content stringlengths 1 1.05M | input_ids listlengths 1 883k | ratio_char_token float64 1 22.9 | token_count int64 1 883k |
|---|---|---|---|
import os
import matplotlib as mpl
import torch
import torchvision
from data_management import IPDataset, Jitter, SimulateMeasurements
from networks import IterativeNet, Tiramisu
from operators import Radon
# ----- load configuration -----
import config # isort:skip
# ----- global configuration -----
mpl.use("agg")
device = torch.device("cuda:0")
torch.cuda.set_device(0)
# ----- measurement configuration -----
theta = torch.linspace(0, 180, 61)[:-1] # 60 lines, exclude endpoint
OpA = Radon(config.n, theta)
# ----- network configuration -----
subnet_params = {
"in_channels": 1,
"out_channels": 1,
"drop_factor": 0.0,
"down_blocks": (5, 7, 9, 12, 15),
"up_blocks": (15, 12, 9, 7, 5),
"pool_factors": (2, 2, 2, 2, 2),
"bottleneck_layers": 20,
"growth_rate": 16,
"out_chans_first_conv": 16,
}
subnet = Tiramisu
it_net_params = {
"num_iter": 1,
"lam": 0.0,
"lam_learnable": False,
"final_dc": False,
"resnet_factor": 1.0,
"operator": OpA,
"inverter": OpA.inv,
}
# ----- training configuration -----
mseloss = torch.nn.MSELoss(reduction="sum")
train_phases = 1
train_params = {
"num_epochs": [19],
"batch_size": [10],
"loss_func": loss_func,
"save_path": [
os.path.join(
config.RESULTS_PATH,
"Radon_Tiramisu_jitter_v6_"
"train_phase_{}".format((i + 1) % (train_phases + 1)),
)
for i in range(train_phases + 1)
],
"save_epochs": 1,
"optimizer": torch.optim.Adam,
"optimizer_params": [{"lr": 8e-5, "eps": 2e-4, "weight_decay": 5e-4}],
"scheduler": torch.optim.lr_scheduler.StepLR,
"scheduler_params": {"step_size": 1, "gamma": 1.0},
"acc_steps": [1],
"train_transform": torchvision.transforms.Compose(
[SimulateMeasurements(OpA), Jitter(5e2, 0.0, 1.0)]
),
"val_transform": torchvision.transforms.Compose(
[SimulateMeasurements(OpA)],
),
"train_loader_params": {"shuffle": True, "num_workers": 0},
"val_loader_params": {"shuffle": False, "num_workers": 0},
}
# ----- data configuration -----
train_data_params = {
"path": config.DATA_PATH,
"device": device,
}
train_data = IPDataset
val_data_params = {
"path": config.DATA_PATH,
"device": device,
}
val_data = IPDataset
# ------ save hyperparameters -------
os.makedirs(train_params["save_path"][-1], exist_ok=True)
with open(
os.path.join(train_params["save_path"][-1], "hyperparameters.txt"), "w"
) as file:
for key, value in subnet_params.items():
file.write(key + ": " + str(value) + "\n")
for key, value in it_net_params.items():
file.write(key + ": " + str(value) + "\n")
for key, value in train_params.items():
file.write(key + ": " + str(value) + "\n")
for key, value in train_data_params.items():
file.write(key + ": " + str(value) + "\n")
for key, value in val_data_params.items():
file.write(key + ": " + str(value) + "\n")
file.write("train_phases" + ": " + str(train_phases) + "\n")
# ------ construct network and train -----
subnet_tmp = subnet(**subnet_params).to(device)
it_net_tmp = IterativeNet(
subnet_tmp,
**{
"num_iter": 1,
"lam": 0.0,
"lam_learnable": False,
"final_dc": False,
"resnet_factor": 1.0,
"operator": OpA,
"inverter": OpA.inv,
}
).to(device)
it_net_tmp.load_state_dict(
torch.load(
"results/Radon_Tiramisu_jitter_v4_train_phase_1/model_weights.pt",
map_location=torch.device(device),
)
)
subnet = it_net_tmp.subnet
it_net = IterativeNet(subnet, **it_net_params).to(device)
train_data = train_data("train", **train_data_params)
val_data = val_data("val", **val_data_params)
for i in range(train_phases):
train_params_cur = {}
for key, value in train_params.items():
train_params_cur[key] = (
value[i] if isinstance(value, (tuple, list)) else value
)
print("Phase {}:".format(i + 1))
for key, value in train_params_cur.items():
print(key + ": " + str(value))
it_net.train_on(train_data, val_data, **train_params_cur)
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#import pdb
T = (0.1, 0.1)
x = 0.0
for i in range(len(T)):
for j in T:
x += i + j
print x
print i
#pdb.set_trace()
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import os
import numpy as np
import random as rd
import time
import argparse
import torch
import torch.utils.data
from torch import optim
import dataset
import gifploter
from trainer.InvML_trainer import InvML_trainer
from generator.samplegenerater import SampleIndexGenerater
def PlotLatenSpace(model, batch_size, datas, labels, loss_caler, gif_ploter, device,
path='./', name='no name', indicator=True, full=True, save_plot=True):
"""use to test the model and plot the latent space
Arguments:
model {torch model} -- a model need to train
batch_size {int} -- batch size
datas {tensor} -- the train data
labels {label} -- the train label, for unsuprised method, it is only used in plot fig
Keyword Arguments:
path {str} -- the path to save the fig (default: {'./'})
name {str} -- the name of current fig (default: {'no name'})
indicator {bool} -- a flag to calculate the indicator (default: {True})
"""
model.eval()
train_loss_sum = [0, 0, 0, 0, 0, 0]
num_train_sample = datas.shape[0]
if full == True:
for batch_idx in torch.arange(0, (num_train_sample-1)//batch_size + 1):
start_number = (batch_idx * batch_size).int()
end_number = torch.min(torch.tensor(
[batch_idx*batch_size+batch_size, num_train_sample])).int()
data = datas[start_number:end_number].float()
label = labels[start_number:end_number]
data = data.to(device)
label = label.to(device)
# train info
train_info = model(data)
loss_dict = loss_caler.CalLosses(train_info)
if type(train_info) == type(dict()):
train_info = train_info['output']
for i, k in enumerate(list(loss_dict.keys())):
train_loss_sum[i] += loss_dict[k].item()
if batch_idx == 0:
latent_point = []
for train_info_item in train_info:
latent_point.append(train_info_item.detach().cpu().numpy())
label_point = label.cpu().detach().numpy()
else:
for i, train_info_item in enumerate(train_info):
latent_point_c = train_info_item.detach().cpu().numpy()
latent_point[i] = np.concatenate(
(latent_point[i], latent_point_c), axis=0)
label_point = np.concatenate(
(label_point, label.cpu().detach().numpy()), axis=0)
gif_ploter.AddNewFig(
latent_point, label_point,
title_=path+'/'+name +
'__AE_' + str(4)[:4] + '__MAE_'+ str(4)[:4],
loss=train_loss_sum,
save=save_plot
)
else:
data = datas.to(device)
label = labels.to(device)
eval_info = model(data)
if type(eval_info) == type(dict()):
eval_info = eval_info['output']
latent_point = []
for info_item in eval_info:
latent_point.append(info_item.detach().cpu().numpy())
label_point = label.cpu().detach().numpy()
gif_ploter.AddNewFig(
latent_point, label_point,
title_=path+'/'+'result', loss=None, save=save_plot
)
def SaveParam(path, param):
"""save the current param in the path """
for v, k in param.items():
print('{v}:{k}'.format(v=v, k=k))
print('{v}:{k}'.format(v=v, k=k), file=open(path+'/param.txt', 'a'))
def SetSeed(seed):
"""function used to set a random seed """
SEED = seed
torch.manual_seed(SEED)
torch.cuda.manual_seed(SEED)
rd.seed(SEED)
np.random.seed(SEED)
if __name__ == '__main__':
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# 1. test encoder
new_param = {}
expName, testName = single_test(new_param, "encoder", device)
# expName, testName = "Orth", 1 # decoder for 1
# expName, testName = "Orth2", 2 # decoder for 2
# expName, testName = "Orth3", 3 # decoder for 3
# expName, testName = "Orth4", 4 # decoder for 4
# test decoder based on encoder param
new_param = {"ExpName": expName+"_Dec", "Test": testName}
# 2. test decoder
_,_ = single_test(new_param, "decoder", device) | [
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import json
import time
import requests
from cleep.exception import CommandError, MissingParameter
from cleep.libs.internals.task import Task
from cleep.core import CleepModule
from cleep.common import CATEGORIES
__all__ = ["Openweathermap"]
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from pywps import Service
from pywps.tests import assert_response_success
from .common import client_for, get_output
from emu.processes.wps_dummy import Dummy
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6... | 3.285714 | 49 |
#!/usr/bin/env python
import rospy
from std_msgs.msg import String
if __name__ == '__main__':
try:
talker()
except rospy.ROSInterruptException:
pass
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# AUTOGENERATED! DO NOT EDIT! File to edit: nbs/02_model.ipynb (unless otherwise specified).
__all__ = ['EfficientLoc', 'CIoU']
# Cell
#export
from efficientnet_pytorch import EfficientNet
import copy
import time
import math
import torch
import torch.optim as opt
from torch.utils.data import DataLoader
from torchvision import transforms
# Cell
# Cell | [
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#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Sat Jun 2 13:09:55 2018
@author: mali
"""
#import time
import pickle
import pyNN.utility.plotting as plot
import matplotlib.pyplot as plt
import comn_conversion as cnvrt
import prnt_plt_anmy as ppanmy
# file and folder names =======================================================
fldr_name = 'rslts/icub64x64/'
pickle_filename = 'TDXY.pickle'
file_pth = cnvrt.read_flenfldr_ncrntpth(fldr_name, pickle_filename )
with open(file_pth , 'rb') as tdxy:
TDXY = pickle.load( tdxy )
print '### lenght of TDXY : {}'.format( len(TDXY) ) # 2+ 2*n_orn )
pop = TDXY[0]
t_ist = 1040
print 'check pop: L_rtna_TDXY'
print '### T : {}'.format(pop[0][t_ist]) # dimension 4 x t_stp x depend
print '### 1D : {}'.format(pop[1][t_ist]) # dimension 4 x t_stp x depend
print '### X : {}'.format(pop[2][t_ist]) # dimension 4 x t_stp x depend
print '### Y : {}'.format(pop[3][t_ist]) # dimension 4 x t_stp x depend
print pop[0]
print pop[1]
#required variables============================================================
n_rtna = 2 # till now should be two
n_orn = 4
rtna_w = 64
rtna_h = 64
krnl_sz = 5
rf_w = rtna_w - krnl_sz +1
rf_h = rtna_h - krnl_sz +1
subplt_rws = n_rtna
subplt_cls = n_orn+1
########### to make animation fast as scale now in micro second ###############
#first to scale be divide over 10 or 100 ======================================
T=TDXY[0][0]
t10u=T [0:T[-1]:100]
#print '### t_10u : {}'.format(t10u)
# second find all times has spikes any one of the rtna or rf ==================
t_spks=[]
for pop in range ( len(TDXY) ):
for inst in range( len(TDXY[pop][0]) ):
if TDXY[pop][2][inst]!=[] :
t_spks.append( TDXY[pop][0][inst] )
print pop, TDXY[pop][0][inst]
t_spks.sort()
for each in t_spks:
count = t_spks.count(each)
if count > 1:
t_spks.remove(each)
print 't_spks : {}'.format( t_spks )
#animate the rtna_rf =========================================================
#print 'abplt_rw, sbplt_cl, rtna_w, rtna_h, rf_w, rf_h: {}, {}, {}, {}, {}, {} '.format(subplt_rws, subplt_cls, rtna_w, rtna_h, rf_w, rf_h)
fig, axs = plt.subplots(subplt_rws, subplt_cls, sharex=False, sharey=False) #, figsize=(12,5))
axs = ppanmy.init_fig_mxn_sbplt_wxh_res (fig, axs, rtna_h, rtna_w, rf_w, rf_h, subplt_rws, subplt_cls)
plt.grid(True)
plt.show(block=False)
plt.pause(.01)
#for i in t_spks: #t10u:
# axs = ppanmy.init_fig_mxn_sbplt_wxh_res (fig, axs, rtna_h, rtna_w, rf_w, rf_h, subplt_rws, subplt_cls)
# plt.suptitle('rtna_rf_orn_3: t= {} usec'.format( i ) )
# if subplt_rws==1:
# axs[0].scatter( TDXY[0][2][i], TDXY[0][3][i] )
# for col in range (subplt_cls):
# axs[col].scatter( TDXY[col+1][2][i], TDXY[col+1][3][i] )
## plt.savefig( 'fgrs/anmy_1/{}_t{}.png'.format(vrjn, i) )
# plt.show(block=False)
# plt.pause(2)
# for col in range(subplt_cls):
# axs[col].cla()
#
# elif subplt_rws==2:
# for col in range (subplt_cls):
# axs[0][0].scatter( TDXY[0][2][i], TDXY[0][3][i] )
# axs[1][0].scatter( TDXY[1][2][i], TDXY[1][3][i] )
# for col in range(1,n_orn+1):
# row=0
# axs[row][col].scatter( TDXY[col+1][2][i], TDXY[col+1][3][i] )
# for col in range(1,n_orn):
# row=1
# axs[row][col].scatter( TDXY[n_orn+1+col][2][i], TDXY[n_orn+1+col][3][i] )
## plt.savefig( 'fgrs/anmy_1/{}_t{}.png'.format(vrjn, i) )
# plt.show(block=False)
# plt.pause(2)
# for row in range(subplt_rws):
# for col in range (subplt_cls):
# axs[row][col].cla()
#
print '##### required variables: \n n_rtna={}, TDXY_len={}, rtna_w={}, rtna_h={}, krnl_sz={}, rf_w={} , rf_h={}'.format( n_rtna , len(TDXY), rtna_w, rtna_h, krnl_sz, rf_w , rf_h )
plt.show(block=False)
last_t_spks=-310
for i in range( len(t_spks) ): #t10u:
# plt.pause(2)
if t_spks[i]-last_t_spks > 300:
#clear
if subplt_rws==2:
for row in range(subplt_rws):
for col in range (subplt_cls):
axs[row][col].cla()
elif subplt_rws==1:
for col in range(subplt_cls):
axs[col].cla()
axs = ppanmy.init_fig_mxn_sbplt_wxh_res (fig, axs, rtna_h, rtna_w, rf_w, rf_h, subplt_rws, subplt_cls)
plt.suptitle('rtna_rf_orn: t= {} usec'.format( t_spks[i] ) )
plt.pause(1.5)
#--------------------------------------------------------------------------
if subplt_rws==1:
axs[0].scatter( TDXY[0][2][t_spks[i]], TDXY[0][3][t_spks[i]] )
for col in range (subplt_cls):
axs[col].scatter( TDXY[col+1][2][t_spks[i]], TDXY[col+1][3][t_spks[i]] )
# plt.savefig( 'fgrs/anmy_1/{}_t{}.png'.format(vrjn, i) )
elif subplt_rws==2:
for col in range (subplt_cls):
axs[0][0].scatter( TDXY[0][2][t_spks[i]], TDXY[0][3][t_spks[i]] )
axs[1][0].scatter( TDXY[1][2][t_spks[i]], TDXY[1][3][t_spks[i]] )
for col in range(1,n_orn+1):
row=0
axs[row][col].scatter( TDXY[col+1][2][t_spks[i]], TDXY[col+1][3][t_spks[i]] )
for col in range(1,n_orn+1):
row=1
axs[row][col].scatter( TDXY[n_orn+1+col][2][t_spks[i]], TDXY[n_orn+1+col][3][t_spks[i]] )
# plt.savefig( 'fgrs/anmy_1/{}_t{}.png'.format(vrjn, i) )
#--------------------------------------------------------------------------
plt.pause(.5)
else: #====================================================================
#--------------------------------------------------------------------------
if subplt_rws==1:
axs[0].scatter( TDXY[0][2][t_spks[i]], TDXY[0][3][t_spks[i]] )
for col in range (subplt_cls):
axs[col].scatter( TDXY[col+1][2][t_spks[i]], TDXY[col+1][3][t_spks[i]] )
# plt.savefig( 'fgrs/anmy_1/{}_t{}.png'.format(vrjn, i) )
elif subplt_rws==2:
for col in range (subplt_cls):
axs[0][0].scatter( TDXY[0][2][t_spks[i]], TDXY[0][3][t_spks[i]] )
axs[1][0].scatter( TDXY[1][2][t_spks[i]], TDXY[1][3][t_spks[i]] )
for col in range(1,n_orn+1):
row=0
axs[row][col].scatter( TDXY[col+1][2][t_spks[i]], TDXY[col+1][3][t_spks[i]] )
for col in range(1,n_orn+1):
row=1
axs[row][col].scatter( TDXY[n_orn+1+col][2][t_spks[i]], TDXY[n_orn+1+col][3][t_spks[i]] )
# plt.savefig( 'fgrs/anmy_1/{}_t{}.png'.format(vrjn, i) )
#--------------------------------------------------------------------------
plt.pause(.5)
last_t_spks = t_spks[i]
# suing builtin animation function ===========================================
#strt_tm = TDXY[0][0][0]
#stop_tm = TDXY[0][0][-1]
#print '\n### n_orn x n_rtna : {}x{}'.format(n_orn, n_rtna)
#print '\n### strt_tm - stop_tm : {} - {}'.format(strt_tm, stop_tm)
#ppanmy.anmy_rtna_rf_orn( TDXY, rtna_h, rtna_w, n_rtna, krnl_sz, strt_tm , stop_tm)
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"""
Django settings for meAdota project.
Generated by 'django-admin startproject' using Django 3.1.1.
For more information on this file, see
https://docs.djangoproject.com/en/3.1/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/3.1/ref/settings/
"""
from pathlib import Path
# Load dotenv
import os
from dotenv import load_dotenv
load_dotenv()
# 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/3.1/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = os.getenv("SECRET_KEY")
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
ALLOWED_HOSTS = ['127.0.0.1', 'localhost']
STATIC_ROOT = ''
STATIC_URL = '/static/'
STATICFILES_DIRS = ('static',)
# Application definition
INSTALLED_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'django.contrib.sites',
'allauth',
'allauth.account',
'allauth.socialaccount',
'django_countries',
'cpf_field',
'django_filters',
# AllAuth [custom providers]
'allauth.socialaccount.providers.facebook',
'allauth.socialaccount.providers.google',
'allauth.socialaccount.providers.twitter',
#my apps
'users',
'pets',
'crispy_forms',
]
CRISPY_TEMPLATE_PACK = 'bootstrap4'
SITE_ID = 1
AUTH_USER_MODEL = 'users.User'
#verify email
EMAIL_BACKEND = 'django.core.mail.backends.console.EmailBackend' #check email on console
ACCOUNT_EMAIL_VERIFICATION = True
ACCOUNT_EMAIL_REQUIRED = True
ACCOUNT_USERNAME_REQUIRED = False
ACCOUNT_AUTHENTICATION_METHOD = 'email'
ACCOUNT_USER_MODEL_USERNAME_FIELD = None
ACCOUNT_LOGOUT_ON_GET = True
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
ROOT_URLCONF = 'meAdota.urls'
ACCOUNT_FORMS = {
'login': 'users.forms.MyLoginForm',
# 'signup': 'allauth.account.forms.SignupForm',
'signup': 'users.forms.MyCustomSignupForm',
'add_email': 'allauth.account.forms.AddEmailForm',
'change_password': 'allauth.account.forms.ChangePasswordForm',
'set_password': 'allauth.account.forms.SetPasswordForm',
'reset_password': 'allauth.account.forms.ResetPasswordForm',
'reset_password_from_key': 'allauth.account.forms.ResetPasswordKeyForm',
'disconnect': 'allauth.socialaccount.forms.DisconnectForm',
}
SOCIALACCOUNT_PROVIDERS = {
'facebook': {
'METHOD': 'oauth2',
'SDK_URL': '//connect.facebook.net/{locale}/sdk.js',
'SCOPE': ['email', 'public_profile'],
'AUTH_PARAMS': {'auth_type': 'reauthenticate'},
'INIT_PARAMS': {'cookie': True},
'FIELDS': [
'id',
'first_name',
'last_name',
'middle_name',
'name',
'name_format',
'picture',
'short_name'
],
'EXCHANGE_TOKEN': True,
'LOCALE_FUNC': lambda request: 'en_US',
'VERIFIED_EMAIL': False,
'VERSION': 'v7.0',
},
'google': {
'SCOPE': [
'profile',
'email',
],
'AUTH_PARAMS': {
'access_type': 'online',
}
}
}
LOGIN_REDIRECT_URL ='/'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [str(BASE_DIR / "templates"), str(BASE_DIR / "templates/account")],
'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',
],
},
},
]
AUTHENTICATION_BACKENDS = [
# Needed to login by username in Django admin, regardless of `allauth`
'django.contrib.auth.backends.ModelBackend',
# `allauth` specific authentication methods, such as login by e-mail
'allauth.account.auth_backends.AuthenticationBackend',
]
WSGI_APPLICATION = 'meAdota.wsgi.application'
# Database
# https://docs.djangoproject.com/en/3.1/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.postgresql_psycopg2',
'NAME': os.getenv("DATABASE_NAME"),
'USER': os.getenv("DATABASE_USER"),
'PASSWORD': os.getenv("DATABASE_PASSWORD"),
'HOST': os.getenv("DATABASE_HOST"),
'PORT': os.getenv("DATABASE_PORT"),
}
}
# Password validation
# https://docs.djangoproject.com/en/3.1/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
},
]
# Internationalization
# https://docs.djangoproject.com/en/3.1/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/3.1/howto/static-files/
STATIC_URL = '/static/'
MEDIA_URL = '/media/'
MEDIA_ROOT = os.path.join(BASE_DIR, 'media')
| [
37811,
198,
35,
73,
14208,
6460,
329,
502,
2782,
4265,
1628,
13,
198,
198,
8645,
515,
416,
705,
28241,
14208,
12,
28482,
923,
16302,
6,
1262,
37770,
513,
13,
16,
13,
16,
13,
198,
198,
1890,
517,
1321,
319,
428,
2393,
11,
766,
198,... | 2.310478 | 2,596 |
from sklearn import preprocessing, svm
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn import cross_validation
import pandas as pd
import numpy as np
import quandl
import math
df = quandl.get('WIKI/GOOGL')
df = df[['Adj. Open', 'Adj. High', 'Adj. Low', 'Adj. Close', 'Adj. Volume']]
df['HL_PCT'] = (df['Adj. High'] - df['Adj. Low']) / df['Adj. Close'] * 100.0
df['PCT_change'] = (df['Adj. Close'] - df['Adj. Open']) / df['Adj. Open'] * 100.0
df = df[['Adj. Close', 'HL_PCT', 'PCT_change', 'Adj. Volume']]
forecast_col = 'Adj. Close'
df.fillna(-99999, inplace = True)
forecast_out = int(math.ceil(0.01 * len(df)))
print(forecast_out)
df['label'] = df[forecast_col].shift(-forecast_out)
df.dropna(inplace = True)
X = np.array(df.drop(['label'],1))
y = np.array(df['label'])
X = preprocessing.scale(X)
y = np.array(df['label'])
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X,y, test_size = 0.2)
clf = LinearRegression()
clf.fit(X_train, y_train)
accuracy = clf.score(X_test,y_test)
print(accuracy)
| [
6738,
1341,
35720,
1330,
662,
36948,
11,
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14761,
198,
6738,
1341,
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13,
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1330,
4512,
62,
9288,
62,
35312,
198,
6738,
1341,
35720,
1330,
327... | 2.39738 | 458 |
import datetime
import os
import yaml
import numpy as np
import pandas as pd
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
from scipy.integrate import solve_ivp
from scipy.optimize import minimize
import plotly.graph_objs as go
ENV_FILE = '../env.yaml'
with open(ENV_FILE) as f:
params = yaml.load(f, Loader=yaml.FullLoader)
# Initialisation des chemins vers les fichiers
ROOT_DIR = os.path.dirname(os.path.abspath(ENV_FILE))
DATA_FILE = os.path.join(ROOT_DIR,
params['directories']['processed'],
params['files']['all_data'])
#Lecture du fihcier de donnes
epidemie_df = (pd.read_csv(DATA_FILE, parse_dates=['Last Update'])
.assign(day=lambda _df:_df['Last Update'].dt.date)
.drop_duplicates(subset=['Country/Region', 'Province/State', 'day'])
[lambda df: df['day'] <= datetime.date(2020,3,20)]
)
# replacing Mainland china with just China
cases = ['Confirmed', 'Deaths', 'Recovered']
# After 14/03/2020 the names of the countries are quite different
epidemie_df['Country/Region'] = epidemie_df['Country/Region'].replace('Mainland China', 'China')
# filling missing values
epidemie_df[['Province/State']] = epidemie_df[['Province/State']].fillna('')
epidemie_df[cases] = epidemie_df[cases].fillna(0)
countries=[{'label':c, 'value': c} for c in epidemie_df['Country/Region'].unique()]
app = dash.Dash('C0VID-19 Explorer')
app.layout = html.Div([
html.H1(['C0VID-19 Explorer'], style={'textAlign': 'center', 'color': 'navy', 'font-weight': 'bold'}),
dcc.Tabs([
dcc.Tab(label='Time', children=[
dcc.Markdown("""
Select a country:
""",style={'textAlign': 'left', 'color': 'navy', 'font-weight': 'bold'} ),
html.Div([
dcc.Dropdown(
id='country',
options=countries,
placeholder="Select a country...",
)
]),
html.Div([
dcc.Markdown("""You can select a second country:""",
style={'textAlign': 'left', 'color': 'navy', 'font-weight': 'bold'} ),
dcc.Dropdown(
id='country2',
options=countries,
placeholder="Select a country...",
)
]),
html.Div([dcc.Markdown("""Cases: """,
style={'textAlign': 'left', 'color': 'navy', 'font-weight': 'bold'} ),
dcc.RadioItems(
id='variable',
options=[
{'label':'Confirmed', 'value': 'Confirmed'},
{'label':'Deaths', 'value': 'Deaths'},
{'label':'Recovered', 'value': 'Recovered'}
],
value='Confirmed',
labelStyle={'display': 'inline-block'}
)
]),
html.Div([
dcc.Graph(id='graph1')
])
]),
dcc.Tab(label='Map', children=[
#html.H6(['COVID-19 in numbers:']),
dcc.Markdown("""
**COVID-19**
This is a graph that shows the evolution of the COVID-19 around the world
** Cases:**
""", style={'textAlign': 'left', 'color': 'navy', 'font-weight': 'bold'} ),
dcc.Dropdown(id="value-selected", value='Confirmed',
options=[{'label': "Deaths ", 'value': 'Deaths'},
{'label': "Confirmed", 'value': 'Confirmed'},
{'label': "Recovered", 'value': 'Recovered'}],
placeholder="Select a country...",
style={"display": "inline-block", "margin-left": "auto", "margin-right": "auto",
"width": "70%"}, className="six columns"),
dcc.Graph(id='map1'),
dcc.Slider(
id='map_day',
min=0,
max=(epidemie_df['day'].max() - epidemie_df['day'].min()).days,
value=0,
marks={i:str(i) for i, date in enumerate(epidemie_df['day'].unique())}
)
]),
dcc.Tab(label='SIR Model', children=[
dcc.Markdown("""
**SIR model**
S(Susceptible)I(Infectious)R(Recovered) is a model describing the dynamics of infectious disease. The model divides the population into compartments. Each compartment is expected to have the same characteristics. SIR represents the three compartments segmented by the model.
**Select a country:**
""", style={'textAlign': 'left', 'color': 'navy'}),
html.Div([
dcc.Dropdown(
id='Country',
value='Portugal',
options=countries),
]),
dcc.Markdown("""Select:""", style={'textAlign': 'left', 'color': 'navy'}),
dcc.Dropdown(id='cases',
options=[
{'label': 'Confirmed', 'value': 'Confirmed'},
{'label': 'Deaths', 'value': 'Deaths'},
{'label': 'Recovered', 'value': 'Recovered'}],
value=['Confirmed','Deaths','Recovered'],
multi=True),
dcc.Markdown("""
**Select your paramaters:**
""", style={'textAlign': 'left', 'color': 'navy'}),
html.Label( style={'textAlign': 'left', 'color': 'navy', "width": "20%"}),
html.Div([
dcc.Markdown(""" Beta:
""", style={'textAlign': 'left', 'color': 'navy'}),
dcc.Input(
id='input-beta',
type ='number',
placeholder='Input Beta',
min =-50,
max =100,
step =0.01,
value=0.45
)
]),
html.Div([
dcc.Markdown(""" Gamma:
""", style={'textAlign': 'left', 'color': 'navy'}),
dcc.Input(
id='input-gamma',
type ='number',
placeholder='Input Gamma',
min =-50,
max =100,
step =0.01,
value=0.55
)
]),
html.Div([
dcc.Markdown(""" Population:
""", style={'textAlign': 'left', 'color': 'navy'}),
dcc.Input(
id='input-pop',placeholder='Population',
type ='number',
min =1000,
max =1000000000000000,
step =1000,
value=1000,
)
]),
html.Div([
dcc.RadioItems(id='variable2',
options=[
{'label':'Optimize','value':'optimize'}],
value='Confirmed',
labelStyle={'display':'inline-block','color': 'navy', "width": "20%"})
]),
html.Div([
dcc.Graph(id='graph2')
]),
])
]),
])
if __name__ == '__main__':
app.run_server(debug=True) | [
11748,
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5589,
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355,
288,
535,
198,
11748,... | 1.764895 | 4,347 |
import pytest
from astropy.io import fits
import numpy as np
from lightkurve.io.kepseismic import read_kepseismic_lightcurve
from lightkurve.io.detect import detect_filetype
| [
11748,
12972,
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62,
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22019,
... | 2.95 | 60 |
__author__ = 'Samara Cardoso'
| [
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16305,
3301,
5172,
28213,
6,
628,
198
] | 2.666667 | 12 |
#
# begin test code
#
from random import randint
if __name__ == '__main__':
aList = [c(1), c(2), c(3), c(4), c(5), c(6)]
print '\n...to be sorted by obj.y.y[1].x[1]'
print ' then, as needed, by obj.y.x'
print ' then, as needed, by obj.x\n\n ',
for i in range(6):
print '(' + str(aList[i].y.y[1].x[1]) + ',',
print str(aList[i].y.x) + ',',
print str(aList[i].x) + ') ',
sortByAttrs(aList, ['y.y[1].x[1]', 'y.x', 'x'])
print '\n\n...now sorted by listed attributes.\n\n ',
for i in range(6):
print '(' + str(aList[i].y.y[1].x[1]) + ',',
print str(aList[i].y.x) + ',',
print str(aList[i].x) + ') ',
print
#
# end test code
#
| [
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8... | 1.985119 | 336 |
# -*- coding: utf-8 -*-
from django import forms
from django.utils.translation import gettext_lazy as _
from django.utils.encoding import smart_bytes
from django.utils import timezone
from ..core import utils
from ..core.utils.forms import NestedModelChoiceField
from ..category.models import Category
from .models import Topic
| [
2,
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12,
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355,
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42625,
14208,
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13,
12... | 3.484211 | 95 |
# constant
import json
import logging
import os
import platform
import subprocess
from datetime import date
from os.path import exists, expanduser, isdir, isfile, join, abspath, dirname
from PIL import Image
logging.basicConfig(
style="{",
format="{threadName:<10s} <{levelname:<7s}> [{asctime:<15s}] {message}",
level=logging.DEBUG
)
# paths
CCCL_PATH = None
SETTING_PATH = None
APP_LOGO = None
OST_SAMPLE = None
DEFAULT_OST_PATH = None
TFONT = None
DEFAULT_COORDINATES_PATH = None
# os
DEVICE_OS = platform.system()
# ost sample
OST_SAMPLE_IMAGE = None
# today
today = None
PRODUCTION_SUCCESS = 0
PRODUCTION_FILE_EXISTS = 1
PRODUCTION_FILE_NOT_RECOGNIZED = 2
update_today()
DEFAULT_DIR = get_desktop_directory()
# "course_code": ["course_title", "course_level", "credit", "compulsory"]
default_common_course_code_library = {
}
default_setting = {
"draw_ost_template": True,
"smart_fill": True,
"train": True,
"json_dir": DEFAULT_DIR,
"img_dir": DEFAULT_DIR,
"last_session": None,
}
default_ost = {
"OST_date_of_issue": today,
"name": ["", ""],
"OEN": "",
"student_number": "",
"gender": "",
"date_of_birth": ["", "", ""],
"name_of_district_school_board": "Toronto Private Inspected",
"district_school_board_number": "",
"name_of_school": "",
"school_number": "",
"date_of_entry": ["", "", ""],
"community_involvement_flag": False,
"provincial_secondary_school_literacy_requirement_flag": False,
"specialized_program": "",
"diploma_or_certificate": "Ontario Secondary School Diploma",
"diploma_or_certificate_date_of_issue": ["", ""],
"authorization": "",
"course_list": [],
"course_font_size": 50,
"course_spacing": 5,
}
default_coordinates = {
"Size": (3300, 2532),
"Offset": (0, 0),
# (x, y, width, height)
"OST_DateOfIssue": (2301, 73, 532, 85, 55),
"Page_1": (2826, 73, 183, 85, 50),
"Page_2": (3046, 73, 183, 85, 50),
"Surname": (85, 204, 645, 94, 50),
"GivenName": (730, 204, 772, 94, 50),
"OEN": (1502, 204, 537, 94, 50),
"StudentNumber": (2039, 204, 538, 94, 50),
"Gender": (2577, 204, 136, 94, 50),
"DateOfBirth_Y": (2713, 228, 202, 70, 40),
"DateOfBirth_M": (2915, 228, 202, 70, 40),
"DateOfBirth_D": (3117, 228, 147, 70, 40),
"NameOfDSB": (85, 336, 1023, 100, 50),
"NumberOfDSB": (1108, 338, 397, 100, 50),
"NameOfSchool": (1505, 338, 807, 100, 50),
"NumberOfSchool": (2311, 338, 402, 100, 50),
"DateOfEntry_Y": (2713, 368, 202, 70, 40),
"DateOfEntry_M": (2915, 368, 202, 70, 40),
"DateOfEntry_D": (3117, 368, 147, 70, 40),
# (x, y, width, height)
"Course": (35, 564, 3230, 1419),
# (x_offset, width)
"Course_date_offset": (35 - 35, 268),
"Course_level_offset": (306 - 35, 183),
"Course_title_offset": (491 - 35, 1637),
"Course_code_offset": (2131 - 35, 244),
"Course_percentage_offset": (2378 - 35, 175),
"Course_credit_offset": (2563 - 35, 183),
"Course_compulsory_offset": (2748 - 35, 207),
"Course_note_offset": (2965 - 35, 299),
"SummaryOfCredit": (2562, 1992, 184, 69, 55),
"SummaryOfCompulsory": (2748, 1992, 207, 69, 55),
"CommunityInvolvement_True": (75, 2125),
"CommunityInvolvement_False": (385, 2125),
"ProvincialSecondarySchoolLiteracy_True": (623, 2125),
"ProvincialSecondarySchoolLiteracy_False": (1173, 2125),
"SpecializedProgram": (1436, 2104, 1828, 96, 40),
"DiplomaOrCertificate": (77, 2240, 1622, 90, 40),
"DiplomaOrCertificate_DateOfIssue_Y": (1702, 2273, 180, 57, 40),
"DiplomaOrCertificate_DateOfIssue_M": (1885, 2273, 180, 57, 40),
"Authorization": (2070, 2240, 1148, 90, 40),
}
COMMON_COURSE_CODE_LIBRARY = None
SETTING = None
DEFAULT_OST_INFO = None
COORDINATES = None
| [
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11,
... | 2.340895 | 1,631 |
from ggplot import aes, geom_point, ggplot, mtcars
import matplotlib.pyplot as plt
from pandas import DataFrame
from bokeh import mpl
from bokeh.plotting import output_file, show
g = ggplot(mtcars, aes(x='wt', y='mpg', color='qsec')) + geom_point()
g.make()
plt.title("Point ggplot-based plot in Bokeh.")
output_file("ggplot_point.html", title="ggplot_point.py example")
show(mpl.to_bokeh())
| [
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148... | 2.518987 | 158 |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
from __future__ import absolute_import, division, print_function
import argparse
import logging
import os
import random
import sys
from io import open
import numpy as np
import torch
import json
from torch.utils.data import (DataLoader, SequentialSampler, RandomSampler, TensorDataset)
from tqdm import tqdm, trange
import ray
from ray import tune
from ray.tune.schedulers import HyperBandScheduler
from models.modeling_bert import QuestionAnswering, Config
from utils.optimization import AdamW, WarmupLinearSchedule
from utils.tokenization import BertTokenizer
from utils.korquad_utils import (read_squad_examples, convert_examples_to_features, RawResult, write_predictions)
from debug.evaluate_korquad import evaluate as korquad_eval
if sys.version_info[0] == 2:
import cPickle as pickle
else:
import pickle
# In[2]:
logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO)
logger = logging.getLogger(__name__)
# In[3]:
# In[4]:
from ray import tune
from ray.tune import track
from ray.tune.schedulers import HyperBandScheduler
from ray.tune.suggest.bayesopt import BayesOptSearch
ray.shutdown()
ray.init(webui_host='127.0.0.1')
# In[5]:
search_space = {
"max_seq_length": 512,
"doc_stride": 128,
"max_query_length": tune.sample_from(lambda _: int(np.random.uniform(50, 100))), #tune.uniform(50, 100),
"train_batch_size": 32,
"learning_rate": tune.loguniform(5e-4, 5e-7, 10),
"num_train_epochs": tune.grid_search([4, 8, 12, 16]),
"max_grad_norm": 1.0,
"adam_epsilon": 1e-6,
"warmup_proportion": 0.1,
"n_best_size": tune.sample_from(lambda _: int(np.random.uniform(50, 100))), #tune.uniform(50, 100),
"max_answer_length": tune.sample_from(lambda _: int(np.random.uniform(12, 25))), #tune.uniform(12, 25),
"seed": tune.sample_from(lambda _: int(np.random.uniform(1e+6, 1e+8)))
}
# In[ ]:
# In[ ]:
def evaluate(predict_file, batch_size, device, output_dir, n_best_size, max_answer_length, model, eval_examples, eval_features):
""" Eval """
all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long)
all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long)
dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_example_index)
sampler = SequentialSampler(dataset)
dataloader = DataLoader(dataset, sampler=sampler, batch_size=batch_size)
logger.info("***** Evaluating *****")
logger.info(" Num features = %d", len(dataset))
logger.info(" Batch size = %d", batch_size)
model.eval()
all_results = []
# set_seed(args) # Added here for reproductibility (even between python 2 and 3)
logger.info("Start evaluating!")
for input_ids, input_mask, segment_ids, example_indices in tqdm(dataloader, desc="Evaluating"):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
with torch.no_grad():
batch_start_logits, batch_end_logits = model(input_ids, segment_ids, input_mask)
for i, example_index in enumerate(example_indices):
start_logits = batch_start_logits[i].detach().cpu().tolist()
end_logits = batch_end_logits[i].detach().cpu().tolist()
eval_feature = eval_features[example_index.item()]
unique_id = int(eval_feature.unique_id)
all_results.append(RawResult(unique_id=unique_id,
start_logits=start_logits,
end_logits=end_logits))
output_prediction_file = os.path.join(output_dir, "predictions.json")
output_nbest_file = os.path.join(output_dir, "nbest_predictions.json")
write_predictions(eval_examples, eval_features, all_results,
n_best_size, max_answer_length,
False, output_prediction_file, output_nbest_file,
None, False, False, 0.0)
expected_version = 'KorQuAD_v1.0'
with open(predict_file) as dataset_file:
dataset_json = json.load(dataset_file)
read_version = "_".join(dataset_json['version'].split("_")[:-1])
if (read_version != expected_version):
logger.info('Evaluation expects ' + expected_version + ', but got dataset with ' + read_version, file=sys.stderr)
dataset = dataset_json['data']
with open(os.path.join(output_dir, "predictions.json")) as prediction_file:
predictions = json.load(prediction_file)
_eval = korquad_eval(dataset, predictions)
logger.info(json.dumps(_eval))
return _eval
# In[6]:
# In[ ]:
analysis = tune.run(train_korquad, config=search_space, scheduler=HyperBandScheduler(metric='f1', mode='max'), resources_per_trial={'gpu':1})
# In[ ]:
dfs = analysis.trial_dataframes
# In[ ]:
# ax = None
# for d in dfs.values():
# ax = d.mean_loss.plot(ax=ax, legend=True)
# ax.set_xlabel("Epochs")
# ax.set_ylabel("Mean Loss")
| [
2,
48443,
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14,
8800,
14,
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21015,
198,
2,
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25,
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69,
12,
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198,
198,
2,
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16,
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628,
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11,
7297,
11,
3601,
62,
8818,
198,
198,
11748,
1822,
29572... | 2.368794 | 2,256 |
from datetime import date
from django import forms
from django.contrib.auth.forms import UserCreationForm
from django.contrib.auth.models import User
from phonenumber_field.formfields import PhoneNumberField
from .models import Profile
from .options import STATE_CHOICES, YEARS
from .utils import AgeValidator
| [
6738,
4818,
8079,
1330,
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198,
198,
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42625,
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1330,
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198,
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8479,
198,
6738,
42625,
14208,
13,
3642,
822,
13,
18439,
13,
27530,
1330,
117... | 3.75 | 84 |
import sys
import pegtree as pg
from pegtree.visitor import ParseTreeVisitor
import random
# from . import verb
import verb
EMPTY = tuple()
#
OPTION = {
'Simple': False, #
'Block': False, # Expression <e> </e>
'EnglishFirst': False, #
'ShuffleSynonym': True, #
'MultipleSentence': False, #
'ShuffleOrder': True, #
'Verbose': True, #
}
# {|}
# :[|] ->
# : -> (synonyms) -> ()
# -> NSuffix()
# [||] ->
# <- BERT
# A B A -> B -> A
# randomize
RandomIndex = 0
# def conjugate(w, mode=0, vpos=None):
# suffix = ''
# if mode & verb.CASE == verb.CASE:
# if RandomIndex % 2 != 0:
# mode = (mode & ~verb.CASE) | verb.NOUN
# suffix = alt('||')
# else:
# suffix = ''
# if mode & verb.THEN == verb.THEN:
# if RandomIndex % 2 != 0:
# mode = (mode & ~verb.THEN) | verb._I
# suffix = ''
# return verb.conjugate(w, mode, vpos) + suffix
# NExpr
def grouping(e):
if isinstance(e, NPhrase):
return '{' + repr(e) + '}'
return repr(e)
neko = NWord('||')
print('@', neko, neko.generate())
wo = NSuffix(neko, '')
print('@', wo, wo.generate())
ni = NSuffix(neko, '')
print('@', ni, ni.generate())
ageru = NVerb('', 'V1', 0)
e = NPhrase(NOrdered(ni, wo), ageru)
print('@', e, e.generate())
##
## NExpr ()
peg = pg.grammar('tokibi.pegtree')
tokibi_parser = pg.generate(peg)
tokibi_reader = TokibiReader()
# t = parse('{|}')
# print(t, t.generate())
# t = parse('{}')
# print(t)
# if __name__ == '__main__':
# if len(sys.argv) > 1:
# read_tsv(sys.argv[1])
# else:
# e = parse('/{[|Puppy]}[|]')
# print(e, e.generate())
# e2 = parse('[|]/[|]')
# #e2, _ = parse('{A/B()//[]}')
# e = parse('A')
# e = e.apply({0: e2})
# print(e, e.generate())
# e = parse('A()')
# e = e.apply({0: e2})
# print(e, e.generate())
| [
11748,
25064,
198,
11748,
613,
13655,
631,
355,
23241,
198,
6738,
613,
13655,
631,
13,
4703,
2072,
1330,
2547,
325,
27660,
15854,
2072,
198,
11748,
4738,
198,
2,
422,
764,
1330,
15942,
198,
11748,
15942,
198,
198,
39494,
9936,
796,
4654... | 1.94499 | 1,018 |
from typing import Callable, Union
from kaggle import KaggleApi
from kaggle.models.kaggle_models_extended import Competition, Kernel
| [
6738,
19720,
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11,
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198,
198,
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293,
1330,
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32,
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198,
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13,
27530,
13,
74,
9460,
293,
62,
27530,
62,
2302,
1631,
1330,
27348,
11,
32169,
628
] | 3.375 | 40 |
from django.urls import path
from . import views
app_name = 'trip'
urlpatterns = [
path('', views.index, name='index'),
path('tripblog/', views.AllTrip.as_view(), name="tripplan"),
path('likereview/', views.like_comment_view, name="like_comment"),
path('tripdetail/<int:pk>/', views.trip_detail, name="tripdetail"),
path('addpost/', views.add_post, name="addpost"),
path('likepost/', views.like_post, name="like_trip"),
path('tripdetail/edit/<int:pk>', views.edit_post, name='editpost'),
path('tripdetail/<int:pk>/remove', views.delete_post, name='deletepost'),
path('category/<category>', views.CatsListView.as_view(), name='category'),
path('addcomment/', views.post_comment, name="add_comment"),
path('action/gettripqueries', views.get_trip_queries, name='get-trip-query'),
# 127.0.0.1/domnfoironkwe_0394
path('place/<str:place_id>/', views.place_info, name='place-detail'),
path('place/<str:place_id>/like', views.place_like, name='place-like'),
path('place/<str:place_id>/dislike', views.place_dislike, name='place-dislike'),
path('place/<str:place_id>/addreview', views.place_review, name='place-review'),
path('place/<str:place_id>/removereview', views.place_remove_review, name='place-remove-review'),
]
| [
6738,
42625,
14208,
13,
6371,
82,
1330,
3108,
198,
6738,
764,
1330,
5009,
198,
198,
1324,
62,
3672,
796,
705,
39813,
6,
198,
6371,
33279,
82,
796,
685,
198,
220,
220,
220,
3108,
10786,
3256,
5009,
13,
9630,
11,
1438,
11639,
9630,
33... | 2.635802 | 486 |
import asyncio
import logging
import pytest
from aio_pika import Message, connect_robust
from aio_pika.exceptions import DeliveryError
from aio_pika.patterns.rpc import RPC, log as rpc_logger
from tests import AMQP_URL
from tests.test_amqp import BaseTestCase
pytestmark = pytest.mark.asyncio
| [
11748,
30351,
952,
198,
11748,
18931,
198,
198,
11748,
12972,
9288,
198,
198,
6738,
257,
952,
62,
79,
9232,
1330,
16000,
11,
2018,
62,
22609,
436,
198,
6738,
257,
952,
62,
79,
9232,
13,
1069,
11755,
1330,
28682,
12331,
198,
6738,
257,... | 3 | 100 |
# coding=utf-8
# Copyright 2014 Pants project contributors (see CONTRIBUTORS.md).
# Licensed under the Apache License, Version 2.0 (see LICENSE).
from __future__ import (absolute_import, division, generators, nested_scopes, print_function,
unicode_literals, with_statement)
import os
import re
from pants.base.cmd_line_spec_parser import CmdLineSpecParser
from pants.build_graph.address import Address
from pants.build_graph.build_file_aliases import BuildFileAliases
from pants.build_graph.target import Target
from pants_test.base_test import BaseTest
| [
2,
19617,
28,
40477,
12,
23,
198,
2,
15069,
1946,
41689,
1628,
20420,
357,
3826,
27342,
9865,
3843,
20673,
13,
9132,
737,
198,
2,
49962,
739,
262,
24843,
13789,
11,
10628,
362,
13,
15,
357,
3826,
38559,
24290,
737,
198,
198,
6738,
1... | 3.238889 | 180 |
#!/usr/bin/env python2
"""\
Run additional filters on a folder of pdbs and copy the results back
into the original pdb.
Usage:
pull_into_place run_additional_metrics <directory> [options]
Options:
--max-runtime TIME [default: 12:00:00]
The runtime limit for each design job. The default value is
set pretty low so that the short queue is available by default. This
should work fine more often than not, but you also shouldn't be
surprised if you need to increase this.
--max-memory MEM [default: 2G]
The memory limit for each design job.
--mkdir
Make the directory corresponding to this step in the pipeline, but
don't do anything else. This is useful if you want to create custom
input files for just this step.
--test-run
Run on the short queue with a limited number of iterations. This
option automatically clears old results.
--clear
Clear existing results before submitting new jobs.
To use this class:
1. You need to initiate it with the directory where your pdb files
to be rerun are.
2. You need to use the setters for the Rosetta executable and the
metric.
"""
from klab import docopt, scripting, cluster
from pull_into_place import pipeline, big_jobs
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
17,
198,
198,
37811,
59,
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319,
257,
9483,
286,
279,
67,
1443,
290,
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262,
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736,
198,
20424,
262,
2656,
279,
9945,
13,
198,
198,
28350,
25,
198,
220,
220,
... | 3.011364 | 440 |
from distutils.core import setup
setup(
name = 'cl3ver',
packages = ['cl3ver'],
license = 'MIT',
install_requires = ['requests'],
version = '0.2',
description = 'A python 3 wrapper for the cleverbot.com API',
author = 'Japhy Bartlett',
author_email = 'cl3ver@pearachute.com',
url = 'https://github.com/japherwocky/cl3ver',
download_url = 'https://github.com/japherwocky/cl3ver/tarball/0.2.tar.gz',
keywords = ['cleverbot', 'wrapper', 'clever', 'chatbot', 'cl3ver'],
classifiers =[
'Programming Language :: Python :: 3 :: Only',
'License :: OSI Approved :: MIT License',
'Intended Audience :: Developers',
'Natural Language :: English',
],
)
| [
6738,
1233,
26791,
13,
7295,
1330,
9058,
198,
40406,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1438,
796,
705,
565,
18,
332,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
10392,
796,
37250,
565,
18,
332,
6,
... | 2.104326 | 393 |
# -*- coding: utf-8 -*-
import re
from Axon.Component import component
from Kamaelia.Util.Backplane import PublishTo, SubscribeTo
from Axon.Ipc import shutdownMicroprocess, producerFinished
from Kamaelia.Protocol.HTTP.HTTPClient import SimpleHTTPClient
from headstock.api.jid import JID
from headstock.api.im import Message, Body
from headstock.api.pubsub import Node, Item, Message
from headstock.api.discovery import *
from headstock.lib.utils import generate_unique
from bridge import Element as E
from bridge.common import XMPP_CLIENT_NS, XMPP_ROSTER_NS, \
XMPP_LAST_NS, XMPP_DISCO_INFO_NS, XMPP_DISCO_ITEMS_NS,\
XMPP_PUBSUB_NS
from amplee.utils import extract_url_trail, get_isodate,\
generate_uuid_uri
from amplee.error import ResourceOperationException
from microblog.atompub.resource import ResourceWrapper
from microblog.jabber.atomhandler import FeedReaderComponent
__all__ = ['DiscoHandler', 'ItemsHandler', 'MessageHandler']
publish_item_rx = re.compile(r'\[(.*)\] ([\w ]*)')
retract_item_rx = re.compile(r'\[(.*)\] ([\w:\-]*)')
geo_rx = re.compile(r'(.*) ([\[\.|\d,|\-\]]*)')
GEORSS_NS = u"http://www.georss.org/georss"
GEORSS_PREFIX = u"georss"
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
11748,
302,
198,
198,
6738,
12176,
261,
13,
21950,
1330,
7515,
198,
6738,
509,
1689,
25418,
13,
18274,
346,
13,
7282,
14382,
1330,
8525,
1836,
2514,
11,
19808,
2514,
198... | 2.670429 | 443 |
import os
import json
from torchblocks.metrics import SequenceLabelingScore
from torchblocks.trainer import SequenceLabelingTrainer
from torchblocks.callback import TrainLogger
from torchblocks.processor import SequenceLabelingProcessor, InputExample
from torchblocks.utils import seed_everything, dict_to_text, build_argparse
from torchblocks.utils import prepare_device, get_checkpoints
from torchblocks.data import CNTokenizer
from torchblocks.data import Vocabulary, VOCAB_NAME
from torchblocks.models.nn.lstm_crf import LSTMCRF
from torchblocks.models.bases import TrainConfig
from torchblocks.models.bases import WEIGHTS_NAME
MODEL_CLASSES = {
'lstm-crf': (TrainConfig, LSTMCRF, CNTokenizer)
}
def build_vocab(data_dir, vocab_dir):
'''
vocab
'''
vocab = Vocabulary()
vocab_path = os.path.join(vocab_dir, VOCAB_NAME)
if os.path.exists(vocab_path):
vocab.load_vocab(str(vocab_path))
else:
files = ["train.json", "dev.json", "test.json"]
for file in files:
with open(os.path.join(data_dir, file), 'r') as fr:
for line in fr:
line = json.loads(line.strip())
text = line['text']
vocab.update(list(text))
vocab.build_vocab()
vocab.save_vocab(vocab_path)
print("vocab size: ", len(vocab))
if __name__ == "__main__":
main()
| [
11748,
28686,
201,
198,
11748,
33918,
201,
198,
6738,
28034,
27372,
13,
4164,
10466,
1330,
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33986,
278,
26595,
201,
198,
6738,
28034,
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13,
2213,
10613,
1330,
45835,
33986,
278,
2898,
10613,
201,
198,
6738,
28034,
27372,
13,
47... | 2.343648 | 614 |
# Create your views here.
from django.urls import reverse_lazy
from django.views import generic
from forms.forms import UserCreateForm
| [
2,
13610,
534,
5009,
994,
13,
198,
6738,
42625,
14208,
13,
6371,
82,
1330,
9575,
62,
75,
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198,
6738,
42625,
14208,
13,
33571,
1330,
14276,
198,
198,
6738,
5107,
13,
23914,
1330,
11787,
16447,
8479,
628
] | 3.702703 | 37 |
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 20 01:33:18 2019
@author: iqbalsublime
"""
from Customer import Customer
from Restaurent import Restaurent
from Reserve import Reserve
from Menu import Menu
from Order import Order
cust1= Customer(1,"Iqbal", "0167****671")
rest1= Restaurent(1,"Farmgate", "102 Kazi Nazrul Islam Ave, Dhaka")
reserve1=Reserve(1, "20-11-2019",cust1, rest1)
"""
print("******Reservation*******")
print("Reserve ID:{}, Date: {} Customer Name: {}, Mobile:{}, Branch: {}".format(reserve1.reserveid,
reserve1.date, reserve1.customer.name, reserve1.customer.mobile, reserve1.restaurent.bname))
#print(reserve1.description())
print("******Reservation*******")
"""
menu1= Menu(1,"Burger", 160,"Fast Food",4)
menu2= Menu(2,"Pizza", 560,"Fast Food",2)
menu3= Menu(3,"Biriani", 220,"Indian",1)
menu4= Menu(4,"Pitha", 50,"Bangla",5)
order1= Order(1,"20-11-2019", cust1)
order1.addMenu(menu1)
order1.addMenu(menu2)
order1.addMenu(menu3)
order1.addMenu(menu4)
print("******Invoice*******")
print("Order ID:{}, Date: {} Customer Name: {}, Mobile:{}".format(order1.oid,
order1.date, order1.Customer.name, order1.Customer.mobile))
totalBill=0.0
serial=1
print("SL---Food----Price---Qy----total")
for order in order1.menus:
print(serial,order.name, order.price, order.quantity, (order.price*order.quantity))
totalBill=totalBill+(order.price*order.quantity)
print("Grand Total :", totalBill)
print("******Invoice*******") | [
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12,
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25,
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12,
23,
532,
9,
12,
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198,
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319,
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25,
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25,
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198,
198,
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22092,
13... | 2.646209 | 554 |
# -*- test-case-name: twisted.logger.test.test_io -*-
# Copyright (c) Twisted Matrix Laboratories.
# See LICENSE for details.
"""
File-like object that logs.
"""
import sys
from typing import AnyStr, Iterable, Optional
from constantly import NamedConstant
from incremental import Version
from twisted.python.deprecate import deprecatedProperty
from ._levels import LogLevel
from ._logger import Logger
def close(self) -> None:
"""
Close this file so it can no longer be written to.
"""
self._closed = True
def flush(self) -> None:
"""
No-op; this file does not buffer.
"""
pass
def fileno(self) -> int:
"""
Returns an invalid file descriptor, since this is not backed by an FD.
@return: C{-1}
"""
return -1
def isatty(self) -> bool:
"""
A L{LoggingFile} is not a TTY.
@return: C{False}
"""
return False
def write(self, message: AnyStr) -> None:
"""
Log the given message.
@param message: The message to write.
"""
if self._closed:
raise ValueError("I/O operation on closed file")
if isinstance(message, bytes):
text = message.decode(self._encoding)
else:
text = message
lines = (self._buffer + text).split("\n")
self._buffer = lines[-1]
lines = lines[0:-1]
for line in lines:
self.log.emit(self.level, format="{log_io}", log_io=line)
def writelines(self, lines: Iterable[AnyStr]) -> None:
"""
Log each of the given lines as a separate message.
@param lines: Data to write.
"""
for line in lines:
self.write(line)
def _unsupported(self, *args: object) -> None:
"""
Template for unsupported operations.
@param args: Arguments.
"""
raise OSError("unsupported operation")
read = _unsupported
next = _unsupported
readline = _unsupported
readlines = _unsupported
xreadlines = _unsupported
seek = _unsupported
tell = _unsupported
truncate = _unsupported
| [
2,
532,
9,
12,
1332,
12,
7442,
12,
3672,
25,
19074,
13,
6404,
1362,
13,
9288,
13,
9288,
62,
952,
532,
9,
12,
198,
2,
15069,
357,
66,
8,
40006,
24936,
46779,
13,
198,
2,
4091,
38559,
24290,
329,
3307,
13,
198,
198,
37811,
198,
... | 2.37069 | 928 |
from dataclasses import dataclass
# from pprint import pprint
import aiohttp
import discord
from discord.ext import commands
from bot import constants
API_URL = "https://livescore6.p.rapidapi.com/matches/v2/"
LIVE_MATCHES_URL = API_URL + "list-live"
HEADERS = {
"x-rapidapi-key": constants.RAPIDAPI_KEY,
"x-rapidapi-host": constants.RAPIDAPI_LIVESCORE6_HOST,
}
def setup(bot: commands.Bot):
"""Add Cricket Cog."""
bot.add_cog(Cricket(bot))
| [
6738,
4818,
330,
28958,
1330,
4818,
330,
31172,
198,
2,
422,
279,
4798,
1330,
279,
4798,
198,
198,
11748,
257,
952,
4023,
198,
11748,
36446,
198,
6738,
36446,
13,
2302,
1330,
9729,
198,
198,
6738,
10214,
1330,
38491,
198,
198,
17614,
... | 2.508108 | 185 |
"""
technique that randomly 0's out the update deltas for each parameter
"""
import theano
import theano.tensor as T
from theano.sandbox.rng_mrg import MRG_RandomStreams
import treeano
import treeano.nodes as tn
fX = theano.config.floatX
| [
37811,
198,
23873,
2350,
326,
15456,
657,
338,
503,
262,
4296,
1619,
83,
292,
329,
1123,
11507,
198,
37811,
198,
198,
11748,
262,
5733,
198,
11748,
262,
5733,
13,
83,
22854,
355,
309,
198,
6738,
262,
5733,
13,
38142,
3524,
13,
81,
7... | 2.963415 | 82 |
import os
import os.path
from datetime import datetime
import time
from stat import *
import pathlib
import json
| [
11748,
28686,
198,
11748,
28686,
13,
6978,
198,
6738,
4818,
8079,
1330,
4818,
8079,
198,
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640,
198,
6738,
1185,
1330,
1635,
198,
11748,
3108,
8019,
198,
11748,
33918,
198
] | 3.766667 | 30 |
from enum import Enum
| [
6738,
33829,
1330,
2039,
388,
628
] | 3.833333 | 6 |
import unittest
import os
from six import StringIO
from package_manager import util
CHECKSUM_TXT = "1915adb697103d42655711e7b00a7dbe398a33d7719d6370c01001273010d069"
DEBIAN_JESSIE_OS_RELEASE = """PRETTY_NAME="Distroless"
NAME="Debian GNU/Linux"
ID="debian"
VERSION_ID="8"
VERSION="Debian GNU/Linux 8 (jessie)"
HOME_URL="https://github.com/GoogleContainerTools/distroless"
SUPPORT_URL="https://github.com/GoogleContainerTools/distroless/blob/master/README.md"
BUG_REPORT_URL="https://github.com/GoogleContainerTools/distroless/issues/new"
"""
DEBIAN_STRETCH_OS_RELEASE = """PRETTY_NAME="Distroless"
NAME="Debian GNU/Linux"
ID="debian"
VERSION_ID="9"
VERSION="Debian GNU/Linux 9 (stretch)"
HOME_URL="https://github.com/GoogleContainerTools/distroless"
SUPPORT_URL="https://github.com/GoogleContainerTools/distroless/blob/master/README.md"
BUG_REPORT_URL="https://github.com/GoogleContainerTools/distroless/issues/new"
"""
DEBIAN_BUSTER_OS_RELEASE = """PRETTY_NAME="Distroless"
NAME="Debian GNU/Linux"
ID="debian"
VERSION_ID="10"
VERSION="Debian GNU/Linux 10 (buster)"
HOME_URL="https://github.com/GoogleContainerTools/distroless"
SUPPORT_URL="https://github.com/GoogleContainerTools/distroless/blob/master/README.md"
BUG_REPORT_URL="https://github.com/GoogleContainerTools/distroless/issues/new"
"""
# VERSION and VERSION_ID aren't set on unknown distros
DEBIAN_UNKNOWN_OS_RELEASE = """PRETTY_NAME="Distroless"
NAME="Debian GNU/Linux"
ID="debian"
HOME_URL="https://github.com/GoogleContainerTools/distroless"
SUPPORT_URL="https://github.com/GoogleContainerTools/distroless/blob/master/README.md"
BUG_REPORT_URL="https://github.com/GoogleContainerTools/distroless/issues/new"
"""
osReleaseForDistro = {
"jessie": DEBIAN_JESSIE_OS_RELEASE,
"stretch": DEBIAN_STRETCH_OS_RELEASE,
"buster": DEBIAN_BUSTER_OS_RELEASE,
"???": DEBIAN_UNKNOWN_OS_RELEASE,
}
if __name__ == '__main__':
unittest.main()
| [
11748,
555,
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395,
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1129,
1314,
324,
65,
40035,
15197,
67,
42780,
41948,
1157,
68,
22... | 2.644138 | 725 |
##########################################################################
# If not stated otherwise in this file or this component's Licenses.txt
# file the following copyright and licenses apply:
#
# Copyright 2016 RDK Management
#
# 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.
##########################################################################
'''
<?xml version='1.0' encoding='utf-8'?>
<xml>
<id></id>
<!-- Do not edit id. This will be auto filled while exporting. If you are adding a new script keep the id empty -->
<version>2</version>
<!-- Do not edit version. This will be auto incremented while updating. If you are adding a new script you can keep the vresion as 1 -->
<name>TS_TAD_Download_SetInvalidDiagnosticsState</name>
<!-- If you are adding a new script you can specify the script name. Script Name should be unique same as this file name with out .py extension -->
<primitive_test_id> </primitive_test_id>
<!-- Do not change primitive_test_id if you are editing an existing script. -->
<primitive_test_name>TADstub_Get</primitive_test_name>
<!-- -->
<primitive_test_version>3</primitive_test_version>
<!-- -->
<status>FREE</status>
<!-- -->
<synopsis>To check if Diagnostics state of download can be set with invalid value. Requested and Canceled are the only writable values.If the test fails,set any writable parameter and check if the DiagnosticsState changes to None</synopsis>
<!-- -->
<groups_id />
<!-- -->
<execution_time>1</execution_time>
<!-- -->
<long_duration>false</long_duration>
<!-- -->
<advanced_script>false</advanced_script>
<!-- execution_time is the time out time for test execution -->
<remarks>RDKB doesn't support Download Diagnostics feature till now</remarks>
<!-- Reason for skipping the tests if marked to skip -->
<skip>false</skip>
<!-- -->
<box_types>
</box_types>
<rdk_versions>
<rdk_version>RDKB</rdk_version>
<!-- -->
</rdk_versions>
<test_cases>
<test_case_id>TC_TAD_34</test_case_id>
<test_objective>To check if Diagnostics state of download can be set with invalid value. Requested and Canceled are the only writable values.If the test fails,set any writable parameter and check if the DiagnosticsState changes to None</test_objective>
<test_type>Positive</test_type>
<test_setup>XB3,Emulator</test_setup>
<pre_requisite>1.Ccsp Components should be in a running state else invoke cosa_start.sh manually that includes all the ccsp components.
2.TDK Agent should be in running state or invoke it through StartTdk.sh script</pre_requisite>
<api_or_interface_used>TADstub_Get</api_or_interface_used>
<input_parameters>Device.IP.Diagnostics.DownloadDiagnostics.DiagnosticsState
Device.IP.Diagnostics.DownloadDiagnostics.Interface
Device.IP.Diagnostics.DownloadDiagnostics.DownloadURL</input_parameters>
<automation_approch>1. Load TAD modules
2. From script invoke TADstub_Set to set all the writable parameters
3. Check whether the result params get changed along with the download DignosticsState
4. Validation of the result is done within the python script and send the result status to Test Manager.
5.Test Manager will publish the result in GUI as PASS/FAILURE based on the response from TAD stub.</automation_approch>
<except_output>CheckPoint 1:
The output should be logged in the Agent console/Component log
CheckPoint 2:
Stub function result should be success and should see corresponding log in the agent console log
CheckPoint 3:
TestManager GUI will publish the result as PASS in Execution/Console page of Test Manager</except_output>
<priority>High</priority>
<test_stub_interface>None</test_stub_interface>
<test_script>TS_TAD_Download_SetInvalidDiagnosticsState</test_script>
<skipped>No</skipped>
<release_version></release_version>
<remarks></remarks>
</test_cases>
<script_tags />
</xml>
'''
# use tdklib library,which provides a wrapper for tdk testcase script
import tdklib;
#Test component to be tested
obj = tdklib.TDKScriptingLibrary("tad","1");
#IP and Port of box, No need to change,
#This will be replaced with correspoing Box Ip and port while executing script
ip = <ipaddress>
port = <port>
obj.configureTestCase(ip,port,'TS_TAD_SetInvalidDownloadDiagnosticsState');
#Get the result of connection with test component and DUT
loadmodulestatus =obj.getLoadModuleResult();
print "[LIB LOAD STATUS] : %s" %loadmodulestatus ;
if "SUCCESS" in loadmodulestatus.upper():
#Set the result status of execution
obj.setLoadModuleStatus("SUCCESS");
tdkTestObj = obj.createTestStep('TADstub_Set');
tdkTestObj.addParameter("ParamName","Device.IP.Diagnostics.DownloadDiagnostics.DiagnosticsState");
tdkTestObj.addParameter("ParamValue","Completed");
tdkTestObj.addParameter("Type","string");
expectedresult="FAILURE";
tdkTestObj.executeTestCase(expectedresult);
actualresult = tdkTestObj.getResult();
details = tdkTestObj.getResultDetails();
if expectedresult in actualresult:
#Set the result status of execution
tdkTestObj.setResultStatus("SUCCESS");
print "TEST STEP 1:Set DiagnosticsState of download as completed";
print "EXPECTED RESULT 1: DiagnosticsState of download must be Requested or Canceled";
print "ACTUAL RESULT 1: Can not set diagnosticsState of download as completed, details : %s" %details;
#Get the result of execution
print "[TEST EXECUTION RESULT] : SUCCESS";
tdkTestObj = obj.createTestStep('TADstub_Set');
tdkTestObj.addParameter("ParamName","Device.IP.Diagnostics.DownloadDiagnostics.Interface");
tdkTestObj.addParameter("ParamValue","Interface_erouter0");
tdkTestObj.addParameter("Type","string");
expectedresult="SUCCESS";
tdkTestObj.executeTestCase(expectedresult);
actualresult = tdkTestObj.getResult();
details = tdkTestObj.getResultDetails();
if expectedresult in actualresult:
#Set the result status of execution
tdkTestObj.setResultStatus("SUCCESS");
print "TEST STEP 2: Set the interface of Download";
print "EXPECTED RESULT 2: Should set the interface of Download ";
print "ACTUAL RESULT 2: %s" %details;
#Get the result of execution
print "[TEST EXECUTION RESULT] : SUCCESS";
tdkTestObj = obj.createTestStep('TADstub_Get');
tdkTestObj.addParameter("paramName","Device.IP.Diagnostics.DownloadDiagnostics.DiagnosticsState");
expectedresult="SUCCESS";
tdkTestObj.executeTestCase(expectedresult);
actualresult = tdkTestObj.getResult();
details= tdkTestObj.getResultDetails();
if expectedresult in actualresult and details=="None":
#Set the result status of execution
tdkTestObj.setResultStatus("SUCCESS");
print "TEST STEP 3 :Get DiagnosticsState of download as None";
print "EXPECTED RESULT 3 :Should get the DiagnosticsState of download as None ";
print "ACTUAL RESULT 3 :The DiagnosticsState of download is , details : %s" %details;
#Get the result of execution
print "[TEST EXECUTION RESULT] : SUCCESS";
else:
#Set the result status of execution
tdkTestObj.setResultStatus("FAILURE");
print "TEST STEP 3 :Get DiagnosticsState of download as None";
print "EXPECTED RESULT 3 :Should get the Diagnostics State of download as None";
print "ACTUAL RESULT 3 :The DiagnosticsState of download is , details : %s" %details;
#Get the result of execution
print "[TEST EXECUTION RESULT] : FAILURE";
else:
#Set the result status of execution
tdkTestObj.setResultStatus("FAILURE");
print "TEST STEP 2: Set the interface of Download";
print "EXPECTED RESULT 2: Should set the interface of Download ";
print "ACTUAL RESULT 2: %s" %details;
#Get the result of execution
print "[TEST EXECUTION RESULT] : FAILURE";
else:
#Set the result status of execution
tdkTestObj.setResultStatus("FAILURE");
print "TEST STEP 1:Set DiagnosticsState of download as completed";
print "EXPECTED RESULT 1: DiagnosticsState of download must be Requested or Canceled";
print "ACTUAL RESULT 1: DiagnosticsState of download is set as completed, details : %s" %details;
#Get the result of execution
print "[TEST EXECUTION RESULT] : FAILURE";
obj.unloadModule("tad");
else:
print "Failed to load tad module";
obj.setLoadModuleStatus("FAILURE");
print "Module loading failed";
| [
29113,
29113,
7804,
2235,
198,
2,
1002,
407,
5081,
4306,
287,
428,
2393,
393,
428,
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338,
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4541,
13,
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2,
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198,
2,
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2,
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... | 2.917813 | 3,200 |
# -*- coding: utf-8 -*-
"""
Created on Fri Sep 01 14:09:03 2017
@author: BJ
"""
import cv2
import os
from matplotlib import pyplot as plt
os.chdir('E:\\GitHub\\openCV\\Tutorials')
# %% IMAGES
# Load color image
# Add the flags 1, 0 or -1, to load a color image, load an image in
# grayscale mode or load image as is, respectively
img = cv2.imread('LegoAd.jpg',1)
# Display an image
# The first argument is the window name (string), the second argument is the image
cv2.imshow('image',img)
# You can display multiple windows using different window names
cv2.imshow('image2',img)
# Displaying an image using Matplotlib
# OpenCV loads color in BGR, while matplotlib displays in RGB, so to use
# matplotlib you need to reverse the order of the color layers
plt.imshow(img[:,:,::-1],interpolation = 'bicubic')
# note there is an argument in imshow to set the colormap, this is ignored if
# the input is 3D as it assumes the third dimension directly specifies the RGB
# values
plt.imshow(img[:,:,::-1], cmap= "Greys", interpolation = 'bicubic')
# you can remove the tick marks using the following code
plt.xticks([]), plt.yticks([])
# Here is a list of the full matplot lib colormaps
# https://matplotlib.org/examples/color/colormaps_reference.html
# Closing an image
# To close a specific window use the following command withi its name as the argument
cv2.destroyWindow('image2')
# To close all windows use the following command with no arguments
cv2.destroyAllWindows()
# Writing an image
# The first argument is the name of the file to write and the second arguemnt
# is the image
cv2.imwrite('testimg.jpg',img)
# Resizing and image
img = cv2.imread('LegoAd.jpg',1)
# need to declare window before showing image
cv2.namedWindow('image',cv2.WINDOW_NORMAL)
cv2.imshow('image',img)
img_height = 600
img_width = int(img_height*float(img.shape[0])/img.shape[1])
cv2.resizeWindow('image', img_height,img_width)
# %% VIDEO
# You first need to create a capture object with the argument either being the
# name of the video file or the index of the capture device (starting from 0)
# only need more indexes when have additional video capture equipment attached
cap = cv2.VideoCapture(0)
while(True):
# Capture frame-by-frame
# frame returns the captured image and ret is a boolean which returns TRUE
# if frame is read correctly
ret, frame = cap.read()
# Our operations on the frame come here
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# gray2 = frame
# Display the resulting frame
cv2.imshow('frame',gray)
# Wait for the signal to stop the capture
# The argument is waitKey is the length of time to wait for the input before
# moving onto the next line of code
# when running on 64-bit you need to add 0xFF
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# When everything done, release the capture
cap.release()
#cv2.destroyWindow('frame')
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
37811,
198,
41972,
319,
19480,
8621,
5534,
1478,
25,
2931,
25,
3070,
2177,
198,
198,
31,
9800,
25,
47204,
198,
37811,
198,
198,
11748,
269,
85,
17,
198,
11748,
28686,
... | 3.076761 | 951 |
# Generated by Django 2.2.1 on 2022-02-25 15:50
from django.db import migrations
| [
2,
2980,
515,
416,
37770,
362,
13,
17,
13,
16,
319,
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12,
2999,
12,
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1120,
198,
198,
6738,
42625,
14208,
13,
9945,
1330,
15720,
602,
628
] | 2.766667 | 30 |
# Apache License Version 2.0
#
# Copyright (c) 2021., Redis Labs
# All rights reserved.
#
# This attribute is the only one place that the version number is written down,
# so there is only one place to change it when the version number changes.
import pkg_resources
PKG_NAME = "redis-benchmarks-specification"
try:
__version__ = pkg_resources.get_distribution(PKG_NAME).version
except (pkg_resources.DistributionNotFound, AttributeError):
__version__ = "99.99.99" # like redis
| [
2,
220,
24843,
13789,
10628,
362,
13,
15,
198,
2,
198,
2,
220,
15069,
357,
66,
8,
33448,
1539,
2297,
271,
23500,
198,
2,
220,
1439,
2489,
10395,
13,
198,
2,
198,
198,
2,
770,
11688,
318,
262,
691,
530,
1295,
326,
262,
2196,
1271... | 3.20915 | 153 |
# coding: utf-8
from __future__ import unicode_literals
from collections import OrderedDict
import six
from django.db.models import Model
from rest_framework import serializers
| [
2,
19617,
25,
3384,
69,
12,
23,
198,
6738,
11593,
37443,
834,
1330,
28000,
1098,
62,
17201,
874,
198,
198,
6738,
17268,
1330,
14230,
1068,
35,
713,
198,
198,
11748,
2237,
198,
6738,
42625,
14208,
13,
9945,
13,
27530,
1330,
9104,
198,
... | 3.6 | 50 |
import requests
from utils import loginFile, dataAnalysis
import os
import datetime
from dateutil.relativedelta import relativedelta
import json
from utils.logCls import Logger
dirpath = os.path.dirname(__file__)
cookieFile = f"{dirpath}/utils/cookies.txt"
dataFile = f"{dirpath}/datas"
if __name__ == '__main__':
demo = DevopsProject("test")
demo.main()
| [
11748,
7007,
198,
6738,
3384,
4487,
1330,
17594,
8979,
11,
1366,
32750,
198,
11748,
28686,
198,
11748,
4818,
8079,
198,
6738,
3128,
22602,
13,
2411,
265,
1572,
12514,
1330,
48993,
1572,
12514,
198,
11748,
33918,
198,
6738,
3384,
4487,
13,... | 2.852713 | 129 |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
# Copyright (c) 2018 - huwei <huwei@gionee.com>
"""
This is a python script for the ezalor tools which is used to io monitor.
You can use the script to open or off the switch, or point the package name which you want to monitor it only.
The core function is to export data what ezalor is record.
"""
import os
import re
import sys, getopt
import sqlite3
import subprocess
import xlsxwriter as xw
from markhelper import MarkHelper
from record import Record
from style import Style
from datetime import datetime
DB_NAME_REG = "^ezalor_{0}(.*).db$"
tableheaders = ["path", "process", "thread", "processId", "threadId",
"readCount", "readBytes", "readTime", "writeCount", "writeBytes", "writeTime", "stacktrace",
"openTime", "closeTime", "mark"]
envDir = "/sdcard/ezalor/"
AUTOCOLUMN_WIDTH_INDEXS = [0, 1, 2, 12, 13, 14]
# os.system("rm " + path + "ezalor.db")
if __name__ == "__main__":
main(sys.argv[1:])
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
198,
2,
532,
9,
12,
19617,
25,
40477,
12,
23,
532,
9,
12,
198,
2,
15069,
357,
66,
8,
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532,
289,
84,
42990,
1279,
13415,
42990,
31,
70,
295,
1453,
13,
785,
29,
198,
37811,
198,
... | 2.677333 | 375 |
# encoding: utf-8
import uuid
import difflib
from datetime import date
from django.db import models
from django.utils.translation import ugettext as _
from django.core.exceptions import ImproperlyConfigured, ValidationError
from django.db import IntegrityError
from django.contrib.contenttypes.models import ContentType
from revisions import managers, utils
import inspect
# the crux of all errors seems to be that, with VersionedBaseModel,
# doing setattr(self, self.pk_name, None) does _not_ lead to creating
# a new object, and thus versioning as a whole doesn't work
# the only thing lacking from the VersionedModelBase is a version id.
# You may use VersionedModelBase if you need to specify your own
# AutoField (e.g. using UUIDs) or if you're trying to adapt an existing
# model to ``django-revisions`` and have an AutoField not named
# ``vid``. | [
2,
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25,
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5239... | 3.556017 | 241 |
import json
import os
from typing import Any, Dict, Optional
import tomlkit
import yaml
from renoir.apis import Renoir, ESCAPES, MODES
from renoir.writers import Writer as _Writer
from .utils import adict, obj_to_adict
def _indent(text: str, spaces: int = 2) -> str:
offset = " " * spaces
rv = f"\n{offset}".join(text.split("\n"))
return rv
def _to_json(obj: Any, indent: Optional[int] = None) -> str:
return json.dumps(obj, indent=indent)
def _to_toml(obj: Any) -> str:
return tomlkit.dumps(obj)
def _to_yaml(obj: Any) -> str:
return yaml.dump(obj)
def base_ctx(ctx: Dict[str, Any]):
ctx.update(
env=obj_to_adict(os.environ),
indent=_indent,
to_json=_to_json,
to_toml=_to_toml,
to_yaml=_to_yaml
)
yaml.add_representer(adict, yaml.representer.Representer.represent_dict)
templater = Templater(mode=MODES.plain, adjust_indent=True, contexts=[base_ctx])
| [
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2969,
... | 2.303178 | 409 |
# Copyright (c) 2021 Adrien Pajon (adrien.pajon@gmail.com)
#
# This software is released under the MIT License.
# https://opensource.org/licenses/MIT
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from api_phidget_n_MQTT.src.lib_global_python import searchLoggerFile
| [
2,
15069,
357,
66,
8,
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350,
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17168,
13789,
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198,
2,
3740,
1378,
44813,
1668,
13,
239... | 3 | 104 |
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
import mock
from oslo_serialization import jsonutils
from oslo_utils.fixture import uuidsentinel as uuids
import six
from nova.objects import resource
from nova.tests.unit.objects import test_objects
fake_resources = resource.ResourceList(objects=[
resource.Resource(provider_uuid=uuids.rp, resource_class='CUSTOM_RESOURCE',
identifier='foo'),
resource.Resource(provider_uuid=uuids.rp, resource_class='CUSTOM_RESOURCE',
identifier='bar')])
fake_vpmems = [
resource.LibvirtVPMEMDevice(
label='4GB', name='ns_0', devpath='/dev/dax0.0',
size=4292870144, align=2097152),
resource.LibvirtVPMEMDevice(
label='4GB', name='ns_1', devpath='/dev/dax0.0',
size=4292870144, align=2097152)]
fake_instance_extras = {
'resources': jsonutils.dumps(fake_resources.obj_to_primitive())
}
| [
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... | 2.669708 | 548 |
from sqlalchemy.engine.base import Connection
from sqlalchemy.sql.selectable import Select, CompoundSelect
from sqlalchemy.engine.result import Result
from sqlalchemy.dialects import postgresql
from .utils import transaction
from ...common_imports import *
from ...util.common_ut import get_uid
from ...core.config import PSQLConfig
################################################################################
### helper functions
################################################################################
################################################################################
### interface to postgres
################################################################################
################################################################################
### fast operations
################################################################################
def fast_select(query:TUnion[str, Select]=None, qual_table:str=None,
index_col:str=None, cols:TList[str]=None,
conn:Connection=None) -> pd.DataFrame:
"""
Some notes:
- loading an empty table with an index (index_col=something) will not
display the index name(s), but they are in the (empty) index
"""
logging.debug('Fastread does not handle dtypes')
# quote table name
if query is None:
assert qual_table is not None
if '.' in qual_table:
schema, table = qual_table.split('.')
quoted_table = f'"{schema}"."{table}"'
else:
quoted_table = f'"{qual_table}"'
if cols is not None:
cols_string = ', '.join([f'"{col}"' for col in cols])
query = f'SELECT {cols_string} FROM {quoted_table}'
else:
query = f'SELECT * FROM {quoted_table}'
head = 'HEADER'
if isinstance(query, (Select, CompoundSelect)):
#! the query object must be converted to a pure postgresql-compatible
#! string for this to work, and in particular to render bound parameters
# in-line using the literal_binds kwarg and the particular dialect
query_string = query.compile(bind=conn.engine,
compile_kwargs={'literal_binds': True},
dialect=postgresql.dialect())
elif isinstance(query, str):
query_string = query
else:
raise NotImplementedError()
copy_sql = f"""COPY ({query_string}) TO STDOUT WITH CSV {head}"""
buffer = io.StringIO()
# Note that we need to use a *raw* connection in this method, which can be
# accessed as conn.connection
with conn.connection.cursor() as curs:
curs.copy_expert(copy_sql, buffer)
buffer.seek(0)
df:pd.DataFrame = pd.read_csv(buffer)
if index_col is not None:
df = df.set_index(index_col)
return df
def fast_insert(df:pd.DataFrame, qual_table:str, conn:Connection=None,
columns:TList[str]=None, include_index:bool=True):
"""
In psycopg 2.9, they changed the .copy_from() method, so that table names
are now quoted. This means that it won't work with a schema-qualified name.
This method fixes this by using copy_expert(), as directed by the psycopg2
docs.
"""
if columns is None:
columns = df.columns
if '.' in qual_table:
schema, table = qual_table.split('.')
quoted_table = f'"{schema}"."{table}"'
else:
quoted_table = f'"{qual_table}"'
start_time = time.time()
# save dataframe to an in-memory buffer
buffer = io.StringIO()
if include_index:
df = df.reset_index()
df.to_csv(buffer, header=False, index=False, columns=columns, na_rep='')
buffer.seek(0)
columns_string = ', '.join('"{}"'.format(k) for k in columns)
query = f"""COPY {quoted_table}({columns_string}) FROM STDIN WITH CSV"""
# Note that we need to use a *raw* connection in this method, which can be
# accessed as conn.connection
with conn.connection.cursor() as curs:
curs.copy_expert(sql=query, file=buffer)
end_time = time.time()
nrows = df.shape[0]
total_time = end_time - start_time
logging.debug(f'Inserted {nrows} rows, {nrows/total_time} rows/second')
def fast_upsert(df:pd.DataFrame, qual_table:str,
index_cols:TList[str], columns:TList[str]=None,
include_index:bool=True, conn:Connection=None):
"""
code based on
https://stackoverflow.com/questions/46934351/python-postgresql-copy-command-used-to-insert-or-update-not-just-insert
"""
if include_index:
df = df.reset_index()
#! importantly, columns are set after potentially resetting the index
if columns is None:
columns = list(df.columns)
if '.' in qual_table:
schema, table = qual_table.split('.')
quoted_table = f'"{schema}"."{table}"'
else:
schema = ''
table = qual_table
quoted_table = f'"{qual_table}"'
# create a temporary table with same columns as target table
# temp_qual_table = f'{schema}.{table}__copy'
temp_uid = get_uid()[:16]
temp_qual_table = f'{schema}_{table}__copy_{temp_uid}'
temp_index_name = f'{schema}_{table}__temp_index_{temp_uid}'
create_temp_table_query = f"""
create temporary table {temp_qual_table} as (select * from {quoted_table} limit
0);
"""
conn.execute(create_temp_table_query)
# if provided, create indices on the table
if index_cols is not None:
create_temp_index_query = f"""
CREATE INDEX {temp_index_name} ON {temp_qual_table}({','.join(index_cols)});
"""
conn.execute(create_temp_index_query)
# copy data into this table
fast_insert(df=df, qual_table=temp_qual_table, conn=conn, columns=columns,
include_index=include_index)
# comma-separated lists of various things
target_cols_string = f"{', '.join(columns)}"
source_cols_string = f"{', '.join([f'{temp_qual_table}.{col}' for col in columns])}"
index_cols_string = f"{', '.join([f'{col}' for col in index_cols])}"
# update existing records
index_conditions = ' AND '.join([f'{qual_table}.{col} = {temp_qual_table}.{col}' for col in index_cols])
update_query = f"""
UPDATE {quoted_table}
SET
({target_cols_string}) = ({source_cols_string})
FROM
{temp_qual_table}
WHERE
{index_conditions}
"""
conn.execute(update_query)
# insert new records
insert_query = f"""
INSERT INTO {quoted_table}({target_cols_string})
(
SELECT {source_cols_string}
FROM
{temp_qual_table} LEFT JOIN {quoted_table} USING({index_cols_string})
WHERE {table} IS NULL);
"""
conn.execute(insert_query) | [
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282... | 2.565725 | 2,655 |
import pytest
from moto import mock_ec2
from moto.ec2.models import AMIS
from aec.command.ami import delete, describe, share
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628,
628,
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] | 3.119048 | 42 |
from django.views.generic import ListView
from rest_framework.permissions import AllowAny
from rest_framework.viewsets import ModelViewSet
from mainapp.models import Manufacturer, Car
from mainapp.serializers import ManufacturerSerializer, CarSerializer
| [
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... | 4.177419 | 62 |
# In this module we will get the brand count and the users per brand as a list.
# Add more details as deemed necessary
# I'm using mergesort here instead of quicksort as the size of data is much larger than for users
import Mergesort
import numpy as np
# Below was using list of tuples for storage, now going to convert to dictionary of np.arrays or lists. This could be more R or database style.
if __name__== "__main__":
customers = [0,0,1,1,1,2,2,2,2]
purchases = [0,3,5,1,2,4,1,3,5]
print(get_brand_purchase_deets(customers, purchases)) | [
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... | 3.038251 | 183 |
import uctypes as ct
MPU_ = {
'TYPE' : ( 0x00, {
'reg' : 0x00 | ct.UINT32,
'SEPARATE' : 0x00 | ct.BFUINT32 | 0 << ct.BF_POS | 1 << ct.BF_LEN,
'DREGION' : 0x00 | ct.BFUINT32 | 8 << ct.BF_POS | 8 << ct.BF_LEN,
'IREGION' : 0x00 | ct.BFUINT32 | 16 << ct.BF_POS | 8 << ct.BF_LEN,
}),
'CTRL' : ( 0x04, {
'reg' : 0x00 | ct.UINT32,
'ENABLE' : 0x00 | ct.BFUINT32 | 0 << ct.BF_POS | 1 << ct.BF_LEN,
'HFNMIENA' : 0x00 | ct.BFUINT32 | 1 << ct.BF_POS | 1 << ct.BF_LEN,
'PRIVDEFENA' : 0x00 | ct.BFUINT32 | 2 << ct.BF_POS | 1 << ct.BF_LEN,
}),
'RNR' : ( 0x08, {
'reg' : 0x00 | ct.UINT32,
'REGION' : 0x00 | ct.BFUINT32 | 0 << ct.BF_POS | 8 << ct.BF_LEN,
}),
'RBAR' : ( 0x0C, {
'reg' : 0x00 | ct.UINT32,
'REGION' : 0x00 | ct.BFUINT32 | 0 << ct.BF_POS | 4 << ct.BF_LEN,
'VALID' : 0x00 | ct.BFUINT32 | 4 << ct.BF_POS | 1 << ct.BF_LEN,
'ADDR' : 0x00 | ct.BFUINT32 | 5 << ct.BF_POS | 27 << ct.BF_LEN,
}),
'RASR' : ( 0x10, {
'reg' : 0x00 | ct.UINT32,
'ENABLE' : 0x00 | ct.BFUINT32 | 0 << ct.BF_POS | 1 << ct.BF_LEN,
'SIZE' : 0x00 | ct.BFUINT32 | 1 << ct.BF_POS | 1 << ct.BF_LEN,
'SRD' : 0x00 | ct.BFUINT32 | 8 << ct.BF_POS | 8 << ct.BF_LEN,
'B' : 0x00 | ct.BFUINT32 | 16 << ct.BF_POS | 1 << ct.BF_LEN,
'C' : 0x00 | ct.BFUINT32 | 17 << ct.BF_POS | 1 << ct.BF_LEN,
'S' : 0x00 | ct.BFUINT32 | 18 << ct.BF_POS | 1 << ct.BF_LEN,
'TEX' : 0x00 | ct.BFUINT32 | 19 << ct.BF_POS | 3 << ct.BF_LEN,
'AP' : 0x00 | ct.BFUINT32 | 24 << ct.BF_POS | 3 << ct.BF_LEN,
'XN' : 0x00 | ct.BFUINT32 | 28 << ct.BF_POS | 1 << ct.BF_LEN,
}),
'RBAR_A1' : ( 0x14, {
'reg' : 0x00 | ct.UINT32,
'REGION' : 0x00 | ct.BFUINT32 | 0 << ct.BF_POS | 4 << ct.BF_LEN,
'VALID' : 0x00 | ct.BFUINT32 | 4 << ct.BF_POS | 1 << ct.BF_LEN,
'ADDR' : 0x00 | ct.BFUINT32 | 5 << ct.BF_POS | 27 << ct.BF_LEN,
}),
'RASR_A1' : ( 0x18, {
'reg' : 0x00 | ct.UINT32,
'ENABLE' : 0x00 | ct.BFUINT32 | 0 << ct.BF_POS | 1 << ct.BF_LEN,
'SIZE' : 0x00 | ct.BFUINT32 | 1 << ct.BF_POS | 1 << ct.BF_LEN,
'SRD' : 0x00 | ct.BFUINT32 | 8 << ct.BF_POS | 8 << ct.BF_LEN,
'B' : 0x00 | ct.BFUINT32 | 16 << ct.BF_POS | 1 << ct.BF_LEN,
'C' : 0x00 | ct.BFUINT32 | 17 << ct.BF_POS | 1 << ct.BF_LEN,
'S' : 0x00 | ct.BFUINT32 | 18 << ct.BF_POS | 1 << ct.BF_LEN,
'TEX' : 0x00 | ct.BFUINT32 | 19 << ct.BF_POS | 3 << ct.BF_LEN,
'AP' : 0x00 | ct.BFUINT32 | 24 << ct.BF_POS | 3 << ct.BF_LEN,
'XN' : 0x00 | ct.BFUINT32 | 28 << ct.BF_POS | 1 << ct.BF_LEN,
}),
'RBAR_A2' : ( 0x1C, {
'reg' : 0x00 | ct.UINT32,
'REGION' : 0x00 | ct.BFUINT32 | 0 << ct.BF_POS | 4 << ct.BF_LEN,
'VALID' : 0x00 | ct.BFUINT32 | 4 << ct.BF_POS | 1 << ct.BF_LEN,
'ADDR' : 0x00 | ct.BFUINT32 | 5 << ct.BF_POS | 27 << ct.BF_LEN,
}),
'RASR_A2' : ( 0x20, {
'reg' : 0x00 | ct.UINT32,
'ENABLE' : 0x00 | ct.BFUINT32 | 0 << ct.BF_POS | 1 << ct.BF_LEN,
'SIZE' : 0x00 | ct.BFUINT32 | 1 << ct.BF_POS | 1 << ct.BF_LEN,
'SRD' : 0x00 | ct.BFUINT32 | 8 << ct.BF_POS | 8 << ct.BF_LEN,
'B' : 0x00 | ct.BFUINT32 | 16 << ct.BF_POS | 1 << ct.BF_LEN,
'C' : 0x00 | ct.BFUINT32 | 17 << ct.BF_POS | 1 << ct.BF_LEN,
'S' : 0x00 | ct.BFUINT32 | 18 << ct.BF_POS | 1 << ct.BF_LEN,
'TEX' : 0x00 | ct.BFUINT32 | 19 << ct.BF_POS | 3 << ct.BF_LEN,
'AP' : 0x00 | ct.BFUINT32 | 24 << ct.BF_POS | 3 << ct.BF_LEN,
'XN' : 0x00 | ct.BFUINT32 | 28 << ct.BF_POS | 1 << ct.BF_LEN,
}),
'RBAR_A3' : ( 0x24, {
'reg' : 0x00 | ct.UINT32,
'REGION' : 0x00 | ct.BFUINT32 | 0 << ct.BF_POS | 4 << ct.BF_LEN,
'VALID' : 0x00 | ct.BFUINT32 | 4 << ct.BF_POS | 1 << ct.BF_LEN,
'ADDR' : 0x00 | ct.BFUINT32 | 5 << ct.BF_POS | 27 << ct.BF_LEN,
}),
'RASR_A3' : ( 0x28, {
'reg' : 0x00 | ct.UINT32,
'ENABLE' : 0x00 | ct.BFUINT32 | 0 << ct.BF_POS | 1 << ct.BF_LEN,
'SIZE' : 0x00 | ct.BFUINT32 | 1 << ct.BF_POS | 1 << ct.BF_LEN,
'SRD' : 0x00 | ct.BFUINT32 | 8 << ct.BF_POS | 8 << ct.BF_LEN,
'B' : 0x00 | ct.BFUINT32 | 16 << ct.BF_POS | 1 << ct.BF_LEN,
'C' : 0x00 | ct.BFUINT32 | 17 << ct.BF_POS | 1 << ct.BF_LEN,
'S' : 0x00 | ct.BFUINT32 | 18 << ct.BF_POS | 1 << ct.BF_LEN,
'TEX' : 0x00 | ct.BFUINT32 | 19 << ct.BF_POS | 3 << ct.BF_LEN,
'AP' : 0x00 | ct.BFUINT32 | 24 << ct.BF_POS | 3 << ct.BF_LEN,
'XN' : 0x00 | ct.BFUINT32 | 28 << ct.BF_POS | 1 << ct.BF_LEN,
}),
}
MPU = ct.struct(0xe000ed90, MPU_)
| [
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6,
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25,
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6,
197,
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220,
220,
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930,
269,
83,
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52,
... | 1.576202 | 2,933 |
import torch
from lib.utils import is_parallel
import numpy as np
np.set_printoptions(threshold=np.inf)
import cv2
from sklearn.cluster import DBSCAN
def build_targets(cfg, predictions, targets, model, bdd=True):
'''
predictions
[16, 3, 32, 32, 85]
[16, 3, 16, 16, 85]
[16, 3, 8, 8, 85]
torch.tensor(predictions[i].shape)[[3, 2, 3, 2]]
[32,32,32,32]
[16,16,16,16]
[8,8,8,8]
targets[3,x,7]
t [index, class, x, y, w, h, head_index]
'''
# Build targets for compute_loss(), input targets(image,class,x,y,w,h)
if bdd:
if is_parallel(model):
det = model.module.det_out_bdd
else:
det = model.det_out_bdd
else:
if is_parallel(model):
det = model.module.det_out_bosch
else:
det = model.det_out_bosch
# print(type(model))
# det = model.model[model.detector_index]
# print(type(det))
na, nt = det.na, targets.shape[0] # number of anchors, targets
tcls, tbox, indices, anch = [], [], [], []
gain = torch.ones(7, device=targets.device) # normalized to gridspace gain
ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt)
targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices
g = 0.5 # bias
off = torch.tensor([[0, 0],
[1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m
# [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm
], device=targets.device).float() * g # offsets
for i in range(det.nl):
anchors = det.anchors[i] #[3,2]
gain[2:6] = torch.tensor(predictions[i].shape)[[3, 2, 3, 2]] # xyxy gain
# Match targets to anchors
t = targets * gain
if nt:
# Matches
r = t[:, :, 4:6] / anchors[:, None] # wh ratio
j = torch.max(r, 1. / r).max(2)[0] < cfg.TRAIN.ANCHOR_THRESHOLD # compare
# j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
t = t[j] # filter
# Offsets
gxy = t[:, 2:4] # grid xy
gxi = gain[[2, 3]] - gxy # inverse
j, k = ((gxy % 1. < g) & (gxy > 1.)).T
l, m = ((gxi % 1. < g) & (gxi > 1.)).T
j = torch.stack((torch.ones_like(j), j, k, l, m))
t = t.repeat((5, 1, 1))[j]
offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
else:
t = targets[0]
offsets = 0
# Define
b, c = t[:, :2].long().T # image, class
gxy = t[:, 2:4] # grid xy
gwh = t[:, 4:6] # grid wh
gij = (gxy - offsets).long()
gi, gj = gij.T # grid xy indices
# Append
a = t[:, 6].long() # anchor indices
indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices
tbox.append(torch.cat((gxy - gij, gwh), 1)) # box
anch.append(anchors[a]) # anchors
tcls.append(c) # class
return tcls, tbox, indices, anch
def morphological_process(image, kernel_size=5, func_type=cv2.MORPH_CLOSE):
"""
morphological process to fill the hole in the binary segmentation result
:param image:
:param kernel_size:
:return:
"""
if len(image.shape) == 3:
raise ValueError('Binary segmentation result image should be a single channel image')
if image.dtype is not np.uint8:
image = np.array(image, np.uint8)
kernel = cv2.getStructuringElement(shape=cv2.MORPH_ELLIPSE, ksize=(kernel_size, kernel_size))
# close operation fille hole
closing = cv2.morphologyEx(image, func_type, kernel, iterations=1)
return closing
def connect_components_analysis(image):
"""
connect components analysis to remove the small components
:param image:
:return:
"""
if len(image.shape) == 3:
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
else:
gray_image = image
# print(gray_image.dtype)
return cv2.connectedComponentsWithStats(gray_image, connectivity=8, ltype=cv2.CV_32S)
| [
11748,
28034,
198,
6738,
9195,
13,
26791,
1330,
318,
62,
1845,
29363,
198,
11748,
299,
32152,
355,
45941,
198,
37659,
13,
2617,
62,
4798,
25811,
7,
400,
10126,
28,
37659,
13,
10745,
8,
198,
11748,
269,
85,
17,
198,
6738,
1341,
35720,
... | 2.015714 | 2,100 |
name = "ada lovelace"
print(name.title())
| [
3672,
796,
366,
4763,
2376,
626,
558,
1,
201,
198,
4798,
7,
3672,
13,
7839,
28955,
201,
198
] | 2.444444 | 18 |
import sys
import subprocess
import os
import shutil
import argparse
import json
import re
from enum import Enum
from dataclasses import dataclass
import gbench
from gbench import util, report
from gbench.util import *
parser = argparse.ArgumentParser(description='test results')
parser.add_argument("-workdir", type=str, help="working directory", default="")
parser.add_argument("-v", help="verbose", action="store_true", default=False)
args = parser.parse_args()
workdir = args.workdir
verbose = args.v
Labs = dict()
Labs["memory_bound"] = dict()
Labs["core_bound"] = dict()
Labs["bad_speculation"] = dict()
Labs["frontend_bound"] = dict()
Labs["data_driven"] = dict()
Labs["misc"] = dict()
Labs["memory_bound"]["data_packing"] = LabParams(threshold=15.0)
Labs["memory_bound"]["loop_interchange_1"] = LabParams(threshold=85.0)
Labs["memory_bound"]["loop_interchange_2"] = LabParams(threshold=75.0)
Labs["misc"]["warmup"] = LabParams(threshold=50.0)
Labs["core_bound"]["function_inlining_1"] = LabParams(threshold=35.0)
Labs["core_bound"]["compiler_intrinsics_1"] = LabParams(threshold=60.0)
Labs["core_bound"]["vectorization_1"] = LabParams(threshold=90.0)
if not workdir:
print ("Error: working directory is not provided.")
sys.exit(1)
os.chdir(workdir)
checkAll = False
benchLabPath = 0
DirLabPathRegex = re.compile(r'labs/(.*)/(.*)/')
try:
outputGitLog = subprocess.check_output("git log -1 --oneline" , shell=True)
# If the commit message has '[CheckAll]' substring, benchmark everything
if b'[CheckAll]' in outputGitLog:
checkAll = True
print("Will benchmark all the labs")
# Otherwise, analyze the changes made in the last commit and identify which lab to benchmark
else:
outputGitShow = subprocess.check_output("git show -1 --dirstat --oneline" , shell=True)
lines = outputGitShow.split(b'\n')
# Expect at least 2 lines in the output
if (len(lines) < 2 or len(lines[1]) == 0):
print("Can't figure out which lab was changed in the last commit. Will benchmark all the labs.")
checkAll = True
elif changedMultipleLabs(lines):
print("Multiple labs changed. Will benchmark all the labs.")
checkAll = True
else:
# Skip the first line that has the commit hash and message
percent, path = lines[1].split(b'%')
GitShowLabPath = DirLabPathRegex.search(str(path))
if (GitShowLabPath):
benchLabPath = LabPath(GitShowLabPath.group(1), GitShowLabPath.group(2))
print("Will benchmark the lab: " + getLabNameStr(benchLabPath))
else:
print("Can't figure out which lab was changed in the last commit. Will benchmark all the labs.")
checkAll = True
except:
print("Error: can't fetch the last commit from git history")
sys.exit(1)
result = False
if checkAll:
if not checkAllLabs(workdir):
sys.exit(1)
print(bcolors.HEADER + "\nLab Assignments Summary:" + bcolors.ENDC)
allSkipped = True
for category in Labs:
print(bcolors.HEADER + " " + category + ":" + bcolors.ENDC)
for lab in Labs[category]:
if ScoreResult.SKIPPED == Labs[category][lab].result:
print(bcolors.OKCYAN + " " + lab + ": Skipped" + bcolors.ENDC)
else:
allSkipped = False
if ScoreResult.PASSED == Labs[category][lab].result:
print(bcolors.OKGREEN + " " + lab + ": Passed" + bcolors.ENDC)
# Return true if at least one lab succeeded
result = True
if ScoreResult.BENCH_FAILED == Labs[category][lab].result:
print(bcolors.FAIL + " " + lab + ": Failed: not fast enough" + bcolors.ENDC)
if ScoreResult.BUILD_FAILED == Labs[category][lab].result:
print(bcolors.FAIL + " " + lab + ": Failed: build error" + bcolors.ENDC)
if allSkipped:
result = True
else:
labdir = os.path.join(workdir, benchLabPath.category, benchLabPath.name)
if not buildLab(labdir, "solution"):
sys.exit(1)
if not checkoutBaseline(workdir):
sys.exit(1)
if not buildLab(labdir, "baseline"):
sys.exit(1)
if noChangesToTheBaseline(labdir):
print(bcolors.OKCYAN + "The solution and the baseline are identical. Skipped." + bcolors.ENDC)
result = True
else:
result = benchmarkLab(benchLabPath)
if not result:
sys.exit(1)
else:
sys.exit(0)
| [
11748,
25064,
198,
11748,
850,
14681,
198,
11748,
28686,
198,
11748,
4423,
346,
198,
11748,
1822,
29572,
198,
11748,
33918,
198,
11748,
302,
198,
6738,
33829,
1330,
2039,
388,
198,
6738,
4818,
330,
28958,
1330,
4818,
330,
31172,
198,
1174... | 2.662711 | 1,601 |
try:
from concurrent.futures import ThreadPoolExecutor
import random, time, os, httpx
from colorama import Fore, Style
except ImportError:
print("Error [!] -> Modules Are not installed")
token, guild = input("Token -> "), input("\nGuild ID -> ")
threads = []
apiv = [6, 7, 8, 9]
codes = [200, 201, 204]
if __name__ == "__main__":
theadpool()
| [
28311,
25,
198,
220,
220,
220,
422,
24580,
13,
69,
315,
942,
1330,
14122,
27201,
23002,
38409,
198,
220,
220,
220,
1330,
4738,
11,
640,
11,
28686,
11,
2638,
87,
198,
220,
220,
220,
422,
3124,
1689,
1330,
4558,
11,
17738,
198,
16341,... | 2.727941 | 136 |
#!/usr/bin/env python3
from skilletlib import SkilletLoader
sl = SkilletLoader('.')
skillet = sl.get_skillet_with_name('panos_cli_example')
context = dict()
context['cli_command'] = 'show system info'
context['username'] = 'admin'
context['password'] = 'NOPE'
context['ip_address'] = 'NOPE'
output = skillet.execute(context)
print(output.get('output_template', 'n/a'))
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
18,
198,
198,
6738,
41306,
8019,
1330,
3661,
32512,
17401,
198,
198,
6649,
796,
3661,
32512,
17401,
10786,
2637,
8,
198,
198,
8135,
32512,
796,
1017,
13,
1136,
62,
8135,
32512,
62,
4480,
62... | 2.892308 | 130 |
from django.views.generic import View
from django.http import HttpResponse
from django.shortcuts import render | [
6738,
42625,
14208,
13,
33571,
13,
41357,
1330,
3582,
198,
6738,
42625,
14208,
13,
4023,
1330,
367,
29281,
31077,
198,
6738,
42625,
14208,
13,
19509,
23779,
1330,
8543
] | 3.928571 | 28 |
""" xssec setup """
import codecs
from os import path
from setuptools import setup, find_packages
from sap.conf.config import USE_SAP_PY_JWT
CURRENT_DIR = path.abspath(path.dirname(__file__))
README_LOCATION = path.join(CURRENT_DIR, 'README.md')
VERSION = ''
with open(path.join(CURRENT_DIR, 'version.txt'), 'r') as version_file:
VERSION = version_file.read()
with codecs.open(README_LOCATION, 'r', 'utf-8') as readme_file:
LONG_DESCRIPTION = readme_file.read()
sap_py_jwt_dep = ''
if USE_SAP_PY_JWT:
sap_py_jwt_dep = 'sap_py_jwt>=1.1.1'
else:
sap_py_jwt_dep = 'cryptography'
setup(
name='sap_xssec',
url='https://github.com/SAP/cloud-pysec',
version=VERSION.strip(),
author='SAP SE',
description=('SAP Python Security Library'),
packages=find_packages(include=['sap*']),
data_files=[('.', ['version.txt', 'CHANGELOG.md'])],
test_suite='tests',
install_requires=[
'deprecation>=2.1.0',
'requests>=2.21.0',
'six>=1.11.0',
'pyjwt>=1.7.0',
'{}'.format(sap_py_jwt_dep)
],
long_description=LONG_DESCRIPTION,
long_description_content_type="text/markdown",
classifiers=[
# http://pypi.python.org/pypi?%3Aaction=list_classifiers
"Development Status :: 5 - Production/Stable",
"Topic :: Security",
"License :: OSI Approved :: Apache Software License",
"Natural Language :: English",
"Operating System :: MacOS :: MacOS X",
"Operating System :: POSIX",
"Operating System :: POSIX :: BSD",
"Operating System :: POSIX :: Linux",
"Operating System :: Microsoft :: Windows",
"Programming Language :: Python",
"Programming Language :: Python :: 2",
"Programming Language :: Python :: 2.7",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.4",
"Programming Language :: Python :: 3.5",
"Programming Language :: Python :: 3.6",
"Programming Language :: Python :: 3.7",
"Programming Language :: Python :: Implementation :: CPython",
"Programming Language :: Python :: Implementation :: PyPy",
],
)
| [
37811,
2124,
82,
2363,
9058,
37227,
198,
11748,
40481,
82,
198,
6738,
28686,
1330,
3108,
198,
6738,
900,
37623,
10141,
1330,
9058,
11,
1064,
62,
43789,
198,
6738,
31841,
13,
10414,
13,
11250,
1330,
23210,
62,
50,
2969,
62,
47,
56,
62,... | 2.405286 | 908 |
# Copyright 2022 @ReneFreingruber
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import utils
import tagging_engine.tagging as tagging
from tagging_engine.tagging import Tag
import mutators.testcase_mutators_helpers as testcase_mutators_helpers
| [
2,
15069,
33160,
2488,
49,
1734,
20366,
278,
622,
527,
198,
2,
198,
2,
49962,
739,
262,
24843,
13789,
11,
10628,
362,
13,
15,
357,
1169,
366,
34156,
15341,
198,
2,
345,
743,
407,
779,
428,
2393,
2845,
287,
11846,
351,
262,
13789,
... | 3.781726 | 197 |
"""Support for SmartHab device integration."""
from datetime import timedelta
import logging
import pysmarthab
from requests.exceptions import Timeout
from homeassistant.components.light import LightEntity
from . import DATA_HUB, DOMAIN
_LOGGER = logging.getLogger(__name__)
SCAN_INTERVAL = timedelta(seconds=60)
| [
37811,
15514,
329,
10880,
39,
397,
3335,
11812,
526,
15931,
198,
6738,
4818,
8079,
1330,
28805,
12514,
198,
11748,
18931,
198,
198,
11748,
279,
893,
3876,
400,
397,
198,
6738,
7007,
13,
1069,
11755,
1330,
3862,
448,
198,
198,
6738,
1363... | 3.333333 | 96 |
#!/usr/bin/env python3
import os, json
print("Content-type: text/html\r\n\r\n")
print()
print("<Title>Test CGI</title>")
print("<p>Hello World cmput404 class!<p/>")
print(os.environ)
json_object = json.dumps(dict(os.environ), indent = 4)
print(json_object)
'''for param in os.environ.keys():
if(param == "HTTP_USER_AGENT"):
print("<b>%20s<b/>: %s<br>" % (param, os.environ[param]))
'''
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
18,
198,
11748,
28686,
11,
33918,
198,
198,
4798,
7203,
19746,
12,
4906,
25,
2420,
14,
6494,
59,
81,
59,
77,
59,
81,
59,
77,
4943,
198,
4798,
3419,
198,
4798,
7203,
27,
19160,
29,
14402... | 2.293785 | 177 |
import os.path as osp
import numpy as np
import cv2
import torch
from torchvision.utils import make_grid
from VCD.utils.camera_utils import project_to_image
import pyflex
import re
import h5py
import os
from softgym.utils.visualization import save_numpy_as_gif
from chester import logger
import random
# Function to extract all the numbers from the given string
################## Pointcloud Processing #################
import pcl
# def get_partial_particle(full_particle, observable_idx):
# return np.array(full_particle[observable_idx], dtype=np.float32)
from softgym.utils.misc import vectorized_range, vectorized_meshgrid
################## IO #################################
def transform_info(all_infos):
""" Input: All info is a nested list with the index of [episode][time]{info_key:info_value}
Output: transformed_infos is a dictionary with the index of [info_key][episode][time]
"""
if len(all_infos) == 0:
return []
transformed_info = {}
num_episode = len(all_infos)
T = len(all_infos[0])
for info_name in all_infos[0][0].keys():
infos = np.zeros([num_episode, T], dtype=np.float32)
for i in range(num_episode):
infos[i, :] = np.array([info[info_name] for info in all_infos[i]])
transformed_info[info_name] = infos
return transformed_info
################## Visualization ######################
def visualize(env, particle_positions, shape_positions, config_id, sample_idx=None, picked_particles=None, show=False):
""" Render point cloud trajectory without running the simulation dynamics"""
env.reset(config_id=config_id)
frames = []
for i in range(len(particle_positions)):
particle_pos = particle_positions[i]
shape_pos = shape_positions[i]
p = pyflex.get_positions().reshape(-1, 4)
p[:, :3] = [0., -0.1, 0.] # All particles moved underground
if sample_idx is None:
p[:len(particle_pos), :3] = particle_pos
else:
p[:, :3] = [0, -0.1, 0]
p[sample_idx, :3] = particle_pos
pyflex.set_positions(p)
set_shape_pos(shape_pos)
rgb = env.get_image(env.camera_width, env.camera_height)
frames.append(rgb)
if show:
if i == 0: continue
picked_point = picked_particles[i]
phases = np.zeros(pyflex.get_n_particles())
for id in picked_point:
if id != -1:
phases[sample_idx[int(id)]] = 1
pyflex.set_phases(phases)
img = env.get_image()
cv2.imshow('picked particle images', img[:, :, ::-1])
cv2.waitKey()
return frames
############################ Other ########################
def updateDictByAdd(dict1, dict2):
'''
update dict1 by dict2
'''
for k1, v1 in dict2.items():
for k2, v2 in v1.items():
dict1[k1][k2] += v2.cpu().item()
return dict1
############### for planning ###############################
| [
11748,
28686,
13,
6978,
355,
267,
2777,
198,
11748,
299,
32152,
355,
45941,
198,
11748,
269,
85,
17,
198,
11748,
28034,
198,
6738,
28034,
10178,
13,
26791,
1330,
787,
62,
25928,
198,
6738,
569,
8610,
13,
26791,
13,
25695,
62,
26791,
1... | 2.421553 | 1,262 |
#! python3
""" Pytest-compatible tests for src/classes.py """
import sys
from pathlib import Path
from copy import deepcopy
from unittest import mock
# A workaround for tests not automatically setting
# root/src/ as the current working directory
path_to_src = Path(__file__).parent.parent / "src"
sys.path.insert(0, str(path_to_src))
from classes import Item, Inventory, Player, Character
from settings import *
def initialiser(testcase):
""" Initialises all test cases with data """
return inner
def test_char_levelmixin():
""" Test for level-up functionality """
char = Character('John Doe', max_level = 5)
assert 1 == char.level
assert 85 == char.next_level
assert char.give_exp(85) == f"Congratulations! You've levelled up; your new level is {char.level}\nEXP required for next level: {int(char.next_level-char.experience)}\nCurrent EXP: {char.experience}"
for _ in range(char.max_level - char.level):
char.give_exp(char.next_level)
assert char.level == char.max_level
assert char.give_exp(char.next_level) == f""
| [
2,
0,
21015,
18,
198,
198,
37811,
9485,
9288,
12,
38532,
5254,
329,
12351,
14,
37724,
13,
9078,
37227,
198,
198,
11748,
25064,
198,
198,
6738,
3108,
8019,
1330,
10644,
198,
6738,
4866,
1330,
2769,
30073,
198,
6738,
555,
715,
395,
1330... | 3.097983 | 347 |
# -*- coding: utf-8 -*-
# BioSTEAM: The Biorefinery Simulation and Techno-Economic Analysis Modules
# Copyright (C) 2020, Yoel Cortes-Pena <yoelcortes@gmail.com>
#
# This module is under the UIUC open-source license. See
# github.com/BioSTEAMDevelopmentGroup/biosteam/blob/master/LICENSE.txt
# for license details.
"""
"""
from ._binary_distillation import BinaryDistillation
import flexsolve as flx
from thermosteam.exceptions import InfeasibleRegion
from thermosteam.equilibrium import DewPoint, BubblePoint
import numpy as np
__all__ = ('ShortcutColumn',)
# %% Functions
# %%
class ShortcutColumn(BinaryDistillation,
new_graphics=False):
r"""
Create a multicomponent distillation column that relies on the
Fenske-Underwood-Gilliland method to solve for the theoretical design
of the distillation column and the separation of non-keys [1]_.The Murphree
efficiency (i.e. column efficiency) is based on the modified O'Connell
correlation [2]_. The diameter is based on tray separation and flooding
velocity [1]_ [3]_. Purchase costs are based on correlations compiled by
Warren et. al. [4]_.
Parameters
----------
ins : streams
Inlet fluids to be mixed into the feed stage.
outs : stream sequence
* [0] Distillate
* [1] Bottoms product
LHK : tuple[str]
Light and heavy keys.
y_top : float
Molar fraction of light key to the light and heavy keys in the
distillate.
x_bot : float
Molar fraction of light key to the light and heavy keys in the bottoms
product.
Lr : float
Recovery of the light key in the distillate.
Hr : float
Recovery of the heavy key in the bottoms product.
k : float
Ratio of reflux to minimum reflux.
Rmin : float, optional
User enforced minimum reflux ratio. If the actual minimum reflux ratio is less than `Rmin`, this enforced value is ignored. Defaults to 0.6.
specification="Composition" : "Composition" or "Recovery"
If composition is used, `y_top` and `x_bot` must be specified.
If recovery is used, `Lr` and `Hr` must be specified.
P=101325 : float
Operating pressure [Pa].
vessel_material : str, optional
Vessel construction material. Defaults to 'Carbon steel'.
tray_material : str, optional
Tray construction material. Defaults to 'Carbon steel'.
tray_type='Sieve' : 'Sieve', 'Valve', or 'Bubble cap'
Tray type.
tray_spacing=450 : float
Typically between 152 to 915 mm.
stage_efficiency=None :
User enforced stage efficiency. If None, stage efficiency is
calculated by the O'Connell correlation [2]_.
velocity_fraction=0.8 : float
Fraction of actual velocity to maximum velocity allowable before
flooding.
foaming_factor=1.0 : float
Must be between 0 to 1.
open_tray_area_fraction=0.1 : float
Fraction of open area to active area of a tray.
downcomer_area_fraction=None : float
Enforced fraction of downcomer area to net (total) area of a tray.
If None, estimate ratio based on Oliver's estimation [1]_.
is_divided=False : bool
True if the stripper and rectifier are two separate columns.
References
----------
.. [1] J.D. Seader, E.J. Henley, D.K. Roper. (2011)
Separation Process Principles 3rd Edition. John Wiley & Sons, Inc.
.. [2] M. Duss, R. Taylor. (2018)
Predict Distillation Tray Efficiency. AICHE
.. [3] Green, D. W. Distillation. In Perrys Chemical Engineers
Handbook, 9 ed.; McGraw-Hill Education, 2018.
.. [4] Seider, W. D., Lewin, D. R., Seader, J. D., Widagdo, S., Gani, R.,
& Ng, M. K. (2017). Product and Process Design Principles. Wiley.
Cost Accounting and Capital Cost Estimation (Chapter 16)
Examples
--------
>>> from biosteam.units import ShortcutColumn
>>> from biosteam import Stream, settings
>>> settings.set_thermo(['Water', 'Methanol', 'Glycerol'])
>>> feed = Stream('feed', flow=(80, 100, 25))
>>> bp = feed.bubble_point_at_P()
>>> feed.T = bp.T # Feed at bubble point T
>>> D1 = ShortcutColumn('D1', ins=feed,
... outs=('distillate', 'bottoms_product'),
... LHK=('Methanol', 'Water'),
... y_top=0.99, x_bot=0.01, k=2,
... is_divided=True)
>>> D1.simulate()
>>> # See all results
>>> D1.show(T='degC', P='atm', composition=True)
ShortcutColumn: D1
ins...
[0] feed
phase: 'l', T: 76.129 degC, P: 1 atm
composition: Water 0.39
Methanol 0.488
Glycerol 0.122
-------- 205 kmol/hr
outs...
[0] distillate
phase: 'g', T: 64.91 degC, P: 1 atm
composition: Water 0.01
Methanol 0.99
-------- 100 kmol/hr
[1] bottoms_product
phase: 'l', T: 100.06 degC, P: 1 atm
composition: Water 0.754
Methanol 0.00761
Glycerol 0.239
-------- 105 kmol/hr
>>> D1.results()
Distillation Units D1
Cooling water Duty kJ/hr -7.9e+06
Flow kmol/hr 5.4e+03
Cost USD/hr 2.64
Low pressure steam Duty kJ/hr 1.43e+07
Flow kmol/hr 368
Cost USD/hr 87.5
Design Theoretical feed stage 8
Theoretical stages 16
Minimum reflux Ratio 1.06
Reflux Ratio 2.12
Rectifier stages 13
Stripper stages 26
Rectifier height ft 31.7
Stripper height ft 50.9
Rectifier diameter ft 4.53
Stripper diameter ft 3.67
Rectifier wall thickness in 0.312
Stripper wall thickness in 0.312
Rectifier weight lb 6.46e+03
Stripper weight lb 7.98e+03
Purchase cost Rectifier trays USD 1.52e+04
Stripper trays USD 2.02e+04
Rectifier tower USD 8.44e+04
Stripper tower USD 1.01e+05
Condenser USD 4.17e+04
Boiler USD 2.99e+04
Total purchase cost USD 2.92e+05
Utility cost USD/hr 90.1
"""
line = 'Distillation'
_ins_size_is_fixed = False
_N_ins = 1
_N_outs = 2
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
2,
16024,
30516,
2390,
25,
383,
8436,
382,
69,
15451,
41798,
290,
5429,
78,
12,
48307,
14691,
3401,
5028,
198,
2,
15069,
357,
34,
8,
12131,
11,
25455,
417,
18418,
274,... | 2.004922 | 3,657 |
"""
ckwg +31
Copyright 2016 by Kitware, 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 name of Kitware, Inc. nor the names of any 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 AUTHORS 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.
==============================================================================
Interface to VITAL camera_intrinsics objects
"""
import collections
import ctypes
import numpy
from vital.types.eigen import EigenArray
from vital.util import VitalErrorHandle, VitalObject
def as_matrix(self):
"""
Access the intrinsics as an upper triangular matrix
**Note:** *This matrix includes the focal length, principal point,
aspect ratio, and skew, but does not model distortion.*
:return: 3x3 upper triangular matrix
"""
f = self.VITAL_LIB['vital_camera_intrinsics_as_matrix']
f.argtypes = [self.C_TYPE_PTR, VitalErrorHandle.C_TYPE_PTR]
f.restype = EigenArray.c_ptr_type(3, 3, ctypes.c_double)
with VitalErrorHandle() as eh:
m_ptr = f(self, eh)
return EigenArray(3, 3, from_cptr=m_ptr, owns_data=True)
def map_2d(self, norm_pt):
"""
Map normalized image coordinates into actual image coordinates
This function applies both distortion and application of the
calibration matrix to map into actual image coordinates.
:param norm_pt: Normalized image coordinate to map to an image
coordinate (2-element sequence).
:type norm_pt: collections.Sequence[float]
:return: Mapped 2D image coordinate
:rtype: EigenArray[float]
"""
assert len(norm_pt) == 2, "Input sequence was not of length 2"
f = self.VITAL_LIB['vital_camera_intrinsics_map_2d']
f.argtypes = [self.C_TYPE_PTR,
EigenArray.c_ptr_type(2, 1, ctypes.c_double),
VitalErrorHandle.C_TYPE_PTR]
f.restype = EigenArray.c_ptr_type(2, 1, ctypes.c_double)
p = EigenArray(2)
p.T[:] = norm_pt
with VitalErrorHandle() as eh:
m_ptr = f(self, p, eh)
return EigenArray(2, 1, from_cptr=m_ptr, owns_data=True)
def map_3d(self, norm_hpt):
"""
Map a 3D point in camera coordinates into actual image coordinates
:param norm_hpt: Normalized coordinate to map to an image coordinate
(3-element sequence)
:type norm_hpt: collections.Sequence[float]
:return: Mapped 2D image coordinate
:rtype: EigenArray[float]
"""
assert len(norm_hpt) == 3, "Input sequence was not of length 3"
f = self.VITAL_LIB['vital_camera_intrinsics_map_3d']
f.argtypes = [self.C_TYPE_PTR,
EigenArray.c_ptr_type(3, 1, ctypes.c_double),
VitalErrorHandle.C_TYPE_PTR]
f.restype = EigenArray.c_ptr_type(2, 1, ctypes.c_double)
p = EigenArray(3)
p.T[:] = norm_hpt
with VitalErrorHandle() as eh:
m_ptr = f(self, p, eh)
return EigenArray(2, 1, from_cptr=m_ptr, owns_data=True)
def unmap_2d(self, pt):
"""
Unmap actual image coordinates back into normalized image coordinates
This function applies both application of the inverse calibration matrix
and undistortion of the normalized coordinates
:param pt: Actual image 2D point to un-map.
:return: Un-mapped normalized image coordinate.
"""
assert len(pt) == 2, "Input sequence was not of length 2"
f = self.VITAL_LIB['vital_camera_intrinsics_unmap_2d']
f.argtypes = [self.C_TYPE_PTR,
EigenArray.c_ptr_type(2, 1, ctypes.c_double),
VitalErrorHandle.C_TYPE_PTR]
f.restype = EigenArray.c_ptr_type(2, 1, ctypes.c_double)
p = EigenArray(2)
p.T[:] = pt
with VitalErrorHandle() as eh:
m_ptr = f(self, p, eh)
return EigenArray(2, 1, from_cptr=m_ptr, owns_data=True)
def distort_2d(self, norm_pt):
"""
Map normalized image coordinates into distorted coordinates
:param norm_pt: Normalized 2D image coordinate.
:return: Distorted 2D coordinate.
"""
assert len(norm_pt) == 2, "Input sequence was not of length 2"
f = self.VITAL_LIB['vital_camera_intrinsics_distort_2d']
f.argtypes = [self.C_TYPE_PTR,
EigenArray.c_ptr_type(2, 1, ctypes.c_double),
VitalErrorHandle.C_TYPE_PTR]
f.restype = EigenArray.c_ptr_type(2, 1, ctypes.c_double)
p = EigenArray(2)
p.T[:] = norm_pt
with VitalErrorHandle() as eh:
m_ptr = f(self, p, eh)
return EigenArray(2, 1, from_cptr=m_ptr, owns_data=True)
def undistort_2d(self, dist_pt):
"""
Unmap distorted normalized coordinates into normalized coordinates
:param dist_pt: Distorted 2D coordinate to un-distort.
:return: Normalized 2D image coordinate.
"""
assert len(dist_pt) == 2, "Input sequence was not of length 2"
f = self.VITAL_LIB['vital_camera_intrinsics_undistort_2d']
f.argtypes = [self.C_TYPE_PTR,
EigenArray.c_ptr_type(2, 1, ctypes.c_double),
VitalErrorHandle.C_TYPE_PTR]
f.restype = EigenArray.c_ptr_type(2, 1, ctypes.c_double)
p = EigenArray(2)
p.T[:] = dist_pt
with VitalErrorHandle() as eh:
m_ptr = f(self, p, eh)
return EigenArray(2, 1, from_cptr=m_ptr, owns_data=True)
| [
37811,
198,
694,
86,
70,
1343,
3132,
198,
15269,
1584,
416,
10897,
1574,
11,
3457,
13,
198,
3237,
2489,
10395,
13,
198,
198,
7738,
396,
3890,
290,
779,
287,
2723,
290,
13934,
5107,
11,
351,
393,
1231,
198,
4666,
2649,
11,
389,
10431... | 2.38217 | 2,894 |
from lanzou.api import LanZouCloud
import urllib.parse
final_files = []
final_share_infos = []
cookies = '''
'''
if __name__ == '__main__':
test()
| [
6738,
26992,
89,
280,
13,
15042,
1330,
14730,
57,
280,
18839,
198,
11748,
2956,
297,
571,
13,
29572,
198,
198,
20311,
62,
16624,
796,
17635,
198,
20311,
62,
20077,
62,
10745,
418,
796,
17635,
628,
628,
628,
198,
198,
27916,
444,
796,
... | 2.439394 | 66 |
import numpy as np
import util.data
| [
11748,
299,
32152,
355,
45941,
198,
11748,
7736,
13,
7890,
628,
628,
628
] | 3.153846 | 13 |
# -*- coding: utf-8 -*-
"""
@author:XuMingxuming624@qq.com)
@description: use bert detect chinese char error
"""
import sys
import time
import numpy as np
import torch
from pytorch_transformers import BertForMaskedLM
from pytorch_transformers import BertTokenizer
sys.path.append('../..')
from pycorrector.detector import ErrorType
from pycorrector.utils.logger import logger
from pycorrector.bert import config
if __name__ == "__main__":
d = BertDetector()
error_sentences = ['',
'',
' ',
' ',
'',
'']
t1 = time.time()
for sent in error_sentences:
err = d.detect(sent)
print("original sentence:{} => detect sentence:{}".format(sent, err))
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
37811,
198,
31,
9800,
25,
55,
84,
44,
278,
87,
12595,
21,
1731,
31,
38227,
13,
785,
8,
198,
31,
11213,
25,
779,
275,
861,
4886,
442,
3762,
1149,
4049,
198,
37811,
... | 2.190736 | 367 |
from pydantic import BaseModel
| [
6738,
279,
5173,
5109,
1330,
7308,
17633,
628
] | 4 | 8 |
test = [
'nop +0',
'acc +1',
'jmp +4',
'acc +3',
'jmp -3',
'acc -99',
'acc +1',
'jmp -4',
'acc +6',
]
actual = [
'acc +17',
'acc +37',
'acc -13',
'jmp +173',
'nop +100',
'acc -7',
'jmp +447',
'nop +283',
'acc +41',
'acc +32',
'jmp +1',
'jmp +585',
'jmp +1',
'acc -5',
'nop +71',
'acc +49',
'acc -18',
'jmp +527',
'jmp +130',
'jmp +253',
'acc +11',
'acc -11',
'jmp +390',
'jmp +597',
'jmp +1',
'acc +6',
'acc +0',
'jmp +588',
'acc -17',
'jmp +277',
'acc +2',
'nop +163',
'jmp +558',
'acc +38',
'jmp +369',
'acc +13',
'jmp +536',
'acc +38',
'acc +39',
'acc +6',
'jmp +84',
'acc +11',
'nop +517',
'acc +48',
'acc +47',
'jmp +1',
'acc +42',
'acc +0',
'acc +2',
'acc +24',
'jmp +335',
'acc +44',
'acc +47',
'jmp +446',
'nop +42',
'nop +74',
'acc +45',
'jmp +548',
'jmp +66',
'acc +1',
'jmp +212',
'acc +18',
'jmp +1',
'acc +4',
'acc -16',
'jmp +366',
'acc +0',
'jmp +398',
'acc +45',
'jmp +93',
'acc +40',
'acc +38',
'acc +21',
'nop +184',
'jmp -46',
'nop -9',
'jmp +53',
'acc +46',
'acc +36',
'jmp +368',
'acc +16',
'acc +8',
'acc -9',
'acc -4',
'jmp +328',
'acc -15',
'acc -5',
'acc +21',
'jmp +435',
'acc -5',
'acc +36',
'jmp +362',
'acc +26',
'jmp +447',
'jmp +1',
'jmp +412',
'acc +11',
'acc +41',
'nop -32',
'acc +17',
'jmp -63',
'jmp +1',
'nop +393',
'jmp +62',
'acc +18',
'acc +30',
'nop +417',
'jmp +74',
'acc +29',
'acc +23',
'jmp +455',
'jmp +396',
'jmp +395',
'acc +33',
'nop +137',
'nop +42',
'jmp +57',
'jmp +396',
'acc +7',
'acc +0',
'jmp +354',
'acc +15',
'acc +50',
'jmp -12',
'jmp +84',
'nop +175',
'acc +5',
'acc -2',
'jmp -82',
'acc +1',
'acc +26',
'jmp +288',
'nop -113',
'nop +366',
'acc +45',
'jmp +388',
'acc +21',
'acc +38',
'jmp +427',
'acc +33',
'jmp -94',
'nop -118',
'nop +411',
'jmp +472',
'nop +231',
'nop +470',
'acc +48',
'jmp -124',
'jmp +1',
'acc +5',
'acc +37',
'acc +42',
'jmp +301',
'acc -11',
'acc -17',
'acc +14',
'jmp +357',
'acc +6',
'acc +20',
'acc +13',
'jmp +361',
'jmp -65',
'acc +29',
'jmp +26',
'jmp +329',
'acc +32',
'acc +32',
'acc +17',
'jmp -102',
'acc -6',
'acc +33',
'acc +9',
'jmp +189',
'acc +3',
'jmp -128',
'jmp -142',
'acc +24',
'acc -5',
'jmp +403',
'acc +28',
'jmp +310',
'acc +34',
'acc +4',
'acc +33',
'acc +18',
'jmp +227',
'acc -8',
'acc -15',
'jmp +112',
'jmp +54',
'acc +21',
'acc +23',
'acc +20',
'jmp +320',
'acc +13',
'jmp -77',
'acc +15',
'nop +310',
'nop +335',
'jmp +232',
'acc -3',
'nop +50',
'acc +41',
'jmp +112',
'nop -10',
'acc +29',
'acc +27',
'jmp +52',
'acc +40',
'nop -132',
'acc -16',
'acc +27',
'jmp +309',
'acc -8',
'nop +147',
'acc +20',
'acc +46',
'jmp +202',
'acc +27',
'jmp -43',
'jmp +1',
'acc +33',
'acc -13',
'jmp +300',
'acc +1',
'jmp -202',
'acc -17',
'acc +0',
'acc +34',
'jmp -5',
'nop +335',
'acc -16',
'acc -17',
'jmp -120',
'acc -19',
'acc -13',
'acc +4',
'jmp +368',
'jmp +21',
'acc +39',
'acc +39',
'acc -18',
'jmp -157',
'nop +280',
'acc +33',
'nop -37',
'jmp +32',
'acc -16',
'acc +18',
'acc +46',
'jmp -121',
'acc -19',
'jmp +195',
'acc +28',
'jmp +124',
'jmp +331',
'jmp -228',
'jmp -146',
'jmp +85',
'jmp +60',
'acc +20',
'acc -9',
'jmp +303',
'jmp -122',
'jmp +111',
'acc +32',
'acc +0',
'acc +39',
'acc +29',
'jmp -31',
'nop +320',
'jmp -63',
'jmp +223',
'nop -149',
'acc -12',
'acc -11',
'acc +32',
'jmp +309',
'jmp -13',
'acc -19',
'jmp -123',
'acc +21',
'acc +18',
'acc +49',
'jmp +175',
'acc -14',
'nop -129',
'acc -2',
'acc +31',
'jmp +79',
'acc +23',
'acc +50',
'acc +39',
'acc +7',
'jmp -235',
'jmp -166',
'acc +9',
'jmp +293',
'acc -11',
'jmp +76',
'acc +44',
'acc +3',
'acc +37',
'jmp +123',
'nop -104',
'jmp -157',
'acc +14',
'acc +10',
'acc +28',
'jmp +25',
'acc +37',
'jmp +188',
'jmp -49',
'acc -11',
'jmp -90',
'acc -8',
'jmp +197',
'acc +5',
'jmp +115',
'acc +44',
'jmp -228',
'nop -2',
'acc +46',
'jmp +130',
'nop +183',
'nop +106',
'acc +27',
'acc +37',
'jmp -309',
'acc +28',
'acc -4',
'acc -12',
'acc +38',
'jmp +93',
'acc +8',
'acc +23',
'acc -9',
'acc +6',
'jmp -42',
'acc +10',
'acc +35',
'acc +4',
'jmp -231',
'acc +19',
'acc +7',
'acc +23',
'acc +11',
'jmp -90',
'acc +0',
'nop +158',
'nop -150',
'acc +33',
'jmp +107',
'acc +48',
'acc -2',
'jmp -104',
'acc +6',
'nop -57',
'nop +172',
'acc -11',
'jmp -7',
'acc +6',
'acc +50',
'acc -9',
'acc +12',
'jmp -171',
'acc +3',
'jmp +26',
'acc +42',
'acc +31',
'acc +20',
'acc +32',
'jmp -48',
'acc +13',
'jmp -6',
'jmp +178',
'acc +47',
'jmp -153',
'acc +28',
'nop +74',
'jmp -162',
'acc -15',
'nop -104',
'acc -9',
'jmp -227',
'acc +49',
'acc -19',
'acc +41',
'jmp -318',
'acc +9',
'acc +12',
'acc +7',
'jmp +34',
'jmp +137',
'nop -143',
'acc -8',
'acc +5',
'acc +31',
'jmp -20',
'jmp -237',
'acc +39',
'acc +0',
'jmp -298',
'acc +45',
'acc -19',
'acc +11',
'jmp -151',
'acc +40',
'acc +27',
'nop +150',
'nop -391',
'jmp -341',
'acc +1',
'acc +11',
'acc +18',
'nop -234',
'jmp +77',
'nop +104',
'jmp -65',
'acc +32',
'jmp -27',
'nop -317',
'nop +159',
'acc +14',
'acc -10',
'jmp -348',
'acc +29',
'jmp +32',
'acc +48',
'acc -19',
'jmp +17',
'jmp -201',
'jmp -224',
'nop +26',
'acc -7',
'acc +23',
'acc +46',
'jmp -6',
'acc +22',
'acc +39',
'acc +9',
'acc +23',
'jmp -30',
'jmp -243',
'acc +47',
'acc -15',
'jmp -298',
'jmp -393',
'jmp +1',
'acc +3',
'nop -24',
'acc +7',
'jmp -59',
'acc -6',
'acc +26',
'jmp -102',
'acc +34',
'acc +24',
'jmp -207',
'acc +36',
'acc +40',
'acc +41',
'jmp +1',
'jmp -306',
'jmp +57',
'jmp +1',
'nop +99',
'acc +28',
'jmp -391',
'acc +50',
'jmp -359',
'acc -5',
'jmp +9',
'jmp -355',
'acc +5',
'acc +2',
'jmp -77',
'acc +40',
'acc +28',
'acc +22',
'jmp -262',
'nop -287',
'acc +34',
'acc -4',
'nop +112',
'jmp -195',
'acc +29',
'nop -94',
'nop -418',
'jmp +24',
'jmp -190',
'acc +2',
'jmp -311',
'jmp -178',
'jmp -276',
'acc -12',
'acc -18',
'jmp +62',
'jmp -174',
'nop +31',
'acc +33',
'nop -158',
'jmp -417',
'acc +3',
'acc +21',
'acc +47',
'jmp +87',
'acc +45',
'jmp -77',
'acc +6',
'acc -10',
'jmp +1',
'jmp -240',
'acc +7',
'acc +47',
'jmp -379',
'acc -14',
'acc +50',
'nop -75',
'acc +30',
'jmp +70',
'jmp -392',
'jmp -430',
'acc +22',
'acc -2',
'jmp -492',
'jmp +1',
'acc -6',
'acc +38',
'jmp -36',
'nop -336',
'jmp -32',
'jmp +61',
'acc +20',
'acc -9',
'acc +2',
'jmp -175',
'acc +21',
'acc -2',
'jmp -6',
'jmp -527',
'acc +11',
'acc +16',
'jmp -262',
'jmp +1',
'nop -327',
'acc +29',
'jmp -114',
'acc +11',
'acc +17',
'acc +26',
'nop -104',
'jmp -428',
'nop -178',
'nop -242',
'acc +29',
'acc +5',
'jmp -245',
'jmp -417',
'jmp -278',
'acc +35',
'acc +21',
'jmp +1',
'nop -263',
'jmp +8',
'acc +42',
'jmp -95',
'nop -312',
'acc -11',
'acc +34',
'acc +0',
'jmp +19',
'acc +8',
'acc -13',
'acc +32',
'acc +21',
'jmp -208',
'acc +15',
'acc +39',
'nop -194',
'jmp -280',
'jmp +24',
'nop -516',
'acc +21',
'acc +48',
'jmp -367',
'jmp -121',
'acc +49',
'acc -16',
'jmp -136',
'acc +0',
'jmp -148',
'jmp -85',
'jmp -103',
'nop -446',
'jmp -242',
'acc -12',
'acc +13',
'acc +31',
'acc -1',
'jmp -435',
'nop -420',
'acc +22',
'acc -5',
'jmp -567',
'nop -354',
'acc +11',
'acc +33',
'acc +45',
'jmp -76',
'acc -2',
'acc +0',
'acc +25',
'acc +46',
'jmp -555',
'acc +0',
'acc +11',
'nop -2',
'jmp -394',
'jmp -395',
'acc +8',
'acc +14',
'acc +47',
'acc +22',
'jmp +1',]
| [
9288,
796,
685,
198,
220,
220,
220,
705,
77,
404,
1343,
15,
3256,
198,
220,
220,
220,
705,
4134,
1343,
16,
3256,
198,
220,
220,
220,
705,
73,
3149,
1343,
19,
3256,
198,
220,
220,
220,
705,
4134,
1343,
18,
3256,
198,
220,
220,
22... | 1.59459 | 6,026 |
#----------------------------------#
######### ARTMETK LELER #######
#----------------------------------#
# + toplama
# - karma
# * arpma
# / blme
# ** kuvvet
# % modls/kalan bulma
# // taban blme/ tam blme
#aritmetik ileler saysal ilemler yapmamz salar
print(45+57)#102
#yalnz + ve * iaretleri karakter dizileri iinde kullanlabilir
#karakter dizilerini birletirmek iin + iareti
print("Selam "+"Bugn "+"Hava ok gzel.")#Selam Bugn Hava ok gzel.
# * iareti karakter dizileri tekrarlamak iin kullanlabilir
print("w"*3+".tnbc1"+".com")#www.tnbc1.com
# % ileci saynn blmnden kalan bulur
print(30 % 4)#2
#saynn kalann bularak tek mi ift mi olduunu bulabiliriz
sayi = int(input("Bir say giriniz: "))
if sayi % 2 == 0:
print("Girdiiniz say bir ift saydr.")
else:
print("Girdiiniz say bir tek saydr.")
#eer bir saynn 2 ye blmnden kalan 0 ise o say ift bir saydr
#veya bu % ileci ile saynn baka bir say ile tam blnp blnmediini
# bulabiliriz
print(36 % 9)#0 #yani 36 9 a tam blnyor
#program yazalm:
bolunen = int(input("Herhangi bir say giriniz: "))
bolen = int(input("Herhangi bir say daha giriniz: "))
sablon = "{} says {} saysna tam".format(bolunen,bolen)
if bolunen % bolen == 0:
print(sablon,"blnyor!")
else:
print(sablon,"blnmyor!")
#kt:
#Herhangi bir say giriniz: 2876
#Herhangi bir say daha giriniz: 123
#2876 says 123 saysna tam blnmyor!
# bir saynn son basaman elde etmek iinde kullanabiliriz
#bu yzden bir saynn 10 blmnde kalann buluruz
print(65 % 10)#5
print(543 % 10)#3
#----------------#
#--//-tam blme--#
#----------------#
a = 6 / 3
print(type(a))#float # 2.0
#pythonda saylarn blmelerin sonucu kesirli olur yani float tipinde
b = 6 // 3
print(b)#3
print(type(b))#int #tam blebildik
print(int(a))#2 # bu ekilde de float tipini inte evirebildik
#----------------#
# ROUND #
#----------------#
#round() bir gml fonksiyondur
#bu fonksiyonun bir saynn deerini yuvarlamamz salar
print(round(2.70))#3
print(round(2.30))#2
print(round(5.68,1))#5.7
print(round(5.68,2))#5.68
print(round(7.9,2))#7.9
#-----------------#
# ** #
#-----------------#
#bir saynn karesini bulmak
#bunun iin 2 rakamna ihityacmz vardr
print(124**2)#15376
#bir saynn karakkn bulmak
#karakkn bulmak iin 0.5 e ihtiyacmz vardr
print(625 ** 0.5)#25.0
#eer ondalkl say yani float tipli say istemiyorsak
#ifadeyi ilemi int tipine evirmemiz gerekir
print(int(625 ** 0.5))#25
#bir saynn kpn bulmak
#kpn bulmak iin 3 rakamna ihtiyacmz vardr
print(124 ** 3)#1906624
#bu ilemleri pow() fonksiyonlar ile de yapabiliriz
print(pow(24,3))#13824
print(pow(96,2))#9216
#-------------------------------------#
# KARILATIRMA LELER #
#-------------------------------------#
#ilenenler arasnda bir karlatrma ilikisi kuran ilelerdir
# == eittir
# != eit deildir
# > byktr
# < kktr
# >= byk eittir
# <= kk eittir
parola = "xyz05"
soru = input("parolanz: ")
if soru == parola:
print("doru parola!")
elif soru != parola:
print("yanl parola!")
#baka bir rnek:
sayi = input("say: ")
if int(sayi) <= 100:
print("say 100 veya 100'den kk")
elif int(sayi) >= 100:
print("say 100 veya 100'den byk")
#-------------------------#
# BOOL LELER #
#-------------------------#
#bool da sadece iki deer vardr true ve false
#bilgisayar biliminde olduu gibi 0 false dir 1 true dur
a = 1
print(a == 1)#a deeri 1 e eit midir?
#True
print(a == 2)#False
# o deeri ve bo veri tipleri False'Dir
# bunun haricinde kalan her ey True
#bu durumu bool() adl fonksiyondan yararlanarak renebiliriz
print(bool(4))#True
print(bool("armut"))#True
print(bool(" "))#True
print(bool(2288281))#True
print(bool("0"))#True
print(bool(0))#False
print(bool(""))#False
#bool deerleri yazlm dnyasnda nemli bir yeri vardr
#daha nce kullandm koul bloglarnda koulun gereklemesi
#veya gereklememesi bool a baldr yan, true ve false
isim = input("isminiz: ")
if isim == "Ferhat":
print("Ne gzel bir isminiz vardr")
else:
print(isim,"ismini pek sevmem!")
#isminiz: caner
#caner ismini pek sevmem!
# eer diyoruz isim ferhat ifadesi true ise unu gster diyoruz
# eer true deeri dnda herhangi bir ey yani false ise unu gster diyoruz
isim = input("isminiz: ")
print(isim == "Ferhat")#True
#
b = ""
print(bool(b))#False
#ii bo veri tiplerin her zaman false olacan bilerek yle
#program yazabiliriz:
kullanici = input("Kullanc adnz: ")
if bool(kullanici) == True:
print("Teekkrler")
else:
print("Kullanc ad alan bo braklamaz!")
# eer kullanc bir eyler yazarsa bool(kullanici) komutu true verecek
# ekrana teekkrler yazs yazlacak
# eer kullanc bir ey yazmadan entera tklar ise false olacak ve else alacaktr
#bu ilemi genellikle u ekilde yazarz:
kullaniciOne = input("Kullanc adnz yaznz: ")
if kullaniciOne:
print("Teekkrler")
else:
print("kullanc ad bo braklamaz")
#---------------------------------#
# BOOL LELER #
#---------------------------------#
#AND
#OR
#NOT
#and
#gmail giri sistemi yazalm
#gmail giri sisteminde kullanc ad ve parola yani her ikisi de doru olmaldr
kullaniciAdi = input("Kullanc adnz: ")
parola = input("Parolannz: ")
if kullaniciAdi == "AliVeli":
if parola == "123456":
print("Sisteme hogeldiniz")
else :
print("Yanl kullanc ad veya parola!")
else:
print("Yanl kullanc ad veya parola")
#bu ilemi daha kolay yazabiliriz
kullanici = input("Kullanc adnz yaznz: ")
sifre = input("ifrenizi yaznz: ")
if kullanici == "aliveli" and sifre == "12345":
print("programa hogeldiniz")
else:
print("Yanl kullanc ad veya parola")
#and ilecini kullanarak iki durumu baladk
#and ilecinin mant her iki durumun gereklemesidir
#btn koullar gerekleiyorsa true dner
#onun haricinde tm sonular false dir
a = 23
b = 10
print(a == 23)#True
print(b == 10)#True
print(a == 23 and b == 10)#True
print(a == 23 and b == 15)#False
# OR
#or veya demektir
#her iki kouldan biri true olursa yine de alr
c = 10
d = 100
print(c == 10)#True
print(d == 100)#True
print(c == 1 or d == 100)#True
# c koulu yanl olsa da d koulu doru olduu iin kt True oldu
# snavdan alnan notlarn harf karln gsteren program
x = int(input("Notunuz: "))
if x > 100 or x < 0:
print("Byle bir not yok")
elif x >= 90 and x <= 100:
print("A aldnz")
elif x >= 80 and x <= 89:
print("B aldnz.")
elif x >= 70 and x <= 79:
print("C aldnz")
elif x >=60 and x <= 69:
print("D aldnz.")
elif x >= 0 and x <= 59:
print("F aldnz.")
#u ekilde daha ksa biimde yazabiliriz
z = int(input("notunuz: "))
if x > 100 or x < 0:
print("Byle bir not yoktur.")
elif z >= 90 <= 100:
print("A aldnz")
elif z >=80 <= 89:
print("B aldnz")
elif z >= 70 <= 79:
print("C aldnz")
elif z >= 60 <=69:
print("D aldnz")
elif z >=0 <=59:
print("F aldnz")
# and i kaldrdmzda ayn sonucu alabiliyoruz
## not ##
# not bir bool ilecidir. trke karl deil demektir
# zellikle kullanc tarafndan deer girilip girilmediini
#denetlmek iin kullanlr
#eer kullanc deer girilise not deeri alacak
#eer kullanc bo braklsa true deeri alacak
parola = input("ifrenizi giriniz Ltfen: ")
if not parola:
print("ifre bo braklamaz")
#ifrenizi giriniz yazs geldiinizde cevap vermeyip entera tkladm
#deer true olunca print fonksiyonu alt
print(bool(parola))#false
#makineye unu soruyoruz aslnda:
#parola bo braklmam deil mi?
#makinede bize: hayr bo braklm diyor
print(bool(not parola))#True
#makineye parola bo braklm deil mi? sorusunu soruyoruz
#makine de bize true evet bo braklm diyor
#yani ikisinin arasndaki fark braklmam/braklm deil? midir
#yani not islei makineye "bo braklm deil mi?" sorusunu soruyor
#eer bo brakldysa cevap True oluyor evet braklm demek oluyor
#----------------------------------#
# Deer Atama leleri #
#----------------------------------#
# deer atama ilemi "=" ileciyle yaplr
a = 25
#a deikenin iine 25 deerini atadk
## += ileci
#deikenin deerine deer eklemek iin kullanlr
a += 10 # a deikenin deerine 10 deeri daha ekledik
print(a) # 35
## -=
#deikenin deerinin drmek yani karmak iin kullanlr
a -= 5 #a deikeninden 5 deer kardk
print(a)#30
## /=
# deikenin deeriyle blme ilemi yapmak iin kullanlr
a /= 2 #a deikenin deerini 2 saysyla bldk
print(a)#15.0
## *=
#deikenin deerini arpmak iin kullanlr
a *= 4 # a deikenin deerini 4 ile arptk
print(a)#60.0
## %=
#deikenin deerinin blme ileminde kalann bulmak iin kullanlr
a %= 7 #a deikenin deerinin 7 ile blnmesinden kalann bulduk
print(a)#4.0
## **=
#deikenin deerinin kuvvetini, kpn ve karakkn bulmak iin kullanlr
a **= 2#a deikenin kuvvetini bulduk
print(a)#16.0
## //=
#deikenin deerinin tam blnmesini bulmak iin kullanlr
a //= 2
print(a)#8
#bu ileler normalde u ilemi yapar rnein
#a = a + 5
#print(a)#5
#fakat bu ilem hzl bir seenek deildir ama mantksal olarak bu ekilde ilem yapar
#ilelerin sa ve solda olma fark
# += veya =+ -= veya =-
a =- 5
print(a) # -5
# a deerine -5 deerini verdik
## := (walrus operatr)
#rnek:
giris = len(input("Adn ne?"))
if giris < 4:
print("Adn ksaym")
elif giris < 6:
print("Adn biraz uzunmu")
else:
print("Uzun bir adn varm.")
#bu kodu := ilecini kullanarakta yazabiliriz
if (giris := len(input("Adnz nedir?"))) < 4:
print("Adn ksaym")
elif giris < 6:
print("Adn biraz uzunmu")
else:
print("ok uzun bir adn varm.")
# := tek avantaj ilemimizi tek satra sdrmas
# ok kullanlmaz
#zaten yeni bir ile olduundan sadece python 3.8.1 de alr
#--------------------------------#
# ATLK LELER #
#--------------------------------#
#bir karakter dizisinin deikenin iinde bulunup bulunmadn
#kontrol edebilmemizi salar
#bu ilemi in adl ile sayesinde yaparz
a = "asdfg"
print("a" in a)#True
#makineye "a" deeri a deikenin iinde var m? sorruyoruz
print("A" in a)#False
print("j" in a)#False
# "j" deeri a deikenin iinde var m? cevap: Hayr yok False
#--------------------------------#
# KMLK LELER #
#--------------------------------#
#pythonda her eyin yani her nesnenin arka planda bir kimlik numaras vardr
#bunu renmek iin id() adl fonskiyondan yararlanrz
a = 50
print(id(a))#140705130925248
# a nn kimlik numarasn yazdr dedik
name = "Hello my name is Murat"
print(id(name))#2704421625648
#pythonda her nesenin esiz tek ve benzersiz bir kimlikleri vardr
#python belli bir deere kadar nbellekte ayn kimlik numarasyla tutar
nameOr = 100
print(id(nameOr))#140705130926848
nameOrOne = 100
print(id(nameOrOne))#140705130926848
#belli bir deeri artan deerleri nbellekte farkl kimlik no laryla tutar
y = 1000
print(id(y))#2467428862544
u = 1000
print(id(u))#1586531830352
#ayn deere sahip olarak gzkselerde python farkl kimlikle tantyor
#bunun nedeni python sadece ufak nesneleri nbellekte tutar
#dier byk nesneleri ise yeni bir depolama ilemi yapar
#ufak ve byk deerleri renmek iin:
for k in range(-1000,1000):
for v in range(-1000,1000):
if k is v:
print(k)
#kan sonuca gre -5 ila 256 arasndaki deerleri nbellekte tutabiliyor
## is
number = 1000
numberOne = 1000
print(id(number))#2209573079632
print(id(numberOne))#2756858382928
print(number is 1000)#False
print(numberOne is 1000)#False
#is kimlikliklerine gre eit midir ayn mdr sorusunu sorar
#is ve == ileci ok kere kartlr ikisinin arasndaki fark:
#is nesnelerin kimliklerine bakarak ayn m olduklarn inceler
# == ise nesnelerin deerlerine bakarak ayn m olduklarn inceler
print(number is 1000)#false
#ayr kimlikleri olduklarndan cevap false
print(number == 1000)#True
#a 1000 deerine sahip olduklar iin cevap true
#is in arka planda yapt ey kabaca bu:
print(id(number)==id(1000))#false
ornek = "Python"
print(ornek is "Python") #True
ornekOne = "Python gl ve kolay bir proglama dilidir"
print(ornekOne is "Python gl ve kolay bir proglama dilidir")#False
print(ornekOne == "Python gl ve kolay bir proglama dilidir")#True
#saysal deerlerde olduu gibi karakter dizilerinde de kk olanlar nbellekte
#byk olan karakter dizileri iinde yeni bir kimlik ve depolama tannmaktadr
## UYGULAMA RNEKLER ##
#------------------------------------#
# BAST BR HESAP MAKNES #
#------------------------------------#
#programmz bir hesap makinesi olacak
#kullanya bir say girecek ve bu say ile topla m karma m yapacak karar verecek
#buna gre ise ilemler yapacak
#kullancya baz seenekler sunalm:
giris = """
(1) topla
(2) kar
(3) arp
(4) bl
(5) karesini hesapla
(6) karakkn hesapla
"""
print(giris)
soru = input("Yapmak istediiniz ilemin numarasn giriniz: ")#kullancan hangi ilemi yapacan soracaz
if soru == "1":
sayi1 = int(input("Toplama ilemi iin ilk sayy giriniz: "))
sayi2 = int(input("Toplama ilemi iin ikinci sayy giriniz: "))
print(sayi1,"+",sayi2,"=",sayi1+sayi2)
elif soru == "2":
sayi3 = int(input("karma ilemi iin ilk sayy giriniz: "))
sayi4 = int(input("karma ilemi iin ikinci sayy giriniz: "))
print(sayi3,"-",sayi4,"=",sayi3-sayi4)
elif soru == "3":
sayi5 = int(input("arpma ilemi iin ilk sayy giriniz: "))
sayi6 = int(input("arpma ilemi iin ikinci sayy giriniz:"))
print(sayi5,"*",sayi6,"=",sayi5*sayi6)
elif soru == "4":
sayi7 = int(input("Blme ilemi iin ilk sayy giriniz: "))
sayi8 = int(input("Blme ilemi iin ikinci sayy giriniz: "))
print(sayi7,"/",sayi8,"=",sayi7/sayi8)
elif soru == "5":
sayi9 = int(input("Karesini hesaplamak istediiniz bir sayy giriniz: "))
print(sayi9,"saynn karesi =",sayi9 ** 2)
elif soru == "6":
sayi10 = int(input("Karekkn hesaplamak iin istediiniz sayy giriniz: "))
print(sayi10,"saysnn karakk =",sayi10 ** 0.5)
else:
print("Yanl giri.")
print("Aadaki seeneklerden birini giriniz: ",giris)
"""
Temel olarak program u ekilde:
eer byle bir durum varsa:
yle bir ilem yap
yok eer yle bir durum varsa:
byle bir ilem yap
eer bambaka bir durum varsa:
yle bir ey yap
"""
#-----------------------------------#
# SRME GRE LEM YAPAN PROGRAM
#-----------------------------------#
#Pythonda 3.x serisinde yazlan kodlar 2.x serinde almaz
#yazdmz kodlarn hangi python srmnde altrlmasn isteyebilirz
#veya 3.x de yazdmz kodlarn 2.x altrlmas haline kullanya hata mesaj verdilebiliriz
#sys moduln aralm ie aktaralm
import sys
#modl iindeki istediimiz deikene erielim
print(sys.version_info)
#sys.version_info(major=3, minor=7, micro=4, releaselevel='final', serial=0)
#birde version deikenin verecei ktya bakalm
print(sys.version)#3.7.4 (default, Aug 9 2019, 18:34:13) [MSC v.1915 64 bit (AMD64)]
#fakat iimize version_info deikeni yaryor
#version_info nun verdii kt gzken baz eyler:
#major, python serisinin ana srm numaras
#minor, alt srm numaras
#micro, en alt srm numarasn verir
#bu deerlere ulamak iin:
print(sys.version_info.major)#3
print(sys.version_info.minor)#7
print(sys.version_info.micro)#4
#Programmz hangi srm ile altrlmas gerektiini kontrol eden bir program yazalm
#bu program iin major ve minor u kullanacaz ihtiya dahilinde micro da kullanabiliriz
import sys
_2x_metni = """
Python'n 2.x srmlerinden birini kullanyorsunuz
Program altrabilmek iin sisteminizde Python'n
3.x srmlerinden biri kurulu olmal."""
_3x_metni = "Programa Hogeldiniz!"
if sys.version_info.major < 3:
print(_2x_metni)
else:
print(_3x_metni)
#burada ilk bata modl iindeki aralar kullanmak iin import ediyoruz
#daha sonra 2.x serisini kullanan biri iin hata mesaj oluturuyoruz
#deikenlerin adlar sayyla balayamayaca iin alt izgi ile baladk
#sonra python3 kullanclar iin merhaba metni yarattk
#eer dedik major numaras yani ana srm 3 ten kkse unu yazdr
#bunun dndaki btn durumlar iin ise _3x_metnini bastr dedik
# 2.x srmlerinde trke karakterleri makine alglayamyordu
#bunu zmek iin ise :
# -*- coding: utf-8 -*-
#bu kodu yaptryorduk 3.x te bu sorun kalkmt
#fakat bu sadece programn kmesini engeller trke karakterler bozuk gzkr
#rnein _2x_metin 2.x srmlerinde alnca yle gzkr:
"""
Python'n 2.x srmlerinden birini kullanyorsunuz.
Program altrabilmek iin sisteminizde Python'n
3.x srmlerinden biri kurulu olmal."""
#bunu engellemek iin karakter dizimizin nne u eklemek
# u ise unicode kavramndan gelmektedir
_2x_metni = u"""
Python'n 2.x srmlerinden birini kullanyorsunuz.
Program altrabilmek iin sisteminizde Python'n
3.x srmlerinden biri kurulu olmal."""
#3 ten kk srmlere hata mesaj yazdrabildik
#imdi ise 3.4 gibi kk srmlere hata mesaj yazdrabiliriz
hataMesaj3 = u"""
uan Python'un eski srmn kullanyorsunuz.
Ltfen gncelleyiniz!
"""
if sys.version_info.major == 3 and sys.version_info.minor == 8:
print("bla bla")
else:
print(hataMesaj3)
#bylece 3.8 alt kullanan kullanclara bir heta mesaj gsterdik
#bu ilemi iin version deikenini de kullanabiliriz
if "3.7" in sys.version:
print("Gncel versiyondasnz")
else:
print(hataMesaj3)
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2611,
220,
1... | 2.237482 | 7,449 |
# -*- coding: utf-8 -*-
"""
Module defines modifier that compresses a stream with gzip
"""
from contextlib import contextmanager
from subprocess import Popen, PIPE
from twindb_backup.modifiers.base import Modifier
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
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198,
26796,
15738,
23157,
326,
552,
16746,
257,
4269,
351,
308,
13344,
198,
37811,
198,
6738,
4732,
8019,
1330,
4732,
37153,
198,
6738,
850,
14681,
1330,
8099,
268,... | 3.323077 | 65 |
# Copyright 2014 Intel Corp.
#
# 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.
"""Resource object."""
from oslo_config import cfg
from oslo_serialization import jsonutils
from oslo_versionedobjects import base
from oslo_versionedobjects import fields
import retrying
import six
from heat.common import crypt
from heat.common import exception
from heat.common.i18n import _
from heat.db import api as db_api
from heat.objects import base as heat_base
from heat.objects import fields as heat_fields
from heat.objects import resource_data
cfg.CONF.import_opt('encrypt_parameters_and_properties', 'heat.common.config')
| [
2,
15069,
1946,
8180,
11421,
13,
198,
2,
198,
2,
49962,
739,
262,
24843,
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11,
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2845,
287,
11846,
351,
262,
13789,
13,
921,
743,
7330,
19... | 3.763514 | 296 |
from .PyrezException import PyrezException
| [
6738,
764,
20519,
21107,
16922,
1330,
9485,
21107,
16922,
198
] | 4.3 | 10 |
import struct
from six import binary_type
from capnpy import ptr
from capnpy.packing import mychr
from capnpy.printer import print_buffer
from capnpy.segment._copy_pointer import copy_pointer, _copy_struct_inline
from capnpy.segment._copy_list import copy_from_list
| [
11748,
2878,
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3849,
1330,
3601,
62,
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198,
198,
... | 3.228916 | 83 |
# -*- coding: utf-8 -*-
# Generated by Django 1.11.13 on 2018-05-14 07:20
from __future__ import unicode_literals
from django.db import migrations, models
| [
2,
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9,
12,
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25,
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12,
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9,
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2,
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13,
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25,
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198,
6738,
11593,
37443,
834,
1330,
28000,
1098,
62,
17201,
874,
198,... | 2.754386 | 57 |
'''
objective of file.py:
define the object class for file,
information to include:
name
size of file
chunk list (should automaticlly be splitted into chunks, each chunk should have indicator)
'''
import base64
import sys
import hashlib
JPG, PNG, PDF, MP3, MP4, UNKNOWN = 1, 2, 3, 4, 5, 0
| [
7061,
6,
198,
220,
220,
220,
9432,
286,
2393,
13,
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25,
628,
220,
220,
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2134,
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11,
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220,
220,
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220,
220,
220,
1321,
284,
2291,
25,
628,
220,
220,
220,
1438,
198,
220,
220,
220,
2546,
28... | 2.786325 | 117 |
import unittest.mock as mock
from app import utils
| [
11748,
555,
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395,
13,
76,
735,
355,
15290,
198,
198,
6738,
598,
1330,
3384,
4487,
628
] | 3.117647 | 17 |
#!/usr/bin/env python3
import argparse
import datetime
import os
import matplotlib.pyplot as plt
import pandas as pd
if __name__ == '__main__':
main()
| [
2,
48443,
14629,
14,
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14,
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18,
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198,
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29487,
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458,
83,
198,
11748,
19798,
292,
355,
279,
67,
628,
19... | 2.666667 | 60 |
# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""WikipediaToxicitySubtypes from Jigsaw Toxic Comment Classification Challenge."""
import csv
import os
import tensorflow.compat.v2 as tf
import tensorflow_datasets.public_api as tfds
_CITATION = """
@inproceedings{10.1145/3038912.3052591,
author = {Wulczyn, Ellery and Thain, Nithum and Dixon, Lucas},
title = {Ex Machina: Personal Attacks Seen at Scale},
year = {2017},
isbn = {9781450349130},
publisher = {International World Wide Web Conferences Steering Committee},
address = {Republic and Canton of Geneva, CHE},
url = {https://doi.org/10.1145/3038912.3052591},
doi = {10.1145/3038912.3052591},
booktitle = {Proceedings of the 26th International Conference on World Wide Web},
pages = {1391-1399},
numpages = {9},
keywords = {online discussions, wikipedia, online harassment},
location = {Perth, Australia},
series = {WWW '17}
}
"""
_DESCRIPTION = """
This version of the Wikipedia Toxicity Subtypes dataset provides access to the
primary toxicity label, as well the five toxicity subtype labels annotated by
crowd workers. The toxicity and toxicity subtype labels are binary values
(0 or 1) indicating whether the majority of annotators assigned that
attributes to the comment text.
The comments in this dataset come from an archive of Wikipedia talk pages
comments. These have been annotated by Jigsaw for toxicity, as well as a variety
of toxicity subtypes, including severe toxicity, obscenity, threatening
language, insulting language, and identity attacks. This dataset is a replica of
the data released for the Jigsaw Toxic Comment Classification Challenge on
Kaggle, with the training set unchanged, and the test dataset merged with the
test_labels released after the end of the competition. Test data not used for
scoring has been dropped. This dataset is released under CC0, as is the
underlying comment text.
See the Kaggle documentation or
https://figshare.com/articles/Wikipedia_Talk_Labels_Toxicity/4563973 for more
details.
"""
_DOWNLOAD_URL = 'https://storage.googleapis.com/jigsaw-unintended-bias-in-toxicity-classification/wikipedia_toxicity_subtypes.zip'
| [
2,
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28,
40477,
12,
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2,
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12131,
383,
309,
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15,
357,
1169,
366,
34156,
15341,
198,
2,
345,
743,
407,
77... | 3.628989 | 752 |
import pandas as pd
import seaborn as sns
sns.set()
# + tags=["parameters"]
upstream = ["download_buildings"]
product = None
# -
buildings = pd.read_csv(upstream["download_buildings"])
buildings["wall_uvalue"].plot.hist(bins=30)
buildings["roof_uvalue"].plot.hist(bins=30)
buildings["window_uvalue"].plot.hist(bins=30)
buildings["wall_uvalue"].to_csv(product["wall"])
buildings["roof_uvalue"].to_csv(product["roof"])
buildings["window_uvalue"].to_csv(product["window"])
| [
11748,
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384,
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198,
929,
5532,
796,
14631,
15002,
62,
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8973,
19... | 2.474227 | 194 |
#!/usr/bin/python
'''
Copyright 2018 Albert Monfa
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
'''
import logging
| [
2,
48443,
14629,
14,
8800,
14,
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198,
7061,
6,
198,
15269,
2864,
9966,
2892,
13331,
198,
198,
26656,
15385,
739,
262,
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11,
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362,
13,
15,
357,
1169,
366,
34156,
15341,
198,
5832,
743,
407,
779,
428,
2393,
2845,
... | 3.789809 | 157 |
from IzVerifier.izspecs.containers.izclasses import IzClasses
__author__ = 'fcanas'
import unittest
from IzVerifier.izspecs.containers.izconditions import IzConditions
from IzVerifier.izspecs.containers.izstrings import IzStrings
from IzVerifier.izspecs.containers.izvariables import IzVariables
from IzVerifier.izverifier import IzVerifier
from IzVerifier.izspecs.containers.constants import *
path1 = 'data/sample_installer_iz5/izpack/'
path2 = 'data/sample_installer_iz5/resources/'
source_path2 = 'data/sample_code_base/src/'
pom = 'data/sample_installer_iz5/pom.xml'
if __name__ == '__main__':
unittest.main()
| [
6738,
28493,
13414,
7483,
13,
528,
4125,
6359,
13,
3642,
50221,
13,
528,
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1330,
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9487,
274,
198,
198,
834,
9800,
834,
796,
705,
69,
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292,
6,
198,
198,
11748,
555,
715,
395,
198,
198,
6738,
28493,
13414,
7483,
13,
52... | 2.792035 | 226 |
from psycopg2.pool import ThreadedConnectionPool
import psycopg2.extras
import psycopg2.sql as sql
from contextlib import contextmanager
from threading import Semaphore
from ..db.exceptions import DatabaseException
from ..utils.logs import Log
from ..constants import MIN_CONN, MAX_CONN, TIMEOUT_CONN, DEFAULT_OFFSET_LIMIT, DEFAULT_CHUNK_SIZE
from ..asserts import assertz
| [
6738,
17331,
22163,
70,
17,
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1330,
14122,
276,
32048,
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198,
11748,
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70,
17,
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2302,
8847,
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198,
6738,
4732,
8019,
1330,
4732,
37153,
198,
6738,
4704,
... | 3.241379 | 116 |
"""Kills all connections from a given treadmill server."""
import logging
import re
import click
import kazoo
from treadmill import presence
from treadmill import utils
from treadmill import zkutils
from treadmill import context
from treadmill import cli
from treadmill import zknamespace as z
_LOGGER = logging.getLogger(__name__)
_ON_EXCEPTIONS = cli.handle_exceptions([
(kazoo.exceptions.NoAuthError, 'Error: not authorized.'),
(context.ContextError, None),
])
def _gen_formatter(mapping, formatter):
"""Generate real formatter to have item index in position."""
pattern = re.compile(r'(%(\w))')
match = pattern.findall(formatter)
# (symbol, key) should be ('%t', 't')
for (symbol, key) in match:
index = mapping[key]
formatter = formatter.replace(symbol, '{%d}' % index, 1)
return formatter
def _list_server_blackouts(zkclient, fmt):
"""List server blackouts."""
# List currently blacked out nodes.
blacked_out = []
try:
blacked_out_nodes = zkclient.get_children(z.BLACKEDOUT_SERVERS)
for server in blacked_out_nodes:
node_path = z.path.blackedout_server(server)
data, metadata = zkutils.get(zkclient, node_path,
need_metadata=True)
blacked_out.append((metadata.created, server, data))
except kazoo.client.NoNodeError:
pass
# [%t] %h %r will be printed as below
# [Thu, 05 May 2016 02:59:58 +0000] <hostname> -
mapping = {'t': 0, 'h': 1, 'r': 2}
formatter = _gen_formatter(mapping, fmt)
for when, server, reason in reversed(sorted(blacked_out)):
reason = '-' if reason is None else reason
print(formatter.format(utils.strftime_utc(when), server, reason))
def _clear_server_blackout(zkclient, server):
"""Clear server blackout."""
path = z.path.blackedout_server(server)
zkutils.ensure_deleted(zkclient, path)
def _blackout_server(zkclient, server, reason):
"""Blackout server."""
if not reason:
raise click.UsageError('--reason is required.')
path = z.path.blackedout_server(server)
zkutils.ensure_exists(
zkclient,
path,
acl=[zkutils.make_host_acl(server, 'rwcda')],
data=str(reason)
)
presence.kill_node(zkclient, server)
def _blackout_app(zkclient, app, clear):
"""Blackout app."""
# list current blacklist
blacklisted_node = z.path.blackedout_app(app)
if clear:
zkutils.ensure_deleted(zkclient, blacklisted_node)
else:
zkutils.ensure_exists(zkclient, blacklisted_node)
def _list_blackedout_apps(zkclient):
"""List blackedout apps."""
try:
for blacklisted in zkclient.get_children(z.BLACKEDOUT_APPS):
print(blacklisted)
except kazoo.client.NoNodeError:
pass
def init():
"""Top level command handler."""
del server_cmd
del app_cmd
return blackout
| [
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198,
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198,
11748,
302,
198,
198,
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479,
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198,
198,
6738,
49246,
1330,
4931,
198,
6738,
49246,
1330,
3384,
4487,
198... | 2.441322 | 1,210 |
import os
from functools import partial
from django.conf.urls.i18n import i18n_patterns
from django.urls import include, path, re_path
from django.utils.translation import gettext_lazy as _
from django.views import defaults, i18n, static
from . import views
base_dir = os.path.dirname(os.path.abspath(__file__))
media_dir = os.path.join(base_dir, "media")
locale_dir = os.path.join(base_dir, "locale")
urlpatterns = [
path("", views.index_page),
# Default views
path("nonexistent_url/", partial(defaults.page_not_found, exception=None)),
path("server_error/", defaults.server_error),
# a view that raises an exception for the debug view
path("raises/", views.raises),
path("raises400/", views.raises400),
path("raises400_bad_request/", views.raises400_bad_request),
path("raises403/", views.raises403),
path("raises404/", views.raises404),
path("raises500/", views.raises500),
path("custom_reporter_class_view/", views.custom_reporter_class_view),
path("technical404/", views.technical404, name="my404"),
path("classbased404/", views.Http404View.as_view()),
# i18n views
path("i18n/", include("django.conf.urls.i18n")),
path("jsi18n/", i18n.JavaScriptCatalog.as_view(packages=["view_tests"])),
path("jsi18n/app1/", i18n.JavaScriptCatalog.as_view(packages=["view_tests.app1"])),
path("jsi18n/app2/", i18n.JavaScriptCatalog.as_view(packages=["view_tests.app2"])),
path("jsi18n/app5/", i18n.JavaScriptCatalog.as_view(packages=["view_tests.app5"])),
path(
"jsi18n_english_translation/",
i18n.JavaScriptCatalog.as_view(packages=["view_tests.app0"]),
),
path(
"jsi18n_multi_packages1/",
i18n.JavaScriptCatalog.as_view(packages=["view_tests.app1", "view_tests.app2"]),
),
path(
"jsi18n_multi_packages2/",
i18n.JavaScriptCatalog.as_view(packages=["view_tests.app3", "view_tests.app4"]),
),
path(
"jsi18n_admin/",
i18n.JavaScriptCatalog.as_view(packages=["django.contrib.admin", "view_tests"]),
),
path("jsi18n_template/", views.jsi18n),
path("jsi18n_multi_catalogs/", views.jsi18n_multi_catalogs),
path("jsoni18n/", i18n.JSONCatalog.as_view(packages=["view_tests"])),
# Static views
re_path(
r"^site_media/(?P<path>.*)$",
static.serve,
{"document_root": media_dir, "show_indexes": True},
),
]
urlpatterns += i18n_patterns(
re_path(_(r"^translated/$"), views.index_page, name="i18n_prefixed"),
)
urlpatterns += [
path("template_exception/", views.template_exception, name="template_exception"),
path(
"raises_template_does_not_exist/<path:path>",
views.raises_template_does_not_exist,
name="raises_template_does_not_exist",
),
path("render_no_template/", views.render_no_template, name="render_no_template"),
re_path(
r"^test-setlang/(?P<parameter>[^/]+)/$",
views.with_parameter,
name="with_parameter",
),
# Patterns to test the technical 404.
re_path(r"^regex-post/(?P<pk>[0-9]+)/$", views.index_page, name="regex-post"),
path("path-post/<int:pk>/", views.index_page, name="path-post"),
]
| [
11748,
28686,
198,
6738,
1257,
310,
10141,
1330,
13027,
198,
198,
6738,
42625,
14208,
13,
10414,
13,
6371,
82,
13,
72,
1507,
77,
1330,
1312,
1507,
77,
62,
33279,
82,
198,
6738,
42625,
14208,
13,
6371,
82,
1330,
2291,
11,
3108,
11,
3... | 2.340892 | 1,367 |
from sympy import (Derivative, Symbol)
from sympy.core.numbers import I
from sympy.core.relational import Eq
from sympy.core.symbol import Dummy
from sympy.functions import exp, im, cos, sin, re
from sympy.functions.combinatorial.factorials import factorial
from sympy.matrices import zeros, Matrix
from sympy.simplify import simplify, collect
from sympy.solvers.deutils import ode_order
from sympy.solvers.solveset import NonlinearError
from sympy.utilities import numbered_symbols, default_sort_key
from sympy.utilities.iterables import ordered, uniq
def linear_ode_to_matrix(eqs, funcs, t, order):
r"""
Convert a linear system of ODEs to matrix form
Explanation
===========
Express a system of linear ordinary differential equations as a single
matrix differential equation [1]. For example the system $x' = x + y + 1$
and $y' = x - y$ can be represented as
.. math:: A_1 X' + A_0 X = b
where $A_1$ and $A_0$ are $2 \times 2$ matrices and $b$, $X$ and $X'$ are
$2 \times 1$ matrices with $X = [x, y]^T$.
Higher-order systems are represented with additional matrices e.g. a
second-order system would look like
.. math:: A_2 X'' + A_1 X' + A_0 X = b
Examples
========
>>> from sympy import (Function, Symbol, Matrix, Eq)
>>> from sympy.solvers.ode.systems import linear_ode_to_matrix
>>> t = Symbol('t')
>>> x = Function('x')
>>> y = Function('y')
We can create a system of linear ODEs like
>>> eqs = [
... Eq(x(t).diff(t), x(t) + y(t) + 1),
... Eq(y(t).diff(t), x(t) - y(t)),
... ]
>>> funcs = [x(t), y(t)]
>>> order = 1 # 1st order system
Now ``linear_ode_to_matrix`` can represent this as a matrix
differential equation.
>>> (A1, A0), b = linear_ode_to_matrix(eqs, funcs, t, order)
>>> A1
Matrix([
[1, 0],
[0, 1]])
>>> A0
Matrix([
[-1, -1],
[-1, 1]])
>>> b
Matrix([
[1],
[0]])
The original equations can be recovered from these matrices:
>>> eqs_mat = Matrix([eq.lhs - eq.rhs for eq in eqs])
>>> X = Matrix(funcs)
>>> A1 * X.diff(t) + A0 * X - b == eqs_mat
True
If the system of equations has a maximum order greater than the
order of the system specified, a ODEOrderError exception is raised.
>>> eqs = [Eq(x(t).diff(t, 2), x(t).diff(t) + x(t)), Eq(y(t).diff(t), y(t) + x(t))]
>>> linear_ode_to_matrix(eqs, funcs, t, 1)
Traceback (most recent call last):
...
ODEOrderError: Cannot represent system in 1-order form
If the system of equations is nonlinear, then ODENonlinearError is
raised.
>>> eqs = [Eq(x(t).diff(t), x(t) + y(t)), Eq(y(t).diff(t), y(t)**2 + x(t))]
>>> linear_ode_to_matrix(eqs, funcs, t, 1)
Traceback (most recent call last):
...
ODENonlinearError: The system of ODEs is nonlinear.
Parameters
==========
eqs : list of sympy expressions or equalities
The equations as expressions (assumed equal to zero).
funcs : list of applied functions
The dependent variables of the system of ODEs.
t : symbol
The independent variable.
order : int
The order of the system of ODEs.
Returns
=======
The tuple ``(As, b)`` where ``As`` is a tuple of matrices and ``b`` is the
the matrix representing the rhs of the matrix equation.
Raises
======
ODEOrderError
When the system of ODEs have an order greater than what was specified
ODENonlinearError
When the system of ODEs is nonlinear
See Also
========
linear_eq_to_matrix: for systems of linear algebraic equations.
References
==========
.. [1] https://en.wikipedia.org/wiki/Matrix_differential_equation
"""
from sympy.solvers.solveset import linear_eq_to_matrix
if any(ode_order(eq, func) > order for eq in eqs for func in funcs):
msg = "Cannot represent system in {}-order form"
raise ODEOrderError(msg.format(order))
As = []
for o in range(order, -1, -1):
# Work from the highest derivative down
funcs_deriv = [func.diff(t, o) for func in funcs]
# linear_eq_to_matrix expects a proper symbol so substitute e.g.
# Derivative(x(t), t) for a Dummy.
rep = {func_deriv: Dummy() for func_deriv in funcs_deriv}
eqs = [eq.subs(rep) for eq in eqs]
syms = [rep[func_deriv] for func_deriv in funcs_deriv]
# Ai is the matrix for X(t).diff(t, o)
# eqs is minus the remainder of the equations.
try:
Ai, b = linear_eq_to_matrix(eqs, syms)
except NonlinearError:
raise ODENonlinearError("The system of ODEs is nonlinear.")
As.append(Ai)
if o:
eqs = [-eq for eq in b]
else:
rhs = b
return As, rhs
def matrix_exp(A, t):
r"""
Matrix exponential $\exp(A*t)$ for the matrix ``A`` and scalar ``t``.
Explanation
===========
This functions returns the $\exp(A*t)$ by doing a simple
matrix multiplication:
.. math:: \exp(A*t) = P * expJ * P^{-1}
where $expJ$ is $\exp(J*t)$. $J$ is the Jordan normal
form of $A$ and $P$ is matrix such that:
.. math:: A = P * J * P^{-1}
The matrix exponential $\exp(A*t)$ appears in the solution of linear
differential equations. For example if $x$ is a vector and $A$ is a matrix
then the initial value problem
.. math:: \frac{dx(t)}{dt} = A \times x(t), x(0) = x0
has the unique solution
.. math:: x(t) = \exp(A t) x0
Examples
========
>>> from sympy import Symbol, Matrix, pprint
>>> from sympy.solvers.ode.systems import matrix_exp
>>> t = Symbol('t')
We will consider a 2x2 matrix for comupting the exponential
>>> A = Matrix([[2, -5], [2, -4]])
>>> pprint(A)
[2 -5]
[ ]
[2 -4]
Now, exp(A*t) is given as follows:
>>> pprint(matrix_exp(A, t))
[ -t -t -t ]
[3*e *sin(t) + e *cos(t) -5*e *sin(t) ]
[ ]
[ -t -t -t ]
[ 2*e *sin(t) - 3*e *sin(t) + e *cos(t)]
Parameters
==========
A : Matrix
The matrix $A$ in the expression $\exp(A*t)$
t : Symbol
The independent variable
See Also
========
matrix_exp_jordan_form: For exponential of Jordan normal form
References
==========
.. [1] https://en.wikipedia.org/wiki/Jordan_normal_form
.. [2] https://en.wikipedia.org/wiki/Matrix_exponential
"""
P, expJ = matrix_exp_jordan_form(A, t)
return P * expJ * P.inv()
def matrix_exp_jordan_form(A, t):
r"""
Matrix exponential $\exp(A*t)$ for the matrix *A* and scalar *t*.
Explanation
===========
Returns the Jordan form of the $\exp(A*t)$ along with the matrix $P$ such that:
.. math::
\exp(A*t) = P * expJ * P^{-1}
Examples
========
>>> from sympy import Matrix, Symbol
>>> from sympy.solvers.ode.systems import matrix_exp, matrix_exp_jordan_form
>>> t = Symbol('t')
We will consider a 2x2 defective matrix. This shows that our method
works even for defective matrices.
>>> A = Matrix([[1, 1], [0, 1]])
It can be observed that this function gives us the Jordan normal form
and the required invertible matrix P.
>>> P, expJ = matrix_exp_jordan_form(A, t)
Here, it is shown that P and expJ returned by this function is correct
as they satisfy the formula: P * expJ * P_inverse = exp(A*t).
>>> P * expJ * P.inv() == matrix_exp(A, t)
True
Parameters
==========
A : Matrix
The matrix $A$ in the expression $\exp(A*t)$
t : Symbol
The independent variable
References
==========
.. [1] https://en.wikipedia.org/wiki/Defective_matrix
.. [2] https://en.wikipedia.org/wiki/Jordan_matrix
.. [3] https://en.wikipedia.org/wiki/Jordan_normal_form
"""
N, M = A.shape
if N != M:
raise ValueError('Needed square matrix but got shape (%s, %s)' % (N, M))
elif A.has(t):
raise ValueError('Matrix A should not depend on t')
def jordan_chains(A):
'''Chains from Jordan normal form analogous to M.eigenvects().
Returns a dict with eignevalues as keys like:
{e1: [[v111,v112,...], [v121, v122,...]], e2:...}
where vijk is the kth vector in the jth chain for eigenvalue i.
'''
P, blocks = A.jordan_cells()
basis = [P[:,i] for i in range(P.shape[1])]
n = 0
chains = {}
for b in blocks:
eigval = b[0, 0]
size = b.shape[0]
if eigval not in chains:
chains[eigval] = []
chains[eigval].append(basis[n:n+size])
n += size
return chains
eigenchains = jordan_chains(A)
# Needed for consistency across Python versions:
eigenchains_iter = sorted(eigenchains.items(), key=default_sort_key)
isreal = not A.has(I)
blocks = []
vectors = []
seen_conjugate = set()
for e, chains in eigenchains_iter:
for chain in chains:
n = len(chain)
if isreal and e != e.conjugate() and e.conjugate() in eigenchains:
if e in seen_conjugate:
continue
seen_conjugate.add(e.conjugate())
exprt = exp(re(e) * t)
imrt = im(e) * t
imblock = Matrix([[cos(imrt), sin(imrt)],
[-sin(imrt), cos(imrt)]])
expJblock2 = Matrix(n, n, lambda i,j:
imblock * t**(j-i) / factorial(j-i) if j >= i
else zeros(2, 2))
expJblock = Matrix(2*n, 2*n, lambda i,j: expJblock2[i//2,j//2][i%2,j%2])
blocks.append(exprt * expJblock)
for i in range(n):
vectors.append(re(chain[i]))
vectors.append(im(chain[i]))
else:
vectors.extend(chain)
fun = lambda i,j: t**(j-i)/factorial(j-i) if j >= i else 0
expJblock = Matrix(n, n, fun)
blocks.append(exp(e * t) * expJblock)
expJ = Matrix.diag(*blocks)
P = Matrix(N, N, lambda i,j: vectors[j][i])
return P, expJ
def _neq_linear_first_order_const_coeff_homogeneous(match_):
r"""
System of n first-order constant-coefficient linear homogeneous differential equations
.. math:: y'_k = a_{k1} y_1 + a_{k2} y_2 +...+ a_{kn} y_n; k = 1,2,...,n
or that can be written as `\vec{y'} = A . \vec{y}`
where `\vec{y}` is matrix of `y_k` for `k = 1,2,...n` and `A` is a `n \times n` matrix.
Since these equations are equivalent to a first order homogeneous linear
differential equation. So the general solution will contain `n` linearly
independent parts and solution will consist some type of exponential
functions. Assuming `y = \vec{v} e^{rt}` is a solution of the system where
`\vec{v}` is a vector of coefficients of `y_1,...,y_n`. Substituting `y` and
`y' = r v e^{r t}` into the equation `\vec{y'} = A . \vec{y}`, we get
.. math:: r \vec{v} e^{rt} = A \vec{v} e^{rt}
.. math:: r \vec{v} = A \vec{v}
where `r` comes out to be eigenvalue of `A` and vector `\vec{v}` is the eigenvector
of `A` corresponding to `r`. There are three possibilities of eigenvalues of `A`
- `n` distinct real eigenvalues
- complex conjugate eigenvalues
- eigenvalues with multiplicity `k`
1. When all eigenvalues `r_1,..,r_n` are distinct with `n` different eigenvectors
`v_1,...v_n` then the solution is given by
.. math:: \vec{y} = C_1 e^{r_1 t} \vec{v_1} + C_2 e^{r_2 t} \vec{v_2} +...+ C_n e^{r_n t} \vec{v_n}
where `C_1,C_2,...,C_n` are arbitrary constants.
2. When some eigenvalues are complex then in order to make the solution real,
we take a linear combination: if `r = a + bi` has an eigenvector
`\vec{v} = \vec{w_1} + i \vec{w_2}` then to obtain real-valued solutions to
the system, replace the complex-valued solutions `e^{rx} \vec{v}`
with real-valued solution `e^{ax} (\vec{w_1} \cos(bx) - \vec{w_2} \sin(bx))`
and for `r = a - bi` replace the solution `e^{-r x} \vec{v}` with
`e^{ax} (\vec{w_1} \sin(bx) + \vec{w_2} \cos(bx))`
3. If some eigenvalues are repeated. Then we get fewer than `n` linearly
independent eigenvectors, we miss some of the solutions and need to
construct the missing ones. We do this via generalized eigenvectors, vectors
which are not eigenvectors but are close enough that we can use to write
down the remaining solutions. For a eigenvalue `r` with eigenvector `\vec{w}`
we obtain `\vec{w_2},...,\vec{w_k}` using
.. math:: (A - r I) . \vec{w_2} = \vec{w}
.. math:: (A - r I) . \vec{w_3} = \vec{w_2}
.. math:: \vdots
.. math:: (A - r I) . \vec{w_k} = \vec{w_{k-1}}
Then the solutions to the system for the eigenspace are `e^{rt} [\vec{w}],
e^{rt} [t \vec{w} + \vec{w_2}], e^{rt} [\frac{t^2}{2} \vec{w} + t \vec{w_2} + \vec{w_3}],
...,e^{rt} [\frac{t^{k-1}}{(k-1)!} \vec{w} + \frac{t^{k-2}}{(k-2)!} \vec{w_2} +...+ t \vec{w_{k-1}}
+ \vec{w_k}]`
So, If `\vec{y_1},...,\vec{y_n}` are `n` solution of obtained from three
categories of `A`, then general solution to the system `\vec{y'} = A . \vec{y}`
.. math:: \vec{y} = C_1 \vec{y_1} + C_2 \vec{y_2} + \cdots + C_n \vec{y_n}
"""
eq = match_['eq']
func = match_['func']
fc = match_['func_coeff']
n = len(eq)
t = list(list(eq[0].atoms(Derivative))[0].atoms(Symbol))[0]
constants = numbered_symbols(prefix='C', cls=Symbol, start=1)
# This needs to be modified in future so that fc is only of type Matrix
M = -fc if type(fc) is Matrix else Matrix(n, n, lambda i,j:-fc[i,func[j],0])
P, J = matrix_exp_jordan_form(M, t)
P = simplify(P)
Cvect = Matrix(list(next(constants) for _ in range(n)))
sol_vector = P * (J * Cvect)
sol_vector = [collect(s, ordered(J.atoms(exp)), exact=True) for s in sol_vector]
sol_dict = [Eq(func[i], sol_vector[i]) for i in range(n)]
return sol_dict
def _matrix_is_constant(M, t):
"""Checks if the matrix M is independent of t or not."""
return all(coef.as_independent(t, as_Add=True)[1] == 0 for coef in M)
def _canonical_equations(eqs, funcs, t):
"""Helper function that solves for first order derivatives in a system"""
from sympy.solvers.solvers import solve
# For now the system of ODEs dealt by this function can have a
# maximum order of 1.
if any(ode_order(eq, func) > 1 for eq in eqs for func in funcs):
msg = "Cannot represent system in {}-order canonical form"
raise ODEOrderError(msg.format(1))
canon_eqs = solve(eqs, *[func.diff(t) for func in funcs], dict=True)
if len(canon_eqs) != 1:
raise ODENonlinearError("System of ODEs is nonlinear")
canon_eqs = canon_eqs[0]
canon_eqs = [Eq(func.diff(t), canon_eqs[func.diff(t)]) for func in funcs]
return canon_eqs
def neq_nth_linear_constant_coeff_match(eqs, funcs, t):
r"""
Returns a dictionary with details of the eqs if every equation is constant coefficient
and linear else returns None
Explanation
===========
This function takes the eqs, converts it into a form Ax = b where x is a vector of terms
containing dependent variables and their derivatives till their maximum order. If it is
possible to convert eqs into Ax = b, then all the equations in eqs are linear otherwise
they are non-linear.
To check if the equations are constant coefficient, we need to check if all the terms in
A obtained above are constant or not.
To check if the equations are homogeneous or not, we need to check if b is a zero matrix
or not.
Parameters
==========
eqs: List
List of ODEs
funcs: List
List of dependent variables
t: Symbol
Independent variable of the equations in eqs
Returns
=======
match = {
'no_of_equation': len(eqs),
'eq': eqs,
'func': funcs,
'order': order,
'is_linear': is_linear,
'is_constant': is_constant,
'is_homogeneous': is_homogeneous,
}
Dict or None
Dict with values for keys:
1. no_of_equation: Number of equations
2. eq: The set of equations
3. func: List of dependent variables
4. order: A dictionary that gives the order of the
dependent variable in eqs
5. is_linear: Boolean value indicating if the set of
equations are linear or not.
6. is_constant: Boolean value indicating if the set of
equations have constant coefficients or not.
7. is_homogeneous: Boolean value indicating if the set of
equations are homogeneous or not.
This Dict is the answer returned if the eqs are linear and constant
coefficient. Otherwise, None is returned.
"""
# Error for i == 0 can be added but isn't for now
# Removing the duplicates from the list of funcs
# meanwhile maintaining the order. This is done
# since the line in classify_sysode: list(set(funcs)
# cause some test cases to fail when gives different
# results in different versions of Python.
funcs = list(uniq(funcs))
# Check for len(funcs) == len(eqs)
if len(funcs) != len(eqs):
raise ValueError("Number of functions given is not equal to the number of equations %s" % funcs)
# ValueError when functions have more than one arguments
for func in funcs:
if len(func.args) != 1:
raise ValueError("dsolve() and classify_sysode() work with "
"functions of one variable only, not %s" % func)
# Getting the func_dict and order using the helper
# function
order = _get_func_order(eqs, funcs)
if not all(order[func] == 1 for func in funcs):
return None
else:
# TO be changed when this function is updated.
# This will in future be updated as the maximum
# order in the system found.
system_order = 1
# Not adding the check if the len(func.args) for
# every func in funcs is 1
# Linearity check
try:
canon_eqs = _canonical_equations(eqs, funcs, t)
As, b = linear_ode_to_matrix(canon_eqs, funcs, t, system_order)
# When the system of ODEs is non-linear, an ODENonlinearError is raised.
# When system has an order greater than what is specified in system_order,
# ODEOrderError is raised.
# This function catches these errors and None is returned
except (ODEOrderError, ODENonlinearError):
return None
A = As[1]
is_linear = True
# Constant coefficient check
is_constant = _matrix_is_constant(A, t)
# Homogeneous check
is_homogeneous = True if b.is_zero_matrix else False
match = {
'no_of_equation': len(eqs),
'eq': eqs,
'func': funcs,
'order': order,
'is_linear': is_linear,
'is_constant': is_constant,
'is_homogeneous': is_homogeneous,
}
# The match['is_linear'] check will be added in the future when this
# function becomes ready to deal with non-linear systems of ODEs
if match['is_constant']:
# Converting the equation into canonical form if the
# equation is first order. There will be a separate
# function for this in the future.
if all([order[func] == 1 for func in funcs]) and match['is_homogeneous']:
match['func_coeff'] = A
match['type_of_equation'] = "type1"
return match
return None
| [
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13... | 2.34708 | 8,511 |
import os
EAGER_MODE_DEBUG = os.environ.get("EAGER_MODE_DEBUG", 'False').lower() in ('true', '1', 't')
| [
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11537... | 2.418605 | 43 |
import sys
import os
import tempfile
from pathlib import Path
import pytest
sys.path.insert(1, os.path.join(sys.path[0], "../../"))
import rips
import dataroot
| [
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366,... | 2.859649 | 57 |
"""
Entry point for the CLI
"""
import logging
import click
from samcli import __version__
from .options import debug_option
from .context import Context
from .command import BaseCommand
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO, format='%(asctime)s %(message)s', datefmt='%Y-%m-%d %H:%M:%S')
pass_context = click.make_pass_decorator(Context)
def common_options(f):
"""
Common CLI options used by all commands. Ex: --debug
:param f: Callback function passed by Click
:return: Callback function
"""
f = debug_option(f)
return f
| [
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... | 2.884058 | 207 |
import ast
import typing as t
import numbers
import json
from wisepy.talking import Talking
from Redy.Tools.PathLib import Path
talking = Talking()
if __name__ == '__main__':
talking.on()
| [
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... | 3.177419 | 62 |
# Copyright 2021 Pants project contributors (see CONTRIBUTORS.md).
# Licensed under the Apache License, Version 2.0 (see LICENSE).
from __future__ import annotations
from textwrap import dedent
from typing import Any, ContextManager
import pytest
from pants.backend.docker.goals import package_image
from pants.backend.docker.subsystems import dockerfile_parser
from pants.backend.docker.subsystems.dockerfile_parser import DockerfileInfo
from pants.backend.docker.target_types import DockerImageTarget
from pants.backend.docker.util_rules import (
dependencies,
docker_binary,
docker_build_args,
docker_build_context,
docker_build_env,
dockerfile,
)
from pants.backend.docker.util_rules.docker_build_args import DockerBuildArgs
from pants.backend.docker.util_rules.docker_build_context import (
DockerBuildContext,
DockerBuildContextRequest,
)
from pants.backend.docker.util_rules.docker_build_env import DockerBuildEnvironment
from pants.backend.docker.value_interpolation import (
DockerBuildArgsInterpolationValue,
DockerInterpolationContext,
DockerInterpolationValue,
)
from pants.backend.python import target_types_rules
from pants.backend.python.goals import package_pex_binary
from pants.backend.python.goals.package_pex_binary import PexBinaryFieldSet
from pants.backend.python.target_types import PexBinary
from pants.backend.python.util_rules import pex_from_targets
from pants.backend.shell.target_types import ShellSourcesGeneratorTarget, ShellSourceTarget
from pants.backend.shell.target_types import rules as shell_target_types_rules
from pants.core.goals.package import BuiltPackage
from pants.core.target_types import FilesGeneratorTarget
from pants.core.target_types import rules as core_target_types_rules
from pants.engine.addresses import Address
from pants.engine.fs import EMPTY_DIGEST, EMPTY_SNAPSHOT, Snapshot
from pants.engine.internals.scheduler import ExecutionError
from pants.testutil.pytest_util import no_exception
from pants.testutil.rule_runner import QueryRule, RuleRunner
def test_create_docker_build_context() -> None:
context = DockerBuildContext.create(
build_args=DockerBuildArgs.from_strings("ARGNAME=value1"),
snapshot=EMPTY_SNAPSHOT,
build_env=DockerBuildEnvironment.create({"ENVNAME": "value2"}),
dockerfile_info=DockerfileInfo(
address=Address("test"),
digest=EMPTY_DIGEST,
source="test/Dockerfile",
putative_target_addresses=(),
version_tags=("base latest", "stage1 1.2", "dev 2.0", "prod 2.0"),
build_args=DockerBuildArgs.from_strings(),
from_image_build_arg_names=(),
copy_sources=(),
),
)
assert list(context.build_args) == ["ARGNAME=value1"]
assert dict(context.build_env.environment) == {"ENVNAME": "value2"}
assert context.dockerfile == "test/Dockerfile"
assert context.stages == ("base", "dev", "prod")
| [
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198,
... | 2.953419 | 1,009 |
import enum
from sqlalchemy import Column, ForeignKey, String, JSON, Integer, Enum
from sqlalchemy.orm import relationship
from build_you.models.base import BaseModel
from build_you.database import Base
| [
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730... | 3.867925 | 53 |
from .model import DeepLabResNetModel
from .hc_deeplab import HyperColumn_Deeplabv2
from .image_reader import ImageReader, read_data_list, get_indicator_mat, get_batch_1chunk, read_an_image_from_disk, tf_wrap_get_patch, get_batch
from .utils import decode_labels, inv_preprocess, prepare_label | [
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62,
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11,
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62,
7890,
62,... | 2.959596 | 99 |
# classification related details
classification_datasets = ['imagenet', 'coco']
classification_schedulers = ['fixed', 'clr', 'hybrid', 'linear', 'poly']
classification_models = ['espnetv2', 'dicenet', 'shufflenetv2']
classification_exp_choices = ['main', 'ablation']
# segmentation related details
segmentation_schedulers = ['poly', 'fixed', 'clr', 'linear', 'hybrid']
segmentation_datasets = ['pascal', 'city']
segmentation_models = ['espnetv2', 'dicenet']
segmentation_loss_fns = ['ce', 'bce']
# detection related details
detection_datasets = ['coco', 'pascal']
detection_models = ['espnetv2', 'dicenet']
detection_schedulers = ['poly', 'hybrid', 'clr', 'cosine']
| [
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# encoding: utf-8
# module win32profile
# from C:\Python27\lib\site-packages\win32\win32profile.pyd
# by generator 1.147
# no doc
# no imports
# Variables with simple values
PI_APPLYPOLICY = 2
PI_NOUI = 1
PT_MANDATORY = 4
PT_ROAMING = 2
PT_TEMPORARY = 1
# functions
def CreateEnvironmentBlock(*args, **kwargs): # real signature unknown
""" Retrieves environment variables for a user """
pass
def DeleteProfile(*args, **kwargs): # real signature unknown
""" Remove a user's profile """
pass
def ExpandEnvironmentStringsForUser(*args, **kwargs): # real signature unknown
""" Replaces environment variables in a string with per-user values """
pass
def GetAllUsersProfileDirectory(*args, **kwargs): # real signature unknown
""" Retrieve All Users profile directory """
pass
def GetDefaultUserProfileDirectory(*args, **kwargs): # real signature unknown
""" Retrieve profile path for Default user """
pass
def GetEnvironmentStrings(*args, **kwargs): # real signature unknown
""" Retrieves environment variables for current process """
pass
def GetProfilesDirectory(*args, **kwargs): # real signature unknown
""" Retrieves directory where user profiles are stored """
pass
def GetProfileType(*args, **kwargs): # real signature unknown
""" Returns type of current user's profile """
pass
def GetUserProfileDirectory(*args, **kwargs): # real signature unknown
""" Returns profile directory for a logon token """
pass
def LoadUserProfile(*args, **kwargs): # real signature unknown
""" Load user settings for a login token """
pass
def UnloadUserProfile(*args, **kwargs): # real signature unknown
""" Unload profile loaded by LoadUserProfile """
pass
# no classes
| [
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... | 3.387283 | 519 |
# !/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2020/3/4 0004 2:09
# @Author : Gpp
# @File : obtain_url.py
from app.web import api
from flask_restful import Resource
from flask import make_response, send_from_directory, jsonify
from app.helper.encrypt import two_encrypting
from app.crud.proxy_crud import ProtocolCrud
from app.helper.get_one_encrypt import get_one_encrypt_data
from app.helper.update_subscribe import add_proxy
# @api.resource('/generate')
# class Generate(Resource):
# def get(self):
# proxies = ProtocolCrud.get_all_share()
# one_encrypt = get_one_encrypt_data(proxies)
# result = add_proxy(two_encrypting(''.join(one_encrypt)))
# return jsonify(result)
| [
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... | 2.5189 | 291 |
import logging
from argparse import ArgumentParser
from dotenv import load_dotenv, find_dotenv
from .config import load
from .core.heart import start_loop
logger = logging.getLogger(__name__)
try:
load_dotenv(find_dotenv())
except Exception as ex:
logger.error("Error while loading .env: '{}'. Ignoring.".format(ex))
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument("--config", help="Config file", default="config.yaml")
parser.add_argument("--noop", action="store_true", default=False, help="Events will be processed, but not sent to Riemann")
parser.add_argument("--debug", action="store_true", default=False, help="Debug mode")
main(parser.parse_args())
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26... | 3.077253 | 233 |
#!/usr/bin/env python
# coding=utf-8
import glob
import click
import os
import json
import datetime
import re
import csv
from requests.exceptions import ConnectionError
from exchangelib import DELEGATE, IMPERSONATION, Account, Credentials, ServiceAccount, \
EWSDateTime, EWSTimeZone, Configuration, NTLM, CalendarItem, Message, \
Mailbox, Attendee, Q, ExtendedProperty, FileAttachment, ItemAttachment, \
HTMLBody, Build, Version
sendmail_secret = None
with open(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'secrets.json')) as data_file:
sendmail_secret = (json.load(data_file))['sendmail_win']
TO_REGISTER = 'Confirmed (to register)'
if __name__ == '__main__':
sendmail_win_cs() | [
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