD)
Flag for Lachin (AZ-LAC)
Flag for Lankaran (AZ-LA)
Flag for Ho Chi Minh City (VN-SG)
Flag for Lerik (AZ-LER)
Flag for Mingachevir (AZ-MI)
Family - Woman: Medium-Dark Skin Tone, Man: Medium-Dark Skin Tone, Girl: Medium-Dark Skin Tone, Girl: Medium-Dark Skin Tone
Flag for Naftalan (AZ-NA)
Flag for Masally (AZ-MAS)
Couple With Heart - Man, Woman
Flag for Lankaran District (AZ-LAN)
Family - Woman: Medium-Light Skin Tone, Man: Medium-Light Skin Tone, Girl: Medium-Light Skin Tone, Girl: Medium-Light Skin Tone
Kiss - Woman: Medium Skin Tone, Man: Medium-Dark Skin Tone
Family - Woman: Dark Skin Tone, Girl: Dark Skin Tone, Baby: Dark Skin Tone
Flag for Neftchala (AZ-NEF)
Flag for Nakhchivan AR (AZ-NX)
Flag for Celje (SI-011)
Flag for Panevio Municipality (LT-32)
Kiss - Woman: Dark Skin Tone, Woman: Medium Skin Tone
Couple With Heart - Man: Light Skin Tone, Woman: Dark Skin Tone
Flag for Ismailli (AZ-ISM)
Tag Latin Small Letter H
Couple With Heart - Woman: Medium-Dark Skin Tone, Man: Light Skin Tone
Family - Woman: Light Skin Tone, Baby: Light Skin Tone
Flag for Nana-Mambr (CF-NM)
Flag for Gobustan (AZ-QOB)
Kiss - Woman: Dark Skin Tone, Man: Light Skin Tone
Kiss - Woman: Dark Skin Tone, Woman: Dark Skin Tone
Flag for Qubadli (AZ-QBI)
Flag for Qazakh (AZ-QAZ)
Flag for Braov (RO-BV)
Family: Man, Woman, Girl, Baby
Flag for Quba (AZ-QBA)
Flag for Qabala (AZ-QAB)
Flag for Uri (CH-UR)
Flag for Oghuz (AZ-OGU)
Flag for Qakh (AZ-QAX)
Flag for marjeke Toplice (SI-206)
Kiss - Man: Medium-Dark Skin Tone, Woman: Dark Skin Tone
Flag for Saint Peter (AG-07)
Family - Man: Light Skin Tone, Woman: Light Skin Tone, Girl: Light Skin Tone, Girl: Light Skin Tone
Flag for Maryland (LR-MY)
Flag for South Australia (AU-SA)
Flag for Qusar (AZ-QUS)
Flag for Sabirabad (AZ-SAB)
Couple With Heart - Man, Woman: Medium Skin Tone
Couple With Heart - Man, Woman: Medium-Light Skin Tone
Flag for Saatly (AZ-SAT)
Flag for Shabran (AZ-SBN)
Couple With Heart - Man: Medium-Light Skin Tone, Woman: Medium Skin Tone
Flag for Shaki District (AZ-SAK)
Flag for Casanare (CO-CAS)
Family: Man, Woman, Baby, Baby
Flag for Shirvan (AZ-SR)
Flag for Shusha (AZ-SUS)
Flag for Valais (CH-VS)
Family - Woman: Medium Skin Tone, Baby: Medium Skin Tone
Kiss - Woman: Light Skin Tone, Man: Dark Skin Tone
Flag for Shaki (AZ-SA)
Flag for Martinique (FR-MQ)
Flag for Sumqayit (AZ-SM)
Flag for Siazan (AZ-SIY)
Flag for Shamakhi (AZ-SMI)
Kiss - Woman: Dark Skin Tone, Man
Flag for Samukh (AZ-SMX)
Family - Man: Light Skin Tone, Woman: Light Skin Tone, Girl: Light Skin Tone, Baby: Light Skin Tone
Flag for Tovuz (AZ-TOV)
Flag for Khachmaz (AZ-XAC)
Flag for Ujar (AZ-UCA)
Flag for Tartar (AZ-TAR)
Kiss - Man: Dark Skin Tone, Man: Light Skin Tone
Family - Woman: Medium-Light Skin Tone, Girl: Medium-Light Skin Tone, Girl: Medium-Light Skin Tone
Family - Man: Medium Skin Tone, Woman: Medium Skin Tone, Girl: Medium Skin Tone, Baby: Medium Skin Tone
Flag for Khizi (AZ-XIZ)
Couple With Heart - Man: Medium Skin Tone, Man: Medium-Light Skin Tone
Flag for Khojali (AZ-XCI)
Flag for Delta Amacuro (VE-Y)
Flag for Stepanakert (AZ-XA)
Flag for Yardymli (AZ-YAR)
Flag for Yevlakh District (AZ-YEV)
Flag for Zaqatala (AZ-ZAQ)
Family - Woman: Medium-Dark Skin Tone, Baby: Medium-Dark Skin Tone
Flag for Yevlakh (AZ-YE)
Flag for Federation of Bosnia and Herzegovina (BA-BIH)
Flag for Zardab (AZ-ZAR)
Flag for Salyan (AZ-SAL)
Flag for Zug (CH-ZG)
Family - Man: Medium-Dark Skin Tone, Woman: Medium-Dark Skin Tone, Girl: Medium-Dark Skin Tone, Baby: Medium-Dark Skin Tone
Family - Man: Dark Skin Tone, Woman: Dark Skin Tone, Girl: Dark Skin Tone, Baby: Dark Skin Tone
Family - Woman: Dark Skin Tone, Baby: Dark Skin Tone
Flag for Republika Srpska (BA-SRP)
Couple With Heart - Man: Medium Skin Tone, Woman
Family - Man: Light Skin Tone, Woman: Light Skin Tone, Baby: Light Skin Tone
Flag for Andalusia (ES-AN)
Family - Man: Medium-Light Skin Tone, Woman: Medium-Light Skin Tone, Baby: Medium-Light Skin Tone
Flag for Saint James (BB-04)
Couple With Heart - Man: Medium-Dark Skin Tone, Woman: Medium-Light Skin Tone
Flag for Saint George (BB-03)
Flag for Saint Andrew (BB-02)
Family: Man, Woman, Baby, Boy
Family - Man: Medium Skin Tone, Woman: Medium Skin Tone, Baby: Medium Skin Tone
Flag for Saint John (BB-05)
Family - Man: Medium-Dark Skin Tone, Woman: Medium-Dark Skin Tone, Baby: Medium-Dark Skin Tone
Flag for Saint Joseph (BB-06)
Flag for Western (LK-1)
Flag for Brest (BY-BR)
Flag for Shamkir (AZ-SKR)
Flag for Saint Lucy (BB-07)
Family - Woman: Light Skin Tone, Baby: Light Skin Tone, Boy: Light Skin Tone
Flag for Castile-La Mancha (ES-CM)
Flag for Saint Philip (BB-10)
Flag for Saint George (VC-04)
Family - Man: Light Skin Tone, Woman: Light Skin Tone, Baby: Light Skin Tone, Boy: Light Skin Tone
Family - Woman: Light Skin Tone, Girl: Light Skin Tone, Girl: Light Skin Tone
Flag for Barisal (BD-A)
Flag for Zangilan (AZ-ZAN)
Flag for Kingston (JM-01)
Family - Man: Medium-Light Skin Tone, Woman: Medium-Light Skin Tone, Baby: Medium-Light Skin Tone, Boy: Medium-Light Skin Tone
Flag for Rajshahi Division (BD-E)
Flag for Rangpur Division (BD-F)
Flag for Dhaka Division (BD-C)
Flag for Khulna Division (BD-D)
Flag for Saint Peter (BB-09)
Flag for Lenart (SI-058)
Family - Woman: Medium-Light Skin Tone, Baby: Medium-Light Skin Tone
Flag for Cascades (BF-02)
Flag for Mymensingh Division (BD-H)
Flag for Wallonia (BE-WAL)
Flag for Beau-Bassin Rose-Hill (MU-BR)
Flag for Centre-Est (BF-04)
Flag for Hong Kong SAR China (CN-91)
Flag for Boucle du Mouhoun (BF-01)
Flag for Centre (BF-03)
Flag for Central Denmark (DK-82)
Flag for Centre-Sud (BF-07)
Family - Man: Medium Skin Tone, Woman: Medium Skin Tone, Baby: Medium Skin Tone, Boy: Medium Skin Tone
Flag for Centre-Ouest (BF-06)
Flag for Centre-Nord (BF-05)
Flag for Saint Michael (BB-08)
Flag for Saint Thomas (BB-11)
Couple With Heart - Man: Medium Skin Tone, Woman: Dark Skin Tone
Flag for Est (BF-08)
Flag for Brussels (BE-BRU)
Flag for Sylhet Division (BD-G)
Flag for Plateau-Central (BF-11)
Flag for Chittagong Division (BD-B)
Flag for Sud-Ouest (BF-13)
Family - Man: Medium-Dark Skin Tone, Woman: Medium-Dark Skin Tone, Baby: Medium-Dark Skin Tone, Boy: Medium-Dark Skin Tone
Flag for Vidin (BG-05)
Flag for Varna (BG-03)
Couple With Heart - Man: Dark Skin Tone, Woman: Medium Skin Tone
Flag for Burgas (BG-02)
Flag for Nord (BF-10)
Flag for Veliko Tarnovo (BG-04)
Family - Man: Medium Skin Tone, Woman: Medium Skin Tone, Girl: Medium Skin Tone, Girl: Medium Skin Tone
Flag for Gabrovo (BG-07)
Family - Man: Dark Skin Tone, Woman: Dark Skin Tone, Baby: Dark Skin Tone, Boy: Dark Skin Tone
Flag for Dobrich (BG-08)
Flag for Sahel (BF-12)
Flag for Tasmania (AU-TAS)
Couple With Heart - Man: Dark Skin Tone, Woman: Light Skin Tone
Family - Woman: Light Skin Tone, Girl: Light Skin Tone, Boy: Light Skin Tone
Family - Man: Light Skin Tone, Woman: Light Skin Tone, Baby: Light Skin Tone, Girl: Light Skin Tone
Family - Man: Medium-Light Skin Tone, Woman: Medium-Light Skin Tone, Baby: Medium-Light Skin Tone, Girl: Medium-Light Skin Tone
Kiss - Man: Medium-Dark Skin Tone, Woman: Medium-Dark Skin Tone
Flag for Khojavend (AZ-XVD)
Flag for Lovech (BG-11)
Flag for Libertador General Bernardo OHiggins (CL-LI)
Flag for Pazardzhik (BG-13)
Couple With Heart - Man: Dark Skin Tone, Woman: Dark Skin Tone
Flag for Pernik (BG-14)
Flag for Kyustendil (BG-10)
Flag for Red Sea (EG-BA)
Flag for Zanzibar Central/South (TZ-11)
Family - Man: Dark Skin Tone, Woman: Dark Skin Tone, Girl: Dark Skin Tone, Boy: Dark Skin Tone
Flag for Pleven (BG-15)
Family - Man: Dark Skin Tone, Man: Dark Skin Tone, Boy: Dark Skin Tone, Boy: Dark Skin Tone
Family - Man: Medium Skin Tone, Woman: Medium Skin Tone, Baby: Medium Skin Tone, Girl: Medium Skin Tone
Flag for Smolyan (BG-21)
Flag for Blagoevgrad (BG-01)
Flag for Bordj Bou Arrridj (DZ-34)
Flag for Plovdiv (BG-16)
Flag for Valle du Bandama (CI-VB)
Flag for Silistra (BG-19)
Couple With Heart - Woman, Man: Medium-Light Skin Tone
Flag for Razgrad (BG-17)
Couple With Heart - Man: Medium-Dark Skin Tone, Man
Flag for Cunene (AO-CNN)
Flag for Sliven (BG-20)
Woman With Headscarf: Light Skin Tone
Flag for Targovishte (BG-25)
Family - Woman: Medium-Light Skin Tone, Woman: Medium-Light Skin Tone, Baby: Medium-Light Skin Tone, Girl: Medium-Light Skin Tone
Family - Man: Medium-Dark Skin Tone, Woman: Medium-Dark Skin Tone, Baby: Medium-Dark Skin Tone, Girl: Medium-Dark Skin Tone
Flag for Sofia District (BG-23)
Flag for Sofia (BG-22)
Family - Man: Dark Skin Tone, Woman: Dark Skin Tone, Girl: Dark Skin Tone, Girl: Dark Skin Tone
Kiss - Man: Light Skin Tone, Woman: Medium-Dark Skin Tone
Woman With Headscarf: Medium Skin Tone
Flag for Yambol (BG-28)
Flag for Capital (BH-13)
Flag for Haskovo (BG-26)
Flag for Schaan (LI-07)
Family - Man: Dark Skin Tone, Woman: Dark Skin Tone, Baby: Dark Skin Tone, Girl: Dark Skin Tone
Flag for Muharraq (BH-15)
Flag for Southern (BH-14)
Woman With Headscarf: Medium-Dark Skin Tone
Flag for Sibiu (RO-SB)
Woman With Headscarf: Medium-Light Skin Tone
Couple With Heart - Woman: Light Skin Tone, Man: Dark Skin Tone
Flag for Northern (BH-17)
Flag for Bubanza (BI-BB)
Couple With Heart - Woman: Light Skin Tone, Woman
Flag for Flanders (BE-VLG)
Family - Woman: Medium Skin Tone, Girl: Medium Skin Tone
Family - Man: Light Skin Tone, Woman: Light Skin Tone, Baby: Light Skin Tone, Baby: Light Skin Tone
Flag for Bujumbura (BI-BM)
Woman With Headscarf: Dark Skin Tone
Flag for Bujumbura Rural (BI-BL)
Kiss - Man: Medium-Dark Skin Tone, Woman: Medium Skin Tone
Family - Man: Medium-Light Skin Tone, Woman: Medium-Light Skin Tone, Baby: Medium-Light Skin Tone, Baby: Medium-Light Skin Tone
Family - Man: Light Skin Tone, Man: Light Skin Tone, Boy: Light Skin Tone, Baby: Light Skin Tone
Flag for Cankuzo (BI-CA)
Flag for Montana (BG-12)
Flag for Sala (LV-085)
Combining Enclosing Keycap
Flag for Bururi (BI-BR)
Flag for Kardzhali (BG-09)
Flag for Rumonge (BI-RM)
Flag for Aruba (NL-AW)
Flag for Muyinga (BI-MY)
Flag for Rutana (BI-RT)
Flag for Ruyigi (BI-RY)
Flag for Kirundo (BI-KI)
Flag for Kayanza (BI-KY)
Flag for Mwaro (BI-MW)
Flag for Shumen (BG-27)
Flag for Ngozi (BI-NG)
Flag for Karuzi (BI-KR)
Flag for Muramvya (BI-MU)
Flag for Layoune-Boujdour-Sakia El Hamra (MA-15)
Family - Man: Medium Skin Tone, Woman: Medium Skin Tone, Baby: Medium Skin Tone, Baby: Medium Skin Tone
Family - Woman: Medium-Dark Skin Tone, Man: Medium-Dark Skin Tone, Girl: Medium-Dark Skin Tone
Family - Man: Medium-Dark Skin Tone, Woman: Medium-Dark Skin Tone, Baby: Medium-Dark Skin Tone, Baby: Medium-Dark Skin Tone
Flag for Donga (BJ-DO)
Family - Woman: Medium Skin Tone, Man: Medium Skin Tone, Baby: Medium Skin Tone, Boy: Medium Skin Tone
Kiss - Man: Medium Skin Tone, Woman: Medium-Light Skin Tone
Flag for Hauts-de-France (FR-HDF)
Flag for Alibori (BJ-AL)
Flag for Atakora (BJ-AK)
Family - Man: Dark Skin Tone, Woman: Dark Skin Tone, Baby: Dark Skin Tone, Baby: Dark Skin Tone
Flag for Littoral (BJ-LI)
Flag for Borgou (BJ-BO)
Family: Woman, Woman, Girl, Baby
Flag for North Dakota (US-ND)
Kiss - Man: Medium-Light Skin Tone, Man: Medium-Dark Skin Tone
Flag for Kouffo (BJ-KO)
Flag for Plateau (BJ-PL)
Flag for Carriacou and Petite Martinique (GD-10)
Flag for Zou (BJ-ZO)
Couple With Heart - Woman: Medium-Light Skin Tone, Man: Light Skin Tone
Couple With Heart - Woman: Medium Skin Tone, Man: Medium Skin Tone
Couple With Heart - Man: Medium Skin Tone, Woman: Medium-Light Skin Tone
Couple With Heart - Woman: Medium Skin Tone, Man: Light Skin Tone
Flag for Beqaa (LB-BI)
Flag for Temburong (BN-TE)
Family - Woman: Light Skin Tone, Boy: Light Skin Tone
Flag for Tutong (BN-TU)
Flag for Brunei-Muara (BN-BM)
Family - Man: Light Skin Tone, Woman: Light Skin Tone, Boy: Light Skin Tone, Boy: Light Skin Tone
Flag for Vratsa (BG-06)
Couple With Heart - Woman: Medium Skin Tone, Man: Medium-Light Skin Tone
Flag for Beni (BO-B)
Flag for Belait (BN-BE)
Couple With Heart - Woman: Medium-Light Skin Tone, Man
Flag for Oum (BJ-OU)
Family - Woman: Medium-Light Skin Tone, Boy: Medium-Light Skin Tone
Flag for Roche Caiman (SC-25)
Couple With Heart - Woman: Light Skin Tone, Man: Medium-Dark Skin Tone
Flag for Cochabamba (BO-C)
Family - Man: Medium-Dark Skin Tone, Woman: Medium-Dark Skin Tone, Girl: Medium-Dark Skin Tone, Boy: Medium-Dark Skin Tone
Flag for Pando (BO-N)
Couple With Heart - Woman: Medium Skin Tone, Woman: Light Skin Tone
Couple With Heart - Woman: Medium-Dark Skin Tone, Man: Medium Skin Tone
Flag for Chuquisaca (BO-H)
Flag for La Paz (BO-L)
Flag for Khentii (MN-039)
Woman in Business Suit Levitating: Medium Skin Tone
Flag for Dolneni (MK-27)
Flag for Stara Zagora (BG-24)
Family - Woman: Medium Skin Tone, Boy: Medium Skin Tone
Flag for Sistan and Baluchestan (IR-13)
Couple With Heart - Woman: Medium-Dark Skin Tone, Man: Medium-Light Skin Tone
Flag for Potos (BO-P)
Flag for Bonaire (BQ-BO)
Kiss - Woman, Man: Light Skin Tone
Couple With Heart - Woman: Medium-Dark Skin Tone, Man
Family - Woman: Medium-Light Skin Tone, Boy: Medium-Light Skin Tone, Boy: Medium-Light Skin Tone
Flag for Brko District (BA-BRC)
Flag for Saba (BQ-SA)
Couple With Heart - Woman: Medium Skin Tone, Man: Medium-Dark Skin Tone
Couple With Heart - Woman: Medium-Dark Skin Tone, Man: Dark Skin Tone
Flag for Acre (BR-AC)
Flag for Gitega (BI-GI)
Family - Woman: Dark Skin Tone, Boy: Dark Skin Tone
Couple With Heart - Woman: Dark Skin Tone, Man: Light Skin Tone
Flag for Amazonas (BR-AM)
Flag for Buenos Aires (AR-C)
Family - Man: Medium-Light Skin Tone, Woman: Medium-Light Skin Tone, Girl: Medium-Light Skin Tone, Baby: Medium-Light Skin Tone
Kiss - Man: Medium-Light Skin Tone, Man: Medium-Light Skin Tone
Flag for Esprito Santo (BR-ES)
Kiss - Man: Dark Skin Tone, Man: Medium-Dark Skin Tone
Kiss - Man: Medium-Light Skin Tone, Man: Medium Skin Tone
Family - Woman: Medium-Dark Skin Tone, Boy: Medium-Dark Skin Tone, Boy: Medium-Dark Skin Tone
Couple With Heart - Man: Light Skin Tone, Woman
Kiss - Man: Dark Skin Tone, Woman: Medium-Dark Skin Tone
Kiss - Woman: Light Skin Tone, Woman: Medium Skin Tone
Kiss - Man: Medium-Light Skin Tone, Man: Dark Skin Tone
Family - Woman: Medium Skin Tone, Boy: Medium Skin Tone, Boy: Medium Skin Tone
Couple With Heart - Woman: Dark Skin Tone, Woman: Medium-Light Skin Tone
Flag for Maranho (BR-MA)
Couple With Heart - Woman: Dark Skin Tone, Woman: Medium Skin Tone
Couple With Heart - Woman: Dark Skin Tone, Woman
Flag for Amap (BR-AP)
Couple With Heart - Man: Medium Skin Tone, Man: Light Skin Tone
Kiss - Woman: Light Skin Tone, Man: Light Skin Tone
Kiss - Man: Medium Skin Tone, Man: Medium Skin Tone
Kiss - Woman: Dark Skin Tone, Woman: Light Skin Tone
Kiss - Man: Medium Skin Tone, Woman: Dark Skin Tone
Kiss - Woman: Medium-Light Skin Tone, Man: Medium-Dark Skin Tone
Kiss - Man: Dark Skin Tone, Man: Medium-Light Skin Tone
Kiss - Man: Medium-Dark Skin Tone, Man: Dark Skin Tone
Kiss - Woman: Medium Skin Tone, Woman: Dark Skin Tone
Kiss - Woman: Medium-Light Skin Tone, Man: Dark Skin Tone
Kiss - Man: Medium Skin Tone, Woman: Medium Skin Tone
Kiss - Man: Medium-Dark Skin Tone, Man: Medium-Light Skin Tone
Kiss - Man: Medium Skin Tone, Woman: Light Skin Tone
Kiss - Man: Medium-Dark Skin Tone, Man
Kiss - Man: Medium-Dark Skin Tone, Man: Medium-Dark Skin Tone
Kiss - Woman, Man: Medium-Dark Skin Tone
Kiss - Woman, Woman: Light Skin Tone
Kiss - Woman: Medium Skin Tone, Man: Light Skin Tone
Kiss - Woman: Dark Skin Tone, Man: Dark Skin Tone
Kiss - Woman: Light Skin Tone, Woman: Dark Skin Tone
Kiss - Woman: Light Skin Tone, Woman: Medium-Light Skin Tone
Kiss - Woman: Medium-Dark Skin Tone, Woman: Dark Skin Tone
Kiss - Woman: Medium-Dark Skin Tone, Woman
Kiss - Woman: Medium-Dark Skin Tone, Woman: Light Skin Tone
Couple With Heart - Woman: Light Skin Tone, Man
Family - Woman: Light Skin Tone, Woman: Light Skin Tone, Boy: Light Skin Tone
Kiss - Woman: Medium-Dark Skin Tone, Man: Medium-Dark Skin Tone
Couple With Heart - Man: Light Skin Tone, Man: Medium Skin Tone
Flag for Mato Grosso (BR-MT)
Couple With Heart - Man: Medium Skin Tone, Woman: Light Skin Tone
Couple With Heart - Man, Man: Dark Skin Tone
Kiss - Woman: Dark Skin Tone, Man: Medium-Light Skin Tone
Kiss - Woman: Dark Skin Tone, Woman: Medium-Dark Skin Tone
Family - Woman: Light Skin Tone, Boy: Light Skin Tone, Girl: Light Skin Tone
Flag for Santa Cruz (BO-S)
Couple With Heart - Man: Light Skin Tone, Woman: Medium Skin Tone
Couple With Heart - Man: Medium Skin Tone, Woman: Medium Skin Tone
Kiss - Woman: Medium-Dark Skin Tone, Woman: Medium Skin Tone
Flag for Collines (BJ-CO)
Family - Man: Light Skin Tone, Woman: Light Skin Tone, Boy: Light Skin Tone
Couple With Heart - Man, Man: Medium Skin Tone
Family - Man: Medium-Dark Skin Tone, Woman: Medium-Dark Skin Tone, Boy: Medium-Dark Skin Tone, Girl: Medium-Dark Skin Tone
Couple With Heart - Man: Medium-Light Skin Tone, Man
Couple With Heart - Man: Medium-Dark Skin Tone, Woman: Medium Skin Tone
Flag for Par (BR-PA)
Family - Woman: Medium Skin Tone, Boy: Medium Skin Tone, Girl: Medium Skin Tone
Couple With Heart - Man: Medium-Light Skin Tone, Man: Medium-Light Skin Tone
Couple With Heart - Man: Dark Skin Tone, Man: Light Skin Tone
Couple With Heart - Woman: Medium Skin Tone, Woman: Medium Skin Tone
Couple With Heart - Man: Medium-Dark Skin Tone, Man: Medium Skin Tone
Couple With Heart - Man: Medium Skin Tone, Man: Medium Skin Tone
Couple With Heart - Man: Light Skin Tone, Woman: Medium-Light Skin Tone
Couple With Heart - Man: Medium-Dark Skin Tone, Woman: Dark Skin Tone
Couple With Heart - Man: Medium-Dark Skin Tone, Man: Medium-Light Skin Tone
Kiss - Woman: Medium-Dark Skin Tone, Woman: Medium-Dark Skin Tone
Couple With Heart - Woman: Medium-Light Skin Tone, Woman: Light Skin Tone
Couple With Heart - Man: Dark Skin Tone, Woman: Medium-Light Skin Tone
Couple With Heart - Man: Medium-Light Skin Tone, Man: Medium-Dark Skin Tone
Couple With Heart - Man: Medium Skin Tone, Man: Medium-Dark Skin Tone
Couple With Heart - Woman, Man: Medium-Dark Skin Tone
Flag for Alagoas (BR-AL)
Couple With Heart - Woman, Man: Light Skin Tone
Flag for Hauts-Bassins (BF-09)
Family - Man: Medium-Light Skin Tone, Boy: Medium-Light Skin Tone
Couple With Heart - Woman: Medium-Dark Skin Tone, Woman: Medium-Dark Skin Tone
Flag for Rio de Janeiro (BR-RJ)
Kiss - Man: Medium-Dark Skin Tone, Woman: Light Skin Tone
Flag for Rondnia (BR-RO)
Couple With Heart - Man: Medium-Dark Skin Tone, Man: Dark Skin Tone
Family - Man: Medium Skin Tone, Boy: Medium Skin Tone
Couple With Heart - Man: Medium-Light Skin Tone, Man: Medium Skin Tone
Flag for Piau (BR-PI)
Family - Man: Medium Skin Tone, Woman: Medium Skin Tone, Boy: Medium Skin Tone, Boy: Medium Skin Tone
Flag for Rio Grande do Norte (BR-RN)
Couple With Heart - Woman: Light Skin Tone, Man: Light Skin Tone
Family - Man: Light Skin Tone, Boy: Light Skin Tone
Couple With Heart - Woman: Medium-Light Skin Tone, Woman: Medium-Dark Skin Tone
Couple With Heart - Man: Dark Skin Tone, Woman: Medium-Dark Skin Tone
Flag for Sergipe (BR-SE)
Flag for Paran (BR-PR)
Family - Man: Dark Skin Tone, Boy: Dark Skin Tone
Couple With Heart - Woman: Medium-Light Skin Tone, Woman: Medium Skin Tone
Couple With Heart - Woman: Medium-Dark Skin Tone, Woman: Medium-Light Skin Tone
Flag for Moscow Province (RU-MOS)
Kiss - Woman: Medium Skin Tone, Woman: Medium Skin Tone
Family - Woman: Dark Skin Tone, Boy: Dark Skin Tone, Boy: Dark Skin Tone
Flag for So Paulo (BR-SP)
Flag for East Azerbaijan (IR-01)
Flag for Rio Grande do Sul (BR-RS)
Couple With Heart - Woman: Medium-Light Skin Tone, Man: Dark Skin Tone
Flag for Sogn og Fjordane (NO-14)
Flag for Tocantins (BR-TO)
Flag for Sveti Andra v Slovenskih Goricah (SI-182)
Couple With Heart - Man: Medium-Light Skin Tone, Woman: Light Skin Tone
Couple With Heart - Man: Dark Skin Tone, Man: Medium Skin Tone
Family - Man: Medium Skin Tone, Boy: Medium Skin Tone, Boy: Medium Skin Tone
Family - Man: Dark Skin Tone, Boy: Dark Skin Tone, Boy: Dark Skin Tone
Flag for Bimini (BS-BI)
Couple With Heart - Man: Dark Skin Tone, Woman
Family - Woman: Light Skin Tone, Boy: Light Skin Tone, Boy: Light Skin Tone
Flag for Roraima (BR-RR)
Flag for Oruro (BO-O)
Flag for Exuma (BS-EX)
Family - Man: Medium-Dark Skin Tone, Boy: Medium-Dark Skin Tone
Couple With Heart - Woman: Medium Skin Tone, Man
Flag for Central Eleuthera (BS-CE)
Flag for Berry Islands (BS-BY)
Flag for Makamba (BI-MA)
Flag for Federal District (BR-DF)
Couple With Heart - Woman: Light Skin Tone, Woman: Medium-Dark Skin Tone
Kiss - Man: Medium-Light Skin Tone, Woman: Medium-Light Skin Tone
Flag for Central Abaco (BS-CO)
Flag for East Grand Bahama (BS-EG)
Flag for Central Andros (BS-CS)
Family - Man: Light Skin Tone, Boy: Light Skin Tone, Girl: Light Skin Tone
Flag for Crooked Island (BS-CK)
Flag for Black Point (BS-BP)
Family - Man: Medium-Light Skin Tone, Boy: Medium-Light Skin Tone, Girl: Medium-Light Skin Tone
Family - Man: Medium Skin Tone, Boy: Medium Skin Tone, Girl: Medium Skin Tone
Couple With Heart - Woman: Dark Skin Tone, Man: Medium-Dark Skin Tone
Kiss - Woman: Medium-Dark Skin Tone, Man: Medium-Light Skin Tone
Flag for North Eleuthera (BS-NE)
Flag for North Abaco (BS-NO)
Flag for Mayaguana (BS-MG)
Family - Man: Medium-Dark Skin Tone, Boy: Medium-Dark Skin Tone, Girl: Medium-Dark Skin Tone
Kiss - Man: Medium-Light Skin Tone, Woman: Light Skin Tone
Flag for Grand Cay (BS-GC)
Flag for Freeport (BS-FP)
Flag for Inagua (BS-IN)
Flag for Hope Town (BS-HT)
Couple With Heart - Woman: Medium-Dark Skin Tone, Woman: Dark Skin Tone
Flag for Long Island (BS-LI)
Family - Man: Dark Skin Tone, Boy: Dark Skin Tone, Girl: Dark Skin Tone
Couple With Heart - Man: Medium-Dark Skin Tone, Woman
Couple With Heart - Woman: Dark Skin Tone, Man: Dark Skin Tone
Family - Man: Light Skin Tone, Boy: Light Skin Tone, Baby: Light Skin Tone
Family: Man, Man, Baby
Family: Woman, Girl, Baby
Family: Man, Boy, Baby
Family: Man, Man, Baby, Boy
Family: Man, Boy, Girl
Family: Man, Baby, Baby
Flag for Ragged Island (BS-RI)
Couple With Heart - Woman: Dark Skin Tone, Woman: Dark Skin Tone
Couple With Heart - Woman: Dark Skin Tone, Man: Medium Skin Tone
Couple With Heart - Woman: Medium-Light Skin Tone, Man: Medium-Light Skin Tone
Flag for North Andros (BS-NS)
Couple With Heart - Woman: Dark Skin Tone, Woman: Light Skin Tone
Kiss - Man: Light Skin Tone, Man
Flag for South Andros (BS-SA)
Kiss - Man: Light Skin Tone, Man: Medium-Light Skin Tone
Flag for South Eleuthera (BS-SE)
Family - Man: Medium-Light Skin Tone, Boy: Medium-Light Skin Tone, Baby: Medium-Light Skin Tone
Kiss - Man: Light Skin Tone, Woman: Light Skin Tone
Kiss - Man: Medium-Light Skin Tone, Woman: Medium-Dark Skin Tone
Kiss - Man: Medium-Dark Skin Tone, Woman: Medium-Light Skin Tone
Kiss - Man: Medium-Dark Skin Tone, Man: Light Skin Tone
Flag for Santa Catarina (BR-SC)
Family: Woman, Woman, Boy, Girl
Kiss - Man, Woman: Medium-Dark Skin Tone
Flag for Rum Cay (BS-RC)
Family: Woman, Woman, Baby, Boy
Kiss - Man: Light Skin Tone, Woman: Medium Skin Tone
Flag for Cat Island (BS-CI)
Couple With Heart - Woman: Medium Skin Tone, Woman
Family - Man: Medium Skin Tone, Boy: Medium Skin Tone, Baby: Medium Skin Tone
Family: Woman, Man, Boy, Baby
Kiss - Man: Medium-Dark Skin Tone, Woman
Kiss - Man, Man: Light Skin Tone
Kiss - Man: Light Skin Tone, Man: Dark Skin Tone
Kiss - Man: Medium-Light Skin Tone, Woman: Medium Skin Tone
Family - Man: Medium-Dark Skin Tone, Boy: Medium-Dark Skin Tone, Baby: Medium-Dark Skin Tone
Flag for South Abaco (BS-SO)
Kiss - Woman: Medium-Dark Skin Tone, Woman: Medium-Light Skin Tone
Couple With Heart - Man: Light Skin Tone, Man: Dark Skin Tone
Kiss - Man: Dark Skin Tone, Man: Dark Skin Tone
Kiss - Woman: Medium-Dark Skin Tone, Man: Dark Skin Tone
Kiss - Woman: Medium-Light Skin Tone, Man: Medium Skin Tone
Kiss - Woman: Medium-Dark Skin Tone, Man: Light Skin Tone
Kiss - Woman: Medium Skin Tone, Man
Family: Man, Girl, Baby
Kiss - Woman: Light Skin Tone, Man: Medium-Dark Skin Tone
Couple With Heart - Man, Man: Medium-Light Skin Tone
Kiss - Woman: Medium-Light Skin Tone, Woman: Medium-Light Skin Tone
Kiss - Man: Dark Skin Tone, Woman: Dark Skin Tone
Kiss - Man, Woman: Medium-Light Skin Tone
Flag for Abidjan (CI-AB)
Kiss - Woman: Light Skin Tone, Man
Kiss - Woman: Medium-Light Skin Tone, Woman: Medium-Dark Skin Tone
Kiss - Man: Light Skin Tone, Woman: Medium-Light Skin Tone
Kiss - Woman: Medium Skin Tone, Man: Dark Skin Tone
Kiss - Woman: Dark Skin Tone, Woman: Medium-Light Skin Tone
Kiss - Woman: Dark Skin Tone, Man: Medium-Dark Skin Tone
Kiss - Woman: Medium-Light Skin Tone, Man
Couple With Heart - Woman, Woman: Medium-Dark Skin Tone
Couple With Heart - Man: Dark Skin Tone, Man: Medium-Light Skin Tone
Family - Man: Dark Skin Tone, Boy: Dark Skin Tone, Baby: Dark Skin Tone
Couple With Heart - Man: Medium-Light Skin Tone, Woman: Medium-Light Skin Tone
Couple With Heart - Woman: Medium-Light Skin Tone, Man: Medium Skin Tone
Flag for Spanish Wells (BS-SW)
Couple With Heart - Man: Dark Skin Tone, Man: Dark Skin Tone
Couple With Heart - Man: Medium-Light Skin Tone, Man: Dark Skin Tone
Couple With Heart - Man: Medium-Light Skin Tone, Woman
Couple With Heart - Woman: Medium-Light Skin Tone, Woman: Medium-Light Skin Tone
Couple With Heart - Man: Medium-Light Skin Tone, Man: Light Skin Tone
Couple With Heart - Man: Medium-Dark Skin Tone, Man: Medium-Dark Skin Tone
Couple With Heart - Woman, Woman: Medium-Light Skin Tone
Couple With Heart - Man: Medium-Light Skin Tone, Woman: Dark Skin Tone
Couple With Heart - Man: Light Skin Tone, Man: Medium-Dark Skin Tone
Couple With Heart - Man: Medium Skin Tone, Woman: Medium-Dark Skin Tone
Couple With Heart - Woman, Woman: Dark Skin Tone
Couple With Heart - Man: Medium Skin Tone, Man: Dark Skin Tone
Family: Man, Man, Boy, Baby
Couple With Heart - Man: Dark Skin Tone, Man
Couple With Heart - Woman: Light Skin Tone, Woman: Dark Skin Tone
Flag for San Salvador (BS-SS)
Flag for Samtse (BT-14)
Couple With Heart - Woman: Light Skin Tone, Man: Medium Skin Tone
Couple With Heart - Woman: Medium-Light Skin Tone, Woman: Dark Skin Tone
Couple With Heart - Man, Woman: Dark Skin Tone
Flag for Paro (BT-11)
Couple With Heart - Man: Light Skin Tone, Woman: Medium-Dark Skin Tone
Family - Man: Medium-Light Skin Tone, Girl: Medium-Light Skin Tone
Flag for Thimphu (BT-15)
Couple With Heart - Woman: Medium-Dark Skin Tone, Woman: Medium Skin Tone
Flag for West Grand Bahama (BS-WG)
Flag for Haa (BT-13)
Flag for Chukha (BT-12)
Kiss - Man: Light Skin Tone, Man: Medium Skin Tone
Family - Man: Medium-Dark Skin Tone, Girl: Medium-Dark Skin Tone
Family - Man: Medium Skin Tone, Girl: Medium Skin Tone
Flag for Acklins (BS-AK)
Flag for Trongsa (BT-32)
Flag for Trashigang (BT-41)
Flag for Punakha (BT-23)
Flag for Wangdue Phodrang (BT-24)
Flag for Bumthang (BT-33)
Flag for Zhemgang (BT-34)
Kiss - Woman: Medium-Light Skin Tone, Man: Medium-Light Skin Tone
Flag for Mongar (BT-42)
Flag for Paraba (BR-PB)
Couple With Heart - Woman: Dark Skin Tone, Man: Medium-Light Skin Tone
Flag for Zrich (CH-ZH)
Flag for Sarpang (BT-31)
Flag for Dagana (BT-22)
Kiss - Woman: Light Skin Tone, Man: Medium Skin Tone
Family - Man: Dark Skin Tone, Man: Dark Skin Tone, Girl: Dark Skin Tone, Girl: Dark Skin Tone
Flag for Central (BW-CE)
Flag for Gasa (BT-GA)
Flag for Chobe (BW-CH)
Flag for Samdrup Jongkhar (BT-45)
Flag for Francistown (BW-FR)
Flag for Lhuntse (BT-44)
Flag for Trashiyangtse (BT-TY)
Flag for Tsirang (BT-21)
Flag for Pemagatshel (BT-43)
Family - Man: Dark Skin Tone, Girl: Dark Skin Tone
Flag for North East (BW-NE)
Flag for Kgatleng (BW-KL)
Flag for Kgalagadi (BW-KG)
Flag for South East (BW-SE)
Flag for Kweneng (BW-KW)
Family - Man: Light Skin Tone, Girl: Light Skin Tone, Boy: Light Skin Tone
Flag for North West (BW-NW)
Flag for Jwaneng (BW-JW)
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Family - Man: Dark Skin Tone, Girl: Dark Skin Tone, Boy: Dark Skin Tone
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Family - Man: Medium Skin Tone, Girl: Medium Skin Tone, Boy: Medium Skin Tone
Couple With Heart - Woman: Medium-Dark Skin Tone, Woman
Family: Man, Woman, Baby, Girl
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Family - Man: Medium-Light Skin Tone, Girl: Medium-Light Skin Tone, Baby: Medium-Light Skin Tone
Family - Man: Medium Skin Tone, Girl: Medium Skin Tone, Baby: Medium Skin Tone
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Family - Man: Medium Skin Tone, Baby: Medium Skin Tone, Girl: Medium Skin Tone
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Flag for Moneghetti (MC-MG)
Flag for Murzuq (LY-MQ)
Flag for Mekns-Tafilalet (MA-06)
Flag for Fontvieille (MC-FO)
Flag for Wadi al Hayaa (LY-WD)
Flag for La Colle (MC-CL)
Flag for Sirte (LY-SR)
Flag for Misrata (LY-MI)
Flag for Fs-Boulemane (MA-05)
Flag for Tripoli (LY-TB)
Flag for La Gare (MC-GA)
Family - Woman: Medium-Dark Skin Tone, Woman: Medium-Dark Skin Tone, Boy: Medium-Dark Skin Tone
Flag for Edine (MD-ED)
Flag for Hnceti (MD-HI)
Flag for Fleti (MD-FA)
Flag for Criuleni (MD-CR)
Flag for Sngerei (MD-SI)
Flag for Soroca (MD-SO)
Flag for Cantemir (MD-CT)
Flag for Rezina (MD-RE)
Flag for oldneti (MD-SD)
Flag for Briceni (MD-BR)
Flag for Vallon de la Rousse (MC-VR)
Flag for Bli (MD-BA)
Flag for Dubsari (MD-DU)
Flag for Clrai (MD-CL)
Flag for Splugues (MC-SP)
Flag for Cahul (MD-CA)
Flag for Ialoveni (MD-IA)
Flag for Orhei (MD-OR)
Flag for Drochia (MD-DR)
Flag for Gagauzia (MD-GA)
Flag for Cimilia (MD-CM)
Flag for Ocnia (MD-OC)
Flag for Basarabeasca (MD-BS)
Flag for Streni (MD-ST)
Flag for Anenii Noi (MD-AN)
Flag for Moulins (MC-MU)
Flag for Bender (MD-BD)
Flag for Glodeni (MD-GL)
Flag for La Source (MC-SO)
Flag for Chiinu (MD-CU)
Flag for Dondueni (MD-DO)
Flag for Floreti (MD-FL)
Flag for Port Hercules (MC-PH)
Flag for Nisporeni (MD-NI)
Flag for Rcani (MD-RI)
Flag for Leova (MD-LE)
Flag for tefan Vod (MD-SV)
Flag for Ungheni (MD-UN)
Flag for Toamasina (MG-A)
Flag for Antananarivo (MG-T)
Flag for Cetinje (ME-06)
Flag for Bogdanci (MK-05)
Flag for Ulcinj (ME-20)
Flag for Kolain (ME-09)
Flag for Bosilovo (MK-07)
Flag for Pljevlja (ME-14)
Flag for Teleneti (MD-TE)
Flag for Bogovinje (MK-06)
Flag for abljak (ME-21)
Flag for Herceg Novi (ME-08)
Flag for Petnjica (ME-23)
Flag for Roaje (ME-17)
Flag for Budva (ME-05)
Flag for Bar (ME-02)
Flag for Berovo (MK-03)
Flag for Tivat (ME-19)
Flag for Pluine (ME-15)
Flag for Kotor (ME-10)
Flag for Ralik Chain (MH-L)
Flag for Danilovgrad (ME-07)
Flag for Plav (ME-13)
Flag for Bitola (MK-04)
Flag for Bijelo Polje (ME-04)
Flag for Andrijevica (ME-01)
Family - Woman: Dark Skin Tone, Woman: Dark Skin Tone, Boy: Dark Skin Tone
Flag for Niki (ME-12)
Flag for Taraclia (MD-TA)
Flag for Mojkovac (ME-11)
Flag for Mahajanga (MG-M)
Flag for Gusinje (ME-22)
Flag for Fianarantsoa (MG-F)
Flag for avnik (ME-18)
Flag for Podgorica (ME-16)
Flag for Toliara (MG-U)
Flag for Antsiranana (MG-D)
Flag for Kratovo (MK-43)
Flag for Kriva Palanka (MK-44)
Flag for Makedonski Brod (MK-52)
Flag for Jegunovce (MK-35)
Flag for Lozovo (MK-49)
Flag for Kumanovo (MK-47)
Flag for Vevani (MK-12)
Flag for Demir Kapija (MK-24)
Flag for Vasilevo (MK-11)
Flag for elino (MK-30)
Flag for Kavadarci (MK-36)
Flag for Zelenikovo (MK-32)
Flag for Kone (MK-41)
Flag for Vinica (MK-14)
Flag for Valandovo (MK-10)
Flag for Novaci (MK-55)
Flag for Novo Selo (MK-56)
Flag for Ilinden (MK-34)
Flag for Makedonska Kamenica (MK-51)
Flag for Vrapite (MK-16)
Flag for Brvenica (MK-08)
Flag for Gradsko (MK-20)
Flag for Mavrovo and Rostua (MK-50)
Flag for Debarca (MK-22)
Flag for Gostivar (MK-19)
Flag for Mogila (MK-53)
Flag for Lipkovo (MK-48)
Flag for Karbinci (MK-37)
Flag for Zrnovci (MK-33)
Flag for Negotino (MK-54)
Flag for Kievo (MK-40)
Flag for Debar (MK-21)
Flag for Veles (MK-13)
Flag for Dojran (MK-26)
Flag for Gevgelija (MK-18)
Flag for Koani (MK-42)
Flag for Krivogatani (MK-45)
Flag for Delevo (MK-23)
Flag for Kruevo (MK-46)
Flag for uer-Sandevo (MK-82)
Flag for Prilep (MK-62)
Flag for Centar upa (MK-78)
Flag for Mandalay (MM-04)
Flag for Sgou (ML-4)
Flag for Petrovec (MK-59)
Flag for einovo-Obleevo (MK-81)
Flag for Kidal (ML-8)
Flag for Bago (MM-02)
Flag for Struga (MK-72)
Flag for Tearce (MK-75)
Flag for Studeniani (MK-74)
Flag for Ohrid (MK-58)
Flag for Sveti Nikole (MK-69)
Flag for Strumica (MK-73)
Flag for Sikasso (ML-3)
Flag for Kachin (MM-11)
Flag for Resen (MK-66)
Flag for Bamako (ML-BKO)
Flag for Magway (MM-03)
Flag for Sopite (MK-70)
Flag for Staro Nagoriane (MK-71)
Flag for Ayeyarwady (MM-07)
Flag for Gao (ML-7)
Flag for Mopti (ML-5)
Flag for tip (MK-83)
Flag for Kayah (MM-12)
Flag for Tanintharyi (MM-05)
Flag for Koulikoro (ML-2)
Flag for Probitip (MK-63)
Flag for Pehevo (MK-60)
Flag for Sagaing (MM-01)
Flag for aka (MK-80)
Flag for Rankovce (MK-65)
Flag for Yangon (MM-06)
Flag for Tetovo (MK-76)
Flag for Rosoman (MK-67)
Flag for Assaba (MR-03)
Flag for Shan (MM-17)
Flag for Rakhine (MM-16)
Flag for Khvsgl (MN-041)
Flag for Bayan-lgii (MN-071)
Flag for Bayankhongor (MN-069)
Flag for Dornod (MN-061)
Flag for Selenge (MN-049)
Flag for Ulaanbaatar (MN-1)
Flag for Darkhan-Uul (MN-037)
Flag for Tv (MN-047)
Flag for Mon (MM-15)
Flag for Trarza (MR-06)
Flag for Skhbaatar (MN-051)
Flag for Gorgol (MR-04)
Flag for vrkhangai (MN-055)
Flag for Chin (MM-14)
Flag for Bulgan (MN-067)
Flag for Zavkhan (MN-057)
Flag for Dornogovi (MN-063)
Flag for mngovi (MN-053)
Flag for Kayin (MM-13)
Flag for Govi-Altai (MN-065)
Flag for Tiris Zemmour (MR-11)
Flag for Dundgovi (MN-059)
Flag for Arkhangai (MN-073)
Flag for Tagant (MR-09)
Flag for Khovd (MN-043)
Flag for Uvs (MN-046)
Flag for Govismber (MN-064)
Flag for Brakna (MR-05)
Flag for Dakhlet Nouadhibou (MR-08)
Flag for Hodh Ech Chargui (MR-01)
Flag for Orkhon (MN-035)
Flag for Hodh El Gharbi (MR-02)
Flag for Naypyidaw (MM-18)
Flag for Adrar (MR-07)
Flag for Inchiri (MR-12)
Flag for Iklin (MT-19)
Flag for Garb (MT-14)
Flag for Mqabba (MT-33)
Flag for Kerem (MT-22)
Flag for Gasri (MT-16)
Flag for Lija (MT-24)
Flag for Birebbua (MT-05)
Flag for Birkirkara (MT-04)
Flag for Marr (MT-31)
Flag for Balzan (MT-02)
Flag for Munxar (MT-36)
Flag for Gajnsielem (MT-13)
Flag for Naxxar (MT-38)
Flag for Floriana (MT-09)
Flag for Marsa (MT-26)
Flag for Dingli (MT-07)
Flag for Gudja (MT-11)
Flag for Kirkop (MT-23)
Flag for Marsaskala (MT-27)
Flag for Paola (MT-39)
Flag for Fontana (MT-10)
Flag for Msida (MT-34)
Flag for Nadur (MT-37)
Flag for Mosta (MT-32)
Flag for Imtarfa (MT-35)
Flag for Cospicua (MT-06)
Flag for Birgu (MT-03)
Flag for Nouakchott Nord (MR-14)
Flag for Gira (MT-12)
Flag for Melliea (MT-30)
Flag for Gaxaq (MT-17)
Flag for amrun (MT-18)
Flag for Fgura (MT-08)
Flag for Attard (MT-01)
Flag for Gargur (MT-15)
Flag for Kalkara (MT-21)
Flag for Nouakchott Sud (MR-15)
Flag for Marsaxlokk (MT-28)
Flag for Victoria (MT-45)
Flag for Qala (MT-42)
Flag for abbar (MT-64)
Flag for Agalga (MU-AG)
Flag for Ta Xbiex (MT-58)
Flag for Piet (MT-41)
Flag for Sannat (MT-52)
Flag for Port Louis District (MU-PL)
Flag for Xagra (MT-61)
Flag for Rivire Noire (MU-BL)
Flag for Sliema (MT-56)
Flag for Safi (MT-47)
Flag for Flacq (MU-FL)
Flag for Pembroke (MT-40)
Flag for Swieqi (MT-57)
Flag for Curepipe (MU-CU)
Flag for urrieq (MT-68)
Flag for San wann (MT-49)
Flag for Grand Port (MU-GP)
Flag for Cargados Carajos (MU-CC)
Flag for Qrendi (MT-44)
Flag for Valletta (MT-60)
Flag for Pamplemousses (MU-PA)
Flag for Qormi (MT-43)
Flag for Port Louis (MU-PU)
Flag for Tarxien (MT-59)
Flag for ebbu Gozo (MT-65)
Flag for Saint Lawrence (MT-50)
Flag for ejtun (MT-67)
Flag for St. Pauls Bay (MT-51)
Flag for Santa Luija (MT-53)
Flag for ebbu (MT-66)
Flag for Rabat (MT-46)
Flag for Siiewi (MT-55)
Family - Woman: Medium Skin Tone, Woman: Medium Skin Tone, Girl: Medium Skin Tone
Flag for Santa Venera (MT-54)
Flag for Xgajra (MT-63)
Flag for Moka (MU-MO)
Flag for Michoacn (MX-MIC)
Flag for Northern (MW-N)
Flag for Upper North Province (MV-UN)
Flag for Colima (MX-COL)
Flag for Rodrigues (MU-RO)
Flag for Guanajuato (MX-GUA)
Flag for Ciudad de Mexico (MX-CMX)
Flag for Puebla (MX-PUE)
Flag for Quatre Bornes (MU-QB)
Flag for Oaxaca (MX-OAX)
Flag for Central (MW-C)
Flag for Savanne (MU-SA)
Flag for Morelos (MX-MOR)
Flag for Hidalgo (MX-HID)
Flag for Aguascalientes (MX-AGU)
Flag for Campeche (MX-CAM)
Flag for Nuevo Len (MX-NLE)
Flag for Mal (MV-MLE)
Flag for Guerrero (MX-GRO)
Flag for Vacoas-Phoenix (MU-VP)
Family - Man: Light Skin Tone, Man: Light Skin Tone, Boy: Light Skin Tone, Girl: Light Skin Tone
Flag for North Central Province (MV-NC)
Flag for Mexico State (MX-MEX)
Flag for Plaines Wilhems (MU-PW)
Flag for Central Province (MV-CE)
Flag for Coahuila (MX-COA)
Flag for South Province (MV-SU)
Flag for Chiapas (MX-CHP)
Flag for Southern (MW-S)
Flag for Sofala (MZ-S)
Flag for Perlis (MY-09)
Flag for Veracruz (MX-VER)
Flag for Sarawak (MY-13)
Flag for Kelantan (MY-03)
Flag for Zambezi (NA-CA)
Flag for Manica (MZ-B)
Flag for Labuan (MY-15)
Flag for Cabo Delgado (MZ-P)
Flag for Hardap (NA-HA)
Flag for Tete (MZ-T)
Flag for Kedah (MY-02)
Flag for Pahang (MY-06)
Flag for Penang (MY-07)
Flag for Perak (MY-08)
Flag for Maputo Province (MZ-L)
Flag for Gois (BR-GO)
Flag for Terengganu (MY-11)
Flag for Inhambane (MZ-I)
Flag for Malacca (MY-04)
Flag for Erongo (NA-ER)
Flag for Tlaxcala (MX-TLA)
Flag for Negeri Sembilan (MY-05)
Flag for Zacatecas (MX-ZAC)
Flag for Tamaulipas (MX-TAM)
Flag for Niassa (MZ-A)
Flag for Maputo (MZ-MPM)
Flag for Nampula (MZ-N)
Flag for Putrajaya (MY-16)
Flag for Sinaloa (MX-SIN)
Flag for Yucatn (MX-YUC)
Flag for Sabah (MY-12)
Family - Woman: Medium-Light Skin Tone, Woman: Medium-Light Skin Tone, Girl: Medium-Light Skin Tone, Girl: Medium-Light Skin Tone
Flag for Zambezia (MZ-Q)
Flag for Quertaro (MX-QUE)
Flag for Gaza (MZ-G)
Flag for Otjozondjupa (NA-OD)
Flag for Maradi (NE-4)
Flag for Kunene (NA-KU)
Flag for Akwa Ibom (NG-AK)
Flag for Tahoua (NE-5)
Flag for Rivire du Rempart (MU-RR)
Flag for Imo (NG-IM)
Flag for Katsina (NG-KT)
Flag for Dosso (NE-3)
Flag for Tillabri (NE-6)
Flag for Ekiti (NG-EK)
Flag for Omaheke (NA-OH)
Flag for Bauchi (NG-BA)
Flag for Karas (NA-KA)
Flag for Bayelsa (NG-BY)
Flag for Ohangwena (NA-OW)
Flag for Benue (NG-BE)
Flag for Enugu (NG-EN)
Flag for Oshana (NA-ON)
Flag for Kaduna (NG-KD)
Family - Man: Light Skin Tone, Baby: Light Skin Tone, Boy: Light Skin Tone
Flag for Kebbi (NG-KE)
Flag for Jigawa (NG-JI)
Flag for Niamey (NE-8)
Flag for Anambra (NG-AN)
Flag for Gombe (NG-GO)
Flag for Agadez (NE-1)
Flag for Khomas (NA-KH)
Flag for Diffa (NE-2)
Flag for Johor (MY-01)
Flag for Kano (NG-KN)
Flag for Omusati (NA-OS)
Flag for Kogi (NG-KO)
Flag for Edo (NG-ED)
Flag for Abia (NG-AB)
Flag for Oshikoto (NA-OT)
Flag for Kavango West (NA-KW)
Flag for Ebonyi (NG-EB)
Flag for Zinder (NE-7)
Flag for Jinotega (NI-JI)
Flag for Nasarawa (NG-NA)
Flag for Friesland (NL-FR)
Flag for Sokoto (NG-SO)
Flag for Rivas (NI-RI)
Flag for Nueva Segovia (NI-NS)
Flag for Plateau (NG-PL)
Flag for Yobe (NG-YO)
Flag for Bonaire (NL-BQ1)
Flag for Atlntico Norte (NI-AN)
Flag for Zamfara (NG-ZA)
Flag for Gelderland (NL-GE)
Flag for Oyo (NG-OY)
Flag for Madriz (NI-MD)
Flag for Chinandega (NI-CI)
Flag for Ondo (NG-ON)
Family - Man: Medium Skin Tone, Man: Medium Skin Tone, Boy: Medium Skin Tone, Girl: Medium Skin Tone
Flag for North Rhine-Westphalia (DE-NW)
Flag for Lagos (NG-LA)
Flag for Managua (NI-MN)
Flag for Atlntico Sur (NI-AS)
Flag for Curaao (NL-CW)
Flag for Boaco (NI-BO)
Flag for Rivers (NG-RI)
Flag for Granada (NI-GR)
Flag for Chontales (NI-CO)
Flag for Groningen (NL-GR)
Flag for Sint Eustatius (NL-BQ3)
Flag for Ro San Juan (NI-SJ)
Flag for Osun (NG-OS)
Flag for Taraba (NG-TA)
Flag for Flevoland (NL-FL)
Flag for Matagalpa (NI-MT)
Flag for Drenthe (NL-DR)
Flag for Carazo (NI-CA)
Flag for Kwara (NG-KW)
Flag for Niger (NG-NI)
Flag for Estel (NI-ES)
Flag for South Holland (NL-ZH)
"""
for line in emojis.splitlines():
words = line.split()
char = words[0]
desc = " ".join(words[1:])
print("{}\t:{}".format(desc, char))
| 35.752537 | 141 | 0.63422 |
e832546effca406bca8fea44d6997bf80564cc29 | 19,186 | py | Python | src/ngc/main.py | HubTou/ngc | 9917adfbaff61e7b20b9f06a4be6b51237e4e768 | [
"BSD-3-Clause"
] | null | null | null | src/ngc/main.py | HubTou/ngc | 9917adfbaff61e7b20b9f06a4be6b51237e4e768 | [
"BSD-3-Clause"
] | null | null | null | src/ngc/main.py | HubTou/ngc | 9917adfbaff61e7b20b9f06a4be6b51237e4e768 | [
"BSD-3-Clause"
] | null | null | null | #!/usr/bin/env python
""" ngc - n-grams count
License: 3-clause BSD (see https://opensource.org/licenses/BSD-3-Clause)
Author: Hubert Tournier
"""
import getopt
import logging
import os
import re
import string
import sys
import unicode2ascii
# Version string used by the what(1) and ident(1) commands:
ID = "@(#) $Id: ngc - n-grams count v1.0.2 (September 26, 2021) by Hubert Tournier $"
# Default parameters. Can be superseded by command line options
parameters = {
"Convert": {
"Unicode to ASCII": False,
"Upper to lower case": False,
"Lower to upper case": False,
"Spaces to one space": False,
},
"Discard": {
"Unicode characters": False,
"Upper case letters": False,
"Lower case letters": False,
"Connection symbols": False, # ' -
"Digits": False,
"Punctuation": False, # . , ; : ! ?
"Other printable symbols": False,
"Spaces": False, # space tab return formfeed vtab
"Control characters": False,
},
"Length": 1,
"Fixed block": False, # Sliding-window mode by default
"Word boundary": False,
"Partial": {
"Discard": False,
"Keep": True,
"Justify": False,
},
"Show": {
"Text": False,
"N-grams": True,
"Summary": False,
},
}
occurrences = {}
summary = {
"Upper case letters": 0,
"Lower case letters": 0,
"Connection symbols": 0,
"Digits": 0,
"Punctuation": 0,
"Other printable symbols": 0,
"Spaces": 0,
"Other spaces": 0,
"Control characters": 0,
"Unicode letters": 0,
"Unicode marks": 0,
"Unicode numbers": 0,
"Unicode punctuations": 0,
"Unicode symbols": 0,
"Unicode separators": 0,
"Unicode others": 0,
"All unicode characters": 0,
"All characters": 0,
"All n-grams": 0
}
################################################################################
def initialize_debugging(program_name):
"""Debugging set up"""
console_log_format = program_name + ": %(levelname)s: %(message)s"
logging.basicConfig(format=console_log_format, level=logging.DEBUG)
logging.disable(logging.INFO)
################################################################################
def display_help():
"""Displays usage and help"""
print("usage: ngc [-b|--block] [-c|--convert ARGS] [--debug]", file=sys.stderr)
print(" [-d|--discard ARGS] [--help|-?] [-l|--length ARG]", file=sys.stderr)
print(" [-p|--partial ARG] [-q|--quiet] [-s|--summary] [-t|--text]", file=sys.stderr)
print(" [--version] [-w|--word] [--] [filename ...]", file=sys.stderr)
print(" ----------------- ----------------------------------------------------",
file=sys.stderr
)
print(" -b|--block Use fixed- instead of sliding-windows blocks", file=sys.stderr)
print(" -c|--convert ARGS Convert text input. A combination of:", file=sys.stderr)
print(" ARG = a - Unicode characters to ASCII (remove accents)", file=sys.stderr)
print(" ARG = l - Upper case letters to lower", file=sys.stderr)
print(" ARG = u - Lower case letters to upper", file=sys.stderr)
print(" ARG = s - Spaces-like characters to 1 space", file=sys.stderr)
print(" ARGS l and u can't be used at the same time", file=sys.stderr)
print(" -d|--discard ARGS Discard characters. A combination of:", file=sys.stderr)
print(" ARG = U - Unicode characters", file=sys.stderr)
print(" ARG = u - Upper case letters", file=sys.stderr)
print(" ARG = l - Lower case letters", file=sys.stderr)
print(" ARG = L - All letters", file=sys.stderr)
print(" ARG = c - Connection symbols ('-)", file=sys.stderr)
print(" ARG = d - Digits", file=sys.stderr)
print(" ARG = p - Punctuation (.,;:!?)", file=sys.stderr)
print(" ARG = o - Other printable symbols", file=sys.stderr)
print(" ARG = s - Spaces (space, tab, return, formfeed, vtab)", file=sys.stderr)
print(" ARG = n - Non printable Control characters", file=sys.stderr)
print(" -l|--length ARG Length of the n-gram. Defaults to 1", file=sys.stderr)
print(" -p|--partial ARG What to do with partial blocks? One among:", file=sys.stderr)
print(" ARG = d - Discard", file=sys.stderr)
print(" ARG = k - Keep as-is", file=sys.stderr)
print(" ARG = j - Keep but right-justify with spaces", file=sys.stderr)
print(" -q|--quiet Don't show occurrences and frequency by n-gram", file=sys.stderr)
print(" -s|--summary Show a summary of what was processed", file=sys.stderr)
print(" -t|--text Show modified text input", file=sys.stderr)
print(" -w|--word Respect Word boundaries (delimited by spaces)", file=sys.stderr)
print(" --debug Enable debug mode", file=sys.stderr)
print(" --help|-? Print usage and this help message and exit", file=sys.stderr)
print(" --version Print version and exit", file=sys.stderr)
print(" -- Options processing terminator", file=sys.stderr)
print(file=sys.stderr)
################################################################################
def process_environment_variables():
"""Process environment variables"""
if "NGC_DEBUG" in os.environ.keys():
logging.disable(logging.NOTSET)
################################################################################
def process_command_line():
"""Process command line"""
# pylint: disable=C0103
global parameters
# pylint: enable=C0103
# option letters followed by : expect an argument
# same for option strings followed by =
character_options = "bc:d:l:p:qstw?"
string_options = [
"block",
"convert=",
"debug",
"discard=",
"help",
"length=",
"partial=",
"quiet",
"summary",
"text",
"version",
"word",
]
try:
options, remaining_arguments = getopt.getopt(
sys.argv[1:], character_options, string_options
)
except getopt.GetoptError as error:
logging.critical(error)
display_help()
sys.exit(1)
for option, argument in options:
if option in ("-b", "--block"):
parameters["Fixed block"] = True
elif option in ("-c", "--convert"):
if 'l' in argument and 'u' in argument:
logging.critical("-c|--convert parameter can't contain [lu] at the same time")
sys.exit(1)
if 'a' in argument:
parameters["Convert"]["Unicode to ASCII"] = True
if 'l' in argument:
parameters["Convert"]["Upper to lower case"] = True
if 'u' in argument:
parameters["Convert"]["Lower to upper case"] = True
if 's' in argument:
parameters["Convert"]["Spaces to one space"] = True
elif option in ("-d", "--discard"):
if 'U' in argument:
parameters["Discard"]["Unicode characters"] = True
if 'u' in argument:
parameters["Discard"]["Upper case letters"] = True
if 'l' in argument:
parameters["Discard"]["Lower case letters"] = True
if 'L' in argument:
parameters["Discard"]["Upper case letters"] = True
parameters["Discard"]["Lower case letters"] = True
if 'c' in argument:
parameters["Discard"]["Connection symbols"] = True
if 'd' in argument:
parameters["Discard"]["Digits"] = True
if 'p' in argument:
parameters["Discard"]["Punctuation"] = True
if 'o' in argument:
parameters["Discard"]["Other printable symbols"] = True
if 's' in argument:
parameters["Discard"]["Spaces"] = True
if 'n' in argument:
parameters["Discard"]["Control characters"] = True
elif option in ("-l", "--length"):
if argument.isdigit() and int(argument) >= 0:
parameters["Length"] = int(argument)
else:
logging.critical("-l|--length parameter must be a strictly positive integer")
sys.exit(1)
elif option in ("-p", "--partial"):
if len(argument) > 1 or argument not in ('d', 'k', 'j'):
logging.critical("-p|--partial parameter must be a single character among [dkj]")
sys.exit(1)
if argument == 'd':
parameters["Partial"]["Discard"] = True
parameters["Partial"]["Keep"] = False
elif argument == 'j':
parameters["Partial"]["Justify"] = True
parameters["Partial"]["Keep"] = False
elif option in ("-q", "--quiet"):
parameters["Show"]["N-grams"] = False
elif option in ("-s", "--summary"):
parameters["Show"]["Summary"] = True
elif option in ("-t", "--text"):
parameters["Show"]["Text"] = True
elif option in ("-w", "--word"):
parameters["Word boundary"] = True
elif option == "--debug":
logging.disable(logging.NOTSET)
elif option in ("--help", "-?"):
display_help()
sys.exit(0)
elif option == "--version":
print(ID.replace("@(" + "#)" + " $" + "Id" + ": ", "").replace(" $", ""))
sys.exit(0)
logging.debug("process_command_line(): parameters:")
logging.debug(parameters)
logging.debug("process_command_line(): remaining_arguments:")
logging.debug(remaining_arguments)
return remaining_arguments
################################################################################
def handle_partial_n_gram(text):
"""Analyze n-grams frequency in a string"""
# pylint: disable=C0103
global occurrences, summary
# pylint: enable=C0103
if not parameters["Partial"]["Discard"]:
if parameters["Partial"]["Justify"]:
for _ in range(parameters["Length"] - len(text)):
text += " "
if text in occurrences:
occurrences[text] += 1
else:
occurrences[text] = 1
summary["All n-grams"] += 1
################################################################################
def frequency_analysis(text):
"""Analyze n-grams frequency in a string"""
# pylint: disable=C0103
global occurrences, summary
# pylint: enable=C0103
if parameters["Show"]["Summary"]:
for character in text:
if ord(character) < 128:
if character in string.ascii_uppercase:
summary["Upper case letters"] += 1
elif character in string.ascii_lowercase:
summary["Lower case letters"] += 1
elif character in ("'", "-"):
summary["Connection symbols"] += 1
elif character in string.digits:
summary["Digits"] += 1
elif character in (".", ",", ";", ":", "!", "?"):
summary["Punctuation"] += 1
elif character == " ":
summary["Spaces"] += 1
elif character in string.whitespace:
summary["Other spaces"] += 1
elif (ord(character) < 32 and ord(character) not in (9, 11, 12, 13)) \
or ord(character) == 127:
summary["Control characters"] += 1
else:
summary["Other printable symbols"] += 1
else:
summary["All unicode characters"] += 1
if unicode2ascii.is_unicode_letter(character):
summary["Unicode letters"] += 1
elif unicode2ascii.is_unicode_mark(character):
summary["Unicode marks"] += 1
elif unicode2ascii.is_unicode_number(character):
summary["Unicode numbers"] += 1
elif unicode2ascii.is_unicode_punctuation(character):
summary["Unicode punctuations"] += 1
elif unicode2ascii.is_unicode_symbol(character):
summary["Unicode symbols"] += 1
elif unicode2ascii.is_unicode_separator(character):
summary["Unicode separators"] += 1
else:
summary["Unicode others"] += 1
if len(text) <= parameters["Length"]:
if text:
handle_partial_n_gram(text)
else:
i = 0
while i < len(text) + 1 - parameters["Length"]:
sequence = text[i:i + parameters["Length"]]
if sequence in occurrences:
occurrences[sequence] += 1
else:
occurrences[sequence] = 1
summary["All n-grams"] += 1
if parameters["Fixed block"]:
i += parameters["Length"]
else:
i += 1
if i < len(text):
handle_partial_n_gram(text[i:])
################################################################################
def process_line(line):
"""Process a text line"""
# pylint: disable=C0103
global summary
# pylint: enable=C0103
line = line.rstrip(os.linesep)
# Conversions:
if parameters["Convert"]["Unicode to ASCII"]:
line = unicode2ascii.unicode_to_ascii_string(line)
if parameters["Convert"]["Upper to lower case"]:
line = line.lower()
if parameters["Convert"]["Lower to upper case"]:
line = line.upper()
# Discards:
if parameters["Discard"]["Unicode characters"]:
line = "".join([c for c in line if ord(c) < 128])
if parameters["Discard"]["Upper case letters"]:
line = re.sub(r"[A-Z]+", "", line)
if parameters["Discard"]["Lower case letters"]:
line = re.sub(r"[a-z]+", "", line)
if parameters["Discard"]["Connection symbols"]:
line = re.sub(r"[-']+", "", line)
if parameters["Discard"]["Digits"]:
line = re.sub(r"[0-9]+", "", line)
if parameters["Discard"]["Punctuation"]:
line = re.sub(r"[\.,;:!\?]+", "", line)
if parameters["Discard"]["Other printable symbols"]:
line = re.sub(r"[\"#$&@\[\\\]_`{|}~%()\*+/<=>^]+", "", line)
if parameters["Discard"]["Spaces"]:
line = re.sub(r"[" + string.whitespace + r"]+", "", line)
if parameters["Discard"]["Control characters"]:
line = "".join(
[c for c in line if not (ord(c) < 9 or (ord(c) > 13 and ord(c) < 32) or ord(c) == 127)]
)
# Late conversions:
if parameters["Convert"]["Spaces to one space"]:
line = re.sub(r"[" + string.whitespace + r"]+", " ", line)
if parameters["Show"]["Text"]:
print(line)
if parameters["Word boundary"]:
# Splitting words on all kind of whitespaces:
for word in line.split():
if word:
frequency_analysis(word)
summary["All characters"] += len(word)
else:
frequency_analysis(line)
summary["All characters"] += len(line)
################################################################################
def process_file(filename):
"""Process the file designated by filename, line by line"""
with open(filename, "r") as file:
for line in file.readlines():
process_line(line)
################################################################################
def compute_kappa_plaintext():
"""Return kappa_plaintext for the processed input stream"""
# pylint: disable=C0103
global occurrences, summary
# pylint: enable=C0103
# See https://en.wikipedia.org/wiki/Index_of_coincidence
index = 0.0
for occurrence in occurrences.values():
index += occurrence * (occurrence - 1)
return index / (summary["All n-grams"] * (summary["All n-grams"] - 1))
################################################################################
def compute_coincidence_index(kappa_plaintext):
"""Return coincidence index for a given kappa_plaintext and alphabet"""
# pylint: disable=C0103
global summary
# pylint: enable=C0103
if summary["Unicode separators"]:
# Unknown alphabet size
return 0
alphabet_size = 0
if summary["Upper case letters"]:
alphabet_size += len(string.ascii_uppercase)
if summary["Lower case letters"]:
alphabet_size += len(string.ascii_lowercase)
if summary["Digits"]:
alphabet_size += len(string.digits)
if summary["Connection symbols"]:
alphabet_size += len("'-")
if summary["Punctuation"]:
alphabet_size += len(".,;:?!")
if summary["Other printable symbols"]:
alphabet_size += len("\"#$&@[\\]_`{|}~%()*+/<=>^")
if summary["Spaces"]:
alphabet_size += 1
if summary["Other spaces"]:
alphabet_size += len(string.whitespace) - 1
if summary["Control characters"]:
alphabet_size += 29
return kappa_plaintext * alphabet_size
################################################################################
def main():
"""The program's main entry point"""
program_name = os.path.basename(sys.argv[0])
initialize_debugging(program_name)
process_environment_variables()
arguments = process_command_line()
exit_status = 0
# Reading from files whose name were given as arguments:
if len(arguments):
for filename in arguments:
if os.path.isfile(filename):
process_file(filename)
else:
logging.error("The argument '%s' is not a filename", filename)
exit_status = 1
# Reading from standard input as there are no arguments:
else:
for line in sys.stdin:
process_line(line)
# Displaying occurrences and frequency by n-gram:
if parameters["Show"]["N-grams"]:
if parameters["Show"]["Text"]:
print("--")
decreasing_occurrences = dict(sorted(occurrences.items(), key=lambda t: t[1], reverse=True))
for key, value in decreasing_occurrences.items():
print("'{}'\t{}\t{:.2f}%".format(key, value, (value/summary["All n-grams"])*100))
# Displaying summary:
if parameters["Show"]["Summary"]:
print("==")
for key, value in summary.items():
print("{:23s}\t{:d}".format(key, value))
print()
kappa_plaintext = compute_kappa_plaintext()
coincidence_index = compute_coincidence_index(kappa_plaintext)
print("{:23s}\t{}".format("Kappa-plaintext", kappa_plaintext))
print("{:23s}\t{}".format("Index of coincidence", coincidence_index))
sys.exit(exit_status)
if __name__ == "__main__":
main()
| 37.472656 | 100 | 0.526686 |
e8327e8e634b6bfa67228d199235d028b776af03 | 3,104 | py | Python | openquake.hazardlib/openquake/hazardlib/tests/gsim/campbell_2003_test.py | rainzhop/ConvNetQuake | a3e6de3f7992eac72f1b9883fec36b8c7fdefd48 | [
"MIT"
] | null | null | null | openquake.hazardlib/openquake/hazardlib/tests/gsim/campbell_2003_test.py | rainzhop/ConvNetQuake | a3e6de3f7992eac72f1b9883fec36b8c7fdefd48 | [
"MIT"
] | null | null | null | openquake.hazardlib/openquake/hazardlib/tests/gsim/campbell_2003_test.py | rainzhop/ConvNetQuake | a3e6de3f7992eac72f1b9883fec36b8c7fdefd48 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2012-2016 GEM Foundation
#
# OpenQuake is free software: you can redistribute it and/or modify it
# under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# OpenQuake is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with OpenQuake. If not, see <http://www.gnu.org/licenses/>.
from openquake.hazardlib.gsim.campbell_2003 import (
Campbell2003,
Campbell2003SHARE,
Campbell2003MblgAB1987NSHMP2008,
Campbell2003MblgJ1996NSHMP2008,
Campbell2003MwNSHMP2008
)
from openquake.hazardlib.tests.gsim.utils import BaseGSIMTestCase
import numpy
# Test data generated from OpenSHA implementation.
| 31.673469 | 79 | 0.715206 |
e834ba6213863414f344960900a8a67a2181902b | 3,713 | py | Python | sktime/regression/interval_based/_tsf.py | khrapovs/sktime | 1589d007ef5dbcdc1f42f2c8278919ebed516358 | [
"BSD-3-Clause"
] | 1 | 2021-12-22T02:45:39.000Z | 2021-12-22T02:45:39.000Z | sktime/regression/interval_based/_tsf.py | khrapovs/sktime | 1589d007ef5dbcdc1f42f2c8278919ebed516358 | [
"BSD-3-Clause"
] | null | null | null | sktime/regression/interval_based/_tsf.py | khrapovs/sktime | 1589d007ef5dbcdc1f42f2c8278919ebed516358 | [
"BSD-3-Clause"
] | null | null | null | # -*- coding: utf-8 -*-
"""Time Series Forest Regressor (TSF)."""
__author__ = ["Tony Bagnall", "kkoziara", "luiszugasti", "kanand77", "Markus Lning"]
__all__ = ["TimeSeriesForestRegressor"]
import numpy as np
from joblib import Parallel, delayed
from sklearn.ensemble._forest import ForestRegressor
from sklearn.tree import DecisionTreeRegressor
from sktime.regression.base import BaseRegressor
from sktime.series_as_features.base.estimators.interval_based._tsf import (
BaseTimeSeriesForest,
_transform,
)
| 30.434426 | 86 | 0.647724 |
e834f207abc6dbf424b5c37c56b17f80d4a4a1f6 | 7,766 | py | Python | vectorc2/vectorc2/settings.py | sebastiankruk/vectorc2 | 13232cd63ebed32346fb4a669511b102b8ed24c0 | [
"Apache-2.0"
] | 11 | 2019-02-27T01:38:47.000Z | 2020-11-13T02:14:58.000Z | vectorc2/vectorc2/settings.py | sebastiankruk/vectorc2 | 13232cd63ebed32346fb4a669511b102b8ed24c0 | [
"Apache-2.0"
] | 20 | 2019-02-27T21:22:59.000Z | 2022-01-13T01:22:16.000Z | vectorc2/vectorc2/settings.py | sebastiankruk/vectorc2 | 13232cd63ebed32346fb4a669511b102b8ed24c0 | [
"Apache-2.0"
] | 1 | 2020-01-14T09:14:28.000Z | 2020-01-14T09:14:28.000Z | """
Django settings for vectorc2 project.
Copyright 2019 Sebastian Ryszard Kruk <vectorc2@kruk.me>
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 os
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/2.1/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = '#9iml9@=i%x#i57qi1zm)&)p46hrf(g=pn7jioagsh*))6+z9('
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
ALLOWED_HOSTS = [
"localhost",
"127.0.0.1",
"0.0.0.0"
]
# Application definition
INSTALLED_APPS = [
'space',
'command',
'bootstrap4',
'octicons',
'nonicons',
'blocks',
'photos',
'morse',
'webview.apps.WebviewConfig',
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'channels',
# 'compressor',
]
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.locale.LocaleMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
ROOT_URLCONF = 'vectorc2.urls'
STATICFILES_DIRS = [
os.path.join(BASE_DIR, "static")
]
MEDIA_ROOT = os.path.join(BASE_DIR, 'media')
MEDIA_URL = '/media/'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [
os.path.join(BASE_DIR, 'webview', 'templates')
],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.contrib.auth.context_processors.auth',
'django.template.context_processors.debug',
'django.template.context_processors.i18n',
'django.template.context_processors.media',
'django.template.context_processors.static',
'django.template.context_processors.request',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = 'vectorc2.wsgi.application'
# Database
# https://docs.djangoproject.com/en/2.1/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),
}
}
# Password validation
# https://docs.djangoproject.com/en/2.1/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/2.1/topics/i18n/
LANGUAGE_CODE = 'en'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/2.1/howto/static-files/
STATIC_URL = '/static/'
# languages
from django.utils.translation import gettext_lazy as _
LANGUAGES = [
('pl', _('Polish')),
('en', _('English')),
]
# Default settings
BOOTSTRAP4 = {
# The complete URL to the Bootstrap CSS file
# Note that a URL can be either a string,
# e.g. "https://stackpath.bootstrapcdn.com/bootstrap/4.1.1/css/bootstrap.min.css",
# or a dict like the default value below.
"css_url": {
"href": "/static/style/bootstrap/bootstrap.min.css",
# "integrity": "sha384-WskhaSGFgHYWDcbwN70/dfYBj47jz9qbsMId/iRN3ewGhXQFZCSftd1LZCfmhktB",
"crossorigin": "anonymous",
},
# The complete URL to the Bootstrap JavaScript file
"javascript_url": {
"url": "/static/script/bootstrap/bootstrap.min.js",
# "integrity": "sha384-smHYKdLADwkXOn1EmN1qk/HfnUcbVRZyYmZ4qpPea6sjB/pTJ0euyQp0Mk8ck+5T",
"crossorigin": "anonymous",
},
# The complete URL to the Bootstrap CSS file (None means no theme)
"theme_url": None,
# The URL to the jQuery JavaScript file (full)
"jquery_url": {
"url": "/static/script/bootstrap/jquery-3.3.1.min.js",
# "integrity": "sha384-tsQFqpEReu7ZLhBV2VZlAu7zcOV+rXbYlF2cqB8txI/8aZajjp4Bqd+V6D5IgvKT",
"crossorigin": "anonymous",
},
# The URL to the jQuery JavaScript file (slim)
"jquery_slim_url": {
"url": "/static/script/bootstrap/jquery-3.3.1.slim.min.js",
# "integrity": "sha384-q8i/X+965DzO0rT7abK41JStQIAqVgRVzpbzo5smXKp4YfRvH+8abtTE1Pi6jizo",
"crossorigin": "anonymous",
},
# The URL to the Popper.js JavaScript file (slim)
"popper_url": {
"url": "/static/script/bootstrap/popper.min.js",
# "integrity": "sha384-ZMP7rVo3mIykV+2+9J3UJ46jBk0WLaUAdn689aCwoqbBJiSnjAK/l8WvCWPIPm49",
"crossorigin": "anonymous",
},
# Put JavaScript in the HEAD section of the HTML document (only relevant if you use bootstrap4.html)
'javascript_in_head': False,
# Include jQuery with Bootstrap JavaScript False|falsy|slim|full (default=False)
# False - means tag bootstrap_javascript use default value - `falsy` and does not include jQuery)
'include_jquery': False,
# Label class to use in horizontal forms
'horizontal_label_class': 'col-md-3',
# Field class to use in horizontal forms
'horizontal_field_class': 'col-md-9',
# Set placeholder attributes to label if no placeholder is provided
'set_placeholder': True,
# Class to indicate required (better to set this in your Django form)
'required_css_class': '',
# Class to indicate error (better to set this in your Django form)
'error_css_class': 'has-error',
# Class to indicate success, meaning the field has valid input (better to set this in your Django form)
'success_css_class': 'has-success',
# Renderers (only set these if you have studied the source and understand the inner workings)
'formset_renderers':{
'default': 'bootstrap4.renderers.FormsetRenderer',
},
'form_renderers': {
'default': 'bootstrap4.renderers.FormRenderer',
},
'field_renderers': {
'default': 'bootstrap4.renderers.FieldRenderer',
'inline': 'bootstrap4.renderers.InlineFieldRenderer',
},
}
ASGI_APPLICATION = "vectorc2.routing.application"
CHANNEL_LAYERS = {
'default': {
'BACKEND': 'channels_redis.core.RedisChannelLayer',
'CONFIG': {
"hosts": [('127.0.0.1', 6379)],
},
},
}
VECTOR = { }
# #TODO
# STATICFILES_FINDERS = [
# 'compressor.finders.CompressorFinder'
# ]
# COMPRESS_ENABLED = False
# COMPRESS_ROOT = os.path.join(BASE_DIR, 'static_collected') | 29.984556 | 107 | 0.676796 |
e83506e2b2f1a14d61d7ea9ee2da6803dd09cbf8 | 7,802 | py | Python | datahandlers/wgisd.py | mikewoodson/ssl-transfer | 524e2d57e9ffdbf0497cd4a1404eb1f85dc9fca7 | [
"MIT"
] | null | null | null | datahandlers/wgisd.py | mikewoodson/ssl-transfer | 524e2d57e9ffdbf0497cd4a1404eb1f85dc9fca7 | [
"MIT"
] | null | null | null | datahandlers/wgisd.py | mikewoodson/ssl-transfer | 524e2d57e9ffdbf0497cd4a1404eb1f85dc9fca7 | [
"MIT"
] | null | null | null | from torchvision.datasets.folder import pil_loader, accimage_loader, default_loader
from torch import Tensor
from pathlib import Path
from enum import Enum
from collections import namedtuple
from torchvision import transforms as T
import os
import numpy as np
import pdb
import functools
import torch.utils.data as data
import torch
def get_fname(self, img_id):
return self.id_to_fname[img_id.item()]
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (sample, target) where target is class_index of the target class.
"""
path, target = self.samples[index]
sample = self.loader(path)
if self.transform is not None:
sample, target = self.transform(sample, target)
return sample, target
def __len__(self):
return len(self.samples)
def __repr__(self):
fmt_str = 'Dataset ' + self.__class__.__name__ + '\n'
fmt_str += ' Number of datapoints: {}\n'.format(self.__len__())
fmt_str += ' Root Location: {}\n'.format(self.root)
tmp = ' Transforms (if any): '
fmt_str += '{0}{1}\n'.format(tmp,
self.transform.__repr__().replace('\n',
'\n' + ' ' * len(tmp)))
return fmt_str
def _check_exists(self):
return self.root.exists() and self.classes_file.exists()
def download(self):
"""Download the wgisd data if it doesn't exist already."""
import requests
import tarfile
from git import Repo
if self._check_exists():
return
print('Downloading %s ... (may take a few minutes)' % self.url)
self.root.mkdir()
Repo.clone_from(self.url, str(self.root))
print('Done!')
| 33.34188 | 94 | 0.564214 |
e8361342f0f0d1a04e77282debfd9e4a5b53ef3e | 16,223 | py | Python | SimpleCV/MachineLearning/query_imgs/get_imgs_geo_gps_search.py | nikhilgk/SimpleCV | ee64451c16db1f40b4da221115273020a6a7b01a | [
"BSD-3-Clause"
] | 2 | 2016-04-30T12:23:05.000Z | 2022-03-02T00:01:10.000Z | SimpleCV/MachineLearning/query_imgs/get_imgs_geo_gps_search.py | nikhilgk/SimpleCV | ee64451c16db1f40b4da221115273020a6a7b01a | [
"BSD-3-Clause"
] | null | null | null | SimpleCV/MachineLearning/query_imgs/get_imgs_geo_gps_search.py | nikhilgk/SimpleCV | ee64451c16db1f40b4da221115273020a6a7b01a | [
"BSD-3-Clause"
] | 2 | 2015-04-16T12:14:55.000Z | 2019-08-07T14:12:04.000Z | #!/usr/bin/python
#
# So this script is in a bit of a hack state right now.
# This script reads
#
#
#
# Graciously copied and modified from:
# http://graphics.cs.cmu.edu/projects/im2gps/flickr_code.html
#Image querying script written by Tamara Berg,
#and extended heavily James Hays
#9/26/2007 added dynamic timeslices to query more efficiently.
#8/18/2008 added new fields and set maximum time slice.
#8/19/2008 this is a much simpler function which gets ALL geotagged photos of
# sufficient accuracy. No queries, no negative constraints.
# divides up the query results into multiple files
# 1/5/2009
# now uses date_taken instead of date_upload to get more diverse blocks of images
# 1/13/2009 - uses the original im2gps keywords, not as negative constraints though
import sys, string, math, time, socket
from flickrapi2 import FlickrAPI
from datetime import datetime
import pycurl
import os
import shutil
socket.setdefaulttimeout(30) #30 second time out on sockets before they throw
#an exception. I've been having trouble with urllib.urlopen hanging in the
#flickr API. This will show up as exceptions.IOError.
#the time out needs to be pretty long, it seems, because the flickr servers can be slow
#to respond to our big searches.
#returns a query and the search times to attempt to get a desired number of photos
#this needs serious refactoring -KAS
###########################################################################
# Modify this section to reflect your data and specific search
###########################################################################
# flickr auth information:
# change these to your flickr api keys and secret
flickrAPIKey = "fa33550d413b36b3fddc473a931a3b3b" # API key
flickrSecret = "7fd481bff0916055" # shared "secret"
rootpath = "../data/" #where do you want the data
desired_photos = 1000 #how many photos do you want to try and get
query_file_name = 'query.dat' #The file to get the queries from
#query_file_name = 'place_rec_queries_fall08.txt'
query_file = open(query_file_name, 'r')
#aggregate all of the positive and negative queries together.
pos_queries = [] #an empty list
neg_queries = '' #a string
num_queries = 0
for line in query_file:
if line[0] != '#' and len(line) > 1: #line end character is 2 long?
print line[0:len(line)-1]
if line[0] != '-':
pos_queries = pos_queries + [line[0:len(line)-1]]
num_queries = num_queries + 1
if line[0] == '-':
neg_queries = neg_queries + ' ' + line[0:len(line)-1]
query_file.close()
print 'positive queries: '
print pos_queries
print 'negative queries: ' + neg_queries
print 'num_queries = ' + str(num_queries)
#this is the desired number of photos in each block
# make a new FlickrAPI instance
fapi = FlickrAPI(flickrAPIKey, flickrSecret)
for current_tag in range(0, num_queries):
print('TOP OF LOOP')
# change this to the location where you want to put your output file
try:
stats = os.stat(rootpath)
except OSError:
os.mkdir(rootpath)
outpath = rootpath+pos_queries[current_tag]+'/'
try:
os.mkdir(outpath)
except OSError:
shutil.rmtree(outpath,True)
os.mkdir(outpath)
out_file = open(rootpath + pos_queries[current_tag] + '.txt','w')
###########################################################################
#form the query string.
query_string = pos_queries[current_tag] + ' ' + neg_queries
print '\n\nquery_string is ' + query_string
total_images_queried = 0;
[mintime,maxtime,total_images,rsp] = DoSearch(fapi,query_string,desired_photos)
print('GETTING TOTATL IMAGES:'+str(total_images))
s = '\nmintime: ' + str(mintime) + ' maxtime: ' + str(maxtime)
print s
out_file.write(s + '\n')
i = getattr(rsp,'photos',None)
if i:
s = 'numimgs: ' + total_images
print s
out_file.write(s + '\n')
current_image_num = 1;
num = 4 # CHANGE THIS BACK int(rsp.photos[0]['pages'])
s = 'total pages: ' + str(num)
print s
out_file.write(s + '\n')
#only visit 16 pages max, to try and avoid the dreaded duplicate bug
#16 pages = 4000 images, should be duplicate safe. Most interesting pictures will be taken.
num_visit_pages = min(16,num)
s = 'visiting only ' + str(num_visit_pages) + ' pages ( up to ' + str(num_visit_pages * 250) + ' images)'
print s
out_file.write(s + '\n')
total_images_queried = total_images_queried + min((num_visit_pages * 250), int(total_images))
#print 'stopping before page ' + str(int(math.ceil(num/3) + 1)) + '\n'
pagenum = 1;
counter = -1
while( pagenum <= num_visit_pages ):
#for pagenum in range(1, num_visit_pages + 1): #page one is searched twice
print ' page number ' + str(pagenum)
try:
print("PAGE")
print(pagenum)
# WARNING THIS QUERY HAS TO MATCH THE SEARCH QUERY!!!!
rsp = fapi.photos_search(api_key=flickrAPIKey,
ispublic="1",
media="photos",
per_page="250",
page=str(pagenum),
has_geo = "0",
text=query_string,
#extras = "tags, original_format, license, geo, date_taken, date_upload, o_dims, views",
#accuracy="6", #6 is region level.
min_upload_date=str(1121832000),#mintime),
max_upload_date=str(1192165200))#maxtime))
#rsp = fapi.photos_search(api_key=flickrAPIKey,
# ispublic="1",
# media="photos",
# per_page="250",
# page='0', #str(pagenum),
# sort="interestingness-desc",
# has_geo = "0", #bbox="-180, -90, 180, 90",
# text=query_string,
# #accuracy="6", #6 is region level. most things seem 10 or better.
# extras = "tags, original_format, license, geo, date_taken, date_upload, o_dims, views",
# min_upload_date=str(mintime),
# max_upload_date=str(maxtime))
##min_taken_date=str(datetime.fromtimestamp(mintime)),
##max_taken_date=str(datetime.fromtimestamp(maxtime)))
time.sleep(1)
fapi.testFailure(rsp)
except KeyboardInterrupt:
print('Keyboard exception while querying for images, exiting\n')
raise
except:
print sys.exc_info()[0]
#print type(inst) # the exception instance
#print inst.args # arguments stored in .args
#print inst # __str__ allows args to printed directly
print ('Exception encountered while querying for images\n')
else:
print('got a response')
# and print them
k = getattr(rsp,'photos',None)
if k:
print('In K')
m = getattr(rsp.photos[0],'photo',None)
if m:
print('In M')
for b in rsp.photos[0].photo:
print('In b')
if b!=None:
counter = counter + 1
##print(http://farm{farm-id}.static.flickr.com/{server-id}/{id}_{secret}.jpg)
myurl = 'http://farm'+b['farm']+".static.flickr.com/"+b['server']+"/"+b['id']+"_"+b['secret']+'.jpg'
fname = outpath+pos_queries[current_tag]+str(counter)+'.jpg' #b['id']+"_"+b['secret']+'.jpg'
print(myurl)
print(fname)
mycurl = pycurl.Curl()
mycurl.setopt(pycurl.URL, str(myurl))
myfile = open(fname,"wb")
mycurl.setopt(pycurl.WRITEDATA, myfile)
mycurl.setopt(pycurl.FOLLOWLOCATION, 1)
mycurl.setopt(pycurl.MAXREDIRS, 5)
mycurl.setopt(pycurl.NOSIGNAL, 1)
mycurl.perform()
mycurl.close()
myfile.close()
out_file.write('URL: '+myurl+'\n')
out_file.write('File: '+ fname+'\n')
out_file.write('photo: ' + b['id'] + ' ' + b['secret'] + ' ' + b['server'] + '\n')
out_file.write('owner: ' + b['owner'] + '\n')
out_file.write('title: ' + b['title'].encode("ascii","replace") + '\n')
out_file.write('originalsecret: ' + b['originalsecret'] + '\n')
out_file.write('originalformat: ' + b['originalformat'] + '\n')
out_file.write('o_height: ' + b['o_height'] + '\n')
out_file.write('o_width: ' + b['o_width'] + '\n')
out_file.write('datetaken: ' + b['datetaken'].encode("ascii","replace") + '\n')
out_file.write('dateupload: ' + b['dateupload'].encode("ascii","replace") + '\n')
out_file.write('tags: ' + b['tags'].encode("ascii","replace") + '\n')
out_file.write('license: ' + b['license'].encode("ascii","replace") + '\n')
out_file.write('latitude: ' + b['latitude'].encode("ascii","replace") + '\n')
out_file.write('longitude: ' + b['longitude'].encode("ascii","replace") + '\n')
out_file.write('accuracy: ' + b['accuracy'].encode("ascii","replace") + '\n')
out_file.write('views: ' + b['views'] + '\n')
out_file.write('interestingness: ' + str(current_image_num) + ' out of ' + str(total_images) + '\n');
out_file.write('\n')
current_image_num = current_image_num + 1;
print('')
pagenum = pagenum + 1; #this is in the else exception block. Itwon't increment for a failure.
#this block is indented such that it will only run if there are no exceptions
#in the original query. That means if there are exceptions, mintime won't be incremented
#and it will try again
timeskip = maxtime - mintime #used for initializing next binary search
mintime = maxtime
out_file.write('Total images queried: ' + str(total_images_queried) + '\n')
out_file.close
| 47.023188 | 133 | 0.513715 |
e836603f27c307f1405743aabfd1cf7f2bab9fdf | 2,695 | py | Python | Chapter04/python/2.0.0/com/sparksamples/util.py | quguiliang/Machine-Learning-with-Spark-Second-Edition | 0ba131e6c15a3de97609c6cb5d976806ccc14f09 | [
"MIT"
] | 112 | 2017-05-13T15:44:29.000Z | 2022-02-19T20:14:14.000Z | Chapter04/python/2.0.0/com/sparksamples/util.py | tophua/Machine-Learning-with-Spark-Second-Edition | 0d93e992f6c79d55ad5cdcab735dbe6674143974 | [
"MIT"
] | 1 | 2017-05-25T00:10:43.000Z | 2017-05-25T00:10:43.000Z | Chapter04/python/2.0.0/com/sparksamples/util.py | tophua/Machine-Learning-with-Spark-Second-Edition | 0d93e992f6c79d55ad5cdcab735dbe6674143974 | [
"MIT"
] | 115 | 2017-05-06T10:49:00.000Z | 2022-03-08T07:48:54.000Z | import os
import sys
from pyspark.sql.types import *
PATH = "/home/ubuntu/work/ml-resources/spark-ml/data"
SPARK_HOME = "/home/ubuntu/work/spark-2.0.0-bin-hadoop2.7/"
os.environ['SPARK_HOME'] = SPARK_HOME
sys.path.append(SPARK_HOME + "/python")
from pyspark import SparkContext
from pyspark import SparkConf
from pyspark.sql import SparkSession
conf = SparkConf().setAppName("First Spark App").setMaster("local")
sc = SparkContext(conf=conf)
spark = SparkSession(sc)
| 35.460526 | 89 | 0.656401 |
e8367fbdc5ace072208dddf385f21c1015f73383 | 384 | py | Python | safemasks/resources/rest/router.py | Safemasks/safemasks-app | 44c1cf16f81b15b74fa5eb38d36eaa078180e975 | [
"BSD-3-Clause"
] | 1 | 2020-11-04T09:42:29.000Z | 2020-11-04T09:42:29.000Z | safemasks/resources/rest/router.py | Safemasks/safemasks-app | 44c1cf16f81b15b74fa5eb38d36eaa078180e975 | [
"BSD-3-Clause"
] | null | null | null | safemasks/resources/rest/router.py | Safemasks/safemasks-app | 44c1cf16f81b15b74fa5eb38d36eaa078180e975 | [
"BSD-3-Clause"
] | 1 | 2022-02-16T12:58:28.000Z | 2022-02-16T12:58:28.000Z | """
"""
from rest_framework import routers
from safemasks.resources.rest.serializers import SupplierViewSet, TrustedSupplierViewSet
# Routers provide an easy way of automatically determining the URL conf.
ROUTER = routers.DefaultRouter()
ROUTER.register(r"suppliers", SupplierViewSet, "suppliers")
ROUTER.register(r"suppliers-trusted", TrustedSupplierViewSet, "suppliers-trusted")
| 32 | 88 | 0.815104 |
e836afc7bcf3544821610837e954b4a6cbeab66b | 47,925 | py | Python | src/zope/testrunner/formatter.py | jamesjer/zope.testrunner | af8bfec49d90613633b76e914a6f54884463ba94 | [
"ZPL-2.1"
] | 1 | 2021-03-05T17:27:37.000Z | 2021-03-05T17:27:37.000Z | src/zope/testrunner/formatter.py | jamesjer/zope.testrunner | af8bfec49d90613633b76e914a6f54884463ba94 | [
"ZPL-2.1"
] | 91 | 2015-01-12T05:27:27.000Z | 2022-03-07T07:03:09.000Z | src/zope/testrunner/formatter.py | jamesjer/zope.testrunner | af8bfec49d90613633b76e914a6f54884463ba94 | [
"ZPL-2.1"
] | 14 | 2015-04-03T09:53:36.000Z | 2021-05-10T15:51:53.000Z | ##############################################################################
#
# Copyright (c) 2004-2008 Zope Foundation and Contributors.
# All Rights Reserved.
#
# This software is subject to the provisions of the Zope Public License,
# Version 2.1 (ZPL). A copy of the ZPL should accompany this distribution.
# THIS SOFTWARE IS PROVIDED "AS IS" AND ANY AND ALL EXPRESS OR IMPLIED
# WARRANTIES ARE DISCLAIMED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF TITLE, MERCHANTABILITY, AGAINST INFRINGEMENT, AND FITNESS
# FOR A PARTICULAR PURPOSE.
#
##############################################################################
"""Output formatting.
"""
from __future__ import print_function
try:
from collections.abc import MutableMapping
except ImportError:
from collections import MutableMapping
from contextlib import contextmanager
import doctest
import os
import re
import sys
import tempfile
import traceback
from datetime import datetime, timedelta
from zope.testrunner.exceptions import DocTestFailureException
try:
unicode
except NameError:
unicode = str
doctest_template = """
File "%s", line %s, in %s
%s
Want:
%s
Got:
%s
"""
def tigetnum(attr, default=None):
"""Return a value from the terminfo database.
Terminfo is used on Unix-like systems to report various terminal attributes
(such as width, height or the number of supported colors).
Returns ``default`` when the ``curses`` module is not available, or when
sys.stdout is not a terminal.
"""
try:
import curses
except ImportError:
# avoid reimporting a broken module in python 2.3
sys.modules['curses'] = None
else:
# If sys.stdout is not a real file object (e.g. in unit tests that
# use various wrappers), you get an error, different depending on
# Python version:
expected_exceptions = (curses.error, TypeError, AttributeError)
if sys.version_info >= (3,):
import io
expected_exceptions += (io.UnsupportedOperation, )
try:
curses.setupterm()
except expected_exceptions:
# You get curses.error when $TERM is set to an unknown name
pass
else:
try:
return curses.tigetnum(attr)
except expected_exceptions:
# You get TypeError on PyPy3 due to a bug:
# https://bitbucket.org/pypy/pypy/issue/2016/pypy3-cursestigetnum-raises-ctype
pass
return default
def terminal_has_colors():
"""Determine whether the terminal supports colors.
Some terminals (e.g. the emacs built-in one) don't.
"""
return tigetnum('colors', -1) >= 8
# Conditional imports: we don't want zope.testrunner to have a hard
# dependency on subunit.
try:
import subunit
from subunit.iso8601 import Utc
subunit.StreamResultToBytes
except (ImportError, AttributeError):
subunit = None
# testtools is a hard dependency of subunit itself, but we guard it
# separately for richer error messages.
try:
import testtools
from testtools.content import (
Content,
ContentType,
content_from_file,
text_content,
)
testtools.StreamToExtendedDecorator
except (ImportError, AttributeError):
testtools = None
| 35.952738 | 94 | 0.584768 |
e8372d789b4ce7a9e159e03cce618e0ad5219646 | 410 | py | Python | waterApp/migrations/0011_auto_20210911_1043.py | csisarep/groundwater_dashboard | 4f93d7b7c9fd6b48ace54e7b62cae717decc98d2 | [
"MIT"
] | null | null | null | waterApp/migrations/0011_auto_20210911_1043.py | csisarep/groundwater_dashboard | 4f93d7b7c9fd6b48ace54e7b62cae717decc98d2 | [
"MIT"
] | null | null | null | waterApp/migrations/0011_auto_20210911_1043.py | csisarep/groundwater_dashboard | 4f93d7b7c9fd6b48ace54e7b62cae717decc98d2 | [
"MIT"
] | null | null | null | # Generated by Django 2.2 on 2021-09-11 04:58
from django.db import migrations, models
| 21.578947 | 73 | 0.617073 |
08fa086cfe8def53819d28aebd9cf2fb43f1e8d2 | 45 | py | Python | geomstats/geometry/stratified/__init__.py | shubhamtalbar96/geomstats | 9c17ccede7e3f0fddf31487c59227c677216a2b9 | [
"MIT"
] | null | null | null | geomstats/geometry/stratified/__init__.py | shubhamtalbar96/geomstats | 9c17ccede7e3f0fddf31487c59227c677216a2b9 | [
"MIT"
] | null | null | null | geomstats/geometry/stratified/__init__.py | shubhamtalbar96/geomstats | 9c17ccede7e3f0fddf31487c59227c677216a2b9 | [
"MIT"
] | null | null | null | """The Stratified Space Geometry Package."""
| 22.5 | 44 | 0.733333 |
08fa0ae611479cbc0414a8eece26a08f791be531 | 928 | py | Python | src/exporter/management/commands/test_export.py | xmdy/h9eNi8F5Ut | 4128d7cbc6105ec0fe69157bd88ef8e30415d6ca | [
"Unlicense"
] | null | null | null | src/exporter/management/commands/test_export.py | xmdy/h9eNi8F5Ut | 4128d7cbc6105ec0fe69157bd88ef8e30415d6ca | [
"Unlicense"
] | null | null | null | src/exporter/management/commands/test_export.py | xmdy/h9eNi8F5Ut | 4128d7cbc6105ec0fe69157bd88ef8e30415d6ca | [
"Unlicense"
] | null | null | null | from django.core.management import BaseCommand
import logging
# These two lines enable debugging at httplib level (requests->urllib3->http.client)
# You will see the REQUEST, including HEADERS and DATA, and RESPONSE with HEADERS but without DATA.
# The only thing missing will be the response.body which is not logged.
try:
import http.client as http_client
except ImportError:
# Python 2
import httplib as http_client
http_client.HTTPConnection.debuglevel = 1
# You must initialize logging, otherwise you'll not see debug output.
logging.basicConfig()
logging.getLogger().setLevel(logging.DEBUG)
requests_log = logging.getLogger("requests.packages.urllib3")
requests_log.setLevel(logging.DEBUG)
requests_log.propagate = True | 37.12 | 99 | 0.774784 |
08fa457de4bf935aa74ea49389d3610e34fb81f5 | 4,179 | py | Python | dask/tests/test_highgraph.py | ianthomas23/dask | 7968e85e2edab95565ebbab1f936c9c549e29126 | [
"BSD-3-Clause"
] | null | null | null | dask/tests/test_highgraph.py | ianthomas23/dask | 7968e85e2edab95565ebbab1f936c9c549e29126 | [
"BSD-3-Clause"
] | null | null | null | dask/tests/test_highgraph.py | ianthomas23/dask | 7968e85e2edab95565ebbab1f936c9c549e29126 | [
"BSD-3-Clause"
] | null | null | null | from functools import partial
import os
import pytest
import dask
import dask.array as da
from dask.utils_test import inc
from dask.highlevelgraph import HighLevelGraph, BasicLayer, Layer
from dask.blockwise import Blockwise
from dask.array.utils import assert_eq
def annot_map_fn(key):
return key[1:]
| 27.313725 | 94 | 0.61139 |
08fa7b2b56dd9ac93cd0f3c88381b55adb14d1bc | 4,612 | py | Python | transference.py | webpwnized/cryptography | 52660c14aa71afe087cf380bb1e9d067fa639bc6 | [
"MIT"
] | 13 | 2017-02-20T21:42:08.000Z | 2021-08-01T21:18:51.000Z | transference.py | webpwnized/cryptography | 52660c14aa71afe087cf380bb1e9d067fa639bc6 | [
"MIT"
] | 1 | 2017-03-24T22:52:25.000Z | 2017-03-26T23:14:08.000Z | transference.py | webpwnized/cryptography | 52660c14aa71afe087cf380bb1e9d067fa639bc6 | [
"MIT"
] | 2 | 2019-02-19T05:01:58.000Z | 2020-04-11T04:10:32.000Z | # Requires pip install bitarray
from bitarray import bitarray
import argparse, math
if __name__ == '__main__':
lArgParser = argparse.ArgumentParser(description='Transference: A tool to help visualize s-boxes (substitution boxes or transfer functions)')
lArgParser.add_argument('-tft', '--transfer-function-table', help='Print the transfer function table for the s-box', action='store_true')
lArgParser.add_argument('-lat', '--linear-approximation-table', help='Calculate the linear transformation table for the s-box', action='store_true')
lArgParser.add_argument('-all', '--all', help='Calculate the linear transformation table for the s-box', action='store_true')
lArgParser.add_argument('-v', '--verbose', help='Enables verbose output', action='store_true')
lArgParser.add_argument('INPUT', action='store', type=str, help='The substitution table (s-box) represented as a comma delimted list of integers. The length of the list is the number of bits in the substitution. Required. Example: 3,2,0,1 means substitute 3 for 0, 2 for 1, 0 for 2 and 1 for 3. ')
lArgs = lArgParser.parse_args()
lTransferFunction = derive_transfer_function(lArgs.INPUT)
if lArgs.all:
lArgs.transfer_function_table = lArgs.linear_approximation_table = True
if lArgs.transfer_function_table:
print_transfer_function_table(lTransferFunction)
if lArgs.linear_approximation_table:
print_linear_approximation_table(lTransferFunction)
| 36.03125 | 301 | 0.645056 |
08fbd2caaba4bfcce30f566e05b356d741dbf22e | 6,264 | py | Python | tests/test_client.py | mjcaley/spamc | 67c4f2b13d569238ea24794eb5253a1416226a2a | [
"MIT"
] | null | null | null | tests/test_client.py | mjcaley/spamc | 67c4f2b13d569238ea24794eb5253a1416226a2a | [
"MIT"
] | null | null | null | tests/test_client.py | mjcaley/spamc | 67c4f2b13d569238ea24794eb5253a1416226a2a | [
"MIT"
] | null | null | null | import pytest
from aiospamc.client import Client
from aiospamc.exceptions import (
BadResponse,
UsageException,
DataErrorException,
NoInputException,
NoUserException,
NoHostException,
UnavailableException,
InternalSoftwareException,
OSErrorException,
OSFileException,
CantCreateException,
IOErrorException,
TemporaryFailureException,
ProtocolException,
NoPermissionException,
ConfigException,
ServerTimeoutException,
ResponseException,
)
from aiospamc.responses import Response
| 30.115385 | 88 | 0.773467 |
08fc5d8c5d3505334ae9288303604f1b7aea6a2c | 3,534 | py | Python | sunpy/conftest.py | tacaswell/sunpy | 1e06d75408d1a621749a5d4e743ae44a31886100 | [
"BSD-2-Clause"
] | null | null | null | sunpy/conftest.py | tacaswell/sunpy | 1e06d75408d1a621749a5d4e743ae44a31886100 | [
"BSD-2-Clause"
] | null | null | null | sunpy/conftest.py | tacaswell/sunpy | 1e06d75408d1a621749a5d4e743ae44a31886100 | [
"BSD-2-Clause"
] | null | null | null | import os
import tempfile
import importlib
import pytest
import astropy
import astropy.config.paths
# Force MPL to use non-gui backends for testing.
try:
import matplotlib
except ImportError:
pass
else:
matplotlib.use('Agg')
# Don't actually import pytest_remotedata because that can do things to the
# entrypoints code in pytest.
remotedata_spec = importlib.util.find_spec("pytest_remotedata")
HAVE_REMOTEDATA = remotedata_spec is not None
# Do not collect the sample data file because this would download the sample data.
collect_ignore = ["data/sample.py"]
def pytest_runtest_setup(item):
"""
pytest hook to skip all tests that have the mark 'remotedata' if the
pytest_remotedata plugin is not installed.
"""
if isinstance(item, pytest.Function):
if 'remote_data' in item.keywords and not HAVE_REMOTEDATA:
pytest.skip("skipping remotedata tests as pytest-remotedata is not installed")
| 27.184615 | 90 | 0.695812 |
08fd1346d7002eaa8e3e4a99ba4673e257dc45fd | 10,165 | py | Python | qtcalendar/models.py | asmateus/PyQtCalendar | b8e5e468082f08159744f692e8edaf2ad52fccbb | [
"MIT"
] | 7 | 2018-01-30T19:23:18.000Z | 2022-02-04T13:07:57.000Z | qtcalendar/models.py | asmateus/PyQtCalendar | b8e5e468082f08159744f692e8edaf2ad52fccbb | [
"MIT"
] | 1 | 2020-11-13T14:58:41.000Z | 2021-04-27T16:58:46.000Z | qtcalendar/models.py | asmateus/PyQtCalendar | b8e5e468082f08159744f692e8edaf2ad52fccbb | [
"MIT"
] | 3 | 2020-05-20T04:40:35.000Z | 2021-02-24T08:58:40.000Z | '''
Models for QtWidgets
'''
from collections import deque
from math import ceil
import datetime as dt
import calendar
| 30.61747 | 92 | 0.59331 |
08fddb52b339d532616e5502ab7e8a544bac55d1 | 5,457 | py | Python | python/orca/src/bigdl/orca/data/tf/data.py | Forest216/BigDL | 840da9a2eaf395978dd83730b02aa5e5dfbd7989 | [
"Apache-2.0"
] | null | null | null | python/orca/src/bigdl/orca/data/tf/data.py | Forest216/BigDL | 840da9a2eaf395978dd83730b02aa5e5dfbd7989 | [
"Apache-2.0"
] | null | null | null | python/orca/src/bigdl/orca/data/tf/data.py | Forest216/BigDL | 840da9a2eaf395978dd83730b02aa5e5dfbd7989 | [
"Apache-2.0"
] | null | null | null | #
# Copyright 2016 The BigDL Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License 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 tensorflow as tf
from bigdl.orca.tfpark.tf_dataset import TensorMeta
from bigdl.dllib.utils import nest
from bigdl.orca.data import SparkXShards
from bigdl.dllib.utils import log4Error
class TensorSliceDataset(Dataset):
class MapDataset(Dataset):
| 36.139073 | 98 | 0.646876 |
08fed17a5871561cc999432b467a8117b686ae74 | 20,180 | py | Python | tools/generate_serialization_header.py | StableCoder/vulkan-mini-libs-2 | e048f45149816e100d3f4f51306626ebf547b032 | [
"Apache-2.0"
] | 1 | 2022-02-28T20:58:44.000Z | 2022-02-28T20:58:44.000Z | tools/generate_serialization_header.py | StableCoder/vulkan-mini-libs-2 | e048f45149816e100d3f4f51306626ebf547b032 | [
"Apache-2.0"
] | null | null | null | tools/generate_serialization_header.py | StableCoder/vulkan-mini-libs-2 | e048f45149816e100d3f4f51306626ebf547b032 | [
"Apache-2.0"
] | null | null | null | #!/usr/bin/env python3
import sys
import getopt
import xml.etree.ElementTree as ET
if __name__ == "__main__":
main(sys.argv[1:])
| 30.345865 | 101 | 0.607334 |
08fee5df6d43bb80284a92b04bcc18e2811bda85 | 5,629 | py | Python | ampel/cli/AbsStockCommand.py | AmpelProject/Ampel-core | dcbfbe38ba400b7f8e44e641b90217ca1bed4f8f | [
"BSD-3-Clause"
] | 5 | 2021-04-15T07:43:26.000Z | 2022-03-04T09:25:09.000Z | ampel/cli/AbsStockCommand.py | AmpelProject/Ampel-core | dcbfbe38ba400b7f8e44e641b90217ca1bed4f8f | [
"BSD-3-Clause"
] | 67 | 2021-02-23T21:43:20.000Z | 2021-12-15T23:28:32.000Z | ampel/cli/AbsStockCommand.py | AmpelProject/Ampel-core | dcbfbe38ba400b7f8e44e641b90217ca1bed4f8f | [
"BSD-3-Clause"
] | 1 | 2021-04-26T07:52:19.000Z | 2021-04-26T07:52:19.000Z | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# File : Ampel-core/ampel/cli/AbsStockCommand.py
# License : BSD-3-Clause
# Author : vb <vbrinnel@physik.hu-berlin.de>
# Date : 25.03.2021
# Last Modified Date: 25.03.2021
# Last Modified By : vb <vbrinnel@physik.hu-berlin.de>
from typing import Dict, Any, Optional, Union, Literal
from ampel.cli.ArgParserBuilder import ArgParserBuilder
from ampel.cli.MaybeIntAction import MaybeIntAction
from ampel.cli.LoadJSONAction import LoadJSONAction
from ampel.cli.AbsCoreCommand import AbsCoreCommand
from ampel.mongo.utils import maybe_match_array
from ampel.model.UnitModel import UnitModel
from ampel.model.time.UnixTimeModel import UnixTimeModel
from ampel.model.time.TimeStringModel import TimeStringModel
from ampel.model.time.TimeLastRunModel import TimeLastRunModel
from ampel.model.time.TimeDeltaModel import TimeDeltaModel
from ampel.model.time.TimeConstraintModel import TimeConstraintModel
| 39.921986 | 109 | 0.688399 |
08ff6b2659f5dee9c411e3cb6cfb927747bc30ea | 682 | py | Python | programmers/lv2/42888.py | KLumy/Basic-Algorithm | e52e4200c1955a9062569814ff3418dd06666845 | [
"MIT"
] | 1 | 2021-01-22T15:58:32.000Z | 2021-01-22T15:58:32.000Z | programmers/lv2/42888.py | KLumy/Basic-Algorithm | e52e4200c1955a9062569814ff3418dd06666845 | [
"MIT"
] | null | null | null | programmers/lv2/42888.py | KLumy/Basic-Algorithm | e52e4200c1955a9062569814ff3418dd06666845 | [
"MIT"
] | null | null | null | from typing import List
if __name__ == "__main__":
i = [
"Enter uid1234 Muzi",
"Enter uid4567 Prodo",
"Leave uid1234",
"Enter uid1234 Prodo",
"Change uid4567 Ryan",
]
print(solution(i)) | 22 | 59 | 0.532258 |
08ffd799a57ae50947297bcb41ac6ea2c30df0ab | 7,081 | py | Python | app/nets.py | bobosoft/intrepyd | 13f0912b31f86f9bcc50f52ef4ad870e33f0cf65 | [
"BSD-3-Clause"
] | 2 | 2021-04-25T17:38:03.000Z | 2022-03-20T20:48:50.000Z | app/nets.py | bobosoft/intrepyd | 13f0912b31f86f9bcc50f52ef4ad870e33f0cf65 | [
"BSD-3-Clause"
] | 1 | 2016-11-30T22:25:00.000Z | 2017-01-16T22:43:39.000Z | app/nets.py | bobosoft/intrepyd | 13f0912b31f86f9bcc50f52ef4ad870e33f0cf65 | [
"BSD-3-Clause"
] | null | null | null | """
Implementation of REST API for nets creation
"""
from flask import Blueprint, request
from .utils import typename_to_type
from .contexts import contexts
nr = Blueprint('nets', __name__)
| 26.82197 | 71 | 0.609942 |
08fffc77755fbb25f85fc2fc8357c667133dc97d | 3,091 | py | Python | tensorflow_addons/image/utils.py | Soroosh129/addons | d92ae02d04e9052f6ca5ea272873efd15eaa35ce | [
"Apache-2.0"
] | 1 | 2020-09-09T14:51:22.000Z | 2020-09-09T14:51:22.000Z | tensorflow_addons/image/utils.py | pkanwar23/addons | de99c6dd904c475c5c95726911f4aac447c13361 | [
"Apache-2.0"
] | null | null | null | tensorflow_addons/image/utils.py | pkanwar23/addons | de99c6dd904c475c5c95726911f4aac447c13361 | [
"Apache-2.0"
] | null | null | null | # Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# 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.
# ==============================================================================
"""Image util ops."""
import tensorflow as tf
def to_4D_image(image):
"""Convert 2/3/4D image to 4D image.
Args:
image: 2/3/4D tensor.
Returns:
4D tensor with the same type.
"""
with tf.control_dependencies(
[
tf.debugging.assert_rank_in(
image, [2, 3, 4], message="`image` must be 2/3/4D tensor"
)
]
):
ndims = image.get_shape().ndims
if ndims is None:
return _dynamic_to_4D_image(image)
elif ndims == 2:
return image[None, :, :, None]
elif ndims == 3:
return image[None, :, :, :]
else:
return image
def from_4D_image(image, ndims):
"""Convert back to an image with `ndims` rank.
Args:
image: 4D tensor.
ndims: The original rank of the image.
Returns:
`ndims`-D tensor with the same type.
"""
with tf.control_dependencies(
[tf.debugging.assert_rank(image, 4, message="`image` must be 4D tensor")]
):
if isinstance(ndims, tf.Tensor):
return _dynamic_from_4D_image(image, ndims)
elif ndims == 2:
return tf.squeeze(image, [0, 3])
elif ndims == 3:
return tf.squeeze(image, [0])
else:
return image
| 30.303922 | 81 | 0.586218 |
1c00a5891e5e90872bc713f8239c14e5c3378b1b | 5,624 | py | Python | tests/test_charge.py | fossabot/MolVS | dc5afca7fcea93ebb0a342b766d70e88d2c0b841 | [
"MIT"
] | 1 | 2019-03-15T03:42:37.000Z | 2019-03-15T03:42:37.000Z | tests/test_charge.py | fossabot/MolVS | dc5afca7fcea93ebb0a342b766d70e88d2c0b841 | [
"MIT"
] | null | null | null | tests/test_charge.py | fossabot/MolVS | dc5afca7fcea93ebb0a342b766d70e88d2c0b841 | [
"MIT"
] | null | null | null | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Tests for charge.py"""
from __future__ import print_function
from __future__ import unicode_literals
from __future__ import division
import logging
from rdkit import Chem
from molvs.standardize import Standardizer, standardize_smiles
from molvs.charge import Reionizer
logging.basicConfig(level=logging.DEBUG)
def charge_parent_smiles(smiles, prefer_organic=False):
"""Utility function that returns the charge parent SMILES for given a SMILES string."""
mol = Chem.MolFromSmiles(smiles.encode('utf8'), sanitize=False)
mol = Standardizer(prefer_organic=prefer_organic).charge_parent(mol)
if mol:
return Chem.MolToSmiles(mol, isomericSmiles=True)
def test_charge_parent():
"""Test neutralization of ionized acids and bases."""
assert charge_parent_smiles('C(C(=O)[O-])(Cc1n[n-]nn1)(C[NH3+])(C[N+](=O)[O-])') == 'NCC(Cc1nn[nH]n1)(C[N+](=O)[O-])C(=O)O'
def test_charge_parent2():
"""Test preservation of zwitterion."""
assert charge_parent_smiles('n(C)1cc[n+]2cccc([O-])c12') == 'Cn1cc[n+]2cccc([O-])c12'
def test_charge_parent3():
"""Choline should be left with a positive charge."""
assert charge_parent_smiles('C[N+](C)(C)CCO') == 'C[N+](C)(C)CCO'
def test_charge_parent4():
"""This should have the hydrogen removed to give deanol as a charge parent."""
assert charge_parent_smiles('C[NH+](C)CCO') == 'CN(C)CCO'
def test_charge_parent5():
"""Sodium benzoate to benzoic acid."""
assert charge_parent_smiles('[Na+].O=C([O-])c1ccccc1') == 'O=C(O)c1ccccc1'
def test_charge_parent6():
"""Benzoate ion to benzoic acid."""
assert charge_parent_smiles('O=C([O-])c1ccccc1') == 'O=C(O)c1ccccc1'
def test_charge_parent7():
"""Charges in histidine should be neutralized."""
assert charge_parent_smiles('[NH3+]C(Cc1cnc[nH]1)C(=O)[O-]') == 'NC(Cc1cnc[nH]1)C(=O)O'
def test_charge_parent8():
""""""
assert charge_parent_smiles('C[NH+](C)(C).[Cl-]') == 'CN(C)C'
def test_charge_parent9():
"""No organic fragments."""
assert charge_parent_smiles('[N+](=O)([O-])[O-]') == 'O=[N+]([O-])[O-]'
def test_charge_parent10():
"""No organic fragments."""
assert charge_parent_smiles('[N+](=O)([O-])[O-]', prefer_organic=True) == 'O=[N+]([O-])[O-]'
def test_charge_parent11():
"""Larger inorganic fragment should be chosen."""
assert charge_parent_smiles('[N+](=O)([O-])[O-].[CH2]') == 'O=[N+]([O-])[O-]'
def test_charge_parent12():
"""Smaller organic fragment should be chosen over larger inorganic fragment."""
assert charge_parent_smiles('[N+](=O)([O-])[O-].[CH2]', prefer_organic=True) == '[CH2]'
def test_standardize():
"""Test table salt."""
assert standardize_smiles('[Na].[Cl]') == '[Cl-].[Na+]'
def test_reionize():
"""Test reionizer moves proton to weaker acid."""
mol = Chem.MolFromSmiles('C1=C(C=CC(=C1)[S]([O-])=O)[S](O)(=O)=O')
r = Reionizer()
mol = r.reionize(mol)
assert Chem.MolToSmiles(mol) == 'O=S(O)c1ccc(S(=O)(=O)[O-])cc1'
def test_reionize2():
"""Test charged carbon doesn't get recognised as alpha-carbon-hydrogen-keto."""
mol = Chem.MolFromSmiles('CCOC(=O)C(=O)[CH-]C#N')
r = Reionizer()
mol = r.reionize(mol)
assert Chem.MolToSmiles(mol) == 'CCOC(=O)C(=O)[CH-]C#N'
def test_reionize3():
""""""
mol = Chem.MolFromSmiles('C[N+]1=C[CH-]N(C(=N)N)/C1=C/[N+](=O)[O-]')
r = Reionizer()
mol = r.reionize(mol)
assert Chem.MolToSmiles(mol) == 'C[N+]1=CCN(C(=N)N)C1=[C-][N+](=O)[O-]'
def test_should_complete():
"""Reionization should not infinitely loop forever on these molecules."""
# GitHub Issue #14
assert standardize_smiles('CCCCCCCCCCCCCCCCCC(=O)CC(=C)C(=O)O[Ti](=O)(OC(C)C)C(C)C') == 'C=C(CC(=O)[CH-]CCCCCCCCCCCCCCCC)C(=O)[O-].CC(C)[O-].CCC.[O-2].[Ti+5]'
assert standardize_smiles('OP(=O)(O)[O-].OP(=O)([O-])[O-].[O-]S(=O)(=O)[O-].[Na+].[Na+].[Na+].[Mg+2].[Cl-].[Cl-].[K+].[K+]') == 'O=P([O-])(O)O.O=P([O-])([O-])O.O=S(=O)([O-])[O-].[Cl-].[Cl-].[K+].[K+].[Mg+2].[Na+].[Na+].[Na+]'
def test_forced_charge1():
"""Test forced charge correction maintaining overall neutral charge."""
assert standardize_smiles('[Na].O=C(O)c1ccccc1') == 'O=C([O-])c1ccccc1.[Na+]'
def test_forced_charge2():
"""Test forced charge correction with no corresponding proton for neutralization."""
# GitHub Issue #15
assert standardize_smiles('[Na].[Na]') == '[Na+].[Na+]'
# TODO: Arguably should become selenite ion... O=[Se]([O-])[O-]. Need an AcidBasePair?
assert standardize_smiles('[Na].[Na].O[Se](O)=O') == 'O=[Se](O)O.[Na+].[Na+]'
# def test_reionize3():
# """Test canonical ionization position when multiple equivalent possibilities."""
# mol = Chem.MolFromSmiles('CC1=CC(=CC=C1S(O)=O)S([O-])=O')
# mol2 = Chem.MolFromSmiles('CC1=CC(=CC=C1S([O-])=O)S(O)=O')
# r = Reionizer()
# mol = r.reionize(mol)
# mol2 = r.reionize(mol2)
# assert Chem.MolToSmiles(mol) == 'Cc1cc(S(=O)[O-])ccc1S(=O)O'
# assert Chem.MolToSmiles(mol2) == 'Cc1cc(S(=O)[O-])ccc1S(=O)O'
# assert Chem.MolToSmiles(mol) == Chem.MolToSmiles(mol2)
#
#
# def test_reionize4():
# """Test canonical ionization position when multiple equivalent possibilities."""
# mol = Chem.MolFromSmiles('CCOC(=O)C(=O)[CH-]C#N')
# mol2 = Chem.MolFromSmiles('[CH2-]COC(=O)C(=O)CC#N')
# r = Reionizer()
# mol = r.reionize(mol)
# mol2 = r.reionize(mol2)
# assert Chem.MolToSmiles(mol) == '[CH2-]COC(=O)C(=O)CC#N'
# assert Chem.MolToSmiles(mol2) == ''
# assert Chem.MolToSmiles(mol) == Chem.MolToSmiles(mol2)
| 35.594937 | 229 | 0.628912 |
1c00c51dbbd7d3ac777974eea9dbea63a30953c4 | 1,921 | py | Python | backend/users/views.py | jochanmin/Blog | 465dcb951ebe2e2fabcd81d0c4e0221decc66ccc | [
"MIT"
] | 11 | 2020-04-26T13:55:08.000Z | 2022-01-27T08:30:03.000Z | backend/users/views.py | jochanmin/Blog | 465dcb951ebe2e2fabcd81d0c4e0221decc66ccc | [
"MIT"
] | 26 | 2020-09-27T16:14:36.000Z | 2022-01-08T13:29:14.000Z | backend/users/views.py | jochanmin/Blog | 465dcb951ebe2e2fabcd81d0c4e0221decc66ccc | [
"MIT"
] | 3 | 2020-05-22T20:01:56.000Z | 2022-01-08T08:40:37.000Z | from django.shortcuts import render
from django.core import serializers
from .models import User
from django.forms.models import model_to_dict
from rest_framework import status
from rest_framework.response import Response
from rest_framework.decorators import api_view, permission_classes
from rest_framework.permissions import IsAuthenticated
# /users/auth/
# /users/register
# /users/users
| 36.942308 | 100 | 0.668922 |
1c00e4c128a365529289973580dd79e35cc9432c | 4,134 | py | Python | src/test/test_Location.py | MrRollyPanda/astral | 5a1b013c945fa902b475ff0fa6769f0d43fe2999 | [
"Apache-2.0"
] | null | null | null | src/test/test_Location.py | MrRollyPanda/astral | 5a1b013c945fa902b475ff0fa6769f0d43fe2999 | [
"Apache-2.0"
] | null | null | null | src/test/test_Location.py | MrRollyPanda/astral | 5a1b013c945fa902b475ff0fa6769f0d43fe2999 | [
"Apache-2.0"
] | null | null | null | # -*- coding: utf-8 -*-
from pytest import raises
from astral import Astral, AstralError, Location
import datetime
import pytz
| 20.984772 | 75 | 0.630866 |