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dd7ea8658ced22af8cde597e98ea9827577d3995 | 343 | py | Python | strings.py | aemreb/telegram_paybot | f973ddd9029f2844901508fa983aa37b6ca93089 | [
"MIT"
] | 2 | 2021-09-17T10:55:14.000Z | 2021-09-17T10:55:38.000Z | strings.py | aemreb/telegram_paybot | f973ddd9029f2844901508fa983aa37b6ca93089 | [
"MIT"
] | null | null | null | strings.py | aemreb/telegram_paybot | f973ddd9029f2844901508fa983aa37b6ca93089 | [
"MIT"
] | 1 | 2021-05-31T18:23:02.000Z | 2021-05-31T18:23:02.000Z | help = '''Hey \n\n /signup nick: enter your nick and sign up \n\n /atm: see your balance \n\n /send nick amount: send this nick that amount of buxx \n '''
signup = '''Hi! Type /signup to sign up.'''
user_created = '''Created user. Welcome to Paybot '''
user_exists = '''User already exists '''
not_enough_buxx = '''Not enough Buxx '''
| 57.166667 | 158 | 0.655977 |
dd7f9dbcfe5bd13ce56beb5ae807d4bb63f3c4df | 1,609 | py | Python | Program_python/Extractfolderimage.py | pection/MN-furniture | 4c796f072662c15b2a263272ef2637e221c42cab | [
"MIT"
] | 1 | 2022-02-22T06:20:56.000Z | 2022-02-22T06:20:56.000Z | Program_python/Extractfolderimage.py | pection/MN-furniture | 4c796f072662c15b2a263272ef2637e221c42cab | [
"MIT"
] | null | null | null | Program_python/Extractfolderimage.py | pection/MN-furniture | 4c796f072662c15b2a263272ef2637e221c42cab | [
"MIT"
] | 1 | 2020-11-24T18:18:42.000Z | 2020-11-24T18:18:42.000Z | import os
import sys
import numpy as np
from PIL import Image
num=1
path ="/Users/pection/Documents/mn_furniture/AddwatermarkProgram/Lastday/"
#we shall store all the file names in this list
filelist=[]
for root, dirs, files in os.walk(path):
for file in files:
if(file.endswith(".jpg")):
filelist.append(os.path.join(root,file))
print (filelist)
logo=Image.open('logo.png')
logo2=Image.open('logo2.png')
watermark = Image.open('WatermarkB5.png')
watermark2 = Image.open('WatermarkB3.png')
logoWidth = watermark.width
logoHeight = watermark.height
watermarkW=watermark.width
watermarkH=watermark.height
logo2Width = watermark2.width
logo2Height = watermark2.height
for filename in filelist:
image = Image.open(filename)
# imageWidth = image.width
# imageHeight = image.height
# if imageWidth<500 :
# img_w, img_h = image.size
# bg_w, bg_h = watermark2.size
# offset = ((bg_w - img_w) // 2, (bg_h - img_h) // 2)
# image.paste(logo2, (0, 0), logo2)
# image2=image.copy()
# image2.paste(watermark2,(int((img_w-logo2Width)/2),int((img_h-logo2Height)/2)),watermark2)
# else :
# img_w, img_h = image.size
# bg_w, bg_h = watermark.size
# offset = ((bg_w - img_w) // 2, (bg_h - img_h) // 2)
# image.paste(logo, (0, 0), logo)
# image2=image.copy()
# image2.paste(watermark,(int((img_w-logoWidth)/2),int((img_h-logoHeight)/2)),watermark)
num += 1
# image.save(filename)
image.save('/Users/pection/Documents/mn_furniture/AddwatermarkProgram/Extract/'+str(num)+'.png') | 35.755556 | 100 | 0.661902 |
dd7fb45e0f3cff64598edf9ddf119adc6b039b8e | 1,986 | py | Python | BrainML/__init__.py | bogdan124/DeepML | ad5e904cc9fcd3c499bbca3538525d83fde003f5 | [
"Apache-2.0"
] | null | null | null | BrainML/__init__.py | bogdan124/DeepML | ad5e904cc9fcd3c499bbca3538525d83fde003f5 | [
"Apache-2.0"
] | null | null | null | BrainML/__init__.py | bogdan124/DeepML | ad5e904cc9fcd3c499bbca3538525d83fde003f5 | [
"Apache-2.0"
] | null | null | null | import tensorflow as tf
from BrainML.activation import Activator
from BrainML.layers import *
from BrainML.optimizer import Optimizer
from tensorflow.python.util import deprecation
##deprecation._PRINT_DEPRECATION_WARNINGS = False
##tf.compat.v1.disable_eager_execution()
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
## if __name__ == "__main__":
## pass | 38.192308 | 268 | 0.735146 |
dd80e90be1c610d2c46bc8b8b02fd6070d94ee6d | 2,900 | py | Python | detection/pixel_link/util/dec.py | HLIG/HUAWEI_OCR2019 | 1070d6291072e0223c2624f686766d0f3065e9c6 | [
"MIT"
] | 211 | 2020-04-13T02:56:57.000Z | 2022-03-30T11:22:46.000Z | detection/pixel_link/util/dec.py | HLIG/HUAWEI_OCR2019 | 1070d6291072e0223c2624f686766d0f3065e9c6 | [
"MIT"
] | 41 | 2020-04-14T20:08:58.000Z | 2022-03-28T15:08:30.000Z | detection/pixel_link/util/dec.py | HLIG/HUAWEI_OCR2019 | 1070d6291072e0223c2624f686766d0f3065e9c6 | [
"MIT"
] | 47 | 2020-04-13T10:17:11.000Z | 2022-02-21T02:06:16.000Z | #encoding=utf-8
import logging
import time
import collections
counter = collections.defaultdict(int)
count_times =collections.defaultdict(int)
| 36.708861 | 137 | 0.589655 |
dd8112f277127d953f0d1ded63b446df7043645e | 2,955 | py | Python | problems/graph-valid-tree.py | sailikhithk/tech-interview-prep | e833764cf98915d56118bddfa0e01871c58de75e | [
"Apache-2.0"
] | null | null | null | problems/graph-valid-tree.py | sailikhithk/tech-interview-prep | e833764cf98915d56118bddfa0e01871c58de75e | [
"Apache-2.0"
] | null | null | null | problems/graph-valid-tree.py | sailikhithk/tech-interview-prep | e833764cf98915d56118bddfa0e01871c58de75e | [
"Apache-2.0"
] | null | null | null | """
First of all a tree of N nodes must have exactly N-1 edges.
2 nodes need 1 edge to connect. 3 nodes need 2 edges to connect...
Just draw one or two, you will know it.
Valid tree don't have cycles, there are two ways to detect it.
DFS. and union find. Union find is more suitable in this sutuation.
1. Union find.
We use an array 'markset' to store the root node of each node. [0]
So if markset[1]==3, it means node1's root is node3.
markset[6]==4, node6's root is node4.
we use find() to find the node's root. [1]
For example if node1's root is node3.
In the recursion, we find out that node3's root is node5.
we return and set node5 as node1's real root.
If a node don't have root then the root is itselves.
Imagine an edge. 1<->6 [2]
union()'s mission is to find out if node1 and node6 share the same root before we know 1<->6 exist.
If node1 and node6 share the same root before we know the edge 1<->6,
There must be a cycle between node1, node6 and their root.
A special situation is that
1<->2, 3<->4, 3<->5 (We have two trees that are not connected)
1 and 3 will share -1 as 'root', this means that they are not connected.
But a valid tree should be connected and only have one and only root.
The time complexity is O(NLogN), becuase we run a loop for every edges.
And the number of edges is equal to N-1
for every edge we use find() to find the root of two nodes
The recursion takes the height of the tree, which is LogN
N is the number of nodes.
Space complexity is O(N).
2. DFS
We use dfs to find if there are cycles in the tree.
If we visit the node that we visited which means there is a cycle.
Since this is an undirected map, we have to add both ways to the adjacent list.
And everytime we use an edge, we need to remove the counter part of it to avoid repeat.
finally, we have to check if we visit all the nodes. because there may be unconnected nodes.
The time complexity is O(N+E). Because this is DFS search in adjacent list.
Space complexity is O(N).
"""
| 31.105263 | 99 | 0.702876 |
dd81524e1e000d2bbdd8e39c55a281ea1c78ab94 | 1,336 | py | Python | config.py | MGorr/icons_updater | aa9f9177a565fbe590cf959f625f049024e01efb | [
"MIT"
] | 1 | 2021-06-18T06:58:15.000Z | 2021-06-18T06:58:15.000Z | config.py | MGorr/icons_updater | aa9f9177a565fbe590cf959f625f049024e01efb | [
"MIT"
] | null | null | null | config.py | MGorr/icons_updater | aa9f9177a565fbe590cf959f625f049024e01efb | [
"MIT"
] | null | null | null | """Configuration class for icons updating."""
import os
from configparser import ConfigParser
_DESTINATION_NAME = 'dst'
_MAGICK_NAME = 'path'
_SOURCES_NAME = 'src'
| 30.363636 | 72 | 0.610778 |
dd820e8c14626563e752d4624969a2315eb254b9 | 8,831 | py | Python | project/settings.py | ziibii88/The_Doe_Agency | 2545aeae71c779166bef78941cac36551498ca76 | [
"MIT"
] | 2 | 2021-07-16T10:25:15.000Z | 2021-08-07T04:44:08.000Z | project/settings.py | ziibii88/The_Doe_Agency | 2545aeae71c779166bef78941cac36551498ca76 | [
"MIT"
] | null | null | null | project/settings.py | ziibii88/The_Doe_Agency | 2545aeae71c779166bef78941cac36551498ca76 | [
"MIT"
] | null | null | null | """
Django settings for project project.
Generated by 'django-admin startproject' using Django 3.2.5.
For more information on this file, see
https://docs.djangoproject.com/en/3.2/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/3.2/ref/settings/
"""
from datetime import timedelta
from pathlib import Path
from decouple import config, Csv
import sentry_sdk
from sentry_sdk.integrations.django import DjangoIntegration
from .logging import CONFIG as LOG_CONFIG
ENVIRON = config("ENVIRON", default="prod")
if ENVIRON == "prod":
sentry_sdk.init(
dsn="https://ad9a9f987fa949a899c3b890ef4cd112"
"@o354850.ingest.sentry.io/5868398",
integrations=[DjangoIntegration()],
environment=ENVIRON,
traces_sample_rate=1.0,
send_default_pii=True,
release="tda@0.1.0", # change in poetry as well
)
LOGGING = LOG_CONFIG
PROJECT_NAME = "The Doe Agency"
PROJECT_SLUG = "the-doe-agency"
PROJECT_CODE = "TDA"
# Build paths inside the project like this: BASE_DIR / 'subdir'.
BASE_DIR = Path(__file__).resolve().parent.parent
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/3.2/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = config("SECRET_KEY")
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = config("DEBUG", default=False, cast=bool)
ALLOWED_HOSTS = config("ALLOWED_HOSTS", default=[], cast=Csv())
# https://django-debug-toolbar.readthedocs.io/en/latest/installation.html#configuring-internal-ips
INTERNAL_IPS = config("INTERNAL_IPS", default=[], cast=Csv())
# Application definition ---------------------------------------------------- #
INSTALLED_APPS = [
# django apps
"django.contrib.admin",
"django.contrib.auth",
"django.contrib.contenttypes",
"django.contrib.sessions",
"django.contrib.messages",
"django.contrib.staticfiles",
"django.contrib.sites",
"django.contrib.humanize",
# 3rd party apps
"django_filters",
"rest_framework",
"rest_framework_simplejwt",
"django_celery_results",
"django_celery_beat",
# health_check
"health_check",
"health_check.db",
"health_check.cache",
"health_check.storage",
"health_check.contrib.migrations",
# 'health_check.contrib.celery',
# 'health_check.contrib.celery_ping',
"health_check.contrib.psutil",
# 'health_check.contrib.s3boto3_storage',
# 'health_check.contrib.rabbitmq',
# 'health_check.contrib.redis',
# project apps
"core",
"scraper",
]
SITE_ID = 1 # Sites framework
if ENVIRON == "dev":
INSTALLED_APPS.append("debug_toolbar")
MIDDLEWARE = [
"debug_toolbar.middleware.DebugToolbarMiddleware",
"django.middleware.security.SecurityMiddleware",
"django.contrib.sessions.middleware.SessionMiddleware",
"django.middleware.common.CommonMiddleware",
"django.middleware.csrf.CsrfViewMiddleware",
"django.contrib.auth.middleware.AuthenticationMiddleware",
"django.contrib.messages.middleware.MessageMiddleware",
"django.middleware.clickjacking.XFrameOptionsMiddleware",
]
ROOT_URLCONF = "project.urls"
TEMPLATES = [
{
"BACKEND": "django.template.backends.django.DjangoTemplates",
"DIRS": [],
"APP_DIRS": True,
"OPTIONS": {
"context_processors": [
"django.template.context_processors.debug",
"django.template.context_processors.request",
"django.contrib.auth.context_processors.auth",
"django.contrib.messages.context_processors.messages",
],
},
},
]
AUTH_USER_MODEL = "core.User"
TEST_URL = "http://httpbin.zube.xyz/ip"
WSGI_APPLICATION = "project.wsgi.application"
# Database ------------------------------------------------------------------ #
# https://docs.djangoproject.com/en/3.2/ref/settings/#databases
DATABASES = {
"default": {
"ENGINE": config("DB_ENGINE", default="django.db.backends.postgresql"),
"HOST": config("DB_HOST", default="127.0.0.1"),
"PORT": config("DB_PORT", default=5432, cast=int),
"NAME": config("DB_NAME", default="tda_db"),
"USER": config("DB_USER", default="tda_user"),
"PASSWORD": config("DB_PASS", default="tda_pass"),
}
}
# Password validation ------------------------------------------------------- #
# https://docs.djangoproject.com/en/3.2/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
"NAME": "django.contrib.auth.password_validation"
".UserAttributeSimilarityValidator"
},
{
"NAME": "django.contrib.auth.password_validation"
".MinimumLengthValidator"
},
{
"NAME": "django.contrib.auth.password_validation"
".CommonPasswordValidator"
},
{
"NAME": "django.contrib.auth.password_validation"
".NumericPasswordValidator"
},
]
# Internationalization ------------------------------------------------------ #
# https://docs.djangoproject.com/en/3.2/topics/i18n/
LANGUAGE_CODE = "en-us"
TIME_ZONE = config("TIME_ZONE", default="UTC")
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images) ------------------------------------ #
# https://docs.djangoproject.com/en/3.2/howto/static-files/
STATIC_URL = "/static/"
STATIC_ROOT = (
BASE_DIR / "static"
) # production, don't forget to run collectstatic
STATICFILES_DIRS = [
BASE_DIR / "staticfiles",
] # development environment
MEDIA_URL = "/media/"
MEDIA_ROOT = BASE_DIR / "media"
# Default primary key field type
# https://docs.djangoproject.com/en/3.2/ref/settings/#default-auto-field
DEFAULT_AUTO_FIELD = "django.db.models.BigAutoField"
# Django REST Framework -------------------------------------------------------
# https://www.django-rest-framework.org/api-guide/settings/
REST_FRAMEWORK = {
"DEFAULT_AUTHENTICATION_CLASSES": [
# https://django-rest-framework-simplejwt.readthedocs.io/en/latest/getting_started.html#installation
"rest_framework_simplejwt.authentication.JWTAuthentication",
# "rest_framework.authentication.TokenAuthentication",
"rest_framework.authentication.SessionAuthentication",
],
"DEFAULT_PERMISSION_CLASSES": [
"rest_framework.permissions.IsAuthenticated"
],
"DEFAULT_FILTER_BACKENDS": [
# django-filters
# https://www.django-rest-framework.org/api-guide/filtering/
# https://django-filter.readthedocs.io/en/latest/guide/rest_framework.html
"django_filters.rest_framework.DjangoFilterBackend",
# https://www.django-rest-framework.org/api-guide/filtering/#searchfilter
"rest_framework.filters.SearchFilter",
# https://www.django-rest-framework.org/api-guide/filtering/#orderingfilter
"rest_framework.filters.OrderingFilter",
],
"DEFAULT_PAGINATION_CLASS": "rest_framework.pagination"
".PageNumberPagination",
"PAGE_SIZE": 100,
}
# DRF SimpleJWT ---------------------------------------------------------------
# https://django-rest-framework-simplejwt.readthedocs.io/en/latest/settings.html
SIMPLE_JWT = {
"ACCESS_TOKEN_LIFETIME": timedelta(minutes=15),
"REFRESH_TOKEN_LIFETIME": timedelta(days=1),
"ROTATE_REFRESH_TOKENS": False,
"BLACKLIST_AFTER_ROTATION": True,
"UPDATE_LAST_LOGIN": True,
"ALGORITHM": "HS256",
"SIGNING_KEY": SECRET_KEY,
"VERIFYING_KEY": None,
"AUDIENCE": None,
"ISSUER": None,
"AUTH_HEADER_TYPES": ("Bearer",),
"AUTH_HEADER_NAME": "HTTP_AUTHORIZATION",
"USER_ID_FIELD": "id",
"USER_ID_CLAIM": "user_id",
"USER_AUTHENTICATION_RULE": "rest_framework_simplejwt.authentication"
".default_user_authentication_rule",
"AUTH_TOKEN_CLASSES": ("rest_framework_simplejwt.tokens.AccessToken",),
"TOKEN_TYPE_CLAIM": "token_type",
"JTI_CLAIM": "jti",
"SLIDING_TOKEN_REFRESH_EXP_CLAIM": "refresh_exp",
"SLIDING_TOKEN_LIFETIME": timedelta(minutes=15),
"SLIDING_TOKEN_REFRESH_LIFETIME": timedelta(days=1),
}
# Celery ----------------------------------------------------------------------
# https://docs.celeryproject.org/en/stable/userguide/configuration.html#configuration-and-defaults
CELERY_BROKER_URL = config(
"CELERY_BROKER_URL", default="amqp://127.0.0.1:5672/"
)
CELERY_RESULT_BACKEND = config("CELERY_RESULT_BACKEND", default="django-db")
CELERY_CACHE_BACKEND = config("CELERY_CACHE_BACKEND", default="django-cache")
CELERY_TIMEZONE = config("CELERY_TIME_ZONE", default=TIME_ZONE)
CELERY_TASK_TRACK_STARTED = config("CELERY_TASK_TRACK_STARTED", default=True)
CELERY_TASK_TIME_LIMIT = config("CELERY_TASK_TIME_LIMIT", default=30 * 60)
| 32.112727 | 108 | 0.669347 |
dd837f67ec7177838bf8a526749af097805f6779 | 15,142 | py | Python | CalsCamera/main.py | NoDrones/Imaging | 555c8aeced98097379b80f448689f2bf2974c3e9 | [
"MIT"
] | 1 | 2019-01-28T21:55:53.000Z | 2019-01-28T21:55:53.000Z | CalsCamera/main.py | NoDrones/Imaging | 555c8aeced98097379b80f448689f2bf2974c3e9 | [
"MIT"
] | null | null | null | CalsCamera/main.py | NoDrones/Imaging | 555c8aeced98097379b80f448689f2bf2974c3e9 | [
"MIT"
] | null | null | null | #Author: Calvin Ryan
import sensor, image, time, pyb, ustruct, math, utime
if __name__ == "__main__":
########### SETUP STUFF
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.skip_frames(time = 2000)
clock = time.clock()
i2c_obj = pyb.I2C(2, pyb.I2C.SLAVE, addr=0x12)
i2c_obj.deinit() # Fully reset I2C device...
i2c_obj = pyb.I2C(2, pyb.I2C.SLAVE, addr=0x12)
#get in focus balance. You have two seconds.
t_start = time.ticks()
t_elapsed = 0
while(t_elapsed < 1): #ignore bc 1 ms
img = sensor.snapshot()
t_elapsed = time.ticks() - t_start
sensor.set_auto_gain(False) # must be turned off for color tracking
sensor.set_auto_whitebal(False) # must be turned off for color tracking
sensor.set_auto_exposure(False)
sensor.set_contrast(+3)
print()
pre_adjust_r_gain = sensor.__read_reg(0x02)
pre_adjust_g_gain = sensor.__read_reg(0x03)
pre_adjust_b_gain = sensor.__read_reg(0x01)
pre_adjust_overall_gain = sensor.__read_reg(0x00)
pre_adjust_exposure = (sensor.__read_reg(0x08) << 8) + sensor.__read_reg(0x10)
print("R gain: " + str(pre_adjust_r_gain))
print("G gain: " + str(pre_adjust_g_gain))
print("B gain: " + str(pre_adjust_b_gain))
print("Overall gain: " + str(pre_adjust_overall_gain))
print("exposure: " + str(pre_adjust_exposure))
print('------------------------------------')
set_l_mean = set_custom_exposure() #default thresholds
print(set_l_mean)
post_adjust_r_gain = sensor.__read_reg(0x02)
post_adjust_g_gain = sensor.__read_reg(0x03)
post_adjust_b_gain = sensor.__read_reg(0x01)
post_adjust_overall_gain = sensor.__read_reg(0x00)
post_adjust_exposure = (sensor.__read_reg(0x08) << 8) + sensor.__read_reg(0x10)
print("R gain: " + str(post_adjust_r_gain))
print("G gain: " + str(post_adjust_g_gain))
print("B gain: " + str(post_adjust_b_gain))
print("Overall gain: " + str(post_adjust_overall_gain))
print("exposure: " + str(post_adjust_exposure))
print()
img = sensor.snapshot()
# should pull img_number from a text file and read the plant_id from a qr code or beaglebone
# default mode is pyb.usb_mode('VCP+MSC')
'''
pyb.usb_mode('VCP+HID')
utime.sleep_ms(1000)
last_photo_id_path = "last_photo_id.txt"
last_photo_id_fd = open(last_photo_id_path, "w+")
img_number_str = last_photo_id_fd.read()
print(img_number_str)
img_number_str = last_photo_id_fd.write("696969")
print("Written bytes: " + str(img_number_str))
img_number_str = last_photo_id_fd.read()
print(img_number_str)
last_photo_id_fd.close()
img_number = 1
plant_id = 1
img_id = str(img_number) + "_plant_" + str(plant_id)
raw_str = "raw_" + str(img_id)
raw_write = image.ImageWriter(raw_str)
raw_write.add_frame(img)
raw_write.close()
img.compress(quality = 100)
img.save("img_" + str(img_id))
raw_read = image.ImageReader(raw_str)
img = raw_read.next_frame(copy_to_fb = True, loop = False)
raw_read.close()
'''
'''
L = Lightness where 0 is black and 100 is white
A = -127 is green and 128 is red
B = -127 is blue and 128 is yellow.
'''
img_stats = img.get_statistics()
########### FIND BAD BLOBS
unhealthy_full_l_mean = 0
unhealthy_full_a_mean = 0
unhealthy_full_b_mean = 0
unhealthy_centroid_l_mean = 0
unhealthy_centroid_a_mean = 0
unhealthy_centroid_b_mean = 0
unhealthy_blob_l_mean = 0
unhealthy_blob_a_mean = 0
unhealthy_blob_b_mean = 0
healthy_full_l_mean = 0
healthy_full_a_mean = 0
healthy_full_b_mean = 0
healthy_centroid_l_mean = 0
healthy_centroid_a_mean = 0
healthy_centroid_b_mean = 0
healthy_blob_l_mean = 0
healthy_blob_a_mean = 0
healthy_blob_b_mean = 0
blob_index = -1
stage_one_bad_thresholds = [(20, 100, -10, 127, 3, 128)]
for blob_index, stage_one_bad_blob in enumerate(img.find_blobs(stage_one_bad_thresholds, pixels_threshold=100, area_threshold=100, merge = False, margin = 15)):
rect_stats = img.get_statistics(roi = stage_one_bad_blob.rect())
print("stage_one_bad_blob: " + str(stage_one_bad_blob))
print("density: " + str(stage_one_bad_blob.density()))
print("full: " + str(rect_stats))
unhealthy_full_l_mean += rect_stats[0]
unhealthy_full_a_mean += rect_stats[8]
unhealthy_full_b_mean += rect_stats[16]
side_l = stage_one_bad_blob.density() * min(stage_one_bad_blob[2], stage_one_bad_blob[3])
partial_hist = img.get_histogram(roi = (stage_one_bad_blob.cx() - round(side_l/2), stage_one_bad_blob.cy() - round(side_l/2), round(side_l), round(side_l)))
partial_stats = partial_hist.get_statistics()
print("partial: "+ str(partial_stats))
unhealthy_centroid_l_mean += partial_stats[0]
unhealthy_centroid_a_mean += partial_stats[8]
unhealthy_centroid_b_mean += partial_stats[16]
blob_stats = img.get_statistics(roi = stage_one_bad_blob.rect(), thresholds = stage_one_bad_thresholds)
print("blob: "+ str(blob_stats))
print("\n")
unhealthy_blob_l_mean += blob_stats[0]
unhealthy_blob_a_mean += blob_stats[8]
unhealthy_blob_b_mean += blob_stats[16]
img.draw_rectangle(stage_one_bad_blob.rect(), color = (255, 255, 255)) #purple
#img.draw_rectangle((stage_one_bad_blob.cx() - round(side_l/2), stage_one_bad_blob.cy() - round(side_l/2), round(side_l), round(side_l)), color = (255, 85, 0))
if blob_index != -1:
unhealthy_full_l_mean = unhealthy_full_l_mean/(blob_index + 1)
unhealthy_full_a_mean = unhealthy_full_a_mean/(blob_index + 1)
unhealthy_full_b_mean = unhealthy_full_b_mean/(blob_index + 1)
unhealthy_centroid_l_mean = unhealthy_centroid_l_mean/(blob_index + 1)
unhealthy_centroid_a_mean = unhealthy_centroid_a_mean/(blob_index + 1)
unhealthy_centroid_b_mean = unhealthy_centroid_b_mean/(blob_index + 1)
unhealthy_blob_l_mean = unhealthy_blob_l_mean/(blob_index + 1)
unhealthy_blob_a_mean = unhealthy_blob_a_mean/(blob_index + 1)
unhealthy_blob_b_mean = unhealthy_blob_b_mean/(blob_index + 1)
print("------------------------------------------------------------------------")
########### FIND GOOD BLOBS
#stage_one_good_thresholds = [(img_stats.l_mean() - 1, 100, -127, img_stats.a_mean() - 4, img_stats.b_mean() - 8, 60)]
stage_one_good_thresholds = [(25, 100, -127, -3, -15, 3)]
for blob_index, stage_one_good_blob in enumerate(img.find_blobs(stage_one_good_thresholds, pixels_threshold=100, area_threshold=100, merge = False, margin = 15)):
rect_stats = img.get_statistics(roi = stage_one_good_blob.rect())
print("stage_one_good_blob: " + str(stage_one_good_blob))
print("density: " + str(stage_one_good_blob.density()))
print("full: "+ str(rect_stats))
healthy_full_l_mean += rect_stats[0]
healthy_full_a_mean += rect_stats[8]
healthy_full_b_mean += rect_stats[16]
side_l = stage_one_good_blob.density() * min(stage_one_good_blob[2], stage_one_good_blob[3])
partial_hist = img.get_histogram(roi = (stage_one_good_blob.cx() - round(side_l/2), stage_one_good_blob.cy() - round(side_l/2), round(side_l), round(side_l)))
partial_stats = partial_hist.get_statistics()
print("partial: "+ str(partial_stats))
healthy_centroid_l_mean += partial_stats[0]
healthy_centroid_a_mean += partial_stats[8]
healthy_centroid_b_mean += partial_stats[16]
blob_stats = img.get_statistics(roi = stage_one_good_blob.rect(), thresholds = stage_one_good_thresholds)
print("blob: "+ str(blob_stats))
print("\n")
healthy_blob_l_mean += blob_stats[0]
healthy_blob_a_mean += blob_stats[8]
healthy_blob_b_mean += blob_stats[16]
img.draw_rectangle(stage_one_good_blob.rect(), color = (0, 0, 0)) #black
#img.draw_rectangle((stage_one_good_blob.cx() - round(side_l/2), stage_one_good_blob.cy() - round(side_l/2), round(side_l), round(side_l)), color = (255, 85, 0))
########## COLOR IT ALL IN
for x in range(stage_one_good_blob[2]):
for y in range(stage_one_good_blob[3]):
pix_location = (stage_one_good_blob[0] + x, stage_one_good_blob[1] + y)
pix_vals = img.get_pixel(pix_location[0], pix_location[1])
lab_pix_vals = image.rgb_to_lab(pix_vals)
if ((lab_pix_vals[1] < (blob_stats.a_mean() + 2 * blob_stats.a_stdev())) & (lab_pix_vals[0] >= (blob_stats.l_mean() - .1 * blob_stats.l_stdev()))): #& (abs(lab_pix_vals[2] - lab_pix_vals[1]) > 10) & (lab_pix_vals[0] > (blob_stats.l_mean() - 10)):
pass
else:
pass
#img.set_pixel(pix_location[0], pix_location[1], (255, 0, 0))
if blob_index != -1:
healthy_full_l_mean = healthy_full_l_mean/(blob_index + 1)
healthy_full_a_mean = healthy_full_a_mean/(blob_index + 1)
healthy_full_b_mean = healthy_full_b_mean/(blob_index + 1)
healthy_centroid_l_mean = healthy_centroid_l_mean/(blob_index + 1)
healthy_centroid_a_mean = healthy_centroid_a_mean/(blob_index + 1)
healthy_centroid_b_mean = healthy_centroid_b_mean/(blob_index + 1)
healthy_blob_l_mean = healthy_blob_l_mean/(blob_index + 1)
healthy_blob_a_mean = healthy_blob_a_mean/(blob_index + 1)
healthy_blob_b_mean = healthy_blob_b_mean/(blob_index + 1)
print(img.compress_for_ide(quality = 100))
print("~~~~~~~~~~~~~~~ RESULTS: ~~~~~~~~~~~~~~~~")
print("good thresholds: " + str(stage_one_good_thresholds))
print("bad thresholds: " + str(stage_one_bad_thresholds))
print("unhealthy full l mean: " + str(unhealthy_full_l_mean))
print("unhealthy full a mean: " + str(unhealthy_full_a_mean))
print("unhealthy full b mean: " + str(unhealthy_full_b_mean))
#print("unhealthy centroid l mean: " + str(unhealthy_centroid_l_mean))
#print("unhealthy centroid a mean: " + str(unhealthy_centroid_a_mean))
#print("unhealthy centroid b mean: " + str(unhealthy_centroid_b_mean))
print("unhealthy blob l mean: " + str(unhealthy_blob_l_mean))
print("unhealthy blob a mean: " + str(unhealthy_blob_a_mean))
print("unhealthy blob b mean: " + str(unhealthy_blob_b_mean))
print("healthy full l mean: " + str(healthy_full_l_mean))
print("healthy full a mean: " + str(healthy_full_a_mean))
print("healthy full b mean: " + str(healthy_full_b_mean))
#print("healthy centroid l mean: " + str(healthy_centroid_l_mean))
#print("healthy centroid a mean: " + str(healthy_centroid_a_mean))
#print("healthy centroid b mean: " + str(healthy_centroid_b_mean))
print("healthy blob l mean: " + str(healthy_blob_l_mean))
print("healthy blob a mean: " + str(healthy_blob_a_mean))
print("healthy blob b mean: " + str(healthy_blob_b_mean))
| 43.636888 | 262 | 0.66616 |
dd83de2fe7b5a8905e65a790566a924385cc7b19 | 297 | py | Python | dados/outside/6-JEL_code.py | correia-marcos/Innovation-on-brazilian-economic-research | 98bc677618ec2dff062db42cda7279a5e7065a32 | [
"MIT"
] | null | null | null | dados/outside/6-JEL_code.py | correia-marcos/Innovation-on-brazilian-economic-research | 98bc677618ec2dff062db42cda7279a5e7065a32 | [
"MIT"
] | null | null | null | dados/outside/6-JEL_code.py | correia-marcos/Innovation-on-brazilian-economic-research | 98bc677618ec2dff062db42cda7279a5e7065a32 | [
"MIT"
] | null | null | null | """
This script was made to anaylse the relation between JEL and areas in ANPEC.
The idea is that checking the JEL code of each paper, it can be vizualized
whether some papers were published in area (from ANPEC meeting) not expected
by their JEL code.
"""
import os
import pandas as pd
import
| 22.846154 | 76 | 0.767677 |
dd88982df37b33dce441276837b7773dc3af6b26 | 1,311 | py | Python | tests/garage/tf/spaces/test_dict_space.py | shadiakiki1986/garage | 095bb5d25b32df1d44b47e99a78a9b01796941d9 | [
"MIT"
] | 3 | 2019-08-11T22:26:55.000Z | 2020-11-28T10:23:50.000Z | tests/garage/tf/spaces/test_dict_space.py | shadiakiki1986/garage | 095bb5d25b32df1d44b47e99a78a9b01796941d9 | [
"MIT"
] | null | null | null | tests/garage/tf/spaces/test_dict_space.py | shadiakiki1986/garage | 095bb5d25b32df1d44b47e99a78a9b01796941d9 | [
"MIT"
] | 2 | 2019-08-11T22:30:14.000Z | 2021-03-25T02:57:50.000Z | """This script tests garage.tf.spaces.dict functionality."""
import unittest
from garage.misc import ext
from garage.tf.envs import TfEnv
from tests.fixtures.envs.dummy import DummyDictEnv
| 31.214286 | 76 | 0.661327 |
dd8913997853973a6abd55f95d60d2c6a230000b | 3,429 | py | Python | utils/compare_MRAE.py | Liuhongzhi2018/SSRGAN | b5be922db1600aabb6a06ee52fb1c83ee738d794 | [
"Apache-2.0"
] | 1 | 2022-01-21T09:01:48.000Z | 2022-01-21T09:01:48.000Z | utils/compare_MRAE.py | Liuhongzhi2018/SSRGAN | b5be922db1600aabb6a06ee52fb1c83ee738d794 | [
"Apache-2.0"
] | 1 | 2021-08-18T11:33:43.000Z | 2021-08-18T11:33:43.000Z | utils/compare_MRAE.py | Liuhongzhi2018/SSRGAN | b5be922db1600aabb6a06ee52fb1c83ee738d794 | [
"Apache-2.0"
] | null | null | null | import argparse
import os
import cv2
import numpy as np
import hdf5storage as hdf5
from scipy.io import loadmat
from matplotlib import pyplot as plt
from SpectralUtils import savePNG, projectToRGB
from EvalMetrics import computeMRAE
BIT_8 = 256
# read path
generated_folder_path = "F:\\NTIRE 2020\\spectral reconstruction\\ensemble\\ensemble\\track1"
generated_folder_path = "F:\\NTIRE 2020\\spectral reconstruction\\ensemble\\ensemble\\track2"
groundtruth_folder_path = "F:\\NTIRE 2020\\spectral reconstruction\\NTIRE2020_Validation_Spectral"
avg_mrae = folder_img_mrae(generated_folder_path, groundtruth_folder_path)
| 36.870968 | 118 | 0.706328 |
dd895eff6bdbc6e4f11421a7c77e8c3865e7d03d | 2,435 | py | Python | board/send_message.py | ben741863140/cfsystem | 227e269f16533719251962f4d8caee8b51091d2f | [
"Apache-2.0"
] | 4 | 2018-02-22T01:59:07.000Z | 2020-07-09T06:28:46.000Z | board/send_message.py | ben741863140/cfsystem | 227e269f16533719251962f4d8caee8b51091d2f | [
"Apache-2.0"
] | null | null | null | board/send_message.py | ben741863140/cfsystem | 227e269f16533719251962f4d8caee8b51091d2f | [
"Apache-2.0"
] | null | null | null | # -*- coding:utf-8 -*-
import gzip
import re
import http.cookiejar
import urllib.request
import urllib.parse
# from logreg.sender import use_sender, sender
| 29.695122 | 93 | 0.588501 |
dd8f4efcbe0f27065a9ebbb9e0dbf84e6d87fb06 | 22,725 | py | Python | preprocess.py | cheery/pytci | 57b526b621da5e6783d15d73bac4ca57b281be70 | [
"MIT"
] | 148 | 2015-10-18T04:47:27.000Z | 2021-11-09T09:36:43.000Z | preprocess.py | cheery/pytci | 57b526b621da5e6783d15d73bac4ca57b281be70 | [
"MIT"
] | 3 | 2015-10-21T21:21:38.000Z | 2017-04-10T00:16:04.000Z | preprocess.py | cheery/pytci | 57b526b621da5e6783d15d73bac4ca57b281be70 | [
"MIT"
] | 10 | 2015-10-18T03:29:39.000Z | 2018-12-18T12:14:01.000Z | import tokenize, operator
macro_list = {}
def pump_token(macro_stack, macro_queue, current, passthrough):
if isinstance(current, list): # an expansion.
for current in current:
pump_token(macro_stack, macro_queue, current, passthrough)
elif len(macro_stack) > 0:
if macro_stack[-1].pump(current):
macro_queue.extend(macro_stack.pop(-1).invoke())
else:
passthrough.append(current)
def pull_identifier(state):
token = state.next_token()
if token and value_of(token) == '(':
token = state.next_token()
stop = state.next_token()
assert stop and value_of(stop) == ')', "bonked 'defined'"
assert token and name_of(token) == 'identifier', "bonked 'defined'"
return token
# This error handling is bit weird. The idea is
# that you could generate stubs instead of halting the compiler.
_init_itself()
# ? - badly implemented, ! - no implementation
#? Integer constants.
#? Character constants, which are interpreted as they would be in normal code.
#? Arithmetic operators for addition, subtraction, multiplication, division,
#?? bitwise operations, shifts, comparisons, and logical operations (&& and ||).
#! The latter two obey the usual short-circuiting rules of standard C.
# Macros. All macros in the expression are expanded before
# actual computation of the expression's value begins.
# Uses of the defined operator, which lets you check whether macros are defined in the middle of an #if.
# Identifiers that are not macros, which are all considered to be the number zero.
# This allows you to write #if MACRO instead of #ifdef MACRO, if you know that MACRO,
# when defined, will always have a nonzero value. Function-like macros used without
# their function call parentheses are also treated as zero.
quick_operator_table = {
'!':(1, operator.not_),
'*':(2, operator.mul), '/':(2, operator.div), '%':(2, operator.mod),
'+':(2, operator.add), '-':(2, operator.sub),
'<<':(2, operator.lshift), '>>':(2, operator.rshift),
'<':(2, operator.lt), '<=':(2, operator.le), '>':(2, operator.gt), '>=':(2, operator.ge),
'==':(2, operator.eq), '!=':(2, operator.ne),
'&':(2, operator.and_), '^':(2, operator.xor), '|':(2, operator.or_),
'&&':(2, (lambda x, y: x and y)), '||':(2, (lambda x, y: x or y)),
}
quick_precedence_table = {
'!':200,
'*':100, '/':100, '%':100,
'+':80, '-':80,
'<<':70, '>>':70,
'<':60, '<=':60, '>':60, '>=':60,
'==':50, '!=':50,
'&':40, '^':35, '|':30, '&&':25, '||':20,
}
def parse_macro_function(stream):
lp = stream.next_token()
assert value_of(lp) == '(', "broken preprocessor"
bind = []
is_variadic = False
current = stream.next_token()
while current and value_of(current) != ')':
if value_of(current) == '...':
is_variadic = True
break
bind.append(value_of(current))
current = stream.next_token()
if not current or value_of(current) != ",":
break
current = stream.next_token()
assert current and value_of(current) == ')', "{1}: {0}: unterminated argument list error".format(position_of(lp))
return MacroFunction(bind, is_variadic, list(stream))
if False:
import character_stream
pull = character_stream.pull
character_stream.pull = new_pull
if __name__=='__main__':
import trigraphs, traceback, os, sys
from character_stream import CharacterStream
local_includes = []
global_includes = ['/usr/lib/gcc/x86_64-linux-gnu/4.8/include', '/usr/local/include', '/usr/lib/gcc/x86_64-linux-gnu/4.8/include-fixed', '/usr/include/x86_64-linux-gnu', '/usr/include']
main()
| 37.561983 | 189 | 0.60374 |
dd8f9e880d1c5b15888f038a47c041322592d1b0 | 2,177 | py | Python | arfit/run_carma_pack.py | farr/arfit | 7ff6def331ef98f43f623da2d9867d1ac967448b | [
"MIT"
] | 5 | 2015-04-29T21:46:52.000Z | 2021-05-13T04:59:23.000Z | arfit/run_carma_pack.py | afcarl/arfit | 7ff6def331ef98f43f623da2d9867d1ac967448b | [
"MIT"
] | null | null | null | arfit/run_carma_pack.py | afcarl/arfit | 7ff6def331ef98f43f623da2d9867d1ac967448b | [
"MIT"
] | 2 | 2015-12-03T12:08:32.000Z | 2018-05-26T16:20:31.000Z | #!/usr/bin/env python
from __future__ import print_function
import argparse
import carmcmc as cm
import numpy as np
import os
import plotutils.autocorr as ac
import sys
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--input', required=True, metavar='FILE', help='input file')
parser.add_argument('--output', required=True, metavar='FILE', help='chain output')
parser.add_argument('--p', default=3, type=int, metavar='P', help='AR order (default: %(default)s)')
parser.add_argument('--q', default=2, type=int, metavar='Q', help='MA order (default: %(default)s)')
parser.add_argument('--neff', default=1000, type=int, metavar='N', help='number of independent samples (default: %(default)s)')
parser.add_argument('--tmax', default=100.0, type=float, metavar='T', help='maximum temperature')
parser.add_argument('--ntemp', default=10, type=int, metavar='N', help='number of temperatures')
args = parser.parse_args()
data = np.loadtxt(args.input)
times, tind = np.unique(data[:,0], return_index=True)
data = data[tind, :]
model = cm.CarmaModel(data[:,0], data[:,1], data[:,2], p=args.p, q=args.q)
thin = 1
nsamp = 10*args.neff
out, ext = os.path.splitext(args.output)
outtemp = out + '.TEMP' + ext
while True:
sample = model.run_mcmc(nsamp, nthin=thin, nburnin=thin*nsamp/2, tmax=args.tmax, ntemperatures=args.ntemp)
np.savetxt(outtemp, np.column_stack((sample.trace, sample.get_samples('loglik'), sample.get_samples('logpost'))))
os.rename(outtemp, args.output)
taus = []
for j in range(sample.trace.shape[1]):
taus.append(ac.autocorrelation_length_estimate(sample.trace[:,j]))
taus = np.array(taus)
if np.any(np.isnan(taus)):
neff_achieved = 0
else:
neff_achieved = sample.trace.shape[0] / np.max(taus)
print('Ran for ', nsamp*thin, ' steps, achieved ', neff_achieved, ' independent samples')
sys.__stdout__.flush()
if neff_achieved >= args.neff:
break
else:
thin *= 2
| 34.015625 | 131 | 0.635278 |
dd90daf3764122311fdf88342d48e04ecb1b9e7d | 3,946 | py | Python | editquality/feature_lists/wikitext.py | paulkernfeld/editquality | 029f21278d89d6e50b0eac7b39d8355f8e4686f4 | [
"MIT"
] | 18 | 2015-09-13T10:47:31.000Z | 2018-08-20T15:00:35.000Z | editquality/feature_lists/wikitext.py | paulkernfeld/editquality | 029f21278d89d6e50b0eac7b39d8355f8e4686f4 | [
"MIT"
] | 98 | 2015-12-13T12:18:24.000Z | 2018-08-07T21:10:46.000Z | editquality/feature_lists/wikitext.py | paulkernfeld/editquality | 029f21278d89d6e50b0eac7b39d8355f8e4686f4 | [
"MIT"
] | 17 | 2015-09-29T20:52:12.000Z | 2018-08-20T11:33:30.000Z | from revscoring.features import Feature, wikitext
from revscoring.features.modifiers import div, log, max, sub
parent = [
log(wikitext.revision.parent.chars + 1),
log(wikitext.revision.parent.tokens + 1),
log(wikitext.revision.parent.words + 1),
log(wikitext.revision.parent.uppercase_words + 1),
log(wikitext.revision.parent.headings + 1),
log(wikitext.revision.parent.wikilinks + 1),
log(wikitext.revision.parent.external_links + 1),
log(wikitext.revision.parent.templates + 1),
log(wikitext.revision.parent.ref_tags + 1),
div(wikitext.revision.parent.chars,
max(wikitext.revision.parent.words, 1),
name="revision.parent.chars_per_word"),
div(wikitext.revision.parent.words,
max(wikitext.revision.parent.tokens, 1),
name="revision.parent.words_per_token"),
div(wikitext.revision.parent.uppercase_words,
max(wikitext.revision.parent.words, 1),
name="revision.parent.uppercase_words_per_word"),
div(wikitext.revision.parent.markups,
max(wikitext.revision.parent.tokens, 1),
name="revision.parent.markups_per_token"),
]
diff = [
wikitext.revision.diff.markup_delta_sum,
wikitext.revision.diff.markup_delta_increase,
wikitext.revision.diff.markup_delta_decrease,
wikitext.revision.diff.markup_prop_delta_sum,
wikitext.revision.diff.markup_prop_delta_increase,
wikitext.revision.diff.markup_prop_delta_decrease,
wikitext.revision.diff.number_delta_sum,
wikitext.revision.diff.number_delta_increase,
wikitext.revision.diff.number_delta_decrease,
wikitext.revision.diff.number_prop_delta_sum,
wikitext.revision.diff.number_prop_delta_increase,
wikitext.revision.diff.number_prop_delta_decrease,
wikitext.revision.diff.uppercase_word_delta_sum,
wikitext.revision.diff.uppercase_word_delta_increase,
wikitext.revision.diff.uppercase_word_delta_decrease,
wikitext.revision.diff.uppercase_word_prop_delta_sum,
wikitext.revision.diff.uppercase_word_prop_delta_increase,
wikitext.revision.diff.uppercase_word_prop_delta_decrease,
sub(wikitext.revision.chars,
wikitext.revision.parent.chars,
name="revision.diff.chars_change"),
sub(wikitext.revision.tokens,
wikitext.revision.parent.tokens,
name="revision.diff.tokens_change"),
sub(wikitext.revision.words,
wikitext.revision.parent.words,
name="revision.diff.words_change"),
sub(wikitext.revision.markups,
wikitext.revision.parent.markups,
name="revision.diff.markups_change"),
sub(wikitext.revision.headings,
wikitext.revision.parent.headings,
name="revision.diff.headings_change"),
sub(wikitext.revision.external_links,
wikitext.revision.parent.external_links,
name="revision.diff.external_links_change"),
sub(wikitext.revision.wikilinks,
wikitext.revision.parent.wikilinks,
name="revision.diff.wikilinks_change"),
sub(wikitext.revision.templates,
wikitext.revision.parent.templates,
name="revision.diff.templates_change"),
sub(wikitext.revision.tags,
wikitext.revision.parent.tags,
name="revision.diff.tags_change"),
sub(wikitext.revision.ref_tags,
wikitext.revision.parent.ref_tags,
name="revision.diff.ref_tags_change"),
Feature("revision.diff.longest_new_token",
_process_new_longest, returns=int,
depends_on=[wikitext.revision.parent.longest_token,
wikitext.revision.longest_token]),
Feature("revision.diff.longest_new_repeated_char",
_process_new_longest, returns=int,
depends_on=[wikitext.revision.parent.longest_repeated_char,
wikitext.revision.longest_repeated_char])
]
| 41.978723 | 71 | 0.727826 |
dd9402b8557bc8fee0baeb9f728d3c332668ae1e | 2,240 | py | Python | test/http2_test/http2_server_health_check.py | miyachu/grpc | a06ea3c3162c10ff90a1578bf82bbbff95dc799d | [
"BSD-3-Clause"
] | 2 | 2021-09-10T00:20:13.000Z | 2021-11-16T11:27:19.000Z | test/http2_test/http2_server_health_check.py | miyachu/grpc | a06ea3c3162c10ff90a1578bf82bbbff95dc799d | [
"BSD-3-Clause"
] | null | null | null | test/http2_test/http2_server_health_check.py | miyachu/grpc | a06ea3c3162c10ff90a1578bf82bbbff95dc799d | [
"BSD-3-Clause"
] | 1 | 2020-11-04T04:19:45.000Z | 2020-11-04T04:19:45.000Z | # Copyright 2017, Google Inc.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above
# copyright notice, this list of conditions and the following disclaimer
# in the documentation and/or other materials provided with the
# distribution.
# * Neither the name of Google Inc. nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import argparse
import hyper
import sys
# Utility to healthcheck the http2 server. Used when starting the server to
# verify that the server is live before tests begin.
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--server_host', type=str, default='localhost')
parser.add_argument('--server_port', type=int, default=8080)
args = parser.parse_args()
server_host = args.server_host
server_port = args.server_port
conn = hyper.HTTP20Connection('%s:%d' % (server_host, server_port))
conn.request('POST', '/grpc.testing.TestService/UnaryCall')
resp = conn.get_response()
if resp.headers.get('grpc-encoding') is None:
sys.exit(1)
else:
sys.exit(0)
| 44.8 | 75 | 0.766964 |
dd9452c189452f40fb4e6f56c43cb761ffc48203 | 3,494 | py | Python | server/droidio/demands/test/test_views.py | lucasOlivio/droid.io | 945b1452eaaa73b4d7f9d1d1a35eaa2900e97e96 | [
"MIT"
] | null | null | null | server/droidio/demands/test/test_views.py | lucasOlivio/droid.io | 945b1452eaaa73b4d7f9d1d1a35eaa2900e97e96 | [
"MIT"
] | null | null | null | server/droidio/demands/test/test_views.py | lucasOlivio/droid.io | 945b1452eaaa73b4d7f9d1d1a35eaa2900e97e96 | [
"MIT"
] | null | null | null | from django.urls import reverse
from rest_framework.test import APITestCase
from rest_framework import status
from nose.tools import eq_
from faker import Faker
import factory
from ..models import Demand
from .factories import DemandFactory
from ..serializers import DemandSerializer
from droidio.users.test.factories import UserFactory
fake = Faker()
| 34.94 | 87 | 0.704637 |
dd94f0230de4472e8494e2e5c028fe0a163fe4d9 | 422 | py | Python | leetcode/python/check_in_n_and_its_double_exists.py | subhadig/leetcode | 9151ea49c342efa228cf82de72736c3445bbfef2 | [
"Unlicense"
] | null | null | null | leetcode/python/check_in_n_and_its_double_exists.py | subhadig/leetcode | 9151ea49c342efa228cf82de72736c3445bbfef2 | [
"Unlicense"
] | null | null | null | leetcode/python/check_in_n_and_its_double_exists.py | subhadig/leetcode | 9151ea49c342efa228cf82de72736c3445bbfef2 | [
"Unlicense"
] | null | null | null | # https://leetcode.com/explore/learn/card/fun-with-arrays/527/searching-for-items-in-an-array/3250/
# time: O(n)
# space: O(n)
| 26.375 | 99 | 0.533175 |
dd95ba5b789b57d2c18cb6c697a4bed1400af969 | 2,743 | py | Python | cloud_functions/trigger-monitor-dag-function/main_test.py | google/feedloader | f6a25569bc3d7d4ee326961fd3b01e45fc3858e4 | [
"Apache-2.0"
] | 5 | 2021-02-15T12:49:12.000Z | 2022-01-12T06:28:41.000Z | cloud_functions/trigger-monitor-dag-function/main_test.py | google/feedloader | f6a25569bc3d7d4ee326961fd3b01e45fc3858e4 | [
"Apache-2.0"
] | null | null | null | cloud_functions/trigger-monitor-dag-function/main_test.py | google/feedloader | f6a25569bc3d7d4ee326961fd3b01e45fc3858e4 | [
"Apache-2.0"
] | 4 | 2021-02-16T17:28:00.000Z | 2021-06-18T15:27:52.000Z | # coding=utf-8
# Copyright 2021 Google LLC.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Unit tests for the Trigger DAG Cloud Function."""
import os
from unittest import mock
from absl.testing import parameterized
import main
_TEST_CLIENT_ID = '12345.apps.googleusercontent.com'
_TEST_DAG_NAME = 'dag-name'
_TEST_WEBSERVER_ID = 'https://12345-tp.appspot.com'
| 31.528736 | 79 | 0.696318 |
dd99b6d2cdd53e9871b02f6e724fb47ac13372e3 | 12,973 | py | Python | programs/loadsheet/loadsheet.py | admin-db/OnboardingTools | 0f9d363d461df8c01e99157386338633828f5f92 | [
"Apache-2.0"
] | 3 | 2021-04-24T14:39:50.000Z | 2021-07-20T17:11:19.000Z | programs/loadsheet/loadsheet.py | admin-db/OnboardingTools | 0f9d363d461df8c01e99157386338633828f5f92 | [
"Apache-2.0"
] | 2 | 2020-07-22T21:34:33.000Z | 2021-01-14T19:26:12.000Z | programs/loadsheet/loadsheet.py | admin-db/OnboardingTools | 0f9d363d461df8c01e99157386338633828f5f92 | [
"Apache-2.0"
] | 2 | 2020-07-16T03:34:35.000Z | 2020-07-22T21:18:12.000Z | #Copyright 2020 DB Engineering
#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.
__version__ = '0.0.3'
__author__ = 'Trevor S., Shane S., Andrew K.'
# Standard Packages
import os
import sys
import string
from typing import Optional
from typing import Union
from typing import Dict
from typing import List
from typing import Any
# Open-source Packages
import openpyxl
import pandas as pd
sys.path.append('../')
# Proprietary Packages
from rules.rules import Rules
# Module GOBAL and CONTRAINTS
# 01132021: bms specific
_REQ_INPUT_HEADERS_BMS = [
'objectid',
'deviceid',
'objectname'
]
# 01132021: general
_REQ_INPUT_HEADERS = _REQ_INPUT_HEADERS_BMS + ['units', 'objecttype']
_REQ_OUTPUT_HEADERS = [
'required',
'manuallymapped',
'building',
'generaltype',
'typename',
'assetname',
'fullassetpath',
'standardfieldname'
]
def get_std_header(
self,
header: str
) -> str:
"""
Returns standardized header used internally based on the document
header passed in
"""
return self._std_header_map[header]
def get_data_row(
self,
row: int
) -> Dict[str, Any]:
pass
def get_data_row_generator(self):
pass
def export_to_loadsheet(self, output_filepath):
"""
exports data in Loadsheet object to excel file
args:
output_filepath - location and name of excel file output
"""
df = pd.DataFrame.from_records(self._data)
df.columns = [self._std_header_map[c] for c in df.columns]
df.to_excel(output_filepath, index=False)
def validate(
self,
non_null_fields: Optional[List[str]]= None
):
""" Perform loadsheet validation. It will not validate the
contents of the loadsheet, in terms of validity of entries, but
will validate that all required fields are filled in and that
no data is missing; the representations layer will handle the
ontology checks.
Checks:
1) Required is always in {YES, NO}
2) non-null fields are filled in where required is YES
3) there are no duplicate fullAssetPath-standardFieldName pairs
Args:
non_null_fields - fields that are checked to have values in step 2
by default set to None to use the following:
'building',
'generalType',
'assetName',
'fullAssetPath',
'standardFieldName',
'deviceId',
'objectType',
'objectId',
'units'
Returns:
None, but throws errors if any issues encountered
"""
# non_null_fields arg included for future user definied check to
# be implemented. Initial commit does not implement this feature
# Therefore we use the hardcoded non_null_fields below
if non_null_fields is None:
non_null_fields = [
'building',
'generaltype',
'assetname',
'fullassetpath',
'standardfieldname',
'deviceid',
'objecttype',
'objectid',
'units'
]
# convert self._data to pd.DataFrame (we will transistion to
# using only dataframes internally in a future update)
df = pd.DataFrame.from_records(self._data)
#required is always in [YES, NO]
assert self._ensure_required_correct(df), "Unacceptable values in required column"
#check for null field_details
null_fields = self._find_null_fields(df, non_null_fields)
assert len(null_fields) == 0, '\n'.join(
["There are rows with missing fields:"]+
[f"\t\t{uid + 2}" for uid in null_fields]
)
#check for duplicate fullAssetPath-standardFieldName combos
repeat_uid = self._get_duplicate_asset_fields(df)
assert len(repeat_uid) == 0, '\n'.join(
["There are duplicated asset-field combinations:"]+
[f"\t\t{uid}" for uid in repeat_uid]
)
def validate_without_errors(
self,
non_null_fields: Optional[List[str]]= None
):
"""
Perform loadsheet validation as in validate
but prints error messages instead of throwing errors
"""
# non_null_fields arg included for future user definied check to
# be implemented. Initial commit does not implement this feature
# Therefore we use the hardcoded non_null_fields below
if non_null_fields is None:
non_null_fields = [
'building',
'generaltype',
'assetname',
'fullassetpath',
'standardfieldname',
'deviceid',
'objecttype',
'objectid',
'units'
]
# convert self._data to pd.DataFrame (we will transistion to
# using only dataframes internally in a future update)
df = pd.DataFrame.from_records(self._data)
#required is always in [YES, NO]
if not self._ensure_required_correct(df):
print("[ERROR]\tUnacceptable values in required column")
#check for null field_details
null_fields = self._find_null_fields(df, non_null_fields)
if len(null_fields) > 0:
print(f"[ERROR]\tThere are rows with missing fields:")
for uid in null_fields:
print(f"\t\t{uid}")
#check for duplicate fullAssetPath-standardFieldName combos
repeat_uid = self._get_duplicate_asset_fields(df)
if len(repeat_uid) > 0:
print(f"[ERROR]\tThere are duplicated asset-field combinations:")
for uid in repeat_uid:
print(f"\t\t{uid}")
def apply_rules(
self,
rule_file: Dict
) -> None:
"""
Apply rules to the dataset. Will ignore any field where
manuallyMapped is set to YES.
args:
- rule_file: path to the rule file
returns: N/A
Note - See rules/rules.py for further information
"""
r = Rules(rule_file)
for row in self._data:
#add output headers
for output_header in _REQ_OUTPUT_HEADERS:
if output_header not in row.keys():
row[output_header] = ""
self._std_header_map[output_header] = output_header
#add manuallyMapped
if 'manuallymapped'not in row.keys():
row['manuallymapped'] = ''
self._std_header_map['manuallymapped'] = "manuallymapped"
#skip manuallyMapped rows
if row['manuallymapped'] == 'YES':
continue
#apply rules
else:
r.ApplyRules(row)
if __name__ == '__main__':
k = Loadsheet.from_bms(r'C:\Users\ShaneSpencer\Downloads\OnboardingTool-master\OnboardingTool-master\resources\bms_exports\alc\US-MTV-1395.csv') | 27.427061 | 145 | 0.697757 |
dd9b08662b0371253cd734386bb9a5108ab7a965 | 59,491 | py | Python | anita.py | utkarsh009/Bnita | 0f8ea0b389907f835685c572194b1eb5580a6286 | [
"BSD-3-Clause"
] | 2 | 2017-06-12T05:40:48.000Z | 2017-06-23T17:09:51.000Z | anita.py | utkarsh009/Bnita | 0f8ea0b389907f835685c572194b1eb5580a6286 | [
"BSD-3-Clause"
] | null | null | null | anita.py | utkarsh009/Bnita | 0f8ea0b389907f835685c572194b1eb5580a6286 | [
"BSD-3-Clause"
] | null | null | null | #
# This is the library part of Anita, the Automated NetBSD Installation
# and Test Application.
#
import os
import pexpect
import re
import string
import subprocess
import sys
import time
import urllib
import urlparse
__version__='1.40'
# Your preferred NetBSD FTP mirror site.
# This is used only by the obsolete code for getting releases
# by number, not by the recommended method of getting them by URL.
# See http://www.netbsd.org/mirrors/#ftp for the complete list.
netbsd_mirror_url = "ftp://ftp.netbsd.org/pub/NetBSD/"
#netbsd_mirror_url = "ftp://ftp.fi.NetBSD.org/pub/NetBSD/"
arch_qemu_map = {
'i386': 'qemu-system-i386',
'amd64': 'qemu-system-x86_64',
'sparc': 'qemu-system-sparc',
# The following ones don't actually work
'sparc64': 'qemu-system-sparc64',
'macppc': 'qemu-system-ppc',
}
# External commands we rely on
if os.uname()[0] == 'NetBSD':
makefs = ["makefs", "-t", "cd9660", "-o", "rockridge"]
elif os.uname()[0] == 'FreeBSD':
makefs = ["mkisofs", "-r", "-o"]
elif os.uname()[0] == 'Darwin':
makefs = ["hdiutil", "makehybrid", "-iso", "-o"]
else:
# Linux distributions differ. Ubuntu has genisoimage
# and mkisofs (as an alias of genisoimage); CentOS has
# mkisofs only. Debian 7 has genisoimage only.
if os.path.isfile('/usr/bin/genisoimage'):
makefs = ["genisoimage", "-r", "-o"]
else:
makefs = ["mkisofs", "-r", "-o"]
fnull = open(os.devnull, 'w')
# Return true if the given program (+args) can be successfully run
# Create a directory if missing
# Run a shell command safely and with error checking
# Subclass pexpect.spawn to add logging of expect() calls
# Subclass urllib.FancyURLopener so that we can catch
# HTTP 404 errors
def my_urlretrieve(url, filename):
r = MyURLopener().retrieve(url, filename)
if sys.version_info >= (2, 7, 12):
# Work around https://bugs.python.org/issue27973
urllib.urlcleanup()
return r
# Download a file, cleaning up the partial file if the transfer
# fails or is aborted before completion.
# Create a file of the given size, containing NULs, without holes.
# Parse a size with optional k/M/G/T suffix and return an integer
# Download "url" to the local file "file". If the file already
# exists locally, do nothing. If "optional" is true, ignore download
# failures and cache the absence of a missing file by creating a marker
# file with the extension ".MISSING".
# As above, but download a file from the download directory tree
# rooted at "urlbase" into a mirror tree rooted at "dirbase". The
# file name to download is "relfile", which is relative to both roots.
# Map a URL to a directory name. No two URLs should map to the same
# directory.
# Inverse of the above; not used, but included just to show that the
# mapping is invertible and therefore collision-free
# Expect any of a set of alternatives. The *args are alternating
# patterns and actions; an action can be a string to be sent
# or a function to be called with no arguments. The alternatives
# will be expected repeatedly until the last one in the list has
# been selected.
#############################################################################
# A NetBSD version.
#
# Subclasses should define:
#
# dist_url(self)
# the top-level URL for the machine-dependent download tree where
# the version can be downloaded, for example,
# ftp://ftp.netbsd.org/pub/NetBSD/NetBSD-5.0.2/i386/
#
# mi_url(self)
# The top-level URL for the machine-independent download tree,
# for example, ftp://ftp.netbsd.org/pub/NetBSD/NetBSD-5.0.2/
#
# default_workdir(self)
# a file name component identifying the version, for use in
# constructing a unique, version-specific working directory
#
# arch(self)
# the name of the machine architecture the version is for,
# e.g., i386
# Subclass for versions where we pass in the version number explicitly
# An official NetBSD release
# A daily build
# A local build
# The top-level URL of a release tree
# A local release directory
# An URL or local file name pointing at an ISO image
#############################################################################
# Helper class for killing the DomU when the last reference to the
# child process is dropped
def vmm_is_xen(vmm):
return vmm == 'xm' or vmm == 'xl'
def slog(fd, tag, data):
print >>fd, "%s(%s)" % (tag, repr(data))
def slog_info(fd, data):
slog(fd, 'info', data)
# A file-like object that escapes unprintable data and prefixes each
# line with a tag, for logging I/O.
# http://stackoverflow.com/questions/616645/how-do-i-duplicate-sys-stdout-to-a-log-file-in-python
def console_interaction(child):
# We need this in pexpect 2.x or everything will be printed twice
child.logfile = None
child.interact()
# Calling this directly is deprecated, use Anita.login()
# Generate a root shell prompt string that is less likely to appear in
# the console output by accident than the default of "# ". Must end with "# ".
# Quote a prompt in /bin/sh syntax, with some extra quotes
# in the middle so that an echoed command to set the prompt is not
# mistaken for the prompt itself.
# Calling this directly is deprecated, use Anita.shell_cmd()
#############################################################################
| 34.933059 | 120 | 0.599351 |
dd9b53c1d51423e3d8a448dcde0235338b5a8f9d | 5,356 | py | Python | solitude/testing/context.py | incerto-crypto/solitude | 1b21a2ca4912da212d413322953ceb4ec2983c17 | [
"BSD-3-Clause"
] | 7 | 2019-03-25T21:48:42.000Z | 2022-02-25T08:21:35.000Z | solitude/testing/context.py | incerto-crypto/solitude | 1b21a2ca4912da212d413322953ceb4ec2983c17 | [
"BSD-3-Clause"
] | null | null | null | solitude/testing/context.py | incerto-crypto/solitude | 1b21a2ca4912da212d413322953ceb4ec2983c17 | [
"BSD-3-Clause"
] | null | null | null | # Copyright (c) 2019, Solitude Developers
#
# This source code is licensed under the BSD-3-Clause license found in the
# COPYING file in the root directory of this source tree
from typing import Mapping, Union, Optional # noqa
import os
import shutil
import tempfile
from functools import wraps
from collections import OrderedDict
from solitude._internal.error_util import type_assert, RaiseForParam
from solitude.server import ETHTestServer, kill_all_servers # noqa
from solitude.client import ContractBase, ETHClient, EventLog # noqa
from solitude.common import ContractObjectList
from solitude.compiler import Compiler # noqa
from solitude import Factory, read_config_file
def SOL_new(
cfg: Union[dict, str]="solitude.yaml",
relative_to: Optional[str]=None) -> TestingContext:
"""Create a new testing context
:param cfg: configuration dictionary or path. If `cfg` is a string, it is interpreted
as a path to the yaml or json file containing the configuration dictionary.
:param relative_to: a path, or None; if `cfg` is a path and `relative_to` is not None,
make the path of the configuration file `cfg` relative to the parent directory of
`relative_to`. This can be used with `__file__` to make the configuration file
location relative to the test script.
"""
with RaiseForParam("cfg"):
type_assert(cfg, (dict, str))
if isinstance(cfg, str):
path = cfg
if relative_to is not None:
rel_dir = os.path.dirname(
os.path.abspath(relative_to))
path = os.path.join(rel_dir, path)
cfg_dict = read_config_file(path)
else:
cfg_dict = cfg
try:
return TestingContext(cfg_dict)
except Exception:
kill_all_servers()
raise
| 32.460606 | 92 | 0.668969 |
dd9c212b2612a151f4e10e08866ba944cee12a2b | 2,883 | py | Python | openwater/zone/model.py | jeradM/openwater | 740b7e76622a1ee909b970d9e5c612a840466cec | [
"MIT"
] | null | null | null | openwater/zone/model.py | jeradM/openwater | 740b7e76622a1ee909b970d9e5c612a840466cec | [
"MIT"
] | null | null | null | openwater/zone/model.py | jeradM/openwater | 740b7e76622a1ee909b970d9e5c612a840466cec | [
"MIT"
] | null | null | null | from abc import ABC, abstractmethod
from datetime import datetime
from typing import TYPE_CHECKING, Dict, Any, List, Optional
if TYPE_CHECKING:
from openwater.core import OpenWater
def __hash__(self):
return hash(self.id)
| 25.289474 | 78 | 0.539369 |
dd9c57daac230a88704ee45525c7ddcffe34b216 | 2,339 | py | Python | shrinkaddrspace/fix_preinit.py | vusec/midfat | 9eb8d3c5188c0c262ad5f5de84f9e26530e38a4c | [
"Apache-2.0"
] | 6 | 2017-06-02T00:16:57.000Z | 2018-12-08T20:49:19.000Z | shrinkaddrspace/fix_preinit.py | vusec/midfat | 9eb8d3c5188c0c262ad5f5de84f9e26530e38a4c | [
"Apache-2.0"
] | null | null | null | shrinkaddrspace/fix_preinit.py | vusec/midfat | 9eb8d3c5188c0c262ad5f5de84f9e26530e38a4c | [
"Apache-2.0"
] | 3 | 2019-05-12T08:52:48.000Z | 2019-08-08T11:17:52.000Z | #!/usr/bin/env python
import struct
from elftools.elf.elffile import ELFFile
if __name__ == '__main__':
main()
| 32.486111 | 80 | 0.607525 |
dd9d9e745268a05b2a5397b1a32f3f6189d22b3b | 41 | py | Python | ppq/utils/__init__.py | openppl-public/ppq | 0fdea7d4982bc57feb6bb8548c7f012707fbd607 | [
"Apache-2.0"
] | 100 | 2021-12-31T09:34:06.000Z | 2022-03-25T02:54:51.000Z | ppq/utils/__init__.py | openppl-public/ppq | 0fdea7d4982bc57feb6bb8548c7f012707fbd607 | [
"Apache-2.0"
] | 12 | 2021-12-31T10:28:15.000Z | 2022-03-31T07:08:44.000Z | ppq/utils/__init__.py | openppl-public/ppq | 0fdea7d4982bc57feb6bb8548c7f012707fbd607 | [
"Apache-2.0"
] | 21 | 2021-12-31T09:51:02.000Z | 2022-03-30T12:21:55.000Z | from .attribute import process_attribute
| 20.5 | 40 | 0.878049 |
dda05eca52f0bd879e75366f591fdb92e3e9abbd | 855 | py | Python | tests/test_views.py | pennlabs/django-shortener | a8f362863d4d8f13916e9e924ed316384f588373 | [
"MIT"
] | 3 | 2018-11-04T15:46:01.000Z | 2020-01-06T13:49:46.000Z | tests/test_views.py | pennlabs/shortener | a8f362863d4d8f13916e9e924ed316384f588373 | [
"MIT"
] | 1 | 2019-07-30T04:31:19.000Z | 2019-07-30T04:31:19.000Z | tests/test_views.py | pennlabs/shortener | a8f362863d4d8f13916e9e924ed316384f588373 | [
"MIT"
] | 2 | 2021-02-22T18:12:27.000Z | 2021-09-16T18:51:47.000Z | import hashlib
from django.test import TestCase
from django.urls import reverse
from shortener.models import Url
| 34.2 | 84 | 0.691228 |
dda073c654623fd4431b83697b75b0c9003f460a | 1,758 | py | Python | Models/Loss/__init__.py | bobo0810/classification | b27397308c5294dcc30a5aaddab4692becfc45d3 | [
"MIT"
] | null | null | null | Models/Loss/__init__.py | bobo0810/classification | b27397308c5294dcc30a5aaddab4692becfc45d3 | [
"MIT"
] | null | null | null | Models/Loss/__init__.py | bobo0810/classification | b27397308c5294dcc30a5aaddab4692becfc45d3 | [
"MIT"
] | null | null | null | import torch
import torch.nn as nn
from timm.loss import LabelSmoothingCrossEntropy
from pytorch_metric_learning import losses
| 27.46875 | 80 | 0.609215 |
dda150ca1f3fdf6e22fc599876192f67eb4fbf8e | 1,195 | py | Python | tests/main.py | caspervg/geontology | e5660629f57747d05cfe6e15721bca23039a672d | [
"MIT"
] | 1 | 2015-12-25T10:46:27.000Z | 2015-12-25T10:46:27.000Z | tests/main.py | caspervg/geontology | e5660629f57747d05cfe6e15721bca23039a672d | [
"MIT"
] | null | null | null | tests/main.py | caspervg/geontology | e5660629f57747d05cfe6e15721bca23039a672d | [
"MIT"
] | null | null | null | from rdflib import URIRef
from rdflib.namespace import DC, RDF, OWL, RDFS
from geontology import GeoOntology
ont = GeoOntology("geo_ontology.ttl", frmt='n3')
# print ont.serialize()
graph = ont.graph
print "InfoColumns"
for s, p, o in graph.triples(
(None, RDFS.subClassOf, URIRef('http://move.ugent.be/geodata/ontology/InfoColumn'))):
print "[%s, %s, %s]" % (s, p, o)
for _s, _p, _o in graph.triples(
(None, RDF.type, s)):
print " [%s, %s, %s]" % (_s, _p, _o)
for __s, __p, __o in graph.triples(
(_s, None, None)):
print " [%s, %s, %s]" % (__s, __p, __o)
print "\n\nGeoColumns"
for s, p, o in graph.triples(
(None, RDFS.subClassOf, URIRef('http://move.ugent.be/geodata/ontology/GeoColumn'))):
print "[%s, %s, %s]" % (s, p, o)
for _s, _p, _o in graph.triples(
(None, RDF.type, s)):
print " [%s, %s, %s]" % (_s, _p, _o)
for __s, __p, __o in graph.triples(
(_s, None, None)):
print " [%s, %s, %s]" % (__s, __p, __o)
if (None, URIRef('http://move.ugent.be/geodata/ontology/defines'), DC.date) in graph:
print "some defines" | 29.146341 | 93 | 0.551464 |
dda2bd0af7d3de24450c99ea2968e3067f121da2 | 1,397 | py | Python | VGG_GRU/TrainTestlist/Emotiw/getTraintest_Emotiw.py | XiaoYee/emotion_classification | 6122e1b575bce5235169f155295b549a8f721ca1 | [
"MIT"
] | 74 | 2018-06-29T06:46:33.000Z | 2022-02-26T19:15:55.000Z | VGG_GRU/TrainTestlist/Emotiw/getTraintest_Emotiw.py | JIangjiang1108/emotion_classification | 6122e1b575bce5235169f155295b549a8f721ca1 | [
"MIT"
] | 6 | 2018-07-02T09:29:05.000Z | 2020-01-30T14:21:26.000Z | VGG_GRU/TrainTestlist/Emotiw/getTraintest_Emotiw.py | JIangjiang1108/emotion_classification | 6122e1b575bce5235169f155295b549a8f721ca1 | [
"MIT"
] | 23 | 2018-06-29T12:52:40.000Z | 2020-12-02T12:55:13.000Z | import os
import os.path as osp
import argparse
import random
parser = argparse.ArgumentParser(description='Emotiw dataset list producer')
args = parser.parse_args()
train = "/home/quxiaoye/disk/FR/Emotiw2018/data/Train_AFEW_all/Emotiw-faces"
test = "/home/quxiaoye/disk/FR/Emotiw2018/data/Val_AFEW/Emotiw-faces"
train_path = osp.join(train)
test_path = osp.join(test)
Face_category = open("./TrainTestlist/Emotiw/Emotiw_TRAIN.txt","w")
Face_category_test = open("./TrainTestlist/Emotiw/Emotiw_VAL.txt","w")
train_img_folders = os.listdir(train_path)
train_img_folders.sort()
for i in range(len(train_img_folders)):
path_folder = osp.join(train_path,train_img_folders[i])
emotion_folders = os.listdir(path_folder)
emotion_folders.sort()
for emotion_folder in emotion_folders:
path_write = osp.join(path_folder,emotion_folder)
Face_category.write(path_write+" "+train_img_folders[i]+"\n")
Face_category.close()
test_img_folders = os.listdir(test_path)
test_img_folders.sort()
for i in range(len(test_img_folders)):
path_folder = osp.join(test_path,test_img_folders[i])
emotion_folders = os.listdir(path_folder)
emotion_folders.sort()
for emotion_folder in emotion_folders:
path_write = osp.join(path_folder,emotion_folder)
Face_category_test.write(path_write+" "+test_img_folders[i]+"\n")
Face_category_test.close()
| 29.723404 | 76 | 0.7602 |
06b3cc12c35c6a81cb3cf69f89310f5caed75723 | 141 | py | Python | config_default.py | wecassidy/pishow | 310f935688e0b3ceeb8fe11bd0fda902c041dd45 | [
"MIT"
] | null | null | null | config_default.py | wecassidy/pishow | 310f935688e0b3ceeb8fe11bd0fda902c041dd45 | [
"MIT"
] | null | null | null | config_default.py | wecassidy/pishow | 310f935688e0b3ceeb8fe11bd0fda902c041dd45 | [
"MIT"
] | null | null | null | IMG_DIR = "/path/to/pictures"
IMG_POLL_RATE = 1 # minute
DWELL_TIME = 5 # seconds
FADE_TIME = 1 # seconds
REFRESH_RATE = 10 # milliseconds
| 17.625 | 32 | 0.723404 |
06b3ddf241a1d83947506562980f958105dbb002 | 5,050 | py | Python | src/stdlib.py | tweakoz/zed64 | c0231444418999191182d53d9319bf7978422bfb | [
"CC-BY-3.0"
] | 4 | 2015-06-04T01:14:43.000Z | 2018-06-16T05:45:57.000Z | src/stdlib.py | tweakoz/zed64 | c0231444418999191182d53d9319bf7978422bfb | [
"CC-BY-3.0"
] | null | null | null | src/stdlib.py | tweakoz/zed64 | c0231444418999191182d53d9319bf7978422bfb | [
"CC-BY-3.0"
] | null | null | null | #######################################################################
##
## Zed64 MetroComputer
##
## Unless a module otherwise marked,
## Copyright 2014, Michael T. Mayers (michael@tweakoz.com
## Provided under the Creative Commons Attribution License 3.0
## Please see https://creativecommons.org/licenses/by/3.0/us/legalcode
##
#######################################################################
#!/usr/bin/python
from myhdl import *
import sys, os, md5, string
import localopts
hvcount_bits = 12
################################################33
def Sel2(output,inpa,inpb,sel):
__verilog__ = \
"""assign %(output)s = %(sel)s ? %(inpb)s : %(inpa)s;"""
output.driven = "wire"
return logic
################################################33
################################################33
################################################33
def pushdir(newdir):
olddir = os.getcwd()
os.chdir(newdir)
return olddir
def popdir(a):
os.chdir(a)
################################################33
################################################33
states = enum( "Reset",
"StateA",
"StateB",
"StateC",
encoding="one_hot" )
timescale = "1ns/1ps"
__all__ = [ 'timescale',
"verilog_params",
"SigReset",
"SigWord",
"SigMod",
'pixdimval',
'SigColorChannel',
'SigBool',
'SigMod64',
'SigPixCnt',
'SigByte',
'SigNyb',
'SigAdr16',
'Sel2',
'Gen2',
'Gen3',
'UcfGen',
'pushdir','popdir']
| 24.278846 | 86 | 0.50198 |
06bb33b3d53b354d7a98d017485acac1da8698a5 | 1,127 | py | Python | py/1081. Smallest Subsequence of Distinct Characters.py | longwangjhu/LeetCode | a5c33e8d67e67aedcd439953d96ac7f443e2817b | [
"MIT"
] | 3 | 2021-08-07T07:01:34.000Z | 2021-08-07T07:03:02.000Z | py/1081. Smallest Subsequence of Distinct Characters.py | longwangjhu/LeetCode | a5c33e8d67e67aedcd439953d96ac7f443e2817b | [
"MIT"
] | null | null | null | py/1081. Smallest Subsequence of Distinct Characters.py | longwangjhu/LeetCode | a5c33e8d67e67aedcd439953d96ac7f443e2817b | [
"MIT"
] | null | null | null | # https://leetcode.com/problems/smallest-subsequence-of-distinct-characters/
# Return the lexicographically smallest subsequence of s that contains all the
# distinct characters of s exactly once.
# Note: This question is the same as 316: https://leetcode.com/problems/remove-
# duplicate-letters/
################################################################################
# record last postion of each char
# use stack and pop previous chars when i) new char is smaller and ii) we can add the popped char back later
| 38.862069 | 108 | 0.585626 |
06bbd57d69031c72132de965a30424f3da29237c | 2,207 | py | Python | ebs-snapshot-manager.py | phanirajendra/aws-ebs-snapshots-lambda | 0464ec54de7e812163e33db73d1cb9d46c35c029 | [
"Apache-2.0"
] | null | null | null | ebs-snapshot-manager.py | phanirajendra/aws-ebs-snapshots-lambda | 0464ec54de7e812163e33db73d1cb9d46c35c029 | [
"Apache-2.0"
] | null | null | null | ebs-snapshot-manager.py | phanirajendra/aws-ebs-snapshots-lambda | 0464ec54de7e812163e33db73d1cb9d46c35c029 | [
"Apache-2.0"
] | 1 | 2020-05-17T14:45:51.000Z | 2020-05-17T14:45:51.000Z | import boto3
import re
import datetime
import time
ec = boto3.client('ec2')
iam = boto3.client('iam')
"""
This function looks at *all* snapshots that have the tags "Type:Automated" and
"DeleteOn" containing the current day formatted as YYYY-MM-DD. The function will
delete the if the todays date is > than the "DeleteOn".
"""
| 34.484375 | 100 | 0.593113 |
06bd33902db8a6c06128727e29c0eae037cf9894 | 378 | py | Python | Cartwheel/lib/Python26/Lib/site-packages/OpenGL/GL/NV/fragment_program.py | MontyThibault/centre-of-mass-awareness | 58778f148e65749e1dfc443043e9fc054ca3ff4d | [
"MIT"
] | null | null | null | Cartwheel/lib/Python26/Lib/site-packages/OpenGL/GL/NV/fragment_program.py | MontyThibault/centre-of-mass-awareness | 58778f148e65749e1dfc443043e9fc054ca3ff4d | [
"MIT"
] | null | null | null | Cartwheel/lib/Python26/Lib/site-packages/OpenGL/GL/NV/fragment_program.py | MontyThibault/centre-of-mass-awareness | 58778f148e65749e1dfc443043e9fc054ca3ff4d | [
"MIT"
] | null | null | null | '''OpenGL extension NV.fragment_program
This module customises the behaviour of the
OpenGL.raw.GL.NV.fragment_program to provide a more
Python-friendly API
'''
from OpenGL import platform, constants, constant, arrays
from OpenGL import extensions, wrapper
from OpenGL.GL import glget
import ctypes
from OpenGL.raw.GL.NV.fragment_program import *
### END AUTOGENERATED SECTION | 31.5 | 56 | 0.812169 |
06be7d0ae668828822753247461cfec9b2e4f3d3 | 675 | py | Python | kpm/commands/push.py | ericchiang/kpm | 3653b1dba8359f086a6a21d3a5003e80a46083a7 | [
"Apache-2.0"
] | 121 | 2016-08-05T17:54:27.000Z | 2022-02-21T14:21:59.000Z | kpm/commands/push.py | ericchiang/kpm | 3653b1dba8359f086a6a21d3a5003e80a46083a7 | [
"Apache-2.0"
] | 82 | 2016-08-07T01:42:41.000Z | 2017-05-05T17:35:45.000Z | kpm/commands/push.py | ericchiang/kpm | 3653b1dba8359f086a6a21d3a5003e80a46083a7 | [
"Apache-2.0"
] | 30 | 2016-08-15T13:12:10.000Z | 2022-02-21T14:22:00.000Z | from appr.commands.push import PushCmd as ApprPushCmd
from kpm.manifest_jsonnet import ManifestJsonnet
| 32.142857 | 66 | 0.638519 |
06be9a64516dc7d92276fa93579a7f01f96fc979 | 360 | py | Python | QASMToQuEST/errors.py | oerc0122/QASMParser | 701b6f25f498ea67670f2d85ae0f2e6920aea267 | [
"MIT"
] | 5 | 2019-05-10T08:17:57.000Z | 2021-12-19T05:06:18.000Z | QASMToQuEST/errors.py | oerc0122/QASMParser | 701b6f25f498ea67670f2d85ae0f2e6920aea267 | [
"MIT"
] | 14 | 2019-04-11T11:28:08.000Z | 2020-02-13T15:18:56.000Z | QASMToQuEST/errors.py | oerc0122/QASMParser | 701b6f25f498ea67670f2d85ae0f2e6920aea267 | [
"MIT"
] | 2 | 2019-05-10T08:17:23.000Z | 2021-12-18T16:37:02.000Z | """
Define the errors which may occur
"""
langMismatchWarning = "Classical language {} does not match output language {}"
langNotDefWarning = "Language {0} translation not found, check QASMToQuEST/langs/{0}.py exists"
noLangSpecWarning = "No language specified for screen print"
noSpecWarning = "Neither language nor output with recognised language specified"
| 45 | 95 | 0.786111 |
06bfd7cd414a9434b1f295b51c26d7407c29f08d | 383 | py | Python | problem_29/distinct_powers.py | plilja/project-euler | 646d1989cf15e903ef7e3c6e487284847d522ec9 | [
"Apache-2.0"
] | null | null | null | problem_29/distinct_powers.py | plilja/project-euler | 646d1989cf15e903ef7e3c6e487284847d522ec9 | [
"Apache-2.0"
] | null | null | null | problem_29/distinct_powers.py | plilja/project-euler | 646d1989cf15e903ef7e3c6e487284847d522ec9 | [
"Apache-2.0"
] | null | null | null | from common.matrix import Matrix
| 21.277778 | 40 | 0.488251 |
06c21871e9ad89697d51562c488828bc64f7390f | 1,436 | py | Python | 1-python/python/transpose.py | Domin-Imperial/Domin-Respository | 2e531aabc113ed3511f349107695847b5c4e4320 | [
"MIT"
] | null | null | null | 1-python/python/transpose.py | Domin-Imperial/Domin-Respository | 2e531aabc113ed3511f349107695847b5c4e4320 | [
"MIT"
] | null | null | null | 1-python/python/transpose.py | Domin-Imperial/Domin-Respository | 2e531aabc113ed3511f349107695847b5c4e4320 | [
"MIT"
] | 1 | 2021-05-24T20:09:38.000Z | 2021-05-24T20:09:38.000Z | # exercism exercise "transpose"
print(transpose("AB\nC"))
print(repr(transpose("AB\nC").split('\n')))
| 27.615385 | 67 | 0.612117 |
06c29fb490c1f0aa4c9251b922a903b4e7ce1750 | 548 | py | Python | tests/create_file.py | stompsjo/RadClass | 3bce664fbc1ea48c067e035adb18cc064db5df4b | [
"BSD-3-Clause"
] | null | null | null | tests/create_file.py | stompsjo/RadClass | 3bce664fbc1ea48c067e035adb18cc064db5df4b | [
"BSD-3-Clause"
] | 2 | 2021-05-11T15:11:17.000Z | 2021-07-02T17:42:35.000Z | tests/create_file.py | stompsjo/RadClass | 3bce664fbc1ea48c067e035adb18cc064db5df4b | [
"BSD-3-Clause"
] | null | null | null | import h5py | 30.444444 | 71 | 0.642336 |
06c2aad42518b04959fd06448a4c2d1ef11c34fe | 4,318 | py | Python | core/models.py | mcflydesigner/innorussian | 70bec97ad349f340bd66cd8234d94f8829540397 | [
"MIT"
] | 1 | 2021-04-12T18:54:37.000Z | 2021-04-12T18:54:37.000Z | core/models.py | mcflydesigner/InnoRussian | 70bec97ad349f340bd66cd8234d94f8829540397 | [
"MIT"
] | null | null | null | core/models.py | mcflydesigner/InnoRussian | 70bec97ad349f340bd66cd8234d94f8829540397 | [
"MIT"
] | null | null | null | from django.db import models
from django.utils.timezone import now
from django.core.validators import FileExtensionValidator
from django.contrib.auth import get_user_model
from django.contrib.postgres.fields import ArrayField
from django.db.models import (Func, Value, CharField, IntegerField)
from .shortcuts import upload_to
"""
Models of core app.
The architecture is done in the following way.
An user accesses the content sequentially:
Category -> Subcategory -> List of words
"""
| 35.393443 | 101 | 0.652154 |
06c565b07d3d63057b60da7cadb5483464d2b8ef | 250 | py | Python | tests/tushare_to_MongoDB.py | sibuzu/OnePy | 464fca1c68a10f90ad128da3bfb03f05d2fc24bc | [
"MIT"
] | null | null | null | tests/tushare_to_MongoDB.py | sibuzu/OnePy | 464fca1c68a10f90ad128da3bfb03f05d2fc24bc | [
"MIT"
] | null | null | null | tests/tushare_to_MongoDB.py | sibuzu/OnePy | 464fca1c68a10f90ad128da3bfb03f05d2fc24bc | [
"MIT"
] | null | null | null | import OnePy as op
instrument = "000001"
ktype = "D"
start = "2017-01-01"
end = None
test = op.Tushare_to_MongoDB(database=instrument, collection=ktype)
test.data_to_db(code=instrument, start=start, end=end, ktype=ktype, autype="qfq", index=False)
| 25 | 94 | 0.748 |
06c71c090d4fa330c0d0b447373205ef201db5b3 | 1,836 | py | Python | costar_models/python/costar_models/datasets/h5f_generator.py | cpaxton/costar_plan | be5c12f9d0e9d7078e6a5c283d3be059e7f3d040 | [
"Apache-2.0"
] | 66 | 2018-10-31T04:58:53.000Z | 2022-03-17T02:32:25.000Z | costar_models/python/costar_models/datasets/h5f_generator.py | cpaxton/costar_plan | be5c12f9d0e9d7078e6a5c283d3be059e7f3d040 | [
"Apache-2.0"
] | 8 | 2018-10-23T21:19:25.000Z | 2018-12-03T02:08:41.000Z | costar_models/python/costar_models/datasets/h5f_generator.py | cpaxton/costar_plan | be5c12f9d0e9d7078e6a5c283d3be059e7f3d040 | [
"Apache-2.0"
] | 25 | 2018-10-19T00:54:17.000Z | 2021-10-10T08:28:15.000Z | from __future__ import print_function
import h5py as h5f
import numpy as np
import six
import os
from .npy_generator import NpzGeneratorDataset
from .image import *
| 30.098361 | 86 | 0.582789 |
06c7a3448e8983e9a265c812e501b174dd35b66d | 5,821 | py | Python | SegmentationAlgorithms/CBSMoT.py | JRose6/TrajLib | 2a5749bf6e9517835801926d6a5e92564ef2c7f0 | [
"Apache-2.0"
] | null | null | null | SegmentationAlgorithms/CBSMoT.py | JRose6/TrajLib | 2a5749bf6e9517835801926d6a5e92564ef2c7f0 | [
"Apache-2.0"
] | null | null | null | SegmentationAlgorithms/CBSMoT.py | JRose6/TrajLib | 2a5749bf6e9517835801926d6a5e92564ef2c7f0 | [
"Apache-2.0"
] | null | null | null | import Distances as d
import pandas as pd
import numpy as np
| 35.932099 | 104 | 0.489263 |
06c9a2b8530162b36b57399f64b942d4d2384eb7 | 164 | py | Python | apps/presupuesto/admin.py | edilio/cubanoshaciamiami.com | 541a22998b7c7ad3acda784c5657bfdf79cc7f07 | [
"MIT"
] | null | null | null | apps/presupuesto/admin.py | edilio/cubanoshaciamiami.com | 541a22998b7c7ad3acda784c5657bfdf79cc7f07 | [
"MIT"
] | null | null | null | apps/presupuesto/admin.py | edilio/cubanoshaciamiami.com | 541a22998b7c7ad3acda784c5657bfdf79cc7f07 | [
"MIT"
] | null | null | null | from django.contrib import admin
from .models import TipoDeGasto, PresupuestoDeGasto
admin.site.register(TipoDeGasto)
admin.site.register(PresupuestoDeGasto) | 27.333333 | 52 | 0.829268 |
06c9d2978cf880b3371f69c40666eeeea090512c | 13,838 | py | Python | Support/validate.py | sgarbesi/javascript-eslint.tmbundle | b117fe0133582676113a96fc9804795c033d0b78 | [
"BSD-3-Clause"
] | 1 | 2015-05-01T14:24:39.000Z | 2015-05-01T14:24:39.000Z | Support/validate.py | sgarbesi/javascript-eslint.tmbundle | b117fe0133582676113a96fc9804795c033d0b78 | [
"BSD-3-Clause"
] | null | null | null | Support/validate.py | sgarbesi/javascript-eslint.tmbundle | b117fe0133582676113a96fc9804795c033d0b78 | [
"BSD-3-Clause"
] | null | null | null | #!/usr/bin/env python
# encoding: utf-8
"""
Validate a JavaScript file using eslint.
Author: Nate Silva
Copyright 2014 Nate Silva
License: MIT
"""
from __future__ import print_function
import sys
import os
import re
import time
import json
import subprocess
import tempfile
import hashlib
import shutil
def find_up_the_tree(dir_name, filename, max_depth=30):
"""
Search for the named file in the dir_name or any of its parent
directories, up to the root directory.
"""
while True:
if max_depth <= 0:
return None
full_path = os.path.abspath(os.path.join(dir_name, filename))
if os.path.isfile(full_path):
return full_path
(drive, path) = os.path.splitdrive(dir_name)
is_root = (path == os.sep or path == os.altsep)
if is_root:
return None
max_depth -= 1
dir_name = os.path.abspath(os.path.join(dir_name, os.pardir))
def find_eslintrc(start_dir):
"""
Locates the most relevant .eslintrc file. Of the following
locations, the first to be found will be used:
1. An .eslintrc file in the start_dir or any of its parents.
2. If the file has not been saved yet, ~/.eslintrc will be
used.
start_dir is normally set to the directory of the file being
validated.
When start_dir is not provided (which happens with files that
are not saved yet), ~/.eslintrc is the only candidate that is
considered.
If no relevant .eslintrc is found, the return value is None.
"""
if start_dir:
# locate the nearest .eslintrc
eslintrc = find_up_the_tree(start_dir, '.eslintrc')
if eslintrc:
return eslintrc
# last ditch: look for .eslintrc in the users home directory
home_eslintrc = os.path.expanduser('~/.eslintrc')
if os.path.isfile(home_eslintrc):
return home_eslintrc
return None
def get_marker_directory():
"""
Create the directory that will hold "marker" files that we use
to detect which files have a validation window open. Used to
implement the following feature:
Normally, when you hit Cmd-S, the validation window appears
only if there is a warning or error.
Assume you had previously validated a file, and the validation
window showing its errors is still open. Now you fix the
errors and press Cmd-S. We want that validation window to
update to show no errors.
In order to do this, we have to somehow detect if TextMate has
a validation window open for the current file. Its not easy.
We use marker files.
This script creates a marker file before returning the HTML
document that will be shown in the validation window.
When the HTML document detects that it is being hidden (closed),
it runs a TextMate.system command to delete its marker file.
"""
baseDir = os.path.join(tempfile.gettempdir(), 'javascript-eslint-tmbundle')
if not os.path.isdir(baseDir):
os.makedirs(baseDir)
today = time.strftime('%Y-%m-%d')
markerDir = os.path.join(baseDir, today)
if not os.path.isdir(markerDir):
os.makedirs(markerDir)
# Deletion should happen automatically, but to be clean(er),
# delete any previous-day marker dirs.
children = os.listdir(baseDir)
children = [_ for _ in children if _ != today]
children = [os.path.join(baseDir, _) for _ in children]
children = [_ for _ in children if os.path.isdir(_)]
[shutil.rmtree(_, True) for _ in children]
return markerDir
if __name__ == '__main__':
quiet = ('-q' in sys.argv or '--quiet' in sys.argv)
validate(quiet)
| 33.833741 | 98 | 0.568001 |
06cdfe64d4ce6044067a25d0bf7f0ef2fa35cf39 | 240 | py | Python | Home_Work_2_B_Naychuk_Anastasiya/Task2.py | NaychukAnastasiya/goiteens-python3-naychuk | a79d0af238a15f58a822bb5d8e4d48227d4a7bc1 | [
"MIT"
] | null | null | null | Home_Work_2_B_Naychuk_Anastasiya/Task2.py | NaychukAnastasiya/goiteens-python3-naychuk | a79d0af238a15f58a822bb5d8e4d48227d4a7bc1 | [
"MIT"
] | null | null | null | Home_Work_2_B_Naychuk_Anastasiya/Task2.py | NaychukAnastasiya/goiteens-python3-naychuk | a79d0af238a15f58a822bb5d8e4d48227d4a7bc1 | [
"MIT"
] | null | null | null | # y = f(X)
# y = 2*x -10 # x>0
# y = 0# x=0
# y = 2 * abs(x) -1 # x<0
print(" ")
x = float(input())
y = 0
if x > 0:
y = 2*x -10
elif x == 0:
y = 0
else: #x<0
y = 2 * abs(x) -1
print ("y: ",y)
| 13.333333 | 26 | 0.375 |
06d128fc6f207aa019def30c73ff71c2d5f4ad72 | 8,745 | py | Python | imagenet_pytorch/utils.py | lishuliang/Emotion-Recognition | a8aea1b71b2508e6157410089b20ab463fe901f5 | [
"MIT"
] | 1 | 2019-03-16T08:11:53.000Z | 2019-03-16T08:11:53.000Z | imagenet_pytorch/utils.py | lishuliang/Emotion-Recognition | a8aea1b71b2508e6157410089b20ab463fe901f5 | [
"MIT"
] | null | null | null | imagenet_pytorch/utils.py | lishuliang/Emotion-Recognition | a8aea1b71b2508e6157410089b20ab463fe901f5 | [
"MIT"
] | null | null | null | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torch.nn.functional as F
from torch.nn import init
| 43.507463 | 145 | 0.5992 |
06d1d332e24aee96ce48f604359996ef77a12eea | 1,349 | py | Python | setup.py | jopo666/HistoPrep | 1b74c346b38c7ca44f92269246571f5f850836af | [
"MIT"
] | 11 | 2021-04-21T10:37:22.000Z | 2021-12-19T22:32:59.000Z | setup.py | jopo666/HistoPrep | 1b74c346b38c7ca44f92269246571f5f850836af | [
"MIT"
] | 1 | 2021-02-24T09:15:13.000Z | 2021-04-19T06:38:58.000Z | setup.py | jopo666/HistoPrep | 1b74c346b38c7ca44f92269246571f5f850836af | [
"MIT"
] | 1 | 2021-09-16T05:00:21.000Z | 2021-09-16T05:00:21.000Z | import setuptools
exec(open('histoprep/_version.py').read())
with open("README.md", "r", encoding="utf-8") as fh:
long_description = fh.read()
setuptools.setup(
name="histoprep",
version=__version__,
author="jopo666",
scripts=['HistoPrep'],
author_email="jopo@birdlover.com",
description="Preprocessing module for large histological images.",
long_description=long_description,
long_description_content_type="text/markdown",
url="https://github.com/jopo666/HistoPrep",
packages=setuptools.find_packages(include=['histoprep','histoprep.*']),
install_requires=[
'opencv-python==4.5.1.48',
'openslide-python==1.1.2',
'pandas==1.2.1',
'Pillow==8.0.0',
'seaborn==0.11.0',
'numpy==1.19.2',
'tqdm==4.60.0',
'aicspylibczi==2.8.0',
'shapely==1.7.1',
'scikit-learn==0.24.1',
'ipywidgets==7.6.3',
],
classifiers=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
"Development Status :: 3 - Alpha",
"Intended Audience :: Science/Research",
"Topic :: Scientific/Engineering :: Bio-Informatics",
],
keywords='image-analysis preprocessing histology openslide',
python_requires='>=3.8',
) | 32.119048 | 75 | 0.614529 |
06d23d367076f35c30b175f7a472c7335658ae54 | 389 | py | Python | setup.py | cprogrammer1994/python-depth-sort | 5399d6d0791eb897d0cac16d2e38b26041478a82 | [
"MIT"
] | null | null | null | setup.py | cprogrammer1994/python-depth-sort | 5399d6d0791eb897d0cac16d2e38b26041478a82 | [
"MIT"
] | null | null | null | setup.py | cprogrammer1994/python-depth-sort | 5399d6d0791eb897d0cac16d2e38b26041478a82 | [
"MIT"
] | null | null | null | import platform
from setuptools import Extension, setup
target = platform.system().lower()
extra_compile_args = []
if target.startswith('linux'):
extra_compile_args.append('-std=c++11')
ext = Extension(
name='depth_sort',
sources=['depth_sort.cpp'],
extra_compile_args=extra_compile_args,
)
setup(
name='depth_sort',
version='0.1.0',
ext_modules=[ext],
)
| 16.913043 | 43 | 0.691517 |
06d28dfe07994e25ac5013d571490aa1301605ee | 15,260 | py | Python | train.py | Kiwi-PUJ/DataTraining | 706642996e884b47a0aa7dfb19da33a7234a311e | [
"CC0-1.0"
] | 3 | 2021-06-04T00:07:54.000Z | 2021-06-09T01:14:07.000Z | train.py | Kiwi-PUJ/DataTraining | 706642996e884b47a0aa7dfb19da33a7234a311e | [
"CC0-1.0"
] | null | null | null | train.py | Kiwi-PUJ/DataTraining | 706642996e884b47a0aa7dfb19da33a7234a311e | [
"CC0-1.0"
] | null | null | null | ## @package Training_app
# Training code developed with Tensorflow Keras. Content: Unet, Unet++ and FCN
#
# @version 1
#
# Pontificia Universidad Javeriana
#
# Electronic Enginnering
#
# Developed by:
# - Andrea Juliana Ruiz Gomez
# Mail: <andrea_ruiz@javeriana.edu.co>
# GitHub: andrearuizg
# - Pedro Eli Ruiz Zarate
# Mail: <pedro.ruiz@javeriana.edu.co>
# GitHub: PedroRuizCode
#
# With support of:
# - Francisco Carlos Calderon Bocanegra
# Mail: <calderonf@javeriana.edu.co>
# GitHub: calderonf
# - John Alberto Betancout Gonzalez
# Mail: <john@kiwibot.com>
# GitHub: JohnBetaCode
import os
from time import time
import numpy as np
import cv2
from glob import glob
from sklearn.model_selection import train_test_split
import tensorflow as tf
from tensorflow.keras.layers import *
from tensorflow.keras.models import Model
from tensorflow.keras.metrics import Recall, Precision
from tensorflow.keras.callbacks import (EarlyStopping, ModelCheckpoint,
ReduceLROnPlateau, CSVLogger, TensorBoard)
## Load data
# Load the data
# @param path Path of the image
## Read image
# Read the images
# @param path Path of the image
## Read mask
# Read the mask of the images
# @param path Path of the mask
## Parse
# Read images and masks and convert to TensorFlow dataformat
# @param x Images
# @param y Masks
## Dataset
# Read images and masks and convert to TensorFlow format
# @param x Images
# @param y Masks
# @param batch Batch size
## Down sample function
# Make the down sample of the layer
# @param x Input
# @param filters The dimensionality of the output space
# @param kernel_size Height and width of the 2D convolution window
# @param padding Padding
# @param strides Strides of the convolution along the height and width
## Up sample function
# Make the up sample of the layer
# @param x Input
# @param skip The skip connection is made to avoid the loss of accuracy
# in the downsampling layers. In case the image becomes so small that
# it has no information, the weights are calculated with the skip layer.
# @param filters The dimensionality of the output space
# @param kernel_size Height and width of the 2D convolution window
# @param padding Padding
# @param strides Strides of the convolution along the height and width
## Bottleneck function
# Added to reduce the number of feature maps in the network
# @param x Input
# @param filters The dimensionality of the output space
# @param kernel_size Height and width of the 2D convolution window
# @param padding Padding
# @param strides Strides of the convolution along the height and width
## Unet 1
# Unet implementation
# @param f Filters dimensionality
## Unet 2
# Unet implementation
# @param f Filters dimensionality
## Unet++ 1
# Unet++ implementation
# @param f Filters dimensionality
## Unet++ 2
# Unet++ implementation
# @param f Filters dimensionality
## FCN 1
# Fully Convolutional Network implementation
# @param f Filters dimensionality
## FCN 2
# Fully Convolutional Network implementation
# @param f Filters dimensionality
## Training
# CNN training
if __name__ == "__main__":
strategy = tf.distribute.MirroredStrategy()
print('Number of devices: {}'.format(strategy.num_replicas_in_sync))
model_l = ["unetv1", "unetv2", "unetppv1", "unetppv2", "fcnv1", "fcnv2"]
for model in model_l:
with strategy.scope():
print("\n\n\n\n\n Training", model, "model\n\n\n\n\n")
training(model)
| 29.921569 | 80 | 0.602687 |
06d2f75b09d1b2bbd785ce87a1527c0fb7ae9040 | 12,133 | py | Python | src_RealData/FCN_Object.py | XYZsake/DRFNS | 73fc5683db5e9f860846e22c8c0daf73b7103082 | [
"MIT"
] | 42 | 2018-10-07T08:19:01.000Z | 2022-02-08T17:41:24.000Z | src_RealData/FCN_Object.py | XYZsake/DRFNS | 73fc5683db5e9f860846e22c8c0daf73b7103082 | [
"MIT"
] | 11 | 2018-12-22T00:15:46.000Z | 2021-12-03T10:29:32.000Z | src_RealData/FCN_Object.py | XYZsake/DRFNS | 73fc5683db5e9f860846e22c8c0daf73b7103082 | [
"MIT"
] | 14 | 2018-08-26T06:47:06.000Z | 2021-07-24T11:52:58.000Z | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import tensorflow as tf
import copy
from tf_image_segmentation.models.fcn_8s import FCN_8s
from tf_image_segmentation.utils.tf_records import read_tfrecord_and_decode_into_image_annotation_pair_tensors
from tf_image_segmentation.utils.training import get_valid_logits_and_labels
from tf_image_segmentation.utils.inference import adapt_network_for_any_size_input
from tf_image_segmentation.utils.visualization import visualize_segmentation_adaptive
from tf_image_segmentation.utils.augmentation import (distort_randomly_image_color,
flip_randomly_left_right_image_with_annotation,
scale_randomly_image_with_annotation_with_fixed_size_output)
import os
from glob import glob
import numpy as np
from scipy import misc
slim = tf.contrib.slim
from utils import ComputeMetrics
| 45.784906 | 161 | 0.570675 |
06d355973fd78ec8f3b614057e835f98f36682ef | 379 | py | Python | qt__pyqt__pyside__pyqode/qt_ini.py | DazEB2/SimplePyScripts | 1dde0a42ba93fe89609855d6db8af1c63b1ab7cc | [
"CC-BY-4.0"
] | 117 | 2015-12-18T07:18:27.000Z | 2022-03-28T00:25:54.000Z | qt__pyqt__pyside__pyqode/qt_ini.py | DazEB2/SimplePyScripts | 1dde0a42ba93fe89609855d6db8af1c63b1ab7cc | [
"CC-BY-4.0"
] | 8 | 2018-10-03T09:38:46.000Z | 2021-12-13T19:51:09.000Z | qt__pyqt__pyside__pyqode/qt_ini.py | DazEB2/SimplePyScripts | 1dde0a42ba93fe89609855d6db8af1c63b1ab7cc | [
"CC-BY-4.0"
] | 28 | 2016-08-02T17:43:47.000Z | 2022-03-21T08:31:12.000Z | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
__author__ = 'ipetrash'
try:
from PyQt4.QtCore import QSettings
except:
from PyQt5.QtCore import QSettings
if __name__ == '__main__':
config = QSettings('config.ini', QSettings.IniFormat)
counter = int(config.value('counter', 0))
config.setValue('counter', counter + 1)
config.setValue('key2', 'abc')
| 18.95 | 57 | 0.664908 |
06d39356c17a9b2e38d63f8d9aaf9f0140911fdf | 46 | py | Python | wafw00f/__init__.py | nofunofunofunofun/wafw00f | a1c3f3a045077d893cd9ed970f5d687b590abfa5 | [
"BSD-3-Clause"
] | 1 | 2022-03-22T09:15:04.000Z | 2022-03-22T09:15:04.000Z | wafw00f/__init__.py | nofunofunofunofun/wafw00f | a1c3f3a045077d893cd9ed970f5d687b590abfa5 | [
"BSD-3-Clause"
] | null | null | null | wafw00f/__init__.py | nofunofunofunofun/wafw00f | a1c3f3a045077d893cd9ed970f5d687b590abfa5 | [
"BSD-3-Clause"
] | 1 | 2021-01-11T17:26:14.000Z | 2021-01-11T17:26:14.000Z | #!/usr/bin/env python
__version__ = '0.9.6'
| 9.2 | 21 | 0.630435 |
06d4487239efe1a625efc060f8606eba1b8fb684 | 3,410 | py | Python | tests/api_tests.py | muhozi/WeConnect | 46825d6ae27a7addbe8f7f2e298f5ee07cd9cf5f | [
"MIT"
] | 3 | 2018-04-07T07:39:19.000Z | 2018-04-13T14:15:26.000Z | tests/api_tests.py | muhozi/WeConnect | 46825d6ae27a7addbe8f7f2e298f5ee07cd9cf5f | [
"MIT"
] | 8 | 2018-03-01T21:11:05.000Z | 2022-03-21T22:16:43.000Z | tests/api_tests.py | muhozi/WeConnect | 46825d6ae27a7addbe8f7f2e298f5ee07cd9cf5f | [
"MIT"
] | 3 | 2018-04-24T03:34:44.000Z | 2018-07-19T09:53:08.000Z | """
Main test
"""
import unittest
import uuid
from api import APP
from flask import json
from api.models.user import User
from api.models.business import Business
from api.helpers import get_token
| 35.520833 | 79 | 0.550733 |
06d6a4324acde5c358d0bd6522e052a2ede943db | 423 | py | Python | cookbook/c05/p03_print_sepend.py | itpubs/python3-cookbook | 140f5e4cc0416b9674edca7f4c901b1f58fc1415 | [
"Apache-2.0"
] | 3 | 2018-09-19T06:44:13.000Z | 2019-03-24T10:07:07.000Z | cookbook/c05/p03_print_sepend.py | itpubs/python3-cookbook | 140f5e4cc0416b9674edca7f4c901b1f58fc1415 | [
"Apache-2.0"
] | 2 | 2020-09-19T17:10:23.000Z | 2020-10-17T16:43:52.000Z | cookbook/c05/p03_print_sepend.py | itpubs/python3-cookbook | 140f5e4cc0416b9674edca7f4c901b1f58fc1415 | [
"Apache-2.0"
] | 1 | 2020-12-22T06:33:18.000Z | 2020-12-22T06:33:18.000Z | #!/usr/bin/env python
# -*- encoding: utf-8 -*-
"""
Topic: print
Desc :
"""
if __name__ == '__main__':
print_sepend()
| 17.625 | 48 | 0.501182 |
06da3baaeb27b37984af143aca6a52ab058a5588 | 122 | py | Python | src/config/config_loader.py | roybenyosef/loc-to-spot | 1db54a8481317f5cd2f746974dcdda2a632d02ec | [
"MIT"
] | null | null | null | src/config/config_loader.py | roybenyosef/loc-to-spot | 1db54a8481317f5cd2f746974dcdda2a632d02ec | [
"MIT"
] | null | null | null | src/config/config_loader.py | roybenyosef/loc-to-spot | 1db54a8481317f5cd2f746974dcdda2a632d02ec | [
"MIT"
] | null | null | null | import yaml
| 17.428571 | 49 | 0.704918 |
06da67599586f6f3ad731d357524246723f211dc | 331 | py | Python | python/demo-django/code/character/models.py | denisroldan/talentum-2015-examples | 4980f49dca56a55d26722f4f6d1fdd88e06f38dd | [
"MIT"
] | null | null | null | python/demo-django/code/character/models.py | denisroldan/talentum-2015-examples | 4980f49dca56a55d26722f4f6d1fdd88e06f38dd | [
"MIT"
] | null | null | null | python/demo-django/code/character/models.py | denisroldan/talentum-2015-examples | 4980f49dca56a55d26722f4f6d1fdd88e06f38dd | [
"MIT"
] | null | null | null | from django.db import models
from game.models import Game
| 25.461538 | 66 | 0.694864 |
06dacdda970f273fddcf69cadb01b1f2dd499e8c | 296 | py | Python | sim_test_model.py | feiyanke/simpy | bde9d09e47596e0bfe66dc7001f556bafd03acc5 | [
"MIT"
] | 1 | 2019-01-28T09:13:58.000Z | 2019-01-28T09:13:58.000Z | sim_test_model.py | feiyanke/simpy | bde9d09e47596e0bfe66dc7001f556bafd03acc5 | [
"MIT"
] | null | null | null | sim_test_model.py | feiyanke/simpy | bde9d09e47596e0bfe66dc7001f556bafd03acc5 | [
"MIT"
] | 2 | 2019-01-28T09:13:59.000Z | 2020-12-13T09:48:20.000Z | import math
import matplotlib.pyplot as plt
from simpy import model
ax1 = plt.subplot(121)
ax2 = plt.subplot(122)
model_sin = model.TimedFunctionModel(math.sin)
model_cos = model.TimedFunctionModel(math.cos)
scope = model.ScopeModel(ax1, ax2)
| 17.411765 | 46 | 0.75 |
06db5fed30a40e13ffe36a7460de36bf7c61b325 | 655 | py | Python | monthlysal.py | aja512/Python-Lab | 8f9c57d6d7f835e31a595223cdddf9c52ebe1cc9 | [
"Apache-2.0"
] | null | null | null | monthlysal.py | aja512/Python-Lab | 8f9c57d6d7f835e31a595223cdddf9c52ebe1cc9 | [
"Apache-2.0"
] | null | null | null | monthlysal.py | aja512/Python-Lab | 8f9c57d6d7f835e31a595223cdddf9c52ebe1cc9 | [
"Apache-2.0"
] | null | null | null | bs=input("Enter basic salary:")
days=input("Enter no. of working days:")
ovrti=input("Enter no. of overtime working hrs:")
deduct=0
if days<6:
deduct=3500
salary=calci(bs,days,ovrti,deduct)
elif days>=6 and days<=12:
deduct=1000
salary= calci(bs,days,ovrti,deduct)
elif days>=13 and days<=18:
deduct=800
salary=calci(bs,days,ovrti,deduct)
else:
deduct=0
salary=calci(bs,days,ovrti,deduct)
| 22.586207 | 49 | 0.690076 |
06db8ba3ca98cc15e56e2db049c572bc5f7c97a3 | 2,760 | py | Python | Day_10_classes_and_objects/day10_uzd1.py | ValRCS/Python_TietoEvry_Sep2021 | e11dac38deb17ba695ce8ad9dab9cf78b4adb99d | [
"MIT"
] | null | null | null | Day_10_classes_and_objects/day10_uzd1.py | ValRCS/Python_TietoEvry_Sep2021 | e11dac38deb17ba695ce8ad9dab9cf78b4adb99d | [
"MIT"
] | null | null | null | Day_10_classes_and_objects/day10_uzd1.py | ValRCS/Python_TietoEvry_Sep2021 | e11dac38deb17ba695ce8ad9dab9cf78b4adb99d | [
"MIT"
] | null | null | null | # class Song: # Song is name of Class, start with Capital letter
# def __init__(self, title="", author="", lyrics=tuple()): # constructor method called upon creation of object
# self.title = title
# self.author = author
# self.lyrics = lyrics
# # print(f"New Song made by Author: {self.author=} Title: {self.title=}")
#
# def sing(self): # method definition which is function associated with objects
# print(f"New Song Title: {self.title}")
# print(f"Lyrics made by Author: {self.author}")
# for line in self.lyrics:
# print(line)
# return self
#
# def yell(self):
# for line in self.lyrics:
# print(line.upper())
# return self
# can be put inside the class or it's better to make _print_lyrics static inside the class?
ziemelmeita = Song('Ziememeita', 'Jumprava', ['Gju meklt ziememeitu', 'Garu, tlu ceu veicu'])
ziemelmeita.sing(1).yell(10).sing().sing(-3)
zrap = Rap("Ziememeita", "Jumprava", ["Gju meklt ziememeitu", "Garu, tlu ceu veicu"])
zrap.break_it(1, "yah").yell(1)
ziemelmeita.sing().yell().sing(1)
| 34.5 | 115 | 0.595652 |
06dc17868ef7177e219fa324d4ee2030cbe721d0 | 51 | py | Python | appengine/src/greenday_core/settings/__init__.py | meedan/montage | 4da0116931edc9af91f226876330645837dc9bcc | [
"Apache-2.0"
] | 6 | 2018-07-31T16:48:07.000Z | 2020-02-01T03:17:51.000Z | appengine/src/greenday_core/settings/__init__.py | meedan/montage | 4da0116931edc9af91f226876330645837dc9bcc | [
"Apache-2.0"
] | 41 | 2018-08-07T16:43:07.000Z | 2020-06-05T18:54:50.000Z | appengine/src/greenday_core/settings/__init__.py | meedan/montage | 4da0116931edc9af91f226876330645837dc9bcc | [
"Apache-2.0"
] | 1 | 2018-08-07T16:40:18.000Z | 2018-08-07T16:40:18.000Z | """
Modules to configure Django's settings
"""
| 12.75 | 42 | 0.647059 |
06dc1c17dd56d7e1a1011a34a1e1d9b273c1982c | 1,956 | py | Python | rurina2/widgets/text.py | TeaCondemns/rurina | 43725ebea5872953125271a9abb300a4e3a80a64 | [
"MIT"
] | null | null | null | rurina2/widgets/text.py | TeaCondemns/rurina | 43725ebea5872953125271a9abb300a4e3a80a64 | [
"MIT"
] | null | null | null | rurina2/widgets/text.py | TeaCondemns/rurina | 43725ebea5872953125271a9abb300a4e3a80a64 | [
"MIT"
] | null | null | null | from constants import STYLE_NORMAL, STYLE_BOLD, STYLE_ITALIC
from prefabs.text import write_autoline
from widgets.widget import WidgetByRect
from base_node import get_surface
from prefabs.surface import blit
from shape import Rect
import pygame
__all__ = [
'Text'
]
| 25.402597 | 90 | 0.541922 |
06dcdfc975ea640979bb4f316bbb031845b68fa5 | 5,350 | py | Python | Chapter05/B12322_05_code upload/topic_categorization.py | PacktPublishing/Python-Machine-Learning-By-Example-Second-Edition | 830ad0124dc72c3a24929ff1b67081a66894f1f9 | [
"MIT"
] | 31 | 2019-05-25T11:28:23.000Z | 2022-02-09T15:19:20.000Z | Chapter05/B12322_05_code upload/topic_categorization.py | PacktPublishing/Python-Machine-Learning-By-Example-Second-Edition | 830ad0124dc72c3a24929ff1b67081a66894f1f9 | [
"MIT"
] | null | null | null | Chapter05/B12322_05_code upload/topic_categorization.py | PacktPublishing/Python-Machine-Learning-By-Example-Second-Edition | 830ad0124dc72c3a24929ff1b67081a66894f1f9 | [
"MIT"
] | 22 | 2019-02-27T20:11:39.000Z | 2022-03-07T21:46:38.000Z | '''
Source codes for Python Machine Learning By Example 2nd Edition (Packt Publishing)
Chapter 5: Classifying Newsgroup Topic with Support Vector Machine
Author: Yuxi (Hayden) Liu
'''
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.datasets import fetch_20newsgroups
from nltk.corpus import names
from nltk.stem import WordNetLemmatizer
all_names = set(names.words())
lemmatizer = WordNetLemmatizer()
from nltk.corpus import stopwords
stop_words = stopwords.words('english')
# Binary classification
categories = ['comp.graphics', 'sci.space']
data_train = fetch_20newsgroups(subset='train', categories=categories, random_state=42)
data_test = fetch_20newsgroups(subset='test', categories=categories, random_state=42)
cleaned_train = clean_text(data_train.data)
label_train = data_train.target
cleaned_test = clean_text(data_test.data)
label_test = data_test.target
from collections import Counter
Counter(label_train)
tfidf_vectorizer = TfidfVectorizer(stop_words='english', max_features=None)
term_docs_train = tfidf_vectorizer.fit_transform(cleaned_train)
term_docs_test = tfidf_vectorizer.transform(cleaned_test)
from sklearn.svm import SVC
svm = SVC(kernel='linear', C=1.0, random_state=42)
svm.fit(term_docs_train, label_train)
accuracy = svm.score(term_docs_test, label_test)
print('The accuracy of binary classification is: {0:.1f}%'.format(accuracy*100))
# Multiclass classification
categories = [
'alt.atheism',
'talk.religion.misc',
'comp.graphics',
'sci.space',
'rec.sport.hockey'
]
data_train = fetch_20newsgroups(subset='train', categories=categories, random_state=42)
data_test = fetch_20newsgroups(subset='test', categories=categories, random_state=42)
cleaned_train = clean_text(data_train.data)
label_train = data_train.target
cleaned_test = clean_text(data_test.data)
label_test = data_test.target
term_docs_train = tfidf_vectorizer.fit_transform(cleaned_train)
term_docs_test = tfidf_vectorizer.transform(cleaned_test)
svm = SVC(kernel='linear', C=1.0, random_state=42)
svm.fit(term_docs_train, label_train)
accuracy = svm.score(term_docs_test, label_test)
print('The accuracy of 5-class classification is: {0:.1f}%'.format(accuracy*100))
from sklearn.metrics import classification_report
prediction = svm.predict(term_docs_test)
report = classification_report(label_test, prediction)
print(report)
# Grid search
categories = None
data_train = fetch_20newsgroups(subset='train', categories=categories, random_state=42)
data_test = fetch_20newsgroups(subset='test', categories=categories, random_state=42)
cleaned_train = clean_text(data_train.data)
label_train = data_train.target
cleaned_test = clean_text(data_test.data)
label_test = data_test.target
tfidf_vectorizer = TfidfVectorizer(stop_words='english', max_features=None)
term_docs_train = tfidf_vectorizer.fit_transform(cleaned_train)
term_docs_test = tfidf_vectorizer.transform(cleaned_test)
parameters = {'C': [0.1, 1, 10, 100]}
svc_libsvm = SVC(kernel='linear')
from sklearn.model_selection import GridSearchCV
grid_search = GridSearchCV(svc_libsvm, parameters, n_jobs=-1, cv=5)
import timeit
start_time = timeit.default_timer()
grid_search.fit(term_docs_train, label_train)
print("--- %0.3fs seconds ---" % (timeit.default_timer() - start_time))
print(grid_search.best_params_)
print(grid_search.best_score_)
svc_libsvm_best = grid_search.best_estimator_
accuracy = svc_libsvm_best.score(term_docs_test, label_test)
print('The accuracy of 20-class classification is: {0:.1f}%'.format(accuracy*100))
from sklearn.svm import LinearSVC
svc_linear = LinearSVC()
grid_search = GridSearchCV(svc_linear, parameters, n_jobs=-1, cv=5)
start_time = timeit.default_timer()
grid_search.fit(term_docs_train, label_train)
print("--- %0.3fs seconds ---" % (timeit.default_timer() - start_time))
print(grid_search.best_params_)
print(grid_search.best_score_)
svc_linear_best = grid_search.best_estimator_
accuracy = svc_linear_best.score(term_docs_test, label_test)
print('TThe accuracy of 20-class classification is: {0:.1f}%'.format(accuracy*100))
# Pipeline
from sklearn.pipeline import Pipeline
pipeline = Pipeline([
('tfidf', TfidfVectorizer(stop_words='english')),
('svc', LinearSVC()),
])
parameters_pipeline = {
'tfidf__max_df': (0.25, 0.5, 1.0),
'tfidf__max_features': (10000, None),
'tfidf__sublinear_tf': (True, False),
'tfidf__smooth_idf': (True, False),
'svc__C': (0.3, 1, 3),
}
grid_search = GridSearchCV(pipeline, parameters_pipeline, n_jobs=-1, cv=5)
start_time = timeit.default_timer()
grid_search.fit(cleaned_train, label_train)
print("--- %0.3fs seconds ---" % (timeit.default_timer() - start_time))
print(grid_search.best_params_)
print(grid_search.best_score_)
pipeline_best = grid_search.best_estimator_
accuracy = pipeline_best.score(cleaned_test, label_test)
print('The accuracy of 20-class classification is: {0:.1f}%'.format(accuracy*100))
| 31.470588 | 108 | 0.774393 |
06de4f84c6d45b4b9c062aad81f802376fdf7b81 | 9,076 | py | Python | lib/enthought/traits/ui/key_bindings.py | mattfoster/matplotlib | 0b47697b19b77226c633ec6a3d74a2199a153315 | [
"PSF-2.0",
"BSD-3-Clause"
] | 1 | 2016-05-08T18:33:12.000Z | 2016-05-08T18:33:12.000Z | lib/enthought/traits/ui/key_bindings.py | mattfoster/matplotlib | 0b47697b19b77226c633ec6a3d74a2199a153315 | [
"PSF-2.0",
"BSD-3-Clause"
] | null | null | null | lib/enthought/traits/ui/key_bindings.py | mattfoster/matplotlib | 0b47697b19b77226c633ec6a3d74a2199a153315 | [
"PSF-2.0",
"BSD-3-Clause"
] | null | null | null | #-------------------------------------------------------------------------------
#
# Written by: David C. Morrill
#
# Date: 05/20/2005
#
# (c) Copyright 2005 by Enthought, Inc.
#
# Classes defined: KeyBinding, KeyBindings
#
#-------------------------------------------------------------------------------
""" Defines KeyBinding and KeyBindings classes, which manage the mapping of
keystroke events into method calls on controller objects that are supplied by
the application.
"""
#-------------------------------------------------------------------------------
# Imports:
#-------------------------------------------------------------------------------
from enthought.traits.api \
import TraitError, HasStrictTraits, Str, List, Any, Instance, Event
from enthought.traits.ui.api \
import View, Item, ListEditor, KeyBindingEditor, toolkit
#-------------------------------------------------------------------------------
# Key binding trait definition:
#-------------------------------------------------------------------------------
# Trait definition for key bindings
Binding = Str( event = 'binding', editor = KeyBindingEditor() )
#-------------------------------------------------------------------------------
# 'KeyBinding' class:
#-------------------------------------------------------------------------------
#-------------------------------------------------------------------------------
# 'KeyBindings' class:
#-------------------------------------------------------------------------------
| 42.213953 | 80 | 0.368114 |
06e007230d32188f666bcfa817cb0d72deaa62d6 | 639 | py | Python | cocos#275--HTMLLabel/html_label_test.py | los-cocos/etc_code | 71c642a5e0f7ff8049cb5fb4ecac3f166ca20280 | [
"MIT"
] | 2 | 2016-08-28T19:41:47.000Z | 2018-12-14T22:01:26.000Z | cocos#275--HTMLLabel/html_label_test.py | los-cocos/etc_code | 71c642a5e0f7ff8049cb5fb4ecac3f166ca20280 | [
"MIT"
] | null | null | null | cocos#275--HTMLLabel/html_label_test.py | los-cocos/etc_code | 71c642a5e0f7ff8049cb5fb4ecac3f166ca20280 | [
"MIT"
] | 2 | 2015-09-21T06:55:12.000Z | 2020-05-29T14:34:34.000Z | #!/usr/bin/env python3
# -*-coding:utf-8 -*
import cocos
from cocos.text import HTMLLabel
from cocos.director import director
if __name__ == '__main__':
main() | 24.576923 | 68 | 0.640063 |
06e062374abeb59d36c97a31d852af3b3fb9d03c | 4,284 | py | Python | depronoun.py | rui-bettencourt/AutomaticSentenceDivision | 4cb29897103189791c932aaea42c8d5b4ecd8bcd | [
"MIT"
] | null | null | null | depronoun.py | rui-bettencourt/AutomaticSentenceDivision | 4cb29897103189791c932aaea42c8d5b4ecd8bcd | [
"MIT"
] | null | null | null | depronoun.py | rui-bettencourt/AutomaticSentenceDivision | 4cb29897103189791c932aaea42c8d5b4ecd8bcd | [
"MIT"
] | null | null | null | #
from nltk.tokenize import word_tokenize
from xml.dom import minidom
import progressbar
from time import sleep
input_file = 'data/dataset_output.txt'
num_lines = sum(1 for line in open(input_file))
read_file = open(input_file, 'r')
write_output_file = open('data/dataset_output_no_pronouns.txt', 'w')
pronouns = ['him','her']
pronouns_objects = ['it']
names = []
objects = []
special_objects = []
pronoun_error_counter_p = 0
pronoun_error_counter_o = 0
pronoun_misplacements = 0
# parse an xml file by name
mydoc_names = minidom.parse('Names.xml')
mydoc_objects = minidom.parse('Objects.xml')
names_raw = mydoc_names.getElementsByTagName('name')
for elem in names_raw:
names.append(elem.firstChild.data)
objects_raw = mydoc_objects.getElementsByTagName('object')
category_raw = mydoc_objects.getElementsByTagName('category')
for elem in objects_raw:
if ' ' not in elem.attributes['name'].value:
objects.append(elem.attributes['name'].value)
else:
complex_word = []
for word in elem.attributes['name'].value.split(' '):
complex_word.append(word)
special_objects.append(complex_word)
for elem in category_raw:
if ' ' not in elem.attributes['name'].value:
objects.append(elem.attributes['name'].value)
else:
complex_word = []
for word in elem.attributes['name'].value.split(' '):
complex_word.append(word)
special_objects.append(complex_word)
names.sort()
objects.sort()
print("The availabe names are: ")
print(names)
print("\n\n")
print("The availabe objects are: ")
print(objects)
print("\n\n")
print("The availabe special objects are: ")
print(special_objects)
print("\n\n")
bar = progressbar.ProgressBar(maxval=num_lines, \
widgets=[progressbar.Bar('=', '[', ']'), ' ', progressbar.Percentage()])
bar.start()
##### Actual code
i = 0
for line in read_file:
i += 1
used_name = None
used_object = None
words = word_tokenize(line)
if any(pronoun in words for pronoun in pronouns):
#Loop the tokenized line for the pronoun and name
for word in words:
if word in names:
used_name = word
if word in pronouns and used_name is not None:
#if a pronoun was found and previously also a name, replace that pronoun by the name
words[words.index(word)] = used_name
elif word in pronouns and used_name is None:
print("PRONOUN WITH NO NAME!")
pronoun_error_counter_p += 1
if any(pronoun in words for pronoun in pronouns_objects):
#Loop the tokenized line for the pronoun and object
for word in words:
if word in objects:
used_object = word
if word in names:
used_name = word
if word in pronouns_objects and used_object is not None:
words[words.index(word)] = "the " + used_object
elif word in pronouns_objects and used_object is None:
# print("PRONOUN WITH NO NAME!")
success = False
for special in special_objects:
correct_special = True
for item in special:
if item not in words:
correct_special = False
break
if correct_special:
to_add = ' '.join(special)
words[words.index(word)] = "the " + to_add
success = True
if not success and used_name is not None:
words[words.index(word)] = used_name
pronoun_misplacements += 1
elif not success:
pronoun_error_counter_o += 1
#Write the output into a file
write_output_file.write(' '.join(words).replace(' .','.') + '\n')
# print("Iter: " + str(i))
bar.update(i)
bar.finish()
print("Success! With " + str(pronoun_error_counter_p) + " sentences that had a pronoun but no name and " + str(pronoun_error_counter_o) + " with no object.")
print("A total of " + str(pronoun_misplacements) + " were considered as pronoun misplacements and the it was replace by a name")
| 33.46875 | 157 | 0.614613 |
06e4f094f267243cc672eb7459a8a3c1167d18f8 | 2,794 | py | Python | pyaws/utils/userinput.py | mwozniczak/pyaws | af8f6d64ff47fd2ef2eb9fef25680e4656523fa3 | [
"MIT"
] | null | null | null | pyaws/utils/userinput.py | mwozniczak/pyaws | af8f6d64ff47fd2ef2eb9fef25680e4656523fa3 | [
"MIT"
] | null | null | null | pyaws/utils/userinput.py | mwozniczak/pyaws | af8f6d64ff47fd2ef2eb9fef25680e4656523fa3 | [
"MIT"
] | null | null | null | """
Python3 Module
Summary:
User Input Manipulation
"""
import re
from string import ascii_lowercase
def bool_assignment(arg, patterns=None):
"""
Summary:
Enforces correct bool argment assignment
Arg:
:arg (*): arg which must be interpreted as either bool True or False
Returns:
bool assignment | TYPE: bool
"""
arg = str(arg) # only eval type str
try:
if patterns is None:
patterns = (
(re.compile(r'^(true|false)$', flags=re.IGNORECASE), lambda x: x.lower() == 'true'),
(re.compile(r'^(yes|no)$', flags=re.IGNORECASE), lambda x: x.lower() == 'yes'),
(re.compile(r'^(y|n)$', flags=re.IGNORECASE), lambda x: x.lower() == 'y')
)
if not arg:
return '' # default selected
else:
for pattern, func in patterns:
if pattern.match(arg):
return func(arg)
except Exception as e:
raise e
def range_bind(min_value, max_value, value):
""" binds number to a type and range """
if value not in range(min_value, max_value + 1):
value = min(value, max_value)
value = max(min_value, value)
return int(value)
def userchoice_mapping(choice):
"""
Summary:
Maps the number of an option presented to the user to the
correct letters in sequential a-z series when choice parameter
is provided as a number.
When given a letter as an input parameter (choice is a single
letter), returns the integer number corresponding to the letter
in the alphabet (a-z)
Examples:
- userchoice_mapping(3) returns 'c'
- userchoice_mapping('z') returns 26 (integer)
Args:
choice, TYPE: int or str
Returns:
ascii (lowercase), TYPE: str OR None
"""
# prepare mapping dict containing all 26 letters
map_dict = {}
letters = ascii_lowercase
for index in range(1, 27):
map_dict[index] = letters[index - 1]
# process user input
try:
if isinstance(choice, str):
if choice in letters:
for k, v in map_dict.items():
if v == choice.lower():
return k
elif int(choice) in range(1, 27):
# integer string provided
return map_dict[int(choice)]
else:
# not in letters or integer string outside range
return None
elif choice not in range(1, 27):
return None
except KeyError:
# integer outside range provided
return None
except ValueError:
# string outside range provided
return None
return map_dict[choice]
| 30.043011 | 100 | 0.566929 |
06e51ad894ceaca1307a132e1efdb1fe4242fe80 | 17,008 | py | Python | batches.py | NRHelmi/ldbc_snb_data_converter | 42eb5dfbe8b46bcb4d8ad56e1fd988e7635deea3 | [
"Apache-2.0"
] | 2 | 2021-01-22T10:07:18.000Z | 2021-02-09T18:13:28.000Z | batches.py | NRHelmi/ldbc_snb_data_converter | 42eb5dfbe8b46bcb4d8ad56e1fd988e7635deea3 | [
"Apache-2.0"
] | 5 | 2021-02-11T23:12:05.000Z | 2021-05-21T12:16:29.000Z | batches.py | szarnyasg/ldbc-example-graph | 1fd52bc60d50cf5184ee1331369d754db2b8489f | [
"Apache-2.0"
] | 1 | 2022-03-24T20:02:23.000Z | 2022-03-24T20:02:23.000Z | import duckdb
from datetime import date
from dateutil.relativedelta import relativedelta
import os
import shutil
con = duckdb.connect(database='ldbc.duckdb', read_only=False)
# batches are selected from the [network_start_date, network_end_date) interval,
# each batch denotes a [batch_start_date, batch_end_date) where
# batch_end_date = batch_start_date + batch_size
network_start_date = date(2011, 1, 1)
network_end_date = date(2014, 1, 1)
batch_size = relativedelta(years=1)
if os.path.isdir("batches"):
shutil.rmtree("batches")
os.mkdir("batches")
batch_start_date = network_start_date
while batch_start_date < network_end_date:
batch_end_date = batch_start_date + batch_size
interval = [batch_start_date, batch_end_date]
print(f"Batch: {interval}")
######################################## cleanup #######################################
# clean insert tables
con.execute("DELETE FROM Person") # INS1
con.execute("DELETE FROM Person_hasInterest_Tag")
con.execute("DELETE FROM Person_studyAt_University")
con.execute("DELETE FROM Person_workAt_Company")
con.execute("DELETE FROM Person_likes_Post") # INS2
con.execute("DELETE FROM Person_likes_Comment") # INS3
con.execute("DELETE FROM Forum") # INS4
con.execute("DELETE FROM Forum_hasTag_Tag")
con.execute("DELETE FROM Forum_hasMember_Person") # INS5
con.execute("DELETE FROM Post") # INS6
con.execute("DELETE FROM Post_hasTag_Tag")
con.execute("DELETE FROM Comment") # INS7
con.execute("DELETE FROM Comment_hasTag_Tag") # INS8
con.execute("DELETE FROM Person_knows_Person")
# clean delete tables
con.execute("DELETE FROM Person_Delete_candidates") # DEL1
con.execute("DELETE FROM Person_likes_Post_Delete_candidates") # DEL2
con.execute("DELETE FROM Person_likes_Comment_Delete_candidates") # DEL3
con.execute("DELETE FROM Forum_Delete_candidates") # DEL4
con.execute("DELETE FROM Forum_hasMember_Person_Delete_candidates") # DEL5
con.execute("DELETE FROM Post_Delete_candidates") # DEL6
con.execute("DELETE FROM Comment_Delete_candidates") # DEL7
con.execute("DELETE FROM Person_knows_Person_Delete_candidates") # DEL8
######################################## inserts #######################################
insertion_params = [batch_start_date, batch_end_date, batch_end_date]
# INS1
con.execute("""
INSERT INTO Person
SELECT creationDate, id, firstName, lastName, gender, birthday, locationIP, browserUsed, isLocatedIn_City, speaks, email
FROM Raw_Person
WHERE creationDate >= ?
AND creationDate < ?
AND deletionDate >= ?
""",
insertion_params)
con.execute("""
INSERT INTO Person_hasInterest_Tag
SELECT creationDate, id, hasInterest_Tag
FROM Raw_Person_hasInterest_Tag
WHERE creationDate >= ?
AND creationDate < ?
AND deletionDate >= ?
""",
insertion_params)
con.execute("""
INSERT INTO Person_studyAt_University
SELECT creationDate, id, studyAt_University, classYear
FROM Raw_Person_studyAt_University
WHERE creationDate >= ?
AND creationDate < ?
AND deletionDate >= ?
""",
insertion_params)
con.execute("""
INSERT INTO Person_workAt_Company
SELECT creationDate, id, workAt_Company, workFrom
FROM Raw_Person_workAt_Company
WHERE creationDate >= ?
AND creationDate < ?
AND deletionDate >= ?
""",
insertion_params)
# INS2
con.execute("""
INSERT INTO Person_likes_Post
SELECT creationDate, id, likes_Post
FROM Raw_Person_likes_Post
WHERE creationDate >= ?
AND creationDate < ?
AND deletionDate >= ?
""",
insertion_params)
# INS3
con.execute("""
INSERT INTO Person_likes_Comment
SELECT creationDate, id, likes_Comment
FROM Raw_Person_likes_Comment
WHERE creationDate >= ?
AND creationDate < ?
AND deletionDate >= ?
""",
insertion_params)
# INS4
con.execute("""
INSERT INTO Forum
SELECT creationDate, id, title, hasModerator_Person
FROM Raw_Forum
WHERE creationDate >= ?
AND creationDate < ?
AND deletionDate >= ?
""",
insertion_params)
con.execute("""
INSERT INTO Forum_hasTag_Tag
SELECT creationDate, id, hasTag_Tag
FROM Raw_Forum_hasTag_Tag
WHERE creationDate >= ?
AND creationDate < ?
AND deletionDate >= ?
""",
insertion_params)
# INS5
con.execute("""
INSERT INTO Forum_hasMember_Person
SELECT creationDate, id, hasMember_Person
FROM Raw_Forum_hasMember_Person
WHERE creationDate >= ?
AND creationDate < ?
AND deletionDate >= ?
""",
insertion_params)
# INS6
con.execute("""
INSERT INTO Post
SELECT creationDate, id, imageFile, locationIP, browserUsed, language, content, length, hasCreator_Person, Forum_containerOf, isLocatedIn_Country
FROM Raw_Post
WHERE creationDate >= ?
AND creationDate < ?
AND deletionDate >= ?
""",
insertion_params)
con.execute("""
INSERT INTO Post_hasTag_Tag
SELECT creationDate, id, hasTag_Tag
FROM Raw_Post_hasTag_Tag
WHERE creationDate >= ?
AND creationDate < ?
AND deletionDate >= ?
""",
insertion_params)
# INS7
con.execute("""
INSERT INTO Comment
SELECT creationDate, id, locationIP, browserUsed, content, length, hasCreator_Person, isLocatedIn_Country, replyOf_Post, replyOf_Comment
FROM Raw_Comment
WHERE creationDate >= ?
AND creationDate < ?
AND deletionDate >= ?
""",
insertion_params)
con.execute("""
INSERT INTO Comment_hasTag_Tag
SELECT creationDate, id, hasTag_Tag
FROM Raw_Comment_hasTag_Tag
WHERE creationDate >= ?
AND creationDate < ?
AND deletionDate >= ?
""",
insertion_params)
# INS8
con.execute("""
INSERT INTO Person_knows_Person
SELECT creationDate, Person1id, Person2id
FROM Raw_Person_knows_Person
WHERE creationDate >= ?
AND creationDate < ?
AND deletionDate >= ?
""",
insertion_params)
######################################## deletes #######################################
deletion_params = [batch_start_date, batch_start_date, batch_end_date]
# DEL1 (Persons are always explicitly deleted)
con.execute("""
INSERT INTO Person_Delete_candidates
SELECT deletionDate, id
FROM Raw_Person
WHERE creationDate < ?
AND deletionDate >= ?
AND deletionDate < ?
""",
deletion_params)
# DEL2
con.execute("""
INSERT INTO Person_likes_Post_Delete_candidates
SELECT deletionDate, id, likes_Post
FROM Raw_Person_likes_Post
WHERE explicitlyDeleted
AND creationDate < ?
AND deletionDate >= ?
AND deletionDate < ?
""",
deletion_params)
# DEL3
con.execute("""
INSERT INTO Person_likes_Comment_Delete_candidates
SELECT deletionDate, id, likes_Comment
FROM Raw_Person_likes_Comment
WHERE explicitlyDeleted
AND creationDate < ?
AND deletionDate >= ?
AND deletionDate < ?
""",
deletion_params)
# DEL4 (Forums are always explicitly deleted -- TODO: check in generated data for walls/albums/groups)
con.execute("""
INSERT INTO Forum_Delete_candidates
SELECT deletionDate, id
FROM Raw_Forum
WHERE creationDate < ?
AND deletionDate >= ?
AND deletionDate < ?
""",
deletion_params)
# DEL5
con.execute("""
INSERT INTO Forum_hasMember_Person_Delete_candidates
SELECT deletionDate, id, hasMember_Person
FROM Raw_Forum_hasMember_Person
WHERE explicitlyDeleted
AND creationDate < ?
AND deletionDate >= ?
AND deletionDate < ?
""",
deletion_params)
# DEL6
con.execute("""
INSERT INTO Post_Delete_candidates
SELECT deletionDate, id
FROM Raw_Post
WHERE explicitlyDeleted
AND creationDate < ?
AND deletionDate >= ?
AND deletionDate < ?
""",
deletion_params)
# DEL7
con.execute("""
INSERT INTO Comment_Delete_candidates
SELECT deletionDate, id
FROM Raw_Comment
WHERE explicitlyDeleted
AND creationDate < ?
AND deletionDate >= ?
AND deletionDate < ?
""",
deletion_params)
# DEL8
con.execute("""
INSERT INTO Person_knows_Person_Delete_candidates
SELECT deletionDate, Person1id, Person2id
FROM Raw_Person_knows_Person
WHERE explicitlyDeleted
AND creationDate < ?
AND deletionDate >= ?
AND deletionDate < ?
""",
deletion_params)
######################################## export ########################################
batch_dir = f"batches/{batch_start_date}"
os.mkdir(f"{batch_dir}")
os.mkdir(f"{batch_dir}/inserts")
os.mkdir(f"{batch_dir}/deletes")
# inserts
con.execute(f"COPY (SELECT strftime(creationDate, '%Y-%m-%dT%H:%M:%S.%g+00:00') AS creationDate, id, firstName, lastName, gender, birthday, locationIP, browserUsed, isLocatedIn_City, speaks, email FROM Person) TO 'batches/{batch_start_date}/inserts/Person.csv' (HEADER, FORMAT CSV, DELIMITER '|')") #INS1
con.execute(f"COPY (SELECT strftime(creationDate, '%Y-%m-%dT%H:%M:%S.%g+00:00') AS creationDate, id, hasInterest_Tag FROM Person_hasInterest_Tag) TO 'batches/{batch_start_date}/inserts/Person_hasInterest_Tag.csv' (HEADER, FORMAT CSV, DELIMITER '|')")
con.execute(f"COPY (SELECT strftime(creationDate, '%Y-%m-%dT%H:%M:%S.%g+00:00') AS creationDate, id, studyAt_University, classYear FROM Person_studyAt_University) TO 'batches/{batch_start_date}/inserts/Person_studyAt_University.csv' (HEADER, FORMAT CSV, DELIMITER '|')")
con.execute(f"COPY (SELECT strftime(creationDate, '%Y-%m-%dT%H:%M:%S.%g+00:00') AS creationDate, id, workAt_Company, workFrom FROM Person_workAt_Company) TO 'batches/{batch_start_date}/inserts/Person_workAt_Company.csv' (HEADER, FORMAT CSV, DELIMITER '|')")
con.execute(f"COPY (SELECT strftime(creationDate, '%Y-%m-%dT%H:%M:%S.%g+00:00') AS creationDate, id, likes_Post FROM Person_likes_Post) TO 'batches/{batch_start_date}/inserts/Person_likes_Post.csv' (HEADER, FORMAT CSV, DELIMITER '|')") #INS2
con.execute(f"COPY (SELECT strftime(creationDate, '%Y-%m-%dT%H:%M:%S.%g+00:00') AS creationDate, id, likes_Comment FROM Person_likes_Comment) TO 'batches/{batch_start_date}/inserts/Person_likes_Comment.csv' (HEADER, FORMAT CSV, DELIMITER '|')") #INS3
con.execute(f"COPY (SELECT strftime(creationDate, '%Y-%m-%dT%H:%M:%S.%g+00:00') AS creationDate, id, title, hasModerator_Person FROM Forum) TO 'batches/{batch_start_date}/inserts/Forum.csv' (HEADER, FORMAT CSV, DELIMITER '|')") #INS4
con.execute(f"COPY (SELECT strftime(creationDate, '%Y-%m-%dT%H:%M:%S.%g+00:00') AS creationDate, id, hasTag_Tag FROM Forum_hasTag_Tag) TO 'batches/{batch_start_date}/inserts/Forum_hasTag_Tag.csv' (HEADER, FORMAT CSV, DELIMITER '|')")
con.execute(f"COPY (SELECT strftime(creationDate, '%Y-%m-%dT%H:%M:%S.%g+00:00') AS creationDate, id, hasMember_Person FROM Forum_hasMember_Person) TO 'batches/{batch_start_date}/inserts/Forum_hasMember_Person.csv' (HEADER, FORMAT CSV, DELIMITER '|')") #INS5
con.execute(f"COPY (SELECT strftime(creationDate, '%Y-%m-%dT%H:%M:%S.%g+00:00') AS creationDate, id, imageFile, locationIP, browserUsed, language, content, length, hasCreator_Person, Forum_containerOf, isLocatedIn_Country FROM Post) TO 'batches/{batch_start_date}/inserts/Post.csv' (HEADER, FORMAT CSV, DELIMITER '|')") #INS6
con.execute(f"COPY (SELECT strftime(creationDate, '%Y-%m-%dT%H:%M:%S.%g+00:00') AS creationDate, id, hasTag_Tag FROM Post_hasTag_Tag) TO 'batches/{batch_start_date}/inserts/Post_hasTag_Tag.csv' (HEADER, FORMAT CSV, DELIMITER '|')")
con.execute(f"COPY (SELECT strftime(creationDate, '%Y-%m-%dT%H:%M:%S.%g+00:00') AS creationDate, id, locationIP, browserUsed, content, length, hasCreator_Person, isLocatedIn_Country, replyOf_Post, replyOf_Comment FROM Comment) TO 'batches/{batch_start_date}/inserts/Comment.csv' (HEADER, FORMAT CSV, DELIMITER '|')") #INS7
con.execute(f"COPY (SELECT strftime(creationDate, '%Y-%m-%dT%H:%M:%S.%g+00:00') AS creationDate, id, hasTag_Tag FROM Comment_hasTag_Tag) TO 'batches/{batch_start_date}/inserts/Comment_hasTag_Tag.csv' (HEADER, FORMAT CSV, DELIMITER '|')")
con.execute(f"COPY (SELECT strftime(creationDate, '%Y-%m-%dT%H:%M:%S.%g+00:00') AS creationDate, Person1id, Person2id FROM Person_knows_Person) TO 'batches/{batch_start_date}/inserts/Person_knows_Person.csv' (HEADER, FORMAT CSV, DELIMITER '|')") #INS8
# deletes
con.execute(f"COPY (SELECT strftime(deletionDate, '%Y-%m-%dT%H:%M:%S.%g+00:00') AS deletionDate, id FROM Person_Delete_candidates) TO 'batches/{batch_start_date}/deletes/Person.csv' (HEADER, FORMAT CSV, DELIMITER '|')") #DEL1
con.execute(f"COPY (SELECT strftime(deletionDate, '%Y-%m-%dT%H:%M:%S.%g+00:00') AS deletionDate, src, trg FROM Person_likes_Post_Delete_candidates) TO 'batches/{batch_start_date}/deletes/Person_likes_Post.csv' (HEADER, FORMAT CSV, DELIMITER '|')") #DEL2
con.execute(f"COPY (SELECT strftime(deletionDate, '%Y-%m-%dT%H:%M:%S.%g+00:00') AS deletionDate, src, trg FROM Person_likes_Comment_Delete_candidates) TO 'batches/{batch_start_date}/deletes/Person_likes_Comment.csv' (HEADER, FORMAT CSV, DELIMITER '|')") #DEL3
con.execute(f"COPY (SELECT strftime(deletionDate, '%Y-%m-%dT%H:%M:%S.%g+00:00') AS deletionDate, id FROM Forum_Delete_candidates) TO 'batches/{batch_start_date}/deletes/Forum.csv' (HEADER, FORMAT CSV, DELIMITER '|')") #DEL4
con.execute(f"COPY (SELECT strftime(deletionDate, '%Y-%m-%dT%H:%M:%S.%g+00:00') AS deletionDate, src, trg FROM Forum_hasMember_Person_Delete_candidates) TO 'batches/{batch_start_date}/deletes/Forum_hasMember_Person.csv' (HEADER, FORMAT CSV, DELIMITER '|')") #DEL5
con.execute(f"COPY (SELECT strftime(deletionDate, '%Y-%m-%dT%H:%M:%S.%g+00:00') AS deletionDate, id FROM Post_Delete_candidates) TO 'batches/{batch_start_date}/deletes/Post.csv' (HEADER, FORMAT CSV, DELIMITER '|')") #DEL6
con.execute(f"COPY (SELECT strftime(deletionDate, '%Y-%m-%dT%H:%M:%S.%g+00:00') AS deletionDate, id FROM Comment_Delete_candidates) TO 'batches/{batch_start_date}/deletes/Comment.csv' (HEADER, FORMAT CSV, DELIMITER '|')") #DEL7
con.execute(f"COPY (SELECT strftime(deletionDate, '%Y-%m-%dT%H:%M:%S.%g+00:00') AS deletionDate, src, trg FROM Person_knows_Person_Delete_candidates) TO 'batches/{batch_start_date}/deletes/Person_knows_Person.csv' (HEADER, FORMAT CSV, DELIMITER '|')") #DEL8
############################# set interval for next iteration ##########################
batch_start_date = batch_end_date
| 50.619048 | 350 | 0.596954 |
06e980c4f9e4c64a9ff9ed3aae6e787174a3969c | 193 | py | Python | College grade 2/Python 3/Lab_2/Ansel/exercise 1.py | SimonH19009/Lzu_Data-science | bd35c5e156b0db21c3585c11dce15fba0b7003e2 | [
"MIT"
] | 1 | 2022-03-06T05:30:44.000Z | 2022-03-06T05:30:44.000Z | College grade 2/Python 3/Lab_2/Ansel/exercise 1.py | SimonH19009/Lzu_Data-science | bd35c5e156b0db21c3585c11dce15fba0b7003e2 | [
"MIT"
] | null | null | null | College grade 2/Python 3/Lab_2/Ansel/exercise 1.py | SimonH19009/Lzu_Data-science | bd35c5e156b0db21c3585c11dce15fba0b7003e2 | [
"MIT"
] | 1 | 2022-03-06T06:07:40.000Z | 2022-03-06T06:07:40.000Z | try:
a=int(input("Please enter a number:"))
if 9<a<100:
a = str(a)
print(a[0])
print(a[1])
else:
print("no correct")
except:
print("no correct")
| 17.545455 | 42 | 0.487047 |
06eb8e9951d11356b784f19e2310c1cacd28857e | 122 | py | Python | gameLogic/__init__.py | JoelEager/pyTanks-Server | 4455c0475980ea1372f11d7ec41d95990b3c8f44 | [
"MIT"
] | 5 | 2017-09-04T09:36:40.000Z | 2019-10-31T20:52:13.000Z | gameLogic/__init__.py | JoelEager/pyTanks-Server | 4455c0475980ea1372f11d7ec41d95990b3c8f44 | [
"MIT"
] | 3 | 2018-12-28T08:09:51.000Z | 2019-01-04T20:36:47.000Z | gameLogic/__init__.py | JoelEager/pyTanks-Server | 4455c0475980ea1372f11d7ec41d95990b3c8f44 | [
"MIT"
] | 1 | 2018-12-28T08:12:36.000Z | 2018-12-28T08:12:36.000Z | """
Contains all the logic for running the game (game clock, per-frame logic, game state update generation, and so on)
""" | 40.666667 | 114 | 0.737705 |
06f25df0cac2fd1a3b4a6fa7c42cbde635e84014 | 1,131 | py | Python | hypothesis/nn/neuromodulation/base.py | boyali/hypothesis-sre | f44d25eb281d49663d49d134ee73ad542849714b | [
"BSD-3-Clause"
] | 45 | 2019-02-13T14:16:35.000Z | 2022-02-23T21:30:02.000Z | hypothesis/nn/neuromodulation/base.py | boyali/hypothesis-sre | f44d25eb281d49663d49d134ee73ad542849714b | [
"BSD-3-Clause"
] | 1 | 2020-01-13T08:29:50.000Z | 2020-01-22T10:28:02.000Z | hypothesis/nn/neuromodulation/base.py | boyali/hypothesis-sre | f44d25eb281d49663d49d134ee73ad542849714b | [
"BSD-3-Clause"
] | 8 | 2019-04-23T14:25:08.000Z | 2021-07-28T15:05:31.000Z | import hypothesis
import torch
from hypothesis.nn.util import list_modules_with_type
| 26.928571 | 87 | 0.715296 |
06f368aa1460565e63ed4a80b862ae97a70212cf | 1,151 | py | Python | 2.py | zweed4u/dailycodingproblem | 6e40eaad347e283f86a11adeff01c6426211a0be | [
"MIT"
] | null | null | null | 2.py | zweed4u/dailycodingproblem | 6e40eaad347e283f86a11adeff01c6426211a0be | [
"MIT"
] | null | null | null | 2.py | zweed4u/dailycodingproblem | 6e40eaad347e283f86a11adeff01c6426211a0be | [
"MIT"
] | null | null | null | #!/usr/bin/python3
"""
Good morning! Here's your coding interview problem for today.
This problem was asked by Uber.
Given an array of integers, return a new array such that each element at index i of the new array is the product of all the numbers in the original array except the one at i.
For example, if our input was [1, 2, 3, 4, 5], the expected output would be [120, 60, 40, 30, 24]. If our input was [3, 2, 1], the expected output would be [2, 3, 6].
Follow-up: what if you can't use division?
"""
print(division_func([1,2,3,4,5]))
print(division_func([3, 2, 1]))
print(func([1,2,3,4,5]))
print(func([3, 2, 1]))
| 28.775 | 174 | 0.636838 |
06fbcffd6a9d16ac26a727dde7c14ddf62ce182c | 3,685 | py | Python | ldap_login/management/commands/importLDAPusers.py | knightsamar/change_agent | 53e8b6335f5f6856ae12dba6a509e4147c68a004 | [
"Apache-2.0"
] | null | null | null | ldap_login/management/commands/importLDAPusers.py | knightsamar/change_agent | 53e8b6335f5f6856ae12dba6a509e4147c68a004 | [
"Apache-2.0"
] | null | null | null | ldap_login/management/commands/importLDAPusers.py | knightsamar/change_agent | 53e8b6335f5f6856ae12dba6a509e4147c68a004 | [
"Apache-2.0"
] | null | null | null | from django.core.management.base import NoArgsCommand, CommandError;
from ldap_login.ldapUtils import ldapManager;
from ldap_login.models import user,group,Role;
from datetime import datetime;
import traceback;
| 46.64557 | 131 | 0.536228 |
06fe538ead84c59a5f996694a885042a2b6cbe93 | 822 | py | Python | rocketchat_API/APISections/subscriptions.py | dudanogueira/rocketchat_API | 190952f07ce04a356c79ad29e9d1e01dea620ba6 | [
"MIT"
] | 210 | 2017-03-20T13:36:24.000Z | 2022-03-30T17:37:02.000Z | rocketchat_API/APISections/subscriptions.py | dudanogueira/rocketchat_API | 190952f07ce04a356c79ad29e9d1e01dea620ba6 | [
"MIT"
] | 144 | 2017-03-21T13:50:22.000Z | 2022-03-28T09:43:26.000Z | rocketchat_API/APISections/subscriptions.py | dudanogueira/rocketchat_API | 190952f07ce04a356c79ad29e9d1e01dea620ba6 | [
"MIT"
] | 103 | 2017-03-20T13:54:54.000Z | 2022-03-22T05:00:18.000Z | from rocketchat_API.APISections.base import RocketChatBase
| 41.1 | 88 | 0.708029 |
06fefeedf2e0b5eb6fdb9935285ddf733447d30c | 1,081 | py | Python | accounts/models.py | Phist0ne/webvirtcloud | d94ca38e5c8b5bb1323d33067ee6b991775cc390 | [
"Apache-2.0"
] | 2 | 2018-03-14T09:46:49.000Z | 2019-05-14T11:45:14.000Z | accounts/models.py | JamesLinus/webvirtcloud | d94ca38e5c8b5bb1323d33067ee6b991775cc390 | [
"Apache-2.0"
] | 1 | 2018-03-01T04:05:25.000Z | 2018-10-01T08:30:00.000Z | accounts/models.py | caicloud/webvirtcloud | d94ca38e5c8b5bb1323d33067ee6b991775cc390 | [
"Apache-2.0"
] | 1 | 2019-06-11T19:54:08.000Z | 2019-06-11T19:54:08.000Z | from django.db import models
from django.contrib.auth.models import User
from instances.models import Instance
| 30.885714 | 63 | 0.747456 |
6600b5f654aa204f83a56f1407440180c9d9fa01 | 8,744 | py | Python | sdk/python/pulumi_cloudflare/custom_ssl.py | vijayraavi/pulumi-cloudflare | ba61514d650fc7a5a2667147519de213c07344b8 | [
"ECL-2.0",
"Apache-2.0"
] | null | null | null | sdk/python/pulumi_cloudflare/custom_ssl.py | vijayraavi/pulumi-cloudflare | ba61514d650fc7a5a2667147519de213c07344b8 | [
"ECL-2.0",
"Apache-2.0"
] | null | null | null | sdk/python/pulumi_cloudflare/custom_ssl.py | vijayraavi/pulumi-cloudflare | ba61514d650fc7a5a2667147519de213c07344b8 | [
"ECL-2.0",
"Apache-2.0"
] | null | null | null | # coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import json
import warnings
import pulumi
import pulumi.runtime
from typing import Union
from . import utilities, tables
| 60.722222 | 440 | 0.688358 |
66012301064b5709a93a6fcc63e250bae442c6d6 | 1,804 | py | Python | main.py | PortosKo/Python_Lesson_2 | 160c569f17d21cc1f2e48227b526a49594e90d59 | [
"MIT"
] | null | null | null | main.py | PortosKo/Python_Lesson_2 | 160c569f17d21cc1f2e48227b526a49594e90d59 | [
"MIT"
] | null | null | null | main.py | PortosKo/Python_Lesson_2 | 160c569f17d21cc1f2e48227b526a49594e90d59 | [
"MIT"
] | null | null | null | # ------
#----------------------------------------
'''
# 1
, .
'''
print ('1')
x = 0
for x in range (1,6,):
print (x,0)
'''
2
10 . 5.
'''
print ('2')
x = 98535254155
count = 0
while (x // 10) > 0:
if x % 10 == 5:
count += 1
x = x // 10
print(count)
'''
3
1 100. .
'''
print ('3')
sum = 0
for i in range(1,101):
sum+=i
print(sum)
'''
4
1 10. .
'''
print ('4')
for x in range(1,10):
x += x
print(x)
'''
5
# .
'''
print('5')
integer_number = 2129
print(integer_number%10,integer_number//10)
while integer_number>0:
print(integer_number%10)
integer_number = integer_number//10
'''
6
.
'''
print('6')
num = int(input(' :'))
sum = 0
while num:
sum = sum + num % 10
num = num // 10
print(' =', sum)
'''
7
.
'''
print('7')
num = int(input(' :'))
mult = 1
while num:
mult = mult * (num % 10)
num = num // 10
print(' =', mult)
'''
8
: 5?
'''
integer_number = 213413
while integer_number>0:
if integer_number%10 == 5:
print('Yes')
break
integer_number = integer_number//10
else: print('No')
'''
9
'''
'''
10
5
''' | 15.288136 | 92 | 0.636364 |
660235efa168bc77f41850e9696ac1ce83979716 | 5,062 | py | Python | config_loader/loader.py | egabancho/config-loader | af45c7bef3afe4dee930754386a1763a28574d6c | [
"MIT"
] | null | null | null | config_loader/loader.py | egabancho/config-loader | af45c7bef3afe4dee930754386a1763a28574d6c | [
"MIT"
] | null | null | null | config_loader/loader.py | egabancho/config-loader | af45c7bef3afe4dee930754386a1763a28574d6c | [
"MIT"
] | null | null | null | """Configuration loader class."""
import ast
import logging
import os
import types
from operator import attrgetter
import pkg_resources
logger = logging.getLogger(__name__)
| 32.87013 | 79 | 0.633544 |
6603d02ce7ae8ff687520b5fd3b5f402880ef876 | 1,616 | py | Python | tginviter/storage/base_storage.py | cuamckuu/tg-inviter | 80b8d4664d1e2628b46ac1a6d58f8495c408d4b4 | [
"MIT"
] | 20 | 2020-08-24T19:11:38.000Z | 2022-03-17T19:24:50.000Z | tginviter/storage/base_storage.py | bequirky12/tg-inviter | 5cad1bc1afce101be03a2cee805931e77b7f6842 | [
"MIT"
] | null | null | null | tginviter/storage/base_storage.py | bequirky12/tg-inviter | 5cad1bc1afce101be03a2cee805931e77b7f6842 | [
"MIT"
] | 8 | 2021-02-05T11:51:21.000Z | 2022-03-22T08:48:44.000Z | import abc
from typing import AbstractSet
| 31.076923 | 76 | 0.631188 |
660466e84335250fcffeb29f786f70f54055b5a4 | 874 | py | Python | docker/run.py | lvxiaojie111/2019NCCCU- | 5b2d2b3a6e70e5b447ccaecd02fbead4763db82d | [
"Apache-2.0"
] | 3 | 2020-10-18T02:16:37.000Z | 2020-10-18T02:30:03.000Z | docker/run.py | lvxiaojie111/2019NCCCU- | 5b2d2b3a6e70e5b447ccaecd02fbead4763db82d | [
"Apache-2.0"
] | null | null | null | docker/run.py | lvxiaojie111/2019NCCCU- | 5b2d2b3a6e70e5b447ccaecd02fbead4763db82d | [
"Apache-2.0"
] | null | null | null | #! /usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
import os
import pandas as pd
import testmodel
import imageio
if __name__ == "__main__":
#################### ######################
testpath = '/home/data/' #
result_folder_path = '/code/result/submission.csv' #
#################### ######################
# testpath = './test/' #
# result_folder_path = './result/submission.csv' #
print("reading start!")
pic_names = [str(x) + ".jpg" for x in range(1, 5001)]
pics = [imageio.imread(testpath + pic_name) for pic_name in pic_names]
print("reading end!")
### dataframe
result = testmodel.model(testpath)
print(result)
# indexNone
result.to_csv(result_folder_path, index=None)
###
| 32.37037 | 74 | 0.613272 |
6604fb1cbe01eb3c82eaa8f6e34b1bd3c2c37677 | 359 | py | Python | app/api/v1/__init__.py | Ethan-Ceng/Flask-CMS | 2fe2664e9ae60affe277e9c3b50c18a2e32e422b | [
"MIT"
] | null | null | null | app/api/v1/__init__.py | Ethan-Ceng/Flask-CMS | 2fe2664e9ae60affe277e9c3b50c18a2e32e422b | [
"MIT"
] | null | null | null | app/api/v1/__init__.py | Ethan-Ceng/Flask-CMS | 2fe2664e9ae60affe277e9c3b50c18a2e32e422b | [
"MIT"
] | null | null | null | from flask import Blueprint
from app.api.v1 import user, book, client, token
| 25.642857 | 50 | 0.707521 |
66055a1c87077054947e4816c06f4763187ad5d0 | 3,125 | py | Python | draugr/visualisation/seaborn_utilities/seaborn_enums.py | cnHeider/draugr | b95e0bb1fa5efa581bfb28ff604f296ed2e6b7d6 | [
"Apache-2.0"
] | 3 | 2019-09-27T08:04:59.000Z | 2020-12-02T06:14:45.000Z | draugr/visualisation/seaborn_utilities/seaborn_enums.py | cnHeider/draugr | b95e0bb1fa5efa581bfb28ff604f296ed2e6b7d6 | [
"Apache-2.0"
] | 64 | 2019-09-27T08:03:42.000Z | 2022-03-28T15:07:30.000Z | draugr/visualisation/seaborn_utilities/seaborn_enums.py | cnHeider/draugr | b95e0bb1fa5efa581bfb28ff604f296ed2e6b7d6 | [
"Apache-2.0"
] | 1 | 2020-10-01T00:18:57.000Z | 2020-10-01T00:18:57.000Z | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
__author__ = "Christian Heider Nielsen"
__doc__ = r"""
Created on 26-01-2021
"""
from enum import Enum
from typing import Tuple
import numpy
from matplotlib import patheffects, pyplot
__all__ = ["plot_median_labels", "show_values_on_bars"]
from draugr.visualisation.matplotlib_utilities.styles.annotation import (
semi_opaque_round_tight_bbox,
)
def plot_median_labels(
ax: pyplot.Axes,
*,
has_fliers: bool = False,
# text_size: int = 10,
# text_weight: str = "normal",
stroke_width: int = 0,
precision: int = 3,
color: str = "black",
edgecolor: str = "black", # also the stroke color
ha: str = "center",
va: str = "center", # bottom
bbox: Tuple = semi_opaque_round_tight_bbox,
) -> None:
""" """
lines = ax.get_lines()
# depending on fliers, toggle between 5 and 6 lines per box
lines_per_box = 5 + int(has_fliers)
# iterate directly over all median lines, with an interval of lines_per_box
# this enables labeling of grouped data without relying on tick positions
for median_line in lines[4 : len(lines) : lines_per_box]:
# get center of median line
mean_x = sum(median_line._x) / len(median_line._x)
mean_y = sum(median_line._y) / len(median_line._y)
text = ax.text(
mean_x,
mean_y,
f"{round(mean_y, precision)}",
ha=ha,
va=va,
# fontweight=text_weight,
# size=text_size,
color=color,
# edgecolor=edgecolor
bbox=bbox,
) # print text to center coordinates
if stroke_width:
# create small black border around white text
# for better readability on multi-colored boxes
text.set_path_effects(
[
patheffects.Stroke(linewidth=stroke_width, foreground=edgecolor),
patheffects.Normal(),
]
)
def show_values_on_bars(axs: pyplot.Axes, h_v: str = "v", space: float = 0.4) -> None:
""" """
if isinstance(axs, numpy.ndarray):
for idx, ax in numpy.ndenumerate(axs):
_show_on_single_plot(ax)
else:
_show_on_single_plot(axs)
| 28.935185 | 86 | 0.57632 |
66085420dcc9a5728829c81982cb5b8af048ece5 | 1,061 | py | Python | py/callback_dashboard.py | pnvnd/plotly | ede0bb0bb92484c2e3bf4e3631fa97f547e02c16 | [
"Unlicense"
] | null | null | null | py/callback_dashboard.py | pnvnd/plotly | ede0bb0bb92484c2e3bf4e3631fa97f547e02c16 | [
"Unlicense"
] | null | null | null | py/callback_dashboard.py | pnvnd/plotly | ede0bb0bb92484c2e3bf4e3631fa97f547e02c16 | [
"Unlicense"
] | 1 | 2022-01-22T17:19:25.000Z | 2022-01-22T17:19:25.000Z | from dash import dash, dcc, html
from dash.dependencies import Input, Output
import plotly.graph_objs as go
import pandas as pd
url = 'csv/covidtesting.csv'
df = pd.read_csv(url)
app = dash.Dash()
# list = df.columns[1:]
# filter_options = []
# for option in list:
# filter_options.append({'label': str(option), 'value': option})
app.layout = html.Div([
dcc.Graph(id='graphs'),
dcc.Dropdown(
id='option-picker',
options=[{"label": x, "value": x} for x in df.columns[1:]],
value=df.columns[1]
)
])
if __name__ == '__main__':
app.run_server() | 24.113636 | 89 | 0.597549 |
6608592b97fcda5eb8dc5fb8cb22369434750380 | 4,288 | py | Python | appface.py | iljoong/FaceTag | c43fce89c92ce6de2f397580d80aa834d3e2dbb6 | [
"MIT"
] | 2 | 2021-11-12T15:30:55.000Z | 2021-11-14T13:53:13.000Z | appface.py | iljoong/FaceTag | c43fce89c92ce6de2f397580d80aa834d3e2dbb6 | [
"MIT"
] | 1 | 2018-07-31T08:30:33.000Z | 2018-08-01T04:44:52.000Z | appface.py | iljoong/FaceTag | c43fce89c92ce6de2f397580d80aa834d3e2dbb6 | [
"MIT"
] | 1 | 2021-11-12T15:31:00.000Z | 2021-11-12T15:31:00.000Z | ###############################################################################################
from keras.models import Model, load_model
from PIL import Image
import numpy as np
import time
import cv2
import os
import logging
import pymongo
#import dlib
import requests
import appconfig
import json
cascade = cv2.CascadeClassifier('./face/haarcascade_frontalface_default.xml')
eyeCascade = cv2.CascadeClassifier('./face/haarcascade_eye.xml')
img_size = 200
labels = []
"""
hog_face_detector = dlib.get_frontal_face_detector()
def detectFaceHog(gray):
start_time = time.time()
rects = []
try:
rects = hog_face_detector(gray, 1)
faces = [ [rect.left(), rect.top(), rect.right()-rect.left(), rect.bottom()-rect.top()] for rect in rects ]
except Exception as e:
print(e)
return faces, time.time() - start_time
cnn_face_detector = dlib.cnn_face_detection_model_v1("../face/mmod_human_face_detector.dat")
def detectFaceCNN(gray):
start_time = time.time()
rects = []
try:
rects = cnn_face_detector(gray, 1)
faces = [ [rect.rect.left(), rect.rect.top(), rect.rect.right()-rect.rect.left(), rect.rect.bottom()-rect.rect.top()] for rect in rects ]
except Exception as e:
print(e)
return faces, time.time() - start_time
"""
| 28.586667 | 145 | 0.614039 |
6608e0e22e04d214ade1044f1d502d28fa22890a | 5,488 | py | Python | src/object2.py | vmlaker/sherlock | 4827803400907cb892b73f74736b151463fcda27 | [
"MIT"
] | 62 | 2015-01-01T16:26:36.000Z | 2022-03-07T11:38:57.000Z | src/object2.py | vmlaker/sherlock | 4827803400907cb892b73f74736b151463fcda27 | [
"MIT"
] | null | null | null | src/object2.py | vmlaker/sherlock | 4827803400907cb892b73f74736b151463fcda27 | [
"MIT"
] | 13 | 2015-08-01T10:54:13.000Z | 2020-12-05T02:19:23.000Z | """Object detection pipeline."""
import multiprocessing
import datetime
import time
import sys
import cv2
import numpy as np
import socket
import sharedmem
import mpipe
import coils
import util
DEVICE = int(sys.argv[1])
WIDTH = int(sys.argv[2])
HEIGHT = int(sys.argv[3])
DURATION = float(sys.argv[4]) # In seconds, or -(port#) if negative.
# Create a process-shared table keyed on timestamps
# and holding references to allocated image memory.
images = multiprocessing.Manager().dict()
# Monitor framerates for the given seconds past.
framerate = coils.RateTicker((2,))
cv2.namedWindow('object detection 2', cv2.cv.CV_WINDOW_NORMAL)
# Create the detector stages.
detector_stages = list()
for classi in util.cascade.classifiers:
detector_stages.append(
mpipe.Stage(
Detector, 1,
classifier=classi,
color=util.cascade.colors[classi]),
)
# Assemble the image processing pipeline:
#
# detector(s) viewer
# || ||
# filter_detector --> postproc --> filter_viewer
#
filter_detector = mpipe.FilterStage(
detector_stages,
max_tasks=1,
cache_results=True,
)
postproc = mpipe.Stage(Postprocessor)
filter_viewer = mpipe.FilterStage(
(mpipe.Stage(Viewer),),
max_tasks=2,
drop_results=True,
)
filter_detector.link(postproc)
postproc.link(filter_viewer)
pipe_iproc = mpipe.Pipeline(filter_detector)
# Create an auxiliary process (modeled as a one-task pipeline)
# that simply pulls results from the image processing pipeline,
# and deallocates associated shared memory after allowing
# the designated amount of time to pass.
pipe_dealloc = mpipe.Pipeline(mpipe.UnorderedStage(deallocate))
pipe_dealloc.put(datetime.timedelta(microseconds=1e6)) # Start it up right away.
# Create the OpenCV video capture object.
cap = cv2.VideoCapture(DEVICE)
cap.set(3, WIDTH)
cap.set(4, HEIGHT)
# Run the video capture loop, allocating shared memory
# and feeding the image processing pipeline.
# Run for configured duration, or (if duration < 0) until we
# connect to socket (duration re-interpreted as port number.)
now = datetime.datetime.now()
end = now + datetime.timedelta(seconds=abs(DURATION))
while end > now or DURATION < 0:
if DURATION < 0:
# Bail if we connect to socket.
try:
socket.socket().connect(('', int(abs(DURATION))))
print('stopping')
break
except:
pass
# Mark the timestamp. This is the index by which
# image procesing stages will access allocated memory.
now = datetime.datetime.now()
# Capture the image.
hello, image = cap.read()
# Allocate shared memory for a copy of the input image.
shape = np.shape(image)
dtype = image.dtype
image_in = sharedmem.empty(shape, dtype)
# Copy the input image to its shared memory version.
image_in[:] = image.copy()
# Add to the images table.
images[now] = image_in # Input image.
# Put the timestamp on the image processing pipeline.
pipe_iproc.put(now)
# Signal pipelines to stop, and wait for deallocator
# to free all memory.
pipe_iproc.put(None)
pipe_dealloc.put(None)
for result in pipe_dealloc.results():
pass
# The end.
| 29.347594 | 81 | 0.622085 |
6609a45086c57a15190dcecf91e35dfc73259aed | 78 | py | Python | answers/001/answer1.py | PyLadiesTokyo/pyladies-tokyo-homework | 3727451d073eb6e9b48f3cd0561c3b64b109f3a5 | [
"Apache-2.0"
] | null | null | null | answers/001/answer1.py | PyLadiesTokyo/pyladies-tokyo-homework | 3727451d073eb6e9b48f3cd0561c3b64b109f3a5 | [
"Apache-2.0"
] | null | null | null | answers/001/answer1.py | PyLadiesTokyo/pyladies-tokyo-homework | 3727451d073eb6e9b48f3cd0561c3b64b109f3a5 | [
"Apache-2.0"
] | 1 | 2020-12-27T13:05:44.000Z | 2020-12-27T13:05:44.000Z | # 110
for num in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]:
print(num) | 26 | 44 | 0.538462 |
660ac2d8937432f3e9a4b2a9a74404def921da17 | 132 | py | Python | lab2_typyOperacje/2.15.py | Damian9449/Python | dc9091e15356733821bbb6a768b7d5e428640340 | [
"MIT"
] | 1 | 2017-11-15T13:03:40.000Z | 2017-11-15T13:03:40.000Z | lab2_typyOperacje/2.15.py | Damian9449/Python | dc9091e15356733821bbb6a768b7d5e428640340 | [
"MIT"
] | null | null | null | lab2_typyOperacje/2.15.py | Damian9449/Python | dc9091e15356733821bbb6a768b7d5e428640340 | [
"MIT"
] | null | null | null | #!/usr/bin/python
L = [1, 3, 6, 77, 34, 45, 26, 81]
result = ""
for number in L:
result = result + str(number)
print(result)
| 13.2 | 33 | 0.575758 |
660bd601b31c2cdb55bcbc98f8dc987a833cfa02 | 2,743 | py | Python | af_scripts/tmp/blendShapeEditor.py | aaronfang/small-Scripts | 890b10ab19fa9cdf2415aaf2dc08b81cc64fc79d | [
"MIT"
] | 1 | 2018-03-08T16:34:00.000Z | 2018-03-08T16:34:00.000Z | af_scripts/tmp/blendShapeEditor.py | aaronfang/personal_scripts | 890b10ab19fa9cdf2415aaf2dc08b81cc64fc79d | [
"MIT"
] | null | null | null | af_scripts/tmp/blendShapeEditor.py | aaronfang/personal_scripts | 890b10ab19fa9cdf2415aaf2dc08b81cc64fc79d | [
"MIT"
] | null | null | null | import maya.cmds as cmds
blendShapeEditor()._UI()
| 49.872727 | 133 | 0.629603 |
660c4602652eeabae6b9b5b5f22abc8c78653e52 | 134 | py | Python | bugtests/test000.py | doom38/jython_v2.2.1 | 0803a0c953c294e6d14f9fc7d08edf6a3e630a15 | [
"CNRI-Jython"
] | null | null | null | bugtests/test000.py | doom38/jython_v2.2.1 | 0803a0c953c294e6d14f9fc7d08edf6a3e630a15 | [
"CNRI-Jython"
] | null | null | null | bugtests/test000.py | doom38/jython_v2.2.1 | 0803a0c953c294e6d14f9fc7d08edf6a3e630a15 | [
"CNRI-Jython"
] | null | null | null | """
Basic test, just raises a TestWarning
"""
import support
raise support.TestWarning('A test of TestWarning. It is not an error')
| 16.75 | 70 | 0.738806 |
660dfb4d814fae5a38c519d53465f866ae0f301e | 5,511 | py | Python | model.py | braemt/attentive-multi-task-deep-reinforcement-learning | 921feefce98076f88c892f0b7e6db8572f596763 | [
"MIT"
] | 12 | 2019-04-07T02:04:48.000Z | 2022-03-22T12:57:47.000Z | model.py | braemt/attentive-multi-task-deep-reinforcement-learning | 921feefce98076f88c892f0b7e6db8572f596763 | [
"MIT"
] | null | null | null | model.py | braemt/attentive-multi-task-deep-reinforcement-learning | 921feefce98076f88c892f0b7e6db8572f596763 | [
"MIT"
] | 7 | 2019-04-07T02:04:49.000Z | 2020-12-28T10:30:27.000Z | import numpy as np
import tensorflow as tf
| 46.310924 | 119 | 0.577209 |
660ea1a100d2f6604cf5b576d0992caedb50b349 | 632 | py | Python | src/data/852.py | NULLCT/LOMC | 79a16474a8f21310e0fb47e536d527dd5dc6d655 | [
"MIT"
] | null | null | null | src/data/852.py | NULLCT/LOMC | 79a16474a8f21310e0fb47e536d527dd5dc6d655 | [
"MIT"
] | null | null | null | src/data/852.py | NULLCT/LOMC | 79a16474a8f21310e0fb47e536d527dd5dc6d655 | [
"MIT"
] | null | null | null | from collections import deque
n, q = map(int, input().split())
graph = [[] for _ in range(n + 1)]
for i in range(n - 1):
a, b = map(int, input().split())
graph[a].append(b)
graph[b].append(a)
CD = []
for i in range(q):
e, f = map(int, input().split())
CD.append([e, f])
dist = [-1] * (n + 1)
dist[0] = 0
dist[1] = 0
d = deque()
d.append(1)
while d:
v = d.popleft()
for i in graph[v]:
if dist[i] != -1:
continue
dist[i] = dist[v] + 1
d.append(i)
for i, j in CD:
x = dist[i] + dist[j]
if x % 2 == 0:
print("Town")
else:
print("Road")
| 16.631579 | 36 | 0.47943 |
6611e568c85da823794769b3aacdd4d28bc257c0 | 265,414 | py | Python | stashboard/contrib/status_images.py | l1kw1d/stashboard | 3e4b18a8168c102d1e1d7f88fec22bcbfc530d23 | [
"MIT"
] | 761 | 2015-01-01T05:28:26.000Z | 2022-03-24T10:07:31.000Z | stashboard/contrib/status_images.py | l1kw1d/stashboard | 3e4b18a8168c102d1e1d7f88fec22bcbfc530d23 | [
"MIT"
] | 11 | 2015-02-05T23:43:47.000Z | 2019-01-10T13:43:42.000Z | stashboard/contrib/status_images.py | l1kw1d/stashboard | 3e4b18a8168c102d1e1d7f88fec22bcbfc530d23 | [
"MIT"
] | 226 | 2015-01-05T15:04:45.000Z | 2022-03-23T11:41:00.000Z | # Copyright (c) 2010 Twilio Inc.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
images = [
{
"url": "/images/status/address-book--arrow.png",
"name": "address-book--arrow"
},
{
"url": "/images/status/address-book--exclamation.png",
"name": "address-book--exclamation"
},
{
"url": "/images/status/address-book--minus.png",
"name": "address-book--minus"
},
{
"url": "/images/status/address-book--pencil.png",
"name": "address-book--pencil"
},
{
"url": "/images/status/address-book--plus.png",
"name": "address-book--plus"
},
{
"url": "/images/status/address-book-blue.png",
"name": "address-book-blue"
},
{
"url": "/images/status/address-book-open.png",
"name": "address-book-open"
},
{
"url": "/images/status/address-book.png",
"name": "address-book"
},
{
"url": "/images/status/alarm-clock--arrow.png",
"name": "alarm-clock--arrow"
},
{
"url": "/images/status/alarm-clock--exclamation.png",
"name": "alarm-clock--exclamation"
},
{
"url": "/images/status/alarm-clock--minus.png",
"name": "alarm-clock--minus"
},
{
"url": "/images/status/alarm-clock--pencil.png",
"name": "alarm-clock--pencil"
},
{
"url": "/images/status/alarm-clock--plus.png",
"name": "alarm-clock--plus"
},
{
"url": "/images/status/alarm-clock-blue.png",
"name": "alarm-clock-blue"
},
{
"url": "/images/status/alarm-clock-select-remain.png",
"name": "alarm-clock-select-remain"
},
{
"url": "/images/status/alarm-clock-select.png",
"name": "alarm-clock-select"
},
{
"url": "/images/status/alarm-clock.png",
"name": "alarm-clock"
},
{
"url": "/images/status/anchor.png",
"name": "anchor"
},
{
"url": "/images/status/application--arrow.png",
"name": "application--arrow"
},
{
"url": "/images/status/application--exclamation.png",
"name": "application--exclamation"
},
{
"url": "/images/status/application--minus.png",
"name": "application--minus"
},
{
"url": "/images/status/application--pencil.png",
"name": "application--pencil"
},
{
"url": "/images/status/application--plus.png",
"name": "application--plus"
},
{
"url": "/images/status/application-block.png",
"name": "application-block"
},
{
"url": "/images/status/application-blog.png",
"name": "application-blog"
},
{
"url": "/images/status/application-blue.png",
"name": "application-blue"
},
{
"url": "/images/status/application-browser.png",
"name": "application-browser"
},
{
"url": "/images/status/application-detail.png",
"name": "application-detail"
},
{
"url": "/images/status/application-dialog.png",
"name": "application-dialog"
},
{
"url": "/images/status/application-dock-090.png",
"name": "application-dock-090"
},
{
"url": "/images/status/application-dock-180.png",
"name": "application-dock-180"
},
{
"url": "/images/status/application-dock-270.png",
"name": "application-dock-270"
},
{
"url": "/images/status/application-dock-tab.png",
"name": "application-dock-tab"
},
{
"url": "/images/status/application-dock.png",
"name": "application-dock"
},
{
"url": "/images/status/application-document.png",
"name": "application-document"
},
{
"url": "/images/status/application-documents.png",
"name": "application-documents"
},
{
"url": "/images/status/application-export.png",
"name": "application-export"
},
{
"url": "/images/status/application-form.png",
"name": "application-form"
},
{
"url": "/images/status/application-home.png",
"name": "application-home"
},
{
"url": "/images/status/application-icon.png",
"name": "application-icon"
},
{
"url": "/images/status/application-image.png",
"name": "application-image"
},
{
"url": "/images/status/application-import.png",
"name": "application-import"
},
{
"url": "/images/status/application-list.png",
"name": "application-list"
},
{
"url": "/images/status/application-monitor.png",
"name": "application-monitor"
},
{
"url": "/images/status/application-network.png",
"name": "application-network"
},
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{
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{
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},
{
"url": "/images/status/ui-scroll-pane-horizontal.png",
"name": "ui-scroll-pane-horizontal"
},
{
"url": "/images/status/ui-scroll-pane-icon.png",
"name": "ui-scroll-pane-icon"
},
{
"url": "/images/status/ui-scroll-pane-image.png",
"name": "ui-scroll-pane-image"
},
{
"url": "/images/status/ui-scroll-pane-list.png",
"name": "ui-scroll-pane-list"
},
{
"url": "/images/status/ui-scroll-pane-table.png",
"name": "ui-scroll-pane-table"
},
{
"url": "/images/status/ui-scroll-pane-text-image.png",
"name": "ui-scroll-pane-text-image"
},
{
"url": "/images/status/ui-scroll-pane-text.png",
"name": "ui-scroll-pane-text"
},
{
"url": "/images/status/ui-scroll-pane-tree.png",
"name": "ui-scroll-pane-tree"
},
{
"url": "/images/status/ui-scroll-pane.png",
"name": "ui-scroll-pane"
},
{
"url": "/images/status/ui-search-field.png",
"name": "ui-search-field"
},
{
"url": "/images/status/ui-slider-050.png",
"name": "ui-slider-050"
},
{
"url": "/images/status/ui-slider-100.png",
"name": "ui-slider-100"
},
{
"url": "/images/status/ui-slider-vertical-050.png",
"name": "ui-slider-vertical-050"
},
{
"url": "/images/status/ui-slider-vertical-100.png",
"name": "ui-slider-vertical-100"
},
{
"url": "/images/status/ui-slider-vertical.png",
"name": "ui-slider-vertical"
},
{
"url": "/images/status/ui-slider.png",
"name": "ui-slider"
},
{
"url": "/images/status/ui-spin.png",
"name": "ui-spin"
},
{
"url": "/images/status/ui-splitter-horizontal.png",
"name": "ui-splitter-horizontal"
},
{
"url": "/images/status/ui-splitter.png",
"name": "ui-splitter"
},
{
"url": "/images/status/ui-status-bar-blue.png",
"name": "ui-status-bar-blue"
},
{
"url": "/images/status/ui-status-bar.png",
"name": "ui-status-bar"
},
{
"url": "/images/status/ui-tab--arrow.png",
"name": "ui-tab--arrow"
},
{
"url": "/images/status/ui-tab--exclamation.png",
"name": "ui-tab--exclamation"
},
{
"url": "/images/status/ui-tab--minus.png",
"name": "ui-tab--minus"
},
{
"url": "/images/status/ui-tab--pencil.png",
"name": "ui-tab--pencil"
},
{
"url": "/images/status/ui-tab--plus.png",
"name": "ui-tab--plus"
},
{
"url": "/images/status/ui-tab-bottom.png",
"name": "ui-tab-bottom"
},
{
"url": "/images/status/ui-tab-content.png",
"name": "ui-tab-content"
},
{
"url": "/images/status/ui-tab-side.png",
"name": "ui-tab-side"
},
{
"url": "/images/status/ui-tab.png",
"name": "ui-tab"
},
{
"url": "/images/status/ui-text-field-hidden.png",
"name": "ui-text-field-hidden"
},
{
"url": "/images/status/ui-text-field-password.png",
"name": "ui-text-field-password"
},
{
"url": "/images/status/ui-text-field-select.png",
"name": "ui-text-field-select"
},
{
"url": "/images/status/ui-text-field-suggestion.png",
"name": "ui-text-field-suggestion"
},
{
"url": "/images/status/ui-text-field.png",
"name": "ui-text-field"
},
{
"url": "/images/status/ui-toolbar--arrow.png",
"name": "ui-toolbar--arrow"
},
{
"url": "/images/status/ui-toolbar--exclamation.png",
"name": "ui-toolbar--exclamation"
},
{
"url": "/images/status/ui-toolbar--minus.png",
"name": "ui-toolbar--minus"
},
{
"url": "/images/status/ui-toolbar--pencil.png",
"name": "ui-toolbar--pencil"
},
{
"url": "/images/status/ui-toolbar--plus.png",
"name": "ui-toolbar--plus"
},
{
"url": "/images/status/ui-toolbar-bookmark.png",
"name": "ui-toolbar-bookmark"
},
{
"url": "/images/status/ui-toolbar.png",
"name": "ui-toolbar"
},
{
"url": "/images/status/ui-tooltip-balloon.png",
"name": "ui-tooltip-balloon"
},
{
"url": "/images/status/ui-tooltip.png",
"name": "ui-tooltip"
},
{
"url": "/images/status/umbrella--arrow.png",
"name": "umbrella--arrow"
},
{
"url": "/images/status/umbrella--exclamation.png",
"name": "umbrella--exclamation"
},
{
"url": "/images/status/umbrella--minus.png",
"name": "umbrella--minus"
},
{
"url": "/images/status/umbrella--pencil.png",
"name": "umbrella--pencil"
},
{
"url": "/images/status/umbrella--plus.png",
"name": "umbrella--plus"
},
{
"url": "/images/status/umbrella.png",
"name": "umbrella"
},
{
"url": "/images/status/universal.png",
"name": "universal"
},
{
"url": "/images/status/usb-flash-drive--arrow.png",
"name": "usb-flash-drive--arrow"
},
{
"url": "/images/status/usb-flash-drive--exclamation.png",
"name": "usb-flash-drive--exclamation"
},
{
"url": "/images/status/usb-flash-drive--minus.png",
"name": "usb-flash-drive--minus"
},
{
"url": "/images/status/usb-flash-drive--pencil.png",
"name": "usb-flash-drive--pencil"
},
{
"url": "/images/status/usb-flash-drive--plus.png",
"name": "usb-flash-drive--plus"
},
{
"url": "/images/status/usb-flash-drive.png",
"name": "usb-flash-drive"
},
{
"url": "/images/status/user--arrow.png",
"name": "user--arrow"
},
{
"url": "/images/status/user--exclamation.png",
"name": "user--exclamation"
},
{
"url": "/images/status/user--minus.png",
"name": "user--minus"
},
{
"url": "/images/status/user--pencil.png",
"name": "user--pencil"
},
{
"url": "/images/status/user--plus.png",
"name": "user--plus"
},
{
"url": "/images/status/user-black-female.png",
"name": "user-black-female"
},
{
"url": "/images/status/user-black.png",
"name": "user-black"
},
{
"url": "/images/status/user-business-boss.png",
"name": "user-business-boss"
},
{
"url": "/images/status/user-business-gray-boss.png",
"name": "user-business-gray-boss"
},
{
"url": "/images/status/user-business-gray.png",
"name": "user-business-gray"
},
{
"url": "/images/status/user-business.png",
"name": "user-business"
},
{
"url": "/images/status/user-detective-gray.png",
"name": "user-detective-gray"
},
{
"url": "/images/status/user-detective.png",
"name": "user-detective"
},
{
"url": "/images/status/user-female.png",
"name": "user-female"
},
{
"url": "/images/status/user-gray-female.png",
"name": "user-gray-female"
},
{
"url": "/images/status/user-gray.png",
"name": "user-gray"
},
{
"url": "/images/status/user-green-female.png",
"name": "user-green-female"
},
{
"url": "/images/status/user-green.png",
"name": "user-green"
},
{
"url": "/images/status/user-medium-female.png",
"name": "user-medium-female"
},
{
"url": "/images/status/user-medium.png",
"name": "user-medium"
},
{
"url": "/images/status/user-nude-female.png",
"name": "user-nude-female"
},
{
"url": "/images/status/user-nude.png",
"name": "user-nude"
},
{
"url": "/images/status/user-red-female.png",
"name": "user-red-female"
},
{
"url": "/images/status/user-red.png",
"name": "user-red"
},
{
"url": "/images/status/user-silhouette-question.png",
"name": "user-silhouette-question"
},
{
"url": "/images/status/user-silhouette.png",
"name": "user-silhouette"
},
{
"url": "/images/status/user-small-female.png",
"name": "user-small-female"
},
{
"url": "/images/status/user-small.png",
"name": "user-small"
},
{
"url": "/images/status/user-thief-baldie.png",
"name": "user-thief-baldie"
},
{
"url": "/images/status/user-thief-female.png",
"name": "user-thief-female"
},
{
"url": "/images/status/user-thief.png",
"name": "user-thief"
},
{
"url": "/images/status/user-white-female.png",
"name": "user-white-female"
},
{
"url": "/images/status/user-white.png",
"name": "user-white"
},
{
"url": "/images/status/user-worker-boss.png",
"name": "user-worker-boss"
},
{
"url": "/images/status/user-worker.png",
"name": "user-worker"
},
{
"url": "/images/status/user-yellow-female.png",
"name": "user-yellow-female"
},
{
"url": "/images/status/user-yellow.png",
"name": "user-yellow"
},
{
"url": "/images/status/user.png",
"name": "user"
},
{
"url": "/images/status/users.png",
"name": "users"
},
{
"url": "/images/status/validation-document.png",
"name": "validation-document"
},
{
"url": "/images/status/validation-invalid-document.png",
"name": "validation-invalid-document"
},
{
"url": "/images/status/validation-invalid.png",
"name": "validation-invalid"
},
{
"url": "/images/status/validation-label-html.png",
"name": "validation-label-html"
},
{
"url": "/images/status/validation-label.png",
"name": "validation-label"
},
{
"url": "/images/status/validation-valid-document.png",
"name": "validation-valid-document"
},
{
"url": "/images/status/validation-valid.png",
"name": "validation-valid"
},
{
"url": "/images/status/validation.png",
"name": "validation"
},
{
"url": "/images/status/vise-drawer.png",
"name": "vise-drawer"
},
{
"url": "/images/status/vise.png",
"name": "vise"
},
{
"url": "/images/status/wall--arrow.png",
"name": "wall--arrow"
},
{
"url": "/images/status/wall--exclamation.png",
"name": "wall--exclamation"
},
{
"url": "/images/status/wall--minus.png",
"name": "wall--minus"
},
{
"url": "/images/status/wall--pencil.png",
"name": "wall--pencil"
},
{
"url": "/images/status/wall--plus.png",
"name": "wall--plus"
},
{
"url": "/images/status/wall-break.png",
"name": "wall-break"
},
{
"url": "/images/status/wall-brick.png",
"name": "wall-brick"
},
{
"url": "/images/status/wall-small-brick.png",
"name": "wall-small-brick"
},
{
"url": "/images/status/wall-small.png",
"name": "wall-small"
},
{
"url": "/images/status/wall.png",
"name": "wall"
},
{
"url": "/images/status/wallet--arrow.png",
"name": "wallet--arrow"
},
{
"url": "/images/status/wallet--exclamation.png",
"name": "wallet--exclamation"
},
{
"url": "/images/status/wallet--minus.png",
"name": "wallet--minus"
},
{
"url": "/images/status/wallet--pencil.png",
"name": "wallet--pencil"
},
{
"url": "/images/status/wallet--plus.png",
"name": "wallet--plus"
},
{
"url": "/images/status/wallet.png",
"name": "wallet"
},
{
"url": "/images/status/wand--arrow.png",
"name": "wand--arrow"
},
{
"url": "/images/status/wand--exclamation.png",
"name": "wand--exclamation"
},
{
"url": "/images/status/wand--minus.png",
"name": "wand--minus"
},
{
"url": "/images/status/wand--pencil.png",
"name": "wand--pencil"
},
{
"url": "/images/status/wand--plus.png",
"name": "wand--plus"
},
{
"url": "/images/status/wand-hat.png",
"name": "wand-hat"
},
{
"url": "/images/status/wand-small.png",
"name": "wand-small"
},
{
"url": "/images/status/wand.png",
"name": "wand"
},
{
"url": "/images/status/water--arrow.png",
"name": "water--arrow"
},
{
"url": "/images/status/water--exclamation.png",
"name": "water--exclamation"
},
{
"url": "/images/status/water--minus.png",
"name": "water--minus"
},
{
"url": "/images/status/water--pencil.png",
"name": "water--pencil"
},
{
"url": "/images/status/water--plus.png",
"name": "water--plus"
},
{
"url": "/images/status/water.png",
"name": "water"
},
{
"url": "/images/status/weather-cloud.png",
"name": "weather-cloud"
},
{
"url": "/images/status/weather-clouds.png",
"name": "weather-clouds"
},
{
"url": "/images/status/weather-cloudy.png",
"name": "weather-cloudy"
},
{
"url": "/images/status/weather-fog.png",
"name": "weather-fog"
},
{
"url": "/images/status/weather-lightning.png",
"name": "weather-lightning"
},
{
"url": "/images/status/weather-moon-clouds.png",
"name": "weather-moon-clouds"
},
{
"url": "/images/status/weather-moon-fog.png",
"name": "weather-moon-fog"
},
{
"url": "/images/status/weather-moon.png",
"name": "weather-moon"
},
{
"url": "/images/status/weather-rain.png",
"name": "weather-rain"
},
{
"url": "/images/status/weather-snow.png",
"name": "weather-snow"
},
{
"url": "/images/status/weather.png",
"name": "weather"
},
{
"url": "/images/status/webcam--arrow.png",
"name": "webcam--arrow"
},
{
"url": "/images/status/webcam--exclamation.png",
"name": "webcam--exclamation"
},
{
"url": "/images/status/webcam--minus.png",
"name": "webcam--minus"
},
{
"url": "/images/status/webcam--pencil.png",
"name": "webcam--pencil"
},
{
"url": "/images/status/webcam--plus.png",
"name": "webcam--plus"
},
{
"url": "/images/status/webcam-network.png",
"name": "webcam-network"
},
{
"url": "/images/status/webcam.png",
"name": "webcam"
},
{
"url": "/images/status/wheel.png",
"name": "wheel"
},
{
"url": "/images/status/wooden-box--arrow.png",
"name": "wooden-box--arrow"
},
{
"url": "/images/status/wooden-box--exclamation.png",
"name": "wooden-box--exclamation"
},
{
"url": "/images/status/wooden-box--minus.png",
"name": "wooden-box--minus"
},
{
"url": "/images/status/wooden-box--pencil.png",
"name": "wooden-box--pencil"
},
{
"url": "/images/status/wooden-box--plus.png",
"name": "wooden-box--plus"
},
{
"url": "/images/status/wooden-box-label.png",
"name": "wooden-box-label"
},
{
"url": "/images/status/wooden-box.png",
"name": "wooden-box"
},
{
"url": "/images/status/wrench--arrow.png",
"name": "wrench--arrow"
},
{
"url": "/images/status/wrench--exclamation.png",
"name": "wrench--exclamation"
},
{
"url": "/images/status/wrench--minus.png",
"name": "wrench--minus"
},
{
"url": "/images/status/wrench--pencil.png",
"name": "wrench--pencil"
},
{
"url": "/images/status/wrench--plus.png",
"name": "wrench--plus"
},
{
"url": "/images/status/wrench-screwdriver.png",
"name": "wrench-screwdriver"
},
{
"url": "/images/status/wrench.png",
"name": "wrench"
},
{
"url": "/images/status/xfn-colleague-met.png",
"name": "xfn-colleague-met"
},
{
"url": "/images/status/xfn-colleague.png",
"name": "xfn-colleague"
},
{
"url": "/images/status/xfn-friend-met.png",
"name": "xfn-friend-met"
},
{
"url": "/images/status/xfn-friend.png",
"name": "xfn-friend"
},
{
"url": "/images/status/xfn-sweetheart-met.png",
"name": "xfn-sweetheart-met"
},
{
"url": "/images/status/xfn-sweetheart.png",
"name": "xfn-sweetheart"
},
{
"url": "/images/status/xfn.png",
"name": "xfn"
},
{
"url": "/images/status/yin-yang.png",
"name": "yin-yang"
},
{
"url": "/images/status/zone--arrow.png",
"name": "zone--arrow"
},
{
"url": "/images/status/zone--exclamation.png",
"name": "zone--exclamation"
},
{
"url": "/images/status/zone--minus.png",
"name": "zone--minus"
},
{
"url": "/images/status/zone--pencil.png",
"name": "zone--pencil"
},
{
"url": "/images/status/zone--plus.png",
"name": "zone--plus"
},
{
"url": "/images/status/zone-money.png",
"name": "zone-money"
},
{
"url": "/images/status/zone-select.png",
"name": "zone-select"
},
{
"url": "/images/status/zone.png",
"name": "zone"
},
{
"url": "/images/status/zones-stack.png",
"name": "zones-stack"
},
{
"url": "/images/status/zones.png",
"name": "zones"
}
]
| 25.193545 | 79 | 0.472439 |
6613dec8d628000fe7b472846f82eac73bd8f3ea | 49,047 | py | Python | autojail/config/memory.py | ekut-es/autojail | bc16e40e6df55c0a28a3059715851ffa59b14ba8 | [
"MIT"
] | 6 | 2020-08-12T08:16:15.000Z | 2022-03-05T02:25:53.000Z | autojail/config/memory.py | ekut-es/autojail | bc16e40e6df55c0a28a3059715851ffa59b14ba8 | [
"MIT"
] | 1 | 2021-03-30T10:34:51.000Z | 2021-06-09T11:24:00.000Z | autojail/config/memory.py | ekut-es/autojail | bc16e40e6df55c0a28a3059715851ffa59b14ba8 | [
"MIT"
] | 1 | 2021-11-21T09:30:58.000Z | 2021-11-21T09:30:58.000Z | import copy
import logging
import math
import sys
from collections import defaultdict
from functools import reduce
from typing import (
Any,
Callable,
Dict,
Iterable,
List,
Optional,
Sequence,
Tuple,
Union,
)
import tabulate
from ortools.sat.python import cp_model
from ..model import (
Board,
CellConfig,
DeviceMemoryRegion,
HypervisorMemoryRegion,
JailhouseConfig,
MemoryRegion,
MemoryRegionData,
ShMemNetRegion,
)
from ..model.datatypes import HexInt
from ..model.parameters import GenerateConfig, GenerateParameters, ScalarChoice
from ..utils import get_overlap
from .passes import BasePass
| 35.438584 | 140 | 0.538728 |
661499e1d6e03e831ef662b0f10bed15a0e0160c | 806 | py | Python | tests/test_util.py | tsufeki/python-restclientaio | 2af2ded9e22ba5552ace193691ed3a4b520cadf8 | [
"BSD-2-Clause"
] | null | null | null | tests/test_util.py | tsufeki/python-restclientaio | 2af2ded9e22ba5552ace193691ed3a4b520cadf8 | [
"BSD-2-Clause"
] | null | null | null | tests/test_util.py | tsufeki/python-restclientaio | 2af2ded9e22ba5552ace193691ed3a4b520cadf8 | [
"BSD-2-Clause"
] | null | null | null |
import pytest
from restclientaio._util import *
| 25.1875 | 74 | 0.555831 |
6615213f91d714c671d2fcc45dfcb7ea7995394c | 1,112 | py | Python | experiments/test_experiments.py | srikarym/torchrl | fee98e78ac1657a2c9a4063dd8d63ba207a121e2 | [
"Apache-2.0"
] | 3 | 2019-02-27T19:00:32.000Z | 2020-07-19T03:18:28.000Z | experiments/test_experiments.py | srikarym/torchrl | fee98e78ac1657a2c9a4063dd8d63ba207a121e2 | [
"Apache-2.0"
] | null | null | null | experiments/test_experiments.py | srikarym/torchrl | fee98e78ac1657a2c9a4063dd8d63ba207a121e2 | [
"Apache-2.0"
] | null | null | null | # pylint: disable=redefined-outer-name
"""Test Experiments.
This test runs all problems and hyperparameter
pairs for 100 time steps. It only guarantees
correct API compatiblity and not the problem
performance metrics.
"""
import pytest
from torchrl import registry
from torchrl.cli.commands.run import do_run
problem_hparams_tuples = []
for problem_id, hparams_list in registry.list_problem_hparams().items():
for hparam_set_id in hparams_list:
problem_hparams_tuples.append((problem_id, hparam_set_id))
| 24.711111 | 72 | 0.714029 |
661546474c795c91d1a7e8fb806431b8b074972c | 3,289 | py | Python | gw2_price_get.py | rpatricktaylor/gw2_pricepull | ede658cd7cb567d8ab7e6c7698acee83eacc78a3 | [
"MIT"
] | null | null | null | gw2_price_get.py | rpatricktaylor/gw2_pricepull | ede658cd7cb567d8ab7e6c7698acee83eacc78a3 | [
"MIT"
] | null | null | null | gw2_price_get.py | rpatricktaylor/gw2_pricepull | ede658cd7cb567d8ab7e6c7698acee83eacc78a3 | [
"MIT"
] | null | null | null | import json
import time
import bs4
import numpy as np
import pandas as pd
import requests as rqs
url_apiV2 = r'https://api.guildwars2.com/v2'
working_dir = r'/home/ubuntu/'
with open(working_dir + 'item_nums', 'r') as f:
item_nums = [int(line[:-1]) for line in f]
price_data_fetch = []
k = 0
while k < len(item_nums):
price_data_fetch += avg_prices(item_nums[k:(k+200)])
k += 200
price_hist = pd.DataFrame(price_data_fetch,
columns=['item_id', 'time',
'bid_price', 'bid_quantity',
'ask_price', 'ask_quantity'])
price_hist['liquidity_score'] = price_hist['ask_price'] / price_hist['bid_price'] - 1.1
out_path = '{}price_hist_{}.csv'.format(working_dir, time.strftime('%Y-%m-%d_%H00'))
price_hist.to_csv(out_path)
| 29.366071 | 89 | 0.549407 |
66160d3954e8f1108b126460356e2b0740e00e9e | 2,055 | py | Python | create_db.py | vladstorm98/TaskBoard | ebe4244cc5cbdfda2fdaac84c0158692f440517c | [
"MIT"
] | null | null | null | create_db.py | vladstorm98/TaskBoard | ebe4244cc5cbdfda2fdaac84c0158692f440517c | [
"MIT"
] | null | null | null | create_db.py | vladstorm98/TaskBoard | ebe4244cc5cbdfda2fdaac84c0158692f440517c | [
"MIT"
] | null | null | null | from flask import Flask
from flask_sqlalchemy import SQLAlchemy
from config import Config
app = Flask(__name__)
app.config.from_object(Config)
db = SQLAlchemy(app)
db.create_all()
db.session.commit()
# from flask import Flask
# from flask_babel import Babel
# from flask_babel import format_datetime
# from datetime import datetime
# from flask import render_template
#
# ap = Flask(__name__)
# babel = Babel(ap)
#
# @ap.route('/', methods=['GET'])
# def index():
# f = format_datetime(datetime(1987, 3, 5, 17, 12), 'EEEE, d. MMMM yyyy H:mm')
#
# print(f)
# return render_template('1')
#
#
# ap.run()
| 29.357143 | 83 | 0.654988 |
661759e4ea700063225bc588e580457ea479d76a | 1,954 | py | Python | 02_assignment/toolbox/Toolbox_Python02450/Scripts/ex3_1_2.py | LukaAvbreht/ML_projects | 8b36acdeb017ce8a57959c609b96111968852d5f | [
"MIT"
] | null | null | null | 02_assignment/toolbox/Toolbox_Python02450/Scripts/ex3_1_2.py | LukaAvbreht/ML_projects | 8b36acdeb017ce8a57959c609b96111968852d5f | [
"MIT"
] | null | null | null | 02_assignment/toolbox/Toolbox_Python02450/Scripts/ex3_1_2.py | LukaAvbreht/ML_projects | 8b36acdeb017ce8a57959c609b96111968852d5f | [
"MIT"
] | null | null | null | # exercise 3.1.4
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
# Load the textDocs.txt as a long string into raw_file:
with open('../Data/textDocs.txt', 'r') as f:
raw_file = f.read()
# raw_file contains sentences seperated by newline characters,
# so we split by '\n':
corpus = raw_file.split('\n')
# corpus is now list of "documents" (sentences), but some of them are empty,
# because textDocs.txt has a lot of empty lines, we filter/remove them:
corpus = list(filter(None, corpus))
# Display the result
print('Document-term matrix analysis')
print()
print('Corpus (5 documents/sentences):')
print(np.asmatrix(corpus))
print()
# To automatically obtain the bag of words representation, we use sklearn's
# feature_extraction.text module, which has a function CountVectorizer.
# We make a CounterVectorizer:
vectorizer = CountVectorizer(token_pattern=r'\b[^\d\W]+\b')
# The token pattern is a regular expression (marked by the r), which ensures
# that the vectorizer ignores digit/non-word tokens - in this case, it ensures
# the 10 in the last document is not recognized as a token. It's not important
# that you should understand it the regexp.
# The object vectorizer can now be used to first 'fit' the vectorizer to the
# corpus, and the subsequently transform the data. We start by fitting:
vectorizer.fit(corpus)
# The vectorizer has now determined the unique terms (or tokens) in the corpus
# and we can extract them using:
attributeNames = vectorizer.get_feature_names()
print('Found terms:')
print(attributeNames)
print()
# The next step is to count how many times each term is found in each document,
# which we do using the transform function:
X = vectorizer.transform(corpus)
N,M = X.shape
print('Number of documents (data objects, N):\t %i' % N)
print('Number of terms (attributes, M):\t %i' % M )
print()
print('Document-term matrix:')
print(X.toarray())
print()
print('Ran Exercise 3.1.2') | 37.576923 | 79 | 0.746162 |
661950464dde57e4e6f43aa2be8e46d0304ef9f0 | 797 | py | Python | vid 18 dream logo.py | agneay/turtle-projects | 88f7de0c8eb1bb0f37255746c3d11b9c272d1c10 | [
"MIT"
] | null | null | null | vid 18 dream logo.py | agneay/turtle-projects | 88f7de0c8eb1bb0f37255746c3d11b9c272d1c10 | [
"MIT"
] | null | null | null | vid 18 dream logo.py | agneay/turtle-projects | 88f7de0c8eb1bb0f37255746c3d11b9c272d1c10 | [
"MIT"
] | null | null | null | from turtle import *
bgcolor("lime")
color("black","white")
shape("circle")
ht()
speed(1)
width(15)
gt(-365,-380)
begin_fill()
seth(90)
fd(20)
r(5,20)
r(4,60)
r(5,100)
r(5,120)
l(85,50)
r(10,25)
r(15,10)
r(15,40)
r(15,50)
r(10,90)
r(20,50)
r(20,25)
r(10,75)
r(30,20)
r(10,10)
l(30,50)
r(15,10)
r(10,20)
r(10,80)
r(10,40)
for _ in range(7):
r(10,30)
r(10,80)
r(20,35)
l(30,150)
for _ in range(3):
r(5,150)
end_fill()
gt(-290,-70)
l(90,30)
l(10,120)
l(15,60)
l(10,30)
l(5,40)
l(10,40)
gt(-320,50)
r(70,30)
l(10,30)
l(15,30)
l(5,60)
l(5,60)
l(15,40)
r(10,20)
l(20,35)
color("black")
gt(-50,170)
stamp()
gt(-340,135)
stamp()
gt(40,-115)
write("dream", font = ('Arial', 85, 'italic','bold'))
done() | 9.16092 | 53 | 0.567127 |
661ea642755905b0144b877774494043f351d6cb | 1,316 | py | Python | magicauth/otp_forms.py | glibersat/django-magicauth | 545cb0df2b2368b27089e253fafa666ca8a870f1 | [
"MIT"
] | null | null | null | magicauth/otp_forms.py | glibersat/django-magicauth | 545cb0df2b2368b27089e253fafa666ca8a870f1 | [
"MIT"
] | null | null | null | magicauth/otp_forms.py | glibersat/django-magicauth | 545cb0df2b2368b27089e253fafa666ca8a870f1 | [
"MIT"
] | null | null | null | from django import forms
from django.core.validators import RegexValidator
from django.core.exceptions import ValidationError
from django_otp import user_has_device, devices_for_user
from magicauth import settings as magicauth_settings
| 37.6 | 173 | 0.705167 |