Reset23/the-stack-v2-filtered-python · Datasets at Hugging Face

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#!/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')

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#!/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()

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# 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' ] })

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#!/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)

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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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# 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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"""Top-level package for geemap.""" __author__ = """Qiusheng Wu""" __email__ = 'giswqs@gmail.com' __version__ = '0.6.1' from .geemap import * from .basemaps import ee_basemaps

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#!/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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""" 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]

[ "daniel.reich@danielreichs-MacBook-Pro.local" ]

daniel.reich@danielreichs-MacBook-Pro.local

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/supervised_learning/0x08-deep_cnns/3-projection_block.py

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[]

no_license

yasheymateen/holbertonschool-machine_learning

52eaf217e924404e8833e75e8b48e80807d784dc

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2022-12-24T13:51:02.743526

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#!/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)

[ "yasheymateen@gmail.com" ]

yasheymateen@gmail.com

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fe9b1fcf9f44092037a8c14b4c5cbfefa6ad6982

/shoppingsite.py

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[]

no_license

elaineyoung702/shopping-site

702abd1536b8e805dc719377de23f456773039c2

e2dbd14569203c6d4be4119ab075f2210c88b61d

refs/heads/master

2021-06-20T05:48:32.217285

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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)

[ "no-reply@hackbrightacademy.com" ]

no-reply@hackbrightacademy.com

7b67cdaa927798bb7bcd21bf3a652fc430e7d02c

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/0x11-python-network_1/2-post_email.py

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[]

no_license

afarizap/holbertonschool-higher_level_programming

ffe0bf1440726c952f4dd28b908eabc4ccb5225b

ad39e58f9cb20cba4b9e2c14075f216097588f47

refs/heads/master

2023-03-30T15:39:35.184484

2021-03-22T22:55:24

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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'))

[ "afarizap@gmail.com" ]

afarizap@gmail.com

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

191,269,395

0

1

NOASSERTION

2019-07-17T00:51:28

2019-06-11T01:15:19

Python

UTF-8

Python

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',)

[ "root@proscript.ru" ]

root@proscript.ru

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

null

0

null

UTF-8

Python

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

[ "aoirint@gmail.com" ]

aoirint@gmail.com

67ecd1716036514223b44bda3eada5013e806d32

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/setup.py

fc0025b4f83392ce591bf27cf3013ce54c98453b

[]

no_license

svetlyak40wt/colorize

0a2c65059d4832b88ffbe62864328810f47f0750

1e1fd8eff1d2c820105d0655a8df36efac12600a

refs/heads/master

2020-05-20T08:14:22.598684

2014-01-16T12:57:29

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1

null

2014-01-16T12:57:29

2010-12-05T09:34:27

Python

UTF-8

Python

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 = 'svetlyak.40wt@gmail.com', 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 """, )

[ "svetlyak.40wt@gmail.com" ]

svetlyak.40wt@gmail.com

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

102,854,533

0

null

UTF-8

Python

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

[ "283403891@qq.com" ]

283403891@qq.com

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

318,686,394

0

null

UTF-8

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793

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#!/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")

[ "hcseo77@gmail.com" ]

hcseo77@gmail.com

2d770c24fe0c6044d3dc2542a21f7bcb180d892e

842e3cd1266d18752a3baf2b90232ed4ce41eb4f

/grako/test/grammar/semantics_test.py

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[ "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

65,163,853

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6

null

UTF-8

Python

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)

[ "apalala@gmail.com" ]

apalala@gmail.com

b81db8a245a81834aad2e44681142b1dee1f761d

24d8cf871b092b2d60fc85d5320e1bc761a7cbe2

/eXe/rev2735-2877/base-trunk-2735/exe/engine/mathidevice.py

d7c9a57ae1108ccbb969998567d54ab61700910a

[]

no_license

joliebig/featurehouse_fstmerge_examples

af1b963537839d13e834f829cf51f8ad5e6ffe76

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refs/heads/master

2016-09-05T10:24:50.974902

2013-03-28T16:28:47

9,080,611

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2

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""" 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

[ "joliebig@fim.uni-passau.de" ]

joliebig@fim.uni-passau.de

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

354,419,532

0

null

UTF-8

Python

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()

[ "dan@leftcoastfs.com" ]

dan@leftcoastfs.com

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/corehq/apps/api/resources/serializers.py

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[ "BSD-3-Clause" ]

permissive

dimagi/commcare-hq

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refs/heads/master

2023-08-16T22:38:27.853437

2023-08-16T19:07:19

247,278

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BSD-3-Clause

2023-09-14T19:03:24

2009-07-09T17:00:07

Python

UTF-8

Python

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)

[ "czue@dimagi.com" ]

czue@dimagi.com

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/leetcode/39_combination_sum/39_combination_sum.py

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[ "Apache-2.0" ]

permissive

ryangillard/misc

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d1f9919400636e6b988fa933493b94829a73331e

refs/heads/master

2021-11-24T17:26:52.740997

2021-11-16T06:27:42

192,038,898

0

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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

[ "ryangillard@google.com" ]

ryangillard@google.com

8602f6a64d2286cd253d640ee4063c095b9f289a

ca507259c36a4299666f4c064f25832f5c3f45c1

/symbol_openapi_client/models/transaction_status_dto.py

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[]

no_license

fullcircle23/symbol-openapi-python-client

ae38a2537d1f2eebca115733119c444b79ec4962

3728d30eb1b5085a5a5e991402d180fac8ead68b

refs/heads/master

2022-04-15T06:20:47.821281

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# 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: ravi@nem.foundation 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()

[ "fullcircle2324@gmail.com" ]

fullcircle2324@gmail.com

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/LifePictorial/top/api/rest/FenxiaoGradesGetRequest.py

b11baf371f16e5abe3b7f6e328da2e7a1297435a

[]

no_license

poorevil/LifePictorial

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''' 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'

[ "poorevil@gmail.com" ]

poorevil@gmail.com

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/blog1/models.py

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[]

no_license

gitlGl/myblog

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2023-09-01T14:06:04.720407

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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()

[ "you@example.com" ]

you@example.com

099a47f37b2f69649665afc8b56a49ed9d42362d

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/Algorithm/Math/Power-Function.py

f0b6d0d8a9288824dc87f7ac62ccb797cc247d80

[]

no_license

behappyyoung/PythonSampleCodes

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refs/heads/master

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""" 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)))

[ "behappyyoung@gmail.com" ]

behappyyoung@gmail.com

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/products/models.py

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[]

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tsokac2/new-irish-life

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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

[ "tsokac2@gmail.com" ]

tsokac2@gmail.com

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/298-Binary_Tree_Longest_Consecutive_Sequence.py

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[]

no_license

chanyoonzhu/leetcode-python

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2022-05-24T11:20:35.927915

2022-04-16T06:02:33

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# 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

[ "zhuchanyoon@gmail.com" ]

zhuchanyoon@gmail.com

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/tools/sapp/sapp/tests/sharded_files/sharded_files_test.py

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2022-12-19T21:35:09.582761

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#!/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, "foo@00000-of-00002.bar"), os.path.join(test_path, "foo@00001-of-00002.bar"), ], ) def test_returns_two_shards_for_two(self): pattern = os.path.join(test_path, "foo@2.bar") sf = ShardedFile(pattern) self.assertEqual( sf.get_filenames(), [ os.path.join(test_path, "foo@00000-of-00002.bar"), os.path.join(test_path, "foo@00001-of-00002.bar"), ], ) def test_returns_two_shards_for_two_ambiguous(self): pattern = os.path.join(test_path, "ambiguous@2.ext") sf = ShardedFile(pattern) self.assertEqual( sf.get_filenames(), [ os.path.join(test_path, "ambiguous@00000-of-00002.ext"), os.path.join(test_path, "ambiguous@00001-of-00002.ext"), ], ) def test_returns_two_shards_for_one_ambiguous(self): pattern = os.path.join(test_path, "ambiguous@1.ext") sf = ShardedFile(pattern) self.assertEqual( sf.get_filenames(), [os.path.join(test_path, "ambiguous@00000-of-00001.ext")], ) def test_fails_for_bad_sharding_pattern(self): pattern = os.path.join(test_path, "foo@baz.bar") 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, "inconsistent@2.baz") with self.assertRaisesRegex( ValueError, f"Shard {test_path}/inconsistent@00001-of-00002.baz 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}/inconsistent@00001-of-00002.baz does not exist.", ): ShardedFile(pattern)

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2023-03-09T06:50:55.994738

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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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joan.prat@knowtrade.eu

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refs/heads/master

2021-04-09T16:39:40.781634

2016-10-14T16:53:47

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""" 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; """), ]

[ "santisiri@gmail.com" ]

santisiri@gmail.com

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#!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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#!/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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#calss header class _BRAINS(): def __init__(self,): self.name = "BRAINS" self.definitions = brain self.parents = [] self.childen = [] self.properties = [] self.jsondata = {} self.basic = ['brain']

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#!/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="giswqs@gmail.com", 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, )

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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)

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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")

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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

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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()

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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)

[ "dg1732004@smail.nju.edu.cn" ]

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# -*- 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

[ "jenkins@isaac.nl" ]

jenkins@isaac.nl

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

null

0

null

UTF-8

Python

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)

[ "wonderfulsuccess@163.com" ]

wonderfulsuccess@163.com

ef7ace55198ec861fcb052180a141fca5b563670

f1961c86e6da14f35c21d7235f4fc8a89fabdcad

/DailyProgrammer/DP20160527C.py

5e886c195faa15637218b42b98ecb6636fdaec9c

[ "MIT" ]

permissive

DayGitH/Python-Challenges

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bc32f1332a92fcc2dfa6f5ea4d95f8a8d64c3edf

refs/heads/master

2021-01-17T13:01:03.784523

2018-06-29T23:49:04

58,497,683

2

0

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UTF-8

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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()

[ "akber91@gmail.com" ]

akber91@gmail.com

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

224,838,456

0

null

2023-04-21T20:41:35

2019-11-29T11:10:18

Python

UTF-8

Python

false

154

py

from django import forms from .models import Task class TaskForm(forms.ModelForm): class Meta: model = Task fields = ['task', 'done']

[ "apple@Apples-MacBook-Pro.local" ]

apple@Apples-MacBook-Pro.local

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

null

0

null

UTF-8

Python

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), "%")

[ "ijumper@163.com" ]

ijumper@163.com

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

242,975,318

0

null

UTF-8

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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)

[ "szarbartosz@gmail.com" ]

szarbartosz@gmail.com

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

109,560,971

2

0

null

UTF-8

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false

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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)

[ "chelseaju@ucla.edu" ]

chelseaju@ucla.edu

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

122,595,075

0

2

null

2023-01-07T10:42:38

2018-02-23T08:38:36

JavaScript

UTF-8

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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()

[ "wcc3@sina.com" ]

wcc3@sina.com

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

226,925,789

0

null

UTF-8

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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

[ "michellegsld@gmail.com" ]

michellegsld@gmail.com

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

168,918,799

0

null

UTF-8

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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()

[ "you@example.com" ]

you@example.com

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

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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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#!/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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#!/usr/bin/python3.x # -*- coding=utf-8 -*- """ Time : 2021/8/6 16:27 Author : hike Email : hikehaidong@gmail.com File Name : __init__.py.py Description: Software : PyCharm """

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#!/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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# ------------------------------------------------------------------------------ # Copyright (c) Tencent # Licensed under the GPLv3 License. # Created by Kai Ma (makai0324@gmail.com) # ------------------------------------------------------------------------------ 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

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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)

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# 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()

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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}) )

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malkobeidallah@gmail.com

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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

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b.raml@gmx.at

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/Graph Theory/find_town_judge_graph.py

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SurajPatil314/Leetcode-problems

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""" 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

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spatil2@umbc.edu

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# Getting SQLAlchemy to issue CREATE SCHEMA on create_all from sqlalchemy.schema import CreateSchema engine.execute(CreateSchema('my_schema'))

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Beshkov/Python_Advanced

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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()

[ "alexander.beshkov@gmail.com" ]

alexander.beshkov@gmail.com

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/readMotionZone3.py

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[]

no_license

hamhochoi/K59_training

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2021-01-19T08:59:34.938053

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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()

[ "dothanhwork2017@gmail.com" ]

dothanhwork2017@gmail.com

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/planner/progression/utils.py

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hxdaze/pyplanners

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2023-05-13T03:40:21.480863

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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)))

[ "caelan@mit.edu" ]

caelan@mit.edu

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/generated-libraries/python/netapp/volume/volume_qos_attributes.py

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radekg/netapp-ontap-lib-gen

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2016-09-06T17:41:23.263133

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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' }, }

[ "radek@gruchalski.com" ]

radek@gruchalski.com

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/dataset/dataset_loader.py

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[]

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wolf943134497/3D-Aware-Ellipses-for-Visual-Localization

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2023-05-05T07:45:54.831338

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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"]

[ "zins.matthieu@gmail.com" ]

zins.matthieu@gmail.com

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# # 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)}

[ "noreply@github.com" ]

Velocity-Engineering.noreply@github.com

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/102.binary-tree-level-order-traversal.py

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[]

no_license

Junyangz/leetcode

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# # @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

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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 = {}

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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])

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#Meghna Raswan #2337415 #raswan@chapman.edu #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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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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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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charsyam/unnecessary_math

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''' 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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LeBron-Jian/BasicAlgorithmPractice

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#_*_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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#!/usr/bin/env python3 import rubin_jupyter_utils.hub as rh q = rh.SingletonScanner( name="sciplat-lab", owner="lsstsqre", debug=True, experimentals=2, dailies=3, weeklies=4, releases=3, cachefile="/tmp/reposcan.json", ) q.scan() q.get_all_tags()

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def first(instructions): code = "" x, y = 1, 1 keypad = ((1,2,3),(4,5,6),(7,8,9)) for instruction in instructions: for c in instruction: if c == 'U'and y > 0: y-=1 if c == 'D'and y < 2: y+=1 if c == 'L'and x > 0: x-=1 if c == 'R'and x < 2: x+=1 code += str(keypad[y][x]) return code def second(instructions): code = "" x, y = 0, 2 keypad = ((None,None,1 ,None,None), (None,2 ,3 ,4 ,None), (5 ,6 ,7 ,8 ,9 ), (None,'A' ,'B' ,'C' ,None), (None,None,'D' ,None,None)) for instruction in instructions: for c in instruction: if c == 'U'and y > 0: if(keypad[y-1][x] != None): y-=1 if c == 'D'and y < 4: if(keypad[y+1][x] != None): y+=1 if c == 'L'and x > 0: if(keypad[y][x-1] != None): x-=1 if c == 'R'and x < 4: if(keypad[y][x+1] != None): x+=1 code += str(keypad[y][x]) return code with open('input.in', 'r') as file: lines = file.readlines() print(first(lines)) print(second(lines))

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ecly@mailbox.org

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t=int(input()) while t>0: n=int(input()) temp=n start=0 sum=0 if n==1: sum=3 else: temp=temp-1 while temp>0: start=start+2*temp temp=temp-1 for i in range(0,2*n): sum=sum+start+i+1 print(sum)

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# -------------------------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # -------------------------------------------------------------------------------------------- # Generated file, DO NOT EDIT # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------------------------- from msrest.serialization import Model class EndpointUrl(Model): """EndpointUrl. :param depends_on: Gets or sets the dependency bindings. :type depends_on: :class:`DependsOn <task-agent.v4_1.models.DependsOn>` :param display_name: Gets or sets the display name of service endpoint url. :type display_name: str :param help_text: Gets or sets the help text of service endpoint url. :type help_text: str :param is_visible: Gets or sets the visibility of service endpoint url. :type is_visible: str :param value: Gets or sets the value of service endpoint url. :type value: str """ _attribute_map = { 'depends_on': {'key': 'dependsOn', 'type': 'DependsOn'}, 'display_name': {'key': 'displayName', 'type': 'str'}, 'help_text': {'key': 'helpText', 'type': 'str'}, 'is_visible': {'key': 'isVisible', 'type': 'str'}, 'value': {'key': 'value', 'type': 'str'} } def __init__(self, depends_on=None, display_name=None, help_text=None, is_visible=None, value=None): super(EndpointUrl, self).__init__() self.depends_on = depends_on self.display_name = display_name self.help_text = help_text self.is_visible = is_visible self.value = value

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lijiunderstand/BiSeNet-CCP

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import torch import glob import os from torchvision import transforms from torchvision.transforms import functional as F import cv2 from PIL import Image import pandas as pd import numpy as np from imgaug import augmenters as iaa import imgaug as ia from utils import get_label_info, one_hot_it import random def augmentation(): # augment images with spatial transformation: Flip, Affine, Rotation, etc... # see https://github.com/aleju/imgaug for more details pass def augmentation_pixel(): # augment images with pixel intensity transformation: GaussianBlur, Multiply, etc... pass class SUN(torch.utils.data.Dataset): def __init__(self, image_path, depth_path, label_path, csv_path, scale, mode='train'): super().__init__() self.mode = mode self.image_list = glob.glob(os.path.join(image_path, '*.jpg')) self.image_name = [x.split('/')[-1].split('.')[0] for x in self.image_list] self.depth_list = [os.path.join(depth_path, x + '.png') for x in self.image_name] self.label_list = [os.path.join(label_path, x + '.png') for x in self.image_name] self.fliplr = iaa.Fliplr(0.5) self.label_info = get_label_info(csv_path) # resize self.resize_img = transforms.Resize(scale, Image.BILINEAR) self.resize_depth = transforms.Resize(scale, Image.NEAREST) self.resize_label = transforms.Resize(scale, Image.NEAREST) # normalization self.to_tensor = transforms.ToTensor() def __getitem__(self, index): # load image and resize img = Image.open(self.image_list[index]) img = self.resize_img(img) img = np.array(img) # load depth and resize depth = Image.open(self.depth_list[index]) depth = self.resize_depth(depth) depth = np.array(depth) depth = depth[:, :, np.newaxis] # add axis (480,640,1) # load label and resize label = Image.open(self.label_list[index]) label = self.resize_label(label) label = np.array(label) # convert label to one-hot graph label = one_hot_it(label, self.label_info).astype(np.uint8) # augment image and label if self.mode == 'train': seq_det = self.fliplr.to_deterministic() img = seq_det.augment_image(img) depth = seq_det.augment_image(depth) label = seq_det.augment_image(label) # image -> to_tensor [3, H, W] img = Image.fromarray(img).convert('RGB') img = self.to_tensor(img).float() # depth -> to_tensor [1, H, W] depth = depth / 65535 depth = self.to_tensor(depth).float() # image + depth = RGBD rgbd = torch.cat((img, depth), 0) # label -> [num_classes, H, W] label = np.transpose(label, [2, 0, 1]).astype(np.float32) label = torch.from_numpy(label) return rgbd, label def __len__(self): return len(self.image_list) if __name__ == '__main__': data = SUN('/temp_disk/xs/sun/train/image', '/temp_disk/xs/sun/train/label_img', '/temp_disk/xs/sun/seg37_class_dict.csv', (480, 640)) from utils import reverse_one_hot, get_label_info, colour_code_segmentation, compute_global_accuracy label_info = get_label_info('/temp_disk/xs/sun/seg37_class_dict.csv') for i, (img, label) in enumerate(data): print(img.shape) print(label.shape) print()

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masakiaota/kyoupuro

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refs/heads/master

2021-06-27T04:13:52.152582

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# メインアイデア # T分割と||分割で差が小さい方を探す # 90度回転させた場合も考える # 総合的に小さい方を採用する # 図的な解説は別のPDFをアップロードしてある H, W = list(map(int, input().split())) # 3の倍数の場合は即座に0とわかる if (H * W) % 3 == 0: print(0) exit() def try_divi(W, H): # パターン1 err = W # パターン2 w2, w3 = W // 2, W // 2 if W % 2: w2 += 1 for h1 in range(1, H // 2 + 1): h2 = H-h1 S1, S2, S3 = h1*W, w2*h2, w3*h2 new_err = max(S1, S2, S3) - min(S1, S2, S3) if new_err < err: err = new_err return err print(min(try_divi(W, H), try_divi(H, W)))

[ "aotamasakimail@gmail.com" ]

aotamasakimail@gmail.com

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/Maths/Euler Sum_for.py

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[]

no_license

btrif/Python_dev_repo

df34ab7066eab662a5c11467d390e067ab5bf0f8

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refs/heads/master

2020-04-02T13:34:11.655162

2019-11-10T11:08:23

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import math N=int(input("Number of terms you want to sum up : \n")) sum = 0 for x in range(1, N+1, 1): sum = sum + 1/x**2 print("The result is : ", sum, "\n") print("The EXACT result is : ", math.pi**2/6)

[ "bogdan.evanzo@gmail.com" ]

bogdan.evanzo@gmail.com

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/h2o-py/tests/testdir_algos/deeplearning/pyunit_weights_and_biases_deeplearning.py

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KendraFabric/h2o-3

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refs/heads/master

2023-03-15T12:32:02.852026

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import sys, os sys.path.insert(1, os.path.join("..","..")) import h2o from tests import pyunit_utils def weights_and_biases(): print "Test checks if Deep Learning weights and biases are accessible from R" covtype = h2o.upload_file(pyunit_utils.locate("smalldata/covtype/covtype.20k.data")) covtype[54] = covtype[54].asfactor() from h2o.estimators.deeplearning import H2ODeepLearningEstimator dlmodel = H2ODeepLearningEstimator(hidden=[17,191], epochs=1, balance_classes=False, reproducible=True, seed=1234, export_weights_and_biases=True) dlmodel.train(x=range(54),y=54,training_frame=covtype) print dlmodel weights1 = dlmodel.weights(0) weights2 = dlmodel.weights(1) weights3 = dlmodel.weights(2) biases1 = dlmodel.biases(0) biases2 = dlmodel.biases(1) biases3 = dlmodel.biases(2) w1c = weights1.ncol w1r = weights1.nrow assert w1c == 52, "wrong dimensionality! expected {0}, but got {1}.".format(52, w1c) assert w1r == 17, "wrong dimensionality! expected {0}, but got {1}.".format(17, w1r) w2c = weights2.ncol w2r = weights2.nrow assert w2c == 17, "wrong dimensionality! expected {0}, but got {1}.".format(17, w2c) assert w2r == 191, "wrong dimensionality! expected {0}, but got {1}.".format(191, w2r) w3c = weights3.ncol w3r = weights3.nrow assert w3c == 191, "wrong dimensionality! expected {0}, but got {1}.".format(191, w3c) assert w3r == 7, "wrong dimensionality! expected {0}, but got {1}.".format(7, w3r) b1c = biases1.ncol b1r = biases1.nrow assert b1c == 1, "wrong dimensionality! expected {0}, but got {1}.".format(1, b1c) assert b1r == 17, "wrong dimensionality! expected {0}, but got {1}.".format(17, b1r) b2c = biases2.ncol b2r = biases2.nrow assert b2c == 1, "wrong dimensionality! expected {0}, but got {1}.".format(1, b2c) assert b2r == 191, "wrong dimensionality! expected {0}, but got {1}.".format(191, b2r) b3c = biases3.ncol b3r = biases3.nrow assert b3c == 1, "wrong dimensionality! expected {0}, but got {1}.".format(1, b3c) assert b3r == 7, "wrong dimensionality! expected {0}, but got {1}.".format(7, b3r) if __name__ == "__main__": pyunit_utils.standalone_test(weights_and_biases) else: weights_and_biases()

[ "spnrpa@gmail.com" ]

spnrpa@gmail.com

e8858b1d17db014e60ff494d1957a7e6b40abea3

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/group_and_right_life/have_group/point/long_point_or_next_place.py

353072ad6581aa82d34c921d2190fd9605b2db54

[]

no_license

JingkaiTang/github-play

9bdca4115eee94a7b5e4ae9d3d6052514729ff21

51b550425a91a97480714fe9bc63cb5112f6f729

refs/heads/master

2021-01-20T20:18:21.249162

2016-08-19T07:20:12

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py

#! /usr/bin/env python def person_or_next_work(str_arg): problem(str_arg) print('fact') def problem(str_arg): print(str_arg) if __name__ == '__main__': person_or_next_work('last_place')

[ "jingkaitang@gmail.com" ]

jingkaitang@gmail.com

9997b4b6559fd1d70162f2a3a4866a702f635c0f

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/datahub/dataset/core/views.py

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[ "MIT" ]

permissive

alixedi/data-hub-api-cd-poc

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refs/heads/develop

2020-12-26T05:52:39.575158

2020-01-30T15:27:27

237,407,350

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MIT

2020-02-07T12:38:47

2020-01-31T10:25:34

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UTF-8

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py

from rest_framework.views import APIView from config.settings.types import HawkScope from datahub.core.auth import PaaSIPAuthentication from datahub.core.hawk_receiver import ( HawkAuthentication, HawkResponseSigningMixin, HawkScopePermission, ) from datahub.dataset.core.pagination import DatasetCursorPagination class BaseDatasetView(HawkResponseSigningMixin, APIView): """ Base API view to be used for creating endpoints for consumption by Data Flow and insertion into Data Workspace. """ authentication_classes = (PaaSIPAuthentication, HawkAuthentication) permission_classes = (HawkScopePermission, ) required_hawk_scope = HawkScope.data_flow_api pagination_class = DatasetCursorPagination def get(self, request): """Endpoint which serves all records for a specific Dataset""" dataset = self.get_dataset() paginator = self.pagination_class() page = paginator.paginate_queryset(dataset, request, view=self) return paginator.get_paginated_response(page) def get_dataset(self): """Return a list of records""" raise NotImplementedError

[ "nick.ir.ross@gmail.com" ]

nick.ir.ross@gmail.com

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21a7969bea3e4fb4b4734443aa1702000c26336b

/009-Guessing-Game-One.py

57b5a45057dda0866b8a9730d4d8a0efa3f39ef9

[]

no_license

jaapdejong/python-challenges

635823e7c67c6b5d769c22298d385cfa40cc9f9d

04fbb009d6c017bdb17a2a6cc45c543a13494fe0

refs/heads/master

2021-01-15T22:47:23.882155

2017-08-10T11:18:08

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UTF-8

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#!/usr/bin/python3 # # Exercise 9 # Generate a random number between 1 and 9 (including 1 and 9). # Ask the user to guess the number, then tell them whether they guessed too low, too high, or exactly right. # (Hint: remember to use the user input lessons from the very first exercise) # from random import randint computer = randint(1, 9) while True: player = int(input("Please enter a number between 1 and 9: ")) if player == computer: print("Found!!") break elif player < computer: print("Too low") else: print("Too high")

[ "jaap.dejong@nedap.com" ]

jaap.dejong@nedap.com

736d50cd559b4a440abd688caf3c6c50d476faaa

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/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/registry/code_version/delete.py

3e012305c57cb4acd3c379e49fb0e9f5eebf2636

[ "LicenseRef-scancode-generic-cla", "MIT", "LGPL-2.1-or-later" ]

permissive

Azure/azure-sdk-for-python

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c2ca191e736bb06bfbbbc9493e8325763ba990bb

refs/heads/main

2023-09-06T09:30:13.135012

2023-09-06T01:08:06

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2023-09-14T21:48:49

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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.machinelearningservices import MachineLearningServicesMgmtClient """ # PREREQUISITES pip install azure-identity pip install azure-mgmt-machinelearningservices # USAGE python delete.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 = MachineLearningServicesMgmtClient( credential=DefaultAzureCredential(), subscription_id="00000000-1111-2222-3333-444444444444", ) client.registry_code_versions.begin_delete( resource_group_name="test-rg", registry_name="my-aml-registry", code_name="string", version="string", ).result() # x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2023-04-01/examples/Registry/CodeVersion/delete.json if __name__ == "__main__": main()

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Azure.noreply@github.com

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/examples/BLE_Alerts_Secure_2/main.py

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[]

no_license

zerynth/lib-espressif-esp32ble

09f4f9ae28056d49d4062c19c0a330bff61396f3

17ab2a1fbd80e99420cd988c8fc148b8f366e379

refs/heads/master

2021-06-08T04:12:22.808499

2020-09-24T15:31:45

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################################################################################ # BLE Alerts with Security 2 # # Created by Zerynth Team 2019 CC # Author: G. Baldi ############################################################################### import streams #import the ESP32 BLE driver: a BLE capable VM is also needed! from espressif.esp32ble import esp32ble as bledrv # then import the BLE modue from wireless import ble streams.serial() notifications_enabled = True connected = False # Let's define some callbacks def value_cb(status,val): # check incoming commands and enable/disable notifications global notifications_enabled print("Value changed to",val[0],val[1]) if val[0]==0: print("Notifications enabled") notifications_enabled = True elif val[0]==2: notifications_enabled = False print("Notifications disabled") else: print("Notifications unchanged") def connection_cb(address): global connected print("Connected to",ble.btos(address)) connected = True def disconnection_cb(address): global connected print("Disconnected from",ble.btos(address)) # let's start advertising again ble.start_advertising() connected = False # Let's define some security callbacks def match_key_cb(passkey): print("MASTER KEY IS:",passkey,"CAN WE PROCEED? PRESS BUTTON FOR YES") pinMode(BTN0,INPUT) for i in range(5): if digitalRead(BTN0)!=0: ble.confirm_passkey(1) print("Confirmed!") return sleep(1000) ble.confirm_passkey(0) print("Not confirmed!") try: # initialize BLE driver bledrv.init() # Set GAP name and LEVEL 2 security # !!! If security is not set, no secure connection will be possible ble.gap("ZNotifier",security=(ble.SECURITY_MODE_1,ble.SECURITY_LEVEL_2)) # add some GAP callbacks ble.add_callback(ble.EVT_CONNECTED,connection_cb) ble.add_callback(ble.EVT_DISCONNECTED,disconnection_cb) # Create a GATT Service: let's try an Alert Notification Service # (here are the specs: https://www.bluetooth.com/specifications/gatt/viewer?attributeXmlFile=org.bluetooth.service.alert_notification.xml) s = ble.Service(0x1811) # The Alert Notification service has multiple characteristics. Let's add them one by one # Create a GATT Characteristic for counting new alerts. # specs: https://www.bluetooth.com/specifications/gatt/viewer?attributeXmlFile=org.bluetooth.characteristic.supported_new_alert_category.xml cn = ble.Characteristic(0x2A47, ble.NOTIFY | ble.READ,16,"New Alerts",ble.BYTES) # Add the GATT Characteristic to the Service s.add_characteristic(cn) # Create anothr GATT Characteristic for enabling/disabling alerts # specs: https://www.bluetooth.com/specifications/gatt/viewer?attributeXmlFile=org.bluetooth.characteristic.alert_notification_control_point.xml cc = ble.Characteristic(0x2A44, ble.WRITE ,2,"Alerts control",ble.BYTES) # Add the GATT Characteristic to the Service s.add_characteristic(cc) # Add a callback to be notified of changes cc.set_callback(value_cb) # Add the Service. You can create additional services and add them one by one ble.add_service(s) # Configure security. BLE security is very flexible. # In this case we declare that the device has only an output capability with yes o or no input (CAP_DISPLAY_YES_NO), # that we require a bonding (storage of the keys after pairing) # and that we want both secure connection and main in the middle protection. ble.security( capabilities=ble.CAP_DISPLAY_YES_NO, bonding=ble.AUTH_BOND, scheme=ble.AUTH_SC|ble.AUTH_MITM, key_size=16) # To do so, we need a callback to accept the passkey when needed ble.add_callback(ble.EVT_MATCH_PASSKEY,match_key_cb) # Setup advertising to 50ms ble.advertising(50) # Start the BLE stack ble.start() # Now start advertising ble.start_advertising() except Exception as e: print(e) # Uncomment the following lines to delte bonded devices! for bond in ble.bonded(): print("Removing bonded:",ble.btos(bond)) ble.remove_bonded(bond) # loop forever while True: print(".") if random(0,100)<50 and notifications_enabled and connected: value = bytearray(cn.get_value()) value[0]=0 # simple alert type if value[1]<255: value[1]=value[1]+1 # add a notification print("Adding a new notification, total of",value[1]) # the remaining 14 bytes can be some text value[2:10] = "Zerynth!" # set the new value. If ble notifications are enabled, the connected device will receive the change cn.set_value(value) sleep(5000)

[ "dev@zerynth.com" ]

dev@zerynth.com

cc525d1d2d9552b17bd4b2818466de9dc2db4e33

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/EDBRCommon/python/simulation/RunIIDR74X/RSGravToZZToLLQQ_M-4500.py

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[]

no_license

jruizvar/ExoDiBosonResonancesRun2

aa613200725cf6cd825d7bcbde60d2e39ba84e39

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refs/heads/Analysis76X

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2016-05-30T21:30:19

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1

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2016-04-22T18:38:45

2014-09-03T12:41:07

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import FWCore.ParameterSet.Config as cms maxEvents = cms.untracked.PSet( input = cms.untracked.int32(-1) ) readFiles = cms.untracked.vstring() secFiles = cms.untracked.vstring() source = cms.Source ("PoolSource",fileNames = readFiles, secondaryFileNames = secFiles) readFiles.extend( [ '/store/mc/RunIISpring15DR74/RSGravToZZToLLQQ_kMpl01_M-4500_TuneCUETP8M1_13TeV-pythia8/MINIAODSIM/Asympt25ns_MCRUN2_74_V9-v1/50000/0E6BC100-2608-E511-81FE-B083FED7685B.root', '/store/mc/RunIISpring15DR74/RSGravToZZToLLQQ_kMpl01_M-4500_TuneCUETP8M1_13TeV-pythia8/MINIAODSIM/Asympt25ns_MCRUN2_74_V9-v1/50000/2C69D809-2608-E511-9F6F-AC853DA06A1A.root', '/store/mc/RunIISpring15DR74/RSGravToZZToLLQQ_kMpl01_M-4500_TuneCUETP8M1_13TeV-pythia8/MINIAODSIM/Asympt25ns_MCRUN2_74_V9-v1/50000/96B4A5FF-2508-E511-8C7A-AC853D9DACD7.root' ] ); secFiles.extend( [ ] )

[ "jruizvar@cern.ch" ]

jruizvar@cern.ch

a9fc27c1b63235d582097a3ea144208589f0880d

b3b066a566618f49ae83c81e963543a9b956a00a

/Supervised Learning with scikit-learn/01_Classification/05_k-Nearest Neighbors Predict.py

96614384e3f708dc1e9787ed95b2a117a9ba84a8

[]

no_license

ahmed-gharib89/DataCamp_Data_Scientist_with_Python_2020

666c4129c3f0b5d759b511529a365dfd36c12f1a

f3d20b788c8ef766e7c86c817e6c2ef7b69520b8

refs/heads/master

2022-12-22T21:09:13.955273

2020-09-30T01:16:05

289,991,534

2

0

null

2020-08-24T17:15:43

2020-08-24T17:15:42

null

UTF-8

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py

''' k-Nearest Neighbors: Predict Having fit a k-NN classifier, you can now use it to predict the label of a new data point. However, there is no unlabeled data available since all of it was used to fit the model! You can still use the .predict() method on the X that was used to fit the model, but it is not a good indicator of the model's ability to generalize to new, unseen data. In the next video, Hugo will discuss a solution to this problem. For now, a random unlabeled data point has been generated and is available to you as X_new. You will use your classifier to predict the label for this new data point, as well as on the training data X that the model has already seen. Using .predict() on X_new will generate 1 prediction, while using it on X will generate 435 predictions: 1 for each sample. The DataFrame has been pre-loaded as df. This time, you will create the feature array X and target variable array y yourself. INSTRUCTIONS 100XP Create arrays for the features and the target variable from df. As a reminder, the target variable is 'party'. Instantiate a KNeighborsClassifier with 6 neighbors. Fit the classifier to the data. Predict the labels of the training data, X. Predict the label of the new data point X_new. ''' # Import KNeighborsClassifier from sklearn.neighbors from sklearn.neighbors import KNeighborsClassifier # Create arrays for the features and the response variable y = df['party'].values X = df.drop('party', axis=1).values # Create a k-NN classifier with 6 neighbors: knn knn = knn = KNeighborsClassifier(n_neighbors=6) # Fit the classifier to the data knn.fit(X,y) # Predict the labels for the training data X y_pred = knn.predict(X) # Predict and print the label for the new data point X_new new_prediction = knn.predict(X_new) print("Prediction: {}".format(new_prediction)) #========================================================# # DEVELOPER # # BasitAminBhatti # # Github # # https://github.com/basitaminbhatti # #========================================================#

[ "Your-Email" ]

Your-Email

2ff172e2e6211730ddf823e829aa16698fff35b2

2f2667682bb78578445b9e3aac7cc62cfba83d5a

/googlenet/nets/cnn_model/CNN_MODEL.py

cb42ca784f10d89d88a49599ffdab9680d78e45d

[]

no_license

Yorwxue/trt_experence

9c770c2a1cb7c48c9d7f21c46be0107de91f1c41

778a6cef019dd8afdae6b608b3cbacb56480c7b1

refs/heads/master

2022-12-21T12:38:13.108402

2019-08-01T08:11:10

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null

2022-12-08T05:57:26

2019-07-08T07:36:12

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UTF-8

Python

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import tensorflow as tf class cnn_model(object): def __init__(self, input_placeholder, num_classes): self.inputs = input_placeholder self.num_classes = num_classes self._build_model() self.labels = None def build_graph(self, label_place_holder): self.labels = label_place_holder self._build_train_op() return def _build_model(self): with tf.variable_scope('cnn'): with tf.variable_scope('unit-1'): x = self._conv2d(self.inputs, name='cnn-1', filter_size=3, in_channels=3, out_channels=64, strides=1) # x = self._leaky_relu(x, 0.01) x = self._max_pool(x, 2, strides=2) with tf.variable_scope('unit-2'): x = self._conv2d(x, name='cnn-2', filter_size=3, in_channels=64, out_channels=128, strides=1) # x = self._leaky_relu(x, 0.01) x = self._max_pool(x, 2, strides=2) with tf.variable_scope('unit-3'): x = self._conv2d(x, name='cnn-3', filter_size=3, in_channels=128, out_channels=128, strides=1) # x = self._leaky_relu(x, 0.01) x = self._max_pool(x, 2, strides=2) with tf.variable_scope('unit-4'): x = self._conv2d(x, name='cnn-4', filter_size=3, in_channels=128, out_channels=256, strides=1) # x = self._leaky_relu(x, 0.01) x = self._max_pool(x, 2, strides=2) with tf.variable_scope('fc'): # [batch_size, max_stepsize, num_features] batch_size, height, width, channels = x.get_shape().as_list() x = tf.reshape(x, [-1, height * width *channels]) outputs = self._fc(x, input_shape=height * width *channels, output_shape=64, name="fc-1") outputs = self._fc(outputs, input_shape=64, output_shape=self.num_classes, name="fc-2") self.logits = tf.identity(outputs, name="logits") def _build_train_op(self): self.global_step = tf.Variable(0, trainable=False) self.loss = tf.nn.softmax_cross_entropy_with_logits( labels=self.labels, logits=self.logits, ) self.optimizer = tf.train.AdamOptimizer().minimize( self.loss, global_step=self.global_step ) train_ops = [self.optimizer] self.train_op = tf.group(*train_ops) self.cost = tf.reduce_mean(self.loss) self.acc, self.acc_op = tf.metrics.accuracy( tf.argmax(self.labels, 1), tf.argmax(self.logits, 1), name="metrics" ) def _conv2d(self, x, name, filter_size, in_channels, out_channels, strides): with tf.variable_scope(name): kernel = tf.get_variable(name='conv', shape=[filter_size, filter_size, in_channels, out_channels], dtype=tf.float32, initializer=tf.contrib.layers.xavier_initializer()) b = tf.get_variable(name='bais', shape=[out_channels], dtype=tf.float32, initializer=tf.constant_initializer()) con2d_op = tf.nn.conv2d(x, kernel, [1, strides, strides, 1], padding='SAME') return tf.nn.bias_add(con2d_op, b) def _leaky_relu(self, x, leakiness=0.0): return tf.where(tf.less(x, 0.0), leakiness * x, x, name='leaky_relu') def _max_pool(self, x, ksize, strides): return tf.nn.max_pool(x, ksize=[1, ksize, ksize, 1], strides=[1, strides, strides, 1], padding='SAME', name='max_pool') def _fc(self, x, input_shape, output_shape, name): with tf.variable_scope(name): W = tf.get_variable(name='w', shape=[input_shape, output_shape], dtype=tf.float32, initializer=tf.contrib.layers.xavier_initializer()) b = tf.get_variable(name='b', shape=[output_shape], dtype=tf.float32, initializer=tf.constant_initializer()) return tf.matmul(x, W) + b

[ "yorwxue@gmail.com" ]

yorwxue@gmail.com

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d7f45fac46598da9825a404d7511df7474237e4a

/ex.071.py

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[]

no_license

MarceloBCS/Exercicios_Curso_em_video

b4a8cbc8573e1303065c0cf1baad25c47d5a2fd8

a90fd67d83cf154f3554f962815fb791d3508d0c

refs/heads/master

2022-12-29T19:15:50.007022

2020-10-13T05:09:28

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cor = {'amarelo':'\033[1;33m', 'red':'\033[1;31m', 'limp':'\033[m'} print(cor['red'], '='*22) print(f' Bhering Bank') print('='*24, cor['limp']) n = int(input('Valor do Saque: R$ ')) nota50 = n // 50 resto50 = n % 50 nota20 = (resto50) // 20 resto20 = resto50 % 20 nota10 = (resto20) // 10 resto10 = resto20 % 10 nota1 = resto10 print('{1}{0:>3} nota(s){2} R$50'.format(nota50, cor['amarelo'], cor['limp'])) print('{1}{0:>3} nota(s){2} R$20'.format(nota20, cor['amarelo'], cor['limp'])) print('{1}{0:>3} nota(s){2} R$10'.format(nota10, cor['amarelo'], cor['limp'])) print('{1}{0:>3} nota(s){2} R$ 1'.format(nota1, cor['amarelo'], cor['limp'])) from datetime import date print(f'\nProcessado em {date.today()}')

[ "63213758+MarceloBCS@users.noreply.github.com" ]

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/xai/brain/wordbase/verbs/_neglect.py

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cash2one/xai

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2021-01-19T12:33:54.964379

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#calss header class _NEGLECT(): def __init__(self,): self.name = "NEGLECT" self.definitions = [u'to not give enough care or attention to people or things that are your responsibility: ', u'to not do something, often because you forget: '] self.parents = [] self.childen = [] self.properties = [] self.jsondata = {} self.specie = 'verbs' def run(self, obj1 = [], obj2 = []): return self.jsondata

[ "xingwang1991@gmail.com" ]

xingwang1991@gmail.com

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/algorithms/Svm/APG/L2/APG_L2_m22.py

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[]

no_license

yingzhuoy/MRs-of-linear-models

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c3df8299e039a12613f2022b370b8c3e9c2dd822

refs/heads/master

2023-04-07T23:09:37.736952

2021-04-04T05:33:37

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import numpy as np from numpy import linalg import cvxopt from cvxopt import matrix,solvers from algorithms.clf import Clf def backtracking(l0, x0, p, q, low, up): # update x beta = 0.5 l = l0 L0 = 0.5*x0.T*(p*x0) + q.T*x0 g0 = p*x0 + q for k in range(128): #xp = x0 - l * g0 #----bug---- xp = x0 - l *g0+2.4710787568352073 xp[xp < low] = low xp[xp > up] = up Lx = 0.5*xp.T*(p*xp) + q.T*xp gt = (x0-xp) / l if Lx > L0 - l *(g0.T*gt) + 0.5*l*gt.T*(gt): l = beta * l else: break return xp, l def projected_apg(p, q, bounds, step_size=0.1, max_iter=1000): m = p.shape[0] low, up = bounds x = np.ones((m, 1), np.float64) y = x v, w = np.linalg.eigh(p) # v[v<=0] = 1e-10 # p = w*np.diag(v)*w.T l = 1/v[-1] - 1e-10 for k in range(max_iter): # heavy on matrix operations # saving previous x y = x # compute loss and its gradient # gradient = p*x + q # proximal mapping # x = x - l * gradient # x[x < low] = low # x[x > up] = up x, l = backtracking(l, y, p, q, low, up) # if(np.linalg.norm(x1-x)): # print('error', np.linalg.norm(x1-x)) # t1 = (1+np.sqrt(1+4*np.square(t0)))/2 # y = x + (t0-1)/t1* (x - y) # t0 = t1 # stop criteria rnormw = np.linalg.norm(y-x) if k > 1 and rnormw < 1e-6: #print('convergence!') break #print(rnormw) return y #L2-svm class APG_L2_m22(): def fit(self, X, y): m, n = X.shape X = np.column_stack((X, np.ones((m, 1)))) y = y.astype(np.float64) data_num = len(y) C = 1.0 kernel = np.dot(X, np.transpose(X)) p = np.matrix(np.multiply(kernel,np.outer(y, y))) + np.diag(np.ones(data_num, np.float64)) * .5/C q = np.matrix(-np.ones([data_num, 1], np.float64)) bounds = (0, np.inf) alpha_svs = projected_apg(p, q, bounds) # p = matrix(p) # q = matrix(q) # g = matrix(-np.eye(data_num)) # h = matrix(np.zeros([data_num, 1], np.float64)) # solvers.options['show_progress'] = False # sol = solvers.qp(p, q, g, h) # alpha_svs1 = np.array(sol['x']) # print(np.linalg.norm(alpha_svs1 - alpha_svs)) # # alpha_svs = alpha_svs1 y1 = np.reshape(y, (-1, 1)) alpha1 = alpha_svs lambda1 = np.multiply(y1,alpha1) w = np.dot(X.T, lambda1) w = np.array(w).reshape(-1) # b = np.mean(y1-np.reshape(np.dot(w, np.transpose(X)), [-1, 1])) b = w[n] w = w[0:n] clf = Clf(w, b) return clf

[ "yingzhuoy@qq.com" ]

yingzhuoy@qq.com

1b4e72269db5cacbd411ce69d585d1981dee4c84

3449140c29d292c910527cc7163689d215d2f22d

/delivery/urls.py

edddc5b0d4461c75abdbf2a5259fa5461fd3ed6c

[]

no_license

gayatrishivani/ichoose-ead-project-sem6

89afeda33261bb18b3848e9171865409d0cf6b44

ae0f59769950656683bc72d1e76cec2bcfa7fd07

refs/heads/master

2023-03-08T18:43:02.970287

2021-02-25T19:06:25

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from django.contrib.auth import views from django.urls import path from .views import * urlpatterns = [ path('order/', OrderAPIView.as_view(), name="order"), path('deliver/', deliver_verify,name ="deliver"), ]

[ "you@example.com" ]

you@example.com

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/test/functional/walletbackup.py

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[ "MIT" ]

permissive

kenfmcoin/kenfmcoin

d8783b34fcb3ae01067e8d1b33e3a73e3b82b1f9

1fa48487593233f2066757dc54f48b2349e2d9db

refs/heads/master

2020-03-10T17:53:31.569229

2018-04-14T12:28:55

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#!/usr/bin/env python3 # Copyright (c) 2014-2016 The KenFMcoin Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. """Test the wallet backup features. Test case is: 4 nodes. 1 2 and 3 send transactions between each other, fourth node is a miner. 1 2 3 each mine a block to start, then Miner creates 100 blocks so 1 2 3 each have 50 mature coins to spend. Then 5 iterations of 1/2/3 sending coins amongst themselves to get transactions in the wallets, and the miner mining one block. Wallets are backed up using dumpwallet/backupwallet. Then 5 more iterations of transactions and mining a block. Miner then generates 101 more blocks, so any transaction fees paid mature. Sanity check: Sum(1,2,3,4 balances) == 114*50 1/2/3 are shutdown, and their wallets erased. Then restore using wallet.dat backup. And confirm 1/2/3/4 balances are same as before. Shutdown again, restore using importwallet, and confirm again balances are correct. """ from random import randint import shutil from test_framework.test_framework import KenFMcoinTestFramework from test_framework.util import * class WalletBackupTest(KenFMcoinTestFramework): def set_test_params(self): self.num_nodes = 4 self.setup_clean_chain = True # nodes 1, 2,3 are spenders, let's give them a keypool=100 self.extra_args = [["-keypool=100"], ["-keypool=100"], ["-keypool=100"], []] def setup_network(self, split=False): self.setup_nodes() connect_nodes(self.nodes[0], 3) connect_nodes(self.nodes[1], 3) connect_nodes(self.nodes[2], 3) connect_nodes(self.nodes[2], 0) self.sync_all() def one_send(self, from_node, to_address): if (randint(1,2) == 1): amount = Decimal(randint(1,10)) / Decimal(10) self.nodes[from_node].sendtoaddress(to_address, amount) def do_one_round(self): a0 = self.nodes[0].getnewaddress() a1 = self.nodes[1].getnewaddress() a2 = self.nodes[2].getnewaddress() self.one_send(0, a1) self.one_send(0, a2) self.one_send(1, a0) self.one_send(1, a2) self.one_send(2, a0) self.one_send(2, a1) # Have the miner (node3) mine a block. # Must sync mempools before mining. sync_mempools(self.nodes) self.nodes[3].generate(1) sync_blocks(self.nodes) # As above, this mirrors the original bash test. def start_three(self): self.start_node(0) self.start_node(1) self.start_node(2) connect_nodes(self.nodes[0], 3) connect_nodes(self.nodes[1], 3) connect_nodes(self.nodes[2], 3) connect_nodes(self.nodes[2], 0) def stop_three(self): self.stop_node(0) self.stop_node(1) self.stop_node(2) def erase_three(self): os.remove(self.options.tmpdir + "/node0/regtest/wallet.dat") os.remove(self.options.tmpdir + "/node1/regtest/wallet.dat") os.remove(self.options.tmpdir + "/node2/regtest/wallet.dat") def run_test(self): self.log.info("Generating initial blockchain") self.nodes[0].generate(1) sync_blocks(self.nodes) self.nodes[1].generate(1) sync_blocks(self.nodes) self.nodes[2].generate(1) sync_blocks(self.nodes) self.nodes[3].generate(100) sync_blocks(self.nodes) assert_equal(self.nodes[0].getbalance(), 50) assert_equal(self.nodes[1].getbalance(), 50) assert_equal(self.nodes[2].getbalance(), 50) assert_equal(self.nodes[3].getbalance(), 0) self.log.info("Creating transactions") # Five rounds of sending each other transactions. for i in range(5): self.do_one_round() self.log.info("Backing up") tmpdir = self.options.tmpdir self.nodes[0].backupwallet(tmpdir + "/node0/wallet.bak") self.nodes[0].dumpwallet(tmpdir + "/node0/wallet.dump") self.nodes[1].backupwallet(tmpdir + "/node1/wallet.bak") self.nodes[1].dumpwallet(tmpdir + "/node1/wallet.dump") self.nodes[2].backupwallet(tmpdir + "/node2/wallet.bak") self.nodes[2].dumpwallet(tmpdir + "/node2/wallet.dump") self.log.info("More transactions") for i in range(5): self.do_one_round() # Generate 101 more blocks, so any fees paid mature self.nodes[3].generate(101) self.sync_all() balance0 = self.nodes[0].getbalance() balance1 = self.nodes[1].getbalance() balance2 = self.nodes[2].getbalance() balance3 = self.nodes[3].getbalance() total = balance0 + balance1 + balance2 + balance3 # At this point, there are 214 blocks (103 for setup, then 10 rounds, then 101.) # 114 are mature, so the sum of all wallets should be 114 * 50 = 5700. assert_equal(total, 5700) ## # Test restoring spender wallets from backups ## self.log.info("Restoring using wallet.dat") self.stop_three() self.erase_three() # Start node2 with no chain shutil.rmtree(self.options.tmpdir + "/node2/regtest/blocks") shutil.rmtree(self.options.tmpdir + "/node2/regtest/chainstate") # Restore wallets from backup shutil.copyfile(tmpdir + "/node0/wallet.bak", tmpdir + "/node0/regtest/wallet.dat") shutil.copyfile(tmpdir + "/node1/wallet.bak", tmpdir + "/node1/regtest/wallet.dat") shutil.copyfile(tmpdir + "/node2/wallet.bak", tmpdir + "/node2/regtest/wallet.dat") self.log.info("Re-starting nodes") self.start_three() sync_blocks(self.nodes) assert_equal(self.nodes[0].getbalance(), balance0) assert_equal(self.nodes[1].getbalance(), balance1) assert_equal(self.nodes[2].getbalance(), balance2) self.log.info("Restoring using dumped wallet") self.stop_three() self.erase_three() #start node2 with no chain shutil.rmtree(self.options.tmpdir + "/node2/regtest/blocks") shutil.rmtree(self.options.tmpdir + "/node2/regtest/chainstate") self.start_three() assert_equal(self.nodes[0].getbalance(), 0) assert_equal(self.nodes[1].getbalance(), 0) assert_equal(self.nodes[2].getbalance(), 0) self.nodes[0].importwallet(tmpdir + "/node0/wallet.dump") self.nodes[1].importwallet(tmpdir + "/node1/wallet.dump") self.nodes[2].importwallet(tmpdir + "/node2/wallet.dump") sync_blocks(self.nodes) assert_equal(self.nodes[0].getbalance(), balance0) assert_equal(self.nodes[1].getbalance(), balance1) assert_equal(self.nodes[2].getbalance(), balance2) # Backup to source wallet file must fail sourcePaths = [ tmpdir + "/node0/regtest/wallet.dat", tmpdir + "/node0/./regtest/wallet.dat", tmpdir + "/node0/regtest/", tmpdir + "/node0/regtest"] for sourcePath in sourcePaths: assert_raises_rpc_error(-4, "backup failed", self.nodes[0].backupwallet, sourcePath) if __name__ == '__main__': WalletBackupTest().main()

[ "37983255+spineinhalb@users.noreply.github.com" ]

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/Python/Fundamentals/Objects and Classes/04. Exercises.py

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no_license

bMedarski/SoftUni

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class Exercise: def __init__(self,topic,course_name,judge_contest_link,): self.topic = topic self.course_name = course_name self.judge_contest_link = judge_contest_link self.problems = [] def add_problems(self,list_of_problems): for problem in list_of_problems: self.problems.append(problem) def print_problem(self): print(f"Exercises: {self.topic}") print(f"Problems for exercises and homework for the \"{self.course_name}\" course @ SoftUni.") print(f"Check your solutions here: {self.judge_contest_link}") problem_count = len(self.problems) for i in range(0,problem_count): print(f"{i+1}. {self.problems[i]}") list_of_exercises = [] while True: user_input = input() if user_input == "go go go": break exercise_args = user_input.split(" -> ") problems = exercise_args[3].split(", ") exercise = Exercise(exercise_args[0],exercise_args[1],exercise_args[2]) exercise.add_problems(problems) list_of_exercises.append(exercise) for exercise in list_of_exercises: exercise.print_problem()

[ "boyamedarski@mail.bg" ]

boyamedarski@mail.bg

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/healthid/apps/orders/migrations/0019_auto_20191118_1908.py

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[]

no_license

malep2007/healtid-web-api

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refs/heads/dev

2022-12-12T03:24:37.537569

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# Generated by Django 2.2 on 2019-11-18 18:08 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('orders', '0018_supplierrating'), ] operations = [ migrations.AlterField( model_name='supplierorderdetails', name='status', field=models.CharField(choices=[('pending', 'Pending Approval'), ('open', 'Open'), ('closed', 'Closed'), ('approved', 'Approved')], default='pending', max_length=10), ), ]

[ "ephraim.malinga@gmail.com" ]

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refs/heads/master

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def oddnumbers(): n = 1 while True: yield n n += 2 def pi_series(): odds = oddnumbers() approximation = 0 while True: approximation += (4 / next(odds)) yield approximation approximation -= (4 / next(odds)) yield approximation approx_pi = pi_series() for x in range(10000): print(next(approx_pi))

[ "me@jessequinn.info" ]

me@jessequinn.info

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/supervised_learning/0x0F-word_embeddings/4-fasttext.py

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[]

no_license

jgadelugo/holbertonschool-machine_learning

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refs/heads/master

2023-02-01T03:52:43.723569

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#!/usr/bin/env python3 """creates and trains a genism fastText model""" from gensim.models import FastText def fasttext_model(sentences, size=100, min_count=5, negative=5, window=5, cbow=True, iterations=5, seed=0, workers=1): """creates and trains a genism fastText model @sentences: list of sentences to be trained on @size: dimensionality of the embedding layer @min_count: minimum number of occurrences of a word for use in training @window: maximum distance between the current and predicted word within a sentence @negative: size of negative sampling @cbow: boolean to determine the training type True is for CBOW False is for Skip-gram @iterations: number of iterations to train over @seed: seed for the random number generator @workers: number of worker threads to train the model Return: trained model """ model = FastText(sentences=sentences, size=size, window=window, min_count=min_count, workers=workers, sg=cbow, negative=negative, seed=seed) model.train(sentences=sentences, total_examples=model.corpus_count, epochs=iterations) return model

[ "alvarezdelugo.jose@gmail.com" ]

alvarezdelugo.jose@gmail.com