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0f907498ef454193d80fa1dbac7f4ef357cb0485 | f9308d5a8efe2dbb48e9cc87cd06405b60a9dc7b | /samples/python/apidocs/ee_featurecollection_aggregate_total_var.py | 6f681ac3fa4a89f5f1bdf70d9c5cf99b8aef7c56 | [
"Apache-2.0",
"CC-BY-4.0"
] | permissive | google/earthengine-community | 4e054b421f66f03507d58668084aee981062fc24 | ce931040c518860f8788b4888c0acfdebd2952fc | refs/heads/master | 2023-09-01T14:47:54.812703 | 2023-08-31T23:01:00 | 2023-08-31T23:01:39 | 200,732,820 | 428 | 552 | Apache-2.0 | 2023-09-13T21:46:51 | 2019-08-05T21:42:11 | Jupyter Notebook | UTF-8 | Python | false | false | 1,021 | py | # Copyright 2023 The Google Earth Engine Community Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# [START earthengine__apidocs__ee_featurecollection_aggregate_total_var]
# FeatureCollection of power plants in Belgium.
fc = ee.FeatureCollection('WRI/GPPD/power_plants').filter(
'country_lg == "Belgium"')
print('Total variance of power plant capacities (MW):',
fc.aggregate_total_var('capacitymw').getInfo()) # 214307.38335169878
# [END earthengine__apidocs__ee_featurecollection_aggregate_total_var]
| [
"copybara-worker@google.com"
] | copybara-worker@google.com |
e479319f2f40f31a8fdd22b4981b19d8cc53f1f7 | caa7e7d7d60d9f073b67661661ddd898b0bdb506 | /setup.py | 404c0124fe14f511cefcf51ad915fde6f68bf0e2 | [
"MIT"
] | permissive | kanghyojun/s2protocol | aa687ef79c2d8e508fa3a4f9f87adac81c190bac | 6e48a010938aa3adea0c9f7ec6ad9bea7b57540e | refs/heads/master | 2021-10-22T20:10:54.604454 | 2014-02-05T16:46:23 | 2014-02-05T16:46:23 | 16,060,557 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 225 | py | # -*- coding: utf-8 -*-
from setuptools import setup, find_packages
setup(name='s2protocol',
version='0.0.0',
author='Blizzard',
install_requires=[
'mpyq'
],
packages=find_packages())
| [
"hyojun@admire.kr"
] | hyojun@admire.kr |
6793a7e2ed84de8b67e05f62dbff2c37d60be349 | 6c0beed4cd8719bf48982a853183121cea35cadf | /thread_syn_scanr_final.py | a312b10f43247c3ca188d52d82f490d1d797ed92 | [] | no_license | vicky288/pythonScripts | c90406dd2addc7a72275be0526daae7eba7d8c29 | 2b31c726b5924314b31a37f3a4eb86c132816859 | refs/heads/master | 2021-04-30T16:35:30.739238 | 2017-02-09T00:06:16 | 2017-02-09T00:06:16 | 80,077,402 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,356 | py | #!/usr/bin/env python
import threading
import Queue
import time
from scapy.all import *
class WorkerThread(threading.Thread) :
def __init__(self, queue, tid) :
threading.Thread.__init__(self)
self.queue = queue
self.tid = tid
print "Worker %d Reporting for Service Sir!" %self.tid
def run(self) :
total_ports = 0
while True :
port = 0
try :
port = self.queue.get(timeout=1)
except Queue.Empty :
print "Worker %d exiting. Scanned %d ports ..." % (self.tid, total_ports)
return
# port scanning to begin
# we rely on scapy to do this
ip = sys.argv[1]
response = sr1(IP(dst=ip)/TCP(dport=port, flags="S"), verbose=False, timeout=.2)
# only checking for SYN-ACK == flags = 18
# filtererd ports etc. is another story altogether
if response :
if response[TCP].flags == 18 :
print "ThreadId %d: Received port number %d Status: OPEN" %(self.tid, port)
self.queue.task_done()
total_ports += 1
queue = Queue.Queue()
threads = []
for i in range(1, 10) :
print "Creating WorkerThread : %d"%i
worker = WorkerThread(queue, i)
worker.setDaemon(True)
worker.start()
threads.append(worker)
print "WorkerThread %d Created!"%i
for j in range (1,1000) :
queue.put(j)
queue.join()
# wait for all threads to exit
for item in threads :
item.join()
print "Scanning Complete!"
| [
"root@localhost.localdomain"
] | root@localhost.localdomain |
51dd7f85ef5c0d7b69d593c9c71aa7bfd3232709 | 31af83f735a4722ba3b7f6a3864d4b9c5be16d90 | /manage.py | f4c005520e48f7273436e9f6bfd757eda1bcdf32 | [] | no_license | DrewCCannedy/brohaus | 300ba75e42324549d8bbe803338f050f31a5ae38 | 6dea8d22fc27ae2199bedd50211c4a4c49e6e055 | refs/heads/master | 2023-08-31T14:15:22.509059 | 2020-07-01T22:22:23 | 2020-07-01T22:22:23 | 276,479,305 | 0 | 0 | null | 2021-09-22T19:21:04 | 2020-07-01T20:50:44 | Python | UTF-8 | Python | false | false | 648 | py | #!/usr/bin/env python
"""Django's command-line utility for administrative tasks."""
import os
import sys
def main():
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'brohaus.settings')
try:
from django.core.management import execute_from_command_line
except ImportError as exc:
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?"
) from exc
execute_from_command_line(sys.argv)
if __name__ == '__main__':
main()
| [
"dakillac@gmail.com"
] | dakillac@gmail.com |
0fd14d1a2133a3e80d9f1f3ab67434b34c00c4e9 | 76eb7570deba15cd8df8ddd96f52e30623c680f0 | /Proj1_v1.py | 0f2f6a2fc69cf613966d0de6b2b8f4c0745e80fa | [] | no_license | tanveerm176/Flask-Knot | 7978277ac042abd1e6dd10de96c193688eea3633 | 0b897dc4906bfabd4c42f638cec88542bfeedb61 | refs/heads/master | 2021-01-24T22:03:07.656733 | 2015-07-20T16:04:15 | 2015-07-20T16:04:15 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,067 | py | from flask import Flask, render_template, request
from data import sentencegenerator as story
from data import Reader,cipher,markov_v3 as chain
Proj1_v1=Flask(__name__)
@Proj1_v1.route('/')
def root():
return render_template('main.html', title='Main Page')
@Proj1_v1.route('/rotn', methods=["POST","GET"])
def rotn():
if request.method=="GET":
return render_template('rotn.html', title='Caesar Cipher')
elif request.method=="POST":
request_data=request.form
if request_data['key'].isdigit()==True:
cypher=cipher.rotn(request_data['word'],int(request_data['key']))
return render_template('rotn.html', title='Caesar Cipher', error=cypher)
else:
return render_template('rotn.html', title='Caesar Cipher', error="Not a valid key")
else:
return 'yo'
@Proj1_v1.route('/sengen', methods=['POST','GET'])
def sengen():
if request.method=="GET":
return render_template('sengen.html', title='Sentence Generator', Sentences='')
elif request.method=="POST":
sentences=request.form
if int(sentences['numsen'])>0:
return render_template('sengen.html', title='Sentence Generator', Sentences=story.senGen(int(sentences['numsen'])))
else:
return render_template('sengen.html', title='Sentence Generator', Sentences='Invalid number of sentences')
else:
return 'yo'
@Proj1_v1.route('/markov', methods=['POST',"GET"])
def markov_result():
if request.method=='GET':
return render_template('markov.html', title='Markov Text Generator', book='', text='')
elif request.method=='POST':
book=request.form['booklist']
if book!=' ':
return render_template('markov.html', title='Markov Text Generator', book=book, text=chain.markov_generator(book))
else:
yes='yes'
else:
return 'yo'
def validate(u,p):
request_data=request.form
credentials=Reader.getCsvDict('./data/credentials.txt')
if request_data['user'] in credentials and request_data['user']!='':
if request_data['pswd']==credentials[request_data['user']][0]:
return True
else:
return False
else:
return False
@Proj1_v1.route('/login', methods=['POST','GET'])#Allows both Post (going through the form) and Get (going directly to the page)
def log_in():
if request.method=="GET":
return render_template('form.html', title='login')
elif request.method=="POST":#Verifies the username and password against constants at the top
request_data=request.form#Takes the immutable dictionary of the user's inputs and saves it in a variable
credentials=Reader.getCsvDict('./data/credentials.txt')
if validate(request_data['user'],request_data['pswd']):
return render_template('form.html', title='login', error='Successful login')
else:
return render_template('form.html', error='Invalid username or password', title='Login')
else:
return 'yo'
@Proj1_v1.route('/signup', methods=['POST', 'GET'])
def signup():
previousCredentials=Reader.read_file('./data/credentials.txt')
if request.method=="GET":
return render_template('signup.html', title='Sign up')
elif request.method=="POST":
new_user=request.form['nuser']
new_pswd=request.form['npswd']
new_credentials='%s,%s'%(new_user,new_pswd)
if new_user=='' or new_pswd=='' or new_user in previousCredentials or new_user.find(',')!=-1 or new_pswd.find(',')!=-1:
return render_template('form.html', error='Invalid signup credentials', title='Login')
else:
Reader.write_file('./data/credentials.txt',new_credentials,'a')
return render_template('form.html', error='Successfully signed up!', title='Login')
else:
return 'yo'
if __name__=='__main__':
Proj1_v1.debug=True
Proj1_v1.run(host='0.0.0.0')
| [
"benjihofing@gmail.com"
] | benjihofing@gmail.com |
39d76bd576224e3946ff7daa7c0eafa7235abd11 | cab7fbf7e7b1df18459ff46b8f5981eff2745104 | /depth_and_motion_learning/transform_utils.py | 08c12d61bc8cbeaae72f4af4ddc62d331e8ef828 | [] | no_license | bolianchen/unsupervised-depth-predictors | d134d791103bc2f8a990c5bc88820b9e1ba65556 | 082103e6caa70f76559f3b38cd9daa30acafa8fe | refs/heads/main | 2023-08-20T08:22:20.341063 | 2021-09-27T09:11:42 | 2021-09-27T09:11:42 | 387,762,636 | 0 | 2 | null | null | null | null | UTF-8 | Python | false | false | 6,454 | py | # coding=utf-8
# Copyright 2021 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Helper functions for geometric transforms."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.compat.v1 as tf
from tensorflow_graphics.geometry.transformation import euler
from tensorflow_graphics.geometry.transformation import rotation_matrix_3d
def matrix_from_angles(rot):
"""Create a rotation matrix from a triplet of rotation angles.
Args:
rot: a tf.Tensor of shape [..., 3], where the last dimension is the rotation
angles, along x, y, and z.
Returns:
A tf.tensor of shape [..., 3, 3], where the last two dimensions are the
rotation matrix.
This function mimics _euler2mat from struct2depth/project.py, for backward
compatibility, but wraps tensorflow_graphics instead of reimplementing it.
The negation and transposition are needed to bridge the differences between
the two.
"""
rank = tf.rank(rot)
# Swap the two last dimensions
perm = tf.concat([tf.range(rank - 1), [rank], [rank - 1]], axis=0)
return tf.transpose(rotation_matrix_3d.from_euler(-rot), perm)
def angles_from_matrix(matrix):
"""Get a triplet of rotation angles from a rotation matrix.
Args:
matrix: A tf.tensor of shape [..., 3, 3], where the last two dimensions are
the rotation matrix.
Returns:
A tf.Tensor of shape [..., 3], where the last dimension is the rotation
angles, along x, y, and z.
This function mimics _euler2mat from struct2depth/project.py, for backward
compatibility, but wraps tensorflow_graphics instead of reimplementing it.
The negation and transposition are needed to bridge the differences between
the two.
"""
rank = tf.rank(matrix)
# Swap the two last dimensions
perm = tf.concat([tf.range(rank - 2), [rank - 1], [rank - 2]], axis=0)
return -euler.from_rotation_matrix(tf.transpose(matrix, perm))
def unstacked_matrix_from_angles(rx, ry, rz, name=None):
"""Create an unstacked rotation matrix from rotation angles.
Args:
rx: A tf.Tensor of rotation angles abound x, of any shape.
ry: A tf.Tensor of rotation angles abound y (of the same shape as x).
rz: A tf.Tensor of rotation angles abound z (of the same shape as x).
name: A string, name for the op.
Returns:
A 3-tuple of 3-tuple of tf.Tensors of the same shape as x, representing the
respective rotation matrix. The small 3x3 dimensions are unstacked into a
tuple to avoid tensors with small dimensions, which bloat the TPU HBM
memory. Unstacking is one of the recommended methods for resolving the
problem.
"""
with tf.name_scope(name, 'BuildUnstackedRotationMatrix', [rx, ry, rz]):
angles = [-rx, -ry, -rz]
sx, sy, sz = [tf.sin(a) for a in angles]
cx, cy, cz = [tf.cos(a) for a in angles]
m00 = cy * cz
m10 = (sx * sy * cz) - (cx * sz)
m20 = (cx * sy * cz) + (sx * sz)
m01 = cy * sz
m11 = (sx * sy * sz) + (cx * cz)
m21 = (cx * sy * sz) - (sx * cz)
m02 = -sy
m12 = sx * cy
m22 = cx * cy
return ((m00, m01, m02), (m10, m11, m12), (m20, m21, m22))
def invert_rot_and_trans(rot, trans):
"""Inverts a transform comprised of a rotation and a translation.
Args:
rot: a tf.Tensor of shape [..., 3] representing rotatation angles.
trans: a tf.Tensor of shape [..., 3] representing translation vectors.
Returns:
a tuple (inv_rot, inv_trans), representing rotation angles and translation
vectors, such that applting rot, transm inv_rot, inv_trans, in succession
results in identity.
"""
inv_rot = inverse_euler(rot) # inv_rot = -rot for small angles
inv_rot_mat = matrix_from_angles(inv_rot)
inv_trans = -tf.matmul(inv_rot_mat, tf.expand_dims(trans, -1))
inv_trans = tf.squeeze(inv_trans, -1)
return inv_rot, inv_trans
def inverse_euler(angles):
"""Returns the euler angles that are the inverse of the input.
Args:
angles: a tf.Tensor of shape [..., 3]
Returns:
A tensor of the same shape, representing the inverse rotation.
"""
sin_angles = tf.sin(angles)
cos_angles = tf.cos(angles)
sz, sy, sx = tf.unstack(-sin_angles, axis=-1)
cz, _, cx = tf.unstack(cos_angles, axis=-1)
y = tf.asin((cx * sy * cz) + (sx * sz))
x = -tf.asin((sx * sy * cz) - (cx * sz)) / tf.cos(y)
z = -tf.asin((cx * sy * sz) - (sx * cz)) / tf.cos(y)
return tf.stack([x, y, z], axis=-1)
def combine(rot_mat1, trans_vec1, rot_mat2, trans_vec2):
"""Composes two transformations, each has a rotation and a translation.
Args:
rot_mat1: A tf.tensor of shape [..., 3, 3] representing rotation matrices.
trans_vec1: A tf.tensor of shape [..., 3] representing translation vectors.
rot_mat2: A tf.tensor of shape [..., 3, 3] representing rotation matrices.
trans_vec2: A tf.tensor of shape [..., 3] representing translation vectors.
Returns:
A tuple of 2 tf.Tensors, representing rotation matrices and translation
vectors, of the same shapes as the input, representing the result of
applying rot1, trans1, rot2, trans2, in succession.
"""
# Building a 4D transform matrix from each rotation and translation, and
# multiplying the two, we'd get:
#
# ( R2 t2) . ( R1 t1) = (R2R1 R2t1 + t2)
# (0 0 0 1 ) (0 0 0 1 ) (0 0 0 1 )
#
# Where each R is a 3x3 matrix, each t is a 3-long column vector, and 0 0 0 is
# a row vector of 3 zeros. We see that the total rotation is R2*R1 and the t
# total translation is R2*t1 + t2.
rot_mat1 = tf.cast(rot_mat1, tf.float64)
rot_mat2 = tf.cast(rot_mat2, tf.float64)
trans_vec1 = tf.cast(trans_vec1, tf.float64)
r2r1 = tf.matmul(rot_mat2, rot_mat1)
r2t1 = tf.matmul(rot_mat2, tf.expand_dims(trans_vec1, -1))
r2t1 = tf.squeeze(r2t1, axis=-1)
r2r1 = tf.cast(r2r1, tf.float32)
r2t1 = tf.cast(r2t1, tf.float32)
return r2r1, r2t1 + trans_vec2
| [
"bolianchen@gmail.com"
] | bolianchen@gmail.com |
6e2ca1f18a82a23e4f8e995107279bf2358e4e14 | 04599835d6fd478900b2117689f3168e1ee728bc | /qa/rpc-tests/wallet-dump.py | 5391b421d112f5e233678e2b9b78d2c9061a0ca4 | [
"MIT"
] | permissive | cryptonclub/cryptoncoin | 373371122d5986524831badd595c8d7c9b0c9c6a | 70abd6815e30087b3367dca957be63d8bc7cd2fa | refs/heads/master | 2020-12-14T22:58:26.288976 | 2020-01-19T13:55:47 | 2020-01-19T13:55:47 | 234,900,865 | 2 | 1 | null | null | null | null | UTF-8 | Python | false | false | 5,243 | py | #!/usr/bin/env python3
# Copyright (c) 2016 The Bitcoin Core developers
# Distributed under the MIT software license, see the accompanying
# file COPYING or http://www.opensource.org/licenses/mit-license.php.
"""Test the dumpwallet RPC."""
from test_framework.test_framework import BitcoinTestFramework
from test_framework.util import *
def read_dump(file_name, addrs, hd_master_addr_old):
"""
Read the given dump, count the addrs that match, count change and reserve.
Also check that the old hd_master is inactive
"""
with open(file_name, encoding='utf8') as inputfile:
found_addr = 0
found_addr_chg = 0
found_addr_rsv = 0
hd_master_addr_ret = None
for line in inputfile:
# only read non comment lines
if line[0] != "#" and len(line) > 10:
# split out some data
key_label, comment = line.split("#")
# key = key_label.split(" ")[0]
keytype = key_label.split(" ")[2]
if len(comment) > 1:
addr_keypath = comment.split(" addr=")[1]
addr = addr_keypath.split(" ")[0]
keypath = None
if keytype == "inactivehdmaster=1":
# ensure the old master is still available
assert(hd_master_addr_old == addr)
elif keytype == "hdmaster=1":
# ensure we have generated a new hd master key
assert(hd_master_addr_old != addr)
hd_master_addr_ret = addr
else:
keypath = addr_keypath.rstrip().split("hdkeypath=")[1]
# count key types
for addrObj in addrs:
if addrObj['address'] == addr and addrObj['hdkeypath'] == keypath and keytype == "label=":
found_addr += 1
break
elif keytype == "change=1":
found_addr_chg += 1
break
elif keytype == "reserve=1":
found_addr_rsv += 1
break
return found_addr, found_addr_chg, found_addr_rsv, hd_master_addr_ret
class WalletDumpTest(BitcoinTestFramework):
def __init__(self):
super().__init__()
self.setup_clean_chain = False
self.num_nodes = 1
self.extra_args = [["-keypool=90", "-usehd=1"]]
def setup_chain(self):
# TODO remove this when usehd=1 becomes the default
# use our own cache and -usehd=1 as extra arg as the default cache is run with -usehd=0
initialize_chain(self.options.tmpdir, self.num_nodes, self.options.cachedir + "/hd", ["-usehd=1"], redirect_stderr=True)
set_cache_mocktime()
def setup_network(self, split=False):
# Use 1 minute timeout because the initial getnewaddress RPC can take
# longer than the default 30 seconds due to an expensive
# CWallet::TopUpKeyPool call, and the encryptwallet RPC made later in
# the test often takes even longer.
self.nodes = start_nodes(self.num_nodes, self.options.tmpdir, self.extra_args, timewait=60, redirect_stderr=True)
def run_test (self):
tmpdir = self.options.tmpdir
# generate 20 addresses to compare against the dump
test_addr_count = 20
addrs = []
for i in range(0,test_addr_count):
addr = self.nodes[0].getnewaddress()
vaddr= self.nodes[0].validateaddress(addr) #required to get hd keypath
addrs.append(vaddr)
# Should be a no-op:
self.nodes[0].keypoolrefill()
# dump unencrypted wallet
self.nodes[0].dumpwallet(tmpdir + "/node0/wallet.unencrypted.dump")
found_addr, found_addr_chg, found_addr_rsv, hd_master_addr_unenc = \
read_dump(tmpdir + "/node0/wallet.unencrypted.dump", addrs, None)
assert_equal(found_addr, test_addr_count) # all keys must be in the dump
assert_equal(found_addr_chg, 50) # 50 blocks where mined
assert_equal(found_addr_rsv, 180) # keypool size (external+internal)
#encrypt wallet, restart, unlock and dump
self.nodes[0].encryptwallet('test')
bitcoind_processes[0].wait()
self.nodes[0] = start_node(0, self.options.tmpdir, self.extra_args[0])
self.nodes[0].walletpassphrase('test', 10)
# Should be a no-op:
self.nodes[0].keypoolrefill()
self.nodes[0].dumpwallet(tmpdir + "/node0/wallet.encrypted.dump")
found_addr, found_addr_chg, found_addr_rsv, hd_master_addr_enc = \
read_dump(tmpdir + "/node0/wallet.encrypted.dump", addrs, hd_master_addr_unenc)
assert_equal(found_addr, test_addr_count)
# TODO clarify if we want the behavior that is tested below in Cryptoncoin (only when HD seed was generated and not user-provided)
# assert_equal(found_addr_chg, 180 + 50) # old reserve keys are marked as change now
assert_equal(found_addr_rsv, 180) # keypool size
if __name__ == '__main__':
WalletDumpTest().main ()
| [
"support@cryptonclub.com"
] | support@cryptonclub.com |
b66a370d3cf4f34e4b12732477ebe0307503fb8e | 2766d1c9d257a68306d03cca5ac2b05ebea92f50 | /CatPic/views.py | ed197d93be594096be3a7e53d21a69aee730cad2 | [] | no_license | DorkMage/CatPic | c11e59079f11838572ebcc6824711fb3889abdae | 69d91b05a1d42b96ad6dab095df5915f0db69481 | refs/heads/main | 2023-01-29T04:19:10.641432 | 2020-12-13T17:08:02 | 2020-12-13T17:08:02 | 317,402,214 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,104 | py | from django.shortcuts import render
from django.http import HttpResponse
from django.template import loader
from django.utils.safestring import mark_safe
from .models import Pic
from .models import Filter
from random import randint
import json
def index(request):
clowder = dict()
catpics = Pic.objects.all()
for i, pic in enumerate(catpics):
url = pic.pic_url.url
cats = pic.pic_cats
text = pic.pic_name
clowder[i] = ([url, cats, text])
filters_qs = Filter.objects.all()
filters = dict()
for i, fil in enumerate(filters_qs):
fil_text = fil.fil_text
fil_cat = fil.fil_cat
filters[i] = ([fil_text, fil_cat])
template = loader.get_template('catpic/index.html')
try:
catpic = catpics[randint(0, len(catpics) - 1)]
except:
catpic = None
#catpic = None
context = {
'filters_qs': filters_qs,
'filters': mark_safe(json.dumps(filters)),
'catpic': catpic,
'clowder': mark_safe(json.dumps(clowder))
}
return HttpResponse(template.render(context, request))
| [
"kittywright@gmail.com"
] | kittywright@gmail.com |
34c69a2e6e5163f82c16f2066cc150e2915edd2e | 13a416a2694d1f6aa1a68cd47610236bf61cafbc | /CodePractice/Turtle/circleturtle.py | a465b8fe30527faaecef0725052e7c92e49ae8e1 | [] | no_license | Highjune/Python | c637f7d0f9e5d1ac9d6ad87b4e54833b8ff4ae11 | 1be43816d22f5f3b8679cf0cd3939e9d9f54497a | refs/heads/master | 2022-11-24T01:20:54.470172 | 2020-07-27T18:01:47 | 2020-07-27T18:01:47 | 263,271,337 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 170 | py | import turtle as t
t.shape("turtle")
num = int(input('num : '))
go = int((num-1)/2)
for i in range(1,num):
for j in range(1,i):
t.forward(1)
t.right(90)
| [
"highjune37@gmail.com"
] | highjune37@gmail.com |
623020b87f72239243d8c9c47e74679b83117736 | 32d96d224ce074758d0d740e77e97bb30982d4e3 | /test.py | 4c56e3bd72ae85110aa2532e377310cb5a11016c | [] | no_license | Moinahmed7777/Pac_man_Agent | a79c5b6e577f4c2fb61f2756a8d0912eaff0dbc2 | 07a92a16ec9930acfa6fd5f000efeab344945ae0 | refs/heads/master | 2022-11-27T20:52:38.780714 | 2020-08-13T20:34:51 | 2020-08-13T20:34:51 | 287,374,555 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 689 | py | # -*- coding: utf-8 -*-
"""
Created on Wed Jul 15 14:30:29 2020
@author: Necro
"""
X=[(4, 3), 'West', 1, [(5, 3), 'South', 1, [(5, 4), 'South', 1, ((5, 5), None, 0)]]]
#X=[ ((4, 3), 'West', 1), [((5, 3), 'South', 1), [((5, 4), 'South', 1), [((5, 5), None, 0), None]] ] ]
#[(((5, 3), 'South', 1), (((5, 4), 'South', 1), (((5, 5), None, 0), None)))]
Y=[]
#X=list(X)
#popitemlist=
while True:
#X=list(X)
popitem=X.pop()
print(popitem)
print('after pop:',X,len(X))
Y.append(X[1])
#X=list(popitem)
print(popitem)
#X=list(popitem)
if len(popitem)==0:
break
X=list(popitem)
print(Y)
#print(popitem) | [
"noreply@github.com"
] | noreply@github.com |
5095b5341c9fb69f1fd83369f1b8a9b3d1c26d60 | 05f9f0434623bcdc8983eec7510a0ebc80a8e86d | /Classes/DiPrime_Class.py | e25ca63ff3d614f32ff8e22d8c80a004b9f1fa0c | [] | no_license | shoryaconsul/DiPriMeFlip_Forest | e55af955906ca66d059eae732990c5c60b3d0b32 | 9f51f0e8e768b07b25b46da2e0bea235e3a03ca4 | refs/heads/main | 2023-02-27T08:37:09.677725 | 2021-02-05T03:16:37 | 2021-02-05T03:16:37 | 330,514,141 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 16,242 | py | import numpy as np
import numpy.random as rn
import random
from copy import deepcopy
# This file implements the proposed algorithm for classifcation (stores fractions).
# The variables mean refer to fractions in this code.
# %%################################################################
# Querying the value of a scalar attribute
# x: True atrribute value
# s: Sensitivity of attribute
# e: Privacy budget
def query(x, s, e):
if s/e < 0:
print('s = ', s, ' e = ', e)
return rn.laplace(x, s/e)
# Query median of continuous attribute
# Xj: Values of feature for each data point (M,)
# Xrange: Min and max value of Xj (2,)
# num: Number of points to choose from (Deprecated)
# eps: Privacy budget. None indicates no privatization
# Returns: Value of attribute to split at
def query_cont_med(Xj, Xrange, eps=None):
M = np.shape(Xj)[0]
Xj_sort = np.sort(Xj)
# print('M: ',M)
if M <= 1:
return random.uniform(Xrange[0], Xrange[1])
elif eps is None: # Return true median
return np.median(Xj)
else: # Private median
bin_size = np.concatenate(([Xj_sort[0]-Xrange[0]],
np.diff(Xj_sort),
[Xrange[1]-Xj_sort[-1]]))
score_lr = np.abs(np.arange(-M, M+1, 2)) # Scoring function is N_L-N_R
# print('SCORE: ', score)
prob = np.exp(np.log(bin_size)-0.5*eps*score_lr) + 1e-12 # Probability disribution for median selection
prob = prob/sum(prob) # Normalizing distribution
ind_med = rn.choice(M+1, p=prob)
if ind_med == 0:
x_pick = random.uniform(Xrange[0], Xj_sort[0])
elif ind_med == M:
x_pick = random.uniform(Xj_sort[-1], Xrange[1])
else:
x_pick = random.uniform(Xj_sort[ind_med-1], Xj_sort[ind_med])
# h,x = np.histogram(Xj,bins=num+1,range=(Xrange[0],Xrange[1]))
# N_left = np.cumsum(h)[:-1]
# score = -np.abs(2*N_left - M)
# prob = np.exp(0.5*eps*score)+1e-6 # Probability disribution for median selection
# prob = prob/sum(prob) # Normalizing distribution
# x_pick = rn.choice(x[1:-1],p=prob)
return x_pick
# Query median of categorical attribute
# Xj: Values of feature for each data point (M,)
# Xdict: Possible values of Xj (list of variable length)
# eps: Privacy budget. None indicates no privatization
# Returns: List of attributes split for both children
def query_cat_med(Xj, Xdict, eps=None):
M = np.shape(Xj)[0]
Xnum = len(Xdict)
unique, counts = np.unique(Xj, return_counts=True)
num_val = np.zeros(Xnum) # Number of data points with with each possible value
i = 0
for val in Xdict:
if val in unique:
num_val[i] = counts[unique == val][0]
i = i+1
# Function to convert decimal to logical lists
blist = lambda i, n: [False]*(n-int(np.ceil(np.log2(i+1)))-int(i == 0)
) + [bool(int(j)) for j in bin(i)[2:]]
N_child = np.zeros(2**(Xnum-1)-1) # Number of points in child node
for i in range(1, 2**(Xnum-1)): # Look at all possible split
N_child[i-1] = np.sum(num_val[blist(i, Xnum)])
if eps is None: # Return true median
i_min = np.argmin(np.abs(N_child-M/2))
split_log = blist(i_min+1, Xnum) # Membership to left or right chiled
val_min = list(np.array(Xdict)[split_log])
val_comp = list(set(Xdict)-set(val_min))
return val_min, val_comp
else: # Private median
score = -np.abs(2*N_child - M)
prob = np.exp(0.5*eps*score)+1e-6 # Probability disribution for median selection
prob = prob/sum(prob) # Normalizing distribution
i_pick = rn.choice(2**(Xnum-1)-1, p=prob)
split_log = blist(i_pick+1, Xnum) # Membership to left or right chiled
val_pick = list(np.array(Xdict)[split_log])
val_comp = list(set(Xdict)-set(val_pick))
return val_pick, val_comp
# Function to find best split for selected attributes
# X: Data (M,N)
# y: Target (M,)
# By: Max absolute value of target (1,)
# A: Dictionary of feature ranges (for cont)/values (for categorical)
# cat_idx: List of indices for categorical features
# K: Number of features to consider for split
# num_cand: Number of candidates for continuous split (Deprecated)
# eps: Privacy budget
# Returns: Splitting feature index, splitting value/categories
def split_count(X, y, A, By, cat_idx, K, eps=None):
M, N = np.shape(X)
K1 = min(K, len(list(A.keys()))) # In case there are less than K attributes
idx_cand = rn.permutation(list(A.keys()))[:K1] # Select k feature indices
# Finding median splits
val_cand = dict()
if eps is None:
eps_fn = None
else:
eps_fn = eps/2
for idx in idx_cand:
if idx in cat_idx:
val_cand[idx] = query_cat_med(X[:, idx], A[idx], eps_fn)
else:
val_cand[idx] = query_cont_med(X[:, idx], A[idx], eps_fn)
# Finding indices in children
sse_idx = np.zeros(K1)
for idx in idx_cand:
if idx in cat_idx:
ind_upp = np.where(X[:, idx] == np.expand_dims(
np.array(val_cand[idx][0]), axis=1))[1]
ind_low = np.where(X[:, idx] == np.expand_dims(
np.array(val_cand[idx][0]), axis=1))[1]
else:
ind_upp = np.where(X[:, idx] >= val_cand[idx])[0]
ind_low = np.where(X[:, idx] < val_cand[idx])[0]
y_upp = y[ind_upp]
y_low = y[ind_low]
pos = np.where(idx_cand == idx)[0][0]
if len(y_upp) != 0 and len(y_low) != 0: # Checking that children are not empty
sse_idx[pos] = (len(y_upp)*np.var(y_upp) + len(y_low)*np.var(y_low))/len(y)
elif len(y_upp) == 0 and len(y_low) == 0:
sse_idx[pos] = 40*By**2
else:
sse_idx[pos] = np.var(y)
if eps is None:
idx_split = idx_cand[np.argmin(sse_idx)]
else:
# Exponential mechanism to pick split
if len(y) != 0:
score_split = np.exp(-0.5*sse_idx*eps_fn/(4*By**2/len(y)))
else: # Empty node, so all splits are equivalent
score_split = np.ones(K1)
# print('# SPLIT: ',len(y_upp),len(y_low))
if np.any(np.isnan(score_split/np.sum(score_split))):
score_split = np.ones(K1)
idx_split = rn.choice(idx_cand, p=score_split/np.sum(score_split))
return idx_split, val_cand[idx_split]
# %% Base classes for Extremely Random Trees with splits along median
class DiPrimeCTree():
def __init__(self, depth=0, max_depth=np.inf, max_features=None,
parent=None):
self.mean = np.nan
self.count = None # For book-keeping
self.split_ind = None # Index of feature for split
self.split_val = None # Feature value at split
self.max_features = max_features # Features to consider per split
self.left = None
self.right = None
self.parent = parent
if depth > max_depth:
raise ValueError('Depth larger than max depth')
self.depth = depth # Depth = 0 as tree is empty
self.max_depth = max_depth
# %% Fitting random tree to data given target values - only leaf nodes noised
# Left is larger than split, right is smaller than split
# A: Dictionary of feature ranges (for cont)/values (for categorical)
# features. A only contains values for features that can be split on
# cat_idx: List of indices for categorical features
# tbound: Class values. If single value given then classes assumed to be [-tbound, tbound]
# eps: Privacy budget for tree
# b_med: Fraction of privacy budget for determining median split
def fit(self, X, y, A, cat_idx, tbound, eps=None, b_med=0.5):
# X: considered to have M samples and N features (M x N)
# y: value to be predicted (M,)
if eps is not None and b_med is None:
raise ValueError('Budget split for median required')
if tbound is not None: # B_L, B_U are lower and upper bounds on y
if len(tbound) == 2:
# mean_def = np.mean(tbound) # Default value of mean for empty node
B_L = tbound[0]
B_U = tbound[1]
elif isinstance(tbound, int) or isinstance(tbound, float):
# mean_def = 0 # Default value of mean for empty node
B_L = -np.abs(tbound)
B_U = np.abs(tbound)
else:
raise ValueError("Invalid value passed to tbound.")
mean_def = random.uniform(B_L, B_U)
M, N = np.shape(X)
self.count = int(M)
if eps is not None:
eps_med = eps*b_med/self.max_depth # Privacy budget for split
eps_level = eps*(1-b_med) # Privacy budget for mean
else:
eps_med = None
if self.depth == self.max_depth or not A: # Reached leaf node or no more attributes to split on
# Private case
if eps is not None: # Compute and noise sufficient statistics
count_clip = len(y)
# print('Depth: ',self.depth,' True count: ',len(y),' Stored count: ',self.count)
if np.isnan(np.mean(y)): # Empty node
self.mean = mean_def
else:
count_0 = max(0, query(x=np.sum(y == B_L), s=1, e=eps_level)) # Query class counts
count_1 = max(0, query(x=np.sum(y == B_U), s=1, e=eps_level))
try:
self.mean = count_1/(count_0+count_1)
except:
if count_1 > count_0:
self.mean = 1
else:
self.mean = 0.5
# Non-private case
else: # Compute sufficient statistics
if np.isnan(np.mean(y)): # Empty node
self.mean = mean_def
else:
self.mean = np.sum(y == B_U)/len(y)
return
# Finding split
# Number of features to consider at each split
if self.max_features is None:
K = int(N)
else:
K = int(self.max_features)
By = np.amax(np.abs(tbound))
# num_cand = 30 # Number of candidates
self.split_ind, feat_val = split_count(X, y, A, By, cat_idx, K, eps_med)
if self.split_ind in cat_idx: # If categorical
feat_left, feat_right = feat_val
self.split_val = feat_left.copy()
else: # If continuous
self.split_val = 1*feat_val
# Splitting data
if self.split_ind in cat_idx:
ind_upp = np.where(X[:, self.split_ind] == np.expand_dims(
np.array(feat_left), axis=1))[1]
ind_low = np.where(X[:, self.split_ind] == np.expand_dims(
np.array(feat_right), axis=1))[1]
A_upp = deepcopy(A)
if len(feat_left) == 1:
A_upp.pop(self.split_ind, None) # Remove from allowed splits
else:
A_upp[self.split_ind] = feat_left.copy() # Update feature values
A_low = deepcopy(A)
if len(feat_right) == 1:
A_low.pop(self.split_ind, None) # Remove from allowed splits
else:
A_low[self.split_ind] = feat_right.copy() # Update feature values
else:
ind_upp = np.where(X[:, self.split_ind] >= self.split_val)[0]
ind_low = np.where(X[:, self.split_ind] < self.split_val)[0]
A_upp = deepcopy(A)
if A_upp[self.split_ind][1] <= self.split_val: # If split no longer possible
# print('POP: ',A_upp[self.split_ind][1],self.split_val)
A_upp.pop(self.split_ind, None)
else:
A_upp[self.split_ind][0] = self.split_val # Updating lower bound
A_low = deepcopy(A)
if A_low[self.split_ind][0] >= self.split_val: # If split no longer possible
# print('POP: ',A_low[self.split_ind][0],self.split_val)
A_low.pop(self.split_ind, None)
else:
A_low[self.split_ind][1] = self.split_val # Updating upper bound
X_upp = X[ind_upp, :]
y_upp = y[ind_upp]
X_low = X[ind_low, :]
y_low = y[ind_low]
# print('LEFT: ',len(y_upp),'RIGHT: ',len(y_low))
# Recursively splitting
tree_upp = DiPrimeCTree(depth=self.depth+1, max_depth=self.max_depth,
max_features=self.max_features, parent=self)
tree_upp.fit(X_upp, y_upp, A_upp, cat_idx, tbound, eps, b_med)
self.left = tree_upp
tree_low = DiPrimeCTree(depth=self.depth+1, max_depth=self.max_depth,
max_features=self.max_features, parent=self)
tree_low.fit(X_low, y_low, A_low, cat_idx, tbound, eps, b_med)
self.right = tree_low
# %% Predicting target values based on attributes
# X: M samples and N features (M x N)
# cat_idx: List of indices for categorical features
# Returns predicted y
def predict(self, X, cat_idx):
if self.depth == 0 and self.split_ind is None:
raise ValueError('Tree not fit to data')
else:
M, N = np.shape(X)
y = np.zeros(M) # Predicted values
for i in range(M):
y[i] = self.predict_y(X[i, :], cat_idx)
return y
# x: Sample of N features (N,)
def predict_y(self, x, cat_idx):
ind = self.split_ind
if self.split_ind is None: # Leaf node
return self.mean
# If true, go right
if ind in cat_idx:
dirn = (x[ind] not in self.split_val)
else:
dirn = x[ind] < self.split_val
if not dirn: # Go to right child
return self.left.predict_y(x, cat_idx)
else: # Go to left child
return self.right.predict_y(x, cat_idx)
# %############################################################################
# %% Extremely Random Forest
# n_trees: Number of random trees
# partition: If true, grow tree on disjoint subsets (rows) of data
class DiPrimeCForest():
def __init__(self, n_trees=10, max_depth=np.inf, max_features=None,
partition=True):
self.num_trees = n_trees # Numm=ber of trees
self.trees = [] # ExtRandTrees
self.partition = partition
for i in range(n_trees): # Initialize all trees
self.trees.append(DiPrimeCTree(
depth=0, max_depth=max_depth, max_features=max_features,
parent=None))
# %% Fitting random tree to data given target values
# Left is larger than split, right is smaller than split
def fit(self, X, y, A, cat_idx, tbound, eps=None, b_med=0.5):
if self.partition: # Growing trees on disjoint subsets of rows
M, N = np.shape(X)
ind_part = np.array_split(rn.permutation(M), self.num_trees) # Partition
for i, tree in enumerate(self.trees): # Fit tree to partition
if eps is None:
tree.fit(X[ind_part[i], :], y[ind_part[i]], A, cat_idx, tbound)
else:
tree.fit(X[ind_part[i], :], y[ind_part[i]], A, cat_idx, tbound, eps, b_med)
else: # Growing trees on all the data
for (t, tree) in enumerate(self.trees):
if eps is None:
tree.fit(X, y, A, cat_idx, tbound)
else:
tree.fit(X, y, A, cat_idx, tbound, eps/self.num_trees, b_med)
# %% Predicting target values based on attributes
# X: M samples and N features (M x N)
# cat_idx: List of indices for categorical features
# Returns predicted y
def predict(self, X, cat_idx):
M, N = np.shape(X)
pred = np.zeros((M, self.num_trees))
for i in range(self.num_trees): # Prediction from each tree
pred[:, i] = self.trees[i].predict(X, cat_idx)
return np.mean(pred, axis=1)
| [
"shoryaconsul@gmail.com"
] | shoryaconsul@gmail.com |
f546e9e52b380e7d078d7a83b9522f48799bf1fb | 6e172edee44d5985d19327cf61865d861395d595 | /2020/11/y2020_d11_p01.py | 14c61f5a7ba331bb0fdd890b3ea3476e58d1cce5 | [] | no_license | rHermes/adventofcode | bbac479ec1c84c55484effa2cd94889d621b3718 | 4cbe7a952678c5f09438702562b7f6f673a1cf83 | refs/heads/master | 2023-01-14T07:05:35.769426 | 2023-01-01T09:53:11 | 2023-01-01T10:13:29 | 225,170,580 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,699 | py | import fileinput
import itertools as it
# Let's build a jump table
def jumptbl(M, ROWS, COLS, x, y):
arounds = []
for dy, dx in [(-1,-1), (-1, 0), (-1, 1), (0,-1), (0, 1), (1,-1), (1,0), (1,1)]:
zx = x + dx
zy = y + dy
idx = zy*COLS + zx
if 0 <= zx < COLS and 0 <= zy < ROWS and M[idx] != None:
arounds.append(idx)
return arounds
# Creates a compressed version of a jump array
def compress(M, ROWS, COLS):
comp = []
# translate from full to sparse
trans = {}
# Build spare index
for y in range(COLS):
for x in range(ROWS):
idx = y*COLS + x
if M[idx] == None:
continue
trans[idx] = len(comp)
comp.append(M[idx])
# Build jump table
jmp = {}
for oidx, nidx in trans.items():
y = oidx // COLS
x = oidx % COLS
# Second pass, now to create jump table
adj = frozenset(trans[k] for k in jumptbl(M, ROWS, COLS, x, y))
if len(adj) < 4:
comp[nidx] = True
else:
jmp[nidx] = adj
return (comp, jmp)
# Step from M to N uing jmp
def step(M, N, jmp):
changed = False
for idx, adj in jmp.items():
t = sum(M[x] for x in adj)
N[idx] = (M[idx] and t < 4) or ((not M[idx]) and t == 0)
changed |= N[idx] != M[idx]
return changed
lines = [line.rstrip() for line in fileinput.input()]
ROWS = len(lines)
COLS = len(lines[0])
# None takes the spot of Empty
M = [{'L': False, '#': True, '.': None}[x] for x in it.chain(*lines)]
comp, jmp = compress(M, ROWS, COLS)
A = comp
B = A.copy()
while step(A, B, jmp):
B, A = A, B
print(sum(A))
| [
"teodor@spaeren.no"
] | teodor@spaeren.no |
3924fa132bfe328ae6a8faf35578e47d92f3d63e | 2e5cac4d666bfd926112111060059feda98f5743 | /online.py | d96915e0ad8a8f411dd31069315ef10ac790c2cc | [] | no_license | swifton/Template | 014b471cce502b3f997fc5e6f8b269a0fe40288f | 3eea5e31335163dfda0976e3b2ba9ea2bb8e51df | refs/heads/master | 2021-01-01T05:30:58.946791 | 2018-04-11T06:14:06 | 2018-04-11T06:14:06 | 40,803,098 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,444 | py | import tornado.websocket
import os.path
players = []
update_rate = 60
class Application(tornado.web.Application):
def __init__(self):
handlers = [(r"/", MainHandler),
(r"/socket", SocketHandler),
(r"/lib/(.*)", tornado.web.StaticFileHandler, {'path': "../template/js"})]
settings = dict(
cookie_secret="IguyZ4KsTOOL0igwXWHJPnTQaZgULkmNom4uO6xXxrE",
template_path=os.path.join(os.path.dirname(__file__), "templates"),
static_path=os.path.join(os.path.dirname(__file__), "static"),
xsrf_cookies=True,
debug=True # TODO: make it impossible to forget to change this in production
)
super(Application, self).__init__(handlers, **settings)
class MainHandler(tornado.web.RequestHandler):
def get(self):
self.render("index.html")
class SocketHandler(tornado.websocket.WebSocketHandler):
def open(self):
players.append(self)
# handle new player
def on_close(self):
player_index = game.players.index(self)
players.remove(self)
# handle unplugged player
def on_message(self, message):
player_index = players.index(self)
# handle the message
if __name__ == "__main__":
app = Application()
app.listen(80)
loop = tornado.ioloop.IOLoop.current()
tornado.ioloop.PeriodicCallback(game.game_loop, 1000 / update_rate).start()
loop.start()
| [
"anton.swifton@gmail.com"
] | anton.swifton@gmail.com |
94d6e7a4e245070004aceed465e415b48a91fdd8 | 5f7bc18fb99b00e8d47865621c42d19de406ae4f | /page/page_shop_transfer.py | 60030d56af440882b4964de52c648dd2d7e2ee15 | [] | no_license | ZYG-Test/WebTest | 0958652095c59ac73978a8b3efc5ac153e6e050c | e1e6e180561f67e5368a858dff27c3975cb5696e | refs/heads/master | 2022-12-20T08:21:54.969577 | 2020-09-09T09:38:36 | 2020-09-09T09:38:36 | 287,206,592 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,952 | py | import time
import page
from base.base import Base
import faker
f = faker.Faker()
class PageTransfer(Base):
# 点击跨境汇款
def page_click_money_transfer(self):
self.base_click(page.shop_money_transfer)
#收款人
def page_receive(self,name,lastname,account,amount,number):
# 输入收款人姓名
self.base_input(page.shop_receive_name, name)
# 输入姓氏
self.base_input(page.shop_receive_lastname, lastname)
# 输入收款人账户
self.base_input(page.shop_receive_account, account)
# 输入收款金额
self.base_input(page.shop_receive_amount, amount)
# 输入收款人联系电话
self.base_input(page.shop_receive_number, number)
# 选择汇款用途
self.base_click(page.shop_receive_use)
time.sleep(1)
self.base_click(page.shop_receive_use_select)
# 汇款人
def page_certificate(self,country="CHN"):
#输入姓名
self.base_input(page.shop_remitter_name,f.last_name())
#输入姓氏
self.base_input(page.shop_remitter_lastname,f.last_name())
#输入生日
self.base_input(page.shop_remitter_birthday,f.date())
#输入国籍
self.base_input(page.shop_remitter_country,country)
#证件类型选择
self.base_click(page.shop_remitter_certificate)
time.sleep(1)
self.base_click(page.shop_certificate_select)
#证件所属国籍
self.base_input(page.shop_certificate_country, country)
#输入证件号
self.base_input(page.shop_certificate_number, f.credit_card_number())
#输入地址
self.base_input(page.shop_certificate_address,f.street_address())
#输入市区
self.base_input(page.shop_certificate_area, f.city_suffix())
# 勾选认证点击下一步
def page_check_next(self):
self.base_click(page.shop_certification_check)
self.base_click(page.shop_nextBtn)
self.base_click(page.shop_nextBtn_two)
# 输入支付密码
def page_input_pwd(self,pwd):
self.base_input(page.shop_input_pay,pwd)
# 点击确认汇款
def page_click_determine(self):
self.base_click(page.shop_determine)
# 获取跨境汇款成功返回信息
def page_get_remittance_info(self):
return self.base_get_text(page.shop_remittance_info)
'''跨境汇款组合业务'''
def cross_remittance(self,name,lastname,account,amount,number,pwd):
# 点击跨境汇款
self.page_click_money_transfer()
# 输入收款人信息
self.page_receive(name,lastname,account,amount,number)
# 输入汇款人信息
self.page_certificate()
# 勾选已认证点击下一步
self.page_check_next()
# 输入支付密码
self.page_input_pwd(pwd)
# 确认缴费
self.page_click_determine() | [
"18142651995@163.com"
] | 18142651995@163.com |
d8ff5b2120acc3d38e45ebe0b0362c23ee03ccad | 6dfdc65b1d633847763fc5012aeb4ca1438fe6ae | /Python and Advanced Analytics/Database/Mongo/product.py | a612bd231d21867bec183f6463c9296e891fa1d1 | [] | no_license | SaurabhRuikar/CdacRepo | 71537432885c83995ea6f2248d2a20ef08b4c9d4 | 12c2a4be8141443e4a567104506a81f115baeab1 | refs/heads/master | 2022-05-26T02:22:02.748779 | 2021-03-03T15:33:13 | 2021-03-03T15:33:13 | 223,311,490 | 0 | 0 | null | 2022-04-12T21:57:09 | 2019-11-22T02:57:34 | HTML | UTF-8 | Python | false | false | 2,092 | py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Dec 7 16:50:58 2019
@author: student
"""
from pymongo import MongoClient
import sys
client=MongoClient('localhost:27017')
print('connection done')
db=client.Product
def insert():
try:
pid=int(input('Enter Product id : '))
pName=input('Enter Product Name : ')
pquantity=int(input('Enter Product Quantity : '))
price=int(input('Enter Price : '))
db.product.insert_one(
{
"id":pid,
"name":pName,
"pquantity":pquantity,
"price":price
}
)
print('Inserted successfully')
except Exception as e:
print(str(e))
def read():
try:
r=db.product.find()
for i in r:
print(i)
except Exception as e:
print(str(e))
def delete():
try:
pid=int(input("\n Enter Product id to Delete : "))
db.product.delete_many({'id':pid})
print('\n Deletion Successful \n')
except Exception as e:
print(str(e))
def update():
try:
pid=int(input('Enter Product id : '))
pName=input('Enter Product Name : ')
pquantity=int(input('Enter Product Quantity : '))
price=int(input('Enter Price : '))
db.product.update_one(
{'id':pid},
{
"$set":{
"name":pName,
'pquantity':pquantity,
"price":price
}
}
)
print('\n Updated successfully')
except Exception as e:
print(str(e))
choice=0
while(1):
choice=input('\n Select \n 1. Insert \n 2. Update \n 3. Read \n 4. Delete \n 5. Exit \n')
if choice=='1':
insert()
elif choice=='2':
update()
elif choice=='3':
read()
elif choice=='4':
delete()
else:
sys.exit(0) | [
"saurabh2830076@gmail.com"
] | saurabh2830076@gmail.com |
9164e169998f84df100794fc7306eac40e464065 | cd685857125d6649bb226e19bc6de48905457bd1 | /C04Recursion/Creativity/c18.py | c96b0db58152e16942f286b1feb30eff9aa06b8b | [] | no_license | nickramos94/DataStructuresAndAlgorithmsInPython | b37dd22018628125001f2853a8e05812e021db3f | b5d5d322e720d3e2411500257f7befb07b6ee4be | refs/heads/master | 2022-12-17T17:33:14.968439 | 2020-01-14T10:08:59 | 2020-01-14T10:08:59 | 292,007,780 | 0 | 0 | null | 2020-09-01T13:38:24 | 2020-09-01T13:38:23 | null | UTF-8 | Python | false | false | 385 | py | def conson_more(s):
VOWELS = {*"aeiouAEIOU"}
def consonants_minus_vowels(index):
if index == -1:
return 0
count = -1 if s[index] in VOWELS else 1
return count + consonants_minus_vowels(index - 1)
return consonants_minus_vowels(len(s) - 1) > 0
if __name__ == "__main__":
print(conson_more("abcde"))
print(conson_more("abce"))
| [
"axel.brisse@gmail.com"
] | axel.brisse@gmail.com |
9c9dd3628ce7fc7213ab36ddaa75d362eea81405 | c6bc84336e5ccb62494a91a7d79fb8998fb6cf66 | /lottery_ndriver.py | dd3751d0bbb8929c70d94ceac0cea7fbfa814171 | [] | no_license | wonjr/python_lottery | a702d0b287c89b9c9844fddb9b519f0f0c952b65 | bf2f689d6bb52d77ca98da37d808eb5ec9a50761 | refs/heads/main | 2023-04-05T13:08:25.629941 | 2021-04-13T10:34:34 | 2021-04-13T10:34:34 | 357,500,721 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 7,576 | py | import lottery
HEAD_HTML = '''
<head>
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1">
<!-- The above 3 meta tags *must* come first in the head; any other head content must come *after* these tags -->
<title>로또</title>
<style type="text/css" media="screen">
body {
background-color: #FAFAFA;
padding-top: 80px;
}
.navbar {
background-color: #29B895;
border-radius: 0 !important;
}
.navbar-brand {
color: white;
}
.navbar-brand:hover {
color: white;
}
.jumbotron {
border-radius: 10px;
padding: 20px;
}
.jumbotron h1 {
margin: 0 0 20px 0;
font-size: 24px !important;
}
.cash-div {
font-size: 24px;
}
.cash-div b {
margin-right: 10px;
}
.red {
background-color: #D84134;
color: white;
}
.green {
background-color: #6AC83B;
}
.yellow {
background-color: #FBC34B;
}
.blue {
background-color: #528FD2;
color: white;
}
.black {
background-color: #414141;
}
.plus {
font-size: 30px;
margin: 0 10px 0 10px;
}
.ball {
color: white;
font-size: 20px;
border-radius: 50%;
display: inline-block;
width: 60px;
height: 60px;
text-align: center;
line-height: 60px;
}
.attempt-numbers {
font-size: 18px;
outline: 1px solid black;
display: inline-block;
margin-bottom: 20px;
margin-right: 20px;
height: 30px;
}
.attempt-number {
display: inline-block;
width: 30px;
text-align: center;
line-height: 30px;
}
.attempt-prize {
display: inline-block;
font-size: 18px;
}
</style>
<!-- Latest compiled and minified CSS -->
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" crossorigin="anonymous">
<!-- Optional theme -->
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap-theme.min.css" integrity="sha384-fLW2N01lMqjakBkx3l/M9EahuwpSfeNvV63J5ezn3uZzapT0u7EYsXMjQV+0En5r" crossorigin="anonymous">
<!-- Latest compiled and minified JavaScript -->
<script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/js/bootstrap.min.js" integrity="sha384-0mSbJDEHialfmuBBQP6A4Qrprq5OVfW37PRR3j5ELqxss1yVqOtnepnHVP9aJ7xS" crossorigin="anonymous"></script>
</head>
'''
MAIN_HTML = '''
<!DOCTYPE html>
<html>
<body>
<nav class="navbar navbar-fixed-top">
<div class="container-fluid">
<!-- Brand and toggle get grouped for better mobile display -->
<div class="navbar-header">
<a class="navbar-brand" href="#">LOTTO</a>
</div>
</div><!-- /.container-fluid -->
</nav>
<div class="col-xs-10 col-xs-offset-1">
<div class="jumbotron">
<h1><b>당첨 번호</b></h1>
{numbers}
</div>
</div>
<div class="col-xs-5 col-xs-offset-1">
<div class="jumbotron cash-div">
<b>당첨 금액</b>
<span>₩{total_prize}</span>
</div>
</div>
<div class="col-xs-5">
<div class="jumbotron cash-div">
<b>쓴 금액</b>
<span>₩{total_cost}</span>
</div>
</div>
<div class="col-xs-10 col-xs-offset-1">
<div class="jumbotron">
<h1><b>내 번호</b></h1>
{attempts}
</div>
</div>
<!-- jQuery (necessary for Bootstrap's JavaScript plugins) -->
<script src="https://ajax.googleapis.com/ajax/libs/jquery/1.11.3/jquery.min.js"></script>
<!-- Include all compiled plugins (below), or include individual files as needed -->
</body>
</html>
'''
def get_color(number):
if number <= 10:
return "yellow"
elif number <= 20:
return "blue"
elif number <= 30:
return "red"
elif number <= 40:
return "black"
else:
return "green"
def generate_numbers_html(numbers):
# template for winning ball
ball_html = '''
<div class="ball {color}">
{number}
</div>
'''
html = ""
for number in numbers[:6]:
html += ball_html.format(number = number, color = get_color(number))
html += '<span class="plus">+</span>'
html += ball_html.format(number = numbers[-1], color = get_color(numbers[-1]))
return html
def generate_attempt_html(attempt, winning_numbers):
number_html = '''
<span class="attempt-number">{number}</span>
'''
red_number_html = '''
<span class="attempt-number red">{number}</span>
'''
blue_number_html = '''
<span class="attempt-number blue">{number}</span>
'''
attempt_html = ""
for num in attempt[0]:
if num in winning_numbers[:6]:
attempt_html += red_number_html.format(number=num)
elif num in winning_numbers[6:]:
attempt_html += blue_number_html.format(number=num)
else:
attempt_html += number_html.format(number=num)
html = '''
<div class="attempt">
<div class="attempt-numbers">
{attempt}
</div>
<div class="attempt-prize">
₩{prize}
</div>
</div>
'''.format(attempt=attempt_html, prize=attempt[1])
return html
def main(winning_numbers, tries, total_prize, total_cost):
out_file = open('lottery.html', 'w', encoding='utf-8')
winning_numbers_html = generate_numbers_html(winning_numbers)
attempts_html = ""
for attempt in tries:
attempts_html += generate_attempt_html(attempt, winning_numbers)
out_file.write(HEAD_HTML + MAIN_HTML.format(
numbers=winning_numbers_html,
attempts=attempts_html,
total_prize=total_prize,
total_cost=total_cost)
)
out_file.close()
NUM_TRIES = int(input("돈을 넣어주세요 한 개에 천원\n돈: ")) // 1000
WINNING_NUMBERS = lottery.draw_winning_numbers()
tries = []
total_prize = 0
total_cost = 0
for i in range(NUM_TRIES):
select_menu = int(input("1.자동 2.번호 선택\n입력: "))
if select_menu == 1:
attempt = sorted(lottery.generate_numbers(6))
else:
attempt = sorted(lottery.generate_input_numbers(6))
prize = lottery.check(attempt, WINNING_NUMBERS)
tries.append((attempt, prize))
total_prize += prize
total_cost += 1000
main(
WINNING_NUMBERS,
sorted(tries, key=lambda x: -x[1]),
total_prize,
total_cost
)
| [
"noreply@github.com"
] | noreply@github.com |
a0322b2f81ed8ef731da2cc2a758f162c0d92b65 | 9b36652dafb58888b7a584806ee69a33fcb609d5 | /objutils/pickleif.py | fd210c74a9e0aa035425e908bbf6ad39a83c3423 | [] | no_license | pySART/objutils | db33e4576cf68111cb4debbafec06a0204844938 | 5ba4631b2245caae80d4dbe0053db0f2706ba53f | refs/heads/master | 2020-06-29T03:35:24.485977 | 2016-11-21T14:21:56 | 2016-11-21T14:21:56 | 74,451,500 | 5 | 2 | null | 2016-11-22T08:36:10 | 2016-11-22T08:36:10 | null | UTF-8 | Python | false | false | 1,385 | py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
__version__ = "0.1.0"
__copyright__ = """
pyObjUtils - Object file library for Python.
(C) 2010-2013 by Christoph Schueler <github.com/Christoph2,
cpu12.gems@googlemail.com>
All Rights Reserved
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
"""
import abc
DUMMY_PROTOCOL = None
class PickleIF(object):
__metaclass__ = abc.ABCMeta
@abc.abstractmethod
def dump(self, obj, file_, protocol = DUMMY_PROTOCOL): pass
@abc.abstractmethod
def dumps(self, obj, protocol = DUMMY_PROTOCOL): pass
@abc.abstractmethod
def load(self, file_): pass
@abc.abstractmethod
def loads(self, string_): pass
| [
"cpu12.gems@googlemail.com"
] | cpu12.gems@googlemail.com |
a856bc79b318b4773ae61f88951e1698c816a3f8 | 19b93af5cb896d93f5d962cec6b9f177eeff0339 | /zinc-launcher | e90e21f845ca8b3f2f4a5ccb7c3b0b21efa24f3a | [] | no_license | mattn/zinc-launcher | 0acccb454aafbe957d4599ee64891b0a0f7f6359 | c1181cf7313797bf7ccef32aed7cc2f4741d2ad9 | refs/heads/master | 2023-06-27T05:42:18.735780 | 2013-09-16T15:44:06 | 2013-09-16T15:44:06 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,604 | #!/usr/bin/python
import gtk
import os
import subprocess
import threading
class zinc_thread(threading.Thread):
def __init__(self, id):
threading.Thread.__init__(self)
self.id = id
def run(self):
os.system("padsp ./zinc " + self.id)
gtk.gdk.threads_init()
win = gtk.Window()
win.set_title("ZiNc Launcher")
win.set_default_size(400, 500)
win.connect('destroy', gtk.main_quit)
swin = gtk.ScrolledWindow()
swin.set_policy(gtk.POLICY_AUTOMATIC, gtk.POLICY_ALWAYS)
tree_model = gtk.ListStore(str, str)
tree_view = gtk.TreeView(tree_model)
renderer = gtk.CellRendererText()
tree_view.append_column(gtk.TreeViewColumn("ID", renderer, text=0))
tree_view.append_column(gtk.TreeViewColumn("Title", renderer, text=1))
def on_activated(tree_view, path, column):
model = tree_view.get_model()
iter = model.get_iter(path)
id = model.get_value(iter, 0)
t = zinc_thread(id)
t.start()
tree_view.set_sensitive(False)
while t.isAlive():
gtk.main_iteration(False)
tree_view.set_sensitive(True)
tree_view.connect('row-activated', on_activated)
os.putenv("LD_LIBRARY_PATH", ".")
p = subprocess.Popen("./zinc --list-games", shell=True, stdin=subprocess.PIPE,
stdout=subprocess.PIPE, stderr=subprocess.STDOUT,
close_fds=True)
(stdouterr, stdin) = (p.stdout, p.stdin)
stdouterr.readline()
while True:
line = stdouterr.readline().strip()
if not line:
break
iter = tree_model.append()
item = line.split(" ", 1)
tree_model.set_value(iter, 0, item[0])
tree_model.set_value(iter, 1, item[1])
swin.add(tree_view)
win.add(swin)
win.show_all()
gtk.main()
| [
"mattn.jp@gmail.com"
] | mattn.jp@gmail.com | |
2c0f774cf9e57e9947263de535f7d1b98fb4245a | 411e8d3400ec0f3b5b8647a37202a52fa4df2c24 | /wx/wx_views.py | 5430f525f4432f732c822af933ee912134eb0d1c | [] | no_license | skymaxu/abh-tornado | f987fe6b26814d54d8aa220c2b082bfc1f5b2d9e | e1d896d29a6383fc6829235a7e53636fff8a006b | refs/heads/master | 2021-01-17T17:36:42.962059 | 2016-07-06T02:43:17 | 2016-07-06T02:43:17 | 62,541,089 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 6,497 | py | #!/usr/bin/python
#coding: utf-8
import json
import os
import sys
import time
import traceback
import tornado.ioloop
import tornado.options
import tornado.web
from views import BaseHandler
class MainHandler(BaseHandler):
def get(self):
return self.render('wx/index.html')
# class LoginHandler(BaseHandler):
# def get(self):
# self.set_secure_cookie('user', 'xxxxxxxx')
# resp = {}
# resp['ret'] = retcode.OK
# resp['ret_msg'] = retcode.get_ret_msg(resp['ret'])
# jsonstr = json.dumps(resp)
# self.set_header('Content-Type', 'application/json')
# self.write(jsonstr)
#
# class LogoutHandler(BaseHandler):
# @tornado.web.authenticated
# def get(self):
# self.clear_cookie('user')
# ret = 0
# resp = {}
# resp['ret'] = ret
# resp['ret_msg'] = retcode.get_ret_msg(resp['ret'])
# jsonstr = json.dumps(resp)
# self.set_header('Content-Type', 'application/json')
# self.write(jsonstr)
#
# class InterfaceMainHandler(BaseHandler):
# def get(self):
# self.write_log.critical('Ileggal intrution!')
# if ('signature' not in self.request.arguments or
# 'timestamp' not in self.request.arguments or
# 'nonce' not in self.request.arguments or
# 'echostr' not in self.request.arguments):
# return
#
# # check signature
# signature = self.get_argument('signature')
# timestamp = self.get_argument('timestamp')
# nonce = self.get_argument('nonce')
# echostr = self.get_argument('echostr')
# if not interface_logic.check_signature(signature, timestamp, nonce, config.OA_TOKEN):
# self.write_log.critical('Ileggal intrution!')
# return
#
# self.write(echostr)
#
# def post(self):
# if ('signature' not in self.request.arguments or
# 'timestamp' not in self.request.arguments or
# 'nonce' not in self.request.arguments):
# return
#
# # check signature
# signature = self.get_argument('signature')
# timestamp = self.get_argument('timestamp')
# nonce = self.get_argument('nonce')
# if not interface_logic.check_signature(signature, timestamp, nonce, config.OA_TOKEN):
# self.write_log.critical('Ileggal intrution!')
# return
#
# # parse msg
# msg = self.request.body
# self.write_log.info('Recv msg: ' + msg)
# msgdict = msg_helper.parse_input_msg(msg)
#
# # check if send to the right oa
# to_user_name = msg_helper.get_value_by_key(msgdict, 'ToUserName')
# if to_user_name != config.OA_USERNAME:
# self.write_log.critical('Send to the wrong official account!')
# return
#
# # process msg
# reply_msg = interface_logic.process_msg(msgdict)
# if reply_msg != None:
# self.write_log.info('Reply msg: ' + reply_msg)
# self.write(reply_msg)
# return
#
# class UserViewHandler(BaseHandler):
# @tornado.web.authenticated
# def get(self):
# if ('id' not in self.request.arguments):
# resp = {}
# resp['ret'] = retcode.MISS_ARGUMENT
# resp['ret_msg'] = retcode.get_ret_msg(resp['ret'])
# jsonstr = json.dumps(resp)
# self.set_header('Content-Type', 'application/json')
# self.write(jsonstr)
# return
#
# id = int(self.get_argument('id'))
#
# if (id < 0):
# resp = {}
# resp['ret'] = retcode.INVALID_ARGUMENT_VALUE
# resp['ret_msg'] = retcode.get_ret_msg(resp['ret'])
# jsonstr = json.dumps(resp)
# self.set_header('Content-Type', 'application/json')
# self.write(jsonstr)
# return
#
# ret, data = admin_logic.view_user(id)
#
# resp = {}
# resp['ret'] = ret
# resp['ret_msg'] = retcode.get_ret_msg(resp['ret'])
# resp['data'] = data
# jsonstr = json.dumps(resp)
# self.set_header('Content-Type', 'application/json')
# self.write(jsonstr)
class WxHandler(BaseHandler):
def parse_request_xml(self, rootElem):
msg = {}
if rootElem.tag == 'xml':
for child in rootElem:
msg[child.tag] = child.text # 获得内容
return msg
def get(self):
# 获取输入参数
signature = self.get_argument('signature', '')
timestamp = self.get_argument('timestamp', '')
nonce = self.get_argument('nonce', '')
echostr = self.get_argument('echostr', '')
# 自己的token
token = "abhweixin" # 这里改写你在微信公众平台里输入的token
# 字典序排序
list = [token, timestamp, nonce]
list.sort()
sha1 = hashlib.sha1()
map(sha1.update, list)
hashcode = sha1.hexdigest()
# sha1加密算法
# 如果是来自微信的请求，则回复echostr
if hashcode == signature:
self.write(echostr)
def post(self):
rawstr = self.request.body
msg = self.parse_request_xml(ET.fromstring(rawstr))
MsgType = tornado.escape.utf8(msg.get("MsgType"))
Content = tornado.escape.utf8(msg.get("Content"))
FromUserName = tornado.escape.utf8(msg.get("FromUserName"))
CreateTime = tornado.escape.utf8(msg.get("CreateTime"))
ToUserName = tornado.escape.utf8(msg.get("ToUserName"))
if MsgType != "text":
Content = "Sorry，亲，你的style我不懂！"
if not Content:
Content = "感谢您关注爱博华科技有限公司！我们拥有专业的技术团队，为您提供专业的项目外包服务！如果您有什么需要，可以随时联系我们，我们竭诚为您服务！"
data = '''<xml>
<ToUserName><![CDATA[%s]]></ToUserName>
<FromUserName><![CDATA[%s]]></FromUserName>
<CreateTime>%s</CreateTime>
<MsgType><![CDATA[%s]]></MsgType>
<Content><![CDATA[%s]]></Content>
</xml> ''' % (FromUserName, ToUserName, int(time.time()), 'text', Content)
self.write(data) # 提交信息
| [
"xutianhua88@qq.com"
] | xutianhua88@qq.com |
ea787b4ae53065aa2e9d7866bddf6587d52b9eab | a68518f4ae6381ee47628afb0f6c8f3a77ee41a8 | /setup.py | ec9ad8435a7b1f14774a368e082a4ee79803c0c5 | [
"MIT"
] | permissive | theSoenke/pytorch-trainer | d2c26fac6a28bb092bea0237fe671cd83152257e | e2bdd881afcc7619b0abd58006e594dac7daa789 | refs/heads/master | 2020-08-17T05:32:22.853416 | 2019-11-25T22:23:10 | 2019-11-25T22:23:10 | 215,616,393 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 691 | py | import setuptools
with open("README.md", "r") as fh:
long_description = fh.read()
setuptools.setup(
name="pytorch-trainer",
version="0.8.1",
description="Lightweight wrapper around PyTorch ",
long_description=long_description,
long_description_content_type="text/markdown",
url="https://github.com/theSoenke/pytorch-trainer",
packages=setuptools.find_packages(),
install_requires=[
'torch>=1.2.0',
'tqdm>=4.35.0',
'pandas'
],
classifiers=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
],
python_requires='>=3.6',
)
| [
"soenke.behrendt@gmail.com"
] | soenke.behrendt@gmail.com |
f9e1d014f00ad100e068a2d024c3d380291478c1 | 6fa7f99d3d3d9b177ef01ebf9a9da4982813b7d4 | /HBKAGJZ62JkCTgYX3_21.py | 715e80114cfa7428c4cd12fde12de966ea9febf6 | [] | no_license | daniel-reich/ubiquitous-fiesta | 26e80f0082f8589e51d359ce7953117a3da7d38c | 9af2700dbe59284f5697e612491499841a6c126f | refs/heads/master | 2023-04-05T06:40:37.328213 | 2021-04-06T20:17:44 | 2021-04-06T20:17:44 | 355,318,759 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 123 | py |
def last(a, n):
if n == 0:
return []
elif n > len(a):
return "invalid"
elif n <= len(a):
return a[-n:]
| [
"daniel.reich@danielreichs-MacBook-Pro.local"
] | daniel.reich@danielreichs-MacBook-Pro.local |
b7ae2023cd59626ee6d6a26f868b5a4f029c5c81 | a19abd5a7c8d0d963eaf6d86d3118dfb8e54973f | /utils.py | b445fb65836a0b97e46426300eea9a820179797a | [
"MIT"
] | permissive | AK391/vits | 460039c33cba369550487e3e13a88515a7412582 | 1b6db7437cd5224baae9776139f890ca315fad8a | refs/heads/main | 2023-05-26T23:19:52.615763 | 2021-06-14T23:23:56 | 2021-06-14T23:23:56 | 376,908,210 | 1 | 0 | MIT | 2021-06-14T17:42:08 | 2021-06-14T17:42:08 | null | UTF-8 | Python | false | false | 7,396 | py | import os
import glob
import sys
import argparse
import logging
import json
import subprocess
import numpy as np
from scipy.io.wavfile import read
import torch
MATPLOTLIB_FLAG = False
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
logger = logging
def load_checkpoint(checkpoint_path, model, optimizer=None):
assert os.path.isfile(checkpoint_path)
checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
iteration = checkpoint_dict['iteration']
learning_rate = checkpoint_dict['learning_rate']
if optimizer is not None:
optimizer.load_state_dict(checkpoint_dict['optimizer'])
saved_state_dict = checkpoint_dict['model']
if hasattr(model, 'module'):
state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
new_state_dict= {}
for k, v in state_dict.items():
try:
new_state_dict[k] = saved_state_dict[k]
except:
logger.info("%s is not in the checkpoint" % k)
new_state_dict[k] = v
if hasattr(model, 'module'):
model.module.load_state_dict(new_state_dict)
else:
model.load_state_dict(new_state_dict)
logger.info("Loaded checkpoint '{}' (iteration {})" .format(
checkpoint_path, iteration))
return model, optimizer, learning_rate, iteration
def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
logger.info("Saving model and optimizer state at iteration {} to {}".format(
iteration, checkpoint_path))
if hasattr(model, 'module'):
state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
torch.save({'model': state_dict,
'iteration': iteration,
'optimizer': optimizer.state_dict(),
'learning_rate': learning_rate}, checkpoint_path)
def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050):
for k, v in scalars.items():
writer.add_scalar(k, v, global_step)
for k, v in histograms.items():
writer.add_histogram(k, v, global_step)
for k, v in images.items():
writer.add_image(k, v, global_step, dataformats='HWC')
for k, v in audios.items():
writer.add_audio(k, v, global_step, audio_sampling_rate)
def latest_checkpoint_path(dir_path, regex="G_*.pth"):
f_list = glob.glob(os.path.join(dir_path, regex))
f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
x = f_list[-1]
print(x)
return x
def plot_spectrogram_to_numpy(spectrogram):
global MATPLOTLIB_FLAG
if not MATPLOTLIB_FLAG:
import matplotlib
matplotlib.use("Agg")
MATPLOTLIB_FLAG = True
mpl_logger = logging.getLogger('matplotlib')
mpl_logger.setLevel(logging.WARNING)
import matplotlib.pylab as plt
import numpy as np
fig, ax = plt.subplots(figsize=(10,2))
im = ax.imshow(spectrogram, aspect="auto", origin="lower",
interpolation='none')
plt.colorbar(im, ax=ax)
plt.xlabel("Frames")
plt.ylabel("Channels")
plt.tight_layout()
fig.canvas.draw()
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
plt.close()
return data
def plot_alignment_to_numpy(alignment, info=None):
global MATPLOTLIB_FLAG
if not MATPLOTLIB_FLAG:
import matplotlib
matplotlib.use("Agg")
MATPLOTLIB_FLAG = True
mpl_logger = logging.getLogger('matplotlib')
mpl_logger.setLevel(logging.WARNING)
import matplotlib.pylab as plt
import numpy as np
fig, ax = plt.subplots(figsize=(6, 4))
im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
interpolation='none')
fig.colorbar(im, ax=ax)
xlabel = 'Decoder timestep'
if info is not None:
xlabel += '\n\n' + info
plt.xlabel(xlabel)
plt.ylabel('Encoder timestep')
plt.tight_layout()
fig.canvas.draw()
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
plt.close()
return data
def load_wav_to_torch(full_path):
sampling_rate, data = read(full_path)
return torch.FloatTensor(data.astype(np.float32)), sampling_rate
def load_filepaths_and_text(filename, split="|"):
with open(filename, encoding='utf-8') as f:
filepaths_and_text = [line.strip().split(split) for line in f]
return filepaths_and_text
def get_hparams(init=True):
parser = argparse.ArgumentParser()
parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
help='JSON file for configuration')
parser.add_argument('-m', '--model', type=str, required=True,
help='Model name')
args = parser.parse_args()
model_dir = os.path.join("./logs", args.model)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
config_path = args.config
config_save_path = os.path.join(model_dir, "config.json")
if init:
with open(config_path, "r") as f:
data = f.read()
with open(config_save_path, "w") as f:
f.write(data)
else:
with open(config_save_path, "r") as f:
data = f.read()
config = json.loads(data)
hparams = HParams(**config)
hparams.model_dir = model_dir
return hparams
def get_hparams_from_dir(model_dir):
config_save_path = os.path.join(model_dir, "config.json")
with open(config_save_path, "r") as f:
data = f.read()
config = json.loads(data)
hparams =HParams(**config)
hparams.model_dir = model_dir
return hparams
def get_hparams_from_file(config_path):
with open(config_path, "r") as f:
data = f.read()
config = json.loads(data)
hparams =HParams(**config)
return hparams
def check_git_hash(model_dir):
source_dir = os.path.dirname(os.path.realpath(__file__))
if not os.path.exists(os.path.join(source_dir, ".git")):
logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
source_dir
))
return
cur_hash = subprocess.getoutput("git rev-parse HEAD")
path = os.path.join(model_dir, "githash")
if os.path.exists(path):
saved_hash = open(path).read()
if saved_hash != cur_hash:
logger.warn("git hash values are different. {}(saved) != {}(current)".format(
saved_hash[:8], cur_hash[:8]))
else:
open(path, "w").write(cur_hash)
def get_logger(model_dir, filename="train.log"):
global logger
logger = logging.getLogger(os.path.basename(model_dir))
logger.setLevel(logging.DEBUG)
formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
if not os.path.exists(model_dir):
os.makedirs(model_dir)
h = logging.FileHandler(os.path.join(model_dir, filename))
h.setLevel(logging.DEBUG)
h.setFormatter(formatter)
logger.addHandler(h)
return logger
class HParams():
def __init__(self, **kwargs):
for k, v in kwargs.items():
if type(v) == dict:
v = HParams(**v)
self[k] = v
def keys(self):
return self.__dict__.keys()
def items(self):
return self.__dict__.items()
def values(self):
return self.__dict__.values()
def __len__(self):
return len(self.__dict__)
def __getitem__(self, key):
return getattr(self, key)
def __setitem__(self, key, value):
return setattr(self, key, value)
def __contains__(self, key):
return key in self.__dict__
def __repr__(self):
return self.__dict__.__repr__()
| [
"jaywalnut310@gmail.com"
] | jaywalnut310@gmail.com |
830a673b50b9c3faacb8e3f2012031d17ab895e6 | daf830ddbe9a17c499c829eab41e9c483f05aadf | /client/views/Register.py | 5ff3f4a3ed1d29c54d3432323d5b612b9d5cd0b6 | [] | no_license | c475/fileshare | c751ea13e5e3636a29869a8a6c689e07b910361a | 32f90b4ee73d308ea0a6e8cc57732c98ed3dbda4 | refs/heads/master | 2021-01-13T14:44:55.518861 | 2016-12-25T06:28:24 | 2016-12-25T06:28:24 | 76,702,143 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 736 | py | from client.models import User
from client.forms import UserCreationForm
from client.mixins import NotLoggedInRequired
from django.views.generic.edit import CreateView
from django.contrib.auth import (
login,
authenticate
)
class Register(NotLoggedInRequired, CreateView):
model = User
template_name = 'registration/register.html'
success_url = '/'
form_class = UserCreationForm
def get_success_url(self):
new_user = authenticate(
username=self.request.POST.get("username", None),
password=self.request.POST.get("password1", None)
)
if new_user is not None:
login(self.request, new_user)
return super(Register, self).get_success_url()
| [
"danjamesbond@gmail.com"
] | danjamesbond@gmail.com |
115a66c23ea4661c8cc03e3030a4ed6b60a0b04e | bd521d50f33960207d9ad174099630f9ee47e6f8 | /study/python-basic/tentoone.py | e4f7d1b0301c61ecf0b938b20c2dd1d338de6638 | [] | no_license | GGRMR/python-study | c44660552daec255c1724a12c3c0a1dd301b012c | ba74c982283b98ec6bdb4b0569df75fa8a55094f | refs/heads/master | 2020-05-04T21:05:51.427503 | 2019-05-21T15:05:12 | 2019-05-21T15:05:12 | 179,464,009 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 134 | py | # -*- coding: utf-8 -*-
"""
bubble_list
@author: Park Jieun
"""
for i in range(10,0,-1):
print(i)
| [
"wldmsdl9707@naver.com"
] | wldmsdl9707@naver.com |
82eca83d6d1d1d11af6b32a044d2bb6b30cf6621 | b8fa1144dd2fc3202524ec4aebb480ad75f5c316 | /categories/views.py | 4efdd0a20671b10be6d0451b3803bf641b36eefb | [] | no_license | srijan113/Django-flix | 17f7ca79fbfae023cc17075890164929da60162b | 0eaf7a57357813cc93ea574b590ced0270b5d083 | refs/heads/main | 2023-04-21T11:45:18.362208 | 2021-05-11T16:41:48 | 2021-05-11T16:41:48 | 366,449,836 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,136 | py | from django.http.response import Http404
from django.shortcuts import render
from django.db.models import Count
from django.views import generic
from .models import Category
from playlist.models import Playlist
from playlist.mixins import PlaylistMixin
class CategoryList(generic.ListView):
queryset = Category.objects.all().filter(active=True).annotate(pl_count = Count('playlists')).filter(pl_count__gt=0)
class CategoryDetailView(PlaylistMixin, generic.ListView):
"""
Another list view for playlist
"""
def get_context_data(self):
context = super().get_context_data()
try:
obj = Category.objects.get(slug = self.kwargs.get('slug'))
except Category.DoesNotExist:
raise Http404
except Category.MultipleObjectsReturned:
raise Http404
except:
obj = None
context['object'] = obj
if obj is not None:
context['title'] = obj.title
return context
def get_queryset(self):
slug = self.kwargs.get('slug')
return Playlist.objects.filter(category__slug = slug).movie_or_show()
| [
"srijan.pokhrel113@gmail.com"
] | srijan.pokhrel113@gmail.com |
246e039f2cc85f5282bfb659128b315d0ff2d1f9 | 4244d1f3dc2acd98cd31b1db03645a233bc57a3b | /myapp/usertypes.py | 4f4b45458f4f1c8279c495a17df9a1a95ec48684 | [] | no_license | ekoputrapratama/plugin-architecture-pyqt5-qwebengine | 371837ad8e32730b7a682359412edd37df366378 | 330d391b232de352d72c1dba86b3a74125b13f8a | refs/heads/master | 2022-11-26T23:50:48.903616 | 2019-11-22T09:54:56 | 2019-11-22T09:54:56 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 179 | py | import enum
import typing
from PyQt5.QtCore import QUrl
Url = typing.TypeVar('Url', str, QUrl)
LoadEvent = enum.Enum(
"LoadEvent", ["FINISHED", "STARTED", "BEFORE_LOAD"]
)
| [
"muhammad.sayuti94@gmail.com"
] | muhammad.sayuti94@gmail.com |
f8818640916f448155ad0113d32a8f22b2140525 | 988dfbd1a762e40b9717398f7e8d40e3de962180 | /cdnmanager/cdnmanager/cdn/videorepo/migrations/0002_auto__add_projectpolicy__add_videoproject.py | 7a5104dd75908f0e5a7928252b351ffdffcff092 | [] | no_license | carribeiro/vdeli | cf0e594c408ff913355dbd6ea1397cda48f3e77a | cf27b2d17aac2975e3eb48a17132e8bd22325876 | refs/heads/master | 2021-10-27T22:43:40.348580 | 2011-07-01T11:07:52 | 2011-07-01T11:07:52 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 9,082 | py | # encoding: utf-8
import datetime
from south.db import db
from south.v2 import SchemaMigration
from django.db import models
class Migration(SchemaMigration):
def forwards(self, orm):
# Adding model 'ProjectPolicy'
db.create_table('videorepo_projectpolicy', (
('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)),
('video_project', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['videorepo.VideoProject'])),
('cdnregion', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['videorepo.CDNRegion'])),
('protocol', self.gf('django.db.models.fields.CharField')(max_length=5)),
('max_simultaneous_segments', self.gf('django.db.models.fields.IntegerField')()),
('segment_size', self.gf('django.db.models.fields.IntegerField')()),
('max_bandwidth_per_segment_mbps', self.gf('django.db.models.fields.IntegerField')()),
))
db.send_create_signal('videorepo', ['ProjectPolicy'])
# Adding model 'VideoProject'
db.create_table('videorepo_videoproject', (
('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)),
('name', self.gf('django.db.models.fields.CharField')(max_length=30)),
('creation_date', self.gf('django.db.models.fields.DateTimeField')()),
('user', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['auth.User'])),
))
db.send_create_signal('videorepo', ['VideoProject'])
def backwards(self, orm):
# Deleting model 'ProjectPolicy'
db.delete_table('videorepo_projectpolicy')
# Deleting model 'VideoProject'
db.delete_table('videorepo_videoproject')
models = {
'auth.group': {
'Meta': {'object_name': 'Group'},
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '80'}),
'permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'})
},
'auth.permission': {
'Meta': {'ordering': "('content_type__app_label', 'content_type__model', 'codename')", 'unique_together': "(('content_type', 'codename'),)", 'object_name': 'Permission'},
'codename': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'content_type': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['contenttypes.ContentType']"}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '50'})
},
'auth.user': {
'Meta': {'object_name': 'User'},
'date_joined': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'email': ('django.db.models.fields.EmailField', [], {'max_length': '75', 'blank': 'True'}),
'first_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}),
'groups': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['auth.Group']", 'symmetrical': 'False', 'blank': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'is_active': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'is_staff': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'is_superuser': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'last_login': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'last_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}),
'password': ('django.db.models.fields.CharField', [], {'max_length': '128'}),
'user_permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'}),
'username': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '30'})
},
'contenttypes.contenttype': {
'Meta': {'ordering': "('name',)", 'unique_together': "(('app_label', 'model'),)", 'object_name': 'ContentType', 'db_table': "'django_content_type'"},
'app_label': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'model': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '100'})
},
'videorepo.cdnregion': {
'Meta': {'object_name': 'CDNRegion'},
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'region_name': ('django.db.models.fields.CharField', [], {'max_length': '60'})
},
'videorepo.cdnserver': {
'Meta': {'object_name': 'CDNServer'},
'cdn_group': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['videorepo.CDNRegion']"}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'ip_address': ('django.db.models.fields.IPAddressField', [], {'max_length': '15'}),
'node_name': ('django.db.models.fields.CharField', [], {'max_length': '60'}),
'server_port': ('django.db.models.fields.IntegerField', [], {})
},
'videorepo.projectpolicy': {
'Meta': {'object_name': 'ProjectPolicy'},
'cdnregion': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['videorepo.CDNRegion']"}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'max_bandwidth_per_segment_mbps': ('django.db.models.fields.IntegerField', [], {}),
'max_simultaneous_segments': ('django.db.models.fields.IntegerField', [], {}),
'protocol': ('django.db.models.fields.CharField', [], {'max_length': '5'}),
'segment_size': ('django.db.models.fields.IntegerField', [], {}),
'video_project': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['videorepo.VideoProject']"})
},
'videorepo.segmentqueue': {
'Meta': {'object_name': 'SegmentQueue'},
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'queue_entry': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['videorepo.TransferQueue']"}),
'segment_end': ('django.db.models.fields.IntegerField', [], {}),
'segment_start': ('django.db.models.fields.IntegerField', [], {}),
'segment_status': ('django.db.models.fields.CharField', [], {'max_length': '15'})
},
'videorepo.transferqueue': {
'Meta': {'object_name': 'TransferQueue'},
'current_segments': ('django.db.models.fields.IntegerField', [], {}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'max_bandwidth_mbps': ('django.db.models.fields.IntegerField', [], {}),
'max_simultaneous_segments': ('django.db.models.fields.IntegerField', [], {}),
'protocol': ('django.db.models.fields.CharField', [], {'max_length': '5'}),
'segment_size': ('django.db.models.fields.IntegerField', [], {}),
'server': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['videorepo.CDNServer']"}),
'transfer_status': ('django.db.models.fields.CharField', [], {'max_length': '15'}),
'transfer_type': ('django.db.models.fields.CharField', [], {'max_length': '15'}),
'video_file': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['videorepo.VideoFile']"})
},
'videorepo.videofile': {
'Meta': {'object_name': 'VideoFile'},
'file_hash': ('django.db.models.fields.CharField', [], {'max_length': '200'}),
'file_name': ('django.db.models.fields.files.FileField', [], {'max_length': '100'}),
'file_size': ('django.db.models.fields.IntegerField', [], {}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'upload_date': ('django.db.models.fields.DateTimeField', [], {})
},
'videorepo.videoproject': {
'Meta': {'object_name': 'VideoProject'},
'creation_date': ('django.db.models.fields.DateTimeField', [], {}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '30'}),
'user': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['auth.User']"})
}
}
complete_apps = ['videorepo']
| [
"unitoff@gmail.com"
] | unitoff@gmail.com |
8eabc5915442c74698de459405acdb8a6cb90fa6 | 18b3ad3b0e1f7f10969738251e1201d01dfbc6bf | /backup_files/practice/rect.py | 00e007de1004f6dc31ae22f14c65ace2161a43fa | [] | no_license | sahthi/backup2 | 11d509b980e731c73733b1399a8143780779e75a | 16bed38f0867fd7c766c2a008c8d43b0660f0cb0 | refs/heads/master | 2020-03-21T12:39:56.890129 | 2018-07-09T08:12:46 | 2018-07-09T08:12:46 | 138,565,151 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 352 | py | #!/usr/bin/python
class rectangle:
def __init__(self,length,breadth):
self.length=length
self.breadth=breadth
def area(self):
return self.breadth*self.length
a=input("enter the length of rectangle:")
b=input("enter the breadth of rectangle:")
obj=rectangle(a,b)
print("area of rectangle:",obj.area())
| [
"siddamsetty.sahithi@votarytech.com"
] | siddamsetty.sahithi@votarytech.com |
b28fdc07e948fed01cb39d809336fa86c0eb6f57 | 13789dc6b4faa7d708c45564b0ca6a62fe89b87d | /BEAC_ERP/urls.py | b0197cece49216609b13477e9efdbeab2feb1608 | [] | no_license | MbadingaMoudouyi/beac_apc | 343485a9cdddb7a76fa9450bb2dacd6dd5566907 | 6fbd82a62a72449572a6f1f73b19fd4162b2a7b1 | refs/heads/main | 2023-06-28T13:09:44.781339 | 2021-07-27T11:21:58 | 2021-07-27T11:21:58 | 389,919,907 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 798 | py | """BEAC_ERP 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('apc/', include('apc.urls')),
]
| [
"mlionelstephen@gmail.com"
] | mlionelstephen@gmail.com |
1ed418b0c91b56556c45e359d752ea4523d1576b | 97c1558464f6e2c4350c207c9e856dcd76ea05af | /A Star Algorithm/AStarAlgorithm.py | 50eeebcf335bf823433c8e465259841b473857d4 | [] | no_license | harshpatel010202/Path-Finding-Python-Project | 0810dce5e3a44e16be278ef89d51a31299f906ac | c06e39c97647ccaa626c9d2f62a055d90c4a0648 | refs/heads/main | 2023-04-19T10:36:23.482380 | 2021-05-05T03:33:52 | 2021-05-05T03:33:52 | 364,451,485 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 7,327 | py | from queue import PriorityQueue
import pygame
WIDTH = 1000
WINDOW = pygame.display.set_mode((WIDTH, WIDTH))
pygame.display.set_caption("A* SEARCH ALGORITHM")
RED = (255, 0, 0)
LIME = (0, 255, 0)
YELLOW = (255, 255, 0)
WHITE = (255, 255, 255)
BLACK = (0, 0, 0)
GRAY = (128, 128, 128)
NAVY = (0, 0, 128)
class Node:
def __init__(self, row, col, width, sum_rows):
self.row = row
self.col = col
self.x = row * width
self.y = col * width
self.sum_rows = sum_rows
self.width = width
self.color = BLACK
self.nearest = []
def get_its_pos(self):
return self.row, self.col
def is_pos_closed(self):
return self.color == YELLOW
def is_pos_open(self):
return self.color == NAVY
def is_pos_obstacle(self):
return self.color == WHITE
def is_pos_commence(self):
return self.color == YELLOW
def is_pos_finish(self):
return self.color == LIME
def pos_reset(self):
self.color = BLACK
def create_pos_path(self):
self.color = LIME
def create_pos_obstacle(self):
self.color = WHITE
def create_pos_closed(self):
self.color = YELLOW
def create_pos_commence(self):
self.color = RED
def create_pos_open(self):
self.color = NAVY
def create_pos_finish(self):
self.color = LIME
def sketch(self, win):
pygame.draw.rect(win, self.color, (self.x, self.y, self.width, self.width))
def update_nearest(self, grid):
self.nearest = []
if self.row < self.sum_rows - 1 and not grid[self.row + 1][self.col].is_pos_obstacle():
self.nearest.append(grid[self.row + 1][self.col])
if self.col < self.sum_rows - 1 and not grid[self.row][self.col + 1].is_pos_obstacle():
self.nearest.append(grid[self.row][self.col + 1])
if self.row > 0 and not grid[self.row - 1][self.col].is_pos_obstacle():
self.nearest.append(grid[self.row - 1][self.col])
if self.col > 0 and not grid[self.row][self.col - 1].is_pos_obstacle():
self.nearest.append(grid[self.row][self.col - 1])
# lt means less than
def __lt__(self, other):
return False
def area(pos1, pos2):
x1, y1 = pos1
x2, y2 = pos2
return abs(x1 - x2) + abs(y1 - y2)
# algorithm
def algorithm(sketch, grid, commence, finish):
counter = 0
available_set = PriorityQueue()
available_set.put((0, counter, commence))
evolved = {}
past_cost_score = {node: float("inf") for row in grid for node in row}
past_cost_score[commence] = 0
heru_score = {node: float("inf") for row in grid for node in row}
heru_score[commence] = area(commence.get_its_pos(), finish.get_its_pos())
available_set_hash = {commence}
while not available_set.empty():
for event in pygame.event.get():
if event.type == pygame.QUIT:
pygame.quit()
currentNode = available_set.get()[2]
available_set_hash.remove(currentNode)
if currentNode == finish:
final_step(evolved, finish, sketch)
finish.create_pos_finish()
return True
for nearest in currentNode.nearest:
node_past_cost_score = past_cost_score[currentNode] + 1
if node_past_cost_score < past_cost_score[nearest]:
evolved[nearest] = currentNode
past_cost_score[nearest] = node_past_cost_score
heru_score[nearest] = node_past_cost_score + area(nearest.get_its_pos(), finish.get_its_pos())
if nearest not in available_set_hash:
counter += 1
available_set.put((heru_score[nearest], counter, nearest))
available_set_hash.add(nearest)
nearest.create_pos_open()
sketch()
if currentNode != commence:
currentNode.create_pos_closed()
return False
def create_its_grid(MAKE_ROWS, width):
grid = []
space = width // MAKE_ROWS
for i in range(MAKE_ROWS):
grid.append([])
for j in range(MAKE_ROWS):
node = Node(i, j, space, MAKE_ROWS)
grid[i].append(node)
return grid
def sketch_grid(win, MAKE_ROWS, width):
space = width // MAKE_ROWS
for i in range(MAKE_ROWS):
pygame.draw.line(win, GRAY, (0, i * space), (width, i * space))
for j in range(MAKE_ROWS):
pygame.draw.line(win, GRAY, (j * space, 0), (j * space, width))
def sketch(win, grid, MAKE_ROWS, width):
win.fill(BLACK)
for row in grid:
for node in row:
node.sketch(win)
sketch_grid(win, MAKE_ROWS, width)
pygame.display.update()
def pos_clicked(pos, MAKE_ROWS, width):
space = width // MAKE_ROWS
y, x = pos
row = y // space
col = x // space
return row, col
# final step
def final_step(evolved, currentNode, sketch):
while currentNode in evolved:
currentNode = evolved[currentNode]
currentNode.create_pos_path()
sketch()
def main(win, width):
MAKE_ROWS = 40
grid = create_its_grid(MAKE_ROWS, width)
#SET BOTH COMMENCE AND FINISH TO NONE#
commence = None
finish = None
run = True
#THIS MEANS WHILE IT IS RUNNING THE FOLLOWING THING IN THE LOOP WILL OCCUR
while run:
sketch(win, grid, MAKE_ROWS, width)
for event in pygame.event.get():
if event.type == pygame.QUIT:
run = False
#TO POSITION COMMENCE, FINISH AND OBSTACLES POSITIONS --> CAN BE DONE USING LEFT CLICK
if pygame.mouse.get_pressed()[0]:
pos = pygame.mouse.get_pos()
row, col = pos_clicked(pos, MAKE_ROWS, width)
node = grid[row][col]
if not commence and node != finish:
commence = node
commence.create_pos_commence()
elif not finish and node != commence:
finish = node
finish.create_pos_finish()
elif node != finish and node != commence:
node.create_pos_obstacle()
#TO ERASE THE COMMENCE, FINISH AND OBSTACLES POSITIONS --> CAN BE DONE USING RIGHT CLICK
elif pygame.mouse.get_pressed()[2]:
pos = pygame.mouse.get_pos()
row, col = pos_clicked(pos, MAKE_ROWS, width)
node = grid[row][col]
node.pos_reset()
if node == commence:
commence = None
elif node == finish:
finish = None
# if space bar is clicked
if event.type == pygame.KEYDOWN:
if event.key == pygame.K_SPACE and commence and finish:
for row in grid:
for node in row:
node.update_nearest(grid)
algorithm(lambda: sketch(win, grid, MAKE_ROWS, width), grid, commence, finish)
# if space bar is clicked
if event.key == pygame.K_c:
commence = None
finish = None
grid = create_its_grid(MAKE_ROWS, width)
pygame.quit()
main(WINDOW, WIDTH)
| [
"harshpatel@uvic.ca"
] | harshpatel@uvic.ca |
a3d20cd0cb67b56a2cee5a0ae9a699eb96bf1677 | 4f862ab15ad1a5ff562f4e068ac8e8051e5064ee | /training_stuff/cifar10_multi_gpu_train.py | 583e81978bdbe2536fa3a48852d99301db7e793f | [] | no_license | skassam21/CSC411 | cd4a64ed4c9879b85432c33fc293fb44650dca76 | 4bf4685338febabcadbadbef657d1273a0aa2db4 | refs/heads/master | 2020-06-27T10:49:40.620559 | 2016-11-23T22:26:14 | 2016-11-23T22:26:14 | 74,525,062 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 10,163 | py | # Copyright 2015 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.
# ==============================================================================
"""A binary to train CIFAR-10 using multiple GPU's with synchronous updates.
Accuracy:
cifar10_multi_gpu_train.py achieves ~86% accuracy after 100K steps (256
epochs of data) as judged by cifar10_eval.py.
Speed: With batch_size 128.
System | Step Time (sec/batch) | Accuracy
--------------------------------------------------------------------
1 Tesla K20m | 0.35-0.60 | ~86% at 60K steps (5 hours)
1 Tesla K40m | 0.25-0.35 | ~86% at 100K steps (4 hours)
2 Tesla K20m | 0.13-0.20 | ~84% at 30K steps (2.5 hours)
3 Tesla K20m | 0.13-0.18 | ~84% at 30K steps
4 Tesla K20m | ~0.10 | ~84% at 30K steps
Usage:
Please see the tutorial and website for how to download the CIFAR-10
data set, compile the program and train the model.
http://tensorflow.org/tutorials/deep_cnn/
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from datetime import datetime
import os.path
import re
import time
import numpy as np
from six.moves import xrange # pylint: disable=redefined-builtin
import tensorflow as tf
from training_stuff import cifar
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_string('train_dir', '/tmp/cifar10_train',
"""Directory where to write event logs """
"""and checkpoint.""")
tf.app.flags.DEFINE_integer('max_steps', 1000000,
"""Number of batches to run.""")
tf.app.flags.DEFINE_integer('num_gpus', 1,
"""How many GPUs to use.""")
tf.app.flags.DEFINE_boolean('log_device_placement', False,
"""Whether to log device placement.""")
def tower_loss(scope):
"""Calculate the total loss on a single tower running the CIFAR model.
Args:
scope: unique prefix string identifying the CIFAR tower, e.g. 'tower_0'
Returns:
Tensor of shape [] containing the total loss for a batch of data
"""
# Get images and labels for CIFAR-10.
images, labels = cifar.distorted_inputs()
# Build inference Graph.
logits = cifar.inference(images)
# Build the portion of the Graph calculating the losses. Note that we will
# assemble the total_loss using a custom function below.
_ = cifar.loss(logits, labels)
# Assemble all of the losses for the current tower only.
losses = tf.get_collection('losses', scope)
# Calculate the total loss for the current tower.
total_loss = tf.add_n(losses, name='total_loss')
# Attach a scalar summary to all individual losses and the total loss; do the
# same for the averaged version of the losses.
for l in losses + [total_loss]:
# Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training
# session. This helps the clarity of presentation on tensorboard.
loss_name = re.sub('%s_[0-9]*/' % cifar.TOWER_NAME, '', l.op.name)
tf.scalar_summary(loss_name, l)
return total_loss
def average_gradients(tower_grads):
"""Calculate the average gradient for each shared variable across all towers.
Note that this function provides a synchronization point across all towers.
Args:
tower_grads: List of lists of (gradient, variable) tuples. The outer list
is over individual gradients. The inner list is over the gradient
calculation for each tower.
Returns:
List of pairs of (gradient, variable) where the gradient has been averaged
across all towers.
"""
average_grads = []
for grad_and_vars in zip(*tower_grads):
# Note that each grad_and_vars looks like the following:
# ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN))
grads = []
for g, _ in grad_and_vars:
# Add 0 dimension to the gradients to represent the tower.
expanded_g = tf.expand_dims(g, 0)
# Append on a 'tower' dimension which we will average over below.
grads.append(expanded_g)
# Average over the 'tower' dimension.
grad = tf.concat(0, grads)
grad = tf.reduce_mean(grad, 0)
# Keep in mind that the Variables are redundant because they are shared
# across towers. So .. we will just return the first tower's pointer to
# the Variable.
v = grad_and_vars[0][1]
grad_and_var = (grad, v)
average_grads.append(grad_and_var)
return average_grads
def train():
"""Train CIFAR-10 for a number of steps."""
with tf.Graph().as_default(), tf.device('/cpu:0'):
# Create a variable to count the number of train() calls. This equals the
# number of batches processed * FLAGS.num_gpus.
global_step = tf.get_variable(
'global_step', [],
initializer=tf.constant_initializer(0), trainable=False)
# Calculate the learning rate schedule.
num_batches_per_epoch = (cifar.NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN /
FLAGS.batch_size)
decay_steps = int(num_batches_per_epoch * cifar.NUM_EPOCHS_PER_DECAY)
# Decay the learning rate exponentially based on the number of steps.
lr = tf.train.exponential_decay(cifar.INITIAL_LEARNING_RATE,
global_step,
decay_steps,
cifar.LEARNING_RATE_DECAY_FACTOR,
staircase=True)
# Create an optimizer that performs gradient descent.
opt = tf.train.GradientDescentOptimizer(lr)
# Calculate the gradients for each model tower.
tower_grads = []
for i in xrange(FLAGS.num_gpus):
with tf.device('/gpu:%d' % i):
with tf.name_scope('%s_%d' % (cifar.TOWER_NAME, i)) as scope:
# Calculate the loss for one tower of the CIFAR model. This function
# constructs the entire CIFAR model but shares the variables across
# all towers.
loss = tower_loss(scope)
# Reuse variables for the next tower.
tf.get_variable_scope().reuse_variables()
# Retain the summaries from the final tower.
summaries = tf.get_collection(tf.GraphKeys.SUMMARIES, scope)
# Calculate the gradients for the batch of data on this CIFAR tower.
grads = opt.compute_gradients(loss)
# Keep track of the gradients across all towers.
tower_grads.append(grads)
# We must calculate the mean of each gradient. Note that this is the
# synchronization point across all towers.
grads = average_gradients(tower_grads)
# Add a summary to track the learning rate.
summaries.append(tf.scalar_summary('learning_rate', lr))
# Add histograms for gradients.
for grad, var in grads:
if grad is not None:
summaries.append(
tf.histogram_summary(var.op.name + '/gradients', grad))
# Apply the gradients to adjust the shared variables.
apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)
# Add histograms for trainable variables.
for var in tf.trainable_variables():
summaries.append(tf.histogram_summary(var.op.name, var))
# Track the moving averages of all trainable variables.
variable_averages = tf.train.ExponentialMovingAverage(
cifar.MOVING_AVERAGE_DECAY, global_step)
variables_averages_op = variable_averages.apply(tf.trainable_variables())
# Group all updates to into a single train op.
train_op = tf.group(apply_gradient_op, variables_averages_op)
# Create a saver.
saver = tf.train.Saver(tf.all_variables())
# Build the summary operation from the last tower summaries.
summary_op = tf.merge_summary(summaries)
# Build an initialization operation to run below.
init = tf.global_variables_initializer()
# Start running operations on the Graph. allow_soft_placement must be set to
# True to build towers on GPU, as some of the ops do not have GPU
# implementations.
sess = tf.Session(config=tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=FLAGS.log_device_placement))
sess.run(init)
# Start the queue runners.
tf.train.start_queue_runners(sess=sess)
summary_writer = tf.train.SummaryWriter(FLAGS.train_dir, sess.graph)
for step in xrange(FLAGS.max_steps):
start_time = time.time()
_, loss_value = sess.run([train_op, loss])
duration = time.time() - start_time
assert not np.isnan(loss_value), 'Model diverged with loss = NaN'
if step % 10 == 0:
num_examples_per_step = FLAGS.batch_size * FLAGS.num_gpus
examples_per_sec = num_examples_per_step / duration
sec_per_batch = duration / FLAGS.num_gpus
format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
'sec/batch)')
print (format_str % (datetime.now(), step, loss_value,
examples_per_sec, sec_per_batch))
if step % 100 == 0:
summary_str = sess.run(summary_op)
summary_writer.add_summary(summary_str, step)
# Save the model checkpoint periodically.
if step % 1000 == 0 or (step + 1) == FLAGS.max_steps:
checkpoint_path = os.path.join(FLAGS.train_dir, 'model.ckpt')
saver.save(sess, checkpoint_path, global_step=step)
def main(argv=None): # pylint: disable=unused-argument
cifar.maybe_download_and_extract()
if tf.gfile.Exists(FLAGS.train_dir):
tf.gfile.DeleteRecursively(FLAGS.train_dir)
tf.gfile.MakeDirs(FLAGS.train_dir)
train()
if __name__ == '__main__':
tf.app.run()
| [
"skassam21@gmail.com"
] | skassam21@gmail.com |
9d5eed9ac51e2c80d2bf186b88f4046d7c5f5a3a | 080c13cd91a073457bd9eddc2a3d13fc2e0e56ae | /MY_REPOS/awesome-4-new-developers/tensorflow-master/tensorflow/python/lib/io/file_io_test.py | e4767806896723cb6003db317ab58e1c9a241678 | [
"Apache-2.0"
] | permissive | Portfolio-Projects42/UsefulResourceRepo2.0 | 1dccc8961a09347f124d3ed7c27c6d73b9806189 | 75b1e23c757845b5f1894ebe53551a1cf759c6a3 | refs/heads/master | 2023-08-04T12:23:48.862451 | 2021-09-15T12:51:35 | 2021-09-15T12:51:35 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 28,674 | py | # This Python file uses the following encoding: utf-8
# Copyright 2015 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.
# =============================================================================
"""Testing File IO operations in file_io.py."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os.path
from absl.testing import parameterized
import numpy as np
from tensorflow.python.framework import errors
from tensorflow.python.lib.io import file_io
from tensorflow.python.platform import gfile
from tensorflow.python.platform import test
class PathLike(object):
"""Backport of pathlib.Path for Python < 3.6"""
def __init__(self, name):
self.name = name
def __fspath__(self):
return self.name
def __str__(self):
return self.name
run_all_path_types = parameterized.named_parameters(
("str", os.path.join), ("pathlike", lambda *paths: PathLike(os.path.join(*paths)))
)
class FileIoTest(test.TestCase, parameterized.TestCase):
def setUp(self):
self._base_dir = os.path.join(self.get_temp_dir(), "base_dir")
file_io.create_dir(self._base_dir)
def tearDown(self):
file_io.delete_recursively(self._base_dir)
def testEmptyFilename(self):
f = file_io.FileIO("", mode="r")
with self.assertRaises(errors.NotFoundError):
_ = f.read()
@run_all_path_types
def testFileDoesntExist(self, join):
file_path = join(self._base_dir, "temp_file")
self.assertFalse(file_io.file_exists(file_path))
with self.assertRaises(errors.NotFoundError):
_ = file_io.read_file_to_string(file_path)
@run_all_path_types
def testWriteToString(self, join):
file_path = join(self._base_dir, "temp_file")
file_io.write_string_to_file(file_path, "testing")
self.assertTrue(file_io.file_exists(file_path))
file_contents = file_io.read_file_to_string(file_path)
self.assertEqual("testing", file_contents)
def testAtomicWriteStringToFile(self):
file_path = os.path.join(self._base_dir, "temp_file")
file_io.atomic_write_string_to_file(file_path, "testing")
self.assertTrue(file_io.file_exists(file_path))
file_contents = file_io.read_file_to_string(file_path)
self.assertEqual("testing", file_contents)
def testAtomicWriteStringToFileOverwriteFalse(self):
file_path = os.path.join(self._base_dir, "temp_file")
file_io.atomic_write_string_to_file(file_path, "old", overwrite=False)
with self.assertRaises(errors.AlreadyExistsError):
file_io.atomic_write_string_to_file(file_path, "new", overwrite=False)
file_contents = file_io.read_file_to_string(file_path)
self.assertEqual("old", file_contents)
file_io.delete_file(file_path)
file_io.atomic_write_string_to_file(file_path, "new", overwrite=False)
file_contents = file_io.read_file_to_string(file_path)
self.assertEqual("new", file_contents)
@run_all_path_types
def testReadBinaryMode(self, join):
file_path = join(self._base_dir, "temp_file")
file_io.write_string_to_file(file_path, "testing")
with file_io.FileIO(file_path, mode="rb") as f:
self.assertEqual(b"testing", f.read())
@run_all_path_types
def testWriteBinaryMode(self, join):
file_path = join(self._base_dir, "temp_file")
file_io.FileIO(file_path, "wb").write("testing")
with file_io.FileIO(file_path, mode="r") as f:
self.assertEqual("testing", f.read())
def testAppend(self):
file_path = os.path.join(self._base_dir, "temp_file")
with file_io.FileIO(file_path, mode="w") as f:
f.write("begin\n")
with file_io.FileIO(file_path, mode="a") as f:
f.write("a1\n")
with file_io.FileIO(file_path, mode="a") as f:
f.write("a2\n")
with file_io.FileIO(file_path, mode="r") as f:
file_contents = f.read()
self.assertEqual("begin\na1\na2\n", file_contents)
def testMultipleFiles(self):
file_prefix = os.path.join(self._base_dir, "temp_file")
for i in range(5000):
f = file_io.FileIO(file_prefix + str(i), mode="w+")
f.write("testing")
f.flush()
self.assertEqual("testing", f.read())
f.close()
def testMultipleWrites(self):
file_path = os.path.join(self._base_dir, "temp_file")
with file_io.FileIO(file_path, mode="w") as f:
f.write("line1\n")
f.write("line2")
file_contents = file_io.read_file_to_string(file_path)
self.assertEqual("line1\nline2", file_contents)
def testFileWriteBadMode(self):
file_path = os.path.join(self._base_dir, "temp_file")
with self.assertRaises(errors.PermissionDeniedError):
file_io.FileIO(file_path, mode="r").write("testing")
def testFileReadBadMode(self):
file_path = os.path.join(self._base_dir, "temp_file")
file_io.FileIO(file_path, mode="w").write("testing")
self.assertTrue(file_io.file_exists(file_path))
with self.assertRaises(errors.PermissionDeniedError):
file_io.FileIO(file_path, mode="w").read()
@run_all_path_types
def testFileDelete(self, join):
file_path = join(self._base_dir, "temp_file")
file_io.FileIO(file_path, mode="w").write("testing")
file_io.delete_file(file_path)
self.assertFalse(file_io.file_exists(file_path))
def testFileDeleteFail(self):
file_path = os.path.join(self._base_dir, "temp_file")
with self.assertRaises(errors.NotFoundError):
file_io.delete_file(file_path)
def testGetMatchingFiles(self):
dir_path = os.path.join(self._base_dir, "temp_dir")
file_io.create_dir(dir_path)
files = ["file1.txt", "file2.txt", "file3.txt", "file*.txt"]
for name in files:
file_path = os.path.join(dir_path, name)
file_io.FileIO(file_path, mode="w").write("testing")
expected_match = [os.path.join(dir_path, name) for name in files]
self.assertItemsEqual(
file_io.get_matching_files(os.path.join(dir_path, "file*.txt")),
expected_match,
)
self.assertItemsEqual(file_io.get_matching_files(tuple()), [])
files_subset = [
os.path.join(dir_path, files[0]),
os.path.join(dir_path, files[2]),
]
self.assertItemsEqual(file_io.get_matching_files(files_subset), files_subset)
file_io.delete_recursively(dir_path)
self.assertFalse(file_io.file_exists(os.path.join(dir_path, "file3.txt")))
def testGetMatchingFilesWhenParentDirContainsParantheses(self):
dir_path = os.path.join(self._base_dir, "dir_(special)")
file_io.create_dir(dir_path)
files = ["file1.txt", "file(2).txt"]
for name in files:
file_path = os.path.join(dir_path, name)
file_io.FileIO(file_path, mode="w").write("testing")
expected_match = [os.path.join(dir_path, name) for name in files]
glob_pattern = os.path.join(dir_path, "*")
self.assertItemsEqual(file_io.get_matching_files(glob_pattern), expected_match)
@run_all_path_types
def testCreateRecursiveDir(self, join):
dir_path = join(self._base_dir, "temp_dir/temp_dir1/temp_dir2")
file_io.recursive_create_dir(dir_path)
file_io.recursive_create_dir(dir_path) # repeat creation
file_path = os.path.join(str(dir_path), "temp_file")
file_io.FileIO(file_path, mode="w").write("testing")
self.assertTrue(file_io.file_exists(file_path))
file_io.delete_recursively(os.path.join(self._base_dir, "temp_dir"))
self.assertFalse(file_io.file_exists(file_path))
@run_all_path_types
def testCopy(self, join):
file_path = join(self._base_dir, "temp_file")
file_io.FileIO(file_path, mode="w").write("testing")
copy_path = join(self._base_dir, "copy_file")
file_io.copy(file_path, copy_path)
self.assertTrue(file_io.file_exists(copy_path))
f = file_io.FileIO(file_path, mode="r")
self.assertEqual("testing", f.read())
self.assertEqual(7, f.tell())
def testCopyOverwrite(self):
file_path = os.path.join(self._base_dir, "temp_file")
file_io.FileIO(file_path, mode="w").write("testing")
copy_path = os.path.join(self._base_dir, "copy_file")
file_io.FileIO(copy_path, mode="w").write("copy")
file_io.copy(file_path, copy_path, overwrite=True)
self.assertTrue(file_io.file_exists(copy_path))
self.assertEqual("testing", file_io.FileIO(file_path, mode="r").read())
def testCopyOverwriteFalse(self):
file_path = os.path.join(self._base_dir, "temp_file")
file_io.FileIO(file_path, mode="w").write("testing")
copy_path = os.path.join(self._base_dir, "copy_file")
file_io.FileIO(copy_path, mode="w").write("copy")
with self.assertRaises(errors.AlreadyExistsError):
file_io.copy(file_path, copy_path, overwrite=False)
@run_all_path_types
def testRename(self, join):
file_path = join(self._base_dir, "temp_file")
file_io.FileIO(file_path, mode="w").write("testing")
rename_path = join(self._base_dir, "rename_file")
file_io.rename(file_path, rename_path)
self.assertTrue(file_io.file_exists(rename_path))
self.assertFalse(file_io.file_exists(file_path))
def testRenameOverwrite(self):
file_path = os.path.join(self._base_dir, "temp_file")
file_io.FileIO(file_path, mode="w").write("testing")
rename_path = os.path.join(self._base_dir, "rename_file")
file_io.FileIO(rename_path, mode="w").write("rename")
file_io.rename(file_path, rename_path, overwrite=True)
self.assertTrue(file_io.file_exists(rename_path))
self.assertFalse(file_io.file_exists(file_path))
def testRenameOverwriteFalse(self):
file_path = os.path.join(self._base_dir, "temp_file")
file_io.FileIO(file_path, mode="w").write("testing")
rename_path = os.path.join(self._base_dir, "rename_file")
file_io.FileIO(rename_path, mode="w").write("rename")
with self.assertRaises(errors.AlreadyExistsError):
file_io.rename(file_path, rename_path, overwrite=False)
self.assertTrue(file_io.file_exists(rename_path))
self.assertTrue(file_io.file_exists(file_path))
def testDeleteRecursivelyFail(self):
fake_dir_path = os.path.join(self._base_dir, "temp_dir")
with self.assertRaises(errors.NotFoundError):
file_io.delete_recursively(fake_dir_path)
@run_all_path_types
def testIsDirectory(self, join):
dir_path = join(self._base_dir, "test_dir")
# Failure for a non-existing dir.
self.assertFalse(file_io.is_directory(dir_path))
file_io.create_dir(dir_path)
self.assertTrue(file_io.is_directory(dir_path))
file_path = join(str(dir_path), "test_file")
file_io.FileIO(file_path, mode="w").write("test")
# False for a file.
self.assertFalse(file_io.is_directory(file_path))
# Test that the value returned from `stat()` has `is_directory` set.
file_statistics = file_io.stat(dir_path)
self.assertTrue(file_statistics.is_directory)
@run_all_path_types
def testListDirectory(self, join):
dir_path = join(self._base_dir, "test_dir")
file_io.create_dir(dir_path)
files = ["file1.txt", "file2.txt", "file3.txt"]
for name in files:
file_path = join(str(dir_path), name)
file_io.FileIO(file_path, mode="w").write("testing")
subdir_path = join(str(dir_path), "sub_dir")
file_io.create_dir(subdir_path)
subdir_file_path = join(str(subdir_path), "file4.txt")
file_io.FileIO(subdir_file_path, mode="w").write("testing")
dir_list = file_io.list_directory(dir_path)
self.assertItemsEqual(files + ["sub_dir"], dir_list)
def testListDirectoryFailure(self):
dir_path = os.path.join(self._base_dir, "test_dir")
with self.assertRaises(errors.NotFoundError):
file_io.list_directory(dir_path)
def _setupWalkDirectories(self, dir_path):
# Creating a file structure as follows
# test_dir -> file: file1.txt; dirs: subdir1_1, subdir1_2, subdir1_3
# subdir1_1 -> file: file3.txt
# subdir1_2 -> dir: subdir2
file_io.create_dir(dir_path)
file_io.FileIO(os.path.join(dir_path, "file1.txt"), mode="w").write("testing")
sub_dirs1 = ["subdir1_1", "subdir1_2", "subdir1_3"]
for name in sub_dirs1:
file_io.create_dir(os.path.join(dir_path, name))
file_io.FileIO(os.path.join(dir_path, "subdir1_1/file2.txt"), mode="w").write(
"testing"
)
file_io.create_dir(os.path.join(dir_path, "subdir1_2/subdir2"))
@run_all_path_types
def testWalkInOrder(self, join):
dir_path_str = os.path.join(self._base_dir, "test_dir")
dir_path = join(self._base_dir, "test_dir")
self._setupWalkDirectories(dir_path_str)
# Now test the walk (in_order = True)
all_dirs = []
all_subdirs = []
all_files = []
for (w_dir, w_subdirs, w_files) in file_io.walk(dir_path, in_order=True):
all_dirs.append(w_dir)
all_subdirs.append(w_subdirs)
all_files.append(w_files)
self.assertItemsEqual(
all_dirs,
[dir_path_str]
+ [
os.path.join(dir_path_str, item)
for item in ["subdir1_1", "subdir1_2", "subdir1_2/subdir2", "subdir1_3"]
],
)
self.assertEqual(dir_path_str, all_dirs[0])
self.assertLess(
all_dirs.index(os.path.join(dir_path_str, "subdir1_2")),
all_dirs.index(os.path.join(dir_path_str, "subdir1_2/subdir2")),
)
self.assertItemsEqual(all_subdirs[1:5], [[], ["subdir2"], [], []])
self.assertItemsEqual(all_subdirs[0], ["subdir1_1", "subdir1_2", "subdir1_3"])
self.assertItemsEqual(all_files, [["file1.txt"], ["file2.txt"], [], [], []])
self.assertLess(all_files.index(["file1.txt"]), all_files.index(["file2.txt"]))
def testWalkPostOrder(self):
dir_path = os.path.join(self._base_dir, "test_dir")
self._setupWalkDirectories(dir_path)
# Now test the walk (in_order = False)
all_dirs = []
all_subdirs = []
all_files = []
for (w_dir, w_subdirs, w_files) in file_io.walk(dir_path, in_order=False):
all_dirs.append(w_dir)
all_subdirs.append(w_subdirs)
all_files.append(w_files)
self.assertItemsEqual(
all_dirs,
[
os.path.join(dir_path, item)
for item in ["subdir1_1", "subdir1_2/subdir2", "subdir1_2", "subdir1_3"]
]
+ [dir_path],
)
self.assertEqual(dir_path, all_dirs[4])
self.assertLess(
all_dirs.index(os.path.join(dir_path, "subdir1_2/subdir2")),
all_dirs.index(os.path.join(dir_path, "subdir1_2")),
)
self.assertItemsEqual(all_subdirs[0:4], [[], [], ["subdir2"], []])
self.assertItemsEqual(all_subdirs[4], ["subdir1_1", "subdir1_2", "subdir1_3"])
self.assertItemsEqual(all_files, [["file2.txt"], [], [], [], ["file1.txt"]])
self.assertLess(all_files.index(["file2.txt"]), all_files.index(["file1.txt"]))
def testWalkFailure(self):
dir_path = os.path.join(self._base_dir, "test_dir")
# Try walking a directory that wasn't created.
all_dirs = []
all_subdirs = []
all_files = []
for (w_dir, w_subdirs, w_files) in file_io.walk(dir_path, in_order=False):
all_dirs.append(w_dir)
all_subdirs.append(w_subdirs)
all_files.append(w_files)
self.assertItemsEqual(all_dirs, [])
self.assertItemsEqual(all_subdirs, [])
self.assertItemsEqual(all_files, [])
@run_all_path_types
def testStat(self, join):
file_path = join(self._base_dir, "temp_file")
file_io.FileIO(file_path, mode="w").write("testing")
file_statistics = file_io.stat(file_path)
os_statistics = os.stat(str(file_path))
self.assertEqual(7, file_statistics.length)
self.assertEqual(
int(os_statistics.st_mtime), int(file_statistics.mtime_nsec / 1e9)
)
self.assertFalse(file_statistics.is_directory)
def testReadLine(self):
file_path = os.path.join(self._base_dir, "temp_file")
with file_io.FileIO(file_path, mode="r+") as f:
f.write("testing1\ntesting2\ntesting3\n\ntesting5")
self.assertEqual(36, f.size())
self.assertEqual("testing1\n", f.readline())
self.assertEqual("testing2\n", f.readline())
self.assertEqual("testing3\n", f.readline())
self.assertEqual("\n", f.readline())
self.assertEqual("testing5", f.readline())
self.assertEqual("", f.readline())
def testRead(self):
file_path = os.path.join(self._base_dir, "temp_file")
with file_io.FileIO(file_path, mode="r+") as f:
f.write("testing1\ntesting2\ntesting3\n\ntesting5")
self.assertEqual(36, f.size())
self.assertEqual("testing1\n", f.read(9))
self.assertEqual("testing2\n", f.read(9))
self.assertEqual("t", f.read(1))
self.assertEqual("esting3\n\ntesting5", f.read())
def testReadErrorReacquiresGil(self):
file_path = os.path.join(self._base_dir, "temp_file")
with file_io.FileIO(file_path, mode="r+") as f:
f.write("testing1\ntesting2\ntesting3\n\ntesting5")
with self.assertRaises(errors.InvalidArgumentError):
# At present, this is sufficient to convince ourselves that the change
# fixes the problem. That is, this test will seg fault without the change,
# and pass with it. Unfortunately, this is brittle, as it relies on the
# Python layer to pass the argument along to the wrapped C++ without
# checking the argument itself.
f.read(-2)
def testTell(self):
file_path = os.path.join(self._base_dir, "temp_file")
with file_io.FileIO(file_path, mode="r+") as f:
f.write("testing1\ntesting2\ntesting3\n\ntesting5")
self.assertEqual(0, f.tell())
self.assertEqual("testing1\n", f.readline())
self.assertEqual(9, f.tell())
self.assertEqual("testing2\n", f.readline())
self.assertEqual(18, f.tell())
self.assertEqual("testing3\n", f.readline())
self.assertEqual(27, f.tell())
self.assertEqual("\n", f.readline())
self.assertEqual(28, f.tell())
self.assertEqual("testing5", f.readline())
self.assertEqual(36, f.tell())
self.assertEqual("", f.readline())
self.assertEqual(36, f.tell())
def testSeek(self):
file_path = os.path.join(self._base_dir, "temp_file")
with file_io.FileIO(file_path, mode="r+") as f:
f.write("testing1\ntesting2\ntesting3\n\ntesting5")
self.assertEqual("testing1\n", f.readline())
self.assertEqual(9, f.tell())
# Seek to 18
f.seek(18)
self.assertEqual(18, f.tell())
self.assertEqual("testing3\n", f.readline())
# Seek back to 9
f.seek(9)
self.assertEqual(9, f.tell())
self.assertEqual("testing2\n", f.readline())
f.seek(0)
self.assertEqual(0, f.tell())
self.assertEqual("testing1\n", f.readline())
with self.assertRaises(errors.InvalidArgumentError):
f.seek(-1)
with self.assertRaises(TypeError):
f.seek()
# TODO(jhseu): Delete after position deprecation.
with self.assertRaises(TypeError):
f.seek(offset=0, position=0)
f.seek(position=9)
self.assertEqual(9, f.tell())
self.assertEqual("testing2\n", f.readline())
def testSeekFromWhat(self):
file_path = os.path.join(self._base_dir, "temp_file")
with file_io.FileIO(file_path, mode="r+") as f:
f.write("testing1\ntesting2\ntesting3\n\ntesting5")
self.assertEqual("testing1\n", f.readline())
self.assertEqual(9, f.tell())
# Seek to 18
f.seek(9, 1)
self.assertEqual(18, f.tell())
self.assertEqual("testing3\n", f.readline())
# Seek back to 9
f.seek(9, 0)
self.assertEqual(9, f.tell())
self.assertEqual("testing2\n", f.readline())
f.seek(-f.size(), 2)
self.assertEqual(0, f.tell())
self.assertEqual("testing1\n", f.readline())
with self.assertRaises(errors.InvalidArgumentError):
f.seek(0, 3)
def testReadingIterator(self):
file_path = os.path.join(self._base_dir, "temp_file")
data = ["testing1\n", "testing2\n", "testing3\n", "\n", "testing5"]
with file_io.FileIO(file_path, mode="r+") as f:
f.write("".join(data))
actual_data = []
for line in f:
actual_data.append(line)
self.assertSequenceEqual(actual_data, data)
def testReadlines(self):
file_path = os.path.join(self._base_dir, "temp_file")
data = ["testing1\n", "testing2\n", "testing3\n", "\n", "testing5"]
f = file_io.FileIO(file_path, mode="r+")
f.write("".join(data))
f.flush()
lines = f.readlines()
self.assertSequenceEqual(lines, data)
def testUTF8StringPath(self):
file_path = os.path.join(self._base_dir, "UTF8测试_file")
file_io.write_string_to_file(file_path, "testing")
with file_io.FileIO(file_path, mode="rb") as f:
self.assertEqual(b"testing", f.read())
def testEof(self):
"""Test that reading past EOF does not raise an exception."""
file_path = os.path.join(self._base_dir, "temp_file")
f = file_io.FileIO(file_path, mode="r+")
content = "testing"
f.write(content)
f.flush()
self.assertEqual(content, f.read(len(content) + 1))
@run_all_path_types
def testUTF8StringPathExists(self, join):
file_path = join(self._base_dir, "UTF8测试_file_exist")
file_io.write_string_to_file(file_path, "testing")
v = file_io.file_exists(file_path)
self.assertEqual(v, True)
def testFilecmp(self):
file1 = os.path.join(self._base_dir, "file1")
file_io.write_string_to_file(file1, "This is a sentence\n" * 100)
file2 = os.path.join(self._base_dir, "file2")
file_io.write_string_to_file(file2, "This is another sentence\n" * 100)
file3 = os.path.join(self._base_dir, "file3")
file_io.write_string_to_file(file3, u"This is another sentence\n" * 100)
self.assertFalse(file_io.filecmp(file1, file2))
self.assertTrue(file_io.filecmp(file2, file3))
def testFilecmpSameSize(self):
file1 = os.path.join(self._base_dir, "file1")
file_io.write_string_to_file(file1, "This is a sentence\n" * 100)
file2 = os.path.join(self._base_dir, "file2")
file_io.write_string_to_file(file2, "This is b sentence\n" * 100)
file3 = os.path.join(self._base_dir, "file3")
file_io.write_string_to_file(file3, u"This is b sentence\n" * 100)
self.assertFalse(file_io.filecmp(file1, file2))
self.assertTrue(file_io.filecmp(file2, file3))
def testFilecmpBinary(self):
file1 = os.path.join(self._base_dir, "file1")
file_io.FileIO(file1, "wb").write("testing\n\na")
file2 = os.path.join(self._base_dir, "file2")
file_io.FileIO(file2, "wb").write("testing\n\nb")
file3 = os.path.join(self._base_dir, "file3")
file_io.FileIO(file3, "wb").write("testing\n\nb")
file4 = os.path.join(self._base_dir, "file4")
file_io.FileIO(file4, "wb").write("testing\n\ntesting")
self.assertFalse(file_io.filecmp(file1, file2))
self.assertFalse(file_io.filecmp(file1, file4))
self.assertTrue(file_io.filecmp(file2, file3))
def testFileCrc32(self):
file1 = os.path.join(self._base_dir, "file1")
file_io.write_string_to_file(file1, "This is a sentence\n" * 100)
crc1 = file_io.file_crc32(file1)
file2 = os.path.join(self._base_dir, "file2")
file_io.write_string_to_file(file2, "This is another sentence\n" * 100)
crc2 = file_io.file_crc32(file2)
file3 = os.path.join(self._base_dir, "file3")
file_io.write_string_to_file(file3, "This is another sentence\n" * 100)
crc3 = file_io.file_crc32(file3)
self.assertTrue(crc1 != crc2)
self.assertEqual(crc2, crc3)
def testFileCrc32WithBytes(self):
file1 = os.path.join(self._base_dir, "file1")
file_io.write_string_to_file(file1, "This is a sentence\n" * 100)
crc1 = file_io.file_crc32(file1, block_size=24)
file2 = os.path.join(self._base_dir, "file2")
file_io.write_string_to_file(file2, "This is another sentence\n" * 100)
crc2 = file_io.file_crc32(file2, block_size=24)
file3 = os.path.join(self._base_dir, "file3")
file_io.write_string_to_file(file3, "This is another sentence\n" * 100)
crc3 = file_io.file_crc32(file3, block_size=-1)
self.assertTrue(crc1 != crc2)
self.assertEqual(crc2, crc3)
def testFileCrc32Binary(self):
file1 = os.path.join(self._base_dir, "file1")
file_io.FileIO(file1, "wb").write("testing\n\n")
crc1 = file_io.file_crc32(file1)
file2 = os.path.join(self._base_dir, "file2")
file_io.FileIO(file2, "wb").write("testing\n\n\n")
crc2 = file_io.file_crc32(file2)
file3 = os.path.join(self._base_dir, "file3")
file_io.FileIO(file3, "wb").write("testing\n\n\n")
crc3 = file_io.file_crc32(file3)
self.assertTrue(crc1 != crc2)
self.assertEqual(crc2, crc3)
def testMatchingFilesPermission(self):
# Create top level directory test_dir.
dir_path = os.path.join(self._base_dir, "test_dir")
file_io.create_dir(dir_path)
# Create second level directories `noread` and `any`.
noread_path = os.path.join(dir_path, "noread")
file_io.create_dir(noread_path)
any_path = os.path.join(dir_path, "any")
file_io.create_dir(any_path)
files = ["file1.txt", "file2.txt", "file3.txt"]
for name in files:
file_path = os.path.join(any_path, name)
file_io.FileIO(file_path, mode="w").write("testing")
file_path = os.path.join(noread_path, "file4.txt")
file_io.FileIO(file_path, mode="w").write("testing")
# Change noread to noread access.
os.chmod(noread_path, 0)
expected_match = [os.path.join(any_path, name) for name in files]
self.assertItemsEqual(
file_io.get_matching_files(os.path.join(dir_path, "*", "file*.txt")),
expected_match,
)
# Change noread back so that it could be cleaned during tearDown.
os.chmod(noread_path, 0o777)
def testFileSeekableWithZip(self):
# Note: Test case for GitHub issue 27276, issue only exposed in python 3.7+.
filename = os.path.join(self._base_dir, "a.npz")
np.savez_compressed(filename, {"a": 1, "b": 2})
with gfile.GFile(filename, "rb") as f:
info = np.load(
f, allow_pickle=True
) # pylint: disable=unexpected-keyword-arg
_ = [i for i in info.items()]
def testHasAtomicMove(self):
self.assertTrue(file_io.has_atomic_move("/a/b/c"))
if __name__ == "__main__":
test.main()
| [
"bryan.guner@gmail.com"
] | bryan.guner@gmail.com |
70252ccd8d751ddb991b9baf48cccda96d0787ae | 00758be070825c33d9178c8a50d1a59ee2c3c790 | /ppci/format/pefile/pefile.py | 9828d6c93963c5f9a97d85c328a03344460d59d7 | [
"BSD-2-Clause"
] | permissive | jsdelivrbot/ppci-mirror | d2a87f21a735a9495ad1130959b599ab317a62f6 | 67195d628275e2332ceaf44c9e13fc58d0877157 | refs/heads/master | 2020-04-10T06:23:38.964744 | 2018-12-07T17:05:05 | 2018-12-07T17:05:05 | 160,853,011 | 0 | 0 | BSD-2-Clause | 2018-12-07T17:07:00 | 2018-12-07T17:07:00 | null | UTF-8 | Python | false | false | 312 | py | from .headers import DosHeader, CoffHeader, PeOptionalHeader64
from .headers import ImageSectionHeader, PeHeader, DataDirectoryHeader
from .headers import ImportDirectoryTable
class PeFile:
""" Pe (exe) file """
def __init__(self):
self.pe_header = PeHeader()
class ExeFile(PeFile):
pass
| [
"windel@windel.nl"
] | windel@windel.nl |
a6ad73f8fa4ad85278553a44d738a6860b414064 | 913ffcf29991e57c504bc639cfabe471dfd41782 | /Tank Game/iniciar_juego.py | 9b0bb5c20e6eb64d3db20df8b75aa549bc3c4941 | [] | no_license | JaimeGo/PyQt-Projects | ef30761c5c2c025b9f98db7ed7e7d66b32d9b535 | c54eeaff69424ab463d64391422005bba3ceabd7 | refs/heads/master | 2020-03-18T13:52:40.067614 | 2018-05-25T07:01:57 | 2018-05-25T07:01:57 | 134,814,650 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 236 | py | from mainwindow import comenzar_main_window
from PyQt4 import uic, QtCore, QtGui
from menu_inicial import MenuInicial
if __name__ == '__main__':
app = QtGui.QApplication([])
form = MenuInicial()
form.show()
app.exec_()
| [
"jgonzalez1@uc.cl"
] | jgonzalez1@uc.cl |
07c1226fe221bea2d784ad146118712a1e324f3f | fd0def9fe74e934e14839f5f733977a096ef318f | /neural_network_example.py | eb0a671c50644f080fcbbdfc95134ddf20cc284b | [] | no_license | MarcoBz/neural_network | 2043801f3ee9725e1325f62e476b267dea5c223b | 29a70864f868cd208e023686e267414723fbb212 | refs/heads/master | 2020-03-25T08:27:18.858311 | 2018-08-05T13:05:08 | 2018-08-05T13:05:08 | 143,613,212 | 2 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,449 | py | # -*- coding: utf-8 -*-
"""
Created on Sun Aug 5 14:08:44 2018
@author: WinUser
"""
import numpy
# scipy.special for the sigmoid function expit()# scipy
import scipy.special
# neural network class definition# neura
class neuralNetwork:
# initialise the neural network
def __init__(self, inputnodes, hiddennodes, outputnodes, learningrate):
# set number of nodes in each input, hidden, output layer
self.inodes = inputnodes
self.hnodes = hiddennodes
self.onodes = outputnodes
# link weight matrices, wih and who
# weights inside the arrays are w_i_j, where link is from node i to node j in the next layer
# w11 w21
# w12 w22 etc
#self.wih = numpy.random.normal(0.0, pow(self.inodes, -0.5), (self.hnodes, self.inodes))
#self.who = numpy.random.normal(0.0, pow(self.hnodes, -0.5), (self.onodes, self.hnodes))
self.wih = numpy.ones((self.hnodes, self.inodes))
self.who = numpy.ones((self.onodes, self.hnodes))
# learning rate
self.lr = learningrate
# activation function is the sigmoid function
self.activation_function = lambda x: scipy.special.expit(x)
pass
# train the neural network
def train(self, inputs_list, targets_list):
# convert inputs list to 2d array
inputs = numpy.array(inputs_list, ndmin=2).T
targets = numpy.array(targets_list, ndmin=2).T
# calculate signals into hidden layer
hidden_inputs = numpy.dot(self.wih, inputs)
# calculate the signals emerging from hidden layer
hidden_outputs = self.activation_function(hidden_inputs)
# calculate signals into final output layer
final_inputs = numpy.dot(self.who, hidden_outputs)
# calculate the signals emerging from final output layer
final_outputs = self.activation_function(final_inputs)
# output layer error is the (target - actual)
output_errors = targets - final_outputs
# hidden layer error is the output_errors, split by weights, recombined at hidden nodes
hidden_errors = numpy.dot(self.who.T, output_errors)
# update the weights for the links between the hidden and output layers
self.who += self.lr * numpy.dot((output_errors * final_outputs * (1.0 - final_outputs)), numpy.transpose(hidden_outputs))
# update the weights for the links between the input and hidden layers
self.wih += self.lr * numpy.dot((hidden_errors * hidden_outputs * (1.0 - hidden_outputs)), numpy.transpose(inputs))
pass
# query the neural network
def query(self, inputs_list):
# convert inputs list to 2d array
inputs = numpy.array(inputs_list, ndmin=2).T
# calculate signals into hidden layer
hidden_inputs = numpy.dot(self.wih, inputs)
# calculate the signals emerging from hidden layer
hidden_outputs = self.activation_function(hidden_inputs)
# calculate signals into final output layer
final_inputs = numpy.dot(self.who, hidden_outputs)
# calculate the signals emerging from final output layer
final_outputs = self.activation_function(final_inputs)
return final_outputs | [
"noreply@github.com"
] | noreply@github.com |
a4056e610f35a5a1bfbe93990398a2a61a725fde | b7620d0f1a90390224c8ab71774b9c906ab3e8e9 | /aliyun-python-sdk-imm/aliyunsdkimm/request/v20200930/CreateFigureClusteringTaskRequest.py | cfa9780027b8c39cc94abaae16dbba18b98bda90 | [
"Apache-2.0"
] | permissive | YaoYinYing/aliyun-openapi-python-sdk | e9c62940baee1a35b9ec4a9fbd1e4eb0aaf93b2f | e9a93cc94bd8290d1b1a391a9cb0fad2e6c64627 | refs/heads/master | 2022-10-17T16:39:04.515562 | 2022-10-10T15:18:34 | 2022-10-10T15:18:34 | 117,057,304 | 0 | 0 | null | 2018-01-11T06:03:02 | 2018-01-11T06:03:01 | null | UTF-8 | Python | false | false | 2,476 | py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you 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 aliyunsdkcore.request import RpcRequest
from aliyunsdkimm.endpoint import endpoint_data
import json
class CreateFigureClusteringTaskRequest(RpcRequest):
def __init__(self):
RpcRequest.__init__(self, 'imm', '2020-09-30', 'CreateFigureClusteringTask','imm')
self.set_method('POST')
if hasattr(self, "endpoint_map"):
setattr(self, "endpoint_map", endpoint_data.getEndpointMap())
if hasattr(self, "endpoint_regional"):
setattr(self, "endpoint_regional", endpoint_data.getEndpointRegional())
def get_UserData(self): # String
return self.get_query_params().get('UserData')
def set_UserData(self, UserData): # String
self.add_query_param('UserData', UserData)
def get_ProjectName(self): # String
return self.get_query_params().get('ProjectName')
def set_ProjectName(self, ProjectName): # String
self.add_query_param('ProjectName', ProjectName)
def get_NotifyTopicName(self): # String
return self.get_query_params().get('NotifyTopicName')
def set_NotifyTopicName(self, NotifyTopicName): # String
self.add_query_param('NotifyTopicName', NotifyTopicName)
def get_NotifyEndpoint(self): # String
return self.get_query_params().get('NotifyEndpoint')
def set_NotifyEndpoint(self, NotifyEndpoint): # String
self.add_query_param('NotifyEndpoint', NotifyEndpoint)
def get_DatasetName(self): # String
return self.get_query_params().get('DatasetName')
def set_DatasetName(self, DatasetName): # String
self.add_query_param('DatasetName', DatasetName)
def get_Tags(self): # Map
return self.get_query_params().get('Tags')
def set_Tags(self, Tags): # Map
self.add_query_param("Tags", json.dumps(Tags))
| [
"sdk-team@alibabacloud.com"
] | sdk-team@alibabacloud.com |
56f881b70b7d27484498f6ee6da3d31c88d37dc0 | 25ef3ecf50f3807197eb84ab617ad5051c2edfc6 | /tweets/migrations/0003_friends_current_user.py | 6b1cc18382d94c375bb8b609d87d323236f48b35 | [] | no_license | hamiteksi/TwitterClone | bfa30f309d2514d51983dd328c961d56e664636f | e5b7c59015ca79588d40b16801748f5c765d58c0 | refs/heads/master | 2020-07-01T19:06:45.331751 | 2019-08-08T13:38:23 | 2019-08-08T13:38:23 | 201,266,799 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 605 | py | # Generated by Django 2.2.2 on 2019-08-05 10:11
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
('tweets', '0002_friends'),
]
operations = [
migrations.AddField(
model_name='friends',
name='current_user',
field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, related_name='Owner', to=settings.AUTH_USER_MODEL),
),
]
| [
"hamiteksi@outlook.com"
] | hamiteksi@outlook.com |
3e12ff790865811a2ef847850a4264105f4e606d | dfc3d113015ba71756271adf746e4e6976a29ab7 | /apps/aboutus/migrations/0001_initial.py | 86df78ee4264c1b5738200cb632c2454a124e99c | [] | no_license | abinabraham/capitalshipping | 169f9a7be71b53b2fd515906ecfb79a5119bdfb0 | 1ff1950c322ea1221a44e2ce141e1926e6c3169c | refs/heads/main | 2023-05-31T09:57:47.865671 | 2021-06-18T08:02:25 | 2021-06-18T08:02:25 | 378,076,804 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,061 | py | # Generated by Django 3.0.6 on 2020-05-30 11:41
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='AboutIntro',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(max_length=200, verbose_name='Titles')),
('des', models.TextField(verbose_name='Description')),
('image', models.ImageField(blank=True, upload_to='about/images/%y%m%d')),
('is_verified', models.BooleanField(default=True)),
('created_at', models.DateTimeField(auto_now_add=True)),
('updated_at', models.DateTimeField(auto_now=True)),
],
options={
'verbose_name': 'About Intro',
'verbose_name_plural': 'About Intro',
'ordering': ['-id'],
},
),
]
| [
"abinabrahamcs@gmail.com"
] | abinabrahamcs@gmail.com |
cdc1db5c843207dd2d1ee052bc775e821e56eb11 | c639e5c7bb55268a13f756b265a67b18e9f27b52 | /blog/migrations/0002_remove_blog_date.py | f2785517b16148b8fed0cdab49f24b555fca54a5 | [] | no_license | LionMillion/django3-person-portfolio | b6b68456dd3c6ec0b426c1dd867d4a1e1847b0e8 | f1ce466b64e8cf50e0f65b5672b310e277aef394 | refs/heads/master | 2022-11-30T10:04:55.632497 | 2020-03-18T18:01:01 | 2020-03-18T18:01:01 | 248,221,469 | 0 | 0 | null | 2022-11-22T05:24:34 | 2020-03-18T12:09:33 | Python | UTF-8 | Python | false | false | 310 | py | # Generated by Django 3.0.4 on 2020-03-17 18:29
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('blog', '0001_initial'),
]
operations = [
migrations.RemoveField(
model_name='blog',
name='date',
),
]
| [
"lionmilion111@gmail.com"
] | lionmilion111@gmail.com |
4306cf5a501f3de00edb62f24e757973416594b6 | e1794a1da4728bfdedcb84edeb62c5e1362feba4 | /government_covid19/government_covid19/spiders/getLinks_Connecticut.py | 87bc385902e88e1f31f00c9a5203a58a58cdf755 | [] | no_license | djEdwards/Government-Scraper | 5ca871cbf71707cbd497412bee402da8b6b08c42 | 848c2dfaa370f0acf80bd768fc7f530e795ecb15 | refs/heads/master | 2023-02-09T10:47:55.038135 | 2020-11-27T22:56:32 | 2020-11-27T22:56:32 | 327,820,213 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 472 | py | #################
## Connecticut - GET LINKS - Scraper
## 06/29/20
## DJ Edwards
#################
import scrapy
class getLinks_Connecticut(scrapy.Spider):
name = "CT_links"
start_urls = ['https://portal.ct.gov/Coronavirus/Pages/Governors-Press-Releases'
]
def parse(self, response):
filename = 'all_CT_links.txt'
links = response.css('td a::attr(href)').getall()
with open(filename,'a') as f:
f.write(','.join(links)) | [
"DJ@DJ.attlocal.net"
] | DJ@DJ.attlocal.net |
82b9644f113f5b32dff7bbc6ece5ed6b64b7a878 | 503210080fc382f929fd958d97fe56707143faa5 | /DictionaryTagger.py | a1cd639610e258ba816ee3bc376ab1cad517eeb7 | [] | no_license | KaptaanBarbosa/eldorado | dbf63e61cfb7abc5d3461f7141f82423502f170e | b577d9fbcaea6097fb7f3bd384d6c4af8f5cb9fc | refs/heads/master | 2020-05-16T21:50:46.145650 | 2015-04-10T07:05:05 | 2015-04-10T07:05:05 | 32,032,167 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,615 | py | __author__ = 'shrivas'
import yaml
class DictionaryTagger(object):
def __init__(self, dictionary_paths):
files = [open(path, 'r') for path in dictionary_paths]
print(files);
for dict_file in files:
self.dictionaries = yaml.load(dict_file)
# def tag_sentence(self, sentence, tag_with_lemmas=False):
# """
# the result is only one tagging of all the possible ones.
# The resulting tagging is determined by these two priority rules:
# - longest matches have higher priority
# - search is made from left to right
# """
# tag_sentence = []
# N = len(sentence)
# if self.max_key_size == 0:
# self.max_key_size = N
# i = 0
# while (i < N):
# j = min(i + self.max_key_size, N) #avoid overflow
# tagged = False
# while (j > i):
# expression_form = ' '.join([word[0] for word in sentence[i:j]]).lower()
# expression_lemma = ' '.join([word[1] for word in sentence[i:j]]).lower()
# if tag_with_lemmas:
# literal = expression_lemma
# else:
# literal = expression_form
# if literal in self.dictionary:
# #self.logger.debug("found: %s" % literal)
# is_single_token = j - i == 1
# original_position = i
# i = j
# taggings = [tag for tag in self.dictionary[literal]]
# tagged_expression = (expression_form, expression_lemma, taggings)
# if is_single_token: #if the tagged literal is a single token, conserve its previous taggings:
# original_token_tagging = sentence[original_position][2]
# tagged_expression[2].extend(original_token_tagging)
# tag_sentence.append(tagged_expression)
# tagged = True
# else:
# j = j - 1
# if not tagged:
# tag_sentence.append(sentence[i])
# i += 1
# return tag_sentence
contentArray =['Starbucks is not doing very well lately.',
'Overall, while it may seem there is already a Starbucks on every corner, Starbucks still has a lot of room to grow.',
'They just began expansion into food products, which has been going quite well so far for them.',
'I can attest that my own expenditure when going to Starbucks has increased, in lieu of these food products.',
'Starbucks is also indeed expanding their number of stores as well.',
'Starbucks still sees strong sales growth here in the united states, and intends to actually continue increasing this.',
'Starbucks also has one of the more successful loyalty programs, which accounts for 30% of all transactions being loyalty-program-based.',
'As if news could not get any more positive for the company, Brazilian weather has become ideal for producing coffee beans.',
'Brazil is the world\'s #1 coffee producer, the source of about 1/3rd of the entire world\'s supply!',
'Given the dry weather, coffee farmers have amped up production, to take as much of an advantage as possible with the dry weather.',
'Increase in supply... well you know the rules...',]
dicttagger = DictionaryTagger([ '/users/shrivas/Desktop/positive.yml', '/users/shrivas/Desktop/negative.yml'])
| [
"shri.sid@gmail.com"
] | shri.sid@gmail.com |
08aff1c9deeb30d9b508c41d8160a58e126e8571 | 7f2f46167e5c7362a703203a5c35d03ce7236489 | /dtelbot/oop.py | de0e0370ac35779b8699bf041c5eb182538d19c7 | [] | no_license | dalor/dsr-bot | fb15b97ceb2688f0c4455b1432e064de1d4f7886 | a2889fdc2eba520da9d4f56bc8cdfe1ed1569381 | refs/heads/master | 2020-05-26T17:54:41.270765 | 2019-05-24T08:56:15 | 2019-05-24T08:56:15 | 188,327,528 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,271 | py | from .bot_core import BotCore
class Bot(BotCore):
def __init__(self, token, proxy=None):
super().__init__(token, proxy)
self.oop = True
def message(self, text, path=['text']):
def reg(old):
self.register(['message'], old, text, path)
return old
return reg
def edited_message(self, text, path=['text']):
def reg(old):
self.register(['edited_message'], old, text, path)
return old
return reg
def channel_post(self, text, path=['text']):
def reg(old):
self.register(['channel_post'], old, text, path)
return old
return reg
def edited_channel_post(self, text, path=['text']):
def reg(old):
self.register(['edited_channel_post'], old, text, path)
return old
return reg
def inline_query(self, text, path=['query']):
def reg(old):
self.register(['inline_query'], old, text, path)
return old
return reg
def chosen_inline_result(self, text, path=['result_id']):
def reg(old):
self.register(['chosen_inline_result'], old, text, path)
return old
return reg
def callback_query(self, text, path=['data']):
def reg(old):
self.register(['callback_query'], old, text, path)
return old
return reg
| [
"dalor@i.ua"
] | dalor@i.ua |
b92f48348a9611da3ff38531ee8b12c81121f7ca | 16e5c3118faf5184a3a5553d576cf5aa86d51cef | /stats-ms/src/measures/light.py | bde2871d0091f570f375c8e31e7ea2d811a5b1cf | [
"ISC"
] | permissive | 7Rocky/IoT_Microservices | a2913372e3413622c6749e33a7404c815fda6e74 | c42de668609965068f8feb87ee80fc618524b679 | refs/heads/master | 2023-03-15T13:18:32.016924 | 2021-06-12T10:02:13 | 2021-06-12T10:02:13 | 236,727,290 | 3 | 0 | ISC | 2023-03-05T01:11:59 | 2020-01-28T12:12:49 | TypeScript | UTF-8 | Python | false | false | 1,289 | py | from statistics import fmean
from src.measures.measure import Measure
class Light(Measure):
def __init__(self, queue_collection, max_items):
super().__init__(queue_collection, max_items)
def calculate_stats(self, data):
dates = [ d.get('date') for d in data ]
digital_values = [ d.get('digital_value') for d in data ]
timestamps = [ d.get('timestamp') for d in data ]
init_date, end_date = min(dates), max(dates)
init_timestamp, end_timestamp = min(timestamps), max(timestamps)
time_span = end_timestamp - init_timestamp
mean_value = fmean(digital_values)
n_samples = len(data)
sensor = data[0].get('sensor')
username = data[0].get('username')
ip = data[0].get('ip')
measure = data[0].get('measure')
return {
'digital_values': digital_values,
'end_date': end_date,
'end_timestamp': end_timestamp,
'init_date': init_date,
'init_timestamp': init_timestamp,
'ip': ip,
'mean_value': round(mean_value, 1),
'measure': measure,
'n_samples': n_samples,
'sensor': sensor,
'time_span': time_span,
'username': username
}
| [
"rockygitt@gmail.com"
] | rockygitt@gmail.com |
f3202b4a5d15caf6eee16f017b65661b6f612ab3 | dc30b23b8ae7cfc9c217b9d5704622403a35545a | /transfer_learning.py | 2259c6cb20dbb789c8a014f0b80ae29bf559937b | [] | no_license | AlexTaguchi/transfer-learning-tutorial | ef1ee6fa8ac9a00ffa73556a5f2ee31c86ced78a | 405eab6371649eeac2384d95a945b96c78b45248 | refs/heads/master | 2020-12-21T10:37:01.476687 | 2020-01-27T03:15:53 | 2020-01-27T03:15:53 | 236,404,558 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 6,002 | py | # Modules
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
import matplotlib.pyplot as plt
import time
import os
import copy
# Parameters
directory = 'data'
epochs = 20
# Normalize data and augmentation for training
data_transforms = {
'train': transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
'val': transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
}
# Read image data directory
image_datasets = {x: datasets.ImageFolder(os.path.join(directory, x), data_transforms[x])
for x in ['train', 'val']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
class_names = image_datasets['train'].classes
# Initialize dataloader
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=10, shuffle=True, num_workers=4)
for x in ['train', 'val']}
# Run on GPU if available
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Train model
def train_model(model, criterion, optimizer, scheduler, epochs=10):
# Record start time of training
start_time = time.time()
# Preallocate best model weights and accuracy
best_model_weights = copy.deepcopy(model.state_dict())
best_accuracy = 0.0
# Train over multiple epochs
for epoch in range(1, epochs + 1):
print(f'Epoch {epoch}/{epochs}')
print('-' * 10)
# Start with train phase then switch to validation phase
for phase in ['train', 'val']:
if phase == 'train':
model.train()
else:
model.eval()
# Preallocate the running loss and number of correct predictions
running_loss = 0.0
running_corrects = 0
# Pass inputs through model
for inputs, labels in dataloaders[phase]:
inputs = inputs.to(device)
labels = labels.to(device)
# Zero the parameter gradients
optimizer.zero_grad()
# Remember gradients for backpropagation only in training phase
with torch.set_grad_enabled(phase == 'train'):
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
loss = criterion(outputs, labels)
# Optimize model only in training phase
if phase == 'train':
loss.backward()
optimizer.step()
# Increment running statistics
running_loss += loss.item() * inputs.size(0)
running_corrects += torch.sum(preds == labels.data)
# Schedule optimizer learning rate
if phase == 'train':
scheduler.step()
# Report loss and accuracy of current phase
epoch_loss = running_loss / dataset_sizes[phase]
epoch_accuracy = running_corrects.double() / dataset_sizes[phase]
print(f'{phase.capitalize()} Loss: {epoch_loss:.4f} Accuracy: {epoch_accuracy:.4f}')
# Keep track of the best running model on the validation set
if phase == 'val' and epoch_accuracy > best_accuracy:
best_accuracy = epoch_accuracy
best_model_weights = copy.deepcopy(model.state_dict())
print()
# Report final model performance
time_elapsed = time.time() - start_time
print(f'Training complete in {time_elapsed // 60:.0f}m {time_elapsed % 60:.0f}s')
print(f'Best Validation Accuracy: {best_accuracy:4f}')
# Return best model weights
model.load_state_dict(best_model_weights)
return model
# Visualize model
def visualize_model(model):
# Remember model training mode and switch to evaluation mode
training_mode = model.training
model.eval()
# Pass a batch of validation inputs without recording gradients
plt.figure()
with torch.no_grad():
for i, (inputs, labels) in enumerate(dataloaders['val']):
# Predict labels from inputs
inputs = inputs.to(device)
labels = labels.to(device)
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
# Plot images and predicted labels
if i < 9:
ax = plt.subplot(3, 3, i + 1)
ax.axis('off')
ax.set_title('Predicted: {}'.format(class_names[preds[i]]))
inp = inputs.cpu().data[i].numpy().transpose((1, 2, 0))
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
inp = inp * std + mean
inp = np.clip(inp, 0, 1)
plt.imshow(inp)
plt.pause(0.001)
else:
break
# Return model back to original training mode
model.train(mode=training_mode)
# Load pretrained model
model = models.resnet18(pretrained=True)
# Replace the final fully connected layer for one with the correct number of outputs
features = model.fc.in_features
model.fc = nn.Linear(features, len(class_names))
model = model.to(device)
# Set up loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
# Decay learning rate by a factor of 0.1 every 5 epochs
scheduler = lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.1)
# Train model
model = train_model(model, criterion, optimizer, scheduler, epochs=epochs)
# Visualize model performance
visualize_model(model)
plt.show()
| [
"thatflippingfool@gmail.com"
] | thatflippingfool@gmail.com |
9c5292e5596095a44765634c9f056877293f618d | b9a110d34d0de3ef91598c7557d41864de3faedb | /SystemCode/Level_Up_Chatbot/Level_Up_App/careerendpointrules.py | 37da14129b3cc65d0b4626c860c6265b2cd4cc55 | [] | no_license | raymondng76/IRS-CS-2019-07-29-IS1FT-GRP-Team10-Personal-Career-Manager-Software-Agent | d316570e6062e72c306942b19fa77d96406c5fb7 | 3f4dd28906575adc30e25e8a5abef545d27b06fd | refs/heads/master | 2020-06-27T12:11:36.544329 | 2019-08-31T08:24:30 | 2019-08-31T08:24:30 | 199,951,810 | 1 | 2 | null | 2019-08-31T08:24:31 | 2019-08-01T01:10:30 | Python | UTF-8 | Python | false | false | 313 | py | from experta import *
class QuestionaireFact(Fact):
"""Fact input from the questionaire"""
pass
careerendpoint = ''
class CareerEndPointRecommender(KnowledgeEngine):
@Rule()
def recommend(self):
"""Recommend Career End Point"""
global careerendpoint
careerendpoint = ''
| [
"35454616+raymondng76@users.noreply.github.com"
] | 35454616+raymondng76@users.noreply.github.com |
d440eb8ab95e9e6084cfda38196829a2ddf59596 | 4643079e397dff1bfa0e001f9afa00708386da2a | /apps/system/models.py | 50dee401737268ee253bf99b8ceecbe731142cf1 | [] | no_license | 3x7r3m157/python_blackbelt | 7bb27f2bd3e279ece9389a794ba0d5ea97bce974 | fb2a70c3352e2604fe155dab33fc2ab46fea2c64 | refs/heads/master | 2020-03-17T22:13:27.082424 | 2018-05-18T19:09:04 | 2018-05-18T19:09:04 | 133,994,314 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,770 | py | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
import bcrypt, re, random
from django.db import models
class UserManager(models.Manager):
def validation(request, postData):
status = { 'valid' : True, 'error': [] }
if not re.match(r'^[a-zA-Z ]+$', postData['name']) or not re.search(r'[a-zA-Z]{3,50}',postData['name']):
status['valid'] = False
status['error'].append('Your name must be at least 3 characters and no longer than 50, sucka.')
if not re.match(r'^[a-zA-Z0-9 ]+$', postData['username'] or not re.search(r'[a-zA-Z0-9]{3,50}', postData['username'])):
status['valid'] = False
status['error'].append('Your username must be at least 3 characters and no longer than 50, sucka.')
if not re.match(r'^[a-zA-Z0-9.+_-]+@[a-zA-Z0-9._-]+\.[a-zA-Z]+$', postData['email']):
status['valid'] = False
status['error'].append('Your email address sucks. Try again.')
if len(User.objects.filter(email=postData['email'])) > 0:
status['valid'] = False
status['error'].append('That email has been taken.')
if len(postData['password']) < 8:
status['valid'] = False
status['error'].append('Password has to be at least 8 characters long.')
if not postData['password'] == postData['confirmpassword']:
status['valid'] = False
status['error'].append('Your password doesnt match.')
if not postData['birthday']:
status['valid'] = False
status['error'].append('Please enter your birthday.')
return status
def startlogin(self, postData):
user = User.objects.filter(email=postData['email'])
status = {
'verified' : False,
'error' : 'Your email or password doesnt match.',
}
if len(user) > 0 and bcrypt.checkpw(postData['password'].encode(),user[0].password.encode()):
status['user'] = user[0]
status['verified'] = True
return status
def usercreator(self, postData):
self.create(name=postData['name'],username=postData['username'],email=postData['email'],password=bcrypt.hashpw(postData['password'].encode(), bcrypt.gensalt()), birthday=postData['birthday'])
class User(models.Model):
name = models.CharField(max_length=50)
username = models.CharField(max_length=50)
email = models.CharField(max_length=50)
password = models.CharField(max_length=50)
birthday = models.DateField(auto_now=False)
objects = UserManager()
def __repr__(self):
return "<User object: {} {} {} {}>".format(self.name, self.username, self.email, self.password)
class ApptManager(models.Manager):
def createAppt(self, postData, user_id):
me = User.objects.get(id=user_id)
self.create(task=postData['task'],time=postData['time'],date=postData['date'],madefor=me)
def destroyAppt(self, postData):
Appt.objects.get(id=postData['appt_id']).delete()
def updateAppt(self, postData):
appt = Appt.objects.get(id=postData['appt_id'])
appt.task = postData['task']
appt.time = postData['time']
appt.date = postData['date']
appt.status = postData['status']
appt.save()
class Appt(models.Model):
task = models.CharField(max_length=50)
time = models.TimeField(auto_now_add=False)
date = models.DateField(auto_now_add=False)
status = models.CharField(max_length=50, default='pending')
madefor = models.ForeignKey(User, related_name='appointments')
objects = ApptManager()
def __repr__(self):
return "<|||APPOINTMENT - task: {} time: {} date: {} status: {}|||>".format(self.task, self.time, self.date, self.status)
| [
"austin@hackernest.com"
] | austin@hackernest.com |
10a23acbe5b537f474ea2602ec0dc6108d8992ac | 529e713a78e82de2ae5d44cfb8ef209e0894d72a | /subprocess/popen_pipe.py | 35776238852d3f4187f52a4212983d822d6e614e | [
"MIT"
] | permissive | realpython/materials | cd2f548276be2c82f134ca03eadb1cd279e0f26e | d2d62756d3854f54a12a767f2bf9470486c0ceef | refs/heads/master | 2023-09-05T22:12:29.806738 | 2023-08-31T20:56:28 | 2023-08-31T20:56:28 | 132,374,697 | 4,678 | 6,482 | MIT | 2023-09-12T22:22:06 | 2018-05-06T20:46:18 | HTML | UTF-8 | Python | false | false | 393 | py | """
**Only works on Linux or macOS**
Demonstrates using `subprocess.Popen()` to pipe one command into the other.
"""
import subprocess
ls_process = subprocess.Popen(["ls", "/usr/bin"], stdout=subprocess.PIPE)
grep_process = subprocess.Popen(
["grep", "python"], stdin=ls_process.stdout, stdout=subprocess.PIPE
)
for line in grep_process.stdout:
print(line.decode("utf-8").strip())
| [
"iansedano@gmail.com"
] | iansedano@gmail.com |
f982ebb9e9cd158b5998ccb248565e40d9cecb2f | 5ba2e42e54110b985aa4e9e35ad0f74fde750bd2 | /fromsapp/migrations/0006_favorites.py | 609dd28e3e5e678342c01586f8741f92f98c4c19 | [] | no_license | BoulinCamille/afpaProject | 3db33fa17d0a35c9dc05b9f541177471b4a6e75a | 8fbcfdc9e6480836291e189b5257211a18f1db49 | refs/heads/main | 2022-12-28T00:39:56.709098 | 2020-10-13T12:17:59 | 2020-10-13T12:17:59 | 303,692,307 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,205 | py | # Generated by Django 3.0.3 on 2020-05-30 00:53
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
('fromsapp', '0005_auto_20200529_1828'),
]
operations = [
migrations.CreateModel(
name='Favorites',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=100)),
('amazon_url', models.TextField(default='')),
('ldlc_url', models.TextField(default='')),
('maxgaming_url', models.TextField(default='')),
('amazon_price', models.CharField(default='', max_length=100)),
('ldlc_price', models.CharField(default='', max_length=100)),
('maxgaming_price', models.CharField(default='', max_length=100)),
('search_user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
],
),
]
| [
"camille.boulin@outolook.fr"
] | camille.boulin@outolook.fr |
40da64597bcd7c63aa5eb0ecc1d623febf2cbf13 | 5ff2a02cd7de012284e8fbecbab759477fc5af34 | /venv/bin/django-admin | 74aadd3beefb7f044425211421717f75bcb56e66 | [
"MIT"
] | permissive | ezro1245/civilas_web | c34bf1084e414dfd7935f8c761628f305c0d756b | 1195142ab5db94ffd1ad0526c389dcac043331c6 | refs/heads/main | 2023-04-22T07:49:32.464127 | 2021-05-10T06:38:02 | 2021-05-10T06:38:02 | 365,847,523 | 0 | 0 | MIT | 2021-05-10T03:17:11 | 2021-05-09T21:02:29 | Python | UTF-8 | Python | false | false | 307 | #!/Users/ezro/Desktop/repositorios/civilas_web/venv/bin/python
# -*- coding: utf-8 -*-
import re
import sys
from django.core.management import execute_from_command_line
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
sys.exit(execute_from_command_line())
| [
"ezrosoftware@gmail.com"
] | ezrosoftware@gmail.com | |
f8a77d8cbbd2ac50088d68d544b1ec1ffc16f9ec | 1db123951c96f41cf8dde9fa0ad0f08dd9790c50 | /books_authors_app/migrations/0001_initial.py | d0b394ab27f6402a272b3853db22d14a6ab18aa4 | [] | no_license | Eliherc1/django-orm-libros | 6fa7cf8e5e6c984ef50fc6d16112864a34bba866 | 69451c22006694d2a2376de3a44a3fd47b036a9e | refs/heads/main | 2023-07-11T04:31:07.320351 | 2021-08-13T03:38:28 | 2021-08-13T03:38:28 | 395,134,169 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,282 | py | # Generated by Django 3.2.3 on 2021-08-11 00:48
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='Book',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(max_length=255)),
('description', models.TextField()),
('created_at', models.DateTimeField(auto_now_add=True)),
('updated_at', models.DateTimeField(auto_now=True)),
],
),
migrations.CreateModel(
name='Author',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('first_name', models.CharField(max_length=45)),
('last_name', models.CharField(max_length=45)),
('created_at', models.DateTimeField(auto_now_add=True)),
('updated_at', models.DateTimeField(auto_now=True)),
('books', models.ManyToManyField(related_name='authors', to='books_authors_app.Book')),
],
),
]
| [
"elidezx@gmail.com"
] | elidezx@gmail.com |
91cbf485313be09b32b5b61c68eca2dc7f3d26bc | 35e5d3eae65f2c602304cf5a65f4f29be951ab9b | /mysite/firstapp/migrations/0005_blog_post.py | 4860497af483c20d55668d83b96de6f08d34eb4a | [] | no_license | bor1senko/lab3 | 930fadcf51485edd28ad1fdaf6b56699c234bfe9 | 446c83df2f88a6637004549e96e43e050cd254e2 | refs/heads/master | 2021-01-17T10:20:46.086325 | 2016-06-09T20:18:00 | 2016-06-09T20:18:00 | 58,852,360 | 7 | 0 | null | null | null | null | UTF-8 | Python | false | false | 671 | py | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('firstapp', '0004_auto_20160518_0124'),
]
operations = [
migrations.CreateModel(
name='blog_post',
fields=[
('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),
('post_text', models.CharField(max_length=400)),
('image', models.ImageField(upload_to=b'')),
('add_name', models.ForeignKey(to='firstapp.Author')),
],
),
]
| [
"i.borisenko97@gmail.com"
] | i.borisenko97@gmail.com |
cd7a71b8a3f440db5afe0529105e12055373b712 | a112b605c23bd76056e9d8fb2dc506748dfd7106 | /recruiting_analytics_backend/cohort_management/business/queries/api/torre/candidate/fetch_candidate_information_by_id.py | b214b92b35b995fb20a22f329327f5d6714c31a7 | [] | no_license | jmromeroeci/recruiting-analytics-project | ee41cd418901f5cd09825077e04cf7bf53a82138 | 126b0c3a7a01cc536238ef3bf2b4ff6c613d55ec | refs/heads/main | 2023-02-09T01:45:21.779475 | 2021-01-05T04:46:21 | 2021-01-05T04:46:21 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,325 | py | from ..torre_query import TorreQuery
from typing import Dict, List
from cohort_management.business.domain.candidate.candidate_information import CandidateInformation, JobInformation, LinkInformation
from cohort_management.business.domain.cohort.cohort_information import OrganizationInformation
class FetchCandidateById(TorreQuery):
def execute(self, candidate_id: str, **kwargs) -> CandidateInformation:
return self.get(TorreQuery.ROOT_URL_BIO + "bios/" + candidate_id, **kwargs)
def _build_organization_dto(self, organization: Dict) -> OrganizationInformation:
return OrganizationInformation(
name=organization["name"],
picture=organization.get("picture", "no-picture")
)
def _build_job_dto(self, job: Dict) -> JobInformation:
return JobInformation(
name=job["name"],
organizations=list(map(self._build_organization_dto, job["organizations"]))
)
def _build_link_dto(self, link: Dict) -> LinkInformation:
return LinkInformation(
name=link["name"],
url=link.get("address", "")
)
def _build_dto(self, response: Dict, **kwargs) -> CandidateInformation:
print(response)
return CandidateInformation(
username=response["person"].get("name", ""),
country=response["person"]["location"]["country"] if "location" in response["person"] else "No Country",
platform_name="Torre",
public_id=response["person"]["publicId"],
bio=response.get("summaryOfBio", ""),
strengths=list(map(lambda strength: strength["name"], response.get("strengths", []))),
interests=list(map(lambda interest: interest["name"], response.get("interests", []))),
jobs=list(map(self._build_job_dto, response.get("jobs", []))),
links=list(map(self._build_link_dto, response.get("strengths", []))),
number_of_strengths=response["stats"].get("strengths", 0),
number_of_awards=response["stats"].get("awards", 0),
number_of_interests=response["stats"].get("interests", 0),
number_of_jobs=response["stats"].get("jobs", 0),
number_of_projects=response["stats"].get("projects", 0),
cohort_id=kwargs["cohort_id"]
)
| [
"jmromeroe@gmail.com"
] | jmromeroe@gmail.com |
0e007e2feb4aaf894b3051e459134496f45c63e5 | e2173ceff2c17a48251755fce244a814fc893d85 | /trading/backtest/__init__.py | 383d2b4b14173b42ac98700c057e25154727d6d8 | [
"MIT"
] | permissive | webclinic017/qfengine | 687a99534f2ab7011c3a643881ae68d98b44eabe | f71c263becb82ee5b7022c17d7983b40d5df31bb | refs/heads/main | 2023-02-27T12:37:39.021657 | 2021-02-08T01:39:47 | 2021-02-08T01:39:47 | 400,952,906 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,997 | py | #--| Pseudos
from qfengine.trading.backtest.backtest import BacktestTradingSession
''' PriceHandler, Strategy, PortfolioHandler, PositionSizer, RiskManager and ExecutionHandler.
The main components are the
They handle portfolio/order management system and
brokerage connection functionality.
The system is event-driven and communicates via an events queue using subclassed Event
objects. The full list of components is as follows:
• Event - All "messages" of data within the system are encapsulated in an Event object. The various events include TickEvent, BarEvent, SignalEvent, SentimentEvent,
OrderEvent and FillEvent.
• Position - This class encapsulates all data associated with an open position in an asset.
That is, it tracks the realised and unrealised profit and loss (PnL) by averaging the multiple
"legs" of the transaction, inclusive of transaction costs.
• Portfolio - The Portfolio class encapsulates a list of Positions, as well as a cash
balance, equity and PnL. This object is used by the PositionSizer and RiskManager
objects for portfolio construction and risk management purposes.
• PortfolioHandler - The PortfolioHandler class is responsible for the management of
the current Portfolio, interacting with the RiskManager and PositionSizer as well as
submitting orders to be executed by an ExecutionHandler.
• PriceHandler - The PriceHandler and derived subclasses are used to ingest financial
asset pricing data from various sources. In particular, there are separate class hierarchies
for bar and tick data.
• Strategy - The Strategy object and subclasses contain the "alpha generation" code for
creating trading signals.
• PositionSizer - The PositionSizer class provides the PortfolioHandler with guidance
on how to size positions once a strategy signal is received. For instance the PositionSizer
could incorporate a Kelly Criterion approach or carry out monthly rebalancing of a fixedweight portfolio.
• RiskManager - The RiskManager is used by the PortfolioHandler to verify, modify
or veto any suggested trades that pass through from the PositionSizer, based on the
current composition of the portfolio and external risk considerations (such as correlation
to indices or volatility).
• ExecutionHandler - This object is tasked with sending orders to brokerages and receiving
"fills". For backtesting this behaviour is simulated, with realistic fees taken into account.
• Statistics - This is used to produce performance reports from backtests. A "tearsheet" capability has recently been added providing detailed statistics on equity curve performance,
with benchmark comparison.
• Backtest - Encapsulates the event-driven behaviour of the system, including the handling
of the events queue. Requires knowledge of all other components in order to simulate a full
backtest.
'''
| [
"35281717+xphysics@users.noreply.github.com"
] | 35281717+xphysics@users.noreply.github.com |
680915b69a019fc0e3af9cad490ccd1a67d7f264 | 4bc29617a307de54a7fe300c8e863f03321bd003 | /lib/python3.8/site-packages/trytond/tests/test_field_one2many.py | eee8fc6605089b9d591beb06b8f0da5ed94aa0eb | [] | no_license | Davidoff2103/tryton-training | f594970e77646f0ffeb42eb4f903252ff0b6c201 | 8d1ec4f2b623f7ca48f38bfda2ac15c01ded35a7 | refs/heads/master | 2023-06-01T11:55:05.400233 | 2021-06-09T10:06:56 | 2021-06-09T10:06:56 | 375,275,666 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 15,817 | py | # This file is part of Tryton. The COPYRIGHT file at the top level of
# this repository contains the full copyright notices and license terms.
import unittest
from trytond.exceptions import UserError
from trytond.pool import Pool
from trytond.tests.test_tryton import activate_module, with_transaction
class CommonTestCaseMixin:
@with_transaction()
def test_create(self):
"Test create one2many"
One2Many = self.One2Many()
one2many, = One2Many.create([{
'targets': [
('create', [{
'name': "Target",
}]),
],
}])
self.assertEqual(len(one2many.targets), 1)
@with_transaction()
def test_search_equals(self):
"Test search one2many equals"
One2Many = self.One2Many()
one2many, = One2Many.create([{
'targets': [('create', [{'name': "Target"}])],
}])
one2manys = One2Many.search([
('targets', '=', "Target"),
])
self.assertListEqual(one2manys, [one2many])
@with_transaction()
def test_search_equals_no_link(self):
"Test search one2many equals without link"
One2Many = self.One2Many()
one2many, no_link = One2Many.create([{
'targets': [('create', [{'name': "Target"}])],
}, {
}])
one2manys = One2Many.search([
('targets', '=', "Target"),
])
self.assertListEqual(one2manys, [one2many])
@with_transaction()
def test_search_non_equals(self):
"Test search one2many non equals"
One2Many = self.One2Many()
one2many, = One2Many.create([{
'targets': [('create', [{'name': "Target"}])],
}])
one2manys = One2Many.search([
('targets', '!=', "Target"),
])
self.assertListEqual(one2manys, [])
@with_transaction()
def test_search_equals_none(self):
"Test search one2many equals None"
One2Many = self.One2Many()
one2many1, one2many2 = One2Many.create([{
'targets': [('create', [{'name': "Target"}])],
}, {
'targets': None,
}])
one2manys = One2Many.search([
('targets', '=', None),
])
self.assertListEqual(one2manys, [one2many2])
@with_transaction()
def test_search_non_equals_none(self):
"Test search one2many non equals None"
One2Many = self.One2Many()
one2many1, one2many2 = One2Many.create([{
'targets': [('create', [{'name': "Target"}])],
}, {
'targets': None,
}])
one2manys = One2Many.search([
('targets', '!=', None),
])
self.assertListEqual(one2manys, [one2many1])
@with_transaction()
def test_search_non_equals_no_link(self):
"Test search one2many non equals without link"
One2Many = self.One2Many()
one2many, no_link = One2Many.create([{
'targets': [('create', [{'name': "Target"}])],
}, {
}])
one2manys = One2Many.search([
('targets', '!=', "Target"),
])
self.assertListEqual(one2manys, [no_link])
@with_transaction()
def test_search_in(self):
"Test search one2many in"
One2Many = self.One2Many()
one2many, = One2Many.create([{
'targets': [('create', [{'name': "Target"}])],
}])
target, = one2many.targets
one2manys = One2Many.search([
('targets', 'in', [target.id]),
])
self.assertListEqual(one2manys, [one2many])
@with_transaction()
def test_search_in_0(self):
"Test search one2many in [0]"
One2Many = self.One2Many()
one2many, = One2Many.create([{
'targets': [('create', [{'name': "Target"}])],
}])
one2manys = One2Many.search([
('targets', 'in', [0]),
])
self.assertListEqual(one2manys, [])
@with_transaction()
def test_search_in_empty(self):
"Test search one2many in []"
One2Many = self.One2Many()
one2many, = One2Many.create([{
'targets': [('create', [{'name': "Target"}])],
}])
one2manys = One2Many.search([
('targets', 'in', []),
])
self.assertListEqual(one2manys, [])
@with_transaction()
def test_search_not_in(self):
"Test search one2many not in"
One2Many = self.One2Many()
one2many, = One2Many.create([{
'targets': [('create', [{'name': "Target"}])],
}])
target, = one2many.targets
one2manys = One2Many.search([
('targets', 'not in', [target.id]),
])
self.assertListEqual(one2manys, [])
@with_transaction()
def test_search_not_in_0(self):
"Test search one2many not in [0]"
One2Many = self.One2Many()
one2many, = One2Many.create([{
'targets': [('create', [{'name': "Target"}])],
}])
one2manys = One2Many.search([
('targets', 'not in', [0]),
])
self.assertListEqual(one2manys, [one2many])
@with_transaction()
def test_search_not_in_empty(self):
"Test search one2many not in []"
One2Many = self.One2Many()
one2many, = One2Many.create([{
'targets': [('create', [{'name': "Target"}])],
}])
one2manys = One2Many.search([
('targets', 'not in', []),
])
self.assertListEqual(one2manys, [one2many])
@with_transaction()
def test_search_join(self):
"Test search one2many equals"
One2Many = self.One2Many()
one2many, = One2Many.create([{
'targets': [('create', [{'name': "Target"}])],
}])
one2manys = One2Many.search([
('targets.name', '=', "Target"),
])
self.assertListEqual(one2manys, [one2many])
@with_transaction()
def test_search_where(self):
"Test search one2many where"
One2Many = self.One2Many()
one2many, = One2Many.create([{
'targets': [('create', [{'name': "Target"}])],
}])
one2manys = One2Many.search([
('targets', 'where', [('name', '=', "Target")]),
])
self.assertListEqual(one2manys, [one2many])
@with_transaction()
def test_search_not_where(self):
"Test search one2many not where"
One2Many = self.One2Many()
one2many, = One2Many.create([{
'targets': [('create', [{'name': "Target"}])],
}])
one2manys = One2Many.search([
('targets', 'not where', [('name', '=', "Target")]),
])
self.assertListEqual(one2manys, [])
@with_transaction()
def test_write_write(self):
"Test write one2many write"
One2Many = self.One2Many()
one2many, = One2Many.create([{
'targets': [('create', [{'name': "Foo"}])],
}])
target, = one2many.targets
One2Many.write([one2many], {
'targets': [
('write', [target.id], {
'name': "Bar",
}),
],
})
self.assertEqual(target.name, "Bar")
@with_transaction()
def test_write_add(self):
"Test write one2many add"
One2Many = self.One2Many()
Target = self.One2ManyTarget()
one2many, = One2Many.create([{}])
target, = Target.create([{}])
One2Many.write([one2many], {
'targets': [
('add', [target.id]),
],
})
self.assertTupleEqual(one2many.targets, (target,))
@with_transaction()
def test_write_remove(self):
"Test write one2many remove"
One2Many = self.One2Many()
Target = self.One2ManyTarget()
one2many, = One2Many.create([{
'targets': [('create', [{'name': "Foo"}])],
}])
target, = one2many.targets
One2Many.write([one2many], {
'targets': [
('remove', [target.id]),
],
})
targets = Target.search([('id', '=', target.id)])
self.assertTupleEqual(one2many.targets, ())
self.assertListEqual(targets, [target])
@with_transaction()
def test_write_copy(self):
"Test write one2many copy"
One2Many = self.One2Many()
Target = self.One2ManyTarget()
one2many, = One2Many.create([{
'targets': [('create', [{'name': "Foo"}])],
}])
target1, = one2many.targets
One2Many.write([one2many], {
'targets': [
('copy', [target1.id], {'name': "Bar"}),
],
})
target2, = Target.search([('id', '!=', target1.id)])
self.assertTupleEqual(one2many.targets, (target1, target2))
@with_transaction()
def test_write_delete(self):
"Test write one2many delete"
One2Many = self.One2Many()
Target = self.One2ManyTarget()
one2many, = One2Many.create([{
'targets': [(
'create', [{'name': "Foo"}, {'name': "Bar"}])],
}])
target1, target2 = one2many.targets
One2Many.write([one2many], {
'targets': [
('delete', [target1.id]),
],
})
targets = Target.search([])
self.assertTupleEqual(one2many.targets, (target2,))
self.assertListEqual(targets, [target2])
class FieldOne2ManyTestCase(unittest.TestCase, CommonTestCaseMixin):
"Test Field One2Many"
@classmethod
def setUpClass(cls):
activate_module('tests')
def One2Many(self):
return Pool().get('test.one2many')
def One2ManyTarget(self):
return Pool().get('test.one2many.target')
@with_transaction()
def test_create_required_with_value(self):
"Test create one2many required with value"
One2Many = Pool().get('test.one2many_required')
one2many, = One2Many.create([{
'targets': [
('create', [{
'name': "Target",
}]),
],
}])
self.assertEqual(len(one2many.targets), 1)
@with_transaction()
def test_create_required_without_value(self):
"Test create one2many required without value"
One2Many = Pool().get('test.one2many_required')
with self.assertRaises(UserError):
One2Many.create([{}])
@with_transaction()
def test_create_size_valid(self):
"Test create one2many size valid"
One2Many = Pool().get('test.one2many_size')
one2many, = One2Many.create([{
'targets': [
('create', [{}] * 3),
],
}])
self.assertEqual(len(one2many.targets), 3)
@with_transaction()
def test_create_size_invalid(self):
"Test create one2many size invalid"
One2Many = Pool().get('test.one2many_size')
with self.assertRaises(UserError):
One2Many.create([{
'targets': [
('create', [{}] * 4),
],
}])
@with_transaction()
def test_create_size_pyson_valid(self):
"Test create one2many size pyson valid"
One2Many = Pool().get('test.one2many_size_pyson')
one2many, = One2Many.create([{
'limit': 4,
'targets': [
('create', [{}] * 4),
],
}])
self.assertEqual(len(one2many.targets), 4)
@with_transaction()
def test_create_size_pyson_invalid(self):
"Test create one2many size pyson invalid"
One2Many = Pool().get('test.one2many_size_pyson')
with self.assertRaises(UserError):
One2Many.create([{
'limit': 3,
'targets': [
('create', [{}] * 4),
],
}])
@with_transaction()
def test_create_filter(self):
"Test create one2many with filter"
One2Many = Pool().get('test.one2many_filter')
filtered, = One2Many.create([{
'targets': [
('create', [{'value': x} for x in range(4)])],
}])
filtered_target, = filtered.filtered_targets
self.assertEqual(len(filtered.targets), 4)
self.assertEqual(filtered_target.value, 3)
@with_transaction()
def test_create_filter_domain(self):
"Test create one2many with filter and domain"
One2Many = Pool().get('test.one2many_filter_domain')
filtered, = One2Many.create([{
'targets': [
('create', [{'value': x} for x in range(4)])],
}])
filtered_target, = filtered.filtered_targets
self.assertEqual(len(filtered.targets), 4)
self.assertEqual(filtered_target.value, 3)
@with_transaction()
def test_search_non_equals_filter(self):
"Test search one2many non equals with filter"
One2Many = Pool().get('test.one2many_filter')
one2many, = One2Many.create([{
'targets': [('create', [{'value': -1}])],
}])
one2manys = One2Many.search([('targets', '!=', None)])
one2manys_filtered = One2Many.search(
[('filtered_targets', '!=', None)])
self.assertListEqual(one2manys, [one2many])
self.assertListEqual(one2manys_filtered, [])
@with_transaction()
def test_search_join_filter(self):
"Test search one2many join with filter"
One2Many = Pool().get('test.one2many_filter')
one2many, = One2Many.create([{
'targets': [('create', [{'value': -1}])],
}])
one2manys = One2Many.search([('targets.value', '=', -1)])
one2manys_filtered = One2Many.search(
[('filtered_targets.value', '=', -1)])
self.assertListEqual(one2manys, [one2many])
self.assertListEqual(one2manys_filtered, [])
class FieldOne2ManyReferenceTestCase(unittest.TestCase, CommonTestCaseMixin):
"Test Field One2Many Reference"
@classmethod
def setUpClass(cls):
activate_module('tests')
def One2Many(self):
return Pool().get('test.one2many_reference')
def One2ManyTarget(self):
return Pool().get('test.one2many_reference.target')
def suite():
suite_ = unittest.TestSuite()
loader = unittest.TestLoader()
suite_.addTests(loader.loadTestsFromTestCase(FieldOne2ManyTestCase))
suite_.addTests(
loader.loadTestsFromTestCase(FieldOne2ManyReferenceTestCase))
return suite_
| [
"davidoff.d777@gmail.com"
] | davidoff.d777@gmail.com |
208867d87a25d6d9b92890e6301ba8120c5999a2 | 3c6c9373298fca5549d4a753bf7ed3a336d5209d | /src/haptic_device_rotation/src/haptic_device_rotation.py | 88f59fec9e71837053b2f74144a737734a711805 | [] | no_license | JLSchol/omni_marco_gazebo | 70077a4648b186bd09510e29f35664bd28f13fd0 | 050cb6dad674a5ff05b0cd5713d989d0d9bf05ee | refs/heads/master | 2023-01-30T16:13:07.620255 | 2019-12-06T15:39:30 | 2019-12-06T15:39:30 | 211,870,305 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,480 | py | #!/usr/bin/env python
from rospy import init_node, is_shutdown, Rate, loginfo, sleep, get_param
from rospy import ROSInterruptException, Time
#import messages
from geometry_msgs.msg import TransformStamped
from PyKDL import Rotation
# import numpy as np
from tf2_ros import StaticTransformBroadcaster
class hapticDeviceRotation():
def __init__(self):
init_node("haptic_device_rotation", anonymous=True)
self._getParameters()
def run(self):
rosRate = Rate(30)
broadcaster = StaticTransformBroadcaster()
while not is_shutdown():
rot = Rotation(self._rotMatrixArray[0],self._rotMatrixArray[1],self._rotMatrixArray[2],
self._rotMatrixArray[3],self._rotMatrixArray[4],self._rotMatrixArray[5],
self._rotMatrixArray[6],self._rotMatrixArray[7],self._rotMatrixArray[8])
quat = rot.GetQuaternion()
staticTransform = self._setTransform(self._robotBaseFrame,self._HDFrame,quat)
broadcaster.sendTransform(staticTransform)
rosRate.sleep()
def _setTransform(self, parentName, childName, quat):
static_transformStamped = TransformStamped()
static_transformStamped.header.stamp = Time.now()
static_transformStamped.header.frame_id = parentName
static_transformStamped.child_frame_id = childName
static_transformStamped.transform.translation.x = 0
static_transformStamped.transform.translation.y = 0
static_transformStamped.transform.translation.z = 0
static_transformStamped.transform.rotation.x = quat[0]
static_transformStamped.transform.rotation.y = quat[1]
static_transformStamped.transform.rotation.z = quat[2]
static_transformStamped.transform.rotation.w = quat[3]
return static_transformStamped
def _getParameters(self):
self._robotBaseFrame = get_param("~robot_base_frame_name")
self._HDFrame = get_param("~HD_frame_name")
rotMatrixString = get_param("~rot_matrix_array")
self._rotMatrixArray = self._getMatrixList(rotMatrixString)
def _getMatrixList(self, matrixString):
matrixList = matrixString.split(" ")
matrixListFloats = [float(char) for char in matrixList]
return matrixListFloats
if __name__ == "__main__":
try:
node = hapticDeviceRotation()
node.run()
except ROSInterruptException:
pass | [
"jasper.schol@live.nl"
] | jasper.schol@live.nl |
019ceff680a7b5a2b85774d7b7ef83055ad0bb38 | 520ad0df0ad20a764dfc7e5599a23d333d86f5d3 | /day14/main.py | 655eeddd68796139ba7c1c0190b70abd47c7dac5 | [] | no_license | machalvan/advent-of-code-2020 | 9e83743ec52f2ee677a6323b0ca8c03416ef48bb | 34717f2e665c2707e065dc3f980a79cb63ad509d | refs/heads/main | 2023-02-06T11:39:39.802352 | 2020-12-25T06:15:33 | 2020-12-25T06:15:33 | 317,325,530 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,625 | py | def part1(input):
mem = {}
mask = None
for line in input:
words = line.split(" = ")
if words[0] == "mask":
mask = words[1]
continue
adr = words[0][4:-1]
mem[adr] = masked(mask, to_bin(words[1]))
return sum([to_dec(mem[adr]) for adr in mem])
def part2(input):
mem = {}
mask = None
for line in input:
words = line.split(" = ")
if words[0] == "mask":
mask = words[1]
continue
adr = words[0][4:-1]
bin = masked2(mask, to_bin(adr))
for a in get_addresses(bin):
mem[to_dec(a)] = int(words[1])
return sum([mem[adr] for adr in mem])
def to_bin(dec):
return '{:036b}'.format(int(dec))
def to_dec(bin):
return int(bin, 2)
def masked(m, v):
masked = ''
for i, c in enumerate(list(m)):
masked += v[i] if c == 'X' else c
return masked
def masked2(m, v):
masked = ''
for i, c in enumerate(list(m)):
masked += v[i] if m[i] == '0' else '1' if m[i] == '1' else 'X'
return masked
def get_addresses(bin):
addresses = ['']
for i, c in enumerate(list(bin)):
if c == 'X':
addresses = [l + x for l in addresses for x in ('0', '1')]
else:
addresses = [l + c for l in addresses]
return addresses
if __name__ == '__main__':
with open('input.txt') as f:
file_str = f.read()
file = file_str.strip().split('\n')
print(part1(list(file)))
print(part2(list(file)))
| [
"machalvan@hotmail.com"
] | machalvan@hotmail.com |
24397dabfec84500b0351935d23351a69656c0c4 | f5dad47c5f1ec2b89f5e004b3e7075bc33cb8802 | /apps/practice_app/migrations/0001_initial.py | ff2307162a16c911f302854ade3a53c2ffd825b8 | [] | no_license | sai-koushik-macha/Coding-Cubs | e8743b533a4926304c25fe29f853cb90897eec76 | 8df3d2ded2006c8a41a799073ab7bd4d8bee0728 | refs/heads/master | 2023-04-23T01:02:20.141240 | 2021-05-10T10:12:07 | 2021-05-10T10:12:07 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,160 | py | # Generated by Django 3.1.5 on 2021-05-09 06:09
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='practice',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('category', models.CharField(blank=True, choices=[('Array', 'Array'), ('Matrix', 'Matrix'), ('String', 'String'), ('Searching & Sorting', 'Searching & Sorting'), ('LinkedList', 'LinkedList'), ('Binary Trees', 'Binary Trees'), ('Binary Search Trees', 'Binary Search Trees'), ('Greedy', 'Greedy'), ('BackTracking', 'BackTracking'), ('Stacks & Queues', 'Stacks & Queues'), ('Heap', 'Heap'), ('Graph', 'Graph'), ('Trie', 'Trie'), ('Dynamic Programming', 'Dynamic Programming'), ('Bit Manipulation', 'Bit Manipulation')], max_length=32)),
('question', models.TextField()),
('testinputs', models.CharField(blank=True, max_length=32)),
('answersource', models.TextField()),
],
),
]
| [
"motrurishashank000@gmail.com"
] | motrurishashank000@gmail.com |
bf45662e630c6cef37b042f2b344ba3a85276222 | efa652178d6d4d2d8d216dc82588ae5a7c570c19 | /py/pypg_wal_archive_clean.py | 232073db8297e5b4231838c79aea00e9c2d18ef2 | [] | no_license | juliano777/pypg_tools | 40a1993e2fdd931b4fb73aa704075e295875c5ca | 01765d21b09d95987e7cac47e8fe5212d9dba9f5 | refs/heads/master | 2023-01-24T03:15:39.891276 | 2023-01-21T20:05:58 | 2023-01-21T20:05:58 | 101,754,272 | 1 | 0 | null | 2023-01-21T20:05:59 | 2017-08-29T11:35:45 | Python | UTF-8 | Python | false | false | 4,493 | py | # _*_ coding:utf8 _*_
'''
This software is licensed under the New BSD Licence.
*******************************************************************************
Copyright (c) 2013, Juliano Atanazio - juliano777@gmail.com
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
- Redistributions of source code must retain the above copyright notice,
this list of conditions and the following disclaimer.
- Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
- Neither the name of the Juliano Atanazio nor the names of its
contributors may be used to endorse or promote products derived from this
software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*******************************************************************************
'''
import sys # sys built-in library
import os # os built-in library
# Show help function
def show_help(code_err):
'''
Function that shows the application usage and returns a code at exit.
'''
msg = '''
Clean up PostgreSQL WAL archive files.
Use {} ((--list | --remove) (directory or path) | --help)
--list List the files that can be deleted.
--remove Remove the older xlog files that can be deleted.
The second argument can be either a directory or simply the way
for an archived xlog.
If this second argument is a directory, all xlogs contained
therein (including .backup files) will be deleted (--remove) or
listed (--list).
If the path to the archived xlog is this second parameter, which
will be listed (--list) or removed (--remove) all xlogs older than
it.
'''
print(msg.format(sys.argv[0]))
sys.exit(code_err)
# Arguments
try:
arg_1 = sys.argv[1] # First argument
arg_2 = sys.argv[2] # Second argument
except:
pass
# Arguments filtering
if ((len(sys.argv) == 1) or ((len(sys.argv) == 2) and (arg_1 != '--help')) or
(len(sys.argv) > 4)):
show_help(1)
if (arg_1 == '--help'):
show_help(0)
elif ((len(sys.argv) > 2) and (arg_1 not in ('--list', '--remove'))):
show_help(1)
else:
pass
# List or Remove Function
def ls_or_rm(x, y):
'''
Function to list or remove older WAL files from PostgreSQL archives.
'''
try:
if (os.path.isdir(y)):
directory = y
bkp_files = [f for f in os.listdir(directory) if
f.endswith('.backup')]
bkp_files.sort()
ref_file = bkp_files[-1][0:24]
else:
directory = os.path.dirname(y)
ref_file = os.path.basename(y)
ref_file = ref_file[0:24]
except Exception as e:
print('{}\n{}'.format(Exception, e))
files = [f for f in os.listdir(directory) if (f < ref_file)]
if (x == '--list'):
print('\nUnnecessary Files:\n')
for file in files:
print(file)
else:
print('\nRemoving the following files in {}:\n'.format(directory))
for file in files:
try:
os.remove('{}/{}'.format(directory, file))
print(file)
except Exception as e:
print('{}\n{}'.format(Exception, e))
# The main function
def main():
'''
Main function
'''
ls_or_rm(arg_1, arg_2)
# Test: If it is executed in command line
if __name__ == '__main__':
main()
| [
"juliano777@gmail.com"
] | juliano777@gmail.com |
2b15123cf7f9abeb1a80a12b23ceb0cbb34fd739 | e1167fd508a2bae06463dd83c41dc8be2d2d9e6d | /osx/commandPlug.py | dcc9a7c04aaf120ed9fdc49b717fe3622090daf1 | [] | no_license | babache/FreeScript | 56e9fc3e09d0db05d55e60a151ce5aa1ab2b05da | 159b820d3807e7ffb887310ad8c670fa57bae4b7 | refs/heads/master | 2021-08-31T08:21:04.013987 | 2017-12-20T19:06:35 | 2017-12-20T19:06:35 | 114,917,807 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 709 | py | from pyHS100 import SmartPlug, SmartBulb, Discover
from pprint import pformat as pf
import subprocess
import re
import time
ip = ""
for dev in Discover.discover().values():
ip = re.search(r"\d{1,3}.\d{1,3}.\d{1,3}.\d{1,3}", str(dev)).group(0)
exit
if ip == "":
exit
plug = SmartPlug(ip)
output = subprocess.check_output(['pmset', '-g', 'batt'])
percent = re.search(r"\d{1,3}%", str(output)).group(0)
battery = int(percent.replace('%', ''))
if int(battery)>95 and plug.state == "ON":
print ("Turn OFF at %s" % time.strftime("%d/%m/%Y %H:%M:%S"))
plug.turn_off()
if int(battery)<20 and plug.state == "OFF":
print ("Turn ON at %s" % time.strftime("%d/%m/%Y %H:%M:%S"))
plug.turn_on()
| [
"babache@Babache.local"
] | babache@Babache.local |
6d7acb9ce754e362f6171af1764146fff1d16b0b | 16508404abab893e7fb679827eec8e28866f1435 | /blogproject/blog/models.py | 47680a2e423e4caac14598cac62385877b4caeb8 | [] | no_license | yuhuaxianfan/Yuhuaxianfan | 60324571e1ee91d70bf9c4f869d4d4536d2a655c | 29236a28a5d51f5d7862fb2f74e81c3419e96608 | refs/heads/master | 2021-01-21T13:26:15.619934 | 2017-10-20T05:03:29 | 2017-10-20T05:03:29 | 102,122,119 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 832 | py | from django.db import models
from django.contrib.auth.models import User
from django.core.urlresolvers import reverse
class Category(models.Model):
name = models.CharField(max_length=100)
def __str__(self):
return self.name
class Tag(models.Model):
name = models.CharField(max_length=100)
def __str__(self):
return self.name
class Post(models.Model):
title = models.CharField(max_length=70)
body = models.TextField()
created_time = models.DateField()
modified_time = models.DateTimeField()
excerpt = models.CharField(max_length=200,blank=True)
category = models.ForeignKey(Category)
tags = models.ManyToManyField(Tag,blank=True)
author = models.ForeignKey(User)
def __str__(self):
return self.title
def get_absolute_url(self):
return reverse('blog:detail',kwargs={'pk':self.pk})
# Create your models here.
| [
"447724352@qq.com"
] | 447724352@qq.com |
9180b287d75bebfb527e3af7057b4cb7e090a0c2 | 78d31def976fc38898e37d1429a75978848c288b | /soundhound/B.py | 823f35d9a93e39403b56805053e7d4c76c19a5a8 | [] | no_license | RayNakagami/Atcoder | c04ad749b7cda6a2e96d3dac7e549273f34c8a2d | d8c26140f0e769c018877e7e3e8a1867d96feaf0 | refs/heads/master | 2020-03-31T02:15:24.868098 | 2018-10-06T07:36:28 | 2018-10-06T07:36:28 | 151,815,164 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 46 | py | S = input()
w = int(input())
print(S[0::w])
| [
"ray.nakagami@gmail.com"
] | ray.nakagami@gmail.com |
79dc973cef96d3e5eef6f7cd3552f8a91bf78cb4 | b71f656374293c5f1238fcb449aa4dde78632861 | /eudplib/utils/blockstru.py | c3e9a5159a3ca9e5b4b729aab409a5ff123314d1 | [
"MIT"
] | permissive | tobeinged/eudplib | ce1cdc15f7ec6af857b4b64b5c826b3dd95d3e48 | 066c0faa200dc19e70cdb6979daf8f008b8ae957 | refs/heads/master | 2023-05-04T08:49:01.180147 | 2019-03-18T14:30:29 | 2019-03-18T14:30:29 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,549 | py | #!/usr/bin/python
# -*- coding: utf-8 -*-
'''
Copyright (c) 2014 trgk
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
'''
from .eperror import ep_assert
class BlockStruManager:
def __init__(self):
self._blockstru = []
self._lastblockdict = {}
def empty(self):
return not self._blockstru
_current_bsm = BlockStruManager() # Default one
def SetCurrentBlockStruManager(bsm):
global _current_bsm
old_bsm = _current_bsm
_current_bsm = bsm
return old_bsm
def EUDCreateBlock(name, userdata):
_blockstru = _current_bsm._blockstru
_lastblockdict = _current_bsm._lastblockdict
block = (name, userdata)
_blockstru.append(block)
if name not in _lastblockdict:
_lastblockdict[name] = []
_lastblockdict[name].append(block)
def EUDGetLastBlock():
_blockstru = _current_bsm._blockstru
return _blockstru[-1]
def EUDGetLastBlockOfName(name):
_lastblockdict = _current_bsm._lastblockdict
return _lastblockdict[name][-1]
def EUDPeekBlock(name):
lastblock = EUDGetLastBlock()
ep_assert(lastblock[0] == name, 'Block starting/ending mismatch')
return lastblock
def EUDPopBlock(name):
_blockstru = _current_bsm._blockstru
_lastblockdict = _current_bsm._lastblockdict
lastblock = _blockstru.pop()
ep_assert(lastblock[0] == name, """\
Block starting/ending mismatch:
- Started with %s
- Ended with %s\
""" % (lastblock[0], name))
_lastblockdict[name].pop()
return lastblock
def EUDGetBlockList():
return _current_bsm._blockstru
| [
"phu54321@naver.com"
] | phu54321@naver.com |
e661fdec78b7319ffe2fcad7ed550a0469bf8d6d | 564d6a4d305a8ac6a7e01c761831fb2081c02d0f | /sdk/network/azure-mgmt-network/azure/mgmt/network/v2020_05_01/operations/_network_interfaces_operations.py | f2a6c07097ef93c19db4a57b35f48ccad53b3847 | [
"LicenseRef-scancode-generic-cla",
"LGPL-2.1-or-later",
"MIT"
] | permissive | paultaiton/azure-sdk-for-python | 69af4d889bac8012b38f5b7e8108707be679b472 | d435a1a25fd6097454b7fdfbbdefd53e05029160 | refs/heads/master | 2023-01-30T16:15:10.647335 | 2020-11-14T01:09:50 | 2020-11-14T01:09:50 | 283,343,691 | 0 | 0 | MIT | 2020-07-28T22:43:43 | 2020-07-28T22:43:43 | null | UTF-8 | Python | false | false | 59,447 | 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.core.polling import LROPoller, NoPolling, PollingMethod
from azure.mgmt.core.exceptions import ARMErrorFormat
from azure.mgmt.core.polling.arm_polling import ARMPolling
from .. import models
if TYPE_CHECKING:
# pylint: disable=unused-import,ungrouped-imports
from typing import Any, Callable, Dict, Generic, Iterable, Optional, TypeVar, Union
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
class NetworkInterfacesOperations(object):
"""NetworkInterfacesOperations 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.v2020_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 _delete_initial(
self,
resource_group_name, # type: str
network_interface_name, # type: str
**kwargs # type: Any
):
# type: (...) -> None
cls = kwargs.pop('cls', None) # type: ClsType[None]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2020-05-01"
accept = "application/json"
# Construct URL
url = self._delete_initial.metadata['url'] # type: ignore
path_format_arguments = {
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'networkInterfaceName': self._serialize.url("network_interface_name", network_interface_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.delete(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, 202, 204]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
if cls:
return cls(pipeline_response, None, {})
_delete_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkInterfaces/{networkInterfaceName}'} # type: ignore
def begin_delete(
self,
resource_group_name, # type: str
network_interface_name, # type: str
**kwargs # type: Any
):
# type: (...) -> LROPoller[None]
"""Deletes the specified network interface.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param network_interface_name: The name of the network interface.
:type network_interface_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:keyword str continuation_token: A continuation token to restart a poller from a saved state.
:keyword polling: True for ARMPolling, False for no polling, or a
polling object for personal polling strategy
:paramtype polling: bool or ~azure.core.polling.PollingMethod
:keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present.
:return: An instance of LROPoller that returns either None or the result of cls(response)
:rtype: ~azure.core.polling.LROPoller[None]
:raises ~azure.core.exceptions.HttpResponseError:
"""
polling = kwargs.pop('polling', True) # type: Union[bool, PollingMethod]
cls = kwargs.pop('cls', None) # type: ClsType[None]
lro_delay = kwargs.pop(
'polling_interval',
self._config.polling_interval
)
cont_token = kwargs.pop('continuation_token', None) # type: Optional[str]
if cont_token is None:
raw_result = self._delete_initial(
resource_group_name=resource_group_name,
network_interface_name=network_interface_name,
cls=lambda x,y,z: x,
**kwargs
)
kwargs.pop('error_map', None)
kwargs.pop('content_type', None)
def get_long_running_output(pipeline_response):
if cls:
return cls(pipeline_response, None, {})
if polling is True: polling_method = ARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, **kwargs)
elif polling is False: polling_method = NoPolling()
else: polling_method = polling
if cont_token:
return LROPoller.from_continuation_token(
polling_method=polling_method,
continuation_token=cont_token,
client=self._client,
deserialization_callback=get_long_running_output
)
else:
return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
begin_delete.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkInterfaces/{networkInterfaceName}'} # type: ignore
def get(
self,
resource_group_name, # type: str
network_interface_name, # type: str
expand=None, # type: Optional[str]
**kwargs # type: Any
):
# type: (...) -> "models.NetworkInterface"
"""Gets information about the specified network interface.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param network_interface_name: The name of the network interface.
:type network_interface_name: str
:param expand: Expands referenced resources.
:type expand: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: NetworkInterface, or the result of cls(response)
:rtype: ~azure.mgmt.network.v2020_05_01.models.NetworkInterface
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["models.NetworkInterface"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2020-05-01"
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'),
'networkInterfaceName': self._serialize.url("network_interface_name", network_interface_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')
if expand is not None:
query_parameters['$expand'] = self._serialize.query("expand", expand, '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('NetworkInterface', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkInterfaces/{networkInterfaceName}'} # type: ignore
def _create_or_update_initial(
self,
resource_group_name, # type: str
network_interface_name, # type: str
parameters, # type: "models.NetworkInterface"
**kwargs # type: Any
):
# type: (...) -> "models.NetworkInterface"
cls = kwargs.pop('cls', None) # type: ClsType["models.NetworkInterface"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2020-05-01"
content_type = kwargs.pop("content_type", "application/json")
accept = "application/json"
# Construct URL
url = self._create_or_update_initial.metadata['url'] # type: ignore
path_format_arguments = {
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'networkInterfaceName': self._serialize.url("network_interface_name", network_interface_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['Content-Type'] = self._serialize.header("content_type", content_type, 'str')
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
body_content_kwargs = {} # type: Dict[str, Any]
body_content = self._serialize.body(parameters, 'NetworkInterface')
body_content_kwargs['content'] = body_content
request = self._client.put(url, query_parameters, header_parameters, **body_content_kwargs)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200, 201]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
if response.status_code == 200:
deserialized = self._deserialize('NetworkInterface', pipeline_response)
if response.status_code == 201:
deserialized = self._deserialize('NetworkInterface', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
_create_or_update_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkInterfaces/{networkInterfaceName}'} # type: ignore
def begin_create_or_update(
self,
resource_group_name, # type: str
network_interface_name, # type: str
parameters, # type: "models.NetworkInterface"
**kwargs # type: Any
):
# type: (...) -> LROPoller["models.NetworkInterface"]
"""Creates or updates a network interface.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param network_interface_name: The name of the network interface.
:type network_interface_name: str
:param parameters: Parameters supplied to the create or update network interface operation.
:type parameters: ~azure.mgmt.network.v2020_05_01.models.NetworkInterface
:keyword callable cls: A custom type or function that will be passed the direct response
:keyword str continuation_token: A continuation token to restart a poller from a saved state.
:keyword polling: True for ARMPolling, False for no polling, or a
polling object for personal polling strategy
:paramtype polling: bool or ~azure.core.polling.PollingMethod
:keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present.
:return: An instance of LROPoller that returns either NetworkInterface or the result of cls(response)
:rtype: ~azure.core.polling.LROPoller[~azure.mgmt.network.v2020_05_01.models.NetworkInterface]
:raises ~azure.core.exceptions.HttpResponseError:
"""
polling = kwargs.pop('polling', True) # type: Union[bool, PollingMethod]
cls = kwargs.pop('cls', None) # type: ClsType["models.NetworkInterface"]
lro_delay = kwargs.pop(
'polling_interval',
self._config.polling_interval
)
cont_token = kwargs.pop('continuation_token', None) # type: Optional[str]
if cont_token is None:
raw_result = self._create_or_update_initial(
resource_group_name=resource_group_name,
network_interface_name=network_interface_name,
parameters=parameters,
cls=lambda x,y,z: x,
**kwargs
)
kwargs.pop('error_map', None)
kwargs.pop('content_type', None)
def get_long_running_output(pipeline_response):
deserialized = self._deserialize('NetworkInterface', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
if polling is True: polling_method = ARMPolling(lro_delay, lro_options={'final-state-via': 'azure-async-operation'}, **kwargs)
elif polling is False: polling_method = NoPolling()
else: polling_method = polling
if cont_token:
return LROPoller.from_continuation_token(
polling_method=polling_method,
continuation_token=cont_token,
client=self._client,
deserialization_callback=get_long_running_output
)
else:
return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
begin_create_or_update.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkInterfaces/{networkInterfaceName}'} # type: ignore
def update_tags(
self,
resource_group_name, # type: str
network_interface_name, # type: str
parameters, # type: "models.TagsObject"
**kwargs # type: Any
):
# type: (...) -> "models.NetworkInterface"
"""Updates a network interface tags.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param network_interface_name: The name of the network interface.
:type network_interface_name: str
:param parameters: Parameters supplied to update network interface tags.
:type parameters: ~azure.mgmt.network.v2020_05_01.models.TagsObject
:keyword callable cls: A custom type or function that will be passed the direct response
:return: NetworkInterface, or the result of cls(response)
:rtype: ~azure.mgmt.network.v2020_05_01.models.NetworkInterface
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["models.NetworkInterface"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2020-05-01"
content_type = kwargs.pop("content_type", "application/json")
accept = "application/json"
# Construct URL
url = self.update_tags.metadata['url'] # type: ignore
path_format_arguments = {
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'networkInterfaceName': self._serialize.url("network_interface_name", network_interface_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['Content-Type'] = self._serialize.header("content_type", content_type, 'str')
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
body_content_kwargs = {} # type: Dict[str, Any]
body_content = self._serialize.body(parameters, 'TagsObject')
body_content_kwargs['content'] = body_content
request = self._client.patch(url, query_parameters, header_parameters, **body_content_kwargs)
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('NetworkInterface', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
update_tags.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkInterfaces/{networkInterfaceName}'} # type: ignore
def list_all(
self,
**kwargs # type: Any
):
# type: (...) -> Iterable["models.NetworkInterfaceListResult"]
"""Gets all network interfaces in a subscription.
:keyword callable cls: A custom type or function that will be passed the direct response
:return: An iterator like instance of either NetworkInterfaceListResult or the result of cls(response)
:rtype: ~azure.core.paging.ItemPaged[~azure.mgmt.network.v2020_05_01.models.NetworkInterfaceListResult]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["models.NetworkInterfaceListResult"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2020-05-01"
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_all.metadata['url'] # type: ignore
path_format_arguments = {
'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('NetworkInterfaceListResult', 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_all.metadata = {'url': '/subscriptions/{subscriptionId}/providers/Microsoft.Network/networkInterfaces'} # type: ignore
def list(
self,
resource_group_name, # type: str
**kwargs # type: Any
):
# type: (...) -> Iterable["models.NetworkInterfaceListResult"]
"""Gets all network interfaces in a resource group.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: An iterator like instance of either NetworkInterfaceListResult or the result of cls(response)
:rtype: ~azure.core.paging.ItemPaged[~azure.mgmt.network.v2020_05_01.models.NetworkInterfaceListResult]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["models.NetworkInterfaceListResult"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2020-05-01"
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'),
'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('NetworkInterfaceListResult', 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/networkInterfaces'} # type: ignore
def _get_effective_route_table_initial(
self,
resource_group_name, # type: str
network_interface_name, # type: str
**kwargs # type: Any
):
# type: (...) -> Optional["models.EffectiveRouteListResult"]
cls = kwargs.pop('cls', None) # type: ClsType[Optional["models.EffectiveRouteListResult"]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2020-05-01"
accept = "application/json"
# Construct URL
url = self._get_effective_route_table_initial.metadata['url'] # type: ignore
path_format_arguments = {
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'networkInterfaceName': self._serialize.url("network_interface_name", network_interface_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.post(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, 202]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
deserialized = None
if response.status_code == 200:
deserialized = self._deserialize('EffectiveRouteListResult', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
_get_effective_route_table_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkInterfaces/{networkInterfaceName}/effectiveRouteTable'} # type: ignore
def begin_get_effective_route_table(
self,
resource_group_name, # type: str
network_interface_name, # type: str
**kwargs # type: Any
):
# type: (...) -> LROPoller["models.EffectiveRouteListResult"]
"""Gets all route tables applied to a network interface.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param network_interface_name: The name of the network interface.
:type network_interface_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:keyword str continuation_token: A continuation token to restart a poller from a saved state.
:keyword polling: True for ARMPolling, False for no polling, or a
polling object for personal polling strategy
:paramtype polling: bool or ~azure.core.polling.PollingMethod
:keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present.
:return: An instance of LROPoller that returns either EffectiveRouteListResult or the result of cls(response)
:rtype: ~azure.core.polling.LROPoller[~azure.mgmt.network.v2020_05_01.models.EffectiveRouteListResult]
:raises ~azure.core.exceptions.HttpResponseError:
"""
polling = kwargs.pop('polling', True) # type: Union[bool, PollingMethod]
cls = kwargs.pop('cls', None) # type: ClsType["models.EffectiveRouteListResult"]
lro_delay = kwargs.pop(
'polling_interval',
self._config.polling_interval
)
cont_token = kwargs.pop('continuation_token', None) # type: Optional[str]
if cont_token is None:
raw_result = self._get_effective_route_table_initial(
resource_group_name=resource_group_name,
network_interface_name=network_interface_name,
cls=lambda x,y,z: x,
**kwargs
)
kwargs.pop('error_map', None)
kwargs.pop('content_type', None)
def get_long_running_output(pipeline_response):
deserialized = self._deserialize('EffectiveRouteListResult', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
if polling is True: polling_method = ARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, **kwargs)
elif polling is False: polling_method = NoPolling()
else: polling_method = polling
if cont_token:
return LROPoller.from_continuation_token(
polling_method=polling_method,
continuation_token=cont_token,
client=self._client,
deserialization_callback=get_long_running_output
)
else:
return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
begin_get_effective_route_table.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkInterfaces/{networkInterfaceName}/effectiveRouteTable'} # type: ignore
def _list_effective_network_security_groups_initial(
self,
resource_group_name, # type: str
network_interface_name, # type: str
**kwargs # type: Any
):
# type: (...) -> Optional["models.EffectiveNetworkSecurityGroupListResult"]
cls = kwargs.pop('cls', None) # type: ClsType[Optional["models.EffectiveNetworkSecurityGroupListResult"]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2020-05-01"
accept = "application/json"
# Construct URL
url = self._list_effective_network_security_groups_initial.metadata['url'] # type: ignore
path_format_arguments = {
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'networkInterfaceName': self._serialize.url("network_interface_name", network_interface_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.post(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, 202]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
deserialized = None
if response.status_code == 200:
deserialized = self._deserialize('EffectiveNetworkSecurityGroupListResult', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
_list_effective_network_security_groups_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkInterfaces/{networkInterfaceName}/effectiveNetworkSecurityGroups'} # type: ignore
def begin_list_effective_network_security_groups(
self,
resource_group_name, # type: str
network_interface_name, # type: str
**kwargs # type: Any
):
# type: (...) -> LROPoller["models.EffectiveNetworkSecurityGroupListResult"]
"""Gets all network security groups applied to a network interface.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param network_interface_name: The name of the network interface.
:type network_interface_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:keyword str continuation_token: A continuation token to restart a poller from a saved state.
:keyword polling: True for ARMPolling, False for no polling, or a
polling object for personal polling strategy
:paramtype polling: bool or ~azure.core.polling.PollingMethod
:keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present.
:return: An instance of LROPoller that returns either EffectiveNetworkSecurityGroupListResult or the result of cls(response)
:rtype: ~azure.core.polling.LROPoller[~azure.mgmt.network.v2020_05_01.models.EffectiveNetworkSecurityGroupListResult]
:raises ~azure.core.exceptions.HttpResponseError:
"""
polling = kwargs.pop('polling', True) # type: Union[bool, PollingMethod]
cls = kwargs.pop('cls', None) # type: ClsType["models.EffectiveNetworkSecurityGroupListResult"]
lro_delay = kwargs.pop(
'polling_interval',
self._config.polling_interval
)
cont_token = kwargs.pop('continuation_token', None) # type: Optional[str]
if cont_token is None:
raw_result = self._list_effective_network_security_groups_initial(
resource_group_name=resource_group_name,
network_interface_name=network_interface_name,
cls=lambda x,y,z: x,
**kwargs
)
kwargs.pop('error_map', None)
kwargs.pop('content_type', None)
def get_long_running_output(pipeline_response):
deserialized = self._deserialize('EffectiveNetworkSecurityGroupListResult', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
if polling is True: polling_method = ARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, **kwargs)
elif polling is False: polling_method = NoPolling()
else: polling_method = polling
if cont_token:
return LROPoller.from_continuation_token(
polling_method=polling_method,
continuation_token=cont_token,
client=self._client,
deserialization_callback=get_long_running_output
)
else:
return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
begin_list_effective_network_security_groups.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkInterfaces/{networkInterfaceName}/effectiveNetworkSecurityGroups'} # type: ignore
def list_virtual_machine_scale_set_vm_network_interfaces(
self,
resource_group_name, # type: str
virtual_machine_scale_set_name, # type: str
virtualmachine_index, # type: str
**kwargs # type: Any
):
# type: (...) -> Iterable["models.NetworkInterfaceListResult"]
"""Gets information about all network interfaces in a virtual machine in a virtual machine scale
set.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param virtual_machine_scale_set_name: The name of the virtual machine scale set.
:type virtual_machine_scale_set_name: str
:param virtualmachine_index: The virtual machine index.
:type virtualmachine_index: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: An iterator like instance of either NetworkInterfaceListResult or the result of cls(response)
:rtype: ~azure.core.paging.ItemPaged[~azure.mgmt.network.v2020_05_01.models.NetworkInterfaceListResult]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["models.NetworkInterfaceListResult"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2018-10-01"
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_virtual_machine_scale_set_vm_network_interfaces.metadata['url'] # type: ignore
path_format_arguments = {
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'virtualMachineScaleSetName': self._serialize.url("virtual_machine_scale_set_name", virtual_machine_scale_set_name, 'str'),
'virtualmachineIndex': self._serialize.url("virtualmachine_index", virtualmachine_index, '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('NetworkInterfaceListResult', 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_virtual_machine_scale_set_vm_network_interfaces.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/microsoft.Compute/virtualMachineScaleSets/{virtualMachineScaleSetName}/virtualMachines/{virtualmachineIndex}/networkInterfaces'} # type: ignore
def list_virtual_machine_scale_set_network_interfaces(
self,
resource_group_name, # type: str
virtual_machine_scale_set_name, # type: str
**kwargs # type: Any
):
# type: (...) -> Iterable["models.NetworkInterfaceListResult"]
"""Gets all network interfaces in a virtual machine scale set.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param virtual_machine_scale_set_name: The name of the virtual machine scale set.
:type virtual_machine_scale_set_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: An iterator like instance of either NetworkInterfaceListResult or the result of cls(response)
:rtype: ~azure.core.paging.ItemPaged[~azure.mgmt.network.v2020_05_01.models.NetworkInterfaceListResult]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["models.NetworkInterfaceListResult"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2018-10-01"
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_virtual_machine_scale_set_network_interfaces.metadata['url'] # type: ignore
path_format_arguments = {
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'virtualMachineScaleSetName': self._serialize.url("virtual_machine_scale_set_name", virtual_machine_scale_set_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('NetworkInterfaceListResult', 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_virtual_machine_scale_set_network_interfaces.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/microsoft.Compute/virtualMachineScaleSets/{virtualMachineScaleSetName}/networkInterfaces'} # type: ignore
def get_virtual_machine_scale_set_network_interface(
self,
resource_group_name, # type: str
virtual_machine_scale_set_name, # type: str
virtualmachine_index, # type: str
network_interface_name, # type: str
expand=None, # type: Optional[str]
**kwargs # type: Any
):
# type: (...) -> "models.NetworkInterface"
"""Get the specified network interface in a virtual machine scale set.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param virtual_machine_scale_set_name: The name of the virtual machine scale set.
:type virtual_machine_scale_set_name: str
:param virtualmachine_index: The virtual machine index.
:type virtualmachine_index: str
:param network_interface_name: The name of the network interface.
:type network_interface_name: str
:param expand: Expands referenced resources.
:type expand: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: NetworkInterface, or the result of cls(response)
:rtype: ~azure.mgmt.network.v2020_05_01.models.NetworkInterface
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["models.NetworkInterface"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2018-10-01"
accept = "application/json"
# Construct URL
url = self.get_virtual_machine_scale_set_network_interface.metadata['url'] # type: ignore
path_format_arguments = {
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'virtualMachineScaleSetName': self._serialize.url("virtual_machine_scale_set_name", virtual_machine_scale_set_name, 'str'),
'virtualmachineIndex': self._serialize.url("virtualmachine_index", virtualmachine_index, 'str'),
'networkInterfaceName': self._serialize.url("network_interface_name", network_interface_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')
if expand is not None:
query_parameters['$expand'] = self._serialize.query("expand", expand, '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('NetworkInterface', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_virtual_machine_scale_set_network_interface.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/microsoft.Compute/virtualMachineScaleSets/{virtualMachineScaleSetName}/virtualMachines/{virtualmachineIndex}/networkInterfaces/{networkInterfaceName}'} # type: ignore
def list_virtual_machine_scale_set_ip_configurations(
self,
resource_group_name, # type: str
virtual_machine_scale_set_name, # type: str
virtualmachine_index, # type: str
network_interface_name, # type: str
expand=None, # type: Optional[str]
**kwargs # type: Any
):
# type: (...) -> Iterable["models.NetworkInterfaceIPConfigurationListResult"]
"""Get the specified network interface ip configuration in a virtual machine scale set.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param virtual_machine_scale_set_name: The name of the virtual machine scale set.
:type virtual_machine_scale_set_name: str
:param virtualmachine_index: The virtual machine index.
:type virtualmachine_index: str
:param network_interface_name: The name of the network interface.
:type network_interface_name: str
:param expand: Expands referenced resources.
:type expand: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: An iterator like instance of either NetworkInterfaceIPConfigurationListResult or the result of cls(response)
:rtype: ~azure.core.paging.ItemPaged[~azure.mgmt.network.v2020_05_01.models.NetworkInterfaceIPConfigurationListResult]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["models.NetworkInterfaceIPConfigurationListResult"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2018-10-01"
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_virtual_machine_scale_set_ip_configurations.metadata['url'] # type: ignore
path_format_arguments = {
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'virtualMachineScaleSetName': self._serialize.url("virtual_machine_scale_set_name", virtual_machine_scale_set_name, 'str'),
'virtualmachineIndex': self._serialize.url("virtualmachine_index", virtualmachine_index, 'str'),
'networkInterfaceName': self._serialize.url("network_interface_name", network_interface_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')
if expand is not None:
query_parameters['$expand'] = self._serialize.query("expand", expand, '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('NetworkInterfaceIPConfigurationListResult', 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_virtual_machine_scale_set_ip_configurations.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/microsoft.Compute/virtualMachineScaleSets/{virtualMachineScaleSetName}/virtualMachines/{virtualmachineIndex}/networkInterfaces/{networkInterfaceName}/ipConfigurations'} # type: ignore
def get_virtual_machine_scale_set_ip_configuration(
self,
resource_group_name, # type: str
virtual_machine_scale_set_name, # type: str
virtualmachine_index, # type: str
network_interface_name, # type: str
ip_configuration_name, # type: str
expand=None, # type: Optional[str]
**kwargs # type: Any
):
# type: (...) -> "models.NetworkInterfaceIPConfiguration"
"""Get the specified network interface ip configuration in a virtual machine scale set.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param virtual_machine_scale_set_name: The name of the virtual machine scale set.
:type virtual_machine_scale_set_name: str
:param virtualmachine_index: The virtual machine index.
:type virtualmachine_index: str
:param network_interface_name: The name of the network interface.
:type network_interface_name: str
:param ip_configuration_name: The name of the ip configuration.
:type ip_configuration_name: str
:param expand: Expands referenced resources.
:type expand: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: NetworkInterfaceIPConfiguration, or the result of cls(response)
:rtype: ~azure.mgmt.network.v2020_05_01.models.NetworkInterfaceIPConfiguration
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["models.NetworkInterfaceIPConfiguration"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2018-10-01"
accept = "application/json"
# Construct URL
url = self.get_virtual_machine_scale_set_ip_configuration.metadata['url'] # type: ignore
path_format_arguments = {
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'virtualMachineScaleSetName': self._serialize.url("virtual_machine_scale_set_name", virtual_machine_scale_set_name, 'str'),
'virtualmachineIndex': self._serialize.url("virtualmachine_index", virtualmachine_index, 'str'),
'networkInterfaceName': self._serialize.url("network_interface_name", network_interface_name, 'str'),
'ipConfigurationName': self._serialize.url("ip_configuration_name", 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')
if expand is not None:
query_parameters['$expand'] = self._serialize.query("expand", expand, '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('NetworkInterfaceIPConfiguration', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_virtual_machine_scale_set_ip_configuration.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/microsoft.Compute/virtualMachineScaleSets/{virtualMachineScaleSetName}/virtualMachines/{virtualmachineIndex}/networkInterfaces/{networkInterfaceName}/ipConfigurations/{ipConfigurationName}'} # type: ignore
| [
"noreply@github.com"
] | noreply@github.com |
e525e75ccebdd3a3803682e6711866c1e53c3c8c | b69c9571db562908f03ab6e57d3f8d6550bf9fc4 | /main_program.py | f7f323228e0607ece4470e049c4eb46a73cdb12e | [] | no_license | Snow-Crash/pc-host | b7f167727418bc4437d0e26daa6e7c7a1a9ef140 | e4824fde89bdd4ba7b8ee31ce7f6722f04b33cca | refs/heads/master | 2023-02-21T08:35:12.058256 | 2021-01-25T17:11:00 | 2021-01-25T17:11:00 | 192,278,045 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 11,627 | py | import tkinter
import time
import serial
import numpy as np
from timeit import default_timer as timer
import matplotlib.pyplot as plt
import tkinter as Tk
import numpy as np
import matplotlib.animation as animation
from pyqtgraph.Qt import QtCore, QtGui
#import pyqtgraph.opengl as gl
import pyqtgraph as pg
from PyQt5 import QtTest
import sys
import multiprocessing
# UART Com Settings
UART_PORT = 'COM6'
UART_BAUDRATE = 230400
UART_INPUT_SIZE = 243
UART_OUTPUT_SIZE = 4
WINDOW = 450
# FPGA Command values
CMD_START_NEURON = 1
CMD_RESET_NEURON = 2
CMD_SET_SPIKE = 3
CMD_SET_TEST_SPIKE = 4
CMD_CLEAR_BUFFERED_SPIKE = 5
class uart_com:
""" A class that encapsulates the UART connection between a PC and FPGA via a USB UART."""
def __init__(self, port=None, baudrate=None, timeout=0.1):
# Set internal variables
if port is None:
self.port = 'COM6'
else:
self.port = port
if baudrate is None:
self.baudrate = 230400
else:
self.baudrate = baudrate
self.serial = serial.Serial()
self.serial.baudrate = self.baudrate
self.serial.port = self.port
self.serial.timeout = timeout
# Open the serial connection
self.serial.open()
# Clear any excess data sitting in the buffers
self.serial.reset_input_buffer()
self.serial.reset_output_buffer()
def __del__(self):
# Release the serial connection
self.serial.close()
def read_blocking(self):
""" A blocking read. stop until receive four 0xff or timeout"""
stop_frame_count = 0
receive = bytearray()
# receive.append(1)
while(1):
data_read = self.serial.read(1)
#if read() returns empty array, break
if len(data_read) == 0:
print('timeout')
break
# print(data_read)
if data_read[0] != 255:
receive.append(data_read[0])
else:
stop_frame_count += 1
if stop_frame_count == 4:
break
return receive
def write_blocking(self, write_data):
""" A blocking write. The expected number of bytes written is set during initialization. """
self.serial.write(write_data)
def dataInReadBuffer(self):
return True if self.serial.in_waiting > 0 else False
class neuron_controller():
def __init__(self, window, port=None, baudrate=None, timeout=0.1):
self.uart = uart_com(port=port, baudrate=baudrate, timeout=timeout)
self.window = window
self.decoding = 'ascii'
def send_word(self, binary_string):
byte_array = self.bin_str_to_bytes(binary_string)
self.uart.write_blocking(byte_array)
def read_data(self):
data = self.uart.read_blocking()
return data
def bin_str_to_bytes(self, bin_str):
'''
seperate every 8 bits by whitespace
msb lsb
31 0
00000000 00000000 00000000 00000000
store msb in bytearray[3], lsb in bytearray[0]
'''
bin_str = bin_str.split(' ')
byte_array = bytearray()
for field in reversed(bin_str):
int_val = int(field, 2)
byte_array.append(int_val)
return byte_array
def check_for_output(self):
return self.connection.dataInReadBuffer()
# Commands to read from the FPGA
def read_cycle(self):
'''
convert raw output from fpga to numpy
'''
psp = np.zeros(110)
voltage = np.zeros(10)
spikes = np.zeros(10)
data = self.read_data()
if self.decoding == 'ascii':
decoded = data.decode('ascii')
split = decoded.split('\n')
psp_str = split[0].split(',')
voltage_str = split[1].split(',')
#data in fpga is is represented by fixed(16,4) format, so divided by 2^12
for i in range(110):
psp[i] = int(psp_str[i]) / 4096
for i in range(10):
voltage[i] = int(voltage_str[i]) / 4096
spikes[i] = int(voltage[i] > 1)
else:
#data is a list, every 5 bytes belong to a packet
#split data into chunks of 5
split_data = [data[i:i + 5] for i in range(0, len(data), 5)]
#0-110 are psp
psp_bytes = split_data[0:110]
#110 to 120 are voltage
voltage_bytes = split_data[110:]
#convert raw bytes to integers
for i,val_byte in enumerate(psp_bytes):
#microblaze sends lsb fisrt, so lsb stores in [1], msb in [3]
#byte order is little endian
u16 = int.from_bytes(val_byte[1:], byteorder = 'little')
int16 = self.u16toint(u16)
psp[i] = int16
voltage_bytes = split_data[110:]
for i,val_byte in enumerate(psp_bytes):
u16 = int.from_bytes(val_byte[1:], byteorder = 'little')
int16 = self.u16toint(u16)
psp_bytes[i] = int16
return spikes, psp, voltage
# Commands to write to the FPGA
def start_cycle(self):
cmd_start = bytearray([0,0,0,CMD_START_NEURON])
self.uart.write_blocking(cmd_start)
def run_one_step(self):
'''
run neuron for one time step, including send start command, and read results
'''
self.start_cycle();
data = self.read_cycle()
return data
def reset_neuron(self):
'''
reset psp
'''
cmd_reset = bytearray([0,0,0,CMD_RESET_NEURON])
self.uart.write_blocking(cmd_reset)
def set_spikes(self, spike_array: np.array):
'''
spike_array: 1d numpy array, 1 for spike, 0 for nothing, each position
is a synapse input
'''
#find non zero positions
input_spike_index = np.where(spike_array!=0)[0]
for idx in input_spike_index:
spike_packet = bytearray([0,0,idx,CMD_SET_SPIKE])
self.uart.write_blocking(spike_packet)
def set_test_spikes(self):
cmd_test_spikes = bytearray([0,0,0,CMD_SET_TEST_SPIKE])
self.uart.write_blocking(cmd_test_spikes)
def clear_spikes(self):
'''
set spike buffer in microblaze to 0
'''
cmd_clear_spikes = bytearray([0,0,0,CMD_CLEAR_BUFFERED_SPIKE])
self.uart.write_blocking(cmd_clear_spikes)
def chunks(self, l, n):
"""Yield successive n-sized chunks from l.
https://stackoverflow.com/questions/312443/how-do-you-split-a-list-into-evenly-sized-chunks
"""
for i in range(0, len(l), n):
yield l[i:i + n]
def u16toint(self, u16):
'''
assume the u16 is unsigned integer, convert it to int
'''
#value in fpga is represented by fixpoint(16,4), while in microblaze,
#it is converted to u32. so only keep lower 16 bits
u16 = u16 & 0xFFFF
#convert unsigned to signed
if u16 > 32767:
return u16-65536
else:
return u16
def disconnect(self):
self.uart.serial.close()
def run_one_step_fake(self):
return np.random.rand(10), np.random.rand(110), np.random.rand(10)
if __name__ == '__main__':
spike = np.load('D:/islped_demo/snn/noise_train.npy')
test_spike = spike[0]
controller = neuron_controller(WINDOW, port='COM5', baudrate=230400, timeout=0.1)
#manager = multiprocessing.Manager()
#shared_list = manager.list()
#n = 0
#process1 = multiprocessing.Process(
# target=write_only, args=[shared_list,n])
#process2 = multiprocessing.Process(
# target=read_only, args=[shared_list])
#process1.start()
#process2.start()
#process1.join()
#process2.join()
#record voltage[instance_idx, neuron_id, time_step]
v_record = np.zeros([100,10,WINDOW])
controller.reset_neuron()
test_pyqtgraph = False
use_fake_data = False
if test_pyqtgraph:
start = timer()
app = QtGui.QApplication(sys.argv)
w = gl.GLViewWidget()
w.setBackgroundColor('w')
w.opts['azimuth'] = 90
w.opts['elevation'] = 0
w.setGeometry(0, 110, 1920, 1080)
w.show()
traces = dict()
for i in range(10):
x = np.array(range(450))
y = np.zeros(450)
z = np.zeros(450)
pts = np.vstack([x, y, z]).transpose()
traces[i] = gl.GLLinePlotItem(
pos=pts,
color=pg.glColor((i, 10 * 1.3)),
width=(i + 1) / 10,
antialias=True,
)
#if use white background
#reference: https://github.com/pyqtgraph/pyqtgraph/issues/193
traces[i].setGLOptions('translucent')
w.addItem(traces[i])
for j in range(450):
for i in range(10):
if use_fake_data:
s,p,v = controller.run_one_step_fake()
v_record[i,:,j] = v
z = v_record[0,i,0:j] + i*5
else:
controller.set_spikes(spike[i,j,:])
s,p,v = controller.run_one_step()
v_record[i,:,j] = v
# z coordinates represent voltage
# + 5 to plac each trace at different vertical position
z = v_record[0,i,0:j] + i*5
#reset psp at last step
if (j == 450-1):
controller.reset_neuron()
x = np.array(range(0,j))
y = np.zeros(j)
z = np.random.rand(j) + i * 5
pts = np.vstack([x, y, z]).transpose()
traces[i].setData(pos=pts, color=pg.glColor((i, 10 * 1.3)), width=3)
print(j)
# QtTest.QTest.qWait(1000)
app.processEvents()
end = timer()
print(end - start) # Time in seconds, e.g. 5.38091952400282
###############################################################################
else:
fig, ax = plt.subplots()
for i in range(10):
line = ax.plot(np.random.randn(450))
plt.show(block=False)
fig.canvas.draw()
plt.ioff()
#run for multiple samples
start = timer()
for i in range(1):
#for every time step
for j in range(WINDOW):
controller.set_spikes(spike[i,j,:])
s,p,v = controller.run_one_step()
v_record[i,:,j] = v
#reset psp at last step
if (j == 450-1):
controller.reset_neuron()
# if j % 5 == 0:
ax.draw_artist(ax.patch)
for n, l in enumerate(ax.lines):
l.set_ydata( v_record[0,n,:])
#
ax.draw_artist(l)
fig.canvas.update()
fig.canvas.flush_events()
#plt.show()
# ...
end = timer()
print(end - start) # Time in seconds, e.g. 5.38091952400282
| [
"hfang02@syr.edu"
] | hfang02@syr.edu |
219941a73b3a3abb917568111dfa3dca7b917452 | 0c67106d19d29092c02cbfc93e33cb077a8e4386 | /machine_translation/nmt_zh/model_helper.py | e83f20932eeac34b26c463ff4bf2be129b34ffa0 | [] | no_license | iamdsyang/chinese_nlp | b026c0b9c84b044638006bfa063b8523fe51aff9 | 22a47ad9e8cfb18593e8a6e089d7a7b5e1b6a838 | refs/heads/master | 2020-05-01T14:52:44.364216 | 2018-07-09T04:04:09 | 2018-07-09T04:04:09 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 9,106 | py | import six, os
import collections
import tensorflow as tf
import numpy as np
from utils import iterator_utils
from utils import vocab_utils
from utils import misc_utils as utils
class TrainModel(
collections.namedtuple("TrainModel", ("graph", "model", "iterator"))):
pass
def create_train_model(
model_creator, hparams):
"""Create train graph, model, and iterator."""
src_file = "%s.%s" % (hparams.train_prefix, hparams.src)
tgt_file = "%s.%s" % (hparams.train_prefix, hparams.tgt)
src_vocab_file = hparams.src_vocab_file
tgt_vocab_file = hparams.tgt_vocab_file
graph = tf.Graph()
with graph.as_default(), tf.container("train"):
src_vocab_table, tgt_vocab_table = vocab_utils.create_vocab_tables(
src_vocab_file, tgt_vocab_file, hparams.share_vocab)
src_dataset = tf.data.TextLineDataset(src_file)
tgt_dataset = tf.data.TextLineDataset(tgt_file)
iterator = iterator_utils.get_iterator(
src_dataset,
tgt_dataset,
src_vocab_table,
tgt_vocab_table,
batch_size=hparams.batch_size,
sos=hparams.sos,
eos=hparams.eos,
source_reverse=hparams.source_reverse,
random_seed=hparams.random_seed,
num_buckets=hparams.num_buckets,
src_max_len=hparams.src_max_len,
tgt_max_len=hparams.tgt_max_len)
model = model_creator(
hparams,
iterator=iterator,
mode=tf.contrib.learn.ModeKeys.TRAIN,
source_vocab_table=src_vocab_table,
target_vocab_table=tgt_vocab_table)
return TrainModel(
graph=graph,
model=model,
iterator=iterator)
class EvalModel(
collections.namedtuple("EvalModel",
("graph", "model", "src_file_placeholder",
"tgt_file_placeholder", "iterator"))):
pass
def create_eval_model(model_creator, hparams):
"""Create train graph, model, src/tgt file holders, and iterator."""
src_vocab_file = hparams.src_vocab_file
tgt_vocab_file = hparams.tgt_vocab_file
graph = tf.Graph()
with graph.as_default(), tf.container("eval"):
src_vocab_table, tgt_vocab_table = vocab_utils.create_vocab_tables(
src_vocab_file, tgt_vocab_file, hparams.share_vocab)
src_file_placeholder = tf.placeholder(shape=(), dtype=tf.string)
tgt_file_placeholder = tf.placeholder(shape=(), dtype=tf.string)
src_dataset = tf.data.TextLineDataset(src_file_placeholder)
tgt_dataset = tf.data.TextLineDataset(tgt_file_placeholder)
iterator = iterator_utils.get_iterator(
src_dataset,
tgt_dataset,
src_vocab_table,
tgt_vocab_table,
hparams.batch_size,
sos=hparams.sos,
eos=hparams.eos,
source_reverse=hparams.source_reverse,
random_seed=hparams.random_seed,
num_buckets=hparams.num_buckets,
src_max_len=hparams.src_max_len_infer,
tgt_max_len=hparams.tgt_max_len_infer)
model = model_creator(
hparams,
iterator=iterator,
mode=tf.contrib.learn.ModeKeys.EVAL,
source_vocab_table=src_vocab_table,
target_vocab_table=tgt_vocab_table)
return EvalModel(
graph=graph,
model=model,
src_file_placeholder=src_file_placeholder,
tgt_file_placeholder=tgt_file_placeholder,
iterator=iterator)
class InferModel(
collections.namedtuple("InferModel",
("graph", "model", "src_placeholder",
"batch_size_placeholder", "iterator"))):
pass
def create_infer_model(model_creator, hparams):
"""Create inference model."""
src_vocab_file = hparams.src_vocab_file
tgt_vocab_file = hparams.tgt_vocab_file
graph = tf.Graph()
with graph.as_default(), tf.container("infer"):
src_vocab_table, tgt_vocab_table = vocab_utils.create_vocab_tables(
src_vocab_file, tgt_vocab_file, hparams.share_vocab)
reverse_tgt_vocab_table = tf.contrib.lookup.index_to_string_table_from_file(
tgt_vocab_file, default_value=vocab_utils.UNK)
src_placeholder = tf.placeholder(shape=[None], dtype=tf.string)
batch_size_placeholder = tf.placeholder(shape=[], dtype=tf.int64)
src_dataset = tf.data.Dataset.from_tensor_slices(
src_placeholder)
iterator = iterator_utils.get_infer_iterator(
src_dataset,
src_vocab_table,
batch_size=batch_size_placeholder,
eos=hparams.eos,
source_reverse=hparams.source_reverse,
src_max_len=hparams.src_max_len_infer)
model = model_creator(
hparams,
iterator=iterator,
mode=tf.contrib.learn.ModeKeys.INFER,
source_vocab_table=src_vocab_table,
target_vocab_table=tgt_vocab_table,
reverse_target_vocab_table=reverse_tgt_vocab_table)
return InferModel(
graph=graph,
model=model,
src_placeholder=src_placeholder,
batch_size_placeholder=batch_size_placeholder,
iterator=iterator)
def load_model(model, ckpt, session, name):
model.saver.restore(session, ckpt)
session.run(tf.tables_initializer())
utils.log("Load {} model parameters from {}".format(name, ckpt))
return model
def create_or_load_model(model, model_dir, session, name):
"""Create translation model and initialize or load parameters in session."""
latest_ckpt = tf.train.latest_checkpoint(model_dir)
if latest_ckpt:
model.saver.restore(session, latest_ckpt)
session.run(tf.tables_initializer())
utils.log("Load {} model parameters from {}".format(name, latest_ckpt))
else:
session.run(tf.global_variables_initializer())
session.run(tf.tables_initializer())
utils.log("Create {} model with fresh parameters".format(name))
global_step = model.global_step.eval(session=session)
return model, global_step
def avg_checkpoints(model_dir, num_last_checkpoints, global_step,
global_step_name):
"""
Average the last N checkpoints in the model_dir.
"""
checkpoint_state = tf.train.get_checkpoint_state(model_dir)
if not checkpoint_state:
utils.log("No checkpoint file found in directory: {}".format(model_dir))
return None
# Checkpoints are ordered from oldest to newest.
checkpoints = (
checkpoint_state.all_model_checkpoint_paths[-num_last_checkpoints:])
if len(checkpoints) < num_last_checkpoints:
utils.log(
"Skipping averaging checkpoints because not enough checkpoints is "
"avaliable."
)
return None
avg_model_dir = os.path.join(model_dir, "avg_checkpoints")
if not os.path.exists(avg_model_dir):
utils.log(
"Creating new directory {} for saving averaged checkpoints." .format(
avg_model_dir))
os.makedirs(avg_model_dir)
utils.log("Reading and averaging variables in checkpoints:")
var_list = tf.contrib.framework.list_variables(checkpoints[0])
var_values, var_dtypes = {}, {}
for (name, shape) in var_list:
if name != global_step_name:
var_values[name] = np.zeros(shape)
for checkpoint in checkpoints:
utils.log("{}".format(checkpoint))
reader = tf.contrib.framework.load_checkpoint(checkpoint)
for name in var_values:
tensor = reader.get_tensor(name)
var_dtypes[name] = tensor.dtype
var_values[name] += tensor
for name in var_values:
var_values[name] /= len(checkpoints)
# Build a graph with same variables in the checkpoints, and save the averaged
# variables into the avg_model_dir.
with tf.Graph().as_default():
tf_vars = [
tf.get_variable(v, shape=var_values[v].shape, dtype=var_dtypes[name])
for v in var_values
]
placeholders = [tf.placeholder(v.dtype, shape=v.shape) for v in tf_vars]
assign_ops = [tf.assign(v, p) for (v, p) in zip(tf_vars, placeholders)]
global_step_var = tf.Variable(
global_step, name=global_step_name, trainable=False)
saver = tf.train.Saver(tf.all_variables())
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
for p, assign_op, (name, value) in zip(placeholders, assign_ops,
six.iteritems(var_values)):
sess.run(assign_op, {p: value})
# Use the built saver to save the averaged checkpoint. Only keep 1
# checkpoint and the best checkpoint will be moved to avg_best_metric_dir.
saver.save(
sess,
os.path.join(avg_model_dir, "translate.ckpt"))
return avg_model_dir | [
"1181856726@qq.com"
] | 1181856726@qq.com |
6515b896bb03a7ebb2cd72e4073d7b54a04e0c93 | 7e2945510ece2255c4902931edc6c53184043243 | /integrationtest/vm/virtualrouter/ipsec/test_create_ipsec_ike_auth_sha384.py | 13de0821af3cdc9d9d76d5db9742203980a750f7 | [
"Apache-2.0"
] | permissive | mahaibin/zstack-woodpecker | 181410d3ab8aefc5ec98280b1449d3ac84eec4c6 | 7ddd907ae67fa1432ae7c5f2035baabdd960993f | refs/heads/master | 2021-01-23T02:43:55.532559 | 2017-03-23T06:43:09 | 2017-03-23T06:43:59 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,340 | py | '''
Test IPsec creation when ikeAuthAlgorithm is sha384
@author: Glody
'''
import zstackwoodpecker.test_util as test_util
import zstackwoodpecker.test_lib as test_lib
import zstackwoodpecker.test_state as test_state
import zstackwoodpecker.operations.ipsec_operations as ipsec_ops
import os
test_stub = test_lib.lib_get_test_stub()
test_obj_dict1 = test_state.TestStateDict()
test_obj_dict2 = test_state.TestStateDict()
ipsec1 = None
ipsec2 = None
mevoco1_ip = None
mevoco2_ip = None
def test():
global mevoco1_ip
global mevoco2_ip
global ipsec1
global ipsec2
mevoco1_ip = os.environ['ZSTACK_BUILT_IN_HTTP_SERVER_IP']
mevoco2_ip = os.environ['secondZStackMnIp']
test_util.test_dsc('Create test vm in mevoco1')
vm1 = test_stub.create_vlan_vm(os.environ.get('l3VlanNetworkName1'))
test_obj_dict1.add_vm(vm1)
vm1.check()
pri_l3_uuid1 = vm1.vm.vmNics[0].l3NetworkUuid
vr1 = test_lib.lib_find_vr_by_l3_uuid(pri_l3_uuid1)[0]
l3_uuid1 = test_lib.lib_find_vr_pub_nic(vr1).l3NetworkUuid
vip1 = test_stub.create_vip('ipsec1_vip', l3_uuid1)
os.environ['ZSTACK_BUILT_IN_HTTP_SERVER_IP'] = mevoco2_ip
test_util.test_dsc('Create test vm in mevoco2')
vm2 = test_stub.create_vlan_vm(os.environ.get('l3VlanDNATNetworkName'))
test_obj_dict2.add_vm(vm2)
vm2.check()
pri_l3_uuid2 = vm2.vm.vmNics[0].l3NetworkUuid
vr2 = test_lib.lib_find_vr_by_l3_uuid(pri_l3_uuid2)[0]
l3_uuid2 = test_lib.lib_find_vr_pub_nic(vr2).l3NetworkUuid
vip2 = test_stub.create_vip('ipsec2_vip', l3_uuid2)
os.environ['ZSTACK_BUILT_IN_HTTP_SERVER_IP'] = mevoco1_ip
test_util.test_dsc('Create ipsec in mevoco1')
ipsec1 = ipsec_ops.create_ipsec_connection('ipsec1', pri_l3_uuid1, vip2.get_vip().ip, '123456', vip1.get_vip().uuid, [os.environ['secondZStackCidrs']], ike_auth_algorithm="sha384")
os.environ['ZSTACK_BUILT_IN_HTTP_SERVER_IP'] = mevoco2_ip
test_util.test_dsc('Create ipsec in mevoco2')
ipsec2 = ipsec_ops.create_ipsec_connection('ipsec2', pri_l3_uuid2, vip1.get_vip().ip, '123456', vip2.get_vip().uuid, [os.environ['firstZStackCidrs']], ike_auth_algorithm="sha384")
os.environ['ZSTACK_BUILT_IN_HTTP_SERVER_IP'] = mevoco1_ip
if not test_lib.lib_check_ping(vm1.vm, vm2.vm.vmNics[0].ip):
test_util.test_fail('vm in mevoco1[MN:%s] could not connect to vm in mevoco2[MN:%s]' % (mevoco1_ip, mevoco2_ip))
os.environ['ZSTACK_BUILT_IN_HTTP_SERVER_IP'] = mevoco2_ip
if not test_lib.lib_check_ping(vm2.vm, vm1.vm.vmNics[0].ip):
test_util.test_fail('vm in mevoco1[MN:%s] could not connect to vm in mevoco2[MN:%s]' % (mevoco2_ip, mevoco1_ip))
os.environ['ZSTACK_BUILT_IN_HTTP_SERVER_IP'] = mevoco1_ip
ipsec_ops.delete_ipsec_connection(ipsec1.uuid)
if test_lib.lib_check_ping(vm1.vm, vm2.vm.vmNics[0].ip, no_exception=True):
test_util.test_fail('vm in mevoco1[MN:%s] could still connect to vm in mevoco2[MN:%s] after Ipsec is deleted' % (mevoco1_ip, mevoco2_ip))
os.environ['ZSTACK_BUILT_IN_HTTP_SERVER_IP'] = mevoco2_ip
if test_lib.lib_check_ping(vm2.vm, vm1.vm.vmNics[0].ip, no_exception=True):
test_util.test_fail('vm in mevoco2[MN:%s] could still connect to vm in mevoco1[MN:%s] after Ipsec is deleted' % (mevoco2_ip, mevoco1_ip))
os.environ['ZSTACK_BUILT_IN_HTTP_SERVER_IP'] = mevoco1_ip
test_lib.lib_error_cleanup(test_obj_dict1)
os.environ['ZSTACK_BUILT_IN_HTTP_SERVER_IP'] = mevoco2_ip
ipsec_ops.delete_ipsec_connection(ipsec2.uuid)
test_lib.lib_error_cleanup(test_obj_dict2)
os.environ['ZSTACK_BUILT_IN_HTTP_SERVER_IP'] = mevoco1_ip
test_util.test_pass('Create Ipsec Success')
#Will be called only if exception happens in test().
def error_cleanup():
global mevoco1_ip
global mevoco2_ip
os.environ['ZSTACK_BUILT_IN_HTTP_SERVER_IP'] = mevoco1_ip
global test_obj_dict1
test_lib.lib_error_cleanup(test_obj_dict1)
global ipsec1
if ipsec1 != None:
ipsec_ops.delete_ipsec_connection(ipsec1.uuid)
os.environ['ZSTACK_BUILT_IN_HTTP_SERVER_IP'] = mevoco2_ip
global test_obj_dict2
test_lib.lib_error_cleanup(test_obj_dict2)
global ipsec2
if ipsec2 != None:
ipsec_ops.delete_ipsec_connection(ipsec2.uuid)
| [
"glody.guo@gmail.com"
] | glody.guo@gmail.com |
374e5e8a8f650eb8fa7df655eaf0a275bdd2792f | 642cf7d391e1767beeba0c836f45b55f27b81867 | /lib/topic_model.py | 66b464ae200eb9274c6f638aaf9e2ebc88600313 | [] | no_license | dataSuckers/Deep-Learning-models | ad664d341807cd4bb242b847e62d3a989c82f91b | 6bac493d424f047121a9e871b707e110e4c45734 | refs/heads/master | 2021-08-30T20:20:10.218440 | 2017-12-19T09:21:24 | 2017-12-19T09:21:24 | 112,164,577 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,283 | py | from textblob import TextBlob
from collections import Counter
import re
from nltk import ngrams
import nltk
from nltk.tokenize import TweetTokenizer
import string
from sklearn.feature_extraction.text import TfidfVectorizer
from nltk.stem.porter import PorterStemmer
from topics_utils import clean_sentence,train_model,test_model_log,pos_tagger,get_tokens,get_all_files_list
from multitask_utils import multi_work
import os
import pandas as pd
'****************************clean texts*********************************'
#get the data
DIR = './../coinscore_test/output/good_tweets/'
FILES, senti_dfs = get_all_files_list(DIR)
tweets = pd.concat(list(senti_dfs))
#tweets = senti_dfs[0]
#tweets = senti_dfs[term]
#from nltk.stem import WordNetLemmatizer
#lemmatizer = WordNetLemmatizer()
#clean text
#CPU times: user 40 s, sys: 44 ms, total: 40 s
#Wall time: 40 s
#%time texts = [clean_sentence(x) for x in tweets.text]
texts = multi_work(thelist=list(enumerate(tweets.tweet_text.values)),func=clean_sentence,arguments=[],iterable_input=False,scaling_number=4,on_disk=False)
texts = sum(texts,[])
texts = sum(texts,[])
texts = list(dict(sorted(texts)).values())
'****************************train models*********************************'
#model training shouldn't be put together
#model1= multi_work(thelist=texts,func=train_model,arguments=[1,True])
#dict_whole = train_model(texts,N=2,norm=True)
dict_whole = train_model(texts,N=1,norm=True)
model = dict_whole
'****************************tag models*********************************'
#nltk pos tags
#CPU times: user 4min 35s, sys: 1.55 s, total: 4min 36s
#Wall time: 4min 37s
#%time tags_nltk = [nltk.pos_tag(tknzr.tokenize(text)) for text in texts]
tknzr = TweetTokenizer()
#if arguments contain a function, it should be double bracketed
tags = multi_work(thelist=list(enumerate(texts)),func=pos_tagger,arguments=[[tknzr.tokenize]],iterable_input=False)
outs = []
for x in tags:
outs+=x
tags =outs
tags = list(dict(sorted(tags)).values())
#textblob tags
#CPU times: user 5min 58s, sys: 2.18 s, total: 6min
#Wall time: 6min 1s
#%time tags = [TextBlob(x).tags for x in texts]
'****************************train noun models*********************************'
#tag_list=['NN','NNS','NNP','NNPS']
tag_list=['NNP','NNPS']
noun_sentence = [' '.join([w for w,t in one_tag if t in tag_list]) for one_tag in tags]
dict_noun = train_model(noun_sentence,N=1,norm=True)
model = dict_noun
'****************************test models*********************************'
#test
#CPU times: user 1min 51s, sys: 844 ms, total: 1min 52s
#Wall time: 1min 52s
tests = list(tweets.text.values)
#%time outs= test_model_log(tests=tests,model=dict_whole,N=1)
test_outs = multi_work(thelist=list(enumerate(tests)),func=test_model_log,arguments=[[model,1]],iterable_input=False)
test_outs = sum(test_outs,[])
test_outs = list(dict(sorted(test_outs)).values())
'****************************tfidf encoding*********************************'
tfidf = TfidfVectorizer(tokenizer=get_tokens,stop_words='english',analyzer='char',ngram_range=(3,7),smooth_idf=True)
%time tfs = tfidf.fit_transform(texts)
feature_names = tfidf.get_feature_names()
response = tfs
for col in response.nonzero()[1]:
print(feature_names[col], ' - ', max(response[:, 135015][1]))
| [
"noreply@github.com"
] | noreply@github.com |
52a836d0343e1d518ac0e52bf9f10bca23d2531e | 689b4573107af6e6d6d91dba6f81c77ab44f2ee5 | /migrations/0009_auto_20171213_1815.py | e561af149598ef6f5e0fb136f61a7d84ffc8921b | [] | no_license | prajwal041/Provisioning_UI | 3f112da972426cd4d5bd96aa193e3e342159a3f8 | d42eadabaed3252c40314e7e19d0694f2a1cd410 | refs/heads/master | 2020-03-27T13:57:34.243833 | 2018-09-24T19:36:28 | 2018-09-24T19:36:28 | 146,636,759 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 641 | py | # -*- coding: utf-8 -*-
# Generated by Django 1.10.4 on 2017-12-13 12:45
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('app', '0008_auto_20171213_1752'),
]
operations = [
migrations.AddField(
model_name='orchestration',
name='imagelist',
field=models.CharField(blank=True, max_length=255),
),
migrations.AddField(
model_name='orchestration',
name='ssh_name',
field=models.CharField(blank=True, max_length=255),
),
]
| [
"prajwal041@gmail.com"
] | prajwal041@gmail.com |
7df4448674925a4f5236bfda93a13a48ff6499c9 | 8cf82dffebc98a4f2166dc306663415c1a485aae | /trainModel_mine.py | 27556cea603ac392fdbb4f23e0e1e5c71a0eefb5 | [] | no_license | GUUUUG/MACS-Net-Overlapping-chromosome-segmentation-based-on-multi-scale-U-shaped-network | e93ae01a026876599251702a29c7f45ecf7b1a4c | bae91433b0e4251c38e196feb8c173f81be68f61 | refs/heads/main | 2023-01-03T23:59:42.957871 | 2020-11-02T13:45:52 | 2020-11-02T13:45:52 | 309,244,647 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 8,621 | py | import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0" ###
#!/usr/bin/python3.5
# -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
import numpy as np
import utilities
import os
from MACS_Net import MACS_Net
from MACS_Net_1 import MACS_Net_1
from CENet_ceshi import CENet_ceshi
from MACS_Net_3layers import MACS_Net_3layers
#from MACS_Net_3layers_TEST import MACS_Net_3layers_TEST
from MACS_Net_4layers import MACS_Net_4layers
#from UNet import UNet
from ARMSNet_duibi.UNet_R_4l import UNet_R_4l
from ARMSNet_duibi.UNet_AR_4l import UNet_AR_4l
from ARMSNet_duibi.UNet_PR_4l import UNet_PR_4l
from ARMSNet_duibi.UNet_DAR_4l import UNet_DAR_4l
from ARMSNet_duibi.UNet_4l import UNet_4l
from model import Deeplabv3
from keras.models import Model
import tensorflow as tf
from Loss_accuracy import LossHistory
import math
from keras.optimizers import Adam
import random
#import cv2
from keras.layers import Conv2D
#from keras.callbacks import Callback
#from keras.callbacks import EarlyStopping
from keras.callbacks import ModelCheckpoint
#from tensorflow.python.keras.callbacks import ModelCheckpoint
from tensorflow.python.keras.callbacks import EarlyStopping
from tensorflow.python.keras.callbacks import Callback
#import warnings
from keras import backend as K
from test import binary_crossentropy
from keras.utils import multi_gpu_model
from time import *
# Load data
xdata = np.load('/home/guangjie.wang/new/data/xdata_128x128.npy') ############################
labels = np.load('/home/guangjie.wang/new/data/ydata_128x128_0123_onehot.npy')
number = 1
a=np.load('/home/guangjie.wang/new/data_cls_new/'+str(number)+'/data_cls_4.npy') ################ 4
a=a.tolist()
b=np.load('/home/guangjie.wang/new/data_cls_new/'+str(number)+'/data_cls_1.npy') ################ 1
b=b.tolist()
x = xdata[a]
y = labels[a]
x_test = xdata[b]
y_test = labels[b]
#weight = []
#for i in range(4):
# posative = np.sum(labels[:,:,:,i])
# negative = 220102656-posative
# inter = round(float(negative)/float(posative),2)
# mi = math.log(inter,1000)
# if mi < 0:
# mi = (1+np.abs(mi))**-1
# weight.append(mi)
# else:
# weight.append(1+mi)
#ix = []
#for i in range(len(y)):
# #print(i)
# value = y[i,:,:,3].sum()
# if value == 0:
# ix.append(i)
#print(len(ix))
#length = np.arange(len(y)).tolist()
#z = []
#for m in length:
# if m not in ix:
# z.append(m)
#x = x[z]
#y = y[z]
#ix = []
#for i in range(len(y_test)):
# #print(i)
# value = y_test[i,:,:,3].sum()
# if value == 0:
# ix.append(i)
#length = np.arange(len(y_test)).tolist()
#z = []
#for m in length:
# if m not in ix:
# z.append(m)
#x_test = x_test[z]
#y_test = y_test[z]
#x_f = np.fliplr(x)
#y_f = np.fliplr(y)
#x_test_f = np.fliplr(x_test)
#y_test_f = np.fliplr(y_test)
#
#x = np.stack((x,x_f),axis=0).reshape(-1,128,128,1)
#y = np.stack((y,y_f),axis=0).reshape(-1,128,128,4)
##############################################################################################################################################################################
##############################################################################################################################################################################
#Name = './MACSNet_4layers_zidaiweighted_original.h5' ################ model 2
#Name = '/home/guangjie.wang/new/MACSNet_duibi/CENet_1.h5'
Name = '/home/guangjie.wang/new/ARMSNet_duibi/duibi_h5/UNet_4l_1.h5'
Name2 = '/home/guangjie.wang/new/ARMSNet_duibi/duibi_h5/UNet_PR_'+str(number)+'.h5' ################ MODEL
#model = CENet_ceshi(input_shape=(128,128,1))
#model = MACS_Net_1(input_shape=(128,128,1))
#model = Deeplabv3(input_shape=(128,128,1), classes=4)
#model = UNet(input_shape=(128,128,1))
#model = UNet_R_4l(input_shape=(128,128,1))
#model = UNet_AR_4l(input_shape=(128,128,1))
#model = UNet_PR_4l(input_shape=(128,128,1))
#model = UNet_DAR_4l(input_shape=(128,128,1))
model = UNet_4l(input_shape=(128,128,1))
#model = MACS_Net(input_shape=(128,128,1))
#model = MACS_Net_4layers(input_shape=(128,128,1))
#model = MACS_Net_3layers(input_shape=(128,128,1))
#model = MACS_Net_3layers_TEST(input_shape=(128,128,1))
#model = multi_gpu_model(model, gpus=2)
model.compile(loss='binary_crossentropy', optimizer='adam') ##loss='binary_crossentropy' , optimizer='adam' optimizer=Adam(lr=1e-4) default lr=1e-3, beta_1=0.9, beta_2=0.999
model.load_weights(Name) ############################################### 3
############################################### predict img
ix = 119
img = x_test[ix,:,:,0].reshape(1,128,128,1)
label = y_test[ix,:,:,3]
#print(np.sum(label))
img_pred = model.predict(img).round()
plt.xticks(())
plt.yticks(())
plt.imshow(x_test[ix,:,:,0])
plt.savefig('./img.png')
plt.imshow(label)
plt.savefig('./label.png')
plt.show()
plt.imshow(img_pred[0,:,:,3])
plt.savefig('./pred.png')
plt.show()
testIOU = utilities.IOU(img_pred, y_test[ix,:,:,:].reshape(1,128,128,4))
print('Testing IOU: ' + str(testIOU))
############################################## predict iou
#y_pred_test = model.predict(x_test).round()
#testIOU = utilities.IOU(y_pred_test, y_test)
#print('Testing IOU: ' + str(testIOU))
################################################ predict chrom iou
#y_pred_test = model.predict(x_test).round()
#testIOU = utilities.IOU_One(y_pred_test, y_test)
#print('Testing Chrom IOU: ' + str(testIOU))
############################################### predict Accuracy
#y_pred_test = model.predict(x_test).round()
#testIOU = utilities.global_chrom_Accuracy(y_pred_test, y_test)
#print('Testing Chrom Acc: ' + str(testIOU))
############################################### predict iou_set
#y_pred_test = model.predict(x_test).round()
#testIOU = utilities.IOU_set(y_pred_test, y_test)
#np.save('/home/guangjie.wang/new/IOU_SET/IOU_test_UNet_'+str(number),testIOU)
#for layer in model.layers[:-3]:
# layer.trainable = False
#for layer in model.layers[-3:]:
# layer.trainable = True
# Specify the number of epochs to run
#num_epoch = 1 ############################################### 4
#for i in range(num_epoch):
# print('epoch:', i)
# # Fit
# #history = LossHistory()
# check_point = ModelCheckpoint(Name2, monitor='val_loss', verbose=0, save_best_only=True, save_weights_only=True, mode='auto', period=1) #
# callback = EarlyStopping(monitor="val_loss", patience=10, verbose=0, mode='min')
#
# history = model.fit(x, y, epochs=100, validation_split=0.2, batch_size=32, callbacks=[check_point, callback]) ########################## [check_point, callback]
#
# # Calculate mIOU
# model.load_weights(Name2)
# y_pred_train = model.predict(x).round()
# trainIOU = utilities.IOU(y_pred_train, y)
# print('value: ',np.sum(y_pred_train[0,:,:,3]))
# print('value: ',np.sum(y[0,:,:,3]))
# print('Training IOU: ' + str(trainIOU))
# y_pred_test = model.predict(x_test).round()
# testIOU = utilities.IOU(y_pred_test, y_test)
# print('Testing Overlap IOU: ' + str(testIOU))
##
## y_pred_test = model.predict(x_test).round()
## testIOU = utilities.IOU_One(y_pred_test, y_test)
## print('Testing Chrom IOU: ' + str(testIOU))
##
## y_pred_test = model.predict(x_test).round()
## testIOU = utilities.global_chrom_Accuracy(y_pred_test, y_test)
## print('Testing Chrom Acc: ' + str(testIOU))
#
#
#
#
# #fig = plt.figure()
# #plt.plot(history.history['loss'],label='training loss')
# #plt.plot(history.history['val_loss'],label='val loss')
# #plt.title('model loss')
# #plt.ylabel('loss')
# #plt.xlabel('epoch')
# #plt.legend(loc='upper right')
# #fig.savefig('/home/guangjie.wang/new/pic/6_'+str(i)+'.png') ####this is My_loss
#
# #y_pred_train = model.predict(x).round()
# #trainIOU = utilities.IOU_set(y_pred_train, y)
# #np.save('/home/guangjie.wang/new/IOU_SET/IOU_train_better_'+str(i),trainIOU)
# #y_pred_test = model.predict(x_test).round()
# #testIOU = utilities.IOU_set(y_pred_test, y_test)
# #np.save('/home/guangjie.wang/new/IOU_SET/IOU_test_better_'+str(i),testIOU)
| [
"953704875@qq.com"
] | 953704875@qq.com |
4a567d7a7c4e85a615d24491eb179078e930fe16 | e68598b26d1f8d49adc22bab6f85b5a739ebda2c | /multi_weather/settings.py | be6ef288b9d091068116f6e02c63841c992b6cab | [] | no_license | ZackT403/multi_weather-1 | 5b1a1891c8ac8bd201e6802d666800620d3fb9ab | 6f0191320d52e1ecd5dfb59472e980ddafbd07d8 | refs/heads/master | 2022-09-06T20:55:36.497176 | 2020-06-02T21:57:59 | 2020-06-02T21:57:59 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,204 | py | """
Django settings for multi_weather project.
Generated by 'django-admin startproject' using Django 3.0.6.
For more information on this file, see
https://docs.djangoproject.com/en/3.0/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/3.0/ref/settings/
"""
import os
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/3.0/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = 'm+t9z#k&)doq87d_amh@sw^u+(y+x)cr(o0z&1ut41v$b%&f=d'
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
ALLOWED_HOSTS = []
# Application definition
INSTALLED_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
]
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
ROOT_URLCONF = 'multi_weather.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [os.path.join(BASE_DIR,'templates')],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = 'multi_weather.wsgi.application'
# Database
# https://docs.djangoproject.com/en/3.0/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),
}
}
# Password validation
# https://docs.djangoproject.com/en/3.0/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
},
]
# Internationalization
# https://docs.djangoproject.com/en/3.0/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/3.0/howto/static-files/
STATIC_URL = '/static/'
STATICFILES_DIRS = (
os.path.join(BASE_DIR, 'static'),
)
| [
"andrew@loenhouse.com"
] | andrew@loenhouse.com |
7abb5c21dc4c445762a6970a1c524ea1b1234e2c | e73cd01c238ae7931093f61ed4a93a5b6268b49c | /venv/bin/easy_install-3.8 | b5ac80b8d46ffc3adc8569a7cdfa427d55aa6592 | [] | no_license | riyadzaigirdar/django-redis | 8532dd6511fe47e04a88bfea66dd5374f7452825 | 0bb75d6e415adac51599d3225af7dc99d4634d9f | refs/heads/master | 2022-12-15T23:12:34.714320 | 2020-09-19T14:55:26 | 2020-09-19T14:55:26 | 296,893,101 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 255 | 8 | #!/home/riyad/Desktop/redis/venv/bin/python
# -*- coding: utf-8 -*-
import re
import sys
from setuptools.command.easy_install import main
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
sys.exit(main())
| [
"riyadzaigir280@gmail.com"
] | riyadzaigir280@gmail.com |
6b6b8ed46de995cb4125b9f3eae5ad6f987cb563 | 1ff9adfdb9d559e6f81ed9470467bab25e93b5ab | /src/ta_lib/_vendor/tigerml/core/reports/lib.py | e68184e54ad7fe935d6209b79f71931a46f2af5f | [] | no_license | Seemant-tiger/housing-price-prediction | a39dbefcb11bc460edeeee92e6becf77d35ff3a8 | be5d8cca769c7e267cfee1932eb82b70c2855bc1 | refs/heads/main | 2023-06-24T00:25:49.776720 | 2021-07-18T16:44:28 | 2021-07-18T16:44:28 | 387,222,852 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 594 | py | def create_report(
contents, name="", path="", format=".html", split_sheets=True, tiger_template=False
):
if format == ".xlsx":
from .excel import create_excel_report
create_excel_report(contents, name=name, path=path, split_sheets=split_sheets)
elif format == ".pptx":
from .ppt.lib import create_ppt_report
create_ppt_report(contents, name=name, path=path, tiger_template=tiger_template)
if format == ".html":
from .html import create_html_report
create_html_report(contents, name=name, path=path, split_sheets=split_sheets)
| [
"seemantsingh1199@gmail.com"
] | seemantsingh1199@gmail.com |
fd4852650ffaaeb54b696523ab7c48faf644901c | 40e593f1872a6ab4ea2e3f1830bd1919fad80422 | /e_commerce_project/cart/views.py | 31a4447a01d731d2e370aaf00749a802cde1a039 | [] | no_license | avodhaR/Ecommerce-app | b1def2391b5082f8ddf94db78a9b6de7508d3f7d | 1f1865ffd41380983231d7ab4b9a15bb9ed9adfb | refs/heads/master | 2023-04-08T03:17:47.375590 | 2021-04-05T06:47:31 | 2021-04-05T06:47:31 | 354,736,037 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,111 | py | from django.shortcuts import render,redirect,get_object_or_404
from e_commerce_app.models import Product
from . models import Cart,CartItem
from django.core.exceptions import ObjectDoesNotExist
def _cart_id(request):
cart=request.session.session_key
if not cart:
cart=request.session.create()
return cart
def add_cart(request,product_id):
product=Product.objects.get(id=product_id)
try:
cart=Cart.objects.get(cart_id=_cart_id(request))
except Cart.DoesNotExist:
cart=Cart.objects.create(cart_id=_cart_id(request))
cart.save()
try:
cart_item=CartItem.objects.get(product=product,cart=cart)
if cart_item.quantity < cart_item.product.stock:
cart_item.quantity +=1
cart_item.save();
except CartItem.DoesNotExist:
cart_item=CartItem.objects.create(
product=product,
quantity=1,
cart=cart
)
cart_item.save()
return redirect('cart:cart_detail')
def cart_detail(request,total=0,counter=0,cart_items=None):
try:
cart=Cart.objects.get(cart_id=_cart_id(request))
cart_items=CartItem.objects.filter(cart=cart,active=True)
for cart_item in cart_items:
total +=(cart_item.product.price * cart_item.quantity)
counter += cart_item.quantity
except ObjectDoesNotExist:
pass
return render(request,'cart.html',dict(cart_items=cart_items,total=total,counter=counter))
def cart_remove(request,product_id):
cart=Cart.objects.get(cart_id=_cart_id(request))
product=get_object_or_404(Product,id=product_id)
cart_item=CartItem.objects.get(product=product,cart=cart)
if cart_item.quantity >1:
cart_item.quantity-=1
cart_item.save()
else:
cart_item.delete()
return redirect('cart:cart_detail')
def full_remove(request,product_id):
cart=Cart.objects.get(cart_id=_cart_id(request))
product=get_object_or_404(Product,id=product_id)
cart_item=CartItem.objects.get(product=product,cart=cart)
cart_item.delete()
return redirect('cart:cart_detail')
| [
"riyasupri@gmail.com"
] | riyasupri@gmail.com |
7213f10f28062eadf3bbacca50ac7ce3992e7b2e | 854639c9f2ce6a1cf0a412a35a56a93f19f07e63 | /Experimento/gerar_grafico_tempo_execucao_5_subplot.py | 926b46fdf0f14c14769d9832f38bc579274e320b | [
"MIT"
] | permissive | RandelSouza/TCC | 3fd82d94fe88bc00b0d2d6968c0772957ccc4dc7 | aeaee2c26635db26ccc3802720d41d2426a062fa | refs/heads/master | 2020-04-29T20:11:35.723032 | 2020-04-20T02:31:37 | 2020-04-20T02:31:37 | 176,377,555 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,496 | py | # -*- coding:UTF-8-*-
import sys
from glob import glob
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import numpy as np
plt.rcParams.update({'font.size': 25})
plt.rc('legend', fontsize=20)
'''
# Configurações dos Gráficos que podem ser alteradas
SMALL_SIZE = 40
MEDIUM_SIZE = 40
BIGGER_SIZE = 40
plt.rc('font', size=SMALL_SIZE) # controls default text sizes
plt.rc('axes', titlesize=SMALL_SIZE) # fontsize of the axes title
plt.rc('axes', labelsize=MEDIUM_SIZE) # fontsize of the x and y labels
plt.rc('xtick', labelsize=SMALL_SIZE) # fontsize of the tick labels
plt.rc('ytick', labelsize=SMALL_SIZE) # fontsize of the tick labels
plt.rc('legend', fontsize=SMALL_SIZE) # legend fontsize
plt.rc('figure', titlesize=BIGGER_SIZE) # fontsize of the figure title
'''
def calc(df):
mean = df[0].mean()
std = df[0].std()
margemdeerro = 1.96 * (std / np.sqrt(len(df[0])))
return mean, margemdeerro
reload(sys)
sys.setdefaultencoding('utf-8')
path = "resultados_experimento"
controllerSDN = [ "Ryu", "Floodlight", "POX" ]
nodesQuantity = [ 26, 52, 104 ]
dados = []
dados_processados = []
color = ["green", "#ff8c00", "m"]
size = 20
x = [ 20, 30, 40 ]
labels=[ str(nodesQuantity[0]), str(nodesQuantity[1]), str(nodesQuantity[2]) ]
for controller in controllerSDN:
for quantity in nodesQuantity:
arquivo = "{}/{}{}{}/time_execucao_real_{}_{}.txt".format(path, controller, "_Nos_", quantity, controller, quantity)
dados.append( pd.read_csv(glob(arquivo)[0], header=None ) )
for dado in dados:
dados_processados.append( calc( dado ) )
print dados_processados
plt.subplot(221)
#plt.ylim(22, 41)
plt.plot( [x+1 for x in range(len(dados[1]))], dados[1], label=controllerSDN[0], color= color[0], marker="", markersize=2, linestyle='-', lw=3)
plt.plot([1, len(dados[1])], [calc(dados[1])[0], calc(dados[1])[0] ], color='#FFFF00', linewidth=3, linestyle='-', label='Média '+controllerSDN[0])
plt.legend(loc='best', numpoints=1 , fancybox=True, framealpha=0.5)
plt.title('Tempo de execução \n com 52 nós IoT (a)')
plt.ylabel('Tempo (s)')
plt.xlabel('Número de coletas')
plt.subplot(222)
#plt.ylim(22, 41)
plt.plot( [x+1 for x in range(len(dados[4]))], dados[4], label=controllerSDN[1], color= color[1], marker="", markersize=2, linestyle='-', lw=3)
plt.plot([1, len(dados[4])], [calc(dados[4])[0], calc(dados[4])[0] ], color='black', linewidth=3, linestyle='-', label='Média '+controllerSDN[1])
plt.legend(loc='best', numpoints=1, fancybox=True, framealpha=0.5)
plt.title('Tempo de execução \n com 52 nós IoT (b)')
plt.ylabel('Tempo (s)')
plt.xlabel('Número de coletas')
plt.subplot(223)
#plt.ylim(22, 41)
plt.plot( [x+1 for x in range(len(dados[7]))], dados[7], label=controllerSDN[2], color= color[2], marker= "", markersize=2, linestyle='-', lw=3)
plt.plot([1, len(dados[7])], [calc(dados[7])[0], calc(dados[7])[0] ], color='blue', linewidth=3, linestyle='-', label='Média '+controllerSDN[2])
plt.legend(loc='best', numpoints=1, fancybox=True, framealpha=0.5)
plt.title('Tempo de execução \n com 52 nós IoT (c)')
plt.ylabel('Tempo (s)')
plt.xlabel('Número de coletas')
plt.subplot(224)
#plt.ylim(22, 41)
plt.plot( [x+1 for x in range(len(dados[1]))], dados[1], label=controllerSDN[0], color= color[0], marker= "", markersize=2, linestyle='-', lw=3)
plt.plot( [x+1 for x in range(len(dados[4]))], dados[4], label=controllerSDN[1], color= color[1], marker= "", markersize=2, linestyle='-', lw=3)
plt.plot( [x+1 for x in range(len(dados[7]))], dados[7], label=controllerSDN[2], color= color[2], marker= "", markersize=2, linestyle='-', lw=3)
plt.plot([1, len(dados[1])], [calc(dados[1])[0], calc(dados[1])[0] ], color='#FFFF00', linewidth=3, linestyle='-', label='Média '+controllerSDN[0])
plt.plot([1, len(dados[4])], [calc(dados[4])[0], calc(dados[4])[0] ], color='black', linewidth=3, linestyle='-', label='Média '+controllerSDN[1])
plt.plot([1, len(dados[7])], [calc(dados[7])[0], calc(dados[7])[0] ], color='blue', linewidth=3, linestyle='-', label='Média '+controllerSDN[2])
plt.legend(loc='upper center', numpoints=1, fancybox=True, framealpha=0.5, bbox_to_anchor=(0.5, 1.), ncol=2)
plt.title('Tempo de execução \n com 52 nós IoT (d)')
plt.ylabel('Tempo (s)')
plt.xlabel('Número de coletas')
plt.grid(True)
plt.subplots_adjust(top=0.92, bottom=0.08, left=0.10, right=0.95, hspace=0.50, wspace=0.35)
#plt.savefig("/home/randel/manhattan02.png")
plt.show()
| [
"randelsouza88@gmail.com"
] | randelsouza88@gmail.com |
7d99e26a6d7d4b0a7f916ad07f46105c644061c7 | ac2f43c8e0d9649a7f063c59b3dffdfed9fd7ed7 | /tests2/common/base_slaac_test.py | 459b17fe8aebffa9efbf641b36e553aada1068c0 | [] | no_license | facebook/openbmc | bef10604ced226288600f55248b7f1be9945aea4 | 32777c66a8410d767eae15baabf71c61a0bef13c | refs/heads/helium | 2023-08-17T03:13:54.729494 | 2023-08-16T23:24:18 | 2023-08-16T23:24:18 | 31,917,712 | 684 | 331 | null | 2023-07-25T21:19:08 | 2015-03-09T19:18:35 | C | UTF-8 | Python | false | false | 2,578 | py | #!/usr/bin/env python3
#
# Copyright 2018-present Facebook. All Rights Reserved.
#
# This program file is free software; you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by the
# Free Software Foundation; version 2 of the License.
#
# This program is distributed in the hope that it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
# FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
# for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program in a file named COPYING; if not, write to the
# Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor,
# Boston, MA 02110-1301 USA
#
import subprocess
from common.base_interface_test import BaseInterfaceTest
from utils.cit_logger import Logger
class BaseSlaacTest(BaseInterfaceTest):
def get_ipv6_address(self):
"""
Get inet6 address with highest length of a given interface
overriding this method of BaseInterfaceTest class because we want
to have inet6 address with highest length
"""
out = self.get_ip_addr_output_inet6()
# trying to find inet6 address with highest length
ipv6 = ""
for value in out[1:]:
if len(value.split("/")[0]) > len(ipv6):
ipv6 = value.split("/")[0]
Logger.debug("Got ip address for " + str(self.ifname))
return ipv6.lower()
def get_mac_address(self):
"""
Get Ethernet MAC address
"""
f = subprocess.Popen(
["fw_printenv", "-n", "ethaddr"],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
)
out, err = f.communicate()
self.assertEqual(
f.returncode,
0,
"fw_printenv -n ethaddr "
+ "exited with returncode: "
+ str(f.returncode)
+ ", err: "
+ str(err),
)
if out:
out = out.decode("utf-8").rstrip()
return out.lower()
else:
raise Exception("Couldn't find MAC address [FAILED]")
def generate_modified_eui_64_mac_address(self):
"""
Get Modified EUI-64 Mac Address
"""
mac_address = self.get_mac_address().split(":")
# reversing the 7th bit of the mac address
mac_address[0] = hex(int(mac_address[0], 16) ^ 2)[2:]
mac_address[2] = mac_address[2] + "fffe"
return "".join(mac_address)
| [
"facebook-github-bot@users.noreply.github.com"
] | facebook-github-bot@users.noreply.github.com |
48b50a8ac8d47771a70791145c9359e97e3a9752 | 178b24f3d164eb498c03cf51762bd938ef76afab | /db/model.py | 01bc9065564740e417b4729f28c5cc5511db9291 | [] | no_license | yezixigua/homeTE | 73175339370741b48d00ad83f04e598c3727da11 | 514dbb80ed397cc758bc86ae32d6a4c257a110f7 | refs/heads/master | 2020-04-05T03:34:18.032645 | 2018-11-24T07:10:04 | 2018-11-24T07:10:04 | 156,519,657 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 6,978 | py | from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
from sqlalchemy import Column, Integer, String
from sqlalchemy.ext.declarative import declarative_base
import time
Base = declarative_base()
# 保存ip的对象类型， 映射一个数据表
class Visitor(Base):
__tablename__ = 'Visitor'
id = Column('id', Integer, primary_key=True, autoincrement=True)
time = Column('time', String(50))
ip = Column('ip', String(50))
# 用户&密码数据库
class User(Base):
__tablename__ = 'User'
id = Column('id', Integer, primary_key=True, autoincrement=True)
name = Column('name', String(50))
password = Column('password', String(50))
email = Column('email', String(20))
phone = Column('phone', String(20))
createdTime = Column('createdTime', String(50))
lastVisitTime = Column('lastVisitTime', String(50))
isDeleted = Column('isDeleted', String(10))
def __init__(self, name='', pwd='', email='', phone=''):
self.name = name
self.password = pwd
ct = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
self.email = email
self.phone = phone
self.createdTime = ct
self.lastVisitTime = ct
self.isDeleted = 'False'
@staticmethod
def init_data():
Base.metadata.create_all(engine)
def add(self):
session1 = DB_Session()
session1.add(self)
session1.commit()
session1.close()
def __repr__(self):
string = '' \
'id: {} user: {} pwd: {} ct: {} lvt: {} d: {}' \
''.format(
self.id,
self.name,
self.password,
self.createdTime,
self.lastVisitTime,
self.isDeleted
)
return string
@classmethod
def query_data(cls, **kwargs):
# 5. 查询数据
# 5.1 返回结果集的第二项
session1 = DB_Session()
for k, v in kwargs.items():
key, value = k, v
print(key, value)
# user = session1.query(cls).get(key)
user = session1.query(cls).filter_by(id=1).all()
session1.close()
print(user)
@classmethod
def query_by_name(cls, name):
# 5. 查询数据
# 5.1 返回结果集的第二项
# user = session.query(cls).get(key)
session1 = DB_Session()
user = session1.query(cls).filter_by(name=name).all()
print(user)
session1.close()
return user
@classmethod
def is_valid_user(cls, name, pwd):
user = cls.query_by_name(name)
if len(user) == 0:
return False
if user[0].password == pwd:
return True
else:
return False
@staticmethod
def query_all():
# 5. 查询数据
session1 = DB_Session()
users = session1.query(User)[:]
for user in users:
print(user)
session1.close()
# 连接数据库的一些变量， 会需要全局引用
# print(os.getcwd())
DB_CONNECT_STRING = 'sqlite:///db/test2.db'
if __name__ == '__main__':
DB_CONNECT_STRING = 'sqlite:///test2.db'
engine = create_engine(DB_CONNECT_STRING, echo=False)
DB_Session = sessionmaker(bind=engine)
session = DB_Session()
def init_data():
# 1. 创建表（如果表已经存在，则不会创建）
Base.metadata.create_all(engine)
def add_data(data_ip):
# 2. 插入数据
session1 = DB_Session()
localtime = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
u = Visitor(time=localtime, ip=data_ip)
session1.add(u)
session1.commit()
session1.close()
def add_data_user():
# 2. 插入数据
session1 = DB_Session()
ct = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
u = User(name='1', password='34', createdTime=ct, lastVisitTime=ct, isDeleted='False')
session1.add(u)
session1.commit()
session1.close()
def add_data_demo():
# 2. 插入数据
u = Visitor(time='tobi', ip='wdtf')
u1 = Visitor(time='tobi', ip='wdtf')
u2 = Visitor(time='tobi', ip='wdtf')
r = Role(name='user')
# 2.1 使用add，如果已经存在，会报错
session.add(u)
session.add(u1)
session.add(u2)
session.add(r)
session.commit()
print(r.id)
#
# # 3 修改数据
# # 3.1 使用merge方法，如果存在则修改，如果不存在则插入（只判断主键，不判断unique列）
# r.name = 'admin'
# session.merge(r)
#
def update_data(id_data, ip='0.0.0.0'):
# 3.2 也可以通过这种方式修改
localtime = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
session.query(Visitor).filter(Visitor.id == id_data).update({'ip': ip, 'time': localtime})
def delete_data(id_data):
# 4. 删除数据
session.query(Role).filter(Role.id == id_data).delete()
def query_data(id, database=User):
# 5. 查询数据
# 5.1 返回结果集的第二项
# user = session.query(database).filter_by(id=1)
user = session.query(database).filter(database.id == id).all()
print(user)
def query_all(database=User):
# 5. 查询数据
users = session.query(database)[:]
for user in users:
print(user)
#
# # 5.3 查询条件
# user = session.query(User).filter(User.id < 6).first()
#
# # 5.4 排序
# users = session.query(User).order_by(User.name)
#
# # 5.5 降序（需要导入desc方法）
# from sqlalchemy import desc
# users = session.query(User).order_by(desc(User.name))
#
# # 5.6 只查询部分属性
# users = session.query(User.name).order_by(desc(User.name))
# for user in users:
# print user.name
#
# # 5.7 给结果集的列取别名
# users = session.query(User.name.label('user_name')).all()
# for user in users:
# print user.user_name
#
# # 5.8 去重查询（需要导入distinct方法）
# from sqlalchemy import distinct
# users = session.query(distinct(User.name).label('name')).all()
#
# # 5.9 统计查询
# user_count = session.query(User.name).order_by(User.name).count()
# age_avg = session.query(func.avg(User.age)).first()
# age_sum = session.query(func.sum(User.age)).first()
#
# # 5.10 分组查询
# users = session.query(func.count(User.name).label('count'), User.age).group_by(User.age)
# for user in users:
# print 'age:{0}, count:{1}'.format(user.age, user.count)
#
# # 6.1 exists查询(不存在则为~exists())
# from sqlalchemy.sql import exists
# session.query(User.name).filter(~exists().where(User.role_id == Role.id))
# # SELECT name AS users_name FROM users WHERE NOT EXISTS (SELECT * FROM roles WHERE users.role_id = roles.id)
#
# # 6.2 除了exists，any也可以表示EXISTS
# session.query(Role).filter(Role.users.any())
#
# # 7 random
# from sqlalchemy.sql.functions import random
# user = session.query(User).order_by(random()).first()
def my_test():
init_data()
User.query_all()
User.is_valid_user('12234', '123')
if __name__ == '__main__':
my_test()
session.close()
| [
"541889983@qq.com"
] | 541889983@qq.com |
a1d37c6673b71f08cb910ad6171f14c473dacd2e | d2310dd974620eb5af112d056e4518b086dea07d | /Apps/Inventario/migrations/0032_auto_20191209_1322.py | 251ea7187a36e3fe6ebd47a07e968600f4c6ab2c | [] | no_license | diegoquirozramirez/Inventario | de824fbc06e6b64f9ca87c061fbded2a3c3df020 | f3e6e89e03e633acb5729390b1ef5f59a55bdaf2 | refs/heads/master | 2022-03-25T17:02:37.830179 | 2019-12-16T08:22:57 | 2019-12-16T08:22:57 | 226,250,841 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 405 | py | # Generated by Django 2.0 on 2019-12-09 18:22
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('Inventario', '0031_auto_20191209_1321'),
]
operations = [
migrations.AlterField(
model_name='base0',
name='codigo_interno',
field=models.CharField(blank=True, max_length=5),
),
]
| [
"ingquirozramirez@gmail.com"
] | ingquirozramirez@gmail.com |
44b3c3e17a72f62f7418db2902479cb858d1cca6 | c275fc8e52e852a82c240d363fc80d818c938549 | /fastreid/modeling/backbones/resnest.py | 54d052d0d66e2081c3a4416c82bce401e9447f8b | [] | no_license | JinkaiZheng/fast-reid_20200430 | 67c968698e6a1c837e7c1c49f0078afae96152a3 | 6832d28e8ddc9b743d2de2a1a089175b8dd4bfd4 | refs/heads/master | 2022-06-29T09:02:50.443834 | 2020-05-12T12:09:26 | 2020-05-12T12:09:26 | 262,527,154 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 17,224 | py | # encoding: utf-8
# based on:
# https://github.com/zhanghang1989/ResNeSt/blob/master/resnest/torch/resnest.py
"""ResNeSt models"""
import logging
import math
import torch
from torch import nn
from .build import BACKBONE_REGISTRY
from ...layers import SplAtConv2d, IBN, Non_local
_url_format = 'https://hangzh.s3.amazonaws.com/encoding/models/{}-{}.pth'
_model_sha256 = {name: checksum for checksum, name in [
('528c19ca', 'resnest50'),
('22405ba7', 'resnest101'),
('75117900', 'resnest200'),
('0cc87c48', 'resnest269'),
]}
def short_hash(name):
if name not in _model_sha256:
raise ValueError('Pretrained model for {name} is not available.'.format(name=name))
return _model_sha256[name][:8]
model_urls = {name: _url_format.format(name, short_hash(name)) for
name in _model_sha256.keys()
}
class Bottleneck(nn.Module):
"""ResNet Bottleneck
"""
# pylint: disable=unused-argument
expansion = 4
def __init__(self, inplanes, planes, with_ibn=False, stride=1, downsample=None,
radix=1, cardinality=1, bottleneck_width=64,
avd=False, avd_first=False, dilation=1, is_first=False,
rectified_conv=False, rectify_avg=False,
norm_layer=None, dropblock_prob=0.0, last_gamma=False):
super(Bottleneck, self).__init__()
group_width = int(planes * (bottleneck_width / 64.)) * cardinality
self.conv1 = nn.Conv2d(inplanes, group_width, kernel_size=1, bias=False)
if with_ibn:
self.bn1 = IBN(group_width)
else:
self.bn1 = norm_layer(group_width)
self.dropblock_prob = dropblock_prob
self.radix = radix
self.avd = avd and (stride > 1 or is_first)
self.avd_first = avd_first
if self.avd:
self.avd_layer = nn.AvgPool2d(3, stride, padding=1)
stride = 1
if radix > 1:
self.conv2 = SplAtConv2d(
group_width, group_width, kernel_size=3,
stride=stride, padding=dilation,
dilation=dilation, groups=cardinality, bias=False,
radix=radix, rectify=rectified_conv,
rectify_avg=rectify_avg,
norm_layer=norm_layer,
dropblock_prob=dropblock_prob)
elif rectified_conv:
from rfconv import RFConv2d
self.conv2 = RFConv2d(
group_width, group_width, kernel_size=3, stride=stride,
padding=dilation, dilation=dilation,
groups=cardinality, bias=False,
average_mode=rectify_avg)
self.bn2 = norm_layer(group_width)
else:
self.conv2 = nn.Conv2d(
group_width, group_width, kernel_size=3, stride=stride,
padding=dilation, dilation=dilation,
groups=cardinality, bias=False)
self.bn2 = norm_layer(group_width)
self.conv3 = nn.Conv2d(
group_width, planes * 4, kernel_size=1, bias=False)
self.bn3 = norm_layer(planes * 4)
if last_gamma:
from torch.nn.init import zeros_
zeros_(self.bn3.weight)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.dilation = dilation
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
if self.dropblock_prob > 0.0:
out = self.dropblock1(out)
out = self.relu(out)
if self.avd and self.avd_first:
out = self.avd_layer(out)
out = self.conv2(out)
if self.radix == 1:
out = self.bn2(out)
if self.dropblock_prob > 0.0:
out = self.dropblock2(out)
out = self.relu(out)
if self.avd and not self.avd_first:
out = self.avd_layer(out)
out = self.conv3(out)
out = self.bn3(out)
if self.dropblock_prob > 0.0:
out = self.dropblock3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class ResNest(nn.Module):
"""ResNet Variants ResNest
Parameters
----------
block : Block
Class for the residual block. Options are BasicBlockV1, BottleneckV1.
layers : list of int
Numbers of layers in each block
classes : int, default 1000
Number of classification classes.
dilated : bool, default False
Applying dilation strategy to pretrained ResNet yielding a stride-8 model,
typically used in Semantic Segmentation.
norm_layer : object
Normalization layer used in backbone network (default: :class:`mxnet.gluon.nn.BatchNorm`;
for Synchronized Cross-GPU BachNormalization).
Reference:
- He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
- Yu, Fisher, and Vladlen Koltun. "Multi-scale context aggregation by dilated convolutions."
"""
# pylint: disable=unused-variable
def __init__(self, last_stride, with_ibn, with_nl, block, layers, non_layers, radix=1, groups=1,
bottleneck_width=64,
dilated=False, dilation=1,
deep_stem=False, stem_width=64, avg_down=False,
rectified_conv=False, rectify_avg=False,
avd=False, avd_first=False,
final_drop=0.0, dropblock_prob=0,
last_gamma=False, norm_layer=nn.BatchNorm2d):
self.cardinality = groups
self.bottleneck_width = bottleneck_width
# ResNet-D params
self.inplanes = stem_width * 2 if deep_stem else 64
self.avg_down = avg_down
self.last_gamma = last_gamma
# ResNeSt params
self.radix = radix
self.avd = avd
self.avd_first = avd_first
super().__init__()
self.rectified_conv = rectified_conv
self.rectify_avg = rectify_avg
if rectified_conv:
from rfconv import RFConv2d
conv_layer = RFConv2d
else:
conv_layer = nn.Conv2d
conv_kwargs = {'average_mode': rectify_avg} if rectified_conv else {}
if deep_stem:
self.conv1 = nn.Sequential(
conv_layer(3, stem_width, kernel_size=3, stride=2, padding=1, bias=False, **conv_kwargs),
norm_layer(stem_width),
nn.ReLU(inplace=True),
conv_layer(stem_width, stem_width, kernel_size=3, stride=1, padding=1, bias=False, **conv_kwargs),
norm_layer(stem_width),
nn.ReLU(inplace=True),
conv_layer(stem_width, stem_width * 2, kernel_size=3, stride=1, padding=1, bias=False, **conv_kwargs),
)
else:
self.conv1 = conv_layer(3, 64, kernel_size=7, stride=2, padding=3,
bias=False, **conv_kwargs)
self.bn1 = norm_layer(self.inplanes)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0], with_ibn=with_ibn, norm_layer=norm_layer, is_first=False)
self.layer2 = self._make_layer(block, 128, layers[1], stride=2, with_ibn=with_ibn, norm_layer=norm_layer)
if dilated or dilation == 4:
self.layer3 = self._make_layer(block, 256, layers[2], stride=1, with_ibn=with_ibn,
dilation=2, norm_layer=norm_layer,
dropblock_prob=dropblock_prob)
self.layer4 = self._make_layer(block, 512, layers[3], stride=1, with_ibn=with_ibn,
dilation=4, norm_layer=norm_layer,
dropblock_prob=dropblock_prob)
elif dilation == 2:
self.layer3 = self._make_layer(block, 256, layers[2], stride=2, with_ibn=with_ibn,
dilation=1, norm_layer=norm_layer,
dropblock_prob=dropblock_prob)
self.layer4 = self._make_layer(block, 512, layers[3], stride=1, with_ibn=with_ibn,
dilation=2, norm_layer=norm_layer,
dropblock_prob=dropblock_prob)
else:
self.layer3 = self._make_layer(block, 256, layers[2], stride=2, with_ibn=with_ibn,
norm_layer=norm_layer,
dropblock_prob=dropblock_prob)
self.layer4 = self._make_layer(block, 512, layers[3], stride=last_stride, with_ibn=with_ibn,
norm_layer=norm_layer,
dropblock_prob=dropblock_prob)
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))
elif isinstance(m, norm_layer):
m.weight.data.fill_(1)
m.bias.data.zero_()
if with_nl:
self._build_nonlocal(layers, non_layers)
else:
self.NL_1_idx = self.NL_2_idx = self.NL_3_idx = self.NL_4_idx = []
def _make_layer(self, block, planes, blocks, stride=1, with_ibn=False, dilation=1, norm_layer=None,
dropblock_prob=0.0, is_first=True):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
down_layers = []
if self.avg_down:
if dilation == 1:
down_layers.append(nn.AvgPool2d(kernel_size=stride, stride=stride,
ceil_mode=True, count_include_pad=False))
else:
down_layers.append(nn.AvgPool2d(kernel_size=1, stride=1,
ceil_mode=True, count_include_pad=False))
down_layers.append(nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=1, bias=False))
else:
down_layers.append(nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False))
down_layers.append(norm_layer(planes * block.expansion))
downsample = nn.Sequential(*down_layers)
layers = []
if planes == 512:
with_ibn = False
if dilation == 1 or dilation == 2:
layers.append(block(self.inplanes, planes, with_ibn, stride, downsample=downsample,
radix=self.radix, cardinality=self.cardinality,
bottleneck_width=self.bottleneck_width,
avd=self.avd, avd_first=self.avd_first,
dilation=1, is_first=is_first, rectified_conv=self.rectified_conv,
rectify_avg=self.rectify_avg,
norm_layer=norm_layer, dropblock_prob=dropblock_prob,
last_gamma=self.last_gamma))
elif dilation == 4:
layers.append(block(self.inplanes, planes, with_ibn, stride, downsample=downsample,
radix=self.radix, cardinality=self.cardinality,
bottleneck_width=self.bottleneck_width,
avd=self.avd, avd_first=self.avd_first,
dilation=2, is_first=is_first, rectified_conv=self.rectified_conv,
rectify_avg=self.rectify_avg,
norm_layer=norm_layer, dropblock_prob=dropblock_prob,
last_gamma=self.last_gamma))
else:
raise RuntimeError("=> unknown dilation size: {}".format(dilation))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes, with_ibn,
radix=self.radix, cardinality=self.cardinality,
bottleneck_width=self.bottleneck_width,
avd=self.avd, avd_first=self.avd_first,
dilation=dilation, rectified_conv=self.rectified_conv,
rectify_avg=self.rectify_avg,
norm_layer=norm_layer, dropblock_prob=dropblock_prob,
last_gamma=self.last_gamma))
return nn.Sequential(*layers)
def _build_nonlocal(self, layers, non_layers):
self.NL_1 = nn.ModuleList(
[Non_local(256) for _ in range(non_layers[0])])
self.NL_1_idx = sorted([layers[0] - (i + 1) for i in range(non_layers[0])])
self.NL_2 = nn.ModuleList(
[Non_local(512) for _ in range(non_layers[1])])
self.NL_2_idx = sorted([layers[1] - (i + 1) for i in range(non_layers[1])])
self.NL_3 = nn.ModuleList(
[Non_local(1024) for _ in range(non_layers[2])])
self.NL_3_idx = sorted([layers[2] - (i + 1) for i in range(non_layers[2])])
self.NL_4 = nn.ModuleList(
[Non_local(2048) for _ in range(non_layers[3])])
self.NL_4_idx = sorted([layers[3] - (i + 1) for i in range(non_layers[3])])
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
NL1_counter = 0
if len(self.NL_1_idx) == 0:
self.NL_1_idx = [-1]
for i in range(len(self.layer1)):
x = self.layer1[i](x)
if i == self.NL_1_idx[NL1_counter]:
_, C, H, W = x.shape
x = self.NL_1[NL1_counter](x)
NL1_counter += 1
# Layer 2
NL2_counter = 0
if len(self.NL_2_idx) == 0:
self.NL_2_idx = [-1]
for i in range(len(self.layer2)):
x = self.layer2[i](x)
if i == self.NL_2_idx[NL2_counter]:
_, C, H, W = x.shape
x = self.NL_2[NL2_counter](x)
NL2_counter += 1
# Layer 3
NL3_counter = 0
if len(self.NL_3_idx) == 0:
self.NL_3_idx = [-1]
for i in range(len(self.layer3)):
x = self.layer3[i](x)
if i == self.NL_3_idx[NL3_counter]:
_, C, H, W = x.shape
x = self.NL_3[NL3_counter](x)
NL3_counter += 1
# Layer 4
NL4_counter = 0
if len(self.NL_4_idx) == 0:
self.NL_4_idx = [-1]
for i in range(len(self.layer4)):
x = self.layer4[i](x)
if i == self.NL_4_idx[NL4_counter]:
_, C, H, W = x.shape
x = self.NL_4[NL4_counter](x)
NL4_counter += 1
return x
@BACKBONE_REGISTRY.register()
def build_resnest_backbone(cfg):
"""
Create a ResNest instance from config.
Returns:
ResNet: a :class:`ResNet` instance.
"""
# fmt: off
pretrain = cfg.MODEL.BACKBONE.PRETRAIN
last_stride = cfg.MODEL.BACKBONE.LAST_STRIDE
with_ibn = cfg.MODEL.BACKBONE.WITH_IBN
with_se = cfg.MODEL.BACKBONE.WITH_SE
with_nl = cfg.MODEL.BACKBONE.WITH_NL
depth = cfg.MODEL.BACKBONE.DEPTH
num_blocks_per_stage = {50: [3, 4, 6, 3], 101: [3, 4, 23, 3], 200: [3, 24, 36, 3], 269: [3, 30, 48, 8]}[depth]
nl_layers_per_stage = {50: [0, 2, 3, 0], 101: [0, 2, 3, 0]}[depth]
stem_width = {50: 32, 101: 64, 200: 64, 269: 64}[depth]
model = ResNest(last_stride, with_ibn, with_nl, Bottleneck, num_blocks_per_stage, nl_layers_per_stage,
radix=2, groups=1, bottleneck_width=64,
deep_stem=True, stem_width=stem_width, avg_down=True,
avd=True, avd_first=False)
if pretrain:
# if not with_ibn:
# original resnet
state_dict = torch.hub.load_state_dict_from_url(
model_urls['resnest' + str(depth)], progress=True, check_hash=True)
# else:
# raise KeyError('Not implementation ibn in resnest')
# # ibn resnet
# state_dict = torch.load(pretrain_path)['state_dict']
# # remove module in name
# new_state_dict = {}
# for k in state_dict:
# new_k = '.'.join(k.split('.')[1:])
# if new_k in model.state_dict() and (model.state_dict()[new_k].shape == state_dict[k].shape):
# new_state_dict[new_k] = state_dict[k]
# state_dict = new_state_dict
res = model.load_state_dict(state_dict, strict=False)
logger = logging.getLogger(__name__)
logger.info('missing keys is {}'.format(res.missing_keys))
logger.info('unexpected keys is {}'.format(res.unexpected_keys))
return model
| [
"sherlockliao01@gmail.com"
] | sherlockliao01@gmail.com |
afa45ecc7ae9304c49f9038d38efcbbae76a06e2 | 01579f20c71dbf868daac38653947eda5a663081 | /mzitu_spider/mzitu/spiders/mzitu_spider.py | 6492d41782ec9dcbc7075f2cf38ec91a2c2444e4 | [] | no_license | yhxuanmo/meizitu_spider | 652f3b91cadda7cee384e1a4ef6e0aae6fab6a76 | e7ca197827a4f5855c4567839cf8d0606d2bbcfc | refs/heads/master | 2020-03-22T14:19:04.690081 | 2018-07-08T13:28:44 | 2018-07-08T13:28:44 | 140,169,486 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,080 | py | from scrapy.spiders import Spider
from scrapy.selector import Selector
from scrapy import Request
from mzitu.items import ImgBoxItem
class MzituSpiser(Spider):
name = 'mzitu'
start_urls = ['http://www.mzitu.com/',]
def parse(self, response):
sel = Selector(response)
typy_tag = sel.xpath('//ul[@id="menu-nav"]/li')
for tag in typy_tag:
# 排除首页的url
if tag.xpath('./a/text()').extract_first() != '首页':
if tag.xpath('./a/text()').extract_first() == '妹子自拍':
continue
if tag.xpath('./a/text()').extract_first() == '每日更新':
continue
# 分类的url
type_url = tag.xpath('./a/@href').extract_first()
# 分类名
type_name = tag.xpath('./a/text()').extract_first()
yield Request(url=type_url,callback=self.type_url_parse, meta={'type_name':type_name})
def type_url_parse(self, response):
# 分页类页面解析
sel = Selector(response)
curent_url = response.url
max_page = sel.xpath('//div[@class="nav-links"]/a[@class="page-numbers"]')[-1].xpath('./text()').extract_first()
for i in range(1,int(max_page)+1):
yield Request(url= curent_url+'page/'+ str(i) + '/',
callback=self.img_box_url_parse,
meta={'type_name':response.meta.get('type_name')}
)
def img_box_url_parse(self, response):
# 套图url解析
sel = Selector(response)
img_box_lis = sel.xpath('//ul[@id="pins"]/li')
item = ImgBoxItem()
type_name = response.meta.get('type_name')
for li in img_box_lis:
# 套图url
item['img_box_url'] = li.xpath('./a/@href').extract()[0]
# 套图名称
item['img_box_name'] = li.xpath('./span/a/text()').extract()[0]
# 分类名
item['type_name'] = type_name
yield item
| [
"yh_xuanmo@163.com"
] | yh_xuanmo@163.com |
b443d6514d029cd986721f382abe20b94dd68d0a | 991223fcbb355ffa278c708a452b8978e987f6dd | /Mega_Man.py | 90d5e6b0902e30ab3dc47c3da6b0a611f276f28e | [] | no_license | jstambaugh/Mega-Man | 2a4be4871c21768f0355874a8b7c0813b87e8ec5 | 6c380327e51605b72b1aca9ec0409cbac16ac795 | refs/heads/master | 2021-10-20T12:22:53.260645 | 2019-02-27T16:03:28 | 2019-02-27T16:03:28 | 172,946,185 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,313 | py | import pygame
import Sprite_Sheet
import Settings
vector = pygame.math.Vector2
class Player(pygame.sprite.Sprite):
def __init__(self,x,y): # The self class used for the player "Mega-Man"
super().__init__()
self.move_x = 5 # The x moving section
self.move_y = 5 # The y moving section for jumping
# Arrays used to store the sprites for each animation moment
self.moving_left = []
self.moving_right = []
self.not_moving = []
self.direction = "Right" # Start the game off moving to the right like traditional megaman
self.shooting = "False"
# All of the sprites where mega man is facing to the right
sprite_sheet = Sprite_Sheet.SpriteSheet("mega_man_sprite_sheet.png")
image = sprite_sheet.get_image(150, 0, 50, 50)
self.moving_right.append(image)
image = sprite_sheet.get_image(200, 0, 50, 50)
self.moving_right.append(image)
image = sprite_sheet.get_image(250, 0, 50, 50)
self.moving_right.append(image)
# All of the sprites where mega man is facing to the left
image = sprite_sheet.get_image(150, 0, 50, 50)
image = pygame.transform.flip(image, True, False)
self.moving_left.append(image)
image = sprite_sheet.get_image(200, 0, 50, 50)
image = pygame.transform.flip(image, True, False)
self.moving_left.append(image)
image = sprite_sheet.get_image(250, 0, 50, 50)
image = pygame.transform.flip(image, True, False)
self.moving_left.append(image)
# Not moving sprite indicator
image = sprite_sheet.get_image(0,0,50,50)
self.not_moving.append(image)
# Need to different not moving images one for the left and one for the right
image = sprite_sheet.get_image(0,0,50,50)
image = pygame.transform.flip(image, True, False)
self.not_moving.append(image)
# Shooting sprites
image = sprite_sheet.get_image(100,50,50,50)
self.not_moving.append(image)
image = sprite_sheet.get_image(100,50,50,50)
image = pygame.transform.flip(image, True, False)
self.not_moving.append(image)
# sprite to start with
self.image = self.not_moving[0]
self.rect = self.image.get_rect()
def update(self): #This section will blit the correct sprite to the screen depending on which way megaman is moving and or if he is moving
pos = self.rect.x
if self.direction == "Right":
frame = (pos // 30) % len(self.moving_right)
self.image = self.moving_right[frame]
elif self.direction == "Left":
frame = (pos // 30) % len(self.moving_left)
self.image = self.moving_left[frame]
elif self.direction == "None-right":
if self.shooting == "False":
self.image = self.not_moving[0]
elif self.shooting == "True":
self.image = self.not_moving[2]
elif self.direction == "None-left":
if self.shooting == "False":
self.image = self.not_moving[1]
elif self.shooting == "True":
self.image = self.not_moving[3]
| [
"noreply@github.com"
] | noreply@github.com |
ee4cc8f5d99dabc24f70760a8e975fedfe84cf5f | 3cd2ec8afd70235f1271ee483373e0f131f4bf33 | /Datasets/GTOS_mobile_single_size.py | 123fbdfe5e345327fb6254e5a4c6fd1ee027ac86 | [
"LicenseRef-scancode-unknown-license-reference",
"MIT"
] | permissive | joeyee/Histogram_Layer | 74678772210bdcc27f22c154af24c8dbf3c61d83 | d396fc3e066afded3b208588ffccdeb8d1d52cf5 | refs/heads/master | 2023-03-28T17:00:37.421362 | 2021-04-05T18:01:59 | 2021-04-05T18:01:59 | 398,550,467 | 0 | 0 | MIT | 2021-08-21T12:19:12 | 2021-08-21T12:19:12 | null | UTF-8 | Python | false | false | 3,216 | py | # -*- coding: utf-8 -*-
"""
Created on Mon July 01 16:01:36 2019
GTOS data loader
@author: jpeeples
"""
import os
from PIL import Image
from torch.utils.data import Dataset
import pdb
import torch
class GTOS_mobile_single_data(Dataset):
def __init__(self, texture_dir, train = True,image_size = 256, img_transform = None): # numset: 0~5
self.texture_dir = texture_dir
self.img_transform = img_transform
self.files = [] # empty list
self.targets = [] #labels
#pdb.set_trace()
imgset_dir = os.path.join(self.texture_dir)
if train: # train
#Get training file
sample_dir = os.path.join(imgset_dir,'train')
class_names = sorted(os.listdir(sample_dir))
label = 0
#Loop through data frame and get each image
for img_folder in class_names:
#Set class label
#Select folder and remove class number/space in name
temp_img_folder = os.path.join(sample_dir,img_folder)
for image in os.listdir(temp_img_folder):
#Check for correct size image
if image.startswith(str(image_size)):
if(image=='Thumbs.db'):
print('Thumb image')
else:
img_file = os.path.join(temp_img_folder,image)
self.files.append({ # appends the images
"img": img_file,
"label": label
})
self.targets.append(label)
label +=1
else: # test
sample_dir = os.path.join(imgset_dir,'test')
class_names = sorted(os.listdir(sample_dir))
label = 0
#Loop through data frame and get each image
for img_folder in class_names:
#Set class label
#Select folder and remove class number/space in name
temp_img_folder = os.path.join(sample_dir,img_folder)
for image in os.listdir(temp_img_folder):
if(image=='Thumbs.db'):
print('Thumb image')
else:
img_file = os.path.join(temp_img_folder,image)
self.files.append({ # appends the images
"img": img_file,
"label": label
})
self.targets.append(label)
label +=1
def __len__(self):
return len(self.files)
def __getitem__(self, index):
datafiles = self.files[index]
img_file = datafiles["img"]
img = Image.open(img_file).convert('RGB')
label_file = datafiles["label"]
label = torch.tensor(label_file)
if self.img_transform is not None:
img = self.img_transform(img)
return img, label,index
if __name__ == '__main__':
path = 'gtos-mobile'
# train = GTOS_mobile_single_data(path)
test = GTOS_mobile_single_data(path,train=False) | [
"jpeeples@ufl.edu"
] | jpeeples@ufl.edu |
fe7dc646e6803d550245c1e42b84b4e935c02343 | 3611ad1133a1d71448f5390bda1dc711ad3f0214 | /Week_0_Search/tictactoe/tictactoe.py | f87331681677a0dc9542509bfdb98bb786e03b37 | [] | no_license | michaeljs-c/CS50-Artificial-Intelligence | 03de8eb05c5bf5367d06e476cfc78874be314f81 | c2218c82394860533645d0464ab796bd44abb92d | refs/heads/master | 2023-03-16T05:16:33.459488 | 2021-03-05T17:37:11 | 2021-03-05T17:37:11 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,103 | py | """
Tic Tac Toe Player
"""
import math
import copy
X = "X"
O = "O"
EMPTY = None
def initial_state():
"""
Returns starting state of the board.
"""
return [[EMPTY, EMPTY, EMPTY],
[EMPTY, EMPTY, EMPTY],
[EMPTY, EMPTY, EMPTY]]
def player(board):
"""
Returns player who has the next turn on a board.
"""
values = [i for n in board for i in n]
xs = values.count("X")
os = values.count("O")
if xs == os:
return "X"
else:
return "O"
def actions(board):
"""
Returns set of all possible actions (i, j) available on the board.
"""
moves = set()
for i in range(0,3):
for j in range(0,3):
if board[i][j] is None:
moves.add((i,j))
return moves
def result(board, action):
"""
Returns the board that results from making move (i, j) on the board.
"""
a = copy.deepcopy(board)
"""if board[action[0]-1][action[1]-1] is not None:
print("That move is already taken")
return board"""
a[action[0]][action[1]] = player(board)
return a
def winner(board):
"""
Returns the winner of the game, if there is one.
"""
a = (board[0][0],board[1][1],board[2][2])
b = (board[0][2],board[1][1],board[2][0])
c = (board[0][0],board[1][0],board[2][0])
d = (board[0][1],board[1][1],board[2][1])
e = (board[0][2],board[1][2],board[2][2])
f = (board[0][0],board[0][1],board[0][2])
g = (board[1][0],board[1][1],board[1][2])
h = (board[2][0],board[2][1],board[2][2])
for n in [a,b,c,d,e,f,g,h]:
if ("X","X","X") == n:
return "X"
elif ("O","O","O") == n:
return "O"
return None
def terminal(board):
"""
Returns True if game is over, False otherwise.
"""
return winner(board) is not None or None not in [i for n in board for i in n]
def utility(board):
"""
Returns 1 if X has won the game, -1 if O has won, 0 otherwise.
"""
if winner(board) == "X":
return 1
elif winner(board) == "O":
return -1
else:
return 0
def minimax(board):
"""
Returns the optimal action for the current player on the board.
"""
if player(board) == "X":
max_value(board)
return action_
else:
min_value(board)
return action_
action_ = ()
def max_value(board):
v = -100
if terminal(board):
return utility(board)
maxa = [v,None]
for action in actions(board):
v = max(v, min_value(result(board,action)))
if v > maxa[0]:
maxa[1] = action
maxa[0] = v
global action_
if maxa[1] is not None:
action_ = maxa[1]
return v
def min_value(board):
v = 100
if terminal(board):
return utility(board)
mina = [v, None]
for action in actions(board):
v = min(v, max_value(result(board,action)))
if v < mina[0]:
mina[1] = action
mina[0] = v
global action_
if mina[1] is not None:
action_ = mina[1]
return v
| [
""
] | |
23e3ad4e01e0f76661ea461347891416a38d216c | a71a756203a07ccaece6db440410493b3b7ff77f | /helios/plugins/builtin/rpc_websocket_proxy_through_ipc_socket/plugin.py | f25a485fbaf58be719639402aa3e72f7562385ca | [
"MIT"
] | permissive | Helios-Protocol/py-helios-node | 73735dc24cd4c816d55649ed2f5df822efabfdce | 691b378938f0a36bf8774dc1ee4e4370b6cf7c63 | refs/heads/master | 2021-08-19T23:05:18.841604 | 2020-01-18T19:38:33 | 2020-01-18T19:38:33 | 134,452,574 | 21 | 10 | MIT | 2019-06-09T04:43:14 | 2018-05-22T17:39:10 | Python | UTF-8 | Python | false | false | 1,344 | py | from argparse import (
ArgumentParser,
_SubParsersAction,
)
from helios.extensibility import (
BaseIsolatedPlugin,
)
from .websocket_proxy_server import Proxy as rpc_websocket_proxy
###
# This one is not used anymore because it is synchronous. There is a new asynchronous one in the json_rpc folder
# This one connects through IPC as well. So it wont be stopped by admin_stopRPC.
###
class RpcWebsocketProxyPlugin(BaseIsolatedPlugin):
@property
def name(self) -> str:
return "RPC Websocket Proxy"
def should_start(self) -> bool:
return (not self.context.args.disable_rpc_websocket_proxy) and self.context.chain_config.is_main_instance
def configure_parser(self, arg_parser: ArgumentParser, subparser: _SubParsersAction) -> None:
arg_parser.add_argument(
'--disable_rpc_websocket_proxy-NOT_USED',
action="store_true",
help="Should we disable the RPC websocket proxy server?",
)
def start(self) -> None:
self.logger.info('RPC Websocket proxy started')
self.context.event_bus.connect()
proxy_url = "ws://0.0.0.0:" + str(self.context.chain_config.rpc_port)
rpc_websocket_proxy_service = rpc_websocket_proxy(proxy_url, self.context.chain_config.jsonrpc_ipc_path)
rpc_websocket_proxy_service.run()
| [
"admin@hyperevo.com"
] | admin@hyperevo.com |
9d0ceb0a983d177fb194fa88a84647305cb10f4a | 6fa7f99d3d3d9b177ef01ebf9a9da4982813b7d4 | /PE8XQipGLS5bhpLZ5_12.py | 135603cb370bc577f19f614c4e375ea0e5b17c54 | [] | no_license | daniel-reich/ubiquitous-fiesta | 26e80f0082f8589e51d359ce7953117a3da7d38c | 9af2700dbe59284f5697e612491499841a6c126f | refs/heads/master | 2023-04-05T06:40:37.328213 | 2021-04-06T20:17:44 | 2021-04-06T20:17:44 | 355,318,759 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 98 | py |
def check_equals(lst1, lst2):
if lst1[:] == lst2[:]:
return True
else:
return False
| [
"daniel.reich@danielreichs-MacBook-Pro.local"
] | daniel.reich@danielreichs-MacBook-Pro.local |
5c79994e4891957104782181f70c52182e20dc7e | 7f1f51e8c70a6434f209b7dda970a52adff33143 | /renew.py | d5d2defc64ae93ef31f01ef8dac3f2d3a2c28261 | [] | no_license | axel-durham/solar | 534a60e06cac39d7e2c0d305e52542ec3e97a0fc | 1b7c16e93ff7823b1209a7b6308e07462dabb8ab | refs/heads/master | 2020-05-19T05:05:01.200926 | 2019-05-09T18:29:15 | 2019-05-09T18:29:15 | 184,841,482 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 8,952 | py | import math as m
def TimeMinutesToDecimal(time):
"""
This function converts time from the hours.minutes.seconds format to hours.decimal_minutes
Input time as a string formatted 'hour.minute.second'
"""
time = time.split('.')
time = [int(x) for x in time]
return time[0] + time[1]/60 + time[2]/3600
def TimeDecimalToMinutes(x):
"""
This function converts time from the hours.decimal_minutes format to hours:minutes:seconds.decimal_milliseconds
"""
x = float(x)
decimal_minutes1, hours = m.modf(x)
hours = int(hours)
decimal_minutes2 = decimal_minutes1*60
decimal_seconds, minutes = m.modf(decimal_minutes2)
minutes = int(minutes)
seconds = round(decimal_seconds*60, 2)
return str(hours) + ':' + str(minutes) + ':' + str(seconds)
def LatLong(coord):
"""
This function converts Latitude and Longitude from a deg.minute.second format to a decimal degree format
Input is is a string formatted 'deg.minutes.seconds'
"""
coord = coord.split('.')
coord = [int(x) for x in coord]
return coord[0] + coord[1]/60 + coord[2]/3600
def DayOfTheYear(date):
"""
This function return the day of the year
Input is a string formatted 'mm/dd'
Leap years are not taken into account
"""
date = date.split('/')
date = [int(x) for x in date]
days_in_months = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
return sum(days_in_months[0:date[0]-1]) + date[1]
def dateToWords(date):
"""
This function converts a date fronm a 'mm/dd' format to a 'Month dd' format
Intended for use on labels for plots
"""
date = date.split('/')
month_words = ['Jan.', 'Feb.', 'Mar.', 'Apr.', 'May', 'Jun.', 'Jul.', 'Aug', 'Sep.', 'Oct.', 'Nov.', 'Dec.']
return month_words[int(date[0])-1] + ' ' + date[1]
def I_0(N):
"""
This function calculate the daily solar constant as a function of the the day of the year
N=1 on January 1st
This function does not take leap years into account
"""
return 1368*(1+0.034*m.cos(2*m.pi*(N-3)/365))
def AM(theta_z):
"""
This function calculates Air Mass # as a function of theta_z
theta_z is in degrees
"""
theta_z_rad = theta_z/360*2*m.pi
return 1/(m.cos(theta_z_rad)+0.50572*(96.07995-theta_z)**(-1.3636))
def LocalToSolarTime(std_time, long_std, long_loc, N):
"""
This function finds solar time (decimal) when local time, standard longitude, local longitude, and day of the year are inputted
input standard time as a hours.decimal_minutes
input longitude in decimal degrees
input day of the year as a number
"""
#step 1: calculate tau for the day of the year
tau = 2*m.pi*N/365
#step 2: calculate ET
#eq 9.9 coefficients
a = [-7.3412, -9.3795, -0.3179, -0.1739]
b = [0.4944, -3.2568, -0.0774, -0.1283]
ET = []
for j in range(4):
ET.append(a[j]*m.sin((j+1)*tau) + b[j]*m.cos((j+1)*tau))
ET_tot_decimal = sum(ET)/60
#DST correction
if N >= 69 and N < 307:
std_time = std_time - 1
solar_time_decimal = std_time + 4/60*(long_std - long_loc) + ET_tot_decimal
return solar_time_decimal
def SolarToLocalTime(solar_time, long_std, long_loc, N):
"""
This function finds local time (decimal) when solar time, standard longitude, local longitude, and day of the year are inputted
input solar time as a string formatted 'hours.minutes.seconds'
input longitude as a string formatted 'degrees.minutes.seconds'
input day of the year as a number
"""
#step 1: calculate tau for the day of the year
tau = 2*m.pi*N/365
#step 2: calculate ET
#eq 9.9 coefficients
a = [-7.3412, -9.3795, -0.3179, -0.1739]
b = [0.4944, -3.2568, -0.0774, -0.1283]
ET = []
for j in range(4):
ET.append(a[j]*m.sin((j+1)*tau) + b[j]*m.cos((j+1)*tau))
ET_tot_decimal = sum(ET)/60
local_time_decimal = TimeMinutesToDecimal(solar_time) - 4/60*(long_std - long_loc) - ET_tot_decimal
if N < 69 or N >= 307:
local_time_decimal = local_time_decimal + 1
return local_time_decimal
def Declination(N):
"""
This function finds declination (delta) in degrees when the day of the year is inputted
N=1 on January 1st
"""
return 23.45*m.sin(m.radians(360*(284+N)/365))
def AltitudeMax(lat, delta):
"""
This function finds altitude at solar noon (alpha_max) in degrees when the day of the year is inputted
input latitude as a string of the form 'degrees.minutes.seconds'
"""
return 90 - LatLong(lat) + delta
def HourAngle(solar_time):
"""
This function finds the hour angle (omega) in degrees when the solar time is supplied
Input solar time in decimal minutes
"""
return solar_time*15 - 180
def ZenithAngle(delta, lat, omega):
"""
This function finds the zenith angle (theta_z) in degrees when the declination (delta), latitude, and hour angle (omega) are supplied
Input delta in degrees
Input latitude in decimal degrees
Input omega in degrees
"""
delta_radians = m.radians(delta)
lat_radians = m.radians(lat)
omega_radians = m.radians(omega)
theta_z_radians = m.acos(m.sin(delta_radians)*m.sin(lat_radians) + m.cos(delta_radians)*m.cos(lat_radians)*m.cos(omega_radians))
return theta_z_radians/2/m.pi*360
def Altitude(delta, lat, omega):
"""
This function finds the altitude (alpha) in degrees when the declination (delta), latitude, and hour angle (omega) are supplied
Input delta in degrees
Input latitude in decimal degrees
Input omega in degrees
"""
delta_radians = m.radians(delta)
lat_radians = m.radians(lat)
omega_radians = m.radians(omega)
alpha_radians = m.asin(m.sin(delta_radians)*m.sin(lat_radians) + m.cos(delta_radians)*m.cos(lat_radians)*m.cos(omega_radians))
return alpha_radians/2/m.pi*360
def SolarAzimuth(delta, omega, alpha):
"""
This function finds the solar azimuth (gamma_s) in degrees when the declination (delta), hour angle(omega), and altitude (alpha) are supplied
Input delta in degrees
Input omega in degrees
Input alpha in degrees
"""
delta_radians = m.radians(delta)
omega_radians = m.radians(omega)
alpha_radians = m.radians(alpha)
gamma_s_radians = m.asin(m.cos(delta_radians)*m.sin(omega_radians)/m.cos(alpha_radians))
return gamma_s_radians/2/m.pi*360
def AngleOfIncidence(alpha, beta, gamma, gamma_s):
"""
This function finds the angle of incidencee (theta_i) in degrees when the altitude (alpha), tilt angle (beta), azimuth (gamma), and solar azimuth (gamma_s) are supplied
Input alpha in degrees
Input beta in degrees (for a horizontal surface, beta = 0)
Input gamma in degrees (for a south facing surface, gamma = 0)
Input gamma_s in degrees
"""
alpha_radians = m.radians(alpha)
beta_radians = m.radians(beta)
gamma_radians = m.radians(gamma)
gamma_s_radians = m.radians(gamma_s)
theta_i_radians = m.acos(m.sin(alpha_radians)*m.cos(beta_radians) + m.cos(alpha_radians)*m.sin(beta_radians)*m.cos(gamma_radians - gamma_s_radians))
return theta_i_radians/2/m.pi*360
def I_Beta_b(I_n_b, theta_i):
"""
This function finds the hourly beam radiation normal to a tilted surface when the hourly beam radiation normal to the ground (I_n_b) and the angle of incidence (theta_i) is supplied
Input theta_i in degrees
"""
theta_i_radians = m.radians(theta_i)
return I_n_b*m.cos(theta_i_radians)/2/m.pi*360
def BeamTransmissivity(theta_z, N, A=0.149):
"""
This function finds the beam transmissivity, tau_b
Input theta_z in degrees
N is the day of the year
A is the altitude in km (defualts to 0.149 for Austin, TX)
The 23km haze model is used (for mid-latitude)
"""
#Haze Model
if N == 172: #June 21st, middle of summer --- N = 172
r_0 = 0.97
r_1 = 0.99
r_k = 1.02
elif N == 355: #December 21st, middle of winter --- N = 355
r_0 = 1.03
r_1 = 1.01
r_k = 1.00
elif N >= 1 and N < 172:
r_0 = (0.97-1.03)/(172+10)*N + 1.0267
r_1 = (0.99-1.01)/(172+10)*N + 1.0089
r_k = (1.02-1.00)/(172+10)*N + 1.001099
elif N > 172 and N < 355:
r_0 = (1.03-0.97)/(355-172)*N + 0.91361
r_1 = (1.01-0.99)/(355-172)*N + 0.9712
r_k = (1.00-1.02)/(355-172)*N + 1.0388
elif N > 355 and N <= 365:
r_0 = (0.97-1.03)/(536-355)*N + 1.14768
r_1 = (0.99-1.01)/(536-355)*N + 1.04923
r_k = (1.02-1.00)/(536-355)*N + 0.98077
a_0_star = 0.4237 - 0.008216*(6-A)**2
a_1_star = 0.5055 + 0.00595*(6.5-A)**2
k_star = 0.2711 + 0.01858*(2.5-A)**2
a_0 = r_0*a_0_star
a_1 = r_1*a_1_star
k = r_k*k_star
theta_z_radians = m.radians(theta_z)
return a_0 + a_1*m.e**(-k/m.cos(theta_z_radians))
def I_c_b(I_0, tau_b, theta_i):
"""
This function finds the clear-day beam Insolation in W/m^2
Input theta_i in degrees
This works for flat and tilted panels (beta > 0)
"""
theta_i_radians = m.radians(theta_i)
return I_0*tau_b*m.cos(theta_i_radians)
def DiffuseTransmissivity(tau_b):
"""
This function finds the diffuse transmissivity, tau_d
"""
return 0.271 - 0.294*tau_b
def I_c_d(tau_d, I_0, theta_z, beta):
"""
This function find the clear-day diffuse insolation in W/m^2
Input theta_z in degrees
Input beta as radians
"""
theta_z_radians = m.radians(theta_z)
beta_radians = m.radians(beta)
return tau_d*I_0*m.cos(theta_z_radians)*(1+m.cos(beta_radians))/2 | [
"axel.durham@utexas.edu"
] | axel.durham@utexas.edu |
4f0864a0e24ce619fcf9319fc69630fd9e35a830 | 235f15f47ffa9b0534b0e823d5ac95bafdc9325e | /src/tests/test_delete.py | 427b50dd17652ebac7b8cbde49dcd71ca106eda1 | [] | no_license | gyuseok-dev/fastapi-sample-for-docker-compose | fca891255ffcead969f4af245b35cf03de61ce13 | 40b7e8769cf5c164eddd146eb4584557763784c8 | refs/heads/main | 2023-06-06T04:54:43.087769 | 2021-06-30T14:08:17 | 2021-06-30T14:08:17 | 381,697,715 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 803 | py | from starlette.testclient import TestClient
from src import crud
def test_delete_todo(client: TestClient, monkeypatch):
async def mock_is_todo_exist(id, db):
return True
async def mock_delete_todo(id, db):
return None
monkeypatch.setattr(crud, "delete_todo", mock_delete_todo)
monkeypatch.setattr(crud, "is_todo_exist", mock_is_todo_exist)
r = client.delete("/api/v1/todos/1")
assert r.status_code == 204
assert r.text == "null"
def test_delete_todo_invalid_id(client: TestClient, monkeypatch):
async def mock_is_todo_exist(id, db):
return False
monkeypatch.setattr(crud, "is_todo_exist", mock_is_todo_exist)
r = client.delete("/api/v1/todos/1")
assert r.status_code == 404
assert r.json() == {"detail": "Item Not Found"}
| [
"gyskjng@gmail.com"
] | gyskjng@gmail.com |
9f8d390f8b93600719eede63855cb61081c13b1c | 89d3d9438ee0dfbfdfa9e32e5bb83f7ac33de820 | /flask_app.py | 5a39354584e396dbd241eec8f586dddf2b55b817 | [] | no_license | Vaibhav148/Quote-Dispensing-Engine | a33226ca869843e12778396612057cb26124999b | cb36885514f3076c9a27207bdc23a0bb466992be | refs/heads/master | 2022-10-10T05:19:14.584571 | 2020-06-14T19:28:40 | 2020-06-14T19:28:40 | 272,264,399 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,201 | py | from flask import Flask, render_template, request, url_for, Markup
import os
import pandas as pd
import numpy as np
from random import randrange
import nltk
nltk.download('vader_lexicon')
from nltk.sentiment.vader import SentimentIntensityAnalyzer
app = Flask(__name__)
quotes = None
@app.before_request
def prepare_sentiment_quote_stash():
global quotes
quotes = pd.read_csv('quotes.csv')
sid = SentimentIntensityAnalyzer()
all_compounds = []
for sentence in quotes['quote']:
ss = sid.polarity_scores(sentence)
for k in sorted(ss):
if k == 'compound':
all_compounds.append(ss[k])
quotes['sentiment_score'] = all_compounds
quotes = quotes.sort_values('sentiment_score')
quotes['index'] = [ix for ix in range(0, len(quotes))]
def gimme_a_quote(direction = None, current_index = None, max_index_value = 0):
rand_index = randrange(max_index_value)
darker = None
brighter = None
if current_index is None:
brighter = rand_index
if direction == 'brighter':
brighter = current_index
else:
darker = current_index
if darker is not None:
try:
current_index = int(darker)
except ValueError:
current_index = rand_index
if current_index > 0:
rand_index = randrange(0, current_index)
else:
rand_index = rand_index
elif brighter is not None:
try:
current_index = int(brighter)
except ValueError:
current_index = rand_index
if current_index < max_index_value -1:
rand_index = randrange(current_index, max_index_value)
else:
rand_index = rand_index
else:
rand_index = rand_index
return (rand_index)
@app.route("/")
def quote_me():
quote_stash_tmp = quotes.copy()
max_index_value = np.max(quote_stash_tmp['index'].values)
rand_index_value = randrange(max_index_value)
darker = request.args.get("darker")
brighter = request.args.get("brighter")
if darker is not None:
try:
current_index = int(darker)
except ValueError:
current_index = randrange(max_index_value)
new_index = gimme_a_quote(direction = 'darker', current_index = current_index, max_index_value = max_index_value)
elif brighter is not None:
try:
current_index = int(brighter)
except ValueError:
current_index = rand_index_value
new_index = gimme_a_quote(direction = 'brighter', current_index = current_index, max_index_value = max_index_value)
else:
new_index = randrange(max_index_value)
random_quote = quote_stash_tmp.iloc[new_index]
quote=random_quote['quote']
author = random_quote['author']
current_id = random_quote['index']
return render_template("quote.html",
quote=quote,
author=author,
current_id=current_id,)
if __name__ == "__main__":
app.run(debug=True, port=5000) | [
"vaibhavathani2740@gmail.com"
] | vaibhavathani2740@gmail.com |
6d4d1d60f2c789f78d8d5f3257764908e635553d | 809f263b77b525549cd945c39c4c9cf2b8e6a167 | /pqcrypto/sign/sphincs_shake256_192s_simple.py | 6a45aa9c023c21f67bb3b6b83ca198236bb3e8f7 | [
"BSD-3-Clause"
] | permissive | Kayuii/pqcrypto | bdf5014b7590dfe363baedbf47171f4b4cb25349 | dd8c56fd876a397caef06a00d35537a4f9c1db28 | refs/heads/master | 2022-12-14T00:34:36.632689 | 2020-09-08T10:40:26 | 2020-09-08T10:40:26 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 447 | py | from .._sign.sphincs_shake256_192s_simple import ffi as __ffi, lib as __lib
from .common import _sign_generate_keypair_factory, _sign_sign_factory, _sign_verify_factory
PUBLIC_KEY_SIZE = __lib.CRYPTO_PUBLICKEYBYTES
SECRET_KEY_SIZE = __lib.CRYPTO_SECRETKEYBYTES
SIGNATURE_SIZE = __lib.CRYPTO_BYTES
generate_keypair = _sign_generate_keypair_factory(__ffi, __lib)
sign = _sign_sign_factory(__ffi, __lib)
verify = _sign_verify_factory(__ffi, __lib)
| [
"inbox@philonas.net"
] | inbox@philonas.net |
ab4d888257c3c89753a638575b9018d4733a7a00 | 087fdfcd157fa626f63ef22da7813b66531c18e9 | /personality_analysis/vocab/vocab.py | 538bf00325559c827348b3938e404be84db49a56 | [
"MIT"
] | permissive | kayzhou/exp_user_behaviour | 5316c5e04a1c624631219b46fdb32e16a17ef2e2 | 58490763c76880eb84211a86bcadb31b868b5c13 | refs/heads/main | 2023-07-26T00:51:50.486012 | 2021-08-25T02:37:59 | 2021-08-25T02:37:59 | 374,299,484 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 48,603 | py | financial_keywords = ['富有', '富于', '饶富', '超富', '最富', '饶有', '极富', '国最', '最高档', '装富', '贵有', '高富', '财产权', '人财物', '财产权利', '私产', '家庭财产', '个人收入', '巨额财产', '债权人', '私有财产', '共同财产', '合法财产', '家庭收入', '居民家庭', '财产性', '个人财产', '家庭事务', '财政支出', '公共财政', '财权', '地权', '合法权利', '公民权利', '共同利益', '生命安全', '生命权', '财产损失', '谋财害命', '房产信息', '财税', '装穷', '装钱', '有钱有势', '穷光蛋', '脏钱', '木有钱', '钱有', '很富', '搞钱', '太有钱', '有钱有闲', '有权人', '有钱出钱', '真有钱', '赚不赚钱', '钱那', '木有买', '闲钱', '有闲', '钱少', '挣大钱', '钱真', '挣了钱', '赚了钱', '没赚', '钱你', '那钱', '钱当', '没钱买', '钱就会', '钱比', '钱才', '钱让', '钱太', '有了钱', '太抠', '钱还没', '有钱', '贪钱', '莫装', '穷死', '哭穷', '金钱至上', '金钱游戏', '钱为', '价值观念', '钱权', '权钱', '徼', '权钱交易', '诉权', '分权', '印钱', '权宜', '钱与', '金钱观', '拜金主义', '钱而', '奴化', '钱养', '钱来', '钱更', '钱买', '赚回', '玩钱', '钱能', '钱用', '钱用在', '钱要', '钱去', '投资收益', '税收收入', '毛收入', '工人工资', '员工工资', '低工资', '低收入者', '净收入', '平均收入', '销售总额', '全年收入', '营业收入', '业务收入', '净营收', '零售额', '纯收入', '人均收入', '收入水平', '中低收入', '农民收入', '工资收入', '高收入者', '国民收入', '低收入', '居民收入', '总收入', '销售收入', '净利', '营收', '营业额', '外汇收入', '年收入', '实际收入', '增加收入', '财政收入', '财政投入', '中低收入者', '非税', '富人', '穷人家', '有钱人', '尿糖', '狂话', '乍富', '钱多人', '穷人', '钱都让', '贫富不均', '杀富济贫', '平权', '贫富悬殊', '中国贫富', '劫富济贫', '民穷', '为富不仁', '贫弱', '别替', '购房人', '买方', '房屋买卖', '买卖人', '买房者', '供房', '赎人', '买帐', '抵偿', '买房子', '亿万富豪', '大富豪', '富豪榜', '富豪', '新富', '民富国强', '中产阶层', '贫穷落后', '家财万贯', '家财', '腰缠万贯', '腰缠', '万户侯', '家资', '万贯', '家产', '家业', '身家', '贫家', '财阀', '兴家', '聚敛', '农资', '金融资产', '资不抵债', '国家税收', '国有资本', '百万千万', '家贫', '富户', '孩子家', '法家', '病愈', '贫寒', '家翁', '富家子弟', '万利', '本利', '一千亿', '一万四', '土豪', '大款', '大数', '大手大脚', '结款', '税款', '赃款', '货款', '借款人', '工程款', '外逃', '赔偿款', '赔款', '巨款', '煤老板', '富家女', '中国富豪', '大富翁', '大富贵', '富翁', '大富', '斗富', '富贾', '亿万富翁', '财富榜', '85亿', '百万富翁', '首富', '巨商', '富家', '巨富', '王公', '富婆', '官商', '负翁', '胡润', '胡润百富', '排名榜', '新财富', '榜上', '高居榜首', '金牌榜', '国家元首', '富商', '股神', '农家子弟', '中产阶级', '小资产阶级', '资产阶级', '无产', '工薪阶层', '有产者', '负资产', '中奖', '获奖者', '得奖', '名单', '获奖人', '未获奖', '中奖者', '得奖者', '获奖项', '中奖人', '得奖人', '中奖号码', '中头奖', '中奖号', '纪念奖', '颁奖人', '获奖', '未获', '兑奖', '弃奖', '奖池', '号码牌', '抽奖券', '单注', '帐号密码', '发奖', '周奖', '第三波', '抽奖', '选中', '没中', '抽选', '五等奖', '小奖', '四等奖', '刮奖', '机率', '行大运', '发财', '发发发', '万事大吉', '财源滚滚', '财运亨通', '宏图大展', '大吉大利', '发家致富', '脱贫致富', '致富', '发横财', '升官发财', '致富经', '发家', '勤劳致富', '兴旺发达', '升官', '发财致富', '升迁', '发大财', '谋财', '步步高升', '财路', '高升', '官运', '梦而已', '梦者', '益发', '国难财', '梦就', '恭贺新禧', '财神到', '马年行', '招财进宝', '接财神', '财源', '阖家欢乐', '和气生财', '生财有道', '生财之道', '聚财', '财气', '散财', '正财', '顺意', '阖家幸福', '事业兴旺', '恭喜发财', '等奖品', '赢大奖', '大奖', '裸钻', '来赢', '奖券', '巨奖', '中大奖', '来晒单', '礼品券', '等奖项', '等你拿', '拿大奖', '礼品', '单赢', '头奖', '创新奖', '单项奖', '优胜奖', '奖给', '票券', '抽送', '赠券', '欠缴', '发红包', '赚钱', '亏钱', '养家', '管钱', '赚大钱', '好赚', '理亏', '才玩', '钱堆', '大笔', '大钱', '大笔钱', '挣', '就赚', '多赚', '攒点', '挣点', '钱收', '钱得', '赚过', '挣来', '生命财产', '没钱', '粪土', '用钱', '均贫富', '买房人', '穷国', '一本万利', '壕', '傍大款', '携款', '款爷', '中产', '开奖', '抽中', '中奖率', '发财梦', '生财', '发财树', '奖品', '比较发达', '欠发达', '高度发达', '发达国家', '大红包', '抢红包', '挣钱', '赚', '难赚']
# 432
popularity_keywords = ['名作家', '词曲作家', '曲名', '作曲者', '遗作', '名作', '名剧', '剧作家', '词曲创作', '曲作者', '成家立业', '功成名就', '竖子成名', '无成', '成于', '功名富贵', '争名', '家翁', '名臣', '自矜', '成名成家', '小有成就', '功成身退', '功名', '建功立业', '那就成', '巨大成就', '竖子', '臭名', '成钢', '诗名', '褚遂良', '望子成龙', '名讳', '病成', '不成器', '伟大成就', '成批', '便成仁', '成仁', '有名气', '赫赫有名', '鼎鼎有名', '小有名气', '出名', '著名', '盛名', '大名鼎鼎', '有名有姓', '驰名', '系出名门', '倾巢而出', '名闻遐迩', '闻名于世', '遐迩闻名', '驰名于世', '闻名遐尔', '闻名', '世界闻名', '有名', '名气', '有名无实', '颇负盛名', '享有盛名', '着名', '出名门', '名门望族', '名匠', '名宅', '名门', '名牌产品', '名产', '而出名', '衙口', '报了名', '出了门', '出产地', '土产', '名吃', '名优', '生产地', '赤味', '土鸭', '名窑', '农产', '驰名中外', '赫赫的', '享有盛誉', '威名', '鼎鼎大名', '专有名词', '名声在外', '出了名', '知名', '著名演员', '著名品牌', '著名人士', '齐名', '最富', '久负盛名', '享誉', '享誉中外', '盛誉', '久闻大名', '大名府', '久仰大名', '单姓', '大姓', '马姓', '徒有其名', '复姓', '山名', '驰名世界', '远近驰名', '闻名中外', '名闻', '闻名全国', '名扬四海', '名扬中外', '相互理解', '相互信任', '互相爱护', '互爱', '互相理解', '互敬', '相互依赖', '尊重人权', '相互间', '互敬互爱', '值得尊敬', '尊重', '令人尊敬', '敬重', '值得羡慕', '受尊敬', '值得称赞', '尊重知识', '值不值得', '敬佩', '值得尊重', '崇敬', '敬仰', '钦佩', '尊敬', '敬慕', '尊老敬老', '民族尊严', '国格', '尊严感', '健全人格', '多重人格', '保障人权', '重惩', '严肃性', '人格尊严', '道德感', '沉重感', '相互尊重', '荣誉感', '珍存', '疼惜', '珍惜', '自珍', '惜才', '弥足', '怜惜', '爱惜', '轻视', '啧啧称奇', '交口称赞', '赞口不绝', '称赞', '赞叹不已', '夸赞', '叹服', '连称', '称羡', '广为人知', '称颂', '称奇', '津津乐道', '为人所知', '赞誉', '赞赏', '赞许', '广受', '赞扬', '盛赞', '褒奖', '称道', '高度评价', '颂扬', '表扬', '赞美', '石氏', '啧啧称赞', '众口', '赞不绝口', '有口皆碑', '大加', '赏识', '夸她', '夸奖', '令人钦佩', '评得', '值得反思', '阈值', '颇得', '惊叹不已', '惊叹', '令人叹服', '赞道', '叹为观止', '赞颂', '令人称奇', '哑然', '啧', '认同度', '认知度', '诚信度', '信誉度', '可控性', '可靠性', '美誉度', '贡献度', '接受程度', '程度较高', '赞同', '认同感', '认可', '苟同', '同意', '完全同意', '思想认识', '感性认识', '并不认为', '嘉奖', '谬奖', '褒扬', '抬爱', '荣誉奖', '赏赐', '认可度', '契合度', '高度肯定', '增长幅度', '信任感', '信赖感', '认同', '感受力', '安定感', '不信任感', '充分认识', '首肯', '充分反映', '充分条件', '充分考虑', '定给', '敢肯定', '肯请', '定案', '称许', '嘉许', '接受批评', '充分肯定', '稳定度', '高度一致', '高炳义', '高度重视', '肯定句', '威望', '声望', '名位', '企望', '品级', '声名显赫', '权势', '名望', '声誉', '享有声誉', '相差悬殊', '自由度', '权威性', '频度', '望族', '士族', '名居', '衙署', '企求', '物质欲望', '毫无希望', '渴盼', '悲观失望', '热望', '众望', '希求', '冀望', '企图心', '德高', '位高权重', '望众', '品德高尚', '行尊', '位尊', '向老', '望尘', '治民', '民怨', '于国', '生民', '瞻望', '民力', '忧民', '徼', '威廉堡', '德威', '威桑', '高洞', '高城', '威尼斯水城', '海德威', '德格县', '拉纳', '高庙', '王德威', '丘德威', '威拉德', '德威龙', '沃邦', '文德', '芒德', '高威', '武德', '怅望', '望之', '望云', '仰首', '望而', '举目望去', '喟', '迢遥', '南望', '望尽', '民望', '不负重望', '众志', '所望', '众望所归', '所归', '众人皆醉', '主导权', '领先地位', '主导者', '占据主动', '权位', '权欲', '身居高位', '权重', '财权', '官位', '重利', '归其位', '声名', '商誉', '名声', '争权', '利禄', '争权夺利', '本位', '不畏强权', '兴废', '要面子', '丢面子', '碍于面子', '不留情面', '死要面子', '情面', '留钱', '爱面子', '不孝子', '留面子', '没面子', '孙子辈', '甩脸子', '丢饭碗', '兜圈子', '要债', '死不要脸', '要脸', '装孙子', '碍于', '有求于', '不体面', '没规矩', '见过世面', '没脸', '没脑子', '没骨气', '要死不活', '要死要活', '贪财', '给脸', '力哈', '脸子', '挺凶', '抹不掉', '脱不开', '解不开', '晕开', '推不开', '脱开', '抹粉', '拔开', '抹头', '那开', '不讲情面', '忸怩', '不领情', '寡情', '无情无义', '不留情', '场面上', '伤自尊了', '不自重', '妄自尊大', '自尊', '伤害罪', '自伤', '自愧', '暗自神伤', '伤财', '伤自尊', '害己', '自尊心', '作贱自己', '狂妄自大', '骄傲自大', '妄自', '自欺', '自高自大', '自惭', '枉自', '自卑心理', '自信心', '自我中心', '自卑感', '自立自强', '才会爱', '自强自立', '学会爱', '寡断', '自励', '自我牺牲', '严于律己', '自我剖析', '自我解嘲', '自我教育', '自我管理', '自我实现', '自我暗示', '牺牲者', '自我认识', '自我表现', '敝帚自珍', '自悲', '自爱', '自贱', '自谦', '自恃', '自轻自贱', '自毙', '性心理', '逆反心理', '恐惧心理', '自私自利', '心理战', '心理疾病', '过于自信', '自信力', '信仰自由', '缺欠', '缺乏经验', '偏信', '仰慕者', '爱岗', '深恨', '爱慕', '兼爱', '谈爱', '爱生恨', '向爱', '倾慕', '仰慕', '爱慕者', '人心神', '爱怜', '倾吐', '真纯', '眷念', '怜爱', '爱憎', '怜悯之心', '生畏', '楚楚可怜', '虔敬', '敬服', '令人敬畏', '敬虔', '礼敬', '曝光', '照曝光', '网友曝', '被疑', '自曝', '媒曝', '爆出', '大曝光', '遭', '艳照', '谍照', '艳照门', '曝', '爆乳', '曝光量', '曝光率', '曝光度', '溢美', '赞美声', '颂赞', '旋律优美', '赞歌', '朗诵诗', '法语歌', '吟颂', '唱诗', '英文歌曲', '长诗', '传颂', '颂歌', '歌颂', '传诵', '歌吟', '唱国歌', '歌功颂德', '卓有成就', '小成', '学有所成', '学业有成', '杰出成就', '杀身成仁', '居功', '方能成', '头功', '陈立夫', '芮成刚', '成家', '立业', '家业', '功名利禄', '不求名利', '功业', '浮名', '追求名利', '建功', '丰功伟业', '奇功', '战功', '伟绩', '成大业', '辉煌成就', '巨大变化', '铸成大错', '成就动机', '巨大作用', '颇受', '颇受欢迎', '谬赞', '赞叹', '评议', '德高望重', '显赫', '声名大噪', '蜚声中外', '声名鹊起', '名声大噪', '名声鹊起', '名声大振', '蜚声国际', '有声书', '惠誉', '相有声', '悬殊', '相差太多', '贫富悬殊', '相差不多', '不相容', '相差太大', '相距甚远', '不相称', '相异', '篡位', '下位', '圣名', '争名夺利', '争利', '争功', '党争', '污名', '有权有势', '当权', '钱权', '权钱', '有钱有势', '权柄', '成名作', '易图', '名歌手', '早成', '珍视', '主导地位', '给面子', '抹不开', '缺乏自信', '过曝', '曝光台', '赞美诗', '言施']
# 627
image_keywords = ['吸引力', '赶潮流', '时髦', '时尚女装', '时尚饰品', '女装品牌', '时尚女性', '时装节', '时尚服饰', '时装展', '秋冬装', '时装剧', '街头时尚', '时尚秀', '裙装', '裙裤', '下装', '休闲裤', '休闲装', '西装裤', '翻领', '小脚裤', '毛衫', '套衫', '包型', '板型', '廓形', '搭扣', '挺括', '壁布', '斜纹', '短款', '便鞋', '美观大方', '造形', '美观', '运动感', '款型', '外形上', '纤秀', '形貌', '果形', '表面', '外观', '光鲜', '外貌', '内柔', '表相', '华丽却', '外型', '姣美', '身材苗条', '健硕', '细瘦', '纤瘦', '俊俏', '壮硕', '身形', '高挑', '俊朗', '气质佳', '温良', '文品', '形象气质', '年轻美貌', '气质高雅', '样貌', '贤淑', '品德高尚', '容颜', '面容', '姣好', '相貌', '花容月貌', '品貌', '老态', '丽质', '高雅而', '高贵而', '娴雅', '雅淡', '高贵和', '高贵又', '英气', '透气性', '气色好', '气质', '爆发力', '人格魅力', '英气逼人', '气韵', '天生丽质', '端丽', '玉质', '流丽', '清俊', '兰心蕙', '雅丽', '婉丽', '古拙', '匠气', '乡土气息', '文艺腔', '痞气', '范有', '温文儒雅', '良好形象', '形象思维', '形象化', '贵族般', '华丽而', '雍容', '高贵', '贵而', '华而', '穿着者', '裤装', '乔装打扮', '打扮', '衣着', '打底衫', '穿着打扮', '穿插着', '褴褛', '紧身裙', '穿着', '女舞者', '穿衣戴帽', '笔挺', '整身', '装束', '晚装', '得体', '衣衫褴褛', '衣衫不整', '纱衣', '薄衣', '夏衣', '僧袍', '肩披', '破衣', '裙裾', '曳地', '身着', '身穿', '睡裤', '短衣', '穿', '唐装', '皮夹克', '西装', '没穿', '戴帽', '带帽', '长衣', '衣帽', '白帽子', '裤袜', '套头毛衣', '绒裤', '血衣', '貂皮大衣', '衣衫', '遮体', '衣冠不整', '长袖衫', '薄衫', '紧身衣', '民族服装', '服饰文化', '民族风格', '服饰', '服饰店', '民族色彩', '传统服装', '妆饰', '衣裙', '藏装', '戏装', '衣料', '身材', '苗条身材', '魔鬼身材', '保持身材', '洁身自好', '白皙', '瘦削', '脸型', '腰身', '修长', '身型', '瘦小', '体态', '塑身', '好身材', '常保', '保持警惕', '保身', '长期保持', '苗条', '身材高大', '魔鬼般', '纹眉', '扮鬼脸', '身材矮小', '机灵鬼', '鬼气', '瘦身操', '竖条', '人高马大', '瘦高', '高大而', '身居高位', '背身', '娇小玲珑', '娇俏', '娇弱', '娇憨', '容貌', '易老', '娇容', '渐老', '岁月流逝', '枯槁', '老心', '姿容', '面庞', '脸庞', '面露', '面颊', '蜡黄', '长相', '平平的', '面相', '才貌', '美丑', '美眷', '女性美', '恬美', '年轻貌美', '年轻夫妻', '貌美', '美女蛇', '周美青', '年轻夫妇', '年逾花甲', '闭月羞花', '花容', '花月', '貌若', '花想容', '雪月', '春花秋月', '花易', '风花', '疲态', '媚态', '老态龙钟', '窘态', '颇显', '显形', '老气', '渐显', '年老色衰', '势态', '外在', '外显', '外因', '内在联系', '客观存在', '外境', '外化', '内在', '性感女郎', '性感美', '港女', '女裙', '新女性', '扮女', '邦女郎', '东方女性', '童女', '性感女', '野性美', '欧美大', '知性美', '东方美女', '韩范', '金发女郎', '兔女郎', '涩女郎', '摩登女郎', '色女郎', '豪乳', '半裸', '爆灯', '闪爆', '巨乳', '爆炸头', '裸模', '嫩模', '露背', '爆乳', '扮起', '迷死人', '火儿', '烈焰红唇', '狂野', '柔媚', '母性', '粗狂', '妖异', '风与', '抹胸', '包臀裙', '背带裙', '小衫', '吊带裙', '纱裙', '皮裙', '垫肩', '低胸', '丰臀', '御女', '宇豪', '香肩', '陈豪', '帅酷', '舞衣', '女人香', '男人味', '人味', '女人缘', '尚感', '俐落', '柔嫩', '蛙肉', '丰腴', '肥腴', '筋肉', '滑腻', '韧韧', '颗粒感', '光泽感', '细皮嫩肉', '大头贴', '自拍照', '张萌照', '小照', '张照', '照来', '寸照', '靓照', '照片儿', '上镜头', '演出照', '帅滴', '帅照', '张小花', '张小然', '造型照', '果照', '照发', '上照', '素颜照', '近照', '证件照', '裸照', '生活照', '张带', '偷发', '张美照', '洗照片', '照上', '图片吧', '大片儿', '来组', '选片', '纸片儿', '毛片儿', '洗出来', '张自拍', '大头照', '可拍照', '张合照', '才拍', '硬照', '明星照', '定妆照', '私照', '私房话', '新造型', '发型', '头型', '扎头发', '烫个', '发箍', '梳头发', '盘发', '短发', '寸头', '真俊', '新发型', '背头', '头箍', '挺帅', '红头发', '长发', '齐刘海', '刘海', '剪短', '超短', '披肩发', '短衫', '脸形', '高鼻梁', '体型', '唇纹', '造型', '新郎官', '新造', '型仔', '准新郎', '新机型', '发饰', '盘头', '发簪', '发夹', '发髻', '长卷发', '盘扣', '绑带', '美发师', '发型师', '造型各异', '拗', '凹造型', '花型', '人物造型', '烫头发', '留头发', '黄头发', '吹头发', '发剪', '头发丝', '长头发', '理发师', '发型设计', '剪发', '剪头发', '造型师', '美甲师', '民族舞', '彝族人', '畲族', '服装设计', '服装鞋帽', '服装类', '服装师', '服装服饰', '服装店', '服装节', '首饰店', '饰品店', '品牌服饰', '时装店', '名牌服饰', '服饰品牌', '家饰', '内衣店', '国际服装', '服装学院', '时装设计', '流行时尚', '鞋帽', '鞋包', '品牌服装', '皮装', '汉文化', '民族服饰', '龙山文化', '蜀文化', '徽文化', '文化性', '红山文化', '楚文化', '服务化', '奇装异服', '长袍', '韩服', '便服', '藏服', '蒙族', '装饰品', '珠宝饰品', '饰物', '首饰', '家居饰品', '耳饰', '手饰', '银饰', '头饰', '装饰画', '佩饰', '眼线', '唇妆', '眼影', '底妆', '裸妆', '腮红', '眼线笔', '眼线液', '眼唇', '定妆', '小妆', '妆容', '妆点', '打扮起来', '淡妆', '浓妆艳抹', '俏丽', '脱妆', '眼妆', '减龄', '遮瑕', '上妆', '裸色', '修颜', '粉底霜', '贴服', '祛皱', '浓妆', '化浓妆', '烟熏妆', '卸妆后', '卸完', '补妆', '敷上', '化妆水', '卸妆液', '护唇膏', '唇色', '唇线', '塑型', '斜肩', '熟龄', '光采', '妆扮', '装扮成', '装点', '扮萌', '短打', '入时', '跟着打', '撩起来', '抖起来', '梳起来', '踢起来', '捆起来', '拽起来', '滑起来', '演起来', '鼓起来', '梳洗打扮', '梳妆', '梳妆镜', '不得体', '扮酷', '嗲得', '乖得', '丑得', '装萌', '梳妆打扮', '梳洗', '洗洗涮涮', '洗涮', '洗濯', '打毛衣', '打昏', '扮得', '酷男', '装酷', '男扮女装', '军装', '装鬼', '色鬼', '遇鬼', '捣鬼', '扮作', '乔装', '扮成', '化妆', '彩妆师', '化妆间', '摄像师', '萧峻', '冯海', '化妆箱', '化妆棉', '化妆镜', '化妆袋', '卸妆水', '韩妆', '试妆', '彩妆品', '化妆刷', '花妆', '护肤品', '药妆店', '日用品', '美容品', '化妆品行业', '小烟', '美美容', '风小', '化妆盒', '化妆包', '硫化', '过厚', '化脓', '化妆室', '化妆舞会', '梳妆台', '更衣间', '试衣间', '小间', '除皱', '脱色', '湿敷', '防脱发', '护肤霜', '有吸引力的', '诱惑力', '吸引', '赶时髦', '髦', '酷似', '外表', '气场', '浪漫气质', '文气', '着装', '衣装', '娇小', '外在美', '惹火', '野性', '女人味', '肉感', '玉照', '皂片', '私房照', '服装', '饰品', '装扮', '扮鬼', '化妆师', '妆', '化妆品']
# 659
affiliation_keywords = ['父子关系', '夫妻关系', '亲戚关系', '亲密关系', '密切关系', '父女关系', '邻里关系', '互动关系', '相互关系', '关系人', '密切相关', '密切联系', '亲子关系', '两性关系', '紧密联系', '紧密结合', '互相联系', '伙伴关系', '婚姻观', '关系紧张', '姻亲', '婚姻制度', '婚姻状况', '暧昧关系', '休戚相关', '从属关系', '搞关系', '父系', '婆媳关系', '男女关系', '父女情', '正当关系', '友好合作', '友好往来', '友好相处', '搞好关系', '和平友好', '外交关系', '至亲至爱', '亲人或', '无话不谈', '至亲', '至亲好友', '血亲', '志趣相投', '亲亲的', '亲密', '朋友们', '好朋友', '亲戚朋友', '博友', '女性朋友', '老朋友', '男女朋友', '友', '亲朋好友', '戚友', '新朋友', '老友', '旧友', '新老朋友', '老战友', '新朋', '故友', '老同事', '明友', '把兄弟', '盟兄弟', '兄弟', '周兄', '大弟', '兄弟情', '亲兄妹', '难兄难弟', '四兄弟', '互祝', '亲朋', '祝家人', '友朋', '亲友们', '走亲访友', '祝全家', '亲切友好', '同窗好友', '加为好友', '谢友', '崔智友', '友台', '友有', '玩友', '友问', '道友', '文友', '知心朋友', '结识', '狐朋狗友', '良师益友', '良朋', '挚诚', '友聚', '够朋友', '友尽', '探友', '酒肉朋友', '租男友', '串亲戚', '亲戚', '走亲戚', '亲戚家', '志同道合', '同道', '有志者', '志趣', '合作者', '同心同德', '同工同酬', '共患难', '同行者', '伴同', '志同道合者', '相投', '兴趣爱好', '个人兴趣', '相契', '矢志', '相互信任', '臭味相投', '投缘', '相熟', '亲密无间', '道不同不相为谋', '同道中人', '尚道', '同辈', '同流', '携手同心', '意中人', '性情中人', '圈中人', '同路人', '同席', '同甘苦', '同日生', '爱好音乐', '共同之处', '共同话题', '共同利益', '互相爱护', '互勉', '结伴同游', '结伴同行', '游伴', '约同', '同行人', '履约', '搭伴', '商业伙伴', '同甘', '情投意合', '意气相投', '老相识', '同德', '同心协力', '同心结', '共同理想', '共同犯罪', '共苦', '同甘共苦', '同义', '同生共死', '荣辱与共', '同侪', '情义', '战友情', '情意', '深情厚谊', '共叙', '恩情', '情分', '交情', '情份', '增进感情', '增进', '相互促进', '互相促进', '友谊宫', '友谊桥', '之谊', '友谊路', '亲情', '友谊', '伉俪情深', '战友', '老战士', '情深意重', '情感世界', '世态人情', '人情世故', '思想感情', '感情丰富', '情感生活', '情感故事', '感情线', '不谙世事', '感情交流', '真情实感', '动感情', '重感情', '感情用事', '感情投资', '真心真意', '真情实意', '纠葛', '感情破裂', '不合情理', '情绪性', '纠缠不清', '起纠纷', '感情', '情感小说', '情感话题', '情感化', '感情世界', '感情纠葛', '感情戏', '夫妻感情', '真感情', '激情戏', '苦情戏', '言情片', '情色片', '哭戏', '伤感情', '解法', '破裂', '感官世界', '内心世界', '生活感', '婚姻生活', '生命感', '夫妻生活', '陪伴', '陪着', '陪着她', '相陪', '谢谢有', '搀扶着', '伴随', '曾陪我', '都陪', '牵挂着', '陪', '陪伴着', '有人陪', '陪他', '来陪', '作伴', '相随', '相携', '相依', '相扶', '与共', '相守到', '伴', '长相守', '再相逢', '来陪我', '陪到', '陪床', '陪住', '来陪你', '都爱我', '都爱你', '都祝你', '陪我玩', '来家', '奉陪', '来你', '想着她', '牵着手', '陪笑', '不知有汉', '难觅', '善知识', '知彼知己', '知己者', '无知己', '不知所终', '不知为不知', '相思苦', '知遇', '乐卡', '银卡', '知音难觅', '星享卡', '申卡', '运通卡', '铂金卡', '声卡', '卡密', '此卡', '亲眷', '亲人', '亲如家人', '亲疏', '双亲', '亲属', '情侣们', '恋人们', '许下爱', '浓情蜜意', '小情侣', '挚爱', '共度', '新婚夫妇', '向家人', '全家人', '两家人', '傣家人', '来英', '和和睦睦', '牵上', '知心人', '负心人', '扑上去', '美人痣', '心魂', '走上台', '再踏上', '心贴心', '才会爱', '自爱', '成人达', '推己及人', '人不为己', '爱民如子', '才爱', '不如人意', '初恋情人', '爱人', '旧情人', '爱亲', '至死不渝', '备至', '六亲', '至爱', '来亲', '夫妻恩爱', '永浴爱河', '恩爱夫妻', '恩爱', '爱相随', '于爱', '甜甜蜜蜜', '阖家幸福', '合家欢乐', '阖家欢', '家庭幸福', '阖家欢乐', '阖家', '合家', '阖家团圆', '合家幸福', '顺意', '祝全', '祝全国', '祝健康', '祝杨', '祝张', '祝老', '祝您健康', '欢欢乐乐', '阖家团聚', '家团圆', '团团圆圆', '团圆日', '生活美满', '祝你和', '和睦', '美满', '全家欢', '和和美美', '睦睦', '和和气气', '和睦相处', '和睦家医院', '吉祥和', '和平共处', '和平相处', '共处', '合谐', '于与人', '相互理解', '和气生财', '和睦村', '冀连梅', '何睦', '和谐家园', '和家园', '谐和', '侠气', '一团和气', '客客气气', '重情义', '和善', '圆圆满满', '美满生活', '美满婚姻', '气气', '和气', '莫生气', '暖暖和和', '呕气', '父母辈', '岳父母', '给母亲', '赡养父母', '父母双亡', '孝敬父母', '祖父母', '父母心', '父母', '冤亲', '眷属', '非亲非故', '亲恩', '任人唯亲', '那家人', '向家', '向人', '替人家', '乡邻', '自家人', '谨向', '亲如一家', '亲如兄弟', '苗家人', '亲家母', '父母亲', '故去', '守寡', '亲生父母', '老父亲', '畅叙', '把盏', '斟茶', '挚友', '益友', '师友', '良师', '善友', '知友', '叙旧', '叙叙旧', '旧识', '诸友', '诤友', '花友', '寻亲访友', '故交', '一友', '亲友', '宾朋', '真诚待人', '诚能', '真诚相待', '挚交', '心慈', '于诚', '诚诚恳恳', '心诚', '虔敬', '结善缘', '网友聚会', '送友人', '乐聚', '访友', '酒逢知己', '酒逢知己千杯少', '相知相惜', '知己', '贵在知心', '相识已久', '自斟', '知已', '酌酒', '自斟自饮', '才知酒', '自知者', '解己', '知己知彼', '知之者', '知之为知之', '士为知己者死', '谷丽媛', '小色', '红颜薄命', '重色轻友', '红颜', '待字闺中', '知心话', '千杯不醉', '三杯酒', '几杯酒', '知者', '悦己者', '谈谈天', '说古', '谈谈心', '叙谈', '对酌', '婚姻家庭', '婚姻关系', '感情生活', '婚姻', '关系型', '包办婚姻', '婚姻大事', '婚姻登记', '婚恋观', '爱情观', '婚姻法', '婚前婚后', '恋爱观', '家庭事务', '非婚', '友好关系', '亲密度', '朋友', '好兄弟', '耍朋友', '共同爱好', '情谊', '增进友谊', '友情', '友谊医院', '情感', '相伴', '知音卡', '爱侣', '家人', '心上人', '爱人如己', '恋人', '恩恩爱爱', '睦', '蓝颜', '碳粉', '古也']
# 549
selfacceptance_keywords = ['学业有成', '成长', '长成', '成长期', '增长期', '成长性', '成熟期', '成长型', '长期性', '高速成长', '高成长', '中长期', '个人成长', '小型企业', '专家型', '长期投资', '发展型', '专业型', '畸型', '个人经历', '个人努力', '成长史', '个人修养', '个人所有', '年成长', '茁壮成长', '史是', '创业史', '奋斗史', '小环境', '变宽', '环境恶劣', '适应能力', '反应力', '适应性', '应变力', '耐受力', '应变能力', '力弱', '抵抗能力', '应激反应', '适应症', '应变', '适应力', '随机应变', '抗压性', '感悟力', '思维力', '兴奋期', '短时期', '周期短', '自适', '自我调节', '弥合', '内调', '协调性', '安适', '调治', '适时地', '调节作用', '体察', '相适应', '适用性', '应用性', '普适性', '自适应', '融合性', '适应期', '艰难困苦', '窘困', '愁苦', '苦无', '困窘', '忍辱', '苦累', '恚', '适应环境', '聪颖', '聪明能干', '高智慧', '才智', '聪明才智', '聪敏', '明辨', '智性', '聪慧', '不智', '真智慧', '智是', '聪明智慧', '大智若愚', '才是大', '谋略', '胆略', '智慧', '高智', '高智伟', '商业智能', '德智体', '测智商', '智慧之门', '智谋', '普门', '众妙', '文殊师', '方便之门', '戒定慧', '智语', '智之虎', '智珠', '贤慧', '真和尚', '真聪明', '真邪', '理智型', '真善', '真与假', '禀性', '空性', '精神性', '智识', '能动性', '性灵', '寓于', '明心见性', '哲理性', '克服困难', '克服', '改善服务', '敬服', '碰到困难', '不畏艰难', '都佩服', '判断能力', '服输', '重重困难', '困难重重', '解决困难', '灾难深重', '不怕困难', '步履艰难', '遇到困难', '碰到问题', '磕磕碰碰', '碰得', '难以解决', '妥善解决', '解难', '急需解决', '决择', '解决矛盾', '解困', '解决目前', '就地解决', '能克服', '心理障碍', '破除', '摒除', '攻克', '不畏艰险', '艰难险阻', '险阻', '知难而上', '畏难', '耐苦', '艰苦环境', '困苦', '苦境', '困厄', '受苦受难', '危机重重', '十分困难', '不怕苦', '不怕牺牲', '不怕累', '二不怕死', '难缠', '怕累', '天不怕', '攻坚战', '攻坚', '攻无不克', '救难', '攻防战', '战略决策', '阻击战', '力主', '中坚', '纪律性', '规律性', '自主性', '自觉性', '政治性', '自然性', '职业性', '阶级性', '引导性', '规范性', '自怨', '互勉互励', '自惭', '自警', '枉自', '浊者自浊', '不自重', '严以律己', '寡断', '守规矩', '遵守纪律', '自觉遵守', '无规则', '遵行', '坚持原则', '不规矩', '交通规则', '守法者', '显规则', '遵守规则', '自觉自愿', '守法', '自律性', '严格遵守', '知法', '严于', '律己', '严于律已', '于己', '慎于', '自谦', '谦虚谨慎', '于人', '施于人', '自我实现', '自我认识', '自我牺牲', '自我完善', '自我教育', '自我中心', '自我剖析', '自我解嘲', '企业管理者', '严守', '信守', '恪尽职守', '谨守', '遵纪守法', '共守', '守信', '警觉性', '政治觉悟', '自主权', '独立性', '自立自强', '独立自主', '自强不息', '自立', '自学能力', '自爱', '自恃', '独立行使', '自力', '责权利', '财权', '失责', '诉权', '分权', '事权', '职权', '责任制', '责任方', '权利人', '自信力', '缺乏自信', '信心', '没信心', '自卑心理', '自尊心', '信心倍增', '自制力', '自信', '信仰自由', '公信度', '自信心', '自然力', '自理能力', '自控力', '过于自信', '缺欠', '自私自利', '缺乏经验', '偏信', '坚定信心', '充满信心', '信心十足', '满怀信心', '信心百倍', '坚定信念', '奸诈', '狂妄自大', '自贱', '自负盈亏', '自强自立', '自立更生', '坚强意志', '不畏强权', '自由权', '宗教自由', '宗教信仰者', '信仰者', '信仰危机', '公民自由', '自由思想', '自由化', '自欺', '强加于人', '信任他人', '严于律己', '自暴自弃', '惯于', '自珍', '才会爱', '学会爱', '自励', '聪明伶俐', '聪明绝顶', '能干', '学聪明', '少干', '绝顶聪明', '明白事理', '敢想敢干', '敢想敢说', '敢做敢当', '真干', '敢说敢做', '敢骂', '敢作敢当', '敢做敢为', '敢做', '钱少', '常干', '儿少', '敢干', '缺德事', '少想', '少是', '嫌少', '少问', '干出', '才力', '干才', '干好', '才是能', '巧干', '实干家', '办好事', '顾好', '好好儿', '活得好', '干点', '单干', '来干', '肯吃苦', '实干', '实干精神', '实干型', '勤恳', '实干者', '都肯', '肯做', '正事儿', '正事', '专干', '活儿干', '都干', '常跟', '老干', '都在干', '干不了', '才干', '逼出', '混出', '惹出', '蠢事', '祸事', '不干不净', '活不了', '脱不了', '管不了', '都忍', '有所不为', '有所为', '寸有所长', '尺有所短', '无所作为', '无所不为', '有所长', '有所作为', '有所感', '有所不知', '有所为有所不为', '所动', '若有所失', '所失', '所作所为', '一无所成', '无所不谈', '无所遁形', '不耻', '大有可为', '盛大有', '大有希望', '才有大', '大有可观', '都有为', '大有文章', '善为', '有恒', '所怀', '为所欲为', '不以为意', '为情所困', '发挥所长', '化发展', '长足发展', '畸形发展', '促发展', '发展前途', '社会发展', '不为所动', '己所不欲', '妄为', '不以为耻', '在所不惜', '老有所乐', '老有所养', '小有成就', '学棋', '所学', '成成', '有志于', '勤奋学习', '学习心得', '虚心', '学习态度', '学习者', '虚心接受', '业务学习', '互相学习', '心学', '好好学', '学习', '学下', '努力学习', '学得好', '虚心学习', '练下', '学习效果', '重新学习', '学懂', '习得', '学用', '学不完', '比学', '新闻学', '学习会', '学新闻', '新学年', '改学', '重新做人', '业务培训', '专业课程', '业务知识', '勤奋好学', '勤学苦练', '勤学', '勤练', '勤奋努力', '实际效果', '学习成绩', '学门', '学习气氛', '科学态度', '正确态度', '积极态度', '态度端正', '态度改变', '表明态度', '治学', '生态学', '教育者', '科学化', '学习型', '学术研究', '集中学习', '互学', '互勉', '交流学习', '互利', '应承', '承不承认', '承担风险', '担责任', '重责', '应负', '该当', '担负起', '正当权益', '当政者', '肩负起', '负起', '担起', '担负', '担不起', '负有责任', '责任者', '重担', '扛起', '重大责任', '担着', '敢当', '愧不敢当', '敢作敢为', '当得起', '负担得起', '赔得起', '承当', '尽到责任', '责任感', '承担责任', '承受', '更知', '欺辱', '当自强', '吾辈', '自当', '应尽', '更须', '害人者', '敢为人先', '敢为天下先', '健康成长', '环境变', '调适', '适应', '大智慧', '攻坚克难', '自我管理', '恪守', '权责', '自负', '肯干', '干事', '大有作为', '有所发展', '学有所成', '好好学习', '担当不起', '责任', '承担', '更当']
# 561
health_keywords = ['健健康康', '祝健康', '祝您健康', '康健', '都平安', '健康长寿', '健康成长', '身体健康', '康安', '都祝你', '真健康', '有益健康', '益健康', '少健康', '保健康', '健康操', '果蔬汁', '健骨', '健康歌', '健康证', '都健康', '祝张', '祝全家', '祝家人', '祝杨', '祝顺', '泰康人寿', '孙健', '乔健', '整体健康', '健康检查', '心理健康', '身心状态', '身心愉快', '心身', '体健', '长命百岁', '福寿绵长', '祝老', '瘦身操', '健康网', '操操操', '保健操', '操操', '何康康', '健身舞', '平平安安', '快快乐乐', '康美', '健美运动', '美体', '维康', '训练课', '训练馆', '康复训练', '训练法', '锻练', '训练员', '训练师', '冬训', '体能', '训练班', '耗体力', '脚力', '脑力', '体力活', '体力劳动', '精力', '体力不支', '耐力', '毅力', '整体素质', '心理素质', '基本素质', '人员素质', '文化素质', '质素', '国民素质', '道德素质', '提高素质', '科学素养', '加强锻炼', '体育锻炼', '煅', '锻炼', '锻炼身体', '体能训练', '练完', '身体疲劳', '勤练', '练车', '增强体质', '强身健体', '自我保健', '臂力', '费力气', '腰力', '支撑力', '运动量', '脚踏板', '耗氧量', '平衡力', '护脚', '耗力', '流体力学', '集中力', '才力', '体力', '抗力', '壮劳力', '防御力', '抵抗能力', '免疫能力', '吸收能力', '控制能力', '体液免疫', '抗病力', '力弱', '操作能力', '控制力', '掌控力', '控制感', '自控力', '防御能力', '不能抑制', '判断能力', '控制性', '力有', '精神压力', '力有木', '力也', '受限于', '反应力', '应变力', '心有余力', '物力', '财力', '人力', '人力资本', '工人工资', '人财物', '劳力', '为力', '承载力', '心力交瘁', '离心力', '殚', '费尽', '尽力去做', '向心力', '尽心竭力', '气力', '力气活', '没力气', '卖力气', '别费', '费点', '卯足', '憋足', '拼劲', '精神不振', '精神紧张', '精神振奋', '压抑感', '心理压力', '压力感', '抗压性', '应激', '精神力量', '精力充沛', '旺盛期', '活力充沛', '旺盛', '精神力', '干劲', '抗抑郁', '人力物力', '物流费', '空气阻力', '物质财富', '民力', '快生了', '病成', '找病', '药到病除', '久病成医', '病殃殃', '啥病', '生吞活剥', '起病', '犯病', '倒床', '瘫倒', '累倒', '卧病', '腰伤', '病愈', '如山倒', '昏倒', '昏迷不醒', '这病', '妇科病', '怪病', '脑癌', '肾癌', '生了病', '少得', '偏瘫', '要犯', '老毛病', '咽炎', '病倒', '发晕', '脑胀', '昏厥', '请医生', '换药', '拿药', '复诊', '开药', '打点滴', '王医生', '验血', '紧绷着', '累病', '绷着', '病怏怏', '怏怏', '想着她', '累着', '病体', '重感冒', '治感冒', '患感冒', '抗感冒', '风寒感冒', '咳嗽', '伤风感冒', '感冒药', '风热感冒', '鼻塞', '发低烧', '咳得', '发冷', '发高烧', '脑涨', '肠胃炎', '昏昏沉沉', '感冒', '低烧', '热伤风', '流涕', '嗓子疼', '高烧', '发烧时', '持续发烧', '退烧', '退烧药', '祝寿星', '长寿村', '长寿湖', '长山岛', '三长', '长寿菜', '永寿', '长寿果', '长谷', '寿县', '长生果', '圣药', '枇杷果', '圣果', '山楂粥', '降糖', '百果', '粟米羹', '长寿乡', '洪村', '兴寿', '滩村', '长屋', '湾村', '长溪村', '长生', '长命', '老鼠药', '心不老', '未老先衰', '灵药', '方长', '福寿康宁', '添寿', '增福', '福永', '享寿', '寿延', '福如东海', '福寿之', '福满', '祝李', '永葆', '永驻', '常保', '青春永驻', '童贞', '永葆青春', '佑护', '常存', '永在', '益寿延年', '延年', '益寿', '轻身', '老年性', '益肺', '防衰老', '益精', '防老', '抗疲劳', '养生粥', '养生茶', '养生酒', '养生菜', '汤最', '疗疾', '养生餐', '滋益', '养生经', '养脾', '来恋', '补养', '温补', '养神', '养血', '暖身', '养生', '温中', '养生学', '养生法', '温经', '养生之道', '养生方', '养生汤', '摄生', '温饮', '药补', '滋阳', '阴精', '滋肾', '补肾益精', '益胃', '固精', '固肾', '温肺', '杞菊', '生滚', '汤羹', '煲粥', '靓汤', '滋补品', '清补', '平补', '补阳', '食疗', '进补', '姜枣茶', '生茶', '红枣茶', '枣茶', '养生馆', '养肾', '修生', '食疗法', '养气', '营养学', '中医学', '生态学', '药学', '生理学', '理学', '疲劳感', '肌肉疲劳', '视疲劳', '身体虚弱', '疲惫感', '腺体', '淤滞', '体弱', '虚劳', '纾缓', '安康', '气血虚弱', '体虚', '脾胃虚弱', '虚弱者', '虚寒', '气虚', '血虚', '久病', '发肤', '玉体', '身体语言', '自体', '领受', '身教', '体肤', '受损害', '抱恙', '偶感', '欠安', '恙', '四肢无力', '轻微伤', '惫', '酸软', '运动状态', '平衡状态', '心态平衡', '状态栏', '混乱状态', '紧张状态', '中间状态', '动态平衡', '精神状态', '健康状况', '身体检查', '状况良好', '状况不佳', '甲状腺', '身体素质', '病状', '天气状况', '安歇', '微恙', '安份', '慰安', '徐安安', '缺欠', '安安全全', '还安', '健身室', '康乐活动', '商务会议', '健体', '娱乐室', '康年', '泉浴', '会务', '体育设施', '健身活动', '康体', '护理室', '桑拿浴', '按摩房', '蒸汽浴室', '淋浴室', '乒乓球室', '健身器', '健身球', '强身', '丰肌', '热浴', '操房', '健身', '来健身', '健身卡', '健身教练', '健身器材', '健身操', '健身运动', '健美操', '常运动', '室内运动', '热身运动', '全身性', '来运动', '下楼来', '爬下来', '来下', '文体活动', '团体活动', '外交活动', '科普活动', '竞赛活动', '教学活动', '运动器材', '洁身', '器械', '耗材', '铝材', '全钢', '充满活力', '精神饱满', '补充剂', '精力旺盛', '活跃性', '青春活力', '活力', '充氧', '充满热情', '朝气', '充满生机', '充满希望', '朝气蓬勃', '充溢', '青春少女', '青春不老', '魅力四射', '跃动', '蓬勃', '稚气', '勃勃的', '蓬勃向上', '热力四射', '艳光四射', '基情', '激情', '光芒四射', '才运动', '运动过度', '运动损伤', '运化', '运动强度', '运动性', '热热身', '热身', '动身前往', '活动量', '有氧运动', '量减少', '食量', '体育项目', '球类运动', '奥林匹克运动', '水上运动', '体育竞技', '足球运动', '民主运动', '运时', '动火', '肠蠕动', '懒得动', '健美', '肌力', '运动选手', '全面性', '性腺', '内室', '室内外', '室内环境', '室外机', '动力性', '运动感', '能动性', '波动性', '机动性', '操控性', '气动', '冲击性', '性冲动', '跑跑步', '加衣', '多加练习', '炼钢', '泌乳', '皂素', '冶炼', '颞', '吸汗', '阴囊', '慢跑', '晨跑', '跑完', '跑步者', '跑步机', '健步', '跑鞋', '爬山', '磨练', '历炼', '磨砺', '锤炼', '试炼', '磨练意志', '砥砺', '磨难', '淬炼', '受折磨', '刚跑', '快步走', '骑骑', '跑下来', '跑下去', '走步', '散散步', '爬楼梯', '跑不动', '闭门思过', '面壁思过', '常思', '小过', '静坐', '悔过', '梦过', '勤于思考', '苦思冥想', '知过', '救人者', '医病', '庸医', '救命恩人', '未病', '救命钱', '救死扶伤', '草菅', '急病', '治好病', '住院治疗', '诊治', '救治', '治病救人', '脑瘤', '急症', '根治', '病者', '尿毒', '妇科疾病', '肾衰', '疗毒', '病原', '系统疾病', '病毒性', '脑病', '夏治', '治疗法', '难治', '治法', '闭经', '防病毒', '治疗室', '自然疗法', '介入治疗', '药疗', '心理治疗', '舒经', '求医问药', '投医', '寻医', '良医', '转院', '脑血栓', '癫痫病', '血癌', '久治不愈', '喊救命', '钱养', '筹钱', '偷钱', '垫钱', '救人一命', '钱那', '能保命', '取药', '配药', '开给', '开药方', '打药', '喂药', '危重', '病故', '肾衰竭', '该病', '性肝炎', '看病难', '看病', '富贵病', '就医难', '看病贵', '查不出', '开点', '买药', '带药', '看医生', '胃药', '喝药', '买饭', '医务室', '挂号费', '专家号', '候诊', '排号', '挂不上', '挂号处', '特需', '叫号', '就诊', '保外就医', '诊疗费', '筛查', '用药', '就业难', '就医', '实际困难', '医案', '急难', '从医', '入园难', '医疗卫生', '复检', '查血', '尿检', '体格检查', '血常规', '化验', '查体', '检查报告', '血检', '定期检查', '体检表', '孕检', '待产', '妇产', '进产房', '顺产', '产科', '临产', '产检', '早孕', '临检', '不孕症', '验尸', '尸检', '填表', '验伤', '查完', '验尿', '抽血', '复查', '皮试', '胸片', '体检中心', '爱康', '初诊', '诊疗', '会诊', '试验性', '化验单', '检查结果', '洗胃', '消炎药', '退热药', '冲剂', '眼药膏', '止泻药', '喷药', '垫补', '补液', '止咳药', '镇痛药', '止疼药', '止疼', '止痛片', '喉痛', '苦药', '止痛', '颈椎痛', '喝凉水', '喝开水', '喝血', '照喝', '喝盐水', '喝口水', '消炎片', '消毒液', '立消', '止疼片', '止痛药', '药液', '疼疼', '止痒', '退热', '吃错', '吃了亏', '吃不准', '错怪', '吃准', '没治', '吃不香', '吃白饭', '咽喉炎', '咽喉痛', '胃肠炎', '胃炎', '口腔炎', '慢性咽炎', '胸痛', '咽痛', '才健康', '身心健康', '健康美', '精力有限', '心力', '力气', '得病', '病着', '发烧', '长生不老', '永保', '延年益寿', '养身', '食补', '身体发肤', '身体状况', '健身房', '大冲', '四射', '体育新闻', '体育系', '体育比赛', '体育台', '群众体育', '体育产业', '体育频道', '体育界', '体育运动', '跑步', '磨炼', '思过', '医治', '求医', '挂号', '慈铭', '吃错药']
# 833
community_keywords = ['改善社会', '帮助别人', '世界美好', '别人生活', '帮助', '乐于助人', '履行责任', '公益', '团队意识', '关心别人', '拯救灵魂', '世界和平', '服务社会', '世界更美好', '救世', '服务国家', '帮扶', '慈善', '利人利己', '帮助者', '给予帮助', '帮助人', '帮到', '协助', '度过难关', '热情帮助', '支助', '扶助', '助人', '于己', '施于人', '信任他人', '助人为乐', '乐善好施', '真诚待人', '心地善良', '公益行', '公益活动', '公益路', '公益事业', '公益性', '光明行', '公益金', '助学', '微公益', '救世主', '救度', '救难', '救主', '救苦', '宗教界', '世之', '主神', '世世', '中医界', '互帮互助', '守望相助', '受助', '孤残', '助老', '助残', '扶持', '慈善事业', '慈善家', '义拍', '慈善机构', '基金会', '募捐', '善款', '义卖', '损人利己', '利人', '损人不利己', '毫不利己', '损人害己', '谋利益', '求利', '资助者', '受助者', '救助者', '赞助者', '求助者', '辅助性', '捐助人', '捐血', '援手', '互相帮助', '互帮', '交人', '无助于', '助益', '帮上', '帮帮我', '帮帮', '帮不了', '帮个忙', '提供援助', '大力协助', '法律援助', '求助于', '通力协作', '共渡难关', '解决困难', '爱国热情', '帮教', '热情服务', '资助金', '扶助金', '捐资助学', '救助金', '无偿援助', '支援灾区', '扶养', '赡养', '天助', '积善', '正己', '于人', '严于律己', '于事', '勿施于人', '责己', '嫁祸于人', '我为人人', '何乐不为', '舍己为人', '乐善', '养德', '行善积德', '扬善', '心善', '水善利', '善思', '善道', '宽以待人', '纯良', '良善', '热心肠', '善谈', '温良', '捐书', '献爱心', '公共事业', '公益心','筹募', '公共财政', '助学金', '贫困学生', '资助', '捐助', '筹款', '爱心', '打拐', '民主权利', '普度众生', '救苦救难', '普渡众生','救穷', '援救', '相帮', '互助', '团结互助', '互转', '互爱', '相扶', '相助', '相持', '互相爱护', '相互支持', '互相支持', '敬老爱老', '尊老敬老', '助残日','拍品', '义捐', '义买', '拍卖会','竞拍', '义演', '中华慈善总会', '募捐箱', '爱德基金会', '联合国儿童基金会', '中国扶贫基金会', '中国青少年发展基金会', '中国红十字基金会', '捐款捐物', '募款', '捐款', '乐捐', '捐赠', '捐入', '赠款', '捐出', '害己', '损人', '害人者', '人不为己', '害人害己', '人人自危', '害民', '加害人', '利他主义', '利他', '民族利益','公共利益', '求善']
# 207
# print(len(financial_keywords),len(popularity_keywords),len(image_keywords),len(affiliation_keywords),len(selfacceptance_keywords),len(health_keywords)) | [
"zhenkun91@outlook.com"
] | zhenkun91@outlook.com |
21062a70c9c801d0fbd39a73c551d1b344d148ea | 68def49f2d3290c7de76a1f08dc63b7db34b1989 | /kinokorik/main/migrations/0007_comment.py | a4c2906cadd2f9800357182a356c75ed9d2cb3e4 | [] | no_license | amirlanburakhanov/Kinokorik.TV | a7c433d1da63121b6aaf2f68e07d21fecf4b0a3e | db531498ac5445261767905f4229159be04d9efc | refs/heads/master | 2020-09-30T23:35:47.587219 | 2019-12-11T15:51:39 | 2019-12-11T15:51:39 | 227,400,460 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,169 | py | # Generated by Django 2.2.3 on 2019-12-02 07:48
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('main', '0006_auto_20191130_2102'),
]
operations = [
migrations.CreateModel(
name='Comment',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('author', models.CharField(max_length=30, verbose_name='Author')),
('content', models.TextField(verbose_name='Content')),
('is_active', models.BooleanField(db_index=True, default=True, verbose_name='Display on screen?')),
('created_at', models.DateTimeField(auto_now_add=True, db_index=True, verbose_name='Published')),
('bb', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='main.Bb', verbose_name='Movie')),
],
options={
'verbose_name': 'Comment',
'verbose_name_plural': 'Comments',
'ordering': ['created_at'],
},
),
]
| [
"amirlanburahanov@gmail.com"
] | amirlanburahanov@gmail.com |
94d06d040b4b2ab63cd972227cd4465457230360 | 9bad373aba508fd5bdccfd81b527cec2415c4c2a | /FallingBody.py | 49ffc5efbaffd4305b3faba7842eddef1a7a85b2 | [] | no_license | JosephEmmanuel/python_files | a305329d8620028ed55bc4d58d56a0ea85d5091b | 55eaeeebc94edcdf8a16486b73c1d542d3408ea5 | refs/heads/master | 2020-05-27T02:22:05.187329 | 2020-04-05T09:15:07 | 2020-04-05T09:15:07 | 188,450,943 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,706 | py | #Falling body simulation
#Author Ronald Jasper
import math
#earth's acceleration due to gravity, a constant in m/(sec square)
g=9.80665
def distanceTraveledWhileFall(t):
#distance traveled by a body while falling for t seconds
#d is in meters, t is in seconds
d=0.5*g*t*t
return(d)
def timeTakenToFall(d):
#Time taken to fall d distance
#d is in meters, t is in seconds
t=math.sqrt(2*d/g)
return(t)
def instantaneousVelocityAfterFallingElapsedTime(t):
v=g*t
return(v)
def instantaneousVelocityAfterFallingDistance(d):
v=math.sqrt(2*g*d)
return(v)
def mps2kmphr(mpc):
#convert m/s to km/hr
return(mpc/1000*3600)
c=input("choice: enter time or distance. t or d:")
if (c=="t"):
time=float(input("Enter time in seconds:"))
distance=distanceTraveledWhileFall(time)
velocity=instantaneousVelocityAfterFallingElapsedTime(time)
print("")
print("Input time of fall:", time, " seconds or ", time/60.0, " minutes")
print("Distance fell:", distance, " meters or ", distance/1000, " km")
print("Velocity at that time:", velocity, " meters/second or ", mps2kmphr(velocity), " km/hour")
print("")
if (c=="d"):
distance=float(input("Enter distance of fall in meters:"))
time=timeTakenToFall(distance)
velocity=instantaneousVelocityAfterFallingDistance(distance)
print("")
print("Distance of fall:", distance, " meters or ", distance/1000, " km")
print("Time of fall:", time, " seconds or ", time/60.0, " minutes")
print("Velocity at that distance:", velocity, " meters/second or ", mps2kmphr(velocity), " km/hour")
print("") | [
"jet.repository8@gmail.com"
] | jet.repository8@gmail.com |
71c778cc56ea5d0a03b400a5fb63405ab5da9707 | 3a17437dce911b74c102756ce27cfade29e2c19b | /clients.py | a495552b38b4fbbad717c4c1f15c1481d152a162 | [] | no_license | hacksnextdoor/mobility | a3c9208f78ef7e14c4cf7f4654c3856054959b2d | dc04e6106c2dee962247da742751e908db18286e | refs/heads/master | 2020-05-14T07:50:01.721978 | 2019-05-10T05:21:53 | 2019-05-10T05:21:53 | 181,713,295 | 1 | 2 | null | 2019-05-10T16:19:13 | 2019-04-16T15:06:59 | Python | UTF-8 | Python | false | false | 664 | py | import requests
def get_all():
return [Jump()]
class BaseBikeClass:
NAME = ''
BASE_URL = ''
METADATA = {}
def all_bikes():
raise NotImplementedError
class Jump(BaseBikeClass):
def __init__(self):
self.NAME = 'jumpbikes'
self.BASE_URL = 'jumpbikes.com/opendata'
self.METADATA = {}
def all_bikes(self, region):
if region not in ['atx', 'nyc', 'chi', 'sc', 'dc', 'san', 'sac', 'pvd', 'mia']:
raise Exception("There are no bikes in {0}".format(region))
url = "https://{0}.{1}/free_bike_status.json".format(region, self.BASE_URL)
r = requests.get(url)
data = r.json()
return data | [
"brandonsanchez05@gmail.com"
] | brandonsanchez05@gmail.com |
e59d092404ad3bb41f179eac7e955fc6b6e3d84f | 9b0b9ed1fe86355a8e7f71e9af76e21aa86fb17c | /Venter/forms.py | 69543ee08c5508f893bdbac44f2a8e3d4171b252 | [] | no_license | simranmadhok/Venter_CMS | b453d1f4b74bd4a7cfc72c37a1cebaa52c4ea809 | 05bf5e44ebbfbfe63209b496aa25d12c1b2c65a1 | refs/heads/master | 2020-04-26T04:26:38.414249 | 2019-02-04T06:29:27 | 2019-02-04T06:29:27 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,823 | py | from django import forms
from django.contrib.auth.models import User
from Backend import settings
from Venter.models import File, Profile
from .validate import csv_file_header_validation
class CSVForm(forms.ModelForm):
"""
ModelForm, used to facilitate CSV file upload.
Meta class------
1) declares 'File' as the model class to generate the 'csv_form'
2) includes only only field in the 'csv_form 'from the File model
Usage:
1) upload_file.html template: Generates the file form fields in the csv file upload page for logged in users.
"""
class Meta:
model = File
fields = ('csv_file',)
def __init__(self, *args, **kwargs):
"""
It accepts the self.request argument, here for the purpose of accessing the logged-in user's organisation name
"""
self.request = kwargs.pop("request")
super(CSVForm, self).__init__(*args, **kwargs)
def clean_csv_file(self):
"""
It validates specific attributes of 'csv_file' field: csv header, file type, and file size.
"""
# cleaning and retrieving the uploaded csv file to perform further validation on it
uploaded_csv_file = self.cleaned_data['csv_file']
# checks for non-null file upload
if uploaded_csv_file:
# validation of the filetype based on the extension type .csv
# validation of the filesize based on the size limit 5MB
# the csv_file_header_validation() is invoked from validate.py
filename = uploaded_csv_file.name
if filename.endswith(settings.FILE_UPLOAD_TYPE):
if uploaded_csv_file.size < int(settings.MAX_UPLOAD_SIZE):
if csv_file_header_validation(uploaded_csv_file, self.request):
return uploaded_csv_file
else:
raise forms.ValidationError(
"Incorrect headers, please contact your administrator")
else:
raise forms.ValidationError(
"File size must not exceed 5 MB")
else:
raise forms.ValidationError(
"Please upload .csv extension files only")
return uploaded_csv_file
class UserForm(forms.ModelForm):
"""
Modelform, generated from Django's user model.
Meta class------
1) declares 'User' as the model class to generate the 'user_form'
2) includes only five fields in the 'user_form' from the User model
Usage------
1) 'registration.html' template: Generates the user form fields in the signup page for new users
2) 'update_profile.html' template: Generates the user form fields in the update profile page for existing users
"""
class Meta:
model = User
fields = ('username', 'password', 'email', 'first_name', 'last_name')
def save(self): # pylint: disable = W0221
user = super(UserForm, self).save(commit=False)
password = self.cleaned_data.get('password')
user.set_password(password)
user.save()
return user
class ProfileForm(forms.ModelForm):
"""
Modelform, generated from Django's Profile model.
Meta class------
1) declares 'Profile' as the model class to generate the 'profile_form'
2) includes only three fields in the 'profile_form' from the Profile model
Usage------
1) 'registration.html' template: Generates the profile form fields in the signup page for new users
2) 'update_profile.html' template: Generates the profile form fields in the update profile page for existing users
"""
class Meta:
model = Profile
fields = ('organisation_name', 'phone_number', 'profile_picture')
| [
"madhok.simran8@gmail.com"
] | madhok.simran8@gmail.com |
6406b63bcacf384542fd445cf5ca113155b0db63 | 52f558dcd7cbea61e6b370bcf2ec65d3b8990087 | /Observtions/Python/AttachmentVsSlope.py | 2fd544ec9ba84a5ef48f0088d974265f319de0d3 | [] | no_license | gauravcse/Graph-Learning | 0f61abef73c30d6edca3313351e8db20601ea875 | 4098ade242c4b5ba906c335fe66b40174e6ee06f | refs/heads/master | 2021-01-17T19:19:38.978666 | 2016-07-17T17:31:33 | 2016-07-17T17:31:33 | 63,544,016 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 466 | py |
# coding: utf-8
# In[2]:
import networkx as nx
import random
import matplotlib.pyplot as plt
import numpy as np
from sklearn import linear_model
import pandas as pd
get_ipython().magic(u'matplotlib inline')
# In[3]:
data = pd.read_csv("pref_slope.csv")
data = data.as_matrix()
# In[5]:
x,y = data[:,[0]],data[:,[1]]
# In[10]:
plt.xlabel("Prefferential Attachment")
plt.ylabel("Slope")
plt.scatter(x,y)
plt.show()
# In[28]:
# In[ ]:
# In[ ]:
| [
"gauravmitra95@gmail.com"
] | gauravmitra95@gmail.com |
2a488fae216a650166cbcdea5467d76f54a01160 | 1fa678e96bffbb40aaec6eeacdad63d6702768c1 | /part-3 files/generate_input_attributes.py | 646701ff449296096aa6c10987db41a6c158fa05 | [] | no_license | hiteshvaidya/Android-Security | b18f8d7abb94813c88f294d962215c995d7eb8ad | c48eb036c6c0f25978dacc985e68eecee1cf6995 | refs/heads/master | 2020-07-11T15:14:13.896262 | 2019-08-28T00:49:07 | 2019-08-28T00:49:07 | 204,580,179 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,935 | py | """
This program generates dataset for the machine learning model used in this project.
:author: Hitesh Vaidya
"""
import numpy as np
import math
import csv
def generatePrintBinary(n):
# Create an empty queue
from Queue import Queue
q = Queue()
numbers = []
# Enqueu the first binary number
q.put("1")
# This loop is like BFS of a tree with 1 as root
# 0 as left child and 1 as right child and so on
while(n>0):
n-= 1
# Print the front of queue
s1 = q.get()
# print s1
numbers.append(s1)
s2 = s1 # Store s1 before changing it
# Append "0" to s1 and enqueue it
q.put(s1+"0")
# Append "1" to s2 and enqueue it. Note that s2
# contains the previous front
q.put(s2+"1")
return numbers
def main():
numbers = generatePrintBinary(math.pow(2,10)-1)
vectors = []
# print "main function"
for index in numbers:
# temp = index.split()
num = ''
while len(index) < 10:
index = '0' + index
num = [int(x) for x in index]
vectors.append(num)
# if len(vectors)%500:
# print vectors[-1]
vectors.insert(0, [0 for x in range(10)])
print "number of vectors = " + str(len(vectors))
print '\nfinal data\n'
data = []
for index in range(1, 29):
count = 0
for combo in vectors:
temp = [x for x in combo]
temp.append(index)
data.append(temp)
count += 1
print "data size = " + str(len(data)) + "; rise = " + str(count)
# print 'after -> ' + str(data[np.random.randint(0, len(data))])
print '\nfinal'
print 'len = ' + str(len(data))
for index in range(10):
print str(-1*index) + '. ' + str(data[-index])
with open('random.csv', 'w') as csvfile:
fp = csv.writer(csvfile)
for index in data:
fp.writerow(index)
if __name__ == '__main__':
main() | [
"noreply@github.com"
] | noreply@github.com |
2a7bd36e3bdacdc5463a393dd2219babfd2e80ea | 918b7f10e0f662aa756959fd67204405f14d4e00 | /ddpg/Actor.py | 7a66a045692040f70ee349ec1186670ead21f109 | [
"MIT"
] | permissive | fstonezst/deep-rl | 2b98c78dde3e164792aab068d702169f82457911 | a7091badd36fac8a4b1782cf9110eb14b8addfd4 | refs/heads/master | 2021-09-11T14:30:55.766336 | 2018-01-23T14:41:16 | 2018-01-23T14:41:16 | 108,348,333 | 0 | 0 | null | 2017-12-15T06:11:53 | 2017-10-26T01:56:15 | Python | UTF-8 | Python | false | false | 3,956 | py | import tensorflow as tf
import tflearn
import numpy as np
class ActorNetwork(object):
"""
Input to the network is the state, output is the action
under a deterministic policy.
The output layer activation is a tanh to keep the action
between -action_bound and action_bound
"""
def __init__(self, sess, state_dim, action_dim, action_bound, learning_rate, tau):
self.sess = sess
self.s_dim = state_dim
self.a_dim = action_dim
self.action_bound = action_bound
self.learning_rate = learning_rate
self.tau = tau
# Actor Network
self.inputs, self.out, self.scaled_out = self.create_actor_network()
self.network_params = tf.trainable_variables()
# Target Network
self.target_inputs, self.target_out, self.target_scaled_out = self.create_actor_network()
self.target_network_params = tf.trainable_variables()[
len(self.network_params):]
# Op for periodically updating target network with online network
# weights
self.update_target_network_params = \
[self.target_network_params[i].assign(tf.multiply(self.network_params[i], self.tau) +
tf.multiply(self.target_network_params[i], 1. - self.tau))
for i in range(len(self.target_network_params))]
# This gradient will be provided by the critic network
self.action_gradient = tf.placeholder(tf.float32, [None, self.a_dim])
# Combine the gradients here
self.actor_gradients = tf.gradients(
self.scaled_out, self.network_params, -self.action_gradient)
# Optimization Op
self.optimize = tf.train.AdamOptimizer(self.learning_rate). \
apply_gradients(zip(self.actor_gradients, self.network_params))
self.num_trainable_vars = len(
self.network_params) + len(self.target_network_params)
def create_actor_network(self):
times = 1
N_HIDDEN_1, N_HIDDEN_2 = 400 * times, 300 * times
DROPOU_KEEP_PROB = 0.5
inputs = tflearn.input_data(shape=[None, self.s_dim])
inputLayer = tflearn.layers.normalization.batch_normalization(inputs)
w_init = tflearn.initializations.uniform(minval=-1/np.sqrt(self.s_dim), maxval=1/np.sqrt(self.s_dim))
net = tflearn.fully_connected(inputLayer, N_HIDDEN_1, activation='relu',regularizer='L2', weight_decay=1.0E-2, weights_init=w_init)
net = tflearn.layers.normalization.batch_normalization(net)
w_init = tflearn.initializations.uniform(minval=-1/np.sqrt(N_HIDDEN_1), maxval=1/np.sqrt(N_HIDDEN_1))
net = tflearn.fully_connected(net, N_HIDDEN_2, activation='relu',regularizer='L2', weight_decay=1.0E-2, weights_init=w_init)
net = tflearn.layers.normalization.batch_normalization(net)
# Final layer weights are init to Uniform[-3e-3, 3e-3]
w_init = tflearn.initializations.uniform(minval=-3.0E-3, maxval=3.0E-3)
out = tflearn.fully_connected(
net, self.a_dim, activation='tanh', weights_init=w_init, bias=w_init)
# Scale output to -action_bound to action_bound
scaled_out = tf.multiply(out, self.action_bound)
return inputs, out, scaled_out
def train(self, inputs, a_gradient):
self.sess.run(self.optimize, feed_dict={
self.inputs: inputs,
self.action_gradient: a_gradient
})
def predict(self, inputs):
return self.sess.run(self.scaled_out, feed_dict={
self.inputs: inputs
})
def predict_target(self, inputs):
return self.sess.run(self.target_scaled_out, feed_dict={
self.target_inputs: inputs
})
def update_target_network(self):
self.sess.run(self.update_target_network_params)
def get_num_trainable_vars(self):
return self.num_trainable_vars
| [
"fstone.zh@gmail.com"
] | fstone.zh@gmail.com |
be8baba074bc2b06e054dbc1762b979a90b1f361 | c3a5c2886f47f2d5e6c43a315c4edbd252c7c95d | /archivos.py | 23118e3c9819378191567445fe8f461cdad8d42f | [] | no_license | 0danielfrozo/Clases_Phyton | 1999501cc4f4fdac4b08433bd575dc6a73f04918 | 777e9685d6061d6f073000bbf10183eb840b829f | refs/heads/master | 2020-03-25T18:57:32.364042 | 2018-08-08T19:27:53 | 2018-08-08T19:27:53 | 144,058,086 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 32 | py | print "mi primer repositorio"
| [
"dg.melo@uniandes.edu.co"
] | dg.melo@uniandes.edu.co |
d88feee59447268d7756cf064e8f61bfb542edd5 | bbc33e3a01f3e14f12956e40eed43fd668bcf349 | /rubiksolver.py | 741267b1c70c9f56070eb6e450e2995a9297f071 | [] | no_license | TristanKalloniatis/Rubiksolver | 4cd576250646d7252b97a8d84ade4bfa3efeed90 | 3c9d7434d5b21ed6490940500736d2f77b10d0f9 | refs/heads/master | 2021-01-10T15:16:20.979526 | 2016-02-03T19:03:39 | 2016-02-03T19:03:39 | 51,023,071 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 22,039 | py | #Documentation for Rubik's solver
#In this file we specify the effect of each face turn on a 3x3x3 cube and give a solving algorithm
#Later will add computer vision functionality to automatically read state of scrambled cube from photos
#Cube representation: we represent the cube by an array cube[6][3][3] where first index covers the faces, second and third index cover the cubies
#Array is filled with entries 0,1,2,3,4,5 representing the 6 face colours
#Convention on cube orientation and numbering:
#UP, DOWN, LEFT, RIGHT, FRONT, BACK
#Hold cube so that ULF corner is closet to you
#U=cube[0] with first 3-index giving the rows and second 3-index the columns from this view
#L=cube[1] and F=cube[2] with subsidiary indices as above
#Rotate the cube a half turn about UD axis so that URB corner faces
#R=cube[3] and B=cube[4] with subsidiary indices as above
#Finally, rotate the cube so that F is in front, L is left, etc. Then perform a quarter turn around LR axis so D faces
#D=cube[5] with subsidiary indices as above
#Sanity check on numbering convention: here are the indices for the 8 corners
#ULF=cube[0][2][0]=cube[1][0][2]=cube[2][0][0]
#UFR=cube[0][2][2]=cube[2][0][2]=cube[3][0][0]
#URB=cube[0][0][2]=cube[3][0][2]=cube[4][0][0]
#UBL=cube[0][0][0]=cube[4][0][2]=cube[1][0][0]
#FLD=cube[2][2][0]=cube[1][2][2]=cube[5][0][0]
#RFD=cube[3][2][0]=cube[2][2][2]=cube[5][0][2]
#BRD=cube[4][2][0]=cube[3][2][2]=cube[5][2][2]
#LBD=cube[1][2][0]=cube[4][2][2]=cube[5][2][0]
#And for the 12 edges
#UL=cube[0][1][0]=cube[1][0][1]
#UF=cube[0][2][1]=cube[2][0][1]
#UR=cube[0][1][2]=cube[3][0][1]
#UB=cube[0][0][1]=cube[4][0][1]
#LF=cube[1][1][2]=cube[2][1][0]
#FR=cube[2][1][2]=cube[3][1][0]
#RB=cube[3][1][2]=cube[4][1][0]
#BL=cube[4][1][2]=cube[1][1][0]
#LD=cube[1][2][1]=cube[5][1][0]
#FD=cube[2][2][1]=cube[5][0][1]
#RD=cube[3][2][1]=cube[5][1][2]
#BD=cube[4][2][1]=cube[5][2][1]
#Since the centre pieces don't move, we initialise colour choice so that cube[i][1][1] has colour label i for all i
#We now specify the effect of the 6 CLOCKWISE face turns
def Uturn(cube):
face=0
#Track the effect of the U turn on the corner pieces
temp=cube[face][0][0]
#This part can be copied for each face
cube[face][0][0]=cube[face][2][0]
cube[face][2][0]=cube[face][2][2]
cube[face][2][2]=cube[face][0][2]
cube[face][0][2]=temp
temp=cube[1][0][0]
cube[1][0][0]=cube[2][0][0]
cube[2][0][0]=cube[3][0][0]
cube[3][0][0]=cube[4][0][0]
cube[4][0][0]=temp
temp=cube[4][0][2]
cube[4][0][2]=cube[1][0][2]
cube[1][0][2]=cube[2][0][2]
cube[2][0][2]=cube[3][0][2]
cube[3][0][2]=temp
#Track the effect of the U turn on the edge pieces
temp=cube[face][1][0]
#This part can be copied for each face
cube[face][1][0]=cube[face][2][1]
cube[face][2][1]=cube[face][1][2]
cube[face][1][2]=cube[face][0][1]
cube[face][0][1]=temp
temp=cube[1][0][1]
cube[1][0][1]=cube[2][0][1]
cube[2][0][1]=cube[3][0][1]
cube[3][0][1]=cube[4][0][1]
cube[4][0][1]=temp
def Lturn(cube):
face=1
#Track the effect of the L turn on the corner pieces
temp=cube[face][0][0]
#This part can be copied for each face
cube[face][0][0]=cube[face][2][0]
cube[face][2][0]=cube[face][2][2]
cube[face][2][2]=cube[face][0][2]
cube[face][0][2]=temp
temp=cube[4][0][2]
cube[4][0][2]=cube[5][2][0]
cube[5][2][0]=cube[2][2][0]
cube[2][2][0]=cube[0][2][0]
cube[0][2][0]=temp
temp=cube[0][0][0]
cube[0][0][0]=cube[4][2][2]
cube[4][2][2]=cube[5][0][0]
cube[5][0][0]=cube[2][0][0]
cube[2][0][0]=temp
#Track the effect of the L turn on the edge pieces
temp=cube[face][1][0]
#This part can be copied for each face
cube[face][1][0]=cube[face][2][1]
cube[face][2][1]=cube[face][1][2]
cube[face][1][2]=cube[face][0][1]
cube[face][0][1]=temp
temp=cube[4][1][2]
cube[4][1][2]=cube[5][1][0]
cube[5][1][0]=cube[2][1][0]
cube[2][1][0]=cube[0][1][0]
cube[0][1][0]=temp
def Fturn(cube):
face=2
#Track the effect of the F turn on the corner pieces
temp=cube[face][0][0]
#This part can be copied for each face
cube[face][0][0]=cube[face][2][0]
cube[face][2][0]=cube[face][2][2]
cube[face][2][2]=cube[face][0][2]
cube[face][0][2]=temp
temp=cube[1][0][2]
cube[1][0][2]=cube[5][0][0]
cube[5][0][0]=cube[3][2][0]
cube[3][2][0]=cube[0][2][2]
cube[0][2][2]=temp
temp=cube[0][2][0]
cube[0][2][0]=cube[1][2][2]
cube[1][2][2]=cube[5][0][2]
cube[5][0][2]=cube[3][0][0]
cube[3][0][0]=temp
#Track the effect of the F turn on the edge pieces
temp=cube[face][1][0]
#This part can be copied for each face
cube[face][1][0]=cube[face][2][1]
cube[face][2][1]=cube[face][1][2]
cube[face][1][2]=cube[face][0][1]
cube[face][0][1]=temp
temp=cube[1][1][2]
cube[1][1][2]=cube[5][0][1]
cube[5][0][1]=cube[3][1][0]
cube[3][1][0]=cube[0][2][1]
cube[0][2][1]=temp
def Rturn(cube):
face=3
#Track the effect of the R turn on the corner pieces
temp=cube[face][0][0]
#This part can be copied for each face
cube[face][0][0]=cube[face][2][0]
cube[face][2][0]=cube[face][2][2]
cube[face][2][2]=cube[face][0][2]
cube[face][0][2]=temp
temp=cube[2][0][2]
cube[2][0][2]=cube[5][0][2]
cube[5][0][2]=cube[4][2][0]
cube[4][2][0]=cube[0][0][2]
cube[0][0][2]=temp
temp=cube[0][2][2]
cube[0][2][2]=cube[2][2][2]
cube[2][2][2]=cube[5][2][2]
cube[5][2][2]=cube[4][0][0]
cube[4][0][0]=temp
#Track the effect of the R turn on the edge pieces
temp=cube[face][1][0]
#This part can be copied for each face
cube[face][1][0]=cube[face][2][1]
cube[face][2][1]=cube[face][1][2]
cube[face][1][2]=cube[face][0][1]
cube[face][0][1]=temp
temp=cube[2][1][2]
cube[2][1][2]=cube[5][1][2]
cube[5][1][2]=cube[4][1][0]
cube[4][1][0]=cube[0][1][2]
cube[0][1][2]=temp
def Bturn(cube):
face=4
#Track the effect of the B turn on the corner pieces
temp=cube[face][0][0]
#This part can be copied for each face
cube[face][0][0]=cube[face][2][0]
cube[face][2][0]=cube[face][2][2]
cube[face][2][2]=cube[face][0][2]
cube[face][0][2]=temp
temp=cube[3][0][2]
cube[3][0][2]=cube[5][2][2]
cube[5][2][2]=cube[1][2][0]
cube[1][2][0]=cube[0][0][0]
cube[0][0][0]=temp
temp=cube[0][0][2]
cube[0][0][2]=cube[3][2][2]
cube[3][2][2]=cube[5][2][0]
cube[5][2][0]=cube[1][0][0]
cube[1][0][0]=temp
#Track the effect of the B turn on the edge pieces
temp=cube[face][1][0]
#This part can be copied for each face
cube[face][1][0]=cube[face][2][1]
cube[face][2][1]=cube[face][1][2]
cube[face][1][2]=cube[face][0][1]
cube[face][0][1]=temp
temp=cube[3][1][2]
cube[3][1][2]=cube[5][2][1]
cube[5][2][1]=cube[1][1][0]
cube[1][1][0]=cube[0][0][1]
cube[0][0][1]=temp
def Dturn(cube):
face=5
#Track the effect of the D turn on the corner pieces
temp=cube[face][0][0]
#This part can be copied for each face
cube[face][0][0]=cube[face][2][0]
cube[face][2][0]=cube[face][2][2]
cube[face][2][2]=cube[face][0][2]
cube[face][0][2]=temp
temp=cube[1][2][2]
cube[1][2][2]=cube[4][2][2]
cube[4][2][2]=cube[3][2][2]
cube[3][2][2]=cube[2][2][2]
cube[2][2][2]=temp
temp=cube[2][2][0]
cube[2][2][0]=cube[1][2][0]
cube[1][2][0]=cube[4][2][0]
cube[4][2][0]=cube[3][2][0]
cube[3][2][0]=temp
#Track the effect of the D turn on the edge pieces
temp=cube[face][1][0]
#This part can be copied for each face
cube[face][1][0]=cube[face][2][1]
cube[face][2][1]=cube[face][1][2]
cube[face][1][2]=cube[face][0][1]
cube[face][0][1]=temp
temp=cube[1][2][1]
cube[1][2][1]=cube[4][2][1]
cube[4][2][1]=cube[3][2][1]
cube[3][2][1]=cube[2][2][1]
cube[2][2][1]=temp
def turntype(num):
if num==1:
return " "
elif num==2:
return "2"
else:
return "\'"
def name(face):
if face==0:
return "blue"
elif face==1:
return "orange"
elif face==2:
return "white"
elif face==3:
return "red"
elif face==4:
return "yellow"
else:
return "green"
def turn(cube,face,num):
#num=1, 2, or -1 to represent clockwise, half, or anticlockwise turns of the face
if num==-1: num=3
if num==-2: num=2
if num==-3: num=1
if num==-4: num=0
if num==4: num=0
for n in range(num):
if n==0:
print name(face),turntype(num)
if face==0:
Uturn(cube)
elif face==1:
Lturn(cube)
elif face==2:
Fturn(cube)
elif face==3:
Rturn(cube)
elif face==4:
Bturn(cube)
else:
Dturn(cube)
def adjacent(face):
#Returns the 4 adjacent faces to a given face followed by opposite face as an array of size 5, in the order above, to the left, to the right, below, opposite when face is oriented so subsidiary idices increase down and to the right
if face==0:
return [4,1,3,2,5]
elif face==1:
return [0,4,2,5,3]
elif face==2:
return [0,1,3,5,4]
elif face==3:
return [0,2,4,5,1]
elif face==4:
return [0,3,1,5,2]
else:
return [2,1,3,4,0]
def setcube(cube):
#Build a setlike dictionary representation of the cubes state
#Will not use turns on this representation, but will ue to track the position of pieces during the solve more easily
cubedict=dict()
cubedict['ULF']=set([cube[0][2][0],cube[1][0][2],cube[2][0][0]])
cubedict['UFR']=set([cube[0][2][2],cube[2][0][2],cube[3][0][0]])
cubedict['URB']=set([cube[0][0][2],cube[3][0][2],cube[4][0][0]])
cubedict['UBL']=set([cube[0][0][0],cube[4][0][2],cube[1][0][0]])
cubedict['FLD']=set([cube[2][2][0],cube[1][2][2],cube[5][0][0]])
cubedict['RFD']=set([cube[3][2][0],cube[2][2][2],cube[5][0][2]])
cubedict['BRD']=set([cube[4][2][0],cube[3][2][2],cube[5][2][2]])
cubedict['LBD']=set([cube[1][2][0],cube[4][2][2],cube[5][2][0]])
cubedict['UL']=set([cube[0][1][0],cube[1][0][1]])
cubedict['UF']=set([cube[0][2][1],cube[2][0][1]])
cubedict['UR']=set([cube[0][1][2],cube[3][0][1]])
cubedict['UB']=set([cube[0][0][1],cube[4][0][1]])
cubedict['LF']=set([cube[1][1][2],cube[2][1][0]])
cubedict['FR']=set([cube[2][1][2],cube[3][1][0]])
cubedict['RB']=set([cube[3][1][2],cube[4][1][0]])
cubedict['BL']=set([cube[4][1][2],cube[1][1][0]])
cubedict['LD']=set([cube[1][2][1],cube[5][1][0]])
cubedict['FD']=set([cube[2][2][1],cube[5][0][1]])
cubedict['RD']=set([cube[3][2][1],cube[5][1][2]])
cubedict['BD']=set([cube[4][2][1],cube[5][2][1]])
return cubedict
def piecename(i,j,k):
#Returns the 2 or 3 character name of cube[i][j][k] in the dictionary keys above
if (i,j,k)==(0,2,0) or (i,j,k)==(1,0,2) or (i,j,k)==(2,0,0):
return 'ULF'
elif (i,j,k)==(0,2,2) or (i,j,k)==(2,0,2) or (i,j,k)==(3,0,0):
return 'UFR'
elif (i,j,k)==(0,0,2) or (i,j,k)==(3,0,2) or (i,j,k)==(4,0,0):
return 'URB'
elif (i,j,k)==(0,0,0) or (i,j,k)==(4,0,2) or (i,j,k)==(1,0,0):
return 'UBL'
elif (i,j,k)==(2,2,0) or (i,j,k)==(1,2,2) or (i,j,k)==(5,0,0):
return 'FLD'
elif (i,j,k)==(3,2,0) or (i,j,k)==(2,2,2) or (i,j,k)==(5,0,2):
return 'RFD'
elif (i,j,k)==(4,2,0) or (i,j,k)==(3,2,2) or (i,j,k)==(5,2,2):
return 'BRD'
elif (i,j,k)==(1,2,0) or (i,j,k)==(4,2,2) or (i,j,k)==(5,2,0):
return 'LBD'
elif (i,j,k)==(0,1,0) or (i,j,k)==(1,0,1):
return 'UL'
elif (i,j,k)==(0,2,1) or (i,j,k)==(2,0,1):
return 'UF'
elif (i,j,k)==(0,1,2) or (i,j,k)==(3,0,1):
return 'UR'
elif (i,j,k)==(0,0,1) or (i,j,k)==(4,0,1):
return 'UB'
elif (i,j,k)==(1,1,2) or (i,j,k)==(2,1,0):
return 'LF'
elif (i,j,k)==(2,1,2) or (i,j,k)==(3,1,0):
return 'FR'
elif (i,j,k)==(3,1,2) or (i,j,k)==(4,1,0):
return 'RB'
elif (i,j,k)==(4,1,2) or (i,j,k)==(1,1,0):
return 'BL'
elif (i,j,k)==(1,2,1) or (i,j,k)==(5,1,0):
return 'LD'
elif (i,j,k)==(2,2,1) or (i,j,k)==(5,0,1):
return 'FD'
elif (i,j,k)==(3,2,1) or (i,j,k)==(5,1,2):
return 'RD'
else:
return 'BD'
def edgecorrect(cube,face,edge):
#Determines whether an edge on the cube face is positioned AND oriented correctly
#Returns 0 if edge is correctly oriented, 1 if edge is correct but flipped, and -1 otherwise
#Edges are numbered 0,1,2,3 corresponding to subsidiary indices [1][2],[0][1],[1][0],[2][1]
#Numbering is set up so that turning face edge times puts relevant edge on the right when viewing face on
adj=adjacent(face)
if edge==0:
j=1
k=2
a=2
elif edge==1:
j=0
k=1
a=0
elif edge==2:
j=1
k=0
a=1
else:
j=2
k=1
a=3
name=piecename(face,j,k)
otherside=(setcube(cube)[name].difference(set([cube[face][j][k]]))).pop()
if cube[face][j][k]==cube[face][1][1] and otherside==cube[adj[a]][1][1]:
return 0
elif cube[face][j][k]==cube[adj[a]][1][1] and otherside==cube[face][1][1]:
return 1
else:
return -1
def cornercorrect(cube,face,corner):
#Determines whether a corner on the cube face is positioned AND oriented correctly
#Returns 0 if corner is correctly oriented, 1 if corner is correct but needs to be rotated clockwise, 2 if corner is correct but needs to be rotated anticlockwise, and -1 otherwise
#Corners are numbered 0,1,2,3 corresponding to subsidiary indices [0][2],[0][0],[2][0],[2][2]
#Numbering is set up so that turning face corner times puts relevant corner on the top right when viewing face on
adj=adjacent(face)
#Here a1 will specify the face one place clockwise of the given face and a2 the face one place anticlockwise
if corner==0:
j=0
k=2
a1=0
a2=2
elif corner==1:
j=0
k=0
a1=1
a2=0
elif corner==2:
j=2
k=0
a1=3
a2=1
else:
j=2
k=2
a1=2
a2=3
name=piecename(face,j,k)
colours=setcube(cube)[name]
if colours!=set([cube[face][1][1],cube[adj[a1]][1][1],cube[adj[a2]][1][1]]):
return -1
elif cube[face][j][k]==cube[face][1][1]:
return 0
elif cube[face][j][k]==cube[adj[a1]][1][1]:
return 1
else:
return 2
def orientcorners(cube,face,c1,c2):
#Turns c1 clockwise and c2 anticlockwise on a face. Corners are numbered 0,1,2,3 corresponding to subsidiary indices [0][2],[0][0],[2][0],[2][2]
#Numbering is set up so that turning face c1 times puts relevant corner in top right when viewing face on
adj=adjacent(face)
turn(cube,face,c1)
turn(cube,adj[2],1)
turn(cube,adj[4],1)
turn(cube,adj[2],3)
turn(cube,adj[4],3)
turn(cube,adj[2],1)
turn(cube,adj[4],1)
turn(cube,adj[2],3)
turn(cube,face,c2-c1)
turn(cube,adj[2],1)
turn(cube,adj[4],3)
turn(cube,adj[2],3)
turn(cube,adj[4],1)
turn(cube,adj[2],1)
turn(cube,adj[4],3)
turn(cube,adj[2],3)
turn(cube,face,4-c2)
def orientedges(cube,face,e1,e2):
#Flips edges e1 and d2 on a face. Edges are numbered 0,1,2,3 corresponding to subsidiary indices [1][2],[0][1],[1][0],[2][1]
#Numbering is set up so that turning face e1 times puts relevant edge on the right when viewing face on
adj=adjacent(face)
turn(cube,face,e1)
turn(cube,adj[2],1)
turn(cube,adj[4],1)
turn(cube,face,3)
turn(cube,adj[0],1)
turn(cube,face,e2-e1)
turn(cube,adj[0],3)
turn(cube,face,1)
turn(cube,adj[4],3)
turn(cube,adj[2],3)
turn(cube,face,4-e2)
def permuteedges(cube,face,e1,e2,e3):
#Swaps edges e1 and e2, then edges e3 and e4 on a face. Edges are numbered 0,1,2,3 corresponding to subsidiary indices [1][2],[0][1],[1][0],[2][1]
#Assume e4-e3=e2-e1
#Numbering is set up so that turning face e1 times puts relevant edge on the right when viewing face on
adj=adjacent(face)
a=[2,0,1,3]
turn(cube,face,e1)
turn(cube,adj[2],2)
turn(cube,adj[4],e2-e1)
turn(cube,adj[a[e2]],2)
turn(cube,adj[4],(4-e2+e1)%4)
turn(cube,adj[2],2)
turn(cube,face,e3-e1)
turn(cube,adj[2],2)
turn(cube,adj[4],e2-e1)
turn(cube,adj[a[e2]],2)
turn(cube,adj[4],(4-e2+e1)%4)
turn(cube,adj[2],2)
turn(cube,face,4-e3)
def permutecorners(cube,face,c1,c2,c3):
#Performs the 3 cycle c1->c2->c3->c1 on 3 corners. Corners are numbered 0,1,2,3 corresponding to subsidiary indices [0][2],[0][0],[2][0],[2][2]
#Numbering is set up so that turning face c1 times puts relevant corner in top right when viewing face on
#Corners c1 and c2 lie on the face, while c3 lies on the opposite face, with the numbering matching the numbering on the given face
#Assume c3!=3
adj=adjacent(face)
turn(cube,face,c1)
turn(cube,adj[2],1)
if c3==0:
turn(cube,adj[4],1)
else:
turn(cube,adj[4],4-c3)
turn(cube,adj[2],3)
turn(cube,face,c2-c1)
turn(cube,adj[2],1)
if c3==0:
turn(cube,adj[4],3)
else:
turn(cube,adj[4],c3)
turn(cube,adj[2],3)
turn(cube,face,4-c2)
def edgepermutation(cube,face):
#Determines the permutation of edges on a face, assuming all correct edges are already on the face
#Edges are numbered 0,1,2,3 corresponding to subsidiary indices [1][2],[0][1],[1][0],[2][1] when viewing cube face on
perm=[0,0,0,0]
adj=adjacent(face)
a=[2,0,1,3]
for edge in range(4):
if edge==0:
j=1
k=2
elif edge==1:
j=0
k=1
elif edge==2:
j=1
k=0
else:
j=2
k=1
name=piecename(face,j,k)
othercolour=(setcube(cube)[name].difference(set([cube[face][1][1]]))).pop()
for otheredge in range(4):
if othercolour==cube[adj[a[otheredge]]][1][1]:
perm[edge]=otheredge
return perm
def permutationtype(perm):
#Returns the cycle type of a permutation of [0,1,2,3]
ans=[]
for i in range(4):
if i in ans:
break
else:
ans.append(i)
j=i
while True:
j=perm[j]
if j==i:
break
else:
ans.append(j)
ans.append(' ')
return ans
def sign(perm):
#Returns the sign of a permutation
cycles=permutationtype(perm)
cyclelength=0
sign=1
for x in cycles:
if x==" ":
if cyclelength%2==0:
sign=-sign
cyclelength=0
else:
cyclelength=cyclelength+1
return sign
def is3cycle(perm):
#Returns 0 if perm is not a 3 cycle, and otherwise returns the 3 elements of the cycle
#cube=[[[0,0,0],[0,0,0],[0,0,0]],[[0,0,0],[0,0,0],[0,0,0]],[[0,0,0],[0,0,0],[0,0,0]],[[0,0,0],[0,0,0],[0,0,0]],[[0,0,0],[0,0,0],[0,0,0]],[[0,0,0],[0,0,0],[0,0,0]]]
#Sample scrambled state below
cube=[[[3,2,5],[0,0,5],[2,0,3]],[[2,3,0],[1,1,1],[1,1,1]],[[1,4,4],[2,2,1],[4,3,3]],[[0,4,3],[0,3,2],[4,0,5]],[[2,3,0],[5,4,4],[1,3,4]],[[0,4,5],[5,5,2],[5,5,2]]]
while True:
f=raw_input("Face: ")
C1=raw_input("e1: ")
C2=raw_input("e2: ")
C3=raw_input("e3: ")
C4=raw_input("e4: ")
try:
face=int(f)
e1=int(C1)
e2=int(C2)
e3=int(C3)
e4=int(C4)
except:
break
permuteedges(cube,face,e1,e2,e3,e4)
for i in range(6):
print "Face",i
for j in range(3):
for k in range(3):
print cube[i][j][k]
print "-----"
#Read scrambled state
#Later will replace this with computer vision code but for now manually enter the sticker colours
#for i in range(6):
# print "Face",i
# for j in range(3):
# for k in range(3):
# cube[i][j][k]=raw_input("C:")
#cube[i][j][k]=int(cubie)
#May need above line or similar depending on representation of colours. For now keep as integers. Note that if this changes will need to change the labelling of faces
# print "-----"
#Solving algorithm implented will be my intuitive/conjugator solver.
#D layer edges
#Decide which face to start on
face=5
correct=0
for i in range(5,-1,-1):
edgescorrect=0
for edge in range(4):
if edgecorrect(cube,i,edge)==0:
edgescorrect=edgescorrect+1
if edgescorrect>correct:
correct=edgescorrect
face=i
adj=adjacent(face)
#Fix remaining edges on this face
for edge in range(4):
if edgecorrect(cube,face,edge)==0:
continue
elif edgecorrect(cube,face,edge)==1:
turn(cube,face,edge)
turn(cube,adj[2],1)
turn(cube,adj[4],1)
turn(cube,face,3)
turn(cube,adj[0],1)
turn(cube,face,4-edge)
else:
pass
#NEED TO THINK ABOUT CODE HERE
#Middle layer edges
#NEED TO THINK ABOUT CODE HERE
#Permute U layer edges
perm=edgepermutation(cube,adj[4])
if sign(perm)==-1:
turn(cube,adj[4],1)
perm=edgepermutation(cube,adj[4])
#Edges now require an even permutation to position
#CONTINUE FROM HERE
#Worth considering embedding detecting the permutation required in the sign function
#Orient U layer edges
#Permute corners
#Orient corners
#while True:
# f=raw_input("Face: ")
# C=raw_input("edge: ")
# try:
# face=int(f)
# e1=int(C)
# except:
# break
# print edgecorrect(cube,face,e1)
#for i in range(6):
# print "Face",i
# for j in range(3):
# for k in range(3):
# print cube[i][j][k]
# print "-----"
| [
"tristankalloniatis@gmail.com"
] | tristankalloniatis@gmail.com |
b24f887ce614c49d40017dfe367f9809d91a2b74 | 1fbf39b1b51f222961dc2d8018adb5c358b26d22 | /main.py | 9aad821534c05359e1a53905963682d4ee477846 | [] | no_license | mtokarev93/Flask | 2b99fa97c7aa54cbfd89a85ad6944a002b96a9e5 | dfdbaf1815b337c283da161ecd0686ceee34d2d9 | refs/heads/main | 2023-06-30T01:54:41.555045 | 2021-08-05T17:34:31 | 2021-08-05T17:34:31 | 393,123,619 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,860 | py | from flask import Flask, render_template, request, redirect
from flask_sqlalchemy import SQLAlchemy
from cloudipsp import Api, Checkout
app = Flask(__name__) #создание объекта на основе класса Flask. Можем отслеживать те функции, которые будут отслеживать переходы на разные URL адреса.
app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///shop.db'
app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False
db = SQLAlchemy(app)
#Чтобы отследить главную страницу, нам необходимо создать декоратор
@app.route('/')
def index():
items = Item.query.order_by(Item.price).all()
return render_template('index.html', data=items )
# ORM - Object-relational model
class Item(db.Model):
id = db.Column(db.Integer, primary_key=True)
title = db.Column(db.String(100), nullable=False)
price = db.Column(db.Integer, nullable=False)
is_active = db.Column(db.Boolean, default=True)
# item = Item(id=....)
# item.save() => 'INSERT INTO items .....'
# query = 'SELECT * ....'
# db.execute(query)
#text = db.Column(db.Text, nullable=True)
def __repr__(self):
return self.title
@app.route('/about')
def about():
return render_template('about.html')
# GET /item/4 => { Холодильник, белый, 100 000 ₽}
# GET /item/4/buying
@app.route('/item/<int:id>/purchase')
def item_buy(id):
item = Item.query.get(id)
api = Api(merchant_id=1396424,
secret_key='test')
checkout = Checkout(api=api)
data = {
"currency": "RUB",
"amount": str(item.price) + "00"
}
url = checkout.url(data).get('checkout_url')
return redirect(url)
# https://google.ru/products
# REST API
# POST site.ru/item
# GET site.ru/item/3
# site.ru/item
# Это handler (обработчик)
# POST | GET /item
# POST /item
# body:
# title=Tovar&price=100
# GET site.ru/
# @app.route('/') => "<html><head></head><.....></html>"
# Введите название: Товар
# Введите цену: 20 000
# => POST /item
# BODY
# title=Товар&price=20000
# => request.form = {
# 'title': 'Товар',
# 'price': '20000',
# }
# a = dict()
# 'Vasya' => +7985...
# 'Petya' => +7999...
# Petya?
# a['Petya']
@app.route('/item', methods=['POST', 'GET'])
def create():
if request.method == "POST":
title = request.form['title']
price = request.form['price']
item = Item(title=title, price=price)
try:
db.session.add(item)
db.session.commit()
return redirect('/')
except:
return "Получилась ошибка"
else:
return render_template('create.html')
if __name__ == "__main__":
app.run(debug=True) | [
"micheal.93@mail.ru"
] | micheal.93@mail.ru |
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