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bc994eebc5b04d3239183fd006857bffb91a1af8 | de24f83a5e3768a2638ebcf13cbe717e75740168 | /moodledata/vpl_data/171/usersdata/265/81901/submittedfiles/decimal2bin.py | 877f170e5cf66f6b01ad797c8e446c197b5ab56d | []
| no_license | rafaelperazzo/programacao-web | 95643423a35c44613b0f64bed05bd34780fe2436 | 170dd5440afb9ee68a973f3de13a99aa4c735d79 | refs/heads/master | 2021-01-12T14:06:25.773146 | 2017-12-22T16:05:45 | 2017-12-22T16:05:45 | 69,566,344 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 201 | py | # -*- coding: utf-8 -*-
d=int(input('digite o valor de d: '))
contador=0
soma=0
while (d>=0):
if d%10==0:
soma = soma + (0*2*contador)
else :
soma = soma + (i*2*contador)
| [
"[email protected]"
]
| |
f40c73f9d512cabfc6000d3b2f2aba4bcf4280dc | 210ecd63113ce90c5f09bc2b09db3e80ff98117a | /AbletonLive9_RemoteScripts/Oxygen8v2/__init__.py | 31d05f0dde8d5b0ee169a7f5bebf6abf5dce0303 | []
| no_license | ajasver/MidiScripts | 86a765b8568657633305541c46ccc1fd1ea34501 | f727a2e63c95a9c5e980a0738deb0049363ba536 | refs/heads/master | 2021-01-13T02:03:55.078132 | 2015-07-16T18:27:30 | 2015-07-16T18:27:30 | 38,516,112 | 4 | 1 | null | null | null | null | UTF-8 | Python | false | false | 551 | py | #Embedded file name: /Users/versonator/Jenkins/live/Binary/Core_Release_64_static/midi-remote-scripts/Oxygen8v2/__init__.py
from _Generic.GenericScript import GenericScript
import Live
from config import *
def create_instance(c_instance):
""" The generic script can be customised by using parameters (see config.py). """
return GenericScript(c_instance, Live.MidiMap.MapMode.absolute, Live.MidiMap.MapMode.absolute, DEVICE_CONTROLS, TRANSPORT_CONTROLS, VOLUME_CONTROLS, TRACKARM_CONTROLS, BANK_CONTROLS, CONTROLLER_DESCRIPTION, MIXER_OPTIONS) | [
"[email protected]"
]
| |
8b97c1e14adfcb09806e2d37e2f5c4f0b356c009 | 51885da54b320351bfea42c7dd629f41985454cd | /abc088/c.py | 6f50226c2b81dbb528686b2a04839a3d3dee1e8c | []
| no_license | mskt4440/AtCoder | dd266247205faeda468f911bff279a792eef5113 | f22702e3932e129a13f0683e91e5cc1a0a99c8d5 | refs/heads/master | 2021-12-15T10:21:31.036601 | 2021-12-14T08:19:11 | 2021-12-14T08:19:11 | 185,161,276 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,389 | py | #
# abc088 c
#
import sys
from io import StringIO
import unittest
class TestClass(unittest.TestCase):
def assertIO(self, input, output):
stdout, stdin = sys.stdout, sys.stdin
sys.stdout, sys.stdin = StringIO(), StringIO(input)
resolve()
sys.stdout.seek(0)
out = sys.stdout.read()[:-1]
sys.stdout, sys.stdin = stdout, stdin
self.assertEqual(out, output)
def test_入力例_1(self):
input = """1 0 1
2 1 2
1 0 1"""
output = """Yes"""
self.assertIO(input, output)
def test_入力例_2(self):
input = """2 2 2
2 1 2
2 2 2"""
output = """No"""
self.assertIO(input, output)
def test_入力例_3(self):
input = """0 8 8
0 8 8
0 8 8"""
output = """Yes"""
self.assertIO(input, output)
def test_入力例_4(self):
input = """1 8 6
2 9 7
0 7 7"""
output = """No"""
self.assertIO(input, output)
def resolve():
c = []
for _ in range(3):
c.append(list(map(int, input().split())))
a1 = 0
b1 = c[0][0] - a1
b2 = c[0][1] - a1
b3 = c[0][2] - a1
a2 = c[1][0] - b1
a3 = c[2][0] - b1
if a2+b2 == c[1][1] and a2+b3 == c[1][2] and a3+b2 == c[2][1] and a3+b3 == c[2][2]:
print("Yes")
else:
print("No")
if __name__ == "__main__":
# unittest.main()
resolve()
| [
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]
| |
75ff5701bb8130bd4c6b3ed2171d396f007ef5bc | e8cf96d2c1cef71c0bcbe200a0a65dee59d21d65 | /molotov/quickstart/loadtest.py | 4df0a04ebc981811bb4bc1b6b11fb733a40ba6bf | [
"Apache-2.0"
]
| permissive | ronnix/molotov | 11ed38e699bf58ce469b7d96a617ffb98ab7f6f6 | b2684ec13edb6d0ff901398467b16c885c5ff502 | refs/heads/master | 2020-06-20T05:41:36.070912 | 2017-06-09T10:02:01 | 2017-06-09T10:02:01 | 94,195,118 | 0 | 0 | null | 2017-06-13T09:25:58 | 2017-06-13T09:25:58 | null | UTF-8 | Python | false | false | 2,000 | py | """ Molotov-based test.
"""
import json
from molotov import scenario, setup, global_setup, teardown, global_teardown
# This is the service you want to load test
_API = 'http://localhost:8080'
@global_setup()
def test_starts(args):
""" This functions is called before anything starts.
Notice that it's not a coroutine.
"""
pass
@setup()
async def worker_starts(worker_id, args):
""" This function is called once per worker.
If it returns a mapping, it will be used with all requests.
You can add things like Authorization headers for instance,
by setting a "headers" key.
"""
headers = {'SomeHeader': '1'}
return {'headers': headers}
@teardown()
def worker_ends(worker_id):
""" This functions is called when the worker is done.
Notice that it's not a coroutine.
"""
pass
@global_teardown()
def test_ends():
""" This functions is called when everything is done.
Notice that it's not a coroutine.
"""
pass
# each scenario has a weight. Molotov uses it to determine
# how often the scenario is picked.
@scenario(40)
async def scenario_one(session):
async with session.get(_API) as resp:
# if Molotov is called with --statsd
# you will have a statsd client set into the session
# you can use to add metrics
if session.statsd:
session.statsd.incr('BLEH')
# when you read the body, don't forget to use await
res = await resp.json()
assert res['result'] == 'OK'
assert resp.status == 200
# all scenarii are coroutines
@scenario(30)
async def scenario_two(session):
# a call to one of the session method should be awaited
# see aiohttp.Client docs for more info on this
async with session.get(_API) as resp:
assert resp.status == 200
@scenario(30)
async def scenario_three(session):
somedata = json.dumps({'OK': 1})
async with session.post(_API, data=somedata) as resp:
assert resp.status == 200
| [
"[email protected]"
]
| |
2e847e8539a9fe4db57312c83a82af2b3d9222c8 | 0eec43ed5bee5be0d3e0bd952a697ba1d31eeabd | /python/Day14/scope.py | 11f0564e9e5c093f4029a23aa4946eddd2ac6599 | []
| no_license | ssinghaldev/30_days_of_code | ce167613eeeaa30fdfc6451da1d849cf6af0725e | 8a48c89030dde50481298cea9c64b5dfded29df0 | refs/heads/master | 2020-04-04T09:18:11.485394 | 2018-11-28T05:06:00 | 2018-11-28T05:06:00 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 121 | py | # Add your code here
def computeDifference(self):
self.maximumDifference = max(self.__elements) - min(self.__elements)
| [
"[email protected]"
]
| |
673b277e8c1eb534425768123f79ddd2505204f1 | 925f2935b34042abc9161795413031ae68f45b9a | /multimodel_inference/fold_IMisc2sm.py | 291a17afa018e415d33f158ff6cce511b0ce18fa | []
| no_license | Farhad63/AFS-analysis-with-moments | 7e1d17f47c06ed97ebb7c9ec8245fe52a88622c3 | 7874b1085073e5f62d910ef2d79a22b29ff3be84 | refs/heads/master | 2022-04-09T22:11:12.341235 | 2020-03-11T21:15:42 | 2020-03-11T21:15:42 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,074 | py | #!/usr/bin/env python
# split into two diff sizes, then secondary contact with instantaneous size change followed by growth, with symmetric migration
# "genomic islands" of two different migration regimes
# n(para): 12
import matplotlib
matplotlib.use('PDF')
import moments
import random
import pylab
import matplotlib.pyplot as plt
import numpy as np
from numpy import array
from moments import Misc,Spectrum,Numerics,Manips,Integration,Demographics1D,Demographics2D
import sys
infile=sys.argv[1]
pop_ids=[sys.argv[2],sys.argv[3]]
projections=[int(sys.argv[4]),int(sys.argv[5])]
#params=[float(sys.argv[6]),float(sys.argv[7]),float(sys.argv[8]),float(sys.argv[9]),float(sys.argv[10]),float(sys.argv[11]),float(sys.argv[12]),float(sys.argv[13])]
params=array([1,1,1,1,1,1,1,1,1,1,0.5])
# mutation rate per sequenced portion of genome per generation: for A.millepora, 0.02
mu=float(sys.argv[6])
# generation time, in thousand years: 0.005 (5 years)
gtime=float(sys.argv[7])
#infile="5kA3_dadi.data"
#pop_ids=["W","K"]
#projections=[32,38]
dd = Misc.make_data_dict(infile)
data = Spectrum.from_data_dict(dd, pop_ids,projections,polarized=False)
ns=data.sample_sizes
np.set_printoptions(precision=3)
#-------------------
# split with growth and asymmetrical migration; with genomic islands
def IM2iSC(params, ns):
"""
Isolation-with-migration model with split into two arbtrary sizes
p_misid: proportion of misidentified ancestral states
"""
nua,nub,nu1_0,nu2_0,nu1,nu2,T,T0,m,mi,P = params
nu1_func = lambda t: nu1_0 * (nu1/nu1_0)**(t/T)
nu2_func = lambda t: nu2_0 * (nu2/nu2_0)**(t/T)
nu_func = lambda t: [nu1_func(t), nu2_func(t)]
sts = moments.LinearSystem_1D.steady_state_1D(ns[0] + ns[1])
fs = moments.Spectrum(sts)
fs = moments.Manips.split_1D_to_2D(fs, ns[0], ns[1])
fs.integrate([nua, nub], T0, m = np.array([[0, 0], [0, 0]]))
fs.integrate(nu_func, T, dt_fac=0.01, m=np.array([[0, m], [m, 0]]))
stsi = moments.LinearSystem_1D.steady_state_1D(ns[0] + ns[1])
fsi = moments.Spectrum(stsi)
fsi = moments.Manips.split_1D_to_2D(fsi, ns[0], ns[1])
fsi.integrate([nua, nub], T0, m = np.array([[0, 0], [0, 0]]))
fsi.integrate(nu_func, T, dt_fac=0.01, m=np.array([[0, mi], [mi, 0]]))
fs2=P*fsi+(1-P)*fs
return fs2
func=IM2iSC
upper_bound = [100,100,100,100,100, 100, 10,10, 200,200,0.9999]
lower_bound = [1e-3,1e-3,1e-3,1e-3,1e-3,1e-3, 1e-3,1e-3,1e-5,1e-5,1e-4]
params = moments.Misc.perturb_params(params, fold=2, upper_bound=upper_bound,
lower_bound=lower_bound)
# fitting (poptg = optimal parameters):
poptg = moments.Inference.optimize_log(params, data, func,
lower_bound=lower_bound,
upper_bound=upper_bound,
verbose=False, maxiter=30)
# extracting model predictions, likelihood and theta
model = func(poptg, ns)
ll_model = moments.Inference.ll_multinom(model, data)
theta = moments.Inference.optimal_sfs_scaling(model, data)
# random index for this replicate
ind=str(random.randint(0,999999))
# plotting demographic model
plot_mod = moments.ModelPlot.generate_model(func, poptg, ns)
moments.ModelPlot.plot_model(plot_mod, save_file="IMisc2sm_"+ind+"_"+sys.argv[1]+".png",pop_labels=pop_ids, nref=theta/(4*mu), draw_scale=False, gen_time=gtime, gen_time_units="KY", reverse_timeline=True)
# bootstrapping for SDs of params and theta
all_boot=moments.Misc.bootstrap(dd,pop_ids,projections)
uncert=moments.Godambe.GIM_uncert(func,all_boot,poptg,data)
# printing parameters and their SDs
print "RESULT","IMisc2sm",ind,len(params),ll_model,sys.argv[1],sys.argv[2],sys.argv[3],poptg,theta,uncert
# plotting quad-panel figure witt AFS, model, residuals:
moments.Plotting.plot_2d_comp_multinom(model, data, vmin=1, resid_range=3,
pop_ids =pop_ids)
plt.savefig("IMisc2sm_"+ind+"_"+sys.argv[1]+"_"+sys.argv[2]+"_"+sys.argv[3]+"_"+sys.argv[4]+"_"+sys.argv[5]+'.pdf')
| [
"[email protected]"
]
| |
494b730cf575803022fd0cfcf7c6a4255f7c81f4 | 6b2a8dd202fdce77c971c412717e305e1caaac51 | /solutions_5706278382862336_1/Python/urupica/a.py | bacd3f027517e16fe04dada1b43ada1bbcbbb703 | []
| no_license | alexandraback/datacollection | 0bc67a9ace00abbc843f4912562f3a064992e0e9 | 076a7bc7693f3abf07bfdbdac838cb4ef65ccfcf | refs/heads/master | 2021-01-24T18:27:24.417992 | 2017-05-23T09:23:38 | 2017-05-23T09:23:38 | 84,313,442 | 2 | 4 | null | null | null | null | UTF-8 | Python | false | false | 2,570 | py | #!/usr/bin/python
import sys, datetime
# https://code.google.com/p/gmpy/
from gmpy import sqrt
from fractions import Fraction as frac
def solve(pq):
start = (frac(pq[0], pq[1]), 0)
n = 0
y = start[0].denominator
while y % 2 == 0:
n += 1
y /= 2
if y != 1:
return 'impossible'
q = [start]
v = set([start])
while q:
t,g = q.pop()
if g >= 40:
return 'impossible'
anc = set()
x = t.numerator
y = t.denominator
n = 0
while y % 2 == 0:
n += 1
y /= 2
i = 0
n2 = n/2
xx = 2*x
while i <= n2:
b = 2**i
d = 2**(n-i)
a = 0
ad = a*d
while ad <= xx:
if (xx-ad) % b == 0:
c = (xx-ad)/b
t1 = frac(a,b)
t2 = frac(c,d)
if t1 == 1 or t2 == 1:
return g+1
anc.add((t1, g+1))
anc.add((t2, g+1))
a += 1
ad = a*d
i += 1
for u in anc:
if u not in v:
v.add(u)
q.insert(0,u)
def main():
if len(sys.argv) < 2:
print 'Please provide input file'
print 'Usage: %s inputfile [outputfile]' % sys.argv[0]
return
timestart = datetime.datetime.now()
try:
inputFile = open(sys.argv[1])
except:
print 'Failed to read input file %s' % sys.argv[1]
return
try:
outputFile = open(sys.argv[2], 'w') if len(sys.argv) >= 3 else None
except:
print 'Failed to create output file %s' % sys.argv[2]
return
testCases = int(inputFile.readline().strip())
print '-----------------'
print 'Test cases: %d ' % testCases
print 'No output file' if len(sys.argv) < 3 else 'Writing to %s' % sys.argv[2]
print '-----------------'
for testCaseNumber in range(1, testCases+1):
pq = map(int, inputFile.readline().strip().split('/'))
string = 'Case #%d: %s' % (testCaseNumber, solve(pq))
print string
if outputFile:
outputFile.write(string + '\n')
print '-----------------'
print 'Written to %s' % sys.argv[2] if outputFile else 'No output file'
print 'Elapsed time: %s' % (datetime.datetime.now() - timestart)
print '-----------------'
inputFile.close()
if outputFile:
outputFile.close()
if __name__=='__main__':
main()
| [
"[email protected]"
]
| |
49f198411001677c7891235ff88a61cf81bd18d4 | d921253b98a922975709693c411218746af2f017 | /bgx/families/smart_stuff/setup.py | ca54e6e51fa7a6642a979686b71f4d14c11621d2 | [
"Zlib",
"MIT",
"Apache-2.0"
]
| permissive | bestonly125/DGT-Kawartha | 223f88e224c1464fa22a4512e4567ac7ce1bc78f | edfbc18f2c70e813805ec23c28fbc35bf7866ffc | refs/heads/master | 2022-11-22T13:18:21.204906 | 2020-07-24T09:03:57 | 2020-07-24T09:03:57 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,699 | py | # Copyright 2020 NTRLab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ------------------------------------------------------------------------------
from __future__ import print_function
import os
import subprocess
from setuptools import setup, find_packages
data_files = []
if os.path.exists("/etc/default"):
data_files.append(
('/etc/default', ['packaging/systemd/sawtooth-bgt-tp-python']))
if os.path.exists("/lib/systemd/system"):
data_files.append(
('/lib/systemd/system',
['packaging/systemd/sawtooth-bgt-tp-python.service']))
setup(
name='dgt-stuff',
version=subprocess.check_output(
['../../../bin/get_version']).decode('utf-8').strip(),
description='DGT stuff Python ',
author='NTRLab',
url='https://github.com/hyperledger/sawtooth-core',
packages=find_packages(),
install_requires=[
"cbor",
"colorlog",
"sawtooth-sdk",
"sawtooth-signing",
"secp256k1"
],
data_files=data_files,
entry_points={
'console_scripts': [
'stuff = dgt_stuff.client_cli.bgt_cli:main_wrapper',
'stuff-tp = dgt_stuff.processor.main:main'
]
})
| [
"[email protected]"
]
| |
357363e824323766d3fcb3eb32e7192e1d62999e | a65de6ebf74a0ea70fdb4d85a28cc11e641554e0 | /py-scripts/det_keychain_type2.py | 929368c14258f7c70ec8f74e612c98827e8e58aa | [
"MIT"
]
| permissive | VB6Hobbyst7/bbt | e4f63cdd0bb9ef9550edc40b0ee1def7148fe91a | 7b35e4fe9041af9b73bcfb192aeeeb873d730c72 | refs/heads/master | 2023-01-22T03:44:04.506182 | 2020-11-23T23:12:56 | 2020-11-23T23:12:56 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,640 | py | #!/usr/bin/env python3
# Copyright (C) 2017-2020 The btclib developers
#
# This file is part of btclib. It is subject to the license terms in the
# LICENSE file found in the top-level directory of this distribution.
#
# No part of btclib including this file, may be copied, modified, propagated,
# or distributed except according to the terms contained in the LICENSE file.
""" Deterministic Key Sequence (Type-1)"""
import secrets
from hashlib import sha256 as hf
from btclib.curve import mult
from btclib.curve import secp256k1 as ec
from btclib.utils import int_from_bits
# master prvkey in [1, n-1]
mprvkey = 1 + secrets.randbelow(ec.n - 1)
print(f"\nmaster prvkey: {hex(mprvkey).upper()}")
# Master Pubkey:
mpubkey = mult(mprvkey, ec.G)
print(f"Master Pubkey: {hex(mpubkey[0]).upper()}")
print(f" {hex(mpubkey[1]).upper()}")
r = secrets.randbits(ec.nlen)
print(f"\npublic random number: {hex(r).upper()}")
rbytes = r.to_bytes(ec.nsize, "big")
nKeys = 3
for i in range(nKeys):
ibytes = i.to_bytes(ec.nsize, "big")
hd = hf(ibytes + rbytes).digest()
offset = int_from_bits(hd, ec.nlen) % ec.n
q = (mprvkey + offset) % ec.n
Q = mult(q, ec.G, ec)
print(f"\nprvkey #{i}: {hex(q).upper()}")
print(f"Pubkey #{i}: {hex(Q[0]).upper()}")
print(f" {hex(Q[1]).upper()}")
# Pubkeys could also be calculated without using prvkeys
for i in range(nKeys):
ibytes = i.to_bytes(ec.nsize, "big")
hd = hf(ibytes + rbytes).digest()
offset = int_from_bits(hd, ec.nlen) % ec.n
Q = ec.add(mpubkey, mult(offset, ec.G, ec))
assert Q == mult((mprvkey + offset) % ec.n, ec.G, ec)
| [
"[email protected]"
]
| |
6087bac61affd929686678fae8bc6328f0d68c6c | 93c3abdedc8d5571dc2a5241b20a5806d1da59d8 | /dcgan/.ipynb_checkpoints/main-checkpoint.py | 000c110d3963eb3527631a1963b62db4a083e3a3 | []
| no_license | Trccc/5242Project-GAN | bf33b33501b332d9716e2884d821016f5bbfd5aa | 3cd3a7734ae42fb0d3b5efb5bd6839621a9e1b0c | refs/heads/master | 2023-03-18T03:33:23.304285 | 2020-01-14T20:01:18 | 2020-01-14T20:01:18 | 222,341,507 | 4 | 1 | null | null | null | null | UTF-8 | Python | false | false | 6,983 | py | import matplotlib
matplotlib.use('Agg')
import tensorflow as tf
import glob
import imageio
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import os
import PIL
from tensorflow.keras import layers
import time
from IPython import display
import IPython
import tensorflow_datasets as tfds
from pytz import timezone
from datetime import datetime
from config import cfg
from model import *
from utils import *
from loss import *
from Inception_score import *
@tf.function
def train_step(images, showloss = False):
noise = tf.random.normal([cfg.BATCH_SIZE, cfg.NOISE_DIM])
g_loss = generator_loss
d_loss = discriminator_loss
with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
generated_images = generator(noise, training=True)
real_output = discriminator(images, training=True)
fake_output = discriminator(generated_images, training=True)
gen_loss = g_loss(fake_output)
disc_loss = d_loss(real_output, fake_output)
#if showloss:
#print('gen_loss = %.4f|disc_loss = %.4f'%(gen_loss.numpy(),disc_loss.numpy()))
gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)
gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)
generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))
discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))
return gen_loss, disc_loss
def train(dataset, epochs, savedir):
IS_mean = []
IS_std = []
G_loss = []
D_loss = []
for epoch in range(epochs):
start = time.time()
i = 0
g_loss = 0
d_loss = 0
for image_batch in dataset:
i += 1
if (i+1) % cfg.SHOW_LOSS ==0:
g_tensor, d_tensor = train_step(image_batch, showloss = True)
else:
g_tensor, d_tensor = train_step(image_batch)
g_loss += float(g_tensor.numpy())
d_loss += float(d_tensor.numpy())
G_loss.append(g_loss / i)
D_loss.append(d_loss / i)
# Produce images for the GIF
display.clear_output(wait=True)
generate_and_save_images(generator,
epoch + 1,
seed,savedir)
# Save the model every 15 epochs
if (epoch + 1) % 5 == 0:
mean, std = IS(generator, 1000, 100)
IS_mean.append(mean)
IS_std.append(std)
checkpoint.save(file_prefix = checkpoint_prefix)
with train_summary_writer.as_default():
tf.summary.scalar('loss', G_loss[-1], step=epoch)
with test_summary_writer.as_default():
tf.summary.scalar('loss', D_loss[-1], step=epoch)
print ('Time for epoch {} is {} sec'.format(epoch + 1, time.time()-start))
# clear outputs
display.clear_output(wait=True)
# save IS score and Loss plot
IS_mean = np.array(IS_mean)
IS_std = np.array(IS_std)
IS_df = pd.DataFrame({'mean':IS_mean, 'mean+std':IS_mean+IS_std, 'mean-std':IS_mean-IS_std, 'std':IS_std})
IS_df.index = [5 * (x + 1) for x in range(IS_df.shape[0])]
Loss_df = pd.DataFrame({'Generator':G_loss, 'Discriminator':D_loss})
df_path = os.path.join(savedir, 'IS_score.csv')
IS_df.to_csv(path_or_buf=df_path, index=False)
df_path2 = os.path.join(savedir, 'Loss.csv')
Loss_df.to_csv(path_or_buf=df_path2, index=False)
print('Inception score and loss save complete')
path = os.path.join(savedir, 'IS_score_trend.png')
fig = plt.figure(figsize=(6, 6))
plt.plot(IS_df[['mean','mean+std','mean-std']])
plt.title('Inception Score')
plt.legend(IS_df[['mean','mean+std','mean-std']].columns, loc='best')
plt.savefig(path)
#plt.close('all')
path2 = os.path.join(savedir, 'Loss_trend.png')
fig2 = plt.figure(figsize=(6, 6))
plt.plot(Loss_df)
plt.title('Validation Losses')
plt.legend(Loss_df.columns, loc='best')
plt.savefig(path2)
# Generate after the final epoch
generate_and_save_images(generator,
epochs,
seed,savedir)
if __name__ == '__main__':
if cfg.DATA.lower() == 'mnist':
train_data = get_train_data('mnist')
generator = make_generator_model_mnist()
discriminator = make_discriminator_model_mnist()
elif cfg.DATA.lower() == 'svhn':
train_data = get_train_data('svhn')
generator = make_generator_model_svhn()
discriminator = make_discriminator_model_svhn()
noise = tf.random.normal([1, 100])
generator_optimizer = tf.keras.optimizers.Adam(learning_rate=1e-4, beta_1=0.5)
discriminator_optimizer = tf.keras.optimizers.Adam(learning_rate=1e-4, beta_1=0.5)
EPOCHS = cfg.EPOCHS
noise_dim = cfg.NOISE_DIM
num_examples_to_generate = cfg.NUM_EXAMPLES_TO_GENERATE
seed = tf.random.normal([num_examples_to_generate, noise_dim])
now = datetime.now(timezone('US/Eastern'))
subfile = now.strftime("%m_%d_%H_%M")
filedir = os.path.join(cfg.IMAGE_PATH,subfile)
checkpoint_dir = filedir
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")
checkpoint = tf.train.Checkpoint(generator_optimizer=generator_optimizer,
discriminator_optimizer=discriminator_optimizer,
generator=generator,
discriminator=discriminator)
if not os.path.exists(cfg.IMAGE_PATH):
os.mkdir(cfg.IMAGE_PATH)
if not os.path.isfile(filedir):
os.mkdir(filedir)
savedir = filedir
current_time = datetime.now().strftime("%Y%m%d-%H%M%S")
gen_log_dir = 'logs/gradient_tape/' + current_time + '/gen'
disc_log_dir = 'logs/gradient_tape/' + current_time + '/disc'
train_summary_writer = tf.summary.create_file_writer(gen_log_dir)
test_summary_writer = tf.summary.create_file_writer(disc_log_dir)
train(train_data, EPOCHS,savedir)
if cfg.GIF:
anim_file = subfile+'gan.gif'
with imageio.get_writer(anim_file, mode='I') as writer:
filenames = glob.glob(filedir+'/image*.png')
filenames = sorted(filenames)
last = -1
for i,filename in enumerate(filenames):
frame = 2*(i**0.5)
if round(frame) > round(last):
last = frame
else:
continue
image = imageio.imread(filename)
writer.append_data(image)
image = imageio.imread(filename)
writer.append_data(image)
print('finish') | [
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]
| |
17d947a28fb71d6e50670c6f6e7d2ed854795429 | e7c70a02e61f6d4a97c5933f3550bca22afa6acb | /ros_ws/build/original learning_ros/learning_ros/Part_5/arm7dof/arm7dof_traj_as/cmake/arm7dof_traj_as-genmsg-context.py | a4b933642a0c8e90b62feb7114bcb33e3fd069d0 | []
| no_license | amitf82/Final_Proj_Mobile_Robotics | 14cfe7b182df1294a873283c91688c8ca9526fee | 435a6c1562df030fc462fe1b0a84f968a27a2b85 | refs/heads/master | 2021-01-20T03:22:51.387095 | 2017-04-30T08:25:33 | 2017-04-30T08:25:33 | 89,532,221 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,399 | py | # generated from genmsg/cmake/pkg-genmsg.context.in
messages_str = "/home/user/ros_ws/devel/share/arm7dof_traj_as/msg/trajAction.msg;/home/user/ros_ws/devel/share/arm7dof_traj_as/msg/trajActionGoal.msg;/home/user/ros_ws/devel/share/arm7dof_traj_as/msg/trajActionResult.msg;/home/user/ros_ws/devel/share/arm7dof_traj_as/msg/trajActionFeedback.msg;/home/user/ros_ws/devel/share/arm7dof_traj_as/msg/trajGoal.msg;/home/user/ros_ws/devel/share/arm7dof_traj_as/msg/trajResult.msg;/home/user/ros_ws/devel/share/arm7dof_traj_as/msg/trajFeedback.msg"
services_str = ""
pkg_name = "arm7dof_traj_as"
dependencies_str = "roscpp;sensor_msgs;trajectory_msgs;actionlib_msgs;actionlib;std_srvs"
langs = "gencpp;genlisp;genpy"
dep_include_paths_str = "arm7dof_traj_as;/home/user/ros_ws/devel/share/arm7dof_traj_as/msg;roscpp;/opt/ros/indigo/share/roscpp/cmake/../msg;sensor_msgs;/opt/ros/indigo/share/sensor_msgs/cmake/../msg;trajectory_msgs;/opt/ros/indigo/share/trajectory_msgs/cmake/../msg;actionlib_msgs;/opt/ros/indigo/share/actionlib_msgs/cmake/../msg;actionlib;/opt/ros/indigo/share/actionlib/cmake/../msg;geometry_msgs;/opt/ros/indigo/share/geometry_msgs/cmake/../msg;std_msgs;/opt/ros/indigo/share/std_msgs/cmake/../msg"
PYTHON_EXECUTABLE = "/usr/bin/python"
package_has_static_sources = '' == 'TRUE'
genmsg_check_deps_script = "/opt/ros/indigo/share/genmsg/cmake/../../../lib/genmsg/genmsg_check_deps.py"
| [
"[email protected]"
]
| |
0d6b1608e13599f03b3bc14f985dd604ca4101c4 | d7016f69993570a1c55974582cda899ff70907ec | /sdk/sql/azure-mgmt-sql/generated_samples/create_database_default_mode.py | 9fca87c06715de4be793721ff9f2021becd6a493 | [
"LicenseRef-scancode-generic-cla",
"MIT",
"LGPL-2.1-or-later"
]
| permissive | kurtzeborn/azure-sdk-for-python | 51ca636ad26ca51bc0c9e6865332781787e6f882 | b23e71b289c71f179b9cf9b8c75b1922833a542a | refs/heads/main | 2023-03-21T14:19:50.299852 | 2023-02-15T13:30:47 | 2023-02-15T13:30:47 | 157,927,277 | 0 | 0 | MIT | 2022-07-19T08:05:23 | 2018-11-16T22:15:30 | Python | UTF-8 | Python | false | false | 1,935 | 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 azure.identity import DefaultAzureCredential
from azure.mgmt.sql import SqlManagementClient
"""
# PREREQUISITES
pip install azure-identity
pip install azure-mgmt-sql
# USAGE
python create_database_default_mode.py
Before run the sample, please set the values of the client ID, tenant ID and client secret
of the AAD application as environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID,
AZURE_CLIENT_SECRET. For more info about how to get the value, please see:
https://docs.microsoft.com/azure/active-directory/develop/howto-create-service-principal-portal
"""
def main():
client = SqlManagementClient(
credential=DefaultAzureCredential(),
subscription_id="00000000-1111-2222-3333-444444444444",
)
response = client.databases.begin_create_or_update(
resource_group_name="Default-SQL-SouthEastAsia",
server_name="testsvr",
database_name="testdb",
parameters={
"location": "southeastasia",
"properties": {
"collation": "SQL_Latin1_General_CP1_CI_AS",
"createMode": "Default",
"maxSizeBytes": 1073741824,
},
"sku": {"name": "S0", "tier": "Standard"},
},
).result()
print(response)
# x-ms-original-file: specification/sql/resource-manager/Microsoft.Sql/preview/2022-05-01-preview/examples/CreateDatabaseDefaultMode.json
if __name__ == "__main__":
main()
| [
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]
| |
907ff0c5531274923b2904bb253a1409a2028b8b | b87402fa3729a2bee6e8c527dad8f2c09e84c115 | /geemap/__init__.py | 02d54e0f59c9c6c51b062296d246a2b813816863 | [
"MIT"
]
| permissive | tchigher/geemap | 26e3214a7f4e604faf892276d9aad400faffcf67 | 66b2618f31d673230f6ec739fc88da33c50af5a3 | refs/heads/master | 2022-04-13T16:57:47.873576 | 2020-04-08T03:02:18 | 2020-04-08T03:02:18 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 177 | py | """Top-level package for geemap."""
__author__ = """Qiusheng Wu"""
__email__ = '[email protected]'
__version__ = '0.6.1'
from .geemap import *
from .basemaps import ee_basemaps | [
"[email protected]"
]
| |
5d4ffcdcd29c97edfea7392308fd529f0a15b9aa | 4ae178e2f872acba3acdcb06cb145b82e48908f8 | /trial_test_ws/devel/lib/python2.7/dist-packages/moveit_msgs/srv/_GetRobotStateFromWarehouse.py | 61fe624eac24509110f6559ad55373ac79e83dd6 | []
| no_license | ZhikaiZhang1/ros-lbr-repo | 51279a0c1e00f1e1d5f0f3be2e3feb2dc04600df | 8fce59c6145481a0ec58d345cb3caa641c59f78e | refs/heads/master | 2023-06-04T15:55:18.769023 | 2021-06-22T12:34:00 | 2021-06-22T12:34:00 | 380,094,172 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 127 | py | /home/logan/trial_test_ws/devel/.private/moveit_msgs/lib/python2.7/dist-packages/moveit_msgs/srv/_GetRobotStateFromWarehouse.py | [
"[email protected]"
]
| |
b86d96ebe13f417ecffb1d27aed00b6a10b9031d | 6c58da2c54a3d35273e7984313d181f1da9981fc | /DB_Connection/venv/bin/easy_install-2.7 | 785646a1d64db62a32e5e767203f0d9d853dd016 | [
"MIT-0"
]
| permissive | py1-10-2017/rgero215_PY1-10-2017 | e582cb12cc63f84b1c0c14d09a922cb6cb228016 | f455b335ec9c8c850571f3a75dcd95759b4cfdad | refs/heads/master | 2021-09-04T03:23:48.062326 | 2018-01-14T21:07:26 | 2018-01-14T21:07:26 | 105,612,652 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 469 | 7 | #!/Users/RGero13/Desktop/rgero215_PY1-10-2017/DB_Connection/venv/bin/python
# EASY-INSTALL-ENTRY-SCRIPT: 'setuptools==36.6.0','console_scripts','easy_install-2.7'
__requires__ = 'setuptools==36.6.0'
import re
import sys
from pkg_resources import load_entry_point
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
sys.exit(
load_entry_point('setuptools==36.6.0', 'console_scripts', 'easy_install-2.7')()
)
| [
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]
| |
c6707e047dcc8912e3cf72853d8928d25a06a1ee | 8525a60b10c133706fe8303d10b1d272387e62ab | /frites/conn/conn_utils.py | 89375434dc316ba7eb695326c55e67c1840c7405 | [
"BSD-3-Clause"
]
| permissive | brainets/frites | f620b3450aa544484aa941c4cafc4f21020b2a25 | 420421382c6e9c9b411ee458317f5aba7472c4a9 | refs/heads/master | 2023-08-29T06:10:46.775963 | 2023-07-24T08:20:55 | 2023-07-24T08:20:55 | 213,869,364 | 66 | 22 | BSD-3-Clause | 2023-09-05T08:41:55 | 2019-10-09T08:56:55 | Python | UTF-8 | Python | false | false | 28,339 | py | """Utility functions for connectivity."""
import numpy as np
import xarray as xr
import pandas as pd
from frites.utils import nonsorted_unique
from frites.io import set_log_level, logger
from frites.core import ent_nd_g, mi_nd_gg, mi_model_nd_gd
###############################################################################
###############################################################################
# CONN PAIRS
###############################################################################
###############################################################################
def conn_get_pairs(roi, directed=False, nb_min_suj=-np.inf, verbose=None):
"""Get possible connectivity pairs for multiple subjects.
This function returns a DataFrame that contains all of the necessary
informations for managing pairs of brain regions across many subjects.
Parameters
----------
roi : list
List where each item in this list is an array descriving the brain
region names of a single subject.
directed : bool | False
Specify whether the the returned pairs should be for directed (True)
or undirected (default : False) connectivity.
nb_min_suj : int | -np.inf
Specify whether the pairs should be represented by a minimum number of
subjects.
Returns
-------
df_conn : pd.DataFrame
A Pandas DataFrame that describes the connectivity informations at the
group level. The table contains the following entries :
* 'sources' / 'targets' : respectively, the source and target names
* 'subjects' : list of subjects per pair of brain regions
* '#subjects' : number of subjects per pair of brain regions
* 'names' : name of each pair. If undirected, the names are going
to be like 'roi_0-roi_1' or 'roi_0->roi_1' if directed
* 'keep' : booleans indicating whether the number of subjects per
pair of brain regions is over nb_min_suj
df_suj : pd.DataFrame
A Pandas DataFrame that describes the connectivity information per
subject. The table contains the following entries :
* 'subjects' : subject number
* 'keep_roi' / 'drop_roi' : the brain regions respectively to keep
and to remove to fit the input parameters nb_min_suj
* 'keep_suj' : boolean describing if the subject should be dropped
or conserved
* 'conn' : the 2D boolean connectivity array per subject
"""
set_log_level(verbose)
assert isinstance(roi, list)
n_subjects = len(roi)
roi = [np.asarray(k) for k in roi]
# =========================== Conn info per pair ==========================
s_ss, t_ss, ss = [], [], []
for k in range(n_subjects):
# get the unique list of unsorted list of brain regions
u_roi = nonsorted_unique(roi[k], assert_unique=True)
n_u_roi = len(u_roi)
# get all possible pairs
if directed:
pairs = np.where(~np.eye(n_u_roi, dtype=bool))
else:
pairs = np.triu_indices(n_u_roi, k=1)
s_names, t_names = u_roi[pairs[0]], u_roi[pairs[1]]
# if not directed, merge '0-1' and '1-0'
if not directed:
st_names = np.c_[s_names, t_names]
s_names, t_names = np.unique(np.sort(st_names, axis=1), axis=0).T
# keep single-subject source and target names
s_ss += [s_names]
t_ss += [t_names]
ss += [k] * len(s_names)
# fill info in a dataframe
df_ss = pd.DataFrame({
'subjects': ss,
'sources': np.concatenate(s_ss),
'targets': np.concatenate(t_ss)
})
# get the number of subjects per pair
pattern = '->' if directed else '-'
gp = df_ss.groupby(['sources', 'targets'])
df_conn = gp.subjects.aggregate([list]).reset_index()
df_conn = df_conn.rename(columns={'list': 'subjects'})
df_conn['#subjects'] = [len(k) for k in df_conn['subjects']]
df_conn['names'] = [f"{k}{pattern}{i}" for k, i in zip(
df_conn['sources'], df_conn['targets'])]
df_conn['keep'] = df_conn['#subjects'] >= nb_min_suj
# print the info
n_remain = np.sum(list(df_conn['keep']))
n_drop = np.sum(list(~df_conn['keep']))
logger.info(f" {n_remain} remaining pairs of brain regions "
f"(nb_min_suj={nb_min_suj}), {n_drop} dropped")
# ========================= Conn info per subject =========================
# build 2d connectivity array per subject
conn = {}
for n_s in range(n_subjects):
n_roi_s = len(roi[n_s])
_conn = xr.DataArray(
~np.eye(n_roi_s, dtype=bool), dims=('sources', 'targets'),
coords=(roi[n_s], roi[n_s]))
conn[n_s] = _conn
# fill the information
for k in range(len(df_conn)):
_df = df_conn.iloc[k, :]
for s in _df['subjects']:
_s, _t, _k = _df['sources'], _df['targets'], bool(_df['keep'])
conn[s].loc[dict(sources=_s, targets=_t)] = _k
if not directed:
conn[s].loc[dict(sources=_t, targets=_s)] = _k
# get the brain regions to keep / drop per subject
suj, roi_keep, roi_drop, conn_tot = [], [], [], []
for s in range(n_subjects):
_keep = roi[s][np.union1d(*np.where(conn[s]))]
_drop = np.setdiff1d(roi[s], _keep)
suj += [s]
roi_keep += [_keep.tolist()]
roi_drop += [_drop.tolist()]
conn_tot += [conn[s].data]
# create the final dataframe
df_suj = pd.DataFrame({'subjects': suj, 'keep_roi': roi_keep,
'drop_roi': roi_drop}) # , 'conn': conn_tot
df_suj['keep_suj'] = [len(k) > 1 for k in df_suj['keep_roi']]
return df_conn, df_suj
def conn_links(roi, directed=False, net=False, roi_relation='both', sep='auto',
nb_min_links=None, pairs=None, sort=True, triu_k=1,
hemisphere=None, hemi_links='both', categories=None,
source_seed=None, target_seed=None, verbose=None):
"""Construct pairwise links for functional connectivity.
This function can be used for defining the pairwise links for computing
either undirected or directed FC on M/EEG or intracranial EEG.
Parameters
----------
roi : array_like
List of roi (or contacts) names.
directed : bool | False
Specify whether the links should be for undirected (False) or directed
(True) FC
net : bool | False
Specify whether it is for net directed FC (True) or not (False)
roi_relation : {'both', 'inter', 'intra'}
Specify the roi relation between pairs of brain regions. Use either :
* 'intra' : to select only links within a brain region
(e.g. V1-V1)
* 'inter' : to select only links across brain region (e.g. V1-V2)
* 'both' : to select links both within and across brain regions
sep : string | 'auto'
If 'auto', '-' are used to linked brain region names for undirected FC
or '->' for directed FC. Alternatively, you can provide a custom
separator (e.g. sep='/')
nb_min_links : int | None
Threshold for defining a minimum number of links between two brain
regions (e.g. iEEG)
pairs : array_like | None
Force to use certain pairs of brain regions. Should be an array of
shape (n_pairs, 2) where the first column refer to sources and the
second to targets
sort : bool | True
For undirected and net directed FC, sort the names of the brain regions
(e.g. 'V1-M1' -> 'M1-V1')
triu_k : int | 1
Diagonal offset when estimating the undirected links to use. By
default, triu_k=1 means that we skip auto-connections
hemisphere : array_like | None
List of hemisphere names
hemi_links : {'both', 'intra', 'inter'}
Specify whether connectivity links should be :
* 'both': intra-hemispheric and inter-hemispheric (default)
* 'intra': intra-hemispheric
* 'inter': inter-hemispheric
In order to work, you should provide the hemisphere name using the
input `hemisphere`
categories : array_like | list | None
Specify categorical information associated to each region to then
get links only across categories.
source_seed : str, list | None
Brain region name(s) to use as source seed. Can either be the name of a
single brain region or a list of brain regions.
target_seed : str, list | None
Brain region name(s) to use as target seed. Can either be the name of a
single brain region or a list of brain regions.
Returns
-------
indices : tuple
Remaining indices for (sources, targets)
roi_st : list
Name of the pairs of brain regions
"""
set_log_level(verbose)
assert isinstance(roi, (np.ndarray, list, tuple))
if not directed:
assert not net, ("Net computations not supported for undirected "
"connectivity")
roi = np.asarray(roi).astype(str)
n_roi = len(roi)
if isinstance(source_seed, str): source_seed = [source_seed] # noqa
if isinstance(target_seed, str): target_seed = [target_seed] # noqa
logger.info(f"Defining links (n_roi={n_roi}; directed={directed}; "
f"net={net}, nb_min_links={nb_min_links})")
# build separator name
if sep == 'auto':
sep = '->' if directed and not net else '-'
else:
assert isinstance(sep, str)
# get (un)directed pairs
if isinstance(pairs, np.ndarray) and (pairs.shape[1] == 2):
x_s, x_t = pairs[:, 0], pairs[:, 1]
else:
if directed and not net:
x_s, x_t = np.where(~np.eye(n_roi, dtype=bool))
elif (not directed) or (directed and net):
x_s, x_t = np.triu_indices(n_roi, k=triu_k)
# manage roi relation
if roi_relation in ['inter', 'intra']:
logger.info(f" Keeping only {roi_relation}-roi links")
roi_s, roi_t = roi[x_s], roi[x_t]
if roi_relation == 'intra':
keep = [s == t for s, t in zip(roi_s, roi_t)]
elif roi_relation == 'inter':
keep = [s != t for s, t in zip(roi_s, roi_t)]
keep = np.asarray(keep)
logger.info(f" ROI relation selection (dropped={(~keep).sum()} "
"links)")
x_s, x_t = x_s[keep], x_t[keep]
# change roi order for undirected and net directed
if sort and (not directed) or (directed and net):
logger.info(" Sorting roi names")
roi_low = np.asarray([np.char.lower(r.astype(str)) for r in roi])
_xs, _xt = x_s.copy(), x_t.copy()
x_s, x_t = [], []
for s, t in zip(_xs, _xt):
_pair = np.array([roi_low[s], roi_low[t]])
if np.all(_pair == np.sort(_pair)):
x_s.append(s)
x_t.append(t)
else:
x_s.append(t)
x_t.append(s)
x_s, x_t = np.asarray(x_s), np.asarray(x_t)
# keep pairs with a minimum number of links inside
if isinstance(nb_min_links, int):
logger.info(" Thresholding number of links")
roi_st = [f"{s}{sep}{t}" for s, t in zip(roi[x_s], roi[x_t])]
df = pd.DataFrame({'pairs': roi_st})
df = df.groupby('pairs').size()
keep = [df.loc[r] >= nb_min_links for r in roi_st]
x_s, x_t = x_s[keep], x_t[keep]
# hemisphere selection
if isinstance(hemisphere, (list, np.ndarray)):
assert hemi_links in ['both', 'intra', 'inter']
hemisphere = np.asarray(hemisphere)
h_s, h_t = hemisphere[x_s], hemisphere[x_t]
if hemi_links in ['intra', 'inter']:
keep = h_s == h_t if hemi_links == 'intra' else h_s != h_t
x_s, x_t = x_s[keep], x_t[keep]
else:
keep = np.array([True] * len(x_s))
logger.info(f" Hemispheric selection (hemi_links={hemi_links}, "
f"dropped={(~keep).sum()} links)")
# categorical selection
if isinstance(categories, (list, np.ndarray, tuple)):
# reshape categories
categories = np.asarray(categories)
if categories.ndim == 1:
categories = categories[:, np.newaxis]
assert categories.shape[0] == n_roi
# categorical selection
keep = []
for s, t in zip(x_s, x_t):
keep.append(np.all(categories[s, :] != categories[t, :]))
keep = np.asarray(keep)
x_s, x_t = x_s[keep], x_t[keep]
logger.info(f" Categorical selection (dropped={(~keep).sum()} "
"links)")
# seed / target selection
if isinstance(source_seed, (list, tuple, np.ndarray)):
keep = _seed_selection(source_seed, roi, x_s, x_t, directed, 'source')
x_s, x_t = x_s[keep], x_t[keep]
if isinstance(target_seed, (list, tuple, np.ndarray)):
keep = _seed_selection(target_seed, roi, x_s, x_t, directed, 'target')
x_s, x_t = x_s[keep], x_t[keep]
# build pairs of brain region names
roi_st = np.asarray([f"{s}{sep}{t}" for s, t in zip(roi[x_s], roi[x_t])])
return (x_s, x_t), roi_st
def _seed_selection(seed, roi, x_s, x_t, directed, origin):
if directed:
if origin == 'source':
keep = [s in seed for s in roi[x_s]]
elif origin == 'target':
keep = [s in seed for s in roi[x_t]]
else:
keep_s = [s in seed for s in roi[x_s]]
keep_t = [t in seed for t in roi[x_t]]
keep = np.c_[keep_s, keep_t].any(1)
return keep
###############################################################################
###############################################################################
# CONN RESHAPING
###############################################################################
###############################################################################
def conn_reshape_undirected(
da, sep='-', order=None, axis='roi', rm_missing=False,
fill_value=np.nan, fill_diagonal=None, to_dataframe=False,
inplace=False, verbose=None):
"""Reshape a raveled undirected array of connectivity.
This function reshapes a DataArray of connectivity values into a symmetric
matrix. For example, a DataArray of shape (n_pairs,) where n_pairs reflects
pairs of roi (e.g 'roi_1-roi_2') is going to be reshaped into a symmetric
DataArray of shape (n_roi, n_roi). Similarly, a DataArray of shape
(n_pairs, n_times) is going to be reshaped into a symmetric DataArray of
shape (n_roi, n_roi, n_times).
Parameters
----------
da : xarray.DataArray
Flatten DataArray of connectivity values to be reshaped
sep : string | '-'
Separator used to separate the pairs of roi names.
order : list | None
List of roi names to reorder the output.
axis : string | 'roi'
Name of the spatial dimension to use for reshaping
rm_missing : bool | False
When reordering the connectivity array, choose if you prefer to reindex
even if there's missing regions (rm_missing=False) or if missing
regions should be removed (rm_missing=True)
fill_value : float | np.nan
Value to use for filling missing pairs
fill_diagonal : float | None
Value to use in order to fill the diagonal. If None, the diagonal is
untouched
to_dataframe : bool | False
Dataframe conversion. Only possible if the da input does not contains
a time axis.
Returns
-------
da_out : xarray.DataArray
DataArray of shape (n_roi, n_roi, n_times)
See also
--------
conn_dfc
"""
set_log_level(verbose)
assert isinstance(da, xr.DataArray)
if not inplace:
da = da.copy()
assert axis in list(da.dims)
# get sources, targets names and sorted full list
sources, targets, roi_tot = _get_roi_names(da, sep, axis)
# duplicates to make it symmetrical
da = xr.concat((da, da), axis)
s_, t_ = sources + targets, targets + sources
# build the multiindex and unstack it
da, order = _dataarray_unstack(
da, s_, t_, roi_tot, fill_value, order, rm_missing, fill_diagonal, axis
)
# dataframe conversion
if to_dataframe:
da = _dataframe_conversion(da, order, rm_missing)
return da
def conn_reshape_directed(
da, net=False, sep='-', order=None, axis='roi', rm_missing=False,
fill_value=np.nan, fill_diagonal=None, to_dataframe=False,
inplace=False, verbose=None):
"""Reshape a raveled directed array of connectivity.
This function takes a DataArray of shape (n_pairs, n_directions) or
where n_pairs reflects pairs of roi (e.g 'roi_1-roi_2') and n_direction
usually contains bidirected 'x->y' and 'y->x'. At the end, this function
reshape the input array so that rows contains the sources and columns the
targets leading to a non-symmetric DataArray of shape (n_roi, n_roi). A
typical use case for this function would be after computing the covariance
based granger causality.
Parameters
----------
da : xarray.DataArray
Xarray DataArray of shape (n_pairs, n_directions) where
actually the roi dimension contains the pairs (roi_1-roi_2, roi_1-roi_3
etc.). The dimension n_directions should contains the dimensions 'x->y'
and 'y->x'
sep : string | '-'
Separator used to separate the pairs of roi names.
order : list | None
List of roi names to reorder the output.
axis : string | 'roi'
Name of the spatial dimension to use for reshaping
rm_missing : bool | False
When reordering the connectivity array, choose if you prefer to reindex
even if there's missing regions (rm_missing=False) or if missing
regions should be removed (rm_missing=True)
fill_value : float | np.nan
Value to use for filling missing pairs (e.g diagonal)
fill_diagonal : float | None
Value to use in order to fill the diagonal. If None, the diagonal is
untouched
to_dataframe : bool | False
Dataframe conversion. Only possible if the da input does not contains
a time axis.
Returns
-------
da_out : xarray.DataArray
DataArray of shape (n_roi, n_roi)
See also
--------
conn_covgc
"""
set_log_level(verbose)
assert isinstance(da, xr.DataArray)
if not inplace:
da = da.copy()
assert axis in list(da.dims)
# get sources, targets names and sorted full list
sources, targets, roi_tot = _get_roi_names(da, sep, axis)
# transpose, reindex and reorder (if needed)
if 'direction' in list(da.dims):
da_xy, da_yx = da.sel(direction='x->y'), da.sel(direction='y->x')
if net:
da = xr.concat((da_xy - da_yx, da_xy - da_yx), axis)
else:
da = xr.concat((da_xy, da_yx), axis)
s_, t_ = sources + targets, targets + sources
else:
s_, t_ = sources, targets
da, order = _dataarray_unstack(
da, s_, t_, roi_tot, fill_value, order, rm_missing, fill_diagonal, axis
)
# dataframe conversion
if to_dataframe:
da = _dataframe_conversion(da, order, rm_missing)
return da
def _get_roi_names(da, sep, axis):
"""Get the roi names from a DataArray."""
# start by extrating sources / targets names
sources, targets = [], []
for k in da[axis].data:
sources += [k.split(sep)[0]]
targets += [k.split(sep)[1]]
# merge sources and targets to force square matrix
roi_tot = nonsorted_unique(sources + targets)
return sources, targets, roi_tot
def _dataarray_unstack(
da, sources, targets, roi_tot, fill_value, order, rm_missing,
fill_diagonal, axis):
"""Unstack a 1d to 2d DataArray."""
# replace axis by sources and targets
dim_names = list(da.dims)
cut_at = dim_names.index(axis)
dim_names = dim_names[:cut_at] + ['sources', 'targets'] + dim_names[
cut_at + 1:]
# build the multi-index
da[axis] = pd.MultiIndex.from_arrays(
[sources, targets], names=['sources', 'targets'])
# test for duplicated entries
st_names = pd.Series([f"{s}-{t}" for s, t in zip(sources, targets)])
duplicates = np.array(list(st_names.duplicated(keep='first')))
if duplicates.any():
logger.warning(f"Duplicated entries found and removed : "
f"{da[axis].data[duplicates]}")
da = da.sel(roi=~duplicates)
# unstack to be 2D/3D
da = da.unstack(fill_value=fill_value)
# transpose, reindex and reorder (if needed)
da = da.transpose(*tuple(dim_names))
da = da.reindex(dict(sources=roi_tot, targets=roi_tot),
fill_value=fill_value)
# change order
if isinstance(order, (list, np.ndarray)):
if rm_missing:
order = [k for k in order.copy() if k in roi_tot.tolist()]
da = da.reindex(dict(sources=order, targets=order))
# fill diagonal (if needed)
if fill_diagonal is not None:
di = np.diag_indices(da.shape[0])
da.data[di[0], di[1], :] = fill_diagonal
return da, order
def _dataframe_conversion(da, order, rm_missing):
"""Convert a DataArray to a DataFrame and be sure its sorted correctly."""
assert da.data.squeeze().ndim == 2, (
"Dataframe conversion only possible for connectivity arrays when "
"time dimension is missing")
da = da.squeeze().to_dataframe('mi').reset_index()
da = da.pivot(index='sources', columns='targets', values='mi')
if isinstance(order, (list, np.ndarray)):
da = da.reindex(order, axis='index').reindex(order, axis='columns')
# drop empty lines
if rm_missing:
da = da.dropna(axis=0, how='all').dropna(axis=1, how='all')
return da
def conn_ravel_directed(da, sep='-', drop_within=False):
"""Ravel a directed array.
This function reorganize a directed array that contains the coordinates
x->y and y->x to a single coordinate 'x->y'.
Parameters
----------
da : xarray.DataArray
Xarray DataArray that should at least contains the dimensions 'roi'
and 'direction'. The dimension 'direction' should also contains the
coordinates 'x->y' and 'y->x'
sep : string | '-'
Separator used to separate the pairs of roi names.
drop_within : bool | False
Drop within node connections
Returns
-------
da_r : xarray.DataArray
Raveled array of directed connectivity
"""
# inputs testing
assert isinstance(da, xr.DataArray) and isinstance(sep, str)
assert 'direction' in da.dims, "Should be a directed array"
assert 'roi' in da.dims, "Missing roi dimension"
directions = da['direction'].data
assert ('x->y' in directions) and ('y->x' in directions)
# build bidirected roi
roi_xy, roi_yx = [], []
for r in da['roi'].data:
r_s, r_t = r.split(sep)
roi_xy.append(f"{r_s}->{r_t}")
roi_yx.append(f"{r_t}->{r_s}")
# select bidirected arrays
da_xy = da.sel(direction='x->y').drop_vars('direction')
da_yx = da.sel(direction='y->x').drop_vars('direction')
# replace roi names
da_xy['roi'] = roi_xy
da_yx['roi'] = roi_yx
# finally, concat both
da_ravel = xr.concat((da_xy, da_yx), 'roi')
# drop within node connections
if drop_within:
to_keep = []
for r in da_ravel['roi'].data:
r_s, r_t = r.split('->')
to_keep.append(r_s != r_t)
da_ravel = da_ravel.sel(roi=to_keep)
return da_ravel
def conn_net(da, roi='roi', order=None, sep='-', invert=False, verbose=None):
"""Compute the net on directed connectivity.
This function can be used to compute the net difference on directed
connectivity (i.e. A - B = A->B - B->A).
Parameters
----------
da : xr.DataArray
Xarray DataArray containing the connectivity array
roi : 'roi'
Name of the spatial dimension
order : list | None
List of names for specifying the final order
sep : string | '-'
Separator between brain region names (e.g. if 'Insula->Thalamus' then
sep is '->')
invert : bool | False
Specify whether the difference should be computed with A - B or B - A
Returns
-------
out : xr.DataArray
DataArray, with the same dimension names as the input, representing the
net difference of directed connexions.
"""
set_log_level(verbose)
assert roi in da.dims
roi_names = da[roi].data
# get roi order from sources
if order is None:
roi_s, roi_t = [], []
for r in roi_names:
_rs, _rt = r.split(sep)
roi_s.append(_rs)
roi_t.append(_rt)
order = nonsorted_unique(roi_s + roi_t)
order = np.asarray(order)
# build names of the difference
x_s, x_t = np.triu_indices(len(order), k=1)
roi_s, roi_t = order[x_s], order[x_t]
if invert:
_roi_st = roi_s.copy()
roi_s = roi_t
roi_t = _roi_st
# build pairs names
roi_st, p_s, p_t, ignored = [], [], [], []
for s, t in zip(roi_s, roi_t):
name_s, name_t = f"{s}{sep}{t}", f"{t}{sep}{s}"
if (name_s in da[roi]) and (name_t in da[roi]):
roi_st.append(f"{s}-{t}")
p_s.append(name_s)
p_t.append(name_t)
else:
ignored.append(f"{s}-{t}")
# ignored.append(name_s)
if len(ignored):
logger.warning("The following pairs have been ignored in the "
f"subtraction : {ignored}")
# prepare the output
out = da.isel(**{roi: slice(0, len(roi_st))}).copy()
out[roi] = roi_st
out.data = da.sel(**{roi: p_s}).data - da.sel(**{roi: p_t}).data
# update attributes to track operations
out.attrs['net_source'] = p_s
out.attrs['net_target'] = p_t
out.name = da.name + '_net' if da.name else 'Net conn'
return out
###############################################################################
###############################################################################
# CONN MUTUAL INFORMATION
###############################################################################
###############################################################################
def _conn_mi(x, y, mi_type, minorm=False, **kw_mi):
"""Compute the mutual information for connectivity-related functions.
This function compute the mutual information I(x, y) between a continuous x
and a continuous or discret y variable. In addition, we assume here that
the two last axes are multivariate and trials.
Parameters
----------
x : array_like
Array of shape (n_vars, n_mvaxis, n_trials)
y : array_like
Array of shape (n_trials) or (n_vars, n_mvaxis, n_trials)
mi_type : {'cc', 'cd'}
Mutual information type
minorm : bool | False
Normalize the mutual information
kw_mi : dict
Additional arguments are sent to the MI function
Returns
-------
mi : array_like
Array of mutual information of shape (n_vars,)
"""
assert isinstance(x, np.ndarray) and isinstance(y, np.ndarray)
assert (x.ndim == 3) and (1 <= y.ndim <= 3)
assert mi_type in ['cc', 'cd']
kw_mi['traxis'] = -1
kw_mi['mvaxis'] = -2
kw_mi['shape_checking'] = False
# compute mutual information
if mi_type == 'cc':
# reshape y, only if needed
if y.ndim in (1, 2):
y = np.atleast_2d(y)[np.newaxis, ...]
y = np.tile(y, (x.shape[0], 1, 1))
_mi = mi_nd_gg(x, y, **kw_mi)
elif mi_type == 'cd':
_mi = mi_model_nd_gd(x, y, **kw_mi)
# normalize the mi
if minorm:
kw_ent = dict(mvaxis=-2, traxis=-1, biascorrect=kw_mi["biascorrect"],
shape_checking=False)
_ent_x = ent_nd_g(x, **kw_ent)
_ent_y = ent_nd_g(np.atleast_2d(y).astype(float), **kw_ent)
_ent_xy = np.minimum(_ent_x, _ent_y)
_mi /= _ent_xy
return _mi
| [
"[email protected]"
]
| |
4b00204bf1744504a90b22619170f254e3f4633d | f0d713996eb095bcdc701f3fab0a8110b8541cbb | /9ED9zbHHhaPaBz2xi_1.py | 55607ddaf041a60b190183711eb7aeff9bf882d2 | []
| no_license | daniel-reich/turbo-robot | feda6c0523bb83ab8954b6d06302bfec5b16ebdf | a7a25c63097674c0a81675eed7e6b763785f1c41 | refs/heads/main | 2023-03-26T01:55:14.210264 | 2021-03-23T16:08:01 | 2021-03-23T16:08:01 | 350,773,815 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 552 | py | """
Consider a sequence where the first two numbers are `0` and `1` and the next
number of the sequence is the sum of the previous two numbers modulo three.
Create a function that finds the `n`th element of the sequence.
### Examples
normal_sequence(1) ➞ 0
normal_sequence(2) ➞ 1
normal_sequence(3) ➞ 1
# (0+1)%3 = 1
normal_sequence(20) ➞ 2
### Notes
* 1 ≤ N ≤ 10^19
* A hint in comments section.
"""
def normal_sequence(n):
dict = {1:0,2:1,3:1,4:2,5:0,6:2,7:2,0:1}
return dict[n%8]
| [
"[email protected]"
]
| |
4056ce7d0c7d7aa37339d8d6f107d2fc1ba5f3ee | 35e41dab4a3e8eebe17872f5414cf3efb86be54c | /supervised_learning/0x08-deep_cnns/3-projection_block.py | 8cf99029892ce07e6fa99b4b6ec1db75920eeb35 | []
| no_license | yasheymateen/holbertonschool-machine_learning | 52eaf217e924404e8833e75e8b48e80807d784dc | 1329ba88bf9ae50693188e1c85383d9b76c2b1f4 | refs/heads/master | 2022-12-24T13:51:02.743526 | 2020-10-08T06:59:04 | 2020-10-08T06:59:04 | 255,277,037 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 956 | py | #!/usr/bin/env python3
"""identity block maker"""
import tensorflow.keras as K
def projection_block(A_prev, filters, s=2):
"""Return an identity block"""
out = K.layers.Conv2D(filters[0], 1, s,
kernel_initializer='he_normal')(A_prev)
out = K.layers.BatchNormalization()(out)
out = K.layers.Activation('relu')(out)
out = K.layers.Conv2D(filters[1], 3, padding='same',
kernel_initializer='he_normal')(out)
out = K.layers.BatchNormalization()(out)
out = K.layers.Activation('relu')(out)
out = K.layers.Conv2D(filters[2], 1,
kernel_initializer='he_normal')(out)
out = K.layers.BatchNormalization()(out)
out2 = K.layers.Conv2D(filters[2], 1, s,
kernel_initializer='he_normal')(A_prev)
out2 = K.layers.BatchNormalization()(out2)
out = K.layers.add([out, out2])
return K.layers.Activation('relu')(out)
| [
"[email protected]"
]
| |
0b6fa605bb49d021790aa6ee7cb6c301ac938c7d | fe9b1fcf9f44092037a8c14b4c5cbfefa6ad6982 | /shoppingsite.py | 923848ec55b2472541fe4bc960484ee1da3d1163 | []
| no_license | elaineyoung702/shopping-site | 702abd1536b8e805dc719377de23f456773039c2 | e2dbd14569203c6d4be4119ab075f2210c88b61d | refs/heads/master | 2021-06-20T05:48:32.217285 | 2019-07-26T00:51:48 | 2019-07-26T00:51:48 | 198,886,591 | 0 | 0 | null | 2021-03-20T01:19:46 | 2019-07-25T19:00:51 | Python | UTF-8 | Python | false | false | 4,873 | py | """Ubermelon shopping application Flask server.
Provides web interface for browsing melons, seeing detail about a melon, and
put melons in a shopping cart.
Authors: Joel Burton, Christian Fernandez, Meggie Mahnken, Katie Byers.
"""
from flask import Flask, render_template, redirect, flash, session
import jinja2
import melons
app = Flask(__name__)
# A secret key is needed to use Flask sessioning features
app.secret_key = 'this-should-be-something-unguessable'
# Normally, if you refer to an undefined variable in a Jinja template,
# Jinja silently ignores this. This makes debugging difficult, so we'll
# set an attribute of the Jinja environment that says to make this an
# error.
app.jinja_env.undefined = jinja2.StrictUndefined
@app.route("/")
def index():
"""Return homepage."""
return render_template("homepage.html")
@app.route("/melons")
def list_melons():
"""Return page showing all the melons ubermelon has to offer"""
melon_list = melons.get_all()
return render_template("all_melons.html",
melon_list=melon_list)
@app.route("/melon/<melon_id>")
def show_melon(melon_id):
"""Return page showing the details of a given melon.
Show all info about a melon. Also, provide a button to buy that melon.
"""
melon = melons.get_by_id(melon_id)
# print(melon)
return render_template("melon_details.html",
display_melon=melon)
@app.route("/cart")
def show_shopping_cart():
"""Display content of shopping cart."""
# TODO: Display the contents of the shopping cart.
# The logic here will be something like:
#
# - get the cart dictionary from the session
# - create a list to hold melon objects and a variable to hold the total
# cost of the order
# - loop over the cart dictionary, and for each melon id:
# - get the corresponding Melon object
# - compute the total cost for that type of melon
# - add this to the order total
# - add quantity and total cost as attributes on the Melon object
# - add the Melon object to the list created above
# - pass the total order cost and the list of Melon objects to the template
#
# Make sure your function can also handle the case wherein no cart has
# been added to the session
cart = session["cart"]
print(cart)
# melons = []
# total_order_cost = 0
# for melon in cart:
# melons.append[melon]
return render_template("cart.html")
@app.route("/add_to_cart/<melon_id>")
def add_to_cart(melon_id):
"""Add a melon to cart and redirect to shopping cart page.
When a melon is added to the cart, redirect browser to the shopping cart
page and display a confirmation message: 'Melon successfully added to
cart'."""
# TODO: Finish shopping cart functionality
# The logic here should be something like:
#
# - check if a "cart" exists in the session, and create one (an empty
# dictionary keyed to the string "cart") if not
# - check if the desired melon id is the cart, and if not, put it in
# - increment the count for that melon id by 1
# - flash a success message
# - redirect the user to the cart page
if "cart" not in session:
session["cart"] = {}
cart = session["cart"]
cart[melon_id] = cart.get(melon_id, 0) + 1
# print(cart)
flash("Melon successfully added!")
return redirect("/cart")
@app.route("/login", methods=["GET"])
def show_login():
"""Show login form."""
return render_template("login.html")
@app.route("/login", methods=["POST"])
def process_login():
"""Log user into site.
Find the user's login credentials located in the 'request.form'
dictionary, look up the user, and store them in the session.
"""
# TODO: Need to implement this!
# The logic here should be something like:
#
# - get user-provided name and password from request.form
# - use customers.get_by_email() to retrieve corresponding Customer
# object (if any)
# - if a Customer with that email was found, check the provided password
# against the stored one
# - if they match, store the user's email in the session, flash a success
# message and redirect the user to the "/melons" route
# - if they don't, flash a failure message and redirect back to "/login"
# - do the same if a Customer with that email doesn't exist
return "Oops! This needs to be implemented"
@app.route("/checkout")
def checkout():
"""Checkout customer, process payment, and ship melons."""
# For now, we'll just provide a warning. Completing this is beyond the
# scope of this exercise.
flash("Sorry! Checkout will be implemented in a future version.")
return redirect("/melons")
if __name__ == "__main__":
app.run(debug=True)
| [
"[email protected]"
]
| |
7b67cdaa927798bb7bcd21bf3a652fc430e7d02c | a11d83fced34854664fac72e18d48fde6aa967e4 | /0x11-python-network_1/2-post_email.py | 9a6be815e066d3d6642c328bd74da639a6b63b2d | []
| no_license | afarizap/holbertonschool-higher_level_programming | ffe0bf1440726c952f4dd28b908eabc4ccb5225b | ad39e58f9cb20cba4b9e2c14075f216097588f47 | refs/heads/master | 2023-03-30T15:39:35.184484 | 2021-03-22T22:55:24 | 2021-03-22T22:55:24 | 259,437,040 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 458 | py | #!/usr/bin/python3
""" takes in a URL and an email, sends a POST request with the email
as a parameter, and displays the body of the response decoded in utf-8
"""
from urllib import request, parse
from sys import argv
if __name__ == '__main__':
data = parse.urlencode({'email': argv[2]})
data = data.encode('ascii')
req = request.Request(argv[1], data)
with request.urlopen(req) as response:
print(response.read().decode('utf-8'))
| [
"[email protected]"
]
| |
8ab9ca2c71dcf5dd124863ea0ac9caad6daa108a | 01245df7d402532d9729ff933f5ade8c2965760b | /fias/admin.py | 8f9ffe8cfa5ca10b536c6a856b7b8b8a5df040b2 | [
"BSD-2-Clause",
"LicenseRef-scancode-warranty-disclaimer"
]
| permissive | aldev12/django-fias | c653edbef22d7f3fb42e4ebc0a8729fa488e3799 | 53980ada222f1f4f56c268c0489ab9a50a87d9f6 | refs/heads/master | 2020-06-02T18:39:30.285925 | 2020-04-14T02:26:19 | 2020-04-14T02:26:19 | 191,269,395 | 0 | 1 | NOASSERTION | 2019-07-17T00:51:28 | 2019-06-11T01:15:19 | Python | UTF-8 | Python | false | false | 3,549 | py | # coding: utf-8
from __future__ import unicode_literals, absolute_import
from django.contrib import admin
from fias.models import (
AddrObj, House, HouseInt,
LandMark, Room, Stead,
NormDoc,
SocrBase,
NDocType,
ActStat, CenterSt, CurentSt,
EstStat, HSTStat, IntvStat,
OperStat, StrStat,
)
class ViewAdmin(admin.ModelAdmin):
"""
Класс админки только для просмотра данных модели
"""
change_form_template = 'admin/view_form.html'
save_on_top = False
actions = None
def has_add_permission(self, request):
return False
def has_delete_permission(self, request, obj=None):
return False
def save_model(self, request, obj, form, change):
pass
@admin.register(SocrBase)
class SocrBaseAdmin(admin.ModelAdmin):
list_display = ['level', 'scname', 'socrname', 'item_weight']
list_display_links = ('scname', 'socrname')
readonly_fields = ['level', 'scname', 'socrname', 'kod_t_st']
list_editable = ['item_weight']
ordering = ['-item_weight', 'level']
actions = None
def has_add_permission(self, request):
return False
def has_delete_permission(self, request, obj=None):
return False
@admin.register(AddrObj)
class AddrObjAdmin(ViewAdmin):
list_display = ('offname', 'shortname', 'aolevel', 'code', 'aoguid')
@admin.register(House)
class HouseAdmin(ViewAdmin):
list_display = ('aoguid', 'housenum', 'buildnum', 'strucnum', 'houseguid')
raw_id_fields = ('aoguid',)
@admin.register(HouseInt)
class HouseIntAdmin(ViewAdmin):
list_display = ('aoguid', 'intguid', 'houseintid', 'intstart', 'intend')
raw_id_fields = ('aoguid',)
@admin.register(LandMark)
class LandMarkAdmin(ViewAdmin):
list_display = ('aoguid', 'landguid', 'landid')
raw_id_fields = ('aoguid',)
@admin.register(Room)
class RoomAdmin(ViewAdmin):
list_display = ('houseguid', 'flatnumber', 'flattype', 'roomguid', 'roomid')
raw_id_fields = ('houseguid',)
@admin.register(Stead)
class SteadAdmin(ViewAdmin):
list_display = ('steadguid', 'number', 'regioncode')
@admin.register(NDocType)
class NDocTypeAdmin(ViewAdmin):
list_display = ('ndtypeid', 'name')
list_display_links = ('name',)
@admin.register(NormDoc)
class NormDocAdmin(ViewAdmin):
list_display = ('normdocid', 'docdate', 'docnum')
list_display_links = ('normdocid',)
@admin.register(ActStat)
class ActStatAdmin(ViewAdmin):
list_display = ('actstatid', 'name')
list_display_links = ('name',)
@admin.register(CenterSt)
class CenterStatAdmin(ViewAdmin):
list_display = ('centerstid', 'name')
list_display_links = ('name',)
@admin.register(CurentSt)
class CurentStatAdmin(ViewAdmin):
list_display = ('curentstid', 'name')
list_display_links = ('name',)
@admin.register(EstStat)
class EstStatAdmin(ViewAdmin):
list_display = ('eststatid', 'name', 'shortname')
list_display_links = ('name',)
@admin.register(HSTStat)
class HSTStatAdmin(ViewAdmin):
list_display = ('housestid', 'name')
list_display_links = ('name',)
@admin.register(IntvStat)
class IntvStatAdmin(ViewAdmin):
list_display = ('intvstatid', 'name')
list_display_links = ('name',)
@admin.register(OperStat)
class OperStatAdmin(ViewAdmin):
list_display = ('operstatid', 'name')
list_display_links = ('name',)
@admin.register(StrStat)
class StrStatAdmin(ViewAdmin):
list_display = ('strstatid', 'name', 'shortname')
list_display_links = ('name',)
| [
"[email protected]"
]
| |
1699d746364a1a9d683eea8ae286e3c4946fedfd | b9d54c64d4a280703b459b346e42518896e20e0a | /lingvo/core/steps/rnn_steps.py | 6cc1e6b2a8abd40ef87b889efccab03a49964c07 | [
"Apache-2.0"
]
| permissive | zh794390558/lingvo | 55a27a4e241414389f0c7b40f381a672bb164372 | ecdf678179018ca07f4f52d065b9bf3fe2dc7c5a | refs/heads/master | 2020-09-26T18:32:31.631402 | 2019-12-06T04:01:22 | 2019-12-06T04:02:05 | 177,497,272 | 0 | 0 | Apache-2.0 | 2019-03-25T02:05:42 | 2019-03-25T02:05:42 | null | UTF-8 | Python | false | false | 10,097 | py | # Lint as: python2, python3
# Copyright 2019 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.
"""Step APIs for RNN layers."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from lingvo import compat as tf
from lingvo.core import base_layer
from lingvo.core import py_utils
from lingvo.core import rnn_cell
from lingvo.core import step
class RnnStep(step.Step):
"""A step containing an RNNCell."""
@classmethod
def Params(cls):
p = super(RnnStep, cls).Params()
p.Define('cell', rnn_cell.LSTMCellSimple.Params(),
'Params for the RNN cell.')
return p
@base_layer.initializer
def __init__(self, params):
super(RnnStep, self).__init__(params)
p = params
with tf.variable_scope(p.name):
self.CreateChild('cell', p.cell)
def PrepareExternalInputs(self, theta, external_inputs):
"""Does not modify the external_inputs parameter.
This parameter, if provided, is assumed to be a vector that should be
concatenated with the other vectors in step_inputs.inputs.
Args:
theta: unused.
external_inputs: Either a tensor or None.
Returns:
external_inputs, unmodified.
"""
return external_inputs
def ZeroState(self, theta, prepared_inputs, batch_size):
"""Returns the zero_state for the RNN cell.
Args:
theta: Variables used by the RNNCell.
prepared_inputs: unused.
batch_size: An int scalar representing the batch size of per-step inputs.
Returns:
The zero state of the RNNCell.
"""
return self.cell.zero_state(theta.cell, batch_size)
def FProp(self, theta, prepared_inputs, step_inputs, padding, state0):
"""Performs one inference step on the RNN cell.
If external_inputs is not None, it is added as another act input
to the RNNCell.
Args:
theta: Variables used by the RNNCell.
prepared_inputs: If not None, concatenated with step_inputs.input. A
tensor of shape [batch_size, external_input_dim].
step_inputs: A NestedMap containing an 'input' list of [batch_size, dim]
where the sum of dim (including external_inputs) is
p.cell.num_input_nodes.
padding: A 0/1 float tensor of shape [batch_size]; 1.0 means that this
batch element is empty in this step.
state0: A NestedMap of state, either produced by ZeroState or a previous
invocation of FProp.
Returns:
(output, state1), where output is the cell output (GetOutput(state1))
of shape [batch_size, p.cell.num_output_nodes], and state1 is the cell's
recurrent state.
"""
cell_inputs = py_utils.NestedMap(act=step_inputs.inputs)
# An empty NestedMap can act as a None value here.
if prepared_inputs is not None and not isinstance(prepared_inputs,
py_utils.NestedMap):
cell_inputs.act.append(prepared_inputs)
cell_inputs.padding = padding
state1, extra = self.cell.FProp(theta.cell, state0, cell_inputs)
return py_utils.NestedMap(
output=self.cell.GetOutput(state1), extra=extra,
padding=padding), state1
class RnnStackStep(step.Step):
"""A stack of RnnSteps.
Three types of inputs are supported:
step_inputs.input: This is the standard input. It is expected to change
on every step of the sequence, and it is fed only to the first layer.
step_inputs.context: This input changes for each step of the sequence, but
is fed to every layer.
external_inputs: This input is fixed at the beginning of the sequence.
It is fed to every layer.
Residual connections are also supported. When residual_start >= 0, the output
of layer i (i >= residual_start) is added to the output of layer
i - residual_stride.
"""
@classmethod
def Params(cls):
"""Constructs Params for an RnnStackStep."""
p = super(RnnStackStep, cls).Params()
p.Define(
'rnn_cell_tpl', rnn_cell.LSTMCellSimple.Params(),
'RNNCell params template. '
'Can be a single param or '
'a list of rnn_layers params, one for each layer.')
p.Define(
'external_input_dim', 0, 'Size of the external input. '
'The external input is given at the start of the sequence '
'and is given to every layer at every step.')
p.Define(
'step_input_dim', 0, 'Size of the step input. '
'This input is only given to the first layer and is expected to '
'be different for each step.')
p.Define(
'context_input_dim', 0, 'Size of the context input. '
'This input is given to every layer and is expected to be '
'different for each step.')
p.Define(
'rnn_cell_dim', 0, 'Size of the rnn cells. '
'This may be overridden by parameters set in rnn_cell_tpl.')
p.Define(
'rnn_cell_hidden_dim', 0, 'internal size of the rnn cells. When '
'set to > 0 it enables a projection layer at the output of the '
'rnn cell. This may be overridden by parameters set in rnn_cell_tpl.')
p.Define('rnn_layers', 1, 'Number of rnn layers.')
p.Define(
'residual_start', -1,
'Start residual connections from this layer. For this and higher '
'layers, the layer output is the sum of the RNN cell output and '
'input; if the layer also normalizes its output, then the '
'normalization is done over this sum. Set to -1 to disable '
'residual connections.')
p.Define('residual_stride', 1,
'Number of lstm layers to skip per residual connection.')
return p
@base_layer.initializer
def __init__(self, params):
super(RnnStackStep, self).__init__(params)
p = params
sub = []
# Users can either provide a single rnn_cell_tpl or one per layer.
# If only one is provided, we replicate it for each layer.
rnn_cell_tpls = p.rnn_cell_tpl
if not isinstance(rnn_cell_tpls, list):
rnn_cell_tpls = [p.rnn_cell_tpl] * p.rnn_layers
# We may provide up to three tensors as input to the RnnStep:
# the normal input, the context input (from step_inputs.context),
# and the external input (from external_inputs).
arity = 1
if p.context_input_dim:
arity += 1
if p.external_input_dim:
arity += 1
extra_dim = p.context_input_dim + p.external_input_dim
# The first layer's input comes from step_inputs.input. Later layers
# will get their inputs from the previous layer's output.
input_nodes = p.step_input_dim
for i in range(p.rnn_layers):
step_i = RnnStep.Params()
step_i.name = 'rnn_%d' % (i)
step_i.cell = rnn_cell_tpls[i].Copy()
step_i.cell.num_input_nodes = input_nodes + extra_dim
step_i.cell.inputs_arity = arity
# The dimensions of each cell may be specified in the cell template
# but most users will specify them in the stack params.
if step_i.cell.num_output_nodes == 0:
step_i.cell.num_output_nodes = p.rnn_cell_dim
if step_i.cell.num_hidden_nodes == 0:
step_i.cell.num_hidden_nodes = p.rnn_cell_hidden_dim
input_nodes = step_i.cell.num_output_nodes
sub.append(step_i)
stack_params = step.StackStep.Params()
stack_params.name = p.name
stack_params.sub = sub
stack_params.residual_start = p.residual_start
stack_params.residual_stride = p.residual_stride
self.CreateChild('stack', stack_params)
def PrepareExternalInputs(self, theta, external_inputs):
"""Delegates external inputs preparation to sub-layers.
Args:
theta: A `.NestedMap` object containing weight values of this layer and
its children layers.
external_inputs: A `.NestedMap` object. The structure of the internal
fields is defined by the sub-steps.
Returns:
A `.NestedMap` containing a pre-processed version of the external_inputs,
one per sub-step.
"""
return self.stack.PrepareExternalInputs(theta.stack, external_inputs)
def ZeroState(self, theta, prepared_inputs, batch_size):
"""Computes a zero state for each sub-step.
Args:
theta: A `.NestedMap` object containing weight values of this layer and
its children layers.
prepared_inputs: An output from PrepareExternalInputs.
batch_size: The number of items in the batch that FProp will process.
Returns:
A `.NestedMap` containing a state0 object for each sub-step.
"""
return self.stack.ZeroState(theta.stack, prepared_inputs, batch_size)
def FProp(self, theta, prepared_inputs, step_inputs, padding, state0):
"""Performs inference on the stack of sub-steps.
See the documentation for StackStep for the particulars of passing context
information to layers.
Args:
theta: A `.NestedMap` object containing weight values of this layer and
its children layers.
prepared_inputs: An output from PrepareExternalInputs.
step_inputs: A `.NestedMap` containing a list called 'inputs', an
optionally a tensor called 'context'.
padding: A 0/1 float tensor of shape [batch_size]; 1.0 means that this
batch element is empty in this step.
state0: The previous recurrent state.
Returns:
(output, state1):
- output: A `.NestedMap` containing the output of the top-most step.
- state1: The recurrent state to feed to next invocation of this graph.
"""
return self.stack.FProp(theta.stack, prepared_inputs, step_inputs, padding,
state0)
| [
"[email protected]"
]
| |
60d4fed93ac6cca6c2002acc9ab54c8781e1beb1 | a4a540187b93a177331885caee59c3253e91ea7f | /pymcversion/java_version_manifest.py | 340eb9a94fddb23da1d9f3eea8de3417385e38a9 | [
"Unlicense"
]
| permissive | aoirint/pymcversion | ae97123457594ad7741b8f8b69a8deff515eb09c | a3af1d06c9b1ce54530fc9af04b3cb48a0ba1340 | refs/heads/main | 2023-08-25T19:50:52.011370 | 2021-10-22T15:58:48 | 2021-10-22T15:58:48 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 768 | py | import requests
from typing import List
from pydantic import BaseModel
class VersionManifestLatest(BaseModel):
release: str
snapshot: str
class VersionManifestVersion(BaseModel):
id: str
type: str
url: str
time: str
releaseTime: str
class VersionManifest(BaseModel):
latest: VersionManifestLatest
versions: List[VersionManifestVersion]
def get_java_version_manifest() -> VersionManifest:
timeout = 3
useragent = 'aoirint/pymcversion'
headers = {
'User-Agent': useragent,
}
res = requests.get('https://launchermeta.mojang.com/mc/game/version_manifest.json', headers=headers, timeout=timeout)
manifest_dict = res.json()
manifest = VersionManifest.parse_obj(manifest_dict)
return manifest
| [
"[email protected]"
]
| |
a8d7306647943a80193bed286000c317c065f4a4 | 78546bb00bae18dda6f728da61cf91db93715048 | /generate_directory_index_caddystyle.py | f595a133b6734189f84b5dc2719ff0fc99bf3f7d | []
| no_license | scienceacademy/spooky_stories2020 | 32e219f1ab177f6b4e4351c506d3c9fcbf59dc01 | 50a44cb517a318124b00aab2eaa6ef9befba3620 | refs/heads/main | 2023-01-02T16:59:46.885943 | 2020-11-02T03:09:54 | 2020-11-02T03:09:54 | 308,461,571 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 17,614 | py | #!/usr/bin/env python3
# ---
# Copyright 2020 glowinthedark
#
# 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.
# ---
#
# Generate index.html files for
# all subdirectories in a directory tree.
# -handle symlinked files and folders: displayed with custom icons
# By default only the current folder is processed.
# Use -r or --recursive to process nested folders.
import argparse
import datetime
import os
import sys
from pathlib import Path
index_file_name = 'index.html'
def process_dir(top_dir, opts):
glob_patt = opts.filter or '*'
path_top_dir: Path
path_top_dir = Path(top_dir)
index_file = None
index_path = Path(path_top_dir, index_file_name)
if opts.verbose:
print(f'Traversing dir {path_top_dir.absolute()}')
try:
index_file = open(index_path, "w")
except Exception as e:
print('cannot create file %s %s' % (index_path, e))
return
index_file.write("""<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<style>
* { padding: 0; margin: 0; }
body {
font-family: sans-serif;
text-rendering: optimizespeed;
background-color: #ffffff;
}
a {
color: #006ed3;
text-decoration: none;
}
a:hover,
h1 a:hover {
color: #319cff;
}
header,
#summary {
padding-left: 5%;
padding-right: 5%;
}
th:first-child,
td:first-child {
width: 5%;
}
th:last-child,
td:last-child {
width: 5%;
}
header {
padding-top: 25px;
padding-bottom: 15px;
background-color: #f2f2f2;
}
h1 {
font-size: 20px;
font-weight: normal;
white-space: nowrap;
overflow-x: hidden;
text-overflow: ellipsis;
color: #999;
}
h1 a {
color: #000;
margin: 0 4px;
}
h1 a:hover {
text-decoration: underline;
}
h1 a:first-child {
margin: 0;
}
main {
display: block;
}
.meta {
font-size: 12px;
font-family: Verdana, sans-serif;
border-bottom: 1px solid #9C9C9C;
padding-top: 10px;
padding-bottom: 10px;
}
.meta-item {
margin-right: 1em;
}
#filter {
padding: 4px;
border: 1px solid #CCC;
}
table {
width: 100%;
border-collapse: collapse;
}
tr {
border-bottom: 1px dashed #dadada;
}
tbody tr:hover {
background-color: #ffffec;
}
th,
td {
text-align: left;
padding: 10px 0;
}
th {
padding-top: 15px;
padding-bottom: 15px;
font-size: 16px;
white-space: nowrap;
}
th a {
color: black;
}
th svg {
vertical-align: middle;
}
td {
white-space: nowrap;
font-size: 14px;
}
td:nth-child(2) {
width: 80%;
}
td:nth-child(3) {
padding: 0 20px 0 20px;
}
th:nth-child(4),
td:nth-child(4) {
text-align: right;
}
td:nth-child(2) svg {
position: absolute;
}
td .name {
margin-left: 1.75em;
word-break: break-all;
overflow-wrap: break-word;
white-space: pre-wrap;
}
td .goup {
margin-left: 1.75em;
padding: 0;
word-break: break-all;
overflow-wrap: break-word;
white-space: pre-wrap;
}
.icon {
margin-right: 5px;
}
tr.clickable {
cursor: pointer;
}
tr.clickable a {
display: block;
}
@media (max-width: 600px) {
* {
font-size: 1.06rem;
}
.hideable {
display: none;
}
td:nth-child(2) {
width: auto;
}
th:nth-child(3),
td:nth-child(3) {
padding-right: 5%;
text-align: right;
}
h1 {
color: #000;
}
h1 a {
margin: 0;
}
#filter {
max-width: 100px;
}
}
</style>
</head>
<body>
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<defs>
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<!-- File -->
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</svg>
<header>
<h1>"""
f'{path_top_dir.name}'
"""</h1>
</header>
<main>
<div class="listing">
<table aria-describedby="summary">
<thead>
<tr>
<th></th>
<th>Name</th>
<th>Size</th>
<th class="hideable">
Modified
</th>
<th class="hideable"></th>
</tr>
</thead>
<tbody>
<tr class="clickable">
<td></td>
<td><a href=".."><svg width="1.5em" height="1em" version="1.1" viewBox="0 0 24 24"><use xlink:href="#go-up"></use></svg>
<span class="goup">..</span></a></td>
<td>—</td>
<td class="hideable">—</td>
<td class="hideable"></td>
</tr>
""")
# sort dirs first
sorted_entries = sorted(path_top_dir.glob(glob_patt), key= lambda p: (p.is_file(), p.name))
entry: Path
for entry in sorted_entries:
# don't include index.html in the file listing
if entry.name.lower() == index_file_name.lower():
continue
if entry.is_dir() and opts.recursive:
process_dir(entry, opts)
# From Python 3.6, os.access() accepts path-like objects
if (not entry.is_symlink()) and not os.access(str(entry), os.W_OK):
print(f"*** WARNING *** entry {entry.absolute()} is not writable! SKIPPING!")
continue
if opts.verbose:
print(f'{entry.absolute()}')
size_bytes = -1 ## is a folder
size_pretty = '—'
last_modified = '-'
last_modified_human_readable = '-'
last_modified_iso = ''
try:
if entry.is_file():
size_bytes = entry.stat().st_size
size_pretty = pretty_size(size_bytes)
if entry.is_dir() or entry.is_file():
last_modified = datetime.datetime.fromtimestamp(entry.stat().st_mtime).replace(microsecond=0)
last_modified_iso = last_modified.isoformat()
last_modified_human_readable = last_modified.strftime("%c")
except Exception as e:
print('ERROR accessing file name:', e, entry)
continue
entry_path = str(entry.name)
if entry.is_dir() and not entry.is_symlink():
entry_type = 'folder'
entry_path = os.path.join(entry.name, '')
elif entry.is_dir() and entry.is_symlink():
entry_type = 'folder-shortcut'
print('dir-symlink', entry.absolute())
elif entry.is_file() and entry.is_symlink():
entry_type = 'file-shortcut'
print('file-symlink', entry.absolute())
else:
entry_type = 'file'
index_file.write(f"""
<tr class="file">
<td></td>
<td>
<a href="{entry_path}">
<svg width="1.5em" height="1em" version="1.1" viewBox="0 0 265 323"><use xlink:href="#{entry_type}"></use></svg>
<span class="name">{entry.name}</span>
</a>
</td>
<td data-order="{size_bytes}">{size_pretty}</td>
<td class="hideable"><time datetime="{last_modified_iso}">{last_modified_human_readable}</time></td>
<td class="hideable"></td>
</tr>
""")
index_file.write("""
</tbody>
</table>
</div>
</main>
</body>
</html>""")
if index_file:
index_file.close()
# bytes pretty-printing
UNITS_MAPPING = [
(1024 ** 5, ' PB'),
(1024 ** 4, ' TB'),
(1024 ** 3, ' GB'),
(1024 ** 2, ' MB'),
(1024 ** 1, ' KB'),
(1024 ** 0, (' byte', ' bytes')),
]
def pretty_size(bytes, units=UNITS_MAPPING):
"""Human-readable file sizes.
ripped from https://pypi.python.org/pypi/hurry.filesize/
"""
for factor, suffix in units:
if bytes >= factor:
break
amount = int(bytes / factor)
if isinstance(suffix, tuple):
singular, multiple = suffix
if amount == 1:
suffix = singular
else:
suffix = multiple
return str(amount) + suffix
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='''DESCRIPTION:
Generate directory index files (recursive is OFF by default).
Start from current dir or from folder passed as first positional argument.
Optionally filter by file types with --filter "*.py". ''')
parser.add_argument('top_dir',
nargs='?',
action='store',
help='top folder from which to start generating indexes, '
'use current folder if not specified',
default=os.getcwd())
parser.add_argument('--filter', '-f',
help='only include files matching glob',
required=False)
parser.add_argument('--recursive', '-r',
action='store_true',
help="recursively process nested dirs (FALSE by default)",
required=False)
parser.add_argument('--verbose', '-v',
action='store_true',
help='***WARNING: this can take a very long time with complex file tree structures***'
' verbosely list every processed file',
required=False)
config = parser.parse_args(sys.argv[1:])
process_dir(config.top_dir, config)
| [
"[email protected]"
]
| |
67ecd1716036514223b44bda3eada5013e806d32 | 1fcf52f71e4b08b9f6973dcb0a7acf1fe41b9c5f | /setup.py | fc0025b4f83392ce591bf27cf3013ce54c98453b | []
| no_license | svetlyak40wt/colorize | 0a2c65059d4832b88ffbe62864328810f47f0750 | 1e1fd8eff1d2c820105d0655a8df36efac12600a | refs/heads/master | 2020-05-20T08:14:22.598684 | 2014-01-16T12:57:29 | 2014-01-16T12:57:29 | 1,140,039 | 0 | 1 | null | 2014-01-16T12:57:29 | 2010-12-05T09:34:27 | Python | UTF-8 | Python | false | false | 812 | py | from setuptools import setup, find_packages
setup(
name = 'colorizer',
version = '0.1.7',
description = 'Console colorizer, which acts like grep but paint each match in it\'s own color.',
author = 'Alexander Artemenko',
author_email = '[email protected]',
url = 'http://github.com/svetlyak40wt/colorizer/',
license = 'New BSD License',
install_requires = ['termcolor'],
classifiers = [
'Environment :: Console',
'Operating System :: POSIX',
'Operating System :: MacOS :: MacOS X',
'Intended Audience :: Developers',
'Intended Audience :: System Administrators',
'Programming Language :: Python',
],
packages = find_packages(),
entry_points = """
[console_scripts]
colorize = colorizer:main
""",
)
| [
"[email protected]"
]
| |
a1c1382419540480fb893cf3aba8e6f46c76eddc | f8cff1f2c00f000aba61cdf728d8a2b254647f09 | /OpenSpider-master/concurrents/manager.py | b1c32985d3a928292464adbd637ee346c6da18c8 | []
| no_license | vieyahn2017/crawlers | 76097ec9ca71edd128addf341ae02ef26ef383dc | fa584094b026cbf0451e78303c56f614fa5fedde | refs/heads/master | 2021-09-12T21:23:33.265856 | 2018-04-21T03:06:50 | 2018-04-21T03:06:50 | 102,854,533 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 363 | py | __author__ = 'zhangxa'
'''
A concurrentManager should apply a class func who has a run method.
'''
class ConcurrentManger:
def __init__(self,concurrents,runner,*args,**kwargs):
self._concurrents = concurrents
self._runner = runner
self._args = args
self._kwargs = kwargs
def run(self):
raise NotImplementedError
| [
"[email protected]"
]
| |
8b4c344db6561557d19ae3f883fde6d05ab179a4 | d2c80cd70f3220165c7add7ed9a103c0ed1ab871 | /python/9th_session/13dict_popitem+values.py | 8dc5f6d5d6e7468e614605c26b7fad83cfdeaefa | []
| no_license | nervaishere/DashTeam | 2a786af8a871200d7facfa3701a07f97230b706e | a57b34a601f74b06a7be59f2bfe503cbd2a6c15f | refs/heads/master | 2023-08-24T12:24:18.081164 | 2021-10-09T21:10:54 | 2021-10-09T21:10:54 | 393,689,874 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 116 | py | car = {
"brand" : "Ford",
"model" : "Mustang",
"year" : 1964
}
car.popitem()
print(car)
x = car.values()
print(x) | [
"[email protected]"
]
| |
6eac61a01f8b8fcef649ff9f214998ce0fa0545d | 91d1a6968b90d9d461e9a2ece12b465486e3ccc2 | /connect_write_f/contact-recording_resume.py | 934dbeb6b552f992ed8bf1aedd0a3c53fb46b289 | []
| no_license | lxtxl/aws_cli | c31fc994c9a4296d6bac851e680d5adbf7e93481 | aaf35df1b7509abf5601d3f09ff1fece482facda | refs/heads/master | 2023-02-06T09:00:33.088379 | 2020-12-27T13:38:45 | 2020-12-27T13:38:45 | 318,686,394 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 793 | py | #!/usr/bin/python
# -*- codding: utf-8 -*-
import os
import sys
sys.path.append(os.path.dirname(os.path.abspath(os.path.dirname(__file__))))
from common.execute_command import write_parameter
# url : https://awscli.amazonaws.com/v2/documentation/api/latest/reference/ec2/describe-instances.html
if __name__ == '__main__':
"""
start-contact-recording : https://awscli.amazonaws.com/v2/documentation/api/latest/reference/connect/start-contact-recording.html
stop-contact-recording : https://awscli.amazonaws.com/v2/documentation/api/latest/reference/connect/stop-contact-recording.html
suspend-contact-recording : https://awscli.amazonaws.com/v2/documentation/api/latest/reference/connect/suspend-contact-recording.html
"""
write_parameter("connect", "resume-contact-recording") | [
"[email protected]"
]
| |
5e92613605ba6c825cd80bd28a7df6b88eb61d85 | e27333261b8e579564016c71d2061cc33972a8b8 | /development_codes/Backend/.history/PairwiseLTR_20210811104234.py | 2e691d294d6f1595f1741245b7760b35bf29cf1e | []
| no_license | Dustyik/NewsTweet_InformationRetrieval | 882e63dd20bc9101cbf48afa6c3302febf1989b1 | d9a6d92b51c288f5bcd21ea1cc54772910fa58f7 | refs/heads/master | 2023-07-01T09:12:53.215563 | 2021-08-12T08:28:33 | 2021-08-12T08:28:33 | 382,780,359 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 10,089 | py | import pandas as pd
import torchtext
import torch
import random
from torchtext.data.utils import get_tokenizer
from collections import Counter
from sklearn.model_selection import train_test_split
from torch.utils.data import DataLoader
from torch import nn
from gensim.models import KeyedVectors
import gensim.downloader as api
from IPython.display import display
from tfidfImplementation import CosineSimilarity
K = 100
class Ranking_model(nn.Module):
def __init__(self, vocab):
super(Ranking_model, self).__init__()
self.embedding = nn.Embedding(num_embeddings=len(vocab),
embedding_dim=50,
padding_idx=vocab.stoi['<pad>'])
self.encoder = nn.LSTM(50, 50, batch_first=True)
self.nn_layer1 = nn.Linear(in_features=50*2, out_features=1)
def forward(self, qry_tokens, pos_doc_tokens, neg_doc_tokens):
qry_embedded = self.embedding(qry_tokens)
pos_doc_embedded = self.embedding(pos_doc_tokens)
neg_doc_embedded = self.embedding(neg_doc_tokens)
out_qry = torch.mean(self.encoder(qry_embedded)[0],1)
out_pos = torch.mean(self.encoder(pos_doc_embedded)[0],1)
out_neg = torch.mean(self.encoder(neg_doc_embedded)[0],1)
concat_q_pos_doc = torch.cat((out_qry, out_pos),1)
concat_q_neg_doc = torch.cat((out_qry, out_neg),1)
pos_score = torch.relu(self.nn_layer1(concat_q_pos_doc))
neg_score = torch.relu(self.nn_layer1(concat_q_neg_doc))
diff = pos_score - neg_score
return diff
class PairwiseLTR:
def __init__(self, tweets_data, titles_data, max_doc_len=50, max_query_len=50, batch_size=128, method="trim"):
self.data = tweets_data
self.data = self.data.replace({"relevance_score":2}, 1)
self.titles = titles_data
self.articles_id = list(self.data.article_id.unique())
self.get_pos_neg(method)
self.cosineSimilarity = CosineSimilarity(tweets_data)
self.init_model_parameters(max_doc_len, max_query_len) #cretes a train_test_split for the article titles
self.batch_size = batch_size
self.train_dataloader = DataLoader(self.train_dataset, batch_size=self.batch_size,
shuffle=True, collate_fn=self.collate_batch)
self.valid_dataloader = DataLoader(self.valid_dataset, batch_size=self.batch_size,
shuffle=False, collate_fn=self.collate_batch)
try:
self.model = torch.load("PairwiseLTRModel.pt")
self.model.eval()
except:
self.model = Ranking_model(self.vocab)
self.model.to(self.device)
self.init_glove()
self.train()
def return_test_articles(self):
return_df = pd.DataFrame()
for article_id in self.train_articles:
return_df = return_df.append(self.titles.loc[(self.titles['id'] == article_id)])
return return_df
def get_pos_neg(self, method):
self.pos_ = {}
self.neg_ = {}
self.articles_id = list(self.data.article_id.unique())
to_remove = []
for i in self.articles_id:
temp = self.data[self.data["article_id"]==i]
temp["relevance_score"].sum()
num_pos = temp["relevance_score"].sum()
num_neg = temp.shape[0] - num_pos
if method=="trim":
num_keep = min(num_pos, num_neg)
pos_list = list(temp[temp["relevance_score"]==1].clean_text[:num_keep])
neg_list = list(temp[temp["relevance_score"]==0].clean_text[:num_keep])
elif method=="pad":
pos_list = list(temp[temp["relevance_score"]==1].clean_text)
neg_list = list(temp[temp["relevance_score"]==0].clean_text)
if num_pos==0 or num_neg==0:
to_remove.append(i)
continue
if num_pos<num_neg:
for j in range(num_neg-num_pos):
pos_list.append(pos_list[-1])
elif num_neg<num_pos:
for j in range(num_pos-num_neg):
neg_list.append(neg_list[-1])
self.pos_[i] = pos_list
self.neg_[i] = neg_list
for i in to_remove:
self.articles_id.remove(i)
def init_model_parameters(self, max_doc_len, max_query_len):
self.device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
self.tokenizer = get_tokenizer("basic_english")
self.train_articles, self.test_articles = train_test_split(self.articles_id, test_size=1.5/10, random_state=179)
dataset = []
for i in self.train_articles:
for j in range(len(self.pos_[i])):
dataset.append([self.titles[self.titles["id"]==i].title.to_list()[0], self.pos_[i][j], self.neg_[i][j]])
self.train_dataset, self.valid_dataset = train_test_split(dataset, test_size=1/9, random_state=179)
self.counter = Counter()
for (qry, pos, neg) in self.train_dataset:
self.counter.update(self.tokenizer(pos))
self.counter.update(self.tokenizer(neg))
self.vocab = torchtext.vocab.Vocab(self.counter, max_size=10000, specials=('<pad>', '<unk>'), specials_first=True)
self.text_pipeline = lambda x: [self.vocab[token] for token in self.tokenizer(x)]
self.query_padding_pipeline = lambda tokens: [self.vocab.stoi['<pad>'] for p in range(max_query_len - len(tokens))] + tokens[-max_query_len:]
self.doc_padding_pipeline = lambda tokens: [self.vocab.stoi['<pad>'] for p in range(max_doc_len - len(tokens))] + tokens[:max_doc_len]
def collate_batch(self, batch):
query_list, pos_doc_list, neg_doc_list = [], [], []
for (qry, pos, neg) in batch:
qry_ = self.query_padding_pipeline(self.text_pipeline(qry))
pos_ = self.doc_padding_pipeline(self.text_pipeline(pos))
neg_ = self.doc_padding_pipeline(self.text_pipeline(neg))
query_list += [qry_]
pos_doc_list += [pos_]
neg_doc_list += [neg_]
temp = list(zip(query_list, pos_doc_list, neg_doc_list))
random.shuffle(temp)
query_list, pos_doc_list, neg_doc_list = zip(*temp)
query_list = torch.tensor(query_list, dtype=torch.int64)
pos_doc_list = torch.tensor(pos_doc_list, dtype=torch.int64)
neg_doc_list = torch.tensor(neg_doc_list, dtype=torch.int64)
return query_list.to(self.device), pos_doc_list.to(self.device), neg_doc_list.to(self.device)
def init_glove(self):
try:
print("Loading saved word vectors...")
glove_50dim = KeyedVectors.load("./glove_50dim.w2v")
except:
print("Downloading word vectors...")
glove_50dim = api.load("glove-wiki-gigaword-50")
glove_50dim.save('glove_50dim.w2v')
print("Number of word vectors:", glove_50dim.vectors.shape)
#Initialise model embedding with glove
for word in self.vocab.stoi.keys():
if word in glove_50dim.key_to_index.keys():
word_vec = glove_50dim[word]
self.model.embedding.weight.data[self.vocab.stoi[word]] = torch.tensor(word_vec)\
def train(self, num_epochs=10):
optimizer=torch.optim.AdamW(self.model.parameters(), lr=1e-5)
for epoch in range(num_epochs):
print("-->Epoch:{}".format(epoch))
epoch_train_loss = 0.0
self.model.train()
for idx, (qry_tokens, pos_doc_tokens, neg_doc_tokens) in enumerate(self.train_dataloader):
optimizer.zero_grad()
diff = self.model(qry_tokens, pos_doc_tokens, neg_doc_tokens)
loss = torch.log(1 + torch.exp(-1*diff)).mean()
loss.backward()
optimizer.step()
epoch_train_loss += loss.cpu().item()*self.batch_size
print("Batch {}/{}, avg. train loss is {}".format(idx, len(self.train_dataloader), epoch_train_loss/(idx+1)), end='\r')
epoch_val_loss = 0.0
self.model.eval()
with torch.no_grad(): #weights should not update
for idx, (qry_tokens, pos_doc_tokens, neg_doc_tokens) in enumerate(self.valid_dataloader):
diff = self.model(qry_tokens, pos_doc_tokens, neg_doc_tokens)
epoch_val_loss += torch.log(1 + torch.exp(-1*diff)).mean() #same loss as in training
print("\nval loss:{}".format(epoch_val_loss))
torch.save(self.model, "PairwiseLTRModel.pt")
def tfidf_retrieve_K_tweets(self, article_id, article_title):
topKResults = self.cosineSimilarity.query(query_id=article_id, query_text=article_title)[:K]
return topKResults
def rank_docs(self, article_id, qry):
article_title = qry
topKResults = self.tfidf_retrieve_K_tweets(article_id, article_title)
doc_list = topKResults.tweet.tolist()
scores = []
for doc in doc_list:
self.model.eval()
with torch.no_grad():
qry_ = torch.tensor([self.query_padding_pipeline(self.text_pipeline(qry))], dtype=torch.int64).to(self.device)
doc_ = torch.tensor([self.doc_padding_pipeline(self.text_pipeline(doc))], dtype=torch.int64).to(self.device)
score = self.model(qry_, doc_, doc_*0)
scores.append((doc, score.detach().item()))
scores = sorted(scores, key = lambda x: x[1])
results = pd.DataFrame(columns=['article_id', 'tweet_id', 'relevance_score', 'tweet', 'clean_text'])
for doc, score in scores:
doc_filt = self.data[self.data["tweet"]==doc]
if doc not in results["tweet"].to_list():
try:
results = results.append(doc_filt.iloc[0])
except:
pass
return results
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| |
2d770c24fe0c6044d3dc2542a21f7bcb180d892e | 842e3cd1266d18752a3baf2b90232ed4ce41eb4f | /grako/test/grammar/semantics_test.py | 9d520aaa87c70fb7d87067f9de6cfda85bd29469 | [
"BSD-3-Clause",
"BSD-2-Clause"
]
| permissive | apalala/grako | 2786d85eef9799bf614c46c92f19ff183a435d46 | efb373d89e6805930e661758c2cff2b26da4658a | refs/heads/master | 2020-12-25T17:37:05.353167 | 2017-05-02T02:53:11 | 2017-05-02T02:53:11 | 65,163,853 | 16 | 6 | null | null | null | null | UTF-8 | Python | false | false | 1,120 | py | # -*- coding: utf-8 -*-
# Copyright (C) 2017 by Juancarlo Añez
# Copyright (C) 2012-2016 by Juancarlo Añez and Thomas Bragg
from __future__ import absolute_import, division, print_function, unicode_literals
import unittest
from grako.tool import compile
from grako.semantics import ModelBuilderSemantics
class SemanticsTests(unittest.TestCase):
def test_builder_semantics(self):
grammar = '''
start::sum = {number}+ $ ;
number::int = /\d+/ ;
'''
text = '5 4 3 2 1'
semantics = ModelBuilderSemantics()
model = compile(grammar, 'test')
ast = model.parse(text, semantics=semantics)
self.assertEqual(15, ast)
import functools
dotted = functools.partial(type('').join, '.')
dotted.__name__ = 'dotted'
grammar = '''
start::dotted = {number}+ $ ;
number = /\d+/ ;
'''
semantics = ModelBuilderSemantics(types=[dotted])
model = compile(grammar, 'test')
ast = model.parse(text, semantics=semantics)
self.assertEqual('5.4.3.2.1', ast)
| [
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| |
b81db8a245a81834aad2e44681142b1dee1f761d | 24d8cf871b092b2d60fc85d5320e1bc761a7cbe2 | /eXe/rev2735-2877/base-trunk-2735/exe/engine/mathidevice.py | d7c9a57ae1108ccbb969998567d54ab61700910a | []
| no_license | joliebig/featurehouse_fstmerge_examples | af1b963537839d13e834f829cf51f8ad5e6ffe76 | 1a99c1788f0eb9f1e5d8c2ced3892d00cd9449ad | refs/heads/master | 2016-09-05T10:24:50.974902 | 2013-03-28T16:28:47 | 2013-03-28T16:28:47 | 9,080,611 | 3 | 2 | null | null | null | null | UTF-8 | Python | false | false | 1,261 | py | """
MathIdevice: just has a block of text
"""
import logging
from exe.engine.idevice import Idevice
from exe.engine.field import MathField
log = logging.getLogger(__name__)
class MathIdevice(Idevice):
"""
MathIdevice: just has a block of text
"""
def __init__(self, instruc="", latex=""):
Idevice.__init__(self, x_(u"Maths"),
x_(u"University of Auckland"),
x_("""The mathematical language LATEX has been
used to enable your to insert mathematical formula
into your content. It does this by translating
LATEX into an image which is then displayed
within your eXe content. We would recommend that
you use the Free Text iDevice to provide
explanatory notes and learning instruction around
this graphic."""),
"",
"")
self.emphasis = Idevice.NoEmphasis
self.content = MathField(x_(u"Maths"),
x_(u"""You can use the toolbar or enter latex manually into the textarea. """))
self.content.idevice = self
| [
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| |
4f66d4ae6728ef58845bb197375e2267313d0135 | 275a96a33ae1f89e7b2ee0ecdbac7d78abe6d6cc | /test/test_ad_view.py | ea1ebb582cd5d6c22cdaaacd01a964bee35afdb7 | []
| no_license | cascadiarc/cyclos-python-client | 8029ce07174f2fe92350a92dda9a60976b2bb6c2 | a2e22a30e22944587293d51be2b8268bce808d70 | refs/heads/main | 2023-04-03T16:52:01.618444 | 2021-04-04T00:00:52 | 2021-04-04T00:00:52 | 354,419,532 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 805 | py | # coding: utf-8
"""
Cyclos 4.11.5 API
The REST API for Cyclos 4.11.5 # noqa: E501
OpenAPI spec version: 4.11.5
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import unittest
import swagger_client
from swagger_client.models.ad_view import AdView # noqa: E501
from swagger_client.rest import ApiException
class TestAdView(unittest.TestCase):
"""AdView unit test stubs"""
def setUp(self):
pass
def tearDown(self):
pass
def testAdView(self):
"""Test AdView"""
# FIXME: construct object with mandatory attributes with example values
# model = swagger_client.models.ad_view.AdView() # noqa: E501
pass
if __name__ == '__main__':
unittest.main()
| [
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]
| |
42d4548e0211060ef8143593198856e9ef130dfd | 578db86c51d44ebddd0dc7b1738985b3dc69eb74 | /corehq/apps/api/resources/serializers.py | f57a8fc1b707903d74990c7085001cd6ca31691d | [
"BSD-3-Clause"
]
| permissive | dimagi/commcare-hq | a43c7dd32b5f89c89fd5aa1b1359ab7301f4ff6b | e7391ddae1af1dbf118211ecb52c83fc508aa656 | refs/heads/master | 2023-08-16T22:38:27.853437 | 2023-08-16T19:07:19 | 2023-08-16T19:07:19 | 247,278 | 499 | 203 | BSD-3-Clause | 2023-09-14T19:03:24 | 2009-07-09T17:00:07 | Python | UTF-8 | Python | false | false | 489 | py | import json
from tastypie.serializers import Serializer
class ListToSingleObjectSerializer(Serializer):
"""
Serializer class that takes a list of one object and removes the other metadata
around the list view so that just the object is returned.
See IdentityResource for an example.
"""
def to_json(self, data, options=None):
# note: this is not valid if there is ever not exactly one object returned
return json.dumps(data['objects'][0].data)
| [
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| |
350ac6d5f55c221b5552b1ff7e6b0a3063604641 | 6eb318399cea68bc998fc635d7d519e187a24f15 | /leetcode/39_combination_sum/39_combination_sum.py | 3efee07c21fc16d136b49c82665b494a4d2096d8 | [
"Apache-2.0"
]
| permissive | ryangillard/misc | e8eb4813a54bc59babc263087f2d71bdc6af6b7a | d1f9919400636e6b988fa933493b94829a73331e | refs/heads/master | 2021-11-24T17:26:52.740997 | 2021-11-16T06:27:42 | 2021-11-16T06:27:42 | 192,038,898 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 658 | py | class Solution(object):
def combinationSum(self, candidates, target):
"""
:type candidates: List[int]
:type target: int
:rtype: List[List[int]]
"""
paths = []
self.recursive(candidates, target, 0, [], paths)
return paths
def recursive(self, candidates, target, start_index, path, paths):
if target == 0:
paths.append(path)
return None
for i in range(start_index, len(candidates)):
if candidates[i] <= target:
self.recursive(candidates, target - candidates[i], i, path + [candidates[i]], paths)
return None | [
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]
| |
8602f6a64d2286cd253d640ee4063c095b9f289a | ca507259c36a4299666f4c064f25832f5c3f45c1 | /symbol_openapi_client/models/transaction_status_dto.py | f51ff88a06df1f8858bcf03ab41ae5ee63116815 | []
| no_license | fullcircle23/symbol-openapi-python-client | ae38a2537d1f2eebca115733119c444b79ec4962 | 3728d30eb1b5085a5a5e991402d180fac8ead68b | refs/heads/master | 2022-04-15T06:20:47.821281 | 2020-04-16T02:39:14 | 2020-04-16T02:39:14 | 254,701,919 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 7,790 | py | # coding: utf-8
"""
****************************************************************************
Copyright (c) 2016-present,
Jaguar0625, gimre, BloodyRookie, Tech Bureau, Corp. All rights reserved.
This file is part of Catapult.
Catapult is free software: you can redistribute it and/or modify
it under the terms of the GNU Lesser General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
Catapult 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License
along with Catapult. If not, see <http://www.gnu.org/licenses/>.
****************************************************************************
Catapult REST Endpoints
OpenAPI Specification of catapult-rest 1.0.20.22 # noqa: E501
The version of the OpenAPI document: 0.8.9
Contact: [email protected]
NOTE: This file is auto generated by Symbol OpenAPI Generator:
https://github.com/nemtech/symbol-openapi-generator
Do not edit this file manually.
"""
import pprint
import re # noqa: F401
import six
from symbol_openapi_client.configuration import Configuration
class TransactionStatusDTO(object):
"""NOTE: This class is auto generated by OpenAPI Generator.
Ref: https://openapi-generator.tech
Do not edit the class manually.
"""
"""
Attributes:
openapi_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
openapi_types = {
'group': 'TransactionStateTypeEnum',
'code': 'TransactionStatusTypeEnum',
'hash': 'str',
'deadline': 'int',
'height': 'int'
}
attribute_map = {
'group': 'group',
'code': 'code',
'hash': 'hash',
'deadline': 'deadline',
'height': 'height'
}
def __init__(self, group=None, code=None, hash=None, deadline=None, height=None, local_vars_configuration=None): # noqa: E501
"""TransactionStatusDTO - a model defined in OpenAPI""" # noqa: E501
if local_vars_configuration is None:
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._group = None
self._code = None
self._hash = None
self._deadline = None
self._height = None
self.discriminator = None
self.group = group
if code is not None:
self.code = code
self.hash = hash
self.deadline = deadline
if height is not None:
self.height = height
@property
def group(self):
"""Gets the group of this TransactionStatusDTO. # noqa: E501
:return: The group of this TransactionStatusDTO. # noqa: E501
:rtype: TransactionStateTypeEnum
"""
return self._group
@group.setter
def group(self, group):
"""Sets the group of this TransactionStatusDTO.
:param group: The group of this TransactionStatusDTO. # noqa: E501
:type: TransactionStateTypeEnum
"""
if self.local_vars_configuration.client_side_validation and group is None: # noqa: E501
raise ValueError("Invalid value for `group`, must not be `None`") # noqa: E501
self._group = group
@property
def code(self):
"""Gets the code of this TransactionStatusDTO. # noqa: E501
:return: The code of this TransactionStatusDTO. # noqa: E501
:rtype: TransactionStatusTypeEnum
"""
return self._code
@code.setter
def code(self, code):
"""Sets the code of this TransactionStatusDTO.
:param code: The code of this TransactionStatusDTO. # noqa: E501
:type: TransactionStatusTypeEnum
"""
self._code = code
@property
def hash(self):
"""Gets the hash of this TransactionStatusDTO. # noqa: E501
:return: The hash of this TransactionStatusDTO. # noqa: E501
:rtype: str
"""
return self._hash
@hash.setter
def hash(self, hash):
"""Sets the hash of this TransactionStatusDTO.
:param hash: The hash of this TransactionStatusDTO. # noqa: E501
:type: str
"""
if self.local_vars_configuration.client_side_validation and hash is None: # noqa: E501
raise ValueError("Invalid value for `hash`, must not be `None`") # noqa: E501
self._hash = hash
@property
def deadline(self):
"""Gets the deadline of this TransactionStatusDTO. # noqa: E501
Duration expressed in number of blocks. # noqa: E501
:return: The deadline of this TransactionStatusDTO. # noqa: E501
:rtype: int
"""
return self._deadline
@deadline.setter
def deadline(self, deadline):
"""Sets the deadline of this TransactionStatusDTO.
Duration expressed in number of blocks. # noqa: E501
:param deadline: The deadline of this TransactionStatusDTO. # noqa: E501
:type: int
"""
if self.local_vars_configuration.client_side_validation and deadline is None: # noqa: E501
raise ValueError("Invalid value for `deadline`, must not be `None`") # noqa: E501
self._deadline = deadline
@property
def height(self):
"""Gets the height of this TransactionStatusDTO. # noqa: E501
Height of the blockchain. # noqa: E501
:return: The height of this TransactionStatusDTO. # noqa: E501
:rtype: int
"""
return self._height
@height.setter
def height(self, height):
"""Sets the height of this TransactionStatusDTO.
Height of the blockchain. # noqa: E501
:param height: The height of this TransactionStatusDTO. # noqa: E501
:type: int
"""
self._height = height
def to_dict(self):
"""Returns the model properties as a dict"""
result = {}
for attr, _ in six.iteritems(self.openapi_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
return result
def to_str(self):
"""Returns the string representation of the model"""
return pprint.pformat(self.to_dict())
def __repr__(self):
"""For `print` and `pprint`"""
return self.to_str()
def __eq__(self, other):
"""Returns true if both objects are equal"""
if not isinstance(other, TransactionStatusDTO):
return False
return self.to_dict() == other.to_dict()
def __ne__(self, other):
"""Returns true if both objects are not equal"""
if not isinstance(other, TransactionStatusDTO):
return True
return self.to_dict() != other.to_dict()
| [
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]
| |
86afd01855c9b1316420a1200b906414ffe65f9a | f4b60f5e49baf60976987946c20a8ebca4880602 | /lib64/python2.7/site-packages/acimodel-1.3_2j-py2.7.egg/cobra/modelimpl/sts/rtfabinextinatt.py | 7f424a661e3a25b233005511ac9a1699fa0c1b58 | []
| no_license | cqbomb/qytang_aci | 12e508d54d9f774b537c33563762e694783d6ba8 | a7fab9d6cda7fadcc995672e55c0ef7e7187696e | refs/heads/master | 2022-12-21T13:30:05.240231 | 2018-12-04T01:46:53 | 2018-12-04T01:46:53 | 159,911,666 | 0 | 0 | null | 2022-12-07T23:53:02 | 2018-12-01T05:17:50 | Python | UTF-8 | Python | false | false | 4,416 | py | # coding=UTF-8
# **********************************************************************
# Copyright (c) 2013-2016 Cisco Systems, Inc. All rights reserved
# written by zen warriors, do not modify!
# **********************************************************************
from cobra.mit.meta import ClassMeta
from cobra.mit.meta import StatsClassMeta
from cobra.mit.meta import CounterMeta
from cobra.mit.meta import PropMeta
from cobra.mit.meta import Category
from cobra.mit.meta import SourceRelationMeta
from cobra.mit.meta import NamedSourceRelationMeta
from cobra.mit.meta import TargetRelationMeta
from cobra.mit.meta import DeploymentPathMeta, DeploymentCategory
from cobra.model.category import MoCategory, PropCategory, CounterCategory
from cobra.mit.mo import Mo
# ##################################################
class RtFabInExtInAtt(Mo):
"""
"""
meta = TargetRelationMeta("cobra.model.sts.RtFabInExtInAtt", "cobra.model.sts.AFabIn")
meta.moClassName = "stsRtFabInExtInAtt"
meta.rnFormat = "rtfabInExtInAtt"
meta.category = MoCategory.RELATIONSHIP_FROM_LOCAL
meta.label = "Fabric Input"
meta.writeAccessMask = 0x1
meta.readAccessMask = 0x1
meta.isDomainable = False
meta.isReadOnly = True
meta.isConfigurable = False
meta.isDeletable = False
meta.isContextRoot = False
meta.parentClasses.add("cobra.model.sts.ExtIn")
meta.superClasses.add("cobra.model.reln.From")
meta.superClasses.add("cobra.model.reln.Inst")
meta.rnPrefixes = [
('rtfabInExtInAtt', False),
]
prop = PropMeta("str", "childAction", "childAction", 4, PropCategory.CHILD_ACTION)
prop.label = "None"
prop.isImplicit = True
prop.isAdmin = True
prop._addConstant("deleteAll", "deleteall", 16384)
prop._addConstant("deleteNonPresent", "deletenonpresent", 8192)
prop._addConstant("ignore", "ignore", 4096)
meta.props.add("childAction", prop)
prop = PropMeta("str", "dn", "dn", 1, PropCategory.DN)
prop.label = "None"
prop.isDn = True
prop.isImplicit = True
prop.isAdmin = True
prop.isCreateOnly = True
meta.props.add("dn", prop)
prop = PropMeta("str", "lcOwn", "lcOwn", 9, PropCategory.REGULAR)
prop.label = "None"
prop.isImplicit = True
prop.isAdmin = True
prop.defaultValue = 0
prop.defaultValueStr = "local"
prop._addConstant("implicit", "implicit", 4)
prop._addConstant("local", "local", 0)
prop._addConstant("policy", "policy", 1)
prop._addConstant("replica", "replica", 2)
prop._addConstant("resolveOnBehalf", "resolvedonbehalf", 3)
meta.props.add("lcOwn", prop)
prop = PropMeta("str", "modTs", "modTs", 7, PropCategory.REGULAR)
prop.label = "None"
prop.isImplicit = True
prop.isAdmin = True
prop.defaultValue = 0
prop.defaultValueStr = "never"
prop._addConstant("never", "never", 0)
meta.props.add("modTs", prop)
prop = PropMeta("str", "rn", "rn", 2, PropCategory.RN)
prop.label = "None"
prop.isRn = True
prop.isImplicit = True
prop.isAdmin = True
prop.isCreateOnly = True
meta.props.add("rn", prop)
prop = PropMeta("str", "status", "status", 3, PropCategory.STATUS)
prop.label = "None"
prop.isImplicit = True
prop.isAdmin = True
prop._addConstant("created", "created", 2)
prop._addConstant("deleted", "deleted", 8)
prop._addConstant("modified", "modified", 4)
meta.props.add("status", prop)
prop = PropMeta("str", "tCl", "tCl", 12584, PropCategory.REGULAR)
prop.label = "Target-class"
prop.isImplicit = True
prop.isAdmin = True
prop.defaultValue = 2276
prop.defaultValueStr = "stsAFabIn"
prop._addConstant("stsAFabIn", None, 2276)
prop._addConstant("stsFabIn", None, 2279)
prop._addConstant("stsFabInDef", None, 2280)
prop._addConstant("stsFabInRevDef", None, 2285)
prop._addConstant("unspecified", "unspecified", 0)
meta.props.add("tCl", prop)
prop = PropMeta("str", "tDn", "tDn", 100, PropCategory.REGULAR)
prop.label = "Target-dn"
prop.isImplicit = True
prop.isAdmin = True
meta.props.add("tDn", prop)
def __init__(self, parentMoOrDn, markDirty=True, **creationProps):
namingVals = []
Mo.__init__(self, parentMoOrDn, markDirty, *namingVals, **creationProps)
# End of package file
# ##################################################
| [
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]
| |
1621cd025aba6ae82b0151c94d67dbcc0918af22 | 32cb0be487895629ad1184ea25e0076a43abba0a | /LifePictorial/top/api/rest/FenxiaoGradesGetRequest.py | b11baf371f16e5abe3b7f6e328da2e7a1297435a | []
| no_license | poorevil/LifePictorial | 6814e447ec93ee6c4d5b0f1737335601899a6a56 | b3cac4aa7bb5166608f4c56e5564b33249f5abef | refs/heads/master | 2021-01-25T08:48:21.918663 | 2014-03-19T08:55:47 | 2014-03-19T08:55:47 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 289 | py | '''
Created by auto_sdk on 2014-02-10 16:59:30
'''
from top.api.base import RestApi
class FenxiaoGradesGetRequest(RestApi):
def __init__(self,domain='gw.api.taobao.com',port=80):
RestApi.__init__(self,domain, port)
def getapiname(self):
return 'taobao.fenxiao.grades.get'
| [
"[email protected]"
]
| |
ddd78fe9da9398ec8fd58bfc448d03b175f789e7 | c1261b9181d86c418df612dc809af933cfbb2c0d | /blog1/models.py | 72e8cea72ffc9af34805e080119fd81054f69afd | []
| no_license | gitlGl/myblog | 122a598407d12a7397420ce50f9c1ca68a3107d2 | b3d7d1130e81ca625cb9d2b7204e19da6efe7d07 | refs/heads/master | 2023-09-01T14:06:04.720407 | 2022-10-22T08:47:02 | 2022-10-22T08:47:02 | 198,171,669 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,075 | py | from django.db import models
from django.urls import reverse
# Create your models here.
#hjflghjglkhd
# Create your models here.
from django.shortcuts import render, get_object_or_404
#from .models import Post
from django.db import models
from django.contrib.auth.models import User
from tinymce.models import HTMLField
class Category(models.Model):
"""
Django 要求模型必须继承 models.Model 类。
Category 只需要一个简单的分类名 name 就可以了。
CharField 指定了分类名 name 的数据类型，CharField 是字符型，
CharField 的 max_length 参数指定其最大长度，超过这个长度的分类名就不能被存入数据库。
当然 Django 还为我们提供了多种其它的数据类型，如日期时间类型 DateTimeField、整数类型 IntegerField 等等。
Django 内置的全部类型可查看文档：
https://docs.djangoproject.com/en/1.10/ref/models/fields/#field-types
"""
name = models.CharField(max_length=100)
def get_absolute_url(self):
#print(self.pk)
return reverse('blog1:Category_page', kwargs={'category1': self.pk})
'''
class Tag(models.Model):
"""
标签 Tag 也比较简单，和 Category 一样。
再次强调一定要继承 models.Model 类！
"""
name = models.CharField(max_length=100)
'''
class Post(models.Model):
"""
文章的数据库表稍微复杂一点，主要是涉及的字段更多。
"""
# 文章标题
title = models.CharField(max_length=70)
# 文章正文，我们使用了 TextField。
# 存储比较短的字符串可以使用 CharField，但对于文章的正文来说可能会是一大段文本，因此使用 TextField 来存储大段文本。
body = HTMLField()
# 这两个列分别表示文章的创建时间和最后一次修改时间，存储时间的字段用 DateTimeField 类型。
created_time = models.DateTimeField()
modified_time = models.DateTimeField()
# 文章摘要，可以没有文章摘要，但默认情况下 CharField 要求我们必须存入数据，否则就会报错。
# 指定 CharField 的 blank=True 参数值后就可以允许空值了。
excerpt = HTMLField( blank=True)
# 这是分类与标签，分类与标签的模型我们已经定义在上面。
# 我们在这里把文章对应的数据库表和分类、标签对应的数据库表关联了起来，但是关联形式稍微有点不同。
# 我们规定一篇文章只能对应一个分类，但是一个分类下可以有多篇文章，所以我们使用的是 ForeignKey，即一对多的关联关系。
# 而对于标签来说，一篇文章可以有多个标签，同一个标签下也可能有多篇文章，所以我们使用 ManyToManyField，表明这是多对多的关联关系。
# 同时我们规定文章可以没有标签，因此为标签 tags 指定了 blank=True。
# 如果你对 ForeignKey、ManyToManyField 不了解，请看教程中的解释，亦可参考官方文档：
# https://docs.djangoproject.com/en/1.10/topics/db/models/#relationships
category = models.ForeignKey('Category',on_delete=models.CASCADE)
#tags = models.ManyToManyField('Tag' ,blank=True)
# 文章作者，这里 User 是从 django.contrib.auth.models 导入的。
# django.contrib.auth 是 Django 内置的应用，专门用于处理网站用户的注册、登录等流程，User 是 Django 为我们已经写好的用户模型。
# 这里我们通过 ForeignKey 把文章和 User 关联了起来。
# 因为我们规定一篇文章只能有一个作者，而一个作者可能会写多篇文章，因此这是一对多的关联关系，和 Category 类似。
author = models.ForeignKey(User,on_delete=models.CASCADE)
def __str__(self):
return self.title
# 自定义 get_absolute_url 方法
# 记得从 django.urls 中导入 reverse 函数
def get_absolute_url(self):
return reverse('blog1:detail', kwargs={'pk': self.pk})
#Category.get_absolute_url()
| [
"[email protected]"
]
| |
099a47f37b2f69649665afc8b56a49ed9d42362d | c2ae65792af1fab2e7843303ef90790819f872e8 | /Algorithm/Math/Power-Function.py | f0b6d0d8a9288824dc87f7ac62ccb797cc247d80 | []
| no_license | behappyyoung/PythonSampleCodes | 47c224ca76ce509a03c8b75ef6b4bf7f49ebdd7f | f7640467273fa8ea3c7e443e798737ca5bcea6f9 | refs/heads/master | 2023-03-15T00:53:21.034605 | 2023-02-13T17:12:32 | 2023-02-13T17:12:32 | 26,919,763 | 3 | 3 | null | 2023-03-07T12:45:21 | 2014-11-20T15:57:16 | Python | UTF-8 | Python | false | false | 1,804 | py | """
Implement pow(x, n)
"""
from datetime import datetime
def power_function(x, y):
if y == 0:
return 1
else:
for i in range(y):
x *= x
return x
def power_function_s(x, y):
if y == 0:
return 1
elif int(y % 2) == 0:
return power_function_s(x, int(y / 2)) * power_function_s(x, int(y / 2))
else:
return x * power_function_s(x, int(y / 2)) * power_function_s(x, int(y / 2))
def power_function_s_s(x, y):
if y == 0:
return 1
else:
temp = power_function_s_s(x, y // 2)
if int(y % 2) == 0:
return temp * temp
else:
return x * temp * temp
# print(power_function_s(-1, 2))
# print(power_function_s(-1, 3))
# print(power_function_s(-2, 2))
# start_time = datetime.now()
# print(str(power_function_s(7100, 4150))[:50])
# end_time = datetime.now()
# print(end_time - start_time)
# start_time = datetime.now()
# print(str(power_function_s_s(7100, 4150))[:50])
# end_time = datetime.now()
# print(end_time - start_time)
def power_function_f(x, y):
print(y)
if y == 0:
return 1
elif y > 0:
temp = power_function_f(x, y // 2)
print(y, temp)
if int(y % 2) == 0:
return temp * temp
else:
return x * temp * temp
else:
next_y = -(-y // 2)
temp = power_function_f(x, next_y)
print(y, next_y, temp)
if int(-y % 2) == 0:
return 1 / (temp * temp) if temp >1 else (temp * temp)
else:
return 1/(x * temp * temp) if temp >1 else x* (temp * temp)
print(str(power_function_f(2.00000, 2)))
print(str(power_function_f(2.00000, -2)))
print(str(power_function_f(8.84372, -5)))
| [
"[email protected]"
]
| |
5bb0e557dd728812cf61315606915473308c1769 | c4a33b613ffc77dccf96d33c3a5cc127405c0e95 | /products/models.py | a07d41df737ce764165d547ebee4f370ea33ad54 | []
| no_license | tsokac2/new-irish-life | 25f49bd0b74dfa7c0a449772249f6cb51925b643 | d09934b60a1fd4fbd4540d412dc5dab726f5b502 | refs/heads/main | 2023-07-02T09:54:55.082587 | 2021-07-30T04:42:57 | 2021-07-30T04:42:57 | 379,245,725 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,115 | py | from django.db import models
class Category(models.Model):
class Meta:
verbose_name_plural = 'Categories'
name = models.CharField(max_length=254)
friendly_name = models.CharField(max_length=254, null=True, blank=True)
def __str__(self):
return self.name
def get_friendly_name(self):
return self.friendly_name
class Product(models.Model):
category = models.ForeignKey(
'Category', null=True, blank=True, on_delete=models.SET_NULL)
sku = models.CharField(max_length=254, null=True, blank=True)
name = models.CharField(max_length=254)
description = models.TextField()
price = models.DecimalField(max_digits=6, decimal_places=2)
sale_price = models.DecimalField(
max_digits=6, decimal_places=2, null=True, blank=True)
rating = models.DecimalField(
max_digits=2, decimal_places=1, null=True, blank=True)
image_1 = models.ImageField(null=True, blank=True)
image_2 = models.ImageField(null=True, blank=True)
image_3 = models.ImageField(null=True, blank=True)
def __str__(self):
return self.name
| [
"[email protected]"
]
| |
822b4360a54f56d2fbbdea34fed6eb34287dcaa8 | f9ed608c620093b9f6b5058bcedf7ae610c09c8d | /298-Binary_Tree_Longest_Consecutive_Sequence.py | da80a4acedcbf4c90c5005102e1c5612e3ed7da4 | []
| no_license | chanyoonzhu/leetcode-python | 9b88d7f2749e1ae3ed597759b1bf9f7fa4912c35 | 085d868ba0458fc8e6b5549aa00fa151c335fa7f | refs/heads/master | 2022-05-24T11:20:35.927915 | 2022-04-16T06:02:33 | 2022-04-16T06:02:33 | 166,224,197 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 1,998 | py | # Definition for a binary tree node.
# class TreeNode(object):
# def __init__(self, val=0, left=None, right=None):
# self.val = val
# self.left = left
# self.right = right
"""
Questions:
- validity of number (floats, negative integer and 0)
"""
"""
- top-down solution (pre-order traversal)
- O(n): every node is visited once - The time complexity is the same as an in-order traversal of a binary tree with n nodes.
- O(n): The extra space comes from implicit stack space due to recursion. For a skewed binary tree, the recursion could go up to nn levels deep.
"""
class Solution(object):
def longestConsecutive(self, root):
"""
:type root: TreeNode
:rtype: int
"""
return self.dfs(root, -100, 0) # provided that node values are all positive
def dfs(self, node, prev_val, _max):
if node is None:
return _max
if node.val != prev_val + 1:
return max(_max, self.dfs(node.left, node.val, 1), self.dfs(node.right, node.val, 1))
if node.val == prev_val + 1:
return max(self.dfs(node.left, node.val, _max+1), self.dfs(node.right, node.val, _max+1))
"""
- bottom-up solution
- O(n), O(n)
"""
class Solution:
def longestConsecutive(self, root: Optional[TreeNode]) -> int:
self.maximum_length = 0
def helper(root):
if not root:
return 0
length = 1
# easy to miss: need to call helper even if node doesn't connect downwards
l = helper(root.left)
r = helper(root.right)
if root.left and root.left.val == root.val + 1:
length = max(length, 1 + l)
if root.right and root.right.val == root.val + 1:
length = max(length, 1 + r)
self.maximum_length = max(self.maximum_length, length)
return length
helper(root)
return self.maximum_length
| [
"[email protected]"
]
| |
c1a76f770a4973d3e1450afc83bc808df1007e5b | b7174170d50b867050c80129dbde239a948f6f85 | /tools/sapp/sapp/tests/sharded_files/sharded_files_test.py | 1e1903098d02d2e0eba4cf7b97bb287f1d108f76 | [
"MIT"
]
| permissive | GreyElaina/pyre-check | c1a2b7a6ee050f606322eaa588f9bd95cd1b3dbc | abcb5daa64c38a25aed9ab238bb61290444ab06c | refs/heads/master | 2022-12-19T21:35:09.582761 | 2020-09-12T05:54:32 | 2020-09-12T05:58:24 | 295,080,507 | 0 | 0 | MIT | 2020-09-13T04:52:19 | 2020-09-13T04:52:18 | null | UTF-8 | Python | false | false | 3,086 | py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import unittest
from ...sharded_files import ShardedFile
test_path = "tools/sapp/sapp//tests/sharded_files"
class TestShardedFiles(unittest.TestCase):
def test_fails_for_no_sharding(self):
pattern = os.path.join(test_path, "foo.bar")
with self.assertRaisesRegex(ValueError, "Not a sharded file"):
ShardedFile(pattern)
def test_returns_two_shards_for_star(self):
pattern = os.path.join(test_path, "foo@*.bar")
sf = ShardedFile(pattern)
self.assertEqual(
sf.get_filenames(),
[
os.path.join(test_path, "[email protected]"),
os.path.join(test_path, "[email protected]"),
],
)
def test_returns_two_shards_for_two(self):
pattern = os.path.join(test_path, "[email protected]")
sf = ShardedFile(pattern)
self.assertEqual(
sf.get_filenames(),
[
os.path.join(test_path, "[email protected]"),
os.path.join(test_path, "[email protected]"),
],
)
def test_returns_two_shards_for_two_ambiguous(self):
pattern = os.path.join(test_path, "[email protected]")
sf = ShardedFile(pattern)
self.assertEqual(
sf.get_filenames(),
[
os.path.join(test_path, "[email protected]"),
os.path.join(test_path, "[email protected]"),
],
)
def test_returns_two_shards_for_one_ambiguous(self):
pattern = os.path.join(test_path, "[email protected]")
sf = ShardedFile(pattern)
self.assertEqual(
sf.get_filenames(),
[os.path.join(test_path, "[email protected]")],
)
def test_fails_for_bad_sharding_pattern(self):
pattern = os.path.join(test_path, "[email protected]")
with self.assertRaisesRegex(ValueError, "Invalid shard specification: baz"):
ShardedFile(pattern)
def test_fails_for_ambiguous_star_pattern(self):
pattern = os.path.join(test_path, "ambiguous@*.ext")
with self.assertRaisesRegex(
ValueError, "@* matches ambiguous shard sets: @1 and @2"
):
ShardedFile(pattern)
def test_fails_for_inconsistent_set(self):
pattern = os.path.join(test_path, "[email protected]")
with self.assertRaisesRegex(
ValueError,
f"Shard {test_path}/[email protected] does not exist.",
):
ShardedFile(pattern)
def test_fails_for_inconsistent_set_star(self):
pattern = os.path.join(test_path, "inconsistent@*.baz")
with self.assertRaisesRegex(
ValueError,
f"Shard {test_path}/[email protected] does not exist.",
):
ShardedFile(pattern)
| [
"[email protected]"
]
| |
5cb566dab6aec3a2b049d1b835307cf705e2542d | bae75bf1de75fb1b76e19b0d32c778e566de570a | /smodels-database/13TeV/ATLAS/ATLAS-SUSY-2018-32/validation/TChiWW_2EqMassAx_EqMassBy.py | 5ee4f161cc9f5a413fc31f4010ad41cf0b4a88bb | []
| no_license | andlessa/RDM | 78ae5cbadda1875c24e1bb726096b05c61627249 | ac6b242871894fee492e089d378806c2c2e7aad8 | refs/heads/master | 2023-08-16T00:47:14.415434 | 2021-09-21T20:54:25 | 2021-09-21T20:54:25 | 228,639,778 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 101,810 | py | validationData = [{'slhafile': 'TChiWW_100_16_100_16.slha', 'error': 'no results', 'axes': {'x': 100.0, 'y': 16.0}}, {'slhafile': 'TChiWW_100_9_100_9.slha', 'error': 'no results', 'axes': {'x': 100.0, 'y': 9.0}}, {'slhafile': 'TChiWW_101_3_101_3.slha', 'error': 'no results', 'axes': {'x': 101.0, 'y': 3.0}}, {'slhafile': 'TChiWW_101_4_101_4.slha', 'error': 'no results', 'axes': {'x': 101.0, 'y': 4.0}}, {'slhafile': 'TChiWW_103_19_103_19.slha', 'error': 'no results', 'axes': {'x': 103.0, 'y': 19.0}}, {'slhafile': 'TChiWW_103_20_103_20.slha', 'error': 'no results', 'axes': {'x': 103.0, 'y': 20.0}}, {'slhafile': 'TChiWW_104_0_104_0.slha', 'error': 'no results', 'axes': {'x': 104.0, 'y': 0.0}}, {'slhafile': 'TChiWW_104_13_104_13.slha', 'error': 'no results', 'axes': {'x': 104.0, 'y': 13.0}}, {'slhafile': 'TChiWW_104_7_104_7.slha', 'error': 'no results', 'axes': {'x': 104.0, 'y': 7.0}}, {'slhafile': 'TChiWW_107_10_107_10.slha', 'error': 'no results', 'axes': {'x': 107.0, 'y': 10.0}}, {'slhafile': 'TChiWW_107_17_107_17.slha', 'error': 'no results', 'axes': {'x': 107.0, 'y': 17.0}}, {'slhafile': 'TChiWW_107_24_107_24.slha', 'error': 'no results', 'axes': {'x': 107.0, 'y': 24.0}}, {'slhafile': 'TChiWW_107_4_107_4.slha', 'error': 'no results', 'axes': {'x': 107.0, 'y': 4.0}}, {'slhafile': 'TChiWW_109_14_109_14.slha', 'error': 'no results', 'axes': {'x': 109.0, 'y': 14.0}}, {'slhafile': 'TChiWW_110_14_110_14.slha', 'error': 'no results', 'axes': {'x': 110.0, 'y': 14.0}}, {'slhafile': 'TChiWW_110_21_110_21.slha', 'error': 'no results', 'axes': {'x': 110.0, 'y': 21.0}}, {'slhafile': 'TChiWW_110_27_110_27.slha', 'error': 'no results', 'axes': {'x': 110.0, 'y': 27.0}}, {'slhafile': 'TChiWW_110_7_110_7.slha', 'error': 'no results', 'axes': {'x': 110.0, 'y': 7.0}}, {'slhafile': 'TChiWW_111_1_111_1.slha', 'error': 'no results', 'axes': {'x': 111.0, 'y': 1.0}}, {'slhafile': 'TChiWW_112_30_112_30.slha', 'error': 'no results', 'axes': {'x': 112.0, 'y': 30.0}}, {'slhafile': 'TChiWW_113_18_113_18.slha', 'error': 'no results', 'axes': {'x': 113.0, 'y': 18.0}}, {'slhafile': 'TChiWW_113_24_113_24.slha', 'error': 'no results', 'axes': {'x': 113.0, 'y': 24.0}}, {'slhafile': 'TChiWW_113_31_113_31.slha', 'error': 'no results', 'axes': {'x': 113.0, 'y': 31.0}}, {'slhafile': 'TChiWW_114_11_114_11.slha', 'error': 'no results', 'axes': {'x': 114.0, 'y': 11.0}}, {'slhafile': 'TChiWW_114_5_114_5.slha', 'error': 'no results', 'axes': {'x': 114.0, 'y': 5.0}}, {'slhafile': 'TChiWW_115_9_115_9.slha', 'error': 'no results', 'axes': {'x': 115.0, 'y': 9.0}}, {'slhafile': 'TChiWW_116_28_116_28.slha', 'error': 'no results', 'axes': {'x': 116.0, 'y': 28.0}}, {'slhafile': 'TChiWW_116_35_116_35.slha', 'error': 'no results', 'axes': {'x': 116.0, 'y': 35.0}}, {'slhafile': 'TChiWW_117_15_117_15.slha', 'error': 'no results', 'axes': {'x': 117.0, 'y': 15.0}}, {'slhafile': 'TChiWW_117_22_117_22.slha', 'error': 'no results', 'axes': {'x': 117.0, 'y': 22.0}}, {'slhafile': 'TChiWW_117_2_117_2.slha', 'error': 'no results', 'axes': {'x': 117.0, 'y': 2.0}}, {'slhafile': 'TChiWW_117_8_117_8.slha', 'error': 'no results', 'axes': {'x': 117.0, 'y': 8.0}}, {'slhafile': 'TChiWW_118_25_118_25.slha', 'error': 'no results', 'axes': {'x': 118.0, 'y': 25.0}}, {'slhafile': 'TChiWW_119_32_119_32.slha', 'error': 'no results', 'axes': {'x': 119.0, 'y': 32.0}}, {'slhafile': 'TChiWW_119_39_119_39.slha', 'error': 'no results', 'axes': {'x': 119.0, 'y': 39.0}}, {'slhafile': 'TChiWW_120_12_120_12.slha', 'error': 'no results', 'axes': {'x': 120.0, 'y': 12.0}}, {'slhafile': 'TChiWW_120_19_120_19.slha', 'error': 'no results', 'axes': {'x': 120.0, 'y': 19.0}}, {'slhafile': 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'TChiWW_460_40_460_40.slha', 'axes': {'x': 460.0, 'y': 40.0}, 't': 0.021504439200673783, 'signal': 32.3223, 'UL': 43.333999999999996, 'condition': 0.0, 'dataset': None, 'kfactor': 1.0}, {'slhafile': 'TChiWW_460_50_460_50.slha', 'axes': {'x': 460.0, 'y': 50.0}, 't': 0.021504439200673783, 'signal': 32.3223, 'UL': 43.83999999999999, 'condition': 0.0, 'dataset': None, 'kfactor': 1.0}, {'slhafile': 'TChiWW_460_60_460_60.slha', 'axes': {'x': 460.0, 'y': 60.0}, 't': 0.021504439200673783, 'signal': 32.3223, 'UL': 44.346000000000004, 'condition': 0.0, 'dataset': None, 'kfactor': 1.0}, {'slhafile': 'TChiWW_460_70_460_70.slha', 'axes': {'x': 460.0, 'y': 70.0}, 't': 0.021504439200673783, 'signal': 32.3223, 'UL': 43.016, 'condition': 0.0, 'dataset': None, 'kfactor': 1.0}, {'slhafile': 'TChiWW_460_80_460_80.slha', 'axes': {'x': 460.0, 'y': 80.0}, 't': 0.021504439200673783, 'signal': 32.3223, 'UL': 42.34599999999999, 'condition': 0.0, 'dataset': None, 'kfactor': 1.0}, {'slhafile': 'TChiWW_460_90_460_90.slha', 'axes': {'x': 460.0, 'y': 90.0}, 't': 0.021504439200673783, 'signal': 32.3223, 'UL': 42.336, 'condition': 0.0, 'dataset': None, 'kfactor': 1.0}, {'slhafile': 'TChiWW_480_0_480_0.slha', 'error': 'no results', 'axes': {'x': 480.0, 'y': 0.0}}, {'slhafile': 'TChiWW_480_100_480_100.slha', 'axes': {'x': 480.0, 'y': 100.0}, 't': 0.021504439200673783, 'signal': 26.6927, 'UL': 42.07200000000001, 'condition': 0.0, 'dataset': None, 'kfactor': 1.0}, {'slhafile': 'TChiWW_480_10_480_10.slha', 'axes': {'x': 480.0, 'y': 10.0}, 't': 0.021504439200673783, 'signal': 26.6927, 'UL': 37.451375, 'condition': 0.0, 'dataset': None, 'kfactor': 1.0}, {'slhafile': 'TChiWW_480_110_480_110.slha', 'axes': {'x': 480.0, 'y': 110.0}, 't': 0.021504439200673783, 'signal': 26.6927, 'UL': 43.178000000000004, 'condition': 0.0, 'dataset': None, 'kfactor': 1.0}, {'slhafile': 'TChiWW_480_120_480_120.slha', 'axes': {'x': 480.0, 'y': 120.0}, 't': 0.021504439200673783, 'signal': 26.6927, 'UL': 44.284, 'condition': 0.0, 'dataset': None, 'kfactor': 1.0}, {'slhafile': 'TChiWW_480_130_480_130.slha', 'axes': {'x': 480.0, 'y': 130.0}, 't': 0.021504439200673783, 'signal': 26.6927, 'UL': 44.422000000000004, 'condition': 0.0, 'dataset': None, 'kfactor': 1.0}, {'slhafile': 'TChiWW_480_140_480_140.slha', 'axes': {'x': 480.0, 'y': 140.0}, 't': 0.021504439200673783, 'signal': 26.6927, 'UL': 45.194, 'condition': 0.0, 'dataset': None, 'kfactor': 1.0}, {'slhafile': 'TChiWW_480_20_480_20.slha', 'axes': {'x': 480.0, 'y': 20.0}, 't': 0.021504439200673783, 'signal': 26.6927, 'UL': 37.02845833333333, 'condition': 0.0, 'dataset': None, 'kfactor': 1.0}, {'slhafile': 'TChiWW_480_30_480_30.slha', 'axes': {'x': 480.0, 'y': 30.0}, 't': 0.021504439200673783, 'signal': 26.6927, 'UL': 36.827999999999996, 'condition': 0.0, 'dataset': None, 'kfactor': 1.0}, {'slhafile': 'TChiWW_480_40_480_40.slha', 'axes': {'x': 480.0, 'y': 40.0}, 't': 0.021504439200673783, 'signal': 26.6927, 'UL': 37.334, 'condition': 0.0, 'dataset': None, 'kfactor': 1.0}, {'slhafile': 'TChiWW_480_50_480_50.slha', 'axes': {'x': 480.0, 'y': 50.0}, 't': 0.021504439200673783, 'signal': 26.6927, 'UL': 37.839999999999996, 'condition': 0.0, 'dataset': None, 'kfactor': 1.0}, {'slhafile': 'TChiWW_480_60_480_60.slha', 'axes': {'x': 480.0, 'y': 60.0}, 't': 0.021504439200673783, 'signal': 26.6927, 'UL': 38.346, 'condition': 0.0, 'dataset': None, 'kfactor': 1.0}, {'slhafile': 'TChiWW_480_70_480_70.slha', 'axes': {'x': 480.0, 'y': 70.0}, 't': 0.021504439200673783, 'signal': 26.6927, 'UL': 38.852000000000004, 'condition': 0.0, 'dataset': None, 'kfactor': 1.0}, {'slhafile': 'TChiWW_480_80_480_80.slha', 'axes': {'x': 480.0, 'y': 80.0}, 't': 0.021504439200673783, 'signal': 26.6927, 'UL': 39.86, 'condition': 0.0, 'dataset': None, 'kfactor': 1.0}, {'slhafile': 'TChiWW_480_90_480_90.slha', 'axes': {'x': 480.0, 'y': 90.0}, 't': 0.021504439200673783, 'signal': 26.6927, 'UL': 40.965999999999994, 'condition': 0.0, 'dataset': None, 'kfactor': 1.0}, {'slhafile': 'TChiWW_500_0_500_0.slha', 'error': 'no results', 'axes': {'x': 500.0, 'y': 0.0}}, {'slhafile': 'TChiWW_500_100_500_100.slha', 'axes': {'x': 500.0, 'y': 100.0}, 't': 0.021504439200673783, 'signal': 22.1265, 'UL': 38.14, 'condition': 0.0, 'dataset': None, 'kfactor': 1.0}, {'slhafile': 'TChiWW_500_10_500_10.slha', 'axes': {'x': 500.0, 'y': 10.0}, 't': 0.021504439200673783, 'signal': 22.1265, 'UL': 33.88367346938776, 'condition': 0.0, 'dataset': None, 'kfactor': 1.0}, {'slhafile': 'TChiWW_500_110_500_110.slha', 'axes': {'x': 500.0, 'y': 110.0}, 't': 0.021504439200673783, 'signal': 22.1265, 'UL': 38.278, 'condition': 0.0, 'dataset': None, 'kfactor': 1.0}, {'slhafile': 'TChiWW_500_120_500_120.slha', 'axes': {'x': 500.0, 'y': 120.0}, 't': 0.021504439200673783, 'signal': 22.1265, 'UL': 38.416, 'condition': 0.0, 'dataset': None, 'kfactor': 1.0}, {'slhafile': 'TChiWW_500_130_500_130.slha', 'axes': {'x': 500.0, 'y': 130.0}, 't': 0.021504439200673783, 'signal': 22.1265, 'UL': 38.553999999999995, 'condition': 0.0, 'dataset': None, 'kfactor': 1.0}, {'slhafile': 'TChiWW_500_140_500_140.slha', 'axes': {'x': 500.0, 'y': 140.0}, 't': 0.021504439200673783, 'signal': 22.1265, 'UL': 38.69199999999999, 'condition': 0.0, 'dataset': None, 'kfactor': 1.0}, {'slhafile': 'TChiWW_500_20_500_20.slha', 'axes': {'x': 500.0, 'y': 20.0}, 't': 0.021504439200673783, 'signal': 22.1265, 'UL': 33.68775510204081, 'condition': 0.0, 'dataset': None, 'kfactor': 1.0}, {'slhafile': 'TChiWW_500_30_500_30.slha', 'axes': {'x': 500.0, 'y': 30.0}, 't': 0.021504439200673783, 'signal': 22.1265, 'UL': 33.491836734693884, 'condition': 0.0, 'dataset': None, 'kfactor': 1.0}, {'slhafile': 'TChiWW_500_40_500_40.slha', 'axes': {'x': 500.0, 'y': 40.0}, 't': 0.021504439200673783, 'signal': 22.1265, 'UL': 33.29591836734694, 'condition': 0.0, 'dataset': None, 'kfactor': 1.0}, {'slhafile': 'TChiWW_500_50_500_50.slha', 'axes': {'x': 500.0, 'y': 50.0}, 't': 0.021504439200673783, 'signal': 22.1265, 'UL': 33.1, 'condition': 0.0, 'dataset': None, 'kfactor': 1.0}, {'slhafile': 'TChiWW_500_60_500_60.slha', 'axes': {'x': 500.0, 'y': 60.0}, 't': 0.021504439200673783, 'signal': 22.1265, 'UL': 34.108000000000004, 'condition': 0.0, 'dataset': None, 'kfactor': 1.0}, {'slhafile': 'TChiWW_500_70_500_70.slha', 'axes': {'x': 500.0, 'y': 70.0}, 't': 0.021504439200673783, 'signal': 22.1265, 'UL': 35.116, 'condition': 0.0, 'dataset': None, 'kfactor': 1.0}, {'slhafile': 'TChiWW_500_80_500_80.slha', 'axes': {'x': 500.0, 'y': 80.0}, 't': 0.021504439200673783, 'signal': 22.1265, 'UL': 36.123999999999995, 'condition': 0.0, 'dataset': None, 'kfactor': 1.0}, {'slhafile': 'TChiWW_500_90_500_90.slha', 'axes': {'x': 500.0, 'y': 90.0}, 't': 0.021504439200673783, 'signal': 22.1265, 'UL': 37.132, 'condition': 0.0, 'dataset': None, 'kfactor': 1.0}] | [
"[email protected]"
]
| |
66581eb8de97d6811abf4de898df3bae802abcf5 | ba2636f6a21d02015eb3ab806aeeef63a4cd708d | /Python/pyqt5/qt.py | eb6f9f53d288ed674f87405d5f8f8528846850d9 | []
| no_license | ghdic/marinelifeirony | ac317fcd32032e701d5793bdeb5c06bd0f52ca41 | 559ad5618eb99368b4da140cb78609bce2d5da71 | refs/heads/master | 2023-07-21T17:18:16.332222 | 2023-07-08T09:20:15 | 2023-07-08T09:20:15 | 199,758,561 | 6 | 4 | null | 2023-07-08T09:20:16 | 2019-07-31T01:58:32 | Go | UTF-8 | Python | false | false | 27,376 | py | # pyinstaller로 라이브러리 path 오류가 날때
# 환경 변수 추가를 해주거나
# 그래도 안되면 pyinstaller --path C:\Users\ghdic\AppData\Local\Programs\Python\Python35-32\Lib\site-packages\PyQt5\Qt\bin --onefile --noconsole test.py
# 이렇게 직접 path를 설정해준다
# # 1.pyqt5 기본창 실행 + 아이콘 적용
# from PyQt5.QtWidgets import QMainWindow, QApplication
# from PyQt5 import QtGui
# import sys
#
# class Window(QMainWindow):
# def __init__(self):
# super().__init__()
# self.title = "PyQt 5 Window"
# self.top = 100
# self.left = 100
# self.width = 400
# self.height = 300
# self.InitWindow()
#
# def InitWindow(self):
# self.setWindowIcon(QtGui.QIcon("LeetCode_logo.png"))
# self.setWindowTitle(self.title)
# self.setGeometry(self.left, self.top, self.width, self.height)
#
# self.show()
#
# App = QApplication(sys.argv)
# window = Window()
# sys.exit(App.exec())
# # 2.pyqt5 시간을 얻는 여러 방법
# from PyQt5.QtCore import QDateTime, QDate, QTime, Qt
#
# datetime = QDateTime.currentDateTime()
#
# print(datetime.toString())
# print(datetime.toString(Qt.ISODate))
# print(datetime.toString(Qt.DefaultLocaleLongDate))
#
# date = QDate.currentDate()
# print(date.toString())
# print(date.toString(Qt.ISODate))
# print(date.toString(Qt.DefaultLocaleLongDate))
#
# date = QTime.currentTime()
# # 3.local time to UTC
# from PyQt5.QtCore import QDateTime, Qt
#
# datetime = QDateTime.currentDateTime()
#
# print("Local Date And Time Is " + datetime.toString(Qt.DefaultLocaleLongDate))
# print("Universal Date And Time Is " + datetime.toUTC().toString())
#
# print("The Offset From UTC Is {0} : Seconds ".format(datetime.offsetFromUtc()))
# # 4.해당 월, 년에 따라 몇일 있는지 값 얻기
# from PyQt5.QtCore import QDate
#
# date = QDate.currentDate()
#
# d = QDate(2017, 10, 23)
#
# print("Days In A Month: {0}: ".format(d.daysInMonth()))
# print("Days In A Year: {0}: ".format(d.daysInYear()))
# # 5.날짜 데이터 조작하기
# from PyQt5.QtCore import QDateTime, Qt
#
# datetime = QDateTime.currentDateTime()
#
# print("Today Date And Time Is: " + datetime.toString((Qt.ISODate)))
# print("Adding 12 Days To The Date: {0}".format(datetime.addDays(12).toString(Qt.ISODate)))
# print("Subtracting 25 Days: {0}".format(datetime.addDays(-25).toString(Qt.ISODate)))
# print("Adding 50 Seconds: {0}".format(datetime.addSecs(50).toString(Qt.ISODate)))
# print("Adding 3 Months: {0}".format(datetime.addMonths(3).toString(Qt.ISODate)))
# print("Adding 12 Years: {0}".format(datetime.addYears(12).toString(Qt.ISODate)))
# # 6.QButton 사용법, 7.클릭했을때 함수실행 8.QMessage를 이용하여 question물어보는거
# import sys
# from PyQt5 import QtGui
# from PyQt5.QtWidgets import QMainWindow, QApplication, QPushButton, QToolTip, QMessageBox
# from PyQt5.QtCore import QCoreApplication
# class Window(QMainWindow):
# def __init__(self):
# super().__init__()
#
# self.title = "PyQt5 Push Button"
# self.left = 100
# self.top = 100
# self.width = 680
# self.height = 540
#
# self.setWindowIcon(QtGui.QIcon("LeetCode_logo.ico"))
# button = QPushButton("Close", self)
# button.move(200, 200)
# button.setToolTip("<h3>This is Clock Button</h3>")
# button.clicked.connect(self.Close)
#
# button2 = QPushButton("Close QMessage", self)
# button2.setGeometry(400, 400, 150, 100)
# button2.clicked.connect(self.Close_QMessage)
# self.InitUi()
#
# def InitUi(self):
# self.setWindowTitle(self.title)
# self.setGeometry(self.left, self.top, self.width, self.height)
# self.show()
#
# def Close(self):
# QCoreApplication.instance().quit()
#
# def Close_QMessage(self):
# reply = QMessageBox.question(self, "닫는지확인하는창","닫을꺼임?", QMessageBox.Yes | QMessageBox.No, QMessageBox.No)
#
# if reply == QMessageBox.Yes:
# self.close()
#
# App = QApplication(sys.argv)
# window = Window()
# sys.exit(App.exec())
# # 9.QMessage를 통해 About box 띄우기 및 question응용
# import sys
# from PyQt5 import QtGui
# from PyQt5.QtWidgets import QMainWindow, QApplication, QPushButton, QMessageBox
# from PyQt5.QtCore import QCoreApplication
# class Window(QMainWindow):
# def __init__(self):
# super().__init__()
#
# self.title = "PyQt5 Push Button"
# self.left = 100
# self.top = 100
# self.width = 680
# self.height = 540
#
# self.setWindowIcon(QtGui.QIcon("LeetCode_logo.ico"))
#
# button = QPushButton("AboutBox", self)
# button.move(200, 200)
# button.clicked.connect(self.AboutMessage)
#
# button2 = QPushButton("QuestionMessage", self)
# button2.move(100, 100)
# button2.clicked.connect(self.QuestionMessage)
#
# self.InitUi()
#
# def InitUi(self):
# self.setWindowTitle(self.title)
# self.setGeometry(self.left, self.top, self.width, self.height)
# self.show()
#
# def AboutMessage(self):
# QMessageBox.about(self, "About Message", "This is About MessageBox")
#
# def QuestionMessage(self):
# message = QMessageBox.question(self, "Question Message", "Have you Subscribeed My Channel?", QMessageBox.Yes | QMessageBox.No, QMessageBox.No)
#
# if message == QMessageBox.Yes:
# print("Yes I Have Subscribed")
# else:
# print("No I Have not Subtscribed")
# App = QApplication(sys.argv)
# window = Window()
# sys.exit(App.exec())
# # 10. status bus 출력 맨 왼쪽아래에 출력되는 로그나 정보 같은거
# import sys
# from PyQt5 import QtGui
# from PyQt5.QtWidgets import QMainWindow, QApplication, QStatusBar
#
#
# class Window(QMainWindow):
# def __init__(self):
# super().__init__()
#
# self.title = "QStatus Bar"
# self.top = 200
# self.left = 200
# self.width = 600
# self.height = 500
#
# self.setWindowIcon(QtGui.QIcon("LeetCode_logo.ico"))
#
# self.InitUI()
#
#
# def InitUI(self):
#
# self.statusBar().showMessage("This is simple status bar")
#
# self.setWindowTitle(self.title)
# self.setGeometry(self.left, self.top, self.width, self.height)
# self.show()
#
# App = QApplication(sys.argv)
# window = Window()
# sys.exit(App.exec())
# # 11. QMenu Bar 사용 위에 메뉴와 메뉴 선택지(QAction) 사용법
# import sys
# from PyQt5 import QtGui
# from PyQt5.QtWidgets import QMainWindow, QApplication, QAction
#
#
# class Window(QMainWindow):
# def __init__(self):
# super().__init__()
#
# self.title = "QMenuBar"
# self.top = 200
# self.left = 200
# self.width = 600
# self.height = 500
#
# self.setWindowIcon(QtGui.QIcon("LeetCode_logo.ico"))
#
# self.InitUI()
#
#
# def InitUI(self):
#
# mainMenu = self.menuBar()
# fileMenu = mainMenu.addMenu("File")
# viewMenu = mainMenu.addMenu("View")
# editMenu = mainMenu.addMenu("Edit")
# searchMenu = mainMenu.addMenu("Search")
# toolMenu = mainMenu.addMenu("Tool")
# helpMenu = mainMenu.addMenu("Help")
#
# exitButton = QAction(QtGui.QIcon("button_icon.png"), "Exit", self)
# exitButton.setShortcut("Ctrl+E")
# exitButton.setStatusTip("Exit Application")
# exitButton.triggered.connect(self.close)
# fileMenu.addAction(exitButton)
#
# self.setWindowTitle(self.title)
# self.setGeometry(self.left, self.top, self.width, self.height)
# self.show()
#
# App = QApplication(sys.argv)
# window = Window()
# sys.exit(App.exec())
# # 12. 메뉴 체크하여 statusBar 보여줬다 숨겼다 하기
# import sys
# from PyQt5 import QtGui
# from PyQt5.QtWidgets import QMainWindow, QApplication, QMenu, QMenuBar, QAction, QStatusBar
# class Window(QMainWindow):
# def __init__(self):
# super().__init__()
# self.title = "QMenuBar"
# self.top = 200
# self.left = 200
# self.width = 600
# self.height = 500
# self.setWindowIcon(QtGui.QIcon("LeetCode_logo.ico"))
# self.InitUI()
# def InitUI(self):
# self.statusbar = self.statusBar()
# self.statusbar.showMessage("Message is Ready")
# menubar = self.menuBar()
# viewMenu = menubar.addMenu("View")
# viewAction = QAction("View Status", self, checkable = True)
# viewAction.setStatusTip("View StatusBar")
# viewAction.setChecked(True)
# viewAction.triggered.connect(self.toggleMenu)
# viewMenu.addAction(viewAction)
# self.setWindowTitle(self.title)
# self.setGeometry(self.left, self.top, self.width, self.height)
# self.show()
# def toggleMenu(self, state):
# if state:
# self.statusbar.show()
# else:
# self.statusbar.hide()
# App = QApplication(sys.argv)
# window = Window()
# sys.exit(App.exec())
# # 13. ContextMenu, 창 아무데서나 오른쪽 마우스 클릭할때 뜨는 메뉴 만들기
# import sys
# from PyQt5 import QtGui
# from PyQt5.QtWidgets import QMainWindow, QApplication, QMenu
#
#
# class Window(QMainWindow):
# def __init__(self):
# super().__init__()
#
# self.title = "PyQt5 Context Menu"
# self.top = 200
# self.left = 200
# self.width = 600
# self.height = 500
#
# self.setWindowIcon(QtGui.QIcon("LeetCode_logo.ico"))
#
# self.InitUI()
#
#
# def InitUI(self):
# self.setWindowTitle(self.title)
# self.setGeometry(self.left, self.top, self.width, self.height)
# self.show()
#
#
# def contextMenuEvent(self, event): # contextMenuEvent라는걸 재정의 함.. 오른쪽 클릭할때 뜨는 메뉴
# contextMenu = QMenu(self)
# newAct = contextMenu.addAction("New")
# openAct = contextMenu.addAction("Open")
# quitAct = contextMenu.addAction("Quit")
#
# action = contextMenu.exec_(self.mapToGlobal(event.pos())) # 창 전체에서 pos(위치)에 대해 이벤트를 받아옴
#
# if action == quitAct:
# self.close()
#
# App = QApplication(sys.argv)
# window = Window()
# sys.exit(App.exec())
# #14 Toolbars 이거 툴바 떼어서 움직일 수도 있음.. ㄷㄷ
# import sys
# from PyQt5 import QtGui
# from PyQt5.QtWidgets import QMainWindow, QApplication, QAction
#
#
# class Window(QMainWindow):
# def __init__(self):
# super().__init__()
#
# self.title = "PyQt5 Toolbars"
# self.top = 200
# self.left = 200
# self.width = 600
# self.height = 500
#
# self.setWindowIcon(QtGui.QIcon("LeetCode_logo.ico"))
#
# self.InitUI()
#
#
# def InitUI(self):
#
# exitAct = QAction(QtGui.QIcon('exit.png'), 'Exit', self)
# exitAct.setShortcut('Ctrl+Q')
# exitAct.triggered.connect(self.CloseApp)
#
# copyAct = QAction(QtGui.QIcon('copy.png'), 'Copy', self)
# copyAct.setShortcut('Ctrl+C')
#
# pasteAct = QAction(QtGui.QIcon('paste.png'), 'Paste', self)
# pasteAct.setShortcut('Ctrl+V')
#
# deleteAct = QAction(QtGui.QIcon('delete.png'), 'Delete', self)
# deleteAct.setShortcut('Ctrl+D')
#
# saveAct = QAction(QtGui.QIcon('save.png'), 'Save', self)
# saveAct.setShortcut('Ctrl+S')
#
# self.toolbar = self.addToolBar('Toolbar')
#
# self.toolbar.addAction(exitAct)
# self.toolbar.addAction(copyAct)
# self.toolbar.addAction(pasteAct)
# self.toolbar.addAction(deleteAct)
# self.toolbar.addAction(saveAct)
#
# self.setWindowTitle(self.title)
# self.setGeometry(self.left, self.top, self.width, self.height)
# self.show()
#
# def CloseApp(self):
# self.close()
#
#
# App = QApplication(sys.argv)
# window = Window()
# sys.exit(App.exec())
# # 15. LineEdit 한줄로 텍스트 입력 받을때 쓰는것
# import sys
# from PyQt5 import QtGui
# from PyQt5.QtWidgets import QMainWindow, QApplication, QWidget, QMessageBox, QPushButton, QLineEdit
#
#
# class Window(QMainWindow):
# def __init__(self):
# super().__init__()
#
# self.title = "PyQt5 Toolbars"
# self.top = 200
# self.left = 200
# self.width = 600
# self.height = 500
#
# self.setWindowIcon(QtGui.QIcon("LeetCode_logo.ico"))
#
# self.InitUI()
#
#
# def InitUI(self):
#
# self.linedit = QLineEdit(self)
# self.linedit.move(200, 200)
# self.linedit.resize(280, 40)
#
# self.button = QPushButton("Show Text", self)
# self.button.move(270, 250)
# self.button.clicked.connect(self.onClick)
#
# self.setWindowTitle(self.title)
# self.setGeometry(self.left, self.top, self.width, self.height)
# self.show()
#
# def onClick(self):
# textValue = self.linedit.text()
# QMessageBox.question(self, "Line Edit", "You Have Typed" + textValue,
# QMessageBox.Ok, QMessageBox.Ok)
#
# App = QApplication(sys.argv)
# window = Window()
# sys.exit(App.exec())
# # 16 Positioning Widgets Move함수를 이용해 위젯움직이는거
# from PyQt5.QtWidgets import QMainWindow, QApplication, QLabel
# from PyQt5 import QtGui
# import sys
#
# class Window(QMainWindow):
# def __init__(self):
# super().__init__()
# self.title = "PyQt 5 Positioning"
# self.top = 100
# self.left = 100
# self.width = 600
# self.height = 500
# self.InitWindow()
#
# def InitWindow(self):
# self.label1 = QLabel("Please", self)
# self.label1.move(50, 50)
#
# self.label2 = QLabel("Studing", self)
# self.label2.move(100, 100)
#
# self.label3 = QLabel("English", self)
# self.label3.move(150, 150)
#
# self.label4 = QLabel("Please", self)
# self.label4.move(200, 200)
#
# self.setWindowTitle(self.title)
# self.setGeometry(self.left, self.top, self.width, self.height)
#
# self.show()
#
# App = QApplication(sys.argv)
# window = Window()
# sys.exit(App.exec())
# # 17. HBoxLayout, VBoxLayout, GroupBox
# from PyQt5.QtWidgets import QMainWindow, QApplication, QDialog, QPushButton, QMessageBox, QVBoxLayout, QHBoxLayout, QGroupBox
# from PyQt5 import QtGui
# import sys
#
# class Window(QDialog):
# def __init__(self):
# super().__init__()
# self.title = "PyQt 5 Layouts"
# self.top = 100
# self.left = 100
# self.width = 300
# self.height = 100
# self.InitWindow()
#
# def InitWindow(self):
# self.setWindowTitle(self.title)
# self.setGeometry(self.left, self.top, self.width, self.height)
#
# self.HorizontalLayout()
#
# vBox = QVBoxLayout()
# vBox.addWidget(self.groupBox)
# self.setLayout(vBox)
# self.show()
#
# def HorizontalLayout(self):
# self.groupBox = QGroupBox("What is your favorite sport?")
# hBoxlayout = QHBoxLayout()
#
# button1 = QPushButton("Football", self)
# button1.clicked.connect(self.button1Clicked)
# hBoxlayout.addWidget(button1)
#
# button2 = QPushButton("Cricket", self)
# button2.clicked.connect(self.button2Clicked)
# hBoxlayout.addWidget(button2)
#
# button3 = QPushButton("Tennis", self)
# button3.clicked.connect(self.button3Clicked)
# hBoxlayout.addWidget(button3)
#
# self.groupBox.setLayout(hBoxlayout)
#
# def button1Clicked(self):
# QMessageBox.information(self, "Football", "Yes I Like Football")
#
# def button2Clicked(self):
# QMessageBox.information(self, "Cricket", "Yes I Like Cricket")
#
# def button3Clicked(self):
# QMessageBox.information(self, "Tennis", "Yes I Like Tennis")
#
# App = QApplication(sys.argv)
# window = Window()
# sys.exit(App.exec())
# # 18. GridLayout
# from PyQt5.QtWidgets import QMainWindow, QApplication, QDialog, QGridLayout, QGroupBox, QPushButton, QVBoxLayout
# from PyQt5 import QtGui
# import sys
#
# class Window(QDialog):
# def __init__(self):
# super().__init__()
# self.title = "PyQt 5 GridLayOut"
# self.top = 100
# self.left = 100
# self.width = 300
# self.height = 100
# self.InitWindow()
#
# def InitWindow(self):
# self.setWindowTitle(self.title)
# self.setGeometry(self.left, self.top, self.width, self.height)
# self.gridLayoutCreation()
# vboxLayout = QVBoxLayout()
# vboxLayout.addWidget(self.groupBox)
# self.setLayout(vboxLayout)
# self.show()
#
# def gridLayoutCreation(self):
# self.groupBox = QGroupBox("Grid Layout Example")
#
# gridLayout = QGridLayout()
# # 위치 줄 수 있네
# gridLayout.addWidget(QPushButton('1'), 0, 0)
# gridLayout.addWidget(QPushButton('2'), 0, 1)
# gridLayout.addWidget(QPushButton('3'), 0, 2)
#
# gridLayout.addWidget(QPushButton('4'), 2, 0)
# gridLayout.addWidget(QPushButton('5'), 1, 1)
# gridLayout.addWidget(QPushButton('6'), 1, 2)
#
# self.groupBox.setLayout(gridLayout)
#
#
# App = QApplication(sys.argv)
# window = Window()
# sys.exit(App.exec())
# 19. QCheckbox
# from PyQt5.QtWidgets import QApplication, QMainWindow, QLabel, QCheckBox
# from PyQt5.QtCore import Qt
# import sys
# class Window(QMainWindow):
# def __init__(self):
# super().__init__()
# self.title = "PyQt 5 CheckBoxes"
# self.top = 100
# self.left = 100
# self.width = 300
# self.height = 100
# self.InitWindow()
#
# def InitWindow(self):
#
# checkBox = QCheckBox("Do you like Football ?", self)
# checkBox.move(20, 20)
# checkBox.toggle()
#
# checkBox.stateChanged.connect(self.checBoxChanged)
#
# self.label = QLabel("Hello", self)
# self.label.resize(1000, 20)
# self.label.move(20, 40)
#
# self.setWindowTitle(self.title)
# self.setGeometry(self.left, self.top, self.width, self.height)
#
# self.show()
#
#
# def checBoxChanged(self, state):
# if state == Qt.Checked:
# self.label.setText("Yes I like Football")
# else:
# self.label.setText("No I Dont Like FootBall")
#
#
# App = QApplication(sys.argv)
# window = Window()
# sys.exit(App.exec())
# # 20. QSPinkbox + - 마우스로 클릭해서 숫자 조정하는거
# from PyQt5.QtWidgets import QApplication, QMainWindow, QSpinBox, QVBoxLayout, QLabel, QDoubleSpinBox, QPushButton
# import sys
# class Window(QMainWindow):
# def __init__(self):
# super().__init__()
# self.title = "PyQt 5 SPinBoxes"
# self.top = 100
# self.left = 100
# self.width = 300
# self.height = 100
# #self.InitWindow()
# self.btn = QPushButton("안녕", self)
# self.btn.move(150, 0)
# self.setWindowTitle(self.title)
# self.setGeometry(self.left, self.top, self.width, self.height)
# #self.setLayout(vBoxLayout)
# self.show()
# def InitWindow(self):
# vBoxLayout = QVBoxLayout()
# self.label = QLabel("Current Value", self)
# self.label.move(20, 20)
# self.label.resize(200, 40)
# vBoxLayout.addWidget(self.label)
# self.spinBox = QSpinBox(self)
# self.spinBox.move(20, 0)
# self.spinBox.valueChanged.connect(self.valueChanged)
# self.spinBox.setMaximum(500)
# self.doubleSpinBox = QDoubleSpinBox()
# #self.doubleSpinBox.move(150, 0)
# self.setWindowTitle(self.title)
# self.setGeometry(self.left, self.top, self.width, self.height)
# #self.setLayout(vBoxLayout)
# self.show()
# def valueChanged(self):
# self.label.setText("Current Value " + str(self.spinBox.text()))
# App = QApplication(sys.argv)
# window = Window()
# sys.exit(App.exec())
# # 21. QPixmap image add
# from PyQt5.QtWidgets import QApplication, QMainWindow, QLabel
# from PyQt5.QtGui import QPixmap
# import sys
#
#
# class Window(QMainWindow):
# def __init__(self):
# super().__init__()
# self.title = "PyQt 5 Image"
# self.top = 100
# self.left = 100
# self.width = 600
# self.height = 500
# self.InitWindow()
#
# def InitWindow(self):
# self.label = QLabel(self)
# self.label.setPixmap(QPixmap('image.jpg'))
# self.label.setGeometry(60, 50, 1000, 700)
#
# self.setWindowTitle(self.title)
# self.setGeometry(self.left, self.top, self.width, self.height)
#
# self.show()
#
#
#
# App = QApplication(sys.argv)
# window = Window()
# sys.exit(App.exec())
# # 22. QSlider1
# from PyQt5.QtWidgets import QApplication, QMainWindow, QWidget, QLineEdit, QSlider, QVBoxLayout
# from PyQt5.QtCore import Qt
# import sys
#
#
# class Window(QWidget):
# def __init__(self):
# super().__init__()
# self.title = "PyQt 5 QSlider"
# self.top = 100
# self.left = 100
# self.width = 600
# self.height = 500
# self.InitWindow()
#
# def InitWindow(self):
#
# vboxLayout = QVBoxLayout()
# self.lineEdit = QLineEdit(self)
# self.lineEdit.move(100, 50)
# vboxLayout.addWidget(self.lineEdit)
#
# self.slider = QSlider(Qt.Horizontal, self)
# self.slider.move(100, 20)
# self.slider.setMinimum(1)
# self.slider.setMaximum(99)
# self.slider.setValue(20)
# self.slider.setTickPosition(QSlider.TicksBelow)
# self.slider.setTickInterval(10)
# self.slider.valueChanged.connect(self.changedValude)
# vboxLayout.addWidget(self.slider)
#
# self.setWindowTitle(self.title)
# self.setGeometry(self.left, self.top, self.width, self.height)
#
# self.show()
#
# def changedValude(self):
# size = str(self.slider.value())
# self.lineEdit.setText(size)
#
#
# App = QApplication(sys.argv)
# window = Window()
# sys.exit(App.exec())
# # 23.
# from PyQt5.QtWidgets import QApplication, QMainWindow
# import sys
#
#
# class Window(QMainWindow):
# def __init__(self):
# super().__init__()
# self.title = "PyQt 5 "
# self.top = 100
# self.left = 100
# self.width = 600
# self.height = 500
# self.InitWindow()
#
# def InitWindow(self):
# self.setWindowTitle(self.title)
# self.setGeometry(self.left, self.top, self.width, self.height)
#
# self.show()
#
#
#
# App = QApplication(sys.argv)
# window = Window()
# sys.exit(App.exec())
# # QListWidget
# from PyQt5.QtWidgets import QApplication, QWidget, QVBoxLayout, QListWidget, QLabel
# import sys
# from PyQt5 import QtGui
# class Window(QWidget):
# def __init__(self):
# super().__init__()
# self.title = "PyQt5 QListWidget"
# self.left = 500
# self.top = 200
# self.width = 300
# self.height = 500
# self.iconName = 'temp.png'
# self.InitUI()
# def InitUI(self):
# self.setWindowTitle(self.title)
# self.setWindowIcon(QtGui.QIcon(self.iconName))
# self.setGeometry(self.left, self.top, self.width, self.height)
# vbox = QVBoxLayout()
# self.list = QListWidget()
# self.list.insertItem(0, "Python")
# self.list.insertItem(1, "Java")
# self.list.insertItem(1, "C++")
# self.list.insertItem(1, "C#")
# self.list.insertItem(1, "Ruby")
# self.list.insertItem(1, "Kotlin")
# self.list.clicked.connect(self.listview_clicked)
# self.label = QLabel()
# self.label.setFont(QtGui.QFont("Sanserif", 15))
# vbox.addWidget(self.label)
# vbox.addWidget(self.list)
# self.setLayout(vbox)
# self.show()
# def listview_clicked(self):
# item = self.list.currentItem()
# self.label.setText(str(item.text()))
# App = QApplication(sys.argv)
# window = Window()
# sys.exit(App.exec())
# from PyQt5 import QtGui
# from PyQt5.QtWidgets import QApplication, QWidget, QPlainTextEdit, QVBoxLayout
# import sys
# class Window(QWidget):
# def __init__(self):
# super().__init__()
# self.title = "PyQt5 Plain TextEdit"
# self.top = 200
# self.left = 500
# self.width = 400
# self.height = 300
# self.InitWindow()
# def InitWindow(self):
# self.setWindowIcon(QtGui.QIcon("icon.png"))
# self.setWindowTitle(self.title)
# self.setGeometry(self.left, self.top, self.width, self.height)
# vbox = QVBoxLayout()
# plainText = QPlainTextEdit()
# plainText.setPlaceholderText("This is some text for our plaintextedit")
# #plainText.setReadOnly(True)
# text = "Please subscribe the channel and like the videos"
# plainText.appendPlainText(text)
# plainText.setPlaceholderText(text)
# plainText.setUndoRedoEnabled(True)
# vbox.addWidget(plainText)
# self.setLayout(vbox)
# self.show()
# App = QApplication(sys.argv)
# window = Window()
# sys.exit(App.exec())
# # 콘솔 입력 가능 예제
# import platform
# import sys
# from PyQt5 import QtCore, QtGui, QtWidgets
# class NativeMessenger(QtCore.QObject):
# messageChanged = QtCore.pyqtSignal(str)
# def __init__(self, parent=None):
# super().__init__(parent)
# self.m_qin = QtCore.QFile()
# self.m_qin.open(
# sys.stdin.fileno(), QtCore.QIODevice.ReadOnly | QtCore.QIODevice.Unbuffered
# )
# if platform.system() == "Windows":
# import win32api
# if sys.platform == "win32":
# import os
# import msvcrt
# if platform.python_implementation() == "PyPy":
# os.fdopen(fh.fileno(), "wb", 0)
# else:
# msvcrt.setmode(sys.stdin.fileno(), os.O_BINARY)
# self.m_notifier = QtCore.QWinEventNotifier(
# win32api.GetStdHandle(win32api.STD_INPUT_HANDLE)
# )
# else:
# self.m_notifier = QtCore.QSocketNotifier(
# sys.stdin.fileno(), QtCore.QSocketNotifier.Read, self
# )
# self.m_notifier.activated.connect(self.readyRead)
# @QtCore.pyqtSlot()
# def readyRead(self):
# line = self.m_qin.readLine().data().decode().strip()
# self.messageChanged.emit(line)
# if __name__ == "__main__":
# app = QtWidgets.QApplication(sys.argv)
# w = QtWidgets.QLabel(alignment=QtCore.Qt.AlignCenter)
# w.resize(640, 480)
# w.show()
# messenger = NativeMessenger()
# messenger.messageChanged.connect(w.setText)
# sys.exit(app.exec_())
| [
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]
| |
cd28d35b22be1c515cd2b2d8c6067d3bec20ebf5 | 131cf803a1f7b9638ab0a604d61ab2de22906014 | /tests/system/web/api_1_0/resources/test_action_template.py | cb67c0780e6d1e4638116518724615052eadf8a7 | [
"Apache-2.0"
]
| permissive | dimensigon/dimensigon | 757be1e61e57f7ce0a610a9531317761393eaad0 | 079d7c91a66e10f13510d89844fbadb27e005b40 | refs/heads/master | 2023-03-09T06:50:55.994738 | 2021-02-21T11:45:01 | 2021-02-21T11:45:01 | 209,486,736 | 2 | 0 | Apache-2.0 | 2021-02-26T02:59:18 | 2019-09-19T07:11:35 | Python | UTF-8 | Python | false | false | 2,167 | py | from flask import url_for
from dimensigon import defaults
from dimensigon.domain.entities import bypass_datamark_update, ActionType, ActionTemplate
from dimensigon.web import db
from tests.base import TestDimensigonBase
class TestActionTemplate(TestDimensigonBase):
def setUp(self) -> None:
self.initials = dict(self.initials)
self.initials.update(action_template=False)
super().setUp()
def fill_database(self):
self.at1 = ActionTemplate(id="aaaaaaaa-1234-5678-1234-56781234aaa1", action_type=ActionType.SHELL,
code="mkdir {dir}", last_modified_at=defaults.INITIAL_DATEMARK, name="mkdir",
version=1)
self.at2 = ActionTemplate(id="aaaaaaaa-1234-5678-1234-56781234aaa2",
action_type=ActionType.SHELL,
code="rmdir {dir}",
last_modified_at=defaults.INITIAL_DATEMARK,
expected_stdout='output',
expected_stderr='err',
expected_rc=0,
name="rmdir",
system_kwargs={'kwarg1': 1},
pre_process='pre_process',
post_process='post_process',
version=1
)
with bypass_datamark_update():
db.session.add_all([self.at1, self.at2])
db.session.commit()
def test_action_template_list(self):
response = self.client.get(url_for('api_1_0.actiontemplatelist'), headers=self.auth.header)
self.assertListEqual([self.at1.to_json(), self.at2.to_json()],
response.get_json())
def test_action_template(self):
response = self.client.get(
url_for('api_1_0.actiontemplateresource', action_template_id="aaaaaaaa-1234-5678-1234-56781234aaa1"),
headers=self.auth.header)
self.assertDictEqual(
self.at1.to_json(), response.get_json())
| [
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]
| |
173bef981b78afc909cfcef7137f104224d65eba | f7c07caa1210d2a08e8433cdd854b1232efa88e3 | /Basic-Python-Examples/Reading-Input.py | 6a2261758bee80620c94908b12e5dff8ba5e9431 | []
| no_license | rchicoli/ispycode-python | c2fbecc28bf32933150986d24f77b7297f50b78e | fa27f2377943ac2e4d983065406578151091e3f5 | refs/heads/master | 2020-03-20T11:34:59.698618 | 2018-06-14T21:14:02 | 2018-06-14T21:14:02 | 137,407,150 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 70 | py |
var = raw_input("Enter something: ")
print "You entered: ", var
| [
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]
| |
e7a482ab473236d6774c1f06998aecde86703dd1 | e78154abbb8bacf5afccda9da371684cbeabad36 | /popego/popserver/popserver/db/.svn/text-base/migration_004.py.svn-base | b03ff10669d839bab29b9c43a3f116206f18bf1f | [
"BSD-3-Clause"
]
| permissive | enterstudio/popego | 1a196fabc374c0f45764e5c74bd7752236424040 | 2d09e793d9d2f297139edb325b8a70ddda9b2705 | refs/heads/master | 2021-04-09T16:39:40.781634 | 2016-10-14T16:53:47 | 2016-10-14T16:53:47 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 385 | """ agregar columna para grupo distinguido en itemgroups """
__docformat__ = "restructuredtext"
# I'm using a creative whitespace style that makes it readable both here
# and when printed.
migration = [
("""\
ALTER TABLE itemgroups ADD COLUMN is_null_group BOOLEAN NOT NULL;
""",
"""\
ALTER TABLE itemgroups DROP COLUMN is_null_group;
"""),
]
| [
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]
| ||
4aaf0372ce3ad85fcd307858db488117364e0b63 | cb405bbb673711585ec36b9c9cf9e1c255579e83 | /web/service/github/api/v3/users/SshKeys.py | 67d609fd1471f4658286030e24db636d05bb71a7 | [
"BSD-3-Clause",
"CC0-1.0",
"MIT",
"Unlicense",
"Apache-2.0"
]
| permissive | ytyaru/Python.OTP.tools.201704200841 | c5b265a6458d007ad9b1ad032b42a4eacb5944a3 | 120239f96e40467203939492fd7ad9c5967fac0f | refs/heads/master | 2021-01-25T06:23:56.998622 | 2017-06-06T20:26:59 | 2017-06-06T20:26:59 | 93,560,782 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,363 | py | #!python3
#encoding:utf-8
import requests
import datetime
import time
import json
import web.service.github.api.v3.Response
class SshKeys(object):
def __init__(self, reqp, response):
# def __init__(self):
# self.__response = web.service.github.api.v3.Response.Response()
self.__reqp = reqp
self.__response = response
"""
SSH鍵の生成。
@params {string} public_keyはSSH公開鍵。
@params {string} titleはSSH公開鍵。
"""
def Create(self, public_key, title=None):
# def Create(self, mailaddress, public_key):
# def Create(self, token, mailaddress, public_key):
method = 'POST'
endpoint = 'users/:username/keys'
params = self.__reqp.Get(method, endpoint)
# headers=self.__GetHeaders(token)
# data=json.dumps({'title': mailaddress, 'key': public_key})
# params['data'] = json.dumps({'title': mailaddress, 'key': public_key})
params['data'] = json.dumps({'title': title, 'key': public_key})
url = 'https://api.github.com/user/keys'
print(url)
print(data)
r = requests.post(url, **params)
# r = requests.post(url, headers=headers, data=data)
return self.__response.Get(r)
def Gets(self, username):
# def Gets(self, username, token):
method = 'GET'
endpoint = 'users/:username/keys'
params = self.__reqp.Get(method, endpoint)
keys = []
url = 'https://api.github.com/users/{username}/keys'.format(username=username)
# headers=self.__GetHeaders(token)
while None is not url:
print(url)
# r = requests.get(url, headers=headers)
r = requests.get(url, **params)
keys += self.__response.Get(r)
url = self.__response.Headers.Link.Next(r)
params = self.__reqp.Get(method, endpoint)
return keys
def Get(self, key_id):
# def Get(self, token, key_id):
method = 'GET'
endpoint = 'user/keys/:id'
params = self.__reqp.Get(method, endpoint)
url = 'https://api.github.com/user/keys/{key_id}'.format(key_id=key_id)
# headers=self.__GetHeaders(token)
print(url)
r = requests.get(url, **params)
# r = requests.get(url, headers=headers)
return self.__response.Get(r)
"""
GitHubに設定したSSH公開鍵を削除する。
BASIC認証でしか使えない。
"""
def Delete(self, key_id):
# def Delete(self, key_id, username, password, otp=None):
method = 'DELETE'
endpoint = 'user/keys/:id'
params = self.__reqp.Get(method, endpoint)
url = 'https://api.github.com/user/keys/{key_id}'.format(key_id=key_id)
# headers=self.__GetHeaders(otp)
print(url)
r = requests.delete(url, **params)
# r = requests.delete(url, headers=headers, auth=(username, password))
return self.__response.Get(r)
def __GetHeaders(self, token=None, otp=None):
headers = {
'Time-Zone': 'Asia/Tokyo',
'Accept': 'application/vnd.github.v3+json'
}
if None is not token:
headers.update({'Authorization': 'token ' + token})
if None is not otp:
headers.update({'X-GitHub-OTP': otp})
print(headers)
return headers
| [
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]
| |
b65865ec1ef0c354e6434848517d607c33532700 | fb23ef7cffab7c23081d026e4bf6e642746ba5fa | /pytext/models/r3f_models.py | ccae7e70cb022880311ad97ec658d450bc65251a | [
"BSD-3-Clause"
]
| permissive | thomascherickal/pytext | 2558d597f4355e728cd9982dc29f2b857caed77d | da680540176436163ffd34136dd35a22f98dbb3c | refs/heads/master | 2023-02-27T07:20:30.079053 | 2021-02-04T04:56:57 | 2021-02-04T04:58:44 | 336,046,191 | 1 | 0 | NOASSERTION | 2021-02-04T18:28:05 | 2021-02-04T18:28:04 | null | UTF-8 | Python | false | false | 7,228 | py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from contextlib import AbstractContextManager
from enum import Enum
from typing import Dict
import torch
import torch.nn.functional as F
from pytext.common.constants import Stage
from pytext.config import ConfigBase
from pytext.utils.precision import maybe_float
class R3FNoiseType(Enum):
UNIFORM = "uniform"
NORMAL = "normal"
def build_noise_sampler(noise_type: R3FNoiseType, eps: float):
"""
Given a `noise_type` (`R3FNoiseType`): builds a `torch.distribution`
capable of generating noise within the passed in `eps` (`float`).
"""
if noise_type == R3FNoiseType.UNIFORM:
return torch.distributions.uniform.Uniform(low=-eps, high=eps)
elif noise_type == R3FNoiseType.NORMAL:
return torch.distributions.normal.Normal(loc=0.0, scale=eps)
else:
raise Exception(f"Unknown noise type: {noise_type}")
def compute_symmetric_kl(noised_logits, input_logits):
"""
Computes symmetric KL loss by taking the KL for both the input logits
and the noised logits and comparing the two
"""
return F.kl_div(
F.log_softmax(noised_logits, dim=-1, dtype=torch.float32),
F.softmax(input_logits, dim=-1, dtype=torch.float32),
None,
None,
"sum",
) + F.kl_div(
F.log_softmax(input_logits, dim=-1, dtype=torch.float32),
F.softmax(noised_logits, dim=-1, dtype=torch.float32),
None,
None,
"sum",
) # / noised_logits.size(0)
class R3FConfigOptions(ConfigBase):
"""
Configuration options for models using R3F
"""
# for MTL purposes different lambda per loss
r3f_lambda_by_loss: Dict[str, float] = {}
r3f_default_lambda: float = 0.5
eps: float = 1e-5
noise_type: R3FNoiseType = R3FNoiseType.UNIFORM
class R3FNoiseContextManager(AbstractContextManager):
"""
Context manager that adds a forward hook to the embedding module,
to insert noise into the model and detatch embedding when doing
this pass
"""
def __init__(self, context):
self.encoder_hook = None
self.decoder_hook = None
self.context = context
self.hook = self.context.get_embedding_module().register_forward_hook(
self._hook_implementation
)
def __enter__(self):
return self.context
def __exit__(self, type, value, traceback):
self.hook.remove()
self.hook = None
def _hook_implementation(self, module, input, output):
noise = self.context.noise_sampler.sample(sample_shape=output.shape).to(output)
return output.clone().detach() + noise
class R3FPyTextMixin(object):
"""
Mixin class for applying the R3F method, to apply R3F with any model
inherit the class and implement the abstract functions.
For more details: https://arxiv.org/abs/2008.03156
"""
def __init__(self, config: R3FConfigOptions):
self.r3f_lambda_by_loss = config.r3f_lambda_by_loss
self.r3f_default_lambda = config.r3f_default_lambda
self.r3f_eps = config.eps
self.noise_sampler = build_noise_sampler(config.noise_type, self.r3f_eps)
def get_embedding_module(self, *args, **kwargs):
"""
Given the core model outputs, this returns the embedding module that is used
for the R3F loss, in particular noise will be injected to this module.
"""
raise NotImplementedError()
def forward_with_noise(self, *args, **kwargs):
with R3FNoiseContextManager(self):
return self.original_forward(*args, **kwargs)
def original_forward(self, *args, **kwargs):
"""
Runs the traditional forward of this model
"""
raise NotImplementedError()
def get_sample_size(self, model_inputs, targets):
"""
Gets the sample size of the model that is used as a regularization
factor to the model itself
"""
raise NotImplementedError()
def get_r3f_model_output(self, model_output):
"""
Extracts the output from the model.forward() call that is used for the
r3f loss term
"""
return model_output
def forward(self, *args, use_r3f: bool = False, **kwargs):
if use_r3f:
# forward with the normal model
model_output = self.original_forward(
*args,
**kwargs,
)
# compute noised model outputs
noise_model_outputs = self.forward_with_noise(
*args,
**kwargs,
)
return model_output, noise_model_outputs
else:
return self.original_forward(*args, **kwargs)
def get_r3f_loss_terms(
self, model_outputs, noise_model_outputs, sample_size: int
) -> torch.Tensor:
"""
Computes the auxillary loss for R3F, in particular computes a symmetric
KL divergence between the result from the input embedding and the noise
input embedding.
"""
label_symm_kl = compute_symmetric_kl(
self.get_r3f_model_output(noise_model_outputs),
self.get_r3f_model_output(model_outputs),
)
label_symm_kl = label_symm_kl # * sample_size
return (
self.r3f_lambda_by_loss.get("label", self.r3f_default_lambda)
* label_symm_kl
)
@classmethod
def train_batch(cls, model, batch, state=None):
"""
Runs training over a batch with the R3F method, training will use R3F
while eval and test do not.
"""
# Forward pass through the network.
model_inputs = model.arrange_model_inputs(batch)
model_context = model.arrange_model_context(batch)
targets = model.arrange_targets(batch)
sample_size = model.get_sample_size(model_inputs=model_inputs, targets=targets)
# get embedding
r3f_loss_term = torch.tensor(0)
if state and state.stage == Stage.TRAIN:
# during training run R3F forward calls
model_outputs, noise_model_outputs = model(*model_inputs, use_r3f=True)
r3f_loss_term = model.get_r3f_loss_terms(
model_outputs, noise_model_outputs, sample_size=sample_size
)
else:
# during eval and test don't run R3F forward
model_outputs = model(*model_inputs, use_r3f=False)
# Add stage to context.
if state:
if model_context is None:
model_context = {"stage": state.stage, "epoch": state.epoch}
else:
model_context["stage"] = state.stage
model_context["epoch"] = state.epoch
# Compute loss and predictions.
loss = maybe_float(model.get_loss(model_outputs, targets, model_context))
# add R3F loss term
loss = loss + r3f_loss_term.to(loss.device)
predictions, scores = model.get_pred(model_outputs, context=model_context)
# Pack results and return them.
metric_data = (predictions, targets, scores, loss, model_inputs)
return loss, metric_data
| [
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]
| |
d590ce824a5cb5b72f17a9cab92fbb89c815aae1 | 9743d5fd24822f79c156ad112229e25adb9ed6f6 | /xai/brain/wordbase/otherforms/_brains.py | f3368e44fd4ab511605eb85187d524295c0a1b9c | [
"MIT"
]
| permissive | cash2one/xai | de7adad1758f50dd6786bf0111e71a903f039b64 | e76f12c9f4dcf3ac1c7c08b0cc8844c0b0a104b6 | refs/heads/master | 2021-01-19T12:33:54.964379 | 2017-01-28T02:00:50 | 2017-01-28T02:00:50 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 218 | py |
#calss header
class _BRAINS():
def __init__(self,):
self.name = "BRAINS"
self.definitions = brain
self.parents = []
self.childen = []
self.properties = []
self.jsondata = {}
self.basic = ['brain']
| [
"[email protected]"
]
| |
59eac1215433b62efec457f6593d5623cc29ab49 | 933fc538fa7f1b304467b5e1a0ba7e155abbb535 | /setup.py | 9cc53f678f6035c91fc9ed565426d676a71e6390 | [
"MIT"
]
| permissive | PCKK123/geemap | f2f8d3ab59127b4399ecf005b2eca3cd91832791 | ce65675ca4935076793e667f342e7be79a0ef6c5 | refs/heads/master | 2023-07-19T05:47:16.950807 | 2021-09-03T12:55:36 | 2021-09-03T12:55:36 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,024 | py | #!/usr/bin/env python
"""The setup script."""
import platform
from os import path as op
import io
from setuptools import setup, find_packages
with open("README.rst") as readme_file:
readme = readme_file.read()
with open("HISTORY.rst") as history_file:
history = history_file.read()
here = op.abspath(op.dirname(__file__))
# get the dependencies and installs
with io.open(op.join(here, "requirements.txt"), encoding="utf-8") as f:
all_reqs = f.read().split("\n")
if platform.system() == "Windows":
all_reqs.append("pywin32")
install_requires = [x.strip() for x in all_reqs if "git+" not in x]
dependency_links = [x.strip().replace("git+", "") for x in all_reqs if "git+" not in x]
requirements = [
"Click>=7.0",
]
setup_requirements = []
test_requirements = []
setup(
author="Qiusheng Wu",
author_email="[email protected]",
python_requires=">=3.5",
classifiers=[
"Development Status :: 2 - Pre-Alpha",
"Intended Audience :: Developers",
"License :: OSI Approved :: MIT License",
"Natural Language :: English",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.5",
"Programming Language :: Python :: 3.6",
"Programming Language :: Python :: 3.7",
"Programming Language :: Python :: 3.8",
],
description="A Python package for interactive mapping using Google Earth Engine and ipyleaflet",
entry_points={
"console_scripts": [
"geemap=geemap.cli:main",
],
},
install_requires=install_requires,
dependency_links=dependency_links,
license="MIT license",
long_description=readme + "\n\n" + history,
include_package_data=True,
keywords="geemap",
name="geemap",
packages=find_packages(include=["geemap", "geemap.*"]),
setup_requires=setup_requirements,
test_suite="tests",
tests_require=test_requirements,
url="https://github.com/giswqs/geemap",
version="0.8.18",
zip_safe=False,
)
| [
"[email protected]"
]
| |
45b6c019f9d4399a36f8f1ceb7bd52afae6238cf | 86a017dd4c8d4d77c511cc598190aaa9dc0ae3e8 | /study_class.py | 4441fdb2079687168ed046dbee0f5dd42d538569 | []
| no_license | sungguenja/studying | fd7459eb9faa6488d7b63bf3884a92513daf3c54 | 719f4dfbda211c34de2a0c8cf3b9d3001f29fcec | refs/heads/master | 2023-08-17T13:46:44.343780 | 2023-08-10T11:55:15 | 2023-08-10T11:55:15 | 232,306,053 | 0 | 0 | null | 2022-12-16T10:53:26 | 2020-01-07T11:00:28 | Python | UTF-8 | Python | false | false | 756 | py | class Shape:
def __init__(self, length):
self.__length = length
def ar(self):
return self.__length * self.__length
def length(self):
return self.__length
class Squre(Shape):
def __init__(self, length, area):
super(Squre, self).__init__(length)
self.__area=area
def area(self):
return self.__area
def __repr__(self):
return "{0}".format(super(Squre, self).ar())
N1=Squre(3, 9)
print(N1)
class Person:
def __init__(self, gender):
self.__gender = gender
def gender(self):
return self.__gender
def __repr__(self):
return "{0}".format(self.__gender)
class Son(Person):
def __init__(self, gender):
super(Son, self).__init__(gender)
def __repr__(self):
return "{0}".format(super(Son, self).gender())
J1=Son("Male")
J2=Son("Female")
print(J1)
print(J2) | [
"[email protected]"
]
| |
b070add0a3c79d07ec513e5fb177ced969dc9b3b | 7e41fd7f107a771b9bb52f9c58cd4afda6308044 | /tool/modules/ip_misc.py | 097f6e8f3e0122de2126eac3e6ed632e7dd051fc | []
| no_license | gisazae/SpoofingDetector | e6043bef4b8c28af63fd905963abbc89c954f4a8 | 0ba2d6dd7e91f629f8b83dc851faa4f301f53db7 | refs/heads/master | 2021-01-24T08:41:53.820723 | 2018-02-26T15:35:56 | 2018-02-26T15:35:56 | 122,988,293 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,321 | py | import re, os
from scapy.all import *
'''
Check if ip address is correctly formed
'''
def check_IP(ip):
pattern = re.compile("^(([0-9]){1,3}\.([0-9]){1,3}\.([0-9]){1,3}\.([0-9]){1,3})$")
return pattern.match(ip)
'''
Check if port number is between range 1-65535
'''
def check_port(port):
return int(port) in range(1, 65536)
'''
Enables IPv4 forwarding for routing purposes
'''
def enable_forward():
os.system("echo 1 > /proc/sys/net/ipv4/ip_forward")
'''
Disables IPv4 forwarding
'''
def disable_forward():
os.system("echo 0 > /proc/sys/net/ipv4/ip_forward")
'''
Redirects http traffic to this machine's proxy (sslstrip)
'''
def start_http_redirect(port):
os.system("iptables -t nat -A PREROUTING -p tcp --destination-port 80 -j REDIRECT --to-port " + str(port))
'''
Stops http redirect to sslstrip proxy
'''
def stop_http_redirect(port):
os.system("iptables -t nat -D PREROUTING -p tcp --destination-port 80 -j REDIRECT --to-port " + str(port))
'''
Redirects DNS queries to this machine's DNS
'''
def start_dns_redirect():
os.system("iptables -t nat -A PREROUTING -p udp --destination-port 53 -j REDIRECT --to-port 53")
'''
Stops DNS query redirect
'''
def stop_dns_redirect():
os.system("iptables -t nat -D PREROUTING -p udp --destination-port 53 -j REDIRECT --to-port 53")
| [
"[email protected]"
]
| |
bd672e59d39f51800c208d1f7209a8897e8f0889 | c9ddbdb5678ba6e1c5c7e64adf2802ca16df778c | /cases/synthetic/class_def_attr-big-134.py | 466ed590182cc1782fc6ea8ba942f3b937d480e4 | []
| no_license | Virtlink/ccbench-chocopy | c3f7f6af6349aff6503196f727ef89f210a1eac8 | c7efae43bf32696ee2b2ee781bdfe4f7730dec3f | refs/heads/main | 2023-04-07T15:07:12.464038 | 2022-02-03T15:42:39 | 2022-02-03T15:42:39 | 451,969,776 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,300 | py | class A(object):
x:int = 1
class A2(object):
x:int = 1
x2:int = 1
class A3(object):
x:int = 1
x2:int = 1
x3:int = 1
class A4(object):
x:int = 1
x2:int = 1
x3:int = 1
x4:int = 1
class A5(object):
x:int = 1
x2:int = 1
x3:int = $Literal
x4:int = 1
x5:int = 1
class B(A):
def __init__(self: "B"):
pass
class B2(A):
def __init__(self: "B2"):
pass
class B3(A):
def __init__(self: "B3"):
pass
class B4(A):
def __init__(self: "B4"):
pass
class B5(A):
def __init__(self: "B5"):
pass
class C(B):
z:bool = True
class C2(B):
z:bool = True
z2:bool = True
class C3(B):
z:bool = True
z2:bool = True
z3:bool = True
class C4(B):
z:bool = True
z2:bool = True
z3:bool = True
z4:bool = True
class C5(B):
z:bool = True
z2:bool = True
z3:bool = True
z4:bool = True
z5:bool = True
a:A = None
a2:A = None
a3:A = None
a4:A = None
a5:A = None
b:B = None
b2:B = None
b3:B = None
b4:B = None
b5:B = None
c:C = None
c2:C = None
c3:C = None
c4:C = None
c5:C = None
a = A()
a2 = A()
a3 = A()
a4 = A()
a5 = A()
b = B()
b2 = B()
b3 = B()
b4 = B()
b5 = B()
c = C()
c2 = C()
c3 = C()
c4 = C()
c5 = C()
a.x = 1
b.x = a.x
c.z = a.x == b.x
| [
"[email protected]"
]
| |
13385c1bac21e9f6d94674d6956396df3909b0ad | dd3f5a712dbab0d3c4f4526c64c08ba710f78b81 | /Basic/pachong/t04proxy.py | ff16307fbb87fb20b693ba29b62a7d190f6c04e9 | []
| no_license | nameusea/pyGreat | 3988ebcce3f80a7e458a20f9b2e3ccba368efcf8 | dde8b6a1348620ffd3b2d65db3d5b4331e5c78be | refs/heads/master | 2023-04-25T09:02:32.831423 | 2021-05-17T11:31:22 | 2021-05-17T11:31:22 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,590 | py | import requests
from bs4 import BeautifulSoup
import json
# https://wanakiki.github.io/2020/spider-with-proxy/
class GetIp(object):
"""抓取代理IP"""
def __init__(self):
"""初始化变量"""
self.url = 'http://www.xicidaili.com/nt/'
self.check_url = 'https://www.ip.cn/'
self.ip_list = []
@staticmethod
def get_html(url):
"""请求html页面信息"""
header = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.77 Safari/537.36'
}
try:
request = requests.get(url=url, headers=header)
request.encoding = 'utf-8'
html = request.text
return html
except Exception as e:
return ''
def get_available_ip(self, ip_address, ip_port):
"""检测IP地址是否可用"""
header = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.77 Safari/537.36'
}
ip_url_next = '://' + ip_address + ':' + ip_port
proxies = {'http': 'http' + ip_url_next, 'https': 'https' + ip_url_next}
try:
r = requests.get(self.check_url, headers=header, proxies=proxies, timeout=2)
html = r.text
except:
print('fail-%s' % ip_address)
else:
print('success-%s' % ip_address)
soup = BeautifulSoup(html, 'lxml')
div = soup.find(class_='well')
if div:
print(div.text)
ip_info = {'address': ip_address, 'port': ip_port}
self.ip_list.append(ip_info) # 可以用的ip保存到self.ip_list
def main(self):
"""主方法"""
for i in range(1, 10): # 从这个网站上检测n页的ip
web_html = self.get_html(self.url+str(i))
soup = BeautifulSoup(web_html, 'lxml')
ip_list = soup.find(id='ip_list').find_all('tr')
for ip_info in ip_list:
td_list = ip_info.find_all('td')
if len(td_list) > 0:
ip_address = td_list[1].text
ip_port = td_list[2].text
# 检测IP地址是否有效
self.get_available_ip(ip_address, ip_port)
# 写入有效文件
with open('data/ip.txt', 'w') as file:
json.dump(self.ip_list, file)
print(self.ip_list)
# 程序主入口
if __name__ == '__main__':
get_ip = GetIp()
get_ip.main()
| [
"[email protected]"
]
| |
2ce825ca6f6f248267a0b77f24692ef69ed28d5c | f07a42f652f46106dee4749277d41c302e2b7406 | /Data Set/bug-fixing-5/de4a8b83df05373c8295f9771315d9a23bc2b591-<convert_mongo_result_to_valid_json>-fix.py | 9e4aeda3cd4e73fb96be34588cc02ac29c6a781b | []
| no_license | wsgan001/PyFPattern | e0fe06341cc5d51b3ad0fe29b84098d140ed54d1 | cc347e32745f99c0cd95e79a18ddacc4574d7faa | refs/heads/main | 2023-08-25T23:48:26.112133 | 2021-10-23T14:11:22 | 2021-10-23T14:11:22 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 838 | py | def convert_mongo_result_to_valid_json(self, result):
if (result is None):
return result
if isinstance(result, (integer_types + (float, bool))):
return result
if isinstance(result, string_types):
return result
elif isinstance(result, list):
new_list = []
for elem in result:
new_list.append(self.convert_mongo_result_to_valid_json(elem))
return new_list
elif isinstance(result, dict):
new_dict = {
}
for key in result.keys():
value = result[key]
new_dict[key] = self.convert_mongo_result_to_valid_json(value)
return new_dict
elif isinstance(result, datetime.datetime):
return (result - datetime.datetime(1970, 1, 1)).total_seconds()
else:
return '{}'.format(result) | [
"[email protected]"
]
| |
4a0d71573f39fb6949ac84ec04eeedf104afda83 | 17bdba01253b57e0b74b20a44dae067a643c8b86 | /ingenico/connect/sdk/domain/mandates/definitions/mandate_customer.py | e2fca0a6b17e66a4039b901f4e2e929a2e8f319f | [
"MIT"
]
| permissive | BEAM-ZILLOW/connect-sdk-python3 | 04b18733216d1e8b5e5c724e315ed02de6b83aca | 0355389ddb096edc19d770c8b8499bd3351d2b3d | refs/heads/master | 2020-04-12T17:50:03.259747 | 2018-11-08T09:59:43 | 2018-11-08T09:59:43 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,861 | py | # -*- coding: utf-8 -*-
#
# This class was auto-generated from the API references found at
# https://epayments-api.developer-ingenico.com/s2sapi/v1/
#
from ingenico.connect.sdk.data_object import DataObject
from ingenico.connect.sdk.domain.definitions.bank_account_iban import BankAccountIban
from ingenico.connect.sdk.domain.mandates.definitions.mandate_address import MandateAddress
from ingenico.connect.sdk.domain.mandates.definitions.mandate_contact_details import MandateContactDetails
from ingenico.connect.sdk.domain.mandates.definitions.mandate_personal_information import MandatePersonalInformation
class MandateCustomer(DataObject):
__bank_account_iban = None
__company_name = None
__contact_details = None
__mandate_address = None
__personal_information = None
@property
def bank_account_iban(self):
"""
| Object containing IBAN information
Type: :class:`ingenico.connect.sdk.domain.definitions.bank_account_iban.BankAccountIban`
"""
return self.__bank_account_iban
@bank_account_iban.setter
def bank_account_iban(self, value):
self.__bank_account_iban = value
@property
def company_name(self):
"""
| Name of company, as a consumer
Type: str
"""
return self.__company_name
@company_name.setter
def company_name(self, value):
self.__company_name = value
@property
def contact_details(self):
"""
| Object containing contact details like email address and phone number
Type: :class:`ingenico.connect.sdk.domain.mandates.definitions.mandate_contact_details.MandateContactDetails`
"""
return self.__contact_details
@contact_details.setter
def contact_details(self, value):
self.__contact_details = value
@property
def mandate_address(self):
"""
| Object containing billing address details
Type: :class:`ingenico.connect.sdk.domain.mandates.definitions.mandate_address.MandateAddress`
"""
return self.__mandate_address
@mandate_address.setter
def mandate_address(self, value):
self.__mandate_address = value
@property
def personal_information(self):
"""
| Object containing personal information of the consumer
Type: :class:`ingenico.connect.sdk.domain.mandates.definitions.mandate_personal_information.MandatePersonalInformation`
"""
return self.__personal_information
@personal_information.setter
def personal_information(self, value):
self.__personal_information = value
def to_dictionary(self):
dictionary = super(MandateCustomer, self).to_dictionary()
self._add_to_dictionary(dictionary, 'bankAccountIban', self.bank_account_iban)
self._add_to_dictionary(dictionary, 'companyName', self.company_name)
self._add_to_dictionary(dictionary, 'contactDetails', self.contact_details)
self._add_to_dictionary(dictionary, 'mandateAddress', self.mandate_address)
self._add_to_dictionary(dictionary, 'personalInformation', self.personal_information)
return dictionary
def from_dictionary(self, dictionary):
super(MandateCustomer, self).from_dictionary(dictionary)
if 'bankAccountIban' in dictionary:
if not isinstance(dictionary['bankAccountIban'], dict):
raise TypeError('value \'{}\' is not a dictionary'.format(dictionary['bankAccountIban']))
value = BankAccountIban()
self.bank_account_iban = value.from_dictionary(dictionary['bankAccountIban'])
if 'companyName' in dictionary:
self.company_name = dictionary['companyName']
if 'contactDetails' in dictionary:
if not isinstance(dictionary['contactDetails'], dict):
raise TypeError('value \'{}\' is not a dictionary'.format(dictionary['contactDetails']))
value = MandateContactDetails()
self.contact_details = value.from_dictionary(dictionary['contactDetails'])
if 'mandateAddress' in dictionary:
if not isinstance(dictionary['mandateAddress'], dict):
raise TypeError('value \'{}\' is not a dictionary'.format(dictionary['mandateAddress']))
value = MandateAddress()
self.mandate_address = value.from_dictionary(dictionary['mandateAddress'])
if 'personalInformation' in dictionary:
if not isinstance(dictionary['personalInformation'], dict):
raise TypeError('value \'{}\' is not a dictionary'.format(dictionary['personalInformation']))
value = MandatePersonalInformation()
self.personal_information = value.from_dictionary(dictionary['personalInformation'])
return self
| [
"[email protected]"
]
| |
769ee646685638845b9b0dbcf4b77df4a9ac8a52 | 8b124607a1227013b05b9af9c6a6888200d8f67d | /spider/bookinfo/middlewares.py | 18e1e908f8c17208c57e16c7afd0f1051ebc48b9 | [
"Apache-2.0"
]
| permissive | zzzz123321/Broadview-analysing-sales-figures | f32fcd4b41f00af59a44a09e744523199a17a9f6 | bdff4239fd71b5077bc05703757d9f5d2610c536 | refs/heads/master | 2020-04-27T18:06:22.240431 | 2018-03-05T09:17:29 | 2018-03-05T09:17:29 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,319 | py | # -*- coding: utf-8 -*-
# Define here the models for your spider middleware
#
# See documentation in:
# http://doc.scrapy.org/en/latest/topics/spider-middleware.html
from scrapy import signals
import random
from bookinfo.settings import IPPOOL
from scrapy.downloadermiddlewares.httpproxy import HttpProxyMiddleware
class IPPOOLS(HttpProxyMiddleware):
def __init__(self, ip=''):
self.ip = ip
def process_request(self, request, spider):
thisip = random.choice(IPPOOL)
# print('当前的IP是：'+thisip['ipaddr'])
request.meta['proxy']="http://"+thisip['ipaddr']
class BookinfoSpiderMiddleware(object):
# Not all methods need to be defined. If a method is not defined,
# scrapy acts as if the spider middleware does not modify the
# passed objects.
@classmethod
def from_crawler(cls, crawler):
# This method is used by Scrapy to create your spiders.
s = cls()
crawler.signals.connect(s.spider_opened, signal=signals.spider_opened)
return s
def process_spider_input(self, response, spider):
# Called for each response that goes through the spider
# middleware and into the spider.
# Should return None or raise an exception.
return None
def process_spider_output(self, response, result, spider):
# Called with the results returned from the Spider, after
# it has processed the response.
# Must return an iterable of Request, dict or Item objects.
for i in result:
yield i
def process_spider_exception(self, response, exception, spider):
# Called when a spider or process_spider_input() method
# (from other spider middleware) raises an exception.
# Should return either None or an iterable of Response, dict
# or Item objects.
pass
def process_start_requests(self, start_requests, spider):
# Called with the start requests of the spider, and works
# similarly to the process_spider_output() method, except
# that it doesn’t have a response associated.
# Must return only requests (not items).
for r in start_requests:
yield r
def spider_opened(self, spider):
spider.logger.info('Spider opened: %s' % spider.name)
| [
"[email protected]"
]
| |
ef7ace55198ec861fcb052180a141fca5b563670 | f1961c86e6da14f35c21d7235f4fc8a89fabdcad | /DailyProgrammer/DP20160527C.py | 5e886c195faa15637218b42b98ecb6636fdaec9c | [
"MIT"
]
| permissive | DayGitH/Python-Challenges | d4930bdd85cd1a977d8f6192775ca956a375fcde | bc32f1332a92fcc2dfa6f5ea4d95f8a8d64c3edf | refs/heads/master | 2021-01-17T13:01:03.784523 | 2018-06-29T23:49:04 | 2018-06-29T23:49:04 | 58,497,683 | 2 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,641 | py | """
[2016-05-27] Challenge #268 [Hard] Network and Cards: Part 3, The cheaters
https://www.reddit.com/r/dailyprogrammer/comments/4lavv6/20160527_challenge_268_hard_network_and_cards/
#Description
This week we are creating a game playable over network. This will be a 3-parter.
The third part is going to be even more interaction, and some cheating, card players love to cheat.
We are going to play a modified version of Blackjack:
Each player is dealt 1 covered card at the start of the game.
When a player decides to take a card het recieves that card covered and then has to decide which one to play and which
one to hold.
Player send the card open over the network back to the server.
Starting stays the same:
When all connected clients send a `START` command, the game will begin, you don't have to look for other connections
then.
The communication goes as followed:
CLIENT A -> SERVER: START
CLIENT B -> SERVER: START
SERVER -> CLIENT A: Ace of spades
SERVER -> CLIENT B: 4 of clubs
SERVER -> CLIENT A: TAKE or PASS
CLIENT A -> SERVER: TAKE
SERVER -> CLIENT A: Queen of hearts
CLIENT A -> SERVER: PLAY Ace of spades
SERVER -> CLIENT B: TAKE or PASS
CLIENT B -> SERVER: PASS
The client has the option to either respond with a `TAKE` command, folowed by a `PLAY` or `PASS` command, the server
then go to the next client till everyone is done (all passed or everyone has 21 or more in score)
The cards have the following values:
2 -> 2
3 -> 3
4 -> 4
5 -> 5
6 -> 6
7 -> 7
8 -> 8
9 -> 9
Jack -> 10
Queen -> 10
King -> 10
Ace -> 1 or 11 (11 if not over 21 and 1 if over)
#Formal Inputs & Outputs
##Input description
- Server
Server has to accept at least 4 commands: `START`, `TAKE`, `PLAY` and `PASS`
- Client
Clients must be able to recieve the choice for `TAKE` and `PASS` and must be able to recieve cards, format of that is
up to you
##Output description
- Server
No Output required, but I can imagen that some loggin will be handy.
- Client
A decent output for humans to read the cards and see their current score.
Also must know when to type in the option to `TAKE` and `PASS`
#Notes/Hints
## TCP Socket approach
The server needs to able to handle multiple clients in the end, so a multithreaded approach is advised.
It is advised to think of some command like pattern, so you can send messages to the server and back.
For the server and client, just pick some random ports that you can use.
[Here](https://en.wikipedia.org/wiki/List_of_TCP_and_UDP_port_numbers) you have a list off all "reserved" ports.
For the connection, TCP connections are the easiest way to do this in most languages. But you are not limited to that
if you want to use something more high-level if your language of choice supports that.
## REST api approach
Some off you pointed out that this could be done with a webserver. If this is more in the line of what you are used to,
no problem then, as long as it stays in the line of a multiplayer game.
#Bonus
Examine the game logic from a other submissions (or your own) and try to create a cheating bot.
If a programmer forgets to add checks or some sort, you can exploit these.
**HOWEVER**:
**If you are not up for that, put it in your submission. I don't want to see any bragging, I want this to be fun.
Please be respectfull to other people at all time.**
**I will monitor this closely and any hurtful comment will be deleted**
#Finally
Have a good challenge idea?
Consider submitting it to /r/dailyprogrammer_ideas
"""
def main():
pass
if __name__ == "__main__":
main()
| [
"[email protected]"
]
| |
ada2bcb6a2c7f0e34aad00da2a1dec1042a2ce24 | 0be5c80361a12ef7b56d3da8ce64cc1ab80a5ada | /todo_app/forms.py | 807cfe1aa1b023703185a0585df2b8d10847d0f3 | []
| no_license | swaraj70/TaskTodo | a73492afdd0c0920dea315681b400ac61a8ceb1f | 652ef2a096055b2d96aa8122bb9825e5de79da8c | refs/heads/master | 2023-04-29T02:46:11.406996 | 2020-02-14T05:47:41 | 2020-02-14T05:47:41 | 224,838,456 | 0 | 0 | null | 2023-04-21T20:41:35 | 2019-11-29T11:10:18 | Python | UTF-8 | Python | false | false | 154 | py | from django import forms
from .models import Task
class TaskForm(forms.ModelForm):
class Meta:
model = Task
fields = ['task', 'done'] | [
"[email protected]"
]
| |
fa5d2b4c4e8ebff5883ae88aca6ee894eab5a525 | e21599d08d2df9dac2dee21643001c0f7c73b24f | /practice/ai/pic/simular/pil_sample2.py | c5c9cc3432ed46ccad315b22eff21bde3567adb1 | []
| no_license | herolibra/PyCodeComplete | c7bf2fb4ce395737f8c67749148de98a36a71035 | 4ef7d2c3aec6d28a53eed0e649cdeb74df3d783b | refs/heads/master | 2022-07-17T05:39:03.554760 | 2020-05-03T07:00:14 | 2020-05-03T07:00:14 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 571 | py | #!/usr/bin/env python
# coding=utf-8
# author: zengyuetian
from PIL import Image
import math
import operator
from functools import reduce
def image_contrast(img1, img2):
image1 = Image.open(img1)
image2 = Image.open(img2)
h1 = image1.histogram()
h2 = image2.histogram()
result = math.sqrt(reduce(operator.add, list(map(lambda a, b: (a - b) ** 2, h1, h2))) / len(h1))
return result
if __name__ == '__main__':
img1 = "1.png" # 指定图片路径
img2 = "2.png"
result = image_contrast(img1, img2)
print((100 - result), "%")
| [
"[email protected]"
]
| |
58250e8bea0428c8b376e7e5f633cdc6431591e3 | 65f8211fc33eb5f9ac1ff0d68902226ca9a58692 | /sorting_algorithms/quick_sort_cormen.py | 9fdd4284ab2ac365d12a921a94e243eead478568 | []
| no_license | szarbartosz/asd-python | 46869f5699a1ef661e2df02e523af0adcddbbbda | 0130cc3dcbba6ad62e1516c98b5cbab85848d619 | refs/heads/master | 2022-12-13T19:02:53.699381 | 2020-09-11T13:29:31 | 2020-09-11T13:29:31 | 242,975,318 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 602 | py | def partition(arr, left, right):
i = left - 1
pivot = arr[right]
for j in range(left, right):
if arr[j] <= pivot:
i += 1
arr[i], arr[j] = arr[j], arr[i]
arr[i+1], arr[right] = arr[right], arr[i+1]
return i + 1
def quick_sort(arr, left, right):
if right > left:
mid = partition(arr, left, right)
quick_sort(arr, left, mid - 1)
quick_sort(arr, mid + 1, right)
if __name__ == '__main__':
arr = [54,3234,32,54,54376,4,3,52,34,1,43,5,26,37,45,23,432,52,36]
print(arr)
quick_sort(arr,0,len(arr)-1)
print(arr) | [
"[email protected]"
]
| |
8a6ca59bb06502011b10a473553ad89562b21f6f | deb246d7cb1ce4ea57bba7e1b3c9964c503a0852 | /khmer/khmerEnv/lib/python2.7/site-packages/screed/screedRecord.py | 92eeb4728973624c2c3790803d7968f153a2c4dc | []
| no_license | chelseaju/TahcoRoll | 89fec5e7d3f8658b3647cb01fb5d3dba90dc74a4 | 3a5978cd48d728c3ee36afcf95dec82cec58d7e1 | refs/heads/master | 2021-04-25T15:40:53.545677 | 2019-09-25T14:09:17 | 2019-09-25T14:09:17 | 109,560,971 | 2 | 0 | null | null | null | null | UTF-8 | Python | false | false | 6,955 | py | # Copyright (c) 2016, The Regents of the University of California.
from __future__ import absolute_import
from functools import total_ordering
import types
from . import DBConstants
import gzip
import bz2
from io import BytesIO
try:
from collections import MutableMapping
except ImportError:
import UserDict
MutableMapping = UserDict.DictMixin
class Record(MutableMapping):
"""
Simple dict-like record interface with bag behavior.
"""
def __init__(self, name=None, sequence=None, **kwargs):
d = dict()
if name is not None:
d['name'] = name
if sequence is not None:
d['sequence'] = sequence
d.update(kwargs)
if 'quality' in d and d['quality'] is None:
del d['quality']
self.d = d
def __setitem__(self, name, value):
self.d[name] = value
def __getattr__(self, name):
try:
return self.d[name]
except KeyError:
raise AttributeError(name)
def __len__(self):
return len(self.sequence)
def keys(self):
return self.d.keys()
def __getitem__(self, idx):
if isinstance(idx, slice):
trimmed = dict(self.d)
trimmed['sequence'] = trimmed['sequence'][idx]
if 'quality' in trimmed:
trimmed['quality'] = trimmed['quality'][idx]
return Record(**trimmed)
return self.d[idx]
def __delitem__(self, key):
del self.d[key]
def __iter__(self):
return iter(self.d)
def __repr__(self):
return repr(self.d)
@total_ordering
class _screed_attr(object):
"""
Sliceable database object that supports lazy retrieval
"""
def __init__(self, dbObj, attrName, rowName, queryBy):
"""
Initializes database object with specific record retrieval
information
dbOjb = database handle
attrName = name of attr in db
rowName = index/name of row
queryBy = by name or index
"""
self._dbObj = dbObj
self._attrName = attrName
self._rowName = rowName
self._queryBy = queryBy
def __getitem__(self, sliceObj):
"""
Slicing interface. Returns the slice range given.
*.start + 1 to be compatible with sqlite's 1 not 0 scheme
"""
if not isinstance(sliceObj, slice):
raise TypeError('__getitem__ argument must be of slice type')
if not sliceObj.start <= sliceObj.stop: # String reverse in future?
raise ValueError('start must be less than stop in slice object')
length = sliceObj.stop - sliceObj.start
query = 'SELECT substr(%s, %d, %d) FROM %s WHERE %s = ?' \
% (self._attrName, sliceObj.start + 1, length,
DBConstants._DICT_TABLE,
self._queryBy)
cur = self._dbObj.cursor()
result = cur.execute(query, (str(self._rowName),))
try:
subStr, = result.fetchone()
except TypeError:
raise KeyError("Key %s not found" % self._rowName)
return str(subStr)
def __len__(self):
"""
Returns the length of the string
"""
return len(self.__str__())
def __repr__(self):
"""
Prints out the name of the class and the name of the sliceable attr
"""
return "<%s '%s'>" % (self.__class__.__name__, self._attrName)
def __eq__(self, given):
"""
Compares attribute to given object in string form
"""
if isinstance(given, bytes):
return given == self.__str__()
else:
return str(given) == self.__str__()
def __lt__(self, given):
if isinstance(given, bytes):
return self.__str__() < given
else:
return self.__str__() < str(given)
def __str__(self):
"""
Returns the full attribute as a string
"""
query = 'SELECT %s FROM %s WHERE %s = ?' \
% (self._attrName, DBConstants._DICT_TABLE, self._queryBy)
cur = self._dbObj.cursor()
result = cur.execute(query, (str(self._rowName),))
try:
record, = result.fetchone()
except TypeError:
raise KeyError("Key %s not found" % self._rowName)
return str(record)
def _buildRecord(fieldTuple, dbObj, rowName, queryBy):
"""
Constructs a dict-like object with record attribute names as keys and
_screed_attr objects as values
"""
# Separate the lazy and full retrieval objects
kvResult = []
fullRetrievals = []
for fieldname, role in fieldTuple:
if role == DBConstants._SLICEABLE_TEXT:
kvResult.append((fieldname, _screed_attr(dbObj,
fieldname,
rowName,
queryBy)))
else:
fullRetrievals.append(fieldname)
# Retrieve the full text fields from the db
subs = ','.join(fullRetrievals)
query = 'SELECT %s FROM %s WHERE %s=?' % \
(subs, DBConstants._DICT_TABLE, queryBy)
cur = dbObj.cursor()
res = cur.execute(query, (rowName,))
# Add the full text fields to the result tuple list
data = tuple([str(r) for r in res.fetchone()])
kvResult.extend(zip(fullRetrievals, data))
# Hack to make indexing start at 0
hackedResult = []
for key, value in kvResult:
if key == DBConstants._PRIMARY_KEY:
hackedResult.append((key, int(value) - 1))
else:
hackedResult.append((key, value))
return Record(**dict(hackedResult))
def write_fastx(record, fileobj):
"""Write sequence record to 'fileobj' in FASTA/FASTQ format."""
isbytesio = isinstance(fileobj, BytesIO)
iswb = hasattr(fileobj, 'mode') and fileobj.mode == 'wb'
outputvalid = isbytesio or iswb
if not outputvalid:
message = ('cannot call "write_fastx" on object, must be of a file '
'handle with mode "wb" or an instance of "BytesIO"')
raise AttributeError(message)
defline = record.name
if hasattr(record, 'description'):
defline += ' ' + record.description
if hasattr(record, 'quality'):
recstr = '@{defline}\n{sequence}\n+\n{quality}\n'.format(
defline=defline,
sequence=record.sequence,
quality=record.quality)
else:
recstr = '>{defline}\n{sequence}\n'.format(
defline=defline,
sequence=record.sequence)
fileobj.write(recstr.encode('utf-8'))
def write_fastx_pair(read1, read2, fileobj):
"""Write a pair of sequence records to 'fileobj' in FASTA/FASTQ format."""
if hasattr(read1, 'quality'):
assert hasattr(read2, 'quality')
write_record(read1, fileobj)
write_record(read2, fileobj)
| [
"[email protected]"
]
| |
1833fec4a3f82304da532eae0b5d78201329410c | ca7aa979e7059467e158830b76673f5b77a0f5a3 | /Python_codes/p02639/s177403462.py | 63231992385318309a365b6c65bf958be32e4f02 | []
| no_license | Aasthaengg/IBMdataset | 7abb6cbcc4fb03ef5ca68ac64ba460c4a64f8901 | f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8 | refs/heads/main | 2023-04-22T10:22:44.763102 | 2021-05-13T17:27:22 | 2021-05-13T17:27:22 | 367,112,348 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 81 | py | n = input().split(" ")
for i,j in enumerate(n,1):
if 0 == int(j):
print(i) | [
"[email protected]"
]
| |
aa01639593c0e01a1ab90ad30ead31133d9cf437 | 5ff8cefa68d52d2427bb3d35320cd8bd0d072968 | /JSONExamples/json_example4.py | be53a2d1703a54c3dd3e258d42f84b2c9021dfef | []
| no_license | gsudarshan1990/PythonSampleProjects | a65a111454f8dc551f1cd29901cead0798ad6dc3 | 3c1a5174c5f966b0eed2828221add76ec0d019d5 | refs/heads/master | 2020-05-09T16:02:37.743568 | 2019-07-14T06:22:55 | 2019-07-14T06:22:55 | 181,255,262 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 110 | py | import json
data={
'a':True,
'b':'Hello',
'c':None
}
print(json.dumps(data))
| [
"[email protected]"
]
| |
99a3675e71c362c414c8ea7f39d3da678a6336ac | 45614a944ffbdb75a0bef955582a722da5ce7492 | /python/app.py | e7011353a8b938a296f146d092cafebdb5a08972 | []
| no_license | wccgoog/pass | 1c8ab5393547634a27c7543556a75dec771a9e3d | 0ec01536ae10b3d99707002c0e726072acb50231 | refs/heads/2 | 2023-01-15T13:27:26.312648 | 2019-10-23T09:30:45 | 2019-10-23T09:30:45 | 122,595,075 | 0 | 2 | null | 2023-01-07T10:42:38 | 2018-02-23T08:38:36 | JavaScript | UTF-8 | Python | false | false | 639 | py | from flask import Flask,request,render_template
app=Flask(__name__)
@app.route('/',methods=['GET','POST'])
def home():
return render_template('home.html')
@app.route('/signin',methods=['GET'])
def signin_form():
return render_template('form.html')
@app.route('/signin',methods=['POST'])
def signin():
username=request.form['username']
password=request.form['password']
if username=='admin' and password=='password':
return render_template('signin-ok.html',username=username)
return render_template('form.html',message='Bad username or password',username=username)
if __name__=='__main__':
app.run() | [
"[email protected]"
]
| |
351e42f616d21721a870b7c8092d090c713bcead | 2a603f33f109df24e55d392b124dad3f51922075 | /0x11-python-network_1/5-hbtn_header.py | e67a32a20f51e94d671ba94a4ce7735bbdc91470 | []
| no_license | michellegsld/holbertonschool-higher_level_programming | bc010270904ec2638953afea8fcfd650159e38ac | c6a3a4b2eb80b45708becfa3b08249c3e86294e8 | refs/heads/master | 2023-06-08T21:06:34.401385 | 2023-06-06T02:25:03 | 2023-06-06T02:25:03 | 226,925,789 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 282 | py | #!/usr/bin/python3
"""
Task 5:
Sends a request and displays the value of X-Request-Id
5-hbtn_header.py
"""
import requests
from sys import argv
if __name__ == "__main__":
req = requests.get(argv[1])
try:
print(req.headers["X-Request-Id"])
except:
pass
| [
"[email protected]"
]
| |
04c96bdf8c7d21cf82ad54365d7d445b04fd42ef | 71e2ff113d8b0de6708c7b6165d01a7bbb199b41 | /main.py | c273fb85ebc326ecbd9eb80415a83e91f7653d41 | []
| no_license | MJB90/timeSeries | 3b506b4659121fb58984c358d3ef50a66909df21 | 0b54fe1f06df5e12f26b5b3a0d210b4f86492414 | refs/heads/master | 2020-04-20T15:05:22.759585 | 2019-02-03T12:00:36 | 2019-02-03T12:00:36 | 168,918,799 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,761 | py | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from datetime import datetime
from pandas import Series
import barplot as bp
import warnings
warnings.filterwarnings("ignore")
train = pd.read_csv("data/Train_SU63ISt.csv")
test = pd.read_csv("data/Test_0qrQsBZ.csv")
train_original = train.copy()
test_original = test.copy()
##########################################################################
# Exploratory Analysis
train['Datetime'] = pd.to_datetime(train.Datetime, format='%d-%m-%Y %H:%M')
test['Datetime'] = pd.to_datetime(test.Datetime, format='%d-%m-%Y %H:%M')
test_original['Datetime'] = pd.to_datetime(test_original.Datetime, format='%d-%m-%Y %H:%M')
train_original['Datetime'] = pd.to_datetime(train_original.Datetime, format='%d-%m-%Y %H:%M')
for i in (train, test, test_original, train_original):
i['year'] = i.Datetime.dt.year
i['month'] = i.Datetime.dt.month
i['day'] = i.Datetime.dt.day
i['Hour'] = i.Datetime.dt.hour
train['day of week'] = train['Datetime'].dt.dayofweek
temp = train['Datetime']
# This functions adds a boolean value 1 if the current day is a weekend
def is_weekend(row):
if row.dayofweek == 5 or row.dayofweek == 6:
return 1
else:
return 0
temp2 = train['Datetime'].apply(is_weekend)
train['weekend'] = temp2
# train.index = train['Datetime'] # indexing the Datetime to get the time period on the x-axis.
# df = train.drop('ID', 1) # drop ID variable to get only the Datetime on x-axis.
# ts = df['Count']
# # plt.figure(figsize=(16, 8))
# # plt.plot(ts, label='Passenger Count')
# # plt.title('Time Series')
# # plt.xlabel("Time(year-month)")
# # plt.ylabel("Passenger count")
# # plt.legend(loc='best')
# # plt.show()
#
# temp_data = train.groupby('month')['Count'].mean()
# x_axis_data = temp_data.index
# y_axis_data = temp_data[:]
# bp.plot_bar_x(x_axis_data, y_axis_data, 'Month', 'Count', 'Monthly Count')
#######################################################################################
# Splitting and forecasting
train=train.drop('ID', 1)
test.Timestamp = pd.to_datetime(test.Datetime, format='%d-%m-%Y %H:%M')
test.index = test.Timestamp
# Converting to daily mean
test = test.resample('D').mean()
train.Timestamp = pd.to_datetime(train.Datetime, format='%d-%m-%Y %H:%M')
train.index = train.Timestamp
# Converting to daily mean
train = train.resample('D').mean()
Train = train.ix['2012-08-25':'2014-06-24']
valid = train.ix['2014-06-25':'2014-09-25']
Train.Count.plot(figsize=(15,8), title= 'Daily Ridership', fontsize=14, label='train')
valid.Count.plot(figsize=(15,8), title= 'Daily Ridership', fontsize=14, label='valid')
plt.xlabel("Datetime")
plt.ylabel("Passenger count")
plt.legend(loc='best')
plt.show()
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]
| |
505673351e030e7d2af76e424060f7004776000d | 0178e6a705ee8aa6bb0b0a8512bf5184a9d00ded | /Sungjin/Test/20200913/no5.py | f3ff5d569814fd515de2f7def1d85af6ec47365a | []
| no_license | comojin1994/Algorithm_Study | 0379d513abf30e3f55d6a013e90329bfdfa5adcc | 965c97a9b858565c68ac029f852a1c2218369e0b | refs/heads/master | 2021-08-08T14:55:15.220412 | 2021-07-06T11:54:33 | 2021-07-06T11:54:33 | 206,978,984 | 0 | 1 | null | 2020-05-14T14:06:46 | 2019-09-07T14:23:31 | Python | UTF-8 | Python | false | false | 68 | py |
def solution():
pass
if __name__ == '__main__':
solution() | [
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]
| |
fdbf80ce6ac0265975d02e5efdc5009f07131870 | ecf2511642e0e8fb11616249b062a7c8a130d137 | /src/python_files/GenOpSpace.py | ef84cc5eff5f24db805665ab5b9ae3245e96df93 | []
| no_license | zaddan/apx_tool_chain | 61abc9d6958ce17d9823ac7bf71ae3097f76decf | ef81dd6515f110d3c8a4b62a642d44d9a4327a79 | refs/heads/master | 2021-01-24T06:37:12.248020 | 2017-04-08T20:39:08 | 2017-04-08T20:39:08 | 40,195,826 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,515 | py | import itertools
import sys
# Copyright (C)
# 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., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
#
##
# @file GenOpSpace.py
# @brief this file contains the class for generating all the possible apx version of an operation that is defined as a class
# @author Behzad Boroujerdian
# @date 2015-06-30
##
# @brief this class generates all the possible versions of a specific operator. it ueses the name to figure out the type of input. for example, in the case of multipliation, it generates all the multipliactions possible such as accurate and apx version (apx versions can have different kinds)
#some of the inputs are just names and some of the inputs are ranges and need to be first generated and then permutated.
#for example the name passed to this class, is just one element and doesn't need to be expanded, but the rest of the inputs acquire a low bound and high bound
#the way to use this class is the following way:
#set the numberOfInputs. For example in the case of Eta1 the number of inputs equal 8. These inputs include the low bound and high bound of the followi
##
class GenOpSpace():
def __init__(self, name, numberOfInputs, lInput):
self.name = [name]
self.inputList = [] #this list containg the input that user provided (this can vary from Op to Op. for example in the case of GenEta1Input, this list contains
#NtLB, NtHB, NiaLB, NiaHB, msbLB, msbHB, lsbLB, lsbHB):
self.eachCategoryAllValues = [] #this list stores all the values possible for each input catgory. for example in the case of Eta1 the categories are:
#Nia, msb, lsb, Nt
self.numberOfInputs = numberOfInputs
for i in range(0, self.numberOfInputs):
self.inputList.append(lInput[i])
self.combineList = [] #putting all the values of different categories in one list
self.permutedTuples= [] #using itertool to generate all the permutations of the combineList
self.permutedList= [] #converting the permutedTuples from tuple form to listForm
def sweepInput(self):
self.combineList.append(self.name);
for i in range(0, self.numberOfInputs, 2):
self.eachCategoryAllValues.append(range(self.inputList[i], self.inputList[i+1]))
self.combineList.append(self.eachCategoryAllValues[i/2])
self.permutedTuples= list(itertools.product(*(self.combineList)))
for element in self.permutedTuples:
self.permutedList.append(list(element));
#print self.permutedList
def printPermutations(self):
print self.permutedList
#testing framework
#GenOps = [GenOpSpace("Eta", 8,[1,4, 2,6, 3,5, 4, 6]), GenOpSpace("btm", 4,[10, 11, 100, 110])]
#for element in GenOps:
# element.sweepInput()
# element.printPermutations()
#
| [
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]
| |
49bf830762b81466bff733629a5327e18941cf7f | e48704e1529fe6dcfed8328b0195e3869e90241b | /main.py | 170b3e26c8c7effdfbf03d812353ecc4a1256bfb | []
| no_license | samuelitwaru/bar_and_restaurant | 0401191856f29204174bb3259fdf320319344291 | fb07bc128030bbb630b725871fea4454178fd1e6 | refs/heads/master | 2022-04-11T18:20:59.302667 | 2020-02-18T04:09:46 | 2020-02-18T04:09:46 | 241,265,070 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 37 | py | from Application import app
app.run() | [
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]
| |
dd8773b77d7fa2bcd6b31071a9e5977a94c1c361 | ca7aa979e7059467e158830b76673f5b77a0f5a3 | /Python_codes/p04012/s423711812.py | 60a1ba317ede087b3ef712ef71d15a4b45103d18 | []
| no_license | Aasthaengg/IBMdataset | 7abb6cbcc4fb03ef5ca68ac64ba460c4a64f8901 | f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8 | refs/heads/main | 2023-04-22T10:22:44.763102 | 2021-05-13T17:27:22 | 2021-05-13T17:27:22 | 367,112,348 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 116 | py | w = input()
for i in range(26):
if w.count(chr(97+i)) % 2 == 1:
print('No')
exit()
print('Yes')
| [
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]
| |
e18d24a68f51f405a4f8e4d13943f2abec5d6f23 | 6bf995003dfe009129f7d93b63ec2478927324e0 | /backends/ubpf/tests/ptf/tunneling_test.py | cbc00770d83b8bdcef665d28aa9a236075469f62 | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
]
| permissive | p4lang/p4c | e9fd8acfa25a6e1e42adffd136cbf6cd5e94018a | 4a393c951e1f4fdbdc3894a1f8d2929e700adeb7 | refs/heads/main | 2023-09-04T16:30:20.238093 | 2023-09-04T06:55:44 | 2023-09-04T06:55:44 | 55,433,859 | 621 | 555 | Apache-2.0 | 2023-09-14T14:22:46 | 2016-04-04T18:12:32 | C++ | UTF-8 | Python | false | false | 2,190 | py | #!/usr/bin/env python
# Copyright 2019 Orange
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from base_test import P4rtOVSBaseTest
from ptf.packet import MPLS, Ether
from ptf.testutils import send_packet, simple_ip_only_packet, verify_packets
class TunnelingTest(P4rtOVSBaseTest):
def setUp(self):
P4rtOVSBaseTest.setUp(self)
self.del_flows()
self.unload_bpf_program()
self.load_bpf_program(path_to_program="build/test-tunneling.o")
self.add_bpf_prog_flow(1, 2)
self.add_bpf_prog_flow(2, 1)
class MplsDownstreamTest(TunnelingTest):
def setUp(self):
TunnelingTest.setUp(self)
self.update_bpf_map(map_id=1, key="1 1 168 192", value="0 0 0 0")
def runTest(self):
pkt = Ether(dst="11:11:11:11:11:11") / simple_ip_only_packet(ip_dst="192.168.1.1")
exp_pkt = (
Ether(dst="11:11:11:11:11:11")
/ MPLS(label=20, cos=5, s=1, ttl=64)
/ simple_ip_only_packet(ip_dst="192.168.1.1")
)
send_packet(self, (0, 1), pkt)
verify_packets(self, exp_pkt, device_number=0, ports=[2])
class MplsUpstreamTest(TunnelingTest):
def setUp(self):
TunnelingTest.setUp(self)
self.update_bpf_map(map_id=0, key="20 0 0 0", value="0 0 0 0")
def runTest(self):
pkt = (
Ether(dst="11:11:11:11:11:11")
/ MPLS(label=20, cos=5, s=1, ttl=64)
/ simple_ip_only_packet(ip_dst="192.168.1.1")
)
exp_pkt = Ether(dst="11:11:11:11:11:11") / simple_ip_only_packet(ip_dst="192.168.1.1")
send_packet(self, (0, 1), pkt)
verify_packets(self, exp_pkt, device_number=0, ports=[2])
| [
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]
| |
601e2fd0c5ec8bd6a1589b74e3fe3c62cc00a0d1 | c6ee7be1479797788dd9f9d3a29c0e76ea020db8 | /apscheduler_yqt/rabbitmq_selenium/__init__.py | 6aeef9ff6c2d7a54fa444b5060b547c6b8ca913f | []
| no_license | hikekang/apscheduler_yqt | 2966e7231fff1f81c4fa4a75459cf638592aae6a | 0d30c2ef721b8ffeba98dca6b2441613b4ed608d | refs/heads/master | 2023-08-17T05:19:34.345553 | 2021-09-26T03:21:11 | 2021-09-26T03:21:11 | 406,217,786 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 201 | py | #!/usr/bin/python3.x
# -*- coding=utf-8 -*-
"""
Time : 2021/8/6 16:27
Author : hike
Email : [email protected]
File Name : __init__.py.py
Description:
Software : PyCharm
"""
| [
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]
| |
868f1a271bf1e90f4384544b6b79bb9e791ab6b9 | 8d47af9482444b07b52cf44cebcaf4b992df4d09 | /agents/17_DoubleSampling/17.py | 67cf990ddcfc9a4ff45dc4790a458cd55d3247c2 | []
| no_license | w0lv3r1nix/retro-agents | f4dbce2db558c880b161062796e5397be65bdd10 | c7f93a737dc6c6fc5d8343c099e14bd2bc97aaf1 | refs/heads/master | 2020-08-01T01:19:41.660018 | 2018-06-13T04:28:09 | 2018-06-13T04:28:09 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,854 | py | #!/usr/bin/env python
"""
Train an agent on Sonic using an open source Rainbow DQN
implementation.
"""
import tensorflow as tf
from anyrl.algos import DQN
from anyrl.envs import BatchedGymEnv
from anyrl.envs.wrappers import BatchedFrameStack
from anyrl.models import rainbow_models
from anyrl.rollouts import BatchedPlayer, PrioritizedReplayBuffer, NStepPlayer
from anyrl.spaces import gym_space_vectorizer
import gym_remote.exceptions as gre
from sonic_util import AllowBacktracking, make_env
from DoubleSampling import DoubleSampling
def main():
"""Run DQN until the environment throws an exception."""
env = AllowBacktracking(make_env(stack=False, scale_rew=False))
env = BatchedFrameStack(BatchedGymEnv([[env]]), num_images=4, concat=False)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True # pylint: disable=E1101
with tf.Session(config=config) as sess:
dqn = DQN(*rainbow_models(sess,
env.action_space.n,
gym_space_vectorizer(env.observation_space),
min_val=-200,
max_val=200))
player = NStepPlayer(BatchedPlayer(env, dqn.online_net), 3)
optimize = dqn.optimize(learning_rate=1e-4)
sess.run(tf.global_variables_initializer())
dqn.train(num_steps=2000000, # Make sure an exception arrives before we stop.
player=player,
replay_buffer=DoubleSampling(500000, 0.5, 0.4, epsilon=0.1),
optimize_op=optimize,
train_interval=1,
target_interval=8192,
batch_size=32,
min_buffer_size=20000)
if __name__ == '__main__':
try:
main()
except gre.GymRemoteError as exc:
print('exception', exc)
| [
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ea8568e4d91e2982982e1baec28f8eafed53cab4 | 55d5430e266d331320eef97653bca303c775184b | /GAN/lib/dataset/alignDataSet.py | 06f700b5543cbfd0abd70a7eea77b42820ee75d1 | []
| no_license | arn1992/CCX-rayNet_and_CCX-rayGAN | 57059bc8c02e01f2d03ad5047bf93e2cc0ae4e3a | b219ca9e19ee0a87cfd9895d11d6709429bb9f52 | refs/heads/main | 2023-05-09T03:00:21.496350 | 2021-06-03T02:12:24 | 2021-06-03T02:12:24 | 373,348,224 | 3 | 1 | null | null | null | null | UTF-8 | Python | false | false | 6,619 | py | # ------------------------------------------------------------------------------
# Copyright (c) Tencent
# Licensed under the GPLv3 License.
# Created by Kai Ma ([email protected])
# ------------------------------------------------------------------------------
from __future__ import print_function
from __future__ import absolute_import
from __future__ import division
from X2CT.GAN.lib.dataset.baseDataSet import Base_DataSet
from X2CT.GAN.lib.dataset.utils import *
import h5py
import numpy as np
import os
import torch
import cv2
#
# class AlignDataSet(Base_DataSet):
# '''
# DataSet For unaligned data
# '''
# def __init__(self, opt):
# super(AlignDataSet, self).__init__()
# self.opt = opt
# self.ext = '.h5'
# self.dataset_paths = get_dataset_from_txt_file(self.opt.datasetfile)
# self.dataset_paths = sorted(self.dataset_paths)
# self.dataset_size = len(self.dataset_paths)
# self.dir_root = self.get_data_path
# self.data_augmentation = self.opt.data_augmentation(opt)
#
# @property
# def name(self):
# return 'AlignDataSet'
#
# @property
# def get_data_path(self):
# path = os.path.join(self.opt.dataroot)
# return path
#
# @property
# def num_samples(self):
# return self.dataset_size
#
# def get_image_path(self, root, index_name):
# img_path = os.path.join(root, index_name, 'ct_xray_data'+self.ext)
# assert os.path.exists(img_path), 'Path do not exist: {}'.format(img_path)
# return img_path
#
# # def load_file(self, file_path):
# # hdf5 = h5py.File(file_path, 'r')
# # ct_data = np.asarray(hdf5['ct'])
# # x_ray1 = np.asarray(hdf5['xray1'])
# # x_ray1 = np.expand_dims(x_ray1, 0)
# # hdf5.close()
# # return ct_data, x_ray1
# #
# # '''
# # generate batch
# # '''
# # def pull_item(self, item):
# # file_path = self.get_image_path(self.dir_root, self.dataset_paths[item])
# # ct_data, x_ray1 = self.load_file(file_path)
# #
# # # Data Augmentation
# # ct, xray1 = self.data_augmentation([ct_data, x_ray1])
# #
# # return ct, xray1, file_path
#
#
#
# def load_file(self, file_path):
# '''
#
# :param file_path: dir_root/dataset_paths/file.npy ---> CT
# :return:
# '''
# ct_name = os.path.join(file_path)
# xray_name = os.path.join(file_path.replace('3d_numpy_array', 'xray_image').replace('.npy','.png').replace('CT_3D_', 'normal_'))
# ct_data = np.load(ct_name)
# xray_data = cv2.imread(xray_name, 0)
# x_ray1 = np.expand_dims(xray_data, 0)
#
# return ct_data, x_ray1
#
#
#
# '''
# generate batch
# '''
# def pull_item(self, item):
# file_path = self.dataset_paths[item] #self.get_image_path(self.dir_root, self.dataset_paths[item])
# ct_data, x_ray1 = self.load_file(file_path)
# # assert ct_data.shape[0] == x_ray1.shape[1] and ct_data.shape[1] == x_ray1.shape[2]
# # Data Augmentation
# ct, xray1 = self.data_augmentation([ct_data, x_ray1])
#
# return ct, xray1, file_path
#
#
# from torch.utils.data import Dataset
# class My_Align_DataSet(Dataset):
# '''
# Base DataSet
# '''
# @property
# def name(self):
# return 'AlignDataSet'
#
# def __init__(self, opt):
# self.opt = opt
# self.dataset_paths = get_dataset_from_txt_file(self.opt.datasetfile)
# self.dataset_paths = sorted(self.dataset_paths)
# self.dataset_size = len(self.dataset_paths)
# self.dir_root = self.get_data_path
# self.data_augmentation = self.opt.data_augmentation(opt)
#
# def get_data_path(self):
# path = os.path.join(self.opt.dataroot)
# return path
#
# def get_image_path(self, root, index_name):
# img_path = os.path.join(root, index_name, 'ct_xray_data'+self.ext)
# assert os.path.exists(img_path), 'Path do not exist: {}'.format(img_path)
# return img_path
#
# def load_file(self, file_path):
# '''
#
# :param file_path: dir_root/dataset_paths/file.npy ---> CT
# :return:
# '''
# ct_name = os.path.join(file_path)
# xray_name = os.path.join(file_path.replace('3d_numpy_array', 'xray_image').replace('.npy','.png').replace('CT_3D_', 'normal_'))
# seg_name = os.path.join(file_path.replace('3d_numpy_array', 'seg_image').replace('CT_3D_Patient', ''))
# ct_data = np.load(ct_name)
# xray_data = cv2.imread(xray_name, 0)
# x_ray1 = np.expand_dims(xray_data, 0)
# seg_data = np.expand_dims(np.load(seg_name), 0)
# seg_data[seg_data<0.8] = 0
# seg_data[seg_data>=0.8] = 1
#
# return ct_data, x_ray1, seg_data
#
#
# def __getitem__(self, item):
# file_path = self.dataset_paths[item] #self.get_image_path(self.dir_root, self.dataset_paths[item])
# ct_data, x_ray1, seg_data = self.load_file(file_path)
# # print(file_path, ct_data.shape, x_ray1.shape)
# # assert ct_data.shape[0] == x_ray1.shape[1] and ct_data.shape[1] == x_ray1.shape[2]
# # Data Augmentation
# ct, xray1 = self.data_augmentation([ct_data, x_ray1])
# # segmentation_map
# seg = torch.Tensor(seg_data)
# return ct, xray1, seg, file_path
#
# def __len__(self):
# return self.dataset_size
#
class AlignDataSet(Base_DataSet):
'''
DataSet For unaligned data
'''
def __init__(self, opt):
super(AlignDataSet, self).__init__()
self.opt = opt
self.ext = '.h5'
self.dataset_paths = get_dataset_from_txt_file(self.opt.datasetfile)
self.dataset_paths = sorted(self.dataset_paths)
self.dataset_size = len(self.dataset_paths)
self.dir_root = self.get_data_path
self.data_augmentation = self.opt.data_augmentation(opt)
@property
def name(self):
return 'AlignDataSet'
@property
def get_data_path(self):
path = os.path.join(self.opt.dataroot)
return path
@property
def num_samples(self):
return self.dataset_size
def get_image_path(self, root, index_name):
img_path = os.path.join(root, index_name, 'ct_xray_data'+self.ext)
assert os.path.exists(img_path), 'Path do not exist: {}'.format(img_path)
return img_path
def load_file(self, file_path):
hdf5 = h5py.File(file_path, 'r')
ct_data = np.asarray(hdf5['ct'])
x_ray1 = np.asarray(hdf5['xray1'])
x_ray1 = np.expand_dims(x_ray1, 0)
hdf5.close()
return ct_data, x_ray1
'''
generate batch
'''
def pull_item(self, item):
file_path = self.get_image_path(self.dir_root, self.dataset_paths[item])
ct_data, x_ray1 = self.load_file(file_path)
# Data Augmentation
ct, xray1 = self.data_augmentation([ct_data, x_ray1])
return ct, xray1, file_path
| [
"[email protected]"
]
| |
9a55f6475c0faa28d1d4f188a408cd6edb3b8c7b | 8d472f9facb895dda9e1df81f3bb6c2f81b9c357 | /master/bt5/slapos_erp5/SkinTemplateItem/portal_skins/slapos_administration/NotificationMessageModule_updateProductionNotificationId.py | c500db8c5f0f5a55eeea84950922136ec276be23 | []
| no_license | SlapOS/slapos.core | 852485eed9382685f3df6ba8532f8192bb1389c4 | 369e8d56636e1c59a745e68dc68154abfc5b7840 | refs/heads/master | 2023-08-31T04:42:34.722241 | 2023-08-30T15:13:08 | 2023-08-30T15:13:08 | 1,825,920 | 11 | 4 | null | null | null | null | UTF-8 | Python | false | false | 614 | py | if context.getPortalType() != "Notification Message Module":
raise ValueError("This folder is not a Notification Message Module")
for notification_message in context.searchFolder(id="201%", validation_state="validated"):
if notification_message.getValidationState() != 'validated':
continue
new_id = "master_prod_%s_%s_%s" % (notification_message.getReference().replace("-", "_").replace(".", "_"),
notification_message.getLanguage("en"),
notification_message.getVersion("001"))
notification_message.getObject().setId(new_id)
| [
"[email protected]"
]
| |
e9800083acfaca576ffae8cac4c26355760c9560 | 7233716fbf9fff94240d14770b3fc3f3ada10d9b | /devel/.private/gazebo_msgs/lib/python2.7/dist-packages/gazebo_msgs/srv/_SetLinkProperties.py | 6800b54ace209d309c7e834bf37f50f0b9856e68 | []
| no_license | shashankseth01/E-yantra | 58d42dce90667ca37f31f2cf111ee98c39468617 | 23432e058fce7733bd1a8399fd6edc20967fa6a3 | refs/heads/main | 2023-02-04T00:36:57.230996 | 2020-12-21T09:55:23 | 2020-12-21T09:55:23 | 316,716,460 | 2 | 1 | null | null | null | null | UTF-8 | Python | false | false | 13,337 | py | # This Python file uses the following encoding: utf-8
"""autogenerated by genpy from gazebo_msgs/SetLinkPropertiesRequest.msg. Do not edit."""
import codecs
import sys
python3 = True if sys.hexversion > 0x03000000 else False
import genpy
import struct
import geometry_msgs.msg
class SetLinkPropertiesRequest(genpy.Message):
_md5sum = "68ac74a4be01b165bc305b5ccdc45e91"
_type = "gazebo_msgs/SetLinkPropertiesRequest"
_has_header = False # flag to mark the presence of a Header object
_full_text = """string link_name # name of link
# link names are prefixed by model name, e.g. pr2::base_link
geometry_msgs/Pose com # center of mass location in link frame
# and orientation of the moment of inertias
# relative to the link frame
bool gravity_mode # set gravity mode on/off
float64 mass # linear mass of link
float64 ixx # moment of inertia
float64 ixy # moment of inertia
float64 ixz # moment of inertia
float64 iyy # moment of inertia
float64 iyz # moment of inertia
float64 izz # moment of inertia
================================================================================
MSG: geometry_msgs/Pose
# A representation of pose in free space, composed of position and orientation.
Point position
Quaternion orientation
================================================================================
MSG: geometry_msgs/Point
# This contains the position of a point in free space
float64 x
float64 y
float64 z
================================================================================
MSG: geometry_msgs/Quaternion
# This represents an orientation in free space in quaternion form.
float64 x
float64 y
float64 z
float64 w
"""
__slots__ = ['link_name','com','gravity_mode','mass','ixx','ixy','ixz','iyy','iyz','izz']
_slot_types = ['string','geometry_msgs/Pose','bool','float64','float64','float64','float64','float64','float64','float64']
def __init__(self, *args, **kwds):
"""
Constructor. Any message fields that are implicitly/explicitly
set to None will be assigned a default value. The recommend
use is keyword arguments as this is more robust to future message
changes. You cannot mix in-order arguments and keyword arguments.
The available fields are:
link_name,com,gravity_mode,mass,ixx,ixy,ixz,iyy,iyz,izz
:param args: complete set of field values, in .msg order
:param kwds: use keyword arguments corresponding to message field names
to set specific fields.
"""
if args or kwds:
super(SetLinkPropertiesRequest, self).__init__(*args, **kwds)
# message fields cannot be None, assign default values for those that are
if self.link_name is None:
self.link_name = ''
if self.com is None:
self.com = geometry_msgs.msg.Pose()
if self.gravity_mode is None:
self.gravity_mode = False
if self.mass is None:
self.mass = 0.
if self.ixx is None:
self.ixx = 0.
if self.ixy is None:
self.ixy = 0.
if self.ixz is None:
self.ixz = 0.
if self.iyy is None:
self.iyy = 0.
if self.iyz is None:
self.iyz = 0.
if self.izz is None:
self.izz = 0.
else:
self.link_name = ''
self.com = geometry_msgs.msg.Pose()
self.gravity_mode = False
self.mass = 0.
self.ixx = 0.
self.ixy = 0.
self.ixz = 0.
self.iyy = 0.
self.iyz = 0.
self.izz = 0.
def _get_types(self):
"""
internal API method
"""
return self._slot_types
def serialize(self, buff):
"""
serialize message into buffer
:param buff: buffer, ``StringIO``
"""
try:
_x = self.link_name
length = len(_x)
if python3 or type(_x) == unicode:
_x = _x.encode('utf-8')
length = len(_x)
buff.write(struct.Struct('<I%ss'%length).pack(length, _x))
_x = self
buff.write(_get_struct_7dB7d().pack(_x.com.position.x, _x.com.position.y, _x.com.position.z, _x.com.orientation.x, _x.com.orientation.y, _x.com.orientation.z, _x.com.orientation.w, _x.gravity_mode, _x.mass, _x.ixx, _x.ixy, _x.ixz, _x.iyy, _x.iyz, _x.izz))
except struct.error as se: self._check_types(struct.error("%s: '%s' when writing '%s'" % (type(se), str(se), str(locals().get('_x', self)))))
except TypeError as te: self._check_types(ValueError("%s: '%s' when writing '%s'" % (type(te), str(te), str(locals().get('_x', self)))))
def deserialize(self, str):
"""
unpack serialized message in str into this message instance
:param str: byte array of serialized message, ``str``
"""
codecs.lookup_error("rosmsg").msg_type = self._type
try:
if self.com is None:
self.com = geometry_msgs.msg.Pose()
end = 0
start = end
end += 4
(length,) = _struct_I.unpack(str[start:end])
start = end
end += length
if python3:
self.link_name = str[start:end].decode('utf-8', 'rosmsg')
else:
self.link_name = str[start:end]
_x = self
start = end
end += 113
(_x.com.position.x, _x.com.position.y, _x.com.position.z, _x.com.orientation.x, _x.com.orientation.y, _x.com.orientation.z, _x.com.orientation.w, _x.gravity_mode, _x.mass, _x.ixx, _x.ixy, _x.ixz, _x.iyy, _x.iyz, _x.izz,) = _get_struct_7dB7d().unpack(str[start:end])
self.gravity_mode = bool(self.gravity_mode)
return self
except struct.error as e:
raise genpy.DeserializationError(e) # most likely buffer underfill
def serialize_numpy(self, buff, numpy):
"""
serialize message with numpy array types into buffer
:param buff: buffer, ``StringIO``
:param numpy: numpy python module
"""
try:
_x = self.link_name
length = len(_x)
if python3 or type(_x) == unicode:
_x = _x.encode('utf-8')
length = len(_x)
buff.write(struct.Struct('<I%ss'%length).pack(length, _x))
_x = self
buff.write(_get_struct_7dB7d().pack(_x.com.position.x, _x.com.position.y, _x.com.position.z, _x.com.orientation.x, _x.com.orientation.y, _x.com.orientation.z, _x.com.orientation.w, _x.gravity_mode, _x.mass, _x.ixx, _x.ixy, _x.ixz, _x.iyy, _x.iyz, _x.izz))
except struct.error as se: self._check_types(struct.error("%s: '%s' when writing '%s'" % (type(se), str(se), str(locals().get('_x', self)))))
except TypeError as te: self._check_types(ValueError("%s: '%s' when writing '%s'" % (type(te), str(te), str(locals().get('_x', self)))))
def deserialize_numpy(self, str, numpy):
"""
unpack serialized message in str into this message instance using numpy for array types
:param str: byte array of serialized message, ``str``
:param numpy: numpy python module
"""
codecs.lookup_error("rosmsg").msg_type = self._type
try:
if self.com is None:
self.com = geometry_msgs.msg.Pose()
end = 0
start = end
end += 4
(length,) = _struct_I.unpack(str[start:end])
start = end
end += length
if python3:
self.link_name = str[start:end].decode('utf-8', 'rosmsg')
else:
self.link_name = str[start:end]
_x = self
start = end
end += 113
(_x.com.position.x, _x.com.position.y, _x.com.position.z, _x.com.orientation.x, _x.com.orientation.y, _x.com.orientation.z, _x.com.orientation.w, _x.gravity_mode, _x.mass, _x.ixx, _x.ixy, _x.ixz, _x.iyy, _x.iyz, _x.izz,) = _get_struct_7dB7d().unpack(str[start:end])
self.gravity_mode = bool(self.gravity_mode)
return self
except struct.error as e:
raise genpy.DeserializationError(e) # most likely buffer underfill
_struct_I = genpy.struct_I
def _get_struct_I():
global _struct_I
return _struct_I
_struct_7dB7d = None
def _get_struct_7dB7d():
global _struct_7dB7d
if _struct_7dB7d is None:
_struct_7dB7d = struct.Struct("<7dB7d")
return _struct_7dB7d
# This Python file uses the following encoding: utf-8
"""autogenerated by genpy from gazebo_msgs/SetLinkPropertiesResponse.msg. Do not edit."""
import codecs
import sys
python3 = True if sys.hexversion > 0x03000000 else False
import genpy
import struct
class SetLinkPropertiesResponse(genpy.Message):
_md5sum = "2ec6f3eff0161f4257b808b12bc830c2"
_type = "gazebo_msgs/SetLinkPropertiesResponse"
_has_header = False # flag to mark the presence of a Header object
_full_text = """bool success # return true if get info is successful
string status_message # comments if available
"""
__slots__ = ['success','status_message']
_slot_types = ['bool','string']
def __init__(self, *args, **kwds):
"""
Constructor. Any message fields that are implicitly/explicitly
set to None will be assigned a default value. The recommend
use is keyword arguments as this is more robust to future message
changes. You cannot mix in-order arguments and keyword arguments.
The available fields are:
success,status_message
:param args: complete set of field values, in .msg order
:param kwds: use keyword arguments corresponding to message field names
to set specific fields.
"""
if args or kwds:
super(SetLinkPropertiesResponse, self).__init__(*args, **kwds)
# message fields cannot be None, assign default values for those that are
if self.success is None:
self.success = False
if self.status_message is None:
self.status_message = ''
else:
self.success = False
self.status_message = ''
def _get_types(self):
"""
internal API method
"""
return self._slot_types
def serialize(self, buff):
"""
serialize message into buffer
:param buff: buffer, ``StringIO``
"""
try:
_x = self.success
buff.write(_get_struct_B().pack(_x))
_x = self.status_message
length = len(_x)
if python3 or type(_x) == unicode:
_x = _x.encode('utf-8')
length = len(_x)
buff.write(struct.Struct('<I%ss'%length).pack(length, _x))
except struct.error as se: self._check_types(struct.error("%s: '%s' when writing '%s'" % (type(se), str(se), str(locals().get('_x', self)))))
except TypeError as te: self._check_types(ValueError("%s: '%s' when writing '%s'" % (type(te), str(te), str(locals().get('_x', self)))))
def deserialize(self, str):
"""
unpack serialized message in str into this message instance
:param str: byte array of serialized message, ``str``
"""
codecs.lookup_error("rosmsg").msg_type = self._type
try:
end = 0
start = end
end += 1
(self.success,) = _get_struct_B().unpack(str[start:end])
self.success = bool(self.success)
start = end
end += 4
(length,) = _struct_I.unpack(str[start:end])
start = end
end += length
if python3:
self.status_message = str[start:end].decode('utf-8', 'rosmsg')
else:
self.status_message = str[start:end]
return self
except struct.error as e:
raise genpy.DeserializationError(e) # most likely buffer underfill
def serialize_numpy(self, buff, numpy):
"""
serialize message with numpy array types into buffer
:param buff: buffer, ``StringIO``
:param numpy: numpy python module
"""
try:
_x = self.success
buff.write(_get_struct_B().pack(_x))
_x = self.status_message
length = len(_x)
if python3 or type(_x) == unicode:
_x = _x.encode('utf-8')
length = len(_x)
buff.write(struct.Struct('<I%ss'%length).pack(length, _x))
except struct.error as se: self._check_types(struct.error("%s: '%s' when writing '%s'" % (type(se), str(se), str(locals().get('_x', self)))))
except TypeError as te: self._check_types(ValueError("%s: '%s' when writing '%s'" % (type(te), str(te), str(locals().get('_x', self)))))
def deserialize_numpy(self, str, numpy):
"""
unpack serialized message in str into this message instance using numpy for array types
:param str: byte array of serialized message, ``str``
:param numpy: numpy python module
"""
codecs.lookup_error("rosmsg").msg_type = self._type
try:
end = 0
start = end
end += 1
(self.success,) = _get_struct_B().unpack(str[start:end])
self.success = bool(self.success)
start = end
end += 4
(length,) = _struct_I.unpack(str[start:end])
start = end
end += length
if python3:
self.status_message = str[start:end].decode('utf-8', 'rosmsg')
else:
self.status_message = str[start:end]
return self
except struct.error as e:
raise genpy.DeserializationError(e) # most likely buffer underfill
_struct_I = genpy.struct_I
def _get_struct_I():
global _struct_I
return _struct_I
_struct_B = None
def _get_struct_B():
global _struct_B
if _struct_B is None:
_struct_B = struct.Struct("<B")
return _struct_B
class SetLinkProperties(object):
_type = 'gazebo_msgs/SetLinkProperties'
_md5sum = 'd534ce1b36ee99de0ffa806c3a6348f0'
_request_class = SetLinkPropertiesRequest
_response_class = SetLinkPropertiesResponse
| [
"[email protected]"
]
| |
48ddd44b06ec6eca0612ca841b1a3c4c4285e8ef | 09e57dd1374713f06b70d7b37a580130d9bbab0d | /benchmark/startQiskit_QC1758.py | 9251f4bd17de4e61517b4875032d7ca930e91402 | [
"BSD-3-Clause"
]
| permissive | UCLA-SEAL/QDiff | ad53650034897abb5941e74539e3aee8edb600ab | d968cbc47fe926b7f88b4adf10490f1edd6f8819 | refs/heads/main | 2023-08-05T04:52:24.961998 | 2021-09-19T02:56:16 | 2021-09-19T02:56:16 | 405,159,939 | 2 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,787 | py | # qubit number=5
# total number=60
import cirq
import qiskit
from qiskit import IBMQ
from qiskit.providers.ibmq import least_busy
from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister
from qiskit import BasicAer, execute, transpile
from pprint import pprint
from qiskit.test.mock import FakeVigo
from math import log2,floor, sqrt, pi
import numpy as np
import networkx as nx
def build_oracle(n: int, f) -> QuantumCircuit:
# implement the oracle O_f^\pm
# NOTE: use U1 gate (P gate) with \lambda = 180 ==> CZ gate
# or multi_control_Z_gate (issue #127)
controls = QuantumRegister(n, "ofc")
oracle = QuantumCircuit(controls, name="Zf")
for i in range(2 ** n):
rep = np.binary_repr(i, n)
if f(rep) == "1":
for j in range(n):
if rep[j] == "0":
oracle.x(controls[j])
# oracle.h(controls[n])
if n >= 2:
oracle.mcu1(pi, controls[1:], controls[0])
for j in range(n):
if rep[j] == "0":
oracle.x(controls[j])
# oracle.barrier()
return oracle
def make_circuit(n:int,f) -> QuantumCircuit:
# circuit begin
input_qubit = QuantumRegister(n,"qc")
classical = ClassicalRegister(n, "qm")
prog = QuantumCircuit(input_qubit, classical)
prog.h(input_qubit[0]) # number=3
prog.x(input_qubit[4]) # number=53
prog.h(input_qubit[0]) # number=57
prog.cz(input_qubit[2],input_qubit[0]) # number=58
prog.h(input_qubit[0]) # number=59
prog.z(input_qubit[2]) # number=46
prog.h(input_qubit[0]) # number=54
prog.cz(input_qubit[2],input_qubit[0]) # number=55
prog.h(input_qubit[0]) # number=56
prog.h(input_qubit[1]) # number=4
prog.rx(2.664070570244145,input_qubit[1]) # number=39
prog.h(input_qubit[2]) # number=5
prog.h(input_qubit[3]) # number=6
prog.h(input_qubit[2]) # number=49
prog.cz(input_qubit[3],input_qubit[2]) # number=50
prog.h(input_qubit[2]) # number=51
prog.h(input_qubit[4]) # number=21
Zf = build_oracle(n, f)
repeat = floor(sqrt(2 ** n) * pi / 4)
for i in range(repeat):
prog.append(Zf.to_gate(), [input_qubit[i] for i in range(n)])
prog.h(input_qubit[0]) # number=1
prog.h(input_qubit[3]) # number=40
prog.y(input_qubit[4]) # number=35
prog.h(input_qubit[1]) # number=2
prog.h(input_qubit[2]) # number=7
prog.h(input_qubit[3]) # number=8
prog.h(input_qubit[0]) # number=25
prog.cz(input_qubit[1],input_qubit[0]) # number=26
prog.h(input_qubit[0]) # number=27
prog.h(input_qubit[0]) # number=36
prog.cz(input_qubit[1],input_qubit[0]) # number=37
prog.h(input_qubit[0]) # number=38
prog.cx(input_qubit[1],input_qubit[0]) # number=41
prog.x(input_qubit[0]) # number=42
prog.cx(input_qubit[1],input_qubit[0]) # number=43
prog.cx(input_qubit[1],input_qubit[0]) # number=34
prog.cx(input_qubit[1],input_qubit[0]) # number=24
prog.cx(input_qubit[0],input_qubit[1]) # number=29
prog.cx(input_qubit[2],input_qubit[3]) # number=44
prog.x(input_qubit[1]) # number=30
prog.cx(input_qubit[0],input_qubit[1]) # number=31
prog.x(input_qubit[2]) # number=11
prog.x(input_qubit[3]) # number=12
if n>=2:
prog.mcu1(pi,input_qubit[1:],input_qubit[0])
prog.x(input_qubit[0]) # number=13
prog.x(input_qubit[1]) # number=14
prog.x(input_qubit[2]) # number=15
prog.x(input_qubit[3]) # number=16
prog.h(input_qubit[0]) # number=17
prog.h(input_qubit[1]) # number=18
prog.h(input_qubit[2]) # number=19
prog.h(input_qubit[3]) # number=20
prog.z(input_qubit[1]) # number=52
# circuit end
for i in range(n):
prog.measure(input_qubit[i], classical[i])
return prog
if __name__ == '__main__':
key = "00000"
f = lambda rep: str(int(rep == key))
prog = make_circuit(5,f)
IBMQ.load_account()
provider = IBMQ.get_provider(hub='ibm-q')
provider.backends()
backend = least_busy(provider.backends(filters=lambda x: x.configuration().n_qubits >= 2 and not x.configuration().simulator and x.status().operational == True))
sample_shot =7924
info = execute(prog, backend=backend, shots=sample_shot).result().get_counts()
backend = FakeVigo()
circuit1 = transpile(prog,backend,optimization_level=2)
writefile = open("../data/startQiskit_QC1758.csv","w")
print(info,file=writefile)
print("results end", file=writefile)
print(circuit1.depth(),file=writefile)
print(circuit1,file=writefile)
writefile.close()
| [
"[email protected]"
]
| |
c5de003f1e74c18d0f8f566bab3c1c6638eb1c13 | 725abfa74e3800622837e60615dc15c6e91442c0 | /venv/Lib/site-packages/django/contrib/sites/managers.py | 12f8e555d1819f45ba12459ff1e8dedf2b4a4201 | []
| no_license | Malak-Abdallah/TODOlist | 4840e2e0a27e6499ae6b37524bb3e58455d08bfb | fd35754e8eac9b262fae17ec16ad9fb510a12f5d | refs/heads/master | 2023-07-16T11:38:48.759232 | 2021-08-31T09:43:11 | 2021-08-31T09:43:11 | 401,600,246 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,973 | py | from django.conf import settings
from django.core import checks
from django.core.exceptions import FieldDoesNotExist
from django.db import models
class CurrentSiteManager(models.Manager):
"Use this to limit objects to those associated with the current site."
use_in_migrations = True
def __init__(self, field_name=None):
super().__init__()
self.__field_name = field_name
def check(self, **kwargs):
errors = super().check(**kwargs)
errors.extend(self._check_field_name())
return errors
def _check_field_name(self):
field_name = self._get_field_name()
try:
field = self.model._meta.get_field(field_name)
except FieldDoesNotExist:
return [
checks.Error(
"CurrentSiteManager could not find a field named '%s'."
% field_name,
obj=self,
id="sites.E001",
)
]
if not field.many_to_many and not isinstance(field, (models.ForeignKey)):
return [
checks.Error(
"CurrentSiteManager cannot use '%s.%s' as it is not a foreign key or a many-to-many field."
% (self.model._meta.object_name, field_name),
obj=self,
id="sites.E002",
)
]
return []
def _get_field_name(self):
""" Return self.__field_name or 'site' or 'sites'. """
if not self.__field_name:
try:
self.model._meta.get_field("site")
except FieldDoesNotExist:
self.__field_name = "sites"
else:
self.__field_name = "site"
return self.__field_name
def get_queryset(self):
return (
super()
.get_queryset()
.filter(**{self._get_field_name() + "__id": settings.SITE_ID})
)
| [
"[email protected]"
]
| |
1f0ac06c86ccc934a38d76d04471bc0b6512f58e | d36ac2218dd70e9d1f26f0e82bc32cd2d0166e17 | /tests/test_e_policy.py | e6cad3826d533caa3cf6f6ad7ea876072d51aec9 | [
"MIT"
]
| permissive | dre2004/reinforcement | b8b1524fb81ec2e540b0d8e64e5a77f6fff2ded8 | 50d59a49106ffecc26c0fd97fa240bddfff1384a | refs/heads/master | 2020-05-20T04:38:22.524054 | 2018-10-28T18:59:55 | 2018-10-28T18:59:55 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,064 | py | import random
import pytest
from reinforcement.policies.e_greedy_policies import EpsilonGreedyPolicy
from tests.common_doubles import MockFilter, Call
from tests.q_doubles import QFunctionWrapper
def make_policy(epsilon, filter=None):
return EpsilonGreedyPolicy(epsilon, filter)
STATE_A = [0]
STATE_B = [1]
@pytest.fixture
def q_function():
return QFunctionWrapper([0, 1])
@pytest.fixture
def zero_policy():
"""Returns a normal e greedy policy with epsilon 0"""
return make_policy(0)
@pytest.fixture
def epsilon_policy():
"""Returns a normal e greedy policy with epsilon 0.2"""
return make_policy(0.2)
@pytest.fixture
def function_policy():
"""Returns a normal e greedy policy with epsilon 0.2 provided by a function"""
return make_policy(lambda: 0.2)
def test_epsilon_zero(zero_policy, q_function):
q_function.set_state_action_values(STATE_A, -1, 1)
assert zero_policy.select(STATE_A, q_function) == 1
def test_multiple_states(zero_policy, q_function):
q_function.set_state_action_values(STATE_A, -1, 1)
q_function.set_state_action_values(STATE_B, 10, -5)
assert zero_policy.select(STATE_A, q_function) == 1
assert zero_policy.select(STATE_B, q_function) == 0
def test_non_zero_epsilon(epsilon_policy, q_function):
random.seed(1)
q_function.set_state_action_values(STATE_A, -1, 1)
assert epsilon_policy.select(STATE_A, q_function) == 0
def test_epsilon_as_function(function_policy, q_function):
random.seed(1)
q_function.set_state_action_values(STATE_A, -1, 1)
assert function_policy.select(STATE_A, q_function) == 0
def test_incomplete_state(zero_policy, q_function):
q_function[STATE_A, 0] = -1
assert zero_policy.select(STATE_A, q_function) == 1
def test_invalid_actions_are_ignored(q_function):
q_function[STATE_A, 0] = 10
q_function[STATE_A, 1] = -1
filter = MockFilter(Call((STATE_A, 0), returns=False), Call((STATE_A, 1), returns=True))
policy = make_policy(0, filter)
assert policy.select(STATE_A, q_function) == 1
| [
"[email protected]"
]
| |
58c87bd862dce94dac7a79671928118b0d26a780 | b01f25b447d5ec3d6bc08380ae2601d5badb6af3 | /Graph Theory/find_town_judge_graph.py | a65dc5f13d930fed2aa087d3129ce35ebbcebc07 | []
| no_license | SurajPatil314/Leetcode-problems | 0b05faab17214437a599d846dd1c9a7ea82b9c4c | 9201a87246842855281c90a9705f83fce24d1137 | refs/heads/master | 2021-09-05T02:20:05.274438 | 2021-08-09T21:24:05 | 2021-08-09T21:24:05 | 203,467,630 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,107 | py | """
In a town, there are N people labelled from 1 to N. There is a rumor that one of these people is secretly the town judge.
If the town judge exists, then:
The town judge trusts nobody.
Everybody (except for the town judge) trusts the town judge.
There is exactly one person that satisfies properties 1 and 2.
You are given trust, an array of pairs trust[i] = [a, b] representing that the person labelled a trusts the person labelled b.
If the town judge exists and can be identified, return the label of the town judge. Otherwise, return -1.
"""
from collections import defaultdict
class Solution:
def findJudge(self, N: int, trust: List[List[int]]) -> int:
qw = defaultdict(list)
qw1 = defaultdict(list)
if N == 1 and len(trust) == 0:
return 1
for i in trust:
qw[i[1]].append(i[0])
qw1[i[0]].append(i[1])
ans = []
for i, j in qw.items():
if len(j) == N - 1:
ans.append(i)
for i in ans:
if len(qw1[i]) == 0:
return i
return -1
| [
"[email protected]"
]
| |
19d37369afbab32c4d2c09847ce2d0f8ce1afa84 | 2f98aa7e5bfc2fc5ef25e4d5cfa1d7802e3a7fae | /python/python_12663.py | 6031a1a7cbb9dc2a4982856ada1abfbb19ec911f | []
| no_license | AK-1121/code_extraction | cc812b6832b112e3ffcc2bb7eb4237fd85c88c01 | 5297a4a3aab3bb37efa24a89636935da04a1f8b6 | refs/heads/master | 2020-05-23T08:04:11.789141 | 2015-10-22T19:19:40 | 2015-10-22T19:19:40 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 143 | py | # Getting SQLAlchemy to issue CREATE SCHEMA on create_all
from sqlalchemy.schema import CreateSchema
engine.execute(CreateSchema('my_schema'))
| [
"[email protected]"
]
| |
2eb4684ce29859ebac9b20e4a8d5a10fb93344dc | dfc827bf144be6edf735a8b59b000d8216e4bb00 | /CODE/postprocessing/readplot/db/SWRF/uhsuperimposedDB.py | b249e02f36921c7b8a559bd41b5ad15ff0e335fa | []
| no_license | jordanpitt3141/ALL | c5f55e2642d4c18b63b4226ddf7c8ca492c8163c | 3f35c9d8e422e9088fe096a267efda2031ba0123 | refs/heads/master | 2020-07-12T16:26:59.684440 | 2019-05-08T04:12:26 | 2019-05-08T04:12:26 | 94,275,573 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 12,403 | py | # -*- coding: utf-8 -*-
"""
Created on Wed Sep 23 16:53:37 2015
@author: jordan
"""
import csv
from numpy.linalg import norm
from scipy import *
from pylab import plot, show, legend,xlim,ylim,savefig,title,xlabel,ylabel,clf, loglog, xticks,yticks
from matplotlib2tikz import save as tikz_save
import os
from subprocess import call
diffs = [10.0]
wdirbase = "../../../../../data/raw/DBSWanew100s/o3/10/"
sdirbase = "../../../../../data/postprocessing/uhcomp300s/DB/o3/10/"
cwdirbase = "../../../../../data/raw/trackleadsola10new/o3/"
for diff in diffs:
#full crop front front back middez middle zz
#ylims = [[1.0,1.8],[1.0,1.8],[1,1.8],[1.0,1.8],[1.32,1.42],[1.32,1.42]]
xlims = [[0-1,1000+1],[200-1,1000+1],[200-1,1000+1],[200-1,1000+1],[550-1,650+1],[590-1,620+1]]
gaps = [10,10,10,10,2,2]
#read the DBSW file
for l in range(len(ylims)):
#for l in range(1):
cylim = ylims[l]
cxlim = xlims[l]
sdir = sdirbase +str(diff)+ "/"
if not os.path.exists(sdir):
os.makedirs(sdir)
sdirf = sdir +str(l) + "/"
if not os.path.exists(sdirf):
os.makedirs(sdirf)
gap = gaps[l]
wdir = wdirbase +str(diff)+ "/"
s = cwdirbase + "outlast.txt"
with open(s,'r') as file1:
readfile = csv.reader(file1, delimiter = ',', quotechar='|', quoting=csv.QUOTE_MINIMAL)
h = []
u = []
x = []
j = -1
for row in readfile:
if (j >= 0):
dx = float(row[0])
dt = float(row[1])
t = float(row[2])
x.append(float(row[4]))
h.append(float(row[5]))
u.append(float(row[7]))
j = j + 1
igap = int(gap)
xbeg = int((cxlim[0])/dx)
xend = int((cxlim[1])/dx)
if(xbeg < 0):
xbeg = 0
if(xend > len(x)):
xend = len(x)
xt = x[xbeg:xend:igap]
ht = h[xbeg:xend:igap]
ut = u[xbeg:xend:igap]
xDB = array(xt)
hDB = array(ht)
uDB = array(ut) + 0.294
m = len(x)
s = str(dx)
plot(xDB,hDB,label=s)
plot(xDB,uDB,label=s)
ylim(cylim)
xlim(cxlim)
#eyticks = [h2]
#yticks(list(yticks()[0]) + eyticks)
xlabel("$x$ ($m$)")
ylabel("$h$ ($m$)")
n = len(xDB)
s = sdirf + "hDBSW.dat"
with open(s,'w') as file1:
for i in range(n):
s ="%3.8f%5s%1.15f\n" %(xDB[i]," ",hDB[i])
file1.write(s)
n = len(xDB)
s = sdirf + "hDB.dat"
with open(s,'w') as file1:
for i in range(n):
s ="%3.8f%5s%1.15f\n" %(xDB[i]," ",uDB[i])
file1.write(s)
s = "Dam Break: diff = " + str(beta)
title(s)
s = sdir +str(l)+".png"
savefig(s, bbox_inches='tight')
legend()
s = sdir +str(l)+ "leg.png"
savefig(s, bbox_inches='tight')
clf()
"""
#legend()
if(l==0):
s = "\\documentclass[]{article} \n\\usepackage{pgfplots} \n\\usepgfplotslibrary{external} \n\\tikzexternalize \n" \
+ "\\usepackage{tikz} \n \\usepackage{amsmath} \n \\usepackage{pgfplots} \n \\usetikzlibrary{calc} \n" \
+ "\\pgfplotsset{compat = newest,every x tick label/.append style={font=\scriptsize},every y tick label/.append style={font=\\scriptsize,color=white}, every axis plot post/.style={line join=round}} \n" \
+ "\\begin{document} \n \\begin{tikzpicture} \n \\begin{axis}[ \n ylabel near ticks,\n xlabel near ticks, \n" \
+ "yticklabel style={/pgf/number format/fixed,/pgf/number format/precision=5,}, \n"\
+ "extra y tick style={yticklabel style={font=\scriptsize,color=black}}, \n" \
+ "xtick={0,200,400,600,800,1000}, \n"\
+ "ytick={0.2,0.4,0.6,0.8,1.0,1.2,1.4,1.6,1.8,2.0,2.2}, \n"\
+ "extra y ticks = {0.1,0.3,0.5,0.7,0.9,1.1,1.3,1.36898,1.5,1.7,1.9,2.1}, \n"\
+ "scaled y ticks=false, \n clip mode=individual,\n "\
+ "xmin=200, \n xmax=1000, \n ymin = 0.1, \n ymax = 2.1,\n"\
+ "xlabel=$x$ ($m$), \n ylabel=value]"\
+ "\\addplot [blue] table {hDBSW.dat}; \n"\
+ "\\addplot [green!80!black] table {hDB.dat}; \n"\
+ "\\end{axis} \n \\end{tikzpicture} \n \\end{document} \n "
#print(s)
elif(l==1):
s = "\\documentclass[]{article} \n\\usepackage{pgfplots} \n\\usepgfplotslibrary{external} \n\\tikzexternalize \n" \
+ "\\usepackage{tikz} \n \\usepackage{amsmath} \n \\usepackage{pgfplots} \n \\usetikzlibrary{calc} \n" \
+ "\\pgfplotsset{compat = newest,every x tick label/.append style={font=\scriptsize},every y tick label/.append style={font=\\scriptsize}, every axis plot post/.style={line join=round}} \n" \
+ "\\begin{document} \n \\begin{tikzpicture} \n \\begin{axis}[ \n ylabel near ticks,\n xlabel near ticks, \n" \
+ "yticklabel style={/pgf/number format/fixed,/pgf/number format/precision=5,}, \n"\
+ "xtick={0,200,400,600,800,1000}, \n"\
+ "ytick={1.0,1.1,1.2,1.3,1.36898,1.4,1.5,1.6,1.7,1.8}, \n"\
+ "scaled y ticks=false, \n clip mode=individual,\n "\
+ "xmin=200, \n xmax=1000, \n ymin = 1.0, \n ymax = 1.8,\n"\
+ "xlabel=$x$ ($m$), \n ylabel=value]"\
+ "\\addplot [blue] table {hDBSW.dat}; \n"\
+ "\\addplot [green!80!black] table {hDB.dat}; \n"\
+ "\\end{axis} \n \\end{tikzpicture} \n \\end{document} \n "
#print(s)
elif(l==2):
s = "\\documentclass[]{article} \n\\usepackage{pgfplots} \n\\usepgfplotslibrary{external} \n\\tikzexternalize \n" \
+ "\\usepackage{tikz} \n \\usepackage{amsmath} \n \\usepackage{pgfplots} \n \\usetikzlibrary{calc} \n" \
+ "\\pgfplotsset{compat = newest,every x tick label/.append style={font=\scriptsize},every y tick label/.append style={font=\\scriptsize,color=white}, every axis plot post/.style={line join=round}} \n" \
+ "\\begin{document} \n \\begin{tikzpicture} \n \\begin{axis}[ \n ylabel near ticks,\n xlabel near ticks, \n" \
+ "yticklabel style={/pgf/number format/fixed,/pgf/number format/precision=5,}, \n"\
+ "extra y tick style={yticklabel style={font=\scriptsize,color=black}}, \n" \
+ "xtick={200,250,300,350,400,450,500,550}, \n"\
+ "ytick={1.31,1.33,1.35,1.37,1.39,1.41,1.43,1.45}, \n"\
+ "extra y ticks = {1.3,1.32,1.34,1.36,1.36898,1.38,1.4,1.42,1.44,1.46}, \n" \
+ "scaled y ticks=false, \n clip mode=individual,\n "\
+ "xmin=200, \n xmax=550, \n ymin = 1.3, \n ymax = 1.46,\n"\
+ "xlabel=$x$ ($m$), \n ylabel=value]"\
+ "\\addplot [blue] table {hDBSW.dat}; \n"\
+ "\\addplot [green!80!black] table {hDB.dat}; \n"\
+ "\\end{axis} \n \\end{tikzpicture} \n \\end{document} \n "
#print(s)
elif(l==3):
s = "\\documentclass[]{article} \n\\usepackage{pgfplots} \n\\usepgfplotslibrary{external} \n\\tikzexternalize \n" \
+ "\\usepackage{tikz} \n \\usepackage{amsmath} \n \\usepackage{pgfplots} \n \\usetikzlibrary{calc} \n" \
+ "\\pgfplotsset{compat = newest,every x tick label/.append style={font=\scriptsize},every y tick label/.append style={font=\\scriptsize,color=white}, every axis plot post/.style={line join=round}} \n" \
+ "\\begin{document} \n \\begin{tikzpicture} \n \\begin{axis}[ \n ylabel near ticks,\n xlabel near ticks, \n" \
+ "yticklabel style={/pgf/number format/fixed,/pgf/number format/precision=5,}, \n"\
+ "extra y tick style={yticklabel style={font=\scriptsize,color=black}}, \n" \
+ "xtick={650,700,750,800,850,900,950}, \n"\
+ "ytick={0.2,0.4,0.6,0.8,1.0,1.2,1.4,1.6,1.8,2.0,2.2}, \n"\
+ "extra y ticks = {0.1,0.3,0.5,0.7,0.9,1.1,1.3,1.36898,1.5,1.7,1.9,2.1}, \n"\
+ "scaled y ticks=false, \n clip mode=individual,\n "\
+ "xmin=650, \n xmax=950, \n ymin = 0.1, \n ymax = 2.1,\n"\
+ "xlabel=$x$ ($m$), \n ylabel=value]"\
+ "\\addplot [blue] table {hDBSW.dat}; \n"\
+ "\\addplot [green!80!black] table {hDB.dat}; \n"\
+ "\\end{axis} \n \\end{tikzpicture} \n \\end{document} \n "
elif(l==4):
s = "\\documentclass[]{article} \n\\usepackage{pgfplots} \n\\usepgfplotslibrary{external} \n\\tikzexternalize \n" \
+ "\\usepackage{tikz} \n \\usepackage{amsmath} \n \\usepackage{pgfplots} \n \\usetikzlibrary{calc} \n" \
+ "\\pgfplotsset{compat = newest,every x tick label/.append style={font=\scriptsize},every y tick label/.append style={font=\\scriptsize,color=white}, every axis plot post/.style={line join=round}} \n" \
+ "\\begin{document} \n \\begin{tikzpicture} \n \\begin{axis}[ \n ylabel near ticks,\n xlabel near ticks, \n" \
+ "yticklabel style={/pgf/number format/fixed,/pgf/number format/precision=5,}, \n"\
+ "extra y tick style={yticklabel style={font=\scriptsize,color=black}}, \n" \
+ "xtick={550,560,570,580,590,600,610,620,630,640,650}, \n"\
+ "ytick={1.31,1.33,1.35,1.37,1.39,1.41,1.43,1.45}, \n"\
+ "extra y ticks = {1.3,1.32,1.34,1.36,1.36898,1.38,1.4,1.42,1.44,1.46}, \n" \
+ "scaled y ticks=false, \n clip mode=individual,\n "\
+ "xmin=550, \n xmax=650, \n ymin = 1.3, \n ymax = 1.46,\n"\
+ "xlabel=$x$ ($m$), \n ylabel=value]"\
+ "\\addplot [blue] table {hDBSW.dat}; \n"\
+ "\\addplot [green!80!black] table {hDB.dat}; \n"\
+ "\\end{axis} \n \\end{tikzpicture} \n \\end{document} \n "
else:
s = "\\documentclass[]{article} \n\\usepackage{pgfplots} \n\\usepgfplotslibrary{external} \n\\tikzexternalize \n" \
+ "\\usepackage{tikz} \n \\usepackage{amsmath} \n \\usepackage{pgfplots} \n \\usetikzlibrary{calc} \n" \
+ "\\pgfplotsset{compat = newest,every x tick label/.append style={font=\scriptsize},every y tick label/.append style={font=\\scriptsize,color=white}, every axis plot post/.style={line join=round}} \n" \
+ "\\begin{document} \n \\begin{tikzpicture} \n \\begin{axis}[ \n ylabel near ticks,\n xlabel near ticks, \n" \
+ "yticklabel style={/pgf/number format/fixed,/pgf/number format/precision=5,}, \n"\
+ "extra y tick style={yticklabel style={font=\scriptsize,color=black}}, \n" \
+ "xtick={590,595,600,605,610,615,620}, \n"\
+ "ytick={1.31,1.33,1.35,1.37,1.39,1.41,1.43,1.45}, \n"\
+ "extra y ticks = {1.3,1.32,1.34,1.36,1.36898,1.38,1.4,1.42,1.44,1.46}, \n" \
+ "scaled y ticks=false, \n clip mode=individual,\n "\
+ "xmin=590, \n xmax=620, \n ymin = 1.3, \n ymax = 1.46,\n"\
+ "xlabel=$x$ ($m$), \n ylabel=value]"\
+ "\\addplot [blue] table {hDBSW.dat}; \n"\
+ "\\addplot [green!80!black] table {hDB.dat}; \n"\
+ "\\end{axis} \n \\end{tikzpicture} \n \\end{document} \n "
#print(s)
filen = sdirf +str(l)+ ".tex"
file1 = open(filen, 'w')
file1.write(s)
file1.close()
#make makefile
s = "LAT = pdflatex \nLATFLAGS = -shell-escape\n\n"
newl = str(l) +":\n\t $(LAT) $(LATFLAGS) " +str(l)+".tex\n\n"
s = s + newl
newl = "clean:\n\t rm -f *~ ./*.log ./*.aux ./*.auxlock ./*.dep ./*.dpth ./*.pdf ./*.gz\n\n"
s = s + newl
newl = "all: " +str(l)
s = s + newl
filen =sdirf + "Makefile"
file1 = open(filen, 'w')
file1.write(s)
file1.close()
call(['make','-C',sdirf,'all'])
""" | [
"[email protected]"
]
| |
6e2c03b6b255c05b1a83bfd12d7a9abac813bc85 | 7ddc47adf330d48bf0b5c602a04c45fa4dc64590 | /Multidimensional Lists/Exercises/Exercise multidimentional lists 04.py | 5209364a7b2049de153e9e53b71c367116c85170 | []
| no_license | Beshkov/Python_Advanced | 634786e5f2de6f19ba74ab2fcf6489f751a774e8 | e1bf1385a03b773b697bc65771193ebd9ef5d226 | refs/heads/main | 2023-03-03T16:19:35.271919 | 2021-02-18T20:45:37 | 2021-02-18T20:45:37 | 329,417,000 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,503 | py | r, c = tuple(int(el) for el in input().split())
matrix = [[el for el in input().split()] for _ in range(r)]
def is_valid(command, matrix):
if not command.split()[0] == 'swap' or not len(command.split()) == 5:
print('Invalid input!')
return
row_one, col_one, row_two, col_two = [int(x) for x in command.split()[1:]]
if not (row_one < len(matrix) and row_two < len(matrix)) or not (
col_one < len(matrix[0]) and col_two < len(matrix[0])):
print('Invalid input!')
return
return (row_one, col_one, row_two, col_two)
def swap(command, matrix):
if is_valid(command, matrix):
r_1, c_1, r_2, c_2 = is_valid(command, matrix)
matrix[r_1][c_1], matrix[r_2][c_2] = matrix[r_2][c_2], matrix[r_1][c_1]
[print(" ".join(map(str, num))) for num in [submatrix for submatrix in matrix]]
command = input()
while not command == "END":
swap(command, matrix)
command = input()
# def check_if_index_is_valid(index_row,index_col, rows, cols):
# if 0 <= index_row < rows and 0<= index_col < cols:
# return True
# return False
#
# def check_if_command_is_valid(command,coordinates, rows, cols):
# if len(coordinates) == 4 and command == "swap":
# for index in range(0,len(coordinates), 2):
# if not check_if_index_is_valid(coordinates[index], coordinates[index+1], rows,cols):
# print('Invalid input!')
# return False
# return True
#
# def print_matrix(matrix):
# for row_index in range(len(matrix)):
# for col_index in range(0,len(matrix[row_index])):
# print(matrix[row_index][col_index], end=' ')
# print()
#
#
# def init_matrix(rows):
#
# matrix = []
# for _ in range(rows):
# matrix.append([el for el in input().split()])
# return matrix
#
# rows, cols = [int(el) for el in input().split()]
# matrix = init_matrix(rows)
# data = input()
#
# while not data == "END":
#
# splitted_data = data.split()
# try:
# command = splitted_data[0]
# coordinates = [int(el) for el in splitted_data[1:]]
# except:
# print("Invalid input")
# if check_if_command_is_valid(command,coordinates, rows, cols):
# temp = matrix[coordinates[0]][coordinates[1]]
# matrix[coordinates[0]][coordinates[1]] = matrix[coordinates[2]][coordinates[3]]
# matrix[coordinates[2]][coordinates[3]] = temp
# print_matrix(matrix)
#
# data = input()
| [
"[email protected]"
]
| |
966bee64272b1e61f7a41d8aa904ff20763d2f3c | 79e1b7919f34ec0425bb98cc4888e092e94e0273 | /readMotionZone3.py | d3e197bc34d632b274850cb613793b6b3042dd07 | []
| no_license | hamhochoi/K59_training | 099e0c193b94677045bd3622f04440d49a3765fb | 845ab3ac64035bf25a72c8dd380cc133092f2e42 | refs/heads/master | 2021-01-19T08:59:34.938053 | 2018-10-24T17:46:29 | 2018-10-24T17:46:29 | 87,705,721 | 0 | 0 | null | 2018-01-13T16:49:23 | 2017-04-09T12:09:21 | null | UTF-8 | Python | false | false | 870 | py | import paho.mqtt.client as mqtt
import time
from datetime import datetime
#f = open("time_pin.txt", "a")
def on_connect(client, userdata, flags, rc):
print("Connected with result code "+str(rc))
# Subscribing in on_connect() means that if we lose the connection and
# reconnect then subscriptions will be renewed.
client.subscribe("zone_3/box_1/motion/id_1")
# The callback for when a PUBLISH message is received from the server.
def on_message(client, userdata, msg):
print(msg.topic+" "+str(msg.payload) + " " + str(datetime.now()))
f = open("time_pin_zone3.txt", "a")
f.write(str(msg.payload) + " " + str(datetime.now()))
f.write("\n")
f.close()
time.sleep(5*60)
client = mqtt.Client()
client.on_connect = on_connect
client.on_message = on_message
client.connect("broker.hivemq.com", 1883, 60)
client.loop_forever()
| [
"[email protected]"
]
| |
764a01fab0d7c00916075b5b3e9e39d50334c0ab | 31c7a5fb038c094abd9d25aa6eb30901aa255e5f | /planner/progression/utils.py | 5352e96dab2b17174f42a3f797b21b240dbfbd26 | [
"MIT"
]
| permissive | hxdaze/pyplanners | 0711d8d7065ea347925bd4395cc22460f8989299 | ef1ae33e233f20cd93ce03cba363b0f14fd078bc | refs/heads/master | 2023-05-13T03:40:21.480863 | 2021-06-02T02:45:24 | 2021-06-02T02:45:24 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,018 | py | from random import random, randint, choice
# TODO: Monte-Carlo Tree Search
DEFAULT_GOAL_PROBABILITY = 0.1
def pop_random(queue):
queue.rotate(randint(0, len(queue) - 1))
return queue.popleft()
def sample_target(sample, goal_sample=None, goal_probability=DEFAULT_GOAL_PROBABILITY):
if (goal_sample is not None) and (random() < goal_probability):
return goal_sample()
return sample()
def pop_min(queue, distance):
minimum, indices = None, []
for i, v in enumerate(queue):
score = distance(v)
if minimum is None or score < minimum:
minimum, indices = score, [i]
elif score == minimum:
indices.append(i)
queue.rotate(choice(indices))
return queue.popleft()
def pop_rrt(distance, sample, goal_sample=None, goal_probability=DEFAULT_GOAL_PROBABILITY):
return lambda queue: pop_min(
queue, lambda sv: distance(sv.state, sample_target(
sample, goal_sample=goal_sample, goal_probability=goal_probability)))
| [
"[email protected]"
]
| |
2a060797c6f0596efb85781e96e662b62b25a605 | e20ed90b9be7a0bcdc1603929d65b2375a224bf6 | /generated-libraries/python/netapp/volume/volume_qos_attributes.py | d9b68e9559d1de7eb232b2af87826e82481b4776 | [
"MIT"
]
| permissive | radekg/netapp-ontap-lib-gen | 530ec3248cff5ead37dc2aa47ced300b7585361b | 6445ebb071ec147ea82a486fbe9f094c56c5c40d | refs/heads/master | 2016-09-06T17:41:23.263133 | 2015-01-14T17:40:46 | 2015-01-14T17:40:46 | 29,256,898 | 2 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,405 | py | from netapp.netapp_object import NetAppObject
class VolumeQosAttributes(NetAppObject):
"""
QoS policy group attached with a volume.
"""
_policy_group_name = None
@property
def policy_group_name(self):
"""
The QoS policy group associated with this volume.
<p>
This optionally specifies which QoS policy group to apply
to the volume. This policy group defines measurable
service level objectives (SLOs) that apply to the storage
objects with which the policy group is associated. If you
do not assign a policy group to a volume, the system will
not monitor and control the traffic to it. This parameter
is not supported on Infinite Volumes. <p>
Attributes: optional-for-create, modifiable
"""
return self._policy_group_name
@policy_group_name.setter
def policy_group_name(self, val):
if val != None:
self.validate('policy_group_name', val)
self._policy_group_name = val
@staticmethod
def get_api_name():
return "volume-qos-attributes"
@staticmethod
def get_desired_attrs():
return [
'policy-group-name',
]
def describe_properties(self):
return {
'policy_group_name': { 'class': basestring, 'is_list': False, 'required': 'optional' },
}
| [
"[email protected]"
]
| |
ed29596b420bf723cd8950b86899310a5b4c08c5 | 8ad9ad16582ad302b121ce9f6cd743dff5187454 | /dataset/dataset_loader.py | 4cb35ca8619a1e7fa17cd3c92077eadf218d5962 | []
| no_license | wolf943134497/3D-Aware-Ellipses-for-Visual-Localization | 0d7d55b2b0997ffdd97f27b31e09de7725790dda | 9caddecd4e175d7528ea6b8d74bb79954c74efef | refs/heads/master | 2023-05-05T07:45:54.831338 | 2021-05-23T19:33:08 | 2021-05-24T21:35:41 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,818 | py | import json
import numpy as np
class Dataset_loader:
"""
Interace with our own dataset file format.
"""
def __init__(self, filename):
with open(filename, "r") as fin:
self.dataset = json.load(fin)
self.nb_images = len(self.dataset)
def __len__(self):
return len(self.dataset)
def get_image_size(self, idx):
"""
Get the image size.
"""
if idx < 0 or idx >= self.nb_images:
print("Invalid index")
return None
return self.dataset[idx]["width"], self.dataset[idx]["height"]
def get_K(self, idx):
"""
Get the K matrix.
"""
if idx < 0 or idx >= self.nb_images:
print("Invalid index")
return None
return np.asarray(self.dataset[idx]["K"])
def get_Rt(self, idx):
"""
Get the extrinsic parameters.
"""
if idx < 0 or idx >= self.nb_images:
print("Invalid index")
return None
R = np.asarray(self.dataset[idx]["R"])
t = np.asarray(self.dataset[idx]["t"])
return np.hstack((R, t.reshape((3, 1))))
def get_rgb_filename(self, idx):
"""
Get the rgb image filename.
"""
if idx < 0 or idx >= self.nb_images:
print("Invalid index")
return None
return self.dataset[idx]["file_name"]
def get_annotations(self, idx):
"""
Get objects annotations.
"""
if idx < 0 or idx >= self.nb_images:
print("Invalid index")
return None
if "annotations" not in self.dataset[idx].keys():
print("No annotations available")
return None
return self.dataset[idx]["annotations"] | [
"[email protected]"
]
| |
e34da30e0fb3817fcdbb7cc9a440c0a0a2569f19 | ec388ce4de7e9268bf65501c0f549632a9b4f448 | /eden/integration/lib/systemd.py | 6f6f83322b20739833d709ce812c6dd76a9815c9 | [
"BSD-3-Clause"
]
| permissive | ExpLife0011/eden | fc789faefd7b6a6a37c34063ffe4faec83c8a9cf | 239a4ae8c4e707a61207ed6a2c41a43838961b46 | refs/heads/master | 2020-04-08T19:39:58.975823 | 2018-11-29T01:31:38 | 2018-11-29T01:33:35 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 17,508 | py | #!/usr/bin/env python3
#
# Copyright (c) 2016-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree. An additional grant
# of patent rights can be found in the PATENTS file in the same directory.
import abc
import contextlib
import errno
import logging
import os
import os.path
import pathlib
import re
import subprocess
import sys
import tempfile
import threading
import types
import typing
from .find_executables import FindExe
from .linux import LinuxCgroup, ProcessID
from .temporary_directory import create_tmp_dir
logger = logging.getLogger(__name__)
SystemdUnitName = str
class SystemdUserServiceManager:
"""A running 'systemd --user' process manageable using 'systemctl --user'."""
def __init__(self, xdg_runtime_dir: pathlib.Path) -> None:
super().__init__()
self.__xdg_runtime_dir = xdg_runtime_dir
@property
def xdg_runtime_dir(self) -> pathlib.Path:
return self.__xdg_runtime_dir
def is_alive(self) -> bool:
result = self._systemctl.run(
["--user", "show-environment"],
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
)
if result.returncode == 0:
return True
if result.returncode == 1:
logger.warning(f'{self} is not alive: {result.stdout.decode("utf-8")}')
return False
result.check_returncode()
return False
def enable_runtime_unit_from_file(self, unit_file: pathlib.Path) -> None:
self._systemctl.check_call(["enable", "--runtime", "--", unit_file])
self._systemctl.check_call(["daemon-reload"])
self.sanity_check_enabled_unit(unit_file=unit_file)
def sanity_check_enabled_unit(self, unit_file: pathlib.Path) -> None:
unit_name = unit_file.name
if "@" in unit_name:
unit_name = unit_name.replace("@", "@testinstance")
self.sanity_check_enabled_unit_fragment(
unit_name=unit_name, expected_unit_file=unit_file
)
self.sanity_check_enabled_unit_sources(
unit_name=unit_name, expected_unit_file=unit_file
)
def sanity_check_enabled_unit_fragment(
self, unit_name: SystemdUnitName, expected_unit_file: pathlib.Path
) -> None:
service = SystemdService(unit_name=unit_name, systemd=self)
actual_unit_file = service.query_fragment_path()
if actual_unit_file != expected_unit_file:
raise Exception(
"Enabled unit's FragmentPath does not match unit file\n"
"Expected: {repr(expected_unit_file)}\n"
"Actual: {repr(actual_unit_file)}"
)
def sanity_check_enabled_unit_sources(
self, unit_name: SystemdUnitName, expected_unit_file: pathlib.Path
) -> None:
actual_unit_sources = self._systemctl.check_output(["cat", "--", unit_name])
expected_unit_sources = b""
for file in [expected_unit_file]:
expected_unit_sources += b"# " + bytes(file) + b"\n"
expected_unit_sources += file.read_bytes()
if actual_unit_sources != expected_unit_sources:
raise Exception(
"Enabled unit does not match unit file\n"
"Expected: {repr(expected_unit_sources)}\n"
"Actual: {repr(actual_unit_sources)}"
)
def systemd_run(
self,
command: typing.Sequence[str],
properties: typing.Mapping[str, str],
extra_env: typing.Mapping[str, str],
unit_name: typing.Optional[SystemdUnitName] = None,
) -> "SystemdService":
systemd_run_command = ["systemd-run", "--user"]
for name, value in properties.items():
systemd_run_command.extend(("--property", f"{name}={value}"))
for name, value in extra_env.items():
systemd_run_command.extend(("--setenv", f"{name}={value}"))
if unit_name is not None:
systemd_run_command.extend(("--unit", unit_name))
systemd_run_command.append("--")
systemd_run_command.extend(command)
output = subprocess.check_output(
systemd_run_command, env=self.env, stderr=subprocess.STDOUT
)
match = re.match(
r"^Running as unit: (?P<unit>.*)$",
output.decode("utf-8"),
flags=re.MULTILINE,
)
if match is None:
raise Exception("Failed to parse unit from command output")
return self.get_service(match.group("unit"))
def get_active_unit_names(self) -> typing.List[SystemdUnitName]:
def parse_line(line: str) -> SystemdUnitName:
parts = re.split(r" +", line)
return parts[0]
stdout = self._systemctl.check_output(
[
"list-units",
"--all",
"--full",
"--no-legend",
"--no-pager",
"--plain",
"--state=active",
]
)
return [parse_line(line) for line in stdout.decode("utf-8").splitlines()]
def get_unit_paths(self) -> typing.List[pathlib.Path]:
stdout = subprocess.check_output(
["systemd-analyze", "--user", "unit-paths"], env=self.env
)
return [pathlib.Path(line) for line in stdout.decode("utf-8").splitlines()]
def get_service(self, unit_name: SystemdUnitName) -> "SystemdService":
return SystemdService(unit_name=unit_name, systemd=self)
@property
def env(self) -> typing.Dict[str, str]:
env = dict(os.environ)
env.update(self.extra_env)
return env
@property
def extra_env(self) -> typing.Dict[str, str]:
return {
"DBUS_SESSION_BUS_ADDRESS": "",
"XDG_RUNTIME_DIR": str(self.xdg_runtime_dir),
}
@property
def _systemctl(self) -> "_SystemctlCLI":
return _SystemctlCLI(env=self.env)
def __str__(self) -> str:
return f"systemd ({self.xdg_runtime_dir})"
def __repr__(self) -> str:
return (
f"SystemdUserServiceManager(xdg_runtime_dir={repr(self.xdg_runtime_dir)})"
)
class SystemdService:
def __init__(
self, unit_name: SystemdUnitName, systemd: SystemdUserServiceManager
) -> None:
super().__init__()
self.__systemd = systemd
self.__unit_name = unit_name
@property
def unit_name(self) -> SystemdUnitName:
return self.__unit_name
def start(self) -> None:
self.__systemctl.check_call(["start", "--", self.unit_name])
def stop(self) -> None:
self.__systemctl.check_call(["stop", "--", self.unit_name])
def restart(self) -> None:
self.__systemctl.check_call(["restart", "--", self.unit_name])
def query_active_state(self) -> str:
return self.__query_property("ActiveState").decode("utf-8")
def query_sub_state(self) -> str:
return self.__query_property("SubState").decode("utf-8")
def query_cgroup(self) -> LinuxCgroup:
return LinuxCgroup(self.__query_property("ControlGroup"))
def query_process_ids(self) -> typing.Sequence[ProcessID]:
return self.query_cgroup().query_process_ids()
def query_fragment_path(self) -> pathlib.Path:
return pathlib.Path(os.fsdecode(self.__query_property("FragmentPath")))
def __query_property(self, property: str) -> bytes:
stdout = self.__systemctl.check_output(
["show", f"--property={property}", "--", self.unit_name]
)
prefix = property.encode("utf-8") + b"="
if not stdout.startswith(prefix):
raise Exception(f"Failed to parse output of systemctl show: {stdout}")
return stdout[len(prefix) :].rstrip(b"\n")
@property
def __systemctl(self) -> "_SystemctlCLI":
return self.__systemd._systemctl
def __str__(self) -> str:
return f"{self.unit_name} (XDG_RUNTIME_DIR={self.__systemd.xdg_runtime_dir})"
def __repr__(self) -> str:
return (
f"SystemdService(unit_name={repr(self.unit_name)}, "
f"systemd={repr(self.__systemd)})"
)
class _SystemctlCLI:
def __init__(self, env: typing.Dict[str, str]) -> None:
super().__init__()
self.__env = env
def check_call(
self, command_arguments: typing.Sequence[typing.Union[str, pathlib.Path]]
) -> None:
"""Run 'systemctl --user' with the given arguments.
See also subprocess.check_call.
"""
subprocess.check_call(self.__command(command_arguments), env=self.__env)
def check_output(
self, command_arguments: typing.Sequence[typing.Union[str, pathlib.Path]]
) -> bytes:
"""Run 'systemctl --user' and return the command's output.
See also subprocess.check_output.
"""
return subprocess.check_output(
self.__command(command_arguments), env=self.__env
)
def run(
self,
command_arguments: typing.Sequence[typing.Union[str, pathlib.Path]],
stdout: "subprocess._FILE" = None,
stderr: "subprocess._FILE" = None,
) -> subprocess.CompletedProcess:
"""Run 'systemctl --user' and return the command's output and exit status.
See also subprocess.run.
"""
return subprocess.run(
self.__command(command_arguments),
env=self.__env,
stdout=stdout,
stderr=stderr,
)
def __command(
self, command_arguments: typing.Sequence[typing.Union[str, pathlib.Path]]
) -> typing.Sequence[str]:
command = ["systemctl", "--user"]
command.extend(str(arg) for arg in command_arguments)
return command
class SystemdUserServiceManagerMixin(metaclass=abc.ABCMeta):
def make_temporary_systemd_user_service_manager(self) -> SystemdUserServiceManager:
context_manager = temporary_systemd_user_service_manager()
exit = context_manager.__exit__
systemd = context_manager.__enter__()
self.addCleanup(lambda: exit(None, None, None)) # pyre-ignore (T36820067)
return systemd
def addCleanup(
self,
function: typing.Callable[..., typing.Any],
*args: typing.Any,
**kwargs: typing.Any,
) -> None:
raise NotImplementedError()
@contextlib.contextmanager
def temporary_systemd_user_service_manager() -> typing.Iterator[
SystemdUserServiceManager
]:
"""Create an isolated systemd instance for tests."""
def should_create_managed() -> bool:
forced_type_variable = "EDEN_TEST_FORCE_SYSTEMD_USER_SERVICE_MANAGER_TYPE"
forced_type = os.getenv(forced_type_variable)
if forced_type is not None and forced_type:
if forced_type == "managed":
return True
if forced_type == "unmanaged":
return False
raise ValueError(
f"Unsupported value for {forced_type_variable}: {forced_type!r}"
)
if not _is_system_booted_with_systemd():
return False
return True
lifetime_duration = 30
with create_tmp_dir() as xdg_runtime_dir:
if should_create_managed():
parent_systemd = SystemdUserServiceManager(
xdg_runtime_dir=_get_current_xdg_runtime_dir()
)
with _transient_managed_systemd_user_service_manager(
xdg_runtime_dir=xdg_runtime_dir,
parent_systemd=parent_systemd,
lifetime_duration=lifetime_duration,
) as child_systemd:
yield child_systemd
else:
with _TransientUnmanagedSystemdUserServiceManager(
xdg_runtime_dir=xdg_runtime_dir, lifetime_duration=lifetime_duration
) as systemd:
yield systemd
def _is_system_booted_with_systemd() -> bool:
"""See the sd_booted(3) manual page."""
return pathlib.Path("/run/systemd/system/").exists()
@contextlib.contextmanager
def _transient_managed_systemd_user_service_manager(
xdg_runtime_dir: pathlib.Path,
parent_systemd: SystemdUserServiceManager,
lifetime_duration: int,
) -> typing.Iterator[SystemdUserServiceManager]:
"""Create an isolated systemd instance using 'systemd-run systemd'."""
child_systemd_service = parent_systemd.systemd_run(
command=["/usr/lib/systemd/systemd", "--user", "--unit=basic.target"],
properties={
"Description": f"Eden test systemd user service manager "
f"({xdg_runtime_dir})",
"CollectMode": "inactive-or-failed",
"Restart": "no",
"RuntimeMaxSec": str(lifetime_duration),
"TimeoutStartSec": str(lifetime_duration),
"Type": "notify",
},
extra_env={"XDG_RUNTIME_DIR": str(xdg_runtime_dir)},
)
child_systemd = SystemdUserServiceManager(xdg_runtime_dir=xdg_runtime_dir)
try:
yield child_systemd
finally:
try:
child_systemd_service.stop()
except Exception:
logger.warning(
f"Failed to stop systemd user service manager ({child_systemd})",
exc_info=True,
)
# Ignore the exception.
class _TransientUnmanagedSystemdUserServiceManager:
"""Create an isolated systemd instance as child process.
This class does not work if a user systemd instance is already running.
"""
__cleanups: contextlib.ExitStack
__lifetime_duration: int
__xdg_runtime_dir: pathlib.Path
def __init__(self, xdg_runtime_dir: pathlib.Path, lifetime_duration: int) -> None:
super().__init__()
self.__xdg_runtime_dir = xdg_runtime_dir
self.__lifetime_duration = lifetime_duration
self.__cleanups = contextlib.ExitStack()
def start_systemd_process(self) -> subprocess.Popen:
cgroup = self.create_cgroup()
env = dict(os.environ)
env["XDG_RUNTIME_DIR"] = str(self.__xdg_runtime_dir)
# HACK(strager): Work around 'systemd --user' refusing to start if the
# system is not managed by systemd.
env["LD_PRELOAD"] = str(
pathlib.Path(FindExe.FORCE_SD_BOOTED).resolve(strict=True)
)
process = subprocess.Popen(
[
"timeout",
f"{self.__lifetime_duration}s",
"/usr/lib/systemd/systemd",
"--user",
"--unit=basic.target",
"--log-target=console",
],
stdin=subprocess.DEVNULL,
env=env,
preexec_fn=lambda: cgroup.add_current_process(),
)
self.__cleanups.callback(lambda: self.stop_systemd_process(process))
return process
def stop_systemd_process(self, systemd_process: subprocess.Popen) -> None:
systemd_process.terminate()
try:
systemd_process.wait(timeout=15)
return
except subprocess.TimeoutExpired:
pass
logger.warning(
"Failed to terminate systemd user service manager. Attempting to kill."
)
systemd_process.kill()
systemd_process.wait(timeout=3)
def create_cgroup(self) -> LinuxCgroup:
parent_cgroup = LinuxCgroup.from_current_process()
path = tempfile.mkdtemp(
prefix="edenfs_test.", dir=str(parent_cgroup.sys_fs_cgroup_path)
)
cgroup = LinuxCgroup.from_sys_fs_cgroup_path(pathlib.PosixPath(path))
self.__cleanups.callback(lambda: cgroup.delete_recursive())
return cgroup
def wait_until_systemd_is_alive(
self,
systemd_process: subprocess.Popen,
child_systemd: SystemdUserServiceManager,
) -> None:
while True:
systemd_did_exit = systemd_process.poll() is not None
if systemd_did_exit:
raise Exception("systemd failed to start")
if child_systemd.is_alive():
return
def __enter__(self) -> SystemdUserServiceManager:
systemd_process = self.start_systemd_process()
child_systemd = SystemdUserServiceManager(
xdg_runtime_dir=self.__xdg_runtime_dir
)
self.wait_until_systemd_is_alive(systemd_process, child_systemd)
return child_systemd
def __exit__(
self,
exc_type: typing.Optional[typing.Type[BaseException]],
exc_value: typing.Optional[BaseException],
traceback: typing.Optional[types.TracebackType],
) -> typing.Optional[bool]:
self.__cleanups.close()
return None
def _get_current_xdg_runtime_dir() -> pathlib.Path:
problems = []
path = None
if path is None:
path_from_env = os.environ.get("XDG_RUNTIME_DIR")
if path_from_env is None or path_from_env == "":
problems.append("$XDG_RUNTIME_DIR is not set")
else:
path = pathlib.Path(path_from_env)
if path is None:
if os.getuid() == 0:
path = pathlib.Path("/run")
else:
path = pathlib.Path("/run/user") / str(os.getuid())
assert path is not None
if not path.exists():
problems.append(f"'{path}' does not exist")
raise Exception(
"Could not determine XDG_RUNTIME_DIR: " + ", and ".join(problems)
)
return path
| [
"[email protected]"
]
| |
f412e114622a62333efacf8bd085912edb0d96e1 | 7eebbfaee45fdc57c4fc6ba32c87c35be1e62b14 | /airbyte-integrations/connectors/source-amazon-ads/source_amazon_ads/streams/report_streams/products_report.py | ea97bf690ae643e105877fc34e221bea461fb5e4 | [
"MIT",
"Elastic-2.0"
]
| permissive | Velocity-Engineering/airbyte | b6e1fcead5b9fd7c74d50b9f27118654604dc8e0 | 802a8184cdd11c1eb905a54ed07c8732b0c0b807 | refs/heads/master | 2023-07-31T15:16:27.644737 | 2021-09-28T08:43:51 | 2021-09-28T08:43:51 | 370,730,633 | 0 | 1 | MIT | 2021-06-08T05:58:44 | 2021-05-25T14:55:43 | Java | UTF-8 | Python | false | false | 8,127 | py | #
# Copyright (c) 2021 Airbyte, Inc., all rights reserved.
#
from copy import copy
from .report_streams import RecordType, ReportStream
METRICS_MAP = {
"campaigns": [
"bidPlus",
"campaignName",
"campaignId",
"campaignStatus",
"campaignBudget",
"campaignRuleBasedBudget",
"applicableBudgetRuleId",
"applicableBudgetRuleName",
"impressions",
"clicks",
"cost",
"attributedConversions1d",
"attributedConversions7d",
"attributedConversions14d",
"attributedConversions30d",
"attributedConversions1dSameSKU",
"attributedConversions7dSameSKU",
"attributedConversions14dSameSKU",
"attributedConversions30dSameSKU",
"attributedUnitsOrdered1d",
"attributedUnitsOrdered7d",
"attributedUnitsOrdered14d",
"attributedUnitsOrdered30d",
"attributedSales1d",
"attributedSales7d",
"attributedSales14d",
"attributedSales30d",
"attributedSales1dSameSKU",
"attributedSales7dSameSKU",
"attributedSales14dSameSKU",
"attributedSales30dSameSKU",
"attributedUnitsOrdered1dSameSKU",
"attributedUnitsOrdered7dSameSKU",
"attributedUnitsOrdered14dSameSKU",
"attributedUnitsOrdered30dSameSKU",
],
"adGroups": [
"campaignName",
"campaignId",
"adGroupName",
"adGroupId",
"impressions",
"clicks",
"cost",
"attributedConversions1d",
"attributedConversions7d",
"attributedConversions14d",
"attributedConversions30d",
"attributedConversions1dSameSKU",
"attributedConversions7dSameSKU",
"attributedConversions14dSameSKU",
"attributedConversions30dSameSKU",
"attributedUnitsOrdered1d",
"attributedUnitsOrdered7d",
"attributedUnitsOrdered14d",
"attributedUnitsOrdered30d",
"attributedSales1d",
"attributedSales7d",
"attributedSales14d",
"attributedSales30d",
"attributedSales1dSameSKU",
"attributedSales7dSameSKU",
"attributedSales14dSameSKU",
"attributedSales30dSameSKU",
"attributedUnitsOrdered1dSameSKU",
"attributedUnitsOrdered7dSameSKU",
"attributedUnitsOrdered14dSameSKU",
"attributedUnitsOrdered30dSameSKU",
],
"keywords": [
"campaignName",
"campaignId",
"adGroupName",
"adGroupId",
"keywordId",
"keywordText",
"matchType",
"impressions",
"clicks",
"cost",
"attributedConversions1d",
"attributedConversions7d",
"attributedConversions14d",
"attributedConversions30d",
"attributedConversions1dSameSKU",
"attributedConversions7dSameSKU",
"attributedConversions14dSameSKU",
"attributedConversions30dSameSKU",
"attributedUnitsOrdered1d",
"attributedUnitsOrdered7d",
"attributedUnitsOrdered14d",
"attributedUnitsOrdered30d",
"attributedSales1d",
"attributedSales7d",
"attributedSales14d",
"attributedSales30d",
"attributedSales1dSameSKU",
"attributedSales7dSameSKU",
"attributedSales14dSameSKU",
"attributedSales30dSameSKU",
"attributedUnitsOrdered1dSameSKU",
"attributedUnitsOrdered7dSameSKU",
"attributedUnitsOrdered14dSameSKU",
"attributedUnitsOrdered30dSameSKU",
],
"productAds": [
"campaignName",
"campaignId",
"adGroupName",
"adGroupId",
"impressions",
"clicks",
"cost",
"currency",
"asin",
"attributedConversions1d",
"attributedConversions7d",
"attributedConversions14d",
"attributedConversions30d",
"attributedConversions1dSameSKU",
"attributedConversions7dSameSKU",
"attributedConversions14dSameSKU",
"attributedConversions30dSameSKU",
"attributedUnitsOrdered1d",
"attributedUnitsOrdered7d",
"attributedUnitsOrdered14d",
"attributedUnitsOrdered30d",
"attributedSales1d",
"attributedSales7d",
"attributedSales14d",
"attributedSales30d",
"attributedSales1dSameSKU",
"attributedSales7dSameSKU",
"attributedSales14dSameSKU",
"attributedSales30dSameSKU",
"attributedUnitsOrdered1dSameSKU",
"attributedUnitsOrdered7dSameSKU",
"attributedUnitsOrdered14dSameSKU",
"attributedUnitsOrdered30dSameSKU",
],
"asins_keywords": [
"campaignName",
"campaignId",
"adGroupName",
"adGroupId",
"keywordId",
"keywordText",
"asin",
"otherAsin",
"sku",
"currency",
"matchType",
"attributedUnitsOrdered1d",
"attributedUnitsOrdered7d",
"attributedUnitsOrdered14d",
"attributedUnitsOrdered30d",
"attributedUnitsOrdered1dOtherSKU",
"attributedUnitsOrdered7dOtherSKU",
"attributedUnitsOrdered14dOtherSKU",
"attributedUnitsOrdered30dOtherSKU",
"attributedSales1dOtherSKU",
"attributedSales7dOtherSKU",
"attributedSales14dOtherSKU",
"attributedSales30dOtherSKU",
],
"asins_targets": [
"campaignName",
"campaignId",
"adGroupName",
"adGroupId",
"asin",
"otherAsin",
"sku",
"currency",
"matchType",
"attributedUnitsOrdered1d",
"attributedUnitsOrdered7d",
"attributedUnitsOrdered14d",
"attributedUnitsOrdered30d",
"attributedUnitsOrdered1dOtherSKU",
"attributedUnitsOrdered7dOtherSKU",
"attributedUnitsOrdered14dOtherSKU",
"attributedUnitsOrdered30dOtherSKU",
"attributedSales1dOtherSKU",
"attributedSales7dOtherSKU",
"attributedSales14dOtherSKU",
"attributedSales30dOtherSKU",
"targetId",
"targetingText",
"targetingType",
],
"targets": [
"campaignName",
"campaignId",
"adGroupName",
"adGroupId",
"targetId",
"targetingExpression",
"targetingText",
"targetingType",
"impressions",
"clicks",
"cost",
"attributedConversions1d",
"attributedConversions7d",
"attributedConversions14d",
"attributedConversions30d",
"attributedConversions1dSameSKU",
"attributedConversions7dSameSKU",
"attributedConversions14dSameSKU",
"attributedConversions30dSameSKU",
"attributedUnitsOrdered1d",
"attributedUnitsOrdered7d",
"attributedUnitsOrdered14d",
"attributedUnitsOrdered30d",
"attributedSales1d",
"attributedSales7d",
"attributedSales14d",
"attributedSales30d",
"attributedSales1dSameSKU",
"attributedSales7dSameSKU",
"attributedSales14dSameSKU",
"attributedSales30dSameSKU",
"attributedUnitsOrdered1dSameSKU",
"attributedUnitsOrdered7dSameSKU",
"attributedUnitsOrdered14dSameSKU",
"attributedUnitsOrdered30dSameSKU",
],
}
class SponsoredProductsReportStream(ReportStream):
"""
https://advertising.amazon.com/API/docs/en-us/sponsored-products/2-0/openapi#/Reports
"""
def report_init_endpoint(self, record_type: str) -> str:
return f"/v2/sp/{record_type}/report"
metrics_map = METRICS_MAP
def _get_init_report_body(self, report_date: str, record_type: str, profile):
metrics_list = self.metrics_map[record_type]
body = {
"reportDate": report_date,
}
if RecordType.ASINS in record_type:
body["campaignType"] = "sponsoredProducts"
if profile.accountInfo.type == "vendor":
metrics_list = copy(metrics_list)
metrics_list.remove("sku")
return {**body, "metrics": ",".join(metrics_list)}
| [
"[email protected]"
]
| |
a8d8af51088b85de2bc2918484ac156679217741 | 6a9f6e6527afb38611a5c695e5845e492d7676ff | /102.binary-tree-level-order-traversal.py | 7eb77a9a8d81de928a9b97a1005e30cb9e67ae71 | []
| no_license | Junyangz/leetcode | 39913d48778d369f9f0a96070352d63b207e7df6 | 5de48fbdcb61ce8a437255a119a4a3ae242ede52 | refs/heads/master | 2020-06-09T08:57:26.361138 | 2019-11-14T08:38:07 | 2019-11-14T08:38:07 | 193,413,271 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 635 | py | #
# @lc app=leetcode id=102 lang=python3
#
# [102] Binary Tree Level Order Traversal
#
# Definition for a binary tree node.
# class TreeNode:
# def __init__(self, x):
# self.val = x
# self.left = None
# self.right = None
class Solution:
res = []
def levelOrder(self, root: TreeNode, l=0) -> List[List[int]]:
if not root: return None
if l == 0: self.res = []
if len(self.res) < l + 1: self.res.append([])
self.res[l].append(root.val)
left, right = self.levelOrder(root.left, l + 1), self.levelOrder(root.right, l + 1)
return self.res
| [
"[email protected]"
]
| |
227324e4b93ca4b8bf74ffa2dd956e21cce31967 | 9743d5fd24822f79c156ad112229e25adb9ed6f6 | /xai/brain/wordbase/adjectives/_primaries.py | e457d57cae006604c84eb17a3f6a03363e756bca | [
"MIT"
]
| permissive | cash2one/xai | de7adad1758f50dd6786bf0111e71a903f039b64 | e76f12c9f4dcf3ac1c7c08b0cc8844c0b0a104b6 | refs/heads/master | 2021-01-19T12:33:54.964379 | 2017-01-28T02:00:50 | 2017-01-28T02:00:50 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 257 | py |
from xai.brain.wordbase.adjectives._primary import _PRIMARY
#calss header
class _PRIMARIES(_PRIMARY, ):
def __init__(self,):
_PRIMARY.__init__(self)
self.name = "PRIMARIES"
self.specie = 'adjectives'
self.basic = "primary"
self.jsondata = {}
| [
"[email protected]"
]
| |
d2cb3437eb8ea8a93580f88ea57e3ac8d9291e75 | 30b97efb2f36f81aa684d16d19e0e2db17f2967d | /09. 기본수학2/3009.py | 9717ea46cc12a0dece6c17b3d29096b76c42fd5b | []
| no_license | jmseb3/bakjoon | 0a784a74c6476ef51864e2ada9d2551c7c7979eb | a38db54e851372059b0e45add92e43e556835e62 | refs/heads/main | 2023-08-25T08:43:04.579785 | 2021-10-01T08:40:37 | 2021-10-01T08:40:37 | 362,287,450 | 2 | 0 | null | null | null | null | UTF-8 | Python | false | false | 259 | py | x = [0,0,0]
y = [0,0,0]
ans = [0,0]
for i in range(3):
x[i], y[i] = map(int, input().split())
for i in range(3):
if x[i%3] == x[(i+1) %3]:
ans[0] = x[(i+2)%3]
if y[i%3] == y[(i+1)%3]:
ans[1] = y[(i+2)%3]
print(ans[0],ans[1]) | [
"[email protected]"
]
| |
fb93d545c4c43801e2e58c194bc06d63dc13116b | 8997a0bf1e3b6efe5dd9d5f307e1459f15501f5a | /tensorflow__deep_dream/main.py | ee4ed831937293c6cb4bb3cd528b1385333935bf | [
"CC-BY-4.0"
]
| permissive | stepik/SimplePyScripts | 01092eb1b2c1c33756427abb2debbd0c0abf533f | 3259d88cb58b650549080d6f63b15910ae7e4779 | refs/heads/master | 2023-05-15T17:35:55.743164 | 2021-06-11T22:59:07 | 2021-06-11T22:59:07 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 14,680 | py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
__author__ = 'ipetrash'
# MOTE: То, что эта нейронная сеть делает называется "Инцепционизм" -- картины, «написанные» нейронными сетями.
# SOURCE: https://github.com/llSourcell/deep_dream_challenge
# OTHER:
# https://github.com/llSourcell/deep_dream_challenge/blob/master/deep_dream.py
# https://github.com/samkit-jain/Data-Science-by-Siraj-Raval/blob/a66b7791a4628f815dc683605dd224acad9bc277/deep_dream_challenge.py#L149
# https://medium.com/@mrubash1/deepdream-accelerating-deep-learning-with-hardware-5085ea415d8a
# https://github.com/mrubash1/DeepDream_Streaming_Video/tree/master/src
# https://github.com/mrubash1/DeepDream_Streaming_Video/blob/master/src/deep_dream.py
# https://github.com/mrubash1/DeepDream_Streaming_Video/blob/master/src/app.py
# Статьи на русском о DeepDream:
# https://habrahabr.ru/company/io/blog/262267/
# https://meduza.io/shapito/2015/06/19/hudozhnik-ot-gugla-neyronnye-seti-nauchilis-pisat-kartiny
# https://meduza.io/galleries/2015/06/19/intseptsionizm
# LAYERS:
# http://storage.googleapis.com/deepdream/visualz/tensorflow_inception/index.html
#
# import requests
# rs = requests.get('http://storage.googleapis.com/deepdream/visualz/tensorflow_inception/index.html')
#
# from bs4 import BeautifulSoup
# root = BeautifulSoup(rs.content, 'html.parser')
# layers = [a.text for a in root.select('a')]
# print(layers) # ['conv2d0_pre_relu', 'conv2d1_pre_relu', 'conv2d2_pre_relu', 'head0_bottleneck_pre_relu', ...
# ABOUT CODE:
# http://nbviewer.jupyter.org/github/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/deepdream/deepdream.ipynb
# NOTE: "FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated"
# pip3 install h5py==2.8.0rc1
import os
import io
from timeit import default_timer
from typing import Union
# pip install tensorflow==1.15
# pip install tensorflow-gpu==1.15
# pip install tensorflow
import tensorflow as tf
import numpy as np
import PIL.Image
# import matplotlib.pyplot as plt
from common import download_tensorflow_model, IMG_NOISE, showarray, savearray
# # Helper functions for TF Graph visualization
# # pylint: disable=unused-variable
# def strip_consts(graph_def, max_const_size=32):
# """Strip large constant values from graph_def."""
# strip_def = tf.GraphDef()
# for n0 in graph_def.node:
# n = strip_def.node.add() # pylint: disable=maybe-no-member
# n.MergeFrom(n0)
# if n.op == 'Const':
# tensor = n.attr['value'].tensor
# size = len(tensor.tensor_content)
# if size > max_const_size:
# tensor.tensor_content = "<stripped %d bytes>" % size
# return strip_def
# def rename_nodes(graph_def, rename_func):
# res_def = tf.GraphDef()
# for n0 in graph_def.node:
# n = res_def.node.add() # pylint: disable=maybe-no-member
# n.MergeFrom(n0)
# n.name = rename_func(n.name)
# for i, s in enumerate(n.input):
# n.input[i] = rename_func(s) if s[0] != '^' else '^' + rename_func(s[1:])
# return res_def
# def showarray(a):
# a = np.uint8(np.clip(a, 0, 1) * 255)
# plt.imshow(a)
# plt.show()
#
# def savearray(a, file_name):
# print('save:', file_name)
#
# a = np.uint8(np.clip(a, 0, 1) * 255)
# PIL.Image.fromarray(a).save(file_name)
#
# def visstd(a, s=0.1):
# '''Normalize the image range for visualization'''
# return (a - a.mean()) / max(a.std(), 1e-4) * s + 0.5
def T(layer):
'''Helper for getting layer output tensor'''
return graph.get_tensor_by_name("import/%s:0" % layer)
# def render_naive(t_obj, img0=img_noise, iter_n=20, step=1.0):
# t_score = tf.reduce_mean(t_obj) # defining the optimization objective
# t_grad = tf.gradients(t_score, t_input)[0] # behold the power of automatic differentiation!
#
# img = img0.copy()
# for _ in range(iter_n):
# g, _ = sess.run([t_grad, t_score], {t_input: img})
# # normalizing the gradient, so the same step size should work
# g /= g.std() + 1e-8 # for different layers and networks
# img += g * step
# showarray(visstd(img))
data_dir = 'data/'
# Step 1 - download google's pre-trained neural network
download_tensorflow_model(data_dir)
model_fn = 'tensorflow_inception_graph.pb'
# Step 2 - Creating Tensorflow session and loading the model
graph = tf.Graph()
sess = tf.compat.v1.InteractiveSession(graph=graph)
with tf.io.gfile.GFile(os.path.join(data_dir, model_fn), 'rb') as f:
graph_def = tf.compat.v1.GraphDef()
graph_def.ParseFromString(f.read())
t_input = tf.compat.v1.placeholder(np.float32, name='input') # define the input tensor
imagenet_mean = 117.0
t_preprocessed = tf.expand_dims(t_input - imagenet_mean, 0)
tf.import_graph_def(graph_def, {'input': t_preprocessed})
layers = [op.name for op in graph.get_operations() if op.type == 'Conv2D' and 'import/' in op.name]
feature_nums = [int(graph.get_tensor_by_name(name + ':0').get_shape()[-1]) for name in layers]
print('Number of layers', len(layers))
print('Total number of feature units:', sum(feature_nums))
# import webbrowser
# for layer in layers:
# # "import/conv2d0_pre_relu/conv" -> "conv2d0_pre_relu"
# name = layer.split('/')[1]
# url = f'http://storage.googleapis.com/deepdream/visualz/tensorflow_inception/{name}.html'
# webbrowser.open(url)
# NOTE: Interesting layers
# layer = 'mixed3b_3x3_bottleneck_pre_relu'
# unit = 109 # Other
#
# layer = 'mixed5a_3x3_pre_relu'
# unit = 174 # Fear
# unit = 190 # Fear
# unit = 299 # Fear
# unit = 304 # Fear
# unit = 316 # Fear
# unit = 317 # Fear
#
# layer = 'mixed4d_3x3_bottleneck_pre_relu'
# unit = 65 # Building
# unit = 66 # Building
# unit = 88 # Fear
# unit = 114 # Building
# unit = 139 # Flowers
#
# layer = 'mixed5b_3x3_bottleneck_pre_relu'
# unit = 91 # Birds
# unit = 119 # Fear
# unit = 166 # Birds
# unit = 167 # Birds
#
# layer = 'mixed4a_pool_reduce_pre_relu'
# unit = 49 # Fear
#
# layer = 'mixed4b_3x3_bottleneck_pre_relu'
# unit = 33 # Fear
#
# layer = 'mixed4b_3x3_pre_relu'
# unit = 95 # Building
#
# layer = 'mixed4b_5x5_pre_relu'
# unit = 55 # Dogs
#
# layer = 'mixed4c_3x3_pre_relu'
# unit = 83 # Flowers
# unit = 230 # Flowers
#
# layer = 'mixed4d_5x5_bottleneck_pre_relu'
# unit = 13 # Building
#
# layer = 'mixed5a_5x5_pre_relu'
# unit = 11 # Animals
# unit = 44 # Village
#
# layer = 'mixed4d_5x5_pre_relu'
# unit = 1 # Cats
#
# layer = 'mixed4e_pool_reduce_pre_relu'
# unit = 26 # Building
# unit = 27 # Animals
# unit = 29 # Dogs
# unit = 50 # Birds
# unit = 57 # Birds
# unit = 68 # Flowers
# unit = 101 # Fear
# unit = 105 # Cats
#
# layer = 'mixed5a_3x3_bottleneck_pre_relu'
# unit = 100 # Dogs
#
# layer = 'mixed5a_pool_reduce_pre_relu'
# unit = 16 # Birds
# unit = 53 # Monkeys
#
# layer = 'mixed5a_1x1_pre_relu'
# unit = 0 # Animals
# unit = 1 # Animals
# unit = 3 # Fear
# unit = 9 # Other
# unit = 47 # Dogs
# unit = 57 # Snakes
# unit = 63 # Butterflies
# unit = 81 # Animals
# unit = 93 # Other
# unit = 97 # Animals
# unit = 158 # Fishes
# unit = 175 # Dogs
# unit = 224 # Birds
#
# layer = 'mixed4d_3x3_pre_relu'
# unit = 88 # Flowers
#
# layer = 'mixed4c_pool_reduce_pre_relu'
# unit = 1 # Other
# unit = 23 # Animals
# unit = 29 # Building
# unit = 41 # Flowers
# unit = 61 # Building
#
# layer = 'mixed4c_5x5_pre_relu'
# unit = 14 # Cars
# unit = 63 # Cars
output_dir = 'output'
if not os.path.exists(output_dir):
os.mkdir(output_dir)
def tffunc(*argtypes):
'''Helper that transforms TF-graph generating function into a regular one.
See "resize" function below.
'''
placeholders = list(map(tf.compat.v1.placeholder, argtypes))
def wrap(f):
out = f(*placeholders)
def wrapper(*args, **kw):
return out.eval(dict(zip(placeholders, args)), session=kw.get('session'))
return wrapper
return wrap
def resize(img, size):
img = tf.expand_dims(img, 0)
return tf.compat.v1.image.resize_bilinear(img, size)[0, :, :, :]
resize = tffunc(np.float32, np.int32)(resize)
def calc_grad_tiled(img, t_grad, sess, tile_size=512):
'''Compute the value of tensor t_grad over the image in a tiled way.
Random shifts are applied to the image to blur tile boundaries over
multiple iterations.'''
sz = tile_size
h, w = img.shape[:2]
sx, sy = np.random.randint(sz, size=2)
img_shift = np.roll(np.roll(img, sx, 1), sy, 0)
grad = np.zeros_like(img)
for y in range(0, max(h - sz // 2, sz), sz):
for x in range(0, max(w - sz // 2, sz), sz):
sub = img_shift[y:y + sz, x:x + sz]
g = sess.run(t_grad, {t_input: sub})
grad[y:y + sz, x:x + sz] = g
return np.roll(np.roll(grad, -sx, 1), -sy, 0)
# TODO: TEST THIS
# SOURCE: https://github.com/samkit-jain/Data-Science-by-Siraj-Raval/blob/a66b7791a4628f815dc683605dd224acad9bc277/deep_dream_challenge.py#L149
def render_deepdreamvideo(sess):
import imageio
reader = imageio.get_reader('cockatoo.mp4')
fps = reader.get_meta_data()['fps']
writer = imageio.get_writer('output.mp4', fps=fps)
for i, image in enumerate(reader):
image = np.float32(image)
# Apply gradient ascent to that layer and append to video
image = writer.append_data(render_deepdream(tf.square(T('mixed4c')), sess, image))
writer.append_data(image)
writer.close()
def render_deepdream(t_obj, sess, img0=IMG_NOISE, iter_n=10, step=1.5, octave_n=4, octave_scale=1.4):
t_score = tf.reduce_mean(t_obj) # defining the optimization objective
t_grad = tf.gradients(t_score, t_input)[0] # behold the power of automatic differentiation!
# split the image into a number of octaves
img = img0
octaves = []
for _ in range(octave_n - 1):
hw = img.shape[:2]
lo = resize(img, np.int32(np.float32(hw) / octave_scale))
hi = img - resize(lo, hw)
img = lo
octaves.append(hi)
# generate details octave by octave
for octave in range(octave_n):
if octave > 0:
hi = octaves[-octave]
img = resize(img, hi.shape[:2]) + hi
for _ in range(iter_n):
g = calc_grad_tiled(img, t_grad, sess)
img += g * (step / (np.abs(g).mean() + 1e-7))
# # this will usually be like 3 or 4 octaves
# # Step 5 output deep dream image via matplotlib
# showarray(img / 255.0)
return img
def render_deepdream_from_layer_by_unit(img0, name: Union[str, io.BytesIO], layer, unit=None) -> Union[str, io.BytesIO]:
t = default_timer()
# t_obj = tf.square(T(layer)[:, :, :, unit])
t_obj = T(layer)
if unit:
t_obj = t_obj[:, :, :, unit]
# t_obj = tf.square(t_obj)
img = render_deepdream(t_obj, sess, img0)
if isinstance(name, str):
if unit:
name = f'{output_dir}/{name}__{layer}__{unit}.jpg'
else:
name = f'{output_dir}/{name}__{layer}.jpg'
else:
name = name
savearray(img / 255.0, name)
print(f'Elapsed {default_timer() - t:.2f} secs\n')
return name
def main():
#
# # PRINT: layer by units
# t_obj_layers = [x.split('/')[1] for x in layers]
# for l in t_obj_layers:
# print(l, int(T(l).get_shape()[-1]))
#
# Layer: mixed4d_5x5
# Url: http://storage.googleapis.com/deepdream/visualz/tensorflow_inception/mixed4d_5x5_pre_relu.html
# Url: https://microscope.openai.com/models/inceptionv1/mixed4d_5x5_0/1
savearray(IMG_NOISE / 255.0, '{}/noise.png'.format(output_dir, 'noise'))
print()
# layer = 'mixed4c'
# t_obj = tf.square(T(layer))
# FROM NOISE
render_deepdream_from_layer_by_unit(IMG_NOISE, 'noise', 'mixed4d_5x5_pre_relu', 61)
render_deepdream_from_layer_by_unit(IMG_NOISE, 'noise', 'head1_bottleneck_pre_relu', 1)
# FROM FILENAME
img0 = PIL.Image.open('pilatus800.jpg')
img0 = np.float32(img0)
render_deepdream_from_layer_by_unit(img0, 'pilatus800', 'mixed4d_1x1_pre_relu', 39)
render_deepdream_from_layer_by_unit(img0, 'pilatus800', 'mixed4d_5x5_pre_relu', 61)
render_deepdream_from_layer_by_unit(img0, 'pilatus800', 'mixed4c_3x3_bottleneck_pre_relu', 64)
render_deepdream_from_layer_by_unit(img0, 'pilatus800', 'mixed4c_3x3_bottleneck_pre_relu', 104)
# Save to memory
bytes_io = io.BytesIO()
render_deepdream_from_layer_by_unit(IMG_NOISE, bytes_io, 'mixed4d_5x5_pre_relu', 61)
bytes_io.seek(0)
print(bytes_io.read(10))
# b'\xff\xd8\xff\xe0\x00\x10JFIF'
# # PROCESS FROM ALL LAYERS
# # ['conv2d0_pre_relu', 'conv2d1_pre_relu', 'conv2d2_pre_relu', 'mixed3a_1x1_pre_relu', ...
# t_obj_layers = [x.split('/')[1] for x in layers]
# for name in t_obj_layers:
# render_deepdream_from_layer_by_unit(img0, name)
# # t_obj = tf.square(T(layer))
# # img = render_deepdream(t_obj, sess, img0)
# # savearray(img / 255.0, '{}/{}_{}.png'.format(output_dir, 'pilatus800', layer))
# # FROM filters
# layer = 'mixed4d_3x3_bottleneck_pre_relu'
# filter_name = {'Tornado': 84, 'Flowers': 139, 'Fireworks': 50, 'Caves': 38, 'Mountains': 142, 'Van Gogh': 1}
# for name, unit in filter_name.items():
# t_obj = tf.square(T(layer)[:, :, :, unit])
# img = render_deepdream(t_obj, sess, img0)
# savearray(img / 255.0, '{}/{}_{}_{}.png'.format(output_dir, 'pilatus800', name, layer))
# TODO: test
# render_deepdreamvideo(sess)
#
#
# # Step 3 - Pick a layer to enhance our image
# layer = 'mixed4d_3x3_bottleneck_pre_relu'
# unit = 139 # picking some feature unit to visualize
#
# # img0 = img_noise
#
# # open image
# img0 = PIL.Image.open('pilatus800.jpg')
# img0 = np.float32(img0)
#
# # showarray(img0)
#
# # # # Step 4 - Apply gradient ascent to that layer
# # # render_deepdream(tf.square(T('mixed4c')), img0)
# # # t_obj = tf.square(T('mixed4c'))
# # t_obj = tf.square(T(layer)[:, :, :, unit])
# # img = render_deepdream(t_obj, sess, img0)
# # # img = render_deepdream(tf.square(T('mixed4c')), sess, img0)
# # # showarray(img / 255.0)
# # savearray(img / 255.0, 'output.png')
#
# print(T('mixed4d_3x3_bottleneck_pre_relu'))
# print(T('mixed4c'))
# # print(T('abc'))
# quit()
if __name__ == '__main__':
main()
| [
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eabdc460c1970ab10e273e221fc427cb01c439ff | 08db753c516c90538fbebe6570b62caa395c6860 | /MRaswan_Assignment8/harry_potter.py | ad18f3ee2f9e8943ef7cfde23a4a6da5a90242f2 | []
| no_license | meghnaraswan/PythonAssignments | 4c717fb90e6a782db8789582408d8ad313bbd000 | 63d6afaa0a6f4086031eaa66b1fbb7a6a533cd8f | refs/heads/master | 2021-07-11T10:39:14.403841 | 2021-04-04T19:40:49 | 2021-04-04T19:40:49 | 237,349,522 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,546 | py | #Meghna Raswan
#2337415
#[email protected]
#CPSC 230-10 (1515)
#Assignment 8
#word count
import operator
def read_file(input_file_name):
input_file_handle = open(input_file_name, 'r') ##opens the file and reads the file
str_of_words = "" #initialize output string
for line_str in input_file_handle: #iterate over every line in the file
line_list = line_str.split() #split the lines into a list of individual words
for word in line_list: #iterates over every word in the list of words
word = word.lower().strip(',.!?"') #make lower case and removes punctuation from list
if word != "": #if after stripping we are left with empty word then don't add
str_of_words += word + " " #add to string
input_file_handle.close() #closes file
return str_of_words
def build_dictionary(string_of_words):
text_list = string_of_words.split() #split the list into a list of individual words
word_dict = {} #initialize output dictionary
for word in text_list: #iderates over every word in the list
if word in word_dict:
word_dict[word] += 1 #adds 1 count for every word repeated
else:
word_dict[word] = 1 #else, if the word is not repeated, it will have a word count of 1
return word_dict
def write_file(word_dict):
sorted_list = sorted(word_dict.items(), key=operator.itemgetter(1), reverse=True) #sorts dictionary in ascending order, and the reverse sorts in descending order
output_file = open("counts.txt", "w") #writes the new text into a new file called counts.txt
for (word, count) in sorted_list: #word is the key and count is the value in the dictionary
print("{}, {}".format(word, count)) #formats the list as word, count
output_file.write("{}, {}\n".format(word, count)) #writes this into the new file and adds new line for every new word and count
output_file.close() #closes file
return (word, count)
if __name__ == '__main__': #script is being run as the main module
input_file = 'harry_potter.txt' #the file we will be reading
word_string = read_file(input_file) #calling the read_file function to read harry_potter.txt file, remove punctuation, and create a string
word_dict = build_dictionary(word_string) #calling the build_dictionary function on word_string to create a dictionary using the string and counting the words
write_file(word_dict) #calling the write_file function to create a new file with the words and word count sorted in descending order
| [
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751151aa43a65c2ae6631e2343285b05224fe7b3 | b9d53e5506ee883d74614cf564e37de53e924b5c | /run.py | 7b84858abcb14586b5bb52e0c378af1bf6e69ace | []
| no_license | Hodo7amShichiYA/VK_Img_V1 | e5eb78d0374d24219183fb98da4b0e9018973611 | 307601cfa23bd150487eb14ecea43e3f0ab0aa0d | refs/heads/master | 2020-06-05T09:00:25.851616 | 2019-06-17T16:50:43 | 2019-06-17T16:50:43 | 192,385,177 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,102 | py | from selenium import webdriver
from lxml import etree
import time
from time import sleep
def vkimg(userid):
email = 'x'
pwd = 'x'
with open('./%s lasturl.txt'%userid, 'r+') as l:
url = l.read()
print('地址获取成功')
chrome_options = webdriver.ChromeOptions()
chrome_options.add_argument('--headless')
browser = webdriver.Chrome(chrome_options=chrome_options)
browser.implicitly_wait(30)
browser.get("https://vk.com/feed")
print('LOGIN...')
elem = browser.find_element_by_id('email')
elem.send_keys(email)
elem = browser.find_element_by_id('pass')
elem.send_keys(pwd)
elem = browser.find_element_by_id('login_button')
elem.click()
print('登录完成')
browser.get(url)
starttime = time.time()
for i in range(1,40000):
pst = time.time()
sleep(3)
response = browser.page_source
html = etree.HTML(response)
html_data = html.xpath('//*[@id="pv_photo"]/img/@src')[0]
print('获取完成')
pageurl = browser.current_url
print('正在抓取第%s页的页面代码:%s' % (i,pageurl))
with open('./%s lasturl.txt'%userid, 'w') as tf:
tf.write(pageurl)
print('获取完成')
with open('./%s url.txt'%userid, 'a') as f:
f.write('%s\n'%html_data)
print('第%s页图片地址抓取完成' % i)
elem = browser.find_element_by_id('pv_photo')
elem.click()
pet = time.time()
print('第%s张图片抓取用时%d秒'%(i,(pet-pst)))
browser.quit()
endtime = time.time()
print('程序执行时长：%d 秒'%(endtime-starttime))
if __name__ == "__main__":
userid = input('输入本次抓取的文件名：')
print('尝试获取上次保存的地址')
try:
with open('./'+userid+' lasturl.txt', 'r+') as f:
aa = f.read()
print(aa)
except:
intro = input('初次抓取请填入第一张图片的地址：')
with open('./%s lasturl.txt' % userid, 'w') as lu:
lu.write(intro)
vkimg(userid)
| [
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]
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22f9942da36f5533775e4cecabe544164b90f9ba | 000a4b227d970cdc6c8db192f4437698cb782721 | /python/helpers/typeshed/stdlib/profile.pyi | 982bcabad40111fe78d27e3f9ba69534cb546a7e | [
"MIT",
"Apache-2.0"
]
| permissive | trinhanhngoc/intellij-community | 2eb2f66a2a3a9456e7a0c5e7be1eaba03c38815d | 1d4a962cfda308a73e0a7ef75186aaa4b15d1e17 | refs/heads/master | 2022-11-03T21:50:47.859675 | 2022-10-19T16:39:57 | 2022-10-19T23:25:35 | 205,765,945 | 1 | 0 | Apache-2.0 | 2019-09-02T02:55:15 | 2019-09-02T02:55:15 | null | UTF-8 | Python | false | false | 1,364 | pyi | from _typeshed import Self, StrOrBytesPath
from typing import Any, Callable, TypeVar
from typing_extensions import ParamSpec
__all__ = ["run", "runctx", "Profile"]
def run(statement: str, filename: str | None = ..., sort: str | int = ...) -> None: ...
def runctx(
statement: str, globals: dict[str, Any], locals: dict[str, Any], filename: str | None = ..., sort: str | int = ...
) -> None: ...
_T = TypeVar("_T")
_P = ParamSpec("_P")
_Label = tuple[str, int, str]
class Profile:
bias: int
stats: dict[_Label, tuple[int, int, int, int, dict[_Label, tuple[int, int, int, int]]]] # undocumented
def __init__(self, timer: Callable[[], float] | None = ..., bias: int | None = ...) -> None: ...
def set_cmd(self, cmd: str) -> None: ...
def simulate_call(self, name: str) -> None: ...
def simulate_cmd_complete(self) -> None: ...
def print_stats(self, sort: str | int = ...) -> None: ...
def dump_stats(self, file: StrOrBytesPath) -> None: ...
def create_stats(self) -> None: ...
def snapshot_stats(self) -> None: ...
def run(self: Self, cmd: str) -> Self: ...
def runctx(self: Self, cmd: str, globals: dict[str, Any], locals: dict[str, Any]) -> Self: ...
def runcall(self, __func: Callable[_P, _T], *args: _P.args, **kw: _P.kwargs) -> _T: ...
def calibrate(self, m: int, verbose: int = ...) -> float: ...
| [
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]
| |
98cbfc244d6321a2b2564a9a471a7607c49aa45d | 943e502b6e293c48564cf6c6d7dd7e39fc7dcf56 | /unnecessary_math/unnecessary_math.py | 8bf025b4cc2960f579adae219e7107e308fd6d53 | []
| no_license | charsyam/unnecessary_math | e3a49f757dcf1a606a53c90fb8be5ba50e5dd412 | fcd6fe4a87b89419bb05886388e9f6342079b5cb | refs/heads/master | 2021-01-20T12:52:19.963717 | 2017-05-13T03:33:19 | 2017-05-13T03:33:19 | 90,422,244 | 0 | 0 | null | 2017-05-13T03:33:19 | 2017-05-05T22:44:48 | Python | UTF-8 | Python | false | false | 443 | py | '''
Module showing how doctests can be included with source code
Each '>>>' line is run as if in a python shell, and counts as a test.
The next line, if not '>>>' is the expected output of the previous line.
If anything doesn't match exactly (including trailing spaces), the test fails.
'''
def multiply(a, b):
"""
>>> multiply(4, 3)
12
>>> multiply('a', 3)
'aaa'
"""
return a * b
def add(a, b):
return a + b
| [
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]
| |
0fd3fb1d4912d8adc8ba824a5d0208ed3da11c1d | a28e1e659e4dd82be5e253443b0c7a808cdcee92 | /DataStructure/tree.py | a5baa7fec37b604010a20f5f471ca8781ac1a306 | []
| no_license | LeBron-Jian/BasicAlgorithmPractice | b2af112e8f1299fe17cf456111276fce874586cb | 51943e2c2c4ec70c7c1d5b53c9fdf0a719428d7a | refs/heads/master | 2023-06-07T19:12:16.362428 | 2023-05-27T06:58:12 | 2023-05-27T06:58:12 | 217,682,743 | 13 | 14 | null | 2020-09-12T01:50:35 | 2019-10-26T08:59:04 | Python | UTF-8 | Python | false | false | 1,892 | py | #_*_coding:utf-8_*_
class Node:
def __init__(self, name, type='dir'):
self.name = name
self.type = type # 'dir' or ; 'file'
self.children = []
self.parent = None
# 链式存储
def __repr__(self):
return self.name
'''
分析列表，假设hello目录下如何找到子目录world的目录呢？
n = Node('hello')
n2 = Node('world')
n.children.append(n2)
那，如何通过world目录找到父亲目录 hello呢？
n2.parent = n
那么这样做就相当于双链表
'''
class FileSystemTree:
def __init__(self):
self.root = Node("/") # 首先我们创建一个根目录
self.now = self.root
def mkdir(self, name):
# 创建一个文件目录，所以我们必须保证name是以 /结尾，如果没有，我们就加
if name[-1] != '/':
name += '/'
node = Node(name)
# 创建一个文件目录
self.now.children.append(node)
node.parent = self.now
def ls(self):
# 展示当前文件夹下的文件
return self.now.children
def cd(self, name):
# 切换到指定目录 注意：支持绝对路径和相对路径
# 相对路径是从now的路径下开始，而绝对路径是从root路径下开始找
if name[-1] != '/':
name += '/'
if name == '../':
self.now = self.now.parent
return
for child in self.now.children:
if child.name == name:
# 如果传入的目录名等于孩子的目录名，我们直接切换
self.now = child
return
raise ValueError("invalid dir")
tree = FileSystemTree()
tree.mkdir('var/')
tree.mkdir('bin/')
tree.mkdir('usr/')
print(tree.ls()) # [var/, bin/, usr/]
tree.cd('bin/')
print(tree.ls()) # []
print(tree.root.children) # [var/, bin/, usr/] | [
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]
|
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