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|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1d79b7801e234433e8bc93bd5f80827c3dfe3bb1
|
5bc2e45a8e8c256ab8ed332ebc5323eaac6631eb
|
/bandit/arch/analyzer/base_analyzer.py
|
64c6f93d9efdb8dffd3ae2e40a03c166f2a16775
|
[] |
no_license
|
ru-fix/permanent_predictor
|
1ed4a31cdaaf42ec8e882d679ff68522845138cc
|
a7662d31f887a5a5edb45e7114a281ba039b8884
|
refs/heads/master
| 2023-06-01T01:28:59.580346
| 2021-06-22T21:16:09
| 2021-06-22T21:16:09
| 379,379,745
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 5,833
|
py
|
from plotly.graph_objects import Figure
from arch.analyzer.utils.graphic_container import GraphicContainer
from consts import html_bases
class BaseAnalyzer:
def __init__(self, **kwargs):
for key in kwargs:
setattr(self, key, kwargs[key])
def __format_graphics(self, metrics):
graphics = {}
for metric_name, metric_data in metrics.results.items():
if metric_name in self.graphics_params:
graphics[metric_name] = GraphicContainer(
metric_data,
self.graphics_params[metric_name],
self.default_graphics_params,
metrics.params[metric_name],
metric_name,
metrics.experiment_name
)
return graphics
def __recursive_dropdown_build(self, current_key, key, dropdown_item, graphic_name):
# leaf nodes
if isinstance(dropdown_item, Figure):
return current_key, key, html_bases.wrap_into_div(
graphic_name+current_key,
dropdown_item.to_html(
include_plotlyjs=False,
full_html=False))
# recursion
current_level_dropdown_ids = []
current_level_dropdown_keys = []
current_level_dropdown_items = []
for index, (key, content) in enumerate(dropdown_item.items()):
full_ids, level_ids, item_htmls = self.__recursive_dropdown_build(
current_key + "_" + key,
key,
content,
graphic_name
)
# inner result
if isinstance(full_ids, list):
# we need all full ids
current_level_dropdown_ids += full_ids
if index == 0:
level_ids.append(list(dropdown_item.keys()))
current_level_dropdown_keys = level_ids
# leaf result
else:
# get only first child info
if index == 0:
current_level_dropdown_ids = [[current_key + "_" + key for key in dropdown_item.keys()]]
current_level_dropdown_keys = [[key for key in dropdown_item.keys()]]
current_level_dropdown_items.append(item_htmls)
return current_level_dropdown_ids, current_level_dropdown_keys, "\n".join(current_level_dropdown_items)
def __build_html(self, graphics):
# creating script
html_script = html_bases.wrap_into_script(
"\n".join(base.get_html_element_script()
for base in [html_bases.Script, html_bases.Buttons, html_bases.Dropdown])
)
html_style = html_bases.Style.get_html_element_base()
graphic_group_prefix = "graphic_group"
graphic_groups = [f"{graphic_group_prefix}_{key}" for key in graphics.keys()]
html_body = [html_bases.Buttons.get_html_element_base(graphic_groups, graphics.keys(), graphic_group_prefix)]
for graphic_group_index, (graphic_name, graphic) in enumerate(graphics.items()):
div_graphic_id = f"{graphic_name}_graph"
content = graphic.get()
if isinstance(content, dict):
dropdown_full_ids, dropdowns_values, graphic_html = self.__recursive_dropdown_build("", "", content, div_graphic_id)
dropdowns_values.reverse()
dropdowns = []
for index, dropdown_type_values in enumerate(dropdowns_values):
dropdown_id = f"{div_graphic_id}_{index}"
dropdown = html_bases.Dropdown.get_html_element_base(dropdown_type_values, dropdown_id, div_graphic_id)
dropdowns.append(dropdown)
dropdowns = "\n".join(dropdowns)
graphic_html = '\n'.join([dropdowns, graphic_html])
else:
graphic_html = content.to_html(
include_plotlyjs=False,
full_html=False
)
html_body.append(html_bases.wrap_into_div(graphic_groups[graphic_group_index], graphic_html))
result_html_body = html_bases.wrap_into_body('\n'.join(html_body))
result_head = html_bases.wrap_into_head('\n'.join([html_style, html_script]))
result_html = html_bases.wrap_into_html('\n'.join([result_head, result_html_body]))
return result_html
def build(self, metrics_path):
metrics = self.state_handler.load_metrics_data(metrics_path)
graphics = self.__format_graphics(metrics)
html = self.__build_html(graphics)
self.state_handler.save_experiment(graphics, html, metrics.run_name, metrics.experiment_name)
def compare_experiments(self, analyze_name, run_name, experiment_locations):
experiments_graphics = self.state_handler.load_experiments_graphics(experiment_locations)
compare_graphics = {}
for experiment_name, experiment_graphics in experiments_graphics.items():
for metric_name, experiment_graphic in experiment_graphics.items():
if metric_name not in compare_graphics:
experiment_graphic.update_graphic_params(
self.default_compare_graphics_params
)
if metric_name in self.compare_graphics_params:
experiment_graphic.update_graphic_params(
self.compare_graphics_params[metric_name]
)
compare_graphics[metric_name] = experiment_graphic
else:
compare_graphics[metric_name].add(experiment_name, experiment_graphic)
html = self.__build_html(compare_graphics)
self.state_handler.save_analyze(html, analyze_name, run_name)
|
[
"rduryagin@fix.ru"
] |
rduryagin@fix.ru
|
eacf58fddef56680e7da230bfefdd089d05d179f
|
b786ad8fdf75f0e132affd40de5a6809628fa071
|
/venv/Scripts/easy_install-script.py
|
6ef1646d4956685bccb239122bfeb7fa8863c96a
|
[] |
no_license
|
Bhuvan-Rm/WebScraping
|
e703c673b321cad716f6289248a1cb1d55434f7f
|
9a3f1c967d7e3dba82a975a72913902774c914c1
|
refs/heads/master
| 2020-09-07T11:32:10.197385
| 2019-12-14T09:56:36
| 2019-12-14T09:56:36
| 220,765,917
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 442
|
py
|
#!A:\PycharmProjects\WebScraping\venv\Scripts\python.exe
# EASY-INSTALL-ENTRY-SCRIPT: 'setuptools==40.8.0','console_scripts','easy_install'
__requires__ = 'setuptools==40.8.0'
import re
import sys
from pkg_resources import load_entry_point
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
sys.exit(
load_entry_point('setuptools==40.8.0', 'console_scripts', 'easy_install')()
)
|
[
"bhuvaneshrm@gmail.com"
] |
bhuvaneshrm@gmail.com
|
e35ef6540b47da980333bbf7d153523adc956efb
|
eb2b13d90d54e6e1e7f348b34e23f19bdbca9e90
|
/9_class.py
|
a3e5c7569600970550504b29070a609f43c5584d
|
[] |
no_license
|
diassor/platzi_pyhton_2019
|
6df0d4d9a996ee454aac05f88e1d5c5aff7e8f76
|
fdf74d41f6077069cdad8f8df291315d86783b40
|
refs/heads/master
| 2020-04-20T10:37:02.171540
| 2019-03-10T04:07:47
| 2019-03-10T04:07:47
| 168,794,052
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 2,662
|
py
|
# -*- coding: utf-8 -*-
"""
Las funciones
Las funciones es lo mas importante en la programacion
En el contexto de la programación, una función es una secuencia
enunciados (statements) con un nombre que realizan un cómputo
Una función tiene un nombre, parámetros (opcional) y valor de regreso
(return value)(opcional)
Python incluye varias built-in functions en su librería estándar
En el contexto de la programacion las funciones son una agrupacion
de statements de enunciados que tienen un nombre
Por que agrupamos nuestras funciones ?
El nombre de las funciones deben ser descriptivos
es muy importante
Deben terner parametros (opcional) Que es lo que recive
y puede regresar un valor
Python() es uno de los lenguajes que se conocen como Battery include
con baterias includas tiene muchas librerias para ser usadas
Las funciones (Built-in functions)
es una lista de constructores de funciones
(https://docs.python.org/3/library/functions.html)
aqui dejo el link de la informacion de
las funciones de los constructores
Para declarar otras funciones debemos declarar (un modulo)
Otras funciones se pueden encontrar en módulos
○ Para utilizarlas es necesario importar el módulo
■ Ej. import math
Para declarar una función, utilizamos el keyword def
○ Ej. def my_fuction(first_arg, second_arg=None)
Las funciones se pueden componer.
○ Ej.
def sum_two_numbers(x, y):
return x + y
other_function(sum_two_numbers(3, 4))
Ejemplo el metodo math que es para ecuaciones matematicas
las funciones se pueden componer osea hacer una funcion y pasar
este valor a otra funcion
El orden las funciones es de arriba to down
and left to right
Los argumentos pueden ser posicionales(positional arguments)
o con nombre (named arguments)
*Los parametros y variables son locales a la funcion
~ global keyword
fin del comunicado
# ejecutando desde la shell
>>> type(1)
<class 'int'>
>>> un_entero = int('5') comillas simples es un str
>>> print(un_entero) pero la funcion int lo combierte
5 en un entero
>>> type(5)
<class 'int'>
>>>
>>> a = bool('a') Booleano
>>> print(a)
True
>>> a = float(3) Flotante
>>> print(a)
3.0
>>> type(a)
<class 'float'>
>>> def suma_de_dos_numeros(x, y): funcion
... return x + y
...
>>> suma_de_dos_numeros(10, 15)
25
>>> type(suma_de_dos_numeros)
<class 'function'>
>>> suma_total = suma_de_dos_numeros(10, 15) tipo entero
>>> print(suma_total)
25
>>> type(suma_total)
<class 'int'>
"""
|
[
"gruasedwin@gmail.com"
] |
gruasedwin@gmail.com
|
2bc9a632cf25a60f9ffb85dca3488916585b641e
|
732cf97fdd3b730938ed8fc383d8f6b0c82625bf
|
/examples/04_decoding/plot_discrete_decoders.py
|
e0b2aac1bf698e1a1b37b1cbe4e19b3321238f4a
|
[
"MIT"
] |
permissive
|
akimbler/NiMARE
|
6c76980d0a008e99a6e2533e4b89de8d96f59ae1
|
717697035b04ff0244aa4aa170f5aa16a9fba69a
|
refs/heads/master
| 2022-12-09T05:17:22.041543
| 2020-08-08T21:10:07
| 2020-08-08T21:10:07
| 286,131,857
| 0
| 0
|
MIT
| 2020-08-08T23:00:19
| 2020-08-08T23:00:19
| null |
UTF-8
|
Python
| false
| false
| 2,291
|
py
|
# emacs: -*- mode: python-mode; py-indent-offset: 4; tab-width: 4; indent-tabs-mode: nil -*-
# ex: set sts=4 ts=4 sw=4 et:
"""
.. _decode1:
==============================================================
Decode regions of interest and subsets of Datasets
==============================================================
We can use the methods in ``nimare.decode.discrete`` to apply functional
characterization analysis to regions of interest or subsets of the Dataset.
"""
import os
import numpy as np
import pandas as pd
import nibabel as nib
from nilearn.plotting import plot_stat_map, plot_roi
import nimare
from nimare.decode import discrete
from nimare.tests.utils import get_test_data_path
###############################################################################
# Load dataset with abstracts
# ---------------------------
# We'll load a small dataset composed only of studies in Neurosynth with
# Angela Laird as a coauthor, for the sake of speed.
dset = nimare.dataset.Dataset.load(
os.path.join(get_test_data_path(), 'neurosynth_laird_studies.pkl.gz'))
dset.annotations.head(5)
###############################################################################
# Create a region of interest
# -------------------------------------------
# First we'll make an ROI
arr = np.zeros(dset.masker.mask_img.shape, int)
arr[65:75, 50:60, 50:60] = 1
mask_img = nib.Nifti1Image(arr, dset.masker.mask_img.affine)
plot_roi(mask_img, draw_cross=False)
# Get studies with voxels in the mask
ids = dset.get_studies_by_mask(mask_img)
###############################################################################
# Decode an ROI image using the Neurosynth method
# -----------------------------------------------
# Run the decoder
decoder = discrete.NeurosynthDecoder(correction=None)
decoder.fit(dset)
decoded_df = decoder.transform(ids=ids)
decoded_df.sort_values(by='probReverse', ascending=False).head()
###############################################################################
# Decode an ROI image using the BrainMap method
# -----------------------------------------------
# Run the decoder
decoder = discrete.BrainMapDecoder(correction=None)
decoder.fit(dset)
decoded_df = decoder.transform(ids=ids)
decoded_df.sort_values(by='probReverse', ascending=False).head()
|
[
"noreply@github.com"
] |
akimbler.noreply@github.com
|
087e986ecd374552b6acb1cab52581815e712c5e
|
573f1901b444e35a616f10923751a52ffe1d66a3
|
/combine-audio-video/cmdline_merging.py
|
ba98b96a23885e1ae2d240756422375ac318113b
|
[
"MIT"
] |
permissive
|
dracogamer123/pytube-implementation
|
9d6280efec214c3b76b3e9003dc3f2a2f8db5b1e
|
84c856441f91b0f15e9ec6918e879e0c720c42a7
|
refs/heads/master
| 2023-08-25T10:37:10.670027
| 2021-10-19T01:47:46
| 2021-10-19T01:47:46
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 881
|
py
|
from pytube import YouTube
def copy(video_path, audio_path, name, final_path):
"""
FFMPEG-COPY
------
video_path: path of video on disk
audio_path: path of audio on disk
name: name of output file
final_path: path of output video on disk
"""
import ffmpeg
video = ffmpeg.input(audio_path)
audio = ffmpeg.input(video_path)
(
ffmpeg
.output(audio, video, f'{name}.mp4', acodec='copy', vcodec='copy')
.run()
)
def interface():
print("Welcome to the Pytube Downloader!")
url = input("URL: ")
yt = YouTube(url)
yt.streams.get_highest_resolution.download(
output_path='/media/pranav/240GB SSD/Youtube Project/Pytube/code/src/downloads')
yt.streams.get_by_itag(139).download(
output_path='/media/pranav/240GB SSD/Youtube Project/Pytube/code/src/downloads')
interface()
|
[
"technotebook@yahoo.com"
] |
technotebook@yahoo.com
|
240e21a8d399705b7c01de6ad24a1a73c2a7b0ad
|
baa9b9544c9e69c8ccd732c47de16f19247e91dd
|
/model/trainer.py
|
1c705156805175156653e4ad9e47eae2e3c5c90d
|
[] |
no_license
|
amirveyseh/rumor-
|
d2a20e29a657c08e6de07113a9c64f2f5155ec08
|
ce501f2827d3b9de076b4607b080c7f87376d5a4
|
refs/heads/master
| 2020-06-07T12:09:17.683256
| 2019-06-21T02:56:06
| 2019-06-21T02:56:06
| 193,019,151
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 4,167
|
py
|
"""
A trainer class.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
from model.gcn import GCNClassifier
from utils import constant, torch_utils
class Trainer(object):
def __init__(self, opt, emb_matrix=None):
raise NotImplementedError
def update(self, batch):
raise NotImplementedError
def predict(self, batch):
raise NotImplementedError
def update_lr(self, new_lr):
torch_utils.change_lr(self.optimizer, new_lr)
def load(self, filename):
try:
checkpoint = torch.load(filename)
except BaseException:
print("Cannot load model from {}".format(filename))
exit()
self.model.load_state_dict(checkpoint['model'])
self.opt = checkpoint['config']
def save(self, filename, epoch):
params = {
'model': self.model.state_dict(),
'config': self.opt,
}
try:
torch.save(params, filename)
print("model saved to {}".format(filename))
except BaseException:
print("[Warning: Saving failed... continuing anyway.]")
def unpack_batch(batch, cuda):
inputs = [Variable(b).cuda() for b in batch[:10]]
labels = Variable(batch[10]).cuda()
lens = batch[1].eq(0).long().sum(2).squeeze()
return inputs, labels, lens
class GCNTrainer(Trainer):
def __init__(self, opt, emb_matrix=None):
self.opt = opt
self.emb_matrix = emb_matrix
self.model = GCNClassifier(opt, emb_matrix=emb_matrix)
self.criterion = nn.CrossEntropyLoss()
self.parameters = [p for p in self.model.parameters() if p.requires_grad]
if opt['cuda']:
self.model.cuda()
self.criterion.cuda()
self.optimizer = torch_utils.get_optimizer(opt['optim'], self.parameters, opt['lr'])
def update(self, batch):
inputs, labels, lens = unpack_batch(batch, self.opt['cuda'])
# step forward
self.model.train()
self.optimizer.zero_grad()
logits, pooling_output, g, sparse_graph, c1, c2 = self.model(inputs)
loss = self.criterion(logits, labels)
# l2 decay on all conv layers
if self.opt.get('conv_l2', 0) > 0:
loss += self.model.conv_l2() * self.opt['conv_l2']
# l2 penalty on output representations
# if self.opt.get('pooling_l2', 0) > 0:
# loss += self.opt['pooling_l2'] * (pooling_output ** 2).sum(1).mean()
# loss += 0.0000001 * self.hloss(g.view(-1,g.shape[1]))
# loss += 0.0000000001 * torch.norm(torch.abs(g.view(-1, g.shape[-1])-sparse_graph.view(-1, sparse_graph.shape[-1])))
# c1l = c1.pow(2).sum(1).sqrt().unsqueeze(1)
# c2l = c2.pow(2).sum(1).sqrt().unsqueeze(1)
# loss_pred = -(torch.mm(c1, c2.transpose(0,1)) / torch.mm(c1l, c2l.transpose(0,1))).diag().abs().mean() + (c1l-c2l).abs().mean()
c2 = torch.max(c2, 1)[1]
loss_pred = self.criterion(c1, c2)
loss += loss_pred
loss_val = loss.item()
# backward
loss.backward()
torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.opt['max_grad_norm'])
self.optimizer.step()
return loss_val, loss_pred.item()
def predict(self, batch, unsort=True):
inputs, labels, lens = unpack_batch(batch, self.opt['cuda'])
# orig_idx = batch[11]
# forward
self.model.eval()
logits, _, _, _, _, _ = self.model(inputs)
loss = self.criterion(logits, labels)
probs = F.softmax(logits, 1).data.cpu().numpy().tolist()
predictions = np.argmax(logits.data.cpu().numpy(), axis=1).tolist()
# if unsort:
# _, predictions, probs = [list(t) for t in zip(*sorted(zip(orig_idx,\
# predictions, probs)))]
return predictions, probs, loss.item()
class HLoss(nn.Module):
def __init__(self):
super(HLoss, self).__init__()
def forward(self, x):
b = x * torch.log(x.clamp(min=1e-8))
b = -1.0 * b.sum(1)
return b.mean()
|
[
"apouranb@iq.cs.uoregon.edu"
] |
apouranb@iq.cs.uoregon.edu
|
ede5626aeb578a8f801688300ee8b55922c731a5
|
36e66b509260bf15266f89a7bcbbc8bc5a413d7f
|
/SinglePageApplication/SinglePageApplication/wsgi.py
|
412a6dd818aae42656f691bdaca7c55df352fc06
|
[] |
no_license
|
Asdor1996/TestTable
|
66c18f54822bc55f71b95a55be2606c0d8cfdf32
|
09ba885af293743d3246fc03fcc34fa4bc5d7781
|
refs/heads/master
| 2023-04-24T06:40:46.580063
| 2021-05-18T12:42:22
| 2021-05-18T12:42:22
| 368,520,122
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 419
|
py
|
"""
WSGI config for SinglePageApplication project.
It exposes the WSGI callable as a module-level variable named ``application``.
For more information on this file, see
https://docs.djangoproject.com/en/3.2/howto/deployment/wsgi/
"""
import os
from django.core.wsgi import get_wsgi_application
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'SinglePageApplication.settings')
application = get_wsgi_application()
|
[
"quazun@yandex.ru"
] |
quazun@yandex.ru
|
b104fecc770ce509c27e2eec648f77b12ab83bd8
|
3b92548f49ead02f3655efc2f867659706dbfa59
|
/MidExamSimulation/Practice/2.py
|
e3925d331872a091ef058586572f76e15367991e
|
[] |
no_license
|
hengkysanjaya123/Computational_Mathematic
|
f50c651bbbfb0d46f92ee10b25a1e28cc671b27e
|
566a827936a971aff1bf0111d184a165db84b46c
|
refs/heads/master
| 2020-05-09T10:10:56.313919
| 2019-07-08T12:40:30
| 2019-07-08T12:40:30
| 181,025,715
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,144
|
py
|
import numpy as np
import matplotlib.pyplot as plt
data = [
5.5, 1.1, 6.5, 4.9, 6.4,
7.0, 1.5, 5.7, 5.9, 5.4,
6.1, 1.2, 7.3, 6.1, 4.4,
]
x = [1]*len(data)
print(x)
# x = [1, 10, 50]
# y = [2, 5, 20]
# plt.plot(x, y, 'red')
# plt.plot(x, y, 'bo')
#
# plt.plot(x, y, color='green', marker='o', linestyle='dashed'
# , linewidth=2,markersize=12)
# plt.plot('xlabel', 'ylabel', data=x)
#https://www.machinelearningplus.com/plots/top-50-matplotlib-visualizations-the-master-plots-python/
# plt.scatter(x, data, alpha=0.8, c='red')
minimum = np.min(data)
maximum = np.max(data)
median = np.median(data)
arrayInfo = [minimum, maximum, median]
ax1 = plt.subplot2grid((1, 1), (0, 0))
for i in range(0,15):
plt.plot(2, data[i], 'co')
plt.errorbar(3, arrayInfo, marker="^")
plt.boxplot(data, positions=[4])
plt.xticks([1, 2, 3, 4], ["","Data", "Error Bar", "Boxplot"])
ax1.spines['bottom'].set_color('w')
ax1.spines['top'].set_color('w')
ax1.spines['right'].set_color('w')
ax1.spines['left'].set_color('w')
ax = plt.gca()
ax.grid(True, color='w', alpha=0.3)
ax1.set_facecolor('lightgrey')
plt.show()
|
[
"hengkysanjaya204@gmail.com"
] |
hengkysanjaya204@gmail.com
|
013b58beacb2a8913cad6063f400be40ffbc1cd4
|
a96874d67911e51e60e903776264350887e8e486
|
/first poroject
|
1ba8f1305b5518116d47c616486a06382bfd9016
|
[] |
no_license
|
MRALAMs/first-project
|
284c1f72056f22ee60723805a888eef9436282a2
|
7db00d4b99a32b21e89979e4ccabc21b22f7abfb
|
refs/heads/master
| 2022-11-11T10:52:09.294997
| 2020-07-02T08:58:01
| 2020-07-02T08:58:01
| 276,602,043
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 364
|
#! /usr/bin/python3
def moadel(math,physics,dini,exersise):
return ((int(math)*3)+(int(physics)*4)+(int(dini)*2)+(int(exersise)*1))*0.1
mylist =["reza","ali","mohamad"]
for i in range(len(mylist)):
a=input("math score ? ")
b=input("physics score ? ")
c=input("dini score ? ")
d=input("exersise score ?")
print (mylist[i],moadel(a,b,c,d))
|
[
"mr.alamshah222@gmail.com"
] |
mr.alamshah222@gmail.com
|
|
046a3e964f46637b57ab5238becb3ebecbc1227f
|
a9878b55be543468cc94758cac5544a48100fa14
|
/TreeIntro.py
|
730647e3d35b997e27b88da1788871f56ea3db7b
|
[] |
no_license
|
Vishalkapoor123/Data-Structures-and-Algorithms
|
b1e392bc189b831f0641c82bc3fd3bf91a984882
|
1a0048d1ed0843cebc7dfaa72901b3e0a24bbc29
|
refs/heads/master
| 2023-09-02T14:36:15.446584
| 2021-10-30T13:23:53
| 2021-10-30T13:23:53
| 417,809,457
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 2,967
|
py
|
class Node:
def __init__(self, data):
self.data = data
self.left = None
self.right = None
class BinaryTree:
def __init__(self, data):
self.root = Node(data)
#Internal insert methpd i.e. not client facing
def insert(self, root, data):
newNode = Node(data)
if(root == None):
return newNode
if(data < root.data):
root.left = self.insert(root.left, data)
elif(data > root.data):
root.right = self.insert(root.right, data)
elif(data == root.data):
return root
return root
#Cliennt facing method
def insert_client(self, data):
temp = self.root
self.root = self.insert(temp,data)
def search(self, data):
temp = self.root
while(temp):
if(temp.data == data):
return True
elif(temp.data > data):
temp = temp.left
elif(temp.data < data):
temp = temp.right
if(temp == None):
return False
def min(self, root):
if(root.left==None): return root
elif(root.left):
return self.min(root.left)
def min_client(self):
temp=self.root
return self.min(temp)
def deleteMin(self,root):
if(root.left==None):
return root.right
root.left = self.deleteMin(root.left)
return root
#Internal delete method i.e. not client facing
def delete(self, root, data):
if(root == None): return None
if(root.data<data):
root.left = self.delete(root.left , data)
elif(root.data > data):
root.right = self.delete(root.right, data)
else:
temp = root
root = self.min(temp.right)
root.right = self.deleteMin(temp.right)
root.left = temp.left
return root
#Cliennt facing delete method
def delete_client(self, data):
temp = self.root
self.root = self.delete(temp,data)
def test(self):
print(self.root.left.data)
print(self.root.left.left.data)
#Internal method
def inorder(self, root):
if(root.left):
self.inorder(root.left)
print(root.data)
if(root.right):
self.inorder(root.right)
#Client facing inorder method
def inorder_client(self):
self.inorder(self.root)
################################Starter code ################################
if __name__ == '__main__':
tree = BinaryTree(5)
tree.insert_client(1)
tree.insert_client(10)
tree.insert_client(3)
tree.insert_client(7)
tree.insert_client(0)
tree.insert_client(11)
tree.inorder_client()
# tree.test()
print(tree.search(1))
tree.delete_client(5)
print("minimum {}".format(tree.min_client().data))
tree.inorder_client()
|
[
"vishal.kapoor@cgi.com"
] |
vishal.kapoor@cgi.com
|
7cdb29f9b285de5b818b392bf4e6f7bffb440a44
|
ed6fb97371218d6c7e779280924ca14ccfa8921c
|
/iris/utils/logging.py
|
4bbc7ba540044c5b5c820cfc615d68b5e51cfc03
|
[
"Apache-2.0"
] |
permissive
|
lowks/lymph
|
d27e35d94f47836ac770158651db74e59aacd170
|
b4c68feb603b2c5347aaa61e112328eb82c22061
|
refs/heads/master
| 2020-12-25T16:02:43.004588
| 2014-06-25T09:25:31
| 2014-06-25T09:25:31
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,019
|
py
|
from __future__ import absolute_import
import logging
import six
import zmq.green as zmq
from iris.utils.sockets import bind_zmq_socket
def get_loglevel(level_name):
level = level_name.upper()
if level not in ('DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'):
raise ValueError("unknown loglevel: %s" % level)
return getattr(logging, level)
class PubLogHandler(logging.Handler):
def __init__(self, endpoint):
super(PubLogHandler, self).__init__()
ctx = zmq.Context.instance()
self.socket = ctx.socket(zmq.PUB)
self.endpoint, port = bind_zmq_socket(self.socket, endpoint)
def emit(self, record):
topic = record.levelname
self.socket.send_multipart([
_encode(topic),
_encode(self.endpoint),
_encode(self.format(record))])
def _encode(potentially_text, encoding='utf-8'):
if isinstance(potentially_text, six.text_type):
return potentially_text.encode(encoding)
return potentially_text
|
[
"johannes.dollinger@deliveryhero.com"
] |
johannes.dollinger@deliveryhero.com
|
ac365906676b84c1bb52b685f557dc14488325b5
|
57cf4f1a6da938b630039b2cab2ada2d017af82e
|
/clothing/migrations/0002_clothingitem_owner.py
|
eb6ae2805f508d12adac155523d61f125fcfdf23
|
[
"MIT"
] |
permissive
|
shailevi23/iWear
|
babe5eb5bb281037bb9c434d25aac1e30185bcd8
|
4f28fd4aa4cc1a50695412bdf21fa5a0f174d554
|
refs/heads/main
| 2023-09-04T00:11:51.970694
| 2021-11-08T09:09:29
| 2021-11-08T09:09:29
| 425,762,550
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 619
|
py
|
# Generated by Django 3.2.5 on 2021-07-28 19:14
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
('clothing', '0001_initial'),
]
operations = [
migrations.AddField(
model_name='clothingitem',
name='owner',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='Item_owner', to=settings.AUTH_USER_MODEL),
),
]
|
[
"aharoni94@gmail.com"
] |
aharoni94@gmail.com
|
16b96c658327248f2648d6e852c807401832b0b5
|
775fdf65590a4947bbbc4055e25f914577e730fd
|
/字符串格式化.py
|
f53a071a4be425978cf2a89815af8edbc52b7d83
|
[] |
no_license
|
focusshell/Python
|
155c823037ddf6d31bf7915d2ebf7a2adbf52bdf
|
70ad14b6413ab97058db051403ccc34a5ee28cc2
|
refs/heads/master
| 2023-02-22T04:38:10.318959
| 2023-02-10T06:54:23
| 2023-02-10T06:54:23
| 143,408,423
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 121
|
py
|
#!/usr/bin/python3
print("我叫%s 今年 %d 岁!" % ( '陈情',22))
print("我是%s 今年 %d岁！" % ( '一菲',27))
|
[
"1106328167@qq.com"
] |
1106328167@qq.com
|
f84af80af296086f1881227d43ec06c829202f8e
|
0b699b4c6081b3b06acd0b64d8bb1f84bbb9c956
|
/manage.py
|
c04731a2df3f9d49fbdeabcd66b213e421d1e6cf
|
[] |
no_license
|
rxy0424/django-learn
|
267d5b947e4d896cc2ddb29ef8b807342d5e0f84
|
735a6629911c1b455c0c68443031973f8b60593d
|
refs/heads/master
| 2021-01-20T06:12:03.705250
| 2017-04-30T13:17:00
| 2017-04-30T13:17:00
| 89,851,760
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 802
|
py
|
#!/usr/bin/env python
import os
import sys
if __name__ == "__main__":
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "ksxt.settings")
try:
from django.core.management import execute_from_command_line
except ImportError:
# The above import may fail for some other reason. Ensure that the
# issue is really that Django is missing to avoid masking other
# exceptions on Python 2.
try:
import django
except ImportError:
raise ImportError(
"Couldn't import Django. Are you sure it's installed and "
"available on your PYTHONPATH environment variable? Did you "
"forget to activate a virtual environment?"
)
raise
execute_from_command_line(sys.argv)
|
[
"rxy0424@gmail.com"
] |
rxy0424@gmail.com
|
8179861a56b00ea0aae727ab31ba65679ea3dcb6
|
5c0e83b07e01983b064980b805e6067cd1123714
|
/rd_caltech.py
|
81e59b15ea802ee43b2828b30359ef9bfbe9dc85
|
[
"MIT"
] |
permissive
|
zyg11/MTCNN-TF
|
750ec7b6533b639deba5126e19a434da615585ac
|
4d41c5fd2dc13008d39b868aa2e921a7ff731e10
|
refs/heads/master
| 2020-08-26T14:24:41.084820
| 2019-04-02T09:02:23
| 2019-04-02T09:02:23
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,656
|
py
|
#author : lxy
#time: 2018.3.23/ 11:30:00
#tool: python3
#version: 0.1
#modify:
#project: pedestrian detection
################################
import numpy as np
import glob
import os
import argparse
def args():
parser = argparse.ArgumentParser(description="read caltech txt")
parser.add_argument('--dir_in',type=str,default="/home/lxy/Downloads/DataSet/trainval/",\
help='annotation files saved dir ')
parser.add_argument('--out_file',type=str,default='train_caltech.txt',\
help='generated outfiles saved')
return parser.parse_args()
def get_fil():
parm = args()
dir_in = parm.dir_in
out_f = parm.out_file
f_wt = open(out_f,'w')
file_txts = glob.glob(dir_in+'annotations/*.txt')
pass_cnt = 0
for file_item in file_txts:
f_rd = open(file_item,'r')
line_list = f_rd.readlines()
if len(line_list)==0:
f_rd.close()
print("empyt file: ",file_item)
pass_cnt+=1
continue
img_split = file_item.split('/')
img_name = img_split[-1][:-4]
img_lists = glob.glob(dir_in+'images/*')
for img_one in img_lists:
img_lists_split = img_one.split('/')
img_one_name = img_lists_split[-1]
if img_name in img_one_name:
img_name = img_one_name
f_wt.write("{} ".format(img_name))
for line in line_list:
line = line.strip()
f_wt.write("{} ".format(line[1:]))
f_wt.write("\n")
f_rd.close()
f_wt.close()
print("pass ",pass_cnt)
if __name__=="__main__":
get_fil()
|
[
"lixiaoyu283284@163.com"
] |
lixiaoyu283284@163.com
|
31d24fc92e38e24e03ce1742b7ceec5868ba5c7e
|
b54b6168ba35ce6ad34f5a26b5a4a3ab8afa124a
|
/kratos/applications/ShapeOptimizationApplication/test_examples/03_Strain_Energy_Minimization_3D_Shell/optimization_settings.py
|
5e1b07aab234b21e628b0a82c683e10309050967
|
[] |
no_license
|
svn2github/kratos
|
e2f3673db1d176896929b6e841c611932d6b9b63
|
96aa8004f145fff5ca6c521595cddf6585f9eccb
|
refs/heads/master
| 2020-04-04T03:56:50.018938
| 2017-02-12T20:34:24
| 2017-02-12T20:34:24
| 54,662,269
| 2
| 1
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 5,125
|
py
|
# ================================================================================================================
# Workspace settings
# ================================================================================================================
# Define directory with a relative or absolute path
design_history_directory = "Design_history"
design_history_file = "design_history.csv"
# ================================================================================================================
# Response functions
# ================================================================================================================
# Define container of objective functions
# Format: objectives = { "unique_func_id": {"gradient_mode": "analytic"},
# "unique_func_id": {"gradient_mode": "semi_analytic", "step_size": 1e-5},
# "unique_func_id": {"gradient_mode": "external"},
# ... }
objectives = { "strain_energy": {"gradient_mode": "semi_analytic", "step_size": 1e-8} }
# Define container of constraint functions
# Format: constraints = { "unique_func_id": {"type": "eq"/"ineq","gradient_mode": "analytic"},
# "unique_func_id": {"type": "eq"/"ineq","gradient_mode": "semi_analytic", "step_size": 1e-5},
# "unique_func_id": {"type": "eq"/"ineq","gradient_mode": "external"},
# ... }
constraints = { }
# ================================================================================================================
# Design variables
# ================================================================================================================
design_control = "vertex_morphing"
# options: "vertex_morphing"
design_output_mode = "relative"
# options: "relative" - X is defined relative to previous design
# "total" - X is defined relative to initial design
# "absolute" - X is defined as absolute values (coordinates)
domain_size = 3
# options: 2 or 3 for 2D or 3D optimization patch
# Case: design_control = "vertex_morphing"
design_surface_name = "design_surface"
filter_function = "linear"
# options: "gaussian"
# "linear"
filter_size = 3
use_mesh_preserving_filter_matrix = False
# options: True - surface normal information used in the filter matrix
# : False - complete filter matrix is used
perform_edge_damping = True
# options: True - edge damping is applied with the settings below
# : False - no edge damping is applied, settings below can be ignored
damped_edges = [ ["support_edges", False, True, True, "linear", 3 ],
["side_edges", False, False, True, "linear", 3 ] ]
# damped_edges = [ [edge_sub_model_part_name_1, damp_in_X, damp_in_Y, damp_in_Z, damping_function, damping_radius ],
# [edge_sub_model_part_name_2, damp_in_X, damp_in_Y, damp_in_Z, damping_function, damping_radius ],
# ... ]
# options for damping function: "cosine"
# "linear"
# ================================================================================================================
# Optimization algorithm
# ================================================================================================================
optimization_algorithm = "steepest_descent"
# options: "steepest_descent",
# "augmented_lagrange",
# "penalized_projection",
# General convergence criterions
max_opt_iterations = 300
# Case: "steepest descent"
relative_tolerance_objective = 1e-1 # [%]
# Case: optimization_algorithm = "augmented_lagrange"
max_sub_opt_iterations = 100
relative_tolerance_sub_opt = 1e-1 # [%]
penalty_fac_0 = 4
gamma = 4
penalty_fac_max = 2000
lambda_0 = 0.0
# ================================================================================================================
# Determination of step size (line-search)
# ================================================================================================================
# Only constant step-size is implemented yet
normalize_search_direction = True
step_size = .1 # e.g. 5 for active normalization or 1e7 for inactive normalization
# ================================================================================================================
# For GID output
# ================================================================================================================
nodal_results=[ "NORMALIZED_SURFACE_NORMAL",
"OBJECTIVE_SENSITIVITY",
"MAPPED_OBJECTIVE_SENSITIVITY",
"DESIGN_UPDATE",
"DESIGN_CHANGE_ABSOLUTE",
"SHAPE_UPDATE",
"SENSITIVITIES_DEACTIVATED",
"SHAPE_UPDATES_DEACTIVATED",
"SHAPE_CHANGE_ABSOLUTE"]
VolumeOutput = True
GiDPostMode = "Binary"
GiDWriteMeshFlag = True
GiDWriteConditionsFlag = True
GiDMultiFileFlag = "Single"
# ================================================================================================================
|
[
"dbaumgaertner@4358b7d9-91ec-4505-bf62-c3060f61107a"
] |
dbaumgaertner@4358b7d9-91ec-4505-bf62-c3060f61107a
|
f099e8563d50a673936df3dfddd48a1bcda5b76d
|
2b3ed6bef2f569448918b8be72c733614c231fce
|
/hdf5_example.py
|
dd3342f3c57d95a4688d33cb9ed830c521fb325f
|
[] |
no_license
|
jackdbd/dask-playground
|
8e67024ba60fbac3ff1ad77b94363731c04c0afd
|
721bc234eadf13e9ef24173bbbc9a68761bf1a7c
|
refs/heads/master
| 2021-04-25T19:58:47.303280
| 2017-11-01T12:49:00
| 2017-11-01T12:49:00
| 109,123,767
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 554
|
py
|
import os
import h5py
import numpy as np
import dask.array as da
h5file_path = 'myfile.hdf5'
if os.path.exists(h5file_path):
os.unlink(h5file_path)
# create a continuous uniform distribution between 0.0 and 1.0
arr = np.random.random(size=(10000, 2000))
with h5py.File(h5file_path, 'w') as h5f:
h5f.create_dataset('dataset_1', data=arr)
with h5py.File(h5file_path, 'r') as h5f:
dset = h5f['dataset_1'][:]
x = da.from_array(dset, chunks=(1000, 1000))
result = x.mean().compute()
print(result) # should be pretty clonse to 0.5
|
[
"jackdebidda@gmail.com"
] |
jackdebidda@gmail.com
|
fa70a0452b646b1a21cf73baf1168baf012e94fb
|
089d3832f25c0e4167acd9f3ac692c78e808e1d6
|
/pymms/data/hapgood.py
|
6300784c2a9483074b59061ca11583b073a782e5
|
[
"MIT"
] |
permissive
|
argallmr/pymms
|
ea135b57dd62370a0065680e573d09317e8575c0
|
69b2a0934f5fac548a47c6d9233cd345ea209b93
|
refs/heads/master
| 2023-07-22T17:48:35.022075
| 2023-07-06T19:49:55
| 2023-07-06T19:49:55
| 124,706,809
| 5
| 17
|
MIT
| 2020-05-31T02:52:15
| 2018-03-10T23:20:50
|
Python
|
UTF-8
|
Python
| false
| false
| 35,757
|
py
|
import numpy as np
import pandas as pd
from scipy.spatial.transform import Rotation as R
from scipy.stats import binned_statistic
from pathlib import Path
# IGRF Coefficients
sample_data_path = Path(__file__).parent / 'swfo' / 'data'
igrf_coeff_file = sample_data_path / 'igrf13coeffs.txt'
class IGRF_Coeff():
# See this link for:
# - https://www.ngdc.noaa.gov/IAGA/vmod/igrf.html
# - IGRF coefficients
# - Python library for IGRF reference field
# - Publication of IGRF-13
def __init__(self):
'''
Initialize the object
'''
# Read in the table of IGRF coefficients
self._coeffs = self.read_coeff()
def closest_year(self, time):
'''
Return the index of the most recent past IGRF year.
Parameters
----------
time : `numpy.datetime64`
Times at which to determine the IGRF coefficients
'''
# Years of input time and of coefficients
years = pd.to_datetime(time).year.values
igrf_years = self._coeffs.columns.values[:-1].astype(np.float32).astype(np.int32)
# Locate the closest IGRF year: equivalent to $T_{t}$
stat, edge, num = binned_statistic(years, years,
statistic='count',
bins=igrf_years,
)
return num
def coeff(self, gh, m, n, time):
'''
Determine the IGRF coefficient at the given times.
Parameters
----------
gh : str
The spherical harmonic coefficient, ('g', 'h')
m : int
The order of the spherical harmonic coefficient
n : int
The degree of the spherical harmonic coefficient
time : `numpy.datetime64`
Times at which to determine the IGRF coefficients
Returns
-------
coeff_t : `numpy.array`
The coefficient g_{n}^{m}(t) or h_{n}^{m}(t)
'''
if (time < np.datetime64('1900', 'Y')).any() | (time > np.datetime64('2026', 'Y')).any():
raise ValueError('time must be in the range 1900-2025')
# Coefficient of interest
coeff = self.get_coeff(gh, m, n)
# Most recent coefficient
iyr = self.closest_year(time)
coeff_T = coeff[:-1][iyr-1]
# Linear change in coefficients over five years
cdot_T = self.time_derivative(coeff, iyr)
# Time difference from IGRF years
dt = self.time_diff(time, iyr)
# Linear interpolation of coefficients
# - Axes cannot be aligned during addition because the index has
# duplicate values
# - Add the values together instead of the DataFrames.
coeff_t = coeff_T + dt * cdot_T
coeff_t.index = pd.to_datetime(time).year
return coeff_t
def get_coeff(self, gh, m, n):
'''
Retrieve the values of a spherical harmonic coefficient from
the look-up table.
Parameters
----------
gh : str
The spherical harmonic coefficient, ('g', 'h')
m : int
The order of the spherical harmonic coefficient
n : int
The degree of the spherical harmonic coefficient
Returns
-------
coeff_T : `numpy.array`
The coefficient g_{n}^{m}(T) or h_{n}^{m}(T)
'''
return self._coeffs.loc[(gh, m, n), :]
@staticmethod
def read_coeff():
'''
Read the look-up table of spherical harmonic coefficients
'''
return pd.read_csv(igrf_coeff_file,
delim_whitespace=True,
header=3,
index_col=('g/h', 'n', 'm')
)
@staticmethod
def test(gh='g', n=1, m=0):
'''
Plot a coefficient and its interpolated values g_{n}^{m}(T)
and g_{n}^{m}(t)
Parameters
----------
gh : str
The spherical harmonic coefficient, ('g', 'h')
m : int
The order of the spherical harmonic coefficient
n : int
The degree of the spherical harmonic coefficient
'''
str_coeff = '${0:s}_{{{1:1d}}}^{{{2:1d}}}$'.format(gh, n, m)
# Define a set of times at which to determine the coefficients
t0 = np.datetime64('1900-01-01', 'Y')
t1 = np.datetime64('2026-01-01', 'Y')
times = np.arange(t0, t1, step=np.timedelta64(1, 'Y'))
# Get the IGRF coefficients and the interpolated coefficients
igrf = IGRF_Coeff()
c = igrf.get_coeff(gh, n, m)[:-1]
c_t = igrf.coeff(gh, n, m, times)
# Plot the results
from matplotlib import pyplot as plt
fig, axes = plt.subplots(nrows=1, ncols=1, squeeze=False)
plt.subplots_adjust(left=0.15, right=0.95, top=0.92)
c.index = c.index.astype(np.float32).astype(np.int32)
c_t.index = c_t.index.astype(np.float32).astype(np.int32)
ax = axes[0,0]
c_t.plot(marker='o', ax=ax, label=str_coeff[:-1]+'(t)$')
c.plot(ax=ax, marker='x', label=str_coeff)
ax.set_xlabel('Year')
ax.set_ylabel(str_coeff + ' (nT)')
ax.set_title('Interpolated IGRF Coefficients')
ax.legend([str_coeff[:-1]+'(t)$', str_coeff[:-1]+'(T)$'])
plt.show()
def time_diff(self, time, iyr):
'''
Time difference from the IGRF look-up table times: dt = t - T
Parameters
----------
time : `numpy.datetime64`
Times at which to determine the IGRF coefficients
iyr : `numpy.array` int
Index into the IGRF look-up table indicating the next
earlier time (see `self.closest_year()`)
Returns
-------
dt : `numpy.datetime64`
Time difference from the IGRF times
'''
# IGRF years as datetimes
igrf_yrs = np.array([c[0:4]
for c in self._coeffs.columns[:-1]],
dtype='datetime64[Y]')
# Compute fractional years
dt = ((time - igrf_yrs[iyr-1]).astype('timedelta64[s]')
/ np.timedelta64(1, 'Y').astype('timedelta64[s]')
)
return dt
@staticmethod
def time_derivative(coeff, iyr):
'''
Time derivative of an IGRF coefficient
Parameters
----------
coeff : `numpy.array`
Coefficient of which the derivative is computed
iyr : `numpy.array` int
Index into the IGRF look-up table indicating the next
earlier time (see `self.closest_year()`)
'''
# Coefficient at nearest IGRF year
c_T = coeff[:-1][iyr-1]
# Coefficient at the next IGRF year, T+5
# - Note that the last column is already the time derivative
c_Tplus5 = coeff[iyr]
# Time derivative: 1/5 * [c(T+5) - c(T)]
cdot = 0.2 * (c_Tplus5.values - c_T.values)
# Create a DataFrame
cdot = pd.Series(cdot, name='cdot', index=c_T.index)
cdot_T = cdot.where((c_Tplus5.index != coeff.index[-1]),
other=c_Tplus5.values)
return cdot_T
def date2mjd(date):
'''
Convert a date to Modified Julian Date -- the number WHOLE of days since
1858-11-17T00:00:00Z.
Calling Sequence:
mjd = date2mjd(year, month, day)
Calculate the Modified Julian Date from the numeric year, month and day.
mjd = date2mjd (date)
Calulate the Modified Julian Date from a date string, Nx10
character array, or 1xN element cell array of strings, where N
represents the number of dates formatted as 'yyyy-mm-dd'.
Parameters
----------
date : array of `numpy.datetime64`
Date to be converted. Dates are converted to datetime64[D] during
calculation
Returns
-------
MJD : int
Modified Julian Date
References:
- https://www.spenvis.oma.be/help/background/coortran/coortran.html
- Hapgood, M. A. (1992). Space physics coordinate transformations:
A user guide. Planetary and Space Science, 40 (5), 711?717.
doi:http://dx.doi.org/10.1016/0032-0633 (92)90012-D
- Hapgood, M. A. (1997). Corrigendum. Planetary and Space Science,
45 (8), 1047 ?. doi:http://dx.doi.org/10.1016/S0032-0633 (97)80261-9
'''
# datetime64:
# Datetimes are always stored based on POSIX time (though having a TAI
# mode which allows for accounting of leap-seconds is proposed), with an
# epoch of 1970-01-01T00:00Z.
#
# MJD:
# The MJD gives the number of days since midnight on November 17, 1858.
# This date corresponds to 2400000.5 days after day 0 of the Julian
# calendar.
mjd0 = np.datetime64('1858-11-17T00:00:00', 's')
return (date - mjd0) / np.timedelta64(86400, 's')
def date2mjd2000(time):
jul_2000 = date2mjd(np.datetime64('2000-01-01T12:00:00', 's'))
mjd = date2mjd(time.astype('datetime64[D]'))
return mjd - jul_2000
def date2ssm(time):
'''
Convert time to seconds since midnight. Note that if `time` contains times
from different dates, that will not be reflected in the results.
Parameters
----------
time : `numpy.datetime64`
Time to be converted
Returns
-------
ssm : int, `numpy.ndarray`
Fractional seconds elapsed since the previous midnight.
'''
return ((time - time.astype('datetime64[D]')).astype('timedelta64[ns]')
).astype(int) / 1e9
def date2ssny(time):
'''
Convert time to seconds since new year. Note that if `time` contains times
from different years, that will not be reflected in the results.
Parameters
----------
time : `numpy.datetime64`
Time to be converted
Returns
-------
ssny : int, `numpy.ndarray`
Fractional seconds elapsed since new year's eve.
'''
days = (time.astype('datetime64[D]') - time.astype('datetime64[Y]')
).astype('timedelta[s]').astype(int)
ssm = date2ssm(time)
return days + ssm
def date2sse(time, epoch=None, unit='s'):
'''
Convert time to seconds since new year. Note that if `time` contains times
from different years, that will not be reflected in the results.
Parameters
----------
time : `numpy.datetime64`
Time to be converted
Returns
-------
ssny : int, `numpy.ndarray`
Fractional seconds elapsed since new year's eve.
'''
if (epoch is None) | (epoch in ('new year', 'ny')):
epoch = time.astype('datetime64[Y]')
elif epoch == 'midnight':
epoch = time.astype('datetime64[D]')
elif epoch == 'first':
epoch = time[0].astype('datetime64[D]')
t_delta = (time - epoch).astype('timedelta64[ns]')
if unit == 'D':
divisor = 1e9 * 86400
elif unit == 'h':
divisor = 1e9 * 3600
elif units == 'm':
divisor = 1e9 * 60
elif unit == 's':
divisor = 1e9
elif unit == 'ms':
divisor = 1e6
elif unit == 'us':
divisor = 1e3
elif unit == 'ns':
divisor = 1
else:
raise ValueError('Unit not recognized. Must be denomination <= D')
return t_delta.astype(float) / divisor
def date2juldays(time):
'''
Convert Gregorian date to Julian days.
Julian days are calculated from noon on 1-Jan. 1900.
Parameters
----------
time : array of datetime64
Gregorian datetimes to be converted to Julian days
Returns
-------
juldays : ndarray
Julian days elapsed since noon on 1-Jan. 1900
'''
# Make sure the date is within range.
if ((time < np.datetime64('1901', 'Y')).any()
| (time > np.datetime64('2099', 'Y')).any()
):
raise ValueError('Year must be between 1901 and 2099')
# We need to convert to Julian centuries since 1900-01-01T12:00:00
# - Start by calculating the number of days
# Whole years since 1900
years = time.astype('datetime64[Y]') - np.datetime64('1900', 'Y')
# Number of leap days since 1900 (4 years per leap day)
# - Julian Year: Exactly 365.25 days of 86,400 SI seconds each.
try:
leap_days = np.asarray(((time.astype('datetime64[Y]')
- np.datetime64('1901', 'Y')
) / np.timedelta64(4, 'Y')
).astype(int), 'timedelta64[D]'
)
except Exception:
import pdb
pdb.set_trace()
# Number of days into the current year
year_day = (time.astype('datetime64[D]') - time.astype('datetime64[Y]')
+ np.timedelta64(1, 'D'))
# Fraction of the current day that has elapsed
frac_day = ((time - time.astype('datetime64[D]'))
/ np.timedelta64(1, 'D').astype('timedelta64[s]'))
# Number of days elapsed since 1900-01-01T12:00:00
# - Note that we subtract 0.5 because the epoch starts at noon
Julian_days = ((365 * years.astype(int) + leap_days + year_day
).astype(float) - 0.5) + frac_day
return Julian_days
def earth_obliquity(T0):
'''
Axial tilt (obliquity) is the angle between an object's rotational axis
and its orbital axis; equivalently, the angle between its equatorial
plane and orbital plane.
Parameters
----------
T0 : `numpy.ndarray`
Time in Julian centuries calculated from 12:00:00 UTC
on 2000-01-01 (known as Epoch 2000) to the previous
midnight. It is computed as:
T0 = (MJD - 51544.5) / 36525.0
Returns
-------
obliquity : `numpy.ndarray`
Obliquity of Earth's ecliptic orbit (degrees).
'''
# 23.439 is the dipole tilt angle in degrees at Earth's position on
# Jan. 1, 2000. 0.013 is the fractional number of degrees that this
# angle changes per day: (1 - 360 degrees / 365 days/year)
return 23.439 - 0.013 * T0
def dipole_igrf_coeffs(time):
'''
Compute the IGRF coefficients required to determine the dipole
tilt axis and angle.
Parameters
----------
time : `numpy.datetime64`
Times at which to determine the IGRF coefficients
Returns
-------
g10 : `numpy.float`
g10 IGRF coefficient
g11 : `numpy.float`
g11 IGRF coefficient
h11 : `numpy.float`
h11 IGRF coefficient
'''
igrf = IGRF_Coeff()
g10 = igrf.coeff('g', 1, 0, time)
g11 = igrf.coeff('g', 1, 1, time)
h11 = igrf.coeff('h', 1, 1, time)
return g10, g11, h11
def dipole_latlon(time):
'''
Compute the latitude and longitude of the dipole axis in GEO coordinates.
Parameters
----------
time : `numpy.datetime64`
Times at which to determine the coordinates of the dipole axis
Returns
-------
lat : `numpy.float`
Latitude of Earth's dipole axis in GEO coordinates in degrees
lon : `numpy.float`
Longitude of Earth's dipole axis in GEO coordinates in degrees
'''
g10, g11, h11 = dipole_igrf_coeffs(time)
lat, lon = dipole_igrf2latlon(g10, g11, h11)
return lat, lon
def dipole_unit_vector(time):
'''
Compute the (x, y, z) GEO coordinates of Earth's dipole axis unit vector.
Parameters
----------
time : `numpy.datetime64`
Times at which to determine the coordinates of the dipole axis
Returns
-------
Q_geo : `numpy.float`
Earth's dipole axis as a unit vector in GEO coordinates
'''
lat, lon = dipole_latlon(time)
Q_geo = dipole_latlon2vector(lat, lon)
return Q_geo
def dipole_igrf2latlon(g10, g11, h11):
'''
Convert IGRF coefficients that define Earth's magnetic dipole axis
to latitude and longitude values in GEO coordinates.
Parameters
----------
g10 : `numpy.float`
g10 IGRF coefficient
g11 : `numpy.float`
g11 IGRF coefficient
h11 : `numpy.float`
h11 IGRF coefficient
Returns
-------
lat : `numpy.float`
Latitude of Earth's dipole axis in GEO coordinates in degrees
lon : `numpy.float`
Longitude of Earth's dipole axis in GEO coordinates in degrees
'''
geo_lon = np.rad2deg(np.arctan2(h11, g11))
geo_lat = 90.0 - np.rad2deg(np.arctan((g11*np.cos(geo_lon)
+ h11*np.sin(geo_lon)
) / g10))
return geo_lat, geo_lon
def dipole_latlon2vector(lat, lon):
'''
Convert the latitude and longitude of Earth's magnetic dipole axis
to a unit vector in GEO coordinates.
Parameters
----------
lat : `numpy.float`
Latitude of Earth's dipole axis in GEO coordinates in degrees
lon : `numpy.float`
Longitude of Earth's dipole axis in GEO coordinates in degrees
Returns
-------
Q_geo : `numpy.float`
Earth's dipole axis as a unit vector in GEO coordinates
'''
dlat = np.deg2rad(lat)
dlon = np.deg2rad(lon)
return np.column_stack([np.cos(dlat)*np.cos(dlon),
np.cos(dlat)*np.sin(dlon),
np.sin(dlon)])
def dipole_time2latlon(time):
'''
Calculate the latitude and longitude of Earth's magnetic dipole axis
in GEO coordinates.
Parameters
----------
time : `numpy.datetime64`
Times at which to determine the coordinates of the dipole axis
Returns
-------
lat : `numpy.float`
Latitude of Earth's dipole axis in GEO coordinates in degrees
lon : `numpy.float`
Longitude of Earth's dipole axis in GEO coordinates in degrees
'''
mjd = time2mjd(time)
lat = 78.8 + 4.283e-2 * (mjd - 46066) / 365.25
lon = 289.1 - 1.413e-2 * (mjd - 46066) / 365.25
return lat, lon
def dipole_axis(lat=None, lon=None,
g10=None, g11=None, h11=None, time=None):
'''
Determine Earth's magnetic dipole axis as a unit vector in
GEO coordinates.
Parameters
----------
lat : `numpy.float`
Latitude of Earth's dipole axis in GEO coordinates
lon : `numpy.float`
Longitude of Earth's dipole axis in GEO coordinates
g10 : `numpy.float`
g10 IGRF coefficient
g11 : `numpy.float`
g11 IGRF coefficient
h11 : `numpy.float`
h11 IGRF coefficient
time : `numpy.datetime64`
Times at which to determine the coordinates of the dipole axis
Returns
-------
Q_geo : `numpy.float`
Earth's dipole axis as a unit vector in GEO coordinates
'''
if time is not None:
Q_geo = dipole_unit_vector(time)
elif (g10 is not None) & (g11 is not None) & (h11 is not None):
lat, lon = dipole_igrf2latlon(g10, g11, h11)
if (lat is not None) & (lon is not None):
Q_geo = dipole_latlon2vector(lat, lon)
return Q_geo
def dipole_inclination(time):
'''
The angle between the GSE Z axis and projection of the magnetic
dipole axis on the GSE YZ plane (i.e. the GSM Z axis) measured
positive for rotations towards the GSE Y axis.
Parameters
----------
time : `numpy.datetime64`
Times at which to calculate the dipole inclination angle.
Returns
-------
psi : float
Inclination angle between z-GSM and z-GSe
'''
Q_geo = dipole_axis(time=time)
# Rotate GEO coordinates to GSE coordinates
r_geo2gse = geo2gse(time)
axis = r_geo2gse.apply(Q_geo)
# Compute the dipole tilt angle
psi = np.arctan2(axis[:,1], axis[:,2])
return psi
def dipole_tilt_angle(time):
'''
Dipole tilt angle, i.e. the angle between the GSM Z axis and the
dipole axis. It is positive for the North dipole pole sunward of GSM Z.
Parameters
----------
time : `numpy.datetime64`
Times at which to calculate the dipole tilt angle.
Returns
-------
mu : float
Tilt angle between z-GSM and the dipole axis
'''
Q_geo = dipole_axis(time=time)
# Rotate GEO coordinates to GSE coordinates
r_geo2gse = geo2gse(time)
Q_gse = r_geo2gse.apply(Q_geo)
# Compute the dipole tilt angle
mu = np.arctan(Q_gse[:,0] / np.sqrt(Q_gse[:,1]**2 + Q_gse[:,2]**2))
return mu
def gei2dsc(time, ra, dec):
'''
Return the transformation to the despun spacecraft frame (SCS) from
Geocentric Equatorial Inertial system (GEI) at the given time, with RA
and dec (in degrees) of the spin vector.
Parameters
----------
YEAR : in, required, type = double
MONTH : in, required, type = double
DAY : in, required, type = double
SECS : in, required, type = double
Seconds into `DAY`.
RA : in, required, type = double
Right-ascention of the spacecraft (degrees).
DEC : in, required, type = double
Declination of the spacecraft (degrees).
Returns
-------
SCS2GSE : out, optional, type = float
Transformation matrix to rotate SCS to GSE.
References
----------
- https://www.spenvis.oma.be/help/background/coortran/coortran.html
- Hapgood, M. A. (1992). Space physics coordinate transformations:
A user guide. Planetary and Space Science, 40 (5), 711?717.
doi:http://dx.doi.org/10.1016/0032-0633 (92)90012-D
- Hapgood, M. A. (1997). Corrigendum. Planetary and Space Science,
45 (8), 1047 ?. doi:http://dx.doi.org/10.1016/S0032-0633 (97)80261-9
'''
# Location of the sun
SUN = sun_position(time) # in what coords? normalized?
# RA and DEC form a spherical coordinate system.
# - RA = number of hours past the vernal equinox (location on the
# celestial equator of sunrise on the first day of spring).
# - DEC = degrees above or below the equator
dec_rad = np.deg2rad(dec)
ra_rad = np.deg2rad(ra)
# [x y z] components of the unit vector pointing in the direction of
# the spin axis.
# - The spin axis points to a location on the suface of the celestial sphere.
# - RA and dec are the spherical coordinates of that location,
# with the center of the earth as the origin.
# - Transforming GEI to SCS transforms [0 0 1] to [x y z] = OMEGA
# - Already normalized: spherical to cartesian with r = 1.
scsz = np.column_stack((np.cos(ra_rad) * np.cos(dec_rad),
np.sin(ra_rad) * np.cos(dec_rad),
np.sin(dec_rad)
))
# Form the X- and Y-vectors
# - X must point in the direction of the sun.
# - To ensure this, Y' = Z' x Sun
# - X' = Y' x Z'
scsy = np.cross(scsz, SUN, axis=1)
scsy /= np.linalg.norm(scsy, axis=1)[:, np.newaxis]
scsx = np.cross(scsy, scsz, axis=1)
# scsy = mrvector_cross(scsz, SUN)
# scsy = mrvector_normalize(scsy)
# scsx = mrvector_cross(scsy, scsz)
# Transformation from GEI to SCS.
GEI2DSC = np.zeros((len(ra), 3, 3))
GEI2DSC[:,0,:] = scsx
GEI2DSC[:,1,:] = scsy
GEI2DSC[:,2,:] = scsz
return R.from_matrix(GEI2DSC)
def gei2geo(time):
T0 = nJulCenturies(date2mjd2000(time))
UT = date2ssm(time)
theta = 100.461 + 36000.770*T0 + 15.04107*UT
# scipy rotates the vector. We want to rotate the coordinate system.
return R.from_euler('Z', theta, degrees=True).inv()
def geo2gse(time):
T1 = gei2geo(time).inv()
T2 = gei2gse(time)
return T2 * T1
def geo2mag(time):
lat, lon = dipole_latlon(time)
# scipy rotates the vector. We want to rotate the coordinate system.
return R.from_euler('Y', latitude-90).inv() * R.from_euler('Z', longitude).inv()
def gei2gse(time):
'''
Produce a rotation matrix from GEI to GSE.
Parameters
----------
MJD : int
Modified Julian Date.
UTC : `numpy.ndarray`
UTC in decimal hours since midnight.
Returns
-------
T3 : out, required, type = double
Totation matrix from GEI to GSE.
References:
See Hapgood Rotations Glossary.txt.
- https://www.spenvis.oma.be/help/background/coortran/coortran.html
- Hapgood, M. A. (1992). Space physics coordinate transformations:
A user guide. Planetary and Space Science, 40 (5), 711?717.
doi:http://dx.doi.org/10.1016/0032-0633 (92)90012-D
- Hapgood, M. A. (1997). Corrigendum. Planetary and Space Science,
45 (8), 1047 ?. doi:http://dx.doi.org/10.1016/S0032-0633 (97)80261-9
'''
# Number of julian centuries since Epoch 2000
jul_cent = nJulCenturies(date2mjd2000(time))
UTC = date2ssm(time) / 3600
# Axial tilt
obliq = earth_obliquity(jul_cent)
eLon = sun_ecliptic_longitude(jul_cent, UTC)
#
# The transformation from GEI to GSE, then is
# - T2 = <eLon, Z> <obliq, X>
# - A pure rotation about X by angle obliq
# - A pure rotation about Z by angle eLon
#
# Scipy rotates the vector. We want to rotate the coordinate system.
T21 = R.from_euler('X', obliq, degrees=True).inv()
T22 = R.from_euler('Z', eLon, degrees=True).inv()
T2 = T22 * T21
return T2
def gse2gsm(time):
phi = dipole_inclination(time)
# scipy rotates the vector. We want to rotate the coordinate system
T3 = R.from_euler('X', -phi, degrees=True).inv()
return T3
def gsm2sm(time):
# Dipole tilt angle
mu = dipole_tilt_angle(time)
# scipy rotates the vector. We want to rotate the coordinate system
return R.from_euler('Y', -mu).inv()
def mjd2epoch2000(mjd):
'''
Convert Modified Julian Date (MJD) to Epoch 2000
MJD: Number of days since midnight on November 17, 1858
Epoch2000: Number of days since noon on January 1, 2000
Parameters
----------
mjd : `numpy.ndarray`
Modified Julian dates
Returns
-------
epoch2000 : `numpy.ndarray`
Epoch 2000 times
'''
return mjd - 51544.5
def nJulCenturies(nDays):
'''
Convert number of days to Julian Centuries. there are exactly 36525 days
in a Julian Century
Parameters
----------
nDays : `numpy.ndarray`
Fractional number of days
Returns
-------
jul_centuries : `numpy.ndarray`
Number of Julian centuries
'''
return nDays / 36525.0
def sun_ecliptic_longitude(T0, UTC):
''''
Determine the ecliptic longitude of the sun
Note:
Strictly speaking, TDT (Terrestrial Dynamical Time) should be used
here in place of UTC, but the difference of about a minute gives a
difference of about 0.0007∞ in lambdaSun.
Calling Sequence:
eLon = sun_ecliptic_longitude (T0, UTC)
Compute the sun's ecliptic longitude (degrees) given the number
of julian centuries (T0) from 12:00 UTC 01-Jan-2000 until
00:00 UTC on the day of interest, and Universal Time (UTC) in
fractional number of hours.
Parameters
----------
T0 : `numpy.ndarray`
Time in Julian centuries calculated from 12:00:00 UTC
on 1 Jan 2000 (known as Epoch 2000) to the previous
midnight. It is computed as:
T0 = (MJD - 51544.5) / 36525.0
UTC : `numpy.ndarray`
UTC decimal hours since midnight
Returns
-------
eLon : `numpy.ndarray`
Mean anomaly of the sun, in degrees
References
----------
See Hapgood Rotations Glossary.txt.
- https://www.spenvis.oma.be/help/background/coortran/coortran.html
- Hapgood, M. A. (1992). Space physics coordinate transformations:
A user guide. Planetary and Space Science, 40 (5), 711?717.
doi:http://dx.doi.org/10.1016/0032-0633 (92)90012-D
- Hapgood, M. A. (1997). Corrigendum. Planetary and Space Science,
45 (8), 1047 ?. doi:http://dx.doi.org/10.1016/S0032-0633 (97)80261-9
'''
# Sun's Mean anomaly
ma = np.deg2rad(sun_mean_anomaly(T0, UTC))
# Mean longitude (degrees)
mLon = sun_mean_longitude(T0, UTC)
# Ecliptic Longitude
# - Force to the range [0, 360)
eLon = (mLon
+ (1.915 - 0.0048 * T0) * np.sin(ma)
+ 0.020 * np.sin(2.0 * ma)
) % 360.0
return eLon
def sun_mean_anomaly(T0, UTC):
'''
Compute the sun's mean anomaly.
Note:
Strictly speaking, TDT (Terrestrial Dynamical Time) should be used
here in place of UTC, but the difference of about a minute gives a
difference of about 0.0007∞ in lambdaSun.
Calling Sequence:
ma = sun_mean_anomaly(T0, UTC)
Compute the sun's mean anomaly (degrees) given the number
of Julian centuries (T0) from 2000-01-01T12:00:00Z until
00:00 UTC on the day of interest, and Universal Time (UTC) in
decimal hours.
Parameters
----------
T0 : `numpy.ndarray`
Time in Julian centuries calculated from 2000-01-01T12:00:00Z
(known as Epoch 2000) to the previous midnight. It is computed as
T0 = (MJD - 51544.5) / 36525.0
UTC : `numpy.ndarray`
UTC decimal hours since midnight
Returns
-------
mean_anomaly : `numpy.ndarray`
Ecliptic longitude of the sun, in degrees.
References
----------
See Hapgood Rotations Glossary.txt.
- https://www.spenvis.oma.be/help/background/coortran/coortran.html
- Hapgood, M. A. (1992). Space physics coordinate transformations:
A user guide. Planetary and Space Science, 40 (5), 711?717.
doi:http://dx.doi.org/10.1016/0032-0633 (92)90012-D
- Hapgood, M. A. (1997). Corrigendum. Planetary and Space Science,
45 (8), 1047 ?. doi:http://dx.doi.org/10.1016/S0032-0633 (97)80261-9
'''
# Sun's Mean anomaly
# - Force to the range [0, 360)
return (357.528 + 35999.050 * T0 + 0.04107 * UTC) % 360.0
def sun_mean_longitude(T0, UTC):
'''
Compute the sun's mean longiude.
Note:
Strictly speaking, TDT (Terrestrial Dynamical Time) should be used
here in place of UTC, but the difference of about a minute gives a
difference of about 0.0007∞ in lambdaSun.
Calling Sequence:
lambda = sun_mean_anomaly(T0, UTC)
Compute the sun's mean longitude (degrees) given the number
of julian centuries (T0) from 2000-01-01T12:00:00Z until
00:00 UTC on the day of interest, and Universal Time (UTC) in
decimal hours.
Parameters
----------
T0: in, required, type = double
Time in Julian centuries calculated from 2000-01-01T12:00:00Z
(known as Epoch 2000) to the previous midnight. It is computed as
T0 = (MJD - 51544.5) / 36525.0
UTC: in, required, type = double
UTC decimal hours since midnight
Returns
-------
LAMBDA: out, required, type = double
Mean longitude of the sun, in degrees.
References
----------
- https://www.spenvis.oma.be/help/background/coortran/coortran.html
- Hapgood, M. A. (1992). Space physics coordinate transformations:
A user guide. Planetary and Space Science, 40 (5), 711?717.
doi:http://dx.doi.org/10.1016/0032-0633 (92)90012-D
- Hapgood, M. A. (1997). Corrigendum. Planetary and Space Science,
45 (8), 1047 ?. doi:http://dx.doi.org/10.1016/S0032-0633 (97)80261-9
'''
# Sun's Mean Longitude
# - Force to the range [0, 360)
return (280.460 + 36000.772 * T0 + 0.04107 * UTC) % 360
def sun_position(time):
'''
Determine the direction of the sun in GEI.
Program to caluclate sidereal time and position of the sun. It is good
for years 1901 through 2099 due to leap-year limitations. Its accuracy
is 0.006 degrees.
Direction of the sun in cartesian coordinates:
X = cos(SRASN) cos(SDEC)
Y = sin(SRASN) cos(SDEC)
Z = sin(SDEC)
Parameters
----------
time : `numpy.datetime64`
Year.
Returns
-------
S : out, optional, type=float
Sidereal time and position of the sun.
References
----------
C.T. Russel, Geophysical Coordinate Transformations.
http://www-ssc.igpp.ucla.edu/personnel/russell/papers/gct1.html/#appendix2
The above link appears to be broken (2021-11-11). Here is another link.
See Appendix 2.
http://jsoc.stanford.edu/~jsoc/keywords/Chris_Russel/Geophysical%20Coordinate%20Transformations.htm#appendix2
See the following reference, page C5, for a brief description of the
algorithm (with updated coefficients?).
(US), N. A. O. (Ed.). (2013). The Astronomical Almanac for the Year
2014. U.S. Government Printing Office. Retrieved from
http://books.google.com/books?id=2E0jXu4ZSQAC
This reference provides an algroithm that reduces the error and extends
the valid time range (i.e. not this algorithm).
Reda, I.; Andreas, A. (2003). Solar Position Algorithm for Solar
Radiation Applications. 55 pp.; NREL Report No. TP-560-34302,
Revised January 2008. http://www.nrel.gov/docs/fy08osti/34302.pdf
'''
# Seconds elapsed into day
frac_day = (time - time.astype('datetime64[D]')) / np.timedelta64(1, 's')
# Number of days elapsed since 1900-01-01T12:00:00
Julian_days = date2juldays(time)
# Constants
# RAD = 57.29578 # 180/pi
# Convert seconds to days.
# FDAY = SECS/86400
# Number of days since noon on 1 Jan 1900.
# DDJ = 365 .* (IYR-1900) + fix((IYR-1901) / 4) + IDAY - 0.5
# DJ = DDJ .* ones(1, length(SECS)) + FDAY
# Convert to Julian centuries from 1900
# - Julian Year: Exactly 365.25 days of 86,400 SI seconds each.
# - Julian Century: 36,525 days
T = nJulCenturies(Julian_days)
# T = DJ / 36525
# Degrees per day
# - It takes 365.2422 days to complete a revolution about the sun.
# - There are 360 degrees in a circle.
# => 360.0 / 365.2422 = 0.9856 degrees/day
# Keep degrees between 0 and 360
# mod(..., 360) will force answer to be in range [0, 360).
# Mean longitude of the sun
VL = (279.696678 + 0.9856473354 * Julian_days) % 360.0
# Greenwhich sidereal time.
GST = (279.690983 + 0.9856473354 * Julian_days
+ 360 * frac_day + 180.) % 360.0
# Mean anomaly
G = np.deg2rad((358.475845 + 0.985600267 * Julian_days) % 360)
# Ecliptic longitude
SLONG = (VL + (1.91946 - 0.004789 * T) * np.sin(G)
+ 0.020094 * np.sin(2 * G))
# Obliquity (Axial tilt)
OBLIQ = np.deg2rad(23.45229 - 0.0130125 * T)
SLP = np.deg2rad(SLONG - 0.005686)
SIND = np.sin(OBLIQ) * np.sin(SLP)
COSD = np.sqrt(1 - SIND**2)
# Solar declination
SDEC = np.rad2deg(np.arctan(SIND / COSD))
# Solar right ascension
SRASN = 180 - np.rad2deg(np.arctan2(SIND / (np.tan(OBLIQ) * COSD),
-np.cos(SLP) / COSD))
# Equatorial rectangular coordinates of the sun
S = np.column_stack((np.cos(np.deg2rad(SRASN)) * COSD,
np.sin(np.deg2rad(SRASN)) * COSD,
SIND))
return S
|
[
"matthew.argall@unh.edu"
] |
matthew.argall@unh.edu
|
e6247400b23975a02a38404955166d70d6f867d6
|
f4e20f45fafae6cb0e6c21e2d47a2a31892c2408
|
/PythonExercicios/ex011.py
|
552c3fcf1c692efc57ffc779bbe4656321464e4c
|
[
"MIT"
] |
permissive
|
andrei406/Meus-PycharmProjects-de-Iniciante
|
6e03accac46d75ee0973ae7e32fd059ddae59a82
|
6d59350eaa6538b68aca90046226cbc7b1f9f621
|
refs/heads/main
| 2023-03-01T22:18:08.141485
| 2021-02-07T14:06:06
| 2021-02-07T14:06:06
| 325,803,845
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 193
|
py
|
h = float(input('Sua parede mede quantos metros de altura?'))
l = float(input('E de comprimento?'))
a = h * l
print('\033[1;30mVocê vai precisar de {} litro(s) de tinta\033[m'.format(h / 2))
|
[
"76476855+andrei406@users.noreply.github.com"
] |
76476855+andrei406@users.noreply.github.com
|
0668cf1f54afe3f62d779d41ce8620b2ba97cb0e
|
c289c9472ce1f6eb80a9831b930674d3b71e85e4
|
/chap_10/client.py
|
bc6efd1313359fa57d236efa16aeaf20313f3c99
|
[] |
no_license
|
Jordan-Rowland/oop-practice
|
a6ba2ad9f6323f58b7e4ac521c2885e5bd5b9571
|
b442c62bbc45ad599bb964b22f98d6237f2e0297
|
refs/heads/master
| 2020-07-10T00:22:51.433643
| 2019-11-20T05:48:17
| 2019-11-20T05:48:17
| 204,117,797
| 1
| 0
| null | 2019-11-20T05:48:18
| 2019-08-24T06:21:08
|
Python
|
UTF-8
|
Python
| false
| false
| 163
|
py
|
import socket
client = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
client.connect(('localhost', 2401))
print(f"Received: {client.recv(1024)}")
client.close
|
[
"36084892+Jordan-Rowland@users.noreply.github.com"
] |
36084892+Jordan-Rowland@users.noreply.github.com
|
25324867b9607040353779c80fb44f1218bdd375
|
86762033dd59308e764bad95acbdcc53f3b74598
|
/d04/guess number.py
|
236d9aad076eb991ef44ed3d1cb78657a9dc1f59
|
[] |
no_license
|
HuaMa001/python001
|
59a992c4c485e4bb1c27bbc64e01d6fd672d612e
|
12b15585e1b3ef569ed01b30660146f72d699518
|
refs/heads/master
| 2022-11-26T22:59:42.499122
| 2020-08-11T10:47:42
| 2020-08-11T10:47:42
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 536
|
py
|
import random
ans=random.randint(1, 99)
min, max=0, 100
amount=5 #可猜五次
while amount>0:
amount-=1
guess=int(input('請在%d-%d之間猜數字:'%(min, max)))
#驗證範圍?
if guess <=min or guess>=max:
print('輸入範圍錯誤')
continue
#是否猜對 ?
if guess > ans:
max=guess
elif guess < ans:
min=guess
else:
print('恭喜答對了')
break
#若都沒猜對
if amount==0:
print("你是非洲人 快看答案:",str(ans))
|
[
"chenenlin2@gmali.com"
] |
chenenlin2@gmali.com
|
fc111a7c2540a79d6929363fbcd5d364985612e9
|
9c04dfec0c7e6a5e82fc5bf0ddf2e06c2308cdef
|
/packages/auto-nlp-deployment/src/deployments/entities/deployment_info.py
|
b5e8275c19aee3ff65293d93f3451400512a6665
|
[
"MIT"
] |
permissive
|
fhswf/tagflip-autonlp
|
8d678c780476d20d4d870a23320e5908a4e8972f
|
f94abb35ed06198567e5d9cbb7abb7e112149d6c
|
refs/heads/main
| 2023-04-07T10:19:01.108884
| 2022-04-10T19:56:48
| 2022-04-10T19:56:48
| 410,777,896
| 5
| 2
|
MIT
| 2022-04-10T12:19:35
| 2021-09-27T07:07:28
|
TypeScript
|
UTF-8
|
Python
| false
| false
| 544
|
py
|
from typing import Any, Literal, Optional, Union
from pydantic import AnyHttpUrl, BaseModel
from deployments.entities.deployment_status import DeploymentStatus
class ModelSignature(BaseModel):
input: Optional[Any]
output: Optional[Any]
class Endpoint(BaseModel):
url: AnyHttpUrl
method: Union[Literal["POST"], Literal["GET"], Literal["PUT"]]
signature: Optional[ModelSignature]
class DeploymentInfo(BaseModel):
deployment_id: str
runtime: str
status: DeploymentStatus
endpoint: Optional[Endpoint]
|
[
"timo@n.euhaus.net"
] |
timo@n.euhaus.net
|
8a9e6a093b51b04808ac6f330219095ad90cc1ac
|
cfdaf5972763d29df295dec0451546e7c3eec629
|
/bpsx-forghp.py
|
1ea2bda407641731e6c7aa95f161a653da01dc97
|
[] |
no_license
|
setosato/myprivatecode
|
a452c3c0635942f0e8188198d8bea2f2f8d5eab8
|
5aa21759a35049cc67571bf888b5372572645ab1
|
refs/heads/master
| 2020-07-19T14:01:02.685777
| 2019-09-06T07:06:04
| 2019-09-06T07:06:04
| 206,461,350
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 5,063
|
py
|
'''
/*
* Copyright 2010-2017 Amazon.com, Inc. or its affiliates. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License").
* You may not use this file except in compliance with the License.
* A copy of the License is located at
*
* http://aws.amazon.com/apache2.0
*
* or in the "license" file accompanying this file. This file is distributed
* on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
* express or implied. See the License for the specific language governing
* permissions and limitations under the License.
*/
'''
from AWSIoTPythonSDK.MQTTLib import AWSIoTMQTTClient
import logging
import time
import argparse
import json
AllowedActions = ['both', 'publish', 'subscribe']
# Custom MQTT message callback
def customCallback(client, userdata, message):
print("Received a new message: ")
print(message.payload)
print("from topic: ")
print(message.topic)
print("--------------\n\n")
# Read in command-line parameters
parser = argparse.ArgumentParser()
parser.add_argument("-e", "--endpoint", action="store", required=False, dest="host", help="Your AWS IoT custom endpoint")
parser.add_argument("-r", "--rootCA", action="store", required=False, dest="rootCAPath", help="Root CA file path")
parser.add_argument("-c", "--cert", action="store", dest="certificatePath", help="Certificate file path")
parser.add_argument("-k", "--key", action="store", dest="privateKeyPath", help="Private key file path")
parser.add_argument("-p", "--port", action="store", dest="port", type=int, help="Port number override")
parser.add_argument("-w", "--websocket", action="store_true", dest="useWebsocket", default=False,
help="Use MQTT over WebSocket")
parser.add_argument("-id", "--clientId", action="store", dest="clientId", default="basicPubSub",
help="Targeted client id")
parser.add_argument("-t", "--topic", action="store", dest="topic", default="sdk/test/Python", help="Targeted topic")
parser.add_argument("-m", "--mode", action="store", dest="mode", default="both",
help="Operation modes: %s"%str(AllowedActions))
parser.add_argument("-M", "--message", action="store", dest="message", default="Hello World!",
help="Message to publish")
args = parser.parse_args()
host = args.host
rootCAPath = args.rootCAPath
certificatePath = args.certificatePath
privateKeyPath = args.privateKeyPath
port = args.port
useWebsocket = args.useWebsocket
clientId = args.clientId
topic = args.topic
#This is non-working configuation
clientId="Handson_pc2"
host="xxxxx-ats.iot.ap-northeast-1.amazonaws.com"
rootCAPath="cert4/rootca"
privateKeyPath="cert4/xxxxx-private.pem.key"
certificatePath="cert4/xxxxx-certificate.pem.crt"
"""
#This is working configuation(commented out)
clientId="Handson_pi1"
host="xxxxx-ats.iot.ap-northeast-1.amazonaws.com"
rootCAPath="cert/rootca"
privateKeyPath="cert/yyyyy.private.key"
certificatePath="cert/yyyyy.cert.pem"
"""
# Port defaults
if args.useWebsocket and not args.port: # When no port override for WebSocket, default to 443
port = 443
if not args.useWebsocket and not args.port: # When no port override for non-WebSocket, default to 8883
port = 8883
# Configure logging
logger = logging.getLogger("AWSIoTPythonSDK.core")
logger.setLevel(logging.DEBUG)
streamHandler = logging.StreamHandler()
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
streamHandler.setFormatter(formatter)
logger.addHandler(streamHandler)
# Init AWSIoTMQTTClient
myAWSIoTMQTTClient = None
if useWebsocket:
myAWSIoTMQTTClient = AWSIoTMQTTClient(clientId, useWebsocket=True)
myAWSIoTMQTTClient.configureEndpoint(host, port)
myAWSIoTMQTTClient.configureCredentials(rootCAPath)
else:
myAWSIoTMQTTClient = AWSIoTMQTTClient(clientId)
myAWSIoTMQTTClient.configureEndpoint(host, port)
myAWSIoTMQTTClient.configureCredentials(rootCAPath, privateKeyPath, certificatePath)
# AWSIoTMQTTClient connection configuration
myAWSIoTMQTTClient.configureAutoReconnectBackoffTime(1, 32, 20)
myAWSIoTMQTTClient.configureOfflinePublishQueueing(-1) # Infinite offline Publish queueing
myAWSIoTMQTTClient.configureDrainingFrequency(2) # Draining: 2 Hz
myAWSIoTMQTTClient.configureConnectDisconnectTimeout(10) # 10 sec
myAWSIoTMQTTClient.configureMQTTOperationTimeout(5) # 5 sec
# Connect and subscribe to AWS IoT
myAWSIoTMQTTClient.connect()
if args.mode == 'both' or args.mode == 'subscribe':
myAWSIoTMQTTClient.subscribe(topic, 1, customCallback)
time.sleep(2)
# Publish to the same topic in a loop forever
loopCount = 0
while True:
if args.mode == 'both' or args.mode == 'publish':
message = {}
message['message'] = args.message
message['sequence'] = loopCount
messageJson = json.dumps(message)
myAWSIoTMQTTClient.publish(topic, messageJson, 1)
if args.mode == 'publish':
print('Published topic %s: %s\n' % (topic, messageJson))
loopCount += 1
time.sleep(1)
|
[
"noreply@github.com"
] |
setosato.noreply@github.com
|
23262b3f947db8170a051fb2936e3230dcb7c642
|
96bd5be700b926c598f8c69ee774cc24ca072f1f
|
/pykapa/xls_functions.py
|
1d323bab11e1ba645d2b96e72f2595e333b6d989
|
[
"Apache-2.0"
] |
permissive
|
ikapadata/pykapa
|
b847bb6309300db99bf7db0ab2591b639a6a9ee4
|
24109a16dee380d2d4e688930b2a58883ba116ba
|
refs/heads/master
| 2021-07-14T04:02:55.489870
| 2020-05-22T11:41:06
| 2020-05-22T11:41:06
| 165,073,988
| 0
| 0
|
Apache-2.0
| 2020-05-19T08:11:32
| 2019-01-10T14:28:14
|
Python
|
UTF-8
|
Python
| false
| false
| 8,980
|
py
|
from dateutil import parser
from datetime import datetime, timedelta
import uuid as UUID
# check if the selected field is equal to the expected value
def selected(field,value):
sel = str(field).split(" ")
print('Selected: %s'%sel)
print('Value: %s' %value )
if str(value) in sel:
return True
else:
return False
# determine the length of the string
def string_length(field):
return len(field)
# return the selected value at the position described by the number
def selected_at(field, number):
field = field.split(' ')
try:
return field[number]
except Exception as err:
return err
# count the number of selected items
def count_selected(field):
field = field.split(' ')
return len(field)
# concatenate strings
def concat(field1, field2,*therest):
return str(field1) + str(field2) + ''.join(map(str, therest))
#retrun substring
def substr(fieldorstring, startindex, endindex):
if endindex<len(fieldorstring) and startindex<len(fieldorstring):
return fieldorstring[startindex:endindex]
else:
return 'Error: Specified startindex/endindex is out range'
def coalesce(field1, field2):
if len(field1)!=0:
return field1
else:
return field2
import re
#Returns true or false depending on whether the field matches the regular expression specified
def regex(field, expression):
if re.search(expression,field) != None:
return True
else:
return False
#function to execute an if statement
def IF(expression, valueiftrue, valueiffalse):
if eval(expression)==True:
return valueiftrue
else:
return valueiffalse
# change data-type to float
def number(field):
return float(field)
# change data-type to string
def string(field):
return str(field)
# format dates from inputs
#Converts string into a date
def date(string):
dt = parser.parse(string).date()
return dt
#Converts string into a date and time
def date_time(string):
dt = parser.parse(string)
return dt
#Converts date and/or time into a string
def format_date_time(field, date_format):
dt = parser.parse(str(field)).strftime(date_format)
return dt
#today's date
def today():
dt = datetime.now().date()
return dt
#current date and time
def now():
dt = datetime.now()
return dt
def date_N_days_ago(N):
return datetime.now() - timedelta(days=N)
# return the number of days
def days(timedelta):
try:
days = timedelta.days
return days
except:
Obj = string(type(timedelta))
err_msg = '*Argument Error*: days(arg) accepts datetime.timedelta arguments and not ' + Obj + '\n' +'days('+timedelta+') must take the form ' +'days(date_1 - date_2)'
return err_msg
# function to check if string is float
def is_number(s):
try:
float(s)
return True
except ValueError:
return False
#Check if argument is date
def date_check(dateX):
try:
dt = parser.parse(dateX).date()
return True
except :
return False
# return the label for a select_one or select_multiple field choice
def jr_choice_name(value, field, df_xls):
df_choices = df_xls['choices']
df_select = df_xls['select']
# find the listname of the field
df_row = df_select[df_select.name == field] # find the row containing the field
listname = df_row.loc[df_row.index.values[0], 'list_name'] # return the listname
# find the label from the choices dataframe
df_listname = df_choices[df_choices.choice_list_name == listname] # filter the choice dataframe by the listname
df_list_row = df_listname[df_listname.choice_name == value] # find the row that contains the value
if len(df_list_row)>0:
label = df_list_row.loc[df_list_row.index.values[0],'choice_label'] # return the label from the row
else:
label = 'nan'
return label
# change syntax of a xls function in a string
def str_func(s,func_name):
# add open parentheses to func_name
if func_name[-1]!='(':
func_name = concat(func_name,'(')
# replace colons and hyphens with underscores
func = func_name.replace(':','_') # replace colons
func = func.replace('-','_') # replace hyphens
s_right= ')' # assign closed parantheses
try:
# get the argument and format the function
sub_s = get_substring(func_name,s_right, s)
if sub_s[-1] != ')':
idx = sub_s.rfind(')')
sub_s = sub_s[0:idx+1]
new_func = concat(func,sub_s[1:len(sub_s)-1],',df_xls',')') # the correctly formated function
# form new string
new_str = s.replace(concat(func_name[0:len(func_name)-1],sub_s), new_func) # the new string with function
return new_str
except Exception as err:
err = str(err)
#print(string)
return s
#get substring
def get_substring(s_left,s_right, s):
r = re.search(concat(s_left,'(.*)',s_right),s)
return r.group(1)
# determine if sub string (sub_s) is in string (s)
def is_in(sub_s,s):
return sub_s in s
# check for balanced parentheses in string
def balanced_par(myStr):
open_list = ["[","{","("]
close_list = ["]","}",")"]
stack = []
stack= []
for char in myStr:
#print(char)
if char in open_list:
stack.append(char) # append char in stack list
elif char in close_list:
pos = close_list.index(char) # determine the index of the closed paranthesis
if len(stack) > 0 and open_list[pos] == stack[len(stack)-1]:
stack.pop()
else:
return False
if len(stack) == 0:
return True
# get a function from string
def get_func(string, func):
f_idx = string.index(func) # index of the function
new_str = string[f_idx+len(func) : ] # new short string
char_i = 0 # initialize char counter
open_list = ["[","{","("]
try:
if new_str[0] in open_list:
for char in new_str:
char_i +=1
# check if there are balanced parantheses
bal_par = balanced_par(new_str[0:char_i])
if bal_par == True:
func_0 = concat(func,new_str[0:char_i])
return func_0
except Exception as err:
print(err)
return None
# eval functions in string
def evalfunc_str(string,df_xls,funcs = ['jr_choice_name','date_check','count_selected','is_number','now',
'today','format_date_time','date_time','date','string','number','IF', 'regex',
'coalesce','substr','concat','count_selected','selected_at','string_length',
'selected','uuid','round','int']):
nan = 'nan'
for func in funcs:
#print('evalFuncStr: ',string)
occ = str(string).count(func)# count occurence of func in string
if occ > 0:
for i in range(occ):
if func in string:
func_str = get_func(string, func)
else:
func_str = None
if func_str != None:
try:
result = eval(func_str) # evaluate function
string= string.replace(func_str, str(result)) # replace function with the evluated result
#print('Function: %s Result: %s'%(func_str, result))
except Exception as err:
print(err)
string = string
return string
# change the syntax of xls function to pyhton
def format_funcstr(string,func):
occ = string.count(func)# count occurence of func in string
if occ > 0:
for i in range(occ):
#print('occ: ',i)
if func in string:
func_str = get_func(string, func)
else:
func_str = None
if func_str != None:
arg = func_str[len(func): len(func_str)]
new_arg= concat(arg[0:len(arg)-1],', df_xls)')
#print('index: ',i, ' o_arg: ', arg, ' n_arg: ', new_arg)
new_func = func.replace(':','_')
new_func = new_func.replace('-','_')
string = string.replace(concat(func,arg),concat(new_func,new_arg))
#print(string)
#print('ffs: ', string)
return string
def uuid():
return str(UUID.uuid4())
|
[
"noreply@github.com"
] |
ikapadata.noreply@github.com
|
fa1ba4629c3a01734531a8062cab51667e0fbbe7
|
33a09a5221c4fa748b7cf7d7522e30f1d7068beb
|
/Alzheimer's/feature_selection_alz.py
|
044cca130d04720aca6bf434bd3e4a830535738a
|
[
"MIT"
] |
permissive
|
Claudmj/Microarray-analysis-with-Bayesian-networks
|
17968fe20caf68fd87c3c4e7c9c1a877d20de042
|
4bf6640fa6ffcffb60922269eded83ace0678072
|
refs/heads/main
| 2023-07-14T11:08:01.286613
| 2021-08-20T14:05:12
| 2021-08-20T14:05:12
| 305,335,675
| 5
| 3
| null | 2020-10-20T14:16:18
| 2020-10-19T09:45:33
|
Python
|
UTF-8
|
Python
| false
| false
| 1,682
|
py
|
# -*- coding: utf-8 -*-
"""
Created on Fri Aug 21 11:32:09 2020
@author: claud
"""
import pandas as pd
import numpy as np
import seaborn as sns
from sklearn import preprocessing
#import data into dataframe
#golub = pd.read_csv("Golub_Dataset.txt", delimiter='\t')
filename='processed_alz.csv'
golu=pd.read_csv(filename,header=None,skiprows=0) #no header
n = 50
#transpose dataframe
golub = golu.T
#sort dataframe according to All/AML
golub = golub.sort_values(by = 0) #column 0 contains the labels
#add ideal gene vector
golub['ideal_gene'] = golub[0].apply(lambda x: 0 if x == 'Healthy Control' else 1)
#calculate pearson correlation between all the genes (1:7129) and the ideal gene.
pearson_correlation = []
for i in np.arange(1,golub.shape[1] - 1):
gene_corr = golub['ideal_gene'].corr(pd.to_numeric(golub.iloc[:,i]))
pearson_correlation.append((i,gene_corr))
#sort the list of tuples (pearson_correlation)
sorted_pearson_correlation = sorted(pearson_correlation, key=lambda x: x[1], reverse=True)
#get the top correlated genes from the dataframe
top_features_index = [i[0] for i in sorted_pearson_correlation[0:n]]
top_features_genes = golub.iloc[:,top_features_index]
#visualise
sns.heatmap(top_features_genes.astype('float'))
#add labels
top_features_genes['Class_Type'] = golub[0]
#write to csv
top_features_genes.to_csv('PCC_alz_feature_selection.csv', header = True)
#Select random features for exp2
random = golub.sample(n= n, axis=1, replace=False)
#heatmap for random genes
#sns.heatmap(random.astype('float'),cmap="YlGnBu")
random['0'] = golub[0]
random.to_csv('random_alz.csv', header = True)
|
[
"noreply@github.com"
] |
Claudmj.noreply@github.com
|
27986b86b5af74996374d397e67b3192010b6e91
|
c0561a83b9950d3558d8e2a5dc3335fa68f6ec66
|
/ch6/heap_sort.py
|
961c7978e9cca24f00eed24e5baf5e51a947986b
|
[] |
no_license
|
statco19/doit_pyalgo
|
e19800eb12ff34b89fefb8a88c1612b18ec931e8
|
55dfab8d0b62f73f59268cbef7ed099a21b53817
|
refs/heads/master
| 2023-03-03T09:01:53.229280
| 2021-02-08T06:58:34
| 2021-02-08T06:58:34
| 298,769,383
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 565
|
py
|
def heap_sort(a):
def down_heap(a, left, right):
temp = a[left]
parent = left
while parent < (right + 1)//2:
cl = parent * 2 + 1
cr = cl + 1
child = cr if cr <= right and a[cr]>a[cl] else cl
if temp >= a[child]:
break
a[parent] = a[child]
parent=child
a[parent] = temp
return a
n = len(a)
for i in range((n-1)//2, -1, -1):
down_heap(a, i, n-1)
for i in range(n-1, 0, -1):
a[0],a[i] = a[i],a[0]
down_heap(a, 0, i-1)
return a
if __name__ == "__main__":
a = [6,4,3,7,1,9,8]
print(heap_sort(a))
|
[
"noreply@github.com"
] |
statco19.noreply@github.com
|
9df4c2da6b9587b435ea6909bb0ad98bd2d3b8c8
|
ecdb29762e4554fe9b3202f56688802c6d68683e
|
/blog/views.py
|
70e0d0624346d14d03e18b44270e7fd933f5eeaa
|
[] |
no_license
|
zhangsjv/my-first-blog
|
236603365de0835469b02b5fef0489f0ff7245ec
|
4c2f64e6e30afc6e7b7f41a79e4d2457a1ee27ac
|
refs/heads/master
| 2022-12-27T02:49:10.435076
| 2020-10-10T11:26:09
| 2020-10-10T11:26:09
| 302,367,563
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 302
|
py
|
from django.shortcuts import render
from django.utils import timezone
from .models import Post
# Create your views here
def post_list(request):
posts=Post.objects.filter(publised_date__lte=timezone.now()).order_by('-publised_date')
return render(request,'blog/post_list.html',{'posts':posts})
|
[
"zhangsj0209@gmail.com"
] |
zhangsj0209@gmail.com
|
58e81452937dfd410170e2ea91ccccbc72c18e66
|
99f26a233369d80c37938507bb16e648c640afc6
|
/neutronclient/tests/unit/test_cli20_network_ip_availability.py
|
eb325a8f68856adb41f1a3d136050916f8a0f158
|
[
"Apache-2.0"
] |
permissive
|
starlingx-staging/stx-python-neutronclient
|
2d621ad4bd260474e6cc200bc5188a673cc5ff35
|
c64a68a330f7f938249523492068dd94fc8d46ea
|
refs/heads/master
| 2020-03-18T00:57:13.786897
| 2018-11-23T18:45:06
| 2018-11-24T01:18:08
| 134,120,728
| 0
| 3
|
Apache-2.0
| 2018-11-24T01:18:10
| 2018-05-20T04:51:37
|
Python
|
UTF-8
|
Python
| false
| false
| 2,357
|
py
|
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
import sys
from neutronclient.neutron.v2_0 import network_ip_availability
from neutronclient.tests.unit import test_cli20
class CLITestV20NetworkIPAvailability(test_cli20.CLITestV20Base):
id_field = 'network_id'
def _test_list_network_ip_availability(self, args, query):
resources = "network_ip_availabilities"
cmd = network_ip_availability.ListIpAvailability(test_cli20.MyApp
(sys.stdout), None)
self._test_list_resources(resources, cmd,
base_args=args,
query=query)
def test_list_network_ip_availability(self):
self._test_list_network_ip_availability(args=None,
query='ip_version=4')
def test_list_network_ip_availability_ipv6(self):
self._test_list_network_ip_availability(
args=['--ip-version', '6'], query='ip_version=6')
def test_list_network_ip_availability_net_id_and_ipv4(self):
self._test_list_network_ip_availability(
args=['--ip-version', '4', '--network-id', 'myid'],
query='ip_version=4&network_id=myid')
def test_list_network_ip_availability_net_name_and_tenant_id(self):
self._test_list_network_ip_availability(
args=['--network-name', 'foo', '--tenant-id', 'mytenant'],
query='network_name=foo&tenant_id=mytenant&ip_version=4')
def test_show_network_ip_availability(self):
resource = "network_ip_availability"
cmd = network_ip_availability.ShowIpAvailability(
test_cli20.MyApp(sys.stdout), None)
self._test_show_resource(resource, cmd, self.test_id,
args=[self.test_id])
|
[
"manjeet.s.bhatia@intel.com"
] |
manjeet.s.bhatia@intel.com
|
e88f9fca86593c9f58548a9b9ee9d1d925f8edac
|
d3a836353ff223f76fa005215560bb9a0d5e1250
|
/tensorflow/python/grappler/layout_optimizer_test.py
|
d9c1c3ce41aee5a5a8bac5f9dd164771611413de
|
[
"Apache-2.0"
] |
permissive
|
jhabikal21/tensorflow
|
9ee926adc0217aa379202fd5c714b7c03e4514f6
|
98d20962172301385aae694141801a375debd2bc
|
refs/heads/master
| 2021-07-15T20:10:13.666688
| 2021-06-23T11:12:14
| 2021-06-23T11:12:14
| 117,846,715
| 0
| 0
| null | 2018-01-17T14:22:49
| 2018-01-17T14:22:48
| null |
UTF-8
|
Python
| false
| false
| 45,939
|
py
|
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for Grappler LayoutOptimizer."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.core.protobuf import config_pb2
from tensorflow.core.protobuf import device_properties_pb2
from tensorflow.core.protobuf import rewriter_config_pb2
from tensorflow.core.protobuf import saver_pb2
from tensorflow.python.client import session
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import random_seed
from tensorflow.python.grappler import cluster as gcluster
from tensorflow.python.grappler import tf_optimizer
from tensorflow.python.layers import convolutional as conv_layers
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import functional_ops
from tensorflow.python.ops import gen_array_ops
from tensorflow.python.ops import gen_math_ops
from tensorflow.python.ops import gen_nn_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import state_ops
from tensorflow.python.ops import variables
from tensorflow.python.platform import test
from tensorflow.python.training import gradient_descent
from tensorflow.python.training import saver as saver_lib
def _weight(shape):
"""Generates a weight of a given shape."""
return random_ops.truncated_normal(shape, seed=0, stddev=0.1)
def _bias(shape):
"""Generates a bias of a given shape."""
return constant_op.constant(0.1, shape=shape)
def _conv2d(x, w):
"""Returns a 2d convolution layer with full stride."""
return nn.conv2d(x, w, strides=[1, 1, 1, 1], padding='SAME')
def _max_pool_2x2(x):
"""Downsamples a feature map by 2X."""
return nn.max_pool(
x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
# Taken from tensorflow/examples/tutorials/mnist/mnist_deep.py
def _two_layer_model(x):
x_image = array_ops.reshape(x, [-1, 28, 28, 1])
w_conv1 = _weight([5, 5, 1, 32])
b_conv1 = _bias([32])
h_conv1 = nn.relu(_conv2d(x_image, w_conv1) + b_conv1)
h_pool1 = _max_pool_2x2(h_conv1)
w_conv2 = _weight([5, 5, 32, 64])
b_conv2 = _bias([64])
h_conv2 = nn.relu(_conv2d(h_pool1, w_conv2) + b_conv2)
h_pool2 = _max_pool_2x2(h_conv2)
return h_pool2
def _model_with_second_port():
random_seed.set_random_seed(0)
x = random_ops.truncated_normal([2, 5, 5, 4], seed=0)
scale = constant_op.constant(0.1, shape=[4])
offset = constant_op.constant(0.3, shape=[4])
y, mean, _ = nn.fused_batch_norm(x, scale, offset)
mul = math_ops.add(y, mean)
output = array_ops.identity(mul)
return output
def _model_with_branch(x):
x_image = array_ops.reshape(x, [-1, 28, 28, 1])
w_conv1 = _weight([5, 5, 1, 32])
w_conv2 = _weight([5, 5, 1, 32])
c_conv1 = _conv2d(x_image, w_conv1)
c_conv2 = _conv2d(x_image, w_conv2)
add = math_ops.add(c_conv1, c_conv2)
return add
def _model_with_vec_and_4d(x):
x_image = array_ops.reshape(x, [-1, 28, 28, 1])
w_conv1 = _weight([5, 5, 1, 32])
c_conv1 = _conv2d(x_image, w_conv1)
vector = constant_op.constant(6.4, shape=[32])
add = math_ops.add(c_conv1, vector)
return add
def _loop():
random_seed.set_random_seed(0)
x1 = random_ops.truncated_normal([1, 784], seed=0)
x2 = random_ops.truncated_normal([1, 784], seed=0)
x3 = random_ops.truncated_normal([1, 784], seed=0)
x4 = random_ops.truncated_normal([1, 784], seed=0)
elems = (x1, x2, x3, x4)
outputs = functional_ops.map_fn(_two_layer_model, elems, dtype=dtypes.float32)
return outputs
def _loop_with_branch():
random_seed.set_random_seed(0)
x1 = random_ops.truncated_normal([1, 784], seed=0)
x2 = random_ops.truncated_normal([1, 784], seed=0)
x3 = random_ops.truncated_normal([1, 784], seed=0)
x4 = random_ops.truncated_normal([1, 784], seed=0)
elems = (x1, x2, x3, x4)
outputs = functional_ops.map_fn(
_model_with_branch, elems, dtype=dtypes.float32)
return outputs
def _loop_with_vec_and_4d():
random_seed.set_random_seed(0)
x1 = random_ops.truncated_normal([1, 784], seed=0)
x2 = random_ops.truncated_normal([1, 784], seed=0)
x3 = random_ops.truncated_normal([1, 784], seed=0)
x4 = random_ops.truncated_normal([1, 784], seed=0)
elems = (x1, x2, x3, x4)
outputs = functional_ops.map_fn(
_model_with_vec_and_4d, elems, dtype=dtypes.float32)
return outputs
def _get_config(layout_optimizer=True):
if layout_optimizer:
rewrite_options = rewriter_config_pb2.RewriterConfig(
layout_optimizer=rewriter_config_pb2.RewriterConfig.ON)
else:
rewrite_options = rewriter_config_pb2.RewriterConfig(
layout_optimizer=rewriter_config_pb2.RewriterConfig.OFF)
graph_options = config_pb2.GraphOptions(
rewrite_options=rewrite_options, build_cost_model=1)
config = config_pb2.ConfigProto(graph_options=graph_options)
return config
def _simple_metagraph(depthwise=False):
random_seed.set_random_seed(0)
x = variables.Variable(random_ops.truncated_normal([1, 200, 200, 3], seed=0))
conv = conv_layers.separable_conv2d if depthwise else conv_layers.conv2d
y = conv(x, 32, [3, 3])
z = conv(y, 32, [3, 3])
optimizer = gradient_descent.GradientDescentOptimizer(1e-4)
loss = math_ops.reduce_mean(z)
train_op = optimizer.minimize(loss)
graph = ops.get_default_graph()
graph.add_to_collection('train_op', train_op)
meta_graph = saver_lib.export_meta_graph(graph_def=graph.as_graph_def())
return meta_graph
def _get_cluster():
named_device = device_properties_pb2.NamedDevice()
named_device.name = '/GPU:0'
named_device.properties.type = 'GPU'
named_device.properties.environment['architecture'] = '4'
cluster = gcluster.Cluster(devices=[named_device])
return cluster
class LayoutOptimizerTest(test.TestCase):
"""Tests the Grappler layout optimizer."""
def _train(self, checkpoint_path, layout_optimizer=False, restore=False):
ops.reset_default_graph()
graph = ops.get_default_graph()
with session.Session(
config=_get_config(layout_optimizer), graph=graph) as sess:
batch = 2
height = 6
width = 7
input_channels = 3
shape = [batch, height, width, input_channels]
image = array_ops.placeholder(dtype='float32', shape=shape)
conv1 = conv_layers.conv2d(image, 32, [3, 3])
conv2 = conv_layers.conv2d(conv1, 32, [3, 3])
optimizer = gradient_descent.GradientDescentOptimizer(0.01)
loss = math_ops.reduce_mean(conv2)
train_op = optimizer.minimize(loss)
saver = saver_lib.Saver(write_version=saver_pb2.SaverDef.V2)
if restore:
saver.restore(sess, checkpoint_path)
else:
sess.run(variables.global_variables_initializer())
np.random.seed(0)
for _ in range(2):
image_val = np.random.rand(*shape).astype(np.float32)
sess.run([loss, train_op], feed_dict={image: image_val})
if restore:
all_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
all_vars_values = [var.eval(session=sess) for var in all_vars]
return all_vars_values
else:
saver.save(sess, checkpoint_path)
def testTwoConvLayers(self):
if test.is_gpu_available(cuda_only=True):
random_seed.set_random_seed(0)
x = random_ops.truncated_normal([1, 784], seed=0)
output = _two_layer_model(x)
with session.Session() as sess:
output_val_ref = sess.run(output)
with session.Session(config=_get_config()) as sess:
metadata = config_pb2.RunMetadata()
output_val = sess.run(output, run_metadata=metadata)
nodes = []
num_transposes = 0
for node in metadata.cost_graph.node:
if node.name.startswith('LayoutOptimizerTranspose'):
num_transposes += 1
nodes.append(node.name)
# Four transposes were initially added in the Expand phase of
# LayoutOptimizer; two of them are cancelled out in the Collapse phase.
expected_num_transposes = 2
self.assertEqual(expected_num_transposes, num_transposes)
self.assertIn('LayoutOptimizerTransposeNHWCToNCHW-Conv2D-0', nodes)
self.assertIn('LayoutOptimizerTransposeNCHWToNHWC-Relu_1-0-0', nodes)
self.assertAllClose(output_val_ref, output_val, atol=1e-3)
def testSplitWithNonConstAxis(self):
if test.is_gpu_available(cuda_only=True):
random_seed.set_random_seed(0)
x = random_ops.truncated_normal([1, 784], seed=0)
conv = _two_layer_model(x)
dim = array_ops.placeholder(dtype='int32')
split = array_ops.split(conv, 2, axis=dim)
output = math_ops.reduce_sum(split[0])
with session.Session() as sess:
output_val_ref = sess.run(output, feed_dict={dim: 3})
with session.Session(config=_get_config()) as sess:
metadata = config_pb2.RunMetadata()
output_val = sess.run(output, run_metadata=metadata, feed_dict={dim: 3})
nodes = []
num_transposes = 0
for node in metadata.cost_graph.node:
if node.name.startswith('LayoutOptimizerTranspose'):
num_transposes += 1
nodes.append(node.name)
# Four transposes were initially added in the Expand phase of
# LayoutOptimizer; two of them are cancelled out in the Collapse phase.
expected_num_transposes = 2
self.assertEqual(expected_num_transposes, num_transposes)
self.assertIn('LayoutOptimizerTransposeNHWCToNCHW-Conv2D-0', nodes)
self.assertIn('LayoutOptimizerTransposeNCHWToNHWC-split-0-0', nodes)
self.assertIn('LayoutOptimizerDimMapNHWCToNCHW_split_0', nodes)
self.assertAllClose(output_val_ref, output_val, atol=1e-3)
def testSplitVWithNonConstAxis(self):
if test.is_gpu_available(cuda_only=True):
random_seed.set_random_seed(0)
x = random_ops.truncated_normal([1, 784], seed=0)
conv = _two_layer_model(x)
dim = array_ops.placeholder(dtype='int32')
sizes = constant_op.constant([50, 10, 4], shape=[3])
split = gen_array_ops._split_v(
value=conv, size_splits=sizes, axis=dim, num_split=3)
output = math_ops.reduce_sum(split[0])
with session.Session() as sess:
output_val_ref = sess.run(output, feed_dict={dim: 3})
with session.Session(config=_get_config()) as sess:
metadata = config_pb2.RunMetadata()
output_val = sess.run(output, run_metadata=metadata, feed_dict={dim: 3})
nodes = []
num_transposes = 0
for node in metadata.cost_graph.node:
if node.name.startswith('LayoutOptimizerTranspose'):
num_transposes += 1
nodes.append(node.name)
# Four transposes were initially added in the Expand phase of
# LayoutOptimizer; two of them are cancelled out in the Collapse phase.
expected_num_transposes = 2
self.assertEqual(expected_num_transposes, num_transposes)
self.assertIn('LayoutOptimizerTransposeNHWCToNCHW-Conv2D-0', nodes)
self.assertIn('LayoutOptimizerTransposeNCHWToNHWC-SplitV-0-0', nodes)
self.assertIn('LayoutOptimizerDimMapNHWCToNCHW_SplitV_2', nodes)
self.assertAllClose(output_val_ref, output_val, atol=1e-3)
def testPadWithConstPaddings(self):
if test.is_gpu_available(cuda_only=True):
random_seed.set_random_seed(0)
x = random_ops.truncated_normal([1, 784], seed=0)
conv = _two_layer_model(x)
paddings_val = [[1, 2], [3, 4], [5, 6], [7, 8]]
paddings = constant_op.constant(
paddings_val, dtype='int32', name='PaddingsConst')
pad = array_ops.pad(conv, paddings)
output = array_ops.identity(pad)
with session.Session() as sess:
output_val_ref = sess.run(output)
with session.Session(config=_get_config()) as sess:
metadata = config_pb2.RunMetadata()
output_val = sess.run(output, run_metadata=metadata)
nodes = []
num_transposes = 0
for node in metadata.cost_graph.node:
if node.name.startswith('LayoutOptimizerTranspose'):
num_transposes += 1
nodes.append(node.name)
# Four transposes were initially added in the Expand phase of
# LayoutOptimizer; two of them are cancelled out in the Collapse phase.
expected_num_transposes = 2
self.assertEqual(expected_num_transposes, num_transposes)
self.assertIn('LayoutOptimizerTransposeNHWCToNCHW-Conv2D-0', nodes)
self.assertIn('LayoutOptimizerTransposeNCHWToNHWC-Pad-0-0', nodes)
self.assertIn('LayoutOptimizer-Pad-PaddingsConst', nodes)
self.assertAllClose(output_val_ref, output_val, atol=1e-3)
def testReduceSum(self):
if test.is_gpu_available(cuda_only=True):
random_seed.set_random_seed(0)
x = random_ops.truncated_normal([1, 784], seed=0)
conv = _two_layer_model(x)
reduce_sum = math_ops.reduce_sum(conv)
output = array_ops.identity(reduce_sum)
with session.Session() as sess:
output_val_ref = sess.run(output)
with session.Session(config=_get_config()) as sess:
metadata = config_pb2.RunMetadata()
output_val = sess.run(output, run_metadata=metadata)
nodes = []
num_transposes = 0
for node in metadata.cost_graph.node:
if node.name.startswith('LayoutOptimizerTranspose'):
num_transposes += 1
nodes.append(node.name)
# Three transposes were initially added in the Expand phase of
# LayoutOptimizer; two of them are cancelled out in the Collapse phase.
expected_num_transposes = 1
self.assertEqual(expected_num_transposes, num_transposes)
self.assertIn('LayoutOptimizerTransposeNHWCToNCHW-Conv2D-0', nodes)
self.assertAllClose(output_val_ref, output_val, atol=1e-3)
def testReduceSumAlongHWC(self):
if test.is_gpu_available(cuda_only=True):
random_seed.set_random_seed(0)
x = random_ops.truncated_normal([1, 784], seed=0)
conv = _two_layer_model(x)
reduce_sum = math_ops.reduce_sum(conv, axis=[1, 2, 3])
output = array_ops.identity(reduce_sum)
with session.Session() as sess:
output_val_ref = sess.run(output)
with session.Session(config=_get_config()) as sess:
metadata = config_pb2.RunMetadata()
output_val = sess.run(output, run_metadata=metadata)
nodes = []
num_transposes = 0
for node in metadata.cost_graph.node:
if node.name.startswith('LayoutOptimizerTranspose'):
num_transposes += 1
nodes.append(node.name)
# Three transposes were initially added in the Expand phase of
# LayoutOptimizer; two of them are cancelled out in the Collapse phase.
expected_num_transposes = 1
self.assertEqual(expected_num_transposes, num_transposes)
self.assertIn('LayoutOptimizerTransposeNHWCToNCHW-Conv2D-0', nodes)
self.assertAllClose(output_val_ref, output_val, atol=1e-3)
def testReduceSumAlongNHW(self):
if test.is_gpu_available(cuda_only=True):
random_seed.set_random_seed(0)
x = random_ops.truncated_normal([1, 784], seed=0)
conv = _two_layer_model(x)
reduce_sum = math_ops.reduce_sum(conv, axis=[0, 1, 2])
output = array_ops.identity(reduce_sum)
with session.Session() as sess:
output_val_ref = sess.run(output)
with session.Session(config=_get_config()) as sess:
metadata = config_pb2.RunMetadata()
output_val = sess.run(output, run_metadata=metadata)
nodes = []
num_transposes = 0
for node in metadata.cost_graph.node:
if node.name.startswith('LayoutOptimizerTranspose'):
num_transposes += 1
nodes.append(node.name)
# Three transposes were initially added in the Expand phase of
# LayoutOptimizer; two of them are cancelled out in the Collapse phase.
expected_num_transposes = 1
self.assertEqual(expected_num_transposes, num_transposes)
self.assertIn('LayoutOptimizerTransposeNHWCToNCHW-Conv2D-0', nodes)
self.assertAllClose(output_val_ref, output_val, atol=1e-3)
def testReduceSumAlongC(self):
if test.is_gpu_available(cuda_only=True):
random_seed.set_random_seed(0)
x = random_ops.truncated_normal([1, 784], seed=0)
conv = _two_layer_model(x)
reduce_sum = math_ops.reduce_sum(conv, axis=[3])
output = array_ops.identity(reduce_sum)
with session.Session() as sess:
output_val_ref = sess.run(output)
with session.Session(config=_get_config()) as sess:
metadata = config_pb2.RunMetadata()
output_val = sess.run(output, run_metadata=metadata)
nodes = []
num_transposes = 0
for node in metadata.cost_graph.node:
if node.name.startswith('LayoutOptimizerTranspose'):
num_transposes += 1
nodes.append(node.name)
# Three transposes were initially added in the Expand phase of
# LayoutOptimizer; two of them are cancelled out in the Collapse phase.
expected_num_transposes = 1
self.assertEqual(expected_num_transposes, num_transposes)
self.assertIn('LayoutOptimizerTransposeNHWCToNCHW-Conv2D-0', nodes)
self.assertAllClose(output_val_ref, output_val, atol=1e-3)
def testConcatWithControlDependency(self):
if test.is_gpu_available(cuda_only=True):
random_seed.set_random_seed(0)
x = random_ops.truncated_normal([1, 784], seed=0)
conv = _two_layer_model(x)
axis = constant_op.constant(3)
var = variables.Variable(3)
assign = state_ops.assign(var, 6)
with ops.control_dependencies([assign]):
concat = array_ops.concat([conv, conv], axis)
output = array_ops.identity(concat)
with session.Session() as sess:
output_val_ref = sess.run(output)
with session.Session(config=_get_config()) as sess:
metadata = config_pb2.RunMetadata()
output_val = sess.run(output, run_metadata=metadata)
nodes = []
num_transposes = 0
for node in metadata.cost_graph.node:
if node.name.startswith('LayoutOptimizerTranspose'):
num_transposes += 1
nodes.append(node.name)
# Four transposes were initially added in the Expand phase of
# LayoutOptimizer; two of them are cancelled out in the Collapse phase.
expected_num_transposes = 2
self.assertEqual(expected_num_transposes, num_transposes)
self.assertIn('LayoutOptimizerTransposeNHWCToNCHW-Conv2D-0', nodes)
self.assertIn('LayoutOptimizerTransposeNCHWToNHWC-concat-0-0', nodes)
self.assertIn('LayoutOptimizer-concat-Const_2', nodes)
self.assertAllClose(output_val_ref, output_val, atol=1e-3)
def testFill(self):
if test.is_gpu_available(cuda_only=True):
random_seed.set_random_seed(0)
x = array_ops.placeholder(dtype='float32')
conv = _two_layer_model(x)
shape = array_ops.shape(conv)
scalar = array_ops.constant(5.7)
fill = array_ops.fill(shape, scalar)
output = array_ops.identity(fill)
x_val = [3.4] * 784
with session.Session() as sess:
output_val_ref = sess.run(output, feed_dict={x: x_val})
with session.Session(config=_get_config()) as sess:
metadata = config_pb2.RunMetadata()
output_val = sess.run(
output, run_metadata=metadata, feed_dict={
x: x_val
})
nodes = []
num_transposes = 0
num_vec_permute = 0
for node in metadata.cost_graph.node:
if node.name.startswith('LayoutOptimizerTranspose'):
num_transposes += 1
if node.name.startswith('LayoutOptimizerVecPermute'):
num_vec_permute += 1
nodes.append(node.name)
# Four transposes were initially added in the Expand phase of
# LayoutOptimizer; two of them are cancelled out in the Collapse phase.
expected_num_transposes = 2
self.assertEqual(expected_num_transposes, num_transposes)
# Two vector permute nodes were initially added in the Expand phase of
# LayoutOptimizer; they cancelled out each other in the Collapse phase.
expected_vec_permute = 0
self.assertEqual(expected_vec_permute, num_vec_permute)
self.assertIn('LayoutOptimizerTransposeNHWCToNCHW-Conv2D-0', nodes)
self.assertIn('LayoutOptimizerTransposeNCHWToNHWC-Fill-0-0', nodes)
self.assertAllClose(output_val_ref, output_val, atol=1e-3)
def testTile(self):
if test.is_gpu_available(cuda_only=True):
random_seed.set_random_seed(0)
x = random_ops.truncated_normal([1, 784], seed=0)
conv = _two_layer_model(x)
multiple = array_ops.placeholder(dtype='int32')
tile = array_ops.tile(conv, multiple)
output = array_ops.identity(tile)
multiple_val = [2, 3, 4, 1]
with session.Session() as sess:
output_val_ref = sess.run(output, feed_dict={multiple: multiple_val})
with session.Session(config=_get_config()) as sess:
metadata = config_pb2.RunMetadata()
output_val = sess.run(
output, run_metadata=metadata, feed_dict={
multiple: multiple_val
})
nodes = []
num_transposes = 0
for node in metadata.cost_graph.node:
if node.name.startswith('LayoutOptimizerTranspose'):
num_transposes += 1
nodes.append(node.name)
# Four transposes were initially added in the Expand phase of
# LayoutOptimizer; two of them are cancelled out in the Collapse phase.
expected_num_transposes = 2
self.assertEqual(expected_num_transposes, num_transposes)
self.assertIn('LayoutOptimizerTransposeNHWCToNCHW-Conv2D-0', nodes)
self.assertIn('LayoutOptimizerTransposeNCHWToNHWC-Tile-0-0', nodes)
self.assertIn('LayoutOptimizerVecPermuteNHWCToNCHW_Tile_1', nodes)
self.assertAllClose(output_val_ref, output_val, atol=1e-3)
def testReverseWithConstDims(self):
if test.is_gpu_available(cuda_only=True):
random_seed.set_random_seed(0)
x = random_ops.truncated_normal([1, 784], seed=0)
conv = _two_layer_model(x)
dims = constant_op.constant([3, 1], name='DimsConst')
reverse = array_ops.reverse(conv, dims)
output = array_ops.identity(reverse)
with session.Session() as sess:
output_val_ref = sess.run(output)
with session.Session(config=_get_config()) as sess:
metadata = config_pb2.RunMetadata()
output_val = sess.run(output, run_metadata=metadata)
nodes = []
num_transposes = 0
for node in metadata.cost_graph.node:
if node.name.startswith('LayoutOptimizerTranspose'):
num_transposes += 1
nodes.append(node.name)
# Four transposes were initially added in the Expand phase of
# LayoutOptimizer; two of them are cancelled out in the Collapse phase.
expected_num_transposes = 2
self.assertEqual(expected_num_transposes, num_transposes)
self.assertIn('LayoutOptimizerTransposeNHWCToNCHW-Conv2D-0', nodes)
self.assertIn('LayoutOptimizerTransposeNCHWToNHWC-ReverseV2-0-0', nodes)
self.assertIn('LayoutOptimizer-ReverseV2-DimsConst', nodes)
self.assertAllClose(output_val_ref, output_val, atol=1e-3)
def testReverseWithNonConstDims(self):
if test.is_gpu_available(cuda_only=True):
random_seed.set_random_seed(0)
x = random_ops.truncated_normal([1, 784], seed=0)
conv = _two_layer_model(x)
dims = array_ops.placeholder(dtype='int32')
reverse = array_ops.reverse(conv, dims)
output = array_ops.identity(reverse)
dims_val = [2, 3]
with session.Session() as sess:
output_val_ref = sess.run(output, feed_dict={dims: dims_val})
with session.Session(config=_get_config()) as sess:
metadata = config_pb2.RunMetadata()
output_val = sess.run(
output, run_metadata=metadata, feed_dict={
dims: dims_val
})
nodes = []
num_transposes = 0
for node in metadata.cost_graph.node:
if node.name.startswith('LayoutOptimizerTranspose'):
num_transposes += 1
nodes.append(node.name)
# Four transposes were initially added in the Expand phase of
# LayoutOptimizer; two of them are cancelled out in the Collapse phase.
expected_num_transposes = 2
self.assertEqual(expected_num_transposes, num_transposes)
self.assertIn('LayoutOptimizerTransposeNHWCToNCHW-Conv2D-0', nodes)
self.assertIn('LayoutOptimizerTransposeNCHWToNHWC-ReverseV2-0-0', nodes)
self.assertIn('LayoutOptimizerDimMapNHWCToNCHW_ReverseV2_1', nodes)
self.assertAllClose(output_val_ref, output_val, atol=1e-3)
def testSelectOp(self):
if test.is_gpu_available(cuda_only=True):
random_seed.set_random_seed(0)
x = random_ops.truncated_normal([1, 784], seed=0)
conv = _two_layer_model(x)
add = math_ops.add(conv, conv)
mean = math_ops.reduce_mean(conv)
condition = math_ops.less(conv, mean)
select = gen_math_ops._select(condition, conv, add)
output = array_ops.identity(select)
with session.Session() as sess:
output_val_ref = sess.run(output)
with session.Session(config=_get_config()) as sess:
metadata = config_pb2.RunMetadata()
output_val = sess.run(output, run_metadata=metadata)
nodes = []
num_transposes = 0
for node in metadata.cost_graph.node:
if node.name.startswith('LayoutOptimizerTranspose'):
num_transposes += 1
nodes.append(node.name)
expected_num_transposes = 2
self.assertEqual(expected_num_transposes, num_transposes)
self.assertIn('LayoutOptimizerTransposeNHWCToNCHW-Conv2D-0', nodes)
self.assertIn('LayoutOptimizerTransposeNCHWToNHWC-Select-0-0', nodes)
self.assertAllClose(output_val_ref, output_val, atol=1e-3)
def testSelectOpScalarCondition(self):
if test.is_gpu_available(cuda_only=True):
random_seed.set_random_seed(0)
x = random_ops.truncated_normal([1, 784], seed=0)
conv = _two_layer_model(x)
add = math_ops.add(conv, conv)
condition = constant_op.constant(True)
select = gen_math_ops._select(condition, conv, add)
output = array_ops.identity(select)
with session.Session() as sess:
output_val_ref = sess.run(output)
with session.Session(config=_get_config()) as sess:
metadata = config_pb2.RunMetadata()
output_val = sess.run(output, run_metadata=metadata)
nodes = []
num_transposes = 0
for node in metadata.cost_graph.node:
if node.name.startswith('LayoutOptimizerTranspose'):
num_transposes += 1
nodes.append(node.name)
expected_num_transposes = 2
self.assertEqual(expected_num_transposes, num_transposes)
self.assertIn('LayoutOptimizerTransposeNHWCToNCHW-Conv2D-0', nodes)
self.assertIn('LayoutOptimizerTransposeNCHWToNHWC-Select-0-0', nodes)
self.assertAllClose(output_val_ref, output_val, atol=1e-3)
def testPadWithNonConstPaddings(self):
if test.is_gpu_available(cuda_only=True):
random_seed.set_random_seed(0)
x = random_ops.truncated_normal([1, 784], seed=0)
conv = _two_layer_model(x)
paddings = array_ops.placeholder(dtype='int32')
pad = array_ops.pad(conv, paddings)
output = array_ops.identity(pad)
paddings_val = [[1, 2], [3, 4], [5, 6], [7, 8]]
with session.Session() as sess:
output_val_ref = sess.run(output, feed_dict={paddings: paddings_val})
with session.Session(config=_get_config()) as sess:
metadata = config_pb2.RunMetadata()
output_val = sess.run(
output, run_metadata=metadata, feed_dict={
paddings: paddings_val
})
nodes = []
num_transposes = 0
for node in metadata.cost_graph.node:
if node.name.startswith('LayoutOptimizerTranspose'):
num_transposes += 1
nodes.append(node.name)
# Four transposes were initially added in the Expand phase of
# LayoutOptimizer; two of them are cancelled out in the Collapse phase.
expected_num_transposes = 2
self.assertEqual(expected_num_transposes, num_transposes)
self.assertIn('LayoutOptimizerTransposeNHWCToNCHW-Conv2D-0', nodes)
self.assertIn('LayoutOptimizerTransposeNCHWToNHWC-Pad-0-0', nodes)
self.assertIn('LayoutOptimizerVecPermuteNHWCToNCHW_Pad_1', nodes)
self.assertAllClose(output_val_ref, output_val, atol=1e-3)
def testMaxPoolV2(self):
if test.is_gpu_available(cuda_only=True):
random_seed.set_random_seed(0)
x = random_ops.truncated_normal([1, 784], seed=0)
conv = _two_layer_model(x)
ksize = constant_op.constant([1, 2, 3, 1], shape=[4])
strides = array_ops.placeholder(dtype='int32', shape=[4])
max_pool = gen_nn_ops._max_pool_v2(conv, ksize, strides, 'VALID')
output = array_ops.identity(max_pool)
strides_val = [1, 3, 2, 1]
with session.Session() as sess:
output_val_ref = sess.run(output, feed_dict={strides: strides_val})
with session.Session(config=_get_config()) as sess:
metadata = config_pb2.RunMetadata()
output_val = sess.run(
output, run_metadata=metadata, feed_dict={
strides: strides_val
})
nodes = []
num_transposes = 0
for node in metadata.cost_graph.node:
if node.name.startswith('LayoutOptimizerTranspose'):
num_transposes += 1
nodes.append(node.name)
expected_num_transposes = 2
self.assertEqual(expected_num_transposes, num_transposes)
self.assertIn('LayoutOptimizerTransposeNHWCToNCHW-Conv2D-0', nodes)
self.assertIn('LayoutOptimizerTransposeNCHWToNHWC-MaxPoolV2-0-0', nodes)
self.assertIn('LayoutOptimizerVecPermuteNHWCToNCHW_MaxPoolV2_2', nodes)
self.assertIn('LayoutOptimizer-MaxPoolV2-Const_2', nodes)
self.assertAllClose(output_val_ref, output_val, atol=1e-3)
def testMaxPoolGradV2(self):
if test.is_gpu_available(cuda_only=True):
random_seed.set_random_seed(0)
x = random_ops.truncated_normal([1, 784], seed=0)
conv = _two_layer_model(x)
ksize = constant_op.constant([1, 2, 3, 1], shape=[4])
strides = array_ops.placeholder(dtype='int32', shape=[4])
max_pool_grad = gen_nn_ops.max_pool_grad_v2(conv, conv, conv, ksize,
strides, 'VALID')
output = array_ops.identity(max_pool_grad)
strides_val = [1, 3, 2, 1]
with session.Session() as sess:
output_val_ref = sess.run(output, feed_dict={strides: strides_val})
with session.Session(config=_get_config()) as sess:
metadata = config_pb2.RunMetadata()
output_val = sess.run(
output, run_metadata=metadata, feed_dict={
strides: strides_val
})
nodes = []
num_transposes = 0
for node in metadata.cost_graph.node:
if node.name.startswith('LayoutOptimizerTranspose'):
num_transposes += 1
nodes.append(node.name)
expected_num_transposes = 2
self.assertEqual(expected_num_transposes, num_transposes)
self.assertIn('LayoutOptimizerTransposeNHWCToNCHW-Conv2D-0', nodes)
self.assertIn('LayoutOptimizerTransposeNCHWToNHWC-MaxPoolGradV2-0-0',
nodes)
self.assertIn('LayoutOptimizerVecPermuteNHWCToNCHW_MaxPoolGradV2_4',
nodes)
self.assertIn('LayoutOptimizer-MaxPoolGradV2-Const_2', nodes)
self.assertAllClose(output_val_ref, output_val, atol=1e-3)
def testSliceWithNonConstAxis(self):
if test.is_gpu_available(cuda_only=True):
random_seed.set_random_seed(0)
x = random_ops.truncated_normal([1, 784], seed=0)
conv = _two_layer_model(x)
size = array_ops.placeholder(dtype='int32')
s = array_ops.slice(conv, [0, 0, 0, 0], size)
output = array_ops.identity(s)
size_val = [1, 2, 3, 4]
with session.Session() as sess:
output_val_ref = sess.run(output, feed_dict={size: size_val})
with session.Session(config=_get_config()) as sess:
metadata = config_pb2.RunMetadata()
output_val = sess.run(
output, run_metadata=metadata, feed_dict={
size: size_val
})
nodes = []
num_transposes = 0
for node in metadata.cost_graph.node:
if node.name.startswith('LayoutOptimizerTranspose'):
num_transposes += 1
nodes.append(node.name)
# Four transposes were initially added in the Expand phase of
# LayoutOptimizer; two of them are cancelled out in the Collapse phase.
expected_num_transposes = 2
self.assertEqual(expected_num_transposes, num_transposes)
self.assertIn('LayoutOptimizerTransposeNHWCToNCHW-Conv2D-0', nodes)
self.assertIn('LayoutOptimizerTransposeNCHWToNHWC-Slice-0-0', nodes)
self.assertIn('LayoutOptimizerVecPermuteNHWCToNCHW_Slice_2', nodes)
self.assertAllClose(output_val_ref, output_val, atol=1e-3)
def testStridedSliceWithNonConstAxis(self):
if test.is_gpu_available(cuda_only=True):
random_seed.set_random_seed(0)
x = random_ops.truncated_normal([1, 784], seed=0)
conv = _two_layer_model(x)
end = array_ops.placeholder(dtype='int32')
s = array_ops.strided_slice(conv, [0, 0, 0, 0], end, strides=[1, 2, 3, 1])
output = array_ops.identity(s)
end_val = [1, 2, 3, 4]
with session.Session() as sess:
output_val_ref = sess.run(output, feed_dict={end: end_val})
with session.Session(config=_get_config()) as sess:
metadata = config_pb2.RunMetadata()
output_val = sess.run(
output, run_metadata=metadata, feed_dict={
end: end_val
})
nodes = []
num_transposes = 0
for node in metadata.cost_graph.node:
if node.name.startswith('LayoutOptimizerTranspose'):
num_transposes += 1
nodes.append(node.name)
# Four transposes were initially added in the Expand phase of
# LayoutOptimizer; two of them are cancelled out in the Collapse phase.
expected_num_transposes = 2
self.assertEqual(expected_num_transposes, num_transposes)
self.assertIn('LayoutOptimizerTransposeNHWCToNCHW-Conv2D-0', nodes)
self.assertIn('LayoutOptimizerTransposeNCHWToNHWC-StridedSlice-0-0',
nodes)
self.assertIn('LayoutOptimizerVecPermuteNHWCToNCHW_StridedSlice_2', nodes)
self.assertIn('LayoutOptimizer-StridedSlice-StridedSlice/begin', nodes)
self.assertIn('LayoutOptimizer-StridedSlice-StridedSlice/strides', nodes)
self.assertAllClose(output_val_ref, output_val, atol=1e-3)
def testStridedSliceWithMask(self):
if test.is_gpu_available(cuda_only=True):
random_seed.set_random_seed(0)
x = random_ops.truncated_normal([1, 784], seed=0)
conv = _two_layer_model(x)
# This will generate a StridedSlice op with begin mask and end mask.
s = conv[:, :, 1:-1, :]
output = array_ops.identity(s)
with session.Session() as sess:
output_val_ref = sess.run(output)
with session.Session(config=_get_config()) as sess:
metadata = config_pb2.RunMetadata()
output_val = sess.run(output, run_metadata=metadata)
nodes = []
num_transposes = 0
for node in metadata.cost_graph.node:
if node.name.startswith('LayoutOptimizerTranspose'):
num_transposes += 1
nodes.append(node.name)
# Four transposes were initially added in the Expand phase of
# LayoutOptimizer; two of them are cancelled out in the Collapse phase.
expected_num_transposes = 2
self.assertEqual(expected_num_transposes, num_transposes)
self.assertIn('LayoutOptimizerTransposeNHWCToNCHW-Conv2D-0', nodes)
self.assertIn('LayoutOptimizerTransposeNCHWToNHWC-strided_slice-0-0',
nodes)
self.assertIn('LayoutOptimizer-strided_slice-strided_slice/stack', nodes)
self.assertIn('LayoutOptimizer-strided_slice-strided_slice/stack_1',
nodes)
self.assertIn('LayoutOptimizer-strided_slice-strided_slice/stack_2',
nodes)
self.assertAllClose(output_val_ref, output_val, atol=1e-3)
def testStridedSliceGradWithNonConstAxis(self):
if test.is_gpu_available(cuda_only=True):
random_seed.set_random_seed(0)
x = random_ops.truncated_normal([1, 784], seed=0)
conv = _two_layer_model(x)
end = array_ops.placeholder(dtype='int32')
shape = array_ops.shape(conv)
end_val = [1, 2, 3, 4]
s = array_ops.strided_slice(
conv, [0, 0, 0, 0], end_val, strides=[1, 2, 3, 1])
s_grad = array_ops.strided_slice_grad(shape, [0, 0, 0, 0], end,
[1, 2, 3, 1], s)
output = array_ops.identity(s_grad)
with session.Session() as sess:
output_val_ref = sess.run(output, feed_dict={end: end_val})
with session.Session(config=_get_config()) as sess:
metadata = config_pb2.RunMetadata()
output_val = sess.run(
output, run_metadata=metadata, feed_dict={
end: end_val
})
nodes = []
num_transposes = 0
for node in metadata.cost_graph.node:
if node.name.startswith('LayoutOptimizerTranspose'):
num_transposes += 1
nodes.append(node.name)
# Four transposes were initially added in the Expand phase of
# LayoutOptimizer; two of them are cancelled out in the Collapse phase.
expected_num_transposes = 2
self.assertEqual(expected_num_transposes, num_transposes)
self.assertIn('LayoutOptimizerTransposeNHWCToNCHW-Conv2D-0', nodes)
self.assertIn('LayoutOptimizerTransposeNCHWToNHWC-StridedSliceGrad-0-0',
nodes)
self.assertIn('LayoutOptimizerVecPermuteNHWCToNCHW_StridedSliceGrad_2',
nodes)
self.assertIn('LayoutOptimizer-StridedSlice-StridedSliceGrad/begin',
nodes)
self.assertIn('LayoutOptimizer-StridedSlice-StridedSliceGrad/strides',
nodes)
self.assertAllClose(output_val_ref, output_val, atol=1e-3)
def testShapeN(self):
if test.is_gpu_available(cuda_only=True):
x = array_ops.placeholder(dtype='float32')
conv = _two_layer_model(x)
shapen = array_ops.shape_n([conv, conv])
output = math_ops.add(shapen[0], shapen[1])
x_val = [1.7] * 784
with session.Session() as sess:
output_val_ref = sess.run(output, feed_dict={x: x_val})
with session.Session(config=_get_config()) as sess:
metadata = config_pb2.RunMetadata()
output_val = sess.run(
output, run_metadata=metadata, feed_dict={
x: x_val
})
nodes = []
num_transposes = 0
for node in metadata.cost_graph.node:
if node.name.startswith('LayoutOptimizerTranspose'):
num_transposes += 1
nodes.append(node.name)
expected_num_transposes = 1
self.assertEqual(expected_num_transposes, num_transposes)
self.assertIn('LayoutOptimizerTransposeNHWCToNCHW-Conv2D-0', nodes)
self.assertIn('LayoutOptimizerVecPermuteNCHWToNHWC-ShapeN-0-0', nodes)
self.assertAllEqual(output_val_ref, output_val)
def testLoop(self):
if test.is_gpu_available(cuda_only=True):
output = _loop()
with session.Session() as sess:
output_val_ref = sess.run(output)
with session.Session(config=_get_config()) as sess:
metadata = config_pb2.RunMetadata()
output_val = sess.run(output, run_metadata=metadata)
nodes = []
num_transposes = 0
for node in metadata.cost_graph.node:
if node.name.startswith('LayoutOptimizerTranspose'):
num_transposes += 1
nodes.append(node.name)
# Four transposes were initially added in the Expand phase of
# LayoutOptimizer; two of them are cancelled out in the Collapse phase.
expected_num_transposes = 2
self.assertEqual(expected_num_transposes, num_transposes)
self.assertEqual(expected_num_transposes, num_transposes)
self.assertIn('LayoutOptimizerTransposeNHWCToNCHW-map/while/Conv2D-0',
nodes)
self.assertIn(
'LayoutOptimizerTransposeNCHWToNHWC-map/while/MaxPool_1-0-2', nodes)
self.assertAllClose(output_val_ref, output_val, atol=1e-3)
def testLoopWithBranch(self):
if test.is_gpu_available(cuda_only=True):
output = _loop_with_branch()
with session.Session() as sess:
output_val_ref = sess.run(output)
with session.Session(config=_get_config()) as sess:
metadata = config_pb2.RunMetadata()
output_val = sess.run(output, run_metadata=metadata)
nodes = []
num_transposes = 0
for node in metadata.cost_graph.node:
if node.name.startswith('LayoutOptimizerTranspose'):
num_transposes += 1
nodes.append(node.name)
expected_num_transposes = 2
self.assertEqual(expected_num_transposes, num_transposes)
self.assertIn('LayoutOptimizerTransposeNHWCToNCHW-map/while/Conv2D-0',
nodes)
self.assertIn('LayoutOptimizerTransposeNCHWToNHWC-map/while/Add-0-2',
nodes)
self.assertAllClose(output_val_ref, output_val, atol=1e-3)
def testLoopWithVecAnd4D(self):
if test.is_gpu_available(cuda_only=True):
output = _loop_with_vec_and_4d()
with session.Session() as sess:
output_val_ref = sess.run(output)
with session.Session(config=_get_config()) as sess:
metadata = config_pb2.RunMetadata()
output_val = sess.run(output, run_metadata=metadata)
nodes = []
num_transposes = 0
for node in metadata.cost_graph.node:
if node.name.startswith('LayoutOptimizerTranspose'):
num_transposes += 1
nodes.append(node.name)
expected_num_transposes = 2
self.assertEqual(expected_num_transposes, num_transposes)
self.assertIn('LayoutOptimizerTransposeNHWCToNCHW-map/while/Conv2D-0',
nodes)
self.assertIn('LayoutOptimizerTransposeNCHWToNHWC-map/while/Add-0-2',
nodes)
self.assertAllClose(output_val_ref, output_val, atol=1e-3)
def testBinaryOpSecondPort(self):
if test.is_gpu_available(cuda_only=True):
output = _model_with_second_port()
with session.Session() as sess:
output_val_ref = sess.run(output)
with session.Session(config=_get_config()) as sess:
metadata = config_pb2.RunMetadata()
output_val = sess.run(output, run_metadata=metadata)
nodes = []
num_transposes = 0
for node in metadata.cost_graph.node:
if node.name.startswith('LayoutOptimizerTranspose'):
num_transposes += 1
nodes.append(node.name)
expected_num_transposes = 2
self.assertEqual(expected_num_transposes, num_transposes)
self.assertIn('LayoutOptimizerTransposeNHWCToNCHW-FusedBatchNorm-0',
nodes)
self.assertIn('LayoutOptimizerTransposeNCHWToNHWC-Add-0-0', nodes)
self.assertAllClose(output_val_ref, output_val, atol=1e-3)
def testGradient(self):
meta_graph = _simple_metagraph()
rewrite_options = rewriter_config_pb2.RewriterConfig(
layout_optimizer=rewriter_config_pb2.RewriterConfig.ON)
optimized_graph = tf_optimizer.OptimizeGraph(
rewrite_options, meta_graph, cluster=_get_cluster())
found = 0
for node in optimized_graph.node:
if node.op in ['Conv2D', 'Conv2DBackpropFilter', 'Conv2DBackpropInput']:
found += 1
self.assertEqual(node.attr['data_format'].s, b'NCHW')
self.assertEqual(found, 5)
def testDepthwise(self):
meta_graph = _simple_metagraph(depthwise=True)
rewrite_options = rewriter_config_pb2.RewriterConfig(
layout_optimizer=rewriter_config_pb2.RewriterConfig.ON)
optimized_graph = tf_optimizer.OptimizeGraph(
rewrite_options, meta_graph, cluster=_get_cluster())
found = 0
for node in optimized_graph.node:
if node.op in [
'DepthwiseConv2dNative', 'DepthwiseConv2dNativeBackpropFilter',
'DepthwiseConv2dNativeBackpropInput'
]:
found += 1
self.assertEqual(node.attr['data_format'].s, b'NCHW')
self.assertEqual(found, 6)
def testCheckpointCompatibility(self):
if not test.is_gpu_available(cuda_only=True):
self.skipTest('GPU required')
checkpoint_path = self.get_temp_dir()
self._train(checkpoint_path)
vars_expected = self._train(checkpoint_path, restore=True)
vars_layout_optimized = self._train(
checkpoint_path, restore=True, layout_optimizer=True)
for var_expected, var_layout_optimized in zip(vars_expected,
vars_layout_optimized):
self.assertAllClose(var_expected, var_layout_optimized, atol=1e-6)
if __name__ == '__main__':
test.main()
|
[
"gardener@tensorflow.org"
] |
gardener@tensorflow.org
|
9dd7670dd482869fa273d037703679c2bf9427de
|
f66e717c600c0bd1e3272e4501491ec99185fd4e
|
/Andelabs Day 2/CarClass/CarClass.py
|
b0340dc623d8edc76241cadc7f08fa491a52a7da
|
[] |
no_license
|
Andretalik/Adrian-Otieno-bc17-week-1
|
8e1814769d3498b1de6fc2c71aec67ca6bf58722
|
555420ec71a524053629e39f6dcf0601a661553d
|
refs/heads/master
| 2021-01-18T23:03:58.841109
| 2017-04-06T16:00:15
| 2017-04-06T16:00:15
| 87,086,288
| 0
| 0
| null | 2017-04-06T16:00:16
| 2017-04-03T14:54:48
|
Python
|
UTF-8
|
Python
| false
| false
| 964
|
py
|
class Car(object):
def __init__(self, name='General', model='GM', type_car='saloon', num_of_doors=4, num_of_wheels=4, speed=0):
self.name = name
self.model = model
self.num_of_doors = num_of_doors
self.num_of_wheels = num_of_wheels
self.type_car = type_car
self.speed = speed
if self.name == 'Porshe' or self.name == 'Koenigsegg':
self.num_of_doors = 2
if self.type_car == 'trailer':
self.num_of_wheels = 8
else:
self.num_of_wheels = 4
self.type_car = 'saloon'
def is_saloon(self):
if self.type_car == 'saloon':
return True
else:
return False
def drive(self, moving_speed):
if 3 < moving_speed <= 7:
self.speed = 77
return self
elif 0 < moving_speed <= 3:
self.speed = 1000
return self
else:
return self
|
[
"noreply@github.com"
] |
Andretalik.noreply@github.com
|
d624ff4893bd574e6c0d2ea5f9b242a26fa6d711
|
33698eb3976bbc8a64bc68787a24a7adb53c5c33
|
/projetojogo/venv/projetoflask.py
|
9cd4ee9eb1b48bd7dbf2e62d2b41de1e438e4ee0
|
[] |
no_license
|
jacksonwsup/study_flask-microframework
|
f225470cbdf887b5345648aa3c52220b04e71c8d
|
72f263510ea7f52d5768bef1beea2d81574c404d
|
refs/heads/main
| 2023-08-31T17:03:39.816094
| 2021-11-02T04:37:13
| 2021-11-02T04:37:13
| 423,698,814
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 274
|
py
|
from flask import Flask, render_template #funcção render_template utilizamos para renderizar qualqer formato
app = Flask(__name__)
#app.run(host='0.0.0.0', port=8080)
@app.route('/inicio')
def hellow():
return render_template('lista.html', titulo='Jogos')
app.run()
|
[
"86334378+jacksonwsup@users.noreply.github.com"
] |
86334378+jacksonwsup@users.noreply.github.com
|
6e2270dae209181b8af5ff3dcc9eea8dc4033c64
|
f6ed7bc808f5536bc77166fe5c3571e5c028f308
|
/neptune/internal/cli/commands/executing/null_executor.py
|
b7ba6ca006a08ffa645f0c58a15b0419a5cec32f
|
[
"Apache-2.0"
] |
permissive
|
jiji-online/neptune-cli
|
d086bb59725b7545f3e0f80bd89e8f99ff3851a0
|
50cf680a80d141497f9331ab7cdaee49fcb90b0c
|
refs/heads/main
| 2023-07-18T17:56:10.671562
| 2021-09-14T07:54:13
| 2021-09-14T07:54:13
| 406,275,162
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 742
|
py
|
#
# Copyright (c) 2017, deepsense.io
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from future.builtins import object
class NullExecutor(object):
def execute(self, experiment, args):
pass
def abort(self):
pass
|
[
"serhii.freidin@jiji.ng"
] |
serhii.freidin@jiji.ng
|
e2a012ec71783edc7049afbffecf672773bea279
|
b71cbb9604d3ff09af51fb7d6df771a1198f545e
|
/home/bin/find_burnable_quantities.py
|
946fa92c784d624aadeac96e04e903cf805373ad
|
[
"LicenseRef-scancode-unknown-license-reference",
"MIT"
] |
permissive
|
ssokolow/profile
|
74ed45f2eccd692da92b05cf5069b3d4e28d4ef2
|
0e1bb67e1c83b58e2c49d89b06b1fd3928273614
|
refs/heads/master
| 2022-09-18T06:05:59.220154
| 2022-09-04T19:13:25
| 2022-09-04T19:13:25
| 367,158
| 9
| 9
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 8,524
|
py
|
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""A simple little script to walk a filesystem subtree from the leaves upwards
and draw a tree of of folders large enough to be backed up to DVD+R.
(Displayed size values for parent directories do not include content which
matches the exclusion filter)
"""
from __future__ import (absolute_import, division, print_function,
with_statement)
__author__ = "Stephan Sokolow (deitarion/SSokolow)"
__appname__ = "Find Burnable Quantities"
__version__ = "0.0pre0"
__license__ = "MIT"
MIN_SIZE = 4480 * (1024 ** 2) # 4480MiB
OMITTED_NAMES = ['VIDEO_TS'] # Only show DVD-Video dumps as whole entries
# Avoid thrashing the disk by descending into directories with tons of tiny
# files that we're unlikely to want to size for burning anyway.
TRAVERSAL_EXCLUSIONS = [
'.backups',
'.git', '.hg', '.bzr', '.svn',
'incomplete',
'ACTION_NEEDED_BEFORE_BURNING'
]
import logging
log = logging.getLogger(__name__)
import itertools, math, os, re
def walk(top, topdown=True, onerror=None, followlinks=False,
traversal_filter_cb=None):
"""Python 2.7.3's os.walk(), modified to allow directory exclusion when
using topdown=True.
If a function is passed in via the traversal_filter_cb argument, call it
with the same arguments that will be yielded before descending.
Users may then mutate `dirs` to control traversal order or skip folders.
"""
islink, join, isdir = os.path.islink, os.path.join, os.path.isdir
# We may not have read permission for top, in which case we can't
# get a list of the files the directory contains. os.path.walk
# always suppressed the exception then, rather than blow up for a
# minor reason when (say) a thousand readable directories are still
# left to visit. That logic is copied here.
try:
names = os.listdir(top)
except os.error, err:
if onerror is not None:
onerror(err)
return
dirs, nondirs = [], []
for name in names:
if isdir(join(top, name)):
dirs.append(name)
else:
nondirs.append(name)
if traversal_filter_cb:
traversal_filter_cb(top, dirs, nondirs)
if topdown:
yield top, dirs, nondirs
for name in dirs:
new_path = join(top, name)
if followlinks or not islink(new_path):
for x in walk(new_path, topdown, onerror, followlinks,
traversal_filter_cb=traversal_filter_cb):
yield x
if not topdown:
yield top, dirs, nondirs
def humansort_key(strng):
"""Human/natural sort key-gathering function for sorted()
Source: http://stackoverflow.com/a/1940105
"""
if isinstance(strng, tuple):
strng = strng[0]
return [w.isdigit() and int(w) or w.lower()
for w in re.split(r'(\d+)', strng)]
def format_file_size(size, unit='', precision=0):
"""Take a size in bits or bytes and return it all prettied
up and rounded to whichever unit gives the smallest number.
A fixed unit can be specified. Possible units are B, KiB,
MiB, GiB, TiB, and PiB so far. Case-insensitive.
Works on both negative and positive numbers. In the event
that the given value is in bits, the user will have to
use result = result[:-1] + 'b' to make it appear correct.
Will calculate using integers unless precision is != 0.
Will display using integers unless precision is > 0.
"""
# Each unit's position in the list is crucial.
# units[2] = 'MiB' and size / 1024**2 = size in MiB
# units[3] = 'GiB' and size / 1024**3 = size in GiB
units = ['B', 'KiB', 'MiB', 'GiB', 'TiB', 'PiB']
increment = 1024.0 # Must be float in Python 2.x to avoid floor division
# Did the calling function specify a valid unit of measurement?
if unit and unit.upper() in [x.upper() for x in units]:
unit_idx = units.index(unit)
else:
unit_idx = min(int(math.log(abs(size), increment)), len(units) - 1)
size /= increment ** unit_idx
return '%.*f%s' % (precision, size, units[unit_idx])
class StatGatherer(object):
"""Gathers size-filtered disk usage stats"""
def __init__(self, omitted_names=None, traversal_exclusions=None):
"""
param omitted_names: Names to be omitted from the listing for clarity
(eg. VIDEO_TS)
param traversal_exclusions: Directories to be skipped when descending
for efficiency (eg. .git)
"""
self.size_cache = {}
self.count_cache = {}
self.omitted_names = omitted_names or []
self.traversal_exclusions = traversal_exclusions or []
def _exclusion_cb(self, top, dirs, nondirs):
"""Used with my custom os.walk() to implement traversal exclusions
when using topdown=False
"""
for name in self.traversal_exclusions:
while name in dirs:
dirs.remove(name)
def examine(self, root, min_size=MIN_SIZE):
"""Generator to walk a filesystem from the leaves in and print a tree
of folders larger than C{min_size} bytes."""
for path, dirs, files in walk(root, topdown=False,
traversal_filter_cb=self._exclusion_cb):
path = os.path.normpath(os.path.normcase(path))
# Recursively sum file sizes
size, fcount = 0, len(files)
for fname in files:
try:
size += os.path.getsize(os.path.join(path, fname))
except OSError:
pass
for dname in dirs:
dpath = os.path.join(path, dname)
if dpath in self.size_cache:
size += self.size_cache[dpath]
if dpath in self.count_cache:
fcount += self.count_cache[dpath]
self.size_cache[path] = size
self.count_cache[path] = fcount
if size >= min_size and (
os.path.split(path)[1] not in self.omitted_names):
yield (path, size, fcount)
# pylint: disable=no-self-use
def render(self, results):
"""Generator to render a list of (path, size, count) tuples as a
treeview using indents."""
# Commented out to allow efficient generator stream processing
# results.sort(key=humansort_key)
for (path, size, files) in results:
# print(path, size)
yield '%s%s (%s in %s files)' % (
' ' * (path.count(os.sep)),
os.path.split(path)[1], format_file_size(size, precision=2),
files)
def main():
"""The main entry point, compatible with setuptools entry points."""
# pylint: disable=bad-continuation
from optparse import OptionParser
parser = OptionParser(version="%%prog v%s" % __version__,
usage="%prog [options] <path> ...",
description=__doc__.replace('\r\n', '\n').split('\n--snip--\n')[0])
parser.add_option('-v', '--verbose', action="count", dest="verbose",
default=2, help="Increase the verbosity. Use twice for extra effect")
parser.add_option('-q', '--quiet', action="count", dest="quiet",
default=0, help="Decrease the verbosity. Use twice for extra effect")
parser.add_option('-r', '--reverse', action="store_true", dest="reverse",
default=0, help="Show results as they are gathered (in reverse order)")
# Allow pre-formatted descriptions
parser.formatter.format_description = lambda description: description
opts, args = parser.parse_args()
# Set up clean logging to stderr
log_levels = [logging.CRITICAL, logging.ERROR, logging.WARNING,
logging.INFO, logging.DEBUG]
opts.verbose = min(opts.verbose - opts.quiet, len(log_levels) - 1)
opts.verbose = max(opts.verbose, 0)
logging.basicConfig(level=log_levels[opts.verbose],
format='%(levelname)s: %(message)s')
statter = StatGatherer(
omitted_names=OMITTED_NAMES,
traversal_exclusions=TRAVERSAL_EXCLUSIONS)
results = [statter.render(statter.examine(x)) for x in args]
# Implement reverse() use with the minimum possible loss of
# streaming display
for resultset in results:
if not opts.reverse:
resultset = reversed(list(resultset))
for line in resultset:
print(line)
if __name__ == '__main__':
main()
|
[
"http://www.ssokolow.com/ContactMe"
] |
http://www.ssokolow.com/ContactMe
|
b70471b30ed693024129232b607386dcc2056eed
|
4d05be863b63a56a90b4c46b15069827b33ecaae
|
/Algorithms/leetcode/088_merge_sorted_array.py
|
cdc7c4756f42f6563a0e1d9faa78195016a55fbc
|
[] |
no_license
|
leeo1116/PyCharm
|
e532fa9754056019508cc454214ee1a8ad9b26a9
|
b6942c05c27556e5fe47879e8b823845c84c5430
|
refs/heads/master
| 2022-11-06T00:43:14.882453
| 2017-07-13T04:50:00
| 2017-07-13T04:50:00
| 36,851,636
| 0
| 1
| null | 2022-10-20T10:44:39
| 2015-06-04T06:09:09
|
Python
|
UTF-8
|
Python
| false
| false
| 775
|
py
|
__author__ = 'Liang Li'
class Solution:
# @param {integer[]} nums1
# @param {integer} m
# @param {integer[]} nums2
# @param {integer} n
# @return {void} Do not return anything, modify nums1 in-place instead.
def merge(self, nums1, m, nums2, n):
i = m-1
j = n-1
k = m+n-1
while i >= 0 and j >= 0:
if nums1[i] > nums2[j]:
nums1[k] = nums1[i]
i -= 1
k -= 1
else:
nums1[k] = nums2[j]
j -= 1
k -= 1
while j >= 0:
nums1[k] = nums2[j]
j -= 1
k -= 1
s = Solution()
nums1 = [2, 5, 8, 12, 0, 0, 0, 0]
nums2 = [1, 3, 4, 10]
s.merge(nums1, 4, nums2, 4)
print(nums1)
|
[
"leeo1116@gmail.com"
] |
leeo1116@gmail.com
|
24516fbfe12de0980aa098f7de5d754300afa400
|
5fb8b98ed56253954cce5b82727f48a90ea89807
|
/standalone-modules/OneHotEncoder/run.py
|
1fe2ecd48f77ed379678ff1c818f1b80b73a5570
|
[] |
no_license
|
gitter-badger/datacanvas-modules
|
2d96c922dcc866b3326c667b8467891484b9df2b
|
a4c50defe84166b282961a0f1d5601b27dea5434
|
refs/heads/master
| 2021-05-02T07:04:30.121612
| 2018-02-09T06:43:14
| 2018-02-09T06:43:14
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 378
|
py
|
import pandas as pd
import category_encoders as ce
def main(params, inputs, outputs):
columns_param = params.columns
data = inputs.data
data_new = outputs.data_new
data_0 = pd.read_pickle(data)
encoder = ce.OneHotEncoder(cols=[col for col in columns_param.split(",")])
data_1 = encoder.fit_transform(data_0)
data_1.to_pickle(data_new)
|
[
"yunky.dong@gmail.com"
] |
yunky.dong@gmail.com
|
2b40da75789f55119d250b7354c69c87b1f8de71
|
b23d294fdffabe72c336644f119860f5ce704eef
|
/python_1000phone/预科/day1-turtle/01-第一个python代码.py
|
bc791ddb4d9c798be10d6dc7d7d522d3d4d2a228
|
[] |
no_license
|
ikaros274556330/my_code
|
65232758fd20820e9f4fa8cb5a6c91a1969862a2
|
92db21c4abcbd88b7bd77e78d9f660b4534b5071
|
refs/heads/master
| 2020-11-26T09:43:58.200990
| 2019-12-23T02:08:39
| 2019-12-23T02:08:39
| 229,032,315
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 42
|
py
|
# 打印hello world!
print('hello world!')
|
[
"274556330@qq.com"
] |
274556330@qq.com
|
100bf1b290d658349122b8ccac599bd77cba14c7
|
2f6511ae1ebe993754a5ec056186259cae213658
|
/selenium/main/login/main.py
|
88e4ba2f30fa47b36df77da91f33696754092ab2
|
[] |
no_license
|
arvigupt/connect
|
508f16b2fcec03b1ecad90697d0f02730fd83af4
|
7dca766c8aae9bdb2f97366ed101dcc0f25a39c2
|
refs/heads/main
| 2023-03-28T04:10:34.990081
| 2021-03-31T07:15:46
| 2021-03-31T07:15:46
| 350,322,413
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 5,875
|
py
|
from core.common import commoncomponent
from core.common.repository import data_platform
from core.common.repository import dp_applicant_login_info
from core.common.models.UserCredential import UserCredential
import uvicorn
import datetime
import json
import os
from typing import Optional
from fastapi import FastAPI, Header
from selenium.common.exceptions import TimeoutException
from selenium.webdriver.common.by import By
from selenium import webdriver
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.chrome.options import Options
from selenium.webdriver import Firefox, DesiredCapabilities
from selenium.webdriver.firefox.options import Options as FirefoxOptions
login_status = 'login_status'
app = FastAPI()
grid_url = "http://localhost:4444/wd/hub"
def default(o):
if isinstance(o, (datetime.date, datetime.datetime)):
return o.isoformat()
def convert_response_into_json(result):
return json.dumps(result, indent = 2, default = default)
def check_authorization_value(authorization):
if authorization != "success":
raise "invalid authorization"
@app.get("/platforms")
def get_platforms(authorization: Optional[str] = Header(None)):
check_authorization_value(authorization)
result = data_platform.fetch_data_platforms()
return convert_response_into_json(result)
@app.get("/platforms/{platform_name}")
def get_platform_by_name(platform_name, authorization: Optional[str] = Header(None)):
check_authorization_value(authorization)
result = data_platform.fetch_data_platform_by_name(platform_name)
return convert_response_into_json(result)
@app.post("/platforms")
def get_platforms(credential: UserCredential, authorization: Optional[str] = Header(None)):
check_authorization_value(authorization)
driver = get_firefox_driver()
result = start_login(driver, credential.tenant_id, credential.data_platform_id, credential.applicant_id, credential.username,
credential.password, credential.otp, credential.relogin)
return convert_response_into_json(result)
def get_firefox_driver():
print("executing get_firefox_driver")
options = webdriver.FirefoxOptions()
# options.add_argument( "no-sandbox" )
# options.add_argument( "--disable-gpu" )
options.add_argument( "-private" )
# options.add_argument("--disable-infobars")
# options.add_argument( "--disable-dev-shm-usage" )
# options.add_argument("start-maximized");
# options.add_argument("ignore-certificate-errors");
# options.add_argument("disable-popup-blocking");
# options.add_argument("disable-extensions");
# options.add_argument("disable-notifications");
driver = webdriver.Remote(command_executor=grid_url, desired_capabilities=DesiredCapabilities.FIREFOX, options=options )
return driver
def get_chrome_driver():
print("executing get_chrome_driver")
options = webdriver.ChromeOptions()
options.add_argument( "no-sandbox" )
options.add_argument( "--disable-gpu" )
options.add_argument( "--incognito" )
options.add_argument("--disable-infobars")
# options.add_argument( "--window-size=800,600" )
options.add_argument( "--disable-dev-shm-usage" )
options.add_argument("start-maximized");
options.add_argument("ignore-certificate-errors");
options.add_argument("disable-popup-blocking");
options.add_argument("disable-extensions");
options.add_argument("disable-notifications");
# options.add_experimental_option("prefs", {"profile.allow_all_cookies": True});
options.add_experimental_option("prefs", {"profile.enable-cookies": True});
# options.add_argument("enable-cookies");
# options.add_experimental_option("prefs", {"profile.default_content_settings.cookies": 2});
driver = webdriver.Remote(command_executor=grid_url, desired_capabilities=DesiredCapabilities.CHROME, options=options )
return driver
def start_login(driver, tenant_id, data_platform_id, applicant_id, username, password, otp, relogin):
applicant_login_info = dp_applicant_login_info.fetch_dp_applicant_login_info(tenant_id, data_platform_id, applicant_id)
applicant_username = username
applicant_pwd = password
applicant_otp = otp
if applicant_login_info == None:
dp_applicant_login_info.insert_dp_applicant_login_info(tenant_id, data_platform_id, applicant_id)
dp_applicant_login_info.update_login_status(tenant_id, data_platform_id, applicant_id, '', "none")
applicant_login_info = dp_applicant_login_info.fetch_dp_applicant_login_info(tenant_id, data_platform_id, applicant_id)
elif applicant_login_info[login_status] == "in-progress" :
applicant_username = applicant_login_info['username']
applicant_pwd = applicant_login_info['pwd']
elif applicant_login_info[login_status] == "completed":
if relogin == True:
dp_applicant_login_info.delete_dp_applicant_login_info(tenant_id, data_platform_id, applicant_id)
dp_applicant_login_info.insert_dp_applicant_login_info(tenant_id, data_platform_id, applicant_id)
dp_applicant_login_info.update_login_status(tenant_id, data_platform_id, applicant_id, '', "none")
applicant_login_info = dp_applicant_login_info.fetch_dp_applicant_login_info(tenant_id, data_platform_id,
applicant_id)
else:
print("Operation completed successfully.")
exit(1)
commoncomponent.login_to_application(driver, tenant_id, data_platform_id, applicant_id, applicant_username, applicant_pwd,
applicant_otp, applicant_login_info)
if __name__ == '__main__':
uvicorn.run(app, host="0.0.0.0", port=8000)
|
[
"arvind@Arvinds-MacBook-Pro.local"
] |
arvind@Arvinds-MacBook-Pro.local
|
0bb311e3db55cf3f8c9eb6227d012f33756a20b9
|
f19ba7a733013aa9897f2ce9a1d25268bc26ba97
|
/Robot_e grafi_solutions/es_grafo_orientato_bassignana.py
|
d0c5579552d8043dc0598b934644a40fb6aef82a
|
[] |
no_license
|
My-Students/Python-robotics
|
d876cb5a01aca583dbe48c4885e892a86e31e6ed
|
7b70f5dd1a62ef6f1ad305a3bd096b6eb1f87bd5
|
refs/heads/master
| 2021-02-13T14:32:37.153082
| 2020-03-30T15:31:45
| 2020-03-30T15:31:45
| 244,704,397
| 0
| 1
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 2,193
|
py
|
import network as nx
import matplotlib.pyplot as plt
def main():
num_nodi = int(input("Inserire il numero di nodi"))
dict = creaDictDaNumNodi(num_nodi)
stampaDict(dict)
stampaMatrice(creaMatriceDaDict(dict, num_nodi), num_nodi)
disegnaGrafo(dict)
def creaMatriceDaNumNodi(num_nodi):
matrix = []
for i in range(1, num_nodi + 1):
e = [int(i) for i in input(f"Inserire le num_nodiicinanze del nodo {i} (usare la '.' come separatore): ").split('.')]
colonna = [0 for dim in range(0, num_nodi)]
for j in e:
if j != i:
colonna[j - 1] = 1
matrix.append(colonna)
return matrix
def creaDictDaNumNodi(num_nodi):
dict = {}
for r in range(0, num_nodi):
chianum_nodi = r + 1
occ = [int(i) for i in
input(f"Inserire le num_nodiicinanze del nodo {chianum_nodi} (usare la '.' come separatore): ").split('.')]
dict[chianum_nodi] = occ
return dict
def creaDictDaMatrice(grafo):
dict = {}
for r in range(0, len(grafo)):
num_nodi = r + 1
occ = []
for c in range(0, len(grafo)):
if grafo[r][c] == 1:
occ.append(c + 1)
dict[num_nodi] = occ
return dict
def creaMatriceDaDict(dict, num_nodi):
matrix = []
for key, num_nodi in dict.items():
colonna = [0 for dim in range(0, num_nodi)]
for link in num_nodi:
colonna[link - 1] = 1
matrix.append(colonna)
return matrix
def stampaDict(dict):
print("\n{")
for key, num_nodi in dict.items():
print(f"\t{key}: {num_nodi},")
print("}")
def disegnaGrafo(dict):
G = nx.Graph()
for key, num_nodi in dict.items():
G.add_node(key)
for i in num_nodi:
G.add_edge(int(key), int(i))
print(f"\n{nx.info(G)}")
nx.draw(G)
plt.show()
def stampaMatrice(matrix, num_nodi):
for r in range(0, num_nodi):
print(" ")
for c in range(0, num_nodi):
print(matrix[r][c], end=' ')
if __name__ == '__main__':
main()
|
[
"noreply@github.com"
] |
My-Students.noreply@github.com
|
392ab4ad3209f4bb37e55cbada92691ad36b9f56
|
294f1ae78a6b0edf6857e3316ace22a7503c40fd
|
/app/crud/__init__.py
|
a0da9acee5fb1366c9bec782d7c2ad813a8473db
|
[
"Apache-2.0"
] |
permissive
|
vampire725/EchoProxy
|
ecc535f7f9ea61fec208e858ecf28ac58c266d47
|
0273f47397b76fa0292db267d99eeb9dccc4e869
|
refs/heads/master
| 2022-04-06T05:54:14.824916
| 2020-03-08T13:41:09
| 2020-03-08T13:41:09
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 128
|
py
|
# !/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2020/3/4 0004 21:25
# @Author : Gpp
# @File : __init__.py.py
|
[
"gpp0725@outlook.com"
] |
gpp0725@outlook.com
|
9910357a988f65c5b80d36a05e1c1bf939455313
|
9d2bb59a7c7528750be78fc9b44081648f63364a
|
/pytils/date.py
|
1f50531f420b2ddb4003fa15057640fbcd114227
|
[] |
no_license
|
johanlundahl/pytils
|
368ece19cf666a319811e269d3798dc1b681b10f
|
bae78dbe2e5025254b01d901134667ee7ac37454
|
refs/heads/master
| 2023-01-03T17:57:54.317305
| 2022-12-28T20:10:20
| 2022-12-28T20:10:20
| 143,716,827
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 3,591
|
py
|
from abc import ABC, abstractmethod, abstractproperty
from datetime import datetime, timedelta
import calendar
class Period(ABC):
def __init__(self, dt=datetime.now()):
self._datetime = datetime(dt.year, dt.month, dt.day)
@classmethod
def now(cls):
return cls(datetime.now())
@classmethod
def parse(cls, date_str):
date = datetime.strptime(date_str, cls().date_pattern)
return cls(date)
@classmethod
def of(cls, period):
return cls(period._datetime)
@staticmethod
@abstractproperty
def date_pattern(cls):
pass
@property
def name(self):
return str(self)
@property
@abstractmethod
def number(self):
pass
@abstractmethod
def prev(self):
pass
@abstractmethod
def next(self):
pass
@abstractmethod
def range(self):
pass
def __str__(self):
return self._datetime.strftime(self.date_pattern)
class Date(Period):
@property
def date_pattern(cls):
return '%Y-%m-%d'
@property
def number(self):
return self._datetime.day
def prev(self):
yesterday = self._datetime - timedelta(days=1)
return Date(yesterday)
def next(self):
tomorrow = self._datetime + timedelta(days=1)
return Date(tomorrow)
def range(self):
end = self._datetime.replace(hour=23,
minute=59,
second=59,
microsecond=999)
return (self._datetime, end)
def __sub__(self, obj):
if isinstance(obj, int):
new_date = self._datetime - timedelta(days=obj)
return Date(new_date)
class Week(Period):
@property
def date_pattern(self):
return '%V %Y'
@property
def number(self):
return self._datetime.isocalendar()[1]
def range(self):
weekday = self._datetime.weekday()
monday = self._datetime - timedelta(days=weekday)
sunday = self._datetime + timedelta(days=(7-weekday))
return (Date(monday), Date(sunday))
def prev(self):
monday, sunday = self.range()
return Week.of(monday.prev())
def next(self):
monday, sunday = self.range()
return Week.of(sunday.next())
class Month(Period):
@property
def date_pattern(self):
return '%B %Y'
@property
def number(self):
return self._datetime.month
@property
def days(self):
year = self._datetime.year
month = self._datetime.month
return calendar.monthrange(year, month)[1]
def prev(self):
first, last = self.range()
return Month.of(first.prev())
def next(self):
first, last = self.range()
return Month.of(last.next())
def range(self):
year = self._datetime.year
month = self._datetime.month
last_day = calendar.monthrange(year, month)[1]
return (Date(datetime(year, month, 1)),
Date(datetime(year, month, last_day)))
class Year(Period):
@property
def date_pattern(self):
return '%Y'
@property
def number(self):
return self._datetime.year
def prev(self):
first, last = self.range()
return Year.of(first.prev())
def next(self):
first, last = self.range()
return Year.of(last.next())
def range(self):
year = self._datetime.year
return (Date(datetime(year, 1, 1)), Date(datetime(year, 12, 31)))
|
[
"johan.t.lundahl@gmail.com"
] |
johan.t.lundahl@gmail.com
|
d71d23c9bd97b0cc2e634b08122a66d17abfe9d2
|
08ab8bfeee89992e294a9ae34310ff349a104885
|
/pyserini/dsearch/_dsearcher.py
|
d1dffbeb57114bb7c527305cc12774db1818bfeb
|
[
"Apache-2.0"
] |
permissive
|
RootofalleviI/pyserini
|
9b80cf3743dfb9331b7c37e239717870f6e01169
|
5023349d2ad1e8900d866b3e37b6afb3b74ef970
|
refs/heads/master
| 2023-08-08T08:24:58.811915
| 2021-09-15T22:18:52
| 2021-09-15T22:18:52
| 367,443,046
| 0
| 0
|
Apache-2.0
| 2021-09-15T21:59:43
| 2021-05-14T18:03:08
|
Python
|
UTF-8
|
Python
| false
| false
| 22,902
|
py
|
#
# Pyserini: Reproducible IR research with sparse and dense representations
#
# 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.
#
"""
This module provides Pyserini's dense search interface to FAISS index.
The main entry point is the ``SimpleDenseSearcher`` class.
"""
import os
from dataclasses import dataclass
from typing import Dict, List, Union, Optional
import numpy as np
import pandas as pd
from transformers import (AutoModel, AutoTokenizer, BertModel, BertTokenizer, BertTokenizerFast,
DPRQuestionEncoder, DPRQuestionEncoderTokenizer, RobertaTokenizer)
from transformers.file_utils import is_faiss_available, requires_backends
from pyserini.util import (download_encoded_queries, download_prebuilt_index,
get_dense_indexes_info, get_sparse_index)
from pyserini.search import SimpleSearcher, Document
from ._model import AnceEncoder
import torch
if is_faiss_available():
import faiss
class QueryEncoder:
def __init__(self, encoded_query_dir: str = None):
self.has_model = False
self.has_encoded_query = False
if encoded_query_dir:
self.embedding = self._load_embeddings(encoded_query_dir)
self.has_encoded_query = True
def encode(self, query: str):
return self.embedding[query]
@classmethod
def load_encoded_queries(cls, encoded_query_name: str):
"""Build a query encoder from a pre-encoded query; download the encoded queries if necessary.
Parameters
----------
encoded_query_name : str
pre encoded query name.
Returns
-------
QueryEncoder
Encoder built from the pre encoded queries.
"""
print(f'Attempting to initialize pre-encoded queries {encoded_query_name}.')
try:
query_dir = download_encoded_queries(encoded_query_name)
except ValueError as e:
print(str(e))
return None
print(f'Initializing {encoded_query_name}...')
return cls(encoded_query_dir=query_dir)
@staticmethod
def _load_embeddings(encoded_query_dir):
df = pd.read_pickle(os.path.join(encoded_query_dir, 'embedding.pkl'))
return dict(zip(df['text'].tolist(), df['embedding'].tolist()))
class TctColBertQueryEncoder(QueryEncoder):
def __init__(self, encoder_dir: str = None, tokenizer_name: str = None,
encoded_query_dir: str = None, device: str = 'cpu'):
super().__init__(encoded_query_dir)
if encoder_dir:
self.device = device
self.model = BertModel.from_pretrained(encoder_dir)
self.model.to(self.device)
self.tokenizer = BertTokenizer.from_pretrained(tokenizer_name or encoder_dir)
self.has_model = True
if (not self.has_model) and (not self.has_encoded_query):
raise Exception('Neither query encoder model nor encoded queries provided. Please provide at least one')
def encode(self, query: str):
if self.has_model:
max_length = 36 # hardcode for now
inputs = self.tokenizer(
'[CLS] [Q] ' + query + '[MASK]' * max_length,
max_length=max_length,
truncation=True,
add_special_tokens=False,
return_tensors='pt'
)
inputs.to(self.device)
outputs = self.model(**inputs)
embeddings = outputs.last_hidden_state.detach().cpu().numpy()
return np.average(embeddings[:, 4:, :], axis=-2).flatten()
else:
return super().encode(query)
class DprQueryEncoder(QueryEncoder):
def __init__(self, encoder_dir: str = None, tokenizer_name: str = None,
encoded_query_dir: str = None, device: str = 'cpu'):
super().__init__(encoded_query_dir)
if encoder_dir:
self.device = device
self.model = DPRQuestionEncoder.from_pretrained(encoder_dir)
self.model.to(self.device)
self.tokenizer = DPRQuestionEncoderTokenizer.from_pretrained(tokenizer_name or encoder_dir)
self.has_model = True
if (not self.has_model) and (not self.has_encoded_query):
raise Exception('Neither query encoder model nor encoded queries provided. Please provide at least one')
def encode(self, query: str):
if self.has_model:
input_ids = self.tokenizer(query, return_tensors='pt')
input_ids.to(self.device)
embeddings = self.model(input_ids["input_ids"]).pooler_output.detach().cpu().numpy()
return embeddings.flatten()
else:
return super().encode(query)
class BprQueryEncoder(QueryEncoder):
def __init__(self, encoder_dir: str = None, tokenizer_name: str = None,
encoded_query_dir: str = None, device: str = 'cpu'):
self.has_model = False
self.has_encoded_query = False
if encoded_query_dir:
self.embedding = self._load_embeddings(encoded_query_dir)
self.has_encoded_query = True
if encoder_dir:
self.device = device
self.model = DPRQuestionEncoder.from_pretrained(encoder_dir)
self.model.to(self.device)
self.tokenizer = DPRQuestionEncoderTokenizer.from_pretrained(tokenizer_name or encoder_dir)
self.has_model = True
if (not self.has_model) and (not self.has_encoded_query):
raise Exception('Neither query encoder model nor encoded queries provided. Please provide at least one')
def encode(self, query: str):
if self.has_model:
input_ids = self.tokenizer(query, return_tensors='pt')
input_ids.to(self.device)
embeddings = self.model(input_ids["input_ids"]).pooler_output.detach().cpu()
dense_embeddings = embeddings.numpy()
sparse_embeddings = self.convert_to_binary_code(embeddings).numpy()
return {'dense':dense_embeddings.flatten(), 'sparse':sparse_embeddings.flatten()}
else:
return super().encode(query)
def convert_to_binary_code(self, input_repr: torch.Tensor):
return input_repr.new_ones(input_repr.size()).masked_fill_(input_repr < 0, -1.0)
@staticmethod
def _load_embeddings(encoded_query_dir):
df = pd.read_pickle(os.path.join(encoded_query_dir, 'embedding.pkl'))
ret = {}
for text, dense, sparse in zip(df['text'].tolist(), df['dense_embedding'].tolist(), df['sparse_embedding'].tolist()):
ret[text] = {'dense': dense, 'sparse': sparse}
return ret
class DkrrDprQueryEncoder(QueryEncoder):
def __init__(self, encoder_dir: str = None, encoded_query_dir: str = None, device: str = 'cpu', prefix: str = "question:"):
super().__init__(encoded_query_dir)
self.device = device
self.model = BertModel.from_pretrained(encoder_dir)
self.model.to(self.device)
self.tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
self.has_model = True
self.prefix = prefix
@staticmethod
def _mean_pooling(model_output, attention_mask):
model_output = model_output[0].masked_fill(1 - attention_mask[:, :, None], 0.)
model_output = torch.sum(model_output, dim=1) / torch.clamp(torch.sum(attention_mask, dim=1), min=1e-9)[:, None]
return model_output.flatten()
def encode(self, query: str):
if self.has_model:
if self.prefix:
query = f'{self.prefix} {query}'
inputs = self.tokenizer(query, return_tensors='pt', max_length=40, padding="max_length")
inputs.to(self.device)
outputs = self.model(input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"])
embeddings = self._mean_pooling(outputs, inputs['attention_mask']).detach().cpu().numpy()
return embeddings.flatten()
else:
return super().encode(query)
class AnceQueryEncoder(QueryEncoder):
def __init__(self, encoder_dir: str = None, tokenizer_name: str = None,
encoded_query_dir: str = None, device: str = 'cpu'):
super().__init__(encoded_query_dir)
if encoder_dir:
self.device = device
self.model = AnceEncoder.from_pretrained(encoder_dir)
self.model.to(self.device)
self.tokenizer = RobertaTokenizer.from_pretrained(tokenizer_name or encoder_dir)
self.has_model = True
if (not self.has_model) and (not self.has_encoded_query):
raise Exception('Neither query encoder model nor encoded queries provided. Please provide at least one')
def encode(self, query: str):
if self.has_model:
inputs = self.tokenizer(
[query],
max_length=64,
padding='longest',
truncation=True,
add_special_tokens=True,
return_tensors='pt'
)
inputs.to(self.device)
embeddings = self.model(inputs["input_ids"]).detach().cpu().numpy()
return embeddings.flatten()
else:
return super().encode(query)
class AutoQueryEncoder(QueryEncoder):
def __init__(self, encoder_dir: str = None, tokenizer_name: str = None,
encoded_query_dir: str = None, device: str = 'cpu',
pooling: str = 'cls', l2_norm: bool = False):
super().__init__(encoded_query_dir)
if encoder_dir:
self.device = device
self.model = AutoModel.from_pretrained(encoder_dir)
self.model.to(self.device)
self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name or encoder_dir)
self.has_model = True
self.pooling = pooling
self.l2_norm = l2_norm
if (not self.has_model) and (not self.has_encoded_query):
raise Exception('Neither query encoder model nor encoded queries provided. Please provide at least one')
@staticmethod
def _mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] # First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1)
sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9)
return sum_embeddings / sum_mask
def encode(self, query: str):
if self.has_model:
inputs = self.tokenizer(
query,
padding='longest',
truncation=True,
add_special_tokens=True,
return_tensors='pt'
)
inputs.to(self.device)
outputs = self.model(**inputs)
if self.pooling == "mean":
embeddings = self._mean_pooling(outputs, inputs['attention_mask']).detach().cpu().numpy()
else:
embeddings = outputs[0][:, 0, :].detach().cpu().numpy()
if self.l2_norm:
faiss.normalize_L2(embeddings)
return embeddings.flatten()
else:
return super().encode(query)
@dataclass
class DenseSearchResult:
docid: str
score: float
class SimpleDenseSearcher:
"""Simple Searcher for dense representation
Parameters
----------
index_dir : str
Path to faiss index directory.
"""
def __init__(self, index_dir: str, query_encoder: Union[QueryEncoder, str], prebuilt_index_name: Optional[str] = None):
requires_backends(self, "faiss")
if isinstance(query_encoder, QueryEncoder):
self.query_encoder = query_encoder
else:
self.query_encoder = self._init_encoder_from_str(query_encoder)
self.index, self.docids = self.load_index(index_dir)
self.dimension = self.index.d
self.num_docs = self.index.ntotal
assert self.docids is None or self.num_docs == len(self.docids)
if prebuilt_index_name:
sparse_index = get_sparse_index(prebuilt_index_name)
self.ssearcher = SimpleSearcher.from_prebuilt_index(sparse_index)
@classmethod
def from_prebuilt_index(cls, prebuilt_index_name: str, query_encoder: QueryEncoder):
"""Build a searcher from a pre-built index; download the index if necessary.
Parameters
----------
query_encoder: QueryEncoder
the query encoder, which has `encode` method that convert query text to embedding
prebuilt_index_name : str
Prebuilt index name.
Returns
-------
SimpleDenseSearcher
Searcher built from the prebuilt faiss index.
"""
print(f'Attempting to initialize pre-built index {prebuilt_index_name}.')
try:
index_dir = download_prebuilt_index(prebuilt_index_name)
except ValueError as e:
print(str(e))
return None
print(f'Initializing {prebuilt_index_name}...')
return cls(index_dir, query_encoder, prebuilt_index_name)
@staticmethod
def list_prebuilt_indexes():
"""Display information about available prebuilt indexes."""
get_dense_indexes_info()
def search(self, query: str, k: int = 10, threads: int = 1) -> List[DenseSearchResult]:
"""Search the collection.
Parameters
----------
query : str
query text
k : int
Number of hits to return.
threads : int
Maximum number of threads to use for intra-query search.
Returns
-------
List[DenseSearchResult]
List of search results.
"""
emb_q = self.query_encoder.encode(query)
assert len(emb_q) == self.dimension
emb_q = emb_q.reshape((1, len(emb_q)))
faiss.omp_set_num_threads(threads)
distances, indexes = self.index.search(emb_q, k)
distances = distances.flat
indexes = indexes.flat
return [DenseSearchResult(self.docids[idx], score)
for score, idx in zip(distances, indexes) if idx != -1]
def batch_search(self, queries: List[str], q_ids: List[str], k: int = 10, threads: int = 1) \
-> Dict[str, List[DenseSearchResult]]:
"""
Parameters
----------
queries : List[str]
List of query texts
q_ids : List[str]
List of corresponding query ids.
k : int
Number of hits to return.
threads : int
Maximum number of threads to use.
Returns
-------
Dict[str, List[DenseSearchResult]]
Dictionary holding the search results, with the query ids as keys and the corresponding lists of search
results as the values.
"""
q_embs = np.array([self.query_encoder.encode(q) for q in queries])
n, m = q_embs.shape
assert m == self.dimension
faiss.omp_set_num_threads(threads)
D, I = self.index.search(q_embs, k)
return {key: [DenseSearchResult(self.docids[idx], score)
for score, idx in zip(distances, indexes) if idx != -1]
for key, distances, indexes in zip(q_ids, D, I)}
def load_index(self, index_dir: str):
index_path = os.path.join(index_dir, 'index')
docid_path = os.path.join(index_dir, 'docid')
index = faiss.read_index(index_path)
docids = self.load_docids(docid_path)
return index, docids
def doc(self, docid: Union[str, int]) -> Optional[Document]:
"""Return the :class:`Document` corresponding to ``docid``. Since dense indexes don't store documents
but sparse indexes do, route over to corresponding sparse index (according to prebuilt_index_info.py)
and use its doc API
Parameters
----------
docid : Union[str, int]
Overloaded ``docid``: either an external collection ``docid`` (``str``) or an internal Lucene ``docid``
(``int``).
Returns
-------
Document
:class:`Document` corresponding to the ``docid``.
"""
return self.ssearcher.doc(docid) if self.ssearcher else None
@staticmethod
def _init_encoder_from_str(encoder):
encoder = encoder.lower()
if 'dpr' in encoder:
return DprQueryEncoder(encoder_dir=encoder)
elif 'tct_colbert' in encoder:
return TctColBertQueryEncoder(encoder_dir=encoder)
elif 'ance' in encoder:
return AnceQueryEncoder(encoder_dir=encoder)
elif 'sentence' in encoder:
return AutoQueryEncoder(encoder_dir=encoder, pooling='mean', l2_norm=True)
else:
return AutoQueryEncoder(encoder_dir=encoder)
@staticmethod
def load_docids(docid_path: str) -> List[str]:
id_f = open(docid_path, 'r')
docids = [line.rstrip() for line in id_f.readlines()]
id_f.close()
return docids
class BinaryDenseSearcher(SimpleDenseSearcher):
"""Simple Searcher for binary-dense representation
Parameters
----------
index_dir : str
Path to faiss index directory.
"""
def __init__(self, index_dir: str, query_encoder: Union[QueryEncoder, str], prebuilt_index_name: Optional[str] = None):
super().__init__(index_dir, query_encoder, prebuilt_index_name)
def search(self, query: str, k: int = 10, binary_k: int = 100, rerank: bool = True, threads: int = 1) -> List[DenseSearchResult]:
"""Search the collection.
Parameters
----------
query : str
query text
k : int
Number of hits to return at second stage.
binary_k : int
Number of hits to return at first stage.
rerank: bool
Whether to use dense repr to rerank the binary ranking results.
threads : int
Maximum number of threads to use for intra-query search.
Returns
-------
List[DenseSearchResult]
List of search results.
"""
ret = self.query_encoder.encode(query)
dense_emb_q = ret['dense']
sparse_emb_q = ret['sparse']
assert len(dense_emb_q) == self.dimension
assert len(sparse_emb_q) == self.dimension
dense_emb_q = dense_emb_q.reshape((1, len(dense_emb_q)))
sparse_emb_q = sparse_emb_q.reshape((1, len(sparse_emb_q)))
faiss.omp_set_num_threads(threads)
distances, indexes = self.binary_dense_search(k, binary_k, rerank, dense_emb_q, sparse_emb_q)
distances = distances.flat
indexes = indexes.flat
return [DenseSearchResult(str(idx), score)
for score, idx in zip(distances, indexes) if idx != -1]
def batch_search(self, queries: List[str], q_ids: List[str], k: int = 10, binary_k: int = 100, \
rerank: bool = True, threads: int = 1) -> Dict[str, List[DenseSearchResult]]:
"""
Parameters
----------
queries : List[str]
List of query texts
q_ids : List[str]
List of corresponding query ids.
k : int
Number of hits to return.
binary_k : int
Number of hits to return at first stage.
rerank: bool
Whether to use dense repr to rerank the binary ranking results.
threads : int
Maximum number of threads to use.
Returns
-------
Dict[str, List[DenseSearchResult]]
Dictionary holding the search results, with the query ids as keys and the corresponding lists of search
results as the values.
"""
dense_q_embs = []
sparse_q_embs = []
for q in queries:
ret = self.query_encoder.encode(q)
dense_q_embs.append(ret['dense'])
sparse_q_embs.append(ret['sparse'])
dense_q_embs = np.array(dense_q_embs)
sparse_q_embs = np.array(sparse_q_embs)
n, m = dense_q_embs.shape
assert m == self.dimension
faiss.omp_set_num_threads(threads)
D, I = self.binary_dense_search(k, binary_k, rerank, dense_q_embs, sparse_q_embs)
return {key: [DenseSearchResult(str(idx), score)
for score, idx in zip(distances, indexes) if idx != -1]
for key, distances, indexes in zip(q_ids, D, I)}
def binary_dense_search(self, k, binary_k, rerank, dense_emb_q, sparse_emb_q):
num_queries = dense_emb_q.shape[0]
sparse_emb_q = np.packbits(np.where(sparse_emb_q > 0, 1, 0)).reshape(num_queries, -1)
if not rerank:
distances, indexes = self.index.search(sparse_emb_q, k)
else:
raw_index = self.index.index
_, indexes = raw_index.search(sparse_emb_q, binary_k)
sparse_emb_p = np.vstack(
[np.unpackbits(raw_index.reconstruct(int(id_))) for id_ in indexes.reshape(-1)]
)
sparse_emb_p = sparse_emb_p.reshape(
dense_emb_q.shape[0], binary_k, dense_emb_q.shape[1]
)
sparse_emb_p = sparse_emb_p.astype(np.float32)
sparse_emb_p = sparse_emb_p * 2 - 1
distances = np.einsum("ijk,ik->ij", sparse_emb_p, dense_emb_q)
sorted_indices = np.argsort(-distances, axis=1)
indexes = indexes[np.arange(num_queries)[:, None], sorted_indices]
indexes = np.array([self.index.id_map.at(int(id_)) for id_ in indexes.reshape(-1)], dtype=np.int)
indexes = indexes.reshape(num_queries, -1)[:, :k]
distances = distances[np.arange(num_queries)[:, None], sorted_indices][:, :k]
return distances, indexes
def load_index(self, index_dir: str):
index_path = os.path.join(index_dir, 'index')
index = faiss.read_index_binary(index_path)
return index, None
@staticmethod
def _init_encoder_from_str(encoder):
encoder = encoder.lower()
if 'bpr' in encoder:
return BprQueryEncoder(encoder_dir=encoder)
else:
raise NotImplementedError
|
[
"noreply@github.com"
] |
RootofalleviI.noreply@github.com
|
7b8c22e8a79c520dc513ac590e68f17e2239679c
|
e2be1907175772072e41781373afc7f82974e0f1
|
/src/factories/trainer_factory/GAN.py
|
ba89719767893b17426c78a6a58753948c3f3e2c
|
[] |
no_license
|
MiuLab/TaylorGAN
|
6cdd40d0f035832e68cf22ee8bf5ce6bffdb2bcc
|
2f4b62aaa50e6d0b485bf3d33e1a19892d50527e
|
refs/heads/main
| 2023-04-08T10:22:53.161981
| 2021-01-26T02:54:57
| 2021-01-26T02:54:57
| 306,371,483
| 40
| 3
| null | 2021-01-26T03:30:18
| 2020-10-22T14:55:35
|
Python
|
UTF-8
|
Python
| false
| false
| 3,218
|
py
|
import tensorflow as tf
from core.objectives.GAN import (
BCE,
GANObjective,
GANLossTuple,
ReinforceEstimator,
StraightThroughEstimator,
TaylorEstimator,
GumbelSoftmaxEstimator,
)
from core.train import DiscriminatorUpdater, GANTrainer
from factories.modules import discriminator_factory
from flexparse import create_action, LookUp, IntRange
from library.utils import cached_property
from ..utils import create_factory_action
from .trainer_factory import TrainerCreator
from . import optimizers
class GANCreator(TrainerCreator):
def create_trainer(self, placeholder, generator_updater) -> GANTrainer:
loss_tuple, _, d_steps = self.args[GAN_ARGS]
return GANTrainer(
placeholder=placeholder,
generator_updater=generator_updater,
discriminator_updater=self.create_discriminator_updater(
self._discriminator,
discriminator_loss=loss_tuple.discriminator_loss,
),
d_steps=d_steps,
)
def create_discriminator_updater(self, discriminator, discriminator_loss):
return DiscriminatorUpdater(
discriminator,
optimizer=self.args[D_OPTIMIZER_ARG],
losses=[
discriminator_loss,
*self.args[discriminator_factory.REGULARIZER_ARG],
],
)
@cached_property
def objective(self):
loss_tuple, estimator = self.args[GAN_ARGS[:2]]
return GANObjective(
discriminator=self._discriminator,
generator_loss=loss_tuple.generator_loss,
estimator=estimator,
)
@cached_property
def _discriminator(self):
return discriminator_factory.create(self.args, self.meta_data)
@classmethod
def model_args(cls):
return discriminator_factory.MODEL_ARGS
@classmethod
def objective_args(cls):
return GAN_ARGS
@classmethod
def regularizer_args(cls):
return [discriminator_factory.REGULARIZER_ARG]
@classmethod
def optimizer_args(cls):
return [D_OPTIMIZER_ARG]
D_OPTIMIZER_ARG = optimizers.create_action_of('discriminator')
GAN_ARGS = [
create_action(
'--loss',
type=LookUp({
'alt': GANLossTuple(lambda fake_score: BCE(fake_score, labels=1.)), # RKL - 2JS
'JS': GANLossTuple(lambda fake_score: -BCE(fake_score, labels=0.)), # 2JS
'KL': GANLossTuple(lambda fake_score: -tf.exp(fake_score)), # -sig / (1 - sig)
'RKL': GANLossTuple(lambda fake_score: -fake_score), # log((1 - sig) / sig)
}),
default='RKL',
help='loss function pair of GAN.',
),
create_factory_action(
'--estimator',
registry={
'reinforce': ReinforceEstimator,
'st': StraightThroughEstimator,
'taylor': TaylorEstimator,
'gumbel': GumbelSoftmaxEstimator,
},
default='taylor',
help_prefix="gradient estimator for discrete sampling.\n",
),
create_action(
'--d-steps',
type=IntRange(minval=1),
default=1,
help='update generator every n discriminator steps.',
),
]
|
[
"jsaon92@gmail.com"
] |
jsaon92@gmail.com
|
436a25cdd1f502b59ba9088db92cf9788b40dfa7
|
0d041257a0633bb1654cad262462acb898616008
|
/Tech_Hub/urls.py
|
d5cb584db429be209a43c548bef12c60f9a0fa13
|
[] |
no_license
|
SibghatShaikh/Tech
|
dd725ae3f54e758bcbf33cc675b80187015a7c1e
|
4da96f6cb5019bb026e284c3aa6afaee417714ce
|
refs/heads/master
| 2023-04-27T17:29:40.895915
| 2021-05-25T07:02:37
| 2021-05-25T07:02:37
| 366,473,992
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 824
|
py
|
"""Tech_Hub URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/3.2/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: path('', views.home, name='home')
Class-based views
1. Add an import: from other_app.views import Home
2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')
Including another URLconf
1. Import the include() function: from django.urls import include, path
2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))
"""
from django.contrib import admin
from django.urls import path, include
urlpatterns = [
path('admin/', admin.site.urls),
path('', include('Tech.urls'))
]
|
[
"noreply@github.com"
] |
SibghatShaikh.noreply@github.com
|
8975a6bd1fdea6404bbd69a7286fd7c7f358b085
|
7b164b810f84988ef0bb2016caf07433f12d4e8a
|
/library_management_system/Lib MS/LIBRAY_MANAGEMENT/ret.py
|
74e0f1519acf9851ade81d392a63bb2e0fed5b20
|
[] |
no_license
|
iAnas19/Library-Management-System-project
|
b96b891a5cbf9d2a2ba2fa48d3fa48c5c0f326f0
|
52249b300020a33573fbf4c98fe639907fa7bf9e
|
refs/heads/main
| 2023-05-07T10:20:38.139817
| 2021-04-08T21:28:24
| 2021-04-08T21:28:24
| 356,052,279
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 2,647
|
py
|
from tkinter import *
from tkinter import messagebox
import os,sys
import mysql.connector
from mysql.connector import Error
from datetime import datetime,date
py = sys.executable
class ret(Tk):
def __init__(self):
super().__init__()
self.iconbitmap(r'libico.ico')
self.title("Return")
self.maxsize(420,280)
self.canvas = Canvas(width=500, height=417, bg='gray')
self.canvas.pack()
self.cal = 0
a = StringVar()
def qui():
if len(a.get()) == '0':
messagebox.showerror("Error","Please Enter The Book Id")
else:
try:
self.conn = mysql.connector.connect(host='localhost',
database='library',
user='root',
password='')
self.mycursor = self.conn.cursor()
self.mycursor.execute("Select book_id from issue_book where return_date = '' and book_id = %s",[a.get()])
temp = self.mycursor.fetchone()
if temp:
self.mycursor.execute("update book set availability ='YES' where book_id = %s", [a.get()])
self.conn.commit()
now = datetime.now()
idate = now.strftime('%Y-%m-%d %H:%M:%S')
self.mycursor.execute("update issue_book set return_date = %s where book_id = %s", [idate,a.get()])
self.conn.commit()
self.conn.close()
messagebox.showinfo('Info', 'Succesfully Returned')
d = messagebox.askyesno("Confirm", "Return more books?")
if d:
self.destroy()
os.system('%s %s' % (py, 'ret.py'))
else:
self.destroy()
else:
messagebox.showinfo("Oop's", "Book not yet issued")
except Error:
messagebox.showerror("Error","Something Goes Wrong")
Label(self, text='Return Book', fg='red',font=('arial', 35, 'bold')).pack()
Label(self, text='Enter Book ID', font=('Comic Scan Ms', 15, 'bold')).place(x=20, y=120)
Entry(self, textvariable=a, width=40).place(x=165, y=124)
Button(self, text="Return", width=25, command=qui).place(x=180, y=180)
ret().mainloop()
|
[
"noreply@github.com"
] |
iAnas19.noreply@github.com
|
48b118b90caf47d3f1600ff81ab6b6d79a875e12
|
891cc1e3e9e452e9bf104fb19aa47c96077efd7d
|
/finding_cipher_key.py
|
1220c645661476cd730b28906274905475570ba4
|
[] |
no_license
|
saemsheth/Co-relation-attack-on-A5-stream-cipher
|
4ef689e08eec35265758a69adabefbdba290d9fe
|
01aa095f978e47dbb65fd4545edaa10f60784aa0
|
refs/heads/master
| 2022-12-02T01:08:06.526733
| 2020-08-20T18:29:16
| 2020-08-20T18:29:16
| 289,075,676
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 5,971
|
py
|
# -*- coding: utf-8 -*-
"""Finding_cipher_key.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1a_ydQwmXrzCV8F4GVzFh1mLt-zHkw4ni
"""
ai=[]
bi=[]
ci=[]
m1=[]
m2=[]
m3=[]
a=[]
b=[]
c1=[]
def LFSR1(x):
for i in range(0,520):
x3=x&1
x = (x>>1)|(((x&1)^((x>>1)&1))<<6)
ai.append(x3)
def LFSR2(x):
for i in range(0,520):
x3=x&1
x = (x >> 1) | (((x & 1) ^ ((x >> 2) & 1) ^ ((x >> 3) &
1) ^ ((x >> 4) & 1)) << 7)
bi.append(x3)
def LFSR3(x):
for i in range(0,520):
x3=x&1
x = (x >> 1) | (((x & 1) ^ ((x >> 4) & 1)) << 8)
ci.append(x3)
def shift(seq, n):
return seq[n:]+seq[:n]
def com1():
c=0
zi = [0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0,
1,1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1,1,0,
1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 0, 0, 0, 1,
0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1,
1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1,
1,
1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0,
1, 0, 1, 1, 1, 0, 1, 0, 1,
0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0,
1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1,
1,
1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1,
1, 0, 0, 1, 0, 0, 1, 0, 1,
1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0,
1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1,
0,
0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0,
1, 1, 0, 0, 0, 1, 0, 1, 1,
0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1,
0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0,
1,
1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0,
0, 0, 0, 1, 1, 0, 1, 0, 1,
0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0,
1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0,
0,
1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0,
1, 0, 1, 1, 1, 0, 1, 0, 0,
0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,
1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1,
0,
1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0,
0, 1, 0, 1, 1, 1, 1, 0, 0,
0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1,
0,
1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0,
0, 0, 1, 1, 1, 1, 0, 1,0]
for i in range(0,520):
if(zi[i]==a[i]):
c=c+1
else:
c=c-1
return c
def com2():
c=0
zi = [0,0,1,1,1,1,0,1,0,0,0,0,1,1,0,1,0,1,1,1,0,1,1,0,1,0,1,1,1,1,0,1
,0,1,0,0,1,1,0,1,1,0,0,1,0,0,0,0,0,0,1,1,0,1,0,1,0,1,0,1,0,0,0,0
,1,
0,1,0,1,1,0,0,1,1,0,1,1,1,0,1,1,1,1,1,0,0,1,0,0,0,1,1,0,0,0,1,0,
0,0,0,0,1,1,1,1,0,0,1,1,1,1,0,0,0,0,0,1,0,1,0,0,1,0,1,1,1,0,1,0,
1,
0,0,1,1,1,0,0,0,0,1,1,1,1,1,1,0,1,0,1,0,1,0,0,1,1,1,1,0,1,0,0,0,
1,0,0,0,1,1,1,0,1,1,1,0,1,1,0,1,0,0,0,0,1,0,1,1,1,0,0,1,0,0,1,0,
1,
1,1,1,1,0,0,0,1,1,1,0,1,0,0,1,1,1,0,1,0,1,0,0,0,0,0,1,1,0,0,0,1,
0,0,1,1,1,0,0,1,0,0,1,0,1,0,1,1,1,1,0,1,0,0,0,0,1,1,0,0,0,1,0,1,
1,
0,0,1,1,0,0,1,1,1,1,0,1,0,1,1,0,1,1,0,0,0,1,1,1,1,1,0,1,1,0,0,0,
1,0,1,1,0,1,1,0,0,1,0,0,1,1,1,0,0,1,1,1,0,0,1,0,0,0,0,1,1,0,1,0,
1,
0,0,1,0,1,1,0,0,1,1,0,1,1,0,0,1,0,0,1,0,1,0,0,1,0,0,0,1,0,0,0,1,
1,1,0,1,0,0,1,1,0,0,0,0,0,1,1,0,0,0,0,0,0,1,1,0,1,0,1,1,1,0,1,0,
0,
0,0,0,0,1,1,1,1,1,0,1,1,1,1,0,1,1,1,1,1,1,0,1,0,1,0,1,0,0,0,0,1,
1,0,1,1,1,0,1,0,1,1,1,0,0,0,0,1,0,1,1,0,0,0,1,0,0,1,0,1,1,1,1,0,
0,
0,1,1,1,0,1,1,0,1,1,0,0,0,0,0,1,0,0,0,0,0,1,1,0,1,1,0,0,1,1,1,0,
1,0,0,0,1,0,1,0,0,1,1,0,1,0,1,1,0,0,1,1,0,1,0,0,0,0,1,1,1,1,0,1,
0]
for i in range(0,520):
if(zi[i]==b[i]):
c=c+1
else:
c=c-1
return c
def com3():
c=0
zi = [0,0,1,1,1,1,0,1,0,0,0,0,1,1,0,1,0,1,1,1,0,1,1,0,1,0,1,1,1,1,0,1
,0,1,0,0,1,1,0,1,1,0,0,1,0,0,0,0,0,0,1,1,0,1,0,1,0,1,0,1,0,0,0,0
,1,
0,1,0,1,1,0,0,1,1,0,1,1,1,0,1,1,1,1,1,0,0,1,0,0,0,1,1,0,0,0,1,0,
0,0,0,0,1,1,1,1,0,0,1,1,1,1,0,0,0,0,0,1,0,1,0,0,1,0,1,1,1,0,1,0,
1,
0,0,1,1,1,0,0,0,0,1,1,1,1,1,1,0,1,0,1,0,1,0,0,1,1,1,1,0,1,0,0,0,
1,0,0,0,1,1,1,0,1,1,1,0,1,1,0,1,0,0,0,0,1,0,1,1,1,0,0,1,0,0,1,0,
1,
1,1,1,1,0,0,0,1,1,1,0,1,0,0,1,1,1,0,1,0,1,0,0,0,0,0,1,1,0,0,0,1,
0,0,1,1,1,0,0,1,0,0,1,0,1,0,1,1,1,1,0,1,0,0,0,0,1,1,0,0,0,1,0,1,
1,
0,0,1,1,0,0,1,1,1,1,0,1,0,1,1,0,1,1,0,0,0,1,1,1,1,1,0,1,1,0,0,0,
1,0,1,1,0,1,1,0,0,1,0,0,1,1,1,0,0,1,1,1,0,0,1,0,0,0,0,1,1,0,1,0,
1,
0,0,1,0,1,1,0,0,1,1,0,1,1,0,0,1,0,0,1,0,1,0,0,1,0,0,0,1,0,0,0,1,
1,1,0,1,0,0,1,1,0,0,0,0,0,1,1,0,0,0,0,0,0,1,1,0,1,0,1,1,1,0,1,0,
0,
0,0,0,0,1,1,1,1,1,0,1,1,1,1,0,1,1,1,1,1,1,0,1,0,1,0,1,0,0,0,0,1,
1,0,1,1,1,0,1,0,1,1,1,0,0,0,0,1,0,1,1,0,0,0,1,0,0,1,0,1,1,1,1,0,
0,
0,1,1,1,0,1,1,0,1,1,0,0,0,0,0,1,0,0,0,0,0,1,1,0,1,1,0,0,1,1,1,0,
1,0,0,0,1,0,1,0,0,1,1,0,1,0,1,1,0,0,1,1,0,1,0,0,0,0,1,1,1,1,0,1,
0]
for i in range(0,520):
if(zi[i]==c1[i]):
c=c+1
else:
c=c-1
return c
i1=int(input("Enter the initial state for LFSR1 in range(1,127)"))
i2=int(input("Enter the initial state for LFSR2 in range(1,255)"))
i3=int(input("Enter the initial state for LFSR3 in range(1,511)"))
LFSR1(i1)
LFSR2(i2)
LFSR3(i3)
for i in range(0,520):
T1= i % 128
a=shift(ai,T1)
z1 = com1()
m1.append(z1)
v1=max(m1)
max_index1=m1.index(v1)
k1=shift(ai,max_index1)
k1=shift(k1,128-64)
k1=k1[:7]
for i in range(0,520):
T2= i % 256
b=shift(bi,T2)
z2 = com2()
m2.append(z2)
v2=max(m2)
max_index2=m2.index(v2)
k2=shift(bi,max_index2)
k2= shift(k2,256-64)
k2=k2[:8]
for i in range(0,520):
T3= i % 512
c1=shift(ci,T3)
z3 = com3()
m3.append(z3)
v3=max(m3)
max_index3=m3.index(v3)
k3=shift(ci,max_index3)
k3= shift(k3,512-64)
k3=k3[:9]
print(k1)
print(k2)
print(k3)
|
[
"saemsheth11196@gmail.com"
] |
saemsheth11196@gmail.com
|
c88410f5381d6ce6f915c44be912399406094dbc
|
2a08e70a1b893cb6b621635d83a86f15598cbd0c
|
/file.py
|
16800c32f5b8256526db2999dfc1c30c153d0833
|
[] |
no_license
|
AlinaPy/python
|
b7543aba987f198f95b9984ccf714cf12388ae4d
|
db180579c12d9a8fd7bb1eccef3a83599a5b51a2
|
refs/heads/master
| 2020-06-17T19:09:44.023284
| 2019-07-09T17:45:36
| 2019-07-09T17:45:36
| 196,019,600
| 1
| 0
| null | null | null | null |
WINDOWS-1251
|
Python
| false
| false
| 364
|
py
|
print('Hello world!')
print('Hi Marina!')
answer = input("А ты в курсе, что можно запускать питоновский файл напрямую и вводить туда значения? (Y/N)")
if answer == 'Y':
print("умничка")
else:
print("А я не знаю как запустить файл напрямую((((((((")
|
[
"ino4kayo1@mail.ru"
] |
ino4kayo1@mail.ru
|
bcf372a4ab7f0066d27d9f6f396a77ddb6dfb14b
|
da1037bdf6620861cf971115a9ad6ed8115a25cc
|
/backend/app/fastapi-tutorial/sample/tutorial/06-07-path-parameters-and-numeric-validations.py.py
|
893e2a79374dfff44e151f0e748202e8c916e3a1
|
[] |
no_license
|
nukopy/past-application-architectures
|
b814124f21c581979e2e26533429ebb3010bd812
|
4f20a11ea7d0dd1d6c2278e8e82a3ca801da0c3a
|
refs/heads/master
| 2023-01-11T17:47:08.290825
| 2020-11-10T02:06:52
| 2020-11-10T02:06:52
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,954
|
py
|
from typing import Any, Dict, List, Optional, Type, TypeVar
from fastapi import FastAPI, Path, Query
from pydantic import BaseModel
T = TypeVar("T", bound="ObjectBase")
class ObjectBase(BaseModel):
@classmethod
def create(cls: Type[T], data: Dict[str, Any]) -> T:
""" Factory method: Create pydantic model from dict """
return cls(**data)
def dump(self: T) -> Dict[str, Any]:
""" Dump pydantic model to dict """
return self.dict()
class ItemConfig:
TAX_RATIO: float = 0.10
class Item(ObjectBase):
name: str
description: Optional[str] = None
price: float
def total_price(self) -> float:
tax = self.price * ItemConfig.TAX_RATIO
return self.price + tax
app = FastAPI()
item_list = [
{"name": "Indian Curry", "price": "1000"},
{"name": "Coffee", "price": "500"},
{"name": "Orange Juice", "price": "450"},
]
item_models = [Item.create(item) for item in item_list]
@app.get("/items")
async def get_items(
q: Optional[str] = Query(..., min_length=3, max_length=50, regex="^[a-z]+$")
):
response = {"items": item_models}
if q:
response.update({"query": q})
return response
@app.get("/multiple-query")
async def multiple_query(
q: Optional[List[str]] = Query(
...,
title="List of Query String",
description='複数のクエリパラメータ "q" を送信することができます．',
alias="item-query", # Python の変数名として有効じゃないクエリパラメータ名を alias として使える
deprecated=True,
)
):
query_items = {"q": q}
return query_items
@app.get("/items/{item_id}")
async def get_item(
item_id: int = Path(..., title="The ID of the item to get", ge=100, le=500),
q: Optional[str] = Query(None, alias="item-query"),
):
response = {"item_id": item_id}
if q:
response.update({"query": q})
return response
|
[
"pytwbf201830@gmail.com"
] |
pytwbf201830@gmail.com
|
d53eaa87254e74faa3a0cef995970e6ac910efe1
|
b728bf1e9dc90b1ae6d56aa70b5f5c598b87c097
|
/0x01-python_async_function/1-concurrent_coroutines.py
|
1ed8cdf6ce6faa0e5099e34058ab3df9dd71a049
|
[] |
no_license
|
usfbelhadj/holbertonschool-web_back_end
|
2d0ad2271368abb380dc13c9ce1371ddc580767b
|
c3c9174b79539069d9f830381dff217e8d9688b5
|
refs/heads/master
| 2023-06-01T20:26:12.359948
| 2021-06-13T01:21:09
| 2021-06-13T01:21:09
| 310,302,595
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 747
|
py
|
#!/usr/bin/env python3
'''
Let's execute multiple coroutines at the same time with async
'''
import asyncio
import random
from typing import List
wait_random = __import__('0-basic_async_syntax').wait_random
async def wait_n(n: int, max_delay: int) -> List[float]:
'''
Import wait_random from the previous python file/
that you’ve written and write an async routine called/
wait_n that takes in 2 int arguments (in this order): n and max_delay./
You will spawn wait_random n times with the specified max_delay.
'''
n_wait = []
comp_wait = []
for i in range(n):
n_wait.append(wait_random(max_delay))
for i in asyncio.as_completed(n_wait):
comp_wait.append(await i)
return comp_wait
|
[
"usf.belhadj@gmail.com"
] |
usf.belhadj@gmail.com
|
4f8393409c232a000d3656181248c1ee0309bd84
|
c85c42d2d2bd565b462a0a0fe689488ed296cea8
|
/Studentinfoproject.py
|
7970dc551390204b80447392d2ea25f9ab0d3f71
|
[] |
no_license
|
maryniya/hello
|
bd8b84429dbe386e68cc7374d31e63632e614ad9
|
de5a72675d6636c0c9f542837a7ffa1d999454d4
|
refs/heads/main
| 2023-06-04T01:13:27.836538
| 2021-07-04T15:17:35
| 2021-07-04T15:17:35
| 376,457,779
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,628
|
py
|
#Project1: Basic school administartion tool
import csv
def write_into_csv(info_list):
with open('student_info_list.csv','a', newline='') as csv_file:
writer = csv.writer(csv_file)
if csv_file.tell() == 0:
writer.writerow(["Name", "Age", "Contact Number", "E-mail ID"])
writer.writerow(info_list)
if __name__=='__main__':
condition = True
student_num = 1
while(condition):
student_info = input("Enter student information in the following format(Name Age Contact_Number E-mail_ID): ".format(student_num))
print("Entered information:" + student_info)
#split
student_info_list = student_info.split(' ')
print("Entered split up information is: "+str(student_info_list))
print("\nThe entered information is - \nName: {}\nAge: {}\nContact_number:{}\nE-mail ID: {}"
.format(student_info_list[0], student_info_list[1],student_info_list[2],student_info_list[3]))
write_into_csv(student_info_list)
choice_check = input("Is the entered information correct? (yes/no): ")
if choice_check =="yes":
write_into_csv(student_info_list)
condition_check = input("Enter (yes/no) if you want to enter information for another student: ")
if condition_check == "yes":
condition = True
student_num = student_num + 1
elif condition_check == "no":
condition = False
elif choice_check =="no":
print("\nPlease re-enter the values!")
|
[
"noreply@github.com"
] |
maryniya.noreply@github.com
|
c82abc0f8f4240cc626ac3743057ba07fbf408e2
|
a65f9b9c40f7919004cb6e740bc9173a69487094
|
/oblig2/Exercise2.py
|
ce46c0d6eb191dd76e511f99cba0f591579685dc
|
[] |
no_license
|
andrthu/mek4250
|
e99a66347fdfc68b8b70fd47ea0e6cca05f2b2b9
|
d8dbf2afa1e5bc2185cc8cf6f3fb86bf18eb08e9
|
refs/heads/master
| 2021-01-24T20:48:20.168198
| 2017-07-27T12:02:11
| 2017-07-27T12:02:11
| 51,002,555
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 7,580
|
py
|
from dolfin import *
from numpy import pi,matrix,sqrt,diagflat,zeros,vstack,ones,log,array,size,exp
from scipy import linalg
import matplotlib.pyplot as plt
#defining lists with h values and lambdas, and setting my=1
Lam = [1,100,10000]
my = 1
H = [8,16,32,64]
#Expression u exact
ue = Expression(("pi*x[0]*cos(pi*x[0]*x[1])","-pi*x[1]*cos(pi*x[0]*x[1])"),degree=3)
#source term found by my*laplace(ue)
f = Expression(("(-pow(pi*x[0],3)*cos(pi*x[0]*x[1]) - pow(pi,3)*x[0]*pow(x[1],2)*cos(pi*x[0]*x[1]) - 2*pow(pi,2)*x[1]*sin(pi*x[0]*x[1]))", "pow(pi,3)*pow(x[0],2)*x[1]*cos(pi*x[0]*x[1]) + 2*pow(pi,2)*x[0]*sin(pi*x[0]*x[1]) + pow(pi*x[1],3)*cos(pi*x[0]*x[1])"),degree=2)
#lists to store errors
L2_error = [[[],[],[]],[[],[],[]]]
H1_error = [[[],[],[]],[[],[],[]]]
#lists to store convergence rates aqnd their constants
con = [[[],[]],[[],[]]]
#solving the equation starts here. Loop over two types of element degrees
for p in [1,2]:
#loop over our lambda values
for i in range(len(Lam)):
#define a list to store minimum mesh resolution
hv = []
#loop over different mesh resolutions
for j in range(len(H)):
#defone our mesh
mesh = UnitSquareMesh(H[j],H[j])
#define our vectorspace, and an extra space to measure error
V = VectorFunctionSpace(mesh,"Lagrange",p)
V2= VectorFunctionSpace(mesh,"Lagrange",p+3)
#test and trial
u = TrialFunction(V)
v = TestFunction(V)
#fenics function that is our current lambda
l = Constant(Lam[i])
#interpolta our source term into our space
F = -interpolate(f,V)
#write up our variational form
a = inner(grad(u),grad(v))*dx + l*div(u)*div(v)*dx
L = dot(F,v)*dx
#fix our Dirichlet BCs
bc = DirichletBC(V,ue,"on_boundary")
#solve our equation
u = Function(V)
solve(a==L,u,bc)
#interpolate the exact solution to a our higher degree space
Ue = interpolate(ue,V2)
#find the errors
L2_error[p-1][i].append(errornorm(Ue,u))
H1_error[p-1][i].append(errornorm(Ue,u,'H1'))
hv.append(mesh.hmax())
#plot(u-Ue)
#interactive()
#plot(Ue)
#interactive()
#calculate convergence using least square for each set of
#parameters.
Q1 = vstack([log(array(hv)),ones(len(hv))]).T
con[p-1][0].append(linalg.lstsq(Q1, log(array(L2_error[p-1][i])))[0])
con[p-1][1].append(linalg.lstsq(Q1, log(array(H1_error[p-1][i])))[0])
#plt.plot(log(array(hv)),log(array(L2_error[p-1][i])))
#plt.plot(log(array(hv)),log(array(H1_error[p-1][i])))
#plt.plot((log(array(hv))),p*(log(array(hv))),"r--")
#plt.plot((log(array(hv))),(p+1)*(log(array(hv))),"y--")
#plt.legend(["L2","H1",str(p)+"*log(h)",str(2*p)+"*log(h)" ])
#plt.show()
#do some fancy output print
for i in range(2):
print "**********************************"
p=i+1
print "Polynomial order = %d" %p
print
for j in range(len(Lam)):
print "-------------------lam=%d-------------------" % Lam[j]
print
print "L2 error: ", L2_error[i][j]
print
print "H1 error: ", H1_error[i][j]
print
print "L2 con-rate=%f C=%f" % (con[i][0][j][0] , exp(con[i][0][j][1]))
print
print "H1 con-rate=%f C=%f" % (con[i][1][j][0] , exp(con[i][1][j][1]))
print
print "---------------------------------------------"
print
print
#plot to justify least squares.
for j in range(3):
fig,ax = plt.subplots(2, 1,sharex=True)
for l in range(2):
p=l+1
ax[l].plot(log(array(hv)),log(array(L2_error[l][j])))
ax[l].plot(log(array(hv)),log(array(H1_error[l][j])))
ax[l].plot((log(array(hv))),(l+1)*(log(array(hv))),"g--")
ax[l].plot((log(array(hv))),(l+2)*(log(array(hv))),"b--")
ax[l].set_title("P"+str(p)+" element, lambda="+str(Lam[j]))
ax[l].legend(["L2","H1",str(p)+"*log(h)",str(l+2)+"*log(h)" ],loc=4)
ax[l].set_xlabel("log(h)")
ax[l].set_ylabel("log(error)")
plt.show()
"""
terminal>> python Exercise2.py
Solving linear variational problem.
Solving linear variational problem.
Solving linear variational problem.
Solving linear variational problem.
Solving linear variational problem.
Solving linear variational problem.
Solving linear variational problem.
Solving linear variational problem.
Solving linear variational problem.
Solving linear variational problem.
Solving linear variational problem.
Solving linear variational problem.
Solving linear variational problem.
Solving linear variational problem.
Solving linear variational problem.
Solving linear variational problem.
Solving linear variational problem.
Solving linear variational problem.
Solving linear variational problem.
Solving linear variational problem.
Solving linear variational problem.
Solving linear variational problem.
Solving linear variational problem.
Solving linear variational problem.
**********************************
Polynomial order = 1
-------------------lam=1-------------------
L2 error: [0.07143564353036871, 0.01858894010975571, 0.004697079781323455, 0.001177468248156252]
H1 error: [1.3517242507219713, 0.6790867417407639, 0.3398473366000941, 0.1699577953467868]
L2 con-rate=1.975326 C=2.213178
H1 con-rate=0.997337 C=7.621186
---------------------------------------------
-------------------lam=100-------------------
L2 error: [0.29985169058331784, 0.16406710708851321, 0.06076135418164534, 0.017653643817245494]
H1 error: [2.630391833510895, 1.423505893625182, 0.5774530212031616, 0.2179137174299302]
L2 con-rate=1.369169 C=3.795261
H1 con-rate=1.208202 C=23.611052
---------------------------------------------
-------------------lam=10000-------------------
L2 error: [0.444750039708708, 0.45628947339193204, 0.43298295714847634, 0.3519439457224945]
H1 error: [3.7443848306183782, 3.6589796437836535, 3.4092084266679863, 2.7338766907550016]
L2 con-rate=0.108859 C=0.567093
H1 con-rate=0.146335 C=5.043682
---------------------------------------------
**********************************
Polynomial order = 2
-------------------lam=1-------------------
L2 error: [0.0020804727431950787, 0.0002521449774644887, 3.124455010249258e-05, 3.896910893232253e-06]
H1 error: [0.12333779284666148, 0.031097865213979455, 0.007791325005348004, 0.0019488967225476028]
L2 con-rate=3.019367 C=0.386373
H1 con-rate=1.994832 C=3.920336
---------------------------------------------
-------------------lam=100-------------------
L2 error: [0.014397270633258623, 0.0014981362081414133, 0.00011945190272137094, 8.7436893209144e-06]
H1 error: [0.45195247197065147, 0.09708626269773965, 0.01665272098869556, 0.0028154805090628452]
L2 con-rate=3.570446 C=7.716594
H1 con-rate=2.452345 C=33.981253
---------------------------------------------
-------------------lam=10000-------------------
L2 error: [0.029893235368636906, 0.007174945881791682, 0.0015772288060908345, 0.00027219372347456357]
H1 error: [0.9452508877864363, 0.4515093401616464, 0.19729045871526465, 0.06764907638113875]
L2 con-rate=2.252270 C=1.596042
H1 con-rate=1.260810 C=9.058619
---------------------------------------------
"""
|
[
"andrthu@student.matnat.uio.no"
] |
andrthu@student.matnat.uio.no
|
5fdd9607564a70b74cbab892584f6c9f6a83d532
|
3ee67adff6e3b08a821b51ac9935ca8c06b844b6
|
/Recursividade/elefantes.py
|
10fceabced39d590d7b666366cc82b49bafe4a2b
|
[] |
no_license
|
vinaud/Exercicios-Python
|
5952b11701604b2068709e2fd86f3b62ad3ccc09
|
7575b8b90b52bf77f4b20c63224808466d187d40
|
refs/heads/master
| 2021-05-17T15:03:47.796947
| 2020-05-01T19:29:10
| 2020-05-01T19:29:10
| 250,834,430
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,044
|
py
|
"""
Implemente a função incomodam(n) que devolve uma string contendo "incomodam "
(a palavra seguida de um espaço) n vezes. Se n não for um inteiro estritamente positivo,
a função deve devolver uma string vazia. Essa função deve ser implementada utilizando recursão.
Utilizando a função acima, implemente a função elefantes(n) que devolve uma string contendo
a letra da música "Um elefante incomoda muita gente" de 1 até n elefantes. Se n não for maior que 1,
a função deve devolver uma string vazia. Essa função também deve ser implementada utilizando recursão.
"""
def incomodam(n):
if n < 1:
return ""
return "incomodam " + incomodam(n-1)
def elefantes(n):
if n < 1:
return ""
if n == 1:
return "Um elefante incomoda muita gente\n"
if n == 2:
return elefantes(n-1)+str(n)+" elefantes "+incomodam(n)+"muito mais\n"
return elefantes(n-1)+str(n-1)+" elefantes incomodam muita gente\n"+str(n)+" elefantes " + incomodam(n)+"muito mais\n"
print(elefantes(4))
|
[
"leyenasd@gmail.com"
] |
leyenasd@gmail.com
|
5a8545f7845617099448aca9ffa37ec6059aadcb
|
b8bf409d1d1e38950c1b194c2d3c460920807bf0
|
/Python/Madlibs.py
|
0f5f1dbac134411c6ac06e45c4c9fa5bfc2c1aea
|
[
"MIT"
] |
permissive
|
hhaslam11/SmallProjects
|
caf0e0c16a2f174361e549f9ceea2f3a98c841b1
|
3c861b1b42fdd564739717d2deae49bc0cdc24b4
|
refs/heads/master
| 2021-01-10T10:02:26.245979
| 2017-10-06T21:58:41
| 2017-10-06T21:58:41
| 55,207,559
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,703
|
py
|
"""
Madlibs.py
Kaleb Haslam
https://www.codecademy.com
"""
print("Mad Libs || Kaleb Haslam")
print("Enter a name: ")
name = raw_input()
adj_1 = raw_input("Enter an adjective: ")
adj_2 = raw_input("Enter another adjective: ")
adj_3 = raw_input("Enter a final adjective: ")
verb_1 = raw_input("Enter a verb: ")
verb_2 = raw_input("Enter another verb: ")
verb_3 = raw_input("Enter a final verb: ")
noun_1 = raw_input("Enter a noun: ")
noun_2 = raw_input("Enter another noun: ")
noun_3 = raw_input("Enter a third noun: ")
noun_4 = raw_input("Enter a final noun: ")
animal = raw_input("Enter an animal: ")
food = raw_input("Enter a food: ")
fruit = raw_input("Enter a fruit: ")
number = raw_input("Enter a number: ")
superhero = raw_input("Enter a name of a superhero: ")
country = raw_input("Enter a country: ")
dessert = raw_input("Enter a type of dessert: ")
year = raw_input("Enter a year:")
#The template for the story
STORY = "This morning I woke up and felt %s because %s was going to finally %s over the big %s %s. On the other side of the %s were many %ss protesting to keep %s in stores. The crowd began to %s to the rythym of the %s, which made all of the %ss very %s. %s tried to %s into the sewers and found %s rats. Needing help, %s quickly called %s. %s appeared and saved %s by flying to %s and dropping %s into a puddle of %s. %s then fell asleep and woke up in the year %s, in a world where %ss ruled the world."
print(STORY % (adj_1, name, verb_1, adj_2, noun_1, noun_2, animal, food, verb_2, noun_3, fruit, adj_3, name, verb_3, number, name, superhero, superhero, name, country, name, dessert, name, year, noun_4))
|
[
"kalebmarc@hotmail.com"
] |
kalebmarc@hotmail.com
|
6e35a820c430f2cff165c270e40d72afcb703803
|
7f7445e378de3ccb51387714607aa11949bf8f53
|
/config.py
|
7d492aeaee4ee968462f4fcf6f7a61604b1294bd
|
[] |
no_license
|
HienB1812267/disaster_tweet
|
7e0b0d9fd4ba232e6bb069629cf7049a18359bcb
|
fd9ea29b37f73b57b1ef93133b23336450bcb49e
|
refs/heads/main
| 2023-09-03T08:49:44.942076
| 2021-10-27T15:35:07
| 2021-10-27T15:35:07
| 407,450,381
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 190
|
py
|
DATA_TRAIN_PATH = "./train.csv"
DATA_TEST_PATH = "./test.csv"
SAMPE_SUBMISSTION_PATH ="./sample_submission.csv"
ACRONYMS_PATH = "./acronyms_tweets.txt"
TRUE_TARGET_PATH = "./real_target.csv"
|
[
"kgha2099@gmail.com"
] |
kgha2099@gmail.com
|
2316ed9192f542f72a25d3038b16c60e3271862f
|
68b7d7b72a9d87123373f1e4523bf3655564769d
|
/backend/course/migrations/0001_initial.py
|
0ce22a04074cfc9aad1aacd1a19265b0239921a5
|
[] |
no_license
|
crowdbotics-apps/help-procrastinatio-22418
|
c5a85b31e85b87e9d4e39f402ca3f037d916c990
|
b2a967a5b930ba5cacbeeea702ca9aba71899687
|
refs/heads/master
| 2023-01-09T12:19:42.589420
| 2020-11-08T23:45:22
| 2020-11-08T23:45:22
| 311,177,250
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 5,517
|
py
|
# Generated by Django 2.2.17 on 2020-11-08 23:44
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
name='Category',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=256)),
],
),
migrations.CreateModel(
name='Course',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(blank=True, max_length=256, null=True)),
('description', models.TextField(blank=True, null=True)),
('author', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='course_author', to=settings.AUTH_USER_MODEL)),
('categories', models.ManyToManyField(blank=True, related_name='course_categories', to='course.Category')),
],
),
migrations.CreateModel(
name='Event',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=256)),
('date', models.DateTimeField()),
('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='event_user', to=settings.AUTH_USER_MODEL)),
],
),
migrations.CreateModel(
name='Group',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=256)),
],
),
migrations.CreateModel(
name='SubscriptionType',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=256)),
],
),
migrations.CreateModel(
name='Subscription',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('subscription_type', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='subscription_subscription_type', to='course.SubscriptionType')),
('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='subscription_user', to=settings.AUTH_USER_MODEL)),
],
),
migrations.CreateModel(
name='Recording',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('media', models.URLField()),
('published', models.DateTimeField()),
('event', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='recording_event', to='course.Event')),
('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='recording_user', to=settings.AUTH_USER_MODEL)),
],
),
migrations.CreateModel(
name='PaymentMethod',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('primary', models.BooleanField()),
('token', models.CharField(max_length=256)),
('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='paymentmethod_user', to=settings.AUTH_USER_MODEL)),
],
),
migrations.CreateModel(
name='Module',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(max_length=256)),
('description', models.TextField()),
('course', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='module_course', to='course.Course')),
],
),
migrations.CreateModel(
name='Lesson',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(max_length=256)),
('description', models.TextField()),
('media', models.URLField()),
('module', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='lesson_module', to='course.Module')),
],
),
migrations.CreateModel(
name='Enrollment',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('course', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='enrollment_course', to='course.Course')),
('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='enrollment_user', to=settings.AUTH_USER_MODEL)),
],
),
]
|
[
"team@crowdbotics.com"
] |
team@crowdbotics.com
|
e2e47c06e98fbfb0bdd1cc0fa413a8b3deb1e123
|
99b4956b0026c1c4cb023424342fc75f523aa4fa
|
/04.adventure/18.ui_preparation/scene.py
|
3032ff988bbdaff179596bf01bfd8d648f09c58b
|
[
"MIT"
] |
permissive
|
Gaetz/python-training
|
37c234128d834ee29e0e625f265cf0b68158c228
|
542f658883c66aaa932fb9e385225cfd573bb6de
|
refs/heads/master
| 2021-05-09T03:44:22.990658
| 2020-10-02T18:29:50
| 2020-10-02T18:29:50
| 119,250,880
| 2
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 3,399
|
py
|
import pygame
from sprite_controlled import SpriteControlled
from sprite import Sprite
from warp import Warp
from ui_group import UiGroup
from ui_panel import UiPanel
class Scene:
path = 'D:\\Code\\ArtFx\\Python\\python-training\\01.adventure\\15.scene_amelioration\\'
def __init__(self, filename):
self.filename = filename
self.load(filename)
def load(self, filename):
file = open(Scene.path + filename)
data = file.read().splitlines()
ground_height = 0
self.cursor = Sprite(0, 0, 'cursor.png', False)
self.sprites = []
self.warps = []
self.ui_top = UiGroup()
panel = UiPanel(0, 0, 800, 100)
self.ui_top.add_element(panel)
for line in data:
cell = line.split(";")
# Ground
if(cell[0] == "ground"):
self.ground = Sprite(0, 0, cell[1]+".png", False)
_, screen_h = pygame.display.get_surface().get_size()
ground_height = screen_h - self.ground.surface.get_height()
self.ground.y = ground_height
# Background
elif(cell[0] == "background"):
self.background = Sprite(0, 0, cell[1]+".png", False)
# Player
elif(cell[0] == "player"):
height = 0
if cell[3] == "ground":
height = -1
self.player = SpriteControlled(int(cell[2]), height, cell[1]+".png", True, int(cell[4]))
# Sprites
elif(cell[0] == "sprite"):
height = 0
if cell[3] == "ground":
height = -1
sprite = Sprite(int(cell[2]), height, cell[1]+".png", True)
self.sprites.append(sprite)
# Warps
elif(cell[0] == "warp"):
height = 0
if cell[3] == "ground":
height = -1
warp = Warp(int(cell[2]), height, cell[1]+".png", False, eval(cell[4]))
self.warps.append(warp)
# Set heights
if(self.player.y == -1):
self.player.y = ground_height
for s in self.sprites:
if(s.y == -1):
s.y = ground_height
for w in self.warps:
if(w.y == -1):
w.y = ground_height - w.surface.get_height() / 2
def inputs(self, events):
for event in events:
if event.type == pygame.MOUSEBUTTONDOWN:
mouse_click = pygame.mouse.get_pos()
self.player.move_to(mouse_click[0])
if event.type == pygame.KEYDOWN:
keys = pygame.key.get_pressed()
if keys[pygame.K_F5]:
self.load(self.filename)
def update(self, change_scene):
self.cursor.set_position(pygame.mouse.get_pos())
self.player.update()
for w in self.warps:
if(self.player.intersects(w)):
change_scene(w.to_scene, w.to_scene_x)
self.ui_top.update()
def draw(self, screen):
self.background.draw(screen)
self.ground.draw(screen)
for w in self.warps:
w.draw(screen)
for s in self.sprites:
s.draw(screen)
self.player.draw(screen)
self.ui_top.draw(screen)
self.cursor.draw(screen)
|
[
"gaetan.blaisecazalet@gmail.com"
] |
gaetan.blaisecazalet@gmail.com
|
fad3a00b467541c57ee6edf27a6e4a28dee2abe7
|
54c3e0f24c1eb82fafd4d8f4ef7037f2a656f8a9
|
/extract.py
|
c64ef1de563c1cac223e6758b1da3a333787c6ca
|
[] |
no_license
|
zeeker999/music-source-separation
|
4accd950675efdc81fa9ced2139bb368de1de69c
|
3e1067b7fbb91ceefa3c616ec9063ddd3617b9ad
|
refs/heads/master
| 2022-01-29T10:22:54.634784
| 2019-07-28T01:57:15
| 2019-07-28T01:58:31
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 5,306
|
py
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
'''
By Dabi Ahn. andabi412@gmail.com.
https://www.github.com/andabi
'''
import os
import librosa
import numpy as np
import tensorflow as tf
from config import EvalConfig, ModelConfig
from model import Model
from preprocess import soft_time_freq_mask, to_wav, write_wav
from preprocess import to_spectrogram, get_magnitude, get_phase, to_wav_mag_only
def decode_input(filename):
data, rate = librosa.load(filename, mono=False, sr=ModelConfig.SR)
print ('channels: %d samples: %d' % data.shape)
n_channels = data.shape[0]
total_samples = data.shape[1]
result = []
for ch in range(n_channels):
result.append(np.array([data[ch, :]]).flatten())
return total_samples, data, np.array(result, dtype=np.float32)
def separate(filename, channel):
with tf.Graph().as_default():
# Model
model = Model(ModelConfig.HIDDEN_LAYERS, ModelConfig.HIDDEN_UNITS)
global_step = tf.Variable(0, dtype=tf.int32, trainable=False, name='global_step')
total_samples, origin_samples, samples = decode_input(filename)
channels = origin_samples.shape[0]
with tf.Session(config=EvalConfig.session_conf) as sess:
# Initialized, Load state
sess.run(tf.global_variables_initializer())
model.load_state(sess, EvalConfig.CKPT_PATH)
mixed_wav, src1_wav, src2_wav = samples, samples, samples
mixed_spec = to_spectrogram(mixed_wav)
mixed_mag = get_magnitude(mixed_spec)
mixed_batch, padded_mixed_mag = model.spec_to_batch(mixed_mag)
mixed_phase = get_phase(mixed_spec)
(pred_src1_mag, pred_src2_mag) = sess.run(model(), feed_dict={model.x_mixed: mixed_batch})
seq_len = mixed_phase.shape[-1]
pred_src1_mag = model.batch_to_spec(pred_src1_mag, 1)[:, :, :seq_len]
pred_src2_mag = model.batch_to_spec(pred_src2_mag, 1)[:, :, :seq_len]
# Time-frequency masking
mask_src1 = soft_time_freq_mask(pred_src1_mag, pred_src2_mag)
# mask_src1 = hard_time_freq_mask(pred_src1_mag, pred_src2_mag)
mask_src2 = 1. - mask_src1
pred_src1_mag = mixed_mag * mask_src1
pred_src2_mag = mixed_mag * mask_src2
# (magnitude, phase) -> spectrogram -> wav
if EvalConfig.GRIFFIN_LIM:
pred_src1_wav = to_wav_mag_only(pred_src1_mag, init_phase=mixed_phase,
num_iters=EvalConfig.GRIFFIN_LIM_ITER)
pred_src2_wav = to_wav_mag_only(pred_src2_mag, init_phase=mixed_phase,
num_iters=EvalConfig.GRIFFIN_LIM_ITER)
else:
pred_src1_wav = to_wav(pred_src1_mag, mixed_phase)
pred_src2_wav = to_wav(pred_src2_mag, mixed_phase)
def stack(data):
size = data.shape[0] // channels
elements = []
for i in range(channels):
elements.append(data[size * i:size * (i + 1)])
return np.dstack(elements)[0]
music_data = pred_src1_wav
voice_data = pred_src2_wav
if channel >= 0:
def filter_samples(data):
for i in range(origin_samples.shape[0]):
if i != channel:
data[i, :] = origin_samples[i, 0:data.shape[1]]
return data
music_data = filter_samples(music_data)
voice_data = filter_samples(voice_data)
music_wav = np.dstack(music_data)[0]
voice_wav = np.dstack(voice_data)[0]
return music_wav, voice_wav
return None
def extract(filename, channel):
music_wav, voice_wav = separate(filename, channel)
base_file_name = os.path.splitext(filename)[0]
write_wav(music_wav, base_file_name + '-h%d-music' % ModelConfig.HIDDEN_UNITS)
write_wav(voice_wav, base_file_name + '-h%d-voice' % ModelConfig.HIDDEN_UNITS)
if __name__ == '__main__':
from optparse import OptionParser
parser = OptionParser(usage='%prog music.wav')
parser.add_option('-c', dest='channel', default=-1, type=int,
help="extract voice from specified channel, -1 to extract all channels")
parser.add_option('-p', dest='check_point', default=EvalConfig.CKPT_PATH,
help="the path to checkpoint")
parser.add_option('--hidden-units', dest='hidden_units', default=ModelConfig.HIDDEN_UNITS, type=int,
help='the hidden units per GRU cell')
parser.add_option('--hidden-layers', dest='hidden_layers', default=ModelConfig.HIDDEN_LAYERS, type=int,
help='the hidden layers of network')
parser.add_option('--case-name', dest='case_name', default=EvalConfig.CASE,
help='the name of this setup')
options, args = parser.parse_args()
if options.check_point:
EvalConfig.CKPT_PATH = options.check_point
ModelConfig.HIDDEN_UNITS = options.hidden_units
ModelConfig.HIDDEN_LAYERS = options.hidden_layers
EvalConfig.CASE = options.case_name
for arg in args:
extract(arg, options.channel)
|
[
"sunmoon1997@gmail.com"
] |
sunmoon1997@gmail.com
|
3b769885947b5d6041a315b1fcab32fb408af28c
|
cd85ea1a17cb0f3ec5b47e0c020c28c508c650ff
|
/020.finance.py
|
9b730a1a435184f22d6215f3603f50480b4c1d2e
|
[] |
no_license
|
HappyPM/proj1
|
6181ee054e0c4a1bbfba669986e28f9127b9a293
|
08c0805f63f428ac381ccdbee602cd26a95e0932
|
refs/heads/master
| 2020-04-05T14:03:31.230156
| 2016-08-31T00:12:27
| 2016-08-31T00:12:27
| 34,797,301
| 2
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 3,419
|
py
|
#-*- coding: utf-8 -*-
import json
import urllib2
from pymongo import MongoClient
try:
from BeautifulSoup import BeautifulSoup
except ImportError:
from bs4 import BeautifulSoup # exception import
gnUrl = 'http://companyinfo.stock.naver.com/v1/company/cF1001.aspx?finGubun=MAIN&cmp_cd='
gnOpener = urllib2.build_opener()
gnOpener.addheaders = [('User-agent', 'Mozilla/5.0')] # header define
_client = MongoClient()
_db = _client.hpm
gnCompanyColl = _db.company
gnFinanceColl = _db.finance
def get_days_to_json(soup):
script = soup.findAll('script')[4].string
day = script.split("changeFin = ", 1)[1].split(";",1)[0]
soup = BeautifulSoup(day)
day = soup.text
day = json.loads(day)
return day
#print script
def get_data_to_json(soup):
script = soup.findAll('script')[4].string
data = script.split("changeFinData = ", 1)[1].split(";",1)[0]
data = json.loads(data)
return data
#print script
def set_year_and_quater(days, data, year_data_list, quater_data_list) :
year_day = days[0]
quater_day = days[1]
for data1 in data:
yy_dat = data1[0]
qt_dat = data1[1]
jj = 0
for yy_dat1 in yy_dat:
dnam = yy_dat1[0]
qt_dat1 = qt_dat[jj]
jj = jj + 1
ii = 0
for yy_dat2 in yy_dat1[1:]:
#print len(qt_dat1[ii])
qt_dat2 = qt_dat1[ii]
year_data = {}
year_data["day"] = year_day[ii]
year_data["item_name"] = dnam
year_data["item_value"] = yy_dat2.replace(',', '')
year_data_list.append(year_data);
quater_data = {}
quater_data["day"] = quater_day[ii]
quater_data["item_name"] = dnam
quater_data["item_value"] = qt_dat2.replace(',', '')
#print quater_data
quater_data_list.append(quater_data);
ii = ii + 1;
#print iid
def insert_finance(ncode):
nUrl = gnUrl + ncode
nResponse = gnOpener.open(nUrl)
nPage = nResponse.read()
# page2
# r = requests.get(gnUrl)
nSoup = BeautifulSoup(nPage)
days = get_days_to_json(nSoup)
data = get_data_to_json(nSoup)
#print days
# embedded document 구조하기 위해서 root 에 code 저장
finance_list = [];
finance = {};
finance["code"] = ncode
finance_list.append(finance);
#print finance_list
gnFinanceColl.insert(finance_list)
# year, quater 1차 json 데이터를 finance 구조에 맞게 다시 저장함
year_data_list = [];
quater_data_list = [];
set_year_and_quater(days, data, year_data_list, quater_data_list)
gnFinanceColl.update({"code": ncode}, { "$push": {"year" : year_data_list, "quater" : quater_data_list } })
print ncode
def load_company_all():
_companys = gnCompanyColl.find();
return _companys;
#############3 main ################################
#insert_finance('192400')
nCompanys = load_company_all()
for company in nCompanys[:]
insert_finance(company['code'][1:])
|
[
"runnablek@gmail.com"
] |
runnablek@gmail.com
|
a04be61f7ffc613e9dba0bc3abe9e559b9929aee
|
4dc66e732ccd873490b79cf97b8dafa09fba803c
|
/chapter_9/car.py
|
e2773cf8ba3836004dbacccf24265ef1cde17fac
|
[] |
no_license
|
AaronLaw/Python-Crash-Course-Bootcamp
|
18bf5e8cb2aa77af7c9e78afccb5f38941a50dd9
|
233cc7d82db7a90a11dd5c3dfce894b8dac939de
|
refs/heads/master
| 2020-06-07T14:45:50.410474
| 2019-07-06T22:24:31
| 2019-07-06T22:24:31
| 193,043,412
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 2,759
|
py
|
"""A class that represents car."""
class Car():
"""A simple simulation of car."""
def __init__(self, make, model, year):
"""Initial attributes of car."""
self.make = make
self.model = model
self.year = year
self.odometer_reading = 0
def get_descriptive_name(self):
"""Return a simplely descriptive info of car."""
long_name = f"{self.year} {self.make} {self.model}"
return long_name.title()
def read_odometer(self):
"""Print an info about car's odometer."""
print(f"This car has {self.odometer_reading} miles on it.")
def update_odometer(self, mileage):
"""Set odometer to mileage."""
if mileage >= self.odometer_reading:
self.odometer_reading = mileage
else:
print("You can't roll back an odometer!")
def increment_odometer(self, miles):
"""Increment miles to odometer."""
if miles >= 0:
self.odometer_reading += miles
else:
print("You can't roll back an odometer!")
def fill_gas_tank(self):
"""Fill the gas tank of car."""
print(f"The gas tank is filled.")
class ElectricCar(Car):
"""Represent aspects of a car, specific to electric vehicles."""
def __init__(self, make, model, year):
"""Initial of parent class."""
super().__init__(make, model, year)
# self.battery_size = 70
self.battery = Battery()
def get_battery(self):
"""Return the size of battery."""
return self.battery.battery_size
def describe_battery(self):
"""Print an info about battery."""
# print(f"This car has a {self.battery.battery_size}-kWh battery.")
self.battery.describe_battery()
def fill_gas_tank(self):
"""Electric car has no gas tank."""
print("This car doesn't need a gas tank!")
def get_range(self):
"""Return the running range from battery."""
self.battery.get_range()
class Battery():
"""A simulation of battery in car."""
def __init__(self, battery_size=70):
self.battery_size = battery_size
def describe_battery(self):
"""Print an info about battery."""
print(f"This car has a {self.battery_size}-kWh battery.")
def get_range(self):
"""Print a message to show how long it can run."""
if self.battery_size == 70:
range = 240
elif self.battery_size == 85:
range = 270
print(f"This car can go approximately {range} miles on a full charge.")
def upgrade_battery(self):
"""Upgrade the battery when it's < 85."""
if self.battery_size < 85:
self.battery_size = 85
|
[
"aaron.law@gmail.com"
] |
aaron.law@gmail.com
|
39890e2873138702a15f6a3eb09242e782e318da
|
4d4d99a79e27eb4950724935caf64cf6298e9558
|
/touchpoint_distribution.py
|
b1189b34b8bfa22cdb10839c134a317921307730
|
[] |
no_license
|
mathycee/Digital-Data-Analysis
|
6cf645fb66aaf77c8a1bf72e52801327e902971a
|
ce6f8332d93d4591c6519cdcdcb4d7d2c7b98a0b
|
refs/heads/master
| 2021-01-15T15:37:37.739991
| 2016-09-09T01:17:07
| 2016-09-09T01:17:07
| 37,562,612
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 3,761
|
py
|
##################################################################################
# Touch Point Distribution
# The purpose of this script is to create touchpoint distribution.
#Output: touchpoint_distribution.txt file
#Author: Xiaomeng Chai <chaixiaomeng@gmail.com>
#Date: 08/18/15, 08/24/15
#
##################################################################################
import time as t
##################################################################################
##PARAMETERS##
start_date = 20150401 #this should be the analysis period begin date
end_date = 20150731 #this should be the analysis period end date
#set to True if printing progress to screen is desired
verbose = True
#base output directory
output_directory = "E:\\users\\xchai\\output\\"
###################################################################################
start = t.time()
############################################################################################################################
#load tmp_final_model_dataset file
fh_in = "E:\\xhcai\\output\\tmp_final_model_dataset_100_95_" + str(start_date) + "_" + str(end_date) + ".txt"
# create an output file that is used later as input
fh_out = output_directory + "SID_plength_convflag_" + str(start_date) + "_" + str(end_date) + ".txt"
counter = 0
fh_out = open(fh_out,'w')
with open(fh_in,'r') as fh:
for line in fh.readlines()[1:]:
v = line.strip().split('|')
SID = v[0]
converter_flag = v[2]
if converter_flag == '1':
converter_flag =1
else:
converter_flag = 0
path_length = int(float(v[12]))+int(float(v[13]))+int(float(v[14]))+int(float(v[15]))+int(float(v[16]))+int(float(v[17]))+int(float(v[18]))+int(float(v[19]))+int(float(v[20]))+int(float(v[21]))
print >> fh_out, '%s|%s|%s' % (SID,path_length, converter_flag)
counter += 1
if (counter % 10000000 == 0):
print '%d events processed from tmp_final_dataset file' % (counter)
print 'finish pulling path length for each SID '
fh_out.close()
#08/18/2015 Publisher Distribution
cnt_0 = 0
cnt_1 = 0
cnt_2_10 = 0
cnt_11_50 = 0
cnt_51_100 = 0
cnt_101plus = 0
conv_0 = 0
conv_1 = 0
conv_2_10 = 0
conv_11_50 = 0
conv_51_100 = 0
conv_101plus = 0
fn_in = output_directory + "SID_plength_convflag_" + str(start_date) + "_" + str(end_date) + ".txt"
#fn_in = output_directory + "SID_test.txt"
fn_out = output_directory + "touchpoint_distribution_"+ str(start_date) + "_" + str(end_date) + ".txt"
fn = open (fn_in, 'r')
fh_out = open(fn_out,'w')
for line in fn.readlines():
try:
v = line.strip().split('|')
SID = v[0]
path_length = int(float(v[1]))
conv_flag = int(float(v[2]))
except:
print '%s' %(v)
if path_length == 0:
cnt_0 += 1
conv_0 += conv_flag
elif path_length == 1:
cnt_1 += 1
conv_1 += conv_flag
elif (path_length >=2 and path_length <= 10):
cnt_2_10 += 1
conv_2_10 += conv_flag
elif (path_length >= 11 and path_length <= 50):
cnt_11_50 += 1
conv_11_50 += conv_flag
elif (path_length >= 51 and path_length <= 100):
cnt_51_100 += 1
conv_51_100 += conv_flag
else:
cnt_101plus += 1
conv_101plus += conv_flag
print >> fh_out, 'cnt_0|cnt_1|cnt_2_10|cnt_11_50|cnt_51_100|cnt_101plus|conv_0|conv_1|conv_2_10|conv_11_50|conv_51_100|conv_101plus'
print >> fh_out, '%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s' % (str(cnt_0),str(cnt_1), str(cnt_2_10),str(cnt_11_50),str(cnt_51_100),str(cnt_101plus),str(conv_0),str(conv_1), str(conv_2_10),str(conv_11_50),str(conv_51_100),str(conv_101plus))
fh_out.close()
fn.close()
#end of script actions
end = t.time()
elapsed = end - start
#if verbose == True:
print "done creating touchpoint distribution"
print "Run time: ", elapsed, " seconds\n"
print 'done'
#execfile("E:/Users/xchai/touchpoint_distribution.py")
|
[
"chaixiaomeng@gmail.com"
] |
chaixiaomeng@gmail.com
|
57982798e64264e70d17cad7d0d66871381e92be
|
f9dbf7d57606ded7f7800ee3c2eb50880d45a4f7
|
/python/daaltk/doc/docutils.py
|
d2f2ac936f94d27ce03c09fb5f6594e67b9a9a39
|
[
"Apache-2.0"
] |
permissive
|
dmsuehir/daal-tk
|
bc96976719dc0918152c838be347d5927f7fe6bd
|
f2ed2307fb436fc4f976c46317d367f8c2aa56db
|
refs/heads/master
| 2021-04-30T18:48:31.301277
| 2016-11-07T17:19:18
| 2016-11-07T17:19:18
| 75,002,896
| 0
| 0
| null | 2016-12-13T01:48:07
| 2016-11-28T18:37:19
|
Scala
|
UTF-8
|
Python
| false
| false
| 9,526
|
py
|
"""
Post-processes HTML generated by pdoc
"""
import re
import datetime
import os
import sys
import shutil
import tempfile
import logging
logger = logging.getLogger(__file__)
def pre_process_py(path):
def go(full_name, reader, writer):
text = reader.read()
output = str(DocExamplesPreprocessor(text, mode='doc', file_name=full_name))
writer.write(output)
walk_path(path, '.py', go)
def post_process_html(path):
def go(full_name, reader, writer):
for line in reader.readlines():
processed_line = process_html_line(line, full_name)
writer.write(processed_line)
walk_path(path, '.html', go)
def walk_path(path, suffixes, processor):
"""walks the path_to_examples and creates paths to all the .rst files found"""
logger.debug("walk_path(path='%s', suffixes=%s)", path, suffixes)
for root, dir_names, file_names in os.walk(path):
logger.debug("walk_path: file_names=%s", file_names)
for file_name in file_names:
if file_name.endswith(suffixes):
full_name = os.path.join(root, file_name)
logger.debug("walk_path: processing file %s", full_name)
with open(full_name, 'r') as r:
with tempfile.NamedTemporaryFile(delete=False) as w:
tmp_name = w.name
logger.debug("walk_path: tmp_name=%s", tmp_name)
processor(full_name, r, w)
os.remove(full_name)
shutil.move(tmp_name, full_name)
def process_html_line(line, full_name):
# Repair the "Up" link for certain files (this needs to match the doc/templates/css.mako)
if full_name.endswith("/index.html") and '<a href="index.html" id="fixed_top_left">Up</a>' in line:
if full_name.endswith("/daaltk/index.html"):
return ' <!-- No Up for root level index.html -->\n'
return '<a href="../index.html" id="fixed_top_left">Up</a>\n'
# clean doctest flags
return line
def parse_for_doc(text, file_name=None):
return str(DocExamplesPreprocessor(text, mode='doc', file_name=file_name))
def parse_for_doctest(text, file_name=None):
return str(DocExamplesPreprocessor(text, mode='doctest', file_name=file_name))
class DocExamplesException(Exception):
"""Exception specific to processing documentation examples"""
pass
class DocExamplesPreprocessor(object):
"""
Processes text (intended for Documentation Examples) and applies daal-tk doc markup, mostly to enable doctest testing
"""
doctest_ellipsis = '-etc-' # override for the doctest ELLIPSIS_MARKER
# multi-line tags
hide_start_tag = '<hide>'
hide_stop_tag = '</hide>'
skip_start_tag = '<skip>'
skip_stop_tag = '</skip>'
# replacement tags
doc_replacements = [('<progress>', '[===Job Progress===]'),
('<connect>', 'Connected ...'),
('<datetime.datetime>', repr(datetime.datetime.now())),
('<blankline>', '<BLANKLINE>')] # sphinx will ignore this for us
doctest_replacements = [('<progress>', doctest_ellipsis),
('<connect>', doctest_ellipsis),
('<datetime.datetime>', doctest_ellipsis),
('<blankline>', '<BLANKLINE>')]
# Two simple fsms, each with 2 states: Keep, Drop
keep = 0
drop = 1
def __init__(self, text, mode='doc', file_name=None):
"""
:param text: str of text to process
:param mode: preprocess mode, like 'doc' or 'doctest'
:return: object whose __str__ is the processed example text
"""
if mode == 'doc':
# process for human-consumable documentation
self.replacements = self.doc_replacements
self.is_state_keep = self._is_hide_state_keep
self._disappear = '' # in documentation, we need complete disappearance
elif mode == 'doctest':
# process for doctest execution
self.replacements = self.doctest_replacements
self.is_state_keep = self._is_skip_state_keep
self._disappear = '\n' # disappear means blank line for doctests, to preserve line numbers for error report
else:
raise DocExamplesException('Invalid mode "%s" given to %s. Must be in %s' %
(mode, self.__class__, ", ".join(['doc', 'doctest'])))
self.skip_state = self.keep
self.hide_state = self.keep
self.processed = ''
self._file_name = file_name
if text:
lines = text.splitlines(True)
self.processed = ''.join(self._process_line(line) for line in lines)
if self.hide_state != self.keep:
raise DocExamplesException("unclosed tag %s found%s" % (self.hide_start_tag, self._in_file()))
if self.skip_state != self.keep:
raise DocExamplesException("unclosed tag %s found" % self.skip_start_tag, self._in_file())
def _in_file(self):
return (" in file %s" % self._file_name) if self._file_name else ''
def _is_skip_state_keep(self):
return self.skip_state == self.keep
def _is_hide_state_keep(self):
return self.hide_state == self.keep
def _process_line(self, line):
"""processes line and advances fsms as necessary, returns processed line text"""
stripped = line.lstrip()
if stripped:
# Repair the "Up" link for certain files (this needs to match the doc/templates/css.mako)
if self._file_name and self._file_name.endswith("/index.html") and '<a href="index.html" id="fixed_top_left">Up</a>' in line:
if self._file_name.endswith("/daaltk/index.html"):
return ' <!-- No Up for root level index.html -->\n'
return '<a href="../index.html" id="fixed_top_left">Up</a>\n'
stripped = DocExamplesPreprocessor._strip_markdown_comment(stripped)
if stripped[0] == '<':
if self._process_if_tag_pair_tag(stripped):
return self._disappear # tag-pair markup should disappear appropriately
# check for keyword replacement
for keyword, replacement in self.replacements:
if stripped.startswith(keyword):
line = line.replace(keyword, replacement, 1)
break
return line if self.is_state_keep() else self._disappear
def _process_if_tag_pair_tag(self, stripped):
"""determines if the stripped line is a tag pair start or stop, advances fsms accordingly"""
if stripped.startswith(self.skip_start_tag):
if self.skip_state == self.drop:
raise DocExamplesException("nested tag %s found%s" % (self.skip_start_tag, self._in_file()))
self.skip_state = self.drop
return True
elif stripped.startswith(self.skip_stop_tag):
if self.skip_state == self.keep:
raise DocExamplesException("unexpected tag %s found%s" % (self.skip_stop_tag, self._in_file()))
self.skip_state = self.keep
return True
elif stripped.startswith(self.hide_start_tag):
if self.hide_state == self.drop:
raise DocExamplesException("nested tag %s found%s" % (self.hide_start_tag, self._in_file()))
self.hide_state = self.drop
return True
elif stripped.startswith(self.hide_stop_tag):
if self.hide_state == self.keep:
raise DocExamplesException("unexpected tag %s found%s" % (self.hide_stop_tag, self._in_file()))
self.hide_state = self.keep
return True
return False
markdown_comment_tell = r'[//]:'
markdown_comment_re = r'^\[//\]:\s*#\s*\"(.+)\"$'
markdown_comment_pattern = re.compile(markdown_comment_re)
@staticmethod
def _strip_markdown_comment(s):
"""
Checks if the given string is formatted as a Markdown comment per Magnus' response here:
http://stackoverflow.com/questions/4823468/comments-in-markdown/32190021#32190021
If it is, the formatting is stripped and only the comment's content is returned
If not, the string is returned untouched
"""
if s.startswith(DocExamplesPreprocessor.markdown_comment_tell):
m = DocExamplesPreprocessor.markdown_comment_pattern.match(s)
if m:
return m.group(1)
return s
def __str__(self):
return self.processed
##########################################################
def main():
script_name = os.path.basename(__file__)
usage = "Usage: %s <-html=HTML_DIR|-py=PY_DIR>" % script_name
if len(sys.argv) < 2:
raise RuntimeError(usage)
option = sys.argv[1]
html_flag = '-html='
py_flag = '-py='
if option.startswith(html_flag):
value = option[len(html_flag):]
html_dir = os.path.abspath(value)
print "[%s] processing HTML at %s" % (script_name, html_dir)
post_process_html(html_dir)
elif option.startswith(py_flag):
value = option[len(py_flag):]
py_dir = os.path.abspath(value)
print "[%s] processing Python at %s" % (script_name, py_dir)
pre_process_py(py_dir)
else:
raise RuntimeError(usage)
if __name__ == "__main__":
main()
|
[
"rene.o.dorado@intel.com"
] |
rene.o.dorado@intel.com
|
4639ffa783d2b6bac7000b63fbf2f0edebce684a
|
f61ac85822e491007d7459504c9cf94fd5d2f79c
|
/fluent_python/decorator/average_oo.py
|
1385225d466a47517a174ecd6778da28b9831070
|
[
"MIT"
] |
permissive
|
ftconan/python3
|
9b587d17d125159b9b0b3bf1a6dc5817e7cd2838
|
74628fcfcfed439ee8dc5498d138b4d019f1ea58
|
refs/heads/master
| 2023-07-06T23:17:59.962048
| 2023-06-28T08:15:45
| 2023-06-28T08:15:45
| 138,450,461
| 1
| 1
|
MIT
| 2022-11-22T09:15:49
| 2018-06-24T03:28:02
|
Jupyter Notebook
|
UTF-8
|
Python
| false
| false
| 1,198
|
py
|
"""
@author: magician
@file: average_oo.py
@date: 2020/10/20
"""
class Averager():
"""
Averager
"""
def __init__(self):
self.series = []
def __call__(self, new_value):
self.series.append(new_value)
total = sum(self.series)
return total / len(self.series)
def make_averager():
"""
make_averager
@return:
"""
series = []
def averager(new_value):
series.append(new_value)
total = sum(series)
return total / len(series)
return averager
def make_averager1():
"""
make_averager1
@return:
"""
count = 0
total = 0
def averager(new_value):
nonlocal count, total
count += 1
total += new_value
return total / count
return averager
if __name__ == '__main__':
avg = Averager()
print(avg(10))
print(avg(11))
print(avg(12))
avg1 = make_averager()
print(avg1(10))
print(avg1(11))
print(avg1(12))
print(avg1.__code__.co_varnames)
print(avg1.__code__.co_freevars)
print(avg1.__closure__)
print(avg1.__closure__[0].cell_contents)
avg2 = make_averager1()
print(avg2(10))
|
[
"1508907793@qq.com"
] |
1508907793@qq.com
|
e737f30ee336c86d11634c5ffc31dd2d27b89f09
|
313b7c5dbd4e028f4ae5b27f7f293bc5b991fcd4
|
/Excercise5_2_Sinlawat_S.py
|
b832755dcf4d035f0fbc836c71a96440f70303c8
|
[] |
no_license
|
Sinlawat/Laland
|
ae72a0e47142dbe13f9503dffb550f9ffeb5ac95
|
f8fd74dc0efda969e6529994407ec501e19bdabd
|
refs/heads/master
| 2020-11-28T22:58:41.593540
| 2020-06-22T00:03:02
| 2020-06-22T00:03:02
| 229,943,145
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 110
|
py
|
S = int(input("insert your Speeds = " ))
T = int(input("insert your Times = " ))
V = int(S/T)
print(V , "m/s")
|
[
"Sinlawats58@gmail.com"
] |
Sinlawats58@gmail.com
|
ce4d36e018e297df509215b25d6af8cbe17f9cd9
|
53b61ab08427f5e7a246bc2752b98be97cf299c1
|
/segmentation_framework/models/deeplabv3p/aspp.py
|
19d2e5179650cf326f48ebcda7017d4aacd68778
|
[] |
no_license
|
branislavhesko/segmentation_framework
|
0822e1a367d21cf205c7cdaa479aa425171acb0e
|
8d4c55a219786930e97f6e57d98cbc10fe4b4da5
|
refs/heads/master
| 2022-08-27T12:47:09.160324
| 2022-08-01T21:28:35
| 2022-08-01T21:28:35
| 189,777,229
| 1
| 0
| null | 2020-11-01T22:48:41
| 2019-06-01T21:01:58
|
Python
|
UTF-8
|
Python
| false
| false
| 3,611
|
py
|
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.deeplabv3p.sync_batchnorm.batchnorm import SynchronizedBatchNorm2d
class _ASPPModule(nn.Module):
def __init__(self, inplanes, planes, kernel_size, padding, dilation, BatchNorm):
super(_ASPPModule, self).__init__()
self.atrous_conv = nn.Conv2d(inplanes, planes, kernel_size=kernel_size,
stride=1, padding=padding, dilation=dilation, bias=False)
self.bn = BatchNorm(planes)
self.relu = nn.ReLU()
self._init_weight()
def forward(self, x):
x = self.atrous_conv(x)
x = self.bn(x)
return self.relu(x)
def _init_weight(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
torch.nn.init.kaiming_normal_(m.weight)
elif isinstance(m, SynchronizedBatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
class ASPP(nn.Module):
def __init__(self, backbone, output_stride, BatchNorm):
super(ASPP, self).__init__()
if backbone == 'drn':
inplanes = 512
elif backbone == 'mobilenet':
inplanes = 320
else:
inplanes = 2048
if output_stride == 16:
dilations = [1, 6, 12, 18]
elif output_stride == 8:
dilations = [1, 12, 24, 36]
else:
raise NotImplementedError
self.aspp1 = _ASPPModule(inplanes, 256, 1, padding=0, dilation=dilations[0], BatchNorm=BatchNorm)
self.aspp2 = _ASPPModule(inplanes, 256, 3, padding=dilations[1], dilation=dilations[1], BatchNorm=BatchNorm)
self.aspp3 = _ASPPModule(inplanes, 256, 3, padding=dilations[2], dilation=dilations[2], BatchNorm=BatchNorm)
self.aspp4 = _ASPPModule(inplanes, 256, 3, padding=dilations[3], dilation=dilations[3], BatchNorm=BatchNorm)
self.global_avg_pool = nn.Sequential(nn.AdaptiveAvgPool2d((1, 1)),
nn.Conv2d(inplanes, 256, 1, stride=1, bias=False),
BatchNorm(256),
nn.ReLU())
self.conv1 = nn.Conv2d(1280, 256, 1, bias=False)
self.bn1 = BatchNorm(256)
self.relu = nn.ReLU()
self.dropout = nn.Dropout(0.5)
self._init_weight()
def forward(self, x):
x1 = self.aspp1(x)
x2 = self.aspp2(x)
x3 = self.aspp3(x)
x4 = self.aspp4(x)
x5 = self.global_avg_pool(x)
x5 = F.interpolate(x5, size=x4.size()[2:], mode='bilinear', align_corners=True)
x = torch.cat((x1, x2, x3, x4, x5), dim=1)
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
return self.dropout(x)
def _init_weight(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
# n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
# m.weight.data.normal_(0, math.sqrt(2. / n))
torch.nn.init.kaiming_normal_(m.weight)
elif isinstance(m, SynchronizedBatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def build_aspp(backbone, output_stride, BatchNorm):
return ASPP(backbone, output_stride, BatchNorm)
|
[
"branislavh@protonmail.ch"
] |
branislavh@protonmail.ch
|
99681c36be3784e520c6d493f540d54bbb5b6ac4
|
d8a5fc2195165c970e2340eee87ae2ad5322da29
|
/{{cookiecutter.repo_name}}/{{cookiecutter.project_name}}/photos/views.py
|
48573cfdc703cc624f8d89eccaf4fa0037280c73
|
[
"BSD-3-Clause"
] |
permissive
|
lendlsmith/chrisdev-cookiecutter
|
b76e6194aa8369c2dbf1dac73e3282e025d2b146
|
e0ab2d16bd1a066800ce46bb1740b1254c259a70
|
refs/heads/master
| 2021-10-11T22:20:02.391847
| 2014-07-21T16:57:32
| 2014-07-21T16:57:32
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 644
|
py
|
from django.views.generic import ListView, DetailView
from filer.models import Folder
class GalleryListView(ListView):
#context_object_name = "gallery_list"
try:
queryset = Folder.objects.get(
name='Gallery').children.all().order_by('-created_at')
except Folder.DoesNotExist:
queryset = None
template_name = "gallery/gallery_archive.html"
class GalleryDetailView(DetailView):
#context_object_name = "gallery"
try:
queryset = Folder.objects.get(name='Gallery').children.all()
except Folder.DoesNotExist:
queryset = None
template_name = "gallery/gallery_detail.html"
|
[
"lendl.smith@gmail.com"
] |
lendl.smith@gmail.com
|
86158901edcbeb0e119d14f7e1d9a3dbc67a7ab7
|
beead56b100b562eb4eb03262a5fb6d688309166
|
/pydw/__init__.py
|
abdb214ea9ec9389d7b10b7fed00dd7e116f3188
|
[
"Apache-2.0"
] |
permissive
|
quzhengpeng/pydw
|
b01f64c3a00ffdac53393ed977972ff87728b66c
|
afc003fdd0bc3ca243c81134fb6c660cc26d7c2e
|
refs/heads/master
| 2020-05-09T22:43:24.580879
| 2019-12-25T10:37:39
| 2019-12-25T10:37:39
| 181,480,242
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 298
|
py
|
from pydw.utils import config
from pydw.utils import connection
def connect(db_conn):
conf = config.get_config(db_conn)
conn = connection.get_conn(conf)
return conn.conn
def create(db_conn):
conf = config.get_config(db_conn)
conn = connection.get_conn(conf)
return conn
|
[
"quzhengpeng@gmail.com"
] |
quzhengpeng@gmail.com
|
4528a59aa0db7486bbbf2a3cb6b8db98636d7a1b
|
17e60f61fc82e7369802a1c597b58b0206ad9bec
|
/lib/poolLoop.py
|
0a25964115c941e48f0dbddf08013eda3d965d6c
|
[] |
no_license
|
SLB-DeN/opensvc
|
5e06d42947f51662fa16203a00670a88b9e1fea9
|
75baeb19e0d26d5e150e770aef4d615c2327f32e
|
refs/heads/master
| 2021-05-17T05:35:18.585791
| 2020-03-19T15:20:05
| 2020-03-19T15:20:05
| 250,651,667
| 1
| 0
| null | 2020-03-27T21:29:22
| 2020-03-27T21:29:22
| null |
UTF-8
|
Python
| false
| false
| 1,366
|
py
|
from __future__ import print_function
import os
import pool
import rcExceptions as ex
from rcUtilities import lazy, justcall
class Pool(pool.Pool):
type = "loop"
capabilities = ["rox", "rwx", "roo", "rwo", "blk"]
@lazy
def path(self):
return self.oget("path")
def translate(self, name=None, size=None, fmt=True, shared=False):
data = [
{
"rtype": "disk",
"type": "loop",
"file": os.path.join(self.path, "%s.img" % name),
"size": size,
}
]
if fmt:
data += self.add_fs(name, shared)
return data
def pool_status(self):
from converters import convert_size
if not os.path.exists(self.path):
os.makedirs(self.path)
data = {
"name": self.name,
"type": self.type,
"capabilities": self.capabilities,
}
cmd = ["df", "-P", self.path]
out, err, ret = justcall(cmd)
if ret != 0:
return data
l = out.splitlines()[-1].split()
data["free"] = convert_size(l[3], default_unit="K", _to="k")
data["used"] = convert_size(l[2], default_unit="K", _to="k")
data["size"] = convert_size(l[1], default_unit="K", _to="k")
data["head"] = self.path
return data
|
[
"christophe.varoqui@opensvc.com"
] |
christophe.varoqui@opensvc.com
|
1fbcda7f3f1f05a9abcfa9a66315a7410e7cebba
|
cc38367d207e28e76fa087d0098757235208bbef
|
/Code/irc/banterbot_plugins/xkcd.py
|
f081cd7a7afa4612c3fdca06e8b46e56f0469c22
|
[] |
no_license
|
RussellChamp/tilde-projects
|
c1943ac2120977f9bb6eb47b779266ed06326115
|
210200a8832e58f6197fe264567862a567b13d1a
|
refs/heads/master
| 2020-12-09T07:36:33.170952
| 2020-03-02T21:31:58
| 2020-03-02T21:31:58
| 28,719,355
| 8
| 4
| null | 2019-01-28T16:49:32
| 2015-01-02T16:48:52
|
HTML
|
UTF-8
|
Python
| false
| false
| 189
|
py
|
#!/usr/bin/python3
import pinhook.plugin
import util.xkcdApropos
@pinhook.plugin.register('!xkcd')
def xkcd_plugin(msg):
return pinhook.plugin.message(util.xkcdApropos.xkcd(msg.arg))
|
[
"RussellChamp@gmail.com"
] |
RussellChamp@gmail.com
|
f57a5a411bc4bd9daee914c2fc13faf4310bdc9b
|
97ca8aedfc7959f99bf5add51c2fbb9d535c5aff
|
/tcml_tools/slurmer/parse/group.py
|
6142cd427c107f81a3ddab7c8eda3c9d7558ae77
|
[] |
no_license
|
cogsys-tuebingen/tcml_tools
|
74b930b8109ef0ad559584bb51808edb83fe4e8c
|
4eabeb08e34993143c729136dc4349043dde00ad
|
refs/heads/main
| 2023-06-02T02:27:13.915943
| 2021-06-09T07:01:23
| 2021-06-09T07:01:23
| 359,801,189
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 6,582
|
py
|
import numpy as np
from typing import Union
from collections import OrderedDict, defaultdict
from tcml_tools.slurmer.parse import Result, Metrics
class Group:
"""
A group of slurm jobs that share parameters (except e.g. seed)
metrics will be computed over groups
"""
default_result = Result("__default__", -1, float_acc=1)
all_param_keys = OrderedDict()
all_result_keys = OrderedDict()
def __init__(self, name: str, ids: list, **kwargs):
self.name = name
self.ids = [int(i) for i in ids]
self.params = kwargs
self.data = defaultdict(dict)
self.results = OrderedDict()
for k in kwargs.keys():
Group.all_param_keys[k] = True
def get(self, key: str, default=None):
""" get param/result, or default otherwise """
if key in self.params:
return self.params.get(key)
if key in self.results:
return self.results.get(key).value
return default
def get_param_tuple(self, skip_keys=()) -> tuple:
""" get a tuple of all parameter-values, except for the skipped ones """
return tuple([self.params.get(k, '') for k in self.all_param_keys.keys() if k not in skip_keys])
@staticmethod
def __filter(dct: OrderedDict, ignore_keys=()) -> OrderedDict:
new_dct = dct.copy()
for key in ignore_keys:
new_dct.pop(key, None)
return new_dct
@staticmethod
def sorted_param_keys(**filter_kwargs):
""" all known parameter keys of all groups """
return sorted([k for k in Group.__filter(Group.all_param_keys, **filter_kwargs).keys()])
def merge(self, other: 'Group'):
""" merge another group into this one, keep this name """
self.ids.extend(other.ids)
self.params.update(other.params)
self.data.update(other.data)
l0, l1 = len(self.results), len(other.results)
self.results.update(other.results)
assert len(self.results) == l0+l1, "Some results were overwritten by merging!"
def update_all_data(self, data: {str: dict}):
""" updates the data of all group members that are in the data dict """
for id_ in self.ids:
if id_ in data:
self.data[id_].update(data.get(id_))
def update_data(self, id_: int, data: dict):
""" updates the data of group member with slurm id """
self.data[id_].update(data)
def update_results(self, metrics: [Metrics]):
for m in metrics:
values, missing = self._values(key=m.get_key(), last_k=m.last_k)
try:
for result in m.from_values(values):
self.results[result.name] = max([result, self.results.get(result.name)])
Group.all_result_keys[result.name] = True
except KeyError:
raise KeyError('Missing key "%s" in: %s, but the metric requires it' % (m.get_key(), missing))
def _values(self, key: str, last_k=-1) -> (Union[np.array, None], list):
"""
all values, different group members on axis 0, time series on axis 1, (can be None)
and a list of slurm ids where the values are missing
"""
values = []
missing = []
for id_, data in self.data.items():
if key not in data:
missing.append(id_)
continue
v = np.array([v[2] for v in data.get(key)]) # tensorboard has (step, time, value) triplets
if isinstance(last_k, int) and (last_k > 0):
v = v[-last_k:]
values.append(v)
assert all([len(v) == len(values[0]) for v in values]), "different value-array lengths for key=%s" % key
if len(values) > 0:
return np.stack(values, axis=0), missing
return None, missing
def __header_dict(self, separator: str, **filter_kwargs) -> dict:
# param_keys = Group.sorted_param_keys(**filter_kwargs)
param_keys = list(self.__filter(self.all_param_keys, **filter_kwargs).keys())
value_keys = list(self.__filter(self.all_result_keys, **filter_kwargs).keys())
return {
'n': 'name',
'ids': 'slurm_ids',
'params': separator.join(param_keys),
'values': separator.join(value_keys),
}
def __table_dict(self, separator: str, **filter_kwargs) -> dict:
# param_keys = Group.sorted_param_keys(**filter_kwargs)
param_keys = list(self.__filter(self.all_param_keys, **filter_kwargs).keys())
value_keys = list(self.__filter(self.all_result_keys, **filter_kwargs).keys())
return {
'n': self.name,
'ids': str(self.ids),
'params': separator.join([str(self.params.get(k, '')) for k in param_keys]),
'values': separator.join([self.results.get(k, self.default_result).str for k in value_keys]),
}
def get_csv_str_header(self, **filter_kwargs) -> str:
""" table csv header, e.g. for libre office calc """
return '{n};{ids};;{params};;{values};'.format(**self.__header_dict(';', **filter_kwargs))
def get_csv_str(self, **filter_kwargs) -> str:
""" table csv row, e.g. for libre office calc, printing params and the metric values """
return '{n};{ids};;{params};;{values};'.format(**self.__table_dict(';', **filter_kwargs))
def get_latex_str_header(self, **filter_kwargs) -> str:
""" table header for latex """
return '{n} & {params} & {values} \\\\'.format(**self.__header_dict(' & ', **filter_kwargs)).replace('_', '\_')
def get_latex_str(self, **filter_kwargs) -> str:
""" table row for latex, printing params and the metric values """
return '{n} & {params} & {values} \\\\'.format(**self.__table_dict(' & ', **filter_kwargs)).replace('_', '\_')
class GroupSeparator(Group):
"""
simple hack to just insert a midrule into latex tables, and empty rows into csv data
will probably break everything if added first to a GroupManager, so don't do that
"""
_id = -1
def __init__(self, **kwargs):
self._id += 1
super().__init__('separator %d' % self._id, [], **kwargs)
def update_results(self, metrics):
pass
def get_csv_str(self, **filter_kwargs) -> str:
""" table row for libre office calc, printing params and the metric values """
return ''
def get_latex_str(self, **filter_kwargs) -> str:
""" table row for latex, printing params and the metric values """
return '\\midrule'
|
[
"kevin.laube@uni-tuebingen.de"
] |
kevin.laube@uni-tuebingen.de
|
6fd918e202a3968d5ca574c8f67d5791cf00f0a3
|
2785249737774981c54448096bfeb145aeedf08f
|
/run.py
|
273f2056a542cda4462cb1b00ab137c2e1192627
|
[
"MIT"
] |
permissive
|
ninoseki/hash-hush
|
44e0029bb807e7c00cffc0b9068fdc7d40c7683a
|
20212ab23fec922dec4542acd2e20a6487da9e83
|
refs/heads/master
| 2022-07-18T06:34:22.380257
| 2019-10-25T04:41:46
| 2019-10-25T04:41:46
| 216,987,219
| 1
| 1
|
MIT
| 2020-08-07T00:43:50
| 2019-10-23T06:46:40
|
TypeScript
|
UTF-8
|
Python
| false
| false
| 50
|
py
|
import os
from app import app
app.run(port=5000)
|
[
"noreply@github.com"
] |
ninoseki.noreply@github.com
|
9498aefa8f146488465c0dc49bcdcfecb6c2c61c
|
3b84c4b7b16ccfd0154f8dcb75ddbbb6636373be
|
/google-cloud-sdk/lib/googlecloudsdk/surface/compute/resource_views/resources/add.py
|
91a1766af1560f7ca696cc64491c0c49bb5e745d
|
[
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] |
permissive
|
twistedpair/google-cloud-sdk
|
37f04872cf1ab9c9ce5ec692d2201a93679827e3
|
1f9b424c40a87b46656fc9f5e2e9c81895c7e614
|
refs/heads/master
| 2023-08-18T18:42:59.622485
| 2023-08-15T00:00:00
| 2023-08-15T12:14:05
| 116,506,777
| 58
| 24
| null | 2022-02-14T22:01:53
| 2018-01-06T18:40:35
|
Python
|
UTF-8
|
Python
| false
| false
| 2,988
|
py
|
# Copyright 2014 Google Inc. All Rights Reserved.
"""'resourceviews resources add' command."""
from apiclient import errors
from googlecloudsdk.api_lib.compute import rolling_updates_util as util
from googlecloudsdk.calliope import base
from googlecloudsdk.calliope import exceptions
from googlecloudsdk.core import log
from googlecloudsdk.core import properties
class Add(base.Command):
"""Add resources to a resource view."""
@staticmethod
def Args(parser):
"""Args is called by calliope to gather arguments for this command.
Args:
parser: An argparse parser that you can use to add arguments that go
on the command line after this command. Positional arguments are
allowed.
"""
parser.add_argument(
'resource',
nargs='+',
help=('A list of fully-qualified URLs to each resource that should '
'be added to this view. For example: '
'https://www.googleapis.com/compute/v1/projects/myproject/'
'zones/us-central1-a/instances/instance-1'))
def Run(self, args):
"""Run 'resourceviews resources add'.
Args:
args: argparse.Namespace, The arguments that this command was invoked
with.
Raises:
HttpException: A http error response was received while executing api
request.
ToolException: An error other than http error occured while executing
the command.
"""
zone_views_client = self.context['zoneViewsClient']
region_views_client = self.context['regionViewsClient']
project = properties.VALUES.core.project.Get(required=True)
request_body = {'resources': args.resource}
if 'v1beta1' in self.context['api_version']:
if args.region:
request = region_views_client.addresources(
projectName=project,
region=args.region,
resourceViewName=args.resourceview,
body=request_body)
else:
request = zone_views_client.addresources(
projectName=project,
zone=args.zone,
resourceViewName=args.resourceview,
body=request_body)
else:
request = zone_views_client.addResources(
project=project,
zone=args.zone,
resourceView=args.resourceview,
body=request_body)
try:
request.execute()
log.Print('Resources added to resource view {0}.'.format(
args.resourceview))
except errors.HttpError as error:
raise exceptions.HttpException(util.GetError(error))
except errors.Error as error:
raise exceptions.ToolException(error)
Add.detailed_help = {
'brief': 'Add resources to a resource view.',
'DESCRIPTION': """\
This command adds resources to a resource view. You must provide a
list of fully-qualified URLs for each resource.
Alternatively, you can also use the addinstances command and provide
resource names rather than URLs.
""",
}
|
[
"joe@longreen.io"
] |
joe@longreen.io
|
8ac9eb56ba0571fcfba55fe637d4b74150921482
|
67b69fc833f5dc9b32146b8bc3261f7bb84324bb
|
/sns/forms.py
|
f7a761da1158bd2f7c74fc95efe22d513d7ea524
|
[] |
no_license
|
SotaEndo0214/My-SNS-app
|
08caf43244fba878a9153a2fc91c5f367472b9e7
|
72597f3689f976d46e9fb59198351a067896ea56
|
refs/heads/master
| 2022-11-05T04:59:18.222051
| 2020-06-26T10:30:05
| 2020-06-26T10:30:05
| 275,092,035
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,794
|
py
|
from django import forms
from django.contrib.auth.forms import UserCreationForm
from django.contrib.auth.forms import AuthenticationForm
from .models import Message, User
class UserCreateForm(UserCreationForm):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.fields['username'].widget.attrs['class'] = 'form-control'
self.fields['email'].widget.attrs['class'] = 'form-control'
self.fields['password1'].widget.attrs['class'] = 'form-control'
self.fields['password2'].widget.attrs['class'] = 'form-control'
class Meta:
model = User
fields = ("username", "email", "password1", "password2",)
class LoginForm(AuthenticationForm):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.fields['username'].widget.attrs['class'] = 'form-control'
self.fields['password'].widget.attrs['class'] = 'form-control'
class PostForm(forms.ModelForm):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.fields['context'].widget.attrs['class'] = 'form-control'
self.fields['image'].widget.attrs['class'] = 'form-control'
class Meta:
model = Message
fields = ['context', 'image']
class ProfileEditForm(forms.ModelForm):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.fields['username'].widget.attrs['class'] = 'form-control'
self.fields['email'].widget.attrs['class'] = 'form-control'
self.fields['icon_image'].widget.attrs['class'] = 'form-control'
self.fields['biography'].widget.attrs['class'] = 'form-control'
class Meta:
model = User
fields = ['username', 'email', 'icon_image', 'biography']
|
[
"sotapea@gmail.com"
] |
sotapea@gmail.com
|
4a515d5d729fa2b04b9fa633eeb2936a85a81110
|
ce392e553ddf78d6d52781db93f2ba128020f770
|
/docentes/forms.py
|
933c752a221176e7471604eba0d9cf3988ceb1ae
|
[] |
no_license
|
orionkmc/sigpa
|
d7b3834ad1dc8dd64750733fe9bdc762da5aaf57
|
37a10df9096295c89fd6115ea32b8d784d2e5661
|
refs/heads/master
| 2023-07-06T11:39:22.253318
| 2020-01-21T06:16:07
| 2020-01-21T06:16:07
| 38,455,955
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 531
|
py
|
# -*- coding: utf-8 -*-
from django.forms import ModelForm
from docentes.models import Docentes
from django.forms import formset_factory
class DocenteForm(ModelForm):
class Meta:
model = Docentes
fields = '__all__'
def __init__(self, *args, **kwargs):
super(DocenteForm, self).__init__(*args, **kwargs)
for field in self.fields:
self.fields[field].widget.attrs['class'] =\
'form-control form-control-sm'
DocenteFormSet = formset_factory(DocenteForm, extra=2)
|
[
"orionkmc@gmail.com"
] |
orionkmc@gmail.com
|
5a57d2d08af4a4dd8ad93e96047312d2f03db104
|
6e9a1c5f5095b5ebc78d264ce3adbdfeea113905
|
/affordance_gym/src/affordance_gym/trajectory_parser.py
|
37ac0d4de5c4391e7cbbbd2198db0a856aee4e45
|
[
"MIT"
] |
permissive
|
AXINLETTER/affordance_gym
|
051fd2b085ec81130626bb80318638b45e7aefae
|
e2e6eeafd4d725c5c912ac97d4d5ad1cf318af5f
|
refs/heads/master
| 2020-06-19T19:16:04.150276
| 2019-04-08T17:40:12
| 2019-04-08T17:40:12
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,811
|
py
|
import numpy as np
import os
import pickle
'''
This is used in generating a trajectory dataset.
'''
def parse_trajectory(trajectory):
trajectory = trajectory.joint_trajectory.points
time_steps_raw = np.array([motion.time_from_start.to_sec() for motion in trajectory])
positions_raw = np.stack(np.array(motion.positions) for motion in trajectory)
velocities_raw = np.stack(np.array(motion.velocities) for motion in trajectory)
accelerations_raw = np.stack(np.array(motion.accelerations) for motion in trajectory)
return time_steps_raw, positions_raw, velocities_raw, accelerations_raw
class TrajectoryParser(object):
def __init__(self, save_path, save_file, num_joints):
self.save_path = save_path
if not(os.path.exists(self.save_path)):
os.makedirs(self.save_path)
self.save_file = save_file
self.num_joints = num_joints
self.time_steps_raw = []
self.positions_raw = []
self.velocities_raw = []
self.accelerations_raw = []
self.end_poses = []
def add_trajectory(self, trajectory, end_pose):
time_steps_raw, positions_raw, velocities_raw, accelerations_raw = parse_trajectory(trajectory)
self.time_steps_raw.append(time_steps_raw)
self.positions_raw.append(positions_raw)
self.velocities_raw.append(velocities_raw)
self.accelerations_raw.append(accelerations_raw)
self.end_poses.append(end_pose)
def save(self):
f = open(os.path.join(self.save_path, '{}.pkl'.format(self.save_file)), 'wb')
pickle.dump([
np.array(self.time_steps_raw), np.array(self.positions_raw),
np.array(self.velocities_raw), np.array(self.accelerations_raw), np.array(self.end_poses)
], f)
f.close()
|
[
"aleksijonathanhamalainen@gmail.com"
] |
aleksijonathanhamalainen@gmail.com
|
bf827dd87006c899990aacccbb562772fcbfd3e6
|
78d35bb7876a3460d4398e1cb3554b06e36c720a
|
/sdk/network/azure-mgmt-network/azure/mgmt/network/v2021_05_01/operations/_load_balancer_frontend_ip_configurations_operations.py
|
be7b0ac5d709fb10ed6105bb3d5c250f99998522
|
[
"MIT",
"LicenseRef-scancode-generic-cla",
"LGPL-2.1-or-later"
] |
permissive
|
catchsrinivas/azure-sdk-for-python
|
e35f59b60318a31b3c940a7a3a07b61b28118aa5
|
596227a7738a5342274486e30489239d539b11d1
|
refs/heads/main
| 2023-08-27T09:08:07.986249
| 2021-11-11T11:13:35
| 2021-11-11T11:13:35
| 427,045,896
| 0
| 0
|
MIT
| 2021-11-11T15:14:31
| 2021-11-11T15:14:31
| null |
UTF-8
|
Python
| false
| false
| 9,049
|
py
|
# coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
from typing import TYPE_CHECKING
import warnings
from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.paging import ItemPaged
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import HttpRequest, HttpResponse
from azure.mgmt.core.exceptions import ARMErrorFormat
from .. import models as _models
if TYPE_CHECKING:
# pylint: disable=unused-import,ungrouped-imports
from typing import Any, Callable, Dict, Generic, Iterable, Optional, TypeVar
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
class LoadBalancerFrontendIPConfigurationsOperations(object):
"""LoadBalancerFrontendIPConfigurationsOperations operations.
You should not instantiate this class directly. Instead, you should create a Client instance that
instantiates it for you and attaches it as an attribute.
:ivar models: Alias to model classes used in this operation group.
:type models: ~azure.mgmt.network.v2021_05_01.models
:param client: Client for service requests.
:param config: Configuration of service client.
:param serializer: An object model serializer.
:param deserializer: An object model deserializer.
"""
models = _models
def __init__(self, client, config, serializer, deserializer):
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config
def list(
self,
resource_group_name, # type: str
load_balancer_name, # type: str
**kwargs # type: Any
):
# type: (...) -> Iterable["_models.LoadBalancerFrontendIPConfigurationListResult"]
"""Gets all the load balancer frontend IP configurations.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param load_balancer_name: The name of the load balancer.
:type load_balancer_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: An iterator like instance of either LoadBalancerFrontendIPConfigurationListResult or the result of cls(response)
:rtype: ~azure.core.paging.ItemPaged[~azure.mgmt.network.v2021_05_01.models.LoadBalancerFrontendIPConfigurationListResult]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.LoadBalancerFrontendIPConfigurationListResult"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2021-05-01"
accept = "application/json"
def prepare_request(next_link=None):
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
if not next_link:
# Construct URL
url = self.list.metadata['url'] # type: ignore
path_format_arguments = {
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'loadBalancerName': self._serialize.url("load_balancer_name", load_balancer_name, 'str'),
'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str')
request = self._client.get(url, query_parameters, header_parameters)
else:
url = next_link
query_parameters = {} # type: Dict[str, Any]
request = self._client.get(url, query_parameters, header_parameters)
return request
def extract_data(pipeline_response):
deserialized = self._deserialize('LoadBalancerFrontendIPConfigurationListResult', pipeline_response)
list_of_elem = deserialized.value
if cls:
list_of_elem = cls(list_of_elem)
return deserialized.next_link or None, iter(list_of_elem)
def get_next(next_link=None):
request = prepare_request(next_link)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
return pipeline_response
return ItemPaged(
get_next, extract_data
)
list.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/loadBalancers/{loadBalancerName}/frontendIPConfigurations'} # type: ignore
def get(
self,
resource_group_name, # type: str
load_balancer_name, # type: str
frontend_ip_configuration_name, # type: str
**kwargs # type: Any
):
# type: (...) -> "_models.FrontendIPConfiguration"
"""Gets load balancer frontend IP configuration.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param load_balancer_name: The name of the load balancer.
:type load_balancer_name: str
:param frontend_ip_configuration_name: The name of the frontend IP configuration.
:type frontend_ip_configuration_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FrontendIPConfiguration, or the result of cls(response)
:rtype: ~azure.mgmt.network.v2021_05_01.models.FrontendIPConfiguration
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FrontendIPConfiguration"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2021-05-01"
accept = "application/json"
# Construct URL
url = self.get.metadata['url'] # type: ignore
path_format_arguments = {
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'loadBalancerName': self._serialize.url("load_balancer_name", load_balancer_name, 'str'),
'frontendIPConfigurationName': self._serialize.url("frontend_ip_configuration_name", frontend_ip_configuration_name, 'str'),
'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str')
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
request = self._client.get(url, query_parameters, header_parameters)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
deserialized = self._deserialize('FrontendIPConfiguration', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/loadBalancers/{loadBalancerName}/frontendIPConfigurations/{frontendIPConfigurationName}'} # type: ignore
|
[
"noreply@github.com"
] |
catchsrinivas.noreply@github.com
|
abca959b9a298575138870b50ef3fb6c239191e7
|
6cb52959dbadaa610e91a6769079ff0ddee2422e
|
/exercices/008/solution.py
|
a61f843cd4dadeaafbb9e3579c749b05006babef
|
[] |
no_license
|
LOUISvASS/hackinscience
|
9042e4f1a204c9cfd08ebcfc8178a93fa4d4f027
|
a2ec85cdf2a0851ee7e220481d17a7c44fa01b90
|
refs/heads/master
| 2021-01-13T15:35:11.209514
| 2016-12-19T19:23:03
| 2016-12-19T19:23:03
| 76,887,580
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 73
|
py
|
! /usr/bin/python
string = "Hello World !"
for x in string:
print(x)
|
[
"louis.vasselin@cri-paris.org"
] |
louis.vasselin@cri-paris.org
|
7aa3530f559c27d7df393c936929fb4de6ee002b
|
ae101d2e6d75fb21243c90a9de99bcb0738e7b06
|
/keeper/views.py
|
1c4ee7fd8cd952d7fc7fffaaab3a6933641d41c4
|
[] |
no_license
|
mikhail-vlasenko/Romanov_Key_Keeper
|
5fbc8cde8ea3b3b760a99abf63e4c3d017a1a76a
|
a228195518d310929178417469a558c3c02015b8
|
refs/heads/master
| 2022-12-12T17:36:13.956979
| 2019-11-03T10:05:43
| 2019-11-03T10:05:43
| 168,011,927
| 0
| 0
| null | 2022-12-08T01:45:59
| 2019-01-28T18:17:16
|
Python
|
UTF-8
|
Python
| false
| false
| 13,499
|
py
|
from .models import *
# from django.contrib.auth.models import User
from django.contrib.auth import authenticate, login, logout
from django.shortcuts import get_object_or_404, render
from .forms import *
from django.contrib.auth.decorators import login_required
from django.core.exceptions import ObjectDoesNotExist
from django.http import HttpResponse, HttpResponseRedirect
from django.db import IntegrityError
# timezone = pytz.timezone("Europe/Moscow")
REG_CODE = '12345' # code to enable registration
PASSWORD_CHANGE_CODE = '54321' # code to change password
CARD_TAKE_USERS = ['Guard', 'romanov_admin'] # who is allowed to use card reader
@login_required
def index(request):
"""
Main page rendering function.
Lets users to transfer key to each other.
Displays interface with owned keys and a form for key transfer.
Login is required.
:param request: request object
:return: request answer object, contains *HTML* file
:rtype: :class: `django.http.HttpResponse`
"""
context = {}
if request.method == 'POST':
f = TransferForm(request.POST)
if 'transfer_req' in request.POST:
if f.is_valid():
try:
tran_user = CustomUser.objects.filter(username=f.data['username']).get()
if tran_user != request.user:
try:
if tran_user.key_tran_last != -1:
tran_user_last = CustomUser.objects.filter(username=tran_user.user_tran_last).get()
hist = History.objects.filter(key=tran_user.key_tran_last, user_id=tran_user_last,
active='Ожидает передачи').get()
hist.active = 'Не сдан'
hist.save()
tran_user.key_tran_last = -1
tran_user.user_tran_last = 'никто'
tran_user.save()
hist = History.objects.filter(key=f.data['key_num'], user_id=request.user,
active='Не сдан').get()
hist.active = 'Ожидает передачи'
hist.save()
tran_user.user_tran_last = str(request.user.username)
tran_user.key_tran_last = f.data['key_num']
tran_user.save()
context['message'] = 'Ожидайте подтверждения'
except ObjectDoesNotExist:
context['message'] = 'У вас нет такого ключа'
else:
context['message'] = 'Нельзя передать ключ себе'
except ObjectDoesNotExist:
context['message'] = 'Нет такого пользователя'
elif 'accept' in request.POST:
user_tran = CustomUser.objects.filter(username=request.user.user_tran_last).get()
hist = History.objects.filter(key=request.user.key_tran_last, user_id=user_tran,
active='Ожидает передачи').get()
try:
hist_check = History.objects.filter(key=request.user.key_tran_last, user_id=request.user,
active='Не сдан').get()
context['message'] = 'У вас уже есть ключ от этого кабинета'
except ObjectDoesNotExist:
hist.active = 'Сдан'
hist.time_back = datetime.datetime.now()
hist.save()
hist = History(key=request.user.key_tran_last, user_id=request.user, active='Не сдан')
hist.save()
request.user.key_tran_last = -1
request.user.user_tran_last = 'никто'
request.user.save()
context['message'] = 'Вы получили ключ'
elif 'reject' in request.POST:
user_tran = CustomUser.objects.filter(username=request.user.user_tran_last).get()
hist = History.objects.filter(key=request.user.key_tran_last, user_id=user_tran,
active='Ожидает передачи').get()
hist.active = 'Не сдан'
hist.save()
request.user.key_tran_last = -1
request.user.user_tran_last = 'никто'
request.user.save()
context['message'] = 'Вы отказались получать ключ'
else:
f = TransferForm()
key_list = History.objects.filter(user_id=request.user, active='Не сдан')
context['key_list'] = key_list
if request.user.key_tran_last != -1:
context['key_receive'] = request.user.key_tran_last
context['user_receive'] = request.user.user_tran_last
context['form'] = f
context['user_id'] = str(request.user.last_name) + ' ' + str(request.user.first_name)
context['user_name'] = str(request.user.username)
return render(request, 'transfer.html', context)
@login_required
def card_take(request):
"""
Card using page rendering function.
Lets users to take/return keys using their cards.
User should be Guard or Admin to enable this method.
:param request: request object
:return: request answer object, contains *HTML* file
:rtype: :class: `django.http.HttpResponse`
"""
context = {}
if request.method == 'POST':
f = CardForm(request.POST)
if f.is_valid():
if request.user.username in CARD_TAKE_USERS:
try:
card_user = CustomUser.objects.filter(card_id=f.data['card']).get()
try:
hist_unit = History.objects.filter(key=f.data['key_num'], user_id=card_user, active='Не сдан').get()
hist_unit.active = 'Сдан'
hist_unit.time_back = datetime.datetime.now()
hist_unit.save()
context['action'] = 'ОТДАЛ'
except ObjectDoesNotExist:
hist = History(key=f.data['key_num'], time_cr=datetime.datetime.now(), user_id=card_user)
hist.active = 'Не сдан'
hist.save()
context['action'] = 'ВЗЯЛ'
context['user_name'] = str(card_user.username)
context['key_num'] = str(f.data['key_num'])
except ObjectDoesNotExist:
context['message'] = 'Нет такой карты'
else:
context['message'] = 'У вас недостаточно прав. Вы можете использовать карту только на охране'
else:
f = CardForm()
context['form'] = f
return render(request, 'card_reader.html', context)
@login_required
def history(request, page_id=1):
"""
History page rendering function.
Lets users to see history or search through it.
For security purposes login is also required.
:param request: request object
:param page_id: page number, influence displaying interval
:return: request answer object, contains *HTML* file
:rtype: :class: `django.http.HttpResponse`
"""
context = {}
hist_list = []
hist_min_time = 0
for x in History.objects.all()[::-1]:
if x.active == 'Не сдан':
hist_min_time = x
break
if request.method == 'POST':
f = SearchForm(request.POST)
if f.is_valid():
hist_list = History.objects.all()[::-1]
if f.data['key_num'] != '':
hist_list2 = []
for x in hist_list:
if x.key == int(f.data['key_num']):
hist_list2.append(x)
hist_list = hist_list2
if f.data['last_name'] != '':
hist_list3 = []
for x in hist_list:
if x.user_id.last_name == f.data['last_name']:
hist_list3.append(x)
hist_list = hist_list3
if f.data['is_active'] == 'true':
context['select_active'] = 'true'
hist_list4 = []
for x in hist_list:
if x.active == 'Не сдан':
hist_list4.append(x)
hist_list = hist_list4
elif f.data['is_active'] == 'false':
context['select_active'] = 'false'
hist_list5 = []
for x in hist_list:
if x.active == 'Сдан':
hist_list5.append(x)
hist_list = hist_list5
elif f.data['is_active'] == 'waiting':
context['select_active'] = 'waiting'
hist_list6 = []
for x in hist_list:
if x.active == 'Ожидает передачи':
hist_list6.append(x)
hist_list = hist_list6
else:
context['select_active'] = 'none'
else:
hist_list = History.objects.all()[::-1]
f = SearchForm()
context['hist_list'] = hist_list[(page_id - 1) * 100:(page_id * 100)]
context['min_time_elem'] = hist_min_time
context['page_id_p'] = page_id + 1 # can be improved
if page_id != 1:
context['page_id_m'] = page_id - 1
else:
context['page_id_m'] = page_id
context['form'] = f
return render(request, 'history.html', context)
@login_required
def register(request):
"""
Register page rendering function.
Lets users to register on the website. Only works on special accounts (as taking by card).
This prevents unwanted access to log database.
:param request: request object
:return: request answer object, contains *HTML* file
:rtype: :class: `django.http.HttpResponse`
"""
context = {}
if request.method == 'POST':
f = RegisterForm(request.POST)
if f.is_valid() and f.data['password'] == f.data['password2']:
if request.user.username in CARD_TAKE_USERS:
try:
if CustomUser.objects.filter(card_id=f.data['card_id']).exists():
context['message'] = 'Такая карта уже есть'
else:
user = CustomUser.objects.create_user(username=f.data['username'], password=f.data['password'],
card_id=f.data['card_id'], last_name=f.data['last_name'],
first_name=f.data['first_name'])
user.save()
context['message'] = 'Вы успешно зарегистрированы!'
user = authenticate(request, username=f.data['username'], password=f.data['password'])
if user is not None:
return HttpResponseRedirect('/card')
except IntegrityError:
context['message'] = 'Такое имя пользователя уже есть'
else:
context['message'] = 'У вас недостаточно прав. Вы можете зарегистрироваться на охране'
else:
context['message'] = 'Пароли не совпадают'
else:
f = RegisterForm()
context['users'] = CustomUser.objects.all()
context['form'] = f
return render(request, 'register.html', context)
def login_user(request):
"""
Login page rendering function.
Lets users to login on the website.
:param request: request object
:return: request answer object, contains *HTML* file
:rtype: :class: `django.http.HttpResponse`
"""
context = {}
if request.method == 'POST':
f = LoginForm(request.POST)
if f.is_valid():
user = authenticate(request, username=f.data['username'], password=f.data['password'])
if user is not None:
login(request, user)
context['message'] = 'Вход выполнен!'
return HttpResponseRedirect('/')
else:
context['message'] = 'Не получилось войти:('
else:
f = LoginForm()
context['user_name'] = str(request.user.username)
context['form'] = f
return render(request, 'login.html', context)
def logout_user(request):
"""
Logout function
:param request: request object
:return: Redirect to login page
:rtype: :class: `django.http.HttpResponseRedirect`
"""
logout(request)
return HttpResponseRedirect('/login')
def about(request):
"""
About page rendering function
:param request: request object
:return: request answer object, contains *HTML* file
:rtype: :class: `django.http.HttpResponse`
"""
context = {}
return render(request, 'doc_cap.html', context)
|
[
"medvedek2002@gmail.com"
] |
medvedek2002@gmail.com
|
1107fd771f87376fcefbb02c4824430d8b657594
|
77a432201ab9e8429de1f6f2190d8048ea6c920b
|
/metREx/app/main/controller/scheduler_controller.py
|
bb859f9d2ce6f163dca35c2746bf83a5b49c6eb1
|
[
"Apache-2.0"
] |
permissive
|
homedepot/metREx
|
0f794f12e803a89992a4658de29839e791a39139
|
d30f084fb0bf906372aa14b448d5d8a8fc9fe833
|
refs/heads/master
| 2023-04-03T02:21:26.706911
| 2023-03-29T14:45:41
| 2023-03-29T14:45:41
| 246,633,031
| 9
| 3
|
Apache-2.0
| 2023-03-29T14:45:43
| 2020-03-11T17:11:48
|
Python
|
UTF-8
|
Python
| false
| false
| 1,637
|
py
|
from flask_restx import Resource
from flask_apscheduler import api as aps_api
from ..util.dto import SchedulerDto
api = SchedulerDto.api
@api.route('')
class Scheduler(Resource):
@api.doc('get_scheduler_info')
def get(self):
"""Gets the scheduler info."""
return aps_api.get_scheduler_info()
@api.route('/jobs')
class SchedulerJobList(Resource):
@api.doc('get_jobs')
def get(self):
"""Gets all scheduled jobs."""
return aps_api.get_jobs()
# @api.doc('add_job')
# def post(self):
# """Adds a new job."""
# return aps_api.add_job()
@api.route('/jobs/<job_id>')
class SchedulerJob(Resource):
@api.doc('get_job')
def get(self, job_id):
"""Gets a job."""
return aps_api.get_job(job_id)
# @api.doc('delete_job')
# def delete(self, job_id):
# """Deletes a job."""
# return aps_api.delete_job(job_id)
# @api.doc('update_job')
# def patch(self, job_id):
# """Updates a job."""
# return aps_api.update_job(job_id)
@api.route('/jobs/<job_id>/pause')
class SchedulerJobPause(Resource):
@api.doc('pause_job')
def get(self, job_id):
"""Pauses a job."""
return aps_api.pause_job(job_id)
@api.route('/jobs/<job_id>/resume')
class SchedulerJobResume(Resource):
@api.doc('resume_job')
def get(self, job_id):
"""Resumes a job."""
return aps_api.resume_job(job_id)
@api.route('/jobs/<job_id>/run')
class SchedulerJobRun(Resource):
@api.doc('run_job')
def get(self, job_id):
"""Runs a job."""
return aps_api.run_job(job_id)
|
[
"MICHAEL_PHILLIPSON1@homedepot.com"
] |
MICHAEL_PHILLIPSON1@homedepot.com
|
c5fc0a190aeb1eaa17bd948694bd6a152fcbd0d0
|
0c08f2101d6be5fc40f14d6b761fcd883b880035
|
/main1.py
|
34f4a017ca43eb0b0c0990e534a6e07d67a542c6
|
[] |
no_license
|
shashank0786/Notification-alert-system
|
641ccc4006d3559d92cd6e1591adb1cc70ab0f95
|
11470902249c951b8354cd185372e8a0c28fa0a5
|
refs/heads/master
| 2023-04-22T10:05:55.164384
| 2021-05-01T18:23:00
| 2021-05-01T18:23:00
| 363,478,628
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,269
|
py
|
from plyer import notification
import requests
from bs4 import BeautifulSoup
import time
def notifyMe(title,message):
notification.notify(
title=title,
message=message,
app_icon="G:\covid project new\images.ico",
timeout=7
)
def getData(url):
r=requests.get(url)
return r.text
if __name__ == "__main__":
#notifyMe("shashank","plz stop this covid19")
myHTMLData=getData('https://www.mohfw.gov.in/')
soup = BeautifulSoup(myHTMLData,'html.parser')
#print(soup.prettify())
Mydatast=""
for tr in soup.find_all('thead')[1].find_all('tr'):
Mydatastr +=tr.get_text()
Mydatastr=Mydatastr[1:]
itemList=Mydatastr.split('\n\n')
states=['uttar pradesh','telangana','west bangal']
for item in itemList[0:35]:
dataList=item.split('\n')
if dataList[1] in states:
nTitle='cases of Covid-19'
nText=f"{States:{dataList[1]} \n IND :{dataList2} \n deaths:{dataList[3]}"
notifyMe(nTitle,nText)
time.sleep(2)
# if you want to break out this notifiaction per hour then you can put a while loop on it and at last time.sleep(3600).then it will work very fine..
|
[
"srivastavashashank80@gmail.com"
] |
srivastavashashank80@gmail.com
|
ee258a87e04e9ce5067d6218cbcd67df78c5b3f8
|
395b84c81af098192defb3540c48d5983c34cc5d
|
/stock_util/nadex.py
|
25442de5a58518fc84aaa9d3dc8011d169a0bb03
|
[
"MIT"
] |
permissive
|
mpthomas/CNN-Forex-Trader
|
c41a25ae94cfeed9048fae516d7c730196bc1022
|
58f11e2c2b47c8c3cf570af8165c22707969fbdb
|
refs/heads/master
| 2022-12-08T06:17:15.677533
| 2020-09-26T19:18:08
| 2020-09-26T19:18:08
| 195,927,480
| 1
| 0
|
MIT
| 2022-12-08T05:51:48
| 2019-07-09T03:40:45
|
Jupyter Notebook
|
UTF-8
|
Python
| false
| false
| 3,458
|
py
|
from selenium import webdriver
from selenium.webdriver.common.desired_capabilities import DesiredCapabilities
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
import configparser
import time
class Nadex():
def __init__(self):
self.in_results_frame=False
try:
config=configparser.RawConfigParser()
config.read('config/nadex.cfg')
self.chrome=config.get('nadex','chrome')
self.login_url=config.get('nadex','login_url')
self.demo=config.get('nadex','demo')
config=configparser.RawConfigParser()
config.read('config/passwd.cfg')
self.username=config.get('nadex','username')
print ("Finished getting login")
self.passwd=config.get('nadex','password')
except Exception as e:
print(e)
return None
#self.driver=webdriver.Chrome(self.chrome)
self.driver=webdriver.Remote(
command_executor='http://127.0.0.1:4444/wd/hub',
desired_capabilities=DesiredCapabilities.CHROME)
#self.driver.start()
def login(self):
self.driver.get(str(self.login_url))
#time.sleep(5)
el=self.driver.find_element_by_id("account_id")
if(el):
el.send_keys(self.username)
else:
print("login form not found. Might already be logged in")
return False
self.driver.find_element_by_id("password").send_keys(self.passwd)
if(self.demo):
self.driver.find_element_by_id("demo-toggle").click()
self.driver.find_element_by_id("loginbutton").click()
def get_contracts_old(self):
self.driver.refresh()
time.sleep(10)
try:
wait=WebDriverWait(self.driver,100)
wait.until(EC.frame_to_be_available_and_switch_to_it((By.ID,'ifrMarketFilter')))
except Exception as e:
print(e)
return []
try:
self.driver.find_element_by_id("ddDropDown_filterNameSelect").click()
self.driver.find_element_by_css_selector("div[data='EUR/USD']").click()
#self.driver.execute_script("arguments[0].setAttribute('data','EUR/USD')",e)
self.driver.find_element_by_id("btnSearchFilter").click()
except Exception as e:
print(e)
return []
try:
self.driver.switch_to_default_content()
wait.WebDriverWait(self.driver,10)
wait.until(EC.frame_to_be_available_and_switch_to_it((By.ID,'ifrFilterResults')))
except Exception as e:
print("Couldn't get results iframe")
return []
contracts=self.driver.find_elements(By.CLASS_NAME,"contract")
if(len(contracts) == 0):
print("No contracts found")
return []
for contract in contracts:
print(contract.text)
#contracts[0].click()
def get_contracts(self):
contracts = []
try:
if not self.in_results_frame:
wait=WebDriverWait(self.driver,300).until(EC.frame_to_be_available_and_switch_to_it((By.ID,'ifrFilterResults')))
print("Found results iframe")
self.in_results_frame=True
except Exception as e:
print(e)
print("Couldn't get filter results frame")
return []
try:
el=WebDriverWait(self.driver,300).until(
EC.presence_of_all_elements_located((By.CLASS_NAME,"contract"))
)
print("Wait: located elements by css contract")
count=len(el)
except Exception as e:
print(e)
print("Couldn't get contracts elements")
return []
print("found "+str(len(el))+" contracts")
for i in range(0,count):
els=self.driver.find_elements_by_class_name("contract")
contracts.append(els[i].text)
return contracts
def logout(self):
return True
|
[
"matthomas@gmail.com"
] |
matthomas@gmail.com
|
8f4e42b65ce09e4a562f2d4b298babce0fd4be3b
|
2417d9f6afe95ba19354c65bfb400556f2eb2e19
|
/setup.py
|
2a91c65f19fbb3863f4728098116fca13710074a
|
[
"Apache-2.0"
] |
permissive
|
rakeshnb/pixiedust
|
39f1249a867719919441488f085e1f60519dae58
|
fb5198c7564589c267147d7bdee1f798e7b361ef
|
refs/heads/master
| 2020-05-23T08:09:42.603871
| 2016-10-07T22:08:10
| 2016-10-07T22:08:10
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 732
|
py
|
from setuptools import setup
setup(name='pixiedust',
version='0.38',
description='Misc helpers for Spark Python Notebook',
url='https://github.com/ibm-cds-labs/pixiedust',
install_requires=['maven-artifact','mpld3'],
author='David Taieb',
author_email='david_taieb@us.ibm.com',
license='Apache 2.0',
packages=['pixiedust','pixiedust.packageManager','pixiedust.display',
'pixiedust.display.table','pixiedust.display.graph','pixiedust.display.chart','pixiedust.display.chart.plugins',
'pixiedust.display.tests','pixiedust.display.download',
'pixiedust.services',
'pixiedust.utils'],
include_package_data=True,
zip_safe=False)
|
[
"david_taieb@us.ibm.com"
] |
david_taieb@us.ibm.com
|
f68cd316f77807458ca751c95bccb75bd365fa5b
|
227038c529ce9ad5dbc3040555724afa910f2aeb
|
/dialogue_manager.py
|
aaf84f31be676faac9eb316979fb055df8554e85
|
[] |
no_license
|
EriChen0615/QAQ_chatbot
|
a5a78deb1049cf53d4820b9524baf682d7811f96
|
ecd7cb547a6ce78b02d8f32b64c8b0472e1db750
|
refs/heads/main
| 2023-01-06T20:01:22.279267
| 2020-10-25T14:55:34
| 2020-10-25T14:55:34
| 306,833,382
| 0
| 1
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 12,126
|
py
|
from dumb_nlu import Dumb_NLU
from component import Component
import pandas as pd
import numpy as np
"""
Intellegent: if one field is empty but only one candidate, can autofill
gives suggestions based on historical features
feature to be added: 1. suggestions refer to online forum
2. add top search list
3. maintaince
feature to be tested:1. add customized measurements
"""
class DialogueManager(Component):
def __init__(self):
"""
create a Dialogue Manager object
:param
:attributes
input: get input from nlu
input is a dictionary with three attributes: parts, error, states
states[None=detect parts/error, yes/no=feedback from the user for the validity of the solution]
setup1: transfer input to inner attributes(see function)
last_state, state: None(to identify the problem), yes(we're done!), or no(the pass the next solution)
state_counter: count how many states has been provided but failed
filename: fixed variable for file path
df: dataframe to the data file
df_stats: a list of solutions, ranked based on success frequency
:return None
"""
super().__init__()
# self.tracker = tracker
# self.agent = agent
# self.tracker.connect(agent)
self.input = None
# self.setup1()
self.last_state = None
self.state = None
# self.learnparameter=1
self.state_counter = 0
self.filename = 'data/cnc_troubleshooting.xlsx'
self.df = self.excel_to_df()
self.df_stats = []
self.msg = ""
self.new_solution = ""
def setup(self):
pass
def setup1(self):
"""
deal with input and update existing variables.
if input contains an previously unknown attribute, then update the attribute
else, create a placeholder/leave nothing
DO NOT PERFORM COLLISION DETECTION: will keep the newest valid solution
:return: None
"""
# print("Dialogue Manager is setup!")
if "parts" in self.input and self.input['parts']:
self.part = self.input["parts"]
if "error" in self.input and self.input['error']:
self.error = self.input["error"]
self.last_state = self.state
if "solution" in self.input:
self.new_solution = self.input["solution"]
# print(self.input)
self.state = self.input["state"]
try:
self.part
except AttributeError:
self.part = None
try:
self.error
except AttributeError:
self.error = None
def input_debug(self, intm):
"""
for debug and using the object as a function
e.g. object.input_debug(<input dictionary>)
get_result=object.do_step()
:param intm: input
:return: none
"""
self.input = intm
self.setup1()
return 0
def do_step(self):
# self.tracker.input = self.input
# self.tracker.run()
# self.agent.run()
# if self.state == "greeting":
# response_msg = self.greeting()
# elif self.state == "thanks":
# response_msg = self.thanks()
"""
the main control of the program
If either part is missing, request user to describe the problem using trouble_shooting
If both parts are presented, provide the solution by solution_provider()
(if problem solved, it will jump to thanks() as indicated in the solution_provider())
Additionally, if it is "yes", case closed and object inits itself.
:return: response_msg to the UI
"""
if ((not self.part or not self.error) and self.state == 'no') or not self.state:
response_msg = self.trouble_shooting()
else:
response_msg = self.solution_provider()
self.output = response_msg
print(response_msg) # , self.state_counter, self.state)
self.msg = response_msg
self.last_state = self.state
if self.state == "yes":
pass # self.__init__()
return response_msg
def to_front(self, action):
"""
aborted!
"""
return {'response': action['response'], 'browser_action': 'reply'}
def greeting(self):
"""
Aborted! Replaced by greeting in UI
:return:
"""
return "Hello, how can I help you?"
def thanks(self):
"""
Run this function when the user solved the problem
Update the methods on a sheet
:return: Answer politely with a speck of pride. Our chatbot is well-educated gentleman/lady
"""
# print(self.part, self.error, self.state_counter)
i = 1
while self.df.loc[i, 'Parts'] != self.part or self.df.loc[i, 'Error'] != self.error or self.df.loc[
i, 'Solution'] != self.df_stats[self.state_counter - 2]:
i += 1
self.df.loc[i, 'appear_time'] += 1
self.df.to_excel(self.filename, sheet_name='Sheet1', index=False, header=True)
self.df.to_excel(self.filename, index=False)
# self.__init__()
return "My pleasure."
def trouble_shooting(self):
"""
Try to find some information form our poor dumb master. Hopefully he/she will say something reasonable
:return: if none of "parts" and "error" is understood, give it another try (or am I)?
if one part is missing: list all I know that fits another parameter. That's the best I can do.
hope there is something for my poor master
"""
"""
Intellegent: if one field is empty but only one candidate, can autofill
gives suggestions based on historical features
feature to be added: 1. suggestions refer to online forum
feature to be tested:1. add customized measurements
"""
if not self.state:
return "I'm sorry, TROUBLE!!"
elif self.state == 'greeting':
return "Please tell me more about your issue!"
elif not self.part and not self.error:
return "I'm sorry, I couldn't understand. Good luck :-D"
elif not self.part:
candidates = self.df.loc[(self.df['Error'] == self.error)]
candidates = candidates.Parts.tolist()
candidates = list(set(candidates))
if len(candidates) == 1:
self.part = candidates[0]
self.do_step()
return 0
else:
return "Which part has this problem? Is it " + ','.join(candidates) + "?"
elif not self.error:
self.df = self.excel_to_df()
candidates = self.df.loc[(self.df['Parts'] == self.part)]
candidates = candidates.Error.tolist()
candidates = list(set(candidates))
if len(candidates) == 1:
self.error = candidates[0]
self.do_step()
return 0
else:
return "What's the problem with " + self.part + "? Is " + ', '.join(candidates).lower() + "?"
else:
return
def solution_provider(self):
"""
Solution time!
Trace how many solutions I have provided and provide the next possible solution based on the knowledge.
If there's no more I can provide, sorry!
If my master solved the problem, hooray! Jump to the wrap up thanks()
:return: solution!
"""
if self.state == "no":
if self.part and self.error:
self.df_stats = self.get_solutions(self.part, self.error)
self.state_counter = self.state_counter + 1
# print(self.error, self.part, self.state_counter, self.df_stats)
if self.state_counter > len(self.df_stats):
# self.__init__()
return "Sorry, it is beyond my scope. Please tell me how you solve this problem if possible, " \
"so that I can help next time! $Enter your solution with the dollar signs$" # provide
# user-defined manual
return "Try to " + self.df_stats[self.state_counter - 1].lower() + ". Does it work?"
elif self.state == "solution":
self.study()
return "Thank you for your information!"
elif self.state == "yes":
print(self.df_stats)
print(self.state_counter)
return self.thanks()
elif self.state == 'greeting':
return "Please tell me more about your issue!"
def excel_to_df(self):
"""
as indicated
:return:
"""
return pd.read_excel(self.filename, 0)
def read_sorted_solution(self, df, part, error):
"""read file"""
df = df.loc[(df['Parts'] == part) & (df['Error'] == error)]
df = df.sort_values('appear_time')
# print(df)
solutions = df.Solution.tolist()
return solutions[::-1]
def get_solutions(self, part, error):
"""
Look for possible solutions from database
:return:
A list of solutions based on given part and error from most frequent to least frequent.
"""
return self.read_sorted_solution(self.df, part, error)
def study(self):
"""
Add a new solution from user feedback if all the possible solutions are declined.
"""
df = pd.read_excel(self.filename, 0)
df2 = pd.DataFrame([self.part,self.error,self.new_solution,0])
df = df.append(df2, ignore_index=True)
df.to_excel(self.filename, index=False)
def mergeprocess(nlu, m, text):
input = nlu.process(text)
print(input)
m.input_debug(input)
m.run()
console_msg = m.msg
if console_msg == "Sorry, it is beyond my scope." or console_msg == "My pleasure.":
m.__init__()
return console_msg
def mergeprocess_fake(nlu, m, text):
input = {"parts": "Tool magazine (Umbrella type)", "error": "Tool number in chaos", "state": 'study'}
m.input_debug(input)
m.run()
console_msg = m.msg
if console_msg == "Sorry, it is beyond my scope." or console_msg == "My pleasure.":
m.__init__()
return console_msg
if __name__ == '__main__':
"""
input is a dictionary with three attributes: parts, error, states
states[None=detect parts/error, yes/no=feedback from the user for the validity of the solution]
"""
m = DialogueManager()
"""test for local file"""
"""input = {"parts": None, "error": "Noise for tool changing umbrella", "state": "no"}
m.input_debug(input)
m.run()"""
input = {"parts": None, "error": None, "state": 'greeting'}
m.input_debug(input)
m.run()
"""
input = {"parts": "Tool magazine(Umbrella type)", "error": "Noise for tool changing", "state": "no"}
m = DialogueManager()
m.input_debug(input)
m.run()"""
"""testing for MERGING"""
"""
natural_language = Dumb_NLU()
m = DialogueManager()
user_input = 'hi'
mergeprocess(natural_language, m, user_input)
user_input = 'The change 4 noise milling is not working.,,,'
mergeprocess(natural_language, m, user_input)
user_input = 'yes'
mergeprocess(natural_language, m, user_input)
"""
"""
flow chart
0. initialize
1. input.debug()
2. run():
if attributes not complete: ask trouble_shooting()
if attributes complete: solution_provider()->previous solution not correct, have another method,
go through the list and suggest(use state_counter, get_solutions())
->previous solutionnot correct no more method,
sorry, and initialise
->previous solution correct
go to thanks(), and initialize
3. waiting for input.
"""
|
[
"wonnor-pro@gmail.com"
] |
wonnor-pro@gmail.com
|
56264f51c911aa35397825287da9b3ea40fae6b1
|
a9ddbd4a48fbaffabcae12c1ead1ac21416c6f32
|
/server.py
|
51af13766bb51d9398847203797173cbf03328be
|
[] |
no_license
|
Krzysztof-Wojtczak/Allegro-Task
|
625790281461e5c2c90d02224a9e0fbc0b43a0da
|
0d1921c0403d83c06357502fa0680df8cfcb8770
|
refs/heads/master
| 2020-05-17T15:48:14.304053
| 2019-12-08T19:29:15
| 2019-12-08T19:29:15
| 183,801,968
| 2
| 0
| null | 2019-07-21T16:01:04
| 2019-04-27T17:22:16
|
Python
|
UTF-8
|
Python
| false
| false
| 2,242
|
py
|
from http.server import HTTPServer, BaseHTTPRequestHandler
import re
from urllib.request import urlopen
import cv2
import numpy as np
from mozaika import Mozaika
class Serv(BaseHTTPRequestHandler):
def do_GET(self):
w = 2048 # default width
h = 2048 # default height
losowo = 1 # random image placement = true
urls = [] # images URLs
if self.path.startswith("/mozaika?"): # keyword for getting mosaic, URL should be put in format:
parameters = self.path.split("&") # http://localhost:8080/mozaika?losowo=Z&rozdzielczosc=XxY&zdjecia=URL1,URL2,URL3..
for par in parameters:
if par.find("losowo") == -1:
pass
else:
losowo_index = par.find("losowo")
try:
losowo = int(par[losowo_index + 7])
except:
pass
if par.find("rozdzielczosc") == -1:
pass
else:
try:
w, h = re.findall('\d+', par)
except:
pass
if par.find("zdjecia=") == -1:
pass
else:
urls = self.path[self.path.find("zdjecia=") + 8 :]
urls = urls.split(",")
try:
image_list = create_images_list(urls)
# call mosaic creator
# 1 required attribute: list of images in cv2 format,
# 3 optional attributes: random image positioning, width of output image, height of output image
mozaika = Mozaika(image_list, losowo, w, h)
img = mozaika.output_image # store output image
f = cv2.imencode('.jpg', img)[1].tostring() # encode to binary format
self.send_response(200)
self.send_header('Content-type', 'image/jpg')
except:
self.send_response(404)
self.end_headers()
self.wfile.write(f) # send output image
#return
def url_to_image(url):
# gets image from URL and converts it to cv2 color image format
resp = urlopen(url)
image = np.asarray(bytearray(resp.read()), dtype="uint8")
image = cv2.imdecode(image, cv2.IMREAD_COLOR)
return image
def create_images_list(urls):
# takes URLs list and creates list of images
image_list = []
for url in urls:
image = url_to_image(url)
if image is not None:
image_list.append(image)
return image_list
httpd = HTTPServer(("localhost", 8080), Serv)
httpd.serve_forever()
|
[
"noreply@github.com"
] |
Krzysztof-Wojtczak.noreply@github.com
|
ffb4d6b50b4e12caf33c65ea3356284a98460588
|
e9978cdfe6754a1f9712bdd8d20221313b4c1ea4
|
/com/jefferson/test/test_operacoes.py
|
dcb2d61f3f176bcdd8132493b0e0f42fc981c688
|
[] |
no_license
|
jefferson-paixao/devops_ac02
|
94b93eea987418d321befbd9810ce701074307fb
|
571bcfc581a9700df51922544a859673149c7767
|
refs/heads/master
| 2021-05-22T00:01:23.574384
| 2020-04-04T02:52:38
| 2020-04-04T02:52:38
| 252,871,635
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 282
|
py
|
from unittest import TestCase
from com.jefferson.operacoes import Operacoes
class TestOperacoes(TestCase):
def setUp(self):
self.operacoes = Operacoes()
def test_multiplicacao(self):
self.assertEqual(self.operacoes.multiplicacao(5,5),25,"Deveria ser 25!")
|
[
"jefferson.paixao@aluno.faculdadeimpacta.com.br"
] |
jefferson.paixao@aluno.faculdadeimpacta.com.br
|
e5f98ccd8f816d3621fa6a5b9fd0132e0965826b
|
30a89ae47ca79e4ced151908f4059cd77ade30ef
|
/order/forms.py
|
0c700dce796932a329f19a1102f5113624a6fcd8
|
[] |
no_license
|
harshit8858/mindful_project1_salesapp
|
0bd80c40b2349fe08744dcd0625283c5b6ba4029
|
66f7c7af868518898aa6422d1b17ca9f7cf433ef
|
refs/heads/master
| 2020-03-24T00:02:49.972583
| 2018-08-18T07:56:49
| 2018-08-18T07:56:49
| 142,269,897
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 328
|
py
|
from django import forms
from .models import *
class OrderForm(forms.ModelForm):
class Meta:
model = Order
fields = [
'customer',
'remark',
'product',
'quantity',
'price',
'discount',
'tax',
'total',
]
|
[
"harshit8858@gmail.com"
] |
harshit8858@gmail.com
|
a233dcc9c0864f3ff92942cdb4396157abb00221
|
436c40d8965274a32e48b0d92553efa14177db25
|
/intrusion-v2/src/estimator.py
|
33cf6c907625831eba850165f30ed96a0a08e78f
|
[
"Apache-2.0"
] |
permissive
|
anuj-kh-quantela/end-to-end
|
daa1fad9beb96b2eb664ca38e9762a223b3ca1d7
|
b00009b80f680c3d1f79f56eee9cdb949963dfb2
|
refs/heads/master
| 2020-03-25T12:06:50.716637
| 2018-08-06T17:41:48
| 2018-08-06T17:41:48
| 143,760,774
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 17,476
|
py
|
import itertools
import logging
import math
from collections import namedtuple
import cv2
import numpy as np
import tensorflow as tf
from scipy.ndimage import maximum_filter, gaussian_filter
import common
from common import CocoPairsNetwork, CocoPairs, CocoPart
logger = logging.getLogger('TfPoseEstimator')
logger.setLevel(logging.INFO)
ch = logging.StreamHandler()
formatter = logging.Formatter('[%(asctime)s] [%(name)s] [%(levelname)s] %(message)s')
ch.setFormatter(formatter)
logger.addHandler(ch)
class Human:
"""
body_parts: list of BodyPart
"""
__slots__ = ('body_parts', 'pairs', 'uidx_list')
def __init__(self, pairs):
self.pairs = []
self.uidx_list = set()
self.body_parts = {}
for pair in pairs:
self.add_pair(pair)
@staticmethod
def _get_uidx(part_idx, idx):
return '%d-%d' % (part_idx, idx)
def add_pair(self, pair):
self.pairs.append(pair)
self.body_parts[pair.part_idx1] = BodyPart(Human._get_uidx(pair.part_idx1, pair.idx1),
pair.part_idx1,
pair.coord1[0], pair.coord1[1], pair.score)
self.body_parts[pair.part_idx2] = BodyPart(Human._get_uidx(pair.part_idx2, pair.idx2),
pair.part_idx2,
pair.coord2[0], pair.coord2[1], pair.score)
self.uidx_list.add(Human._get_uidx(pair.part_idx1, pair.idx1))
self.uidx_list.add(Human._get_uidx(pair.part_idx2, pair.idx2))
def is_connected(self, other):
return len(self.uidx_list & other.uidx_list) > 0
def merge(self, other):
for pair in other.pairs:
self.add_pair(pair)
def part_count(self):
return len(self.body_parts.keys())
def get_max_score(self):
return max([x.score for _, x in self.body_parts.items()])
def __str__(self):
return ' '.join([str(x) for x in self.body_parts.values()])
class BodyPart:
"""
part_idx : part index(eg. 0 for nose)
x, y: coordinate of body part
score : confidence score
"""
__slots__ = ('uidx', 'part_idx', 'x', 'y', 'score')
def __init__(self, uidx, part_idx, x, y, score):
self.uidx = uidx
self.part_idx = part_idx
self.x, self.y = x, y
self.score = score
def get_part_name(self):
return CocoPart(self.part_idx)
def __str__(self):
return 'BodyPart:%d-(%.2f, %.2f) score=%.2f' % (self.part_idx, self.x, self.y, self.score)
class PoseEstimator:
heatmap_supress = False
heatmap_gaussian = False
adaptive_threshold = False
NMS_Threshold = 0.15
Local_PAF_Threshold = 0.2
PAF_Count_Threshold = 5
Part_Count_Threshold = 4
Part_Score_Threshold = 4.5
PartPair = namedtuple('PartPair', [
'score',
'part_idx1', 'part_idx2',
'idx1', 'idx2',
'coord1', 'coord2',
'score1', 'score2'
], verbose=False)
def __init__(self):
pass
@staticmethod
def non_max_suppression(plain, window_size=3, threshold=NMS_Threshold):
under_threshold_indices = plain < threshold
plain[under_threshold_indices] = 0
return plain * (plain == maximum_filter(plain, footprint=np.ones((window_size, window_size))))
@staticmethod
def estimate(heat_mat, paf_mat):
if heat_mat.shape[2] == 19:
heat_mat = np.rollaxis(heat_mat, 2, 0)
if paf_mat.shape[2] == 38:
paf_mat = np.rollaxis(paf_mat, 2, 0)
if PoseEstimator.heatmap_supress:
heat_mat = heat_mat - heat_mat.min(axis=1).min(axis=1).reshape(19, 1, 1)
heat_mat = heat_mat - heat_mat.min(axis=2).reshape(19, heat_mat.shape[1], 1)
if PoseEstimator.heatmap_gaussian:
heat_mat = gaussian_filter(heat_mat, sigma=0.5)
if PoseEstimator.adaptive_threshold:
_NMS_Threshold = max(np.average(heat_mat) * 4.0, PoseEstimator.NMS_Threshold)
_NMS_Threshold = min(_NMS_Threshold, 0.3)
else:
_NMS_Threshold = PoseEstimator.NMS_Threshold
# extract interesting coordinates using NMS.
coords = [] # [[coords in plane1], [....], ...]
for plain in heat_mat[:-1]:
nms = PoseEstimator.non_max_suppression(plain, 5, _NMS_Threshold)
coords.append(np.where(nms >= _NMS_Threshold))
# score pairs
pairs_by_conn = list()
for (part_idx1, part_idx2), (paf_x_idx, paf_y_idx) in zip(CocoPairs, CocoPairsNetwork):
pairs = PoseEstimator.score_pairs(
part_idx1, part_idx2,
coords[part_idx1], coords[part_idx2],
paf_mat[paf_x_idx], paf_mat[paf_y_idx],
heatmap=heat_mat,
rescale=(1.0 / heat_mat.shape[2], 1.0 / heat_mat.shape[1])
)
pairs_by_conn.extend(pairs)
# merge pairs to human
# pairs_by_conn is sorted by CocoPairs(part importance) and Score between Parts.
humans = [Human([pair]) for pair in pairs_by_conn]
while True:
merge_items = None
for k1, k2 in itertools.combinations(humans, 2):
if k1 == k2:
continue
if k1.is_connected(k2):
merge_items = (k1, k2)
break
if merge_items is not None:
merge_items[0].merge(merge_items[1])
humans.remove(merge_items[1])
else:
break
# reject by subset count
humans = [human for human in humans if human.part_count() >= PoseEstimator.PAF_Count_Threshold]
# reject by subset max score
humans = [human for human in humans if human.get_max_score() >= PoseEstimator.Part_Score_Threshold]
return humans
@staticmethod
def score_pairs(part_idx1, part_idx2, coord_list1, coord_list2, paf_mat_x, paf_mat_y, heatmap, rescale=(1.0, 1.0)):
connection_temp = []
cnt = 0
for idx1, (y1, x1) in enumerate(zip(coord_list1[0], coord_list1[1])):
for idx2, (y2, x2) in enumerate(zip(coord_list2[0], coord_list2[1])):
score, count = PoseEstimator.get_score(x1, y1, x2, y2, paf_mat_x, paf_mat_y)
cnt += 1
if count < PoseEstimator.PAF_Count_Threshold or score <= 0.0:
continue
connection_temp.append(PoseEstimator.PartPair(
score=score,
part_idx1=part_idx1, part_idx2=part_idx2,
idx1=idx1, idx2=idx2,
coord1=(x1 * rescale[0], y1 * rescale[1]),
coord2=(x2 * rescale[0], y2 * rescale[1]),
score1=heatmap[part_idx1][y1][x1],
score2=heatmap[part_idx2][y2][x2],
))
connection = []
used_idx1, used_idx2 = set(), set()
for candidate in sorted(connection_temp, key=lambda x: x.score, reverse=True):
# check not connected
if candidate.idx1 in used_idx1 or candidate.idx2 in used_idx2:
continue
connection.append(candidate)
used_idx1.add(candidate.idx1)
used_idx2.add(candidate.idx2)
return connection
@staticmethod
def get_score(x1, y1, x2, y2, paf_mat_x, paf_mat_y):
__num_inter = 10
__num_inter_f = float(__num_inter)
dx, dy = x2 - x1, y2 - y1
normVec = math.sqrt(dx ** 2 + dy ** 2)
if normVec < 1e-4:
return 0.0, 0
vx, vy = dx / normVec, dy / normVec
xs = np.arange(x1, x2, dx / __num_inter_f) if x1 != x2 else np.full((__num_inter,), x1)
ys = np.arange(y1, y2, dy / __num_inter_f) if y1 != y2 else np.full((__num_inter,), y1)
xs = (xs + 0.5).astype(np.int8)
ys = (ys + 0.5).astype(np.int8)
# without vectorization
pafXs = np.zeros(__num_inter)
pafYs = np.zeros(__num_inter)
for idx, (mx, my) in enumerate(zip(xs, ys)):
pafXs[idx] = paf_mat_x[my][mx]
pafYs[idx] = paf_mat_y[my][mx]
# vectorization slow?
# pafXs = pafMatX[ys, xs]
# pafYs = pafMatY[ys, xs]
local_scores = pafXs * vx + pafYs * vy
thidxs = local_scores > PoseEstimator.Local_PAF_Threshold
return sum(local_scores * thidxs), sum(thidxs)
class TfPoseEstimator:
ENSEMBLE = 'addup' # average, addup
def __init__(self, graph_path, target_size=(320, 240)):
self.target_size = target_size
# load graph
with tf.gfile.GFile(graph_path, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
self.graph = tf.get_default_graph()
tf.import_graph_def(graph_def, name='TfPoseEstimator')
self.persistent_sess = tf.Session(graph=self.graph)
# for op in self.graph.get_operations():
# print(op.name)
self.tensor_image = self.graph.get_tensor_by_name('TfPoseEstimator/image:0')
self.tensor_output = self.graph.get_tensor_by_name('TfPoseEstimator/Openpose/concat_stage7:0')
self.heatMat = self.pafMat = None
# warm-up
self.persistent_sess.run(
self.tensor_output,
feed_dict={
self.tensor_image: [np.ndarray(shape=(target_size[1], target_size[0], 3), dtype=np.float32)]
}
)
def __del__(self):
self.persistent_sess.close()
@staticmethod
def _quantize_img(npimg):
npimg_q = npimg + 1.0
npimg_q /= (2.0 / 2**8)
# npimg_q += 0.5
npimg_q = npimg_q.astype(np.uint8)
return npimg_q
@staticmethod
def draw_humans(npimg, humans, imgcopy=False, draw_pose=False):
if imgcopy:
npimg = np.copy(npimg)
image_h, image_w = npimg.shape[:2]
centers_humans = {}
for idx,human in enumerate(humans):
centers = {}
# draw point
for i in range(common.CocoPart.Background.value):
if i not in human.body_parts.keys():
continue
body_part = human.body_parts[i]
center = (int(body_part.x * image_w + 0.5), int(body_part.y * image_h + 0.5))
centers[i] = center
if draw_pose:
cv2.circle(npimg, center, 3, common.CocoColors[i], thickness=3, lineType=8, shift=0)
centers_humans[idx] = centers
# draw line
for pair_order, pair in enumerate(common.CocoPairsRender):
if pair[0] not in human.body_parts.keys() or pair[1] not in human.body_parts.keys():
continue
#npimg = cv2.line(npimg, centers[pair[0]], centers[pair[1]], common.CocoColors[pair_order], 3)
if draw_pose:
cv2.line(npimg, centers[pair[0]], centers[pair[1]], common.CocoColors[pair_order], 3)
return npimg,centers_humans
def _get_scaled_img(self, npimg, scale):
get_base_scale = lambda s, w, h: max(self.target_size[0] / float(w), self.target_size[1] / float(h)) * s
img_h, img_w = npimg.shape[:2]
if scale is None:
if npimg.shape[:2] != (self.target_size[1], self.target_size[0]):
# resize
npimg = cv2.resize(npimg, self.target_size)
return [npimg], [(0.0, 0.0, 1.0, 1.0)]
elif isinstance(scale, float):
# scaling with center crop
base_scale = get_base_scale(scale, img_w, img_h)
npimg = cv2.resize(npimg, dsize=None, fx=base_scale, fy=base_scale)
ratio_x = (1. - self.target_size[0] / float(npimg.shape[1])) / 2.0
ratio_y = (1. - self.target_size[1] / float(npimg.shape[0])) / 2.0
roi = self._crop_roi(npimg, ratio_x, ratio_y)
return [roi], [(ratio_x, ratio_y, 1.-ratio_x*2, 1.-ratio_y*2)]
elif isinstance(scale, tuple) and len(scale) == 2:
# scaling with sliding window : (scale, step)
base_scale = get_base_scale(scale[0], img_w, img_h)
base_scale_w = self.target_size[0] / (img_w * base_scale)
base_scale_h = self.target_size[1] / (img_h * base_scale)
npimg = cv2.resize(npimg, dsize=None, fx=base_scale, fy=base_scale)
window_step = scale[1]
rois = []
infos = []
for ratio_x, ratio_y in itertools.product(np.arange(0., 1.01 - base_scale_w, window_step),
np.arange(0., 1.01 - base_scale_h, window_step)):
roi = self._crop_roi(npimg, ratio_x, ratio_y)
rois.append(roi)
infos.append((ratio_x, ratio_y, base_scale_w, base_scale_h))
return rois, infos
elif isinstance(scale, tuple) and len(scale) == 3:
# scaling with ROI : (want_x, want_y, scale_ratio)
base_scale = get_base_scale(scale[2], img_w, img_h)
npimg = cv2.resize(npimg, dsize=None, fx=base_scale, fy=base_scale)
ratio_w = self.target_size[0] / float(npimg.shape[1])
ratio_h = self.target_size[1] / float(npimg.shape[0])
want_x, want_y = scale[:2]
ratio_x = want_x - ratio_w / 2.
ratio_y = want_y - ratio_h / 2.
ratio_x = max(ratio_x, 0.0)
ratio_y = max(ratio_y, 0.0)
if ratio_x + ratio_w > 1.0:
ratio_x = 1. - ratio_w
if ratio_y + ratio_h > 1.0:
ratio_y = 1. - ratio_h
roi = self._crop_roi(npimg, ratio_x, ratio_y)
return [roi], [(ratio_x, ratio_y, ratio_w, ratio_h)]
def _crop_roi(self, npimg, ratio_x, ratio_y):
target_w, target_h = self.target_size
h, w = npimg.shape[:2]
x = max(int(w*ratio_x-.5), 0)
y = max(int(h*ratio_y-.5), 0)
cropped = npimg[y:y+target_h, x:x+target_w]
cropped_h, cropped_w = cropped.shape[:2]
if cropped_w < target_w or cropped_h < target_h:
npblank = np.zeros((self.target_size[1], self.target_size[0], 3), dtype=np.uint8)
copy_x, copy_y = (target_w - cropped_w) // 2, (target_h - cropped_h) // 2
npblank[copy_y:copy_y+cropped_h, copy_x:copy_x+cropped_w] = cropped
else:
return cropped
def inference(self, npimg, scales=None):
if npimg is None:
raise Exception('The image is not valid. Please check your image exists.')
if not isinstance(scales, list):
scales = [None]
if self.tensor_image.dtype == tf.quint8:
# quantize input image
npimg = TfPoseEstimator._quantize_img(npimg)
pass
rois = []
infos = []
for scale in scales:
roi, info = self._get_scaled_img(npimg, scale)
# for dubug...
# print(roi[0].shape)
# cv2.imshow('a', roi[0])
# cv2.waitKey()
rois.extend(roi)
infos.extend(info)
logger.debug('inference+')
output = self.persistent_sess.run(self.tensor_output, feed_dict={self.tensor_image: rois})
heatMats = output[:, :, :, :19]
pafMats = output[:, :, :, 19:]
logger.debug('inference-')
output_h, output_w = output.shape[1:3]
max_ratio_w = max_ratio_h = 10000.0
for info in infos:
max_ratio_w = min(max_ratio_w, info[2])
max_ratio_h = min(max_ratio_h, info[3])
mat_w, mat_h = int(output_w/max_ratio_w), int(output_h/max_ratio_h)
resized_heatMat = np.zeros((mat_h, mat_w, 19), dtype=np.float32)
resized_pafMat = np.zeros((mat_h, mat_w, 38), dtype=np.float32)
resized_cntMat = np.zeros((mat_h, mat_w, 1), dtype=np.float32)
resized_cntMat += 1e-12
for heatMat, pafMat, info in zip(heatMats, pafMats, infos):
w, h = int(info[2]*mat_w), int(info[3]*mat_h)
heatMat = cv2.resize(heatMat, (w, h))
pafMat = cv2.resize(pafMat, (w, h))
x, y = int(info[0] * mat_w), int(info[1] * mat_h)
if TfPoseEstimator.ENSEMBLE == 'average':
# average
resized_heatMat[max(0, y):y + h, max(0, x):x + w, :] += heatMat[max(0, -y):, max(0, -x):, :]
resized_pafMat[max(0,y):y+h, max(0, x):x+w, :] += pafMat[max(0, -y):, max(0, -x):, :]
resized_cntMat[max(0,y):y+h, max(0, x):x+w, :] += 1
else:
# add up
resized_heatMat[max(0, y):y + h, max(0, x):x + w, :] = np.maximum(resized_heatMat[max(0, y):y + h, max(0, x):x + w, :], heatMat[max(0, -y):, max(0, -x):, :])
resized_pafMat[max(0,y):y+h, max(0, x):x+w, :] += pafMat[max(0, -y):, max(0, -x):, :]
resized_cntMat[max(0, y):y + h, max(0, x):x + w, :] += 1
if TfPoseEstimator.ENSEMBLE == 'average':
self.heatMat = resized_heatMat / resized_cntMat
self.pafMat = resized_pafMat / resized_cntMat
else:
self.heatMat = resized_heatMat
self.pafMat = resized_pafMat / (np.log(resized_cntMat) + 1)
humans = PoseEstimator.estimate(self.heatMat, self.pafMat)
return humans
|
[
"anuj.khandelwal@quantela.com"
] |
anuj.khandelwal@quantela.com
|
510b99d849b64f9db7ba0753063b53024e27520a
|
1a8dea7c14d807413d4e2ab38e72df78138778b3
|
/ao3_downloader/gui.py
|
595d76ec0b312eb77bd718e94293da55ab8da021
|
[] |
no_license
|
ivywong/ao3-downloader
|
aa95f605b4c3a5bac5fbfe66bdc610108b201dac
|
46981482103d146818094393946ed5a3f64ad6cb
|
refs/heads/master
| 2022-04-08T03:42:40.906296
| 2020-02-27T10:20:01
| 2020-02-27T10:20:01
| 217,447,252
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 2,314
|
py
|
import PySimpleGUI as sg
import pathlib
import sys
from multiprocessing import Process
import downloader
# hacky debug window for showing progress
print = sg.Print
downloader.print = sg.Print
class Gui():
def __init__(self):
sg.theme('SystemDefault') # Add a touch of color
user_home = str(pathlib.Path.home())
# All the stuff inside your window.
layout = [ [sg.Text('Ao3 Download URL', size=(20, 1)), sg.InputText('https://archiveofourown.org/series/XXXXXX')],
[sg.Text('Download Destination', size=(20,1)), sg.InputText(user_home), sg.FolderBrowse(initial_folder=user_home)],
[sg.Text('File format', size=(20, 1)), sg.Combo(['azw3', 'mobi', 'epub', 'pdf', 'html'], default_value='epub')],
# [sg.Text('Output:')],
# [sg.Output(size=(88, 20), key='Output')],
[sg.Button('Download'), sg.Button('Cancel')] ]
# Create the Window
self.window = sg.Window('AO3 Downloader', layout)
def start_download(series_url, download_path, file_format):
proc = Process(target=downloader.download_series, args=(series_url, download_path, file_format))
proc.start()
proc.join()
if __name__ == '__main__':
gui = Gui()
# Event Loop to process "events" and get the "values" of the inputs
while True:
event, values = gui.window.read()
if event in (None, 'Cancel'): # if user closes window or clicks cancel
break
gui.window['Browse'].update(disabled=True)
gui.window['Download'].update(disabled=True)
print('You entered ', values)
series_url = values[0]
download_path = pathlib.Path(values[1])
file_format = values[2]
print("Series URL: {}".format(series_url))
print("Download destination: {}".format(download_path))
print("File format: {}".format(file_format))
if series_url and download_path:
try:
downloader.download_series(series_url, download_path, file_format)
except ValueError as e:
print(e)
sys.exit(1)
print("done.")
gui.window['Browse'].update(disabled=False)
gui.window['Download'].update(disabled=False)
gui.window.close()
|
[
"ivy.wong022@gmail.com"
] |
ivy.wong022@gmail.com
|
b169441fb11c3aeb41ab3908e6be615f9254a595
|
14a5610f114b87485233b90f393f73ab377e0edb
|
/cart/.~c9_invoke_MERYI.py
|
f4af78caec2edbcd912fb47fafcbd685e9f6c2be
|
[] |
no_license
|
Code-Institute-Submissions/ves-weather-app-1
|
8a15eb3fa2d54fdc371a971b66b03d942e322b01
|
77ae1eca6b7c9d0da8a8881bd66f3bb94815e686
|
refs/heads/master
| 2022-11-10T20:59:53.377370
| 2020-07-01T23:55:34
| 2020-07-01T23:55:34
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 231
|
py
|
from django.shortcuts import render
from .models import Cart
def cart_home(request):
cart_obj, new_obj = Cart.objects.new_or_get(request)
products = cart_obj.products.all()
return render(request, "cart/cart.html", {})
|
[
"ubuntu@ip-172-31-83-24.ec2.internal"
] |
ubuntu@ip-172-31-83-24.ec2.internal
|
80304d1407e829f6971a6388dcbebcba61816075
|
4664e47fda25736687d8ab958356fcaa9447f139
|
/techa/momentum.py
|
c985d822a368d45b9aee6284233b2a356462d6b3
|
[
"Unlicense"
] |
permissive
|
kkc-krish/techa
|
eb67d0ecf41ca3020d4a91676b852a827a6513ba
|
518f90805b0728466836993b88e820bc8b0405b1
|
refs/heads/master
| 2020-12-13T18:17:53.692915
| 2019-08-23T04:14:26
| 2019-08-23T04:14:26
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 31,981
|
py
|
# -*- coding: utf-8 -*-
"""
Momentum Indicators
"""
import pandas as pd
from finta import TA
from talib.abstract import Function
from overlap import MA
from volatility import ATR
__all__ = ['ADX', 'ADXR', 'APO', 'AROON', 'AROONOSC', 'ATR', 'BASP', 'BASPN', 'BOP', 'CCI', 'CFI', 'CMO', 'COPP', 'DX',
'EBBP', 'EFI', 'EMV', 'Function', 'IFT_RSI', 'IMI', 'KST', 'MA', 'MACD', 'MACDEXT', 'MACDFIX', 'MFI', 'MI',
'MINUS_DI', 'MINUS_DM', 'MOM', 'PLUS_DI', 'PLUS_DM', 'PPO', 'ROC', 'ROCP', 'ROCR', 'ROCR100', 'RSI', 'STOCH',
'STOCHD', 'STOCHF', 'STOCHRSI', 'TA', 'TRIX', 'TSI', 'ULTOSC', 'UO', 'VZO', 'WILLR', 'WTO']
def MI(data, period=9):
"""
Mass Index
MI uses the high-low range to identify trend reversals based on range expansions.
In this sense, the Mass Index is a volatility indicator that does not have a directional bias.
Instead, the Mass Index identifies range bulges that can foreshadow a reversal of the current trend.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: period used for indicator calculation
:return pd.Series: with indicator data calculation results
"""
return TA.MI(data, period=period)
def COPP(data):
"""
Coppock Curve
COPP is a momentum indicator, it signals buying opportunities when the indicator moved from negative territory
to positive territory.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:return pd.Series: with indicator data calculation results
"""
return TA.COPP(data)
def VZO(data, period=14):
"""
VZO
VZO uses price, previous price and moving averages to compute its oscillating value.
It is a leading indicator that calculates buy and sell signals based on oversold / overbought conditions.
Oscillations between the 5% and 40% levels mark a bullish trend zone, while oscillations between -40% and 5%
mark a bearish trend zone.
Meanwhile, readings above 40% signal an overbought condition, while readings above 60% signal an extremely
overbought condition.
Alternatively, readings below -40% indicate an oversold condition, which becomes extremely oversold below -60%.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: period used for indicator calculation
:return pd.Series: with indicator data calculation results
"""
return TA.VZO(data, period)
def KST(data, r1=10, r2=15, r3=20, r4=30):
"""
Know Sure Thing
KST is a momentum oscillator based on the smoothed rate-of-change for four different time frames.
KST measures price momentum for four different price cycles. It can be used just like any momentum oscillator.
Chartists can look for divergences, overbought/oversold readings, signal line crossovers and center-line
crossovers.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int r1: period used at first ROC calculation
:param int r2: period used at second ROC calculation
:param int r3: period used at third ROC calculation
:param int r4: period used at last ROC calculation
:return pd.Series: with indicator data calculation results
"""
return TA.KST(data, r1, r2, r3, r4)
def TSI(data, long=25, short=13, signal=13):
"""
True Strength Index
TSI is a momentum oscillator based on a double smoothing of price changes.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int long: long period used for indicator calculation
:param int short: short period used for indicator calculation
:param int signal: signal period used for indicator calculation
:return pd.Series: with indicator data calculation results
"""
return TA.TSI(data, long, short, signal)
def EFI(data, period=13):
"""
Elder Force Index
EFI is an indicator that uses price and volume to assess the power behind a move or identify possible turning
points.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: period used for indicator calculation
:return pd.Series: with indicator data calculation results
"""
return TA.EFI(data, period=period)
def IFT_RSI(data, rsi_period=14, wma_period=9):
"""
Modified Inverse Fisher Transform applied on RSI.
Suggested method to use any IFT indicator is to buy when the indicator crosses over –0.5 or crosses over +0.5
if it has not previously crossed over –0.5 and to sell short when the indicators crosses under +0.5 or crosses
under –0.5 if it has not previously crossed under +0.5.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int rsi_period: pandas DataFrame with open, high, low, close data
:param int wma_period: pandas DataFrame with open, high, low, close data
:return pd.Series: with indicator data calculation results
"""
return TA.IFT_RSI(data, rsi_period, wma_period)
def BASP(data, period=40):
"""
Buy and Sell Pressure
BASP indicator serves to identify buying and selling pressure.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: period used for indicator calculation
:return pd.Series: with indicator data calculation results
"""
return TA.BASP(data, period)
def BASPN(data, period=40):
"""
Buy and Sell Pressure Normalized
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: period used for indicator calculation
:return pd.Series: with indicator data calculation results
"""
return TA.BASPN(data, period)
def UO(data):
"""
Ultimate Oscillator
UO is a momentum oscillator designed to capture momentum across three different time frames.
The multiple time frame objective seeks to avoid the pitfalls of other oscillators.
Many momentum oscillators surge at the beginning of a strong advance and then form bearish divergence as the
advance continues.
This is because they are stuck with one time frame. The Ultimate Oscillator attempts to correct this fault by
incorporating longer time frames into the basic formula.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:return pd.Series: with indicator data calculation results
"""
return TA.UO(data)
def CFI(data):
"""
Cumulative Force Index.
Adopted from Elder's Force Index (EFI).
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:return pd.Series: with indicator data calculation results
"""
return TA.CFI(data)
def EBBP(data):
"""
Bull power and bear power by Dr. Alexander Elder
EBBP show where today’s high and low lie relative to the a 13-day EMA
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:return pd.Series: with indicator data calculation results
"""
return TA.EBBP(data)
def EMV(data, period=14):
"""
Ease of Movement
EMV is a volume-based oscillator that fluctuates above and below the zero line.
As its name implies, it is designed to measure the "ease" of price movement.
Prices are advancing with relative ease when the oscillator is in positive territory.
Conversely, prices are declining with relative ease when the oscillator is in negative territory.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: period used for indicator calculation
:return:pd.Series: with indicator data calculation results
"""
return TA.EMV(data, period)
def WTO(data, channel_length=10, average_length=21):
"""
Wave Trend Oscillator
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int channel_length: channel length value
:param int average_length: average length value
:return pd.Series: with indicator data calculation results
"""
return TA.WTO(data, channel_length, average_length)
def STOCHD(data, period=3):
"""
Stochastic Oscillator %D
STOCH %D is a 3 period simple moving average of %K.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: period used for indicator calculation
:return pd.Series: with indicator data calculation results
"""
return TA.STOCHD(data, period)
def ADX(data, period=14):
"""
ADX
ADX is 100 * smoothed moving average of absolute value (DMI +/-) divided by (DMI+ + DMI-).
ADX does not indicate trend direction or momentum, only trend strength.
Generally, ADX readings below 20 indicate trend weakness, and readings above 40 indicate trend strength.
An extremely strong trend is indicated by readings above 50
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: period used for indicator calculation
:return pd.Series: with indicator data calculation results
"""
temp_df = pd.DataFrame()
temp_df['up_move'] = data['high'].diff()
temp_df['down_move'] = data['low'].diff()
positive_dm = []
negative_dm = []
for row in temp_df.itertuples():
if row.up_move > row.down_move and row.up_move > 0:
positive_dm.append(row.up_move)
else:
positive_dm.append(0)
if row.down_move > row.up_move and row.down_move > 0:
negative_dm.append(row.down_move)
else:
negative_dm.append(0)
temp_df['positive_dm'] = positive_dm
temp_df['negative_dm'] = negative_dm
atr = ATR(data, period=period * 6)
dir_plus = pd.Series(100 * (temp_df['positive_dm'] / atr).ewm(span=period, min_periods=period - 1).mean())
dir_minus = pd.Series(100 * (temp_df['negative_dm'] / atr).ewm(span=period, min_periods=period - 1).mean())
return pd.concat([dir_plus, dir_minus])
# fn = Function('ADX')
# return fn(data, period)
def ADXR(data, period=14):
"""
Average Directional Movement Index Rating
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: period used for indicator calculation
:return pd.Series: with indicator data calculation results
"""
fn = Function('ADXR')
return fn(data, period)
def IMI(data):
"""
Inter-day Momentum Index
source: http://www.fmlabs.com/reference/default.htm?url=IMI.htm
"""
imi_list = []
up_sum = .0
down_sum = .0
i = 0
while i < len(data['close']):
if data['close'][i] > data['open'][i]:
up_sum = up_sum + (data['close'][i] - data['open'][i])
else:
down_sum = down_sum + (data['open'][i] - data['close'][i])
imi = 100 * (up_sum / (up_sum + down_sum))
imi_list.append(imi)
i += 1
return imi_list
def APO(data, fast_period=12, slow_period=26, ma_type=0, price='close'):
"""
Absolute Price Oscillator
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int fast_period: fast period used for indicator calculation
:param int slow_period: slow period used for indicator calculation
:param int ma_type: moving average type (0 simple, 1 exponential)
:param str price: column used for indicator calculation (default = "close")
:return pd.Series: with indicator data calculation results
"""
apo_list = []
i = 0
while i < len(data[price]):
if i + 1 < slow_period:
apo = float('NaN')
else:
start_fast = i + 1 - fast_period
end = i + 1
sma_fast = MA(data[price][start_fast:end], period=fast_period, ma_type=ma_type)
start_slow = i + 1 - slow_period
end = i + 1
sma_slow = MA(data[price][start_slow:end], period=slow_period, ma_type=ma_type)
apo = sma_slow - sma_fast
# apo *= -1
apo_list.append(apo)
i += 1
return pd.Series(apo_list, name='APO')
def AROON(data, period=14):
"""
Aroon indicator
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: period used for indicator calculation
:return pd.Series: up and down indicators from data calculation
"""
fn = Function('AROON')
return fn(data, period)
def AROONOSC(data, period=14):
"""
Aroon Oscillator
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: period used for indicator calculation
:return pd.Series: with indicator data calculation results
"""
fn = Function('AROONOSC')
return fn(data, period)
def BOP(data):
"""
Balance of Power Indicator
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:return pd.Series: with indicator data calculation results
"""
fn = Function('BOP')
return fn(data)
def CCI(data, period=14):
"""
Commodity Index Channel
CCI is a versatile indicator that can be used to identify a new trend or warn of extreme conditions.
CCI measures the current price level relative to an average price level over a given period of time.
The CCI typically oscillates above and below a zero line. Normal oscillations will occur within the range of
+100 and −100.
Readings above +100 imply an overbought condition, while readings below −100 imply an oversold condition.
As with other overbought/oversold indicators, this means that there is a large probability that the price will
correct to more representative levels.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: period used for indicator calculation
:return pd.Series: with indicator data calculation results
"""
fn = Function('CCI')
return fn(data, period)
def CMO(data, period=14):
"""
Chaikin Momentum Oscillator
CMO is an oscillator that measures the accumulation/distribution line of the moving average convergence
divergence (MACD).
The Chaikin oscillator is calculated by subtracting a 10-day exponential moving average (EMA) of the
ADL from a three-day EMA of the ADL, and highlights the momentum implied by the ADL.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: period used for indicator calculation
:return pd.Series: with indicator data calculation results
"""
fn = Function('CMO')
return fn(data, period)
def DX(data, period=14):
"""
Directional Movement Index
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: period used for indicator calculation
:return pd.Series: with indicator data calculation results
"""
fn = Function('DX')
return fn(data, period)
def MACD(data, slow_period=10, fast_period=21, signal=9, price='close'):
"""
Moving Average Convergence Divergence
The MACD Line oscillates above and below the zero line, which is also known as the center-line.
These crossovers signal that the 12-day EMA has crossed the 26-day EMA. The direction, of course, depends on
the direction of the moving average cross.
Positive MACD indicates that the 12-day EMA is above the 26-day EMA. Positive values increase as the shorter
EMA diverges further from the longer EMA.
This means upside momentum is increasing. Negative MACD values indicates that the 12-day EMA is below the
26-day EMA.
Negative values increase as the shorter EMA diverges further below the longer EMA. This means downside momentum
is increasing.
Signal line crossovers are the most common MACD signals. The signal line is a 9-day EMA of the MACD Line.
As a moving average of the indicator, it trails the MACD and makes it easier to spot MACD turns.
A bullish crossover occurs when the MACD turns up and crosses above the signal line.
A bearish crossover occurs when the MACD turns down and crosses below the signal line.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int slow_period: slow period used for indicator calculation
:param int fast_period: fast period used for indicator calculation
:param int signal: period used for signal calculation
:param str price: column used for indicator calculation (default = "close")
:return pd.Series: with indicator data calculation results
"""
ema_fast = pd.Series(data[price].ewm(ignore_na=False,
min_periods=fast_period - 1, span=fast_period).mean(), index=data.index)
ema_slow = pd.Series(data[price].ewm(ignore_na=False,
min_periods=slow_period - 1, span=slow_period).mean(), index=data.index)
macd_series = pd.Series(ema_fast - ema_slow, index=data.index, name='macd')
macd_signal_series = macd_series.ewm(ignore_na=False, span=signal).mean()
macd_signal_series = pd.Series(macd_signal_series, index=data.index, name='macd_signal')
macd_df = pd.concat([macd_signal_series, macd_series], axis=1)
return pd.DataFrame(macd_df)
def MACDEXT(data, fast_period=12, fast_ma_type=0, slow_period=26, slow_ma_type=0, signal_period=9,
signal_ma_type=0):
"""
Moving Average Convergence Divergence Extended
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int fast_period: fast period used for indicator calculation
:param int fast_ma_type: fast moving average type (0 simple, 1 exponential)
:param int slow_period: slow period used for indicator calculation
:param int slow_ma_type: slow moving average type (0 simple, 1 exponential)
:param int signal_period: period used for signal calculation
:param int signal_ma_type: signal moving average type (0 simple, 1 exponential)
:return pd.Series: with indicator data calculation results with indicator data calculation results
"""
fn = Function('MACDEXT')
return fn(data, fastperiod=fast_period, fastmatype=fast_ma_type,
slowperiod=slow_period,
slowmatype=slow_ma_type, signalperiod=signal_period,
signalmatype=signal_ma_type)
def MACDFIX(data, signal_period=9):
"""
Moving Average Convergence/Divergence Fix 12/26
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int signal_period: period used for signal calculation
:return pd.Series: with indicator data calculation results
"""
fn = Function('MACDFIX')
return fn(data, signalperiod=signal_period)
def MFI(data, period=14):
"""
Money Flow Indicator
MFI is a momentum indicator that measures the inflow and outflow of money into a security over a specific
period of time.
MFI can be understood as RSI adjusted for volume.
The money flow indicator is one of the more reliable indicators of overbought and oversold conditions, perhaps
partly because it uses the higher readings of 80 and 20 as compared to the RSI's overbought/oversold readings
of 70 and 30.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: period used for indicator calculation
:return pd.Series: with indicator data calculation results
"""
fn = Function('MFI')
return fn(data, period)
def MINUS_DI(data, period=14):
"""
Minus Directional indicator
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: period used for indicator calculation
:return pd.Series: with indicator data calculation results
"""
fn = Function('MINUS_DI')
return fn(data, period)
def MINUS_DM(data, period=14):
"""
Minus Directional Movement indicator
DM is a valuable tool for assessing price direction and strength.
This indicator was created in 1978 by J. Welles Wilder, who also created the popular relative strength index.
DMI tells you when to be long or short.
It is especially useful for trend trading strategies because it differentiates between strong and weak trends,
allowing the trader to enter only the strongest trends.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: period used for indicator calculation
:return pd.Series: with indicator data calculation results
"""
fn = Function('MINUS_DM')
return fn(data, period)
def MOM(data, period=9):
"""
Momentum Indicator
MOM is measured by continually taking price differences for a fixed time interval.
To construct a 10-day momentum line, simply subtract the closing price 10 days ago from the last closing price.
This positive or negative value is then plotted around a zero line.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: period used for indicator calculation
:return pd.Series: with indicator data calculation results
"""
fn = Function('MOM')
return fn(data, period)
def PLUS_DI(data, period=14):
"""
Plus Directional Index indicator
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: period used for indicator calculation
:return pd.Series: with indicator data calculation results
"""
fn = Function('PLUS_DI')
return fn(data, period)
def PLUS_DM(data, period=14):
"""
Plus Directional Movement indicator
DM is a valuable tool for assessing price direction and strength.
This indicator was created in 1978 by J. Welles Wilder, who also created the popular relative strength index.
DMI tells you when to be long or short.
It is especially useful for trend trading strategies because it differentiates between strong and weak trends,
allowing the trader to enter only the strongest trends.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: period used for indicator calculation
:return pd.Series: with indicator data calculation results
"""
fn = Function('PLUS_DM')
return fn(data, period)
def PPO(data, fast_period=12, slow_period=26, ma_type=0):
"""
Percentage Price Oscillator
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int fast_period: fast period used for indicator calculation
:param int slow_period: slow period used for indicator calculation
:param int ma_type: moving average type (0 simple, 1 exponential)
:return pd.Series: with indicator data calculation results
"""
fn = Function('PPO')
return fn(data, fastperiod=fast_period, slowperiod=slow_period, matype=ma_type)
def ROC(data, period=10):
"""
Rate of Change
ROC is a pure momentum oscillator that measures the percent change in price from one period to the next.
The ROC calculation compares the current price with the price “n” periods ago.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: period used for indicator calculation
:return pd.Series: with indicator data calculation results
"""
fn = Function('ROC')
return fn(data, timeperiod=period)
def ROCP(data, period=10):
"""
Rate of Change
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: period used for indicator calculation
:return pd.Series: with indicator data calculation results
"""
fn = Function('ROCP')
return fn(data, period)
def ROCR(data, period=10):
"""
Rate of Change
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: period used for indicator calculation
:return pd.Series: with indicator data calculation results
"""
fn = Function('ROCR')
return fn(data, period)
def ROCR100(data, period=10):
"""
Rate of Change as 100 percentage
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: period used for indicator calculation
:return pd.Series: with indicator data calculation results
"""
fn = Function('ROCR100')
return fn(data, period)
def RSI(data, period=14, price='close'):
"""
Relative Strength Index
RSI is a momentum oscillator that measures the speed and change of price movements.
RSI oscillates between zero and 100. Traditionally, and according to Wilder, RSI is considered overbought when
above 70 and oversold when below 30.
Signals can also be generated by looking for divergences, failure swings and center-line crossovers.
RSI can also be used to identify the general trend.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: period used for indicator calculation period used for indicator calculation
:param str price: column used for indicator calculation (default = "close")
:return pd.Series: with indicator data calculation results
"""
rsi_series = pd.DataFrame(index=data.index)
gain = [0]
loss = [0]
for row, shifted_row in zip(data[price], data[price].shift(-1)):
if row - shifted_row > 0:
gain.append(row - shifted_row)
loss.append(0)
elif row - shifted_row < 0:
gain.append(0)
loss.append(abs(row - shifted_row))
elif row - shifted_row == 0:
gain.append(0)
loss.append(0)
rsi_series['gain'] = gain
rsi_series['loss'] = loss
avg_gain = rsi_series['gain'].rolling(window=period).mean()
avg_loss = rsi_series['loss'].rolling(window=period).mean()
relative_strength = avg_gain / avg_loss
rsi_ = 100 - (100 / (1 + relative_strength))
return pd.Series(rsi_, index=data.index, name='RSI')
# fn = Function('RSI')
# return fn(data, period)
def STOCH(data, fastk_period=5, slowk_period=3, slowk_ma_type=0, slowd_period=3, slowd_ma_type=0):
"""
Stochastic Oscillator
The Stochastic Oscillator is a momentum indicator comparing the closing price of a security to the range of it's
prices over a certain period of time.
The sensitivity of the oscillator to market movements is reducible by adjusting that time period or by taking a
moving average of the result.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int fastk_period: period used for K fast indicator calculation
:param int slowk_period: period used for K slow indicator calculation
:param int slowk_ma_type: slow K moving average type (0 simple, 1 exponential)
:param int slowd_period: period used for D slow indicator calculation
:param int slowd_ma_type: slow D moving average type (0 simple, 1 exponential)
:return pd.Series: with indicator data calculation results
"""
fn = Function('STOCH')
return fn(data, fastk_period=fastk_period, slowk_period=slowk_period,
slowk_matype=slowk_ma_type, slowd_matype=slowd_ma_type,
slowd_period=slowd_period)
def STOCHF(data, fastk_period=5, fastd_period=3, fastd_ma_type=0):
"""
Stochastic %F
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int fastk_period: period used for K fast indicator calculation
:param int fastd_period: period used for D fast indicator calculation
:param int fastd_ma_type: fast D moving average type (0 simple, 1 exponential)
:return pd.Series: with indicator data calculation results
"""
fn = Function('STOCHF')
return fn(data, fastk_period=fastk_period, fastd_period=fastd_period,
fastd_matype=fastd_ma_type)
def STOCHRSI(data, period=14, fastk_period=5, fastd_period=3, fastd_ma_type=0):
"""
Stochastic RSI
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: RSI period
:param int fastk_period: period used for K fast indicator calculation
:param int fastd_period: period used for D fast indicator calculation
:param int fastd_ma_type: fast D moving average type (0 simple, 1 exponential)
:return pd.Series: with indicator data calculation results
"""
fn = Function('STOCHRSI')
return fn(data, period, fastk_period=fastk_period,
fastd_period=fastd_period,
fastd_matype=fastd_ma_type)
def TRIX(data, period=30):
"""
Triple Exponential Moving Average Oscillator
The TRIX is a momentum indicator that oscillates around zero.
It displays the percentage rate of change between two triple smoothed exponential moving averages.
To calculate TRIX we calculate triple smoothed EMA3 of n periods and then subtracts previous period EMA3 value
from last EMA3 value and divide the result with yesterdays EMA3 value.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: period used for indicator calculation period used for indicator calculation
:return pd.Series: with indicator data calculation results
"""
fn = Function('TRIX')
return fn(data, period)
def ULTOSC(data, period1=7, period2=14, period3=28):
"""
Ultimate Oscillator
UO or ULTOSC is a momentum oscillator designed to capture momentum across three different time frames.
The multiple time frame objective seeks to avoid the pitfalls of other oscillators.
Many momentum oscillators surge at the beginning of a strong advance and then form bearish divergence as the
advance continues.
This is because they are stuck with one time frame. The Ultimate Oscillator attempts to correct this fault by
incorporating longer time frames into the basic formula.
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period1: first period used for indicator calculation period used for indicator calculation
:param int period2: second period used for indicator calculation period used for indicator calculation
:param int period3: third period used for indicator calculation period used for indicator calculation
:return pd.Series: with indicator data calculation results
"""
fn = Function('ULTOSC')
return fn(data, timeperiod1=period1, timeperiod2=period2, timeperiod3=period3)
def WILLR(data, period=14):
"""
Williams %R
Williams %R, or just %R, is a technical analysis oscillator showing the current closing price in relation to
the high and low of the past N days (for a given N).
It was developed by a publisher and promoter of trading materials, Larry Williams.
It's purpose is to tell whether a stock or commodity market is trading near the high or the low, or somewhere in
between, of its recent trading range.
The oscillator is on a negative scale, from −100 (lowest) up to 0 (highest).
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:param int period: period used for indicator calculation period used for indicator calculation
:return pd.Series: with indicator data calculation results
"""
willr_list = []
i = 0
close, high, low = data['close'], data['high'], data['low']
while i < len(close):
if i + 1 < period:
willr = float(' NaN')
else:
start = i + 1 - period
end = i + 1
willr = (max(high[start:end]) - close[i]) / (max(high[start:end]) - min(low[start:end])) * 100
# willr *= -1
willr_list.append(willr)
i += 1
return willr_list
if __name__ == '__main__':
print(str(getattr(__import__('momentum'), 'TRIX')))
|
[
"danielumpierrezdelrio@gmail.com"
] |
danielumpierrezdelrio@gmail.com
|
9e970d6b5a4196876c2d30a1e3a820a778e6aabc
|
4fe0d37eb4810d3aa5fca50a60bd8f57c2558673
|
/build/ros_arduino_bridge/ros_arduino_python/catkin_generated/pkg.develspace.context.pc.py
|
cfcd6131b4b1dad06511bc5b36495eba4e78533a
|
[] |
no_license
|
jim1949/gpsbot_ws
|
f0aa961472d65633f1d385426e6e0fd489a8e104
|
0dfa36223620ae226f6a40735179b6cae265693d
|
refs/heads/master
| 2021-05-07T05:55:08.584882
| 2017-11-22T08:45:06
| 2017-11-22T08:45:06
| 103,118,141
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 382
|
py
|
# generated from catkin/cmake/template/pkg.context.pc.in
CATKIN_PACKAGE_PREFIX = ""
PROJECT_PKG_CONFIG_INCLUDE_DIRS = "".split(';') if "" != "" else []
PROJECT_CATKIN_DEPENDS = "".replace(';', ' ')
PKG_CONFIG_LIBRARIES_WITH_PREFIX = "".split(';') if "" != "" else []
PROJECT_NAME = "ros_arduino_python"
PROJECT_SPACE_DIR = "/home/relaybot/gpsbot_ws/devel"
PROJECT_VERSION = "0.2.0"
|
[
"jim1949@163.com"
] |
jim1949@163.com
|
aea5124f9f2718dae828e8f08e419c0c88fa27e0
|
1d60c5a7b8ce6277bff514e376f79848f706344c
|
/Data Analyst with Python - Career Track/01. Introduction to Data Science in Python/04. Different Types of Plots/05. Modifying histograms.py
|
1ce32da1b97d613df25adfdb3b264ed5dbd7b8c8
|
[] |
no_license
|
DidiMilikina/DataCamp
|
338c6e6d3b4f5b6c541c1aba155a36e9ee24949d
|
3bf2cf3c1430190a7f8e54efda7d50a5fd66f244
|
refs/heads/master
| 2020-12-15T13:16:54.178967
| 2020-05-06T17:30:54
| 2020-05-06T17:30:54
| 235,113,616
| 4
| 3
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 764
|
py
|
'''
Modifying histograms
Let's explore how changes to keyword parameters in a histogram can change the output. Recall that:
range sets the minimum and maximum datapoints that we will include in our histogram.
bins sets the number of points in our histogram.
We'll be exploring the weights of various puppies from the DataFrame puppies. matplotlib has been loaded under the alias plt.
Instructions 1/3
35 XP
Create a histogram of the column weight from the DataFrame puppies.
Change the number of bins to 50.
Change the range to start at 5 and end at 35.
'''
SOLUTION
# Change the range to start at 5 and end at 35
plt.hist(puppies.weight,
range=(5, 35))
# Add labels
plt.xlabel('Puppy Weight (lbs)')
plt.ylabel('Number of Puppies')
# Display
plt.show()
|
[
"didimilikina8@gmail.com"
] |
didimilikina8@gmail.com
|
857a4621182c97e535493ea4340e8ca458dd3fdf
|
39ce19349f32f62cdd87cbc86b89dee0fe145b90
|
/namespace/graph/comp_printer.py
|
06da72fc07b68d2427149d52832bbd133eaf683c
|
[
"MIT"
] |
permissive
|
justjazz903/TFTCompEnumerator
|
cb01e5430061fb2b56447ccbda8af3c132cadf5b
|
a94d257c9fe4b670bcc17dc646c67ae9c113f32d
|
refs/heads/main
| 2023-09-01T02:15:42.885804
| 2021-10-22T02:47:59
| 2021-10-22T02:47:59
| 401,531,492
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,291
|
py
|
import json
import numpy as np
from namespace.graph.coder import bit_code_to_comp_id
from constant import CONSTANT
def get_comp_matrix(comp_id, champion_matrix, trait_matrix):
cost = champion_matrix[comp_id][:,-1]
comp_trait = np.sum(champion_matrix[comp_id], axis=0)[:-1]
comp_trait = comp_trait * trait_matrix.T
trait = np.zeros((trait_matrix.shape[0]), dtype=np.int8)
for trait_num in range(comp_trait.shape[0]):
if trait_num == 0:
continue
trait += np.where(comp_trait[trait_num] >= trait_num, 1, 0)
return trait, cost
def print_comp(bit_code, champion_matrix, trait_matrix):
with open(CONSTANT['FILE']['JSON'], 'r') as f:
json_file = json.load(f)
comp_id = bit_code_to_comp_id(bit_code, len(json_file['champions']))
trait, cost = get_comp_matrix(comp_id, champion_matrix, trait_matrix)
champ_names = list()
for champ_id in comp_id:
champ_names.append(json_file['champions'][champ_id]['name'])
trait_names = list()
for trait_id in range(len(trait)):
if trait[trait_id] != 0:
trait_names.append(json_file['traits'][trait_id]['name'] + ': ' + str(json_file['traits'][trait_id]['level'][trait[trait_id] - 1]))
return champ_names, trait_names, round(np.mean(cost), 2)
|
[
"31698772+justjazz903@users.noreply.github.com"
] |
31698772+justjazz903@users.noreply.github.com
|
4642e5f6dd8a3f91769fb2903013e098207ac562
|
5a22ff4bf71dde6ade4ddbe9d688c8a6370baa80
|
/src/_ctype_c.py
|
d588b7f46643038cab0bcac448761ae8c32ffa3f
|
[] |
no_license
|
sorenmulli/pyspeed
|
58ac7cb141954c1f8823ebef2089718a3d314117
|
1a51615713e32a2f5aac28fc1dc7b84ec93a4e96
|
refs/heads/master
| 2022-12-31T22:19:18.822724
| 2020-10-24T17:17:39
| 2020-10-24T19:07:13
| 288,703,022
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 209
|
py
|
import sys, os
from ctypes import CDLL, c_double
so_file = os.path.join( os.path.dirname( sys.argv[0] ), 'src', 'c_src', 'lib.so' )
c_lib = CDLL(so_file)
rootloop = c_lib.rootloop
rootloop.restype = c_double
|
[
"swholm@protonmail.com"
] |
swholm@protonmail.com
|
823ffb55f2f8f86439af50c36e239d023bb8afd2
|
e7196fd6e0fc1cab8ffc0417fca30947f673f774
|
/Remember/config.py
|
3c27d0a99411da95ef55720640a4ede50b3465de
|
[
"Apache-2.0"
] |
permissive
|
maxis1314/pyutils
|
6298dc054bd9585e33179755b10e7723713ee053
|
7e0666c650209155b3da186d09c54cf14825df1e
|
refs/heads/master
| 2020-12-23T20:30:09.406487
| 2017-07-06T10:11:36
| 2017-07-06T10:11:36
| 92,569,049
| 2
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 302
|
py
|
'''
this is the config file of Remember
'''
import os
BASE_DIR = os.path.abspath(os.path.dirname(__file__))
DB_BASE_DIR = os.path.join(BASE_DIR, "db")
SQLALCHEMY_DATABASE_URI = "sqlite:///" + os.path.join(DB_BASE_DIR, "app.db")
SQLALCHEMY_MIGRATE_REPO = os.path.join(DB_BASE_DIR, "db_repository")
|
[
"maxis1314@163.com"
] |
maxis1314@163.com
|
bc46d8ec26ee147ba88221d428f5a54885ec59e4
|
e061ab21018ac80573d03ef0c3cba8f448c4b7cc
|
/backend/alembic/versions/2023_01_05_1144-d4161e384f83_added_messagetreestate_table.py
|
778808cac77d094c4b4896e12badab8b30fb55dd
|
[
"Apache-2.0"
] |
permissive
|
LAION-AI/Open-Assistant
|
8b82c24fac954da421d66c3e90fbae6776ae6280
|
8c0e1a31bea1542dd39716b1dbbecd46785d9d23
|
refs/heads/main
| 2023-08-25T23:33:38.114219
| 2023-08-22T21:04:33
| 2023-08-22T21:04:33
| 577,603,990
| 34,014
| 3,206
|
Apache-2.0
| 2023-09-11T19:13:48
| 2022-12-13T05:24:17
|
Python
|
UTF-8
|
Python
| false
| false
| 1,899
|
py
|
"""added MessageTreeState table
Revision ID: d4161e384f83
Revises: 8d269bc4fdbd
Create Date: 2023-01-05 11:44:02.630633
"""
import sqlalchemy as sa
import sqlmodel
from alembic import op
from sqlalchemy.dialects import postgresql
# revision identifiers, used by Alembic.
revision = "d4161e384f83"
down_revision = "8d269bc4fdbd"
branch_labels = None
depends_on = None
def upgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
op.create_table(
"message_tree_state",
sa.Column("id", postgresql.UUID(as_uuid=True), server_default=sa.text("gen_random_uuid()"), nullable=False),
sa.Column("created_date", sa.DateTime(), server_default=sa.text("CURRENT_TIMESTAMP"), nullable=False),
sa.Column("deleted", sa.Boolean(), server_default=sa.text("false"), nullable=False),
sa.Column("message_tree_id", sqlmodel.sql.sqltypes.GUID(), nullable=False),
sa.Column("state", sqlmodel.sql.sqltypes.AutoString(length=128), nullable=False),
sa.Column("goal_tree_size", sa.Integer(), nullable=False),
sa.Column("current_num_non_filtered_messages", sa.Integer(), nullable=False),
sa.Column("max_depth", sa.Integer(), nullable=False),
sa.PrimaryKeyConstraint("id"),
)
op.create_index(
op.f("ix_message_tree_state_message_tree_id"), "message_tree_state", ["message_tree_id"], unique=False
)
op.create_index("ix_message_tree_state_tree_id", "message_tree_state", ["message_tree_id"], unique=True)
# ### end Alembic commands ###
def downgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
op.drop_index("ix_message_tree_state_tree_id", table_name="message_tree_state")
op.drop_index(op.f("ix_message_tree_state_message_tree_id"), table_name="message_tree_state")
op.drop_table("message_tree_state")
# ### end Alembic commands ###
|
[
"noreply@github.com"
] |
LAION-AI.noreply@github.com
|
84000dc843dc3b9bda62c43a3739cf9e263d6ba1
|
ecf2fa7f946b631d61c4ae88499976f1749e8b20
|
/P3B2/lstm_text_synthsis.py
|
14f673a21753cce27ba6b41c1cf2484ed59e780d
|
[
"MIT"
] |
permissive
|
tianxiangchen2015/Benchmarks
|
92cae3059f29ac5860332ccef7d658d0270f98be
|
93359c5e33aed01fb0bc089198c1b194a452e0c8
|
refs/heads/master
| 2021-01-19T01:50:56.284600
| 2017-03-30T19:37:46
| 2017-03-30T19:37:46
| 84,397,274
| 0
| 0
| null | 2017-03-09T04:13:28
| 2017-03-09T04:13:28
| null |
UTF-8
|
Python
| false
| false
| 4,115
|
py
|
import keras
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout
from keras.layers import LSTM
from keras.optimizers import RMSprop
import numpy as np
import os
import datetime
import cPickle
class LossHistory( keras.callbacks.Callback ):
def on_train_begin( self, logs= {} ):
self.losses = []
def on_batch_end( self, batch, logs= {} ):
self.losses.append( logs.get( 'loss' ) )
rnn_size = 256
# load data from pickle
f = open( 'data.pkl', 'r' )
classes = cPickle.load( f )
chars = cPickle.load( f )
char_indices = cPickle.load( f )
indices_char = cPickle.load( f )
maxlen = cPickle.load( f )
step = cPickle.load( f )
X_ind = cPickle.load( f )
y_ind = cPickle.load( f )
f.close()
[ s1, s2 ] = X_ind.shape
X = np.zeros( ( s1, s2, len( chars ) ), dtype=np.bool )
y = np.zeros( ( s1, len( chars ) ), dtype=np.bool )
for i in range( s1 ):
for t in range( s2 ):
X[ i, t, X_ind[ i, t ] ] = 1
y[ i, y_ind[ i ] ] = 1
# build the model: a single LSTM
print( 'Build model...' )
model = Sequential()
model.add( LSTM( rnn_size, input_shape=( maxlen, len( chars ) ) ) )
model.add( Dense( len( chars ) ) )
model.add( Activation( 'softmax' ) )
optimizer = RMSprop( lr= 0.001 )
model.compile( loss= 'categorical_crossentropy', optimizer= optimizer )
def sample(preds, temperature=1.0):
# helper function to sample an index from a probability array
preds = np.asarray(preds).astype('float64')
preds = np.log(preds) / temperature
exp_preds = np.exp(preds)
preds = exp_preds / np.sum(exp_preds)
probas = np.random.multinomial(1, preds, 1)
return np.argmax(probas)
# train the model, output generated text after each iteration
min_loss = 1e15
loss_count = 0
for iteration in range(1, 100):
print()
print('-' * 50)
print('Iteration', iteration)
history = LossHistory()
model.fit( X, y, batch_size= 100, nb_epoch= 1, callbacks= [ history ] )
loss = history.losses[ -1 ]
print( loss )
if loss < min_loss:
min_loss = loss
loss_count = 0
else:
loss_count = loss_count + 1
if loss_count > 4:
break
dirname = str( rnn_size ) + "/" + str( maxlen )
if not os.path.exists( dirname ):
os.makedirs( dirname )
# serialize model to JSON
model_json = model.to_json()
with open( dirname + "/model_" + str( iteration ) + "." + str( round( loss, 6 ) ) + ".json", "w" ) as json_file:
json_file.write( model_json )
# serialize weights to HDF5
model.save_weights( dirname + "/model_" + str( iteration ) + "." + str( round( loss, 6 ) ) + ".h5" )
print( "Checkpoint saved." )
outtext = open( dirname + "/example_" + str( iteration ) + "." + str( round( loss, 6 ) ) + ".txt", "w" )
for diversity in [0.2, 0.5, 1.0, 1.2]:
outtext.write('----- diversity:' + str( diversity ) + "\n" )
generated = ''
seedstr = "Diagnosis"
outtext.write('----- Generating with seed: "' + seedstr + '"' + "\n" )
sentence = " " * maxlen
# class_index = 0
generated += sentence
outtext.write( generated )
for c in seedstr:
sentence = sentence[1:] + c
x = np.zeros( ( 1, maxlen, len( chars ) ) )
for t, char in enumerate(sentence):
x[ 0, t, char_indices[ char ] ] = 1.
preds = model.predict(x, verbose=0)[0]
next_index = sample(preds, diversity)
next_char = indices_char[next_index]
generated += c
outtext.write( c )
for i in range( 400 ):
x = np.zeros( ( 1, maxlen, len( chars ) ) )
for t, char in enumerate(sentence):
x[ 0, t, char_indices[ char ] ] = 1.
preds = model.predict(x, verbose=0)[0]
next_index = sample(preds, diversity)
next_char = indices_char[next_index]
generated += next_char
sentence = sentence[1:] + next_char
outtext.write(next_char)
outtext.write( "\n" )
outtext.close()
|
[
"rarvind@arvind2.ornl.gov"
] |
rarvind@arvind2.ornl.gov
|
5b9ce34590b5d06b5110ae260649e03585017ebf
|
7189f6cdbdf2fe9a374c7aeb59034043edfbca08
|
/pythonProjects/fileMoverGUI2.0/fileMoverGui.py
|
689cc4073b2b37614225311f2bcc979f8b9ca5ed
|
[] |
no_license
|
tayharrison/AcademyProjects
|
072d42c9b5225c307decdc88e6ff99b619c186b8
|
22c1d3553384e2acba409d76d369f32f85f78fae
|
refs/heads/master
| 2020-06-24T23:16:13.908263
| 2017-10-24T20:05:40
| 2017-10-24T20:05:40
| 96,948,747
| 0
| 1
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 3,157
|
py
|
# File Mover GUI
# Taylor Harrison
# Python 3.6
from tkinter import *
from tkinter import messagebox
import tkinter as ttk
from datetime import *
from datetime import timedelta
import shutil
import os
import fileMoverGuiMain
import fileMoverGuifunc
def load_gui(self):
# Create and place the header
self.lbl_header = ttk.Label(self.master, bg='LIGHTGOLDENRODYELLOW', font=('Roboto',18,'bold'), text='Transfer Recently Modified Files')
self.lbl_header.grid(row=0, column=0, columnspan=5, padx=(25,5), pady=(10,10), sticky=N)
# Create and place the labels for the entry text fields
self.lbl_transfer_from = ttk.Label(self.master, bg='LIGHTGOLDENRODYELLOW', font=('Roboto',12), text='Transfer from:')
self.lbl_transfer_from.grid(row=1, column=0, columnspan=2, padx=(25,0), pady=(10,0), sticky=N+W)
self.lbl_transfer_to = ttk.Label(self.master, bg='LIGHTGOLDENRODYELLOW', font=('Roboto',12), text='Transfer to:')
self.lbl_transfer_to.grid(row=3, column=0, columnspan=2, padx=(25,0), pady=(10,0), sticky=N+W)
# Create and place the entry text fields
self.entry_transfer_from = ttk.Entry(self.master, width=50, font=('Arial', 10))
self.entry_transfer_from.grid(row=2, column=0, columnspan=4, padx=(25, 0), pady=(10, 0), sticky=N+W)
self.entry_transfer_to = ttk.Entry(self.master, width=50, font=('Arial', 10))
self.entry_transfer_to.grid(row=4, column=0, columnspan=4, padx=(25, 0), pady=(10, 0), sticky=N+W)
# Create and place the Browse buttons, that will look for a directory on the user's computer
self.btn_browse_from = ttk.Button(self.master, width=10, height=1, bg='LIGHTGOLDENRODYELLOW', text='Browse', command=lambda: fileMoverGuifunc.browse_folder_from(self))
self.btn_browse_from.grid(row=2, column=4, padx=(25, 0), pady=(7, 0), sticky=N+W)
self.btn_browse_to = ttk.Button(self.master, width=10, height=1, bg='LIGHTGOLDENRODYELLOW', text='Browse',command=lambda: fileMoverGuifunc.browse_folder_to(self))
self.btn_browse_to.grid(row=4, column=4, padx=(25, 0), pady=(7, 0), sticky=N+W)
# Create and place the Run button, that will execute the file check and transfer
self.btn_run = ttk.Button(self.master,width=8, height=2, bg='LIGHTGOLDENRODYELLOW', text='Run', command=lambda: fileMoverGuifunc.run_file_transfer(self))
self.btn_run.grid(row=5, column=3, rowspan = 2, padx=(0, 0), pady=(12, 10))
# Create and place label for the last time the file check was run
self.lbl_last_run = ttk.Label(self.master, bg='LIGHTGOLDENRODYELLOW', font=('Courier',12), text='Last Run:')
self.lbl_last_run.grid(row=5, column=0, columnspan=2, padx=(25,0), pady=(10,0), sticky=N+W)
# Create and place the time the last file check was run
self.lr_time = StringVar()
self.lr_time.set('XX-XX-XXXX XX:XX:XX XX')
self.txt_last_run_time = ttk.Label(self.master, bg='LIGHTGOLDENRODYELLOW', font=('Courier',12), width=22, textvariable=self.lr_time)
self.txt_last_run_time.grid(row=6, column=0, columnspan=2, padx=(25,0), pady=(0,0), sticky=N+W)
if __name__ == "__main__":
pass
|
[
"noreply@github.com"
] |
tayharrison.noreply@github.com
|
5e3ba54958a37cce2e223b5d076283b9591fe10e
|
c4551a6266e2a92fad6964f5764ebd849a4e3f4d
|
/runPboc_online.py
|
0f9ddf609c677851c44167065f2fc702dddf1b63
|
[] |
no_license
|
xueyizhiyong-UUU/featureEngineerOnline
|
eaedf1557019053c4041130a81704b18a06922a1
|
1eb61e911a623910021fc4e71f110e0abab658db
|
refs/heads/master
| 2023-08-17T22:09:52.277120
| 2021-10-27T03:38:04
| 2021-10-27T03:42:13
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 389,035
|
py
|
# coding:utf-8
import pboc_features
from model_handle import *
import features_func
import bh_data
from new_bhData import *
from main_score import *
import datetime
from dateutil.relativedelta import relativedelta
from pandas import json_normalize
from handle_third import *
from joblib import Parallel, delayed, load, dump
import sys
sys.path.append(r'/home/youzhengjie/WorkHome/generalFunction')
# 人行特征
feature = [
"loan_second_by03_classify5_giniimpurity",
"loan_second_m24_ncycle_month60_State_num_mean_mean",
"loan_rating_worst",
"loan_second_hdue1R_month60_to_report_mean_min",
"loan_second_by06_month60_Amount_num_mean_mean",
"loan_second_by06_ncycle_month60_Amount_num_mean_mean",
"age_idcard",
"blacn_lac",
"business_loan_amount_max",
"business_loan_amount_sum",
"card_second_m06_maxUsed_vs_sumAmount",
"card_second_m12_sumUsed_vs_sumAmount",
"CardCount_count",
"CardCount_ratio",
"clacn_lac",
"consume_loan_account_count",
"consume_loan_amount_sum_now",
"consume_loan_balance_max_now",
"consume_loan_planRepayAmount_max_now",
"credit_tips_total_count",
"creditCardCount_vs_TotalCount",
"debit_card_five_years_normal_ratio",
"debit_card_lastmonth_normal_balance_avg",
"debit_card_lastmonth_normal_planRepayAmount_count",
"debit_card_lastmonth_normal_repayedAmount_count",
"debit_card_repayment_billday_count",
"debit_card_repayment_m1_count",
"debit_card_repayment_normal_ratio",
"diploma",
"first_loan_amount",
"first_loan_time_till_now",
"firstCreditCardMonth_to_report",
"laco03_lacn",
"laco06_lac",
"lc_month60_Amount_num_mean",
"lc_month60_C_count",
"lc_month60_m01_C_ratio",
"lc_month60_m01_N_ratio",
"lc_month60_m01_Null_count",
"lc_month60_m03_Amount_num_mean",
"lc_month60_m03_N_count",
"lc_month60_m03_Null_ratio",
"lc_month60_m06_Amount_num_mean",
"lc_month60_m06_C_ratio",
"lc_month60_m06_N_count",
"lc_month60_m06_N_ratio",
"lc_month60_m06_Null_ratio",
"lc_month60_m06_State_num_mean",
"lc_month60_m12_C_count",
"lc_month60_m12_C_ratio",
"lc_month60_m12_N_count",
"lc_month60_m12_N_ratio",
"lc_month60_m12_Null_count",
"lc_month60_m12_Null_ratio",
"lc_month60_m12_State_num_mean",
"lc_month60_m24_1_ratio",
"lc_month60_m24_Amount_num_mean",
"lc_month60_m24_C_count",
"lc_month60_m24_N_count",
"lc_month60_m24_State_giniimpurity",
"lc_month60_m24_State_num_mean",
"lc_month60_m36_1_ratio",
"lc_month60_m36_Amount_num_mean",
"lc_month60_m36_C_count",
"lc_month60_m36_N_count",
"lc_month60_m36_N_ratio",
"lc_month60_m36_Null_count",
"lc_month60_m36_Null_ratio",
"lc_month60_m36_State_big0_mean",
"lc_month60_m36_State_giniimpurity",
"lc_month60_m48_C_count",
"lc_month60_m48_N_count",
"lc_month60_m48_Null_count",
"lc_month60_m48_Null_ratio",
"lc_month60_m48_State_num_mean",
"lc_month60_m60_C_count",
"lc_month60_m60_N_count",
"lc_month60_m60_Null_count",
"lc_month60_N_count",
"lc_month60_N_ratio",
"lc_month60_Null_count",
"lc_month60_State_big0_mean",
"liv_36m_cnt",
"liveaddr_num",
"ln_1m_expiresum",
"loan_account_count",
"loan_amount_sum_open_03m",
"loan_card_amount_count",
"loan_card_balance_mean",
"loan_card_card_total_amount_ratio",
"loan_card_credit_card_ratio",
"loan_card_debt_total_amount_ratio",
"loan_card_gm06_credit_card_ratio",
"loan_card_gm06_other_consumer_ratio",
"loan_card_gm06_planRepayAmount_count",
"loan_card_gm06_remainingTerms_max",
"loan_card_gm12_balance_max",
"loan_card_gm12_credit_card_ratio",
"loan_card_gm12_finance_lease_ratio",
"loan_card_gm12_other_consumer_org_cnt",
"loan_card_gm12_other_consumer_ratio",
"loan_card_gm12_planRepayAmount_count",
"loan_card_gm12_planRepayAmount_max",
"loan_card_gm12_planRepayAmount_mean",
"loan_card_gm12_remainingTerms_max",
"loan_card_gm12_remainingTerms_sum",
"loan_card_gm12_RepayedAmount_max",
"loan_card_gm12_type_card_ratio",
"loan_card_gm12_type_loan_ratio",
"loan_card_loan_total_amount_ratio",
"loan_card_m01_amount_count",
"loan_card_m01_amount_maxCardVsMaxComsum",
"loan_card_m01_comsum_amount_max",
"loan_card_m01_comsumOrCard_amount_max",
"loan_card_m01_operation_loan_ratio",
"loan_card_m01_other_loan_ratio",
"loan_card_m01_planRepayAmount_count",
"loan_card_m01_planRepayAmount_max",
"loan_card_m01_planRepayAmount_mean",
"loan_card_m01_remainingTerms_max",
"loan_card_m01_RepayedAmount_max",
"loan_card_m01_RepayedAmount_mean",
"loan_card_m01_RepayedAmount_sum",
"loan_card_m01_startdata_min_to_report_days",
"loan_card_m06_amount_count",
"loan_card_m06_other_loan_org_cnt",
"loan_card_m06_planRepayAmount_mean",
"loan_card_m06_remainingTerms_max",
"loan_card_m06_RepayedAmount_max",
"loan_card_m06_RepayedAmount_mean",
"loan_card_notcycle_avgRepaymentAmount_ratio",
"loan_card_now_other_consumer_org_cnt",
"loan_card_now_other_loan_count",
"loan_card_now_other_loan_org_cnt",
"loan_card_now_other_loan_ratio",
"loan_card_now_planRepayAmount_max",
"loan_card_now_planRepayAmount_mean",
"loan_card_now_remainingTerms_sum",
"loan_card_now_RepayedAmount_mean",
"loan_card_other_consumer_count",
"loan_card_other_consumer_org_cnt",
"loan_card_other_consumer_ratio",
"loan_card_other_loan_org_cnt",
"loan_card_planRepayAmount_sum",
"loan_card_r_card_ratio",
"loan_card_r_finance_lease_ratio",
"loan_card_r_guaranty_combine_nhave_count",
"loan_card_r_guaranty_combine_nhave_ratio",
"loan_card_r_guaranty_credit_no_count",
"loan_card_r_guaranty_credit_no_ratio",
"loan_card_r_guaranty_pledge_count",
"loan_card_r_ncycle_count",
"loan_card_r_other_consumer_count",
"loan_card_RepayedAmount_count",
"loan_card_RepayedAmount_max",
"loan_card_RepayedAmount_mean",
"loan_card_startdata_min_to_report_days",
"loan_card_total_amount_sum",
"loan_card_type_loan_count",
"loan_card_type_loan_org_cnt",
"loan_current_ndue_account_count",
"loan_current_ndue_balance_sum",
"loan_GrantOrg_CD",
"loan_GrantOrg_CD_now",
"loan_is_vouch_loanAmount_max",
"loan_ndue_account_count",
"loan_ndue_account_count_03m",
"loan_ndue_account_count_24m",
"loan_ndue_amount_sum",
"loan_second_as_settle_count",
"loan_second_balance_mean",
"loan_second_bt_other_loan_ratio",
"loan_second_bt_other_person_count",
"loan_second_bt_other_person_ratio",
"loan_second_businessType_giniimpurity",
"loan_second_by03_accountStatus_giniimpurity",
"loan_second_by03_balance_min",
"loan_second_by03_byDate_to_report_mean",
"loan_second_by03_gf_combine_nowarranty_ratio",
"loan_second_by03_gf_crdit_count",
"loan_second_by03_leftRepayTerms_range",
"loan_second_by03_month60_State_countCr_max",
"loan_second_by03_month60_State_countNull_mean",
"loan_second_by03_month60_State_num_size_sum",
"loan_second_by03_month60_to_report_max_range",
"loan_second_by03_month60_to_report_mean_range",
"loan_second_by03_month60_to_report_min_mean",
"loan_second_by03_ncycle_balance_min",
"loan_second_by03_ncycle_balance_ratio_mean",
"loan_second_by03_ncycle_balance_ratio_range",
"loan_second_by03_ncycle_bt_finance_lease_ratio",
"loan_second_by03_ncycle_bt_other_loan_ratio",
"loan_second_by03_ncycle_gf_crdit_ratio",
"loan_second_by03_ncycle_leftRepayTerms_mean",
"loan_second_by03_ncycle_month60_State_count2r_max",
"loan_second_by03_ncycle_month60_State_count2r_sum",
"loan_second_by03_ncycle_month60_State_count3r_max",
"loan_second_by03_ncycle_month60_State_count3r_mean",
"loan_second_by03_ncycle_month60_State_countC_sum",
"loan_second_by03_ncycle_month60_State_countCr_sum",
"loan_second_by03_ncycle_month60_State_countN_mean",
"loan_second_by03_ncycle_month60_State_countN_sum",
"loan_second_by03_ncycle_month60_State_countNullr_range",
"loan_second_by03_ncycle_org_commercial_bank_ratio",
"loan_second_by03_ncycle_org_consumer_finance_ratio",
"loan_second_by03_ncycle_planRepayAmount_mean",
"loan_second_by03_ncycle_planRepayAmount_range",
"loan_second_by03_ncycle_repayAmt_max",
"loan_second_by03_ncycle_repayedAmount_min",
"loan_second_by03_ncycle_repayMons_mean",
"loan_second_by03_ncycle_repayMons_range",
"loan_second_by03_ncycle_repayTerms_mean",
"loan_second_by03_ncycle_rf_month_ratio",
"loan_second_by03_ncycle_rf_other_ratio",
"loan_second_by03_ncycle_startDate_to_report_max",
"loan_second_by03_ncycleR_balance_range",
"loan_second_by03_ncycleR_month60_State_countN_max",
"loan_second_by03_ncycleR_month60_State_countNr_max",
"loan_second_by03_ncycleR_month60_State_countNull_max",
"loan_second_by03_ncycleR_month60_State_num_size_sum",
"loan_second_by03_ncycleR_month60_to_report_mean_sum",
"loan_second_by03_ncycleR_startDate_to_report_sum",
"loan_second_by03_now_balance_ratio_mean",
"loan_second_by03_now_bt_other_person_ratio",
"loan_second_by03_now_loanAmount_max",
"loan_second_by03_now_loanGrantOrg_giniimpurity",
"loan_second_by03_now_month60_State_count2_max",
"loan_second_by03_now_month60_State_count2_mean",
"loan_second_by03_now_month60_State_count2r_sum",
"loan_second_by03_now_month60_State_countNr_range",
"loan_second_by03_now_month60_State_countNullr_mean",
"loan_second_by03_now_org_consumer_finance_count",
"loan_second_by03_now_org_lease_finance_ratio",
"loan_second_by03_now_repayTerm_ratio_min",
"loan_second_by03_now_rf_month_ratio",
"loan_second_by03_now_rf_other_count",
"loan_second_by03_now_rt_unknow_count",
"loan_second_by03_nowR_balance_range",
"loan_second_by03_nowR_bt_other_person_count",
"loan_second_by03_nowR_byDate_to_report_sum",
"loan_second_by03_nowR_gf_crdit_ratio",
"loan_second_by03_nowR_gf_other_ratio",
"loan_second_by03_nowR_loanAmount_max",
"loan_second_by03_nowR_loanAmount_min",
"loan_second_by03_nowR_loanGrantOrg_nunique",
"loan_second_by03_nowR_month60_State_countC_mean",
"loan_second_by03_nowR_month60_State_countN_mean",
"loan_second_by03_nowR_month60_State_countNull_sum",
"loan_second_by03_nowR_month60_State_countNullr_max",
"loan_second_by03_nowR_month60_State_countNullr_mean",
"loan_second_by03_nowR_month60_State_countNullr_range",
"loan_second_by03_nowR_month60_State_num_size_max",
"loan_second_by03_nowR_month60_State_num_size_range",
"loan_second_by03_nowR_month60_to_report_max_mean",
"loan_second_by03_nowR_month60_to_report_max_range",
"loan_second_by03_nowR_month60_to_report_mean_mean",
"loan_second_by03_nowR_month60_to_report_mean_range",
"loan_second_by03_nowR_month60_to_report_min_mean",
"loan_second_by03_nowR_month60_to_report_min_sum",
"loan_second_by03_nowR_org_commercial_bank_ratio",
"loan_second_by03_nowR_org_consumer_finance_count",
"loan_second_by03_nowR_org_consumer_finance_ratio",
"loan_second_by03_nowR_org_micro_loan_count",
"loan_second_by03_nowR_planRepayAmount_range",
"loan_second_by03_nowR_repayAmt_max",
"loan_second_by03_nowR_repayAmt_range",
"loan_second_by03_nowR_repayedAmount_range",
"loan_second_by03_nowR_repayFrequency_giniimpurity",
"loan_second_by03_nowR_repayMons_range",
"loan_second_by03_nowR_repayMons_ratio_max",
"loan_second_by03_nowR_repayMons_ratio_range",
"loan_second_by03_nowR_repayMons_sum",
"loan_second_by03_nowR_repayTerms_sum",
"loan_second_by03_nowR_rf_other_count",
"loan_second_by03_nowR_startDate_to_report_max",
"loan_second_by03_nowR_startDate_to_report_mean",
"loan_second_by03_org_micro_loan_ratio",
"loan_second_by03_org_other_ratio",
"loan_second_by03_planRepayAmount_max",
"loan_second_by03_planRepayAmount_mean",
"loan_second_by03_repayAmt_mean",
"loan_second_by03_repayTerm_ratio_min",
"loan_second_by03_rf_month_ratio",
"loan_second_by03_startDate_to_report_max",
"loan_second_by03_startDate_to_report_mean",
"loan_second_by03_startDate_to_report_sum",
"loan_second_by03_vouchR_month60_to_report_mean_sum",
"loan_second_by06_bt_other_person_ratio",
"loan_second_by06_byDate_to_report_sum",
"loan_second_by06_classify5_giniimpurity",
"loan_second_by06_classify5_num_range",
"loan_second_by06_cycleR_repayMons_ratio_sum",
"loan_second_by06_hdue1_month60_State_num_mean_mean",
"loan_second_by06_hdue1R_month60_State_countNull_sum",
"loan_second_by06_hdue1R_month60_State_countNullr_sum",
"loan_second_by06_hdue1R_repayMons_ratio_sum",
"loan_second_by06_month60_State_countCr_max",
"loan_second_by06_month60_State_countN_range",
"loan_second_by06_month60_State_countN_sum",
"loan_second_by06_month60_State_countNr_range",
"loan_second_by06_month60_State_countNull_mean",
"loan_second_by06_month60_State_countUnknow_mean",
"loan_second_by06_month60_State_countUnknowr_mean",
"loan_second_by06_month60_State_num_size_sum",
"loan_second_by06_month60_to_report_max_sum",
"loan_second_by06_month60_to_report_mean_max",
"loan_second_by06_month60_to_report_mean_range",
"loan_second_by06_ncycle_balance_max",
"loan_second_by06_ncycle_balance_ratio_mean",
"loan_second_by06_ncycle_bt_other_loan_count",
"loan_second_by06_ncycle_bt_person_business_ratio",
"loan_second_by06_ncycle_byDate_to_report_mean",
"loan_second_by06_ncycle_classify5_num_range",
"loan_second_by06_ncycle_gf_combine_nowarranty_ratio",
"loan_second_by06_ncycle_gf_other_ratio",
"loan_second_by06_ncycle_is_now_min",
"loan_second_by06_ncycle_month60_State_count2_max",
"loan_second_by06_ncycle_month60_State_countCr_max",
"loan_second_by06_ncycle_month60_State_countCr_mean",
"loan_second_by06_ncycle_month60_State_countCr_range",
"loan_second_by06_ncycle_month60_State_countN_sum",
"loan_second_by06_ncycle_month60_State_countNull_sum",
"loan_second_by06_ncycle_month60_State_countUnknow_mean",
"loan_second_by06_ncycle_month60_State_countUnknowr_mean",
"loan_second_by06_ncycle_month60_to_report_max_sum",
"loan_second_by06_ncycle_month60_to_report_mean_mean",
"loan_second_by06_ncycle_month60_to_report_mean_range",
"loan_second_by06_ncycle_month60_to_report_min_mean",
"loan_second_by06_ncycle_org_commercial_bank_count",
"loan_second_by06_ncycle_org_commercial_bank_ratio",
"loan_second_by06_ncycle_org_giniimpurity",
"loan_second_by06_ncycle_org_other_ratio",
"loan_second_by06_ncycle_repayAmt_mean",
"loan_second_by06_ncycle_repayAmt_range",
"loan_second_by06_ncycle_repayedAmount_mean",
"loan_second_by06_ncycle_repayMons_mean",
"loan_second_by06_ncycle_repayMons_range",
"loan_second_by06_ncycle_repayMons_ratio_range",
"loan_second_by06_ncycle_repayMons_sum",
"loan_second_by06_ncycle_repayTerm_ratio_min",
"loan_second_by06_ncycle_repayTerm_ratio_range",
"loan_second_by06_ncycle_rf_other_count",
"loan_second_by06_ncycle_rf_other_ratio",
"loan_second_by06_ncycle_rt_unknow_count",
"loan_second_by06_ncycle_startDate_to_report_sum",
"loan_second_by06_ncycleR_balance_range",
"loan_second_by06_ncycleR_month60_State_countNullr_sum",
"loan_second_by06_ncycleR_RepayedAmount_ratio_max",
"loan_second_by06_ncycleR_repayTerm_ratio_max",
"loan_second_by06_now_balance_ratio_mean",
"loan_second_by06_now_bt_other_loan_count",
"loan_second_by06_now_byDate_to_report_mean",
"loan_second_by06_now_month60_State_countN_mean",
"loan_second_by06_now_month60_State_countNr_range",
"loan_second_by06_now_month60_State_num_size_mean",
"loan_second_by06_now_org_commercial_bank_count",
"loan_second_by06_now_org_myself_count",
"loan_second_by06_now_planRepayAmount_sum",
"loan_second_by06_now_rf_other_ratio",
"loan_second_by06_now_rt_unknow_count",
"loan_second_by06_now_startDate_to_report_range",
"loan_second_by06_nowR_balance_ratio_range",
"loan_second_by06_nowR_bt_other_person_ratio",
"loan_second_by06_nowR_byDate_to_report_mean",
"loan_second_by06_nowR_byDate_to_report_sum",
"loan_second_by06_nowR_gf_combine_nowarranty_ratio",
"loan_second_by06_nowR_gf_other_ratio",
"loan_second_by06_nowR_guaranteeForm_giniimpurity",
"loan_second_by06_nowR_loanAmount_range",
"loan_second_by06_nowR_loanGrantOrg_giniimpurity",
"loan_second_by06_nowR_loanGrantOrg_nunique",
"loan_second_by06_nowR_month60_State_countN_max",
"loan_second_by06_nowR_month60_State_countNull_mean",
"loan_second_by06_nowR_month60_State_countNullr_mean",
"loan_second_by06_nowR_month60_State_countUnknowr_mean",
"loan_second_by06_nowR_month60_to_report_max_max",
"loan_second_by06_nowR_month60_to_report_max_mean",
"loan_second_by06_nowR_month60_to_report_max_range",
"loan_second_by06_nowR_month60_to_report_max_sum",
"loan_second_by06_nowR_month60_to_report_mean_max",
"loan_second_by06_nowR_month60_to_report_mean_mean",
"loan_second_by06_nowR_month60_to_report_mean_range",
"loan_second_by06_nowR_month60_to_report_min_sum",
"loan_second_by06_nowR_org_commercial_bank_count",
"loan_second_by06_nowR_org_commercial_bank_ratio",
"loan_second_by06_nowR_repayAmt_max",
"loan_second_by06_nowR_repayAmt_range",
"loan_second_by06_nowR_repayMons_ratio_mean",
"loan_second_by06_nowR_repayMons_ratio_range",
"loan_second_by06_nowR_rf_month_ratio",
"loan_second_by06_nowR_rf_once_ratio",
"loan_second_by06_nowR_startDate_to_report_max",
"loan_second_by06_nowR_startDate_to_report_range",
"loan_second_by06_org_commercial_bank_ratio",
"loan_second_by06_org_micro_loan_ratio",
"loan_second_by06_org_other_ratio",
"loan_second_by06_repayAmt_mean",
"loan_second_by06_repayedAmount_sum",
"loan_second_by06_repayMons_mean",
"loan_second_by06_repayMons_ratio_mean",
"loan_second_by06_repayMons_ratio_sum",
"loan_second_by06_repayTerm_ratio_min",
"loan_second_by06_repayTerms_range",
"loan_second_by06_rf_other_ratio",
"loan_second_by06_startDate_to_report_range",
"loan_second_by06_startDate_to_report_sum",
"loan_second_by06_vouch_loanAmount_sum",
"loan_second_by06_vouchR_loanGrantOrg_nunique",
"loan_second_by06_vouchR_month60_State_num_size_sum",
"loan_second_by06_vouchR_repayTerm_ratio_min",
"loan_second_by12_accountStatus_giniimpurity",
"loan_second_by12_as_settle_count",
"loan_second_by12_balance_min",
"loan_second_by12_balance_ratio_range",
"loan_second_by12_bt_other_loan_count",
"loan_second_by12_byDate_to_report_max",
"loan_second_by12_byDate_to_report_mean",
"loan_second_by12_byDate_to_report_sum",
"loan_second_by12_c5_unknow_count",
"loan_second_by12_c5_unknow_ratio",
"loan_second_by12_class_ncycle_count",
"loan_second_by12_classify5_num_sum",
"loan_second_by12_gf_combine_nowarranty_ratio",
"loan_second_by12_gf_crdit_count",
"loan_second_by12_hdue1_month60_State_countNr_mean",
"loan_second_by12_hdue1R_bt_other_person_count",
"loan_second_by12_hdue1R_loanGrantOrg_nunique",
"loan_second_by12_hdue1R_month60_State_countNullr_sum",
"loan_second_by12_hdue1R_month60_State_num_max_mean",
"loan_second_by12_hdue1R_month60_to_report_mean_min",
"loan_second_by12_hdue1R_repayAmt_sum",
"loan_second_by12_hdue1R_repayMons_ratio_sum",
"loan_second_by12_leftRepayTerms_min",
"loan_second_by12_loanAmount_range",
"loan_second_by12_loanGrantOrg_nunique",
"loan_second_by12_month60_Amount_num_mean_mean",
"loan_second_by12_month60_Amount_num_sum_mean",
"loan_second_by12_month60_State_count1r_mean",
"loan_second_by12_month60_State_countCr_mean",
"loan_second_by12_month60_State_countN_max",
"loan_second_by12_month60_State_countN_mean",
"loan_second_by12_month60_State_countN_sum",
"loan_second_by12_month60_State_countNr_range",
"loan_second_by12_month60_State_countNr_sum",
"loan_second_by12_month60_State_countNull_max",
"loan_second_by12_month60_State_countNull_sum",
"loan_second_by12_month60_State_countNullr_range",
"loan_second_by12_month60_State_num_size_mean",
"loan_second_by12_month60_State_num_sum_mean",
"loan_second_by12_month60_to_report_max_mean",
"loan_second_by12_month60_to_report_mean_max",
"loan_second_by12_month60_to_report_min_max",
"loan_second_by12_month60_to_report_min_sum",
"loan_second_by12_ncycle_balance_min",
"loan_second_by12_ncycle_balance_ratio_max",
"loan_second_by12_ncycle_balance_ratio_mean",
"loan_second_by12_ncycle_balance_ratio_min",
"loan_second_by12_ncycle_byDate_to_report_max",
"loan_second_by12_ncycle_byDate_to_report_sum",
"loan_second_by12_ncycle_class_ncycle_count",
"loan_second_by12_ncycle_classify5_giniimpurity",
"loan_second_by12_ncycle_gf_combine_nowarranty_ratio",
"loan_second_by12_ncycle_guaranteeForm_giniimpurity",
"loan_second_by12_ncycle_leftRepayTerms_mean",
"loan_second_by12_ncycle_loanAmount_range",
"loan_second_by12_ncycle_month60_Amount_num_mean_mean",
"loan_second_by12_ncycle_month60_State_count1_mean",
"loan_second_by12_ncycle_month60_State_count1r_mean",
"loan_second_by12_ncycle_month60_State_countCr_mean",
"loan_second_by12_ncycle_month60_State_countN_mean",
"loan_second_by12_ncycle_month60_State_countN_sum",
"loan_second_by12_ncycle_month60_State_countNr_mean",
"loan_second_by12_ncycle_month60_State_countNr_sum",
"loan_second_by12_ncycle_month60_State_countNullr_sum",
"loan_second_by12_ncycle_month60_State_countUnknow_max",
"loan_second_by12_ncycle_month60_State_num_mean_mean",
"loan_second_by12_ncycle_month60_State_num_size_mean",
"loan_second_by12_ncycle_month60_State_num_sum_mean",
"loan_second_by12_ncycle_month60_to_report_max_mean",
"loan_second_by12_ncycle_month60_to_report_mean_max",
"loan_second_by12_ncycle_month60_to_report_mean_sum",
"loan_second_by12_ncycle_month60_to_report_min_max",
"loan_second_by12_ncycle_month60_to_report_min_sum",
"loan_second_by12_ncycle_org_commercial_bank_ratio",
"loan_second_by12_ncycle_org_micro_loan_count",
"loan_second_by12_ncycle_org_micro_loan_ratio",
"loan_second_by12_ncycle_org_myself_count",
"loan_second_by12_ncycle_org_trust_company_ratio",
"loan_second_by12_ncycle_repayAmt_mean",
"loan_second_by12_ncycle_repayAmt_range",
"loan_second_by12_ncycle_repayedAmount_sum",
"loan_second_by12_ncycle_repayMons_ratio_mean",
"loan_second_by12_ncycle_repayMons_ratio_range",
"loan_second_by12_ncycle_repayTerm_ratio_mean",
"loan_second_by12_ncycle_repayTerm_ratio_range",
"loan_second_by12_ncycle_repayTerms_mean",
"loan_second_by12_ncycle_rf_month_count",
"loan_second_by12_ncycle_rf_month_ratio",
"loan_second_by12_ncycle_rf_other_ratio",
"loan_second_by12_ncycle_startDate_to_report_max",
"loan_second_by12_ncycle_startDate_to_report_mean",
"loan_second_by12_ncycle_startDate_to_report_range",
"loan_second_by12_ncycleR_month60_State_countNull_sum",
"loan_second_by12_ncycleR_month60_State_countUnknowr_max",
"loan_second_by12_ncycleR_org_giniimpurity",
"loan_second_by12_now_balance_ratio_mean",
"loan_second_by12_now_balance_ratio_min",
"loan_second_by12_now_balance_ratio_range",
"loan_second_by12_now_bt_other_loan_ratio",
"loan_second_by12_now_byDate_to_report_mean",
"loan_second_by12_now_classify5_num_range",
"loan_second_by12_now_gf_other_ratio",
"loan_second_by12_now_month60_State_countN_mean",
"loan_second_by12_now_month60_State_countNull_mean",
"loan_second_by12_now_repayAmt_max",
"loan_second_by12_now_repayAmt_min",
"loan_second_by12_now_repayedAmount_mean",
"loan_second_by12_now_repayMons_ratio_mean",
"loan_second_by12_now_repayTerm_ratio_range",
"loan_second_by12_now_rf_other_count",
"loan_second_by12_nowR_bt_other_loan_count",
"loan_second_by12_nowR_byDate_to_report_mean",
"loan_second_by12_nowR_loanAmount_max",
"loan_second_by12_nowR_loanAmount_mean",
"loan_second_by12_nowR_loanAmount_sum",
"loan_second_by12_nowR_loanGrantOrg_nunique",
"loan_second_by12_nowR_month60_State_countN_sum",
"loan_second_by12_nowR_month60_State_countNull_max",
"loan_second_by12_nowR_month60_State_countNull_sum",
"loan_second_by12_nowR_month60_State_countUnknow_mean",
"loan_second_by12_nowR_month60_State_num_size_mean",
"loan_second_by12_nowR_month60_State_num_size_range",
"loan_second_by12_nowR_month60_to_report_mean_mean",
"loan_second_by12_nowR_month60_to_report_mean_range",
"loan_second_by12_nowR_month60_to_report_min_mean",
"loan_second_by12_nowR_org_commercial_bank_count",
"loan_second_by12_nowR_org_commercial_bank_ratio",
"loan_second_by12_nowR_org_consumer_finance_ratio",
"loan_second_by12_nowR_org_micro_loan_count",
"loan_second_by12_nowR_planRepayAmount_range",
"loan_second_by12_nowR_repayAmt_max",
"loan_second_by12_nowR_repayAmt_range",
"loan_second_by12_nowR_repayAmt_sum",
"loan_second_by12_nowR_repayFrequency_giniimpurity",
"loan_second_by12_nowR_repayMons_max",
"loan_second_by12_nowR_repayMons_ratio_mean",
"loan_second_by12_nowR_repayTerms_sum",
"loan_second_by12_nowR_startDate_to_report_mean",
"loan_second_by12_nowR_startDate_to_report_range",
"loan_second_by12_org_consumer_finance_count",
"loan_second_by12_org_consumer_finance_ratio",
"loan_second_by12_org_micro_loan_ratio",
"loan_second_by12_org_trust_company_ratio",
"loan_second_by12_repayAmt_max",
"loan_second_by12_repayFrequency_giniimpurity",
"loan_second_by12_repayMons_max",
"loan_second_by12_repayMons_ratio_mean",
"loan_second_by12_repayMons_ratio_sum",
"loan_second_by12_repayTerm_ratio_mean",
"loan_second_by12_repayTerm_ratio_range",
"loan_second_by12_repayTerms_max",
"loan_second_by12_repayTerms_mean",
"loan_second_by12_rf_month_ratio",
"loan_second_by12_rf_other_ratio",
"loan_second_by12_vouch_loanAmount_min",
"loan_second_by12_vouch_org_micro_loan_ratio",
"loan_second_by12_vouch_repayAmt_min",
"loan_second_by12_vouchR_loanGrantOrg_nunique",
"loan_second_by12_vouchR_month60_State_countNr_sum",
"loan_second_by12_vouchR_month60_State_countNullr_sum",
"loan_second_by12_vouchR_month60_to_report_mean_sum",
"loan_second_by12_vouchR_org_micro_loan_ratio",
"loan_second_by12_vouchR_repayMons_ratio_mean",
"loan_second_byDate_to_report_sum",
"loan_second_c5_unknow_count",
"loan_second_class_ncycle_count",
"loan_second_class_ncycle_ratio",
"loan_second_cycle_repayTerms_range",
"loan_second_cycleR_month60_State_countNull_sum",
"loan_second_gf_crdit_count",
"loan_second_gf_other_ratio",
"loan_second_hdue1_month60_Amount_num_max_mean",
"loan_second_hdue1_month60_Amount_num_mean_mean",
"loan_second_hdue1_month60_State_count1r_min",
"loan_second_hdue1_month60_State_countN_sum",
"loan_second_hdue1_month60_State_countNr_min",
"loan_second_hdue1_month60_State_num_mean_mean",
"loan_second_hdue1_month60_State_num_mean_min",
"loan_second_hdue1_month60_State_num_size_min",
"loan_second_hdue1_month60_to_report_max_min",
"loan_second_hdue1_month60_to_report_mean_max",
"loan_second_hdue1_month60_to_report_mean_mean",
"loan_second_hdue1_repayAmt_mean",
"loan_second_hdue1_repayMons_ratio_mean",
"loan_second_hdue1R_class_ncycle_count",
"loan_second_hdue1R_loanAmount_mean",
"loan_second_hdue1R_loanGrantOrg_nunique",
"loan_second_hdue1R_month60_State_countCr_sum",
"loan_second_hdue1R_month60_State_countN_sum",
"loan_second_hdue1R_month60_State_countNr_sum",
"loan_second_hdue1R_month60_State_countNullr_sum",
"loan_second_hdue1R_month60_to_report_max_min",
"loan_second_hdue1R_month60_to_report_max_range",
"loan_second_hdue1R_month60_to_report_mean_max",
"loan_second_hdue1R_org_micro_loan_ratio",
"loan_second_hdue1R_org_trust_company_ratio",
"loan_second_hdue1R_repayMons_ratio_min",
"loan_second_hdue1R_repayTerms_sum",
"loan_second_hdue1R_rt_unknow_count",
"loan_second_hdue1R_startDate_to_report_max",
"loan_second_leftRepayTerms_min",
"loan_second_loanGrantOrg_giniimpurity",
"loan_second_loanGrantOrg_nunique",
"loan_second_m06_balance_ratio_mean",
"loan_second_m06_businessType_giniimpurity",
"loan_second_m06_byDate_to_report_mean",
"loan_second_m06_guaranteeForm_giniimpurity",
"loan_second_m06_leftRepayTerms_range",
"loan_second_m06_loanAmount_min",
"loan_second_m06_month60_Amount_num_mean_max",
"loan_second_m06_month60_Amount_num_sum_max",
"loan_second_m06_month60_State_countCr_mean",
"loan_second_m06_month60_State_countNull_mean",
"loan_second_m06_month60_State_countNullr_mean",
"loan_second_m06_month60_State_num_mean_mean",
"loan_second_m06_month60_to_report_max_range",
"loan_second_m06_month60_to_report_mean_mean",
"loan_second_m06_ncycle_balance_min",
"loan_second_m06_ncycle_balance_ratio_max",
"loan_second_m06_ncycle_balance_ratio_mean",
"loan_second_m06_ncycle_balance_ratio_min",
"loan_second_m06_ncycle_balance_ratio_range",
"loan_second_m06_ncycle_byDate_to_report_mean",
"loan_second_m06_ncycle_due_class_mean",
"loan_second_m06_ncycle_guaranteeForm_giniimpurity",
"loan_second_m06_ncycle_leftRepayTerms_max",
"loan_second_m06_ncycle_loanAmount_min",
"loan_second_m06_ncycle_month60_Amount_num_max_max",
"loan_second_m06_ncycle_month60_Amount_num_sum_mean",
"loan_second_m06_ncycle_month60_State_countCr_mean",
"loan_second_m06_ncycle_month60_State_countNr_mean",
"loan_second_m06_ncycle_month60_State_countNullr_mean",
"loan_second_m06_ncycle_month60_State_num_max_sum",
"loan_second_m06_ncycle_month60_State_num_mean_max",
"loan_second_m06_ncycle_month60_State_num_size_sum",
"loan_second_m06_ncycle_month60_to_report_max_mean",
"loan_second_m06_ncycle_month60_to_report_mean_sum",
"loan_second_m06_ncycle_org_commercial_bank_ratio",
"loan_second_m06_ncycle_org_consumer_finance_ratio",
"loan_second_m06_ncycle_org_giniimpurity",
"loan_second_m06_ncycle_org_other_ratio",
"loan_second_m06_ncycle_repayedAmount_max",
"loan_second_m06_ncycle_repayedAmount_mean",
"loan_second_m06_ncycle_repayedAmount_sum",
"loan_second_m06_ncycle_repayFrequency_giniimpurity",
"loan_second_m06_ncycle_repayMons_ratio_min",
"loan_second_m06_ncycle_repayTerm_ratio_range",
"loan_second_m06_ncycle_startDate_to_report_mean",
"loan_second_m06_ncycleR_guaranteeForm_giniimpurity",
"loan_second_m06_ncycleR_repayTerms_mean",
"loan_second_m06_now_balance_ratio_mean",
"loan_second_m06_now_balance_ratio_range",
"loan_second_m06_now_bt_other_loan_ratio",
"loan_second_m06_now_businessType_giniimpurity",
"loan_second_m06_now_c5_normal_ratio",
"loan_second_m06_now_due_class_max",
"loan_second_m06_now_due_class_mean",
"loan_second_m06_now_gf_other_ratio",
"loan_second_m06_now_is_now_sum",
"loan_second_m06_now_leftRepayTerms_mean",
"loan_second_m06_now_leftRepayTerms_range",
"loan_second_m06_now_loanAmount_min",
"loan_second_m06_now_month60_Amount_num_mean_sum",
"loan_second_m06_now_month60_Amount_num_meanbig0_sum",
"loan_second_m06_now_month60_Amount_num_sum_max",
"loan_second_m06_now_month60_State_count1r_mean",
"loan_second_m06_now_month60_State_countNr_mean",
"loan_second_m06_now_month60_State_countNull_mean",
"loan_second_m06_now_month60_State_countNullr_mean",
"loan_second_m06_now_month60_State_num_sum_sum",
"loan_second_m06_now_month60_to_report_max_mean",
"loan_second_m06_now_month60_to_report_mean_mean",
"loan_second_m06_now_org_giniimpurity",
"loan_second_m06_now_org_other_ratio",
"loan_second_m06_now_org_trust_company_ratio",
"loan_second_m06_now_planRepayAmount_mean",
"loan_second_m06_now_planRepayAmount_min",
"loan_second_m06_now_repayedAmount_max",
"loan_second_m06_now_repayedAmount_mean",
"loan_second_m06_now_repayedAmount_range",
"loan_second_m06_now_repayedAmount_sum",
"loan_second_m06_now_repayMons_range",
"loan_second_m06_now_repayTerms_range",
"loan_second_m06_now_rf_once_ratio",
"loan_second_m06_now_rt_onschedule_ratio",
"loan_second_m06_now_startDate_to_report_mean",
"loan_second_m06_nowR_balance_range",
"loan_second_m06_nowR_balance_ratio_range",
"loan_second_m06_nowR_bt_other_loan_ratio",
"loan_second_m06_nowR_businessType_giniimpurity",
"loan_second_m06_nowR_byDate_to_report_mean",
"loan_second_m06_nowR_byDate_to_report_sum",
"loan_second_m06_nowR_month60_State_countNull_mean",
"loan_second_m06_nowR_month60_State_countNullr_mean",
"loan_second_m06_nowR_month60_State_num_size_min",
"loan_second_m06_nowR_month60_to_report_max_mean",
"loan_second_m06_nowR_month60_to_report_mean_max",
"loan_second_m06_nowR_month60_to_report_mean_mean",
"loan_second_m06_nowR_month60_to_report_mean_range",
"loan_second_m06_nowR_month60_to_report_mean_sum",
"loan_second_m06_nowR_month60_to_report_min_max",
"loan_second_m06_nowR_org_giniimpurity",
"loan_second_m06_nowR_repayAmt_max",
"loan_second_m06_nowR_repayAmt_mean",
"loan_second_m06_nowR_repayMons_mean",
"loan_second_m06_nowR_repayMons_min",
"loan_second_m06_nowR_repayMons_range",
"loan_second_m06_nowR_repayMons_ratio_mean",
"loan_second_m06_nowR_repayMons_ratio_range",
"loan_second_m06_nowR_repayMons_sum",
"loan_second_m06_nowR_repayTerms_mean",
"loan_second_m06_nowR_startDate_to_report_mean",
"loan_second_m06_org_consumer_finance_count",
"loan_second_m06_org_consumer_finance_ratio",
"loan_second_m06_org_micro_loan_ratio",
"loan_second_m06_org_other_ratio",
"loan_second_m06_repayAmt_range",
"loan_second_m06_repayedAmount_max",
"loan_second_m06_repayedAmount_mean",
"loan_second_m06_repayMons_ratio_mean",
"loan_second_m06_startDate_to_report_mean",
"loan_second_m12_balance_ratio_min",
"loan_second_m12_bt_other_person_ratio",
"loan_second_m12_businessType_giniimpurity",
"loan_second_m12_classify5_num_range",
"loan_second_m12_gf_combine_nowarranty_ratio",
"loan_second_m12_gf_other_ratio",
"loan_second_m12_month60_Amount_num_max_sum",
"loan_second_m12_month60_Amount_num_mean_mean",
"loan_second_m12_month60_Amount_num_meanbig0_sum",
"loan_second_m12_month60_Amount_num_sum_max",
"loan_second_m12_month60_Amount_num_sum_mean",
"loan_second_m12_month60_State_count1r_max",
"loan_second_m12_month60_State_countCr_mean",
"loan_second_m12_month60_State_countN_mean",
"loan_second_m12_month60_State_countNr_range",
"loan_second_m12_month60_State_countUnknowr_mean",
"loan_second_m12_month60_State_num_mean_max",
"loan_second_m12_month60_State_num_mean_mean",
"loan_second_m12_month60_State_num_size_mean",
"loan_second_m12_month60_State_num_size_range",
"loan_second_m12_month60_State_num_size_sum",
"loan_second_m12_month60_State_num_sum_max",
"loan_second_m12_month60_State_num_sum_mean",
"loan_second_m12_month60_to_report_max_mean",
"loan_second_m12_ncycle_balance_max",
"loan_second_m12_ncycle_balance_ratio_max",
"loan_second_m12_ncycle_balance_ratio_mean",
"loan_second_m12_ncycle_balance_ratio_min",
"loan_second_m12_ncycle_balance_ratio_range",
"loan_second_m12_ncycle_balance_sum",
"loan_second_m12_ncycle_bt_other_loan_ratio",
"loan_second_m12_ncycle_bt_other_person_count",
"loan_second_m12_ncycle_byDate_to_report_max",
"loan_second_m12_ncycle_byDate_to_report_mean",
"loan_second_m12_ncycle_classify5_num_range",
"loan_second_m12_ncycle_due_class_max",
"loan_second_m12_ncycle_gf_combine_nowarranty_ratio",
"loan_second_m12_ncycle_gf_crdit_ratio",
"loan_second_m12_ncycle_guaranteeForm_giniimpurity",
"loan_second_m12_ncycle_leftRepayTerms_max",
"loan_second_m12_ncycle_loanGrantOrg_nunique",
"loan_second_m12_ncycle_month60_Amount_num_mean_mean",
"loan_second_m12_ncycle_month60_State_count1r_mean",
"loan_second_m12_ncycle_month60_State_count2r_mean",
"loan_second_m12_ncycle_month60_State_countCr_mean",
"loan_second_m12_ncycle_month60_State_countNr_mean",
"loan_second_m12_ncycle_month60_State_countNr_range",
"loan_second_m12_ncycle_month60_State_num_max_mean",
"loan_second_m12_ncycle_month60_State_num_max_sum",
"loan_second_m12_ncycle_month60_State_num_mean_max",
"loan_second_m12_ncycle_month60_State_num_mean_mean",
"loan_second_m12_ncycle_month60_State_num_sum_max",
"loan_second_m12_ncycle_month60_State_num_sum_mean",
"loan_second_m12_ncycle_month60_to_report_mean_range",
"loan_second_m12_ncycle_org_consumer_finance_count",
"loan_second_m12_ncycle_org_micro_loan_ratio",
"loan_second_m12_ncycle_org_other_ratio",
"loan_second_m12_ncycle_org_trust_company_count",
"loan_second_m12_ncycle_org_trust_company_ratio",
"loan_second_m12_ncycle_repayAmt_mean",
"loan_second_m12_ncycle_repayAmt_min",
"loan_second_m12_ncycle_repayedAmount_max",
"loan_second_m12_ncycle_repayedAmount_mean",
"loan_second_m12_ncycle_repayedAmount_range",
"loan_second_m12_ncycle_RepayedAmount_ratio_max",
"loan_second_m12_ncycle_RepayedAmount_ratio_range",
"loan_second_m12_ncycle_repayedAmount_sum",
"loan_second_m12_ncycle_repayMons_range",
"loan_second_m12_ncycle_rf_other_count",
"loan_second_m12_ncycle_startDate_to_report_sum",
"loan_second_m12_ncycleR_balance_range",
"loan_second_m12_ncycleR_byDate_to_report_mean",
"loan_second_m12_ncycleR_month60_State_countUnknow_mean",
"loan_second_m12_ncycleR_month60_State_countUnknowr_mean",
"loan_second_m12_ncycleR_repayTerm_ratio_range",
"loan_second_m12_now_balance_ratio_mean",
"loan_second_m12_now_bt_other_person_ratio",
"loan_second_m12_now_bt_person_business_ratio",
"loan_second_m12_now_businessType_giniimpurity",
"loan_second_m12_now_byDate_to_report_mean",
"loan_second_m12_now_byDate_to_report_sum",
"loan_second_m12_now_gf_combine_warranty_count",
"loan_second_m12_now_gf_crdit_ratio",
"loan_second_m12_now_gf_other_ratio",
"loan_second_m12_now_leftRepayTerms_range",
"loan_second_m12_now_loanAmount_range",
"loan_second_m12_now_month60_Amount_num_mean_max",
"loan_second_m12_now_month60_Amount_num_mean_sum",
"loan_second_m12_now_month60_State_count1r_sum",
"loan_second_m12_now_month60_State_countN_max",
"loan_second_m12_now_month60_State_countN_mean",
"loan_second_m12_now_month60_State_countN_range",
"loan_second_m12_now_month60_State_countNr_sum",
"loan_second_m12_now_month60_State_countNullr_mean",
"loan_second_m12_now_month60_State_countUnknowr_mean",
"loan_second_m12_now_month60_State_num_size_mean",
"loan_second_m12_now_month60_to_report_min_sum",
"loan_second_m12_now_org_consumer_finance_ratio",
"loan_second_m12_now_repayAmt_max",
"loan_second_m12_now_repayAmt_mean",
"loan_second_m12_now_repayAmt_min",
"loan_second_m12_now_repayedAmount_mean",
"loan_second_m12_now_repayedAmount_range",
"loan_second_m12_now_RepayedAmount_ratio_max",
"loan_second_m12_now_RepayedAmount_ratio_range",
"loan_second_m12_now_repayMons_range",
"loan_second_m12_now_repayMons_ratio_mean",
"loan_second_m12_now_repayMons_ratio_range",
"loan_second_m12_now_repayTerm_ratio_mean",
"loan_second_m12_now_repayTerm_ratio_min",
"loan_second_m12_now_rf_other_count",
"loan_second_m12_now_rf_other_ratio",
"loan_second_m12_now_startDate_to_report_mean",
"loan_second_m12_nowR_balance_ratio_range",
"loan_second_m12_nowR_businessType_giniimpurity",
"loan_second_m12_nowR_byDate_to_report_sum",
"loan_second_m12_nowR_gf_crdit_count",
"loan_second_m12_nowR_gf_crdit_ratio",
"loan_second_m12_nowR_loanAmount_min",
"loan_second_m12_nowR_month60_State_countN_sum",
"loan_second_m12_nowR_month60_State_countNr_mean",
"loan_second_m12_nowR_month60_State_countNull_mean",
"loan_second_m12_nowR_month60_State_countNullr_mean",
"loan_second_m12_nowR_month60_State_countNullr_sum",
"loan_second_m12_nowR_month60_State_countUnknow_mean",
"loan_second_m12_nowR_month60_State_countUnknowr_mean",
"loan_second_m12_nowR_month60_State_num_size_range",
"loan_second_m12_nowR_month60_State_num_size_sum",
"loan_second_m12_nowR_month60_to_report_mean_range",
"loan_second_m12_nowR_org_commercial_bank_ratio",
"loan_second_m12_nowR_org_consumer_finance_count",
"loan_second_m12_nowR_org_consumer_finance_ratio",
"loan_second_m12_nowR_org_micro_loan_ratio",
"loan_second_m12_nowR_org_trust_company_ratio",
"loan_second_m12_nowR_repayAmt_max",
"loan_second_m12_nowR_repayAmt_mean",
"loan_second_m12_nowR_repayedAmount_mean",
"loan_second_m12_nowR_repayMons_ratio_max",
"loan_second_m12_nowR_repayMons_ratio_mean",
"loan_second_m12_nowR_repayMons_ratio_min",
"loan_second_m12_nowR_repayMons_ratio_range",
"loan_second_m12_nowR_repayTerms_sum",
"loan_second_m12_nowR_rf_once_ratio",
"loan_second_m12_nowR_rf_other_count",
"loan_second_m12_org_micro_loan_ratio",
"loan_second_m12_org_trust_company_count",
"loan_second_m12_org_trust_company_ratio",
"loan_second_m12_planRepayAmount_mean",
"loan_second_m12_repayAmt_max",
"loan_second_m12_repayAmt_mean",
"loan_second_m12_RepayedAmount_ratio_max",
"loan_second_m12_repayMons_range",
"loan_second_m12_repayMons_ratio_sum",
"loan_second_m12_repayTerm_ratio_range",
"loan_second_m12_repayTerms_mean",
"loan_second_m12_rf_irregular_ratio",
"loan_second_m12_rf_month_ratio",
"loan_second_m12_rf_other_ratio",
"loan_second_m24_balance_min",
"loan_second_m24_bt_other_loan_count",
"loan_second_m24_businessType_giniimpurity",
"loan_second_m24_byDate_to_report_max",
"loan_second_m24_gf_combine_nowarranty_ratio",
"loan_second_m24_hdue1_month60_State_num_mean_min",
"loan_second_m24_hdue1R_loanGrantOrg_nunique",
"loan_second_m24_hdue1R_month60_State_countNullr_sum",
"loan_second_m24_hdue1R_month60_State_num_size_sum",
"loan_second_m24_leftRepayTerms_max",
"loan_second_m24_month60_Amount_num_mean_sum",
"loan_second_m24_month60_State_count2r_mean",
"loan_second_m24_month60_State_countCr_max",
"loan_second_m24_month60_State_countNull_max",
"loan_second_m24_month60_State_countNull_mean",
"loan_second_m24_month60_State_countNull_sum",
"loan_second_m24_month60_State_countNullr_mean",
"loan_second_m24_month60_State_countNullr_range",
"loan_second_m24_month60_State_num_mean_mean",
"loan_second_m24_month60_to_report_max_max",
"loan_second_m24_month60_to_report_max_mean",
"loan_second_m24_month60_to_report_max_sum",
"loan_second_m24_month60_to_report_min_max",
"loan_second_m24_ncycle_balance_mean",
"loan_second_m24_ncycle_balance_ratio_max",
"loan_second_m24_ncycle_balance_ratio_min",
"loan_second_m24_ncycle_bt_other_person_ratio",
"loan_second_m24_ncycle_businessType_giniimpurity",
"loan_second_m24_ncycle_byDate_to_report_range",
"loan_second_m24_ncycle_classify5_num_min",
"loan_second_m24_ncycle_gf_crdit_ratio",
"loan_second_m24_ncycle_guaranteeForm_giniimpurity",
"loan_second_m24_ncycle_is_now_range",
"loan_second_m24_ncycle_leftRepayTerms_max",
"loan_second_m24_ncycle_loanAmount_min",
"loan_second_m24_ncycle_loanAmount_range",
"loan_second_m24_ncycle_loanGrantOrg_giniimpurity",
"loan_second_m24_ncycle_loanGrantOrg_nunique",
"loan_second_m24_ncycle_logo_max",
"loan_second_m24_ncycle_logo_mean",
"loan_second_m24_ncycle_month60_Amount_num_mean_sum",
"loan_second_m24_ncycle_month60_State_countC_sum",
"loan_second_m24_ncycle_month60_State_countCr_max",
"loan_second_m24_ncycle_month60_State_countCr_sum",
"loan_second_m24_ncycle_month60_State_countNr_mean",
"loan_second_m24_ncycle_month60_State_countNr_sum",
"loan_second_m24_ncycle_month60_State_countNull_mean",
"loan_second_m24_ncycle_month60_State_num_mean_max",
"loan_second_m24_ncycle_month60_State_num_size_mean",
"loan_second_m24_ncycle_month60_State_num_size_min",
"loan_second_m24_ncycle_month60_State_num_size_range",
"loan_second_m24_ncycle_month60_to_report_max_max",
"loan_second_m24_ncycle_month60_to_report_max_mean",
"loan_second_m24_ncycle_month60_to_report_mean_max",
"loan_second_m24_ncycle_month60_to_report_mean_mean",
"loan_second_m24_ncycle_month60_to_report_mean_sum",
"loan_second_m24_ncycle_month60_to_report_min_max",
"loan_second_m24_ncycle_org_consumer_finance_count",
"loan_second_m24_ncycle_org_consumer_finance_ratio",
"loan_second_m24_ncycle_org_other_ratio",
"loan_second_m24_ncycle_org_trust_company_count",
"loan_second_m24_ncycle_org_trust_company_ratio",
"loan_second_m24_ncycle_planRepayAmount_mean",
"loan_second_m24_ncycle_repayAmt_max",
"loan_second_m24_ncycle_repayAmt_mean",
"loan_second_m24_ncycle_repayAmt_range",
"loan_second_m24_ncycle_RepayedAmount_ratio_mean",
"loan_second_m24_ncycle_repayFrequency_giniimpurity",
"loan_second_m24_ncycle_repayMons_min",
"loan_second_m24_ncycle_repayMons_ratio_mean",
"loan_second_m24_ncycle_repayTerm_ratio_range",
"loan_second_m24_ncycle_repayTerms_mean",
"loan_second_m24_ncycle_repayTerms_range",
"loan_second_m24_ncycle_rf_month_ratio",
"loan_second_m24_ncycle_rf_once_count",
"loan_second_m24_ncycle_rf_other_ratio",
"loan_second_m24_ncycle_startDate_to_report_max",
"loan_second_m24_ncycle_startDate_to_report_sum",
"loan_second_m24_ncycleR_byDate_to_report_max",
"loan_second_m24_ncycleR_month60_State_countCr_max",
"loan_second_m24_ncycleR_month60_State_countCr_range",
"loan_second_m24_ncycleR_month60_State_countNr_mean",
"loan_second_m24_ncycleR_month60_State_countNr_min",
"loan_second_m24_ncycleR_month60_State_countNr_sum",
"loan_second_m24_ncycleR_month60_to_report_min_range",
"loan_second_m24_ncycleR_org_trust_company_count",
"loan_second_m24_ncycleR_repayFrequency_giniimpurity",
"loan_second_m24_ncycleR_rf_once_ratio",
"loan_second_m24_now_balance_max",
"loan_second_m24_now_balance_ratio_min",
"loan_second_m24_now_gf_other_count",
"loan_second_m24_now_guaranteeForm_giniimpurity",
"loan_second_m24_now_leftRepayTerms_max",
"loan_second_m24_now_leftRepayTerms_range",
"loan_second_m24_now_loanAmount_range",
"loan_second_m24_now_month60_State_count2r_sum",
"loan_second_m24_now_month60_State_countN_mean",
"loan_second_m24_now_month60_State_countNr_mean",
"loan_second_m24_now_month60_State_countNull_mean",
"loan_second_m24_now_month60_State_countNullr_range",
"loan_second_m24_now_month60_State_countUnknowr_max",
"loan_second_m24_now_month60_State_countUnknowr_mean",
"loan_second_m24_now_month60_to_report_max_range",
"loan_second_m24_now_month60_to_report_mean_range",
"loan_second_m24_now_month60_to_report_min_sum",
"loan_second_m24_now_org_micro_loan_ratio",
"loan_second_m24_now_org_trust_company_ratio",
"loan_second_m24_now_planRepayAmount_min",
"loan_second_m24_now_repayAmt_mean",
"loan_second_m24_now_repayedAmount_mean",
"loan_second_m24_now_repayedAmount_min",
"loan_second_m24_now_RepayedAmount_ratio_range",
"loan_second_m24_now_repayFrequency_giniimpurity",
"loan_second_m24_now_repayMons_range",
"loan_second_m24_now_repayMons_ratio_sum",
"loan_second_m24_now_repayTerm_ratio_min",
"loan_second_m24_now_repayTerm_ratio_range",
"loan_second_m24_now_repayTerms_mean",
"loan_second_m24_now_rf_other_ratio",
"loan_second_m24_now_rt_onschedule_ratio",
"loan_second_m24_nowR_balance_ratio_range",
"loan_second_m24_nowR_bt_other_loan_count",
"loan_second_m24_nowR_bt_person_business_ratio",
"loan_second_m24_nowR_byDate_to_report_mean",
"loan_second_m24_nowR_byDate_to_report_sum",
"loan_second_m24_nowR_loanAmount_max",
"loan_second_m24_nowR_loanAmount_mean",
"loan_second_m24_nowR_loanAmount_range",
"loan_second_m24_nowR_month60_State_countN_max",
"loan_second_m24_nowR_month60_State_countN_mean",
"loan_second_m24_nowR_month60_State_countN_sum",
"loan_second_m24_nowR_month60_State_countNull_max",
"loan_second_m24_nowR_month60_State_countNull_mean",
"loan_second_m24_nowR_month60_State_countNullr_mean",
"loan_second_m24_nowR_month60_to_report_max_max",
"loan_second_m24_nowR_month60_to_report_max_sum",
"loan_second_m24_nowR_month60_to_report_mean_max",
"loan_second_m24_nowR_month60_to_report_mean_mean",
"loan_second_m24_nowR_month60_to_report_mean_range",
"loan_second_m24_nowR_month60_to_report_min_max",
"loan_second_m24_nowR_month60_to_report_min_mean",
"loan_second_m24_nowR_org_commercial_bank_ratio",
"loan_second_m24_nowR_org_consumer_finance_count",
"loan_second_m24_nowR_org_consumer_finance_ratio",
"loan_second_m24_nowR_repayFrequency_giniimpurity",
"loan_second_m24_nowR_repayMons_max",
"loan_second_m24_nowR_repayMons_ratio_mean",
"loan_second_m24_nowR_repayMons_ratio_sum",
"loan_second_m24_nowR_repayTerms_min",
"loan_second_m24_nowR_repayTerms_range",
"loan_second_m24_nowR_repayTerms_sum",
"loan_second_m24_nowR_rf_month_ratio",
"loan_second_m24_nowR_rf_once_count",
"loan_second_m24_nowR_rf_once_ratio",
"loan_second_m24_nowR_rf_other_ratio",
"loan_second_m24_nowR_startDate_to_report_max",
"loan_second_m24_org_consumer_finance_count",
"loan_second_m24_org_other_count",
"loan_second_m24_org_other_ratio",
"loan_second_m24_org_trust_company_count",
"loan_second_m24_org_trust_company_ratio",
"loan_second_m24_repayAmt_max",
"loan_second_m24_repayAmt_range",
"loan_second_m24_RepayedAmount_ratio_range",
"loan_second_m24_RepayedAmount_ratio_sum",
"loan_second_m24_repayMons_ratio_sum",
"loan_second_m24_repayTerm_ratio_range",
"loan_second_m24_repayTerms_min",
"loan_second_m24_rf_once_count",
"loan_second_m24_rf_once_ratio",
"loan_second_m24_rf_other_count",
"loan_second_m24_rf_other_ratio",
"loan_second_m24_vouch_balance_max",
"loan_second_m24_vouch_loanAmount_max",
"loan_second_m24_vouch_loanAmount_sum",
"loan_second_m24_vouch_month60_State_countCr_max",
"loan_second_m24_vouch_month60_State_countN_max",
"loan_second_m24_vouch_month60_State_countNr_max",
"loan_second_m24_vouch_month60_State_countNr_mean",
"loan_second_m24_vouch_month60_State_countNr_min",
"loan_second_m24_vouch_repayMons_mean",
"loan_second_m24_vouch_repayMons_ratio_sum",
"loan_second_m24_vouch_repayTerms_mean",
"loan_second_m24_vouchR_bt_other_person_count",
"loan_second_m24_vouchR_bt_other_person_ratio",
"loan_second_m24_vouchR_byDate_to_report_mean",
"loan_second_m24_vouchR_leftRepayTerms_sum",
"loan_second_m24_vouchR_loanAmount_max",
"loan_second_m24_vouchR_loanAmount_sum",
"loan_second_m24_vouchR_loanGrantOrg_nunique",
"loan_second_m24_vouchR_month60_State_countN_sum",
"loan_second_m24_vouchR_month60_State_countNr_max",
"loan_second_m24_vouchR_month60_State_countNr_sum",
"loan_second_m24_vouchR_month60_State_countNull_mean",
"loan_second_m24_vouchR_month60_State_num_size_sum",
"loan_second_m24_vouchR_org_micro_loan_count",
"loan_second_m24_vouchR_repayMons_min",
"loan_second_m24_vouchR_repayTerms_sum",
"loan_second_m24_vouchR_startDate_to_report_max",
"loan_second_month60_1_ratio",
"loan_second_month60_Amount_big0_mean",
"loan_second_month60_Amount_num_max_mean",
"loan_second_month60_Amount_num_mean_max",
"loan_second_month60_C_count",
"loan_second_month60_C_ratio",
"loan_second_month60_N_count",
"loan_second_month60_N_ratio",
"loan_second_month60_Null_count",
"loan_second_month60_Null_ratio",
"loan_second_month60_State_countCr_mean",
"loan_second_month60_State_countN_max",
"loan_second_month60_State_countN_mean",
"loan_second_month60_State_countN_sum",
"loan_second_month60_State_countNr_max",
"loan_second_month60_State_countNr_mean",
"loan_second_month60_State_countNr_sum",
"loan_second_month60_State_countNull_max",
"loan_second_month60_State_countNull_mean",
"loan_second_month60_State_countNull_sum",
"loan_second_month60_State_countNullr_max",
"loan_second_month60_State_countNullr_mean",
"loan_second_month60_State_countNullr_sum",
"loan_second_month60_State_countUnknow_sum",
"loan_second_month60_State_giniimpurity",
"loan_second_month60_State_num_max_mean",
"loan_second_month60_State_num_mean",
"loan_second_month60_State_num_mean_max",
"loan_second_month60_State_num_mean_mean",
"loan_second_month60_State_num_size_max",
"loan_second_month60_State_num_size_sum",
"loan_second_month60_to_report_max_max",
"loan_second_month60_to_report_max_range",
"loan_second_month60_to_report_max_sum",
"loan_second_month60_to_report_mean_max",
"loan_second_month60_to_report_mean_mean",
"loan_second_month60_to_report_mean_range",
"loan_second_month60_to_report_mean_sum",
"loan_second_month60_to_report_min_max",
"loan_second_month60_to_report_min_sum",
"loan_second_ncycle_as_settle_count",
"loan_second_ncycle_as_settle_ratio",
"loan_second_ncycle_balance_mean",
"loan_second_ncycle_bt_other_loan_count",
"loan_second_ncycle_bt_other_person_count",
"loan_second_ncycle_businessType_giniimpurity",
"loan_second_ncycle_byDate_to_report_mean",
"loan_second_ncycle_byDate_to_report_sum",
"loan_second_ncycle_c5_unknow_count",
"loan_second_ncycle_class_ncycle_count",
"loan_second_ncycle_due_class_mean",
"loan_second_ncycle_gf_combine_nowarranty_count",
"loan_second_ncycle_gf_crdit_count",
"loan_second_ncycle_gf_crdit_ratio",
"loan_second_ncycle_loanAmount_min",
"loan_second_ncycle_loanAmount_range",
"loan_second_ncycle_loanGrantOrg_giniimpurity",
"loan_second_ncycle_loanGrantOrg_nunique",
"loan_second_ncycle_month60_State_countC_sum",
"loan_second_ncycle_month60_State_countCr_mean",
"loan_second_ncycle_month60_State_countCr_sum",
"loan_second_ncycle_month60_State_countN_max",
"loan_second_ncycle_month60_State_countN_sum",
"loan_second_ncycle_month60_State_countNr_max",
"loan_second_ncycle_month60_State_countNr_sum",
"loan_second_ncycle_month60_State_countNull_max",
"loan_second_ncycle_month60_State_countNull_mean",
"loan_second_ncycle_month60_State_countNull_sum",
"loan_second_ncycle_month60_State_countNullr_max",
"loan_second_ncycle_month60_State_countNullr_sum",
"loan_second_ncycle_month60_State_countUnknow_max",
"loan_second_ncycle_month60_State_countUnknow_mean",
"loan_second_ncycle_month60_State_countUnknow_range",
"loan_second_ncycle_month60_State_countUnknow_sum",
"loan_second_ncycle_month60_State_countUnknowr_max",
"loan_second_ncycle_month60_State_num_mean_max",
"loan_second_ncycle_month60_State_num_mean_mean",
"loan_second_ncycle_month60_State_num_size_max",
"loan_second_ncycle_month60_State_num_size_min",
"loan_second_ncycle_month60_State_num_size_range",
"loan_second_ncycle_month60_State_num_size_sum",
"loan_second_ncycle_month60_to_report_max_mean",
"loan_second_ncycle_month60_to_report_max_sum",
"loan_second_ncycle_month60_to_report_mean_max",
"loan_second_ncycle_month60_to_report_mean_sum",
"loan_second_ncycle_month60_to_report_min_range",
"loan_second_ncycle_month60_to_report_min_sum",
"loan_second_ncycle_org_commercial_bank_count",
"loan_second_ncycle_org_commercial_bank_ratio",
"loan_second_ncycle_org_consumer_finance_count",
"loan_second_ncycle_org_giniimpurity",
"loan_second_ncycle_org_trust_company_ratio",
"loan_second_ncycle_repayAmt_max",
"loan_second_ncycle_repayAmt_range",
"loan_second_ncycle_repayedAmount_range",
"loan_second_ncycle_RepayedAmount_ratio_sum",
"loan_second_ncycle_repayFrequency_giniimpurity",
"loan_second_ncycle_repayMons_ratio_max",
"loan_second_ncycle_repayMons_ratio_mean",
"loan_second_ncycle_repayMons_ratio_sum",
"loan_second_ncycle_repayMons_sum",
"loan_second_ncycle_repayTerms_min",
"loan_second_ncycle_rf_month_count",
"loan_second_ncycle_rf_once_count",
"loan_second_ncycle_rf_once_ratio",
"loan_second_ncycle_rf_other_ratio",
"loan_second_ncycle_rt_unknow_count",
"loan_second_ncycle_startDate_to_report_sum",
"loan_second_ncycleR_month60_State_countNr_mean",
"loan_second_ncycleR_month60_State_countNull_sum",
"loan_second_ncycleR_month60_State_countNullr_mean",
"loan_second_ncycleR_month60_to_report_max_min",
"loan_second_ncycleR_month60_to_report_max_sum",
"loan_second_ncycleR_org_giniimpurity",
"loan_second_ncycleR_repayMons_ratio_mean",
"loan_second_ncycleR_repayTerms_mean",
"loan_second_now_bt_other_person_ratio",
"loan_second_now_byDate_to_report_mean",
"loan_second_now_gf_other_count",
"loan_second_now_gf_other_ratio",
"loan_second_now_month60_State_count2r_mean",
"loan_second_now_month60_to_report_min_mean",
"loan_second_now_org_commercial_bank_ratio",
"loan_second_now_planRepayAmount_mean",
"loan_second_now_repayMons_range",
"loan_second_now_rf_other_count",
"loan_second_nowR_bt_other_loan_count",
"loan_second_nowR_bt_other_person_ratio",
"loan_second_nowR_bt_person_business_ratio",
"loan_second_nowR_byDate_to_report_max",
"loan_second_nowR_byDate_to_report_mean",
"loan_second_nowR_c5_normal_ratio",
"loan_second_nowR_gf_crdit_count",
"loan_second_nowR_gf_other_count",
"loan_second_nowR_gf_other_ratio",
"loan_second_nowR_guaranteeForm_giniimpurity",
"loan_second_nowR_loanAmount_mean",
"loan_second_nowR_loanAmount_range",
"loan_second_nowR_month60_State_countN_max",
"loan_second_nowR_month60_State_countN_mean",
"loan_second_nowR_month60_State_countN_range",
"loan_second_nowR_month60_State_countNr_mean",
"loan_second_nowR_month60_State_countNull_max",
"loan_second_nowR_month60_State_countNull_mean",
"loan_second_nowR_month60_State_countNull_range",
"loan_second_nowR_month60_State_countNullr_mean",
"loan_second_nowR_month60_State_countNullr_sum",
"loan_second_nowR_month60_State_countUnknow_sum",
"loan_second_nowR_month60_State_countUnknowr_sum",
"loan_second_nowR_month60_to_report_min_max",
"loan_second_nowR_org_commercial_bank_count",
"loan_second_nowR_org_consumer_finance_count",
"loan_second_nowR_org_consumer_finance_ratio",
"loan_second_nowR_org_trust_company_count",
"loan_second_nowR_planRepayAmount_mean",
"loan_second_nowR_planRepayAmount_range",
"loan_second_nowR_repayAmt_mean",
"loan_second_nowR_repayedAmount_mean",
"loan_second_nowR_repayFrequency_giniimpurity",
"loan_second_nowR_repayMons_max",
"loan_second_nowR_repayMons_range",
"loan_second_nowR_repayMons_ratio_max",
"loan_second_nowR_repayMons_ratio_mean",
"loan_second_nowR_repayMons_ratio_range",
"loan_second_nowR_repayTerms_mean",
"loan_second_nowR_repayTerms_sum",
"loan_second_nowR_rf_irregular_ratio",
"loan_second_nowR_rf_month_ratio",
"loan_second_nowR_rf_once_count",
"loan_second_nowR_rf_once_ratio",
"loan_second_nowR_rf_other_count",
"loan_second_nowR_rf_other_ratio",
"loan_second_nowR_startDate_to_report_mean",
"loan_second_nowR_startDate_to_report_range",
"loan_second_org_commercial_bank_count",
"loan_second_org_commercial_bank_ratio",
"loan_second_org_consumer_finance_count",
"loan_second_org_consumer_finance_ratio",
"loan_second_org_micro_loan_count",
"loan_second_org_micro_loan_ratio",
"loan_second_org_trust_company_count",
"loan_second_planRepayAmount_mean",
"loan_second_planRepayAmount_sum",
"loan_second_repayAmt_max",
"loan_second_repayAmt_min",
"loan_second_repayedAmount_mean",
"loan_second_RepayedAmount_ratio_mean",
"loan_second_repayMons_mean",
"loan_second_repayMons_ratio_mean",
"loan_second_repayMons_ratio_sum",
"loan_second_repayTerm_ratio_range",
"loan_second_repayTerms_max",
"loan_second_rf_once_count",
"loan_second_rf_once_ratio",
"loan_second_rt_unknow_count",
"loan_second_startDate_to_report_mean",
"loan_second_startDate_to_report_range",
"loan_second_startDate_to_report_sum",
"loan_second_vouch_loanAmount_mean",
"loan_second_vouch_loanAmount_min",
"loan_second_vouch_month60_State_countCr_sum",
"loan_second_vouch_month60_State_countNr_mean",
"loan_second_vouch_month60_State_countNr_min",
"loan_second_vouch_month60_to_report_max_sum",
"loan_second_vouchR_balance_ratio_mean",
"loan_second_vouchR_class_ncycle_count",
"loan_second_vouchR_classify5_num_sum",
"loan_second_vouchR_loanGrantOrg_nunique",
"loan_second_vouchR_month60_State_countN_sum",
"loan_second_vouchR_month60_State_countNull_sum",
"loan_second_vouchR_month60_State_num_size_sum",
"loan_second_vouchR_month60_to_report_max_sum",
"loan_second_vouchR_repayAmt_min",
"loan_second_vouchR_repayMons_ratio_mean",
"loan_second_vouchR_repayMons_sum",
"loan_second_vouchR_repayTerms_sum",
"loan_second_vouchR_rf_month_count",
"loan_second_vouchR_rf_month_ratio",
"loan_special_settlement_count",
"LoanCount_count",
"m01_query_detail_loan_financing_guarantee_count",
"m03_query_detail_card_approval_ratio",
"m03_query_detail_date_to_report_mean",
"m03_query_detail_guarantee_ratio",
"m03_query_detail_loan_commercial_bank_count",
"m03_query_detail_loan_commercial_bank_ratio",
"m03_query_detail_loan_consumer_finance_ratio",
"m03_query_detail_loan_date_to_report_mean",
"m06_query_detail_card_approval_count",
"m06_query_detail_card_approval_ratio",
"m06_query_detail_card_cnt",
"m06_query_detail_card_date_to_report_mean",
"m06_query_detail_date_to_report_mean",
"m06_query_detail_loan_commercial_bank_count",
"m06_query_detail_loan_commercial_bank_ratio",
"m06_query_detail_loan_date_to_report_max",
"m06_query_detail_loan_date_to_report_mean",
"m06_query_detail_loan_financing_guarantee_ratio",
"m06_query_detail_reason_giniimpurity",
"m12_loan_special_settlement_amt",
"m12_loan_special_settlement_ratio",
"m12_query_detail_card_approval_ratio",
"m12_query_detail_card_date_to_report_mean",
"m12_query_detail_cnt",
"m12_query_detail_date_to_report_mean",
"m12_query_detail_funding_approval_ratio",
"m12_query_detail_funding_date_to_report_mean",
"m12_query_detail_guarantee_date_to_report_max",
"m12_query_detail_guarantee_ratio",
"m12_query_detail_loan_cnt",
"m12_query_detail_loan_consumer_finance_ratio",
"m12_query_detail_loan_date_to_report_mean",
"m12_query_detail_loan_financing_guarantee_ratio",
"m12_query_detail_loan_mon_report_06_count",
"m12_query_detail_loan_mon_report_06_ratio",
"m12_query_detail_loan_queryDate_to_report_06_ratio",
"m12_query_detail_loan_small_loan_count",
"m12_query_detail_mon_report_12_count",
"m12_query_detail_mon_report_12_ratio",
"m12_query_detail_mon_report_giniimpurity",
"m12_query_detail_reason_giniimpurity",
"m12_query_detail_small_loan_count",
"m120_loan_special_other_ratio",
"m18_loan_special_guarantor_amt",
"m18_loan_special_guarantor_count",
"m18_loan_special_other_amt",
"m18_loan_special_prepayment_ratio",
"m18_loan_special_settlement_amt",
"m18_loan_special_settlement_ratio",
"m180_loan_special_settlement_amt",
"m24_loan_special_guarantor_amt",
"m24_loan_special_prepayment_ratio",
"m24_loan_special_roll_count",
"m24_loan_special_settlement_amt",
"m24_loan_special_settlement_count",
"m24_loan_special_settlement_ratio",
"m24_query_detail_card_mon_report_06_count",
"m24_query_detail_card_mon_report_06_ratio",
"m24_query_detail_card_queryDate_to_report_06_ratio",
"m24_query_detail_cnt",
"m24_query_detail_commercial_bank_count",
"m24_query_detail_commercial_bank_ratio",
"m24_query_detail_date_to_report_mean",
"m24_query_detail_funding_approval_ratio",
"m24_query_detail_guarantee_cnt",
"m24_query_detail_lease_finance_ratio",
"m24_query_detail_loan_approval_count",
"m24_query_detail_loan_consumer_finance_ratio",
"m24_query_detail_loan_date_to_report_max",
"m24_query_detail_loan_date_to_report_mean",
"m24_query_detail_loan_financing_guarantee_ratio",
"m24_query_detail_loan_mon_report_06_ratio",
"m24_query_detail_loan_mon_report_12_count",
"m24_query_detail_loan_mon_report_12_ratio",
"m24_query_detail_loan_mon_report_18_count",
"m24_query_detail_loan_mon_report_24_count",
"m24_query_detail_loan_mon_report_24_ratio",
"m24_query_detail_loan_mon_report_giniimpurity",
"m24_query_detail_loan_queryDate_to_report_06_ratio",
"m24_query_detail_loan_queryDate_to_report_12_ratio",
"m24_query_detail_loan_trust_company_count",
"m24_query_detail_loan_trust_company_ratio",
"m24_query_detail_mon_report_12_ratio",
"m24_query_detail_mon_report_24_ratio",
"m24_query_detail_mon_report_giniimpurity",
"m24_query_detail_queryDate_to_report_06_ratio",
"m24_query_detail_small_loan_count",
"m24_query_detail_small_loan_ratio",
"m240_loan_special_other_ratio",
"m3_loan_special_other_amt",
"m3_loan_special_settlement_amt",
"m30_loan_special_prepayment_ratio",
"m30_loan_special_settlement_count",
"m300_loan_special_other_ratio",
"m36_loan_special_settlement_count",
"m36_query_detail_card_approval_ratio",
"m36_query_detail_card_mon_report_06_count",
"m36_query_detail_card_mon_report_06_ratio",
"m36_query_detail_commercial_bank_count",
"m36_query_detail_financing_guarantee_ratio",
"m36_query_detail_loan_approval_count",
"m36_query_detail_loan_date_to_report_max",
"m36_query_detail_loan_date_to_report_mean",
"m36_query_detail_loan_financing_guarantee_ratio",
"m36_query_detail_loan_mon_report_06_ratio",
"m36_query_detail_loan_mon_report_18_count",
"m36_query_detail_loan_mon_report_24_count",
"m36_query_detail_loan_mon_report_24_ratio",
"m36_query_detail_loan_mon_report_giniimpurity",
"m36_query_detail_loan_mon_report_nunique",
"m36_query_detail_loan_queryDate_to_report_06_ratio",
"m36_query_detail_loan_trust_company_count",
"m36_query_detail_loan_trust_company_ratio",
"m36_query_detail_mon_report_06_ratio",
"m36_query_detail_mon_report_12_count",
"m36_query_detail_mon_report_24_count",
"m36_query_detail_small_loan_ratio",
"m360_loan_special_other_ratio",
"m42_loan_special_other_ratio",
"m42_loan_special_prepayment_amt",
"m42_loan_special_prepayment_ratio",
"m48_loan_special_prepayment_ratio",
"m48_loan_special_settlement_ratio",
"m48_query_detail_card_approval_ratio",
"m48_query_detail_commercial_bank_count",
"m48_query_detail_insurance_company_ratio",
"m48_query_detail_lease_finance_ratio",
"m48_query_detail_loan_date_to_report_max",
"m48_query_detail_loan_date_to_report_mean",
"m48_query_detail_loan_financing_guarantee_ratio",
"m48_query_detail_loan_mon_report_06_ratio",
"m48_query_detail_loan_mon_report_18_count",
"m48_query_detail_loan_mon_report_24_count",
"m48_query_detail_loan_mon_report_giniimpurity",
"m48_query_detail_loan_queryDate_to_report_06_ratio",
"m48_query_detail_loan_small_loan_count",
"m48_query_detail_loan_trust_company_ratio",
"m48_query_detail_mon_report_24_ratio",
"m48_query_detail_mon_report_giniimpurity",
"m48_query_detail_operator_giniimpurity",
"m48_query_detail_queryDate_to_report_06_ratio",
"m48_query_detail_queryDate_to_report_12_ratio",
"m48_query_detail_reason_giniimpurity",
"m48_query_detail_trust_company_ratio",
"m54_loan_special_other_ratio",
"m54_loan_special_prepayment_amt",
"m54_loan_special_prepayment_ratio",
"m54_loan_special_settlement_ratio",
"m6_loan_special_other_amt",
"m60_query_detail_card_mon_report_06_count",
"m60_query_detail_card_mon_report_06_ratio",
"m60_query_detail_card_queryDate_to_report_06_ratio",
"m60_query_detail_commercial_bank_ratio",
"m60_query_detail_date_to_report_mean",
"m60_query_detail_guarantee_ratio",
"m60_query_detail_loan_cnt",
"m60_query_detail_loan_consumer_finance_ratio",
"m60_query_detail_loan_date_to_report_max",
"m60_query_detail_loan_date_to_report_mean",
"m60_query_detail_loan_financing_guarantee_ratio",
"m60_query_detail_loan_mon_report_06_ratio",
"m60_query_detail_loan_mon_report_12_ratio",
"m60_query_detail_loan_mon_report_18_count",
"m60_query_detail_loan_mon_report_24_count",
"m60_query_detail_loan_mon_report_24_ratio",
"m60_query_detail_loan_mon_report_giniimpurity",
"m60_query_detail_loan_queryDate_to_report_06_ratio",
"m60_query_detail_loan_queryDate_to_report_12_ratio",
"m60_query_detail_loan_small_loan_count",
"m60_query_detail_mon_report_06_ratio",
"m60_query_detail_mon_report_giniimpurity",
"m60_query_detail_queryDate_to_report_12_ratio",
"nloc_6mpay_amt",
"other_fstmth",
"otherFirstMonth",
"otherLoanCount",
"otherLoanCount_vs_TotalCount",
"query_12m_lnsum",
"query_24m_reasonsum",
"query_detail_card_queryDate_to_report_01_ratio",
"query_detail_card_queryDate_to_report_03_ratio",
"query_detail_date_to_report_mean",
"query_detail_financing_guarantee_count",
"query_detail_lease_finance_ratio",
"query_detail_loan_approval_count",
"query_detail_loan_date_to_report_max",
"query_detail_loan_date_to_report_mean",
"query_detail_loan_financing_guarantee_ratio",
"query_detail_loan_mon_report_06_ratio",
"query_detail_loan_mon_report_12_ratio",
"query_detail_loan_mon_report_18_count",
"query_detail_loan_mon_report_18_ratio",
"query_detail_loan_mon_report_24_count",
"query_detail_loan_mon_report_24_ratio",
"query_detail_loan_mon_report_giniimpurity",
"query_detail_loan_mon_report_nunique",
"query_detail_loan_queryDate_to_report_01_ratio",
"query_detail_loan_queryDate_to_report_03_ratio",
"query_detail_loan_queryDate_to_report_06_ratio",
"query_detail_loan_queryDate_to_report_12_ratio",
"query_detail_loan_small_loan_count",
"query_detail_mon_report_06_ratio",
"query_detail_mon_report_12_count",
"query_detail_mon_report_18_count",
"query_detail_mon_report_18_ratio",
"query_detail_queryDate_to_report_01_ratio",
"query_detail_queryDate_to_report_03_ratio",
"query_detail_small_loan_count",
"query_detail_trust_company_ratio",
"query_summary_loanAfterQueryCount",
"query_summary_selfQueryCount",
"recent_loan_rating_worst",
"recent_loan_time_till_now",
"self_2y_qrynum",
"consume_loan_amount_max_now",
"consume_loan_planRepayAmount_sum_now",
"laco06_lacn",
"loan_card_amount_sum",
"loan_card_gm06_planRepayAmount_max",
"loan_card_gm06_remainingTerms_sum",
"loan_card_gm12_RepayedAmount_mean",
"loan_card_gm12_RepayedAmount_sum",
"loan_card_m01_balance_sum",
"loan_card_now_startdata_min_to_report_days",
"loan_card_r_operation_loan_ratio",
"loan_card_RepayedAmount_sum",
"loan_ndue_planRepayAmount_max_24m_now",
"loan_second_by03_ncycleR_gf_crdit_ratio",
"loan_second_by03_now_month60_State_count1r_mean",
"loan_second_by03_now_month60_State_countN_max",
"loan_second_by03_now_month60_to_report_mean_sum",
"loan_second_by03_now_org_consumer_finance_ratio",
"loan_second_by03_nowR_loanAmount_mean",
"loan_second_by03_org_giniimpurity",
"loan_second_by03_repayFrequency_giniimpurity",
"loan_second_by03_startDate_to_report_range",
"loan_second_by06_bt_other_loan_ratio",
"loan_second_by06_ncycle_guaranteeForm_giniimpurity",
"loan_second_by06_ncycleR_bt_other_person_ratio",
"loan_second_by06_ncycleR_month60_to_report_min_sum",
"loan_second_by06_ncycleR_repayedAmount_sum",
"loan_second_by06_now_leftRepayTerms_range",
"loan_second_by06_now_month60_State_countNull_range",
"loan_second_by06_nowR_repayAmt_sum",
"loan_second_by06_RepayedAmount_ratio_mean",
"loan_second_by12_month60_to_report_max_max",
"loan_second_by12_ncycle_bt_other_person_count",
"loan_second_by12_ncycle_bt_other_person_ratio",
"loan_second_by12_ncycle_loanGrantOrg_giniimpurity",
"loan_second_by12_ncycle_org_consumer_finance_count",
"loan_second_by12_ncycle_repayTerm_ratio_min",
"loan_second_by12_ncycleR_month60_to_report_mean_mean",
"loan_second_by12_now_month60_to_report_min_mean",
"loan_second_by12_now_repayMons_min",
"loan_second_by12_now_repayMons_ratio_max",
"loan_second_by12_now_rf_other_ratio",
"loan_second_by12_nowR_balance_ratio_range",
"loan_second_by12_nowR_class_ncycle_ratio",
"loan_second_by12_nowR_month60_to_report_min_sum",
"loan_second_by12_nowR_repayMons_ratio_min",
"loan_second_by12_org_commercial_bank_count",
"loan_second_m06_loanAmount_max",
"loan_second_m06_month60_State_countNullr_range",
"loan_second_m06_month60_to_report_min_sum",
"loan_second_m06_ncycle_gf_other_count",
"loan_second_m06_ncycle_month60_to_report_mean_mean",
"loan_second_m06_ncycleR_balance_sum",
"loan_second_m06_ncycleR_gf_crdit_ratio",
"loan_second_m06_ncycleR_leftRepayTerms_mean",
"loan_second_m06_ncycleR_org_commercial_bank_ratio",
"loan_second_m06_now_balance_range",
"loan_second_m06_now_byDate_to_report_mean",
"loan_second_m06_now_month60_State_countNr_max",
"loan_second_m06_now_month60_to_report_min_sum",
"loan_second_m06_now_repayAmt_min",
"loan_second_m06_now_repayMons_mean",
"loan_second_m06_now_repayMons_ratio_sum",
"loan_second_m06_nowR_loanAmount_range",
"loan_second_m06_nowR_month60_to_report_min_mean",
"loan_second_m06_org_giniimpurity",
"loan_second_m06_RepayedAmount_ratio_mean",
"loan_second_m12_month60_State_countNr_mean",
"loan_second_m12_month60_to_report_min_mean",
"loan_second_m12_ncycle_month60_State_countNullr_mean",
"loan_second_m12_ncycle_planRepayAmount_mean",
"loan_second_m12_ncycle_repayAmt_range",
"loan_second_m12_ncycle_RepayedAmount_ratio_min",
"loan_second_m12_ncycle_repayTerms_mean",
"loan_second_m12_now_repayedAmount_min",
"loan_second_m12_now_repayTerms_range",
"loan_second_m12_nowR_month60_to_report_min_mean",
"loan_second_m12_nowR_planRepayAmount_mean",
"loan_second_m12_nowR_repayedAmount_range",
"loan_second_m12_nowR_repayTerms_mean",
"loan_second_m12_nowR_rf_month_ratio",
"loan_second_m24_month60_State_countNr_max",
"loan_second_m24_ncycle_month60_State_countCr_mean",
"loan_second_m24_ncycle_month60_State_countNr_range",
"loan_second_m24_ncycle_month60_State_countUnknowr_mean",
"loan_second_m24_ncycle_org_giniimpurity",
"loan_second_m24_ncycle_repayTerm_ratio_max",
"loan_second_m24_ncycleR_month60_State_countNullr_mean",
"loan_second_m24_now_loanAmount_sum",
"loan_second_m24_nowR_bt_other_person_ratio",
"loan_second_m24_nowR_repayAmt_mean",
"loan_second_m24_planRepayAmount_mean",
"loan_second_m24_RepayedAmount_ratio_mean",
"loan_second_month60_State_count1r_mean",
"loan_second_ncycle_month60_to_report_max_range",
"loan_second_ncycle_org_consumer_finance_ratio",
"loan_second_ncycle_org_micro_loan_count",
"loan_second_ncycle_org_trust_company_count",
"loan_second_ncycle_repayAmt_min",
"loan_second_ncycle_RepayedAmount_ratio_range",
"loan_second_ncycle_repayMons_ratio_min",
"loan_second_ncycleR_balance_mean",
"loan_second_ncycleR_gf_crdit_ratio",
"loan_second_ncycleR_month60_to_report_min_sum",
"loan_second_ncycleR_org_commercial_bank_ratio",
"loan_second_now_month60_State_countNull_sum",
"loan_second_now_month60_State_countUnknowr_mean",
"loan_second_nowR_loanGrantOrg_giniimpurity",
"loan_second_rf_month_ratio",
"loan_second_rf_other_ratio",
"m36_query_detail_mon_report_06_count",
"m60_loan_special_other_ratio",
"m60_query_detail_loan_mon_report_12_count",
"m60_query_detail_reason_giniimpurity",
"otherloan_num",
"query_detail_loan_commercial_bank_count",
"loan_second_by12_month60_State_num_mean_mean",
]
def getMonList_new(month60Desc):
date_list = []
if len(month60Desc) > 5:
begin_end_date = re.findall('[0-9]{4}年[0-9]{2}月', month60Desc)
begin_date = datetime.datetime.strptime(begin_end_date[0], '%Y年%m月')
end_date = datetime.datetime.strptime(begin_end_date[1], '%Y年%m月')
while begin_date <= end_date:
date_str = begin_date.strftime("%Y.%m")
date_list.append(date_str)
begin_date += relativedelta(months=1)
if len(date_list) > 0:
return date_list[-1]
else:
return ''
# 人行数据处理
def rhDataOnline(applyInfo, rhInfo, channel):
dict_feature = {}
error_rh = []
# data_info = json.loads(request_body)
# data_info = request_body
# 逐步判断每个数据集内是否都存在数据
pboc_info = rhInfo
apply_info = applyInfo
# 安徽征信
if (channel == 2) and (len(pboc_info['cc_rh_report_detail_loan_second']) > 0):
for i in pboc_info['cc_rh_report_detail_loan_second']:
if 'byDate' in i.keys():
pass
else:
i['byDate'] = getMonList_new(i['month60Desc']) if 'month60Desc' in i else np.nan
if (channel == 2) and (len(pboc_info['cc_rh_report_detail_debit_card_second']) > 0):
for i in pboc_info['cc_rh_report_detail_debit_card_second']:
if 'byDate' in i.keys():
pass
else:
i['byDate'] = getMonList_new(i['month60Desc']) if 'month60Desc' in i else np.nan
if 'statementDate' in i.keys():
pass
else:
i['statementDate'] = i['cardGrantDate'] if 'cardGrantDate' in i else np.nan
if len(pboc_info) > 0 and len(apply_info) > 0:
try:
# 人行数据返回
dict_out1 = pboc_features.pboc_f(pboc_info=pboc_info, apply_info=apply_info)
except Exception as e:
error_rh.append(str(e))
dict_out1 = {}
# print(dict_out1)
# 剔出正确的特征
for key, value in dict_out1.items():
if key in feature:
dict_feature[key] = value
customer_info = {"loanNo": apply_info["loanNo"],
"businessChannel": apply_info['businessChannel'],
"name": apply_info['name'],
"idcardNo": apply_info['idcardNo'],
"mobile": apply_info['mobile'],
"loan_time": apply_info['loan_time'],
"totalAmount":apply_info['totalAmount']}
# 对每个 特征进行辨识
customer_info.update(dict_feature)
rh_data = pd.DataFrame([customer_info])
# print(rh_data['self_2y_qrynum'])
rh_data = rh_data.add_prefix('rhData_')
rh_data = rh_data.rename(columns={"rhData_loanNo": "loanNo", "rhData_businessChannel": "businessChannel",
"rhData_name": "name", "rhData_idcardNo": "idcardNo", "rhData_mobile": "mobile"})
else:
error_rh.append("人行数据为空!")
return {"code":1,"data":-1,"msg":"人行数据为空!"}
# 对error_rhData数据进行增加
rh_data["error_rhData"] = [str(error_rh)]
# rh_data.fillna(-99, inplace=True)
json_records = rh_data.to_json(orient="records", force_ascii=False)
json_records = json.loads(json_records)
# print(json_records)
# response_data = {"code": 0,
# "data": json_records[0],
# "msg": "Success!"}
response_data = pd.DataFrame(json_records)
# response = lgh_rh_20211001(response_data)
return response_data
# 百融数据处理
def bairongDataOnline(applyInfo, bairongInfo):
error_bairong = []
if len(bairongInfo) > 0:
bairong_respBody = bairongInfo
# 百融数据
try:
bairong_data = features_func.BAIRONG_APPLY_LOAN_feature_online(dict_out={}, json_dict=bairong_respBody)
except Exception as e:
error_bairong.append(str(e))
bairong_data = {}
else:
error_bairong.append("No bairong_respBody")
print('{"error":1,"data":"-1","msg":"No bairong_respBody!"}')
bairong_respBody = {}
try:
bairong_data = features_func.BAIRONG_APPLY_LOAN_feature_online(dict_out={}, json_dict={})
except Exception as e:
error_bairong.append(str(e))
bairong_data = {}
bairong_data = pd.DataFrame([bairong_data])
bairong_data["loanNo"] = applyInfo["loanNo"] if 'loanNo' in applyInfo.keys() else np.nan
bairong_data["businessChannel"] = applyInfo["businessChannel"] if 'businessChannel' in applyInfo.keys() else np.nan
bairong_data["name"] = applyInfo["name"] if 'name' in applyInfo.keys() else np.nan
bairong_data["idcardNo"] = applyInfo["idcardNo"] if 'idcardNo' in applyInfo.keys() else np.nan
bairong_data["mobile"] = applyInfo["mobile"] if 'mobile' in applyInfo.keys() else np.nan
bairong_data["loan_time"] = applyInfo["loan_time"] if 'loan_time' in applyInfo.keys() else np.nan
bairong_data = bairong_data.add_prefix('bairongData_')
bairong_data["error_bairongData"] = [str(error_bairong)]
# print(bairong_data)
# bairong_data.fillna(-99, inplace=True)
json_records = bairong_data.to_json(orient="records", force_ascii=False)
json_records = json.loads(json_records)
response_data = pd.DataFrame(json_records)
# response_data = {"code": 0,
# "data": json_records[0],
# "msg": "Success!"}
return response_data
# 新颜数据处理
def xinyanDataOnline(applyInfo, xinyanInfo):
error_xinyan = []
apply_info = applyInfo
# 新颜数据
if len(xinyanInfo) > 0:
xinyan_respBody = xinyanInfo
try:
xinyan_data = features_func.XINYAN_RADAR_feature_func_online(dict_add={}, json_dict=xinyan_respBody, appl_time=apply_info['loan_time'])
except Exception as e:
error_xinyan.append(str(e))
xinyan_data = {}
else:
error_xinyan.append("No xinyan_respBody")
print('{"error":1,"data":"-1","msg":"No xinyan_respBody!"}')
xinyan_respBody = {}
try:
xinyan_data = features_func.XINYAN_RADAR_feature_func_online(dict_add={}, json_dict={}, appl_time=apply_info['loan_time'])
except Exception as e:
error_xinyan.append(str(e))
xinyan_data = {}
xinyan_data = pd.DataFrame([xinyan_data])
xinyan_data["loanNo"] = applyInfo["loanNo"] if 'loanNo' in applyInfo.keys() else np.nan
xinyan_data["businessChannel"] = applyInfo["businessChannel"] if 'businessChannel' in applyInfo.keys() else np.nan
xinyan_data["name"] = applyInfo["name"] if 'name' in applyInfo.keys() else np.nan
xinyan_data["idcardNo"] = applyInfo["idcardNo"] if 'idcardNo' in applyInfo.keys() else np.nan
xinyan_data["mobile"] = applyInfo["mobile"] if 'mobile' in applyInfo.keys() else np.nan
xinyan_data["loan_time"] = applyInfo["loan_time"] if 'loan_time' in applyInfo.keys() else np.nan
xinyan_data = xinyan_data.add_prefix('xinyanData_')
xinyan_data["error_xinyanData"] = [str(error_xinyan)]
# print(xinyan_data)
# xinyan_data.fillna(-99, inplace=True)
json_records = xinyan_data.to_json(orient="records", force_ascii=False)
json_records = json.loads(json_records)
# response_data = {"code": 0,
# "data": json_records[0],
# "msg": "Success!"}
response_data = pd.DataFrame(json_records)
return response_data
# 京东数据处理
def jingdongDataOnline(applyInfo, jingdongInfo):
error_jingdong = []
if len(jingdongInfo) > 0:
jingdong_respBody = jingdongInfo["data"]
# 京东数据
try:
jingdong_data = features_func.JD_feature_func_online(dict_add={}, json_dict=jingdong_respBody)
except Exception as e:
error_jingdong.append(str(e))
jingdong_data = {}
else:
error_jingdong.append("No JD_CUSTOM_YHHXZB")
print('{"error":1,"data":"-1","msg":"No JD_CUSTOM_YHHXZB!"}')
jingdong_respBody = {}
# 京东数据
try:
jingdong_data = features_func.JD_feature_func_online(dict_add={}, json_dict={})
except Exception as e:
error_jingdong.append(str(e))
jingdong_data = {}
jingdong_data = pd.DataFrame([jingdong_data])
jingdong_data["loanNo"] = applyInfo["loanNo"] if 'loanNo' in applyInfo.keys() else np.nan
jingdong_data["businessChannel"] = applyInfo["businessChannel"] if 'businessChannel' in applyInfo.keys() else np.nan
jingdong_data["name"] = applyInfo["name"] if 'name' in applyInfo.keys() else np.nan
jingdong_data["idcardNo"] = applyInfo["idcardNo"] if 'idcardNo' in applyInfo.keys() else np.nan
jingdong_data["mobile"] = applyInfo["mobile"] if 'mobile' in applyInfo.keys() else np.nan
jingdong_data["loan_time"] = applyInfo["loan_time"] if 'loan_time' in applyInfo.keys() else np.nan
jingdong_data = jingdong_data.add_prefix('jingdongData_')
jingdong_data["error_jingdongData"] = [str(error_jingdong)]
# jingdong_data.fillna(-99, inplace=True)
json_records = jingdong_data.to_json(orient="records", force_ascii=False)
json_records = json.loads(json_records)
# response_data = {"code": 0,
# "data": json_records[0],
# "msg": "Success!"}
response_data = pd.DataFrame(json_records)
return response_data
# 华道数据处理
def huadaoDataOnline(applyInfo, huadaoInfo):
error_huadao = []
apply_info = applyInfo
# 华道数据
if len(huadaoInfo) > 0:
huadao_respBody = huadaoInfo
try:
huadao_data = features_func.huadao_feature_func_online(dict_add={}, json_dict=huadao_respBody, appl_time=apply_info['loan_time'])
except Exception as e:
error_huadao.append(str(e))
huadao_data = {}
else:
error_huadao.append("No huadao_respBody")
print('{"error":1,"data":"-1","msg":"No huadao_respBody!"}')
huadao_respBody = {}
try:
# 华道数据
huadao_data = features_func.huadao_feature_func_online(dict_add={}, json_dict={}, appl_time=apply_info['loan_time'])
except Exception as e:
error_huadao.append(str(e))
huadao_data = {}
huadao_data = pd.DataFrame([huadao_data])
huadao_data["loanNo"] = applyInfo["loanNo"] if 'loanNo' in applyInfo.keys() else np.nan
huadao_data["businessChannel"] = applyInfo["businessChannel"] if 'businessChannel' in applyInfo.keys() else np.nan
huadao_data["name"] = applyInfo["name"] if 'name' in applyInfo.keys() else np.nan
huadao_data["idcardNo"] = applyInfo["idcardNo"] if 'idcardNo' in applyInfo.keys() else np.nan
huadao_data["mobile"] = applyInfo["mobile"] if 'mobile' in applyInfo.keys() else np.nan
huadao_data["loan_time"] = applyInfo["loan_time"] if 'loan_time' in applyInfo.keys() else np.nan
huadao_data = huadao_data.add_prefix('huadaoData_')
huadao_data["error_huadaoData"] = [str(error_huadao)]
# print(huadao_data)
# huadao_data.fillna(-99, inplace=True)
json_records = huadao_data.to_json(orient="records", force_ascii=False)
json_records = json.loads(json_records)
# response_data = {"code": 0,
# "data": json_records[0],
# "msg": "Success!"}
response_data = pd.DataFrame(json_records)
return response_data
# 百行数据处理
def bhDataOnline(applyInfo, bhInfo):
error_baihang = []
if len(bhInfo) > 0:
try:
baihang_info = bhInfo
# 百行数据返回
baihang_data = bh_data.get_data_set(baihang_info=baihang_info)
# 新百行特征-1
dict_in, dict_out = bh_data.get_data(baihang_info=baihang_info)
data_new1 = newBhData(dict_in, dict_out) # 特征加工
baihang_data.update(data_new1)
baihang_data = pd.DataFrame([baihang_data])
except Exception as e:
error_baihang.append(str(e))
baihang_data = pd.DataFrame()
else:
error_baihang.append("No baihanginfo")
print('{"error":1,"data":"-1","msg":"No baihanginfo!"}')
try:
baihang_info = {}
# 百行数据返回
baihang_data = bh_data.get_data_set(baihang_info=baihang_info)
# 新百行特征-1
dict_in, dict_out = bh_data.get_data(baihang_info=baihang_info)
data_new1 = newBhData(dict_in, dict_out) # 特征加工
baihang_data.update(data_new1)
baihang_data = pd.DataFrame([baihang_data])
except Exception as e:
error_baihang.append(str(e))
baihang_data = pd.DataFrame()
baihang_data["loanNo"] = applyInfo["loanNo"] if 'loanNo' in applyInfo.keys() else np.nan
baihang_data["businessChannel"] = applyInfo["businessChannel"] if 'businessChannel' in applyInfo.keys() else np.nan
baihang_data["name"] = applyInfo["name"] if 'name' in applyInfo.keys() else np.nan
baihang_data["idcardNo"] = applyInfo["idcardNo"] if 'idcardNo' in applyInfo.keys() else np.nan
baihang_data["mobile"] = applyInfo["mobile"] if 'mobile' in applyInfo.keys() else np.nan
baihang_data["loan_time"] = applyInfo["loan_time"] if 'loan_time' in applyInfo.keys() else np.nan
baihang_data = baihang_data.add_prefix('baihangData_')
baihang_data['error_baihangData'] = [str(error_baihang)]
# baihang_data.fillna(-99, inplace=True)
json_records = baihang_data.to_json(orient="records", force_ascii=False)
json_records = json.loads(json_records)
# print(json_records[0])
# print(type(json_records))
# response_data = {"code": 0,
# "data": json_records[0],
# "msg": "Success!"}
response_data = pd.DataFrame(json_records)
return response_data
# 百行普惠-优分黑名单-优分多头-安徽社保数据处理
def bhph_black_duotou_shebao(bhphInfo, youfenInfo, shebaoInfo):
"""
:param bhphInfo: 百行普惠数据
:param youfenInfo: 优分黑名单，优分多头
:param shebaoInfo: 安徽社保
:return:
"""
# 字典处理黑名单数据
def dict_handle_black(x):
if x['data']['statusCode'] == "2012":
data = json_normalize(x['data']['result'])
data = data.rename(columns=lambda x: x.replace(".", "_") if type(x) is str else x)
return data
else:
return intal_init_black(pd.DataFrame())
# 字典处理社保数据
def dict_handle_shebao(x):
return pd.DataFrame([x['levelInfo']])
# 字典处理百行普惠数据
def dict_handle_bh_ph(x):
return pd.DataFrame([{"score": x['score']}])
# 字典处理多头数据
def dict_handle_duotou(x):
if x['data']['statusCode'] == "2012":
data = json_normalize(x['data']['result'])
data = data.rename(columns=lambda x: x.replace(".", "_") if type(x) is str else x)
return data
else:
return intal_init_duotou(pd.DataFrame())
if 'black' in youfenInfo.keys():
df_black = dict_handle_black(youfenInfo['black'])
if df_black.empty:
pass
else:
df_black = df_black.add_prefix('black_')
else:
df_black = intal_init_black(pd.DataFrame())
if 'risk' in youfenInfo.keys():
df_duotou = dict_handle_duotou(youfenInfo['risk'])
if df_duotou.empty:
pass
else:
df_duotou = df_duotou.add_prefix('duotou_')
else:
df_duotou = intal_init_duotou(pd.DataFrame())
if len(shebaoInfo) > 0:
df_shebao = dict_handle_shebao(shebaoInfo)
df_shebao = df_shebao.add_prefix('shebao_')
else:
df_shebao = intal_init_shebao(pd.DataFrame())
if len(bhphInfo) > 0:
df_bhph = dict_handle_bh_ph(bhphInfo)
df_bhph = df_bhph.add_prefix('bhph_')
else:
df_bhph = intal_init_bhph(pd.DataFrame())
data = pd.concat([df_black, df_duotou, df_shebao,df_bhph],axis=1)
# 特征工程
finnal_data = data.replace('', np.nan)
col = finnal_data.select_dtypes(include='object').columns.tolist()
for i in col:
try:
finnal_data[i] = finnal_data[i].astype("float64")
except:
pass
finnal_data = handle_new_third(finnal_data)
return finnal_data
# 数据处理
def runMain(request_body=None, channel=0):
# 取三要素
# 特征返回处理
if 'applyInfo' in request_body.keys():
applyInfo = request_body['applyInfo']
loanNo = request_body['applyInfo']['loanNo']
businessChannel = request_body['applyInfo']['businessChannel']
idcardNo = applyInfo['idcardNo']
mobile = applyInfo['mobile']
name = applyInfo['name']
data = {
"idcardNo": applyInfo['idcardNo'],
"mobile": applyInfo['mobile'],
"name": applyInfo['name']
}
else:
applyInfo = {}
loanNo = np.nan
data = {}
businessChannel = np.nan
idcardNo = np.nan
mobile = np.nan
name = np.nan
if 'rhInfo' in request_body.keys():
rhInfo = request_body['rhInfo']
else:
rhInfo = {}
if 'bhInfo' in request_body.keys():
bhInfo = request_body['bhInfo']
else:
bhInfo = {}
if 'jingdongInfo' in request_body.keys():
jdInfo = request_body['jingdongInfo']
else:
jdInfo = {}
if 'xinyanInfo' in request_body.keys():
xinyanInfo = request_body['xinyanInfo']
else:
xinyanInfo = {}
if 'bairongInfo' in request_body.keys():
brInfo = request_body['bairongInfo']
else:
brInfo = {}
if 'huadaoInfo' in request_body.keys():
huadaoInfo = request_body['huadaoInfo']
else:
huadaoInfo = {}
if 'bhphInfo' in request_body.keys():
bhphInfo = request_body['bhphInfo']
else:
bhphInfo = {}
if 'youfenInfo' in request_body.keys():
youfenInfo = request_body['youfenInfo']
else:
youfenInfo = {}
if 'shebaoInfo' in request_body.keys():
shebaoInfo = request_body['shebaoInfo']
else:
shebaoInfo = {}
# 加载数据object数据格式
col_object = load('./joblib/col_object.joblib')
# lgh_rh_20211001 人行模型处理
rh_feature_df = rhDataOnline(applyInfo, rhInfo, channel)
# lgh_bh_20211001 百行模型处理
bh_feature_df = bhDataOnline(applyInfo, bhInfo)
# lgh_br_20211001 百融模型处理
br_feature_df = bairongDataOnline(applyInfo, brInfo)
# lgh_jd_20211001 京东模型处理
jd_feature_df = jingdongDataOnline(applyInfo, jdInfo)
# lgh_hd_20211001 华道模型处理
hd_feature_df = huadaoDataOnline(applyInfo, huadaoInfo)
# lgh_xy_20211001 新颜模型处理
xinyan_feature_df = xinyanDataOnline(applyInfo, xinyanInfo)
# lgh_all_20211001 全数据模型
all_feature_df = pd.concat([br_feature_df, jd_feature_df, hd_feature_df, xinyan_feature_df, bh_feature_df, rh_feature_df], axis=1)
# 转数据格式
all_col = all_feature_df.select_dtypes(include='object').columns.tolist()
all_object = list(set(all_col)-set(col_object))
for i in all_object:
all_feature_df[i] = pd.to_numeric(all_feature_df[i], errors='ignore')
test1_time = time()
print("特征加工计算时间:", test1_time - begin_time)
try:
lgh_rh_20211001_score = lgh_rh_20211001(all_feature_df)
except Exception as e:
print("人行模型报错:", e)
lgh_rh_20211001_score = {'rh_dpd60_raw_score': -1,'pboc2_fpd20_raw_coor_score': -1}
rh_time = time()
print("人行模型计算时间:", rh_time - test1_time)
try:
lgh_br_20211001_score = lgh_br_20211001(all_feature_df)
except Exception as e:
print("百融模型报错:", e)
lgh_br_20211001_score = {'bairong_dpd60_raw_score': -1}
br_time = time()
print("百融模型计算时间:", br_time - rh_time)
try:
lgh_jd_20211001_score = lgh_jd_20211001(all_feature_df)
except Exception as e:
print("京东模型报错:", e)
lgh_jd_20211001_score = {'jingdong_fpd20_raw_block3_score': -1}
jd_time = time()
print("京东模型计算时间:", jd_time - br_time)
try:
# lgh_big_20211001 大数据模型
lgh_big_20211001_score = lgh_big_20211001(all_feature_df)
except Exception as e:
print("大数据模型报错:", e)
lgh_big_20211001_score = {'big3_dpd60_raw_block3_score': -1,
'big3_dpd60_raw_n3_score': -1,
'big3_fpd20_raw_coor_score': -1,
'big_fpd20_raw_coor_1_score': -1,
'big_fpd20_raw_score': -1}
big_time = time()
print("大数据模型计算时间:", big_time - jd_time)
try:
lgh_all_20211001_score = lgh_all_20211001(all_feature_df)
except Exception as e:
print("全数据模型报错:", e)
lgh_all_20211001_score = {'all3_dpd60_raw_block3_score': -1,
'all3_fpd20_raw_block3_score': -1,
'all3_fpd20_raw_coor_score': -1,
'all_dpd60_raw_score': -1}
all_time = time()
print("全数据模型计算时间:", all_time - big_time)
# yzj_all_20211001 全数据模型
yzj_all_20211001_score = yzj_all_20211001(all_feature_df)
yzj_time = time()
print("yzj模型计算时间:", yzj_time - all_time)
# cxt_all_20211001 全数据模型
cxt_all_20211001_score = cxt_all_20211001(all_feature_df)
cxt_time = time()
print("cxt模型计算时间:", cxt_time - yzj_time)
# zgl 全数据模型
# zgl_all_20211001_score = zgl_main(request_body)
# 百行普惠-优分黑名单-优分多头-安徽社保数据处理
new_third_data = bhph_black_duotou_shebao(bhphInfo, youfenInfo, shebaoInfo)
try:
lgh_youfen_20211001_score = lgh_youfen_20211001(new_third_data)
except Exception as e:
print("百行普惠-优分黑名单-优分多头-安徽社保数据模型报错:", e)
lgh_youfen_20211001_score = {'youfen_fpd20_raw_coor3_score': -1}
test2_time = time()
print("新三方模型计算时间:", test2_time - cxt_time)
print("模型计算时间:",test2_time-test1_time)
# final_data = pd.concat([all_feature_df, new_third_data], axis=1)
if new_third_data['black_cellphoneDimension_seriousCondition_hit'].values.tolist():
black_cellphoneDimension_seriousCondition_hit = new_third_data['black_cellphoneDimension_seriousCondition_hit'].values[0]
else:
black_cellphoneDimension_seriousCondition_hit = np.nan
if new_third_data['black_idcardDimension_seriousCondition_hit'].values.tolist():
black_idcardDimension_seriousCondition_hit = new_third_data['black_idcardDimension_seriousCondition_hit'].values[0]
else:
black_idcardDimension_seriousCondition_hit = np.nan
if new_third_data['bhph_score'].values.tolist():
bhph_score = new_third_data['bhph_score'].values[0]
else:
bhph_score = np.nan
if new_third_data['shebao_IncomeLevel'].values.tolist():
shebao_IncomeLevel = new_third_data['shebao_IncomeLevel'].values[0]
else:
shebao_IncomeLevel = np.nan
if new_third_data['shebao_StabilityLevel'].values.tolist():
shebao_StabilityLevel = new_third_data['shebao_StabilityLevel'].values[0]
else:
shebao_StabilityLevel = np.nan
if new_third_data['shebao_CreditLevel'].values.tolist():
shebao_CreditLevel = new_third_data['shebao_CreditLevel'].values[0]
else:
shebao_CreditLevel = np.nan
# 拼接返回
dict_a = {'loanNo': loanNo,
'businessChannel': businessChannel,
'name': name,
'idcardNo': idcardNo,
'mobile': mobile,
'black_cellphoneDimension_seriousCondition_hit': black_cellphoneDimension_seriousCondition_hit,
'black_idcardDimension_seriousCondition_hit': black_idcardDimension_seriousCondition_hit,
'bhph_score': bhph_score,
'shebao_IncomeLevel': shebao_IncomeLevel,
'shebao_StabilityLevel': shebao_StabilityLevel,
'shebao_CreditLevel': shebao_CreditLevel}
dict_a.update(lgh_rh_20211001_score)
dict_a.update(lgh_br_20211001_score)
dict_a.update(lgh_jd_20211001_score)
dict_a.update(lgh_youfen_20211001_score)
dict_a.update(lgh_big_20211001_score)
dict_a.update(lgh_all_20211001_score)
dict_a.update(yzj_all_20211001_score)
dict_a.update(cxt_all_20211001_score)
# dict_a.update(zgl_all_20211001_score)
return dict_a
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23:18:39","ipProvince":"广东省"},{"appVersion":"0.0.1","timeLong":0,"ipCity":"深圳市","openId":"2088712359801808","mobileVersion":"10","uuid":"kQCrQeHHFn5cYtBAdY7w7SdJkmSCsHTa","mobileBrand":"SAMSUNG","mobileModel":"SAMSUNG SM-N960U","net":"WIFI","businessChannel":11,"mobileSystem":"android","ip":"27.38.141.134","eventType":1,"eventCode":"100001","createTime":"2021-07-20 23:18:36","ipProvince":"广东省"},{"appVersion":"0.0.1","timeLong":0,"ipCity":"深圳市","openId":"2088712359801808","mobileVersion":"10","uuid":"kQCrQeHHFn5cYtBAdY7w7SdJkmSCsHTa","mobileBrand":"SAMSUNG","mobileModel":"SAMSUNG SM-N960U","net":"WIFI","businessChannel":11,"mobileSystem":"android","ip":"27.38.141.134","eventType":3,"eventCode":"300001","createTime":"2021-07-20 23:18:12","ipProvince":"广东省"},{"appVersion":"0.0.1","timeLong":0,"ipCity":"深圳市","openId":"2088712359801808","mobileVersion":"10","uuid":"kQCrQeHHFn5cYtBAdY7w7SdJkmSCsHTa","mobileBrand":"SAMSUNG","mobileModel":"SAMSUNG SM-N960U","net":"WIFI","businessChannel":11,"mobileSystem":"android","ip":"27.38.141.134","eventType":2,"eventCode":"200001","createTime":"2021-07-20 23:17:36","ipProvince":"广东省"},{"appVersion":"0.0.1","timeLong":0,"ipCity":"深圳市","openId":"2088712359801808","mobileVersion":"10","uuid":"kQCrQeHHFn5cYtBAdY7w7SdJkmSCsHTa","mobileBrand":"SAMSUNG","mobileModel":"SAMSUNG SM-N960U","net":"WIFI","businessChannel":11,"mobileSystem":"android","ip":"27.38.141.134","eventType":2,"eventCode":"200010","createTime":"2021-07-20 23:17:32","ipProvince":"广东省"},{"appVersion":"0.0.1","timeLong":0,"ipCity":"深圳市","openId":"2088712359801808","mobileVersion":"10","uuid":"kQCrQeHHFn5cYtBAdY7w7SdJkmSCsHTa","mobileBrand":"SAMSUNG","mobileModel":"SAMSUNG SM-N960U","net":"WIFI","businessChannel":11,"mobileSystem":"android","ip":"27.38.141.134","eventType":3,"eventCode":"300001","createTime":"2021-07-20 23:17:06","ipProvince":"广东省"},{"appVersion":"0.0.1","timeLong":0,"ipCity":"深圳市","openId":"2088712359801808","mobileVersion":"10","uuid":"kQCrQeHHFn5cYtBAdY7w7SdJkmSCsHTa","mobileBrand":"SAMSUNG","mobileModel":"SAMSUNG SM-N960U","net":"WIFI","businessChannel":11,"mobileSystem":"android","ip":"27.38.141.134","eventType":2,"eventCode":"200003","createTime":"2021-07-20 23:17:04","ipProvince":"广东省"},{"appVersion":"0.0.1","timeLong":0,"ipCity":"深圳市","openId":"2088712359801808","mobileVersion":"10","uuid":"kQCrQeHHFn5cYtBAdY7w7SdJkmSCsHTa","mobileBrand":"SAMSUNG","mobileModel":"SAMSUNG SM-N960U","net":"WIFI","businessChannel":11,"mobileSystem":"android","ip":"27.38.141.134","eventType":2,"eventCode":"200002","createTime":"2021-07-20 23:16:55","ipProvince":"广东省"},{"appVersion":"0.0.1","timeLong":0,"ipCity":"深圳市","openId":"2088712359801808","mobileVersion":"10","uuid":"kQCrQeHHFn5cYtBAdY7w7SdJkmSCsHTa","mobileBrand":"SAMSUNG","mobileModel":"SAMSUNG SM-N960U","net":"WIFI","businessChannel":11,"mobileSystem":"android","ip":"27.38.141.134","eventType":2,"eventCode":"200002","createTime":"2021-07-20 23:16:54","ipProvince":"广东省"},{"appVersion":"0.0.1","timeLong":0,"ipCity":"深圳市","openId":"2088712359801808","mobileVersion":"10","uuid":"kQCrQeHHFn5cYtBAdY7w7SdJkmSCsHTa","mobileBrand":"SAMSUNG","mobileModel":"SAMSUNG SM-N960U","net":"WIFI","businessChannel":11,"mobileSystem":"android","ip":"27.38.141.134","eventType":2,"eventCode":"200002","createTime":"2021-07-20 23:16:51","ipProvince":"广东省"},{"appVersion":"0.0.1","timeLong":0,"ipCity":"深圳市","openId":"2088712359801808","mobileVersion":"10","uuid":"kQCrQeHHFn5cYtBAdY7w7SdJkmSCsHTa","mobileBrand":"SAMSUNG","mobileModel":"SAMSUNG SM-N960U","net":"WIFI","businessChannel":11,"mobileSystem":"android","ip":"27.38.141.134","eventType":2,"eventCode":"200001","createTime":"2021-07-20 23:16:49","ipProvince":"广东省"},{"appVersion":"0.0.1","timeLong":0,"ipCity":"深圳市","openId":"2088712359801808","mobileVersion":"10","uuid":"kQCrQeHHFn5cYtBAdY7w7SdJkmSCsHTa","mobileBrand":"SAMSUNG","mobileModel":"SAMSUNG SM-N960U","net":"WIFI","businessChannel":11,"mobileSystem":"android","ip":"27.38.141.134","eventType":1,"eventCode":"100005","createTime":"2021-07-20 23:16:39","ipProvince":"广东省"},{"appVersion":"0.0.1","timeLong":0,"ipCity":"深圳市","openId":"2088712359801808","mobileVersion":"10","uuid":"kQCrQeHHFn5cYtBAdY7w7SdJkmSCsHTa","mobileBrand":"SAMSUNG","mobileModel":"SAMSUNG SM-N960U","net":"WIFI","businessChannel":11,"mobileSystem":"android","ip":"27.38.141.134","eventType":1,"eventCode":"100001","createTime":"2021-07-20 23:16:29","ipProvince":"广东省"},{"appVersion":"0.0.1","timeLong":0,"ipCity":"深圳市","openId":"2088712359801808","mobileVersion":"10","uuid":"kQCrQeHHFn5cYtBAdY7w7SdJkmSCsHTa","mobileBrand":"SAMSUNG","mobileModel":"SAMSUNG SM-N960U","net":"WIFI","businessChannel":11,"mobileSystem":"android","ip":"163.125.39.94","eventType":1,"eventCode":"100001","createTime":"2021-07-19 13:03:34","ipProvince":"广东省"},{"appVersion":"0.0.1","timeLong":0,"ipCity":"深圳市","openId":"2088712359801808","mobileVersion":"10","uuid":"kQCrQeHHFn5cYtBAdY7w7SdJkmSCsHTa","mobileBrand":"SAMSUNG","mobileModel":"SAMSUNG SM-N960U","net":"WIFI","businessChannel":11,"mobileSystem":"android","ip":"163.125.39.94","eventType":4,"eventCode":"400001","createTime":"2021-07-19 12:34:17","ipProvince":"广东省"},{"appVersion":"0.0.1","timeLong":0,"ipCity":"深圳市","openId":"2088712359801808","mobileVersion":"10","uuid":"kQCrQeHHFn5cYtBAdY7w7SdJkmSCsHTa","mobileBrand":"SAMSUNG","mobileModel":"SAMSUNG SM-N960U","net":"WIFI","businessChannel":11,"mobileSystem":"android","ip":"163.125.39.94","eventType":3,"eventCode":"300001","createTime":"2021-07-19 12:33:00","ipProvince":"广东省"},{"appVersion":"0.0.1","timeLong":0,"ipCity":"深圳市","openId":"2088712359801808","mobileVersion":"10","uuid":"kQCrQeHHFn5cYtBAdY7w7SdJkmSCsHTa","mobileBrand":"SAMSUNG","mobileModel":"SAMSUNG SM-N960U","net":"WIFI","businessChannel":11,"mobileSystem":"android","ip":"163.125.39.94","eventType":1,"eventCode":"100001","createTime":"2021-07-19 12:32:58","ipProvince":"广东省"},{"appVersion":"0.0.1","timeLong":0,"ipCity":"深圳市","openId":"2088712359801808","mobileVersion":"10","uuid":"kQCrQeHHFn5cYtBAdY7w7SdJkmSCsHTa","mobileBrand":"SAMSUNG","mobileModel":"SAMSUNG SM-N960U","net":"WIFI","businessChannel":11,"mobileSystem":"android","ip":"163.125.39.94","eventType":1,"eventCode":"100005","createTime":"2021-07-19 12:32:51","ipProvince":"广东省"},{"appVersion":"0.0.1","timeLong":0,"ipCity":"深圳市","openId":"2088712359801808","mobileVersion":"10","uuid":"kQCrQeHHFn5cYtBAdY7w7SdJkmSCsHTa","mobileBrand":"SAMSUNG","mobileModel":"SAMSUNG SM-N960U","net":"WIFI","businessChannel":11,"mobileSystem":"android","ip":"163.125.39.94","eventType":1,"eventCode":"100001","createTime":"2021-07-19 12:32:43","ipProvince":"广东省"},{"appVersion":"0.0.1","timeLong":0,"ipCity":"深圳市","openId":"2088712359801808","mobileVersion":"10","uuid":"kQCrQeHHFn5cYtBAdY7w7SdJkmSCsHTa","mobileBrand":"SAMSUNG","mobileModel":"SAMSUNG SM-N960U","net":"WIFI","businessChannel":11,"mobileSystem":"android","ip":"163.125.39.94","eventType":3,"eventCode":"300001","createTime":"2021-07-18 13:38:16","ipProvince":"广东省"},{"appVersion":"0.0.1","timeLong":0,"ipCity":"深圳市","openId":"2088712359801808","mobileVersion":"10","uuid":"kQCrQeHHFn5cYtBAdY7w7SdJkmSCsHTa","mobileBrand":"SAMSUNG","mobileModel":"SAMSUNG SM-N960U","net":"WIFI","businessChannel":11,"mobileSystem":"android","ip":"163.125.39.94","eventType":1,"eventCode":"100001","createTime":"2021-07-18 13:38:14","ipProvince":"广东省"},{"appVersion":"0.0.1","timeLong":0,"ipCity":"深圳市","openId":"2088712359801808","mobileVersion":"10","uuid":"kQCrQeHHFn5cYtBAdY7w7SdJkmSCsHTa","mobileBrand":"SAMSUNG","mobileModel":"SAMSUNG SM-N960U","net":"WIFI","businessChannel":11,"mobileSystem":"android","ip":"163.125.39.94","eventType":2,"eventCode":"200002","createTime":"2021-07-18 13:38:00","ipProvince":"广东省"},{"appVersion":"0.0.1","timeLong":0,"ipCity":"深圳市","openId":"2088712359801808","mobileVersion":"10","uuid":"kQCrQeHHFn5cYtBAdY7w7SdJkmSCsHTa","mobileBrand":"SAMSUNG","mobileModel":"SAMSUNG SM-N960U","net":"WIFI","businessChannel":11,"mobileSystem":"android","ip":"163.125.39.94","eventType":2,"eventCode":"200001","createTime":"2021-07-18 13:37:55","ipProvince":"广东省"},{"appVersion":"0.0.1","timeLong":0,"ipCity":"深圳市","openId":"2088712359801808","mobileVersion":"10","uuid":"kQCrQeHHFn5cYtBAdY7w7SdJkmSCsHTa","mobileBrand":"SAMSUNG","mobileModel":"SAMSUNG SM-N960U","net":"WIFI","businessChannel":11,"mobileSystem":"android","ip":"163.125.39.94","eventType":1,"eventCode":"100005","createTime":"2021-07-18 13:37:54","ipProvince":"广东省"},{"appVersion":"0.0.1","timeLong":0,"ipCity":"深圳市","openId":"2088712359801808","mobileVersion":"10","uuid":"kQCrQeHHFn5cYtBAdY7w7SdJkmSCsHTa","mobileBrand":"SAMSUNG","mobileModel":"SAMSUNG SM-N960U","net":"WIFI","businessChannel":11,"mobileSystem":"android","ip":"163.125.39.94","eventType":3,"eventCode":"300001","createTime":"2021-07-18 13:37:49","ipProvince":"广东省"},{"appVersion":"0.0.1","timeLong":0,"ipCity":"深圳市","openId":"2088712359801808","mobileVersion":"10","uuid":"kQCrQeHHFn5cYtBAdY7w7SdJkmSCsHTa","mobileBrand":"SAMSUNG","mobileModel":"SAMSUNG SM-N960U","net":"WIFI","businessChannel":11,"mobileSystem":"android","ip":"163.125.39.94","eventType":1,"eventCode":"100001","createTime":"2021-07-18 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from time import *
begin_time = time()
a = runMain(request_body=request_body, channel=0)
end_time = time()
run_time = end_time-begin_time
print(begin_time)
print(end_time)
print ('该循环程序运行时间：',run_time) #该循环程序运行时间： 1.4201874732
print(a)
|
[
"youzhengjie@qnvip.com"
] |
youzhengjie@qnvip.com
|
86df1e55a9124a5ef39b58a13013e3833bb34158
|
a4575a8628de6cf49fd2a8901d7f75e65fedbecf
|
/setup.py
|
3ce490f30cdf003ac59b8194d4031d4431757d8c
|
[
"MIT"
] |
permissive
|
Skorii/Henallux-Project-2019
|
6a3228797cd33e32aa81091b87599315dc88e2e1
|
1376f4f143963b3309f545f7f80e8ded32fa21f4
|
refs/heads/master
| 2022-12-23T04:15:49.632171
| 2020-09-24T18:05:33
| 2020-09-24T18:05:33
| 229,773,337
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,038
|
py
|
from setuptools import setup
def readme():
with open('README.md') as f:
README = f.read()
return README
setup(
name='master-slave',
version='1.0.0',
description='package to communicate with infected devices trought a local network',
long_description=readme(),
long_description_content_type='text/markdown',
url='https://github.com/Skorii/ProjetPython2019',
author='Arnaud "Skorii" Gony',
license="MIT",
classifiers=[
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.7",
],
packages=['master_slave'],
include_package_data=True,
install_requires=['argparse', 'datetime', 'logging', 'os', 'platform', 'psutil', 're', 'requests', 'shutil', 'socket', 'threading', 'time', 'uuid'],
entry_points={
"console_scripts": [
"master = master_slave.master:main",
"slave = master_slave.slave:main",
],
},
)
|
[
"noreply@github.com"
] |
Skorii.noreply@github.com
|
cc2f87dd75c15312568839c8506425e329400ef6
|
7815090f077e0c83e13a41b15aab324d0d6e89fa
|
/python_example_1/download_file.py
|
3ea5ac4495d7088db3272a2504e6c8157a9482d9
|
[
"BSD-3-Clause"
] |
permissive
|
namolo2017/hackZEISS
|
61e525bcaa3de469a5f0e86649691524c61bd526
|
fdb2c7342fbe0adf9b137f90bf54e8899b1924fb
|
refs/heads/master
| 2021-01-11T18:08:19.065875
| 2017-01-19T23:35:48
| 2017-01-19T23:35:48
| 79,501,786
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 259
|
py
|
#!/usr/bin/python3
import boto3
s3 = boto3.resource('s3')
# Print out bucket names
for bucket in s3.buckets.all():
print(bucket.name)
# download a file
s3_client = boto3.client('s3')
s3_client.download_file('z0001-bucket', 't_0002.png', 't_0002.png')
|
[
"oliver@momo.Speedport_W_724V_Typ_A_05011603_00_009"
] |
oliver@momo.Speedport_W_724V_Typ_A_05011603_00_009
|
0f57847960a06f7e8f4b1adc58374dbc48ad303d
|
29976a5db2873bf084137ca5dc0920efe0b5a1f2
|
/lora/__init__.py
|
32c547656e83fa88e0ef64782f40460e6e997670
|
[
"MIT"
] |
permissive
|
jieter/python-lora
|
874540b3c68588022afaae8a1b0954e0c82b28cc
|
5452c5aad29247d1bb0c1c57b409c976592a4190
|
refs/heads/master
| 2023-02-21T13:09:57.342877
| 2022-10-27T20:15:24
| 2022-10-27T20:15:24
| 53,316,778
| 67
| 24
|
MIT
| 2023-02-08T00:43:15
| 2016-03-07T10:27:45
|
Python
|
UTF-8
|
Python
| false
| false
| 18
|
py
|
VERSION = "1.1.9"
|
[
"noreply@github.com"
] |
jieter.noreply@github.com
|
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